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.

Application of the Conditional Nonlinear Local Lyapunov Exponent to Second-Kind Predictability

In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz model.The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external forcings.On this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields.The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method.This similarity supports ENSO as the major predictable signal for weather and climate prediction.In addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was proposed.The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit.For instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean.Moreover,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.

Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites

Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.

Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network

Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.

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.

Ideal Case Study of Adaptive Localization in Storm-scale Ensemble Kalman Filter Assimilation

This study explores the use of the hierarchical ensemble filter to determine the localized influence of observations in the Weather Research and Forecasting ensemble square root filtering(WRF-EnSRF)assimilation system.With error correlations between observations and background field state variables considered,the adaptive localization approach is applied to conduct a series of ideal storm-scale data assimilation experiments using simulated Doppler radar data.Comparisons between adaptive and empirical localization methods are made,and the feasibility of adaptive localization for storm-scale ensemble Kalman filter assimilation is demonstrated.Unlike empirical localization,which relies on prior knowledge of distance between observations and background field,the hierarchical ensemble filter provides continuously updating localization influence weights adaptively.The adaptive scheme improves assimilation quality during rapid storm development and enhances assimilation of reflectivity observations.The characteristics of both the observation type and the storm development stage should be considered when identifying the most appropriate localization method.Ultimately,combining empirical and adaptive methods can optimize assimilation quality.

One-and Multi-dimensional CWENOZ Reconstructions for Implementing Boundary Conditions Without Ghost Cells

We address the issue of point value reconstructions from cell averages in the context of third-order finite volume schemes,focusing in particular on the cells close to the boundaries of the domain.In fact,most techniques in the literature rely on the creation of ghost cells outside the boundary and on some form of extrapolation from the inside that,taking into account the boundary conditions,fills the ghost cells with appropriate values,so that a standard reconstruction can be applied also in the boundary cells.In Naumann et al.(Appl.Math.Comput.325:252–270.https://doi.org/10.1016/j.amc.2017.12.041,2018),motivated by the difficulty of choosing appropriate boundary conditions at the internal nodes of a network,a different technique was explored that avoids the use of ghost cells,but instead employs for the boundary cells a different stencil,biased towards the interior of the domain.In this paper,extending that approach,which does not make use of ghost cells,we propose a more accurate reconstruction for the one-dimensional case and a two-dimensional one for Cartesian grids.In several numerical tests,we compare the novel reconstruction with the standard approach using ghost cells.

Lack of Evidence for Local Adaptation of the Endangered Karner Blue Butterfly to Its Sole Larval Hostplant—The Wild Lupine

Local adaptation is an important process that drives the evolution of populations within species, and it can be generally expressed by the higher fitness of individuals raised in their native habitats versus in a foreign location. The influence of local adaptation is especially prominent in species that subsist in small and/or highly isolated populations. This study evaluated whether the federally endangered Karner blue butterfly, Lycaeides melissa samuelis (Lepidoptera: Lycaenidae) is locally adapted to its exclusive larval host plant, the wild lupine (Lupinus perennis). To test for local adaptation, individuals from a laboratory-raised colony were reared on wild lupine plants from populations belonging to either their native (Indiana) or a foreign (Michigan and Wisconsin) region. For this purpose, lupine plants from the different populations were grown in a common garden in growth chambers, and one Karner blue larva was placed on each plant. Fitness traits related to growth and development were recorded for each butterfly across populations. Days from hatching to pupation and eclosion showed gender-specific significant differences across wild lupine populations and plant genotypes (within populations). The percent survival of butterflies (from hatching to eclosion) also differed among plants from different populations. These results indicate that wild lupine sources can affect some developmental traits of Karner blue butterflies. However, growth-related traits, such as pupal and adult weight of individuals reared in plants from native populations did not differ from those of foreign regions. The apparent absence of local adaptation to wild lupine suggests that, at least, some individuals of this species could be translocated from native populations to foreign reintroduction sites without experiencing decreased fitness levels. However, future studies including more populations across the geographical range of this butterfly are recommended to evaluate other environmental factors that could influence adaptation on a wider spatial scale.

