Exercise-induced adaptation of neurons in the vertebrate locomotor system

Vertebrate neurons are highly dynamic cells that undergo several alterations in their functioning and physiologies in adaptation to various external stimuli.In particular,how these neurons respond to physical exercise has long been an area of active research.Studies of the vertebrate locomotor system’s adaptability suggest multiple mechanisms are involved in the regulation of neuronal activity and properties during exercise.In this brief review,we highlight recent results and insights from the field with a focus on the following mechanisms:(a)alterations in neuronal excitability during acute exercise;(b)alterations in neuronal excitability after chronic exercise;(c)exercise-induced changes in neuronal membrane properties via modulation of ion channel activity;(d)exercise-enhanced dendritic plasticity;and(e)exercise-induced alterations in neuronal gene expression and protein synthesis.Our hope is to update the community with a cellular and molecular understanding of the recent mechanisms underlying the adaptability of the vertebrate locomotor system in response to both acute and chronic physical exercise.

Whole-genome analysis reveals distinct adaptation signatures to diverse environments in Chinese domestic pigs

Background Long-term natural and artificial selection has resulted in many genetic footprints within the genomes of pig breeds across distinct agroecological zones.Nevertheless,the mechanisms by which these signatures contribute to phenotypic diversity and facilitate environmental adaptation remain unclear.Results Here,we leveraged whole-genome sequencing data from 82 individuals from 6 domestic pig breeds originating in tropical,high-altitude,and frigid regions.Population genetic analysis suggested that habitat isolation significantly shaped the genetic diversity and contributed to population stratification in local Chinese pig breeds.Analysis of selection signals revealed regions under selection for adaptation in tropical(55.5 Mb),high-altitude(43.6 Mb),and frigid(17.72 Mb)regions.The potential functions of the selective sweep regions were linked to certain complex traits that might play critical roles in different geographic environments,including fat coverage in frigid environments and blood indicators in tropical and high-altitude environments.Candidate genes under selection were significantly enriched in biological pathways involved in environmental adaptation.These pathways included blood circulation,protein degradation,and inflammation for adaptation to tropical environments;heart and lung development,hypoxia response,and DNA damage repair for high-altitude adaptation;and thermogenesis,cold-induced vasodilation(CIVD),and the cell cycle for adaptation to frigid environments.By examining the chromatin state of the selection signatures,we identified the lung and ileum as two candidate functional tissues for environmental adaptation.Finally,we identified a mutation(chr1:G246,175,129A)in the cis-regulatory region of ABCA1 as a plausible promising variant for adaptation to tropical environments.Conclusions In this study,we conducted a genome-wide exploration of the genetic mechanisms underlying the adaptability of local Chinese pig breeds to tropical,high-altitude,and frigid environments.Our findings shed light on the prominent role of cis-regulatory elements in environmental adaptation in pigs and may serve as a valuable biological model of human plateau-related disorders and cardiovascular diseases.

The ABA synthesis enzyme allele OsNCED2^(T)promotes dryland adaptation in upland rice

Upland rice shows dryland adaptation in the form of a deeper and denser root system and greater drought resistance than its counterpart,irrigated rice.Our previous study revealed a difference in the frequency of the OsNCED2 gene between upland and irrigated populations.A nonsynonymous mutation(C to T,from irrigated to upland rice)may have led to functional variation fixed by artificial selection,but the exact biological function in dryland adaptation is unclear.In this study,transgenic and association analysis indicated that the domesticated fixed mutation caused functional variation in OsNCED2,increasing ABA levels,root development,and drought tolerance in upland rice under dryland conditions.OsNCED2-overexpressing rice showed increased reactive oxygen species-scavenging abilities and transcription levels of many genes functioning in stress response and development that may regulate root development and drought tolerance.OsNCED2^(T)-NILs showed a denser root system and drought resistance,promoting the yield of rice under dryland conditions.OsNCED2^(T)may confer dryland adaptation in upland rice and may find use in breeding dryland-adapted,water-saving rice.

