1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal ...1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.展开更多
Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that c...Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.展开更多
Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve thro...Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.展开更多
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an uns...Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an unsupervised subdomain adaptation method based on a time-frequency feature attention mechanism,LMMD-based subdomain alignment,and contrastive local alignment.This enables the application of the diagnosis model across different working conditions and equipment types.First,a novel time-frequency feature attention mechanism assigns weights to vibration signals of varying dimensions.Second,the time series is transformed to obtain a three-channel time-frequency diagram.This diagram is input into the proposed dimension-segmentation cross-channel multihead self-attention framework to extract high-dimensional frequencydomain fault features.These features are concatenated with the time-domain features to obtain a global feature representation.Then,the extracted high-dimensional features are sent to the classification module to obtain the predicted labels for the source and target domains.Finally,after confidence filtering,the true labels from the source domain and the prediction labels from the target domain are fed into a dynamically weighted multilevel feature alignment module to promote proximity between similar fault features across domains while enhancing separation among different fault types.The validity and superiority of the proposed method were demonstrated through simulation experiments conducted on two types of manned escalator systems under multiple working conditions.For the most challenging transfer task,the proposed method achieved higher accuracy on the target domain test set than DANN,ADDA,C-CLCN,TFA-CCN,and TFA-LCN by 26.87%,24.72%,11.44%,28.94%,and 16.85%,respectively.展开更多
The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a tra...The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a transformative pathway for climate change adaptation and peacebuilding, with emphasis on a holistic approach to managing resource conflicts and environmental challenges. This paper explores the synergies between emerging technologies and strategic framework to mitigate climate-induced tensions and foster resilience. It focuses on the application of renewable energy systems to reduce dependence on contested resources, blockchain technology to ensure transparency in climate finance, equitable resource allocation and Artificial Intelligence (AI) to enhance early warning systems for climate-related disaster and conflicts. Additionally, technologies such as precision agriculture and remote sensing empower communities to optimize resource use, adapt to shifting environmental conditions, and reduce competition over scares resources. These innovations with inclusive governance and local capacity-building are very primordial. Ultimately, the convergence of technology, policy, and local participation offers a scalable and replicable model for addressing the dual challenges of environmental degradation and instability, thereby paving the way for a more sustainable and peaceful future.展开更多
Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,...Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method.展开更多
This study explores the cultural,social,and academic adaptation experiences of international students in Wenzhou,China.Based on surveys and interviews with 52 students from 20 countries—predominantly Morocco—the res...This study explores the cultural,social,and academic adaptation experiences of international students in Wenzhou,China.Based on surveys and interviews with 52 students from 20 countries—predominantly Morocco—the research investigates key challenges and coping strategies related to local integration.The findings indicate that while Wenzhou offers a generally supportive academic environment—enhanced by AI integration and practical teaching methods—language barriers continue to hinder students’daily life,academic engagement,and social interactions.Limited Mandarin proficiency made it difficult for many students to build friendships with locals and navigate everyday tasks.Cultural adaptation also presented obstacles,particularly in adjusting to local food and social norms.Despite these challenges,students employed various strategies to facilitate integration,such as attending HSK language courses,watching Chinese media,and initiating conversations with local peers.While most participants described the local community as welcoming,perceptions varied based on individual experiences and language ability.The study highlights the importance of enhanced language support and structured cross-cultural exchange initiatives in improving international students’experiences.It contributes to the broader discourse on international student mobility by offering insights from a second-tier Chinese city,emphasizing the role of institutional practices in shaping adaptation outcomes.展开更多
