In order to accelerate the research on the property optimization of titanium alloy based on high-throughput methods,it is necessary to reveal the relationship between hardness and other mechanical properties which is ...In order to accelerate the research on the property optimization of titanium alloy based on high-throughput methods,it is necessary to reveal the relationship between hardness and other mechanical properties which is still unclear.In this work,taking Ti20C alloy as research object,almost all the microstructure of dual-phase titanium alloys were covered by traversing over 100 heat treatment schemes.Then,massive experiments including microstructure characterization and performance test were conducted,obtaining 51,590 pieces of microstructure data and 3591 pieces of mechanical property data.Subsequently,based on large-scale data-driven technology,the quantitative mapping relationship between hardness and other mechanical properties was deeply discussed.The results of random forest models showed that the correlation between hardness(H)and Charpy impact energy(A_(k))(or elongation,A)was hardly dependent on the microstructure types,while the relationship between H and tensile strength(R_(m))(or yield strength,R_(p0.2))was highly dependent on microstructure types.Specifically,combined with statistical analysis,it was found that the relationship between H and Ak(or A)were negatively linear.Interestingly,the relationship between H and strength was positively linear for equiaxed microstructure,and strength was linked to d^(−1/2)(d,equivalent circle diameter)ofα-grains in the form of classical Hall–Petch formula;but for other microstructures,the relationships were quadratic.Furthermore,the above rules were nearly the same in the rolling direction and transverse direction.Finally,a"four-quadrant partition map"between H and R_(p0.2)/R_(m) was established as a versatile material-screening tool,which can provide guidance for on-demand selection of titanium alloys.展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
The liver performs several vital functions such as metabolism,toxin removal,and glucose storage through the coordination of various cell types.With the recent breakthrough of the single-cell/single-nucleus RNAseq(sc/s...The liver performs several vital functions such as metabolism,toxin removal,and glucose storage through the coordination of various cell types.With the recent breakthrough of the single-cell/single-nucleus RNAseq(sc/snRNA-seq)techniques,there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features.In this study,we build a unified liver cell atlas uniLIVER(http://lifeome.net/database/uniliver)by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets.Moreover,we introduce LiverCT,a machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of“variant”cellular states analogous to the sequence variants in genomic analysis.Applying LiverCT on liver cancer datasets,we find that the“deviated”states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma,and the enrichments of tumor cells with the hepatocyte-cholangiocyte“intermediate”states significantly indicate poor prognosis.Besides,we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis.This reference atlas mapping framework can also be extended to any other tissues.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
目的探讨心脏磁共振组织追踪(cardiac magnetic resonance tissue tracing,CMR-TT)技术及T1 mapping技术在2型糖尿病(type 2 diabetes mellitus,T2DM)患者心肌损伤评估中的应用价值。材料与方法前瞻性收集2023年12月至2025年4月在我院...目的探讨心脏磁共振组织追踪(cardiac magnetic resonance tissue tracing,CMR-TT)技术及T1 mapping技术在2型糖尿病(type 2 diabetes mellitus,T2DM)患者心肌损伤评估中的应用价值。材料与方法前瞻性收集2023年12月至2025年4月在我院进行心脏磁共振检查的T2DM患者64例,健康对照(healthy controls,HC)32例。所有心脏磁共振图像数据导入专用软件进行分析,获取全心心肌应变参数、双心室功能参数以及左心室T1 mapping参数,采用t检验,Mann-Whitney U检验及卡方检验对两组间上述参数进行比较,采用Pearson及Spearman相关性分析心肌结构、功能与心肌应变的关联。