Animal body size variation is of particular interest in evolutionary biology,but the genetic basis remains largely unknown.Previous studies have shown the presence of two parallel evolutionary genetic clusters within ...Animal body size variation is of particular interest in evolutionary biology,but the genetic basis remains largely unknown.Previous studies have shown the presence of two parallel evolutionary genetic clusters within the fish genus Epinephelus with evident divergence in body size,providing an excellent opportunity to investigate the genetic basis of body size variation in vertebrates.Herein,we performed phylotranscriptomic analysis and reconstructed the phylogeny of 13 epinephelids originating from the South China Sea.Two genetic clades with an estimated divergence time of approximately 15.4 million years ago were correlated with large and small body size,respectively.A total of 180 rapidly evolving genes and two positively selected genes were identified between the two groups.Functional enrichment analyses of these candidate genes revealed distinct enrichment categories between the two groups.These pathways and genes may play important roles in body size variation in groupers through complex regulatory networks.Based on our results,we speculate that the ancestors of the two divergent groups of groupers may have adapted to different environments through habitat selection,leading to genetic variations in metabolic patterns,organ development,and lifespan,resulting in body size divergence between the two locally adapted populations.These findings provide important insights into the genetic mechanisms underlying body size variation in groupers and species differentiation.展开更多
Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system.EEG based neonatal sleep staging provides valuable information about an infant’s growth and h...Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system.EEG based neonatal sleep staging provides valuable information about an infant’s growth and health,but is challenging due to the unique characteristics of EEG and lack of standardized protocols.This study aims to develop and compare 18 machine learning models using Automated Machine Learning(autoML)technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification.The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning.The data is obtained from neonates at post-menstrual age 37±05 weeks.352530-s EEG segments from 19 infants are used to train and test the proposed models.There are twelve time and frequency domain features extracted from each channel.Each model receives the common features of nine channels as an input vector of size 108.Each model’s performance was evaluated based on a variety of evaluation metrics.The maximum mean accuracy of 84.78%and kappa of 69.63%has been obtained by the AutoML-based Random Forest estimator.This is the highest accuracy for EEG-based sleep-wake classification,until now.While,for the AutoML-based Adaboost Random Forest model,accuracy and kappa were 84.59%and 69.24%,respectively.High performance achieved in the proposed autoML-based approach can facilitate early identification and treatment of sleep-related issues in neonates.展开更多
基金supported by the National Natural Science Foundation of China (32273136,31872572)Agriculture Research System of China (ARS-47)+1 种基金Science and Technology Planning Project of Guangdong Province (2023B1212060023)Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (SML2023SP201)。
文摘Animal body size variation is of particular interest in evolutionary biology,but the genetic basis remains largely unknown.Previous studies have shown the presence of two parallel evolutionary genetic clusters within the fish genus Epinephelus with evident divergence in body size,providing an excellent opportunity to investigate the genetic basis of body size variation in vertebrates.Herein,we performed phylotranscriptomic analysis and reconstructed the phylogeny of 13 epinephelids originating from the South China Sea.Two genetic clades with an estimated divergence time of approximately 15.4 million years ago were correlated with large and small body size,respectively.A total of 180 rapidly evolving genes and two positively selected genes were identified between the two groups.Functional enrichment analyses of these candidate genes revealed distinct enrichment categories between the two groups.These pathways and genes may play important roles in body size variation in groupers through complex regulatory networks.Based on our results,we speculate that the ancestors of the two divergent groups of groupers may have adapted to different environments through habitat selection,leading to genetic variations in metabolic patterns,organ development,and lifespan,resulting in body size divergence between the two locally adapted populations.These findings provide important insights into the genetic mechanisms underlying body size variation in groupers and species differentiation.
文摘Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system.EEG based neonatal sleep staging provides valuable information about an infant’s growth and health,but is challenging due to the unique characteristics of EEG and lack of standardized protocols.This study aims to develop and compare 18 machine learning models using Automated Machine Learning(autoML)technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification.The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning.The data is obtained from neonates at post-menstrual age 37±05 weeks.352530-s EEG segments from 19 infants are used to train and test the proposed models.There are twelve time and frequency domain features extracted from each channel.Each model receives the common features of nine channels as an input vector of size 108.Each model’s performance was evaluated based on a variety of evaluation metrics.The maximum mean accuracy of 84.78%and kappa of 69.63%has been obtained by the AutoML-based Random Forest estimator.This is the highest accuracy for EEG-based sleep-wake classification,until now.While,for the AutoML-based Adaboost Random Forest model,accuracy and kappa were 84.59%and 69.24%,respectively.High performance achieved in the proposed autoML-based approach can facilitate early identification and treatment of sleep-related issues in neonates.
