These 10 alleles were examined for evidence of IHR by comparing them to the previously described non-IHR X. fastidiosa subsp. multiplex alleles and to the known X. fastidiosa subsp. fastidiosa and sandyi alleles . Of these 10, 4 alleles were found to be derived in their entirety from X. fastidiosa subsp. fastidiosa, and 3 were found to be chimeric for X. fastidiosa subsp. multiplex and fastidiosa sequences, with significant evidence of one or more recombination breakpoints. These 7 alleles encompassed 4 loci: leuA, cysG, holC, and pilU. The locus most strongly implicated in IHR wascysG, since all of the 9 recombinant-group STs were characterized at this locus by 1 of 3 cysG alleles unique to the group. The involvement of IHR in the genesis of all 3 of these alleles is illustrated by their close genetic relationship to X. fastidiosa subsp.fastidiosa and sandyi alleles . Allele 12, apart from being found in the recombinant group, is an X. fastidiosa subsp. fastidiosa allele . The other two alleles were found to be chimeric: allele 18 contains a single recombinant region at the 3= end of 342 bp, while allele 6 has two short recombinant regions, one at the 5= end of at least 23 bp and another toward the 5= end of at least 35 bp . The DNA sequence variation defining these patterns is shown in Table 2. The patterns seen in the DNA sequences of the 3 cysGalleles are consistent with the hypothesis of a single IHR that introgressed donor allele 12 into X. fastidiosa subsp. multiplex, followed by subsequent intrasubspecific recombination reintroducing X. fastidiosa subsp. multiplex sequence to create alleles 6 and 18 . There are no inconsistent sites, container size for blueberries provided the 5= recombination breakpoint in allele 18 starts after position 71. Introgression of X. fastidiosa subsp. fastidiosa sequence into X. fastidiosa subsp. multiplexwas found in alleles at 3 other loci .

In the case of pilU, 7 of the 9 recombinant STs carried either an allele identical to a known X. fastidiosa subsp. fastidiosa allele or 1 bp different from it . Allele 1 is an allele that characterizes most U.S. isolates as well as several STs found in Costa Rica, while allele 9 is unique to the recombinant group. The leuA locus has a single statistically significant recombinant allele, allele 4 . It differed by 2 bp from the X. fastidiosa subsp. fastidiosa allele 9 but by 8 bp from the most similar nonIHR X. fastidiosa subsp. multiplex allele. X. fastidiosa subsp. fastidiosa allele 9 could be the donor for its entirety , although if the recombination region started after site 10 but before position 520 , then only one site would be unexplained. That remaining site carries a base unique to this allele and is probably a novel mutation. If the recombination breakpoint was 3= of position 295 then X. fastidiosa subsp. fastidiosa allele 1 provides as good a match as allele 9 . Similarly, holC allele 7 was also 8 bp different from the most similar non-IHR X. fastidiosa subsp. multiplex allele, providing clear evidence that the 5= end was derived from X. fastidiosa subsp. fastidiosa . The pattern can be explained if X. fastidiosasubsp. fastidiosa allele 19 is the donor of the 5= region ending somewhere between positions 183 and 286, since it leaves no inconsistent bases . The loci leuA and holC each had an additional allele that were unique to the recombinant group, as was an allele at another locus, nuoL4. Although these last 3 alleles did not show statistically significant evidence of introgression , they all showed a grouping of 2 or 3 nucleotide changes that were not found in non-IHR X. fastidiosa subsp. multiplex but were present in X. fastidiosa subsp. fastidiosa. Of these 3, the strongest case for IHR is holC9, where a region of possible IHR can be seen at the 3= end of the sequence .Analysis of the recombinant group ofX. fastidiosa subsp. multiplex showed three important results. First, intersubspecific recombination was shown to have occurred in 50% of 8 loci scattered throughout the genome that were chosen independently of the data . Second, it was shown that the donor of the introgressed sequence was X. fastidiosa subsp. fastidiosa, a subspecies introduced from Central America into the United States as a single strain .

However, the introgressed sequence at two of the loci did not come from any of the X. fastidiosa subsp. fastidiosa genotypes that have been found in the United States. This result suggests that another introduction of X. fastidiosa subsp. fastidiosa must have occurred, an introduction that resulted in successful IHR, after which the donor genotype seems to have disappeared. This involvement of an unexpected X. fastidiosa subsp. fastidiosa strain supports the hypothesis that the members of the recombinant group share a single ancestral IHR event. Third, the hypothesis that IHR has facilitated a shift to new hosts is strongly supported by the example of blueberry, where 10 isolates have been typed and potentially supported by the example of blackberry .Of course, the direct causation of this link can never be proved without knowledge of the genetic changes driving this shift. It can always be argued that the link is fortuitous and that one or more point mutations in the nonrecombined X. fastidiosa subsp. multiplex genome are causal in the host shift. Arguing against this possibility are 2 additional pieces of information. First, both blueberry and blackberry are native to the United States, so if only a simple genetic change was required to infect these species, why did the native non-recombinant X. fastidiosa subsp. multiplex apparently never acquire these changes? Second, a similar but even more extensive mixing of the genomes of X. fastidiosa subspp. fastidiosa and multiplex is found in the only form of X. fastidiosa that infects another U.S. native plant, mulberry . Furthermore, in other bacterial species, it has been demonstrated that recombination can drive rapid evolution, both in the laboratory and, in the case of Helicobacter pylori, in mice . Similarly, McCarthy et al. concluded that lineages of Campylobacter jejuni in chickens versus cattle and sheep were able to shift host type, because rapid adaptation was facilitated by recombination with the resident host population.

In the study by Nunney et al. , it was shown that the recombinant genotypes formed a well-defined group , demonstrating that intersubspecific homologous recombination was not randomly distributed across the X. fastidiosa subsp. multiplex isolates. This work was based on a survey of 143X. fastidiosa subsp. multiplex isolates using just 8 loci. There were 33 isolates that showed some evidence of IHR in at least 1 locus: all but 2 showed statistically significant evidence in at least 2 loci, while the remaining 110 showed no such evidence . The generality of this discrete group of recombinant forms was supported by our analysis presented here of the sequence data from 9 more loci sequenced by Parker et al. . These loci divided isolates into 2 groups that appeared to correspond to the recombinant and non-IHR groups, respectively ,raspberry grow in pots even though Parker et al. found no evidence of IHR. Upon reanalysis, we found statistically significant IHR in 6 of the 9 loci in the clade A data but no evidence of IHR in the clade B data. Clade A included 6 isolates that we had typed in the present study, and each of these showed evidence of IHR in 4 or 5 of the additional 9 loci. Thus, in two independent samplings that together examined 17 loci, there was clear evidence of substantial genomewide IHR in the recombinant group isolates, amounting to 50% of the genes showing IHR across the MLST locis plus the pilU locus . The average was higher when based on the loci sequenced by Parker et al. ; however, this was probably biased upwards by the manner in which the loci were chosen . None of the IHR events in 6 of the 9 loci identified using the targeted introgression test, or in the case of complete introgression, a chi-square test, were detected by Parker et al. using PHI and the 9 tests implemented in RDP . This failure of the standard tests of recombination to detect IHR was previously noted by Nunney et al. , motivating the development of their introgression test. We examined the hypothesis that the recombinant group STs were derived from a single IHR event involving a X. fastidiosa subsp. multiplex recipient and an X. fastidiosa subsp. fastidiosa donor. The distribution of allelic differences among the recombinant STs was consistent with them all being derived from a single initial event, but a small number of other intersubspecific and intrasubspecific recombination events would also be needed . More importantly, the genotypes seen in the recombinant group can be accounted for entirely, or very nearly so, based on a single X. fastidiosa subsp. fastidiosa donor genotype. For example, the substantial variation in cysGcan all be accounted for by an ancestral introgression of X. fastidiosa subsp. fastidiosa allele 12 followed by subsequent intrasubspecific recombination of X. fastidiosa subsp. multiplex sequence to form the other two alleles . In contrast, variation at pilU could be accounted for by a second donor contributing the X. fastidiosa subsp. fastidiosa pilU9 allele, but it could also have arisen by a single mutation in pilU1 unique to the recombinant group. A possible single X. fastidiosa subsp. multiplex recipient genotype was also identified . This genotype is consistent with a known ST: setting cysG to allele 3 makes the recipient identical to ST45, which was sampled from the states of California, Kentucky, and Texas .

Elsewhere, we consider a slightly different hypothesis regarding the origin of the recombinant group in which the donor and recipient subspecies are reversed—i.e., that it was derived from a single IHR event, but involving an X. fastidiosa subsp. multiplex donor and an X. fastidiosa subsp. fastidiosa recipient; however, apart from the role reversal, the conclusions are unaltered . The ancestral reconstruction allows us to consider the second question posed earlier: is the donor consistent with the X. fastidiosa subsp. fastidiosa genotypes found in the United States? The answer is very clearly “no.” The original donor carried cysG12 and holC19 . These alleles are both found in isolates from Central America, but no X. fastidiosa subsp. fastidiosa isolate found in the United States comes close to matching this criterion: the most similar U.S. ST has a 12-bp mismatch. There has been extensive sampling of X. fastidiosa subsp. fastidiosa within the United States, based on 85 isolates sampled across the United States from 15 different host plants . There is very little variation within X. fastidiosa subsp. fastidiosa isolates from the United States, consistent with the hypothesis that all X. fastidiosa subsp. fastidiosa isolates currently found in the United States are derived from a single strain introduced from Central America . Based on these data, we conclude that the X. fastidiosa subsp. fastidiosa donor was introduced into the United States from Central America and recombined with a native X. fastidiosa subsp. multiplex genotype similar to ST45; however, this donor lineage of X. fastidiosa subsp. fastidiosa was ultimately unsuccessful and died out. We can never conclusively prove the absence of this genotype from North America. However, X. fastidiosa has been extensively sampled from many plant species throughout the United States, and no isolates of X. fastidiosa subsp. fastidiosa have been found that carry alleles similar to the inferred donor alleles cysG12 and holC19 ; indeed all X. fastidiosa subsp. fastidiosa isolates so far found in the United States are consistent with the introduction into the United States of just a single genotype . The transient presence of the donor genotype is consistent with a single large-scale introgression event founding the recombinant group. This raises the possibility that conjugation might have been involved; however, if this was the case, the genomic DNA was broken into pieces prior to homologous recombination, since the data show short regions of recombination. The data from the MLST loci plus pilU show 7 significant recombination events , and 3 of them included at least one recombination break point.

The values for glutamate reported for pomegranate herein are greater than values reported for grape juice , indicating increased importance for consumers regarding marketing fraud in the pomegranate juice industry because often grape juice is used as the primary adulterating agent in pomegranate juices in the USA, along with apple and pear juice. Glutamine is the amino acid of the highest concentration in human blood , which may play into the folklore regarding pomegranate juice as a “blood tonic” . Ethanol is of importance in the food and beverage industries because it is an indicator of anaerobic respiration and metabolism in post harvest fruit products. Along with other alcohols, ethanol can contribute to off flavors or even enhance the flavor of fruit if concentrations are low . The freshly expressed juice had ethanol values that were far less than the levels prohibited by countries for religious and food safety reasons, which is typically required to be less than 0.5% ethanol in the USA for the beverage to be considered non-alcoholic; religious restrictions are typically stricter.This work demonstrates the high level of phenotypic diversity of pomegranate juices that exists in approximately 5% of the USDA available pomegranate germplasm collection. This study is the first of its kind in utilizing 1H NMR coupled with conventional post harvest juice quality methodologies to assess differences among pomegranate cultivars for metabolic, physicochemical and nutritional traits. The results indicated a great complexity of juice quality and nutritional differences among the cultivars analyzed, with many fitting the profile of Wonderful, but others differing greatly from this standard. As a replacement,blueberry production alternate or substitute candidate for ‘Wonderful’ in juice markets, ‘Al Sirin Nar,’ ‘Blaze,’ ‘Desertnyi,’ ‘Parfianka,’ ‘Phoenicia,’ ‘Purple Heart,’ and ‘Sakerdze,’ all meet a host of juice quality parameters and mostly fit the nutrition composition of ‘Wonderful.’

These varieties should be considered for further investigation via cultivar trials to determine phenotypical traits important to growers and taste panels to determine preferences of consumers. There were striking differences between the commercial juice and fresh-squeezed juices as well as significant differences between juice extraction methods for many parameters, including amino acid content, phenolic content and antioxidant activity. Potassium concentration varied greatly among cultivars, which can affect the flavor and nutritional composition of these fruits and their juices. This work also presents further evidence that pomegranate is not only a potentially healthy fruit in terms of phenolics and antioxidant activity, but also for nutrition as it relates to amino acids and mineral nutrition . It is important to note that these juice quality traits and nutritional factors can be significantly different among cultivars. Whether the cultivars with unique quality profiles appeal to consumers will need to be investigated in future research. This study is the first of its kind comparing fresh-pressed juice quality of the industry standard, Wonderful, with 13 other NCGR pomegranate cultivars using 1H NMR coupled with other physicochemical analytical techniques. Pomegranate is a deciduous tree crop that has been domesticated for thousands of years for its fruit, flowers, bark, and leaves , all of which have been believed to possess medicinal properties . Despite its long history of cultivation, limited horticultural information is available for growers, breeders, and the food and beverage industries about when fruit of a given cultivar is ready for harvest, processing and consumption . In the United States, Wonderful, the industry standard, is a tart, acidic, moderately hard-seeded fruit that has been reported to have astringent and bitter juice compared to other cultivars previously analyzed from the collection at the United States Department of Agriculture – Agricultural Research Service National Clonal Germplasm Repository , Davis, CA .

