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.

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.

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.

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.

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.

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.

Research on Chinese-English Translation of Enterprise Publicity Materials from the Perspective of Adaptation Theory

As the process of economic globalization continues to advance,China has undergone earth-shaking changes.High-quality products and promotional materials are crucial for Chinese companies to go global and build their brands.The English translation of external promotional materials is crucial.However,some companies do not pay enough attention to their publicity,resulting in a low international status.This paper analyzes the Chinese-English translation of enterprise external publicity materials from the perspective of adaptation theory,thereby exploring the translation strategies adopted under the guidance of adaptation theory,hoping to provide some references for related translation practices in the future.

Bilateral co-transfer for unsupervised domain adaptation

Labeled data scarcity of an interested domain is often a serious problem in machine learning.Leveraging the labeled data from other semantic-related yet co-variate shifted source domain to facilitate the interested domain is a consensus.In order to solve the domain shift between domains and reduce the learning ambiguity,unsupervised domain adaptation(UDA)greatly promotes the transferability of model parameters.However,the dilemma of over-fitting(negative transfer)and under-fitting(under-adaptation)is always an overlooked challenge and potential risk.In this paper,we rethink the shallow learning paradigm and this intractable over/under-fitting problem,and propose a safer UDA model,coined as Bilateral Co-Transfer(BCT),which is essentially beyond previous well-known unilateral transfer.With bilateral co-transfer between domains,the risk of over/under-fitting is therefore largely reduced.Technically,the proposed BCT is a symmetrical structure,with joint distribution discrepancy(JDD)modeled for domain alignment and category discrimination.Specifically,a symmetrical bilateral transfer(SBT)loss between source and target domains is proposed under the philosophy of mutual checks and balances.First,each target sample is represented by source samples with low-rankness constraint in a common subspace,such that the most informative and transferable source data can be used to alleviate negative transfer.Second,each source sample is symmetrically and sparsely represented by target samples,such that the most reliable target samples can be exploited to tackle underadaptation.Experiments on various benchmarks show that our BCT outperforms many previous outstanding work.

Transcriptomic and physiological analyses identifying Lanzhou lily(Lilium davidii var.unicolor)drought adaptation strategies

Drought stress is the main limiting plant growth factor in arid and semiarid regions.The Lanzhou lily(Lilium davidii var.unicolor)is the only sweet-tasting lily grown in these regions of China that offers highly edible,medicinal,health,and ornamental value.The Tresor lily is an ornamental flower known for its strong resistance.Plants were grown under three different drought intensity treatments,namely,being watered at intervals of 5,15,and 25 d(either throughout the study or during specific growth stages).We measured the biomass,leaf area,photosynthetic response,chlorophyll content(SPAD value),and osmoregulation of both the Lanzhou lily and the Tresor lily(Lilium‘Tresor’).Additionally,we employed RNA sequencing(RNA-Seq)and qRT-PCR to investigate transcriptomic changes of the Lanzhou lily in response to drought stress.Results showed that under drought stress,the decreasing rate in the Lanzhou lily bulb weight was lower than the corresponding Tresor lily bulb rate;the net photosynthetic rate,transpiration rate,and stomatal conductance of the Lanzhou lily were all higher compared to the Tresor lily;osmoregulation constituents,such as glucose,fructose,sucrose,trehalose,and soluble sugar,in the Lanzhou lily were comparatively higher;PYL,NCED,and ERS genes were significantly expressed in the Lanzhou lily.Under moderate drought,the biosynthesis of flavonoids,circadian rhythms,and the tryptophan metabolism pathway of the Lanzhou lily were all significant.Under severe drought stress,fatty acid elongation,photosynthetic antenna protein,plant hormone signal transduction,flavone and flavonol biosynthesis,and the carotenoid biosynthesis pathway were all significant.The Lanzhou lily adapted to drought stress by coordinating its organs and the unique role of its bulb,regulating photosynthesis,increasing osmolyte content,activating circadian rhythms,signal transduction,fatty acid elongation metabolism,and phenylalanine and flavonoid metabolic pathways,which may collectively be the main adaptation strategy and mechanisms used by the Lanzhou lily under drought stress.

