Tag Archives: draining pot

Several mechanisms can cause the formation of a water layer

A ‘water layer’ in the field of ISE research refers to a small water layer that can form between the conductor and transducer. This water layer then acts as an unintentional electrolyte reservoir that re-equilibrates with any change in the bulk sample composition.If the ISM and transducer layer do not have good contact with the subsequent layers and do not form a hydrophobic seal, then it is possible for the bulk solution to ‘fill in’ the space by capillary force, not unlike water soaking into a napkin or paper towel. However, if there is a good seal in different layers, it is still possible for a water layer to form. For example, if the micro-structure of the ISM contains ‘pinholes’ , water can likewise transport through these channels to the layers below. Pinholes can be avoided by careful deposition techniques or by making thicker ISM layers. For the latter, the likelihood of forming a pinhole penetrating through the entire membrane is inversely proportional to the membrane thickness. Finally, even if there is a hydrophobic seal and there are no pinholes, water will still diffuse through the membrane to some degree, as the diffusion coefficient of a typical PVC membrane is on the order of 10−8 cm2/s. This is why PVC and other hydrophobic polymers are frequently chosen as the polymer matrix – their high level of hydrophobicity and small diffusion coefficients make it so the water diffusion rate through the ISM is negligible. A simple test to determine if a water layer is forming within an ISE was designed by Fibbioli et al.and is now widely used within the field of polymeric ISE research.

As it has come to be known,draining pot the’ water layer test’ is a relatively simple three-part potentiometric measurement. First, the ISE is conditioned in a concentrated solution of its primary analyte. Then, the electrodes are moved to a concentrated solution of a known interfering analyte. Finally, the electrodes are placed back in the concentrated solution of the primary analyte. The electrode potential is continuously recorded against a commercial Ag/AgCl RE following each exposure to the different solutions. The duration that the electrodes need to be soaked in each solution depends on the thickness of the membranes and the ISE response. Each exposure lasts several hours, and some experiments lasting up to 45 hours have been reported. A schematic describing the water layer test for a nitrate ISE is shown in Figure 4.14. Figure 4.15 shows the water layer test performed on the nitrate ISE. In this water layer test, 100 mM NaNO3 was used as the primary solution, and 100 mM NaCl was the interfering solution. First, the ISE was conditioned in 100 mM NaNO3 until it was stable. The final hour of stable output in NaNO3 is shown, followed by two hours in the interfering solution, and returning to NaNO3 for 24 hours. The potential shows some drift during both the NaCl step and the NaNO3 return, which could indicate the presence of a water layer on the electrode’s surface, which is not unexpected for this type of coated-wire electrode. However, the electrode’s stability is on par with values reported in the literature, which involved specific modifications for stability. The difference between the potential immediately before and the potential immediately after the NaCl step is 15 mV, the same as found by Chen et. al. for electrodes using gold nanoparticles and Polypyrrole to improve stability. Another technique for investigating the stability of an ISE is current-reversal chronopotentiometry. Recall that in Equation 4.10, potential drift is inversely proportional to the capacitance of the ISE.

Current-reversal chronopotentiometry is a technique that allows one to find the capacitance of an ISE. Current-reversal chronopotentiometry is a three-electrode electrode technique with the ISE as the working electrode , a commercial Ag/AgCl electrode as the RE, and a glassy carbon electrode as the counter electrode . The WE is polarized with a few nanoamps of current while the electrode potential is recorded. Rearranging Equation4.10 allows one to solve for the capacitance from the rate of potential change and the current input. After a short period of time, the current flow is reversed, and the bulk resistance of the electrode can be calculated from the ohmic drop when the current is reversed by rearrangement of Equation 4.9. A nitrate ISEs was configured into the three-electrode system described above and submerged in 100 mM NaNO3. A +1 nA current was applied for 60s, at which point the current was reversed to -1 nA for another 60s. The potential is plotted over time in Figure 4.16. EIS is an electrochemical technique that provides in-depth information about the dielectric properties of solid-state ISE sensors. EIS can also identify water layers, pockets of water in membrane pores, and pinholes. Finally, EIS characterizes the contact resistance of the boundaries between layers, which should be minimized to ensure a hydrophobic seal and reduce the ISE impedance. The nitrate ISEs were configured in a three-electrode system, with the ISE as the WE, a commercial Ag/AgCl electrode as the RE, and a glassy carbon electrode as the CE. The three electrodes were immersed in 100 mM NaNO3 solution and the impedance spectra were recorded in the frequency range of 0.5 Hz – 200 kHz. The Bode plot is shown in Figure 4.17A, and the Nyquist plot is shown in Figure 4.17B. The electrode demonstrated a bulk impedance of 1.72 MΩ. Higher bulk resistance of ISEs with PVC and DBF-based membranes has been previously reported, which could be accounted for by membrane thickness and the lack of a transducer layer in our device.Precision agriculture offers a pathway to increase crop yield while reducing water consumption, carbon footprint, and chemicals leaching into groundwater.