Quantitative detection of locomotive wheel polygonization under non-stationary conditions by adaptive chirp mode decomposition

Wheel polygonal wear is a common and severe defect,which seriously threatens the running safety and reliability of a railway vehicle especially a locomotive.Due to non-stationary running conditions(e.g.,traction and braking)of the locomotive,the passing frequencies of a polygonal wheel will exhibit time-varying behaviors,which makes it too difficult to effectively detect the wheel defect.Moreover,most existing methods only achieve qualitative fault diagnosis and they cannot accurately identify defect levels.To address these issues,this paper reports a novel quantitative method for fault detection of wheel polygonization under non-stationary conditions based on a recently proposed adaptive chirp mode decomposition(ACMD)approach.Firstly,a coarse-to-fine method based on the time–frequency ridge detection and ACMD is developed to accurately estimate a time-varying gear meshing frequency and thus obtain a wheel rotating frequency from a vibration acceleration signal of a motor.After the rotating frequency is obtained,signal resampling and order analysis techniques are applied to an acceleration signal of an axle box to identify harmonic orders related to polygonal wear.Finally,the ACMD is combined with an inertial algorithm to estimate polygonal wear amplitudes.Not only a dynamics simulation but a field test was carried out to show that the proposed method can effectively detect both harmonic orders and their amplitudes of the wheel polygonization under non-stationary conditions.

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.

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.

Multiple Targets Localization Algorithm Based on Covariance Matrix Sparse Representation and Bayesian Learning

The multi-source passive localization problem is a problem of great interest in signal pro-cessing with many applications.In this paper,a sparse representation model based on covariance matrix is constructed for the long-range localization scenario,and a sparse Bayesian learning algo-rithm based on Laplace prior of signal covariance is developed for the base mismatch problem caused by target deviation from the initial point grid.An adaptive grid sparse Bayesian learning targets localization(AGSBL)algorithm is proposed.The AGSBL algorithm implements a covari-ance-based sparse signal reconstruction and grid adaptive localization dictionary learning.Simula-tion results show that the AGSBL algorithm outperforms the traditional compressed-aware localiza-tion algorithm for different signal-to-noise ratios and different number of targets in long-range scenes.

Energy efficiency aware dynamic rate and power adaptation in carrier sensing based WLANs under Rayleigh fading and shadowing

We consider the problem of energy efficiency aware dynamic adaptation of data transmission rate and transmission power of the users in carrier sensing based Wireless Local Area Networks(WLANs)in the presence of path loss,Rayleigh fading and log-normal shadowing.For a data packet transmission,we formulate an optimization problem,solve the problem,and propose a rate and transmission power adaptation scheme with a restriction methodology of data packet transmission for achieving the optimal energy efficiency.In the restriction methodology of data packet transmission,a user does not transmit a data packet if the instantaneous channel gain of the user is lower than a threshold.To evaluate the performance of the proposed scheme,we develop analytical models for computing the throughput and energy efficiency of WLANs under the proposed scheme considering a saturation traffic condition.We then validate the analytical models via simulation.We find that the proposed scheme provides better throughput and energy efficiency with acceptable throughput fairness if the restriction methodology of data packet transmission is included.By means of the analytical models and simulations,we demonstrate that the proposed scheme provides significantly higher throughput,energy efficiency and fairness index than a traditional non-adaptive scheme and an existing most relevant adaptive scheme.Throughput and energy efficiency gains obtained by the proposed scheme with respect to the existing adapting scheme are about 75%and 103%,respectively,for a fairness index of 0.8.We also study the effect of various system parameters on throughput and energy efficiency and provide various engineering insights.

Modeling the Spatio-Temporal Dynamics of Local Context for a Contextualized Diffusion of Agroecological Intensification Options in Niger

Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization;the scale of the “village terroir” seems to be a better option. This is the hypothesis we put forward in the framework of the Global Collaboration for Resilient Food Systems program (CRFS), i.e. local context is spatially defined by village terroir. The study is based on data collected through participatory mapping and surveys in “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri). Then the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of local context are analyzed. This study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agrosocio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio- temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.