Conservation genomics provides insights into genetic resilience and adaptation of the endangered Chinese hazelnut, Corylus chinensis

Global climate change has increased concerns regarding biodiversity loss.However,many key conservation issues still required further research,including demographic history,deleterious mutation load,adaptive evolution,and putative introgression.Here we generated the first chromosome-level genome of the endangered Chinese hazelnut,Corylus chinensis,and compared the genomic signatures with its sympatric widespread C.kwechowensis-C yunnanensis complex.We found large genome rearrangements across all Corylus species and identified species-specific expanded gene families that may be involved in adaptation.Population genomics revealed that both C.chinensis and the C.kwechowensis-C.yunnanensis complex had diverged into two genetic lineages,forming a consistent pattern of southwestern-northern differentiation.Population size of the narrow southwestern lineages of both species have decreased continuously since the late Miocene,whereas the widespread northern lineages have remained stable(C.chinensis) or have even recovered from population bottlenecks(C.kwechowensis-C.yunnanensis complex) during the Quaternary.Compared with C.kwechowensis-C. yunnanensis complex,C.chinensis showed significantly lower genomic diversity and higher inbreeding level.However,C.chinensis carried significantly fewer deleterious mutations than C.kwechowensis-C. yunnanensis complex,as more effective purging selection reduced the accumulation of homozygous variants.We also detected signals of positive selection and adaptive introgression in different lineages,which facilitated the accumulation of favorable variants and formation of local adaptation.Hence,both types of selection and exogenous introgression could have mitigated inbreeding and facilitated survival and persistence of C.chinensis.Overall,our study provides critical insights into lineage differentiation,local adaptation,and the potential for future recovery of endangered trees.

Comparative genomic analysis revealed that dietary habits affected the adaptation of Bifidobacterium bifidum to the intestinal tract in different geographic populations

Recent research on the genome of Bifidobacterium bifidum has mainly focused on the isolation sources(intestinal tract niche)recently,but reports on the isolation region are limited.This study analyzed the differences in the genome of B.bifidum isolated from different geographical populations by comparative genomic analysis.Results at the genome level indicated that the GC content of American isolates was significantly higher than that of Chinese and Russian isolates.The phylogenetic tree,based on 919 core genes showed that B.bifidum might be related to the geographical characteristics of isolation region.Furthermore,functional annotation analysis demonstrated that copy numbers of carbohydrate-active enzymes(CAZys)involved in the degradation of polysaccharide from plant and host sources in B.bifidum were high,and 18 CAZys showed significant differences across different geographical populations,indicating that B.bifidum had adapted to the human intestinal environment,especially in the groups with diets rich in fiber.Dietary habits were one of the main reasons for the differences of B.bifidum across different geographical populations.Additionally,B.bifidum exhibited high diversity,evident in glycoside hydrolases,the CRISPR-Cas system,and prophages.This study provides a genetic basis for further research and development of B.bifidum.

Comparative proteomics reveals the response and adaptation mechanisms of white Hypsizygus marmoreus against the biological stress caused by Penicillium

White Hypsizygus marmoreus is a popular edible mushroom.Its mycelium is easy to be contaminated by Penicillium,which leads to a decrease in its quality and yield.Penicillium could compete for limited space and nutrients through rapid growth and produce a variety of harmful gases,such as benzene,aldehydes,phenols,etc.,to inhibit the growth of H.marmoreus mycelium.A series of changes occurred in H.marmoreus proteome after contamination when detected by the label-free tandem mass spectrometry(MS/MS)technique.Some proteins with up-regulated expression worked together to participate in some processes,such as the non-toxic transformation of harmful gases,glutathione metabolism,histone modification,nucleotide excision repair,clearing misfolded proteins,and synthesizing glutamine,which were mainly used in response to biological stress.The proteins with down-regulated expression are mainly related to the processes of ribosome function,protein processing,spliceosome,carbon metabolism,glycolysis,and gluconeogenesis.The reduction in the function of these proteins affected the production of the cell components,which might be an adjustment to adapt to growth retardation.This study further enhanced the understanding of the biological stress response and the growth restriction adaptation mechanisms in edible fungi.It also provided a theoretical basis for protein function exploration and edible mushroom food safety research.

Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.

