Insights into the genetic architecture of Phytophthora capsici root rot resistance in chile pepper (Capsicum spp.) from multi-locus genome-wide association study.

BACKGROUND: Phytophthora root rot, a major constraint in chile pepper production worldwide, is caused by the soil-borne oomycete, Phytophthora capsici. This study aimed to detect significant regions in the Capsicum genome linked to Phytophthora root rot resistance using a panel consisting of 157 Capsicum spp. genotypes. Multi-locus genome wide association study (GWAS) was conducted using single nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing (GBS). Individual plants were separately inoculated with P. capsici isolates, 'PWB-185', 'PWB-186', and '6347', at the 4-8 leaf stage and were scored for disease symptoms up to 14-days post-inoculation. Disease scores were used to calculate disease parameters including disease severity index percentage, percent of resistant plants, area under disease progress curve, and estimated marginal means for each genotype. RESULTS: Most of the genotypes displayed root rot symptoms, whereas five accessions were completely resistant to all the isolates and displayed no symptoms of infection. A total of 55,117 SNP markers derived from GBS were used to perform multi-locus GWAS which identified 330 significant SNP markers associated with disease resistance. Of these, 56 SNP markers distributed across all the 12 chromosomes were common across the isolates, indicating association with more durable resistance. Candidate genes including nucleotide-binding site leucine-rich repeat (NBS-LRR), systemic acquired resistance (SAR8.2), and receptor-like kinase (RLKs), were identified within 0.5 Mb of the associated markers. CONCLUSIONS: Results will be used to improve resistance to Phytophthora root rot in chile pepper by the development of Kompetitive allele-specific markers (KASP®) for marker validation, genomewide selection, and marker-assisted breeding.

Knowledge, Attitudes, Practices, and Prevention Barriers Related to Childhood Lead Poisoning Among Nepali-Speaking Bhutanese Parents in Northeast Ohio, United States.

The study objectives were: (i) to develop and administer a survey to assess childhood lead poisoning (CLP) knowledge, attitudes, practices and prevention barriers (KAP-B) among the Nepali-Speaking Bhutanese (NSB) community in Northeast Ohio; and (ii) to examine the association between socio-demographic characteristics of NSB parents and their understanding of CLP as measured by the constructs of knowledge and attitudes. A Nepali language KAP-B questionnaire was developed and 200 NSB parents with at least one child ≤ 7 years of age from the Akron Metropolitan Area, Ohio were interviewed. NSB parents demonstrated a low level of knowledge about CLP prevention measures. While 82% lived in pre-1978 houses, only 27.5% perceived their house/neighborhood to be potentially lead contaminated. Only 33% of the parents reported understanding lead-related information provided by their child's healthcare provider. Low-level CLP awareness among NSB community emphasizes a need for culturally tailored and linguistically appropriate community-level CLP educational intervention programs in this vulnerable community.

Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers.

BACKGROUND: Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pepper genotypes evaluated in multi-environment trials in New Mexico, USA. RESULTS: Accuracy of prediction differed across different models under ten-fold cross-validations, where high prediction accuracy was observed for highly heritable traits such as plant height and plant width. No model was superior across traits using 14,922 SNP markers for genomewide selection. Bayesian ridge regression had the highest average accuracy for first pod date (0.77) and total yield per plant (0.33). Multilayer perceptron (MLP) was the most superior for flowering time (0.76) and plant height (0.73), whereas the genomic BLUP model had the highest accuracy for plant width (0.62). Using a subset of 7,690 SNP loci resulting from grouping markers based on linkage disequilibrium coefficients resulted in improved accuracy for first pod date, ten pod weight, and total yield per plant, even under a relatively small training population size for MLP and random forest models. Genomic and ridge regression BLUP models were sufficient for optimal prediction accuracies for small training population size. Combining phenotypic selection and genomewide selection resulted in improved selection response for yield-related traits, indicating that integrated approaches can result in improved gains achieved through selection. CONCLUSIONS: Accuracy values for ridge regression and deep learning prediction models demonstrate the potential of implementing genomewide selection for genetic improvement in chile pepper breeding programs. Ultimately, a large training data is relevant for improved genomic selection accuracy for the deep learning models.

