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Aggressive thoracic hemangiomas are rare, benign tumors that extend into the spinal canal and cause neurological symptoms. Delayed diagnosis and treatment, due to a paucity of literature on optimal treatment strategies, can increase morbidity. This case report describes a 19‐year‐old male patient with aggressive thoracic hemangioma who presented with upper back pain and progressive weakness of the lower extremities. The patient underwent preoperative embolization and sclerotherapy, followed by decompression, posterior instrumentation, and stabilization. The final diagnosis was confirmed by biopsy, and there was a significant improvement in neurology after the surgical intervention. The diagnosis of rare lesions, such as aggressive hemangiomas, requires a high level of clinical suspicion and the assistance of imaging modalities in patients with features of compressive myelopathy. A combination of endovascular and surgical approaches can lead to optimal outcomes. [ABSTRACT FROM AUTHOR]

The rust diseases, including leaf rust caused by Puccinia triticina (Pt), stem rust caused by P. graminis f. sp. tritici (Pgt), and stripe rust caused by P. striiformis f. sp. tritici (Pst), are major limiting factors in wheat production worldwide. Identification of novel sources of rust resistance genes is key to developing cultivars resistant to rapidly evolving pathogen populations. Aegilops longissima is a diploid wild grass native to the Levant and closely related to the modern bread wheat D subgenome. To explore resistance genes in the species, we evaluated a large panel of Ae. longissima for resistance to several races of Pt, Pgt, and Pst, and conducted a genome-wide association study (GWAS) to map rust resistance loci in the species. A panel of 404 Ae. longissima accessions, mostly collected from Israel, were screened for seedling-stage resistance to four races of Pt, four races of Pgt, and three races of Pst. Out of the 404 accessions screened, two were found that were resistant to all 11 races of the three rust pathogens screened. The percentage of all accessions screened that were resistant to a given rust pathogen race ranged from 18.5% to 99.7%. Genotyping-by-sequencing (GBS) was performed on 381 accessions of the Ae. longissima panel, wherein 125,343 single nucleotide polymorphisms (SNPs) were obtained after alignment to the Ae. longissima reference genome assembly and quality control filtering. Genetic diversity analysis revealed the presence of two distinct subpopulations, which followed a geographic pattern of a northern and a southern subpopulation. Association mapping was performed in the genotyped portion of the collection (n = 381) and in each subpopulation (n = 204 and 174) independently via a single-locus mixed-linear model, and two multi-locus models, FarmCPU, and BLINK. A large number (195) of markers were significantly associated with resistance to at least one of 10 rust pathogen races evaluated, nine of which are key candidate markers for further investigation due to their detection via multiple models and/or their association with resistance to more than one pathogen race. The novel resistance loci identified will provide additional diversity available for use in wheat breeding. [ABSTRACT FROM AUTHOR]

THE INNOVATION In response to the COVID-19 pandemic, the Veterans Administration (VA) Greater Los Angeles Healthcare System initiated an innovative approach to providing integrated primary care services (Supplemental Figure 1). [...]

Motivation Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plant breeding programs. While methods to predict yield from genotype or phenotype data have been proposed, improved performance and integrated models are needed. Results We propose a machine learning model that leverages both genotype and phenotype measurements by fusing genetic variants with multiple data sources collected by unmanned aerial systems. We use a deep multiple instance learning framework with an attention mechanism that sheds light on the importance given to each input during prediction, enhancing interpretability. Our model reaches 0.754 ± 0.024 Pearson correlation coefficient when predicting yield in similar environmental conditions; a 34.8% improvement over the genotype-only linear baseline (0.559 ± 0.050). We further predict yield on new lines in an unseen environment using only genotypes, obtaining a prediction accuracy of 0.386 ± 0.010, a 13.5% improvement over the linear baseline. Our multi-modal deep learning architecture efficiently accounts for plant health and environment, distilling the genetic contribution and providing excellent predictions. Yield prediction algorithms leveraging phenotypic observations during training therefore promise to improve breeding programs, ultimately speeding up delivery of improved varieties. Availability and implementation Available at https://github.com/BorgwardtLab/PheGeMIL (code) and https://doi.org/doi:10.5061/dryad.kprr4xh5p (data). [ABSTRACT FROM AUTHOR]

