An investigation of the effects of a professional development on teacher efficacy and cultural competency in working with Latino English language learners

Latino English Language Learner (ELL) students comprise of a large portion of students in the United States (Capps, Fix, Muray, Ost, Passel, Herwantoro, 2005; Suarez-Orozco, Rhodes & Milburn, 2009). Many, Latino students however, have lower levels of academic attainment when compared to other ethnic groups (Suarez-Orozco & Suarez-Orozco, 2010; Swail, Cabrera, & Lee, 2004). Teachers however do not feel fully prepared to teach students from diverse backgrounds (Tucker, Porter, Reinke, Herman, Ivery, Mack, & Johnson, 2005). Two constructs that have been found to be related to student success with diverse populations are teacher efficacy and cultural competency. Utilizing a quasi-experimental, one-group, pre-test, post-test design, this study sought to understand the effect of a monthly, 45 minute, four-part professional development series on both teacher efficacy and cultural competency on participants. Twenty participants from a suburban high school (grades 9-12) located in the mid-Atlantic region completed two scales both pre-test and post-test, and provided demographic data. Quantitative data found both insignificant and significant results. Teacher efficacy was evaluated based on Personal Teaching Efficacy (PTE) and General Teaching Efficacy (GTE). There were no statistically significant findings for PTE in participants based on gender, exposure to previous training, and ethnicity. For GTE there were no statistically significant results for participants with previous training or based on ethnicity. There was however an impact for female participants as a result of the four-part professional development series. For the construct of cultural competency there was an increase, specifically in White participants. These results suggest that that four-part professional development series has an impact on this construct

Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds

Genomic selection (GS) is an emerging methodology that helps select superior lines among experimental cultivars in plant breeding programs. It offers the opportunity to increase the productivity of cultivars by delivering increased genetic gains and reducing the breeding cycles. This methodology requires inexpensive and sufficiently dense marker information to be successful, and with whole genome sequencing, it has become an important tool in many crops. The recent assembly of the pearl millet genome has made it possible to employ GS models to improve the selection procedure in pearl millet breeding programs. Here, three GS models were implemented and compared using grain yield and dense molecular marker information of pearl millet obtained from two different genotyping platforms (C [conventional GBS RAD-seq] and T [tunable GBS tGBS]). The models were evaluated using three different cross-validation (CV) schemes mimicking real situations that breeders face in breeding programs: CV2 resembles an incomplete field trial, CV1 predicts the performance of untested hybrids, and CV0 predicts the performance of hybrids in unobserved environments. We found that (i) adding phenotypic information of parental inbreds to the calibration sets improved predictive ability, (ii) accounting for genotype-by-environment interaction also increased the performance of the models, and (iii) superior strategies should consider the use of the molecular markers derived from the T platform (tGBS)

Evaluating dimensionality reduction for genomic prediction

The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the main effects of line, environment, marker, and the genotype by environment interactions. The methods were applied on a real data set containing 315 lines phenotyped in nine environments with 26,817 markers each. Regardless of the DR method and prediction model used, only a fraction of features was sufficient to achieve maximum correlation. Our results underline the usefulness of DR methods as a key pre-processing step in GS models to improve computational efficiency in the face of ever-increasing size of genomic data

Guías para la atención de las principales emergencias obstétricas

El presente documento pretende ser una herramienta accesible y práctica para el abordaje de las situaciones de emergencia y urgencia obstétricas más frecuentes. Trata las principales causas de mortalidad materna, en el entendido que el correcto diagnóstico y manejo de las mismas puede evitar la muerte de la mujer gestant

School closures during the 2009 influenza pandemic: national and local experiences

published_or_final_versio

Enhancing genomic prediction with stacking ensemble learning in arabica coffee.

Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection. This study explores SEL potential in coffee breeding, aiming to improve prediction accuracy for important traits [yield (YL), total number of the fruits (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer)] in Coffea Arabica. We analyzed data from 195 individuals genotyped for 21,211 single-nucleotide polymorphism (SNP) markers. To comprehensively assess model performance, we employed a cross-validation (CV) scheme. Genomic Best Linear Unbiased Prediction (GBLUP), multivariate adaptive regression splines (MARS), Quantile Random Forest (QRF), and Random Forest (RF) served as base learners. For the meta-learner within the SEL framework, various options were explored, including Ridge Regression, RF, GBLUP, and Single Average. The SEL method was able to predict the predictive ability (PA) of important traits in Coffea Arabica. SEL presented higher PA compared with those obtained for all base learner methods. The gains in PA in relation to GBLUP were 87.44% (the ratio between the PA obtained from best Stacking model and the GBLUP), 37.83%, 199.82%, and 14.59% for YL, NF, LM and Cer, respectively. Overall, SEL presents a promising approach for GS. By combining predictions from multiple models, SEL can potentially enhance the PA of GS for complex traits

Genomic selection of maize test-cross hybrids leveraged by marker sampling.

Maize (Zea mays L.) is a staple crop and the most cultivated cereal worldwide. The expansion of this crop was possible due to efforts in management and breeding. From the breeding standpoint, advances were achieved through field experimental design and analyses, establishing heterotic patterns, and releasing heterotic hybrids. Over the last decade, data analyses have benefited from the surge of genome-based approaches. However, it lacks optimization regarding marker dimensionality, proper selection of tested lines and/or environments, and an indication of promising inbred lines for crosses. This study aimed to convert a high-density single nucleotide polymorphism marker dataset into a low-density dataset and perform genomic selection of maize hybrids tested in drought stress and well-watered environments for grain yield and secondary traits. Single nucleotide polymorphism markers were ranked and selected based on effects from a genome-wide association study. For genomic selection, methods containing general and specific combining abilities (GCA and SCA, respectively) and interaction effects were compared in cross-validation schemes. Accuracies using selected markers were similar to complete marker dataset for all traits under drought nand well-watered conditions. For genomic selection, the model containing the main effects of GCA for inbred lines and testers, SCA for hybrids, and the interaction of GCA and SCA with environments (Model 7) performed better for all traits when information about all environments was included. The model without interaction effects (Model 6) performed better when information about environments was missing

A chickpea genetic variation map based on the sequencing of 3,366 genomes

Zero hunger and good health could be realized by 2030 through effective conservation, characterization and utilization of germplasm resources1 . So far, few chickpea (Cicerarietinum) germplasm accessions have been characterized at the genome sequence level2 . Here we present a detailed map of variation in 3,171 cultivated and 195 wild accessions to provide publicly available resources for chickpea genomics research and breeding. We constructed a chickpea pan-genome to describe genomic diversity across cultivated chickpea and its wild progenitor accessions. A divergence tree using genes present in around 80% of individuals in one species allowed us to estimate the divergence of Cicer over the last 21 million years. Our analysis found chromosomal segments and genes that show signatures of selection during domestication, migration and improvement. The chromosomal locations of deleterious mutations responsible for limited genetic diversity and decreased fitness were identified in elite germplasm. We identified superior haplotypes for improvement-related traits in landraces that can be introgressed into elite breeding lines through haplotype-based breeding, and found targets for purging deleterious alleles through genomics-assisted breeding and/or gene editing. Finally, we propose three crop breeding strategies based on genomic prediction to enhance crop productivity for 16 traits while avoiding the erosion of genetic diversity through optimal contribution selection (OCS)-based pre-breeding. The predicted performance for 100-seed weight, an important yield-related trait, increased by up to 23% and 12% with OCS- and haplotype-based genomic approaches, respectively. On the basis of WGS of 3,366 chickpea germplasm accessions, we report here a rich map of the genetic variation in chickpea. We provide a chickpea pan-genome and offer insights into species divergence, the migration of the cultigen (C. arietinum), rare allele burden and fitness loss in chickpea. We propose three genomic breeding approaches— haplotype-based breeding, genomic prediction and OCS—for developing tailor-made high-yielding and climate-resilient chickpea varieties. We sequenced 3,366 chickpea germplasm lines, including 3,171 cultivated and 195 wild accessions at an average coverage of around 12× (Methods, Extended Data Fig. 1, Supplementary Data 1 Tables 1, 2). Alignment of WGS data to the CDC Frontier reference genome11 identified 3.94 million and 19.57 million single-nucleotide polymorphisms (SNPs) in 3,171 cultivated and 195 wild accessions, respectively (Extended Data Table 1, Supplementary Data 1 Tables 3–7, Supplementary Notes). This SNP dataset was used to assess linkage disequilibrium (LD) decay (Supplementary Data 2 Tables 1, 2, Extended Data Fig. 2, Supplementary Notes) and identify private and population-enriched SNPs (Supplementary Data 3 Tables 1–4, Supplementary Notes). These private and population-enriched SNPs suggest rapid adaptation and can enhance the genetic foundation in the elite gene pool