结果T2DM组左心室心肌质量指数(left ventricular myocardial mass index,LVMI)、左心室重塑指数(left ventricular remodeling index,LVRI)增加(均P<0.001),左心室全局纵向应变(global longitudinal peak strain in the left ventricle,LV GLS)、右心室全局纵向应变降低(均P<0.05),T2DM组周向左心室收缩期峰值应变率(peak systolic strain rate of the left ventricle,LV PSSR)、纵向LV PSSR及左心室舒张期峰值应变率(diastolic peak strain rate of the left ventricle,LV PDSR)绝对值均降低(均P<0.019)。T2DM患者左心房/右心房(leftatrium/rightatrium,LA/RA)储存应变、LA/RA导管应变均降低(均P<0.001)。T2DM患者的细胞外容积(extracellular volume,ECV)较HC组升高(P<0.001)。双心室射血分数、收缩末期容积指数与双心室应变功能均相关(均P<0.003)。LVMI与左心室全局径向应变(global radial strain of the left ventricle,LV GRS)、左心室全局周向应变(global circumferential strain of the left ventricle,LV GCS)、LV GLS、周向LV PSSR、纵向LV PSSR、径向LV PDSR、周向LV PDSR、纵向LV PDSR相关(均P<0.021)。左心室舒张末期容积指数与LV GCS、LV GLS、周向LV PSSR、纵向LV PSSR、周向LV PDSR相关(均P<0.044)。右心室舒张末期容积指数与右心室全局周向应变相关(r=0.331,P=0.007)。LVRI与LV GLS及纵向LV PDSR相关(均P<0.01),且与径向LV PSSR弱相关(r=0.266,P=0.034)。结论T2DM患者全心心肌应变较对照组降低,ECV值升高,双心室心肌结构、功能与心肌应变相互关联,CMR-TT及T1 mapping技术可以有效检测糖尿病心肌损伤。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
Powdery mildew negatively impacts wheat yield and quality.Emmer wheat(Triticum dicoccum),an ancestral species of common wheat,is a gene donor for wheat improvement.Cultivated emmer accession H1-707 exhibited all-stage...Powdery mildew negatively impacts wheat yield and quality.Emmer wheat(Triticum dicoccum),an ancestral species of common wheat,is a gene donor for wheat improvement.Cultivated emmer accession H1-707 exhibited all-stage resistance to powdery mildew over consecutive years.Genetic analysis of H1-707 at the seedling stage revealed a dominant monogenic inheritance pattern,and the underlying gene was designated Pm71.By employing bulked segregant exome sequencing(BSE-Seq)and using 2000 F2:3 families,Pm71 was fine mapped to a 336-kb interval on chromosome arm 6AS by referencing to the durum cv.Svevo RefSeq 1.0.Collinearity analysis revealed high homology in the candidate interval between Svevo and six Triticum species.Among six high-confidence genes annotated within this interval,TRITD6Av1G005050 encoding a GDSL esterase/lipase was identified as a key candidate for Pm71.展开更多
基金This work was financially supported by the National Natural Science Foundation of China(Nos.51901102 and 52101005).
文摘In order to accelerate the research on the property optimization of titanium alloy based on high-throughput methods,it is necessary to reveal the relationship between hardness and other mechanical properties which is still unclear.In this work,taking Ti20C alloy as research object,almost all the microstructure of dual-phase titanium alloys were covered by traversing over 100 heat treatment schemes.Then,massive experiments including microstructure characterization and performance test were conducted,obtaining 51,590 pieces of microstructure data and 3591 pieces of mechanical property data.Subsequently,based on large-scale data-driven technology,the quantitative mapping relationship between hardness and other mechanical properties was deeply discussed.The results of random forest models showed that the correlation between hardness(H)and Charpy impact energy(A_(k))(or elongation,A)was hardly dependent on the microstructure types,while the relationship between H and tensile strength(R_(m))(or yield strength,R_(p0.2))was highly dependent on microstructure types.Specifically,combined with statistical analysis,it was found that the relationship between H and Ak(or A)were negatively linear.Interestingly,the relationship between H and strength was positively linear for equiaxed microstructure,and strength was linked to d^(−1/2)(d,equivalent circle diameter)ofα-grains in the form of classical Hall–Petch formula;but for other microstructures,the relationships were quadratic.Furthermore,the above rules were nearly the same in the rolling direction and transverse direction.Finally,a"four-quadrant partition map"between H and R_(p0.2)/R_(m) was established as a versatile material-screening tool,which can provide guidance for on-demand selection of titanium alloys.