As climate change triggers unprecedented ecological shifts,it becomes imperative to understand the genetic underpinnings of species’adaptability.Adaptive introgression significantly contributes to organismal adaptati...As climate change triggers unprecedented ecological shifts,it becomes imperative to understand the genetic underpinnings of species’adaptability.Adaptive introgression significantly contributes to organismal adaptation to new environments by introducing genetic variation across species boundaries.However,despite growing recognition of its importance,the extent to which adaptive introgression has shaped the evolutionary history of closely related species remains poorly understood.Here we employed population genetic analyses of high-throughput sequencing data to investigate the interplay between genetic introgression and local adaptation in three species of spruce trees in the genus Picea(P.asperata,P.crassifolia,and P.meyeri).We find distinct genetic differentiation among these species,despite a substantial gene flow.Crucially,we find bidirectional adaptive introgression between allopatrically distributed species pairs and unearthed dozens of genes linked to stress resilience and flowering time.These candidate genes most likely have promoted adaptability of these spruces to historical environmental changes and may enhance their survival and resilience to future climate changes.Our findings highlight that adaptive introgression could be prevalent and bidirectional in a topographically complex area,and this could have contributed to rich genetic variation and diverse habitat usage by tree species.展开更多
The environment has an important impact on maize(Zea mays L.)production,making it necessary to identify plant adaptation regions that are suitable for different maize varieties.Traditional methods using field trials a...The environment has an important impact on maize(Zea mays L.)production,making it necessary to identify plant adaptation regions that are suitable for different maize varieties.Traditional methods using field trials are costly and restricted to a limited number of areas.Identifying adaptation regions based on climate data has great potential,but a basic understanding and a prediction approach for diverse maize varieties are lacking.Here,we collected a representative dataset comprising 32,840 data points from the National Maize Variety Trial Data Management Platform.We employed three traits to characterize the adaptability of different maize varieties:PH(plant height),DTS(days to silking),and yield.First,we quantified the contributions of variety(V),environment(E),and V×E to variance in the three adaptationrelated traits.The mean contributions of E to variance in PH,DTS,and yield were 54.50%,82.87%,and 75.92%,respectively,suggesting that environmental effects are crucial for phenotype construction.Second,we analyzed correlations between the three traits and three environmental indices:GDD(growing degree days),PRE(precipitation),and SSD(sunshine duration).The highest absolute correlation coefficients between phenotypes and environmental indices were 0.15–0.69 at the whole-data level.To predict variety adaptation on a national scale,we modeled the three traits using environmental indices and best linear unbiased predictors(BLUPs)via the random forest algorithm.The predictive abilities of our models for PH,DTS,and yield were 0.90(MAE=9.95 cm),0.99(MAE=1.09 d),and 0.95(MAE=0.55 t ha^(−1)),respectively,indicating that our proposed framework can predict adaptationrelated traits for diverse maize varieties in China.展开更多
Understanding the ecological adaptation of tree species can not only reveal the evolutionary potential but also benefit biodiversity conservation under global climate change.Quercus is a keystone genus in Northern Hem...Understanding the ecological adaptation of tree species can not only reveal the evolutionary potential but also benefit biodiversity conservation under global climate change.Quercus is a keystone genus in Northern Hemisphere forests,and its wide distribution in diverse ecosystems and long evolutionary history make it an ideal model for studying the genomic basis of ecological adaptations.Here we used a newly sequenced genome of Quercus gilva,an evergreen oak species from East Asia,with 18 published Fagales genomes to determine how Fagaceae genomes have evolved,identify genomic footprints of ecological adaptability in oaks in general,as well as between evergreen and deciduous oaks.We found that oak species exhibited a higher degree of genomic conservation and stability,as indicated by the absence of large-scale chromosomal structural variations or additional whole-genome duplication events.In addition,we identified expansion and tandem repetitions within gene families that contribute to plant physical and chemical defense(e.g.,cuticle biosynthesis and oxidosqualene cyclase genes),which may represent the foundation for the ecological adaptation of oak species.Circadian rhythm and hormone-related genes may regulate the habits of evergreen and deciduous oaks.This study provides a comprehensive perspective on the ecological adaptations of tree species based on phylogenetic,genome evolutionary,and functional genomic analyses.展开更多
Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One f...Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.展开更多
Background Eccentric training,such as Nordic hamstring exercise(NHE)training,is commonly used as a preventive measure for hamstring strains.Eccentric training is believed to induce lengthening of muscle fascicles and ...Background Eccentric training,such as Nordic hamstring exercise(NHE)training,is commonly used as a preventive measure for hamstring strains.Eccentric training is believed to induce lengthening of muscle fascicles and to be associated with the addition of sarcomeres in series within muscle fibers.However,the difficulty in measuring sarcomere adaptation in human muscles has severely limited information about the precise mechanisms of adaptation.This study addressed this limitation by measuring the multiscale hamstring muscle adaptations in response to 9 weeks of NHE training and 3 weeks of detraining.Methods Twelve participants completed 9 weeks of supervised NHE training,followed by a 3-week detraining period.We assessed biceps femoris long-head(BFlh)muscle fascicle length,sarcomere length,and serial sarcomere number in the central and distal regions of the muscle.Additionally,we measured muscle volume and eccentric strength at baseline,post-training,and post-detraining.Results NHE training over 9 weeks induced significant architectural and strength adaptations in the BFlh muscle.Fascicle length increased by 19%in the central muscle region and 33%in the distal muscle region.NHE also induced increases in serial sarcomere number(25%in the central region and 49%in the distal region).BFlh muscle volume increased by 8%,and knee flexion strength increased by 40%with training.Following 3 weeks of detraining,fascicle length decreased by 12%in the central region and 16%in the distal region along with reductions in serial sarcomere number.Conclusion Nine weeks of NHE training produced substantial,region-specific increases in BFlh muscle fascicle length,muscle volume,and force generation.The direct measurement of sarcomere lengths revealed that the increased fascicle length was accompanied by the addition of sarcomeres in series within the muscle fascicles.展开更多
Extreme heat and chronic water scarcity present formidable challenges to large desert-dwelling mammals.In addition to camels,antelopes within the Hippotraginae and Alcelaphinae subfamilies also exhibit remarkable phys...Extreme heat and chronic water scarcity present formidable challenges to large desert-dwelling mammals.In addition to camels,antelopes within the Hippotraginae and Alcelaphinae subfamilies also exhibit remarkable physiological and genetic specializations for desert survival.Among them,the critically endangered addax(Addax nasomaculatus)represents the most desert-adapted antelope species.However,the evolutionary and molecular mechanisms underlying desert adaptations remain largely unexplored.Herein,a high-quality genome assembly of the addax was generated to investigate the molecular evolution of desert adaptation in camels and desert antelopes.Comparative genomic analyses identified 136 genes harboring convergent amino acid substitutions implicated in crucial biological processes,including water reabsorption,fat metabolism,and stress response.Notably,a convergent R146S amino acid mutation in the prostaglandin EP2 receptor gene PTGER2 significantly reduced receptor activity,potentially facilitating large-mammal adaptation to arid environments.Lineage-specific innovations were also identified in desert antelopes,including previously uncharacterized conserved non-coding elements.Functional assays revealed that several of these elements exerted significant regulatory effects in vitro,suggesting potential roles in adaptive gene expression.Additionally,signals of introgression and variation in genetic load were observed,indicating their possible influence on desert adaptation.These findings provide insights into the sequential evolutionary processes that drive physiological resilience in arid environments and highlight the importance of convergent evolution in shaping adaptive traits in large terrestrial mammals.展开更多