Population genetic analysis and scans for adaptation and contemporary selection footprints provide genomic insight into aus, indica and japonica rice cultivars diversification.

Following domestication, rice cultivars have been spread worldwide to different climates and have experienced selection pressures to improve desirable traits. This has resulted in diverse cultivars that display variations in phenotypic traits, such as stress tolerance, grain size, and yield. To better understand the genomic composition arising from cultivar's development and local adaptation, high-density genotypes (containing 286,183 single-nucleotide polymorphisms after the quality control) of 1284 rice cultivars of aus, indica, and temperate and tropical japonica were scanned for diversifying signatures by applying a pairwise comparison of fixation index (Fst) test. Each cultivar's population was investigated for contemporary selection using the integrated haplotype score test. Signatures of diversifying selection among the pairwise comparisons were found in genomic regions mainly involved in response to stress (pathogens, drought, heat, cold) and development and morphology of various structures, such as root, pollen, spikelet, and grain. The most significant diversification signal between indica and japonica cultivars was detected at the location of ROX2 gene. Aus with indica comparison detected the most divergent signal at important candidate genes of OsEXPA8 and OsEXPA9, whereas temperate with tropical japonica comparison resulted in two well-known candidate genes OsHCT4 and OsGpx4. Recent selection analysis detected different patterns of contemporary selection in genomic regions related to rice breeding standard criteria such as stress tolerance, seed germination, starch content, and flowering time. Our findings highlight the underlying molecular basis of adaptive divergence and propose that modern rice breeding may provide additional diversification among rice cultivars.

Relationship Between Primary Language Spoken at Home and Blood Lead Levels in Children from Northeast Ohio, United States: A Retrospective Cohort Study.

To estimate the prevalence and incidence of blood lead levels (BLL) ≥ 5 and ≥ 3.5 µg/dl and assess their association with primary language spoken at home in Northeast Ohio, U.S. children, a retrospective cohort study was conducted among 19,753 children aged < 6 years. Primary language spoken at home was used to define children from resettled refugee families (RRFs) and non-RRFs. The overall BLL ≥ 5 and ≥ 3.5 µg/dl prevalence were 3.22 and 6.10%, and incidence rates were 2.25 and 3.64 cases per 100 person-years, respectively. Compared to children from non-RRFs children from RRFs were 3.62-times [95% confidence interval (CI): 1.84, 7.13] as likely to have BLL ≥ 5 µg/dl prevalence, and 6.72-times [95% CI 2.60, 17.40] as likely to have BLL ≥ 5 µg/dl incidence during the follow-up period. The higher prevalence and incidence of BLL acquired in the United States among children from RRFs warrant further research to identify specific environmental and sociocultural lead sources for these children.

Determinants of obstetric fistula in Afghanistan: An analysis of the Demographic and Health Survey 2015.

OBJECTIVE: To examine the reproductive, sociodemographic, and geographic factors associated with obstetric fistula, which is a significant but neglected health problem faced by women in low-income countries, in women in Afghanistan, where epidemiologic studies examining the determinants of obstetric fistula are currently lacking. METHODS: This cross-sectional study used data from a nationally representative sample of women collected through the Afghanistan Demographic and Health Survey 2015. Descriptive and binary and multivariable logistic regression analyses were performed to estimate the prevalence of obstetric fistula and describe and assess the risk factors associated with the condition. RESULTS: Among the weighted sample of 29 374 women aged 15-49 years, 3% reported having an obstetric fistula. In a multivariable logistic regression analysis, factors associated with women reporting a history of an obstetric fistula included parity, ethnicity, geography, income level, educational attainment, employment outside the home, decider on medical care, and their husband's education level. CONCLUSION: Our findings highlight the multifactorial nature of the determinants of obstetric fistula in Afghan women and underscore the need for multidisciplinary integrative interventions to address the reproductive, socio-economic, cultural, and healthcare-related factors in reducing the burden of obstetric fistula in women in Afghanistan.

Single nucleotide polymorphisms reveal genetic diversity in New Mexican chile peppers (Capsicum spp.).

