cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20156884.v1
title: Piezo1-mediated fluid shear stress promotes OPG and inhibits RANKL via NOTCH3 in MLO-Y4 osteocytes

description: Piezo1, a mechanosensitive ion channel, participates in a variety of biological processes in maintaining bone homeostasis. As the most abundant cells in bones of the mammals, osteocytes play an essential role in bone formation, remodeling, and bone mass maintenance. Here, by exposing MLO-Y4 osteocytes to the fluid shear stress (FSS) microenvironment, we explored the effect of Piezo1-mediated FSS on the expression of the molecules critical to the process of bone formation and resorption, Receptor Activator of Nuclear Factor-Kappa-B Ligand (RANKL) and Osteoprotegerin (OPG). It was found that 9 dyne/cm<sup>2</sup> loading for 30 minutes showed an upregulation trend on Piezo1 when MLO-Y4 osteocytes were exposed to an FSS microenvironment. FSS promotes the expression of OPG and inhibits the expression of RANKL. The blocker of Piezo1, GsMTx4, downregulates the effect of FSS on the expression of these two molecules. In addition, NOTCH3 was involved in this process. Thus, the results demonstrated that Piezo1-mediated FSS promotes the expression of OPG and inhibits the expression of RANKL via NOTCH3 in MLO-Y4 osteocytes.
DOI: 10.6084/m9.figshare.12292550.v1
title: An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data

description: Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.
DOI: 10.6084/m9.figshare.20156884
title: Piezo1-mediated fluid shear stress promotes OPG and inhibits RANKL via NOTCH3 in MLO-Y4 osteocytes

description: Piezo1, a mechanosensitive ion channel, participates in a variety of biological processes in maintaining bone homeostasis. As the most abundant cells in bones of the mammals, osteocytes play an essential role in bone formation, remodeling, and bone mass maintenance. Here, by exposing MLO-Y4 osteocytes to the fluid shear stress (FSS) microenvironment, we explored the effect of Piezo1-mediated FSS on the expression of the molecules critical to the process of bone formation and resorption, Receptor Activator of Nuclear Factor-Kappa-B Ligand (RANKL) and Osteoprotegerin (OPG). It was found that 9 dyne/cm<sup>2</sup> loading for 30 minutes showed an upregulation trend on Piezo1 when MLO-Y4 osteocytes were exposed to an FSS microenvironment. FSS promotes the expression of OPG and inhibits the expression of RANKL. The blocker of Piezo1, GsMTx4, downregulates the effect of FSS on the expression of these two molecules. In addition, NOTCH3 was involved in this process. Thus, the results demonstrated that Piezo1-mediated FSS promotes the expression of OPG and inhibits the expression of RANKL via NOTCH3 in MLO-Y4 osteocytes.
DOI: 10.6084/m9.figshare.20156885
title: MicroRNA-30c-2-3p represses malignant progression of gastric adenocarcinoma cells via targeting ARHGAP11A

description: MicroRNAs are crucial tumor regulators to tumor development and progression. MiR-30c-2-3p can suppress malignant progression of tumor cells, but no study has reported the modulatory process of miR-30c-2-3p in gastric adenocarcinoma (GA). We herein investigated role of miR-30c-2-3p in GA cells. Here, we evaluated gene level in cancer cells by qRT-PCR. CCK-8, colony formation, flow cytometry, and transwell assays revealed biological functions of miR-30c-2-3p and ARHGAP11A. Genes downstream of miR-30c-2-3p were acquired through bioinformatics analysis. Our results suggested a low level of miR-30c-2-3p in GA tissue and cells, while its high expression could repress the malignant progression and promote cell cycle arrest and apoptosis of GA cells. Besides, ARHGAP11A was downstream of miR-30c-2-3p, with up-regulated ARHGAP11A facilitating malignant progression and repressing cell cycle arrest and apoptosis of GA cells. In addition, changes in GA cell functions caused by high ARHGAP11A expression could be partially offset by enhancing miR-30c-2-3p. Thus, our observations indicated that miR-30c-2-3p was a tumor repressor that could inhibit GA progression via modulating ARHGAP11A.
DOI: 10.6084/m9.figshare.20156885.v1
title: MicroRNA-30c-2-3p represses malignant progression of gastric adenocarcinoma cells via targeting ARHGAP11A

description: MicroRNAs are crucial tumor regulators to tumor development and progression. MiR-30c-2-3p can suppress malignant progression of tumor cells, but no study has reported the modulatory process of miR-30c-2-3p in gastric adenocarcinoma (GA). We herein investigated role of miR-30c-2-3p in GA cells. Here, we evaluated gene level in cancer cells by qRT-PCR. CCK-8, colony formation, flow cytometry, and transwell assays revealed biological functions of miR-30c-2-3p and ARHGAP11A. Genes downstream of miR-30c-2-3p were acquired through bioinformatics analysis. Our results suggested a low level of miR-30c-2-3p in GA tissue and cells, while its high expression could repress the malignant progression and promote cell cycle arrest and apoptosis of GA cells. Besides, ARHGAP11A was downstream of miR-30c-2-3p, with up-regulated ARHGAP11A facilitating malignant progression and repressing cell cycle arrest and apoptosis of GA cells. In addition, changes in GA cell functions caused by high ARHGAP11A expression could be partially offset by enhancing miR-30c-2-3p. Thus, our observations indicated that miR-30c-2-3p was a tumor repressor that could inhibit GA progression via modulating ARHGAP11A.
DOI: 10.6084/m9.figshare.12292550
title: An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data