The paleoenvironmental changes and adaptation strategies of hominins during the Late Pleistocene are crucial for understanding the evolution,dispersal,and behavioral shifts of early modern humans.Despite South China&#...The paleoenvironmental changes and adaptation strategies of hominins during the Late Pleistocene are crucial for understanding the evolution,dispersal,and behavioral shifts of early modern humans.Despite South China's significance as a nexus for hominin dispersal and handaxe technology diffusion,quantitative reconstructions of paleoenvironments linked to archaeological records remain scarce.The Sandinggai site(96.6-13.3 ka BP)in central South China,with its well-preserved stratigraphy and abundant lithic artefacts,is notable for providing valuable insights.In this study,quantitative reconstruction of the vegetation succession and climate change sequences at the site was conducted using palynological and isotopic data.The results indicated a shift from a warm-temperate evergreen and deciduous broadleaf mixed forest to a temperate deciduous broadleaf forest,with the climate transitioning from warm and humid to cooler and drier conditions.During the early phase,an increase in lithic production suggested favorable conditions for hominin survival.In the later phase,decreased lithic production and the replacement of large handaxe tools by smaller flake tools,indicated that hominins adapted to the cooler,drier climate and more open landscapes through lithic miniaturization.These findings highlight the environment-driven adaptation of lithic technology and hominin behavior,thereby shedding light on human survival adaptation strategies.展开更多
In recent years,the heterogeneous SAR image classification task of"training on simulated data and testing on measured data"has garnered increasing attention in the field of Synthetic Aperture Radar Automatic...In recent years,the heterogeneous SAR image classification task of"training on simulated data and testing on measured data"has garnered increasing attention in the field of Synthetic Aperture Radar Automatic Target Recognition(SAR-ATR).Although current mainstream domain adaptation methods have made significant breakthroughs in addressing domain shift problems,the escalating model complexity and task complexity have constrained their deployment in real-world applications.To tackle this challenge,this paper proposes a domain adaptation framework based on linear-kernel Maximum Mean Discrepancy(MMD),integrated with a near-zero-cost pseudo-label denoising technique leveraging deep feature clustering.Our method completely eliminates the need for data augmentation and handcrafted feature design,achieving endto-end pseudo-label self-training.Competitive performance is demonstrated across three typical scenarios in the SAMPLE dataset,with the highest accuracy of 98.65%achieved in ScenarioⅢ.The relevant code is available at:https://github.com/TheGreatTreatsby/SAMPLE_MMD.展开更多
Lactococcus lactis,a major starter culture in the dairy industry,has been widely applied in food fermentation.While current research has primarily focused on evaluating its role during fermentation,genomic investigati...Lactococcus lactis,a major starter culture in the dairy industry,has been widely applied in food fermentation.While current research has primarily focused on evaluating its role during fermentation,genomic investigations into its genetic diversity and functional adaptability remain limited.In this study,199 L.lactis strains isolated from Chinese traditional artisanal cheeses(72 bovine,71 goat,and 56 yak milk cheese isolates)were subjected to comparative genomic analysis.Genomic characteristic analysis indicated that bovine milk strains possess larger genomes and the highest number of unique genes.Functional characterization further demonstrated notable differences in carbohydrate metabolism among strains from different sources,with yak milk strains enriched in enzymes involved in complex polysaccharide degradation,including members of the carbohydrate esterases family.Moreover,strains from different sources exhibit distinct strategies for lactose hydrolysis and metabolic utilization,reflecting adaptive evolution to their specific nutritional niches.Analysis of the antibiotic resistance profile suggests that L.lactis predominantly harbors glycopeptide and lincosamide resistance genes,encompassing four distinct resistance mechanisms.Collectively,this study reveals the genetic diversity and adaptive evolution of L.lactis strains from different sources and identifies key genes associated with carbohydrate degradation,lactose metabolism,and antibiotic resistance,providing concrete genetic evidence for the selection of efficient and safe industrial fermentation strains.展开更多
文摘1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.
基金supported by the Financial Special Fund,grant number XZ202401JD0027National Barley Industry Technology System(CARS-05-01A-08)+3 种基金the Xizang Agri-Tech Innovation Project(XZNKY-2025-CXGC-T01)the Joint Funds of the National Natural Science Foundation of China(No.U20A2026)the Financial Special Fund,grant number(32401784,2017CZZX001/2,XZNKY-2018-C-021 and NYSTC202401)the China Agriculture Research System of Barley(CARS-05).