Low-Rank Optimal Transport for Robust Domain Adaptation

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

Population genomic data reveal low genetic diversity,divergence and local adaptation among threatened Reeves's Pheasant(Syrmaticus reevesii)

Population genomic data could provide valuable information for conservation efforts;however,limited studies have been conducted to investigate the genetic status of threatened pheasants.Reeves’s Pheasant(Syrmaticus reevesii)is facing population decline,attributed to increases in habitat loss.There is a knowledge gap in understanding the genomic status and genetic basis underlying the local adaptation of this threatened bird.Here,we used population genomic data to assess population structure,genetic diversity,inbreeding patterns,and genetic divergence.Furthermore,we identified candidate genes linked with adaptation across the current distribution of Reeves’s Pheasant.The present study assembled the first de novo genome sequence of Reeves’s Pheasant and annotated 19,458 genes.We also sequenced 30 individuals from three populations(Dabie Mountain,Shennongjia,Qinling Mountain)and found that there was clear population structure among those populations.By comparing with other threatened species,we found that Reeves’s Pheasants have low genetic diversity.Runs of homozygosity suggest that the Shennongjia population has experienced serious inbreeding.The demographic history results indicated that three populations experienced several declines during the glacial period.Local adaptative analysis among the populations identified 241 candidate genes under directional selection.They are involved in a large variety of processes,including the immune response and pigmentation.Our results suggest that the three populations should be considered as three different conservation units.The current study provides genetic evidence for conserving the threatened Reeves’s Pheasant and provides genomic resources for global biodiversity management.

Unveiling the adaptation strategies of woody plants in remnant forest patches to spatiotemporal urban expansion through leaf trait networks

Background:With the expansion of urban areas,the remnants of forested areas play a crucial role in preserving biodiversity in urban environments.This study aimed to explore the impact of spatiotemporal urban expansion on the networks of leaf traits in woody plants within remnant forest patches,thereby enhancing our understanding of plant adaptive strategies and contributing to the conservation of urban biodiversity.Methods:Our study examined woody plants within 120 sample plots across 15 remnant forest patches in Guiyang,China.We constructed leaf trait networks (LTNs) based on 26 anatomical,structural,and compositional leaf traits and assessed the effects of the spatiotemporal dynamics of urban expansion on these LTNs.Results and conclusions:Our results indicate that shrubs within these patches have greater average path lengths and diameters than trees.With increasing urban expansion intensity,we observed a rise in the edge density of the LTN-shrubs.Additionally,modularity within the networks of shrubs decreased as road density and urban expansion intensity increased,and increases in the average path length and average clustering coefficient for shrubs were observed with a rise in the composite terrain complexity index.Notably,patches subjected to‘leapfrog’expansion exhibited greater average patch length and diameter than those experiencing edge growth.Stomatal traits were found to have high degree centrality within these networks,signifying their substantial contribution to multiple functions.In urban remnant forests,shrubs bolster their resilience to variable environmental pressures by augmenting the complexity of their leaf trait networks.

Diabetic retinopathy identification based on multi-sourcefree domain adaptation

AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identification from unlabeled data.METHODS:A multi-SFDA method was proposed for DR identification.This method integrates multiple source models,which are trained from the same source domain,to generate synthetic pseudo labels for the unlabeled target domain.Besides,a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances.Validation is performed using three color fundus photograph datasets(APTOS2019,DDR,and EyePACS).RESULTS:The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks.It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains.CONCLUSION:The multi-SFDA method provides an effective approach to overcome the challenges in DR identification.The method not only addresses difficulties in data labeling and privacy issues,but also reduces the need for large amounts of labeled data required by deep learning methods,making it a practical tool for early detection and preservation of vision in diabetic patients.

Comparative DNA methylation reveals epigenetic adaptation to high altitude in snub-nosed monkeys

DNA methylation plays a crucial role in environmental adaptations.Here,using whole-genome bisulfite sequencing,we generated comprehensive genome-wide DNA methylation profiles for the high-altitude Yunnan snub-nosed monkey(Rhinopithecus bieti)and the closely related golden snub-nosed monkey(R.roxellana).Our findings indicated a slight increase in overall DNA methylation levels in golden snub-nosed monkeys compared to Yunnan snub-nosed monkeys,suggesting a higher prevalence of hypermethylated genomic regions in the former.Comparative genomic methylation analysis demonstrated that genes associated with differentially methylated regions were involved in membrane fusion,vesicular formation and trafficking,hemoglobin function,cell cycle regulation,and neuronal differentiation.These results suggest that the high-altitude-related epigenetic modifications are extensive,involving a complete adaptation process from the inhibition of single Ca^(2+)channel proteins to multiple proteins collaboratively enhancing vesicular function or inhibiting cell differentiation and proliferation.Functional assays demonstrated that overexpression or down-regulation of candidate genes,such as SNX10,TIMELESS,and CACYBP,influenced cell viability under stress conditions.Overall,this research suggests that comparing DNA methylation across closely related species can identify novel candidate genomic regions and genes associated with local adaptations,thereby deepening our understanding of the mechanisms underlying environmental adaptations.