Precision agriculture is the practice of collecting spatial and temporal data in an agricultural field to match the inputs to the site-specific conditions. While industrial agriculture seeks to maximize crop yield, there is also the consideration of maintaining a healthy ecosystem. Fortunately, these are not competing interests; Numerous case studies have demonstrated that adopting precision agriculture techniques increases crop yield while lessening detrimental environmental effects. Consider first the use of irrigation in agriculture, which accounts for approximately 36.7% of the freshwater consumption in the U.S., 65% in China, and77% in New Zealand. Part of why so much water is used in agriculture is, quite simply, because crops need a lot of water to grow. For example, high-production maize crops require 600,000 gallons of water per acre per season – that’s an Olympic swimming pool’s worth of fresh water per acre! Adopting precision agriculture practices – such as variable-rate irrigation – have proven to reduce water consumption by 26.3% . Meanwhile, fixing nitrogen from the air to produce fertilizers is an extraordinarily energy-intensive process and accounts for nearly 2% of the U.S.’s annual CO2 emissions. Crops recover only 30-50% of nitrogen in fertilizers, which means that over half of the nitrogen becomes a potential source of environmental pollution, such as groundwater contamination, eutrophication, acid rain, ammonia redeposition, and greenhouse gases. Fortunately, precision agriculture practices have demonstrated an increase in nitrogen use efficiency, thereby reducing both the production volume of fertilizer as well as the amount that is polluted into the environment. We began this exploration from the ground-up. First, we investigated how many sensors are needed to inform a precision agriculture system. The results of that work informed the design of nitrate sensor nodes to fulfill those specifications,round plastic planters and lab-scale versions of those nodes were fabricated and tested in greenhouse experiments. After these WiFi-enabled nitrate sensor nodes were validated, we replaced the components of the nitrate sensor node with naturally-degradable alternatives to realize a no-maintenance version of the sensor node. The fabrication methods were scalable and low cost, while the sensors were comparable to their non-degradable twins. Such sensors could be widely distributed throughout a landscape to map nitrate movement through the watershed, inform the efficient application of fertilizer, or alert residents to elevated nitrate levels in drinking water.Accurate soil data is crucial information for precision agriculture. In particular, the moisture content and the concentration of various chemical analytes in soil have a significant influence on crop health and yield.

These properties vary considerably over short distances, which begs the question: What spatial density does soil need to be sampled to capture soil variability? Half of the spatial range, referred to hereafter as the ‘half-variogram range’, can be used as a “rule-of-thumb” to account for the spatial dependency of agricultural measurements.Similar to how an agricultural field can be defined in the real world as a geographic area at a location, a digital representation – or ’simulation’ – of an agricultural field can be defined as many discrete pixels, where each pixel’s position corresponds to a geographic coordinate and its size to an area. Here, we briefly discuss three methods of expressing an agricultural field in a digital format. For agricultural fields that a simple geometric shape can approximate – such as a rectangular farm or a central-pivot farm – expressing the farm digitally is trivial. For a rectangular-shaped field, we discretize the space into a grid of uniform pixels with dimensions proportional to the length and width of the physical domain. For a central-pivot field, we bound the field in a square grid of uniform pixels, loop through each pixel in the grid, and add the pixel to a list if that pixel’s coordinates are equal to or less than the field’s radius. This technique is demonstrated in Figure 5.2A for a rectangular-shaped field and in Figure 5.2B for a central-pivot field. When the boundaries of the agricultural field are not regularly shaped, we define the field by a list of consecutive coordinate points that, when piece wise connected by polynomial curves, form an enclosed shape. Here, we adopt a simple ray tracing algorithm to determine whether or not a pixel is inside or outside of this boundary. Given an enclosed boundary and a point in space, if one were to draw an infinite vector in any direction originating from that point, it will intersect the boundary an odd-numbered amount of times if-and-only-if the point is within the enclosed space, which is shown in Figure 5.2C. This holds for all points in space except for points on the boundary, which must be determined explicitly. In this way, we use the coordinates of each pixel as a point to determine if a pixel is inside the boundary and append it to a list. Finally, satellite or drone visible-spectra images of agricultural land are already stored in a digital, pixelized format. Such images and datasets are widely available from Google Earth, NASA Earth Observatory, or the USDA cropland data layer. Computer vision techniques can differentiate the arable land on a field from obstructions and store those pixels in a list. This process is visualized in Figure 5.2D. In all cases, it is essential to note the physical dimensions that a single pixel represents. It should also be noted that because each method requires discretization of the field, the results are approximations whose accuracy increases proportionally to the number of pixels used.The optimal layout of sensors in an agricultural field is achieved when, using the fewest number of sensors possible, all points in the field are statistically represented by the data collected by sensors in that field. For a given sensor, the data collected from that sensor is statistically significant for all points within a radial distance equal to the half-variogram range of that sensor. Thus, if we consider an agricultural field a two-dimensional collection of pixels, we can model sensors as circles with a radius equal to the half-variogram range. Using this definition for placement, our problem is similar to the circle packing problem. Circle packing is a well-researched area in mathematics that has many practical applications. Object packing aims to fit as many of some objects within a domain as possible without any overlap. There are several algorithms that aim to optimize object packing, such as random sequential addition, the Metropolis algorithm, and various particle growth schemes. The limit of packing efficiency for equal-size circles in two dimensions is about 91% for a hexagonal grid.