文摘Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.
基金Supported by Chongqing Health Commission and Chongqing Science and Technology Bureau,No.2023MSXM182。
文摘Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金supported by the National Natural Science Foundation of China(Grant Nos.52375255,51935007)the Shanghai Rising-Star Program(Grant No.24QB2705000)。
文摘Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an unsupervised subdomain adaptation method based on a time-frequency feature attention mechanism,LMMD-based subdomain alignment,and contrastive local alignment.This enables the application of the diagnosis model across different working conditions and equipment types.First,a novel time-frequency feature attention mechanism assigns weights to vibration signals of varying dimensions.Second,the time series is transformed to obtain a three-channel time-frequency diagram.This diagram is input into the proposed dimension-segmentation cross-channel multihead self-attention framework to extract high-dimensional frequencydomain fault features.These features are concatenated with the time-domain features to obtain a global feature representation.Then,the extracted high-dimensional features are sent to the classification module to obtain the predicted labels for the source and target domains.Finally,after confidence filtering,the true labels from the source domain and the prediction labels from the target domain are fed into a dynamically weighted multilevel feature alignment module to promote proximity between similar fault features across domains while enhancing separation among different fault types.The validity and superiority of the proposed method were demonstrated through simulation experiments conducted on two types of manned escalator systems under multiple working conditions.For the most challenging transfer task,the proposed method achieved higher accuracy on the target domain test set than DANN,ADDA,C-CLCN,TFA-CCN,and TFA-LCN by 26.87%,24.72%,11.44%,28.94%,and 16.85%,respectively.
文摘The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a transformative pathway for climate change adaptation and peacebuilding, with emphasis on a holistic approach to managing resource conflicts and environmental challenges. This paper explores the synergies between emerging technologies and strategic framework to mitigate climate-induced tensions and foster resilience. It focuses on the application of renewable energy systems to reduce dependence on contested resources, blockchain technology to ensure transparency in climate finance, equitable resource allocation and Artificial Intelligence (AI) to enhance early warning systems for climate-related disaster and conflicts. Additionally, technologies such as precision agriculture and remote sensing empower communities to optimize resource use, adapt to shifting environmental conditions, and reduce competition over scares resources. These innovations with inclusive governance and local capacity-building are very primordial. Ultimately, the convergence of technology, policy, and local participation offers a scalable and replicable model for addressing the dual challenges of environmental degradation and instability, thereby paving the way for a more sustainable and peaceful future.
基金Supported by the Anhui Province Sports Health Information Monitoring Technology Engineering Research Center Open Project (KF2023012)。
文摘Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method.
基金supported by Cultural and Ideological Progress Director Center of Ouhai District of Wenzhou(2024-135F).
文摘This study explores the cultural,social,and academic adaptation experiences of international students in Wenzhou,China.Based on surveys and interviews with 52 students from 20 countries—predominantly Morocco—the research investigates key challenges and coping strategies related to local integration.The findings indicate that while Wenzhou offers a generally supportive academic environment—enhanced by AI integration and practical teaching methods—language barriers continue to hinder students’daily life,academic engagement,and social interactions.Limited Mandarin proficiency made it difficult for many students to build friendships with locals and navigate everyday tasks.Cultural adaptation also presented obstacles,particularly in adjusting to local food and social norms.Despite these challenges,students employed various strategies to facilitate integration,such as attending HSK language courses,watching Chinese media,and initiating conversations with local peers.While most participants described the local community as welcoming,perceptions varied based on individual experiences and language ability.The study highlights the importance of enhanced language support and structured cross-cultural exchange initiatives in improving international students’experiences.It contributes to the broader discourse on international student mobility by offering insights from a second-tier Chinese city,emphasizing the role of institutional practices in shaping adaptation outcomes.