Despite these negative fruit quality traits, pomegranate cultivation in the United States remains predominantly a monoculture of ‘Wonderful.’ It is believed that cultivars in the national germplasm with desirable traits, such as soft seededness and low acidity may be candidates for commercial production. Studies have demonstrated a large variation in mature fruit size within commercial orchards of ‘Wonderful,’ which poses a problem for fresh market growers and packers. Wetzstein et al. reported a greater than five-fold range in mature fruit volume and weight in commercial ‘Wonderful’ pomegranate groves. Factors that influence fruit size and yield include aril number , cultivar , cultural practices , and plant maturity . Finding cultivars with better uniformity than Wonderful would be beneficial to the industry In addition to variable fruit size, dates of fruit maturity can play a major role in fruit quality. Late season harvests run the risk of fall rains, which have been associated with greater numbers of split fruit . Typical commercial harvest windows for ‘Wonderful’ range from late September to early November, but fruit in the Central Valley of California, USA, where the most pomegranate cultivation occurs, are typically ready to harvest starting in late October. Usually by November, effects of weather, especially rains, and pests will begin to damage the fruit. Therefore, harvest date can determine whether a cultivar is a good candidate for commercial production. In addition to the fresh fruit market, pomegranates are also utilized for juice. The beverage and wine industries utilize different fruit juices that have sufficient quantities of organic acids, carbohydrates , and phenolic compounds. Concentrations of total soluble solids , often expressed in ºBrix, for commercial pomegranates range from 12% to 16% at maturity. It is recommended that ‘Wonderful’ have at least 15% TSS at harvest , but above 17% is preferable . Hasnaoui et al. reported that citric acid is the determinant of sour flavor in pomegranate juice, despite sugar concentration. Sweet pomegranates typically have been reported to have citric acid concentrations less than 0.50% . Standards for fruit maturity of sweet cultivars are being investigated because growers often pick early-season cultivars too early in order to increase profits .

There are no known imposed regulations on growers in any country, meaning they can harvest early before fruit maturity without short-term consequence. The effect of picking early on consumer perception and acceptance of pomegranate fresh fruit has been shown to be associated with astringency and a low flavor preference score . Standards have been proposed for titratable acidity and total soluble solids for ‘Wonderful’ pomegranate . Generally, citric acid is the most abundant organic acid in pomegranate juice, so TA is generally expressed in citric acid equivalents. ‘Wonderful’ pomegranate fruit is considered mature when juice is lower than 1.85% TA , so fruit are picked when fruit measure below that threshold. Maturity index is a standardized measure of maturity in many fruit crops. For pomegranate, a commonly used MI is the ratio of °Brix to TA, also known as the sugar to acid ratio. This ratio is often used to determine fruit maturity, but it has been found to not be usable for sweet cultivars. Instead, fresh aril weight is used at the indicator of fruit maturity and consumer acceptance in the sweet cultivars . The optimum MI for ‘Wonderful’ has been calculated to be greater than 8.1, at which point the fruit is ready for harvest. Other cultivars may have significantly different quantities of organic sugars and acids, so MI is logically different for different types of pomegranates . Fruit quality is not only related to sugar content, titratable acidity and spoilage, but also to phenolic compounds that contribute to the fruit’s flavors, antioxidant activity, and color . Cultivar is more influential in determining fruit juice composition than site of cultivation, year of harvest, or length of storage ,blueberry in container so it is important to study differences in traits among cultivars to identify superior cultivars in germplasm resources and make them available to growers and consumers. Determining levels of phenolics and the antioxidant activity of a cultivar’s juice is important to the beverage industry and consumers, because advertisements promote high antioxidant activity as the main selling point of juice products. If any cultivar were to demonstrate similar antioxidant activity to Wonderful, it would possibly be competitive in the pomegranate market were it to meet other consumer preferences. Having lower antioxidant activity than ‘Wonderful’ could make for an undesirable candidate for commercial production, although the public is unlikely to be able to detect differences in antioxidant activities. The objectives of this research were: 1) to evaluate fruit and juice quality traits of NCGR germplasm by comparing commercial quality parameters to the industry standard, Wonderful; and 2) to determine potential harvest windows of ten preselected pomegranate cultivars, based on seasonal trends of potential maturity indices with harvest dates. Fruit were harvested from the USDA-ARS National Clonal Germplasm Repository for Tree Fruit and Nut Crops and Grapes in Davis, CA, USA for two seasons. The trees were all over nine-years-old and in full maturity. The pomegranate cultivars analyzed included: Ambrosia, Desertnyi, Eversweet, Golden Globe, Green Globe, Haku Botan, Loffani, Parfianka, Phoenicia, and Wonderful . The cultivars in this study that have been described as soft-seeded included Desertnyi, Eversweet and Parfianka. Low acid cultivars included Ambrosia, Desertnyi, Eversweet, Golden Globe, Green Globe, and Loffani. Wonderful fresh fruit was included as a control and as the standard to compare the other cultivars in this study. All cultivars are of American origin except for Haku Botan and Desertnyi and Parfianka . Up to twelve fruit of each cultivar were hand-harvested in mid-September, midOctober, and mid-November in 2014. This was repeated in 2015.

Fruit were ground shipped, and then stored at 6 °C and 98% relative humidity for 3-4 weeks until processing. Fresh market quality fruit, as defined by being well-filled, mature, and unblemished, were chosen for juice analysis from each of the 10 cultivars. Three cultivars, Golden Globe, Green Globe and Parfianka, had fruit damage in November, so less fruit were available for these cultivars during November harvest dates. ‘Golden Globe’ and ‘Parfianka’ had no fresh market quality fruit available in November 2015 and ‘Green Globe’ had no fresh market fruit available for 2014 and 2015. At this last season harvest, the trees of these cultivars had fruit that were either cracked or infested with insect pests, especially leaf footed bug.Up to 12 fruit per cultivar were weighed using a tared digital scale. The equatorial diameter and stem- to blossom-end length of each fruit and calyx length and diameter were measured with a digital caliper. After weighing and measuring, fruit were opened by scoring the peels of the fruit longitudinally with a scalpel. Arils were then manually extracted from a subsample of five fruit. Weight of 100 arils and total aril weight was determined with a tared digital scale. Only intact, non-damaged arils were weighed for the weight of 100 arils to reduce error. Edible fruit fraction was determined by dividing weight of all arils by the total weight of fruit and converting the result to percent. For juice processing, fruits were halved and 100 undamaged arils were manually removed and placed in a polyethylene bag. These arils were pressed manually with approximately 480 Newtons of force to express the juice directly into a 15 mL test tube. Raw juice samples were transferred to a centrifuge tube via pipette and were centrifuged at 1000 g for 5 min using a Becton Dickinson DYNAC Centrifuge . Aliquots of the supernatants were used for all chemical analyses. 5.2.3 °Brix Degrees Brix, also known as total soluble solids, were quantified with a Vee Gee Scientific PDX-1 Digital Refractometer , utilizing 0.5 mL sample of expressed, centrifuged juice. Individual juice samples, each one representing a single whole fruit, were transferred to the refractometer sample reservoir with a hand pipettor. The sample reservoir was cleaned by spraying with deionized water with a wash bottle and wiped dry with Kimwipes before each subsequent sample. Results were reported in °Brix and represented the relative percent sugar content of the centrifuged pomegranate juices. One measurement was taken for each juice sample. Juice TA was measured with a Hanna HI 84532 fruit juice TA mini-titrator . Samples were prepared by mixing individual centrifuged juice aliquots with 45 mL of deionized water. Each sample represented the juice of one whole fruit and 3 to 5 samples were analyzed for TA per cultivar. Because the predominant organic acid in most pomegranates is citric acid, results are expressed in citric acid equivalents, which represent percentage citric acid in juice solution.

Some of these early cultivars include Foothill Early, Granada, and Early Wonderful. These cultivars reach maturity long before fall rains, which can be devastating to pomegranate crop yields due to fruit splitting . However, the early cultivars listed have been described as inferior compared to the fruit and juice quality of Wonderful, specifically having lower internal color quality . However, it is important to note that different cultures prefer differing flavor profiles for pomegranates. Pomegranate is utilized for many products, ranging from fresh fruit to various types of value-added products. Value added products can be from the seeds, which contain fleshy, juice-containing arils, which are the outer integument of the seed, or from the fruit peel. Maestre et al. reported that uses for pomegranate fruit include juice, jams, preserves, jellies, refrigerated arils, frozen arils, liquors, syrups and soft drinks. These various uses for pomegranate can be dependent on the cultivar that is being utilized. For example, jellies produced with ‘Borde’ experience fewer losses than ‘Mollar’ pomegranate when stored at higher temperatures. Yoshimura et al. reported that pomegranate is a successful skin whitening agent in mammals, which could be utilized as a plant-source material in the cosmetic or pharmaceutical industries. Per Stover and Mercure , as a fresh fruit, consumer demand for pomegranate may increase with the introduction of cultivars with soft-seeded arils and uniquely colored and sweeter arils. Day and Wilkins also believe that both commercial growers and consumers may express interest in cultivars other than Wonderful if they were to have “unusual” or “unique” traits. As a whole fresh market fruit, pomegranates should be medium to large at harvest and their exterior color should be pink or red , but it should be noted that market preferences have changed in the past, as with Malus and Prunus crop species. In the United States,big plastic pots the most important product produced from pomegranates is juice . The beverage industry has a market value of $187.4 billion, with total fruit juice sales equal to $50 billion .

Previous market research found that pomegranate juice was ranked first in the super premium juice category in grocery store sales and that PomWonderfulTM juice was the leader in pomegranate juice sales, with $36.5 million in sales during the time of the study . Market research also suggested that for juice blends, pomegranate-blueberry blends had the best sales. In addition to consumer acceptance of the fruit, to be a successful commercial cultivar, a cultivar must be easily propagated in the nursery and readily established in a commercial orchard; otherwise, it would be difficult to implement commercial production for that genotype. Compared to all other methods of propagation, utilizing dormant hardwood cuttings is the most inexpensive and convenient method to propagate pomegranate. As a result, it has become the standard for the industry for the production of clonal bare root or container-propagated trees . Perhaps the most important reason pomegranate trees are propagated via stem cuttings is that the seeds of pomegranate are the product of sexual reproduction and if propagated by seed, the progeny are not true to type . As a result, seeds are not utilized in the nursery industry for production of commercial pomegranate trees. Historically, pomegranate was propagated commercially by dormant hardwood cuttings in California long before the first scientific reports on pomegranate were published by the University of California . Although many studies have been conducted on propagation of pomegranate via hardwood cuttings , none of these studies is known to have included any of the cultivars from the USDA-ARS NCGR. In addition, there are no data in the literature regarding dormant cutting propagation of the most important commercial cultivar in the United States, Wonderful. Another omission from the literature is the lack of information on vegetative growth parameters of the cultivars in the collection. These parameters may elucidate important horticultural traits, such as plant vigor, branching habit, and the proportion of buds that produce vegetative shoots, including the proportion of buds below the soil-line that produce both vegetative shoots and roots. Success rate and vegetative growth parameters are important characteristics for professionals in the nursery industry and commercial growers.

Research on pomegranate propagation typically utilizes hardwood cuttings with stem lengths of 20 cm or more . Whereas it may be optimal to use 20 cm of stem length for hardwood cuttings, the most limiting factor to crop expansion of pomegranate in California is the lack of nursery stock . Alikhani et al. reported that cuttings with three buds or more givethe best results for clonal hardwood propagation; Heidari et al. found evidence that single node cuttings worked better than those having two or four buds. This finding was ascribed to the endogenous hormonal balance in the propagule. Reports on the use of the auxin indole-butyric acid to increase rooting are contradictory. Polat and Caliskan reported that 1 g×L -1 of IBA was sufficient to root pomegranate cuttings, but stated that this concentration was not optimal. Saroj et al. reported that 2.5 g×L -1 IBA was an optimum rate of auxin for high rooting success in pomegranate. However, much higher doses of IBA have been demonstrated to increase pomegranate propagation success, with a treatment of 12 g×L -1 resulting in a lower rooting success rate . To be considered for commercial pomegranate production, a pomegranate cultivar would need to produce high yields and be as precocious as Wonderful. Wonderful typically starts producing an economic crop in year three after planting , so any variety that produces fruit in year three or earlier could possibly be a competitive cultivar relative to the industry standard. The cultivar would also need and acceptable establishment rate, i.e. to quickly reach a tree size that partially fills the 4 m x 5.8 m spacing typical of pomegranate groves within the same time or earlier than Wonderful, excluding cultivars with dwarf and semi-dwarf traits that could be farmed in high density plantings. Another trait of benefit to a commercial cultivar is having an earlier harvest date than Wonderful, which is typically harvested for the fresh market in late September, October, or early November, depending on climate and growing conditions. For juice production, ‘Wonderful’ trees are stripped at the end of the commercial fresh fruit production season, which can be as late as November. The cultivar must also maintain good fruit quality over this period to be competitive with Wonderful. Having an earlier harvest date is ideal because it will increase the chances of avoiding fall rains, which are known to cause devastating losses due to fruit splitting.

