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Postemergence herbicides used to control weeds in the space between raised, plastic-covered beds in plasticulture production systems are typically banded, and herbicides are applied to weeds and to where weeds do not occur. To reduce the incidence of off-targeted applications, the University of Florida developed a smart-spray technology for row middles in plasticulture systems. The technology detects weed according to categories and applies herbicides only where the weeds occur. Field experiments were conducted at the Gulf Coast Research and Education Center in Balm, FL, in fall 2021 and spring 2022. The objective was to evaluate the efficacy of postemergence applications of diquat and glyphosate in row middles in jalapeno pepper fields when banded or applied with smart-spray technology. The overall precision of the weed detection model was 0.92 and 0.89 for fall and spring, respectively. The actuation precision achieved was 0.86 and 1 for fall and spring, respectively. No significant differences were observed between banded and targeted applications either with glyphosate or diquat in terms of broadleaf, grass, and nutsedge weed density. No significant pepper damage was observed with either herbicide or application technique. The smart-spray technology reduced herbicide application volume by 26% and 42% in fall and spring, respectively, with no reduction in weed control or pepper yield compared to a banded application. Overall, the smart-spray technology reduced the herbicide volume applied with no reductions in weed control and no significant effects on crop yield. Nomenclature: Diquat; glyphosate; jalapeno pepper; Capsicum annuum L.

Smart spray technology developed at the University of Florida was designed to reduce off-target applications when applying postemergence (POST) herbicides for weed control in plasticulture systems. A trial was conducted in the fall of 2021 and spring of 2022 to evaluate smart spray technology in row middles in a banana pepper field at the Gulf Coast Research and Education Center in Balm, FL. The objective of this study was to evaluate the efficacy of targeted POST-herbicide applications in plasticulture pepper row middles in the presence or absence of a pre-emergent (PRE) herbicide. Flumioxazin reduced broadleaf and overall weed densities in both seasons and lowered grass density in the spring. Two targeted applications reduced the nutsedge density in spring compared to the two banded applications. No significant pepper damage was observed in any treatments. Applied POST herbicide volume following PRE-herbicide was reduced by 84% and 54% for fall and spring respectively. In the absence of a PRE herbicide, targeted applications reduced POST-herbicide volumes by 30% and 45% for fall and spring respectively. No reduction in weed control or pepper yield was observed when comparing targeted with banded applications. Overall, the use of smart spray technology for POST herbicides in row middles reduced applied spray volume with no reduction in weed control, significant injuries on pepper, or negative effects on yield.

One of the most widely used auxinic herbicides in southern Iran’s cereal crop fields is 2,4-D; however, the concurrent growing season of off-season melons in this region potentially leads to herbicide drift from cereal fields to the melon fields. To study the response of some Iranian wild melon accessions to three simulated drift rates of 2,4-D, including 112.1, 11.2, and 3.7 g ae ha−1, a field experiment was conducted during 2019 and 2020 growing seasons. It was found that by increasing the herbicide rate from 3.7 to 112.1 g ae ha−1, the level of visual injury increased in all accessions. However, significant variation in herbicide tolerance was observed among different melon accessions. The MEL-R1 was the most tolerant accession with only 20% injury, while MEL-D8 displayed very high injury rate (ca. 90%) as assessed at 6 weeks after treatment during 2019. The accession MEL-S3 was the most tolerant to 2,4-D drift rates (20% injury) at 6 weeks after treatment during 2020. There was no significant difference between the accessions MEL-R1 and MEL-S3 in terms of their response to 2,4-D treatment during both years of the study, as these accessions fully recovered from injury over 6 weeks after herbicide treatment. In addition, only these two accessions were able to produce yield after the application of 2,4-D at the highest rate tested (112.1 g ae ha−1). Therefore, the melon accessions MEL-R1 and MEL-S3 could be recommended for cultivation and even for breeding programs in order to develop 2,4-D-tolerant commercial cultivars in regions where this herbicide is commonly used in cereal crop production adjacent to the melon fields.

