The giant diploid faba genome unlocks variation in a global protein crop

Publisher Copyright: © 2023, The Author(s).Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emissions and loss of biodiversity1. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value2. Faba bean (Vicia faba L.) has a high yield potential and is well suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has expanded to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, although with substantial copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association analysis to dissect the genetic basis of seed size and hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate the improvement of sustainable protein production across the Mediterranean, subtropical and northern temperate agroecological zones.Peer reviewe

Identification and characterization of antibacterial compound(s) of cockroaches (Periplaneta americana)

Infectious diseases remain a significant threat to human health, contributing to more than 17 million deaths, annually. With the worsening trends of drug resistance, there is a need for newer and more powerful antimicrobial agents. We hypothesized that animals living in polluted environments are potential source of antimicrobials. Under polluted milieus, organisms such as cockroaches encounter different types of microbes, including superbugs. Such creatures survive the onslaught of superbugs and are able to ward off disease by producing antimicrobial substances. Here, we characterized antibacterial properties in extracts of various body organs of cockroaches (Periplaneta americana) and showed potent antibacterial activity in crude brain extract against methicillin-resistant Staphylococcus aureus and neuropathogenic E. coli K1. The size-exclusion spin columns revealed that the active compound(s) are less than 10 kDa in molecular mass. Using cytotoxicity assays, it was observed that pre-treatment of bacteria with lysates inhibited bacteria-mediated host cell cytotoxicity. Using spectra obtained with LC-MS on Agilent 1290 infinity liquid chromatograph, coupled with an Agilent 6460 triple quadruple mass spectrometer, tissues lysates were analyzed. Among hundreds of compounds, only a few homologous compounds were identified that contained isoquinoline group, chromene derivatives, thiazine groups, imidazoles, pyrrole containing analogs, sulfonamides, furanones, flavanones, and known to possess broad-spectrum antimicrobial properties, and possess anti-inflammatory, anti-tumour, and analgesic properties. Further identification, characterization and functional studies using individual compounds can act as a breakthrough in developing novel therapeutics against various pathogens including superbugs

ASSESSMENT OF FINITE ELEMENT ACTIVE LEARNING MODULES: AN UPDATE IN RESEARCH FINDINGS

The landscape of contemporary engineering education is ever changing, adapting and evolving. As an example, finite element theory and application has often been included in graduate-level courses in engineering programs; however, current industry needs bachelor’s-level engineering graduates with skills in applying this essential analysis and design technique. Engineering education is also changing to include more active learning. In response to the need to introduce undergrads to the finite element method as well as the need for engineering curricula to include more active learning, we have developed, implemented and assessed a suite of Active Learning Module (ALMs).The ALMs are designed to improve student learning of difficult engineering concepts while students gain essential knowledge of finite element analysis. We have used the Kolb learning Cycle as a conceptual framework to guide our design of the ALMs. Originally developed using MSC Nastran, followed by development efforts in SolidWorks Simulation, ANSOFT, ANSYS, and other commercial FEA software packages, a team of researchers, with National Science Foundation support, have created over twenty-eight active learning modules. We will discuss the implementation of these learning modules which have been incorporated into undergraduate courses that cover topics such as machine design, mechanical vibrations, heat transfer, bioelectrical engineering, electromagnetic field analysis, structural fatigue analysis, computational fluid dynamics, rocket design, and chip formation during manufacturing, and large scale deformation in machining. This update on research findings includes statistical results for each module which compare performance on pre- and post-learning module quizzes to gauge change in student knowledge related to the difficult engineering concepts that each module addresses. Statistically significant student performance gains provide evidence of module effectiveness. In addition, we present statistical comparisons between different personality types (based on Myers-Briggs Type Indicator, MBTI, subgroups) and different learning styles (based on Felder-Solomon ILS subgroups) in regards to the average gains each group of students have made on quiz performance. Although exploratory, and generally based on small sample sizes at this point in our multi-year effort, the modules for which subgroup differences are found are being carefully reviewed in an attempt to determine whether modifications should be made to better ensure equitable impact of the module across students from specific personality and / or learning styles subgroups (e.g., MBTI Intuitive versus Sensing; ILS Sequential versus Global)