Orchard establishment rates for pomegranate cultivars under drip irrigation are unknown. Day et al. found that ‘Wonderful’ trees will produce a commercial crop by the third year in the field under furrow irrigation in the San Joaquin Valley in Central California, but plant establishment data were not collected. No data are available for tree height, canopy width within a row or across the row or trunk diameter, the traits which were measured by Webster et al. to determine establishment rates in apple cultivars. These traits are important in all tree crops. Comparisons of tree establishment rates across different climates are also not found in the literature. For a commercial fruit tree grove to be sustainable and profitable, the trees need to be precocious, i.e., to produce as many flowers and fruit of marketable quality, as early as possible . Pomegranate is monecious and produces two flower types, functionally “male” and functionally “female.” Male flowers only produce pollen and have an underdeveloped gynoecium, whereas the female flowers are perfect and are able to produce fruit . To ensure that precocity is not a false measure of the ability of a tree to set fruit, the number of male and female flowers during peak bloom must be counted on each tree . Pomegranate is a crop that can survive in relatively harsh conditions and it has been classified as a drought- and heat-tolerant crop . Glozer and Ferguson reported that pomegranate trees tolerate temperatures as low as -11° C. The geographical suitability of cultivars for commercial production at a given site can also be assessed by quantifying physiological parameters influencing tree health, growth and productivity. These parameters include photo inhibition, stomatal conductance, transpiration and photosynthetic rate. To determine satisfactory geographic suitability and success,growing berries in containers a cultivar would have to perform physiologically at an equal or greater level than Wonderful. Hepaksoy et al. found evidence that leaf transpiration and water-use efficiency were correlated with fruit splitting in pomegranate. Additionally, being able to forecast establishment rates and precocity of the cultivars is crucial to assess whether a cultivar is a candidate as a commercial cultivar and it allows growers to anticipate establishment of their trees. In addition, information on tree size and growth habit is important to evaluate a cultivar’s suitability for trellising or high density planting.

The pomegranate fruit is often classified as a berry , or it is considered berry-like , having a “leathery” exocarp that ranges in color from light yellow to black , although in the market, pomegranate fruit are typically a bright red color. Peel texture can range from soft to tough. Mature fruit are spherical with a calyx at the blossom end , which closely resembles a crown. After fertilization, sepal color typically changes from orangered to green, but can vary among cultivars. As the fruitlet develops, the fruit’s color typically changes from green to the color of that genotype at maturity . Pomegranate seeds are arranged in locules that are separated by white to yellowish septa, inedible mesocarp tissue , which are not evenly distributed in the fruit . The septa are inedible because they are comprised of insoluble fiber . Locules at the stem end are generally more numerous than those at the calyx end . The epidermis of each seed is fleshy and is botanically referred to as a sarcotesta or aril. The flavor and color of the sarcotesta is cultivar-dependent, ranging from sour to sweet and white to deep purple, respectively. The hardness of the seeds, amount of juice contained in the arils, and aril size are also cultivar dependent . A large pomegranate fruit can contain up to 1200 to 1300 seeds . Pomegranate is a non-climacteric fruit, meaning that it matures and does not ripen . Differences in seed number may affect a cultivar’s ability to be successful in the prepackaged aril market, which has developed considerably over the last five years. Studies have demonstrated a large variation in mature fruit size within commercial orchards of ‘Wonderful,’ which poses a problem for fresh market growers and packers. Wetzstein et al. reported a greater than five-fold range in mature fruit volume and mass in commercial ‘Wonderful’ pomegranate groves. At maturity, pomegranate fruit are typically 3.5 to 6.5 cm in diameter with a mass of 30 g to several hundred grams , although commercially-valuable pomegranates typically weigh more than 400 g and the largest-sized ‘Wonderful’ fruit from mature trees can have a diameter of over 100 mm . Factors that influence fruit size and yield include aril number , cultivar , cultural practices , and tree age . Cultivars that show greater uniformity in fruit size would be beneficial to the industry due to the fresh market sizing problems reported for Wonderful by Wetzstein et al. . Typical commercial harvest windows for ‘Wonderful’ range from late September to early November, but fruit in the San Joaquin Valley of California, USA, where the most Punica cultivation occurs, are typically ready to harvest starting in late October. Usually by November, the effects of weather and pests will begin to take a toll on mature fruit. Harvest date can determine whether a cultivar is a candidate as a commercial cultivar. Cultivars with an earlier fruit maturity date have a better chance of competing with Wonderful in the market. Later cultivars run the risk of fall rains, which have been associated with greater numbers of split fruit and a loss in fresh market sales . The beverage and wine industries utilize fruit juices that have sufficient quantities of organic acids, carbohydrates , and phenolic compounds. For commercial pomegranate fruit, concentrations of total soluble solids range from 12% to 16% at maturity, but ‘Wonderful’ should have at least 15% TSS at harvest . Hasnaoui et al. reported that citric acid is the determinant of tart flavor in pomegranate juice, and this is said to be independent of the sugar concentration.

In retrospective observational studies on hot topics , thousands of independent analytic teams may approach a similar question—all with different plans. This field-wise multiple hypothesis testing has been shown experimentally to generate both positive and negative statistically significant associations, simply by analytic choices. Even when data sets are standardized, multiple analytic approaches may yield a range of answers to a single question. Finally, randomized controlled trials, the gold standard of causal inference, have historically been immune to questions of multiple hypothesis testing, although this is increasingly being called into question with the emergence of redundant, duplicative, and large trial portfolios. In this commentary, we explore the role of multiplicity in biomedical research—a growing challenge to the interpretation of individual study results.Consider the case of nutritional headlines that dominate the front pages of prominent news outlets such as The New York Times’ health section. One week, researchers may suggest that blueberries or dark chocolate have been shown to reduce your risk of cancer, but the next week, these same exposures may be found to increase your risk. What explains this phenomenon? To begin, for popular topics, it is likely that thousands of individual analyses of a data set will be performed over a relatively short period of time, each controlling for some co-variates—those that researchers believe are plausibly related to an outcome—in an effort to uncover a meaningful correlation. Each of these models will create a new relationship between the investigated variables,planting blueberries in containers as Patel et al. demonstrated by simulating the research community of nutritional epidemiology.The authors used the National Health and Nutrition Examination Survey and probed a series of nutritional exposures, asking if they increased or decreased overall mortality.

For each exposure, the researchers used baseline variables and the 13 most common co-variates adjusted for in the sampled literature [e.g. ‘, hypertension, diabetes, cholesterol, alcohol consumption, education, income, family history of heart disease, heart disease, any cancer, physical activity and race/ethnicity’].Then, the entire research community was simulated. Over 8000 different models were generated for each exposure-mortality association by combining all conceivable combinations of the 13 modifiable demographic factors. They found that the majority of studies showed no significant association. But, what was noteworthy is that for 31% of the variables, there were both statistically significant positive and negative outcomesforthe same hypothesis, indicating that the hazard ratio could be HR>1 or HR ≤1 with a significant p-value depending on the level of co-variant adjustment.Researchers called this the vibration of effects. Schoenfeld and Ioannidis extended this result in an analysis measuring 50 common ingredients randomly selected from a cookbookThen, the researchers conducted a literature search on articles that measured each ingredient’s link to cancer. Most of the ingredients had articles measuring their relation to cancer risk. Despite many weak and non-significant relationships, most ingredients had studies with outcomes contrary to each other, showing either an increased or decreased risk of developing cancer.Zaorsky and colleagues applied the vibration of effect approach to practical questions in cancer medicine. They found that by varying other analytic choices—leftt truncation adjustment, propensity score matching, landmark analysis, and different combinations of co-variates—they were able to generate any desired result.These are all instances of a common theme when dealing with multiplicity:studies measuring the same research question yielding opposite findings.Work by Silberzahn et al. demonstrated a similar situation of multiplicity when they categorized the skin tone of different soccer players and included it in a data set with reports of penalties to 29 research teams.The following question was posed to the teams: Were soccer referees more likely to issue a dark-skinned player a red card, signalling a penalty, than they were a light-skinned player? Twenty of the teams reported significant evidence of bias, whereas nine teams discovered a non-significant relationship, with one team finding a trend in the opposite direction .These different analytical strategies provide researchers with a great deal of latitude, allowing for the potential of a myriad of distinct outcomes.

However, the issue intensifies when one considers that significant findings are more likely to be published,resulting in a dichotomized literature devoid of a middle ground of null results.Randomized controlled trials have historically been thought to be immune to multiplicity as rarely are hundreds or thousands of studies run on a single clinical question, but this fact may be shifting. There are now four critical considerations to examine regarding the relationship between multiplicity and oncology: The United States Food and Drug Administration will approve drugs based on a single positive trial, even if the primary outcome is a surrogate endpoint, and even if other trials are negative; Drug approvals often generate enormous financial windfalls; Pharmaceutical companies tend to conduct large, duplicative trials with little rationale; and The probability value is arbitrary. First, consider neratinib, the only drug ever approved in the adjuvant setting prior to the metastatic. Approval was based on a single placebo-controlled Phase III trial measuring invasive disease-free survival as a primary composite endpoint. The magnitude of benefit was small, with 5.1% and 1.3% improvements in 5-year iDFS rates in patients with hormone receptor-positive breast cancer who began therapy with trastuzumab less than one 1-year ago or more than 1-year ago, respectively.Additionally, there are occasions when a medicine, such as adjuvant sunitinib in renal cancer, is approved despite the existence of a single negative trial and a single positive trial, thus ignoring the study portfolio.The second and third points may be coupled; approvals of cancer drugs are anticipated to yield billion-dollar profits,which encourages the conduction of duplicative studies in many tumour types, despite weak evidence. Consider the genesis of the EVOLVE-1 study, which compared everolimus with placebo in patients with advanced hepatocellular carcinoma following sorafenib failure.The maximum tolerated dose and the disease control rate were tested in early Phase I and Phase I/II studies, respectively, which laid a relatively weak foundation for expediting the EVOLVE-1 trial, rather than conducting a more conservative Phase II trial.Despite the negative outcome of the trial, one reason for taking such an enormous financial risk is because, despite the high upfrontcosts of conducting these large trials, a far larger financial incentive remains, namely drug approval if the trial is successful. However, the case of everolimus is just one example in the broader landscape. Consider that approximately 700 clinical studies were conducted in a single year for pembrolizumab, and that when more and more tumour types are evaluated, the risk/benefit profile of the drug deteriorates, as was shown during the development of sunitinib monotherapy.

Even with negative trials and worsening aggregate risk/benefit profiles, a drug approval’s billiondollar return greatly outweighs the initial expense of conducting million-dollar studies. Fourth, consider the most widely used statistical instrument, the p-value. If researchers run 100 trials to determine the effect of an inert drug on survival and assume a one-tail p-value of p<.05, a distribution of five trials on average will have a false-positive result. This is precisely the definition of the p-value—the probability of seeing this result or a more extreme result if the null hypothesis is assumed. This value is an arbitrary line in the sand, although arguably a necessary one that is admittedly susceptible for misinterpretation. These concepts are illustrated in Figure 1. The left side represents a single, large pan-tumour RCT for a novel cancertherapy that was negative. In an analysis of prespecified subgroups, there were some tumours with positive results. Is it likely that a positive subgroup finding may result in FDA approval for a particular indication? The answer to that question is no. The FDA would perform adjustments for multiplicity, and in the absence of that, the findings are, at most, hypothesis generating. Now consider that instead of conducting a single RCT, several, separate RCTs were conducted in numerous indications, approximating the mentioned subgroups . Some of these studies may be positive in the same subgroups, perhaps even by chance alone, but the overall portfolio may be the same. The difference is that now these findings will result in drug approval. The reality is that, although each of these studies in orange were performed independently, they represent a trial portfolio. Both situations are philosophically equivalent as they test a single hypothesis , and on the left, the bias is clear, but on the right, positive trials appear distinct, and the portfolio is never assessed in aggregate. Because the trial portfolio is not considered in the present oncologic regulatory environment,container growing raspberries multiplicity must be accounted for. One example that illustrates how statisticians and cancer doctors may view a question different is also captured in Figure 1. Some statistical experts have suggested meta-analyses be used for the figure on the Right , rather than multiplicity testing.However, these approaches fall short of answering the pertinent clinical question because the drug is considered in aggregate in multiple tumour types repeatedly, rather than identifying whether a drug works in one tumour or the other.A meta-analysis or pooled estimate focuses on determining whether a drug is effective in all tumour types, rather than the cancer doctor’s question of which tumour the treatment is effective in–a distinct difference. Because this technique does not exclude the possibility of a single positive trial leading to drug approval, multiplicity adjustment is needed to sate the doctor’s and patient’s question, and not a pooled estimate. This scenario also illustrates the importance of content specific experts guiding the framing of the statistical question. Combining all the key points above, businesses are now incentivized to test drugs with marginal benefits in as many indications possible. Consider that when a pharmaceutical firm develops a drug, all translational research costs are expended, leaving just the expense of additional trials. When companies consider this sunk cost, which requires no further investment in research and development but just the expense of the additional trial at the end, it incentivizes the company to test it in every single tumour type as many times as possible.

When combined with the low bar for drug approval, the considerable post-approval revenue, and a generation’s threshold of significance, pharmaceutical companies stand to profit enormously. Because these therapies are likely more effective than an inert substance, both true and false positives are obtained, which when averaged, results in a highly profitable approach. We see this with immune checkpoint inhibitor trials. There are now thousands of studies of largely similar molecules with massive duplication in the same or similar cancer settings,often yielding conflicting results.Hyperkalaemia is a common electrolyte abnormality which occurs most frequently in patients with decreased kidney function, with the highest prevalence observed in patients with end-stage renal disease . Severe hyperkalaemia is a medical emergency, as high serum potassium levels or its abrupt excursions may be a cause of sudden cardiac death. Besides a decrease in potassium excretion by the kidneys or ESRD and often made worse by medications such as inhibitors of the renin-angiotensin-aldosterone system, hyperkalaemia may also be exacerbated by an abnormal redistribution between the intracellular and extracellular space and by increased dietary potassium intake. In the early stages of CKD, even very high potassium intake is not sufficient to cause hyperkalaemia and external potassium balance is generally neutral, unless therapies reducing net intracellular shift or renal excretion capacity are administered. This is an important consideration since high potassium diets are useful in patients with CKD because they have been associated with favourable cardiovascular and renal outcomes. However, in advanced stages of CKD and in ESRD a positive external potassium balance, namely a dietary input that surpasses output, has a crucial role in engendering hyperkalaemia, and its prevention requires a balanced management of dietary potassium load. The present paper aims to review dietary potassium handling and gives information about practical approach to limit potassium load in CKD patients at risk of hyperkalaemia. Whereas the US Food and Nutrition Board of the Institute of Medicine has set an adequate intake for potassium relatively high in healthy adults, i.e. 4.7 g per day, the World Health Organization recommends a dietary potassium intake of 3.9 g per day or at least 90 mmol/day , to reduce blood pressure and the risk of cardiovascular damage, stroke and coronary heart disease. In patients with non-dialysis dependent CKD stages 1–5, the National Kidney Foundation suggests an unrestricted potassium intake unless the serum potassium level is elevated. In hemodialysis patients, potassium intake should be up to 2.7–3.1 g/day and in peritoneal dialysis patients close to 3–4 g/day; in both cases, adjustments based on serum potassium levels are crucial.

