Muscle endurance time estimation during isometric training using electromyogram and supervised learning

Constant-force isometric muscle training is useful for increasing the maximal strength , rehabilitation and work-fatigue assessment. Earlier studies have shown that muscle fatigue characteristics can be used for evaluating muscle endurance limit. Study Objective: To predict muscle endurance time during isometric task using frequency spectrum characteristics of surface electromyography signals along with analysis of frequency spectrum shape and scale during fatigue accumulation. Method: Thirteen subjects performed isometric lateral raise at 60% MVC of deltoid (lateral) till endurance limit. Time windowed sEMG frequency spectrum was modelled using 2-parameter distributions namely Gamma and Weibull for spectrum analysis and endurance prediction. Results: Gamma distribution provided better spectrum fitting (P < 0.001) than Weibull distribution. Spectrum Distribution demonstrated no change in shape but shifted towards lower frequency with increase of magnitude at characteristic mode frequency. Support Vector Regression based algorithm was developed for endurance time estimation using features derived from fitted frequency spectrum. Time taken till endurance limit for acquired dataset 38.53 ± 17.33 s (Mean ± Standard Deviation) was predicted with error of 0.029 ± 4.19 s . R-square: 0.956, training and test sets RMSE was calculated as 3.96 and 4.29 s respectively. The application of the algorithm suggested that model required 70% of sEMG signal from maximum time of endurance for high prediction accuracy. Conclusion: Endurance Limit prediction algorithm was developed for quantification of endurance time for optimizing isometric training and rehabilitation. Our method could help personalize and change conventional training method of same weight and duration for all subjects with optimized training parameters, based upon individual sEMG activity.     

Turbid reefs moderate coral bleaching under climate‐related temperature stress

Thermal‐stress events that cause coral bleaching and mortality have recently increased in frequency and severity. Yet few studies have explored conditions that moderate coral bleaching. Given that high light and high ocean temperature together cause coral bleaching, we explore whether corals at turbid localities, with reduced light, are less likely to bleach during thermal‐stress events than corals at other localities. We analyzed coral bleaching, temperature, and turbidity data from 3,694 sites worldwide with a Bayesian model and found that K_d490, a measurement positively related to turbidity, between 0.080 and 0.127 reduced coral bleaching during thermal‐stress events. Approximately 12% of the world's reefs exist within this “moderating turbidity” range, and 30% of reefs that have moderating turbidity are in the Coral Triangle. We suggest that these turbid nearshore environments may provide some refuge through climate change, but these reefs will need high conservation status to sustain them close to dense human populations.     

大渡河中游干暖河谷区生境对植物群落分布格局和多样性的影响

大渡河中游干暖河谷区滑坡和泥石流灾害频发, 对该区域坡面植物群落的研究有助于揭示植被演替的方向, 为坡面植被生态恢复提供基本依据。本研究沿大渡河中游河谷区每隔约5 km设置典型样地, 调查了植被的物种组成和分布以及样地的地形、土壤等10个生境因子, 探讨河谷区植被的连续性变化, 并通过多元回归树(multivariate regression trees, MRT)、多样性指数和典范对应分析(canonical correspondence analysis, CCA)等方法对植物群落进行分类、比较和排序。结果表明: 大渡河中游干暖河谷植被以土壤碳含量、pH值和C : N等3个因子为节点, 可划分为多花胡枝子(Lespedeza floribunda)-荩草(Arthraxon hispidus)-香薷(Elsholtzia ciliate)(群落A)、地果(Ficus tikoua)-车桑子(Dodonaea viscosa)-川滇薹草(Carex schneideri)(群落B)、云南松(Pinus yunnanensis)-栓皮栎(Quercus variabilis)(群落C)和荩草-扭黄茅(Heteropogon contortus)(群落D)等4种群落。该区域以灌木和草本为主要植被类型(群落A、B、C), 间或有裸地分布, 易成为泥石流灾害产生的物源区; 以多花胡枝子为主的灌草群落A的物种丰富度、优势度与多样性表现一致, 均高于以乔木和草本为主的群落C和D, 但物种多样性优势并不显著, 灌草群落分布广而结构单一, 外来物种占比为8.33%, 是生态系统脆弱和不稳定的表现。多元回归树和典范对应分析结果表明, pH值、C : N、坡向和土壤容重等4个因子对植物群落组成和分布影响最大, 且土壤因子的影响大于地形因子。     