Previous studies have shown that disruption of this gene leads to an albino phenotype

It has been reported that pre-assembled CRISPR/Cas9 ribonucleo proteins can be delivered into protoplasts to induce mutations, without the need for stable integration of CRISPR/Cas9 genes into the host-plant genome. Particle bombardment has also been used to deliver CRISPR/Cas9 ribonucleo proteins to wheat and maize cells, producing non-transgenic mutants. However, working with protoplasts, as well as utilizing biolistics, limits the potential for full-plant regeneration to some species and tissue types. Therefore, it is important to also develop alternative methods to produce non-transgenic CRISPR mutants of perennial crop plant species. In contrast to the limited success of plant regeneration from protoplasts, plant regeneration from leaf, hypocotyl, epicotyl, shoot, root, cotyledon, or callus explants has been well established for the majority of crop plant species, including many that are recalcitrant to regeneration from protoplasts. It has also been shown that proteins can be produced following transient expression of Agrobacterium T-DNA genes. In addition, Agrobacterium inoculation protocols have been developed for many perennial crop species. To circumvent the limited regeneration potential when using CRISPR ribonucleoproteins to produce non-transgenic mutants, we report a method for using Agrobacterium to transiently express the Cas9 and sgRNA genes in plant cells,drainage pot using tobacco as a model plant and PDS as a model target gene. We have also developed a high-throughput screening protocol utilizing next-generation sequencing in combination with high-resolution DNA melting analysis to efficiently identify mutants from a population of shoots regenerated in the absence of selection pressure.

We demonstrate that the combination of Agrobacteriummediated transient CRISPR/Cas9 expression with a highly efficient screening protocol makes it possible to efficiently obtain non-transgenic mutant plants, a method that should be applicable to heterozygous perennial crop species.Using tobacco as a model plant and an intron-containing GUS gene as a marker , we observed that transient expression of T-DNA genes in inoculated leaf discs peaked 3–4 days following Agrobacterium infection in the absence of kanamycin selection. Figure 1d shows the GUS activity in tobacco leaf discs 2–6 days post infection , and Fig. 1e shows the GUS activity in leaf discs after 5 additional days in timentin-containing media. The antibiotic timentin was used to suppress Agrobacterium growth following an initial 2–6 day co-incubation; thus, the GUS activities shown in Fig. 1e should result from stable integration of the GUS gene into the tobacco genome. The difference in GUS expression between explants in Fig. 1d and those in Fig. 1e is indicative of transient GUS expression, demonstrating that there are high levels of transient expression of the genes in the T-DNA region. The results in Fig. 1d, e indicated that a 3-day or 4-day co-incubation was optimal for Agrobacterium-mediated transient expression of the GUS gene. Three days of Agrobacterium co-incubation was subsequently used for transient expression of CRISPR/Cas9 genes. The sgRNA used in these experiments targets the beginning of the fourth exon of the endogenous tobacco phytoene desaturase gene , as shown in Figure 1c.We used this phenotype as a visual marker to identify tobacco mutants whose PDS gene had been edited following the expression of Cas9 and PDS-targeting sgRNA genes. We infected 415 tobacco leaf-disc explants in three independent experiments using 3 days of Agrobacterium co-incubation without any selection for transgenic cells or shoots .