ASSESSMENT OF FINITE ELEMENT ACTIVE LEARNING MODULES: AN UPDATE IN RESEARCH FINDINGS

The landscape of contemporary engineering education is ever changing, adapting and evolving. As an example, finite element theory and application has often been included in graduate-level courses in engineering programs; however, current industry needs bachelor’s-level engineering graduates with skills in applying this essential analysis and design technique. Engineering education is also changing to include more active learning. In response to the need to introduce undergrads to the finite element method as well as the need for engineering curricula to include more active learning, we have developed, implemented and assessed a suite of Active Learning Module (ALMs).The ALMs are designed to improve student learning of difficult engineering concepts while students gain essential knowledge of finite element analysis. We have used the Kolb learning Cycle as a conceptual framework to guide our design of the ALMs. Originally developed using MSC Nastran, followed by development efforts in SolidWorks Simulation, ANSOFT, ANSYS, and other commercial FEA software packages, a team of researchers, with National Science Foundation support, have created over twenty-eight active learning modules. We will discuss the implementation of these learning modules which have been incorporated into undergraduate courses that cover topics such as machine design, mechanical vibrations, heat transfer, bioelectrical engineering, electromagnetic field analysis, structural fatigue analysis, computational fluid dynamics, rocket design, and chip formation during manufacturing, and large scale deformation in machining. This update on research findings includes statistical results for each module which compare performance on pre- and post-learning module quizzes to gauge change in student knowledge related to the difficult engineering concepts that each module addresses. Statistically significant student performance gains provide evidence of module effectiveness. In addition, we present statistical comparisons between different personality types (based on Myers-Briggs Type Indicator, MBTI, subgroups) and different learning styles (based on Felder-Solomon ILS subgroups) in regards to the average gains each group of students have made on quiz performance. Although exploratory, and generally based on small sample sizes at this point in our multi-year effort, the modules for which subgroup differences are found are being carefully reviewed in an attempt to determine whether modifications should be made to better ensure equitable impact of the module across students from specific personality and / or learning styles subgroups (e.g., MBTI Intuitive versus Sensing; ILS Sequential versus Global)

Active Engineering Education Modules: A Summary of Recent Research Findings

Contemporary engineering education is ever changing, adapting and evolving. As an example, finite element theory and application has in the past been included in graduate-level courses in engineering programs, however, current industry needs bachelor\u27s level engineering graduates with skills in applying this essential analysis and design technique

Active Engineering Education Modules: A Summary of Recent Research Findings

Contemporary engineering education is ever changing, adapting and evolving. As an example, finite element theory and application has in the past been included in graduate-level courses in engineering programs, however, current industry needs bachelor\u27s level engineering graduates with skills in applying this essential analysis and design technique

Active engineering education modules: Summary paper of five years of incremental improvements to the Modules

The landscape of contemporary engineering education is ever changing, adapting and evolving. Finite element theory and application has often been the focus of graduate-level courses in engineering programs; however, industry needs more bachelor\u27s-level engineering graduates to have skills in applying this essential analysis and design technique. Today\u27s globally competitive world requires fast redesigns of products/processes that are well-suited to using finite element analysis to reduce the design cycle. We have used the Kolb Learning Cycle as a conceptual framework to improve student learning of difficult engineering concepts, and to gain essential knowledge of finite element analysis (FEA) and design content knowledge. A team of researchers, with a National Science Foundation grant for the past five years, have created and made improvements to seventeen active learning FEA modules which were originally developed using MSC Nastran, following the development efforts in SolidWorks Simulation, ANSOFT, ANSYS, and other commercial FEA software packages. We summarize the incremental improvements of these learning modules during the past five years as we implemented them into undergraduate courses that covered topics such as machine design, mechanical vibrations, heat transfer, bioelectrical engineering, electromagnetic field analysis, structural fatigue analysis, computational fluid dynamics, rocket design, and chip formation during manufacturing, and large scale deformation in machining. This paper summarizes five years of incremental improvements to the modules comparing the student performance on pre- and post-learning module quizzes to gauge change in student knowledge related to the difficult engineering concepts addressed in each module. The researchers made significant changes to their finite element learning modules annually to improve student understanding of these difficult engineering concepts in their classes. Statistically, significant student performance gains provide evidence of module effectiveness by gender and ethnic groups was found to be minimal. In addition, we present statistical comparisons between different personality types (based on Myers-Briggs Type Indicator, MBTI subgroups) and different learning styles (based on Felder-Solomon ILS subgroups) in regards to the average gains each subgroup of students has made on quiz performance. Although exploratory, and generally based on small sample sizes in our five-year formative evaluation process, the modules for which subgroup differences were carefully reviewed and some instances re-administered in a different settings in an attempt to improve student performance across specific personality and/or learning styles subgroups (e.g. MBTI Intuitive versus Sensing; ILS Sequential versus Global)

