The second two chapters describe a novel high precision O2analyzer that was initially developed to measure AQ and a related general purpose data acquisition system that was developed alongside the O2analyzer.Automated image analysis techniques enable the non‐ destructive phenotyping of large plant diversity panels. The 1001 Genomes Project is one example of such a panel; it comprises 1135 sequenced natural accessions of Arabidopsis thaliana Heynh. sampled from a wide range of environments . Combining these high‐quality genetic resources with high‐throughput phenotyping methods enables powerful genome‐wide association studies. One technique for evaluating the developmental traits of such large diversity panels is growing the accessions in agar‐ filled culture dishes. This allows root traits to be quantified quickly using high‐throughput image analysis methods. The plants are not destroyed or contaminated in the process and can therefore be photographed at different stages of growth. One disadvantage of this approach is that the rosettes are askew, so rosette area is usually not assessed even when the leaves are visible in the photographs. Quantifying both root and shoot characteristics is usually preferable because many plant processes involve both organs; for example, nitrogen acquisition and allocation involves root uptake from the rhizosphere, assimilation into organic forms in both the roots and shoots, and translocation throughout the plant . Studying this process requires precise measurements of both the roots and shoots, plastic pot which has previously been technically difficult. Here, we show that leaf area measured from plate images is accurate even when the rosettes are somewhat askew and can therefore be used for rapidly phenotyping large image sets of Arabidopsis seedlings. As part of a larger study to examine the genetic basis of plant adaptation to different nitrogen forms and concentrations in the rhizosphere , we measured leaf area from more than 2000 images of Arabidopsis seedlings on agar plates.

To determine whether rosette area measurements taken from plate images are sufficient for shoot phenotyping, we compared them to both measurements from images of the rosettes photographed from directly overhead and seedling mass. To compare the overhead and plate image rosette area measurements, six different natural Arabidopsis accessions were planted on agar plates containing a base nutrient solution consisting of 2 mM CaCl2, 2 mM KH2PO4, 2 mM MgSO4, 1 mM KCl, 0.75 mM MES, 0.5 μM CuSO4, 2 μM MnSO4, 25 μM H3BO3, 42 μM FeNaDTPA, 2 μM ZnSO4, 0.5 μM H2MoO4, and 0.8% agar. Different concentrations of sucrose were added to the base media to ensure that there would be a variety of different‐sized seedlings. After planting, the plates were kept at 4°C for four days and then placed into a growth chamber with a 14‐h day/10‐h night cycle. After 12 days of growth, rosette area of the plants was measured in two ways, first from photographs of the seedlings in the plates and second from a photograph of the rosettes placed upright on paper. All photographs from this image set were taken with a Pixel 3A cellphone camera . A total of 58 seedlings were grown and measured this way. As part of a larger study investigating plant responses to different nitrogen forms and concentrations in the rhizosphere, we quantified the rosette area from plate images and compared it with seedling mass. A total of 148 Col‐0 seedlings were grown under 10 different nitrogen conditions with either nitrate or ammonium as the sole nitrogen source at concentrations ranging from 0.05 mM to 5 mM. After 12 days of growth, the plates were photographed and the seedlings, including both roots and shoots, were excised and weighed.As another part of the aforementioned study, more than 2000 images of Arabidopsis seedlings on agar plates were collected. This image set was generated from an experiment in which the 1135 natural accessions of the 1001 Genomes Project were grown under four different nitrogen conditions: 0.1 mM and 1 mM nitrate using KNO3 as the sole nitrogen source and 0.1 mM and 1 mM ammonium using NH4HCO3 as the sole nitrogen source.

The seedlings were grown under long‐day conditions . The closed plates were photographed 12 days after planting using an EOS Rebel digital camera fitted with an 18–55 mm EF‐S lens . The root traits, including primary root length and number of lateral roots, were estimated from the images using RootNav, image analysis software that allows the semiautomated quantification of complex root system architectures .Some of the image sets did not have a red two‐dimensional scale present, making them unsuitable for rosette area measurement using existing methods such as Easy Leaf Area . We developed our own image processing workflows in Python, which were able to use a scale if it was present or, alternatively, to detect the area of the agar plate to serve as a scale. These workflows use the PlantCV package for most of the image‐ processing functions. The general steps in the workflow are cropping the image to the plate region, leaf identification and pixel counting, and scale identification. Cropping the image to the region of interest was done to save processing time and eliminate background features that could be mistaken for objects of interest. This was done using binary thresholding or edge detection to separate the agar‐filled culture dish from the background . The choice to use edge detection to identify the plate versus binary thresholding was dependent on the image set used. The detection of the agar plate also allows for the rotation of the image if the plate is not correctly aligned within the image. Leaf identification was performed using binary thresholding and object detection . The specific color channel and threshold value used to identify the leaves varied between the different image sets due to different background and lighting conditions, but as long as the images within a set are taken against the same background and with the same lighting conditions then these values should remain consistent for processing the entire set. For the validation images, the “C” channel of CMYK color space was used to identify the leaves, whereas in the diversity panel image set the “B” channel of L*a*b* color space was used.

To determine the appropriate threshold values for an image set, we used the plot histogram function in PlantCV. This function is used to visualize the range of pixel intensity in the color channel of interest. For image sets with lower contrast, grow bag the histogram equalization function was used to make thresholding easier. To simplify leaf identification, an ROI was defined for the top section of the cropped image where the leaves are found. Objects detected within the ROI were grouped into six shoots using clustering. The image moment of each shoot in the binary image was used to calculate the number of pixels that made up the leaves in each seedling. Scale identification was performed either by using a reference scale that was placed within the image or using the plate itself as a scale. For dedicated reference scales, the same general process that was used to identify and count pixels of the leaves was used for the scale. Once the number of pixels in the scale or the number of pixels making up the plate were measured, the rosette area could be calculated. For the large image set of the diversity panel, we automated the workflow in a Python script, which took approximately four hours to process all 2000 images. We were able to estimate rosette area for over 90% of the seedlings that successfully germinated, resulting in 8964 individual measurements. Many of the seedlings that were not measurable had fallen below the middle of the plate and were not within the defined ROI. It is important to note that the parameters used for the various transformations, such as thresholding, grayscale conversion, and scale calculation, are specific to the image set. These parameters would need to be modified when using a different image set, but the general steps would still apply.The strong positive linear relationship between the rosette area measurements taken from the plate images and those taken from photographs of excised rosettes demonstrates that using plate images for shoot trait analyses can yield meaningful phenotype data with minimal effort. While the correlation between rosette area measured from plate images and seedling mass was not as strong, it was still sufficient to indicate that this is aviable method for estimating plant growth. A lower correlation between these measurements is also to be expected because the seedling mass includes both shoot and root mass and is therefore not as specific to shoot growth as is the rosette area. We were also able to apply this analysis to an image set generated for the purpose of root phenotyping, allowing us to obtain additional valuable phenotypic information. The rosette area measured using this technique across a large Arabidopsis diversity panel was found to be heritable and showed a significant response to rhizosphere nitrogen form and concentration. These results were in line with other developmental traits measured using established techniques, such as primary root length measured using RootNav . Agar plate images are widely used for the non‐ destructive measurement of Arabidopsis root traits. Here, we showed that useful shoot trait information can also be collected from these same images, enabling simultaneous root and shoot phenotyping. This can be done quicklyand is easily automated, making it suitable for large image sets. The images can be captured and analyzed without the need for specialized imaging equipment or dedicated phenotyping facilities. The agar plate itself can be used as a scale, enabling the analysis of image sets without dedicated two‐dimensional scales. With the procedures described here, image sets generated for root phenotyping in other studies might also provide data about shoot phenotypes without much additional effort.

The field of robot guidance has seen great advancement thanks to advances in Machine Vision and Machine Learning. Palletizer systems, comprised of vision guided pick-and place robots along a conveyor have become commonplace in manufacturing and logistics, reducing labor costs and handling heavier loads than humans are capable of handling . In the field of robotic surgery, neural networks have been developed to automate repetitive tasks based on input from cameras, reducing surgeon fatigue during long procedures. Permeant magnets could be a useful positioning aid in cases where clear line of sight is not available. For example, surgical robots have been incorporated in the insertion of pedicle screws during spinal fusion surgery, but only as far as aligning a surgical tool to the spine. The actual insertion of screws is highly dependent on the feel and experience of the surgeon. If some part of the screw could be magnetized, magnetometers could provide useful information about its position in the body. There has been some work on magnetic object tracking. Wahlstrom used an array of 4 magnetometers to track magnets from the opposite side of a piece of plywood using an Extended Kalman Filter, with RMS position error of 4.95mm and orientation error of 1.85 degrees. This work will attempt to calculate magnet positions with greater accuracy using a larger array of magnetic field readings. To avoid the increased cost of using a large number of magnetometers simultaneously, one magnetometer is positioned at different locations in 3D space. With readings taken from a large grid of points, existing nonlinear optimization algorithms can be used to compute the position and orientation of the magnets. In order to carry out this task, a system had to be designed and built to position a magnetometer in 3 dimensions. An alternate use that this system was created for was the characterization of magnetic devices fabricated by other members of the Magnetic Microsystems and Microrobotics lab. Measuring fields surrounding MMM lab devices will help in calculating magnetic forces and experimentally validating simulations.Agriculture is a key human activity in terms of food production, economic importance and impact on the global carbon cycle. As the human population heads toward 9 billion or beyond by 2050, there is an acute need to balance agricultural output with its impact on the environment, especially in terms of greenhouse gas production. An evolving set of tools, approaches and metrics are being employed under the term “climate smart agriculture” to help—from small and industrial scale growers to local and national policy setters—develop techniques at all levels and find solutions that strike that production-environment balance and promote various ecosystem services.

The amino acid metabolism functional GO category is highly enriched in the group of DEGs between BOD and RNO and more specifically in the top 400 BOD DEGs . Some examples of genes involved in amino acid metabolism that have a higher transcript abundance in BOD berries are phenylalanine ammonia lyase 1 , which catalyzes the first step in phenylpropanoid biosynthesis, branched-chainamino-acid aminotransferase 5 , which is involved in isoleucine, leucine and valine biosynthesis, 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase 1 , which catalyzes the first committed step in aromatic amino acid biosynthesis, and tyrosine aminotransferase 7 , which is involved in tyrosine and phenylalanine metabolism. Included in this group were 44 stilbene synthases , which are part of the phenylpropanoid pathway; these STSs had a higher transcript abundance in BOD berries as compared to RNO berries, with very similar transcript abundance profiles to PAL1 .In a previous analysis, WGCNA defined a circadian clock subnetwork that was highly connected to transcript abundance profiles in late ripening grapevine berries. To compare the response of the circadian clock in the two different locations, we plotted all of the genes of the model made earlier. Most core clock genes and light sensing and peripheral clock genes had significantly different transcript abundance in BOD berries than that in RNO berries at the same sugar level . All but one of these had higher transcript abundance in BOD berries relative to RNO berries. The transcript abundance of other genes had nearly identical profiles .

These data are summarized in a simplified clock model , black flower bucket which integrates PHYB as a key photoreceptor and temperature sensor that can regulate the entrainment and rhythmicity of the core circadian clock, although to be clear it is the protein activity of PHYB, not the transcript abundance that is regulating the clock.The common gene set for both locations represented approximately 25% of the genes differentially expressed with sugar level or location. Presumably these gene sets represent genes that were not influenced by location but were influenced by berry development or sugar level. This study is limited in that only two locations in one season were investigated. As more locations are compared in the future, these gene sets will likely be reduced in size even further. The processes involved in these gene sets or modules included the increase of catabolism and the decline of translation and photosynthesis. It is clear that these processes play important roles in berry ripening. Most of the genes in the genome varied in transcript abundance with increasing sugar levels and berry maturation and most of these varied with the vineyard site. Many of the DEGs were enriched with gene ontologies associated with environmental or hormonal stimuli.Plants are exposed to a multitude of factors that influence their physiology even in controlled agricultural fields such as vineyards. The vineyards in BOD and RNO are exposed to very different environments ; these environmental influences were reflected in some of the DEG sets with enriched gene ontologies. The results from this study are consistent with the hypothesis that the transcript abundance of berry skins in the late stages of berry ripening were sensitive to local environmental influences on the grapevine. While most transcript abundances in berries are largely influenced by genetics or genotype, environment also plays a large role.