Particle drift happens during herbicide application when droplets travel outside the intended site. Different nozzles produce various range of droplets, so they play a very important role in coverage and drift occasions. When nozzles produce small droplets, the potential for off-target movement is very high. Another important factor determining particle drift is the distance between crops. Wind velocity gives the energy to herbicide particles to move away from the target place. Therefore, a drift simulation of herbicide (mesotrione and rimsulfuron plus thifensulfuron-methyl mixture) was done in a wind tunnel, using different nozzles Extended Range (XR) and Turbo TeeJet Induction (TTI). The wind speed was set at 4.4 m/s, representing the least favourable conditions where applications are possible. In the wind tunnel, eight crops (cantaloupe, cotton, green bean, pumpkin, soybean, sunflower, wheat, and watermelon) were positioned at 4, 6, 9, and 12 m downwind distances from the nozzle, and drift was simulated. Following treatments, plants were returned to a greenhouse for 28 days, and biomass reduction was recorded. Artificial collectors (Mylar cards) and water sensitive cards were positioned alongside plants. According to obtained results, spraying with XR nozzle influences higher injuries than TTI nozzle. Tracer deposition was higher at all distances when XR nozzle was used. Accordingly, droplet numbers, covered area, Volume Median Diameter (VMD), and deposition were higher on water sensitive cards when spraying were done using XR nozzle. As a consequence, higher biomass reduction occurred using the XR nozzle. The most sensitive crops were cantaloupe, pumpkin and sunflower, while the most tolerant were soybean and wheat.

Background Recent experimental efforts of CRISPR-Cas9 systems have shown that off-target binding and cleavage are a concern for the system and that this is highly dependent on the selected guide RNA (gRNA) design. Computational predictions of off-targets have been proposed as an attractive and more feasible alternative to tedious experimental efforts. However, accurate scoring of the high number of putative off-targets plays a key role for the success of computational off-targeting assessment. Results We present an approximate binding energy model for the Cas9–gRNA–DNA complex, which systematically combines the energy parameters obtained for RNA–RNA, DNA–DNA, and RNA–DNA duplexes. Based on this model, two novel off-target assessment methods for gRNA selection in CRISPR-Cas9 applications are introduced: CRISPRoff to assign confidence scores to predicted off-targets and CRISPRspec to measure the specificity of the gRNA. We benchmark the methods against current state-of-the-art methods and show that both are in better agreement with experimental results. Furthermore, we show significant evidence supporting the inverse relationship between the on-target cleavage efficiency and specificity of the system, in which introduced binding energies are key components. Conclusions The impact of the binding energies provides a direction for further studies of off-targeting mechanisms. The performance of CRISPRoff and CRISPRspec enables more accurate off-target evaluation for gRNA selections, prior to any CRISPR-Cas9 genome-editing application. For given gRNA sequences or all potential gRNAs in a given target region, CRISPRoff -based off-target predictions and CRISPRspec -based specificity evaluations can be carried out through our webserver at https://rth.dk/resources/crispr/ .

Filamentous fungi play an important role in human health and industrial/agricultural production. With the increasing number of full genomes available for fungal species, the study of filamentous fungi has brought about a wider range of genetic manipulation opportunities. However, the utilization of traditional methods to study fungi is time consuming and laborious. Recent rapid progress and wide application of a versatile genome editing technology, i.e., the CRISPR (clustered regularly interspaced short palindromic repeat)–Cas9 (CRISPR-related nuclease 9) system, has revolutionized biological research and has many innovative applications in a wide range of fields showing great promise in research and application of filamentous fungi. In this review, we introduce the CRISPR/Cas9 genome editing technology focusing on its application in research of filamentous fungi and we discuss the general considerations of genome editing using CRISPR/Cas9 system illustrating vector construction, multiple editing strategies, technical consideration of different sizes of homology arms on genome editing efficiency, off-target effects, and different transformation methodologies. In addition, we discuss the challenges encountered using CRISPR/Cas9 technology and give the perspectives of future applications of CRISPR/Cas9 technology for basic research and practical application of filamentous fungi.

Recent years have seen the development of computational tools to assist researchers in performing CRISPR-Cas9 experiment optimally. More specifically, these tools aim to maximize on-target activity (guide efficiency) while also minimizing potential off-target effects (guide specificity) by analyzing the features of the target site. Nonetheless, currently available tools cannot robustly predict experimental success as prediction accuracy depends on the approximations of the underlying model and how closely the experimental setup matches the data the model was trained on. Here, we present an overview of the available computational tools, their current limitations and future considerations. We discuss new trends around personalized health by taking genomic variants into account when predicting target sites as well as discussing other governing factors that can improve prediction accuracy.

