Public Map Service Platforms(PMSPs)provide embedded map services in domains such as forests and rivers.Users from different domains(Domain Users)prefer specific spatial features,and extracting the Browsing Interests o...Public Map Service Platforms(PMSPs)provide embedded map services in domains such as forests and rivers.Users from different domains(Domain Users)prefer specific spatial features,and extracting the Browsing Interests of Domain Users(BIDUs)can help elucidate users’access intentions and provide suitable recommendations.Previous research has found that access frequency of spatial features is an indicator of users’browsing interests;however,highfrequency spatial features are sparsely distributed,resulting in inaccurate extraction of browsing interests.Our objective is to model the spatial co-occurrence of spatial features and employ BIDUs extraction to address this limitation.First,to extract spatial features in tiles,we proposed a k-nearest neighbor method for Point-of-Interest(POI)extraction and a template-based method for Land Uses/Land Covers extraction.Then,we developed the word2vec model to construct a POI semantic space to quantify spatial co-occurrence and employed multi-domain user classification to verify its effectiveness.Finally,a combined word2vec and singular value decomposition model is proposed to perform topic extraction as a representation of BIDUs.Compared with the baseline models,the proposed model integrates spatial co-occurrence from massive POIs to achieve high-accuracy BIDU extraction.Our findings can help construct domain user profiles and support the development of intelligent PMSPs.展开更多
Understanding the fundamental drivers of large-scale species co-occurrence is a critical issue in ecology and conservation research. Here, we assessed foraging guilds, habitat type and disturbances as drivers of bird ...Understanding the fundamental drivers of large-scale species co-occurrence is a critical issue in ecology and conservation research. Here, we assessed foraging guilds, habitat type and disturbances as drivers of bird species co-occurrence in Ghana's Central Region over six months. Birds were sampled in 120 points across six different habitat types (farmland, forest reserve, urban area, coastal savannah, wetland, and mangrove), using the point-centred count technique. In total, 4060 individuals belonging to 216 species were recorded across all six habitat types. We found that co-occurring species were more similar in their foraging behaviour and habitat association. About 60% of the birds were found to co-occur randomly, 15% co-occurred negatively, and 25% co-occurred positively. Carnivores like the Black Heron (Egretta ardesiaca) and Spur-winged Lapwing (Vanellus spinosus) randomly co-occurred with other guild groups and were dominant in the mangroves and wetlands. Frugivores from forest reserves had only a 25% chance of randomly co-occurring with other birds and about a 60% chance of positively co-occurring with other birds. Our findings suggest that foraging guilds and habitat type are major factors driving bird co-occurrence and community assemblages in this West African suburban region.展开更多
Land use influences soil biota community composition and diversity,and then belowground ecosystem processes and functions.To characterize the effect of land use on soil biota,soil nematode communities in crop land,for...Land use influences soil biota community composition and diversity,and then belowground ecosystem processes and functions.To characterize the effect of land use on soil biota,soil nematode communities in crop land,forest land and fallow land were investigated in six regions of northern China.Generic richness,diversity,abundance and biomass of soil nematodes was the lowest in crop land.The richness and diversity of soil nematodes were 28.8and 15.1%higher in fallow land than in crop land,respectively.No significant differences in soil nematode indices were found between forest land and fallow land,but their network keystone genera composition was different.Among the keystone genera,50%of