Climate change risk,resilience,and adaptation among rural farmers in East Africa:A literature review

This study assesses the literature evidence on climate change risk,resilience,and adaptation measures used among rural farmers in East Africa.A systematic literature review was conducted comprising 30 papers from the Web of Science database published during 2000-2022.The results of the literature review showed that climate change risks have direct impacts on agricultural practices,limit rural farmers’resilience,and exacerbate their food insecurity.The most prominent risks are increasingly shorter wet seasons and heat stress,which lead to droughts and food production losses.Responding to climate risks,farmers in East Africa adopt various adaptation strategies such as mixed-and inter-cropping,conservation tillage,early planting,crop diversification,etc.Also,this review summarizes the determinants of climate change adaptation strategy selection by farmers in East Africa,including age,gender,household size,economic status and household assets,landownership and livestock,education and training,etc.Overall,the choice of adaptation strategies to climate change is strongly determined by the gender of household heads,the results of gender as a determinant of adaptation differ greatly between different case studies.Although female-headed households(FHHs)tend to perceive changes in temperature more readily than male-headed households(MHHs),the latter are generally more likely to adopt different adaptation strategies.Despite the resilience and adaptation measures used by rural farmers in East Africa now,improved weather forecasting and early warning systems are needed as a better direction towards the future.

Vultures as a model for testing molecular adaptations of dietary specialization in birds

Vultures are the only obligate scavengers among extant vertebrates.They provide valuable ecological services in ecosystems through removing carcasses,thus preventing the growth of other scavenger populations and the spread of pathogens.Moreover,their specific diets expose them to various deadly pathogens,which makes them potential candidates for studying molecular adaptations required to survive this extremely specialized scavenging habit.In this review,we summarize the morphological characteristics and behavioral habits,origin and phylogeny,and molecular adaptations to scavenging in both Old and New World vultures.The two groups of vultures share a similar appearance,indicative of convergent evolution.Vultures have experienced different degrees of specialization in their sensory organs;Old World vultures depend on sight,while New World ones depend on both smell and sight.Combined fossil records and molecular data suggest that vultures evolved independently,with distinct phylogenetic positions.We also explored their adaptation to scavenging in facial and intestinal microbiomes,gastric acid secretion and immunity.Compared with the facial microbiome,the intestinal microbiome had a lower diversity,dominated by Fusobacteria and Clostridia.The phages and single invertebrate species Adineta vaga,which feeds on dead bacteria and protozoa,present in the gut suggest a possible alternative defense mechanism.Several genes involved in gastric acidic secretion(including ATP4B,SLC26A7 and SST)and immunity(including BCL6,STING,and TLRs) undergoing positive selection likely have essential roles in eliminating invasive pathogens and initiating an innate immune response.Taken together,this review presents the current research status of vultures and highlights the use of vultures as a model for exploring molecular adaptations of dietary specialization in birds.It also provides a theoretical basis for the study of the genetic mechanisms of vultures to scavenging,and contributes to the formulation of vulture conservation strategies.

Selective footprints and genes relevant to cold adaptation and other phenotypic traits are unscrambled in the genomes of divergently selected chicken breeds

Background The genomes of worldwide poultry breeds divergently selected for performance and other phenotypic traits may also be affected by,and formed due to,past and current admixture events.Adaptation to diverse environments,including acclimation to harsh climatic conditions,has also left selection footprints in breed genomes.Results Using the Chicken 50K_CobbCons SNP chip,we genotyped four divergently selected breeds:two aboriginal,cold tolerant Ushanka and Orloff Mille Fleur,one egg-type Russian White subjected to artificial selection for cold tolerance,and one meat-type White Cornish.Signals of selective sweeps were determined in the studied breeds using three methods:(1)assessment of runs of homozygosity islands,(2)F_(ST) based population differential analysis,and(3)haplotype differentiation analysis.Genomic regions of true selection signatures were identified by two or more methods or in two or more breeds.In these regions,we detected 540 prioritized candidate genes supplemented them with those that occurred in one breed using one statistic and were suggested in other studies.Amongst them,SOX5,ME3,ZNF536,WWP1,RIPK2,OSGIN2,DECR1,TPO,PPARGC1A,BDNF,MSTN,and beta-keratin genes can be especially mentioned as candidates for cold adaptation.Epigenetic factors may be involved in regulating some of these important genes(e.g.,TPO and BDNF).Conclusion Based on a genome-wide scan,our findings can help dissect the genetic architecture underlying various phenotypic traits in chicken breeds.These include genes representing the sine qua non for adaptation to harsh environments.Cold tolerance in acclimated chicken breeds may be developed following one of few specific gene expression mechanisms or more than one overlapping response known in cold-exposed individuals,and this warrants further investigation.

Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples well.Existing approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity(AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity(GECS), and adaptive graph embedding(AGE).GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS,the neighborhood samples with the same label are rewarded,while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.

Hierarchical pattern recognition of landform elements considering scale adaptation

Landform elements with varying morphologies and spatial arrangements are recognized as feature indicator of landform classification and play a critical role in geomorphological studies.Differential geometry method has been extensively applied in prior landform element research,while its efficacy in differentiating similar morphological characteristics remains inadequate to date.To reduce reliance on geomorphometric variables and increase awareness of landform patterns,geomorphons method was generated in previous study corresponding to specific landform reclassification map based on lookup table.Besides,to address the problem of feature similarity,hierarchical classification was proposed and effectively utilized for terrain recognition through the analytical strategy of fuzzy gradient features.Thus,combining the advantages of these two aspects,a hierarchical framework was proposed in this study for landform element pattern recognition considering the morphology and hierarchy factors.First,the local triplet patterns derived from geomorphons were enhanced by setting the flatness threshold,and subsequently adopted for the primary landform element recognition.Then,as geomorphic units with the same morphology possess different spatial analytical scales,the unidentified landform elements under the principle of scale adaptation were determined by calculating the spatial correlation and entropy information.To ensure the effectiveness of this proposed method,the sampling points were randomly selected from NASADEM data and then validated against a real 3D terrain model.Quantitative results of landform element pattern recognition demonstrate that our approach can reach above 77%average accuracy.Additionally,it delineates local details more effectively than geomorphons in visual assessment,resulting in a 7%accuracy improvement in overall scale.

Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.

Cross-protective effect of acid adaptation on ethanol tolerance in Salmonella Enteritidis

Cross protection can undermine the effectiveness of control measures on foodborne pathogens,and therefore brings major implications for food safety.In this work,the capacity of Salmonella Enteritidis to mount ethanol tolerance following acid adaptation was characterized by analysis of cell viability and cell membrane property.It was observed that preadaptation to pH 4.5 significantly(P<0.05)increased the tolerance of log-phase cells to ethanol;in contrast,stationary-phase cells displayed reduced ethanol tolerance after acid adaptation.However,acid adaptation did not cause cell leakage and morphological change in both log-phase and stationary-phase S.Enteritidis.Fatty acid analysis further revealed that the amount of C_(14:0),C_(17:0 cyclo) and C_(19:0 cyclo) fatty acids was increased,while that of C_(16:1ω7c) and C_(18:1ω7c) fatty acids was decreased,respectively,in response to acid adaptation,regardless of bacterial growth phase.Notably,acid adaptation significantly(P<0.05)increased the proportion of C_(16:0) fatty acid in log-phase cells,but this effect did not occur in stationary-phase cells.Moreover,exogenous addition of C_(16:0) fatty acid to stationary-phase acid-adapted cultures was able to enhance bacterial ethanol tolerance.Taken together,C_(16:0) fatty acid is involved in the growth-phase-dependent protective effect of acid adaptation on ethanol tolerance in S.Enteritidis.

The genomic and epigenetic footprint of local adaptation to variable climates in kiwifruit

A full understanding of adaptive genetic variation at the genomic level will help address questions of how organisms adapt to diverse climates.Actinidia eriantha is a shade-tolerant species,widely distributed in the southern tropical region of China,occurring in spatially heterogeneous environments.In the present study we combined population genomic,epigenomic,and environmental association analyses to infer population genetic structure and positive selection across a climatic gradient,and to assess genomic offset to climatic change for A.eriantha.The population structure is strongly shaped by geography and influenced by restricted gene f low resulting from isolation by distance due to habitat fragmentation.In total,we identified 102 outlier loci and annotated 455 candidate genes associated with the genomic basis of climate adaptation,which were enriched in functional categories related to development processes and stress response;both temperature and precipitation are important factors driving adaptive variation.In addition to single-nucleotide polymorphisms(SNPs),a total of 27 single-methylation variants(SMVs)had significant correlation with at least one of four climatic variables and 16 SMVswere located in or adjacent to genes,several of whichwere predicted to be involved in plant response to abiotic or biotic stress.Gradient forest analysis indicated that the central/east populations were predicted to be at higher risk of future population maladaptation under climate change.Our results demonstrate that local climate factors impose strong selection pressures and lead to local adaptation.Such information adds to our understanding of adaptive mechanisms to variable climates revealed by both population genome and epigenome analysis.

Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.