In recent years, unmanned aerial vehicle (UAV) technology has expanded to include UAV sprayers capable of applying pesticides. Very little research has been conducted to optimize application parameters and measure the potential of off-target movement from UAV-based pesticide applications. Field experiments were conducted in Raleigh, NC during spring 2018 to characterize the effect of different application speeds and nozzle types on target area coverage and uniformity of UAV applications. The highest coverage was achieved with an application speed of 1 m s–1 and ranged from 30% to 60%, whereas applications at 7 m s–1 yielded 13% to 22% coverage. Coverage consistently decreased as application speed increased across all nozzles, with extended-range flat-spray nozzles declining at a faster rate than air-induction nozzles, likely due to higher drift. Experiments measuring the drift potential of UAV-applied pesticides using extended-range flat spray, air-induction flat-spray, turbo air–induction flat-spray, and hollow-cone nozzles under 0, 2, 4, 7, and 9 m s–1 perpendicular wind conditions in the immediate 1.75 m above the target were conducted in the absence of natural wind. Off-target movement was observed under all perpendicular wind conditions with all nozzles tested but was nondetectable beyond 5 m away from the target. Coverage from all nozzles exhibited a concave-shaped curve in response to the increasing perpendicular wind speed due to turbulence. The maximum target coverage in drift studies was observed when the perpendicular wind was 0 and 8.94 m s–1, but higher turbulence at the two highest perpendicular wind speeds (6.71 and 8.94 m s–1) increased coverage variability, whereas the lowest variability was observed at 2.24 m s–1 wind speed. Results suggested that air-induction flat-spray and turbo air–induction flat-spray nozzles and an application speed of 3 m s–1 provided an adequate coverage of target areas while minimizing off-target movement risk.

PurposeSeveral tracers have been designed for tracking the abnormal accumulation of tau pathology in vivo. Recently, concerns have been raised about the sources of off-target binding for these tracers; inconclusive data propose binding for some tracers to monoamine oxidase B (MAO-B).MethodsMolecular docking and dynamics simulations were used to estimate the affinity and free energy for the binding of several tau tracers (FDDNP, THK523, THK5105, THK5317, THK5351, T807 [aka AV-1451, flortaucipir], T808, PBB3, RO-948, MK-6240, JNJ-311 and PI-2620) to MAO-B. These values were then compared with those for safinamide (MAO-B inhibitor). PET imaging was used with the tau tracer [18F]THK5317 and the MAO-B tracer [11C]DED in five patients with Alzheimer’s disease to investigate the MAO-B binding component of this first generation tau tracer in vivo.ResultsThe computational modelling studies identified a binding site for all the tau tracers on MAO-B; this was the same site as that for safinamide. The binding affinity and free energy of binding for the tau tracers to MAO-B was substantial and in a similar range to those for safinamide. The most recently developed tau tracers MK-6240, JNJ-311 and PI-2620 appeared, in silico, to have the lowest relative affinity for MAO-B. The in vivo investigations found that the regional distribution of binding for [18F]THK5317 was different from that for [11C]DED, although areas of suspected off-target [18F]THK5317 binding were detected. The binding relationship between [18F]THK5317 and [11C]DED depended on the availability of the MAO-B enzyme.ConclusionsThe developed tau tracers show in silico and in vivo evidence of cross-interaction with MAO-B; the MAO-B component of the tracer binding was dependent on the regional concentration of the enzyme.

Background Genome-wide knockout studies, noncoding deletion scans, and other large-scale studies require a simple and lightweight framework that can quickly discover and score thousands of candidate CRISPR guides targeting an arbitrary DNA sequence. While several CRISPR web applications exist, there is a need for a high-throughput tool to rapidly discover and process hundreds of thousands of CRISPR targets. Results Here, we introduce FlashFry, a fast and flexible command-line tool for characterizing large numbers of CRISPR target sequences. With FlashFry, users can specify an unconstrained number of mismatches to putative off-targets, richly annotate discovered sites, and tag potential guides with commonly used on-target and off-target scoring metrics. FlashFry runs at speeds comparable to commonly used genome-wide sequence aligners, and output is provided as an easy-to-manipulate text file. Conclusions FlashFry is a fast and convenient command-line tool to discover and score CRISPR targets within large DNA sequences.