cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19626088.v1
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 7
DOI: 10.6084/m9.figshare.19626091
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 3
DOI: 10.6084/m9.figshare.19626091.v1
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 3
DOI: 10.6084/m9.figshare.19626094
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 6
DOI: 10.6084/m9.figshare.19626094.v1
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 6
DOI: 10.6084/m9.figshare.19626097
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 11
DOI: 10.6084/m9.figshare.19626097.v1
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 11
DOI: 10.6084/m9.figshare.19626100
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 2
DOI: 10.6084/m9.figshare.19626100.v1
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 2
DOI: 10.6084/m9.figshare.19626103
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 8
DOI: 10.6084/m9.figshare.19626103.v1
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 8
DOI: 10.6084/m9.figshare.19626106
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 4
DOI: 10.6084/m9.figshare.19626106.v1
title: Analysis of geochemical patterns in a soil profile over mineralized bedrock

description: Supplementary material 4
DOI: 10.6084/m9.figshare.23290456
title: Additional file 2 of Granzyme B + CD8 + T cells with terminal differentiated effector signature determine multiple sclerosis progression

description: Additional file 2. Table S1.
DOI: 10.6084/m9.figshare.23290535
title: Additional file 2 of A homozygous missense mutation in the fibroblast growth factor 5 gene is associated with the long-hair trait in Angora rabbits

description: Additional file 2. Table S1. KEGG enrichment analysis of the 174 identified candidate genes.
DOI: 10.6084/m9.figshare.23290456.v1
title: Additional file 2 of Granzyme B + CD8 + T cells with terminal differentiated effector signature determine multiple sclerosis progression

description: Additional file 2. Table S1.
DOI: 10.6084/m9.figshare.23290535.v1
title: Additional file 2 of A homozygous missense mutation in the fibroblast growth factor 5 gene is associated with the long-hair trait in Angora rabbits

description: Additional file 2. Table S1. KEGG enrichment analysis of the 174 identified candidate genes.
DOI: 10.6084/m9.figshare.23290676.v1
title: LncRNA AK001796 promotes cell proliferation via acting as a ceRNA of miR-150 in hepatocellular carcinoma

description: Abstract Long non-coding RNA AK001796 was initially identified altered in lung cancer. Recent research showed it could participate in the prognosis of hepatocellular carcinoma (HCC). However, the general biological role of AK001796 and its underlying mechanisms in HCC remain unclear. Here we demonstrated that the expression level of AK001796 in HCC tissues and cell lines was up-regulated. Silencing AK001796 suppressed the proliferation ability of HCC cells. Through dual luciferase reporter assays and loss/gain of functions studies, we identified that AK001796 could bind to miR-150, a star microRNA, promoting HCC proliferation. Furthermore, it was reported that growth factor receptor binding protein 2-associated binder 1 (GAB1) is a target gene of miR-150. Owing to AK001796 being a decoy for miR-150 and binding the same putative sites of miR-150 as GAB1, we presented that inhibition of miR-150 in AK001796 silencing cells reversed the reduction in GAB1. Subsequently, our findings demonstrated that silencing AK001796 can impair phospho-ERK1/2 and phospho-AKT. In conclusion, our investigation revealed that AK001796 promoted proliferation by enhancing phospho-ERK1/2 and phospho-AKT through AK001796/miR-150/GAB1 axis in HCC. These results provided further evidence for the critical roles of AK001796 accumulating HCC and suggested that AK001796 might act as an HCC biomarker in clinical treatment.
DOI: 10.6084/m9.figshare.23290799.v1
title: Prediction of health disorders in dairy cows monitored with collar based on Binary logistic analysis

description: ABSTRACT The objective of this study was to analyze data on physical activity and rumination time monitored via collars at the farm coupled with milk yield recorded by the rotary milking system to predict cows based on several disorders using the binary Logistic regression conducted with R software. Data for metritis (n=60), mastitis (n=98), lameness (n=35), and digestive disorders (n=52) were collected from 1,618 healthy cows used to construct the prediction model. To verify the feasibility and adaptability of the proposed method, we analyzed data of cows in the same herd (herd 1) not used to construct the model, and cows in another herd (herd 2) with data recorded by the same type of automated system, and led to detection of 75.0%, 64.2%, 74.2%, and 76.9% animals in herd 1 correctly predicted to suffer from metritis, mastitis, lameness, and digestive disorders, respectively. For cows in herd 2, 66.6%, 58.8%, 80.7%, and 71.4% were correctly predicted for metritis, mastitis, lameness, and digestive disorders, respectively. Compared with traditional clinical diagnoses by farm personnel, the algorithm developed allowed for earlier prediction of cows with a disorder.
DOI: 10.6084/m9.figshare.23290538
title: Additional file 3 of A homozygous missense mutation in the fibroblast growth factor 5 gene is associated with the long-hair trait in Angora rabbits