It is impossible with the experimental design of this study to determine the amount that each of the environmental factors contributed to the amount of differential expression in these two locations. There were too many variables and too many potential interactions to determine anything conclusively. Replication in other seasons will not aid this analysis as climate is highly variable and will produce different results. All we can say is that these genes were differentially expressed between the two locations, which were likely due to known and unknown factors . As additional studies are conducted indifferent locations and seasons in the future, meta analyses can be employed to provide firmer conclusions. It is possible that some of the DEGs identified in this study resulted from genetic differences between the different Cabernet Sauvignon clones and root stock used in the two locations. Not knowing what these genes might be from previous studies prevents us from drawing any clues. These and other factors most certainly affected the berries to some degree. The data in this study indicated that the grape berry skins responded to multiple potential environmental factors in the two vineyard locations in addition to potential signals coming from the maturing seed. We say potential environmental factors because we did not control for these factors; we associated transcript abundance with the factors that were different in the two locations. The transcript abundance profiles along with functional annotation of the genes gave us clues to factors that were influencing the berries and then associations were made with the known environmental variables. Further experiments are required to follow up on these observations. We were able to associate differences in transcript abundance between the two locations. These DEGs could be associated with temperature, light, moisture, and biotic stress.

Additional factors were associated with transcript abundance involved with physiological responses and berry traits such as seed and embryo development, hormone signaling , phenylpropanoid metabolism, and the circadian clock. In the following sections we discuss in more detail some of the possible environmental factors that were reflected in the enriched gene ontologies found in the gene sets from this study.Light regulates the transcript abundance of many genes in plants. It has been estimated that 20% of the plant transcriptome is regulated by white light and this includes genes from most metabolic pathways. Light is sensed by a variety of photoreceptors in plants; there are red/far red, blue and UV light receptors. PHYB is a key light sensor, regulating most of the light sensitive genes and sensing the environment through red light to far-red light ratios and temperature. PHYB entrains the circadian clock affecting the rate of the daily cycle and the expression of many the circadian clock genes; PHYB induces morning phase genes and represses evening phase genes. Other photoreceptors can entrain the circadian clock as well. PHYB and the circadian clock are central regulators of many aspects of plant development including seed germination, seedling growth, and flowering. The circadian clock influences the daily transcript abundance of genes involved in photosynthesis, sugar transport and metabolism, biotic and abiotic stress, even iron homeostasis. Light signaling was very dynamic in the berry skin transcriptome in the late stages of berry ripening with a higher transcript abundance of many light signaling genes in BOD berries. Many photo receptors that interact with the circadian clock had a higher gene expression in BOD berries. In the circadian clock model, Circadian Clock Associated 1 is an early morning gene and has its highest expression at the beginning of the day. It is at the start of the circadian core clock progression through the day, square black flower bucket whereas the transcript abundance of Timing Of CAB Expression 1 is highest at the end of the day and finishes the core clock progression . In both of these cases, there is a higher transcript abundance of these genes in BOD than in RNO. The evening complex is a multi-protein complex composed of Early Flowering 3 , Early Flowering 4 and Phytoclock 1 that peaks at dusk. None of these proteins, had significant differences in transcript abundance between the two locations . The transcript abundance of ELF3 increased with sugar level and shortening of the day length . ELF3, as part of the evening complex , has direct physical interactions with PHYB, COP1 and TOC1 linking light and temperature signaling pathways directly with the circadian clock. It is interesting that most of the components of the clock showed significant differences in transcript abundance between BOD and RNO, except for the three proteins that make up the evening clock. The transcript abundance profile of PHYB was similar in both BOD and RNO berries , however the changes in transcript abundance with sugar level occurred in BOD berries at a lower sugar level. There was a gradual decline of PHYB transcript abundance with increasing sugar level until the last measurement at the fully mature stage, where there was a large increase in transcript abundance. A very similar profile is observed for Reveille 1 . RVE1 promotes seed dormancy in Arabidopsis and PHYB interacts with RVE1 by inhibiting its expression. PIF7 , interacts directly with PHYB to suppress PHYB protein levels.

Likewise, PIF7 activity is regulated by the circadian clock. PIF7 had higher transcript abundance in the BOD than that of RNO berries and generally increased with increasing sugar level. The transcript abundance of two of the other grape phytochromes did not vary significantly between the two locations or at different sugar levels. PHYC had a higher transcript abundance in RNO berries and did not change much with different sugar levels. Many other light receptors , FAR1 , FRS5 , etc. had higher transcript abundance in BOD berries . Thus, light sensing through the circadian clock is a complicated process with multiple inputs. RVE1 follows a circadian rhythm. It behaves like a morning-phased transcription factor and binds to the EE element, but it is not clear if it is affected directly by the core clock or through effects of PHYB or both. PHYB down regulates RVE1; RVE1 promotes auxin concentrations and decreases gibberellin concentrations. Warmer night temperatures cause more rapid reversion of the active form of PHYB to the inactive form and thus may promote a higher expression/activity of RVE1. Pr appears to accelerate the pace of the clock . It is unclear what role phytochromes might have in seed and fruit development in grapes. Very little is known about the effect of PHY on fruit development in general. In one tomato study, the fruit development of phy mutants was accelerated, suggesting that PHYB as a temperature/light sensor and a regulator of the circadian clock may influence fruit development. Carotenoid concentrations, but not sugar concentrations, also were affected in these mutants. Photoperiod affects the transcript abundance of PHYA and PHYB in grape leaves. In the present study, the transcript abundance of the majority of the photoreceptor genes in berry skins, including red, blue and UV light photoreceptors, had a higher transcript abundance in BOD berries . It is unclear what the effect of PHYB and the circadian clock have on grape berry development. However, there were clear differences between the two locations; it seems likely that PHYB and the circadian clock are key grape berry sensors of the environment, affecting fruit development and composition.The grape berry transcriptome is sensitive to temperature. The RNO berries were exposed to a much larger temperature differential between day and night than BOD berries and were also exposed to chilling temperatures in the early morning hours during the late stages of berry ripening . The transcript abundance of some cold-responsive genes was higher in RNO berry skins than in BOD berry skins , including CBF1. CBF1 transcript abundance is very sensitive to chilling temperatures; it is a master regulator of the cold regulon and improves plant cold tolerance. PIF7 binds to the promoter of CBF1, inhibiting CBF1 transcript abundance, linking phytochrome, the circadian clock and CBF1 expression. Our data are consistent with this model; transcript abundance of PIF7 was higher and CBF1 transcript abundance was lower in BOD berry skins than RNO berry skins .ABA concentrations in plants increase in response to dehydration and ABA triggers a major signaling pathway involved in osmotic stress responses and seed development. ABA concentrations only increase in the seed embryo near the end of seed development when the embryo dehydrates and goes into dormancy. ABA concentrations remain high to inhibit seed germination. The transcript abundance of ABA signaling genes such as ABF2 and SnRK2 kinases increase after application of ABA to cell culture and in response to dehydration in leaves of Cabernet Sauvignon. The data in this study are consistent with the hypothesis that BOD berries are riper at lower sugar levels. The ABA signaling genes in the berry skins had higher transcript abundance in BOD berries indicating that ABA concentrations were higher in BOD than RNO berries even though RNO berries were exposed to drier conditions .

The SQUID interference pattern looks reasonably healthy and corresponds to a diameter that is close to the SEM diameter . It is important to remember that it is possible for the Josephson junctions producing nanoSQUIDs to end up higher on the sensor. These might produce healthy SQUIDs but will not be useful for scanning, and discovery of this failure mode comes dangerously late in the campaign, so SQUIDs high up on the pipette are very destructive failure modes. This failure mode is uncommon but worth remembering. If you have access to a vector magnet, such SQUIDs also usually have large cross sections to in-plane magnetic flux, and this can be useful for identifying them and filtering them out. -The capacitances of the Attocube fine positioners are = µF. These scanners have a range of µm. They creep significantly morethan the piezoelectric scanners used in most commercial STM systems, but their large range is quite useful. Damage to the scanners or the associated wiring will appear as deviations from these capacitances. Small variations around these values are fine. After you are done testing these capacitances, reconnect them. Make sure you’re testing the scanner/cryostat side of the wiring, not the outputs of the box- this is a common silly mistake that can lead to unwarranted panic. If you’re working in Andrea Young’s lab, make sure the Z piezo is ungrounded . If for whatever reason current can flow through the circuit while you’re probing the capacitance, you will see the capacitance rise and then saturate above the range of the multimeter. -Because the nanoSQUID is a sharp piece of metal that will be in close contact with other pieces of metal, plastic flower bucket it sometimes makes sense to ground the nanoSQUID circuit to the top gate of a device, or metallic contacts to a crystal, to prevent electrostatic discharge while scanning or upon touchdown.

If you have decided to set up such a circuit, make sure that the sample, the gates, and the nanoSQUID circuit are all simultaneously grounded. If you forget to float one of these circuits and bias the SQUID or gate the device, you can accidentally pump destructive amounts of current through the nanoSQUID or device. However, you must make sure that the z piezoelectric scanner is not grounded. You can now begin your approach to the surface. You should ground the nanoSQUID and the device. Connect the coarse positioner control cable to the cryostat. If you are in Andrea’s lab, verify that the three high current DB-9 cables going from the coarse positioner controller box to the box-to-cable adapter are plugged in in the correct positions. The cables for each channel all have the same connectors, so it is possible to mix up the x, y, and z axes of the coarse positioners. This is a very destructive mistake, because you will not be advancing to the surface and will likely crash the nanoSQUID into a wirebond, or some other feature away from the device. The remaining instructions assume you are using the nanoSQUID control software developed in Andrea’s lab, primarily by Marec Serlin and Trevor Arp. The software is a complete and self-contained scanning probe microscopy control system and user interface based on Python 3and PyQT. Open the coarse positioner control module. Click the small capacitor symbol. You should hear a little click and see 200 nF next to the symbol . The system has sent a pulse of AC voltage to the coarse positioners; the click comes from the piezoelectric crystal moving in response. Check that you see a number around 1000 µm in the resistive encoder window for axis 3 . Note whether you see a number around 2000-3000 µm in the windows for axis 1 and axis 2. If you are in Andrea’s lab, it is possible that you will not for axis 2. Axis 2 has had problems with its resistive encoder calibration curve at low temperature.

The issue seems to be an inaccurate LUT file in the firmware; new firmware can be uploaded using Attocube’s Daisy software. It is not a significant issue if you cannot use the axis 1 and 2 resistive encoders; however, it is critical that there be an accurate number for axis 3. Set the output voltage frequency to be somewhere in the range 5-25 Hz . Set the output voltage to 50 V to start . Make sure that the check box next to Output is checked. Move 10 µm toward the sample . If Axis 3 doesn’t move, don’t panic! It’s usually the case that the coarse positioners are sticky after cooling down the probe before they’ve been used. Try moving backwards and forwards, then increase the voltage to 55 V, then 60 V. Once they’re moving, decrease the voltage back to 50 V. Note the PLL behavior- if there’s a software issue and pulses aren’t being sent, you won’t see activity in the PLL associated with the coarse positioners. Under normal circumstances you should see considerable crosstalk between the PLL and the coarse positioners while the coarse positioners are firing. There are significant transients in the resistive encoder readings after firing the coarse positioners; this is likely a result of heating, but could also have a contribution from mechanical settling and creep. We have observed that the decay times of transients are significantly longer in the 300 mK system than in the 1.5 K or 4 K systems, likely indicating that these transients are largely limited by heat dissipation, at least at very low temperatures. Go into the General Approach Settings of the Approach Control window. There’s a setting in there for coarse positioner step size- set that to 4 µm or so. This is the amount the coarse positioners will attempt to move between fine scanner extensions. They always overshoot this number . Overshooting is of course dangerous because it can produce crashes if it is too egregious. In the Approach Control window, click Set PLL Threshold, verify that standard deviation of frequency is 0.25 Hz. Enter 5 µm into the height window.