Land cover change is prevalent in the eastern Kentucky Appalachian region, mainly due to increased surface mining activities. This study explored the potential change in land cover and its relationship with stream discharge and sediment yield in a watershed of the Cumberland River near Harlan, Kentucky, between 2001 and 2016, using the Soil and Water Assessment Tool (SWAT). Two land cover scenarios for the years 2001 and 2016 were used separately to simulate the surface runoff and sediment yield at the outlet of the Cumberland River near Harlan. Land cover datasets from the National Land Cover Database (NLCD) were used to reclassify the land cover type into the following classes: water, developed, forest, barren, shrubland, and pasture/grassland. Evaluation of the relationship between the land cover change on discharge and sediment was performed by comparing the average annual basin values of streamflow and sediment from each of the land cover scenarios. The SWAT model output was evaluated based on several statistical parameters, including the Nash–Sutcliffe efficiency coefficient (NSE), RMSE-observations standard deviation ratio (RSR), percent bias (PBIAS), and the coefficient of determination (R²). Moreover, P-factor and R-factor indices were used to measure prediction uncertainty. The model showed an acceptable range of agreement for both calibration and validation between observed and simulated values. The temporal land cover change showed a decrease in forest area by 2.42% and an increase in developed, barren, shrubland, and grassland by 0.11%, 0.34%, 0.53%, and 1.44%, respectively. The discharge increased from 92.34 mm/year to 104.7 mm/year, and sediment increased from 0.83 t/ha to 1.63 t/ha from 2001 to 2016, respectively. Based on results from the model, this study concluded that the conversion of forest land into other land types could contribute to increased surface runoff and sediment transport detached from the soil along with runoff water. The research provides a robust approach to evaluating the effect of temporal land cover change on Appalachian streams and rivers. Such information can be useful for designing land management practices to conserve water and control soil erosion in the Appalachian region of eastern Kentucky. [ABSTRACT FROM AUTHOR]

High spatio-temporal resolution and accurate long-term rainfall estimates are critical in sustainable water resource planning and management, assessment of climate variability and extremes, and hydro-meteorology-related water system decisions. The recent advent of improved higher-resolution open-access satellite-based rainfall products has emerged as a viable complementary to ground-based observations that can often not capture the rainfall variability on a spatial scale. In a developing country such as Nepal, where the rain-gauge monitoring network is sparse and unevenly distributed, satellite rainfall estimates are crucial. However, substantial errors associated with such satellite rainfall estimates pose a challenge to their application, particularly in complex orographic regions such as Nepal. Therefore, these precipitation products must be validated before practical usage to check their accuracy and occurrence consistency. This study aims to assess the reliability of the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) product against ground-based observations from 1986 to 2015 in five medium-sized river basins in Nepal, namely, Babai, Bagmati, Kamala, Kankai, and the West Rapti river basin. A set of continuous evaluation metrics (correlation coefficient, root mean square error, relative bias, and Kling-Gupta efficiency) were used in analyzing the accuracy of CHIRPS and categorical metrics (probability of detection, critical success index, false alarm ratio, and frequency bias index). The Probability of Detection and Critical Success Index values were found to be considerably low (<0.4 on average), while the false alarm ratio was significant (>0.4 on average). It was found that CHIRPS showed better performance in seasonal and monthly time scales with high correlation and indicated greater consistency in non-monsoon seasons. Rainfall amount (less than 10 mm and greater than 150 mm) and rainfall frequency was underestimated by CHIRPS in all basins, while the overestimated rainfall was between 10 and 100 mm in all basins except Kamala. Additionally, CHIRPS overestimated dry days and maximum consecutive dry days in the study area. Our study suggests that CHIRPS rainfall products cannot supplant the ground-based observations but complement rain-gauge networks. However, the reliability of this product in capturing local extreme events (such as floods and droughts) seems less prominent. A high-quality rain gauge network is essential to enhance the accuracy of satellite estimations. [ABSTRACT FROM AUTHOR]