BACKGROUND: Chile peppers (Capsicum spp.) are among the most important horticultural crops in the world due to their number of uses. They are considered a major cultural and economic crop in the state of New Mexico in the United States. Evaluating genetic diversity in current New Mexican germplasm would facilitate genetic improvement for different traits. This study assessed genetic diversity, population structure, and linkage disequilibrium (LD) among 165 chile pepper genotypes using single nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing (GBS). RESULTS: A GBS approach identified 66,750 high-quality SNP markers with known map positions distributed across the 12 chromosomes of Capsicum. Principal components analysis revealed four distinct clusters based on species. Neighbor-joining phylogenetic analysis among New Mexico State University (NMSU) chile pepper cultivars showed two main clusters, where the C. annuum genotypes grouped together based on fruit or pod type. A Bayesian clustering approach for the Capsicum population inferred K = 2 as the optimal number of clusters, where the C. chinense and C. frutescens grouped in a single cluster. Analysis of molecular variance revealed majority of variation to be between the Capsicum species (76.08 %). Extensive LD decay (~ 5.59 Mb) across the whole Capsicum population was observed, demonstrating that a lower number of markers would be required for implementing genome wide association studies for different traits in New Mexican type chile peppers. Tajima's D values demonstrated positive selection, population bottleneck, and balancing selection for the New Mexico Capsicum population. Genetic diversity for the New Mexican chile peppers was relatively low, indicating the need to introduce new alleles in the breeding program to broaden the genetic base of current germplasm. CONCLUSIONS: Genetic diversity among New Mexican chile peppers was evaluated using GBS-derived SNP markers and genetic relatedness on the species level was observed. Introducing novel alleles from other breeding programs or from wild species could help increase diversity in current germplasm. We present valuable information for future association mapping and genomic selection for different traits for New Mexican chile peppers for genetic improvement through marker-assisted breeding.

Long-Acting Injectable Antipsychotic Use in Patients with Schizophrenia and Criminal Justice System Encounters.

Background: Nonadherence to medication is prevalent in persons diagnosed with schizophrenia, thus increasing the likelihood of relapse, poor health outcomes, hospitalization, high treatment costs, and high rates of both violent and non-violent offenses. Objective: To assess the association between long-acting injectable (LAI) antipsychotic use and criminal justice system encounters in patients with schizophrenia or schizoaffective disorder. Methods: This retrospective follow-up study was conducted among patients aged ≥18 years treated for schizophrenia or schizoaffective disorder at a community mental health center in Akron, Ohio, between January 1, 2010, and June 15, 2016. The incidence of criminal justice system encounters at 6 months, 1 year, and 2 years pre- versus post-LAI antipsychotic initiation was assessed. A subanalysis was conducted for individuals with a history of prior arrest. Results: Overall, the risk ratio (RR) of having an encounter with the criminal justice system was significantly lower for patients treated with LAI antipsychotics 1 year after initiation of treatment compared with a similar time period prior to initiation (RR [95% confidence interval (CI)]: 0.74 [0.59-0.93]; P<0.01) and 2 years (0.74 [0.62-0.88]; P<0.0001). Statistically significant reductions in criminal justice system encounters after treatment than before treatment were observed in the once-monthly paliperidone palmitate (PP1M) cohort. The incidence of arrests was lower in the 6-month (27 vs 85 arrests), 1-year (46 vs 132 arrests) and 2-year (88 vs 196 arrests) periods post-index LAI medication than in the corresponding periods pre-index LAI medication among individuals with a history of prior arrest. Conclusions: Patients with schizophrenia or schizoaffective disorder who were initiated on a LAI antipsychotic medication, specifically PP1M, were less likely to have an encounter with the criminal justice system compared with a similar time period before the initiation of LAI treatment.

Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning.