Verify that Z is ungrounded . Click Constant Height. Check that the PID is producing an approach speed of 100 nm/s. It is important that you sit and watch the first few rounds of coarse positioner approach. This is boring, but it is important the first few coarse positioning steps often cause the tuning fork to settle and change, which can cause the approach to accelerate or fail. Also by observing this part of the process you can often find simple, obvious issues that you’ve overlooked while setting up the approach. Getting to the surface will take several hours. Typically you’ll want to leave during this time. When you return, the tip should be at constant height. I’d recommend clicking constant height again and approaching to contact again to verify that you’re at the surface. You should be between 10 µm and 20 µm from the surface. It may be necessary to withdraw, approach with the coarse positioners a few µm, and then approach again to ensure you have enough scanner range in the z direction. Click withdraw until you’re fully withdrawn. Click Frustrate Feedback to enable scanning with tip withdrawn. I will present instructions as if you are attempting to navigate to a device through which you can flow current. This will generate gradients in temperature from dissipation and ambient magnetic fields through the Biot-Savart law, both of which the nanoSQUID sensor can detect. I strongly recommend that you navigate with thermal gradients if at all possible. The magnetic field is a signed quantity, so you need to have a pretty strong model and a clear picture of your starting location to successfully use it to navigate. Thermal gradients can be handled with simple gradient ascent; this will almost always lead you to the region of your circuit with the greatest resistance, which is typically an exfoliated heterostructure if that is what you’re studying. A pressure of a few mBar is plenty, flower buckets wholesale but be advised that this may require that you operate at elevated temperatures.Helium 4 has plenty of vapor pressure at 1.5 K, but this is not really an option at 300 mK, and many 300 mK systems struggle with stable operation at any temperature between 300 mK and 4 K. You should run an AC current through your device at finite frequency. Higher frequencies will generally improve the sensitivity of the nanoSQUID, but if the heterostructure has finite resistance the impedance of the device might prevent operation at very high frequency. It’s worth mentioning that the ‘circuit’ you have made has some extremely nonstandard ‘circuit elements’ in it, because it relies on heat conduction and convection from the device through the helium atmosphere to the nanoSQUID. If you don’t know how to compute the frequency-dependent impedance of heat flow through gaseous helium at 1.5K, then that’s fine, because I don’t either! I only mention it because it’s important to keep in mind that just because your electrical circuit isn’t encountering large phase shifts and high impedance, doesn’t mean the thermal signal is getting to your nanoSQUID without significant impedance.

I recommend operating at a relatively low frequency for these reasons, as long as the noise floor is tolerable. In practice this generally means a few kHz. I’d also like to point out that if you are applying a current to your device at a frequency ω, then generally the dominant component of the thermal signal detected by the nanoSQUID will be at 2 · ω, because dissipation is symmetric in current direction . Next you will perform your first thermal scan, 10-20 µm above the surface near your first touchdown point. If you have performed a thermal characterization, then pick a region with high thermal sensitivity, but generally this is unnecessary- I usually simply attempt to thermally navigate with a point that has good magnetic sensitivity. Bias the SQUID to a region with good sensitivity. Check the transfer function. Set the second oscillator on the Zurich to a frequency that is low noise . Connect the second output of the Zurich to the trigger of one of the transport lock-ins and trigger the transport lock-in off of it. Trigger the second transport lock-in off of the first one. Attach the output of one of the lock-ins to the 1/10 voltage divider, then to a contact of the sample. Attach the current input of one of the lock-ins to another contact as the drain. You can attach the voltage contacts somewhere if you want to, this is not particularly important though. It may be necessary to a apply a voltage to the gates, especially if you are working with semiconducting materials, like the transition metal dichalcogenides.There are a lot of issues that can affect scanning, and it isn’t really possible to cover all of themin this document, so you will have to rely on accumulated experience. Some problems will become obvious if you just sit and think about them- for example, if the thermal gradient is precisely along the x-axis and coarse positioner navigation is failing to find a strong local maximum it likely means that the y-axis scanner is disconnected or damaged. In Andrea’s lab, the basic circuits on the 1.5K and 300 mK systems as currently set up should be pretty close to working, so if there’s a problem I’d recommend observing the relevant circuits and thinking about the situation for at least a few minutes before making big changes. The scanners as currently installed on the 1.5K system do not constitute a healthy right-handed coordinate system, so to navigate you will need a lookup table translating scanner axes into coarse positioner axes. I think this issue is resolved on the 300 mK system, but this is the kind of thing that can get scrambled by upgrades and repair campaigns. In all of our note taking Power points and EndNotes, we have a little blue matrix that relates the scan axes to the coarse positioner axes. Use this to determine and write down the direction you need to move in the coarse positioner axes in your notes. You now have an initial direction in which you can start travelling.

The type of magnet proposed here does not invoke spin-orbit coupling; in fact, it does not even invoke spin. Instead, the two symmetry-broken states are themselves electronic bands that live on the crystal, and they differ from each other in both momentum space and real space. For this reason, orbital magnetism does not need spin-orbit coupling to support hysteresis, and it can couple to a much wider variety of physical phenomena than spin magnetism can- indeed, anything that affects the electronic band structure or real space wave function is fair game. For this reason we can expect to encounter many of the phenomena we normally associate with spin-orbit coupling in orbital magnets that do not possess it. I would also like to talk briefly about magnetic moments. It has already been said that magnetic moments in orbital magnets come from center-of-mass angular momentum of electrons, which makes them in some ways simpler and less mysterious than magnetic moments derived from electron spin. However, I didn’t tell you how to compute the angular momentum of an electronic band, only that it can be done. It is a somewhat more involved process to do at any level of generality than I’m willing to attempt here- it is described briefly in a later chapter- but suffice to say that it depends on details of band structure and interaction effects, which themselves depend on electron density and, in two dimensional materials, ambient conditions like displacement field. For this reason we can expect the magnitude of the magnetic moment of the valley degree of freedom to be much more sensitive to variables we can control than the magnetic moment of the electron spin, plastic pot manufacturers which is almost always close to 1 µB. In particular, the magnetization of an orbital magnet can be vanishingly small, or it can increase far above the maximum possible magnetization of a spin ferromagnet of 1 µB per electron.

Under a very limited and specific set of conditions we can precisely calculate the contribution of the orbital magnetic moment to the magnetization, and that will be discussed in detail later as well. Finally, I want to talk briefly about coercive fields. The more perceptive readers may have already noticed that we have broken the argument we used to understand magnetic inversion in spin magnets. The valley degree of freedom is a pair of electronic bands, and is thus bound to the two dimensional crystalline lattice- there is no sense in which we can continuously cant it into the plane while performing magnetic inversion. But of course, we have to expect that it is possible to apply a large magnetic field, couple to the magnetic moment of the valley µ, and eventually reach an energyµ · BC = EI at which magnetic inversion occurs. But what can we use for the Ising anisotropy energy EI ? It turns out that this model survives in the sense that we can make up a constant for EI and use it to understand some basic features of the coercive fields of orbital magnets, but where EI comes from in these systems remains somewhat mysterious. It is likely that it represents the difference in energy between the valley polarized ground state and some minimal-energy path through the spin and valley degenerate subspace, involving hybridized or intervalley coherent states in the intermediate regime. But we don’t need to understand this aspect of the model to draw some useful insights from it, as we will see later.Real magnets are composed of constituent magnetic moments that can be modelled as infinitesimal circulating currents, or charges with finite angular momentum. It can be shown that the magnetic fields generated by the sum total of a uniform two dimensional distribution of these circulating currents- i.e., by a region of uniform magnetization- is precisely equivalent to the magnetic field generated by the current travelling around the edge of that two dimensional uniformly magnetized region through the Biot-Savart law. It turns out that this analogy is complete; it is also the case that a two dimensional region of uniform magnetization also experiences the same forces and torques in a magnetic field as an equivalent circulating current.

The converse is also true- circulating currents can be modelled as two dimensional regions of uniform magnetization. The two pictures in fact are precisely equivalent. This is illustrated in Fig. 2.9. It is possible to prove this rigorously, but I will not do so here. One can say that in general, every phenomenon that produces a chiral current can be equivalently understood as a magnetization. All of the physical phenomena are preserved, although they need to be relabeled: Chiral edge currents are uniform magnetizations, and bulk gradients in magnetization are variations in bulk current current density.In the same way that the Berry phase impacts the kinematics of free electrons moving through a two slit interferometer, Berry curvature impacts the kinematics of electrons moving through a crystal. You’ll often hear people describe Berry curvature as a ‘magnetic field in momentum space.’ You already know how electrons with finite velocity in an ambient magnetic field acquire momentum transverse to their current momentum vector. We call this the Lorentz force. Well, electrons with finite momentum in ‘ambient Berry curvature’ acquire momentum transverse to their current momentum vector. The difference is that magnetic fields vary in real space, and we like to look at maps of their real space distribution. Magnetic fields do not ‘vary in momentum space,’ at nonrelativistic velocities they are strictly functions of position, not of momentum. Berry curvature does not vary in real space within a crystal. It does, however, vary in momentum space; it is strictly a function of momentum within a band. And of course Berry curvature impacts the kinematics of electrons in crystals. Condensed matter physicists love to say that particular phenomena are ‘quantum mechanical’ in nature. Of course this is a rather poorly-defined description of a phenomenon; all phenomena in condensed matter depend on quantum mechanics at some level. Sometimes this means that a phenomenon relies on the existence of a discrete spectrum of energy eigenstates.

At other times it means that the phenomenon relies on the existence of the mysterious internal degree of freedom wave functions are known to have: the quantum phase. I hope it is clear that Berry curvature and all its associated phenomena are the latter kind of quantum mechanical effect. Berry curvature comes from the evolution of an electron’s quantum phase through the Brillouin zone of a crystal in momentum space. It impacts the kinematics of electrons for the same reason it impacts interferometry experiments on free electrons; the quantum phase has gauge freedom and is thus usually safely neglected, but relative quantum phase does not, so whenever coherent wave functions are being interfered with each other, scattered off each other, or made to match boundary conditions in a ‘standing wave,’ as in a crystal, we can expect the kinematics of electrons to be affected. We will shortly encounter a variety of surprising and fascinating consequences of the presence of this new property of a crystal. Berry curvature is not present in every crystal- in some crystals there exist symmetries that prevent it from arising- but it is very common, and many materials with which the reader is likely familiar have substantial Berry curvature, including transition metal magnets, black plastic plant pots wholesale many III-V semiconductors, and many elemental heavy metals. It is a property of bands in every number of dimensions, although the consequences of finite Berry curvature vary dramatically for systems with different numbers of dimensions. A plot of the Berry curvature in face-centered cubic iron is presented in the following reference: [84, 90]. We will not be discussing this material in any amount of detail,the only point I’d like you to take away from it is that Berry curvature is really quite common. For reasons that have already been extensively discussed, we will focus on Berry curvature in two dimensional systems.Several chapters of this thesis focus on the properties of a particular class of magnetic insulator that can exist in two dimensional crystals. These materials share many of the same properties with the magnetic insulators described in Chapter 2. They can have finite magnetization at zero field, and this property is often accompanied by magnetic hysteresis. The spectrum of quantum states available in the bulk of the crystal is gapped, and as a result they are bulk electrical and thermal insulators. They have magnetic domain walls that can move around in response to the application of an external magnetic field, or alternatively be pinned to structural disorder. And of course they emit magnetic fields which can be detected by magnetometers.Unlike all trivial insulators and, in particular, trivial magnetic insulators, these magnetic insulators support a continuous spectrum of quantum states within the gap, with the significant caveat that these states are highly localized to the edges of the two dimensional crystalline magnet .

This is the primary consequence of a non-zero Chern number. These quantum states are often referred to as ‘edge states’ or ‘chiral edge states,’ and they have a set of properties that are reasonably easy to demonstrate theoretically. I will describe the origin of these basic properties only qualitatively here; a deep theoretical understanding of their origin is not important for understanding this work, so long as the reader is willing to accept that the presence of these quantum states is a simple consequence of the quantized total Berry curvature of the set of filled bands. Many more details are available in [84]. These materials are known collectively as Chern insulators, magnetic Chern insulators, or Chern magnets. They are, as mentioned, restricted to two dimensional crystals; three dimensional analogues exist but have significantly different properties. The vast majority of this thesis will be spent exploring deeper consequences and subtle but significant implications of the presence of these states. We will start, however, with a discussion of the most basic properties of chiral edge states. Astute readers may have already noticed that all real materials have many electronic bands, and every band has its own Berry curvature Ωn, so the definition provided in equation 3.4 seems to assign a Chern number to each of the bands in a material, not to the material itself. The properties of a particular two dimensional crystal are determined by the total Chern number of the set of filled bands within that crystal, obtained by adding up the Chern numbers of each of its filled bands. The total Chern number determines the number of edge states available at the Fermi level within the gap.In the absence of spin-orbit coupling, every band comes with a twofold degeneracy generated by the spin degree of freedom. Every band can be populated either by a spin up or a spin down electron, and as a result every Bloch state is really a twofold degenerate Bloch state. Adding spinorbit coupling may mix these states but does not break this twofold degeneracy. An important property of the Chern number is that Kramers’ pairs must have opposite-signed Chern numbers equal in magnitude. This is a direct consequence of similar restrictions on Berry curvature within bands. For a magnetic insulator the set of filled bands is a spontaneously broken symmetry, with the system’s conduction and valence bands hysteretically swapping two members of a Kramers’ pair in response to excursions in magnetic field. These two facts together imply that magnetic hysteresis loops of Chern magnets generally produce hysteresis in the total Chern number of the filled bands, precisely following hysteresis in the magnetization of the two dimensional crystal. This hysteresis loop switches the total Chern number of the filled bands between positive and negative integers of equal magnitude. These facts also imply that finite Chern numbers cannot exist in these kinds of systems without magnetism- if both members of a Kramers’ pair are occupied, the system will have a total Chern number of zero.As discussed previously, additional symmetries of the crystalline lattice itself can produce additional degeneracies that can support spontaneous symmetry breaking and magnetism. In most cases similar rules apply to the Chern numbers of these magnets. We will have a lot more to say about the Chern numbers associated with the valley degree of freedom in graphene.