forest land genera were omnivores-predators and 36%of fallow land genera were bacterivores.The proportion of fungivores in forest land was 20.8%lower than in fallow land.The network complexity and the stability were lower in crop land than forest land and fallow land.Soil pH,NH_(4)^(+)-N and NO_(3)^(–)-N were the major factors influencing the soil nematode community in crop land while soil organic carbon and moisture were the major factors in forest land.Soil nematode communities in crop land influenced by artificial management practices were more dependent on the soil environment than communities in forest land and fallow land.Land use induced soil environment variation and altered network relationships by influencing trophic group proportions among keystone nematode genera.展开更多
目的基于T2^(*)mapping定量分析业余马拉松运动员足踝部关节软骨的T2^(*)值,并分析其与性别、年龄、身体质量指数(body mass index,BMI)、跑龄、跑量之间的相关性。材料与方法于2023年7月份至2023年9月份招募重庆市长跑运动爱好者48名,...目的基于T2^(*)mapping定量分析业余马拉松运动员足踝部关节软骨的T2^(*)值,并分析其与性别、年龄、身体质量指数(body mass index,BMI)、跑龄、跑量之间的相关性。材料与方法于2023年7月份至2023年9月份招募重庆市长跑运动爱好者48名,其中跑量<300 km/月的36例(中低跑量组),跑量≥300 km/月的12例(高跑量组)。所有受试者均进行单侧无症状踝关节的MRI扫描,扫描序列包括T2^(*)mapping多回波自旋回波(spin echo,SE)序列矢状位、质子密度加权成像脂肪抑制(proton density-weighted imaging fat-saturated,PDWI-FS)序列矢状位、冠状位、横轴位以及T1加权脂肪抑制成像(T1-weighted imaging fat-saturated,T1WI-FS)序列横轴位。沿关节软骨轮廓边缘勾画距骨穹窿、跟骰关节跟骨面、骰骨面及后距下关节跟骨面、距骨面软骨作为感兴趣区(region of interest,ROI),获得相应的T2^(*)值。采用线性回归分析软骨T2^(*)值与年龄、BMI、跑龄的相关性,采用独立样本t检验分析不同跑量及不同性别间的软骨T2^(*)值差异。结果(1)距骨穹窿、跟骰关节跟骨面及骰骨面、后距下关节跟骨面及距骨面软骨T2^(*)值在性别上的差异均具有统计学意义(P=0.001、P<0.001、P=0.002、P=0.008、P=0.004);(2)高跑量组的距骨穹窿、后距下关节跟骨面软骨T2^(*)值高于中低跑量组(P=0.014、0.023),不同跑量的跟骰关节跟骨面及骰骨面、后距下关节距骨面软骨T2^(*)值的差异均无统计学意义(P=0.987、0.072、0.724);(3)距骨穹窿、跟骰关节跟骨面及骰骨面、后距下关节跟骨面、距骨面软骨T2^(*)值均与BMI呈正相关(r=0.376、0.384、0.300、0.422、0.455,P=0.005、0.004、0.019、0.001、0.001)。结论在业余马拉松运动员这一跑步群体中,与中低跑量相比,高跑量更有可能导致距骨穹窿、后距下关节跟骨面软骨损伤;而与较低的BMI相比,高BMI增加了距骨穹窿、跟骰关节跟骨面、骰骨面及后距下关节跟骨面、距骨面软骨损伤的风险。展开更多
【目的】探究丝裂原活化蛋白激酶激酶6(mitogen-activated protein kinase kinase6,MAP2K6)基因在湖羊不同发育阶段背最长肌组织中的表达水平,分析该基因的多态性与湖羊生长性状之间的相关性,以期为湖羊的生长性状分子育种提供新的标记...【目的】探究丝裂原活化蛋白激酶激酶6(mitogen-activated protein kinase kinase6,MAP2K6)基因在湖羊不同发育阶段背最长肌组织中的表达水平,分析该基因的多态性与湖羊生长性状之间的相关性,以期为湖羊的生长性状分子育种提供新的标记资源。【方法】利用实时荧光定量PCR检测MAP2K6基因在湖羊(n=15)不同发育阶段背最长肌组织中的表达情况;通过Illumina OvineSNP 50K BeadChip检测湖羊(n=3024)MAP2K6基因的单核苷酸多态性(SNP),利用一般线性模型分析MAP2K6基因SNP位点与湖羊(n=1974)生长性状间的关联性,并使用R语言corrplot包计算湖羊体重与各体尺指标的相关系数。【结果】实时荧光定量PCR检测结果显示,湖羊背最长肌组织中MAP2K6基因表达量在初生到4月龄阶段逐渐升高,且3、4月龄的表达量均极显著高于初生、45日龄和6月龄(P<0.01)。湖羊MAP2K6基因中共检测到2个位点:rs414959578G>A和rs426057803A>G。关联分析结果显示,MAP2K6基因rs414959578G>A位点对湖羊5月龄体重、体高、体斜长、胸围、胸深、胸宽、十字部高、腰角宽,以及6月龄胸围、背膘厚有显著或极显著影响(P<0.05;P<0.01);rs426057803A>G位点对湖羊3月龄管围,5月龄胸围、管围和十字部高以及6月龄背膘厚有显著或极显著影响(P<0.05;P<0.01)。相关性分析结果显示,湖羊体重与体尺指标间存在显著正相关(P<0.05),但6月龄湖羊体斜长与6月龄胸宽、腰角宽,5月龄管围与6月龄腰角宽均不存在显著相关(P>0.05)。【结论】MAP2K6基因与湖羊背最长肌的发育相关,rs414959578G>A和rs426057803A>G位点对湖羊生长性状有显著影响。研究结果可为湖羊生长性状分子标记的挖掘和利用提供一定的理论依据。展开更多
BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the ...BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
This paper highlights the crucial role of Indonesia’s GNSS receiver network in advancing Equatorial Plasma Bubble(EPB)studies in Southeast and East Asia,as ionospheric irregularities within EPB can disrupt GNSS signa...This paper highlights the crucial role of Indonesia’s GNSS receiver network in advancing Equatorial Plasma Bubble(EPB)studies in Southeast and East Asia,as ionospheric irregularities within EPB can disrupt GNSS signals and degrade positioning accuracy.Managed by the Indonesian Geospatial Information Agency(BIG),the Indonesia Continuously Operating Reference Station(Ina-CORS)network comprises over 300 GNSS receivers spanning equatorial to southern low-latitude regions.Ina-CORS is uniquely situated to monitor EPB generation,zonal drift,and dissipation across Southeast Asia.We provide a practical tool for EPB research,by sharing two-dimensional rate of Total Electron Content(TEC)change index(ROTI)derived from this network.We generate ROTI maps with a 10-minute resolution,and samples from May 2024 are publicly available for further scientific research.Two