Similarly, bile acid molecules such as muricholic acid and taurocholic acid were more abundant in IHH-exposed versus control animals. Bile acids are crucial for not only facilitating transport of dietary fats and cholesterol in the host but also regulating host energy expenditure, glucose homeostasis and anti-inflammatory immune responses . Many metabolic and cardiovascular conditions have been associated with aberrant bile acid profiles, suggesting that prolonged perturbations in these key molecules could contribute to downstream adverse cardiovascular consequences of OSA as well. In summary, our work provides reproducible candidate biomarkers of IHH-exposure in animal models and will be most applicable to designing diagnostic and treatment modalities. Furthermore, by identifying consistent alterations across different model systems, we outline a general pipeline to select for biomarkers and therapeutic targets applicable to other intervention studies as well. We have made these information rich datasets publicly available to promote collaborative progress in this area of research.Intermittent hypoxia and hypercapnia was maintained in a computer-controlled atmosphere chamber system as previously described . IHH exposure was introduced to the mice in short periods of synchronized reduction of O2 and increasing of CO2 separated by alternating periods of normoxia and normocapnia with 1– 2 min ramp intervals for 10 hours per day during the light cycle. This treatment protocol mimics the severe clinical condition observed in obstructive sleep apnea patients. Mice on the same HFD but in room air were used as controls. Fecal samples were collected at baseline and twice each week for 6 weeks or 10 weeks .We performed 16S sequencing on fecal samples from Ldlr-/- and ApoE-/- mice for all the time points. DNA extraction and 16S rRNA amplicon sequencing were done using Earth Microbiome Project standard protocols .

In brief, DNA was extracted using the MO BIO PowerSoil DNA extraction kit . Amplicon PCR was performed on the V4 region of the 16S rRNA gene using the primer pair 515f to 806r with Golay error-correcting barcodes on the reverse primer. Amplicons were barcoded and pooled in equal concentrations for sequencing. The amplicon pool was purified with the MO BIO UltraClean PCR cleanup kit and sequenced on the Illumina HiSeq 2500 sequencing platform. Sequence data were demultiplexed and minimally quality filtered using the QIIME 1.9.1 script split_libraries_fastq.py,nursery pots with a Phred quality threshold of 3 and default parameters to generate perstudy FASTA sequence files. The raw sequence data were processed using the Deblur workflow with default parameters in Qiita . This generated a sub-operational taxonomic unit abundance per sample . Taxonomies for sOTUs were assigned using the sklearn-based taxonomy classifier trained on the Greengenes 13_8 99% OTUs in QIIME 2. The sOTU table was rarefied to a depth of 2,000 sequences/sample to control for sequencing effort. A phylogeny was inferred using SATé-enabled phylogenetic placement, which was used to insert 16S Deblur sOTUs into Greengenes 13_8 at a 99% phylogeny.We acquired LC-MS/MS data on fecal samples from Ldlr-/- and ApoE-/- mice using identical protocol. Details of data acquisition parameters are specified in . Briefly, fecal pellets were extracted in 500 µl of 50:50 methanol-H2O solvent, followed by centrifugation to separate insoluble material. The extracts were dried completely by centrifugal evaporation and resuspended in 150 µl of methanol-H2O . After resuspension, the samples were analysed on a Vanquish ultrahigh-performance liquid chromatography system coupled to a Q Exactive orbital ion trap . A C18 core shell column with a flow rate of 0.5 ml/min was used for chromatographic separation . The raw data sets were converted to m/z extensible markup language in centroid mode using MSConvert . All mzXML files were cropped with an m/z range of 75.00 to 1,000.00 Da. Feature extraction was performed in MZmine2  with a signal intensity threshold of 2.0e5 and minimum peak width of 0.3-s. The maximum allowed mass and retention time tolerances were 10 ppm and 10 s, respectively.

Local minimum search algorithm with a minimum relative peak height of 1% was used for chromatographic deconvolution; maximum peak width was set to 1 min. The detected peaks were aligned across all samples using the above-mentioned retention time and mass tolerances producing the final feature table used in these analyses. We performed molecular networking in GNPS to putatively identify molecular features using MS/MS-based spectral library matches. We analyzed them using the same LC-MS/MS method described above to compare and verify the exact masses, fragmentation patterns, and retention times to ensure level 1 annotations, as defined by the 2007 metabolomics standards initiative .We calculated the sharedness of microbial features as follows. To quality-control the 16S sequences obtained per animal model, we retained only reads that were prevalent within each model i.e. above a sum relative abundance threshold of 10E-06 and present in at least 1% of the samples, thus avoiding sequencing noise. The number of such reads in Ldlr-/- and ApoE-/-animals was 635 and 582, respectively. Out of these, 248 sequences were shared between the two models. Therefore, the percentage of microbiome features shared between the animal models was 39% of unique microbial features found in Ldlr-/- models. For metabolomic data, we quality-controlled the chemical features by retaining those above a sum relative abundance threshold of 10E-01 and present in at least 10% of all samples for each animal model individually. There were 267 and 374 such features in Ldlr-/- and ApoE-/- animals, respectively. Out of these, 137 metabolites were shared between the two models. Thus, the percentage shared between the animal models were 51% of total features in Ldlr-/- knockout models.Effect sizes were calculated over the individual genotype, mice, cage number, age, exposure type. For each of these covariates, we applied the mixed directional FDR methodology to test for the significance of each pairwise comparison among the groups. For each significant pairwise comparison via PERMANOVA , we computed the effect size using Cohen’s d or the absolute difference between the mean of each group divided by the pooled standard deviation. As diversity estimators we used unweighted UniFrac and Bray-Curtis distances matrices for the 16S rRNA sequencing and LC-MS/MS mass-spectrometry, respectively. For the microbiome data layer , when taking both genotypes together, we see that the first three largest effect sizes are mouse number, age and cage number, followed by genotype and exposure type.

It is important to note that the maximum difference on the first three covariates are related to genotype differences. For example, the maximum difference in mouse number is between two mice [mouse numbers 105 vs. 32 ; Figure 4.S1] that belong to two different genotypes and exposure types. To untangle the effect of genotype, we stratified our dataset by genotypes and calculated effect sizes of each of the covariates within each model. It is noteworthy that effects of covariates are ranked differently within each model, hinting towards underlying differences in the characteristics of the microbial community. Nevertheless, the effect of exposure is ranked comparably across models. Similarly, we calculate effect sizes of the above mentioned covariates for the metabolome data layer . When taking both genotypes together, consistent with the microbiome results, mouse number, age and cage number have the largest effect sizes, and the groups with the maximum effects belong to different genotypes [e.g. mouse number 114 vs. 17. We then stratified the data by genotype and observed that different covariates had distinct effects within each genotype. Interestingly, our analysis shows that unlike in Ldlr-/- mice, individual variability was not significant in ApoE-/- mice.Random Forest classifier was trained and evaluated with cross validation for each mouse model, using microbial or chemical features as predictors. During cross validation, all the samples from the same mouse appeared only in either training or validation data but not both to avoid over-optimistic cross-validation accuracy scores as a result of the classifier learning idiosyncrasies of the individual itself rather than the treatment. The classifiers trained for each mouse model were then applied on the samples of the other mouse model for cross-genotype prediction. For the longitudinal prediction, we trained and evaluated a RF classifier on the samples collected at each time point for AUC computation. To assess the capability of individual 16S sequences and metabolites to separate IHH-exposed from control animals,large pots plastic we used the abundance of each feature as the score to plot ROC curve and compute AUC, and highlighted the features that can single-handedly distinguish IHH on ROC plots. These analyses were done using the scikitlearn Python package.Molecular networking , introduced in 2012, was one of the first data organization approaches to visualize the relationships between fragmentation spectra for similar molecules from tandem mass spectrometry data in the context of metadata. It formed the basis for the web-based mass spectrometry infrastructure, Global Natural Products Social Molecular Networking  which sees ~200,000 new accessions per month. Molecular networking is used for a range of applications in drug discovery, environmental monitoring, medicine, and agriculture. While molecular networking is useful for visualizing closely related molecular families, the inference of chemical relationships at a dataset-wide level and in the context of diverse metadata requires complementary representation strategies. To address this need, we developed an approach that uses fragmentation trees and supervised machine learning to calculate all pairwise chemical relationships and visualizes it in the context of sample metadata and molecular annotations. We show that a chemical tree enables the application of various tree-based tools, originally developed for analyzing DNA sequencing data , for exploring mass-spectrometry data. We introduce Qemistree, pronounced chemis-tree, a software that constructs a chemical tree from fragmentation spectra based on predicted molecular fingerprints . Recent methods allow us to predict molecular fingerprints from tandem mass spectra . In Qemistree, we use SIRIUS and CSI:FingerID to obtain predicted molecular fingerprints. The users first perform feature detection to generate a list of observed ions, referred to as chemical features henceforth, to be analyzed by Qemistree .

SIRIUS then determines the molecular formula of each feature using the isotope and fragmentation patterns, and estimates the best fragmentation tree explaining the fragmentation spectrum. Subsequently, CSI:FingerID operates on the fragmentation trees using kernel support vector machines to predict molecular properties . We use these molecular fingerprints to calculate pairwise distances between chemical features that are hierarchically clustered to generate a tree representing their structural relationships. Although alternative approaches to hierarchically cluster features based on cosine similarity of fragmentation spectra exist , we use molecular fingerprints as it allows us to compare features based on a diverse range of structural properties predicted by CSI:FingerID. Additionally, as CSI:FingerID was shown to perform well for automatic in silico structural annotation , we leverage it to search molecular structural databases to provide complementary insights into structures when no match is obtained against spectral libraries. Subsequently, we use ClassyFire to assign a 5-level chemical taxonomyTo verify that molecular fingerprint-based trees correctly capture the chemical relationships between molecules, we generated an evaluation dataset with two human fecal samples, a tomato seedling sample, and a human serum sample. Mixtures of these samples were prepared by combining them in gradually increasing proportions to generate a set of diverse but related metabolite profiles and untargeted tandem mass spectrometry was used to profile the chemical composition of these samples. Mass-spectrometry was performed twice using different chromatographic gradients causing a non-uniform retention time shift between the two runs. The data processing of these two experiments leads to the same molecules being detected as different chemical features in downstream analysis. In Figure 5.1a we highlight how these technical variations make the same samples appear chemically disjointed.Using Qemistree, we map each of the spectra in the two chromatographic conditions to a molecular fingerprint, and organize these in a tree structure . Because molecular fingerprints are independent of retention time shifts, spectra are clustered based on their chemical similarity. This tree structure can be decorated using sample type descriptions, chromatographic conditions, and spectral library matches obtained from molecular networking in GNPS. Figure 5.1 shows that similar chemical features are detected exclusively in one of the two batches. However, based on the molecular fingerprints, these chemical features were arranged as neighboring tips in the tree regardless of the retention time shifts. This result shows how Qemistree can reconcile and facilitate the comparison of datasets acquired on different chromatographic gradients. We demonstrate the use of a chemical hierarchy in performing chemically informed comparisons of metabolomics profiles. In standard metabolomic statistical analyses, each molecule is assumed unrelated to the other molecules in the dataset.

The importance of microbes for xenobiotics metabolism was underscored by a study that demonstrated an increase in hepatic expression of ethanol metabolizing genes in germ-free mice, and exacerbation in hepatic steatosis . Non-alcoholic and alcoholic liver diseases are characterized by increased luminal and circulating levels of ethanol and its metabolites, acetaldehyde and acetate . These metabolites have independently been associated with liver damage . Acetaldehyde has been implicated in weakening the intestinal tight junctions, compromising the gut barrier and enabling translocation of microbial products . It has also been associated with down regulating the expression of antimicrobial peptides in the intestine , and eliciting inflammatory and adaptive host immune responses . Additionally, alcoholic liver disease is marked by reduced levels of intestinal butyrate , which is linked to weakening of intestinal tight junctions and, hence, permeability .NAFLD refers to a spectrum of liver disease that can be broadly classified into two categories: nonalcoholic fatty liver , the non-progressive form of NAFLD, and NASH, the progressive form of NAFLD . NASH is generally linked to type 2 diabetes mellitus, cardiovascular risk factors and obesity , although NAFLD has also been reported in lean individuals, emphasizing that genetic and environmental factors also contribute to disease development . Patients with NAFLD have a higher prevalence of SIBO and microbial dysbiosis . Using 16S amplicon sequencing, Boursier et al. found that the bacterial genera, Bacteroides and Ruminococcus were substantially increased, and Prevotella was reduced in patients with NASH compared to those without NASH. Loomba et al. utilized whole-genome metagenomics to characterize the gut microbiota in patients with NAFLD with and without advanced fibrosis and showed an increased abundance of Escherichia coli and Bacteroides vulgatus in patients with advanced fibrosis. An enrichment of Escherichiawas also seen in paediatric patients with NASH compared with children with obesity but without NASH .