The cardiometabolic benefits from regular consumption of nuts or berries are widely reported and include improved vascular function, reduction of cardiovascular disease risk factors, improved insulin sensitivity, and reduced risk of type 2 diabetes mellitus. Antioxidant and anti-inflammatory capacity and activity have also been noted. Metabolic outcomes may be context-specific and related to the physiologic state of the individual and host microbiome composition, among other factors. Examples include findings of ellagitannin and ellagic acid rich foods resulting in differential responses in healthy individuals compared to those with prediabetes, who are dependent on gut microbial-derived metabolite profiles. Many factors contribute to interindividual variability in response to diet that can extend to context-specific aspects influencing the magnitude of health benefits and reinforces the importance for further research aimed at advancing discoveries in precision nutrition. Additional health outcomes related to nut or berry intake are outlined below.Adding nuts or berries to the daily diet may be advantageous for weight management for several physiological reasons. One is that these foods produce feelings of satiety, helping to reduce the desire to consume calorie-rich snacks that are low in vitamins, minerals, and fibers, ultimately improving body composition over time. A second possibility is due to urolithins, secondary metabolites produced from ellagitannins in nuts and berries. Urolithins increase the activation of the adenosine monophosphate-activated protein kinase pathway, resulting in anti-obesogenic properties in vitro and in animal models. AMPK increases fatty acid oxidation and decreases triglyceride accumulation. Phosphorylation of AMPK may also decrease cholesterol synthesis and lipogenesis by downregulating 3-hydroxy-3-methylglutaryl coenzyme A reductase activity and sterol regulatory-element binding protein expression. In clinical studies exploring the relationship between food and body composition, raspberry grow in pots the incorporation of nuts and berries into the diet was associated with weight loss or maintenance.

Regular consumption of nuts or berries has been reported to support brain health and cognitive function, motor control, mood, and executive function at physiologically relevant intakes. Middle-aged and older adults experienced improvements in balance, gait, and memory, and children experienced higher executive function and positive affect after acute and regular intake of both strawberries and blueberries. These beneficial effects may be the result of direct effects on brain signaling or indirect effects through oxidant defense and anti-inflammatory properties of polyphenols and other bioactive compounds in nuts and berry foods. The gut-brain axis is an emerging area of research. Most studies are preclinical in nature using animal models but are suggestive of a significant role of gut microbial-derived ellagitannin metabolites on brain health and neuroprotection. Regular intake of almonds, a good source of fatty acids and polyphenols, has been associated with a significant decrease in facial hyperpigmentation and wrinkle severity. A walnut protein hydrolysate administered to rats exposed to ultraviolet radiation significantly reduced skin photoaging and enhanced skin elasticity. Supplementation with ellagic acid, a compound found in many berries, prevented ultraviolet B -related inflammation and collagen degradation related to skin wrinkling and aging in a murine model. More human studies, using objective measures of skin wrinkles, skin elasticity and response to low-dose UVB radiation exposure are warranted. Monitoring skin responses to a UVB radiation challenge has been used as a marker of whole-body antioxidant status in response to almond consumption. The response to a UVB challenge has also been used to monitor oxidant defenses and changes in skin microbiome following the intake of pomegranate juice.Age-related macular degeneration is the third leading cause of vision loss worldwide .

Anthocyanins, carotenoids, flavonoids, and vitamins C and E, found in many berries, have been shown to reduce risk of eye-related diseases. Goji berries, containing the highest amount of zeaxanthin of any known food, hold particular promise since this compound binds to receptors in the macula to offer protection from blue and ultraviolet light. Regular supplementation with 28 g/d of goji berries for 3 mo increased macular pigment optical density, a biomarker for AMD, as well as the skin carotenoid index. Nuts may also be protective against AMD since they are a rich source of vitamin E and essential fatty acids. Regular intake of nuts has been associated with a reduced risk and slower progression of AMD in 2 epidemiological studies, thought to be due to the beneficial role of polyunsaturated fatty acids.Identification of new cultivars with traits desirable for growers, processors, and consumers is a continuous effort. As researchers continue to produce new varieties by both conventional and molecular-driven approaches, assessing these varieties for nutritional value is a challenge. A combination of broad targeted and untargeted metabolomic approaches, along with defined functional phenotyping could be used for rapid screening and defining of mechanistic pathways associated with health. However, consumer preferences for new cultivars are often driven by size and appearance of the berry or nut and flavor, rather than its nutritional value . This would further confirm the need to balance improvements to nutritional profiles with enhancement of consumer-driven traits, maintaining the marketable nature of the berries and nuts.Biomedical research, particularly for clinical studies, is expensive and resource intensive. Although the USDA competitive grants program offers funding for outstanding research projects, budget limitations favor animal or in vitro study proposals. Compelling pilot data is needed to be competitive for clinical studies funded by the USDA or NIH, so many researchers submit their initial ideas to commodity groups representing specific nuts or berries. Commodity groups represent farmers, processors, and distributors and have been instrumental in supporting fundamental and applied research focused on their specific berry or nut.

The perception that studies funded by nut and berry commodity groups are inherently biased in favor of the test food is an issue sometimes raised by critics, journalists, and the general public. As in all nutrition research, ethical considerations regarding the structure of research questions, hypotheses, study design, outcome measures, interpretation of data, and conclusions must be rigorously considered. The food and beverage industries have played a key role in providing funds and supporting nutrition research on individual foods and beverages, including berries and nuts. Although this draws scrutiny regarding scientific integrity and data reporting, collaboration between academia and industry compared to exclusive corporate funding may help offset some of these concerns. For example, in multiple reported studies, matching funds were also provided by non-industry sources, including institutional and federal agencies. In other cases, while the food industry provided the test agents, key research personnel and staff were not supported by the same funding source. The academia-industry collaboration has also led to the formation of scientific advisory committees that evaluate and recommend proposals for funding, a peer review process that helps ensure rigorous study designs, data reporting, and dissemination of results. Human studies of sufficient statistical power are expensive, labor-intensive efforts requiring sophisticated and costly laboratory equipment and supplies. In order for research proposals to be competitive for funding from the USDA or NIH, pilot data is required, and for nuts and berries, the only realistic source of funding for these exploratory trials is from industry sources. Critics of industry support for nutrition research have yet to propose realistic alternatives for funding needed to generate initial data. Further, 30 planter pot ongoing industry funding of nuts and berries research has yielded important insights into the molecular and physiological understanding of mechanisms of action. Without industry support, provided in an ethical and transparent manner, advances in our understanding of the role of nuts and berries in a healthy dietary pattern would be limited. A risk-of-bias study of 5675 journal articles used in systematic reviews published between 1930 and 2015, representing a wide variety of nutrition topics, concluded that ROB domains started to significantly decrease after 1990, and particularly after 2000. Another study examined the incidence of favorable outcomes reported in studies funded by the food industry in the 10 most-cited nutrition and dietetics journals in 2018. Of the 1461 articles included in the analysis, 196 reported industry support, with processed food and dietary supplement manufacturers supporting 68% of the studies included. Studies supported by any nut or berry commodity group were not considered due to an incidence lower than 3% of qualifying articles. Studies with food industry support reported favorable results in 56% of their articles, compared to 10% of articles with no industry involvement. The authors offer a number of suggestions to help minimize real or perceived bias, calling on research institutions to enforce strict, regularly updated, and transparent oversight of all research projects involving industry.

Suggestions in support of research transparency and integrity have also been advanced from guidelines adapted from the International Life Sciences Institute North America. This served as the basis for the development of consensus guiding principles for public-private partnerships developed by a group of representatives from academia, scientific societies and organizations, industry scientists, and the USDA, NIH, US Centers for Disease Control, and the US Food and Drug Administration. These provisions include full disclosure of funding and confirmation of no direct industry involvement in the study design, data and statistical analyses, and interpretation of the results and only minimal, if any, involvement of industry coauthor, often given as a courtesy to acknowledge funding and logistical support by the investigators with no intellectual involvement by the study sponsor. This is in contrast to industry-initiated research, where the industry office or commodity group sets predetermined research objectives, provides intellectual collaboration, and often has input on the study design, interpretation of results, and decisions regarding publication. Although some critics may argue that repeated industry funding in support of research groups that report favorable results on a particular nut or berry shows a bias toward positive outcomes, other interpretations are also possible. First, few labs have the infrastructure, detailed methodology and analytical equipment, and trained personnel to conduct clinical studies in an efficient and timely manner. Industry funded studies conducted at major universities have layers of review and accountability within their organizations to guard against malfeasance, and while these layers may not focus directly on precise elements of research design and interpretation of results, faculty members at such institutions generally have a level of integrity and accountability, knowing that administrative review exists. Calls for industry-funded research are often broad in scope, which allows researchers to generate proposals, research questions, and hypotheses that do not have preconceived outcomes. A third consideration is that the nuts or berries under study may simply have sufficient bio-activity to produce favorable outcomes, independent of potential researcher bias.This is not the case for two dimensional systems. Those readers with any exposure to introductory physics have likely encountered parallel plate capacitors; these are highly idealized systems composed of a pair of infinitely thin conducting sheets separated by a small insulating space of consistent thickness. When a voltage is applied to one of these sheets with the other connected to a reservoir of mobile electrons, a uniform charge density per unit area appears on both sheets . Of course, in real metallic capacitors the charge density per unit volume is often still not microscopically uniform because the sheets are not actually infinitely thin, so electrons can redistribute themselves in the out-of-plane direction. To achieve true uniformity one of the plates of the capacitor must be atomically thin, so that electrons simply cannot redistribute themselves in the out-of-plane direction in response to the local electric field. An efficient technique for preparing atomically thin pieces of crystalline graphite was discovered in 2004 by Dr. Andre Geim and Dr. Konstantin Novoselov, an achievement for which they shared the Nobel prize in physics in 2010. The technique involves encapsulating a crystal within a piece of scotch tape and repeatedly ripping the tape apart; it works because the out-of-plane bonds in graphite are much weaker than the in-plane bonds. Graphite represents something of an extreme example of this condition, but it is satisfied to varying extents by a large class of other materials, and as a result the technique was rapidly generalized to produce a variety of other two-dimensional crystals. By constructing a capacitor with one gate replaced with one of these two dimensional crystals, as shown in Fig. 1.1D, researchers can easily access electron density as an independent variable in a condensed matter system. These systems also facilitate an additional degree of control, with no real analogue in three dimensional systems. By placing capacitor plates on both sides of the two dimensional crystal and applying opposite voltages to the opposing gates, researchers can apply out-of-plane electric fields to these systems .

Our results in the second year corroborated those of the first year, showing that the separation in both plant water status and leaf gas exchange between the two zones were consistent. Leaf gas exchange was closely related to plant water status, and this relationship was shown in previous research . The relationships between leaf gas exchange and plant water status were evident in our study, where a higher 9 stem would promote a greater stomatal conductance to increase carbon assimilation capacity and decrease intrinsic water use efficiency. In our study, the lowest 9 stem we observed were around harvest with 9 stem of -1.6 MPa and gs of around 50 mmol H2O m−2 ·s −1 , which were not severe enough to impair berry ripening although the photosynthetic activities were still affected. Overall, the gs and AN reached the maximum values at veraison and declined with decreasing plant water status and leaf age toward the end of the season. This further affirmed that the continuous water deficits during the growing season, especially being more pronounced after irrigation was ended after veraison, would reduce stomatal conductance. The water deficits would act as passive hydraulic signals or active hormonal signals with the upregulation in abscisic acid synthesis to limit plant photosynthetic activities, hence lower gs and AN values .According to the previous research, components of yield may be affected by plant water status, where higher water deficits would result in reductions of yield, berry skin weight, and berry weight . In our study, blueberry production we observed constant separation in plant water status after veraison. However, there was no difference shown in cluster number, yield, berry number, or pruning weight.