description: Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.
DOI: 10.6084/m9.figshare.14906173
title: Additional file 1 of Association of cervical carcinogenesis risk with HPV16 E6 and E7 variants in the Taizhou area, China

description: Additional file 1. Clinical data for HPV16 study in Taizhou area, China.
DOI: 10.6084/m9.figshare.14906173.v1
title: Additional file 1 of Association of cervical carcinogenesis risk with HPV16 E6 and E7 variants in the Taizhou area, China

description: Additional file 1. Clinical data for HPV16 study in Taizhou area, China.
DOI: 10.6084/m9.figshare.21205432.v1
title: Additional file 5 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 5: Table S8. Summary of sequencing depth for PDXs. Table S9: List of mutations identified and validated in breast cancer PDXs. Table S10: Weights of mutational signatures identified from DeconstructSigs for each of the PDX models.
DOI: 10.6084/m9.figshare.21205441
title: Additional file 8 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 8: Table S13. List of genes with common copy number alterations in the PDXs.
DOI: 10.6084/m9.figshare.21205423
title: Additional file 2 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 2: Table S1. Summary of total number of variants in difference cell line models and PDXs from whole genome sequencing. Table S2. List of the 635 additional missense mutations in MCF7 compared to Ben-David et al. Table S3. The complete list of variants in the non-coding regions in these cell lines models. Table S4. Weights of mutational signatures identified from DeconstructSigs for each of the cell lines. Table S5. List of structural variants identified in the cell line models.
DOI: 10.6084/m9.figshare.21205426
title: Additional file 3 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 3: Table S6. List of copy number alterations identified in the cell line models.
DOI: 10.6084/m9.figshare.21205429.v1
title: Additional file 4 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 4: Table S7. Comparison of the genes with copy number alterations in COSMIC, Ben-David et al and this study.
DOI: 10.6084/m9.figshare.18480917
title: Skeletal muscle and adipose tissue at the C3 vertebral level for TCIA HNSCC patients

description: The accurate determination of sarcopenia is critical for disease management in patients with head and neck cancer (HNC). Quantitative determination of sarcopenia is currently dependent on manually-generated segmentations of skeletal muscle derived from computed tomography (CT) cross-sectional imaging. This has prompted the increasing utilization of machine learning models for automated sarcopenia determination. However, extant datasets of head and neck CT imaging currently do not provide the necessary manually-generated skeletal muscle segmentations at the C3 vertebral level needed for building these models. In this data descriptor, we detail the annotation of a large set of head and neck CT images for use in automated sarcopenia-related clinical decision making and body composition analysis. A set of 394 HNC patients were selected from The Cancer Imaging Archive, and their skeletal muscle and adipose tissue was manually segmented at the C3 vertebral level using sliceOmatic in .tag format. Subsequently, using publicly disseminated Python scripts, we generated corresponding segmentations files in Neuroimaging Informatics Technology Initiative format. In addition to segmentation data, additional clinical demographic data germane to body composition analysis have been retrospectively collected for these patients from the MD Anderson databases. These data are a valuable resource for studying sarcopenia and body composition analysis in patients with HNC.<br>Corresponding data descriptor: https://www.nature.com/articles/s41597-022-01587-w.
DOI: 10.6084/m9.figshare.21205435.v1
title: Additional file 6 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 6: Table S11. List of copy number alterations and identified in the PDXs.
DOI: 10.6084/m9.figshare.21205438
title: Additional file 7 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 7: Table S12. List of structural variants identified in the PDXs.
DOI: 10.6084/m9.figshare.21205423.v1
title: Additional file 2 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 2: Table S1. Summary of total number of variants in difference cell line models and PDXs from whole genome sequencing. Table S2. List of the 635 additional missense mutations in MCF7 compared to Ben-David et al. Table S3. The complete list of variants in the non-coding regions in these cell lines models. Table S4. Weights of mutational signatures identified from DeconstructSigs for each of the cell lines. Table S5. List of structural variants identified in the cell line models.
DOI: 10.6084/m9.figshare.21205426.v1
title: Additional file 3 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 3: Table S6. List of copy number alterations identified in the cell line models.
DOI: 10.6084/m9.figshare.21205429
title: Additional file 4 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 4: Table S7. Comparison of the genes with copy number alterations in COSMIC, Ben-David et al and this study.
DOI: 10.6084/m9.figshare.21205435
title: Additional file 6 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 6: Table S11. List of copy number alterations and identified in the PDXs.
DOI: 10.6084/m9.figshare.21205441.v1
title: Additional file 8 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 8: Table S13. List of genes with common copy number alterations in the PDXs.
DOI: 10.6084/m9.figshare.21205432
title: Additional file 5 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 5: Table S8. Summary of sequencing depth for PDXs. Table S9: List of mutations identified and validated in breast cancer PDXs. Table S10: Weights of mutational signatures identified from DeconstructSigs for each of the PDX models.
DOI: 10.6084/m9.figshare.21205438.v1
title: Additional file 7 of Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models