Inferring trait networks from a large volume of genetically correlated diverse phenotypes such as yield, architecture, and disease resistance can provide information on the manner in which complex phenotypes are interrelated. However, studies on statistical methods tailored to multidimensional phenotypes are limited, whereas numerous methods are available for evaluating the massive number of genetic markers. Factor analysis operates at the level of latent variables predicted to generate observed responses. The objectives of this study were to illustrate the manner in which data-driven exploratory factor analysis can map observed phenotypes into a smaller number of latent variables and infer a genomic latent factor network using 45 agro-morphological, disease, and grain mineral phenotypes measured in synthetic hexaploid wheat lines (Triticum aestivum L.). In total, eight latent factors including grain yield, architecture, flag leaf-related traits, grain minerals, yellow rust, two types of stem rust, and leaf rust were identified as common sources of the observed phenotypes. The genetic component of the factor scores for each latent variable was fed into a Bayesian network to obtain a trait structure reflecting the genetic interdependency among traits. Three directed paths were consistently identified by two Bayesian network algorithms. Flag leaf-related traits influenced leaf rust, and yellow rust and stem rust influenced grain yield. Additional paths that were identified included flag leaf-related traits to minerals and minerals to architecture. This study shows that data-driven exploratory factor analysis can reveal smaller dimensional common latent phenotypes that are likely to give rise to numerous observed field phenotypes without relying on prior biological knowledge. The inferred genomic latent factor structure from the Bayesian network provides insights for plant breeding to simultaneously improve multiple traits, as an intervention on one trait will affect the values of focal phenotypes in an interrelated complex trait system.

Need for speed: manipulating plant growth to accelerate breeding cycles.

To develop more productive and resilient crops that are capable of feeding 10 billion people by 2050, we must accelerate the rate of genetic improvement in plant breeding programs. Speed breeding manipulates the growing environment by regulating light and temperature for the purpose of rapid generation advance. Protocols are now available for a range of short-day and long-day species and the approach is highly compatible with other cutting-edge breeding tools such as genomic selection. Here, we highlight how speed breeding hijacks biological processes for applied plant breeding outcomes and provide a case study examining wheat growth and development under speed breeding conditions. The establishment of speed breeding facilities worldwide is expected to provide benefits for capacity building, discovery research, pre-breeding, and plant breeding to accelerate the development of productive and robust crops.

Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.).

Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.

Cervical Cancer and Human Papillomavirus Vaccine Awareness Among Married Bhutanese Refugee and Nepali Women in Eastern Nepal.

This study examined the sexually transmitted infections (STIs), cervical cancer, and human papillomavirus virus (HPV) vaccine-related awareness and knowledge among married Bhutanese refugee and Nepali women living in eastern Nepal. Participants were recruited from a women's health camp in Jhapa District in eastern Nepal. A demographic and health survey with questions on STIs, cervical cancer and HPV vaccine was administered to consenting participants. Women who were born in Bhutan or living in the United Nations administered refugee camps were classified as Bhutanese. Of the 630 participants, 14.3% of participants were Bhutanese and the mean age was 38.8 ± 8.2 years. A higher proportion of Bhutanese than Nepali women reported a lack of cervical cancer awareness (42.0% vs. 30.7%; p = 0.036). Only 21.5% of the participants knew HPV as the cause of cervical cancer; 13.9% were aware of an HPV vaccine; and 96% reported that they would have their children vaccinated against HPV if the vaccine was available free of cost to them. In multivariable analyses, the lack of awareness about STIs was directly associated with the lack of cervical cancer awareness [odds ratio (OR) 4.50; 95% confidence interval (CI) 2.99-6.77] and inversely associated with HPV-vaccine awareness [OR 0.53; 95% CI 0.29-0.97]. Low cervical cancer and HPV vaccine awareness and knowledge among Nepali and Bhutanese women in eastern Nepal highlight the need for increasing awareness and knowledge in the context of STIs and reproductive health education. Increasing awareness and knowledge of HPV, its role in cervical cancer, and prevention modalities is a first critical step for implementing successful targeted primary cervical cancer prevention measures focused on behavior modification and vaccine administration.

Understanding the Genetic Basis of Spike Fertility to Improve Grain Number, Harvest Index, and Grain Yield in Wheat Under High Temperature Stress Environments.