Enhancing Unsupervised Domain Adaptation for Person Re-Identification with the Minimal Transfer Cost Framework

In Unsupervised Domain Adaptation(UDA)for person re-identification(re-ID),the primary challenge is reducing the distribution discrepancy between the source and target domains.This can be achieved by implicitly or explicitly constructing an appropriate intermediate domain to enhance recognition capability on the target domain.Implicit construction is difficult due to the absence of intermediate state supervision,making smooth knowledge transfer from the source to the target domain a challenge.To explicitly construct the most suitable intermediate domain for the model to gradually adapt to the feature distribution changes from the source to the target domain,we propose the Minimal Transfer Cost Framework(MTCF).MTCF considers all scenarios of the intermediate domain during the transfer process,ensuring smoother and more efficient domain alignment.Our framework mainly includes threemodules:Intermediate Domain Generator(IDG),Cross-domain Feature Constraint Module(CFCM),and Residual Channel Space Module(RCSM).First,the IDG Module is introduced to generate all possible intermediate domains,ensuring a smooth transition of knowledge fromthe source to the target domain.To reduce the cross-domain feature distribution discrepancy,we propose the CFCM Module,which quantifies the difficulty of knowledge transfer and ensures the diversity of intermediate domain features and their semantic relevance,achieving alignment between the source and target domains by incorporating mutual information and maximum mean discrepancy.We also design the RCSM,which utilizes attention mechanism to enhance the model’s focus on personnel features in low-resolution images,improving the accuracy and efficiency of person re-ID.Our proposed method outperforms existing technologies in all common UDA re-ID tasks and improves the Mean Average Precision(mAP)by 2.3%in the Market to Duke task compared to the state-of-the-art(SOTA)methods.

Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.

A systematic review of climate change impacts,adaptation strategies,and policy development in West Africa

Climate change studies are diverse with no single study giving a comprehensive review of climate change impacts,adaptation strategies,and policy development in West Africa.The unavailability of an all-inclusive study to serve as a guide for practitioners affects the effectiveness of climate change adaptation strategies proposed and adopted in the West African sub-region.The purpose of this study was to review the impacts of climate change risks on the crop,fishery,and livestock sectors,as well as the climate change adaptation strategies and climate-related policies aimed at helping to build resilient agricultural production systems in West Africa.The review process followed a series of rigorous stages until the final selection of 56 articles published from 2009 to 2023.Generally,the results highlighted the adverse effects of climate change risks on food security.We found a continuous decline in food crop production.Additionally,the livestock sector experienced morbidity and mortality,as well as reduction in meat and milk production.The fishery sector recorded loss of fingerlings,reduction in fish stocks,and destruction of mariculture and aquaculture.In West Africa,climate-smart agriculture technologies,physical protection of fishing,and inclusion of gender perspectives in programs appear to be the major adaptation strategies.The study therefore recommends the inclusion of ecosystem and biodiversity restoration,weather insurance,replacement of unsafe vessels,and strengthening gender equality in all climate change mitigation programs,as these will help to secure enough food for present and future generations.

Sleep Quality and Emotional Adaptation among Freshmen in Elite Chinese Universities during Prolonged COVID-19 Lockdown:The Mediating Role of Anxiety Symptoms

Under the effects of COVID-19 and a number of ongoing lockdown tactics,anxiety symptoms and poor sleep quality have become common mental health issues among college freshmen and are intimately related to their emotional adaptation.To explore this connection,this study gathered data from a sample of 256 freshmen enrolled in an elite university in China in September 2022.The association between sleep quality,anxiety symptoms,and emotional adaptation was clarified using correlation analysis.Additionally,the mediating function of anxiety symptoms between sleep quality and emotional adaptation was investigated using a structural equation model.The results reveal that:(1)Chinese elite university freshmen who were subjected to prolonged lockdown had poor sleep quality and mild anxiety symptoms;(2)a significant positive correlation between poor sleep quality and anxiety symptoms was identified;(3)anxiety symptoms were found to have a significant negative impact on emotional adaptation;(4)poor sleep quality had a negative impact on emotional adaptation through anxiety symptoms.This research makes a valuable contribution by offering insights into the intricate relationship between sleep quality and emotional adaptation among freshmen in elite Chinese universities during prolonged lockdown conditions,and it is beneficial for schools and educators to further improve school schedules and psychological health initiatives.