Assessment of active learning modules: An update of research findings

The landscape of contemporary engineering education is ever changing, adapting and evolving.Finite element theory and application has often been the focus of graduate-level courses inengineering programs; however, industry needs bachelor’s-level engineering graduates to haveskills in applying this essential analysis and design technique. We have used the Kolb LearningCycle as a conceptual framework to improve student learning of difficult engineering concepts,and to gain essential knowledge of finite element analysis (FEA) and design content knowledge.Originally developed using MSC Nastran, followed by development efforts in SolidWorksSimulation, ANSOFT, ANSYS, and other commercial FEA software packages, a team ofresearchers, with National Science Foundation support, have created over twenty-eight activelearning modules. We will discuss the implementation of these learning modules which havebeen incorporated into undergraduate courses that cover topics such as machine design,mechanical vibrations, heat transfer, bioelectrical engineering, electromagnetic field analysis,structural fatigue analysis, computational fluid dynamics, rocket design, chip formation duringmanufacturing, and large scale deformation in machining.This update on research findings includes statistical results for each module which compareperformance on pre- and post-learning module quizzes to gauge change in student knowledgerelated to the difficult engineering concepts that each module addresses. Statistically significantstudent performance gains provide evidence of module effectiveness. In addition, we presentstatistical comparisons between different personality types (based on Myers-Briggs TypeIndicator, MBTI, subgroups) and different learning styles (based on the Felder-Solomon ILSsubgroups) in regards to the average gains each subgroup of students has made on quizperformance. Although exploratory, and generally based on small sample sizes at this point inour multi-year formative evaluation process, the modules for which subgroup differences arefound are being carefully reviewed in an attempt to determine whether modifications should bemade to better ensure equitable impact of the module across students from specific personalityand/or learning styles subgroups (e.g., MBTI Intuitive versus Sensing; ILS Sequential versusGlobal)

Active engineering education modules: Summary paper of five years of incremental improvements to the Modules

The landscape of contemporary engineering education is ever changing, adapting and evolving. Finite element theory and application has often been the focus of graduate-level courses in engineering programs; however, industry needs more bachelor\u27s-level engineering graduates to have skills in applying this essential analysis and design technique. Today\u27s globally competitive world requires fast redesigns of products/processes that are well-suited to using finite element analysis to reduce the design cycle. We have used the Kolb Learning Cycle as a conceptual framework to improve student learning of difficult engineering concepts, and to gain essential knowledge of finite element analysis (FEA) and design content knowledge. A team of researchers, with a National Science Foundation grant for the past five years, have created and made improvements to seventeen active learning FEA modules which were originally developed using MSC Nastran, following the development efforts in SolidWorks Simulation, ANSOFT, ANSYS, and other commercial FEA software packages. We summarize the incremental improvements of these learning modules during the past five years as we implemented them into undergraduate courses that covered topics such as machine design, mechanical vibrations, heat transfer, bioelectrical engineering, electromagnetic field analysis, structural fatigue analysis, computational fluid dynamics, rocket design, and chip formation during manufacturing, and large scale deformation in machining. This paper summarizes five years of incremental improvements to the modules comparing the student performance on pre- and post-learning module quizzes to gauge change in student knowledge related to the difficult engineering concepts addressed in each module. The researchers made significant changes to their finite element learning modules annually to improve student understanding of these difficult engineering concepts in their classes. Statistically, significant student performance gains provide evidence of module effectiveness by gender and ethnic groups was found to be minimal. In addition, we present statistical comparisons between different personality types (based on Myers-Briggs Type Indicator, MBTI subgroups) and different learning styles (based on Felder-Solomon ILS subgroups) in regards to the average gains each subgroup of students has made on quiz performance. Although exploratory, and generally based on small sample sizes in our five-year formative evaluation process, the modules for which subgroup differences were carefully reviewed and some instances re-administered in a different settings in an attempt to improve student performance across specific personality and/or learning styles subgroups (e.g. MBTI Intuitive versus Sensing; ILS Sequential versus Global)