基金the Project of Qinghai provincial central government guides local funds for science and technology development(2024ZY005).
文摘As climate change triggers unprecedented ecological shifts,it becomes imperative to understand the genetic underpinnings of species’adaptability.Adaptive introgression significantly contributes to organismal adaptation to new environments by introducing genetic variation across species boundaries.However,despite growing recognition of its importance,the extent to which adaptive introgression has shaped the evolutionary history of closely related species remains poorly understood.Here we employed population genetic analyses of high-throughput sequencing data to investigate the interplay between genetic introgression and local adaptation in three species of spruce trees in the genus Picea(P.asperata,P.crassifolia,and P.meyeri).We find distinct genetic differentiation among these species,despite a substantial gene flow.Crucially,we find bidirectional adaptive introgression between allopatrically distributed species pairs and unearthed dozens of genes linked to stress resilience and flowering time.These candidate genes most likely have promoted adaptability of these spruces to historical environmental changes and may enhance their survival and resilience to future climate changes.Our findings highlight that adaptive introgression could be prevalent and bidirectional in a topographically complex area,and this could have contributed to rich genetic variation and diverse habitat usage by tree species.
基金funded by the National Science and Technology Major Project(2022ZD0115703)the Beijing Postdoctoral Research Foundation(2023-ZZ-116).
文摘The environment has an important impact on maize(Zea mays L.)production,making it necessary to identify plant adaptation regions that are suitable for different maize varieties.Traditional methods using field trials are costly and restricted to a limited number of areas.Identifying adaptation regions based on climate data has great potential,but a basic understanding and a prediction approach for diverse maize varieties are lacking.Here,we collected a representative dataset comprising 32,840 data points from the National Maize Variety Trial Data Management Platform.We employed three traits to characterize the adaptability of different maize varieties:PH(plant height),DTS(days to silking),and yield.First,we quantified the contributions of variety(V),environment(E),and V×E to variance in the three adaptationrelated traits.The mean contributions of E to variance in PH,DTS,and yield were 54.50%,82.87%,and 75.92%,respectively,suggesting that environmental effects are crucial for phenotype construction.Second,we analyzed correlations between the three traits and three environmental indices:GDD(growing degree days),PRE(precipitation),and SSD(sunshine duration).The highest absolute correlation coefficients between phenotypes and environmental indices were 0.15–0.69 at the whole-data level.To predict variety adaptation on a national scale,we modeled the three traits using environmental indices and best linear unbiased predictors(BLUPs)via the random forest algorithm.The predictive abilities of our models for PH,DTS,and yield were 0.90(MAE=9.95 cm),0.99(MAE=1.09 d),and 0.95(MAE=0.55 t ha^(−1)),respectively,indicating that our proposed framework can predict adaptationrelated traits for diverse maize varieties in China.
基金supported by the National Natural Science Foundation of China(No.31901217)the Special Fund for Scientific Research of Shanghai Landscaping and City Appearance Administrative Bureau(grant numbers G192422,G242414,and G242416).
文摘Understanding the ecological adaptation of tree species can not only reveal the evolutionary potential but also benefit biodiversity conservation under global climate change.Quercus is a keystone genus in Northern Hemisphere forests,and its wide distribution in diverse ecosystems and long evolutionary history make it an ideal model for studying the genomic basis of ecological adaptations.Here we used a newly sequenced genome of Quercus gilva,an evergreen oak species from East Asia,with 18 published Fagales genomes to determine how Fagaceae genomes have evolved,identify genomic footprints of ecological adaptability in oaks in general,as well as between evergreen and deciduous oaks.We found that oak species exhibited a higher degree of genomic conservation and stability,as indicated by the absence of large-scale chromosomal structural variations or additional whole-genome duplication events.In addition,we identified expansion and tandem repetitions within gene families that contribute to plant physical and chemical defense(e.g.,cuticle biosynthesis and oxidosqualene cyclase genes),which may represent the foundation for the ecological adaptation of oak species.Circadian rhythm and hormone-related genes may regulate the habits of evergreen and deciduous oaks.This study provides a comprehensive perspective on the ecological adaptations of tree species based on phylogenetic,genome evolutionary,and functional genomic analyses.
