[Objectives] This study was conducted to further investigate the taxonomic status of Hainan population of Trimeresurus stejnegeri from the morphological point of view.[Methods]The difference coefficients between diffe...[Objectives] This study was conducted to further investigate the taxonomic status of Hainan population of Trimeresurus stejnegeri from the morphological point of view.[Methods]The difference coefficients between different populations were compared using the 75% law,and the relationship between scales and latitudes was analyzed.[Results] The scales(abdominal and subcaudal) of 325 Trimeresurus individuals were counted according to China Animal Fauna,including156 T.stejnegeri individuals.Some difference coefficients between the Hainan population and others were greater than 1.28,and there was no correlation between the number of scales and latitude.It conforms to subclassification criteria.[Conclusions]The view about the subspecies status of T.stejnegeri chenbihuii should be supported.展开更多
人群分布不均、遮挡和背景干扰等问题使得人群计数成为了一项复杂且具有挑战性的任务。针对这些问题,提出了一种多尺度特征融合的位置关注网络(Position-Aware Network based on Multi-Scale Feature Fusion,MSFPANet)。首先,设计了一...人群分布不均、遮挡和背景干扰等问题使得人群计数成为了一项复杂且具有挑战性的任务。针对这些问题,提出了一种多尺度特征融合的位置关注网络(Position-Aware Network based on Multi-Scale Feature Fusion,MSFPANet)。首先,设计了一种多尺度特征融合模块,以在不同感受野下提取并融合人群密度图的多尺度特征,同时提取出前景信息,来应对人群计数中的遮挡和背景干扰问题;然后,通过位置注意力分配网络提高模型对人群区域的关注度,有效地应对人群分布不均的问题;最后,为了辅助模型训练,减小背景噪声带来的干扰,引入了一种结构交叉损失用于强化模型对人群结构的学习。实验结果表明:MSF-PANet在Shanghai Tech Part A、Shanghai Tech Part B、UCF-QNRF和UCF_CC_50上平均绝对误差分别为59.5、7.8、103、182.7,均方误差分别为96.7、13.6、177、237.7,验证了所提模块在提高人群计数准确率上的有效性。展开更多
目的分析血小板相关指标与急性缺血性卒中后抑郁(post-stroke depression,PSD)的关系。方法选取2021年9月至2022年5月在华北理工大学附属唐山市工人医院治疗的急性缺血性卒中病人235例,记录其一般临床资料,根据17项汉密顿抑郁量表(Hamil...目的分析血小板相关指标与急性缺血性卒中后抑郁(post-stroke depression,PSD)的关系。方法选取2021年9月至2022年5月在华北理工大学附属唐山市工人医院治疗的急性缺血性卒中病人235例,记录其一般临床资料,根据17项汉密顿抑郁量表(Hamilton depression rating scale,17 item,HAMD-17)评分将病人分为PSD组和非PSD组;测定各组病人血小板相关指标(血小板计数、血小板平均体积、血小板比容、血小板分布宽度);使用美国国立卫生院卒中量表(national institute of health stroke scale,NIHSS)、日常生活活动能力量表(activity of daily living,ADL)分别评估病人神经功能缺损情况和日常生活能力;采用多因素二元logistic回归模型分析与PSD相关的独立危险因素。结果研究共纳入缺血性脑卒中病人235例,PSD组病人85例,非PSD组病人150例。PSD组身体质量指数、NIHSS评分高于非PSD组,PSD组ADL评分低于非PSD组(P<0.05)。两组血小板计数和平均血小板体积分级分布上均差异有统计学差意义(P<0.05);PSD组血小板计数≤183×10^(9)/L、(>183~<257)×10^(9)/L、≥257×10^(9)/L分别有11例(12.9%)、47例(55.3%)、27例(31.8%),非PSD组分别有47例(31.3%)、72例(48.0%)、31例(20.7%)。多因素二元logistic回归分析结果显示,较高水平血小板计数、身体质量指数及NIHSS评分是PSD的独立危险因素(P<0.05)。结论较高水平的血小板计数是PSD发生的独立危险因素。展开更多
Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural div...Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural diversity of forests in an objective and quantitative way across many forest types and sites are still needed, for example to support biodiversity monitoring. The existing approaches to quantify forest structural diversity are based on small geographical regions or single forest types, typically using only small data sets.Results: Here we developed an index of structural diversity based on National Forest Inventory(NFI) data of BadenWurttemberg, Germany, a state with 1.3 million ha of diverse forest types in different ownerships. Based on a literature review, 11 aspects of structural diversity were identified a priori as crucially important to describe structural diversity. An initial comprehensive list of 52 variables derived from National Forest Inventory(NFI) data related to structural diversity was reduced by applying five selection criteria to arrive at one variable for each aspect of structural diversity. These variables comprise 1) quadratic mean diameter at breast height(DBH), 2) standard deviation of DBH, 3) standard deviation of stand height, 4) number of decay classes, 5) bark-diversity index, 6) trees with DBH ≥ 40 cm, 7) diversity of flowering and fructification, 8) average mean diameter of downed deadwood, 9) mean DBH of standing deadwood, 10) tree species richness and 11) tree species richness in the regeneration layer. These variables were combined into a simple,additive index to quantify the level of structural diversity, which assumes values between 0 and 1. We applied this index in an exemplary way to broad forest categories and ownerships to assess its feasibility to analyse structural diversity in large-scale forest inventories.Conclusions: The forest structure index presented here can be derived in a similar way from standard inventory variables for most other large-scale forest inventories to provide important information about biodiversity relevant forest conditions and thus provide an evidence-base for forest management and planning as well as reporting.展开更多
基金Supported by Scientific Research Project of Colleges and Universities in Hainan Province(Hnky2017-47)Sanya Special Scientific Research Pilot Project(2016KS05)+2 种基金Provincial Key(Supporting)Discipline for Ordinary Colleges and Universities in Hainan Province:Zoology2018 Special Fund for the Development of Institutions of Higher Learning(phaseⅠ)Provincial Characteristic Discipline:Marine Science。
文摘[Objectives] This study was conducted to further investigate the taxonomic status of Hainan population of Trimeresurus stejnegeri from the morphological point of view.[Methods]The difference coefficients between different populations were compared using the 75% law,and the relationship between scales and latitudes was analyzed.[Results] The scales(abdominal and subcaudal) of 325 Trimeresurus individuals were counted according to China Animal Fauna,including156 T.stejnegeri individuals.Some difference coefficients between the Hainan population and others were greater than 1.28,and there was no correlation between the number of scales and latitude.It conforms to subclassification criteria.[Conclusions]The view about the subspecies status of T.stejnegeri chenbihuii should be supported.