The only difference measured in yield components was that berry skin weight was higher in Zone 1 in the second season. Early season water deficit irrigation had higher probability to decrease yield than later season water deficit irrigation . However, a season-long water deficit irrigation would have the lowest yield even despite the season-long water deficit irrigation regime applying double amount of water than the other regimes . Some other studies did not have the same results, as early water deficit irrigation did not show significant influences on yield compared to late water deficit irrigation . Another possible explanation was that Zone 1 had greater water amount held in the soil due to the higher clay content. The clay soil with higher water-holding capacity had a better water status at the early season compared to Zone 2, even though the sandy soil in Zone 2 would benefit the plant growth with irrigation when the season progressed . The later season water deficit was exacerbated in Zone 1 due to its higher clay content, causing Zone 1 lost the benefits from the high water status in the early season, and eventually had similar yield components with Zone 2 at harvest. In our work, we did not see any evidence of Ravaz index being affected by spatial variability of plant water status. These results were corroborated by Terry and Kurtural when grapevine cultivar ‘Syrah’ was exposed to post-veraison water deficits in comparable severity of -1.4 MPa .Water deficits affect advancement of grape berry maturity, they promote TSS accumulation and TA degradation in grape berries . Two factors contributed to these differences between the two zones. First, a greater water deficit advanced the berry maturation, leading to a higher TSS and lower TA . Second, berry dehydration may have occurred and the TSS concentration increased in the berries. In our study, smaller berries were observed in Zone 1, which can confirm the berry dehydration could have led to higher TSS in Zone 1. As for berry TA, one study showed that grape organic acids biodegradation would be faster with more solar radiation and higher temperature .

Although the acid degradation was not related to water deficits, like mentioned above, water deficits would limit the grapevines’ ability to regulate temperature . Thus, waterdeficits could promote the organic acid degradation and this effect was observed in this study.Mild water deficits increased the flavonoid content and concentration of red-skinned grape berry due to the upregulation in flavonoid synthesis and the advancement of berry dehydration during growing season . A positive relationship was noticed between soil bulk EC and total skin anthocyanins in 2017 at both depths of soil bulk EC measurements. A more prolonged severe water deficit would lead to deleterious stomatal and temperature regulation and eventually resulted in flavonoid degradation, specifically anthocyanins . This was a plausible explanation for the non-significant relationship between soil bulk EC and total skin anthocyanins in 2016, wherein harvest took place at higher soluble solids and Zone 1 berry skin anthocyanins were presumably in decline. Furthermore, the berry weights were higher in Zone 2, which was similar to the observations in our previous work , indicating there was less berry dehydration. Thus, the higher anthocyanins in Zone 2 was mainly due to the upregulation in anthocyanins other than anthocyanins degradation. These effects were also observed in the wines of 2016, where Zone 2 had higher anthocyanin concentrations. However, in the second season, the differences in berry skin anthocyanins at harvest did not carry over into the wines. We contributed this to the more advanced berry maturity levels at harvest in the first season, the skin cell walls could have become more porous during ripening and increased the extractability of flavonoid compounds . With relatively greater amounts of flavonoids extracted, there was a higher chance to pass on the separations of anthocyanins from the berries to the wines.

Grape berry skin proanthocyanidins are less sensitive toward water deficits than anthocyanins . Nevertheless, their biosynthesis and concentration may be modified by water deficits . In 2016, wine total proanthocyanidins and all the subunits were greater in Zone 2. These differences were not observed in the second season. We attributed this lack of consistency in proanthocyanidin disparities between the two zones to the more advanced maturity of the berries were harvested in 2016 than in 2017. We suggest that similar to skin anthocyanins, the more advanced berry maturity in 2016 could have promoted the proanthocyanidin extractability in the skin tissues , which may augment the separations in the concentration of all the subunits between the two zones.Monitoring and handling live tissues and cell cultures as well as analyzing their secreted contents are essential tasks in experimental biology and bio-medicine. Advances in microscopy have revolutionized biological studies, allowing scientists to perform observations of cellular processes and organisms’ development and behaviors. Imaging has been pivotal to uncovering cellular mechanisms behind biological processes. Several options exist on the market to perform longitudinal imaging of biological materials. These range from super-resolution microscopes, that allow the imaging of individual bio-molecules, to conventional benchtop microscopes, which are common in academic research, industrial, and teaching laboratories. When deciding between the different technologies for longitudinal live tissue imaging, several factors need to be considered in the experimental design. The image acquisition speed of the microscope should be sufficient for the phenomenon being studied. The microscope should be able to acquire images without damaging or disturbing the specimen, such as photo bleaching. The microscope should be capable of imaging in the environmental conditions needed for the desired experiment, including temperature, light, and humidity. The resolution of the microscope should be sufficient to view the phenomenon being studied. When scaling to simultaneous multi-well longitudinal tissue imaging it is also important that the apparatus not be bulky or expensive. It has been challenging to meet all of these criteria. The use of open-source technology, including 3D printers, laser cutters, blueberry in container and low-cost computer hardware, has democratized access to rapid prototyping tools and dramatically increased the repertoire of biomedical equipment available to laboratories around the world. Through rapid prototyping and the use of open-source platforms, the technology can be replicated and quickly improved. 3D printer technology has been applied to several fields in bio-medicine, including biotechnology, bioengineering, and medical applications including fabrication of tissues and organs, casts, implants, and prostheses. Existing 3D printed microscopes range in complexity from simple low-cost systems with pre-loaded imaging modules to portable confocal microscopes capable of imaging individual molecules and even 3D printed microfluidic bioreactors. The majority of low-cost 3D printed microscopes are not intended for longitudinal imaging of simultaneous biological cultures . They usually have a single imaging unit or perform confocal, and even light-sheet imaging. Other systems have taken advantage of one camera attached to a gantry system to perform imaging of multiple experimental replicates. Few 3D-printed microscopes have been developed that perform multi-well imaging with medium throughput.

Several biological applications exist that would greatly benefit from multi-well, multi-week simultaneous imaging, as it allows for concurrent interrogation of different experimental conditions and the inclusion of biological replicates. These include cell culture applications, in which 2D and 3D culture models can be tracked over multi-week periods, as well as developmental and behavioral biology experiments in which multi-week tracking could be performed on whole organisms. Here, we report a simultaneous multi-well imaging system , which features a low-cost per well and performs longitudinal bright field z-stack imaging of 24-well cell culture plates. Images are uploaded to a server as they are captured allowing the users to view the results in near real time. We used this system to longitudinally track different animal models of development and regeneration, including Xenopus tropicalis , Danio rerio , and planaria worms. Finally, we demonstrate this system’s versatility by imaging human embryonic stem cells and 3D cortical organoids inside a standard tissue culture incubator. We demonstrate that the Picroscope is a robust low-cost, versatile multi-well imaging system for longitudinal live imaging biological studies.System design. The Picroscope is a programmable, data rich, sensor-per-well simultaneous imaging system for longitudinal bright field imaging to automate microscopy . The system simultaneously images in each one of the 24 wells multiple focal planes several times every hour for weeks, a frequency impractical to perform manually. The instrument is made using off-the-shelf components , and 3D printed Polylactic acid components with 100% infill . Cost comparison with other open-source microscope projects can be found in Table 1. A cost breakdown of the materials required can be found in Table 2. The Picroscope has been used to image Planaria worms , Xenopus tropicalis , as well as zebrafish . The system was developed to be operated remotely through the internet. Users can set and change the device settings to modify experiments on the fly. Images captured by the system are uploaded to a server where they become visible on a viewer website. We have also created several image analyses scripts that can directly access images on the server, allowing us to generate timelapse videos and composite images in an automated fashion. While the system receives commands and transmits results through the Internet, it is also capable of running on a Local Area Network if internet access is not available. Figure 2 shows the basic workflow from control console to image viewer. Further details about the software and network architecture developed to implement these features can be found in .Diffused illumination from below results in images that show contours and surface features, this is particularly useful when the sample is opaque. Illumination from above typically works best for samples that are sufficiently translucent and can show internal structures as the light can pass through the sample. The flexibility of using different illumination techniques emulates commercial bright field microscopes. The difference from over and under light is best shown in Supplementary Fig. 1. The 3D printed plate holder supports the biological sample during an experiment. For easy alignment, the holder is attached to a xy sliding stage that consists of two interconnected linear stages . The inner stage translating along the y-axis uses 8 leaf springs to connect a central piece holding the 24-well plate with four rigid elements surrounding it. The outer stage translating along the x-axis uses 8 additional leaf springs to connect the inner stage with the outside 4 rigid elements, two of them being connected to the Picroscope frame using 4 screws . While each stage is flexible along one axis , together they can slide along both, x and y axes. Each stage is actuated by two adjustment screws depicted as gray arrows in Fig. 3f.

Here we show that combining Iso-Seq with Illumina sequencing at high coverage enables expression profiling and sequence error correction of IsoSeq reads, particularly those derived from low-expression genes. The clustering analysis of the SMRT link pipeline discarded  18.5% of the FLNC reads, likely caused by low sequence accuracy. To overcome this technical issue, we applied a hybrid error correction pipeline consisting in performing the error correction of the unclustered FLNC reads, followed by an additional clustering step of both to resolve redundancies. Error correction with Illumina reads recovered a significant amount of Iso-Seq reads that would have otherwise been removed by the standard Iso-Seq pipeline, highlighting the importance of integrating multiple sequencing technologies with complementary features . Transcriptome reconstruction has been widely used to develop references for genome-wide expression profiling in the absence of an annotated genome assembly . Though a genome reference is available for grape, transcriptome reconstruction overcomes the limitations of a cultivar-specific reference that lacks the gene content of other cultivars. Although cultivar-specific genes appear nonessential for berry development, those private genes could contribute to cultivar characteristics. For example, the wine grape Tannat accumulates unusually high levels of polyphenols in the berry; its cultivar specific genes account for more than 80% of the expression of phenolic and polyphenolic compound biosynthetic enzymes . De novo transcriptome assembly from short RNA-seq reads has been used to explore the gene content diversity in Tannat , Corvina , and Nebbiolo . Iso-Seq identified 1,501 Cabernet Sauvignon transcripts expressed during berry development that were found in neither the genome of PN40024 nor the transcriptomes of Tannat, Nebbiolo and Corvina. Some private Cabernet Sauvignon transcripts have functions potentially associated with traits characteristic of Cabernet Sauvignon grapes and wines like their color and sugar content.

These transcripts included three sugar transporter-coding genes, big plastic pots which could be involved in the accumulation of glucose and fructose during berry ripening , and a chalcone synthase, a flavanone 3-hydroxylase, and a flavonoid 39-hydroxylase, all involved in the flavonoid pathway. Chalcone synthases catalyze the first committed step of the flavonoid biosynthesis pathway , which produces different classes of metabolites in grape berry, including flavonols , flavan-3-ols and proanthocyanidins , and anthocyanins . In addition, products of the flavonoid 39-hydroxylase can lead to the synthesis of cyanidin-3-glucoside, a red anthocyanin . The analysis of the gene space in the genome assembly showed that private Cabernet Sauvignon genes identified using Iso-Seq are only a fraction of the private Cabernet Sauvignon transcriptome. As in other transcriptome reconstruction methods, Iso-Seq can only identify transcripts that are expressed in the organs and developmental stages used for RNA sequencing. Obtaining the full set of private transcripts without genome assembly would require sequencing additional organs and developmental stages. In addition, it is challenging to differentiate isoforms derived from close paralogous genes, alleles of the same gene, and alternative splicing variants, in any transcriptome obtained by RNA sequencing ; this potentially leads to an overestimation of the genes in the final transcriptome reference. This study could not resolve isoform redundancy in the final transcriptome for about 37% of the gene loci in the Cabernet Sauvignon genome. This is a limitation of Iso-Seq as well as of all transcriptome references that cannot be overcome without a complete genome assembly. In this study, we tested whether the transcriptome reconstructed using Iso-Seq can be used for expression profiling. Only an approximately 3% difference in read alignment between ISNT and the genome reference was observed, implying that at high coverage, ISNT detects almost all genes expressed during berry development.

The slight difference in mapping rate between the two references can be explained by either the absence of some low-expression transcripts in the ISNT or the residual error rate in isoform sequences. Gene expression analysis using the ISNT as reference showed similar results compared to the Cabernet Sauvignon genome assembly, with a very high correlation of expression level and differential gene expression, and with similar global transcriptomic changes. However, we observed differences in the number of expressed and differentially expressed features that depend on the reference used. Those differences could be explained by the diploid phasing of the Cabernet Sauvignon genome assembly and that multiple ISNT transcripts might correspond to a single gene locus. Nonetheless, similar relative amounts of Biological Process GO terms were found among the differentially expressed genes, confirming that the transcriptome obtained using Iso-Seq captured the transcriptional reprogramming underlying the main physiological and biochemical changes during grape berry development. In addition, gene expression analysis revealed that some private isoforms are significantly modulated during berry development, indicating that in addition to identifying the private gene space, the ISNT reference makesit possible to observe its expression. In conclusion, this study demonstrates that Iso-Seq data can be used to create and refine a comprehensive reference transcriptome that represents most genes expressed in a tissue undergoing extensive transcriptional reprogramming during development. In grapes, this approach can aid developing transcriptome references and is particularly valuable given diverse cultivars with private transcripts and accessions that are genetically distant from available genome references, like the non-vinifera Vitis species used as rootstocks or for breeding. The pipeline described here can be useful in efforts to reconstruct the gene space in plant species with large and complex genomes still unresolved.