Spring bread wheat adaptation to diverse environments is supported by various traits such as phenology and plant architecture. A large-scale genome-wide association study (GWAS) was designed to investigate and dissect the genetic architecture of phenology affecting adaptation. It used 48 datasets from 4,680 spring wheat lines. For 8 years (2014–2021), these lines were evaluated for days to heading (DH) and maturity (DM) at three sites: Jabalpur, Ludhiana, and Samastipur (Pusa), which represent the three major Indian wheat-producing zones: the Central Zone (CZ), North-Western Plain Zone (NWPZ), and North-Eastern Plain Zone (NEPZ), respectively. Ludhiana had the highest mean DH of 103.8 days and DM of 148.6 days, whereas Jabalpur had the lowest mean DH of 77.7 days and DM of 121.6 days. We identified 119 markers significantly associated with DH and DM on chromosomes 5B (76), 2B (18), 7D (10), 4D (8), 5A (1), 6B (4), 7B (1), and 3D (1). Our results clearly indicated the importance of the photoperiod-associated gene (Ppd-B1) for adaptation to the NWPZ and the Vrn-B1 gene for adaptation to the NEPZ and CZ. A maximum variation of 21.1 and 14% was explained by markers 2B_56134146 and 5B_574145576 linked to the Ppd-B1 and Vrn-B1 genes, respectively, indicating their significant role in regulating DH and DM. The results provide important insights into the genomic regions associated with the two phenological traits that influence adaptation to the major wheat-producing zones in India. [ABSTRACT FROM AUTHOR]

Accelerating breeding efforts for developing biofortified bread wheat varieties necessitates understanding the genetic control of grain zinc concentration (GZnC) and grain iron concentration (GFeC). Hence, the major objective of this study was to perform genome-wide association mapping to identify consistently significant genotyping-by-sequencing markers associated with GZnC and GFeC using a large panel of 5,585 breeding lines from the International Maize and Wheat Improvement Center. These lines were grown between 2018 and 2021 in an optimally irrigated environment at Obregon, Mexico, while some of them were also grown in a water-limiting drought-stressed environment and a space-limiting small plot environment and evaluated for GZnC and GFeC. The lines showed a large and continuous variation for GZnC ranging from 27 to 74.5 ppm and GFeC ranging from 27 to 53.4 ppm. We performed 742,113 marker-traits association tests in 73 datasets and identified 141 markers consistently associated with GZnC and GFeC in three or more datasets, which were located on all wheat chromosomes except 3A and 7D. Among them, 29 markers were associated with both GZnC and GFeC, indicating a shared genetic basis for these micronutrients and the possibility of simultaneously improving both. In addition, several significant GZnC and GFeC associated markers were common across the irrigated, water-limiting drought-stressed, and space-limiting small plots environments, thereby indicating the feasibility of indirect selection for these micronutrients in either of these environments. Moreover, the many significant markers identified had minor effects on GZnC and GFeC, suggesting a quantitative genetic control of these traits. Our findings provide important insights into the complex genetic basis of GZnC and GFeC in bread wheat while implying limited prospects for marker-assisted selection and the need for using genomic selection. [ABSTRACT FROM AUTHOR]