preliminary findings from the ROTI maps of Ina-CORS are noteworthy.First,the Ina-CORS ROTI maps reveal that the irregularities within a broader EPB structure persist longer,increasing the potential for these irregularities to migrate farther eastward.Second,we demonstrate that combined ROTI maps from Ina-CORS and GNSS receivers in East Asia and Australia can be used to monitor the development of ionospheric irregularities in Southeast and East Asia.We have demonstrated the combined ROTI maps to capture the development of ionospheric irregularities in the Southeast/East Asian sector during the G5 Geomagnetic Storm on May 11,2024.We observed simultaneous ionospheric irregularities in Japan and Australia,respectively propagating northwestward and southwestward,before midnight,whereas Southeast Asia’s equatorial and low-latitude regions exhibited irregularities post-midnight.By sharing ROTI maps from Indonesia and integrating them with regional GNSS networks,researchers can conduct comprehensive EPB studies,enhancing the understanding of EPB behavior across Southeast and East Asia and contributing significantly to ionospheric research.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers:U20A209141771426]Zhizhuo Research Fund on Spatial-Temporal Artificial Intelligence[grant number ZZJJ202204]LIESMARS Special Research Funding.
文摘Public Map Service Platforms(PMSPs)provide embedded map services in domains such as forests and rivers.Users from different domains(Domain Users)prefer specific spatial features,and extracting the Browsing Interests of Domain Users(BIDUs)can help elucidate users’access intentions and provide suitable recommendations.Previous research has found that access frequency of spatial features is an indicator of users’browsing interests;however,highfrequency spatial features are sparsely distributed,resulting in inaccurate extraction of browsing interests.Our objective is to model the spatial co-occurrence of spatial features and employ BIDUs extraction to address this limitation.First,to extract spatial features in tiles,we proposed a k-nearest neighbor method for Point-of-Interest(POI)extraction and a template-based method for Land Uses/Land Covers extraction.Then,we developed the word2vec model to construct a POI semantic space to quantify spatial co-occurrence and employed multi-domain user classification to verify its effectiveness.Finally,a combined word2vec and singular value decomposition model is proposed to perform topic extraction as a representation of BIDUs.Compared with the baseline models,the proposed model integrates spatial co-occurrence from massive POIs to achieve high-accuracy BIDU extraction.Our findings can help construct domain user profiles and support the development of intelligent PMSPs.
文摘Understanding the fundamental drivers of large-scale species co-occurrence is a critical issue in ecology and conservation research. Here, we assessed foraging guilds, habitat type and disturbances as drivers of bird species co-occurrence in Ghana's Central Region over six months. Birds were sampled in 120 points across six different habitat types (farmland, forest reserve, urban area, coastal savannah, wetland, and mangrove), using the point-centred count technique. In total, 4060 individuals belonging to 216 species were recorded across all six habitat types. We found that co-occurring species were more similar in their foraging behaviour and habitat association. About 60% of the birds were found to co-occur randomly, 15% co-occurred negatively, and 25% co-occurred positively. Carnivores like the Black Heron (Egretta ardesiaca) and Spur-winged Lapwing (Vanellus spinosus) randomly co-occurred with other guild groups and were dominant in the mangroves and wetlands. Frugivores from forest reserves had only a 25% chance of randomly co-occurring with other birds and about a 60% chance of positively co-occurring with other birds. Our findings suggest that foraging guilds and habitat type are major factors driving bird co-occurrence and community assemblages in this West African suburban region.