Farmers’ Knowledge and Perceptions of the Effects of Climate Variability and Pollution on Crop Production and Their Varying Adaptation Strategies in The Gambia

Crop and livestock production is critical to food security in The Gambia. Over the years, the country has experienced a reduced yield due to perceived climate change events with limited studies on how climate change and pollution affect crop production. This study assesses farmers’ knowledge and perceptions of the effects of climate variability and pollution on crop production and their varying adaptation strategies in The Gambia. Both quantitative and qualitative methods were used in this study. The sample size for quantitative data collection was calculated as 432 while the qualitative data involves both the focus group discussions and key informant interviews. The focus group discussions comprised two districts in each of the six agricultural regions and two farming communities engaged in crop production were chosen from each district. Furthermore, eight key informant interviews from relevant institutions were conducted. The study shows that The Gambia is highly vulnerable to extreme climatic events. Although most farmers opined that agricultural land contamination emanates from farm runoff and indiscriminate waste dumping, they had little knowledge of heavy metal pollution and bioremediation. The results showed that farmers experienced constraints such as inadequate access to credit, water, and irrigation facilities, insufficient access to efficient inputs, salt intrusion, etc. which threatened food security. The study concludes that crop farmers acknowledged the existence and impacts of climate change, and therefore recommend the availability and affordability of climate change resilient crops and promote variability awareness campaigns to address climate change impacts in The Gambia.

Irrigated Agriculture Facing the Challenge of Climate Change: Adaptation Strategies for Farmers in the Irrigated Perimeters of Môle Saint-Nicolas, Haiti

Môle Saint-Nicolas, like all other communes in the Republic of Haiti, faces increasing climate variability, impacting agricultural production and water resources. Consequently, there is a pressing need for adaptation to these climatic changes. This research aims to showcase the adaptation strategies deployed by farmers to cope with the increasing climate variability. Surveys were conducted through group and individual discussions with a randomly selected cohort of 150 farmers. Two types of analysis were performed: quantitative and qualitative. The quantitative data analysis was conducted using Statistical Package for the Social Sciences (SPSS) software. The findings reveal that farmers have perceived changes in rainfall patterns, temperature, wind, and their environment. These changes manifest as irregular rainfall, higher temperatures, prolonged drought periods, violent winds accompanied by rain, premature cessation of rains, and reduced flow from water sources. In response, the most common adaptation strategies adopted include selecting new cultivars, early-maturing varieties, crop rotation and diversification, canal dredging, new soil preparation methods, upstream water source protection, and micro-watershed management. The significance of this research lies in its contribution to enhancing farmers’ adaptive capacities by alerting stakeholders in the irrigated perimeters about the consequences of climate change, thereby incorporating the real needs of farmers in future projects.

On Adaptation Measures to Solve Tel-Aviv’s Environmental Problems with “Sunny Rain”

Adaptation measures were proposed using“Sunny Rain”to solve Tel Aviv’s environmental problems to cool the air in the city and save humanity from climate catastrophe and fight air pollution;the Yarkon,“Tel Aviv’s main river”will no longer be a dirty ditch;the fog from Ben-Gurion Airport was cleaned;neighborhoods,parks,around Tel Aviv were greened and all green spaces were irrigated with“Sunny Rain”.

Research Progress on Psychosocial Adaptation of Hemodialysis Patients

From the perspective of the biopsychosocial medical model,hemodialysis patients often suffer from psychosocial maladaptation in the face of a series of changes and pressures,which greatly affect their physical and mental health.This paper reviews the concept of psychosocial adaptation of hemodialysis patients,the current research status,influencing factors,measurement tools,and the current status of interventions,aiming to provide a reference for improving the psychosocial adaptation ability of hemodialysis patients.