Agriculture plays an important role in economic growth, and improving crop yield is of great concern in Vietnam. On the one hand, insect pesticides can affect the metabolic processes of crops to degrade crop yield and quality. On the other hand, fruit flies are known to cause 50 to 100% crop loss unless timely interventions are implemented. There are just a small number of fruit fly species that have been discovered, namely Bactrocera dorsalis, B. correcta, B. cucurbitae, B. tau, B. latifrons, B. zonata, B. tuberculata, B. moroides and B. albistriga, while some species remain unidentified. The species which are harmful to fruits are of the common fruit fly species, namely B. cucurbitae and B. tau. To optimize crop yields, agricultural workers tend to use a pesticide scheduler rather than consider the likelihood of pests’ presence in the crop. Thus, this not only causes many pesticide residues in agricultural commodities but also brings great pressure to the ecological environment. The overuse of pesticides is partly because information about pest species and densities cannot be provided in a timely and accurate way. In contrast, if the information is provided in atimely fashion, it could be possible to take proper prevention steps and adopt suitable pest management strategies including the rational use of pesticides . Traditionally, the information about the environment and pest species is acquired mainly through handcrafted feature engineering such that workers manually use sensors and compare a pest’s shape, color, texture, and other characteristics with justification from the domain experts. Likewise, counting is typically time-consuming, labor intensive, and error-prone. Therefore, it is urgent and significant to establish an autonomous and accurate pest identification system. There is a growing tendency of utilizing machine vision technology to solve these problems with promising performance in the agriculture research field. In this work, growing berries in containers we focus on developing a solution to detect oriental yellow flies which usually harm citrus fruits such as oranges and grapefruits. We implement and evaluate the object detection models by applying the models with test sets simulating potential disturbances occurring in real scenario. Additionally, the work presented in this paper will not only focus on the use of different types of object detection algorithms but also apply the TFLITE format of the models compatible to edge device system such as TPU processors. This direction of study is to develop real-time detection application with the emerging edge computing technology to enhance the performance of the system in terms of detection accuracy, power efficiency, and latency reduction with the purpose of detecting the living fruit flies beside the stuck and dead ones on the trap. Moreover, the article will describe the hardware implementation so that the work can be reproduced and further developed. Our contributions are: We constructed, developed, and provided a more in-depth discussion of the end-to-end camera-equipped trap, named AlertTrap with installation of a Lynfield-inspired sticky trap, to instantly detect fruit flies and the solar-energy powering system controlled by a separate Raspberry Pi. We evaluate three different compact and fast object detection deep learning models, namely SSDMobileNetV1, SSD-MobileNetV2, and the Yolov4-tiny. Nevertheless, we introduce artificial disturbances imitating inference effects which may compromise the detection performance in real-time scenario. Moreover, we also evaluate the SSD-MobileNetV1 and SSD-MobileNetV2 models with their TFLITE format versions on a TPU device. With the results, we compare not only their ability to accurately detect and localize the fruit flies which we had trained them to predict, but also the increase in processing speed as well as the power saving factor.Insect detection techniques can be classified into three system types, namely manual, automatic, or semi-automatic systems.

Manual insect detection techniques are known as a process in which trained workers count the trapped flies on a daily basis. These turn out to be error-prone, time consuming, and labor-intensive, while semi-automatic and automatic systems can address the disadvantages with the replacement of highly accurate and autonomous emerging technological software and hardware. Specifically, the remaining two types of insect detection systems are often called e-traps as they are fueled by electronic components with extensive computer algorithms such as a center-controlled unit connecting with a camera and the trap actuators. Thus, they are also known as vision based insect traps. As suggested in the names, the automatic insect detection systems are fully autonomous, whereas the semi-automatic ones involve human interaction in the loop. For example, in [24], the images of insect body parts are classified to aid humans to better categorize the insects. Generally, the e-traps are equipped with a wide range of post-processing techniques to detect and classify trapped insects. These techniques are recognized by the sensor type that is used to capture the existence of insects in the trap. Particularly, they are image based, spectroscopy-based, and optoacoustic techniques, which correspond respectively to the visible-light camera, the near-infrared camera, and the ultrasound sensor. The image-based techniques consist of three sub-domain techniques, namely deep learning and shallow learning, which both are sub-domains in the machine learning field, and image processing techniques. Shallow learning-wise, Kaya et al. created a machine learning-based classifier that can differentiate between 14 butterfly species. The texture and color characteristics are extracted by the writers. A three-layer neural network is used to process the extracted features. The categorization accuracy achieved is 92.85 percent. The detection approach is based on image processing as described in. While image-processing techniques are simpler than deep learning techniques, their accuracy is reasonable and the system is wired with the illumination environment. However, extensive feature engineering must take place prior to the classification. By using auto-brightness adjustment, the algorithm first reduces the effect of changing lighting and weather conditions. Then, using a coordinate logic filter improves the edges by amplifying the difference between the dark bug and the bright background. Finally, the technique uses a circular Hough transform followed by a noise reduction filter to identify the trap’s limits. The achieved accuracy rate is 75%. In [14], it was reported that a Wireless Sensor Network was created for detecting pests in greenhouses. The image processing technique first removes the effect of light changes from the photos, then denoises them, and finally recognizes the blobs. In [15], it was suggested that insect image processing, segmentation, and sorting algorithms could be used as insect “soup” images. In insect “soup” photos, the insects float on the liquid surface. The method was evaluated on 19 soup images by the authors, and it worked well for many of them. Using McPhail traps, a WSN was developed to detect the olive fruit fly and medfly in the field. WSNs are sensor networks that gather data and may be built to process information and transfer it to humans. WSNs may also have actuators that respond to specific events. The template comparison algorithm is the detection algorithm. The identification is based on the detection of specific anatomical, patterning, and color characteristics. Near Infrared Spectroscopy was used to identify infested olives in harvested crops. The Genetic Algorithm extracts the features from the collected full spectral data. The retrieved features serve as the input for the classifier.

The treatments strongly affected anthocyanin compounds in both seasons. In 2019, at harvest, 50 and 100% ETc increased the proportion of peonidins and 25% ETc had a significantly higher proportion of petunidin derivatives. Flavonol composition was only affected by irrigation treatments during the 2020 growing season at harvest . Myricetin and quercetin derivatives were the main flavonols found in Cabernet Sauvignon berry skins and both accounted for about 75% of the total amount. The most restrictive applied water treatment increased proportion quercetins and kaempferols, while 100% ETc increased myricetins and syringetins.This study evaluated the effect of applied water amounts based on the replacement of fractions of the ETc for maintaining berry quality while minimizing yield losses due to the environmental impact . Results covered two seasons that strongly differed in the precipitation supply. Compared with the average total amount of precipitation received by the area in the last decade , 2019 growing season was a rainy period with 970.3 mm precipitation, while 2020 was a hyperarid growing season with only 234.2 mm of precipitation. In spite of the differences in total precipitation, the response of Cabernet Sauvignon grapevines to water deficits was consistent across both seasons and our results corroborated that deficit irrigation may mitigate the effects of water scarcity . The results achieved in this study indicated that 25% and 50% ETc treatments were effective in improving iWUE compared with previous studies reporting a compilation of data from Cabernet Sauvignon and other cultivars . The iWUE decreased when the fractions of appliedETc increased as previously reported by Keller et al. in a 3-year field experiment conducted on the same Cabernet Sauvignon clone as used in this study. Likewise, planting blueberries in containers WUEc calculated as the ratio between yield and water applied was also enhanced with the decreased water supply.

The berry must δ 13C enhanced under stronger water deficits conditions corroborating previous studies with different grapevine cultivars . The iWUE and the berry must δ 13C also indicated a linear relationship in accordance to previous research . Previous work indicated that δ 13C of grape must is a reliable indicator of plant water status and leaf gas exchange in vineyard systems, which in turn, are crucial for the identification of plant water status zones leading to better irrigation decisions and informed management . The present study also provided evidence that δ 13C is a convenient tool without intensive labor and time inputs for the assessment of environmental impacts of deficit irrigation strategies. Increased applied water amounts led to greater canopy size and yields . There was a strong negative relationship between the berry must δ 13C and grapevine vegetative growth measured as LAI. Previous studies reported a linear relationship between the δ 13C and the carbon assimilation rates and consequently with vegetative growth estimated as pruning mass . Yield achieved in this experiment ranged from 4.8 to 10.4 kg · vine−1 in accordance to a previous study conducted in a vineyard at a similar density . Thus, 100% ETc may double the yield compared with the 25% ETc as previously reported by Keller et al. . This suggested that the effect of applied water on yield components is consistent in spite of the climate difference, planting space, and grapevine age. Under our experimental conditions, primary metabolites were affected by applied water amounts in the second season, where 100% ETc accounted for lower TSS but higher pH. Increased water content in berries was associated with a lower concentration of sugars due to a dilution effect . Conversely, the lower pH in 25% ETc grapevines was related to exacerbated organic acid degradation under high temperatures by water deficit . Berry skin flavonol and anthocyanin contents decreased with the 100% ETc in 2019 but not in 2020.

Although several studies reported increases in berry flavonoid content under mild or moderate water deficit , field research conducted in California resulted in contradictory results when severe water deficits were combined with a long hang time . In general, 100% ETc irrigation treatment reduced the proportion of petunidin derivatives and increased the proportion of peonidin derivatives leading to a decreased ratio between tri-hydroxylated and di-hydroxylated anthocyanins, which was suggested to be less chemically stable for winemaking purposes . Likewise, previous studies have reported an increase in the ratio between tri-hydroxylated and dihydroxylated anthocyanins when grapevines were subjected towater deficits given the upregulation of the relevant anthocyanin biosynthetic genes . In addition, these forms were more persistent through hang time, making trihydroxylated flavonoids more abundant as maturity progressed . Flavonol composition was modified by applied water amounts in 2020 growing, where proportions of myricetin and syringetin derivatives increased and quercetin and kaempferol derivatives decreased with 100% ETc. Given that quercetin and kaempferol are important antioxidants in red wines, this shift in the composition may impact the antioxidant properties of wine . In previous work, it was reported that 100% ETc irrigation increased the net carbon assimilation and improved the grapevine water status, leading to higher soluble sugar and starch contents in leaves with the highest yields, and vegetative biomasses . However, the greatest leaf area to fruit ratios measured in this treatment showed a clear sign of disproportionate leaf biomass growth, which presumably impacted berry metabolism. Thus, both studies highlighted the importance of management of water deficits to ensure grape berry composition optimization, improving water use sustainability by rewarding quality over quantity in arid and semiarid regions .Decreasing irrigation amounts increased AMF colonization in accordance with previous studies .