A total of 197 shoots regenerated from infected explants exhibited the albino phenotype, indicating a mutation in PDS, demonstrating a mutation rate of 0.475 pds mutants per explant. However, due to the lack of chemical selection, the total number of shoots regenerated from each explant was very high, and therefore, the mutation rate per total regenerated shoots was quite low . These results indicate that pds mutant plants can be produced via Agrobacterium-mediated expression of Cas9 and sgRNA genes without using antibiotic selection. Ten independent pds mutant plants were randomly chosen for further analysis, and six are shown in Table 2. The specific genetic mutations in these plant lines were identified via high throughput sequencing, which demonstrated that all plants contained tetra-allelic mutations, meaning that all four alleles were mutagenized and no wild-type alleles could be detected. Microscopic analysis of plant tissues was unable to uncover the presence of green cells in albino pds mutants, suggesting that all PDS genes in all cells were mutated in these plants. The albinism resulting from the disrupted PDS gene enabled us to conveniently identify pds mutant shoots at early stages of shoot development in this study. However, the vast majority of desirable mutations for crop improvement are unlikely to display any visually identifiable phenotypes at the early stages of shoot development. When no selection pressure is applied during callus and shoot regeneration following Agrobacterium infection, the vast majority of regenerated shoots or plantlets should be non-mutant, as demonstrated above. Therefore, the ability to efficiently identify mutants lacking any visually identifiable phenotype from a population of regenerated shoots is essential for using the abovedescribed Agrobacterium-mediated transient mutagenesis system. Toward this end, we tested the effectiveness and efficiency of a two-step screening method using the newly produced pds mutants.

The first step, an initial identification of mutants, takes advantage of the high throughput nature of Illumina sequencing, and the second step, a fine identification of mutants, makes use of the high resolution of HRM analysis.Although our mutagenesis rate was relatively high per explant, without a visible phenotype, it would be diffificult to identify mutant plants due to the high number of regenerated shoots in the absence of chemical selection. Additionally, other CRISPR mutagenesis projects could have an even lower mutation rate than the one reported here. Therefore, we have developed a two-step method for high-throughput screening of shoots to identify the presence of targeted mutations. We first mixed leaf tissue from an albino pds mutant , pds-12, with leaf tissues from independently derived non-mutant shoots , regenerated from Agrobacterium-infected explants, at MT-to-WT ratios of 1:20, 1:41, and 1:83. We isolated genomic DNA from these pooled tissue samples and performed PCR reactions to amplify a 186-bp fragment that contained the sgRNA-target region on the fourth PDS exon . PCR products were sequenced on an Illumina platform to ~×60,000 to ×100,000 coverage. Next, we measured the amount of PCR product derived from the MT-to-WT ratios of 1:20, 1:41, and 1:83 mixed tissues and diluted it with ×6 the amount of PCR product derived from WT plant tissue. The diluted PCR product was also used for Illumina sequencing analysis. We observed that the PCR products derived from a 42- plant pooled tissue sample containing the pds-12 mutant showed a drastically elevated nucleotide variant frequency at positions 45–51 , which is consistent with verified mutations at these nucleotide positions . NVF is a measure of the frequency of abnormal nucleotides detected by DNA sequencing at a given position due to mutations or sequencing error. When we diluted the same PCR products with 6× WT PCR products , we observed significant reductions in NVF at positions 45–51 relative to the undiluted PCR product. The observed elevations and reductions of NVF before and after a 6× WT DNA dilution further verified the presence of mutations at nucleotide positions 45–51, as NVF resulting from sequencing error would be unaffected by dilution. As shown in Table 2, high-throughput sequencing uncovered four types of mutations in the pds-12 mutant line: a 1-bp deletion at position 51, a 5-bp deletion at positions 45–49, a 2-bp deletion at positions 49–50, and a 4-bp deletion at positions 46–49 . Similar results were observed using the 1MT: 20WT and 1MT: 83WT pooled tissue samples, with a more drastic elevation of NVF for the 1MT: 20WT samples and reduced elevated NVF for the 1MT: 83WT samples compared to the 1MT: 41WT samples . Using a single-blind approach ,growing raspberries containers we tested the accuracy of the mutant screening method based on elevations and reductions of NVF before and after a 6× WT DNA dilution as described above. We created eight 42-plant pools, five of which contained plant tissue from a single pds mutant plant line and three of which contained 100% WT plants. The five pds mutants used were pds-9, pds-10, pds- 11, pds-13, and pds-14. We confirmed that the screening method was reliable for identifying all 42-plant pools that contained pds mutant plants at a ratio of 1MT: 41WT , with 100% accuracy . Thus, the elevations and reductions of NVF before and after a 6× WT DNA dilution were excellent indicators of 42-plant pools that contained mutant plants.After identification of mutant-containing 42-plant pools, HRM analysis was used to identify individual mutant plant lines within each of the 42-plant pools .