Existence and Uniqueness of Solution for a Fractional Order Integro-Differential Equation with Non-Local and Global Boundary Conditions

In this paper, we prove an important existence and uniqueness theorem for a fractional order Fredholm – Volterra integro-differential equation with non-local and global boundary conditions by converting it to the corresponding well known Fredholm integral equation of second kind. The considered in this paper has been solved already numerically in [1].

Multiplicity of Solutions for Fractional Hamiltonian Systems under Local Conditions

Under some local superquadratic conditions on W (t, u) with respect to u, the existence of infinitely many solutions is obtained for the nonperiodic fractional Hamiltonian systems, where L (t) is unnecessarily coercive.

Overcoming Investment Risks Chinese investors need to thoroughly understand local conditions before investing in Africa

With abundant labor resources and the increase in policy sup port for economie development and loreign investment, Africa is becoming a new sought-after destination for investment by multinational companics.

Genome sequencing and population genomics modeling provide insights into the local adaptation of weeping forsythia

Understanding the genetic basis underlying the local adaptation of nonmodel species is a fundamental goal in evolutionary biology.In this study,we explored the genetic mechanisms of the local adaptation of Forsythia suspensa using genome sequence and population genomics data obtained from specific-locus amplified fragment sequencing.We assembled a high-quality reference genome of weeping forsythia(Scaffold N50=7.3 Mb)using ultralong Nanopore reads.Then,genome-wide comparative analysis was performed for 15 natural populations of weeping forsythia across its current distribution range.Our results revealed that candidate genes associated with local adaptation are functionally correlated with solar radiation,temperature and water variables across heterogeneous environmental scenarios.In particular,solar radiation during the period of fruit development and seed drying after ripening,cold,and drought significantly contributed to the adaptive differentiation of F.suspensa.Natural selection exerted by environmental factors contributed substantially to the population genetic structure of F.suspensa.Our results supported the hypothesis that adaptive differentiation should be highly pronounced in the genes involved in signal crosstalk between different environmental variables.Our population genomics study of F.suspensa provides insights into the fundamental genetic mechanisms of the local adaptation of plant species to climatic gradients.

Heterosis in locally adapted sorghum genotypes and potential of hybrids for increased productivity in contrasting environments in Ethiopia

Increased productivity in sorghum has been achieved in the developed world using hybrids.Despite their yield advantage,introduced hybrids have not been adopted in Ethiopia due to the lack of adaptive traits,their short plant stature and small grain size.This study was conducted to investigate hybrid performance and the magnitude of heterosis of locally adapted genotypes in addition to introduced hybrids in three contrasting environments in Ethiopia.In total,139 hybrids,derived from introduced seed parents crossed with locally adapted genotypes and introduced R lines,were evaluated.Overall,the hybrids matured earlier than the adapted parents,but had higher grain yield,plant height,grain number and grain weight in all environments.The lowland adapted hybrids displayed a mean better parent heterosis(BPH) of19%,equating to 1160 kg ha-1and a 29% mean increase in grain yield,in addition to increased plant height and grain weight,in comparison to the hybrids derived from the introduced R lines.The mean BPH for grain yield for the highland adapted hybrids was 16% in the highland and 52%in the intermediate environment equating to 698 kg ha-1and 2031 kg ha-1,respectively,in addition to increased grain weight.The magnitude of heterosis observed for each hybrid group was related to the genetic distance between the parental lines.The majority of hybrids also showed superiority over the standard check varieties.In general,hybrids from locally adapted genotypes were superior in grain yield,plant height and grain weight compared to the high parents and introduced hybrids indicating the potential for hybrids to increase productivity while addressing farmers' required traits.

Conditional Diagnosability of the Locally Twisted Cubes under the PMC Model

In a multiprocessor systems, it is important to local and to replace the faulty processors to maintain systempsilas high reliability. The fault diagnosis, which is the process of identifying fault processors in a multiprocessor system through testing. The conditional diagnosis requires that for each processor u in a system, all the processors that are directly connected to u do not fail at the same time. In this paper, we study the conditional diagnosability of the n-dimensional locally twisted cubes. After showing some properties of the locally twisted cubes, we prove that it under the PMC model is 4n – 7 for n ≥ 5.