Multiparametric cranberry (Vaccinium macrocarpon Ait.) fruit textural trait development for harvest and postharvest evaluation in representative cultivars

Fruit texture is a priority trait that guarantees the long-term economic sustainability of the cranberry industry through value-added products such as sweetened dried cranberries (SDCs). To develop a standard methodology to measure texture, we conducted a comparative analysis of 22 textural traits using five different methods under both harvest and postharvest conditions in 10 representative cranberry cultivars. A set of textural traits from the 10%-strain compression and puncture methods were identified that differentiate between cultivars primarily based on hardness/stiffness and elasticity properties. The complementary use of both methodologies allowed for a detailed evaluation by capturing the effect of key texture-determining factors such as structure, flesh, and skin. Furthermore, the high effectiveness of this approach in different conditions and its ability to capture high phenotypic variation in cultivars highlights its great potential for applicability in various areas of the value chain and research. Therefore, this study provides an informed reference for unifying future efforts to enhance cranberry fruit texture and qualit

Identification of Disease Resistance Parents and Genome-Wide Association Mapping of Resistance in Spring Wheat

The likelihood of success in developing modern cultivars depend on multiple factors, including the identification of suitable parents to initiate new crosses, and characterizations of genomic regions associated with target traits. The objectives of the present study were to (a) determine the best economic weights of four major wheat diseases (leaf spot, common bunt, leaf rust, and stripe rust) and grain yield for multi-trait restrictive linear phenotypic selection index (RLPSI), (b) select the top 10% cultivars and lines (hereafter referred as genotypes) with better resistance to combinations of the four diseases and acceptable grain yield as potential parents, and (c) map genomic regions associated with resistance to each disease using genome-wide association study (GWAS). A diversity panel of 196 spring wheat genotypes was evaluated for their reaction to stripe rust at eight environments, leaf rust at four environments, leaf spot at three environments, common bunt at two environments, and grain yield at five environments. The panel was genotyped with the Wheat 90K SNP array and a few KASP SNPs of which we used 23,342 markers for statistical analyses. The RLPSI analysis performed by restricting the expected genetic gain for yield displayed significant (p \u3c 0.05) differences among the 3125 economic weights. Using the best four economic weights, a subset of 22 of the 196 genotypes were selected as potential parents with resistance to the four diseases and acceptable grain yield. GWAS identified 37 genomic regions, which included 12 for common bunt, 13 for leaf rust, 5 for stripe rust, and 7 for leaf spot. Each genomic region explained from 6.6 to 16.9% and together accounted for 39.4% of the stripe rust, 49.1% of the leaf spot, 94.0% of the leaf rust, and 97.9% of the common bunt phenotypic variance combined across all environments. Results from this study provide valuable information for wheat breeders selecting parental combinations for new crosses to develop improved germplasm with enhanced resistance to the four diseases as well as the physical positions of genomic regions that confer resistance, which facilitates direct comparisons for independent mapping studies in the future