description: Additional file 3:Table S2. Exonic SNPs of Dusp1, Ihh, Fam134a, Map3k1 and Spata16.
DOI: 10.6084/m9.figshare.7562561
title: The characteristics of blood glucose fluctuations in patients with fulminant type 1 diabetes mellitus in the stable stage

description: ABSTRACT Objective: The aim was to characterize blood glucose fluctuations in patients with fulminant type 1 diabetes (FT1DM) at the stable stage using continuous blood glucose monitoring systems (CGMSs). Subjects and methods: Ten patients with FT1DM and 20 patients with classic type 1 diabetes mellitus (T1DM) (the control group) were monitored using CGMSs for 72 hours. Results: The CGMS data showed that the mean blood glucose (MBG), the standard deviation of the blood glucose (SDBG), the mean amplitude glycemic excursions (MAGE), the blood glucose areas and the percentages of blood glucose levels below 13.9 mmol/L were similar between the two groups. However, the percentage of blood glucose levels below 3.9 mmol/L was significantly higher in the FT1DM group compared to the T1DM group (p &lt; 0.05). The minimum (Min) blood glucose level in the FT1DM group was significantly lower than that of the T1DM group (p &lt; 0.05). Patients with FT1DM had severe dysfunction of the islet beta cells and alpha cells compared to patients with T1DM, as indicated by lower C-peptide values and higher glucagon/C-peptide values. Conclusion: In conclusion, patients with FT1DM at the stable stage were more prone to hypoglycemic episodes as recorded by CGMSs, and they had a greater association with severe dysfunction of both the beta and alpha islet cells compared to patients with T1DM.
DOI: 10.6084/m9.figshare.7037687
title: Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, Using Sparse Linear Programming

description: We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such subpopulations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is computationally infeasible to solve using existing techniques. Our solution involves a novel approach, in which we first transform the original multiple testing problem into a large, sparse linear program. We then solve this problem using advanced optimization techniques. This general method can solve a variety of multiple testing problems and decision theory problems related to optimal trial design, for which no solution was previously available. In particular, we construct new multiple testing procedures that satisfy minimax and Bayes optimality criteria. For a given optimality criterion, our new approach yields the optimal tradeoff between power to detect an effect in the overall population versus power to detect effects in subpopulations. We demonstrate our approach in examples motivated by two randomized trials of new treatments for HIV. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.23290538.v1
title: Additional file 3 of A homozygous missense mutation in the fibroblast growth factor 5 gene is associated with the long-hair trait in Angora rabbits

description: Additional file 3:Table S2. Exonic SNPs of Dusp1, Ihh, Fam134a, Map3k1 and Spata16.
DOI: 10.6084/m9.figshare.14378577
title: miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data

description: In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, <i>miRSM</i>, for facilitating the procedure of inferring and analysing miRNA sponge modules. <i>miRSM</i> provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. <i>miRSM</i> will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.
DOI: 10.6084/m9.figshare.19092640.v1
title: Additional file 3 of LINC01119 negatively regulates osteogenic differentiation of mesenchymal stem cells via the Wnt pathway by targeting FZD4

description: Additional file 3. The data of differentially expressed genes (DEGs) in osteogenic differentiation of MSC at 2d with that at 0 h. the result identified 1291 DEGs in this group.
DOI: 10.6084/m9.figshare.23290594
title: Raw data and analysis code from Avoidance-motivational intensity modulated the effect of negative emotion on working memory