文摘Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.
基金supported by the Australian Research Council Discovery Project(DP200101476)in part by National Institute of Health grants(R01 AR077604,RO1 EB002524,RO1 AR079431,and P41 EB02706)+1 种基金Stanford Graduate Fellowship,The University of Queensland Graduate Scholarship,National Health and Medical Research Council of Australia Fellowship(#1194937)by Wu Tsai Human Performance Alliance at Stanford University and the Joe and Clara Tsai Foundation。
文摘Background Eccentric training,such as Nordic hamstring exercise(NHE)training,is commonly used as a preventive measure for hamstring strains.Eccentric training is believed to induce lengthening of muscle fascicles and to be associated with the addition of sarcomeres in series within muscle fibers.However,the difficulty in measuring sarcomere adaptation in human muscles has severely limited information about the precise mechanisms of adaptation.This study addressed this limitation by measuring the multiscale hamstring muscle adaptations in response to 9 weeks of NHE training and 3 weeks of detraining.Methods Twelve participants completed 9 weeks of supervised NHE training,followed by a 3-week detraining period.We assessed biceps femoris long-head(BFlh)muscle fascicle length,sarcomere length,and serial sarcomere number in the central and distal regions of the muscle.Additionally,we measured muscle volume and eccentric strength at baseline,post-training,and post-detraining.Results NHE training over 9 weeks induced significant architectural and strength adaptations in the BFlh muscle.Fascicle length increased by 19%in the central muscle region and 33%in the distal muscle region.NHE also induced increases in serial sarcomere number(25%in the central region and 49%in the distal region).BFlh muscle volume increased by 8%,and knee flexion strength increased by 40%with training.Following 3 weeks of detraining,fascicle length decreased by 12%in the central region and 16%in the distal region along with reductions in serial sarcomere number.Conclusion Nine weeks of NHE training produced substantial,region-specific increases in BFlh muscle fascicle length,muscle volume,and force generation.The direct measurement of sarcomere lengths revealed that the increased fascicle length was accompanied by the addition of sarcomeres in series within the muscle fascicles.
基金supported by the National Key R&D Program of China(2022YFF1000100)Shaanxi Program for Support of Top-notch Young ProfessionalsFundamental Research Funds for the Central Universities。
文摘Extreme heat and chronic water scarcity present formidable challenges to large desert-dwelling mammals.In addition to camels,antelopes within the Hippotraginae and Alcelaphinae subfamilies also exhibit remarkable physiological and genetic specializations for desert survival.Among them,the critically endangered addax(Addax nasomaculatus)represents the most desert-adapted antelope species.However,the evolutionary and molecular mechanisms underlying desert adaptations remain largely unexplored.Herein,a high-quality genome assembly of the addax was generated to investigate the molecular evolution of desert adaptation in camels and desert antelopes.Comparative genomic analyses identified 136 genes harboring convergent amino acid substitutions implicated in crucial biological processes,including water reabsorption,fat metabolism,and stress response.Notably,a convergent R146S amino acid mutation in the prostaglandin EP2 receptor gene PTGER2 significantly reduced receptor activity,potentially facilitating large-mammal adaptation to arid environments.Lineage-specific innovations were also identified in desert antelopes,including previously uncharacterized conserved non-coding elements.Functional assays revealed that several of these elements exerted significant regulatory effects in vitro,suggesting potential roles in adaptive gene expression.Additionally,signals of introgression and variation in genetic load were observed,indicating their possible influence on desert adaptation.These findings provide insights into the sequential evolutionary processes that drive physiological resilience in arid environments and highlight the importance of convergent evolution in shaping adaptive traits in large terrestrial mammals.