Consistent with preclinical studies,growing blueberries in pots these studies indicate an association between Gram-negative bacteria and progression of liver fibrosis . Genetically modified mouse models have been used to study NAFLD-associated gut dysbiosis and permeability for mechanistic insights in liver disease progression. Rahman et al. used JAM-A -knockout mice to demonstrate that deficiency in this tight junction protein with a diet rich in saturated fats, fructose and cholesterol leads to increased intestinal permeability and liver inflammation. This inflammation could be alleviated by administering antibiotics, underscoring the importance of microbial translocation in promoting immune response in the liver. Another group used mice deficient in Muc2and found that there was a compensatory increase in intestinal levels of antimicrobial protein-coding genes, Reg3b and leading to an overall protective response against NAFLD . The contribution of liver-damaging inflammation in response to translocation of microbes and MAMPs has been elucidated . Using inflammasome-deficient mouse models , Henao-Mejia et al. conclude that there is an accumulation of MAMPs in portal circulation, which enhanced the expression of hepatic TNF, thereby promoting liver inflammation and NASH progression. Furthermore, cohousing inflammasome-deficient mice with wild-type controls exacerbated diet-induced hepatic steatosis and obesity in healthy cage mates, suggesting transferability of disease via the microbiota. Increasing links between NAFLD and the gut microbiome at both the observational and mechanistic levels make the gut microbiota an attractive source of biomarkers for early diagnosis of NAFLD. In a comparison between children with obesity with and without NASH, Zhu and colleagues observed markedly elevated gut microbial production of ethanol in those with NASH. Adults with NAFLD also show increased serum TMAO and hepatic bile acid synthesis , and decreased production of phosphatidylcholine . Furthermore, Loomba et al. observed differences in carbon and amino acid metabolism in gut microbiome of patients with NAFLDassociated advanced fibrosis . This proof-of-concept study provides preliminary evidence to support the utility of a microbiome-derived metagenomics signature to detect advanced fibrosis as well as candidacy for anti-fibrotic treatment trials in NAFLD.

The manifestation of ALD in patients who chronically abuse alcohol is a consequence of multifactorial interactions involving genetics, immune system, gut microbiome and environmental factors . Like NAFLD, the non-progressive form of ALD is characterized by accumulation of fat inside the liver , whereas the progressive form is marked by inflammation and liver injury . Our understanding of the compositional and mechanistic contributions of the gut microbiota in ALD is improving. As in NAFLD, SIBO has been demonstrated as an important hallmark of alcohol-associated liver disease in humans and mouse models . Intestinal dysbiosis in individuals who abuse alcohol is characterized by marked enrichment of Enterobactericaeae and reduction in abundances of Bacteroidetes and Lactobacillus. It has also been demonstrated that alcohol-induced dysbiosis is only partially reversible by alcohol withdrawal or probiotic treatment . Interestingly, patients dependent on alcohol also displayed reduced fungal diversity and Candida overgrowth, presenting the first evidence of the role of gut mycobiome in pathogenesis of liver diseases . Genetically modified murine models have advanced our mechanistic understanding of the contribution of various components of the gut-barrier in the etiology and progression of ALD. Using Reg3b-/- or Reg3g-/- mice, it was found that REG3 lectins protected against alcoholic steatohepatitis by reducing mucosa-associated microbiota, thereby preventing translocation of viable bacteria . Muc2-deficient mice were protected against alcohol-induced liver inflammation due to a compensatory increase in Reg3g and Reg3b lectins . Furthermore, IgA-knockout in mice led to increased levels of IgM and a net protective effect against ASH progression . In response to ethanol-induced gut-barrier dysfunction and translocation, TLRs and other PRRs activate hepatic Kupffer cells and macrophages, as was demonstrated in male Wistar rats . This step initiates inflammatory cascades releasing TNF, IL-1, IL-10, IL-12 and TGF-β . Using TLR4 chimeric mice, it was shown that endotoxin-induced release of TGF-β is mediated by a MyD88-NF-κB-dependent pathway, providing an explanatory mechanism for endotoxin-induced liver inflammation . Furthermore, increased translocation of fungal β- glucan also induced liver inflammation via CLEC7A receptor on hepatic Kupffer cells such that treatment of mice with antifungal agents reduced intestinal fungal overgrowth, decreased β-glucan translocation and ameliorated ethanol-induced liver disease .

Alongside immunological responses to barrier dysfunction, ALD is also marked by systemwide changes in many bio-active compounds. Alcohol consumption leads to an increase in hepatic bile acid synthesis in humans and mice .This increase could be explained by dysbiosis associated disruption in FXR activation in enterocytes as FXR-deficient mice were more likely to develop ethanol-induced steatohepatitis , and treatment with an FXR agonist had protective effects against liver damage . Alcohol-associated dysbiosis in mice was further linked to reduced LCFA biosynthesis such that LCFA supplementation restored eubiosis. In fact, a statistically significant correlation between Lactobacillus spp. and bacterial LCFA was found in patients with ALD but not in healthy individuals as controls . Butyrate production was also negatively altered following ethanol exposure and administration of butyrate in the form of tributyrin mitigated alcohol-induced liver injury in mice .With increasing evidence of mechanistic links between the gut microbiota and liver disease progression, fecal microbiota transplantation is being explored as a therapeutic option for ALD . However, larger, carefully designed trials across multiple ethnic groups are needed before FMT can be considered safe in routine clinical practice for managing ALD.Cirrhosis is an extreme manifestation of chronic liver injury characterized by loss of liver cells, thick fibrous scar and regenerating nodules; this topic has been extensively reviewed elsewhere so we only provide a brief discussion here . NAFLD, ALD, primary biliary cholangitis , primary sclerosing cholangitis or hepatitis can each progress to cirrhosis and constitute its subtypes. ASH and NASH have emerged as the second and third leading causes of cirrhosis in adults in the USA and based on the etiology there is a variable risk of developing HCC . Alterations in the gut microbiota including dysbiosis and SIBO have been associated with and its complications . Treatment for portal systemic encephalopathy and decompensated cirrhosis includes treatment with nonsystemic antibiotics such as rifaximin to reduce intestinal microbiota overgrowth . Gut microbiome alterations were observed in patients with alcohol-associated and hepatitis-associated cirrhosis in a Chinese cohort ,drainage gutter with an invasion of the lower intestinal tract by microbes associated with the oral cavity such as Veillonella and Steptococcus. Concordant with these findings, Chen and colleagues also found an over-representation of genera including Veillonella, Megasphaera, Dialister, Atopobium and Prevotella in the duodenum of patients with cirrhosis. The genera Neisseria and Gemella were discriminative between HBV-related and PBC-related cirrhosis . In 2017, Bajaj and colleagues observed statistically significant fungal dysbiosis in patients with cirrhosis and showed that Bacteroidetesto Ascomycota ratio could independently predict hospitalization in these patients . All experimental models of liver fibrosis result in gut microbial dysbiosis and increased intestinal permeability, and treatment of gastrointestinal tract with nonabsorbable antibiotics improved survival by immunomodulation, reducing translocation and incidences of infection . Mice with genetic ablations of the receptors for bacterial product ligands are protected from experimental liver fibrosis . The current treatment philosophy involves decreasing the bacterial product ligands or blocking their receptors, which results in decreased inflammatory and fibrogenic signaling in the liver, although no antifibrotic drug is currently available for routine clinical practice.The etiology of non-viral HCC follows a so-called multiplehit pathway, whereby liver steatosis, followed by oxidative stress, ER stress together with intestinal dysbiosis and inflammation contribute to the final manifestation of cancer.

The gut microbiota dramatically changes in composition in hosts with HCC. Clostridium species have been found to be enriched in obesity-induced mouse models of HCC , but clinical studies of patients with HCC detected an overgrowth of intestinal Escherichia coli . Murine models and human studies have reported a migration of Helicobacter species to HCC tumor tissues . Notably, members of this genus are known to promote tumor-development by activating NF-kB and WNT signaling and suppressing anti-tumor immunity, and might have a potential role in HCC development . To get insights into the molecular events explaining the progression of liver disease to HCC, various murine models have been explored. However, most of these models have proven suboptimal because they either do not develop all intermediate pathological & metabolic stages, or they manifest HCC incompletely . We have highlighted some frequently-used rodent models of liver disease, their usage and caveats in Table 1.2.2 to aide future research. Accumulating evidence suggests that HCC-associated dysbiosis is accompanied by gutbarrier dysfunction, bacterial translocation, systemic circulation of their tumor-promoting metabolites and activation of proinflammatory and oncogenic signaling pathways . The intestinal poly-immunoglobulin receptor regulates the transport of IgA into the intestinal lumen and maintains microbial homeostasis . PIgR-/- mice modelling NASH-induced HCC had increased levels of systemic and liver IgA, and a concomitant increase in hepatic tumorigenesis due to localized inhibition of liver cytotoxic T cells that prevent HCC development . Furthermore, the application of broad spectrum antibiotics has been shown to attenuate liver inflammation and HCC-development in mice , highlighting the role of the intestinal microbiome in liver tumorigenesis. In another mouse model in which HCC was induced by diethylnitrosamine , activation of TLR4 due to LPS translocation upregulated the hepatic mitogen EREG in hepatic stellate cells and activated NF-kB, resulting in enhanced tumor cell proliferation . Additionally, the secondary bile acid deoxycholic acid , a metabolic byproduct of gut bacteria, was shown to upregulate proinflammatory genes, such as IL6 and TNF, to provoke a senescence associated secretory phenotype in hepatic stellate cells suggesting that SASP could be playing a key role in at least obesity-linked HCC development . In addition to its role in HCC development, the gut microbiome also modulates protumorigenic adaptive immune response via type 17 T helper cells, which produce the proinflammatory cytokine IL-17A . The therapeutic efficacy of the anticancer drugcyclophosphamide depended on the interplay between Th17 signaling and gut microbiome such that germ-free tumor-bearing mice or mice given non-absorbable antibiotics had reduced Th17 response and a subsequent resistance to therapeutic effects of cyclophosphamide was seen . Increased understanding of the role of the gut microbiota has motivated successful microbiome-based therapeutic modalities for HCC, such as treatment with synthetic bile acids to reduce HCC risk in patients with NAFLD , non-selective beta-blockers in the intestinal mucosa which prevent bacterial translocation and liver inflammation and administration of probiotics in rodents models of HCC slowed tumor growth and reduced tumor size .

The LPP had an extraction rate comparable to the SPP, showing that the particle size had no significant effect on the extraction rate. In summary, when using WPP for antioxidant extraction, higher TEY, TPY, and TPC can be achieved by increasing the extraction temperature, time, and solvent ratio. The DSA was independent of the extraction time and solvent ratio but decreased with the increase in extraction temperature. The two groups of peel particles, with average particle sizes of 0.60 and 0.38 mm, respectively, had no significantly different effects on the extraction of phenolics with varied extraction temperature, time, and solvent ratio. The highest TEY, TPY, and TPC obtained were 57.83%, 12.80%, and 22.06%, respectively. The DSA ranged from 5.37 to 6.35 g g-1. The results indicated that extraction of phenolics from peel particles, produced by grinding to less than 0.6mm with a large cutting head, at a temperature of 20°C for 6 min using a solvent ratio of 4:1 could be the most economical and sustainable approach for industrial-scale production. The chromatogram obtained from the HPLC, showing the peaks of major compounds in the peel extract, including gallic acid, ellagic acid, punicalagin , and punicalagin , is shown in Figure 2.5 a. The contents of the four major phenolic compounds produced by the five groups of extraction conditions are shown in Figure 2.5 b. The gallic acid content significantly improved, from 0.17 to 0.29 mg g-1 , with the increase in solvent ratio from 1:1 to 4:1. Except for the group with a 1:1 solvent ratio , the gallic acid contents obtained with the other groups with different solvent ratios varied from 0.27 to 0.35 mg g-1 and were not significantly different, regardless of the time and temperature. The ellagic acid content varied from 0.77 to 1.51 mg g-1, with no significant differences among groups G3, G4, and G5. The solvent ratio mostly influenced punicalagin. At the same extraction conditions, the punicalagin content increased from 2.13 to 4.48 mg g-1 when the solvent ratio increased from 1:1 to 8:1. In addition to the solvent ratio ,snap clamps ABS pvc pipe clip the extraction time and temperature affected the extraction of punicalagin , which can be seen from the increased punicalagin content from 7.51 mg g-1 to 9.17 mg g-1 .

The punicalagin purity ranged in order of G2 > G4 > G5 > G3 > G1, and the corresponding values were 88.99%, 88.27%, 87.73%, 87.64%, and 85.93%, respectively. Qu et al. compared the gallic acid, punicalagin, punicalagin , and ellagic acid concentrations of different pomegranate products. The results presented in this study showed significantly higher retention of punicalagin than the pomegranate peel extract using dried peel particles used by Qu et al. . The WPP extraction in this study also achieved slightly higher phenolic concentrations compared to Langers 100% pomegranate juice. To study the effects of drying, DPP was produced using HA drying and IR drying, and the peel compositions and phenolic extraction conditions were compared between DPP and WPP. The compositions of WPP and DPP on a dry basis are shown in Table 2.5 Composition of fresh and hot-air dried pomegranate peel. . The WPP had higher contents of protein, ash, and crude fat than the DPP. The loss of these contents was reduced by avoiding the drying process. On the other hand, the HA DPP maintained higher total dietary fiber at 25.44%, and the ratio of insoluble dietary fiber to soluble dietary fiber was 8.84. This was higher than the values for WPP, which maintained 17.19% TDF and an IDF/SDF ratio of 4.93. Morais et al. compared the compositions of raw, freeze-dried, and oven-dried papaya peels. They reported similar findings for the differences in fiber content, but the differences were not statistically significant. Figure 2.6 shows the TEY, TPY, DSA, and color characteristics of IR DPP, HA DPP, and WPP. The DPP resulted in significantly lower TEY, TPY, and TPC than the WPP. The DSA values were similar for both DPP and WPP at about 6.41 g g-1. In other words, extraction from WPP resulted in 10% more extract yield and 2.5% more TPY with similar DSA compared to DPP. Loizzo et al. investigated the phytochemical contents of extracts from fresh and processed peel and pulp. Compared to steamed, baked, and microwaved pulp, extraction with fresh pulp achieved up to 0.9% higher TEY and twice the TPC. Their results demonstrated that extraction with fresh peel or pulp could reduce the phytochemical loss that occurs during the drying process. Similarly, Mphahlele et al. compared the bio-active compounds in fresh peel and hot-air dried peel at 40°C, 50°C, and 60°C. As for color, extraction of WPP achieved significantly higher L* , a* , b* , and C* values.