description: Although many studies have explored the association between negative emotion and working memory, the findings remain controversial. The present study investigated the role of avoidance-motivational intensity in modulating the effect of negative emotion on different processes (maintenance versus manipulation) of verbal and spatial working memory. Two experiments employed the modified delayed match-to-sample paradigms to separate the two processes of verbal and spatial working memory under different emotional states, respectively. In Experiment 1, participants were asked to perform the delayed match-to-sample task with or without reordering the characters (manipulation process of verbal working memory). In Experiment 2, mental rotation was used as the manipulation process of spatial working memory. The results showed that negative emotion only affected the manipulation process, but not the maintenance process. Relative to neutral and low avoidance-motivated negative conditions, the manipulation processes of both types of working memory were impaired under the high avoidance-motivated negative condition. No significant difference was observed between low avoidance-motivated negative condition and neutral condition. Our results are discussed in relation to efficiency processing theory and motivational dimensional model of affect. We conclude that negative emotional states with high avoidance-motivational intensity impair the manipulation process of verbal and spatial working memory.
DOI: 10.6084/m9.figshare.12280565
title: File S1. Character matrix used in the phylogenetic analysis in TNT format from Cretaceous diversity and disparity in a lacewing lineage of predators (Neuroptera: Mantispidae)

description: Mantidflies (Mantispidae) are an unusual and charismatic group of predatory lacewings (Neuroptera), whereby the adults represent a remarkable case of morphological and functional convergence with praying mantises (Mantodea). The evolutionary history of mantidflies remains largely unknown due to a scarcity of fossils. Here, we report the discovery of a highly diverse palaeofauna of mantidflies from the mid-Cretaceous (lowermost Cenomanian) of Myanmar. The raptorial forelegs of these mantidflies possess highly divergent morphological modifications, some of which are unknown among modern mantidflies, e.g. the presence of forked basal profemoral spines or even the complete loss of foreleg spine-like structures. A phylogenetic analysis of Mantispidae revealsa pattern of raptorial foreleg evolution across the family. The high species diversity and disparate foreleg characters might have been driven by diverse niches of predator–prey interplay in the complex tropical forest ecosystem of the mid-Cretaceous.
DOI: 10.6084/m9.figshare.12280565.v1
title: File S1. Character matrix used in the phylogenetic analysis in TNT format from Cretaceous diversity and disparity in a lacewing lineage of predators (Neuroptera: Mantispidae)

description: Mantidflies (Mantispidae) are an unusual and charismatic group of predatory lacewings (Neuroptera), whereby the adults represent a remarkable case of morphological and functional convergence with praying mantises (Mantodea). The evolutionary history of mantidflies remains largely unknown due to a scarcity of fossils. Here, we report the discovery of a highly diverse palaeofauna of mantidflies from the mid-Cretaceous (lowermost Cenomanian) of Myanmar. The raptorial forelegs of these mantidflies possess highly divergent morphological modifications, some of which are unknown among modern mantidflies, e.g. the presence of forked basal profemoral spines or even the complete loss of foreleg spine-like structures. A phylogenetic analysis of Mantispidae revealsa pattern of raptorial foreleg evolution across the family. The high species diversity and disparate foreleg characters might have been driven by diverse niches of predator–prey interplay in the complex tropical forest ecosystem of the mid-Cretaceous.
DOI: 10.6084/m9.figshare.7037687.v1
title: Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, Using Sparse Linear Programming

description: We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such subpopulations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is computationally infeasible to solve using existing techniques. Our solution involves a novel approach, in which we first transform the original multiple testing problem into a large, sparse linear program. We then solve this problem using advanced optimization techniques. This general method can solve a variety of multiple testing problems and decision theory problems related to optimal trial design, for which no solution was previously available. In particular, we construct new multiple testing procedures that satisfy minimax and Bayes optimality criteria. For a given optimality criterion, our new approach yields the optimal tradeoff between power to detect an effect in the overall population versus power to detect effects in subpopulations. We demonstrate our approach in examples motivated by two randomized trials of new treatments for HIV. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.23290691.v1
title: RESEARCH ON IDENTIFICATION AND CLASSIFICATION METHOD OF IMBALANCED DATA SET OF PIG BEHAVIOR