Spot blotch caused by the fungus Bipolaris sorokiniana poses a serious threat to bread wheat production in warm and humid wheat-growing regions of the world. Hence, the major objective of this study was to identify consistent genotyping-by-sequencing (GBS) markers associated with spot blotch resistance using genome-wide association mapping on a large set of 6,736 advanced bread wheat breeding lines from the International Maize and Wheat Improvement Center. These lines were phenotyped as seven panels at Agua Fria, Mexico between the 2013–2014 and 2019–2020 crop cycles. We identified 214 significant spot blotch associated GBS markers in all the panels, among which only 96 were significant in more than one panel, indicating a strong environmental effect on the trait and highlights the need for multiple phenotypic evaluations to identify lines with stable spot blotch resistance. The 96 consistent GBS markers were on chromosomes 1A, 1B, 1D, 2A, 3B, 4A, 5B, 5D, 6B, 7A, 7B, and 7D, including markers possibly linked to the Lr46 , Sb1 , Sb2 and Sb3 genes. We also report the association of the 2NS translocation from Aegilops ventricosa with spot blotch resistance in some environments. Moreover, the spot blotch favorable alleles at the 2NS translocation and two markers on chromosome 3BS (3B_2280114 and 3B_5601689) were associated with increased grain yield evaluated at several environments in Mexico and India, implying that selection for favorable alleles at these loci could enable simultaneous improvement for high grain yield and spot blotch resistance. Furthermore, a significant relationship between the percentage of favorable alleles in the lines and their spot blotch response was observed, which taken together with the multiple minor effect loci identified to be associated with spot blotch in this study, indicate quantitative genetic control of resistance. Overall, the results presented here have extended our knowledge on the genetic basis of spot blotch resistance in bread wheat and further efforts to improve genetic resistance to the disease are needed for reducing current and future losses under climate change. [ABSTRACT FROM AUTHOR]

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical. [ABSTRACT FROM AUTHOR]

Introduction Surgical correction is the only treatment for hypospadias. Complication rate is usually high. Indwelling catheter is kept postoperatively for urinary diversion and proper healing. There is no consensus regarding the duration of catheterization. This study was conducted to compare urethrocutaneous fistula (UCF) rate between one and three weeks of catheterization. Methods This study was a randomized control trial, conducted at Tribhuvan University Teaching Hospital, Kathmandu, Nepal. All children undergoing urethroplasty for hypospadias were randomized into two groups. After surgery, urinary catheter was kept for one and three weeks in group 1 and group 2 respectively. Occurrence of UCF and other complications were noted and compared between the groups. Results A total of 32 patients were randomized in to 2 groups having 16 in each group. Fourteen (43.8%) developed UCF. In one week group, 8 (50%) and in three week group 6(37.5%) developed UCF. It was not statistical different (p = 0.48). Occurrence of UCF was not different in different age of children, type of hypospadias and single or staged procedure. Meatal stenosis was not different in both groups. Conclusion Incidence of UCF was not different in one week or three week of urinary catheterization after surgery for hypospadias. [ABSTRACT FROM AUTHOR]

We untangled key regions of the genetic architecture of grain yield (GY) in CIMMYT spring bread wheat by conducting a haplotype-based, genome-wide association study (GWAS), together with an investigation of epistatic interactions using seven large sets of elite yield trials (EYTs) consisting of a total of 6,461 advanced breeding lines. These lines were phenotyped under irrigated and stress environments in seven growing seasons (2011–2018) and genotyped with genotyping-by-sequencing markers. Genome-wide 519 haplotype blocks were constructed, using a linkage disequilibrium-based approach covering 14,036 Mb in the wheat genome. Haplotype-based GWAS identified 7, 4, 10, and 15 stable (significant in three or more EYTs) associations in irrigated (I), mild drought (MD), severe drought (SD), and heat stress (HS) testing environments, respectively. Considering all EYTs and the four testing environments together, 30 stable associations were deciphered with seven hotspots identified on chromosomes 1A, 1B, 2B, 4A, 5B, 6B, and 7B, where multiple haplotype blocks were associated with GY. Epistatic interactions contributed significantly to the genetic architecture of GY, explaining variation of 3.5–21.1%, 3.7–14.7%, 3.5–20.6%, and 4.4– 23.1% in I, MD, SD, and HS environments, respectively. Our results revealed the intricate genetic architecture of GY, controlled by both main and epistatic effects. The importance of these results for practical applications in the CIMMYT breeding program is discussed. [ABSTRACT FROM AUTHOR]