The results demonstrated statistically higher retention of L* and a* values using WPP. According to Cadena et al. , changed color characteristics indicate the formation of caramel-colored pigments resulting from nonenzymatic processes, which are related to lower sensory acceptance. Therefore, extraction from WPP could be a more suitable method for the extraction of polyphenols for use in food product development and supplementation. PCA was applied to explore the interdependence among variables. Observations consisted of the average sub-sampling results from the 30 aforementioned extraction conditions at different extraction times, temperatures, and solvent ratios. Four vectors were estimated based on the eigenvectors of the correlation matrix of four variables: total extract yield , total phenolic yield , total phenolic content , and DPPH scavenging activity . The eigenvalues of F1 to F4 were 2.548, 0.899, 0.544, and 0.008, and the first three PCs accounted for 63.70%, 22.48%, and 13.61% of the sample variance, respectively, and represented 99.79% of the total variance in cumulation . Biplots of the observations and variables show the data distributions of F1-F2 and F1-F3. Confidence ellipses with 99% confidence intervals were used for each set of experiment groups, and no outliers were detected. PCA weighting scores of the four measured variables are listed in Table 2.6. For instance, TEY and TPC were positively correlated with the F1 axis, whereas TPY and DSA were negatively correlated. In addition, TEY and TPY were positively correlated with the F2 axis, while DSA was negatively correlated with the F3 axis. Figure 2.7 also shows the data distribution for F1-F2 and F1-F3. For instance, the data point with a higher F1-axis value had higher TPC, which was extracted at 60°C for 6 min using a solvent ratio of 8:1. This was following the experimental results, indicating that PCA can be applied for future condition prediction. Extraction of bio-active compounds from waste fruit peel is an efficient approach to improve food system sustainability and industry profitability.

This study developed a novel green process for antioxidant extraction from wet pomegranate peel and investigated the effects of extraction conditions on polyphenol yield and quality, including phenolic composition, DPPH scavenging activity, and color characteristics. PCA condensed the multivariable analysis into three factors, which explained 99.79% of the variance and could be suitable for future process development. Three parameters, including drying preparation, extraction temperature, and solvent ratio, significantly influenced the extraction rate. Considering water usage and energy consumption, WPP extraction at 20°C for 6 min with a solvent ratio of 4:1 is recommended as an economic and sustainable process, resulting in 10.53% total phenolic yield with 88.93% punicalagin purity. Overweight is raising concern worldwide due to its high prevalence and various adverse health outcomes. According to a report from World Health Organization, an adult with Body Mass Index between 25.0 to 30.0 is defined as overweight, and over 1.9 billion adults worldwide were overweight in 2016, accounting for 39% of the population . Being overweight can further induce obesity when BMI increased beyond 30.0. These two statuses are major risk factors of physical and mental illness. Wilson et al. conducted 44-year follow-up research on 5209 participants aged 30 to 62 years from the Framingham cohort . Results showed that being overweight was related to elevated cardiovascular risks, including hypertension , angina pectoris , and coronary heart disease . Calle and Kaaks reviewed obesity and obese-related epidemiological studies. An increased risk of cancers was noted from 1.2-2 folds, including colorectal, endometrial, kidney, and oesophageal cancer . BeLue et al. studied the relation between mental healthiness and overweight in youth aged 12 to years in different races and ethnicity . Their results revealed that overweight white and Hispanic youth possessed a higher percentage of self-reported depression, anxiety,greenhouse snap clamps and other mental and behavioral problems. Various causes contributed to being overweight. Among them, a calorie-dense and nutrient-poor diet is a major contributor along with a lack of exercise. Standard American Diet is a typical western diet that includes excess natural and added carbohydrates, fats, and sodium while lacking in consumption of fruits, vegetables, and whole grains . Improving the diet pattern for weight control is in need. Diet with the addition of phytochemicals, such as polyphenol, demonstrated positive health outcomes on weight management. Studies have suggested that polyphenol modulated the plasma and hepatic cholesterol in a few possible mechanisms, including inhibiting enzymes related to intestinal carbohydrate digestion and glucose absorption , inhibiting pancreatic lipase activity and fat absorption from the intestine , promoting β-oxidation of fatty acids , increasing bile excretion to eliminate the cholesterol , and regulating the gut microbiota towards a leaner composition .

Pomegranate peel is a common underused fruit by-product from the juicing process and consists of up to 53.01% of the fruit weight . The high molecular weight polyphenols in the pomegranate peel are the major high-value phytochemicals and have been proven associated with reduced risks of chronic diseases , including type 2 diabetes and cardiovascular diseases . Among all the polyphenols in pomegranate peel, gallic acid , ellagic acid , punicic acid, and punicalagin-α, -β are responsible for most health benefits . It was worth noting that punicalagin is unique in pomegranate peel and demonstrated the greatest antioxidant activities with abundant hydroxyl groups, which can trap peroxyl radicals to reduce oxidation . Therefore, pomegranate peel is a promising source for polyphenols. By far, nearly all the studies of health benefits in pomegranate peel utilized liquid extractable polyphenol. Limited research focused on the pomegranate peel as a whole. Labib and Hossin characterized the effects of pomegranate peel powders and extracts on obese hypercholesterolemic rats solely from anthropometric and serum lipid. Insights of hepatic lipid profile were missing and the regulating mechanisms were not investigated in their study . Moreover, research has shown that the polyphenol and fiber in the food matrix might have a synergistic effect to promote health . Therefore, the objectives of this research were to compare the hypolipidemic properties of PPP and PPE and investigate the regulating mechanisms by supplementing the high-fat diets with different percentages of PPP and PPE to male Syrian hamsters. Pomegranate peel of Wonderful variety was collected from a juicing plant located in Buttonwillow, California in October of 2017. The peel was processed based on Wu et al. . In summary, the peel was sliced and ground into particles less than 0.6 mm. Then extract was obtained by mixing the peel particles with 4 times of water at 20 ⁰C for 6 mins, then filtered before administration to the hamsters. The male Syrian hamster was used since it possessed similarities of hepatic cholesterol and bile acid metabolism with humans compared to other rodents . The compositions of peel powder and extract were listed in Table 2.5. The study was approved by the Animal Care and Use Committee, Western Regional Research Center, USDA, Albany, CA, USA. 45 male golden hamsters were acclimatized for 2 weeks. They were fed with Purina Rodent Laboratory Chow and individually raised in a 20−22 °C environment with relative humidity at 60 %, and 12 h alternating light/dark cycle. After that, 45 hamsters were randomly divided into five groups for each diet as indicated in. A high-fat diet with 20% fat energy intake was set as a control group. 5 and 10% of lyophilized PPP were supplemented into the HF diet ad libitum to evaluate the dose effects. In correspondence, 2.5 and 5% of lyophilized PPE addition were applied to investigate the effect of supplementation form. In this way, LP/LE group contained nearly 8.68 mg soluble phenolic compounds per kg body weight, and HP/HE group doubled the content. These dosages corresponded to approximately 70 mg and 140 mg per day in a 60 kg human according to the Km factor ratio of 5 and 37 for hamsters and humans , respectively .

Furthermore, previous work on apple and pear flowers has revealed considerable overlap in the identity of microbes associated with each host species . Such overlap, in addition to a reduction in diversity with increasing land cultivation, suggests a role for several key processes in shaping floral microbiomes in tree fruits. First, there is a high degree of shared usage of disease and pest management practices employed in pear and apple production systems, as both can suffer greatly from fire blight disease. Inputs applied in conventional and bIPM orchards, including antibiotics and fungicides , can act as strong environmental filters on potential floral colonists or serve as a source for inocula when applied as biologicals, as observed in organic orchards. Second, both apple and pear systems rely considerably on honeybees for pollination, which are known to leave a distinct imprint on floral microbiome diversity . Increased reliance on a single-pollinator species, combined with chemical and nonchemical inputs, are likely important contributors to patterns observed.Orchard management scheme was a key determinant of bacterial community similarity across sites; however, other predictors often explained high levels of variance in community structure across sites. In particular, geographic distance explained a significant amount of variance in both whole-community and taxon-related beta diversity of bacteria. In contrast, for fungi, geographic distance was a significant predictor of only abundance-related turnover. Beyond geographic distance, climatic conditions also contributed significantly to explained variance in the beta diversity or community turnover of fungal communities. In particular, VPD and temperature were negatively associated with fungal diversity, suggesting both microclimate variables affect either species-specific patterns of growth and/or competition. Moisture availability is also an important determinant of microbial growth on the surface of plant tissues , with free water and humidity often being necessary for conidial germination, germ tube growth,plant pot with drainage and potential penetration of plant tissues, including floral organs. This has been frequently observed in other flowering systems of commercial value, including blueberries , raspberries , strawberries , and cut roses .

Within these systems, infection of the gynoecium can be a primary route of disease development. Alternatively, infection of petals and other organs can facilitate secondary infections of fruits . Of the fungal genera examined in our study, Botrytis has been documented to successfully infect the mesocarp via stamen filaments . For the others of interest, it is unclear if there is a link between flower colonization and resulting development and pre- and post harvest diseases. More broadly, our results provide insight into local- and landscape-level drivers of floral microbiome diversity in an important tree fruit commodity, pear. Given the critical link between flowers, yield, and disease, identifying such drivers across both spatial and temporal scales could improve the understanding of links between management, host microbiome structure, and potentially disease resistance or susceptibility. With growing appreciation for the role of host microbiota in affecting resistance against disease , such information has potential to inform development of sustainable management practices in many different types of agroecosystems.We surveyed 15 orchards throughout the Wenatchee River Valley of central Washington in spring 2018. Within the United States, Washington State is the leading producer of deciduous tree fruit crops such as apples, pears, and cherries. These, as well as other commodities, are grown in variable intermountain river valleys and basins east of the Cascade Mountains. These production areas generally experience temperate, dry conditions, in addition to favorable access to irrigation water originating from streams and rivers fed by snowmelt . Given the diverse topography of this region, however, individual orchards range in elevation from 20 to 1,000 m above sea level . Key stages of fruit production, such as flower bloom, can thus experience considerable variation in microclimatic conditions among orchards, affecting bloom timing, fertilization, and fruit development . As flowers are a habitat for diverse microbiota , including a number of pathogenic species that cause pre- and post harvest diseases of tree fruits , microclimatic conditions could affect habitat quality, as well as colonization dynamics and the resulting structure of the floral microbiome. Our survey assesses microbe communities in orchards that used one of three management schemes, with five replicates per scheme, which include organically certified, conventional, and biological-based integrated pest management .

With each of these broad management types, growers were not restricted to a specific spray schedule, but each used a defined set of tools for pest and disease management . Conventional management followed a standard practice , while organic orchards were all managed following USDA-certified organic standards, which prohibits use of such synthetic chemicals. To control fire blight, organic producers often use Serenade Opti at full bloom, a bio-based fungicide and bactericide that leverages Bacillus subtilis endospores and its metabolic by-products as active ingredients . Serenade is not the only bio-based product leveraged by producers for control of fire blight in pear, however, and other products such as Blossom Protect can be used across organic, bIPM, and conventional schemes. Blossom Protect is derived from air-dried spores of Aureobasidium pullulans , an epiphytic or endophytic fungus associated with a wide range of plant species, including many tree fruits. For those orchards that employed the bIPM scheme, growers used a toolbox of cultural controls combined with pesticides with less documented negative impact on natural enemies and other beneficial organisms.Such products included lime sulfur, kaolin, spinosad, and biologicals applied at various stages of bloom . Orchards were sampled once at peak bloom, either on 30 April or 1 May of 2018. At each orchard, 10 trees were sampled, 5 near the edge of the orchard and 5 in the interior. We chose this approach because previous studies suggest that seminatural habitat in the surrounding landscape can both support and increase rates of visitation by native pollinators such as bees and flies . Moreover, pollinators can be important dispersal agents for microbes ; thus, our aim was to detect potential contributions of pollinator visitation to flower microbiome assembly in orchards. For each site and sampling event, 50 open flowers were collected using aseptic technique and pooled at the site level. Flowers with flat, fully reflexed petals that had been open for ;3 days were collected. Once collected, flowers were placed in a cooler, transferred to the lab, and then stored at 4°C until processing.Genomic DNA was extracted from samples using a ZymoBIOMICS DNA microprep kit following the manufacturer’s protocol. Extracted DNA was then used as the template for library preparation and amplicon sequencing following Comeau et al. , performed at the Centre for Comparative Genomics and Evolutionary Bioinformatics at Dalhousie University . There, amplicon fragments were PCR- amplified from DNA in duplicate, using separate template dilutions and high-fidelity Phusion polymerase . A single round of PCR was performed using “fusion primers” targeting either the 16S V4-V5 or ITS2 regions with multiplexing. PCR products were verified visually by running a high-throughput Invitrogen 96-well E-gel .