To determine the sensitivity of HRM analysis, we performed HRM analysis on PCR products amplified from various DNA templates combined at different ratios. These template mixes were created using one-part pds-12 plant tissue combined with different parts independently regenerated non-mutant tissues in the following ratios: 1:1, 1:6, 1:19, and 1:29. Figure 3a shows that mutant-containing PCR products at ratios of 1:1, 1:6, and 1:19 could be distinguished from a wild-type DNA reference. The plant pool size we chose for subsequent HRM analysis was 7; thus, each 42-plant pool containing DNA from mutants could be divided into six pools of seven plants each. We also used a single-blind experiment approach to test the accuracy of HRM analysis to identify mutant plants. We created eight additional 7-plant pools, five containing a single pds mutant each , and the remaining three negative control pools containing only wild-type plants. The HRM analysis results are shown in Fig. 3b and demonstrate that all pooled samples containing pds mutant plants could be identified with 100% accuracy. Finally, upon the identification of mutant-containing 7-plant pools, individual mutant within each pool were identified via HRM based on a 1:1 mix between each putative mutant and a WT plant. Through this method, we successfully identified all pds mutant plants .To distinguish transgenic from non-transgenic mutants, we performed PCR on 29 randomly selected pds mutant lines using primers targeted to three regions in the TDNA fragment . Mutant plants were considered to be non-transgenic if they lacked a PCR product for all three primer sets . Approximately 17.2% of the tested pds mutant lines were determined to be non-transgenic following PCR analysis . As shown in Supplementary Figure 3, a non-transgenic plant , along with a transgenic plant , was cultured on MS media containing 100 mg/L kanamycin. The nontransgenic pds-7 plant died under kanamycin selection, while the transgenic pds-9 plant grew normally.Producing non-transgenic mutants of heterozygous perennial crop plants using CRISPR/Cas9 technology is highly desirable but challenging. We developed an effective method for producing and identifying CRISPR/Cas9- mediated non-transgenic mutant plants, which should be applicable to many perennial heterozygous crop plants. We have demonstrated that we can use Agrobacterium to transiently express CRISPR/Cas9 genes, and such expression can lead to the production of tetra-allelic, nontransgenic mutant plants. We have also demonstrated that the first step of our mutant identification, based on elevated and reduced nucleotide variance frequencies before and after a WT DNA dilution, using high throughput DNA sequencing analysis, is reliable and highly efficient. Furthermore, the second step of mutant identification, using HRM analysis, is simple and effective. With one sgRNA targeting the tobacco PDS gene, we achieved a 47.5% mutation rate using no selective pressure during callus or shoot regeneration. At least 17.2% of the pds mutant plants produced this way were non-transgenic, for an overall non-transgenic mutation rate of 8.2% . We expect that the rate of recovery for non-transgenic mutant plants following Agrobacterium-mediated transient expression of CRISPR/ Cas9 genes could be much higher than we reported here. One reason is that the albino phenotype caused by pds mutations used in this study can result in cell- or shoot growth disadvantages, which may have contributed to lower rates of mutant shoot production. Additionally, multiple sgRNA sequences may be used to target the same gene to increase the efficiency of mutant production. Protoplast-mediated delivery of CRISPR/Cas9 ribonucleoproteins offers advantages for creating non-transgenic mutant plants. However, regenerating plants from protoplasts can be difficult and has not been demonstrated to be possible for many important crop species. Low efficiency of plant regeneration from protoplasts has been reported in economically important crops such as avocado, grape, and apple. Furthermore, regeneration protocols have not been successfully demonstrated in many other plant species.