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

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

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)

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

CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada

The performance of numerical, statistical, and data-driven diagnostic and predictive crop production modeling relies heavily on data quality for input and calibration or validation processes. This study presents a comprehensive database and the analytics used to consolidate it as a homogeneous, consistent, multidimensional genotype, phenotypic, and environmental database for maize phenotype modeling, diagnostics, and prediction. The data used are obtained from the Genomes to Fields (G2F) initiative, which provides multiyear genomic (G), environmental (E), and phenotypic (P) datasets that can be used to train and test crop growth models to understand the genotype by environment (GxE) interaction phenomenon. A particular advantage of the G2F database is its diverse set of maize genotype DNA sequences (G2F-G), phenotypic measurements (G2F-P), station-based environmental time series (mainly climatic data) observations collected during the maize-growing season (G2F-E), and metadata for each field trial (G2F-M) across the United States (US), the province of Ontario in Canada, and the state of Lower Saxony in Germany. The construction of this comprehensive climate and genomic database incorporates the analytics for data quality control (QC) and consistency control (CC) to consolidate the digital representation of geospatially distributed environmental and genomic data required for phenotype predictive analytics and modeling of the GxE interaction. The two-phase QC–CC preprocessing algorithm also includes a module to estimate environmental uncertainties. Generally, this data pipeline collects raw files, checks their formats, corrects data structures, and identifies and cures or imputes missing data. This pipeline uses machine-learning techniques to fill the environmental time series gaps, quantifies the uncertainty introduced by using other data sources for gap imputation in G2F-E, discards the missing values in G2F-P, and removes rare variants in G2F-G. Finally, an integrated and enhanced multidimensional database was generated. The analytics for improving the G2F database and the improved database called Climate for OMICS (CLIM4OMICS) follow findability, accessibility, interoperability, and reusability (FAIR) principles, and all data and codes are available at https://doi.org/10.5281/zenodo.8002909 (Aslam et al., 2023a) and https://doi.org/10.5281/zenodo.8161662 (Aslam et al., 2023b), respectively.</p

Improving predictive ability in sparse testing designs in soybean populations

The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments

Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones

Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines’ performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha−1 across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario

RNA Aptamer Evolution: Two Decades of SELEction

Aptamers are small non-coding RNAs capable of recognizing, with high specificity and affinity, a wide variety of molecules in a manner that resembles antibodies. This class of nucleic acids is the resulting product of applying a well-established screening method known as SELEX. First developed in 1990, the SELEX process has become a powerful tool to select structured oligonucleotides for the recognition of targets, starting with small molecules, going through protein complexes until whole cells. SELEX has also evolved along with new technologies positioning itself as an alternative in the design of a new class of therapeutic agents in modern molecular medicine. This review is an historical follow-up of SELEX method over the two decades since its first appearance

Genomic-enabled prediction model with genotype × environment interaction in elite chickpea lines

Genomic selection (GS) allows safe phenotyping and reduces cost and shortening selection cycles. Incorporating of genotype × environment (G×E) interactions in genomic prediction models improves the predictive ability of lines performance across environments and in target environments. Phenotyping data on a set of 320 elite chickpea breeding lines on different traits (e.g., plant height, days to maturity, and seed yield), from three consecutive years for two different treatments at two locations were recorded. These lines were genotyped on DArTseq(1.6K) and Genotyping- by-Sequencing (GBS; 89K SNPs) platforms. Five different models were fitted, four of which included genomic information as main effects (baseline model) and/or G×E interactions. Three different cross-validation schemes that mimic real scenarios that breeders might face on fields were considered to assess the predictive ability of the models (CV2: incomplete field trials; CV1: newly developed lines; and CV0: new previously untested environments). Different prediction models gave different results for the different traits; however, some interesting patterns were observed. For CV1, analyzing yield seed interaction models improved baseline counterparts on an average between 55 and 92% using DArT and DArT combined with GBS data, respectively [between 9 and 112% for all traits]. While for CV2 these improvements varied b tween 65 and 102% [between 8 and 130% remaining traits]. In CV0, no clear advantage was observed considering the interaction term. These results suggest that GS models hold potential for breeder’s applications on chickpea cultivar improvements

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

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