COVID-19 has placed an increased burden on the NHS. Changes were made to expand patient capacity including hospital restructuring, cancellation of most elective surgeries and early graduation of final year medical students.1 The UK foundation programme (UKFP) curated a new training position for graduates as foundation interim year 1 (FiY1) doctors, where they voluntarily work in paid positions prior to entering formal foundation year 1 (FY1) roles.2 Expediting the process of fulfilling these positions, the General Medical Council facilitated early provisional registration of doctors. We discuss the positives, pitfalls, and perils of the new roles and the first impressions of three newly qualified FiY1 s in medical, obstetrics and gynaecology and surgical posts, a surgical FY1 doctor and a clinical supervisor in surgery. [ABSTRACT FROM AUTHOR]

Breeding programs for wheat (Triticum aestivum L.) and other crops require one or more generations of seed increase before replicated trials can be sown to assess yield. Extensive phenotyping at this stage is challenging because of the small sizes of plots and large numbers of lines under evaluation, and therefore, breeders typically rely on visual selection to promote lines to yield evaluation. Aerial high‐throughput phenotyping (HTP) enables the rapid acquisition of traits that may be useful for selection among early generation lines. With the objective of assessing the potential for aerial measurements recorded on seed increase plots to improve indirect selection for grain yield (GY), two sets of 1,008 early generation bread wheat breeding lines were sown both as replicated yield trials (YTs) and as small, unreplicated plots (SPs) at the International Maize and Wheat Improvement Center during two breeding cycles. Normalized difference vegetation indices (NDVI) collected with an unmanned aerial vehicle (UAV) in the SPs were observed to be heritable and moderately correlated with GY assessed in YTs. Furthermore, NDVI was more predictive of GY than univariate genomic selection (GS), with still higher overall predictive abilities from multitrait approaches. A related experiment showed that selection based on NDVI would have outperformed visual selection, though this approach would have driven a directional response in phenology because of confounding between phenology, NDVI, and GY. A restricted selection index was proposed to address this issue. These results provide a promising outlook for the use of aerial HTP to improve selection at the early generation, seed‐limited stages of breeding programs. [ABSTRACT FROM AUTHOR]

Genomic breeding technologies offer new opportunities for grain yield (GY) improvement in common wheat. In this study, we have evaluated the potential of genomic selection (GS) in breeding for GY in wheat by modeling a large dataset of 48,562 GY observations from the International Maize and Wheat Improvement Center (CIMMYT), including 36 yield trials evaluated between 2012 and 2019 in Obregón, Sonora, Mexico. Our key objective was to determine the value that GS can add to the current three-stage yield testing strategy at CIMMYT, and we draw inferences from predictive modeling of GY using 420 different populations, environments, cycles, and model combinations. First, we evaluated the potential of genomic predictions for minimizing the number of replications and lines tested within a site and year and obtained mean prediction accuracies (PAs) of 0.56, 0.5, and 0.42 in Stages 1, 2, and 3 of yield testing, respectively. However, these PAs were similar to the mean pedigree-based PAs indicating that genomic relationships added no value to pedigree relationships in the yield testing stages, characterized by small family-sizes. Second, we evaluated genomic predictions for minimizing GY testing across stages/years in Obregón and observed mean PAs of 0.41, 0.31, and 0.37, respectively when GY in the full irrigation bed planting (FI BP), drought stress (DS), and late-sown heat stress environments were predicted across years using genotype × environment (G × E) interaction models. Third, we evaluated genomic predictions for minimizing the number of yield testing environments and observed that in Stage 2, the FI BP, full irrigation flat planting and early-sown heat stress environments (mean PA of 0.37 ± 0.12) and the reduced irrigation and DS environments (mean PA of 0.45 ± 0.07) had moderate predictabilities among them. However, in both predictions across years and environments, the PAs were inconsistent across years and the G × E models had no advantage over the baseline model with environment and line effects. Overall, our results provide excellent insights into the predictability of a quantitative trait like GY and will have important implications on the future design of GS for GY in wheat breeding programs globally. [ABSTRACT FROM AUTHOR]