基金supported by the National Natural Science Foundation of China(U22A20501)the National Key Research and Development Plan of China(2022YFD1500601)+4 种基金the National Science and Technology Fundamental Resources Investigation Program of China(2018FY100304)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28090200)the Liaoning Province Applied Basic Research Plan Program,China(2022JH2/101300184)the Shenyang Science and Technology Plan Program,China(21-109-305)the Liaoning Outstanding Innovation Team,China(XLYC2008015)。
文摘Land use influences soil biota community composition and diversity,and then belowground ecosystem processes and functions.To characterize the effect of land use on soil biota,soil nematode communities in crop land,forest land and fallow land were investigated in six regions of northern China.Generic richness,diversity,abundance and biomass of soil nematodes was the lowest in crop land.The richness and diversity of soil nematodes were 28.8and 15.1%higher in fallow land than in crop land,respectively.No significant differences in soil nematode indices were found between forest land and fallow land,but their network keystone genera composition was different.Among the keystone genera,50%of forest land genera were omnivores-predators and 36%of fallow land genera were bacterivores.The proportion of fungivores in forest land was 20.8%lower than in fallow land.The network complexity and the stability were lower in crop land than forest land and fallow land.Soil pH,NH_(4)^(+)-N and NO_(3)^(–)-N were the major factors influencing the soil nematode community in crop land while soil organic carbon and moisture were the major factors in forest land.Soil nematode communities in crop land influenced by artificial management practices were more dependent on the soil environment than communities in forest land and fallow land.Land use induced soil environment variation and altered network relationships by influencing trophic group proportions among keystone nematode genera.
文摘BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金JSPS KAKENHI Grant Number16H06286 supports global GNSS ionospheric maps (TEC,ROTI,and detrended TEC maps) developed by the Institute for SpaceEarth Environmental Research (ISEE) of Nagoya Universitysupport of the 2024 JASSO Follow-up Research Fellowship Program for a 90-day visiting research at the Institute for Space-Earth Environmental Research (ISEE),Nagoya University+3 种基金the support received from Telkom University under the“Skema Penelitian Terapan Periode I Tahun Anggaran 2024”the Memorandum of Understanding for Research Collaboration on Regional Ionospheric Observation (No:092/SAM3/TE-DEK/2021)the National Institute of Information and Communications Technology (NICT) International Exchange Program 2024-2025(No.2024-007)support for a one-year visiting research at Hokkaido University
文摘This paper highlights the crucial role of Indonesia’s GNSS receiver network in advancing Equatorial Plasma Bubble(EPB)studies in Southeast and East Asia,as ionospheric irregularities within EPB can disrupt GNSS signals and degrade positioning accuracy.Managed by the Indonesian Geospatial Information Agency(BIG),the Indonesia Continuously Operating Reference Station(Ina-CORS)network comprises over 300 GNSS receivers spanning equatorial to southern low-latitude regions.Ina-CORS is uniquely situated to monitor EPB generation,zonal drift,and dissipation across Southeast Asia.We provide a practical tool for EPB research,by sharing two-dimensional rate of Total Electron Content(TEC)change index(ROTI)derived from this network.We generate ROTI maps with a 10-minute resolution,and samples from May 2024 are publicly available for further scientific research.Two preliminary findings from the ROTI maps of Ina-CORS are noteworthy.First,the Ina-CORS ROTI maps reveal that the irregularities within a broader EPB structure persist longer,increasing the potential for these irregularities to migrate farther eastward.Second,we demonstrate that combined ROTI maps from Ina-CORS and GNSS receivers in East Asia and Australia can be used to monitor the development of ionospheric irregularities in Southeast and East Asia.We have demonstrated the combined ROTI maps to capture the development of ionospheric irregularities in the Southeast/East Asian sector during the G5 Geomagnetic Storm on May 11,2024.We observed simultaneous ionospheric irregularities in Japan and Australia,respectively propagating northwestward and southwestward,before midnight,whereas Southeast Asia’s equatorial and low-latitude regions exhibited irregularities post-midnight.By sharing ROTI maps from Indonesia and integrating them with regional GNSS networks,researchers can conduct comprehensive EPB studies,enhancing the understanding of EPB behavior across Southeast and East Asia and contributing significantly to ionospheric research.