文摘人群分布不均、遮挡和背景干扰等问题使得人群计数成为了一项复杂且具有挑战性的任务。针对这些问题,提出了一种多尺度特征融合的位置关注网络(Position-Aware Network based on Multi-Scale Feature Fusion,MSFPANet)。首先,设计了一种多尺度特征融合模块,以在不同感受野下提取并融合人群密度图的多尺度特征,同时提取出前景信息,来应对人群计数中的遮挡和背景干扰问题;然后,通过位置注意力分配网络提高模型对人群区域的关注度,有效地应对人群分布不均的问题;最后,为了辅助模型训练,减小背景噪声带来的干扰,引入了一种结构交叉损失用于强化模型对人群结构的学习。实验结果表明:MSF-PANet在Shanghai Tech Part A、Shanghai Tech Part B、UCF-QNRF和UCF_CC_50上平均绝对误差分别为59.5、7.8、103、182.7,均方误差分别为96.7、13.6、177、237.7,验证了所提模块在提高人群计数准确率上的有效性。
文摘目的分析血小板相关指标与急性缺血性卒中后抑郁(post-stroke depression,PSD)的关系。方法选取2021年9月至2022年5月在华北理工大学附属唐山市工人医院治疗的急性缺血性卒中病人235例,记录其一般临床资料,根据17项汉密顿抑郁量表(Hamilton depression rating scale,17 item,HAMD-17)评分将病人分为PSD组和非PSD组;测定各组病人血小板相关指标(血小板计数、血小板平均体积、血小板比容、血小板分布宽度);使用美国国立卫生院卒中量表(national institute of health stroke scale,NIHSS)、日常生活活动能力量表(activity of daily living,ADL)分别评估病人神经功能缺损情况和日常生活能力;采用多因素二元logistic回归模型分析与PSD相关的独立危险因素。结果研究共纳入缺血性脑卒中病人235例,PSD组病人85例,非PSD组病人150例。PSD组身体质量指数、NIHSS评分高于非PSD组,PSD组ADL评分低于非PSD组(P<0.05)。两组血小板计数和平均血小板体积分级分布上均差异有统计学差意义(P<0.05);PSD组血小板计数≤183×10^(9)/L、(>183~<257)×10^(9)/L、≥257×10^(9)/L分别有11例(12.9%)、47例(55.3%)、27例(31.8%),非PSD组分别有47例(31.3%)、72例(48.0%)、31例(20.7%)。多因素二元logistic回归分析结果显示,较高水平血小板计数、身体质量指数及NIHSS评分是PSD的独立危险因素(P<0.05)。结论较高水平的血小板计数是PSD发生的独立危险因素。
基金supported by a grant from the Ministry of Science,Research and the Arts of Baden-Württemberg(7533-10-5-78)to Jürgen BauhusFelix Storch received additional support through the BBW ForWerts Graduate Program
文摘Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural diversity of forests in an objective and quantitative way across many forest types and sites are still needed, for example to support biodiversity monitoring. The existing approaches to quantify forest structural diversity are based on small geographical regions or single forest types, typically using only small data sets.Results: Here we developed an index of structural diversity based on National Forest Inventory(NFI) data of BadenWurttemberg, Germany, a state with 1.3 million ha of diverse forest types in different ownerships. Based on a literature review, 11 aspects of structural diversity were identified a priori as crucially important to describe structural diversity. An initial comprehensive list of 52 variables derived from National Forest Inventory(NFI) data related to structural diversity was reduced by applying five selection criteria to arrive at one variable for each aspect of structural diversity. These variables comprise 1) quadratic mean diameter at breast height(DBH), 2) standard deviation of DBH, 3) standard deviation of stand height, 4) number of decay classes, 5) bark-diversity index, 6) trees with DBH ≥ 40 cm, 7) diversity of flowering and fructification, 8) average mean diameter of downed deadwood, 9) mean DBH of standing deadwood, 10) tree species richness and 11) tree species richness in the regeneration layer. These variables were combined into a simple,additive index to quantify the level of structural diversity, which assumes values between 0 and 1. We applied this index in an exemplary way to broad forest categories and ownerships to assess its feasibility to analyse structural diversity in large-scale forest inventories.Conclusions: The forest structure index presented here can be derived in a similar way from standard inventory variables for most other large-scale forest inventories to provide important information about biodiversity relevant forest conditions and thus provide an evidence-base for forest management and planning as well as reporting.