Breeding for grain yield (GY) in bread wheat at the International Maize and Wheat Improvement Center (CIMMYT) involves three-stage testing at Obregon, Mexico in different selection environments (SEs). To understand the efficiency of selection in the SEs, we performed a large retrospective quantitative genetics study using CIMMYT’s yield trials evaluated in the SEs (2013–2014 to 2017–2018), the South Asia Bread Wheat Genomic Prediction Yield Trials (SABWGPYTs) evaluated in India, Pakistan, and Bangladesh (2014–2015 to 2017–2018), and the Elite Spring Wheat Yield Trials (ESWYTs) evaluated in several sites globally (2003–2004 to 2016–2017). First, we compared the narrow-sense heritabilities in the Obregon SEs and target sites and observed that the mean heritability in the SEs was 44.2 and 92.3% higher than the mean heritabilities in the SABWGPYT and ESWYT sites, respectively. Second, we observed significant genetic correlations between a SE in Obregon and all the five SABWGPYT sites and 65.1% of the ESWYT sites. Third, we observed high ratios of response to indirect selection in the SEs of Obregon with a mean of 0.80 ± 0.21 and 2.6 ± 5.4 in the SABWGPYT and ESWYT sites, respectively. Furthermore, our results also indicated that for all the SABWGPYT sites and 82% of the ESWYT sites, a response greater than 0.5 can be achieved by indirect selection for GY in Obregon. We also performed genomic prediction for GY in the target sites using the performance of the same lines in the SEs of Obregon and observed moderate mean prediction accuracies of 0.24 ± 0.08 and 0.28 ± 0.08 in the SABWGPYT and ESWYT sites, respectively using the genotype x environment (GxE) model. However, we observed similar accuracies using the baseline model with environment and line effects and no advantage of modeling GxE interactions. Overall, this study provides important insights into the suitability of the Obregon SEs in breeding for GY, while the variable genomic predictabilities of GY and the high year-to-year GY fluctuations reported, highlight the importance of multi-environment testing across time and space to stave off GxE induced uncertainties in varietal yields. [ABSTRACT FROM AUTHOR]

Oomycete plant pathogens are difficult to control and routine genetic research is challenging. A major problem is instability of isolates. Here we characterize >600 field and single zoospore isolates of Phytophthora capsici for inheritance of mating type, sensitivity to mefenoxam, chromosome copy number and heterozygous allele frequencies. The A2 mating type was highly unstable with 26% of 241 A2 isolates remaining A2. The A1 mating type was stable. Isolates intermediately resistant to mefenoxam produced fully resistant single-spore progeny. Sensitive isolates remained fully sensitive. Genome re-sequencing of single zoospore isolates revealed extreme aneuploidy; a phenomenon dubbed Dynamic Extreme Aneuploidy (DEA). DEA is characterized by the asexual inheritance of diverse intra-genomic combinations of chromosomal ploidy ranging from 2N to 3N and heterozygous allele frequencies that do not strictly correspond to ploidy. Isolates sectoring on agar media showed dramatically altered heterozygous allele frequencies. DEA can explain the rapid increase of advantageous alleles (e.g. drug resistance), mating type switches and copy neutral loss of heterozygosity (LOH). Although the mechanisms driving DEA are unknown, it can play an important role in adaptation and evolution and seriously hinders all aspects of P. capsici research. [ABSTRACT FROM AUTHOR]