The symbiotic relationship of AMF with grapevines provided several adaptive advantages, such as improved abiotic and biotic stress resistance, enhanced nutrient uptake, and grapevine growth . Previous research suggested that these effects might be related to the altered regulation of nutrient transport, cell wall-related, phenylpropanoid, and stilbene biosynthesis genes driven by AMF colonization . Additionally, it was recently reported that AMF may enhance the content of flavonoids in berries , leading to improved berry composition and antioxidant properties in spite of the lack of effect on petiole nutrient contents . However, vineyard management practices may affect the soil structure and the composition of the rhizosphere-living microbiota , as well as the microbiota associated with grapevine roots, which is mainly composed by Rhizophagus and Glomus genus , likely affecting the effectiveness of the symbiosis. The relationship between AMF and berry must δ 13C suggested that productivity of high quality grapes could still be sustained in this region with less water input because the root system of the grapevines may perform more efficiently due to greater AMF colonization. The totalWF measured in this study ranged between 484.3 and 1237.7 m3 ·tonne −1 across treatments and growing seasons, in accordance with previous studies assessing the WF of grapevine cultivation . This variation in the totalWF was related to the amount applied and the differences in precipitation between the two seasons . Indeed, the previous research speculated that changes in temperature and precipitation may affect the proportional contribution of blue and green WF to the totalWF . Previous studies reported that vineyards accounted for a higher WF compared with other crops such as olives, wheat, and other fruit trees . Wine grape growers require appropriate irrigation schedules that reduce blueWF and increase greenWF leading to a decreased totalWF for increasing sustainability of vineyards. Under our experimental conditions, 25% ETc strongly decreased the blueWF, however, container growing raspberries this came with a dramatic increase in the grayWF component, which led to increased totalWF. Conversely, 100% ETc decreased totalWF to lower values than those reported in Zotou and Tsihrintzis , presumably because of the differences of standard yields recorded in Mesogeia area , where the authors conducted their research, and Napa Valley, CA, USA where this study was performed. A recent study reported that the current values of blueWF and gray WF are unsustainable . The actual runoff of the surface water is not sufficient to satisfy the irrigation requirements and/or dilute the pollutant load associated with the diffuse and point sources to reduce it below the maximum acceptable concentration . These results highlighted that the management of natural resources, specifically water management, is paramount for the sustainability of the wine industry under future constraints . Thus, our data suggested that values ranging between 600 and 1000 m3 · tonne −1 of the totalWF may ensure a high iWUE of grapevines , optimum LAI, and profitable yields, which maintained the balance between vegetative and reproductive growths . Nevertheless, it is noteworthy to address that WF assessment also presents some limitations given that the water consumed by an irrigated crop is often a mix of residual soil moisture from previous precipitation and irrigation events and that the reference ET is strongly dependent on the local climate .Ants benefit plants . Humans have known this for quite a long time. In fact, ants were described as biological control agents in China around 304 AD . Many plants have also evolved to promote the activity of ants on their tissues. Some plants provide domatia as ant housing structures, while others attract ants to their tissues with extra-floral nectaries. Some plants are hosts to honeydew-producing hemipterans that excrete honeydew, a sugary substance consumed by ants. Still other plants are simply substrates for ant foraging.

The majority of studies conducted across these ant–plant groups show that ants benefit plants by removal of herbivores . Nonetheless, in many agroecosystems, the benefits of pest control services by ants are not recognized. Agricultural managers often view them as pests or annoyances to agricultural production because some ants tend honeydew-producing insects that can damage crops . However, a review of the literature on ant-hemipteran associations suggests that even these associations benefit plants indirectly because ants remove other, more damaging herbivores . Regardless, the literature lacks studies investigating ant–plant interactions in agroecosystems. Here, we broadly survey the pest control services provided by a suite of ant species to better understand the role of ant defense of coffee. Coffee is a tropical crop that occurs as an understory shrub in its native range, and coffee plants are therefore often grown under a canopy of shade trees in agroforestry systems in some parts of the world . This canopy layer provides plantatsions with a forest-like vegetation structure that can help maintain biodiversity . Ant biodiversity is high in many coffee plantations and ants attack and prey on many coffee pests, including the coffee berry borer . For example, Azteca instabilis F. Smith is a competitively dominant ant that aggressively patrols arboreal territories in high densities and previous research has found that it impacts the CBB . Some laboratory and observational field studies have found that Pseudomyrmex spp., Procryptocerus hylaeus Kempf, and Pheidole spp. may limit the CBB . However, other field experiments have not found ants to be biological control agents of the CBB . Further, the pest control effects of many ant species on the CBB have not yet been evaluated and it could be that previously documented effects are specific to only a few species. Natural ant pest control of the CBB is particularly important because chemical insecticides used to control CBB are not always effective. This lack of effectiveness is in part because the CBB lifecycle takes place largely hidden within coffee berries and also because the CBB has developed insecticide resistance . Several of the stages of the CBB life cycle make it vulnerable to attack by ants . First, the CBB hatches from eggs within the coffee berry, where it consumes the seeds . Small ants may enter the berry through the beetle entrance hole and predate the larvae and adults inside . Second, old berries infested with the CBB may not be harvested because they often turn black and remain on the coffee branches or may fall to the ground . These old infested berries may act as a population reservoir of borer populations and ant predation at this stage could be very important for limiting CBB populations in the next season. Third, as adult borers disperse to colonize new berries, ants may prevent them from entering new berries . To date, no field experiment has specifically investigated how coffee-foraging ants limit CBB colonization of berries. Here, we studied the abilities of eight ant species to prevent colonization of berries by the CBB. We hypothesized that only species with high activity on branches would limit CBB colonization of berries.

The SN65LVDT41 chip is configured to use low-voltage differential signaling to reduce the effects of noise and electromagnetic interference and allow increased cable length. However, the Raspberry Pi communicates using complementary metal-oxide-semiconductor level logic. To translate between the two signal types, the expansion shield uses the SN65LVDT41 chip from Texas Instruments. The SN65LVDT41 chip has four LVDS line drivers and one LVDS line receiver to control data lines required to communicate with the Intan chip over its Serial Peripheral Interface . Connection to electrodes—Electrodes are connected to the Intan RHD 32-channel recording head stage. For experiments reported here, we created a connection to a commercially available 6-well multi-electrode array plate from Axion Bio-systems. However, any other electrode system fitting an Omnetics 32-pin connector is compatible. The design can be adapted to custom and commercial MEAs of different form factors using adapter boards. The Axion electrode plate mates its bottom contacts to spring finger pins on our designed adapter board. The parts are aligned using a custom holder consisting of a plastic interior surrounded by aluminum plates and compressed together by screws on four corners. The aluminum plate casing prevents warping of the plastic and ensures even pressure compressing the plate and connector on both sides. The compressing holder provides consistent mating of spring finger pins to electrode contacts on the plate.The Piphys system runs custom software to perform: communication with the Intan RHD2132 bio-amplifier chip, buffering and file storage of recorded voltage data locally, near real-time data streaming and plotting on the online dashboard, plant pots with drainage and experiment control from the dashboard. In order to stream data, interact with data being recorded, and control the device, we deployed Redis, Amazon IoT, and S3, as described in Methods.

To perform an electrophysiology recording, the user can configure the sampling rate and start the experiment from the dashboard. Once started, the neural cell activity is firstly digitized and sampled by the Intan RHD2132 bio-amplifier chip in 32 channels. Raspberry Pi stores the data on local memory and also streams it to Redis for near real-time visualization on the online dashboard. Since the Raspberry Pi computer has a 10 second streaming buffer, the data visualized on the dashboard is offset by 10 seconds from the data recorded. Therefore the data streaming is “near real-time”. During a recording, raw data is saved in chunks of 5 minutes to local memory and streamed in chunks of 10 seconds to Redis. Once the recording ends, all local data files are uploaded to S3 for permanent storage, and data is further backed up to Amazon Glacier for long-term archiving. Local data files on the Pi auto-erase every 14 days to release memory. To view a dated recording, the user can select and pull the data files from S3 to the dashboard for display . The Raspberry Pi has 4 CPU cores, and allows multicore and multi-threading. According to resource monitoring with “htop”, the Piphys program runs on two cores. The software uses four threads to record and stream to the cloud simultaneously. One thread is used for hardware interfacing with the Intan chip; a second thread is for cloud streaming, the third one is for local saving and another is for experiment control that gets MQTT messages. Communication with hardware—Communication between Raspberry Pi and Intan RHD2132 bio-amplifier chip uses Serial Peripheral Interface . SPI is a fast and synchronous interface that is widely used in embedded systems for short-distance data streaming. It is a full-duplex master-slave-based interface where both master and slave can transmit data at the same time. The protocol for both Raspberry Pi and Intan RHD2132 bio-amplifier chip is a four-wire interface: Clock , Chip select CS , Master-OutSlave-In , and Master-In-Slave-Out . In Piphys, the Raspberry Pi acts as the master device and generates a clock signal and recording commands to configure the Intan RHD2132 bio-amplifier chip through MOSI.

The Intan chip responds as a slave and sends the digitized data back by MISO. The chip allows the configuration of sampling rate and bandwidth of the low-noise amplifiers. The 32 channels on the chip are sampled sequentially with available sampling rate options from 2 kHz to 15 kHz per channel. The SPI clock is divided from the core clock on Raspberry Pi. Performance and error of the SPI clock are discussed in the Supplementary Material. The amplifiers give 46 dB midband gain with lower bandwidth from 0.1 Hz to 500 Hz, and upper bandwidth from 100 Hz to 20 kHz.Online dashboard—Users interact with Piphys devices through a web browser application, referred to as the Graphical User Interface . The GUI allows a user to initiate a recorded experiment and monitor electrical activity on each channel. Programatically, the GUI mimics an IoT device that sends messages to other devices and listens to their corresponding data streams in a high-performance Redis database service. The Piphys device produces a single data stream to Redis, and many users can view the stream from the Redis server. Therefore, many users can monitor and interact with a particular Piphys device without additional overhead placed on that device. Users can be located anywhere on the Internet without concern for where the physical Piphys device is or which network it is on. We routinely perform electrophysiology experiments from Santa Cruz on a Piphys-connected device that is located 90 miles away in San Francisco. When a new user opens the browser GUI, the web application queries the AWS IoT service for online Piphys devices to populate a device dropdown list. When the user selects a device from the dropdown, an MQTT ‘ping’ message is sent to the relevant device every 30 seconds, indicating that a user is actively monitoring data from that device. As long as the Piphys device receives these pings, the Piphys device will continue to send its raw data stream to the central Redis service. When the Piphys device has not received any user messages for at least a minute, it will cease sending its raw data stream. This protocol ensures the proper decoupling of users from devices. The Piphys device is not dependent on a user gracefully shutting down. While the Piphys device feeds raw data to the Redis service, data transformations are applied downstream by other IoT-connected processes. For example, the Piphys Control Panel displays a threshold spike sorted transformation of the raw data.

This data transformation is an independent process that listens for MQTT requests for the raw data stream and transforms the raw stream into a stream containing the past ten spike events detected per channel. For channels with no detected spikes, a random sample of the channel is saved to the stream every 30 seconds to provide a sampling of the channel’s activity.We tested the Piphys system for long-term recordings of human primary neurons. The goal of this work is to compare the neural signal recorded by the proposed apparatus to commercially available systems. Therefore, as a reference we choose two neural recording devices: Axion Maestro Edge by Axion Biosystems, as one of the leading commercial instruments for neural recording and Intan RHD interface board as one of the leading commercial open source neural recording instruments. These neurons were cultured in an Axion CytoView MEA 6-well plate ‡ . We designed a set of adapters , plastic plants pots which allowed the same culture plate to be used by Piphys and Intan RHD interface board. As mentioned in the Discussion section, the proposed system can interface with any type of neural recording electrodes using the Omnetics connector. After recording, the raw data was ingested to SpyKING CIRCUS software on a personal computer for analysis. SpyKING CIRCUS is a semi-automatic spike sorting software that uses thresholding, clustering, and greedy template match approaches to detect single cell action potentials. Here, we show two types of results, first for single neuron recordings and second for a bursting neural network.After 14 days in culture, primary neurons were recorded with the Piphys system and two commercially available systems: the Intan RHD USB interface board and the Axion Maestro Edge. After recording, all three datasets were filtered with bandpass filtering from 300 Hz to 6000 Hz and spike sorted with a threshold of ± 6 μV. Figure 5 shows a ten-second spike train from Piphys with dots highlighting detected spikes in the raw data. To further demonstrate the applicability of Piphys to primary neuron recording, we compare the shape of the detected action potential and quality metrics such as amplitude distribution, interspike interval distribution, and firing rate to commercially available systems . The data was recorded from the same channel in the same well of neurons by Piphys, Intan, and Axion systems in sequential order on the same day. The data recorded on Piphys corresponds to the data obtained from both commercial systems, with high similarity to Intan and overall consistency with Axion across metrics in Figure 6. The mean spike waveform, shown in the first column of Figure 6, was determined by averaging the voltage in a 3 ms window centered around the point where the voltage crossed the spike threshold. Differences in Axion’s waveform shape are a flatter starting point and a higher upstroke before settling to resting state. The amplitudes for the mean waveform are −24.67 ± 3.92 μV for Piphys, −26.92 ± 4.96 μV for Intan, and −24.50 ± 1.69 μV for Axion. Axion has a smaller deviation than Piphys and Intan, showing lower noise in the recording system. The amplitudes of the detected spikes over time, shown in the middle column of Figure 6 are more sparse for Axion than for Intan and Piphys.