description: Additional file 7: Table S12. List of structural variants identified in the PDXs.
DOI: 10.6084/m9.figshare.24947487
title: Improving efficiency of ground-truth data collection for UAV-based rice growth estimation models: investigating the effect of sampling size on model accuracy

description: One of the bottlenecks in the development of UAV-based crop growth estimation models has been the need for ground-truth data collection through plant sampling. Thus, we investigated the viability of utilizing datasets derived from reduced sampling size for the development of growth estimation models, with the aim of enhancing the efficiency of ground-truth data collection. Koshihikari, a Japonica rice variety, was grown with various fertilizer conditions and transplanting dates. Once a week from transplanting to the heading date, aerial RGB and multispectral images were collected with a UAV. Subsequently, four adjacent hills from each plot were harvested, and above-ground biomass (AGB) and leaf area index (LAI) measurements were taken for each hill. For each hill, the ground-measured data was linked to the UAV-derived features (plant height, vegetation indices, and texture indices). Three datasets were compiled using the values of single hill, the average values of two adjacent hills, and those of four adjacent hills. Models estimating AGB and LAI from UAV-derived features were developed with each dataset using single regression and machine learning (ML) algorithms, and the prediction accuracy was compared among the three datasets. The prediction accuracy of the single regression models was similar across all datasets. In addition, it was demonstrated that the dataset based on single-harvested hills can contribute to improving the prediction accuracy of the ML models. Our results indicated that the dataset based on single-harvested hills was sufficiently reliable for model development and can be utilized, consequently allowing for more efficient ground-truth data collection.
DOI: 10.6084/m9.figshare.24947487.v1
title: Improving efficiency of ground-truth data collection for UAV-based rice growth estimation models: investigating the effect of sampling size on model accuracy

description: One of the bottlenecks in the development of UAV-based crop growth estimation models has been the need for ground-truth data collection through plant sampling. Thus, we investigated the viability of utilizing datasets derived from reduced sampling size for the development of growth estimation models, with the aim of enhancing the efficiency of ground-truth data collection. Koshihikari, a Japonica rice variety, was grown with various fertilizer conditions and transplanting dates. Once a week from transplanting to the heading date, aerial RGB and multispectral images were collected with a UAV. Subsequently, four adjacent hills from each plot were harvested, and above-ground biomass (AGB) and leaf area index (LAI) measurements were taken for each hill. For each hill, the ground-measured data was linked to the UAV-derived features (plant height, vegetation indices, and texture indices). Three datasets were compiled using the values of single hill, the average values of two adjacent hills, and those of four adjacent hills. Models estimating AGB and LAI from UAV-derived features were developed with each dataset using single regression and machine learning (ML) algorithms, and the prediction accuracy was compared among the three datasets. The prediction accuracy of the single regression models was similar across all datasets. In addition, it was demonstrated that the dataset based on single-harvested hills can contribute to improving the prediction accuracy of the ML models. Our results indicated that the dataset based on single-harvested hills was sufficiently reliable for model development and can be utilized, consequently allowing for more efficient ground-truth data collection.
DOI: 10.6084/m9.figshare.24899749
title: An efficient weighted partial MaxSAT encoding for scheduling in overloaded real-time systems

description: Scheduling tasks in overloaded real-time systems is a challenging problem that has received a significant amount of attention in recent years. The processor is overloaded with more tasks than its capacity, resulting in missed deadlines and degraded system performance. Therefore, scheduling algorithms play a critical role in ensuring that high-priority tasks are completed on time while minimizing the impact of incomplete lower-priority tasks. This paper proposes an efficient Weighted Partial MaxSAT(WPMS) encoding that returns an optimal solution in which the total weight of incomplete tasks is minimized in a single machine environment. To assess the efficiency of our proposed formulation, a comparative analysis is conducted alongside the state-of-the-art encoding. By examining the solving 75 distinct problems, it becomes evident that the WPMS encoding proposed herein exhibits a considerable advantage in terms of both time and memory efficiency.
DOI: 10.6084/m9.figshare.24899749.v1
title: An efficient weighted partial MaxSAT encoding for scheduling in overloaded real-time systems

description: Scheduling tasks in overloaded real-time systems is a challenging problem that has received a significant amount of attention in recent years. The processor is overloaded with more tasks than its capacity, resulting in missed deadlines and degraded system performance. Therefore, scheduling algorithms play a critical role in ensuring that high-priority tasks are completed on time while minimizing the impact of incomplete lower-priority tasks. This paper proposes an efficient Weighted Partial MaxSAT(WPMS) encoding that returns an optimal solution in which the total weight of incomplete tasks is minimized in a single machine environment. To assess the efficiency of our proposed formulation, a comparative analysis is conducted alongside the state-of-the-art encoding. By examining the solving 75 distinct problems, it becomes evident that the WPMS encoding proposed herein exhibits a considerable advantage in terms of both time and memory efficiency.