Corynespora cassiicola attackes diverse agriculturally important plants, including soybean and cotton, in the US. It is a reemerge pathogen on cotton in southeastern US. Whole genome sequences of four cotton and one soybean isolate from Tennessee were used to develop single nucleotide polymorphism markers for cotton isolates. Cotton isolates had little diversity at the genome level and very little differentiation from the soybean isolate. Analysis of 75 isolates from cotton and soybean, using targeted-sequencing of 22 polymorphic SNP sites, revealed eight multi-locus genotypes and it appears a single clonal lineage predominates across the southeastern region. The cotton and soybean genome sequences were significantly different from the public reference genome derived from a rubber isolate and the utility of these novel resources will be discussed. [ABSTRACT FROM AUTHOR]

Frogeye leaf spot (FLS), caused by Cercospora sojina, causes significant damage to soybean in the U.S. One control strategy is the use of quinone outside inhibitor (QoI) fungicides. QoI resistant isolates were first reported in Tennessee (TN) in 2010. To investigate the disease dynamics of C. sojina, we collected 437 C. sojina isolates in 2015 from Jackson and Milan, TN and used 40 historical isolates collected from 2006–2009 from TN and ten additional states for comparison. A subset of 186 isolates, including historical isolates, were genotyped for 49 single nucleotide polymorphism (SNP) markers and the QoI resistance locus, revealing 35 unique genotypes. The genotypes clustered into three groups with two groups containing only sensitive isolates and the remaining group containing all resistant isolates and a dominant clonal lineage of 130 isolates. All 477 C. sojina isolates were genotyped for the QoI locus revealing 344 resistant and 133 sensitive isolates. All isolates collected prior to 2015 were QoI sensitive. Both mating type alleles (MAT1-1-1 and MAT1-2) were found in Jackson and Milan, TN and recovered from single lesions suggesting sexual recombination may play a role in the epidemiology of field populations. Analysis of C. sojina isolates using SNP markers proved useful to investigate population diversity and to elaborate on diversity as it relates to QoI resistance and mating type. [ABSTRACT FROM AUTHOR]

Peronospora effusa is an obligate pathogen that causes downy mildew on spinach and is considered the most economically important disease of spinach. The objective of the current research was to assess genetic diversity of known historical races and isolates collected in 2014 from production fields in Yuma, Arizona and Salinas Valley, California. Candidate neutral single nucleotide polymorphisms (SNPs) were identified by comparing sequence data from reference isolates of known races of the pathogen collected in 2009 and 2010. Genotypes were assessed using targeted sequencing on genomic DNA extracted directly from infected plant tissue. Genotyping 26 historical and 167 contemporary samples at 46 SNP loci revealed 82 unique multi-locus genotypes. The unique genotypes clustered into five groups and the majority of isolates collected in 2014 were genetically closely related, regardless of source location. The historical samples, representing several races, showed greater genetic differentiation. Overall, the SNP data indicate much of the genotypic variation found within fields was produced during asexual development, whereas overall genetic diversity may be influenced by sexual recombination on broader geographical and temporal scales. [ABSTRACT FROM AUTHOR]

The phytohormone auxin regulates nearly all aspects of plant growth and development. Based on the current model in Arabidopsis thaliana, Auxin/indole-3-acetic acid (Aux/IAA) proteins repress auxin-inducible genes by inhibiting auxin response transcription factors (ARFs). Experimental evidence suggests that heterodimerization between Aux/IAA and ARF proteins are related to their unique biological functions. The objective of this study was to generate the Aux/IAA-ARF protein-protein interaction map using full length sequences and locate the interacting protein pairs to specific gene co-expression networks in order to define tissue-specific responses of the Aux/IAA-ARF interactome. Pairwise interactions between 19 ARFs and 29 Aux/IAAs resulted in the identification of 213 specific interactions of which 79 interactions were previously unknown. The incorporation of co-expression profiles with protein-protein interaction data revealed a strong correlation of gene co-expression for 70% of the ARF-Aux/IAA interacting pairs in at least one tissue/organ, indicative of the biological significance of these interactions. Importantly, ARF4-8 and 19, which were found to interact with almost all Aux-Aux/IAA showed broad co-expression relationships with Aux/IAA genes, thus, formed the central hubs of the co-expression network. Our analyses provide new insights into the biological significance of ARF-Aux/IAA associations in the morphogenesis and development of various plant tissues and organs. [ABSTRACT FROM AUTHOR]