Any samples with failed PCRs were reamplified by optimizing PCR conditions to produce correct bands to complete a sample plate before continuing with sequencing. The PCRs from the same samples were pooled in one plate, cleaned, and then normalized using the high-throughput Invitrogen SequalPrep 96-well plate kit . Samples were then pooled to make one library and then quantified fluorometrically before sequencing. Amplicon samples were then run on an Illumina MiSeq using 2 300-bp paired-end V3 chemistry. Demultiplexed sequences were trimmed of trailing low-quality bases using the DADA2 pipeline  in R . Paired-end reads were then quality filtered, error corrected, and assembled into ASVs. Once assembled, chimeras were detected and removed, and taxonomic information was then assigned to each ASV using the Ribosomal Database Project naïve Bayesian classifier trained to either the RDP training set or UNITE general FASTA release for bacteria or fungi, respectively. ASVs that failed to classify to kingdom or identified as chloroplast or mitochondrial sequences were discarded. Further, potential contaminant ASVs were identified through inclusion of negative controls during sample and sequence processing and then removed using the “prevalence” method with the decontam package in R . This filtering resulted in samples sequenced at a mean depth of 43,057 sequences per sample for bacteria and 25,890 for fungi. Samples were then rarefied ,pot with drainage holes with all but one bacterial sample retained in the analyses that follow. Such a low cutoff for bacteria is a consequence of a large proportion of reads being identified as plastid DNA, which were removed from the data set. Despite this, we included bacterial data in our study because sampling curves indicate that we were able to identify the majority of bacterial taxa present in samples . Moreover, previous characterization of microbial communities associated with flowers has frequently observed low species richness .To assess the role of abiotic factors, high-resolution climatic metrics for each site were obtained from publicly accessible PRISM data in April 2018. PRISM data are collected at a spatial resolution of 2.5 arcmin . An arcmin is an angular measurement equal to 1/60 of a degree. PRISM data used included elevation , minimum and maximum temperature , minimum and maximum vapor pressure deficit , and precipitation . Vapor pressure deficit is the difference between the amount of moisture in the air and how much moisture the air can hold when saturated, where high VPD indicates drier conditions. As with land cover, the abiotic conditions where sites were located were variable, with elevation ranging from 1,152 to 1,526 m above sea level, April precipitation ranging from 4.2 to 5.3 cm, minimum temperatures ranging from 2.4 to 3.7°C, and maximum temperature ranging from 13.6 to 15.7°C. Statistical analyses. We used multivariate linear regression to assess effects of land cover, orchard management, and climate on the alpha diversity of pear flower microbiomes, using both the Shannondiversity and inverse Simpson index. We chose to include the latter metric to specifically isolate the evenness/dominance aspect of community structure from the taxonomic richness, which heavily contributes to the Shannon diversity metric. All analyses were conducted using R v3.6.1 . To reduce multicollinearity among predictors, we calculated variance inflation factors and used a threshold of 10 to eliminate variables with problematic covariance. This eliminated temperature, precipitation, and elevation from the alpha diversity models. We calculated multimodel average coefficients based on the 90% confidence interval of top models as well as the importance of each coefficient, which indicated the number of top models in which it appeared. We also assessed effects of landscape, climate, and farm management on the dominance of a few focal genera that are highly important for pre- and postharvest diseases of pear, including putative pathogens and beneficial taxa. These included fungal genera Aureobasidium, Botrytis, Cladosporium, Monilinia, Mycosphaerella, and Penicillium and beneficial bacteria, which included Bacillus, Pantoea, and Pseudomonas . One ASV , identified as an Erwinia sp., was detected at a single orchard in our survey. Given such limited detection, we were unable to perform an analysis of links between variables of interest and Erwinia presence and abundance. However, to examine associations between microbial genera and predictors described earlier, we used canonical correlation analysis , an extension of linear regression that finds linear relationships between combinations of explanatory and response variables which maximize the correlation. Separate models were run on fungi and bacteria of interest. Differences in species composition among sites could be affected by processes including substitution of taxa and variation in abundance of particular taxa, so we further evaluated the effects of farm management, land cover, and climate variables on abundance-related and taxon-related aspects of community turnover and the overall community dissimilarity . Beta diversity was partitioned into abundance-related and taxa-related components of Bray-Curtis dissimilarity using the bray.part function in the betapart R package . The influence of explanatory variables on these two components of community turnover between sites, as well as their cumulative overall Bray-Curtis dissimilarity, was investigated using restricted distance-based analysis and AIC model selection and executed using the capscale and ordiR2step functions in the vegan R package . The variance explained by factors included in the top AIC-selected models is included in the results.Fruit flavor is an elusive trait, influenced by many factors including genetics, environments and cultural practices .

Reverse pharmacokinetics can be used to guide potential target tissues/organs/molecules, and then further physiologically relevant pharmacological models are designed to discover bio-active compounds and reveal their corresponding mechanisms. It is worth noting that many compounds show low solubility, which limits their clinical efficiency and restricts their clinical use. Fortunately, there are multiple ways to enhance the bio-availability, such as cocrystallization and the formation of phospholipid complexes and nanoemulsions.Finally, based on the hypothesis that drugs targeting EMT have both antifibrotic and anticancer effects, many important mediators contributing to EMT have been discovered. Additionally, a great number of compounds suppress EMT in tumor and fibrosis by targeting these mediators. It is hoped that many new drugs are designed and developed in the future based on the aforementioned mediators to treat tumors and fibrosis.The first step in the aerobic nitrification process is the oxidation of ammonia to nitrite, mediated mainly by AOB or AOA in soil environments. The most numerous AOB isolated or detected by non-cultural methods in aerobic agricultural surface soils are consistently members of the Nitrosospira genus. Nitrosospira briensis C-128 is a chemolithoautotrophic ammoniaoxidizing betaproteobacterium isolated from a fertilized soil under cultivation for blueberry in Falmouth, Massachusetts, USA in 1971. The genome of Nitrosospira briensis C-128 is the third genome sequence from the genus Nitrosospira to be published and thus provides an important comparison among Nitrosospira. This report includes a summary of the genome sequence and selected features for Nitrosospira briensis C-128 and results are publically available in GenBank accession CP012371.Nitrosospira briensis was described by Winogradsky and & Winogradsky in 1933 as an ammonia-oxidizing bacterium isolated from soil.

The genus name, Nitrosospira, is derived from two Latin roots: nitrosus, meaning nitrous, and spira, indicating spiral. The species name briensis,black plastic plant pots refers to the original isolation location near Brie, France. The culture described by Winogradsky & Winogradsky was not maintained and reisolation of a replacement strain was reported by Watson in 1971. At approximately the same time, N. briensis strainC-128 was isolated by enrichment culturing from a surface soil sample collected from a fertilized blueberry patch in East Falmouth, Massachusetts in 1971 . In 1993, the genus Nitrosospira was emended to include the former genera of Nitrosovibrio and Nitrosolobus based on the high identities of the 16S rRNA gene sequences. Nitrosospira briensis was designated the type species for the genus with strain C- 76 as the type strain . The full-length 16S rRNA gene sequence of N. briensis C-128 is 99 % identical to the N. briensis strain C-76/ Nsp10 sequence . The culture of N. briensis strain C-128 was received in the Norton laboratory from F. Valois in 1995. Nitrosospira briensis C-128 is presently maintained in a culture collection at WHOI and may be obtained upon request from J.M. Norton. Classification and general features of Nitrosospira briensis C-128 are provided as Minimum Information about the Genome Sequence in Table 1. Electron micrographs of the pure culture organism are shown in Fig. 2 revealing the tight spirals visible with TEM negative staining and the convoluted surface of this Nitrosospira as revealed by SEM.Nitrosospira briensis C-128 was chosen for sequencing through the Community Science Program of the DOE Joint Genome Institute as an important representative of the AOB to improve the scope and quality of intra- and inter-generic comparisons in the Nitrosomonadales. The chemolithotrophic metabolism of the AOB, the pathways for production of nitrous oxide and urea metabolism were additional motivating interests in sequencing this genome. Sequencing, finishing, and annotation were accomplished by JGI. The genome sequence has been deposited in the Genome OnLine Database and is part of the NCBI Reference Sequence Collection.

A summary of the project information is found in Table 2.The genomic DNA of Nitrosospira briensis C-128 was sequenced at the DOE JGI using the Pacific Biosciences sequencing technology. All general aspects of sample handling, library construction and sequencing followed JGI isolate sequencing protocols. A PacBio SMRTbell™ library was constructed and sequenced on the PacBio RS platform, which generated 148,206 reads totaling 519.8Mbp. Raw reads were assembled using HGAP v. 2.2.0.p1 . The final draft assembly contained one contig in one scaffold, totaling 3.2 Mbp in size. The input read coverage was 176.1×. An earlier version of the genome was sequenced using the Illumina Hi-Seq 2000 platform. However, this earlier sequence assembly JHVX00000000.1 remained in 31 scaffolds with the nearly identical repeats of several key catabolic gene clusters remaining unresolved. Previously, genome closure for Nitrosospira was achieved only after extensive directed finishing to correctly assemble long nearly identical repeats of gene clusters encoding key catabolic modules including ammonia monooxygenase for the activation of substrate and hydroxylamine dehydrogense and hemecytochrome c proteins for the extraction of electrons and their delivery to the quinone pool in the membrane. The long read capability of the PacBio platform and our depth of coverage enabled sufficient discrimination of repeats to assemble across multiple nearly identical regions into a single contig representing the chromosome of the bacterium. For predicted genes outside of gaps and repeat regions the PacBio and the Illumina predicted genes were 100 % identical. Therefore, we did not combine the Illumina Hi-Seq data with the PacBio data for the complete genome sequence CP012371 reported here.Genes were identified using Prodigal , as part of the JGI’s Microbial annotation pipeline followed by a round of manual curation using GenePRIMP. The predicted CDSs were translated and used to search the NCBI nonredundant database, UniProt, TIGRFam, Pfam, KEGG, COG, and InterPro databases. Transfer RNA genes were identified using the tRNAScanSE tool. Other non-coding RNAs were found using INFERNA. Further gene prediction and manual curation was performed within the Integrated Microbial Genomes platform developed at JGI.The genome of Nitrosospira briensis C-128 contains 3,210,113-bp in one chromosome with a GC content of 53.25 % and no plasmids .

The genome contains one complete ribosomal RNA operon similar to other AOB. Coding bases comprised 85.93 % of the total. We identified 3018 protein encoding genes, 55 RNA genes and 130 pseudogenes. For the identified genes, 74.23 % had a function prediction associated with them. The two-way average nucleotide identity between the chromosomes of Nitrosospira multiformis ATCC 25196 and Nitrosospira briensis C-128 was found to be 77.2 % confirming species delineation. The genome statistics are summarized in Table 3 and genes associated with COG functional categories are summarized in Table 4.Alcohol consumption has been associated with an increased risk of developing colorectal cancer . One large meta-analysis reported an increased relative risk of 1.1 for developing CRC when consuming more than 2 alcoholic beverages per day . Other studies, including a large pooled analysis and meta-analysis , have shown a similar modest risk of developing CRC associated with alcohol consumption at approximately 2 drinks per day and higher risk associated with higher quantities of alcohol consumption. The specific type of alcoholic beverage consumed in the aforementioned studies was not associated with CRC risk. Total alcohol consumption has been shown to increase the risk of developing CRC in familial cases through an interaction with family history by several investigators, but the effects of wine have not been assessed . Controversy over this issue remains, as it has been reported that the risk of developing CRC may depend on the type of alcoholic beverage consumed. Beer intake has been shown to have a strong association with CRC in several studies . Interestingly, in a large population-based cohort study analyzing 28,000 individuals, alcohol intake was associated with an increased risk of rectal cancer; however, this risk was diminished in alcohol drinkers who consumed at least some wine versus those who did not drink any wine at all . In the same study,black plastic planting pots wine intake was associated with a non-significant trend toward decreased risk of developing colon cancer . Moderate wine consumption has been associated with decreased risk of total mortality, an effect attributed to decreased risk of death from cardiovascular causes and protection from cancer and other causes . Light to moderate wine drinkers have been observed to have a lower risk for death from cancer than those who did not drink wine— an effect not observed for consumers of beer and spirits . In a large U.S. mortality study, alcohol was noted to have a trend toward decreased CRC-specific mortality among women —particularly at light consumption levels . Familial CRC is characterized by multi-factorial inherited susceptibility to CRC and represents approximately 20% of CRC cases; another approximately 79% are considered to be sporadic cases. Based on evidence that there is a decreased risk of developing CRC for wine drinkers, that a decreased cancer-related mortality is associated with wine consumption, and that light to moderate alcohol use among female CRC cases results in a trend toward decreased mortality, we set out to determine if wine consumption was associated with favorable effects on tumor characteristics or survival among CRC cases.Using data from the University of California Irvine CRC gene-environment study , incident cases of invasive colorectal carcinoma during the period 1994–1996 were analyzed. Family history of cancer was ascertained via telephone interview.