Firing rates in events per second over the recording period shown are 8.05 for Piphys, 8.44 for Intan, and 6.86 for Axion. The interspike interval histograms, shown in the middle column of Figure 6, have similar longer-tail distributions for Piphys and Intan centered at 122.79 ms and 118.15 ms, and a tighter distribution for Axion centered at 145.57 ms. However, the interspike interval means for all three systems are significantly close together.On day 42 of culture, we recorded from the neurons with Piphys and found the primary neurons displayed synchronized network bursts, consistent with previous observations. Figure 7 shows the synchronous activity captured across four channels. After spike sorting, most detected spikes were arranged in short intervals with periods of silence in between. The spikes inside the bursts align among the channels, indicating that synchronized activity was present through the network. Quantitatively, the bursting has a general population rate of 0.13 bursts each second, with each burst lasting around 1 second. Within one burst, the number of spikes is 55 ± 17.58.The variation between Piphys and Axion shown in Figure 6 could be attributed to physical differences in the circuity and possible advanced filtering performed by Axion’s proprietary BioCore v4 chip §. The filtering could account for the smoothness and low variability of the signal , resulting in a smaller number of identified firing events with a tighter distribution. Piphys and Intan systems both use the same amplifier chips , where the optional on-chip filtering was disabled during recording ∥. The raw signal, therefore, has a larger noise margin , which may create more false-positive firing events. The tail of the amplitude distributions in Intan and Piphys is skewed towards lower-amplitude events, closer to the noise floor. The interspike intervals for Intan and Piphys register several events with near-zero intervals, likely suggesting false-positive spikes from noise contamination. Contamination from noise, which is likely symmetrical, could affect the shape of the mean waveform calculated by overlaying and averaging all registered spikes. Overall, these results demonstrate that Piphys can record neural activity in a manner comparable to commercially available hardware and software.Comparison to other platforms—Comparing electrophysiology platforms side by side is challenging because each system fits a specific niche and requirements for a particular workflow. Different platforms are targeted to particular problems and, therefore, have specific challenges and user needs. Piphys is intended to integration with other IoT sensors, and flexible recording equipment that can be used in a fleet for longitudinal study of many in vitro replicates. It should be noted that that the system proposed in this paper has an average signal to noise ratio of 4.35 dB above the baseline noise in recording neuron burst, which is comparable with other similar systems. Table 1 summarizes electrophysiology systems comparable to Piphys.

In an effort to improve wine quality, many smaller high-end wineries employ laborers to hand sort individual berries after destemming to remove unwanted material such as raisins, diseased berries, unripe berries, and materials other than grapes such as leaves and stems. This can be costly, labor intensive, and it can slow down the process line. To reduce costs and increase throughput, many wineries have adopted optical sorting technology. Using this technology, MOG can be removed more efficiently, and parameters such as color, shape, and size can be used to sort individual berries. Depending on the type of sorter, processing speeds can range between 2 and 15 tons per hour. Furthermore, fewer workers are needed to operate an optical sorter than to hand sort the respective amount of fruit. In addition to saving time and money, optical sorters have the potential to decrease the impact of inconsistent ripening in grapes. One study successfully sorted Carlos Muscadine grapes into four different ripeness levels using light at two different wavelengths in the visible spectrum. The researchers found that with successive sorting levels, there was an increase in Brix and pH, along with a decrease in titratable acidity in grape samples. In the wines, an increase in tannin and pH and a decrease in titratable acidity was found with increasing sorting. In sensory analysis, the first and fourth sorting levels were found to be inferior compared to the middle two treatments. Even though this study used outdated equipment compared to today’s standards, growing raspberries in pots it shows that white grapes can be sorted into different ripeness levels and this can affect the quality of the wine produced. A recent study used visible near-infrared spectroscopy to classify table grapes into different groups based on soluble solid and phenolic content.

The researchers were able to differentiate berries of different classes with accuracy ranging from 77% to 94%. Another study found that wine made from optically sorted Chardonnay grapes had higher residual sugar, pH, and total phenols than the unsorted control. The wines were analyzed sensorially with descriptive analysis and the judges scored the sorted wines significantly higher in tropical fruit and sweetness. However, with only two significant attributes out of twenty, the wines were determined to be similar in character. Another study investigating the effect of mechanical harvesting and optical berry sorting on Pinot noir grapes found that, in general, wines made from optically sorted fruit were significantly lower in total phenol and tannin, potentially due to the removal of MOG during sorting. In sensory analysis only two significant attributes out of eighteen were found and it was concluded that the wines were similar in character. A study published in 2014 used an optical sorter on Riesling, Müller-Thurgau, and Pinot gris grapes infected with Botrytis cinerea to investigate the effect of optical sorting on sulfur binding compounds in the finished wine. The researchers found that wine made from optically sorted fruit contained significantly less 2-oxoglutaric acid and pyruvic acid . They concluded that optical sorting is an effective method for reducing the amount of sulfur dioxide needed in the winemaking process using these varieties. There is a lack of published research investigating the impact of optical berry sorting on wine composition and only a few cultivars of Vitis vinifera have been tested. The objective of the current study was to provide more information on the effect of optical berry sorting on different varieties and investigate the capabilities of today’s optical sorters to sort for different ripeness levels using red grapes and using color as a sorting parameter. The current study found that although optical sorting can efficiently replace hand sorting, the overall impact on wine sensory attributes was minimal. Therefore, in general, the study supported the findings of previous researchers.Three varieties were tested in 2016: Barbera , Cabernet Sauvignon , and Grenache .

BA was harvested on 19 August 2016, CS was harvested 30 August 30 2016, and GN was harvested 8 September 2016. All three varieties were hand harvested early in the morning from UC Davis campus vineyards and processed the same day. Fruit condition was good with seemingly little variation, although GN fruit showed more variation in color than the other cultivars. Half-ton bins were dumped by a forklift into a receiving hoper. Clusters were carried by a Delta TR elevator into a Delta E2 destemmer . Destemmed berries fell onto a moving belt and were carried onto a ChromaxHD Berrytek Optical Sorter . Rejection parameters were established by capturing color profiles of optimal berries, suboptimal berries , and MOG. These parameters were optimized with the assistance of a WECO technician for removing suboptimal berries and MOG while rejecting as few optimal berries as possible. This process was repeated, and parameters were adjusted for each variety. The must was pumped directly into 200 L stainless steel research fermentors, which were filled incrementally to reduce vineyard variation. The rejected material was collected in buckets and transferred into research fermentors. The grapes were processed in three treatments, control , sort , and reject . The rejection rates were 14.9%, 3.9%, and 1.5% for GN, BA, and CS, respectively. Juice collected in trays from the rolling belts during processing operations was added back to each treatment in proportional amounts. This was done to maintain a consistent solid-to-juice ratio in the must among treatments. Wines were made in the UC Davis Teaching and Research Winery using 200 L stainless steel research fermentors. Duplicate fermentations were used for the reject treatment wines because only a small amount of reject material was obtained during grape processing.

Fermentation replications were kept separate through the entire experiment. Juice samples were taken from each fermentation vessel after mixing. Fifty milligrams per liter of SO2 was added to the must after processing using a 15% potassium metabisulfite solution. Yeast assimilable nitrogen was adjusted to 250 mg/L using diammonium phosphate , titratable acidity was adjusted to 6 g/L using tartaric acid. The must was heated to 25 ◦C before inoculation with Lalvin EC1118 yeast using the manufacturers rehydration procedure. One tank volume was pumped over twice per day using automated pump overs for all wines except for the reject treatment for CS. The volume in these tanks was too low for the automated pumps to create suction, therefore, the wines were punched down manually once per day during the fermentation. Once wines were dry, they were pressed using a basket press and allowed to settle for 5 days before being racked and transferred to a temperature-controlled room held at 20 ◦C. The wines were then inoculated with Viniflora CH16 Oenococcus oeni bacteria by Chr. Hansen . Upon finishing the malolactic fermentation, 50 mg/L SO2 was added to the wines and they were held in a 9 ◦C cold room until bottling. Free SO2 was adjusted to 30 mg/L before bottling. All samples for basic wine chemical analyses were taken at the time of bottling. Ethanol was measured using an Alcolyzer and pH was measured using an Orion 5-star pH meter . Titratable acidity and free SO2 were measured using a Mettler Toledo DL50 auto titrator . Residual sugar, malic acid, and volatile acidity were measured using a Thermo Fisher Scientific Gallery automated analyzer . Wines were sterile filtered using 0.45 µm membrane filters prior to bottling using green Bordeaux style bottles and screw cap closures with Saranex liners by Amcor . Samples for phenolic analysis were taken from bottles at the time of sensory analysis. The modified Adams-Harbertson assay was used to determine levels of anthocyanin , tannin , and total iron-reactive phenolics. A Genesis 10S UV-Vis spectrophotometer was used for this assay.An external calibration was used for the quantification of phenolic compounds and curves were made for gallic acid, -catechin , -epicatechin , caffeic acid , quercetin-3-rhamnoside, plant pot with drainage and malvidin-3- glucoside. Caftaric acid was quantified as caffeic acid equivalents, quercetin-glycosides as quercetin-3-O-rhamnoside units and all pigments as malvidin-3-glucoside units. Bottle duplicates for each fermentation replication were analyzed and the sequence was randomized. Wine aroma compounds were analyzed using head-space solid-phase microextraction gas chromatography mass spectrometry . The method used was adapted from a previous study. Samples used for wine volatile analysis were taken at the time of sensory analysis and stored at 4 ◦C for no more than one month. Identified volatile peaks are normalized against an internal standard and the obtained data is thus semiquantitative only. Twenty mL amber glass headspace vials were used, containing 10 mL milliliters of wine sample, 3 g of NaCl salt and 50 µL of a 10 mg/L solution of 2-undecanone . Twenty millimeter green magnetic caps with 3 mm PTFE silicone septa were crimped onto the vials and the samples were mixed by vortexing. The analysis was done using an Agilent Technologies 7890A GC system with a Gerstel MPS2 multipurpose sampler . The mass analyzer was an Agilent Technologies 5975C inert XL EI/CI MSD. The column used was an Agilent Technologies DB-Waxetr with a temperature range of 30 ◦C to 260 ◦C. The dimensions of the column were 30 m, 0.250 mm, and 0.25 µm. Maestro software was used to control the instrument and data were collected using ChemStation software . During the analysis, the oven was held at 40 ◦C for 5 min and then increased 3 ◦C/min to 180 ◦C, followed by 30 ◦C/min to 250 ◦C and held for 7.67 min. The MSD interface was kept at 260 ◦C. HS-SPME-GC-MS conditions were as previously described. Shortly, samples were heated to 30 ◦C for five minutes while agitating with a speed of 500 rpm prior to exposing the fiber to the sample for 45 min at 30 ◦C with agitation at 250 rpm. The SPME fiber was desorbed in split mode with a 10:1 split ratio and the inlet temperature was kept at 260 ◦C. Bottle duplicates were analyzed in triplicate for each treatment. Compound details are provided in Table S1. Wines were analyzed sensorially using descriptive analysis in the J. Lohr Wine Sensory Room, University of California, Davis, CA. GN, BA, and CS wines were analyzed approximately two, three, and four months, respectively, after bottling. Three separate descriptive analysis panels were utilized, one for each variety. Eleven panelists were recruited for GN, and ten each for BA and CS.

The panelists were offered $30 gift certificates for completion of the study. The study was approved by the International Review Board and all participants reviewed and agreed to the terms of the experiment. None of the panelists knew details of the experiment. Two fermentation replicates were selected from each treatment totaling six wines for each descriptive analysis study. There were six training sessions and three evaluation sessions. The panelists were given 30 mL of each wine sample for both the training sessions and the evaluation sessions. The wines were presented blind using black wine glasses and the order was randomized for each session. In the first training session, panelists generated descriptors used for differentiating the wines. In subsequent sessions, the reference standards for each descriptor were optimized through panel discussions until there was general agreement. The list of descriptors was narrowed down until therewere twenty descriptors for GN , twenty-six for BA , and twenty-two for CS . Panelists were asked to rate the intensity of each attribute using an unmarked line scale. Reference standards were given as an anchor for the high end of the intensity scale of each attribute. In addition to these attributes, panelists also analyzed color by matching each wine with a color chart . Panelists were given 30 mL of wine in a clear glass and instructed to hold the glass at arm’s length with a white background and match with the closest color on the poster. Scores were reported by assigning number values to each color on the poster. Perceived color differences from sensory analysis were compared to wine colors determined using a CR-400 Chroma Meter using the CIELAB color space.Wines were analyzed by the panelists in triplicate using a randomized block design over a one-week period. All analyses were completed in isolated booths with positive air flow and red lighting. Randomized three-digit codes were assigned to the wines to eliminate biases. Panelists were given breaks in between each wine and were encouraged to drink water and eat an unsalted cracker as a pallet cleanser. All samples were expectorated. Data were collected using FIZZ software .All statistical analyses were carried out using XLSTAT .