La autoestima en niños y niñas de 5 años del nivel inicial de la Institución Educativa N.° 59 Pachacútec – Ventanilla, 2014

El presente estudio tuvo por objetivo general establecer el nivel de autoestima en niños y niñas de 5 años del nivel inicial de la Institución Educativa N.° 59 Pachacútec – Ventanilla 2014. Dicho estudio empleó la metodología descriptiva de diseño no experimental, transversal. La población estuvo constituida por los niños y niñas de 5 años del nivel inicial de la Institución Educativa N.° 59 Pachacútec – Ventanilla 2014. Se utilizó una muestra no probabilístico, muestreo censal, Para construir, validar y demostrar la confiabilidad del instrumento se ha considerado la validez de contenido, mediante la Técnica de Opinión de Expertos y su instrumento es el certificado de validez de juicio de Expertos de la variable de estudio: Autoestima; se utilizó la técnica de la observación y su instrumento guía de observación, con preguntas politómicas. Para la confiabilidad del instrumento se usó el coeficiente de confiabilidad de Alfa de Cronbach. Concluyéndose que el 19% presenta un nivel alto, el 57% presenta un nivel medio y el 24% presenta un nivel bajo de autoestima

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

La autoestima en niños y niñas de 5 años del nivel inicial de la Institución Educativa N.° 59 Pachacútec – Ventanilla, 2014

El presente estudio tuvo por objetivo general establecer el nivel de autoestima en niños y niñas de 5 años del nivel inicial de la Institución Educativa N.° 59 Pachacútec – Ventanilla 2014. Dicho estudio empleó la metodología descriptiva de diseño no experimental, transversal. La población estuvo constituida por los niños y niñas de 5 años del nivel inicial de la Institución Educativa N.° 59 Pachacútec – Ventanilla 2014. Se utilizó una muestra no probabilístico, muestreo censal, Para construir, validar y demostrar la confiabilidad del instrumento se ha considerado la validez de contenido, mediante la Técnica de Opinión de Expertos y su instrumento es el certificado de validez de juicio de Expertos de la variable de estudio: Autoestima; se utilizó la técnica de la observación y su instrumento guía de observación, con preguntas politómicas. Para la confiabilidad del instrumento se usó el coeficiente de confiabilidad de Alfa de Cronbach. Concluyéndose que el 19% presenta un nivel alto, el 57% presenta un nivel medio y el 24% presenta un nivel bajo de autoestima

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field