基于GTWR模型的浙江省海岸带三维生态足迹动态变化及其影响因素

厘清海岸带自然资本存量消耗和自然资本流量占用情况及其影响因素,能有效缓解海岸带社会经济发展与自然资源、生态环境之间的矛盾,促进区域协调可持续发展。本研究基于二维和三维生态足迹评价模型,分析浙江省海岸带2006—2016年生态足迹时空变化特征,基于足迹深度和足迹广度探究了区域自然资本存量和资本流量占用情况,并利用时空地理加权模型分析其影响因素的时空异质性。结果表明: 研究期间,浙江省海岸带生态危机加深,人均生态赤字平均值为3.5 hm²·cap^-1,人均生态赤字上升了15.5%,其中,能源消耗账户对人均生态足迹的贡献率大于生物资源账户;各县区人均生态足迹呈增长趋势,且空间分异显著。浙江省海岸带以消耗自然资本存量为主,且对自然资本流量更新起到较大的抑制作用;2016年,自然资本存量的消耗量是资本流量占有量的14.87倍。城市规模和经济发展水平、人民收入水平对浙江省海岸带各县区生态足迹以正向促进作用为主,渔业生产对生态足迹为负向影响,并有向正向影响作用转换的趋势,3个影响因子的影响机制时空分异性显著。     

Energy–water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperm species richness in China

Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. Climate and human influences are the major driving factors that directly influence the large‐scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human‐induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human‐induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km² spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables: (a) all species, (b) endemic species, and (c) nonendemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated: (a) energy–water, (b) climatic seasonality, (c) habitat heterogeneity, and (d) human influences. We performed generalized linear models (GLMs) and variation partitioning analyses to determine the effect of predictors on spatial richness patterns. The results showed that the distribution pattern of species richness was highest in the southwestern mountainous area and Taiwan in China. We found a significant relationship between the predictor variable set and species richness pattern. Further, our findings provide evidence that climatic seasonality is the most important factor in explaining distinct fractions of variations in the species richness patterns of all studied response variables. Moreover, it was found that energy–water was the best predictor set to determine the richness pattern of all species and endemic species, while habitat heterogeneity has a better influence on nonendemic species. Therefore, we conclude that with the current climate fluctuations as a result of climate change and increasing human activities, gymnosperms might face a high risk of extinction.     

The use and misuse of regression models in landscape genetic analyses

The field of landscape genetics has been rapidly evolving, adopting and adapting analytical frameworks to address research questions. Current studies are increasingly using regression‐based frameworks to infer the individual contributions of landscape and habitat variables on genetic differentiation. This paper outlines appropriate and inappropriate uses of multiple regression for these purposes, and demonstrates through simulation the limitations of different analytical frameworks for making correct inference. Of particular concern are recent studies seeking to explain genetic differences by fitting regression models with effective distance variables calculated independently on separate landscape resistance surfaces. When moving across the landscape, organisms cannot respond independently and uniquely to habitat and landscape features. Analyses seeking to understand how landscape features affect gene flow should model a single conductance or resistance surface as a parameterized function of relevant spatial covariates, and estimate the values of these parameters by linking a single set of resistance distances to observed genetic dissimilarity via a loss function. While this loss function may involve a regression‐like step, the associated nuisance parameters are not interpretable in terms of organismal movement and should not be conflated with what is actually of interest: the mapping between spatial covariates and conductance/resistance. The growth and evolution of landscape genetics as a field has been rapid and exciting. It is the goal of this paper to highlight past missteps and demonstrate limitations of current approaches to ensure that future use of regression models will appropriately consider the process being modeled, which will provide clarity to model interpretation.