Moderate heat stress accompanied by short episodes of extreme heat during the post-anthesis stage is common in most US wheat growing areas and causes substantial yield losses. Sink strength (grain number) is a key yield limiting factor in modern wheat varieties. Increasing spike fertility (SF) and improving the partitioning of assimilates can optimize sink strength which is essential to improve wheat yield potential under a hot and humid environment. A genome-wide association study (GWAS) allows identification of novel quantitative trait loci (QTLs) associated with SF and other partitioning traits that can assist in marker assisted breeding. In this study, GWAS was performed on a soft wheat association mapping panel (SWAMP) comprised of 236 elite lines using 27,466 single nucleotide polymorphisms (SNPs). The panel was phenotyped in two heat stress locations over 3 years. GWAS identified 109 significant marker-trait associations (MTAs) (p ≤ 9.99 x 10-5) related to eight phenotypic traits including SF (a major component of grain number) and spike harvest index (SHI, a major component of grain weight). MTAs detected on chromosomes 1B, 3A, 3B, and 5A were associated with multiple traits and are potentially important targets for selection. More than half of the significant MTAs (60 out of 109) were found in genes encoding different types of proteins related to metabolism, disease, and abiotic stress including heat stress. These MTAs could be potential targets for further validation study and may be used in marker-assisted breeding for improving wheat grain yield under post-anthesis heat stress conditions. This is the first study to identify novel QTLs associated with SF and SHI which represent the major components of grain number and grain weight, respectively, in wheat.

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery.

BACKGROUND: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. RESULTS: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58-0.81) was higher than the other lines (r = 0.21-0.59) included in this study with different genetic backgrounds. CONCLUSIONS: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing.

Marker-Trait Associations for Enhancing Agronomic Performance, Disease Resistance, and Grain Quality in Synthetic and Bread Wheat Accessions in Western Siberia.

Exploiting genetically diverse lines to identify genes for improving crop performance is needed to ensure global food security. A genome-wide association study (GWAS) was conducted using 46,268 SNP markers on a diverse panel of 143 hexaploid bread and synthetic wheat to identify potential genes/genomic regions controlling agronomic performance (yield and 26 yield-related traits), disease resistance, and grain quality traits. From phenotypic evaluation, we found large genetic variation among the 35 traits and recommended five lines having a high yield, better quality, and multiple disease resistance for direct use in a breeding program. From a GWAS, we identified a total of 243 significant marker-trait associations (MTAs) for 35 traits that explained up to 25% of the phenotypic variance. Of these, 120 MTAs have not been reported in the literature and are potentially novel MTAs. In silico gene annotation analysis identified 116 MTAs within genes and of which, 21 MTAs were annotated as a missense variant. Furthermore, we were able to identify 23 co-located multi-trait MTAs that were also phenotypically correlated to each other, showing the possibility of simultaneous improvement of these traits. Additionally, most of the co-located MTAs were within genes. We have provided genomic fingerprinting for significant markers with favorable and unfavorable alleles in the diverse set of lines for developing elite breeding lines from useful trait-integration. The results from this study provided a further understanding of genetically complex traits and would facilitate the use of diverse wheat accessions for improving multiple traits in an elite wheat breeding program.

Genetic diversity and population structure analysis of synthetic and bread wheat accessions in Western Siberia.

Recurrent selection and intercrossing between best of the best parents in each generation of breeding cycle resulted in a narrower genetic diversity in elite wheat (Triticum aestivum L.) germplasm. Therefore, we investigated diverse source of 143 synthetic and bread wheat accessions for identifying potentially rich genetic resources for improving the genetic diversity in wheat. This study identified 47,526 genotyping-by-sequencing-derived SNP markers that were nearly evenly distributed across three genomes of wheat. The population structure analysis identified three distinct clusters (Japan synthetics, CIMMYT synthetics, and bread wheat) of wheat genotypes on the basis of type and geographical origin of wheat accessions. Population differentiation using analysis of molecular variance indicated 21% of the total genetic variance among subgroups and the remainder within subgroups. This study also identified that the Japan synthetic group was the most divergent group compared with other subgroups. The genetic diversity comparisons between synthetic and bread wheat lines showed that the gene diversity of synthetic wheat was 33% higher than bread wheat accessions, indicating the potential use of these lines for broadening the genetic diversity of modern wheat cultivars. The results from this study will be helpful in further understanding genomic features of wheat and facilitate their use in wheat breeding programs.