Familial CRC cases were identified as those having at least 1 first-degree relative with CRC. Amsterdam criteria were used to define hereditary non-polyposis colon cancer families . HNPCC cases and 1 case with clinically diagnosed Familial Adenomatous Polyposis were excluded from the analysis . The remaining sporadic and familial CRC cases were included for analysis by wine consumption frequency group. Food consumption was self-reported via a validated 100-item National Cancer Institute -Block food-frequency questionnaire in which cases were asked to report their usual eating habits during the 1 yr prior to diagnosis of CRC . Frequency of wine , beer , and liquor consumption was recorded, and available responses ranged from “never” to “6+” servings a day. Total daily energy intake, total daily fiber intake, total daily dietary calcium intake, vegetable and fruit consumption, and body mass index were analyzed from FFQ data using the NCI-Block Analysis Program , version 4.0, as previously reported . All cases were dichotomized as either infrequent wine consumers or regular wine consumers . In the same manner, cases were classified as regular or infrequent consumers of beer and liquor. Clinical and demographic data from consented cases were obtained from the Cancer Surveillance Programs of Orange County, Imperial County, and San Diego County, California as described previously . Recorded data included demographic information , histology, tumor grade, stage at presentation, and survival status. Therapeutic information related to the first course of treatment was obtained including surgical treatment rendered at the primary site, treatment with radiation therapy, and use of chemotherapy. Data were abstracted from medical and laboratory records by trained tumor registrars according to Cancer Reporting in California: Vol. 1. Abstracting and Coding Procedures for Hospitals . Tumor site and histology were coded according to criteria specified by the World Health Organization in International Classification of Diseases for Oncology . Primary site code was searched as described previously using the Surveillance, Epidemiology, and End Results site code for colon and rectum . Appendiceal cancers were excluded. Histology codes included adenocarcinoma , mucinous adenocarcinoma , carcinoma , and not otherwise specified . Only invasive cases of cancer were included in the analysis. Staging was grouped into 3 broad categories that could be classified from clinical and pathologic records and defined according to SEER summary staging as localized disease, regional disease, and remote disease . Socioeconomic status quintiles were obtained from the SES variable available in the California Cancer Registry as described previously . This index variable utilized for SES includes a combination of 7 indicator variables for census block data including assessments of educational status, income, and housing information .In this observational study, earlier stage at presentation and improved OS were noted for familial CRC cases who were regular wine consumers prior to the time of diagnosis compared to those that were infrequent wine users. The observed survival benefit persisted after adjustment for age, gender, stage at presentation, SES, BMI, treatment status, and consumption of beer and liquor. In contrast, among sporadic CRC cases, no differences in stage at presentation or survival were noted for regular versus infrequent wine consumers. The observed survival differences based on reported wine consumption were not detected for beer or liquor consumption. Greater than 1/2 of the regular wine consumers in this study were moderate wine consumers .

To further scrutinize this unusual reverse EAG responses, we used gas chromatography with electroantennographic detection . In GC-EAD analyses, injected mixtures are separated by GC and subjected to antennal preparations under the same condition thus ruling out any possibility of mechanical interference and minor sample contamination. Here, methyl salicylate responded with regular EAG responses, i.e., with the first phase , which is referred to as rise of the receptor potential, and the second phase starting at the end of the stimulus, commonly referred to as the decline of the receptor potential . This is analogous to the depolarization, repolarization, and hyperpolarization of a nervous impulse. As opposed to methyl salicylate, eucalyptol consistently gave inverse EAD responses  thus corroborating what we observed in EAG analyses . Next, we recorded EAG responses when flies were challenged with odorants and an inhibitor. First, we compared the response of w1118 and Orco-Gal4/UAS-CqOR32 flies to -2-hexenal when it was delivered alone or in combination with eucalyptol. EAG responses from w1118 flies to 0.1% -2-hexenal alone or in combination with 10% eucalyptol did not differ significantly . By contrast, EAG responses from Orco-Gal4/UAS-CqOR32 flies to 0.1% -2-hexenal plus 10% eucalyptol were significantly lower than those elicited by 0.1% -2-hexenal alone . We then examined the dose-dependent effect of this inhibition by using Orco-Gal4/UAS-CqOR32 flies. Robust responses to 0.1% methyl salicylate were reduced in a dose-dependent manner with the addition of eucalyptol but remained unchanged at the end of the tests. Likewise, EAG responses to 0.01% -2-hexenal were reduced when coapplied with eucalyptol . Of note, -2-hexenal does not activate CquiOR32 . Such inhibition presumably results from CquiOR32 indirectly inhibiting responses of the fly endogenous receptors to -2-hexenal. In these continuous experiments, a small difference between EAG responses before and after costimulus tests may be due to loss of this volatile semiochemical from the cartridge rather than adaptation. Similar inhibition was observed when 2-heptanone was applied alone or coapplied with eucalyptol . Taken together, these results further suggest that intrareceptor inhibition occurs in vivo as indicated by the inhibitory effect of eucalyptol on methyl salicylate responses. Additionally,drainage planter pot the effect of eucalyptol on the response to -2-hexenal suggests that intraneuronal inhibition occurred.

A few lines of evidence support this hypothesis. First and foremost, eucalyptol does not cause inhibition in control flies and -2-hexenal does not activate CquiOR32 . The simplest explanation is that, in Orco-Gal4/UAS-CqOR32 flies, all endogenous receptors are coexpressedwith CquiOR32. Thus, CquiOR32 response to eucalyptol interferes with the response of DmelOR7a to -2-hexenal. In short, inhibitor and agonist are likely to be acting on different receptors in the same neurons, thus an intraneuron inhibition. To further test the notion of intraneuronal inhibition, we turned to single sensillum recordings .The best ligand for ab4A, the neuron in ab4 sensilla with a large spike amplitude, is -2-hexenal , although ab4A is also very sensitive to other ligands, including hexanal . Contrary to ab4B, ab4A houses only one OR, namely, DmelOr7a . Because expression of CquiOR32 was driven by DmelOrco, ab4A neurons in our transgenic flies house both DmelOr7a and CquiOR32. Coexpression was confirmed by a significantly stronger response to methyl salicylate recorded from Orco-Gal4/UAS-CquiOR32 than from WT flies , while retaining response to hexanal . It is known that methyl salicylate is the best ligand for DmelOr10a in ab1D but elicits only very low response in ab4A . The low response of WT flies to methyl salicylate did not differ significantly when the odorant was delivered alone or codelivered with eucalyptol . By contrast, responses recorded from Orco-Gal4/UAS-CquiOR32 flies were significantly lower when the two stimuli were delivered simultaneously from two different cartridges . Next, we tested whether CquiOR32 response to eucalyptol would affect DmelOR7a response to a cognate ligand, hexanal. Responses of WT flies to hexanal did not differ significantly when comparing hexanal alone with hexanal plus eucalyptol . Recordings from ab4 sensilla in the Orco-Gal4/UAS-CquiOR32 flies showed a slight, albeit not significant, increase in response to hexanal. This is unlikely to be due to hexanal activation of CquiOR32 . When hexanal and eucalyptol were delivered simultaneously firing of DmelOR7a was completely abolished . We also recorded from ab7 sensilla, which expresses DmelOR98a, in ab7A and for which butyl acetate is one of the best ligands . Eucalyptol elicited inhibitory response in ab7A neurons of Orco-Gal4/UASCquiOR32 flies . In the transgenic flies both methyl salicylate and butyl acetate generated excitatory responses , which were inhibited by eucalyptol . Because methyl salicylate and eucalyptol elicit inward and reverse currents in CquiOR32, this in vivo inhibition is not surprising.

However, the consistent observation that eucalyptol inhibits the response of an endogenous receptor to a cognate ligand supports the notion that intraneuronal inhibition occurs when receptors are colocated in a neuron. Specifically, the inhibitory responses of CquiOR32 interferes with the activation of a collocated receptor by a cognate ligand. For example, activation of DmelOR7a in ab4A neuron by hexanal and activation of DmelOR98a in ab7A neuron by butyl acetate were both inhibited by eucalyptol upon interaction with CquiOR32. Contrary to the fruit fly, which expresses only one receptor per neuron , mosquitoes can coexpress multiple ORs in the same neuron .CquiOR32 has an orthologue in the genome of the yellow fever mosquito, AaegOR71 , with 55.5% identity. We sequenced 20 clones and obtained 19 AaegOR71 sequences. Five clones showed sequences identical to the sequence in VectorBase and were, therefore, considered the WT. We expressedAaegOR71-WT in the Xenopus oocyte recording system and challenged the oocytes with compounds that elicited inward and inhibitory currents in CquiOR32. Cyclohexanone elicited inward currents, but the compounds generating the largest inward currents were 4,5-dimethylthiazole and 2-methyl-2-thiazoline ; no response was observed with methyl salicylate. Although eucalyptol and fenchone did not elicit measurable inhibitory currents, these two compounds reduced AaegOR71 responses to cyclohexanone, DMT, and 2MT . Four clones differed from WT in 7 amino acid residues, and 3 clones differed from WT in 11 amino acid residues. They both showed weak responses to odorants when tested in the Xenopus oocyte recording system. The other 7 clones differed from the WTin 6–12 amino acid residues. AaegOR71-V5 , AaegOR71-V14 , and AaegOR71-V15 differed in 12, 9, and 11 amino acid residues, respectively, and none of them responded to odorants. AaegOR71-V8 differed in 10 amino acid residues and showed very weak response only to the Orco agonist VUAA-1. AaegOR71-V4 , AaegOR71-V9 , AaegOR71- V17 differed in 8, 10, and 6 amino acid residues but gave weak to moderate responses to odorants. Next, we tested whether intrareceptor inhibition might be manifested in vivo in the antennae of the yellow fever mosquito. With 350 contacts, 69 recordings were made from SST-2 sensilla.

SSR showed that cyclohexanone, 2-methyl-2-thiazoline, and 2,4-dimethylthiazole elicited dose-dependent excitatory responses in neuron-A in SST-2, whereas eucalyptol and fenchone showed inhibitory responses . When costimulated with 2-methyl-2-thiazoline and eucalyptol, the response to the odorant decreased markedly . We then analyzed the effect of inhibitors on the responses to the three odorants that caused excitatory responses. Both eucalyptol and fenchone inhibited the responses of ORN-A in SST-2 to cyclohexanone , 2-methyl-2-thiazoline , and 4,5-dimethylthiazole in a dose-dependent manner.In 1970, Philip Bjork described a small fossil bear from the Pliocene Glenn’s Ferry Formation of southwestern Idaho. Based on a single m1 as the holotype, he was understandably perplexed and named it Ursus abstrusus. Additional material has not been forthcoming since its initial description and this bear has remained an enigma. Hence the discovery in the 1990s of a similar bear from more complete fossils in the Pliocene of the Canadian High Arctic throws much needed light onto the mystery . In addition to resolving the riddle of Ursus abstrusus, with a moderately complete skull and lower jaws with associated post cranials,plant pot with drainage the new materials present a rare opportunity to fill a large gap in our knowledge of North American High Arctic at a time in the early Pliocene when mean annual temperatures in the High Arctic were ~22 °C warmer than the present polar temperatures. Such a warm climate supported an extensive boreal-type forest biome, radically different from today’s arid polar tundr. Thus the evidence of this primitive bear in an extinct polar forest offers valuable information about the diet and habitat of this basal ursine. Te fossil records of basal ursines has improved with recent discoveries of three relatively complete specimens of basal ursines from China – a very advanced Ursavus and a very primitive Protarctos . We are now in a position to more tightly bracket the North American Pliocene bears as well as providing a wealth of information about cranial anatomy of basal ursines previously unavailable. Te present description of P. abstrusus and a phylogenetic analysis combining molecular and morphological data of most fossil and living ursines for the first time allows a much more detailed view of the history of bears at the critical juncture of their initial diversifcation. In addition, the presence of dental caries provides insight into the evolutionary history of diet of ursines.Protarctos abstrusus is a basal ursine the size of a small Asian black bear. It has a fat forehead covering an uninfated frontal sinus; very high sagittal crest that projects backward to overhang the occipital condyle ; P4 with a small, distinct protocone situated at the level of carnassial notch; M2 talon modestly developed but not very elongated ; no pre-metaconid on m1, smooth posterior surface of m1 trigonid without zigzagpattern, presence of a distinct pre-entoconid; m2 shorter than m1 . It is about the same size as P. boeckhi and difers from it in the relatively smaller p4, presence of a tiny cuspule on lingual side of posterior crest in p4, and presence of a pre-entoconid on m1. P. abstrusus is also similar in size to P. yinanensis and can be distinguished from the latter in a fattened forehead, posteriorly projected sagittal crest, p4 posterior accessory cuspule on lingual side of posterior crest, an m1 pre-entoconid, and less elongated M1 and M2. P. abstrusus difers from P. ruscinensis by its lack of unique features of the latter such as a deep angular process, reduction of P4 protocone, and a single entoconid on m1.There is much disagreement over the generic taxonomy of ursines. Most mammalogists and some paleontologists include all living black bears , brown bears, and polar bears in the genus Ursus but allow separate generic status for the sloth bear, Melursus, and sun bear, Helarctos, although some include all of above in Ursus and others use Talarctos for the polar bear. With a deep time perspective, vertebrate paleontologists either adopt some subgeneric names, such as Ursus for sloth bear, Ursus for Asian black bear, Ursus for American black bear, Ursus for some extinct bears or elevate some of them to generic status. In his remarks about carnivoran classifcation, Kretzoi erected a new genus, Protarctos, for Ursus boeckhi Schlosser, 1899. Kretzoi’s name has been adopted either at full generic rank or as a subgenus, although many authors still prefer a more inclusive usage of Ursus. In our cladistic framework in this study, some generic reassignment becomes necessary to maintain monophyly, especially in light of the general preference to giving sloth and sun bears distinct generic status.Te Beaver Pond site, 78° 33′N 82° 22′W, is a >20m succession of fne to coarse cross-bedded fuvial sands conformably overlain by cobble gravels interpreted to be glacial outwash and capped by 2m of till on the northeastern edge of an interfuvial plateau southeast of Strathcona Fiord on Ellesmere Island, Nunavut . A peat deposit near the base of the sequence, up to 2.4 m thick, produced exceptionally well-preserved plant, invertebrate and vertebrate remains , and is disconformably overlaying light-colored, tilted Eocene sediments. Abundant beaver-cut branches and cut saplings of larch trees suggest that the peat growth may have been promoted by beaver activity. Further supporting this view are the skeletal remains of multiple beaver individuals, and two clusters of beaver-cut branches found within the peat unit, at least one of which was interpreted to be the core of a dam. Using terrestrial cosmogenic nuclide burial dating, four samples of quartz-rich coarse sand from above the peat unit yielded a weighted mean date of >3.4+0.6/−0.4Ma, suggesting the peat accumulation was formed during a mid-Pliocene warm phase.At 78°N, the Beaver Pond site on Ellesmere Island is presently extremely cold and arid, with ice sheets, permafrost, and sparse vegetation.