description: ABSTRACT To address the problem of the low accuracy and poor robustness of modeling methods for imbalanced data sets of pig behavior identification and classification, the three commonly used re-sampling methods of under-sampling, SMOTE and Borderline-SMOTE are compared, and an adaptive boundary data augmentation algorithm AD-BL-SMOTE is proposed. The activity of the pigs was measured using triaxial accelerometers, which were fixed on the backs of the pigs. A multilayer feed-forward neural network was trained and validated with 21 input features to classify four pig activities: lying, standing, walking, and exploring. The results showed that re-sampling methods are an effective way to improve the performance of pig behavior identification and classification. Moreover, AD-BL-SMOTE could yield greater improvements in classification performance than the other three methods for balancing the training data set. The overall major mean accuracy of lying, standing, walking, and exploring by pigs A, B and C was significantly improved by using AD-BL-SMOTE, reaching 91.8%, 93.0% and 96.0%, respectively.
DOI: 10.6084/m9.figshare.7038083
title: Additional file 2: of Assessing the sensitivity of placental growth factor and soluble fms-like tyrosine kinase 1 at 36 weeks’ gestation to predict small-for-gestational-age infants or late-onset preeclampsia: a prospective nested case-control study

description: S2. Study data. (XLSX 104 kb)
DOI: 10.6084/m9.figshare.23290522
title: Additional file 1 of Deep learning classification of uveal melanoma based on histopathological images and identification of a novel indicator for prognosis of patients

description: Additional file 1: Table S1. Baseline features of three cohorts.
DOI: 10.6084/m9.figshare.14378577.v1
title: miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data

description: In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, <i>miRSM</i>, for facilitating the procedure of inferring and analysing miRNA sponge modules. <i>miRSM</i> provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. <i>miRSM</i> will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.
DOI: 10.6084/m9.figshare.23290522.v1
title: Additional file 1 of Deep learning classification of uveal melanoma based on histopathological images and identification of a novel indicator for prognosis of patients

description: Additional file 1: Table S1. Baseline features of three cohorts.
DOI: 10.6084/m9.figshare.23290528.v1
title: Additional file 3 of Deep learning classification of uveal melanoma based on histopathological images and identification of a novel indicator for prognosis of patients

description: Additional file 3: Table S3. The detail parameters for assessment of seven machine learning models.
DOI: 10.6084/m9.figshare.19092643
title: Additional file 4 of LINC01119 negatively regulates osteogenic differentiation of mesenchymal stem cells via the Wnt pathway by targeting FZD4

description: Additional file 4. The data of differentially expressed genes (DEGs) in osteogenic differentiation of MSC at 3d with that at 0 h. the result identified 1793 DEGs in this group.
DOI: 10.6084/m9.figshare.23290531.v1
title: Additional file 4 of Deep learning classification of uveal melanoma based on histopathological images and identification of a novel indicator for prognosis of patients

description: Additional file 4: Table S4. The differentially expressed genes (DEGs) in TCGA-UVM cohort.
DOI: 10.6084/m9.figshare.7037690
title: Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data

description: In this article, we propose a generalized Gaussian process concurrent regression model for functional data, where the functional response variable has a binomial, Poisson, or other non-Gaussian distribution from an exponential family, while the covariates are mixed functional and scalar variables. The proposed model offers a nonparametric generalized concurrent regression method for functional data with multidimensional covariates, and provides a natural framework on modeling common mean structure and covariance structure simultaneously for repeatedly observed functional data. The mean structure provides overall information about the observations, while the covariance structure can be used to catch up the characteristic of each individual batch. The prior specification of covariance kernel enables us to accommodate a wide class of nonlinear models. The definition of the model, the inference, and the implementation as well as its asymptotic properties are discussed. Several numerical examples with different non-Gaussian response variables are presented. Some technical details and more numerical examples as well as an extension of the model are provided as supplementary materials.
DOI: 10.6084/m9.figshare.7037690.v1
title: Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data

description: In this article, we propose a generalized Gaussian process concurrent regression model for functional data, where the functional response variable has a binomial, Poisson, or other non-Gaussian distribution from an exponential family, while the covariates are mixed functional and scalar variables. The proposed model offers a nonparametric generalized concurrent regression method for functional data with multidimensional covariates, and provides a natural framework on modeling common mean structure and covariance structure simultaneously for repeatedly observed functional data. The mean structure provides overall information about the observations, while the covariance structure can be used to catch up the characteristic of each individual batch. The prior specification of covariance kernel enables us to accommodate a wide class of nonlinear models. The definition of the model, the inference, and the implementation as well as its asymptotic properties are discussed. Several numerical examples with different non-Gaussian response variables are presented. Some technical details and more numerical examples as well as an extension of the model are provided as supplementary materials.