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
基金funded by the National Key Research and Development Program of China(No.2021YFF1200901)the National Natural Science Foundation of China(Nos.61721003,62133006,and 92268104)。
文摘The liver performs several vital functions such as metabolism,toxin removal,and glucose storage through the coordination of various cell types.With the recent breakthrough of the single-cell/single-nucleus RNAseq(sc/snRNA-seq)techniques,there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features.In this study,we build a unified liver cell atlas uniLIVER(http://lifeome.net/database/uniliver)by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets.Moreover,we introduce LiverCT,a machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of“variant”cellular states analogous to the sequence variants in genomic analysis.Applying LiverCT on liver cancer datasets,we find that the“deviated”states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma,and the enrichments of tumor cells with the hepatocyte-cholangiocyte“intermediate”states significantly indicate poor prognosis.Besides,we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis.This reference atlas mapping framework can also be extended to any other tissues.
文摘目的探讨心脏磁共振组织追踪(cardiac magnetic resonance tissue tracing,CMR-TT)技术及T1 mapping技术在2型糖尿病(type 2 diabetes mellitus,T2DM)患者心肌损伤评估中的应用价值。材料与方法前瞻性收集2023年12月至2025年4月在我院进行心脏磁共振检查的T2DM患者64例,健康对照(healthy controls,HC)32例。所有心脏磁共振图像数据导入专用软件进行分析,获取全心心肌应变参数、双心室功能参数以及左心室T1 mapping参数,采用t检验,Mann-Whitney U检验及卡方检验对两组间上述参数进行比较,采用Pearson及Spearman相关性分析心肌结构、功能与心肌应变的关联。结果T2DM组左心室心肌质量指数(left ventricular myocardial mass index,LVMI)、左心室重塑指数(left ventricular remodeling index,LVRI)增加(均P<0.001),左心室全局纵向应变(global longitudinal peak strain in the left ventricle,LV GLS)、右心室全局纵向应变降低(均P<0.05),T2DM组周向左心室收缩期峰值应变率(peak systolic strain rate of the left ventricle,LV PSSR)、纵向LV PSSR及左心室舒张期峰值应变率(diastolic peak strain rate of the left ventricle,LV PDSR)绝对值均降低(均P<0.019)。T2DM患者左心房/右心房(leftatrium/rightatrium,LA/RA)储存应变、LA/RA导管应变均降低(均P<0.001)。T2DM患者的细胞外容积(extracellular volume,ECV)较HC组升高(P<0.001)。双心室射血分数、收缩末期容积指数与双心室应变功能均相关(均P<0.003)。LVMI与左心室全局径向应变(global radial strain of the left ventricle,LV GRS)、左心室全局周向应变(global circumferential strain of the left ventricle,LV GCS)、LV GLS、周向LV PSSR、纵向LV PSSR、径向LV PDSR、周向LV PDSR、纵向LV PDSR相关(均P<0.021)。左心室舒张末期容积指数与LV GCS、LV GLS、周向LV PSSR、纵向LV PSSR、周向LV PDSR相关(均P<0.044)。右心室舒张末期容积指数与右心室全局周向应变相关(r=0.331,P=0.007)。LVRI与LV GLS及纵向LV PDSR相关(均P<0.01),且与径向LV PSSR弱相关(r=0.266,P=0.034)。结论T2DM患者全心心肌应变较对照组降低,ECV值升高,双心室心肌结构、功能与心肌应变相互关联,CMR-TT及T1 mapping技术可以有效检测糖尿病心肌损伤。
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financially supported by National Natural Science Foundation of China(32301800,32301923 and 32072053)Wheat Industrial Technology System of Shandong Province(SDAIT-01-01)Key Research and Development Project of Shandong Province(2022LZG002-4,2023LZGC009-4-4).
文摘Powdery mildew negatively impacts wheat yield and quality.Emmer wheat(Triticum dicoccum),an ancestral species of common wheat,is a gene donor for wheat improvement.Cultivated emmer accession H1-707 exhibited all-stage resistance to powdery mildew over consecutive years.Genetic analysis of H1-707 at the seedling stage revealed a dominant monogenic inheritance pattern,and the underlying gene was designated Pm71.By employing bulked segregant exome sequencing(BSE-Seq)and using 2000 F2:3 families,Pm71 was fine mapped to a 336-kb interval on chromosome arm 6AS by referencing to the durum cv.Svevo RefSeq 1.0.Collinearity analysis revealed high homology in the candidate interval between Svevo and six Triticum species.Among six high-confidence genes annotated within this interval,TRITD6Av1G005050 encoding a GDSL esterase/lipase was identified as a key candidate for Pm71.