Genome-Wide Association Study for Multiple Biotic Stress Resistance in Synthetic Hexaploid Wheat.

Genetic resistance against biotic stress is a major goal in many wheat breeding programs. However, modern wheat cultivars have a limited genetic variation for disease and pest resistance and there is always a possibility of the evolution of new diseases and pests to overcome previously identified resistance genes. A total of 125 synthetic hexaploid wheats (SHWs; 2n = 6x = 42, AABBDD, Triticum aestivum L.) were characterized for resistance to fungal pathogens that cause wheat rusts (leaf; Puccinia triticina, stem; P. graminis f.sp. tritici, and stripe; P. striiformis f.sp. tritici) and crown rot (Fusarium spp.); cereal cyst nematode (Heterodera spp.); and Hessian fly (Mayetiola destructor). A wide range of genetic variation was observed among SHWs for multiple (two to five) biotic stresses and 17 SHWs that were resistant to more than two stresses. The genomic regions and potential candidate genes conferring resistance to these biotic stresses were identified from a genome-wide association study (GWAS). This GWAS study identified 124 significant marker-trait associations (MTAs) for multiple biotic stresses and 33 of these were found within genes. Furthermore, 16 of the 33 MTAs present within genes had annotations suggesting their potential role in disease resistance. These results will be valuable for pyramiding novel genes/genomic regions conferring resistance to multiple biotic stresses from SHWs into elite bread wheat cultivars and providing further insights on a wide range of stress resistance in wheat.

Elevated Blood Lead Levels Among Resettled Refugee Children in Ohio, 2009-2016.

Objectives. To assess the prevalence of and the demographic factors for elevated blood lead level (EBLL; ≥ 5 µg/dL) at resettlement among newly admitted refugee children. Methods. This cross-sectional study used data from the postresettlement refugee medical screening of 5661 children resettled in Ohio from 2009 to 2016. We computed prevalence of EBLL and adjusted prevalence ratio with modified Poisson regression modeling. Results. Overall, 22.3% of children younger than 18 years and 27.1% of those younger than 6 years had an EBLL. Children resettled from a South Asia region including Afghanistan (EBLL prevalence = 56.2%; 95% confidence interval [95% CI] = 48.1%, 64.3%), Nepal (44.0%; 95% CI = 33.7%, 54.1%), Bhutan (32.8%; 95% CI = 30.4%, 35.9%), and Burma (31.8%; 95% CI = 27.5%, 35.9%) had the highest prevalence of EBLLs. In addition, those younger than 6 years (prevalence ratio [PR] = 2.0; 95% CI = 1.6, 2.6), male (PR = 1.3; 95% CI = 1.1, 1.4), and screened within 30 days of arrival (PR = 1.7; 95% CI = 1.1, 2.5) had significantly higher EBLL prevalence than did children aged 13 years and older, female, and screened 90 days after arrival. Conclusions. The overall high proportion of EBLL and variation in EBLL by country of origin among resettled refugee children in the United States warrant comprehensive, yet tailored, guidelines for health professionals and resettlement and government agencies for better prevention and awareness programs targeting these high-risk children.

Factors Associated with the Presence of Strong Social Supports in Bhutanese Refugee Women During Pregnancy.

Social support may mitigate stress related to the refugee experience, including during resettlement. For refugee women, social support can play an important role during pregnancy. In-depth interviews were conducted within a sample of 45 Bhutanese refugee women. Perceived social support was measured using the Norbeck Social Support Questionnaire. Averaged social support scores are reported to account for personal network size. Participants were identified as "low support" and "high support" based on their reported score. The mean social support score reported was 18.9. Participants experiencing a secondary resettlement within the U.S. were 4.52 (95% CI 1.19-17.15) times as likely to report a "high support" network compared to participants who resettled directly from Nepal. Personal social networks are an important source of support for resettled refugee women during pregnancy in the U.S.. Refugee women who experience secondary resettlement may perceive stronger support from their personal connections.

Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS.

As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R² of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R² of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.