The Appalachian Mountain region of eastern Kentucky is unique and contains high proportions of forestland along with coal and natural gas depositaries. Landscape changes due to extreme mining activities can eventually threaten the downstream ecosystems, including soil and water quality, resulting in excessive runoff and sedimentation. The purpose of this study is to assess the impacts of land-use and land-cover (LULC) changes in streamflow and sediment yield in Yellow Creek Watershed, Kentucky, USA, between 1992 and 2016. LULC, digital elevation model, soil, and weather data were inputted into the Soil and Water Assessment Tool (SWAT) to simulate discharge and sediment yield. The model output was evaluated on several statistical parameters, such as the Nash-Sutcliffe efficiency coefficient (NSE), RMSE-observations standard deviation ratio (RSR), percent bias (PBIAS), and the coefficient of determination (R2). In addition, two indices, P-factor and R-factor, were used to measure the prediction uncertainty. The calibrated model showed an increase in surface runoff and sediment yield due to changes in LULC in the Yellow Creek Watershed. The results provided important insights for studying water management strategies to make more informed land management decisions and adaptive practices. [ABSTRACT FROM AUTHOR]

Background Measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genomes and the development of improved, high-yielding crop varieties. Results Here we demonstrate the application of deep learning on proximal imaging from a mobile field vehicle to directly estimate plant morphology and developmental stages in wheat under field conditions. We developed and trained a convolutional neural network with image datasets labeled from expert visual scores and used this "breeder-trained" network to classify wheat morphology and developmental stages. For both morphological (awned) and phenological (flowering time) traits, we demonstrate high heritability and very high accuracy against the "ground-truth" values from visual scoring. Using the traits predicted by the network, we tested genotype-to-phenotype association using the deep learning phenotypes and uncovered novel epistatic interactions for flowering time. Enabled by the time-series high-throughput phenotyping, we describe a new phenotype as the rate of flowering and show heritable genetic control for this trait. Conclusions We demonstrated a field-based high-throughput phenotyping approach using deep learning that can directly measure morphological and developmental phenotypes in genetic populations from field-based imaging. The deep learning approach presented here gives a conceptual advancement in high-throughput plant phenotyping because it can potentially estimate any trait in any plant species for which the combination of breeder scores and high-resolution images can be obtained, capturing the expert knowledge from breeders, geneticists, pathologists, and physiologists to train the networks. [ABSTRACT FROM AUTHOR]

When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2–17.45% (datasets 1–3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel. [ABSTRACT FROM AUTHOR]

Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is highly influenced by environmental stimuli, it is important to accurately model the environment and its interaction with genetic factors in prediction models. Arguably, multi-environmental best linear unbiased prediction (BLUP) may deliver better prediction performance than single-environment genomic BLUP. We evaluated pedigree and genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information as prediction inputs in two different validation schemes. All models included main effects, but some considered interactions between the different types of pedigree and genomic covariates via Hadamard products of similarity kernels. Pedigree models always gave better prediction of new lines in observed environments than genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, genomes, and environments were included. When new lines were predicted in unobserved environments, in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design and prediction of the outcome of future breeding programs. [ABSTRACT FROM AUTHOR]

Downy mildew disease, caused by the obligate oomycete pathogen Peronospora effusa, is the most important economic constraint for spinach production. Three races (races 12, 13, and 14) of P. effusa have been sequenced and assembled. The draft genomes of these three races have been deposited to GenBank and provide useful resources for dissecting the interaction between the host and the pathogen and may provide a framework for determining the mechanism by which new races of the pathogen are rapidly emerging.