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Plant Based Nutrition: Exploring Health Benefits For Atherosclerosis, Chronic Diseases, And Metabolic Syndrome A Comprehensive Review
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Research Note: Spur Pruning Leaving One Bud Is An Interesting Viticultural Strategy To Control Bud Acrotony In Bolivian Vine Yards
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Body Adiposity Partially Mediates The Association Between <I>Fto</I> Rs9939609 And Lower Adiponectin Levels In Chilean Children
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Centro de rehabilitación comunitario de basquetbol
Get PDFProyecto de intervención kinésica conducente al tÃtulo profesional de kinesiólogo.Una de las mayores causas de deserción en el mundo del deporte son las lesiones, las cuales afectan en un gran porcentaje a los jugadores de equipos amateur, debido a que en su mayorÃa no tienen acceso a una preparación supervisada por profesionales y tampoco a tratamientos adecuados que los ayuden a volver a sus actividades deportivas. Debido a la problemática comentada, es que nos enfocamos en una de las ramas deportivas, como es de basquetbol amateur que se realiza en una de las comunas más vulnerables de Santiago como es Puente Alto. En la comuna de Puente Alto el deporte es una de las grandes motivaciones de la juventud, por lo que pretendemos subsanar el mayor motivo de renuncia al deporte, creando un centro de Rehabilitación kinésico comunitario, permitiendo una atención integral gratuita según la su vulnerabilidad ya que también tendrá cobros, pero siempre más bajos que el mercado. Con el fin de mantener el interés y motivación en el desarrollo del deporte lo que genera un impacto positivo en la población, ya que crea espacios de participación, entretención. Y a la vez ayuda en la disminución de la delincuencia, vagancia y drogadicción (participa mucha juventud) dándole otra posibilidad de surgir a esta población
Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach
No full textMaize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize
A Revised View of the LSU Gene Family: New Functions in Plant Stress Responses and Phytohormone Signaling
No full textLSUs (RESPONSE TO LOW SULFUR) are plant-specific proteins of unknown function that were initially identified during transcriptomic studies of the sulfur deficiency response in Arabidopsis. Recent functional studies have shown that LSUs are important hubs of protein interaction networks with potential roles in plant stress responses. In particular, LSU proteins have been reported to interact with members of the brassinosteroid, jasmonate signaling, and ethylene biosynthetic pathways, suggesting that LSUs may be involved in response to plant stress through modulation of phytohormones. Furthermore, in silico analysis of the promoter regions of LSU genes in Arabidopsis has revealed the presence of cis-regulatory elements that are potentially responsive to phytohormones such as ABA, auxin, and jasmonic acid, suggesting crosstalk between LSU proteins and phytohormones. In this review, we summarize current knowledge about the LSU gene family in plants and its potential role in phytohormone responses.J.C., A.A.-M. and E.A.V. were supported by the National Agency for Research and Development (ANID) Chile with Program FONDECYT Regular 1190812, FONDECYT Iniciacion 11220937 and FONDECYT Regular 1211130. J.C., A.A.-M. and E.A.V. were also supported by ANID-Millennium Science Initiative Program-ICN17-022. J.M. was supported by the Ministerio de Ciencia e Innovacion (MCIN) and Agencia Estatal de Investigacion (AEI)/10.13039/501100011033/(PID2020-114165RR-381 C21). We also want to acknowledge the Severo Ochoa Program for Centres of Excellence in R&D (CEX2020-000999-S) supported by MCIN/AEI/10.13039/50110001