基金National Natural Science Foundation of China,No.42471185,No.T2192952National Key Research and Development Program of China,No.2022YFF0801502。
文摘The paleoenvironmental changes and adaptation strategies of hominins during the Late Pleistocene are crucial for understanding the evolution,dispersal,and behavioral shifts of early modern humans.Despite South China's significance as a nexus for hominin dispersal and handaxe technology diffusion,quantitative reconstructions of paleoenvironments linked to archaeological records remain scarce.The Sandinggai site(96.6-13.3 ka BP)in central South China,with its well-preserved stratigraphy and abundant lithic artefacts,is notable for providing valuable insights.In this study,quantitative reconstruction of the vegetation succession and climate change sequences at the site was conducted using palynological and isotopic data.The results indicated a shift from a warm-temperate evergreen and deciduous broadleaf mixed forest to a temperate deciduous broadleaf forest,with the climate transitioning from warm and humid to cooler and drier conditions.During the early phase,an increase in lithic production suggested favorable conditions for hominin survival.In the later phase,decreased lithic production and the replacement of large handaxe tools by smaller flake tools,indicated that hominins adapted to the cooler,drier climate and more open landscapes through lithic miniaturization.These findings highlight the environment-driven adaptation of lithic technology and hominin behavior,thereby shedding light on human survival adaptation strategies.
文摘In recent years,the heterogeneous SAR image classification task of"training on simulated data and testing on measured data"has garnered increasing attention in the field of Synthetic Aperture Radar Automatic Target Recognition(SAR-ATR).Although current mainstream domain adaptation methods have made significant breakthroughs in addressing domain shift problems,the escalating model complexity and task complexity have constrained their deployment in real-world applications.To tackle this challenge,this paper proposes a domain adaptation framework based on linear-kernel Maximum Mean Discrepancy(MMD),integrated with a near-zero-cost pseudo-label denoising technique leveraging deep feature clustering.Our method completely eliminates the need for data augmentation and handcrafted feature design,achieving endto-end pseudo-label self-training.Competitive performance is demonstrated across three typical scenarios in the SAMPLE dataset,with the highest accuracy of 98.65%achieved in ScenarioⅢ.The relevant code is available at:https://github.com/TheGreatTreatsby/SAMPLE_MMD.
基金supported by the National Key Research and Development Project(2022YFD2100703)the National Natural Science Foundation of China(32394051 and U23A20259)the Fundamental Research Funds for the Central Universities(JUSRP622013).
文摘Lactococcus lactis,a major starter culture in the dairy industry,has been widely applied in food fermentation.While current research has primarily focused on evaluating its role during fermentation,genomic investigations into its genetic diversity and functional adaptability remain limited.In this study,199 L.lactis strains isolated from Chinese traditional artisanal cheeses(72 bovine,71 goat,and 56 yak milk cheese isolates)were subjected to comparative genomic analysis.Genomic characteristic analysis indicated that bovine milk strains possess larger genomes and the highest number of unique genes.Functional characterization further demonstrated notable differences in carbohydrate metabolism among strains from different sources,with yak milk strains enriched in enzymes involved in complex polysaccharide degradation,including members of the carbohydrate esterases family.Moreover,strains from different sources exhibit distinct strategies for lactose hydrolysis and metabolic utilization,reflecting adaptive evolution to their specific nutritional niches.Analysis of the antibiotic resistance profile suggests that L.lactis predominantly harbors glycopeptide and lincosamide resistance genes,encompassing four distinct resistance mechanisms.Collectively,this study reveals the genetic diversity and adaptive evolution of L.lactis strains from different sources and identifies key genes associated with carbohydrate degradation,lactose metabolism,and antibiotic resistance,providing concrete genetic evidence for the selection of efficient and safe industrial fermentation strains.
