cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21702146.v1
title: Supplementary Material for: Anti-inflammatory action of antidepressants: Investigating the longitudinal effect of antidepressants on white blood cell count

description: Introduction: Antidepressants have documented anti-inflammatory effects on pro-inflammatory biomarkers. However, the long-term effects of antidepressants on inflammatory markers and effects of different antidepressant classes on pro-inflammatory biomarkers is largely unexplored. Here, we evaluate the short and long-term effects of all antidepressant classes on a clinical immune marker, white blood cell count (WBC). Methods: Using a retrospective study design, we extracted WBC count and prescription medications from electronic health records at Vanderbilt University Medical Center. We created a longitudinal model to evaluate the short-term and long-term effects of these medications on WBC count. We validated our longitudinal model using two known anti-inflammatory medications, biologic immunosuppressants and chemotherapy, and one medication class without known immunomodulatory properties, contraceptives. We used the longitudinal model to determine the effects of antidepressant use on WBC count stratified by drug class. Results: Biologic immunosuppressant and chemotherapy use associated with decreased WBC count, but contraceptive use did not associate with changes in WBC count, validating our longitudinal modeling approach. All antidepressant classes associated with decreased WBC count in the long-term cohorts. SSRI and Atypical use also associated with decreased WBC count in the short-term cohort. Conclusions: Using electronic health record data, we show all anti-depressant classes exhibit anti-inflammatory effects on a clinical immune marker, WBC count. Additionally, our results indicate that in some cases the anti-inflammatory effects of antidepressants persist over at least a one-year time frame. Our work contributes to the immunomodulatory knowledge of antidepressants and motivates future studies investigating alternative therapeutic routes for antidepressants.
DOI: 10.6084/m9.figshare.21989747
title: Monthly inflows in 2022 from Africa to EU

description: These datasets collect all the immigration flows between central Africa and Spain, Greece and Italy in 2022.
DOI: 10.6084/m9.figshare.21989747.v1
title: Monthly inflows in 2022 from Africa to EU

description: These datasets collect all the immigration flows between central Africa and Spain, Greece and Italy in 2022.
DOI: 10.6084/m9.figshare.21989753.v1
title: Supplementary Material for: Confirmation of Natural Hybridization between <b><i>Kengyilia</i></b> (StStYYPP) and <b><i>Campeiostachys</i></b> (StStYYHH) (Triticeae: Poaceae) Based on Morphological and Molecular Cytogenetic Analyses

description: Natural hybridization has been frequently observed in Triticeae; however, few studies have investigated the origin of natural intergeneric Triticeae hybrids. In the present study, we discovered three putative hybrid Triticeae plants in the Western Sichuan Plateau of China. Morphologically, the putative hybrids were intermediate between <i>Kengyilia melanthera</i> (2n = 6x = 42; StStYYPP) and <i>Campeiostachys dahurica</i> var. <i>tangutorum</i> (2n = 6x = 42; StStYYHH) with greater plant height and tiller number. Cytological analyses demonstrated that the hybrids were hexaploid with 42 chromosomes (2n = 6x = 42). At metaphase I, 12.10–12.58 bivalents and 13.81–14.18 univalents per cell were observed in the hybrid plants. Genomic in situ hybridization demonstrated that the hybrids had StStYYHP genomes. Phylogenetic analysis of <i>Acc1</i> sequences indicated that the hybrids were closely related to <i>K. melanthera</i> and <i>C. dahurica</i> var. <i>tangutorum.</i> Our morphological, cytological, and molecular analyses indicate that these hexaploid natural hybrid plants may be hybrids of <i>K. melanthera</i> and <i>C. dahurica</i> var. <i>tangutorum.</i>
DOI: 10.6084/m9.figshare.21989735
title: Cryptocurrency News Datasets

description: A total of 12,750 news headlines are obtained in the period between October 2021 and November 2022 from CoinTelegraph, CryptoNews and CryptoPotato.
DOI: 10.5281/zenodo.7395016
title: Controlled crossed Andreev reflection and elastic co-tunneling mediated by Andreev bound states

description: This repository contains measured data related to the "Controlled crossed Andreev reflection and elastic co-tunneling mediated by Andreev bound states" publication. Code reproducing all paper plots is included as well. Tu run the Jupyter notebook, install Anaconda and the package QCoDeS.
DOI: 10.5281/zenodo.7395015
title: Controlled crossed Andreev reflection and elastic co-tunneling mediated by Andreev bound states

description: This repository contains measured data related to the "Controlled crossed Andreev reflection and elastic co-tunneling mediated by Andreev bound states" publication. Code reproducing all paper plots is included as well. Tu run the Jupyter notebook, install Anaconda and the package QCoDeS.
DOI: 10.5281/zenodo.7594005
title: MetaTransformer trained species model

description: Trained models of our MetaTransformer with size 13 k-mer embedding trained on species microbial data.
DOI: 10.5281/zenodo.7594004
title: MetaTransformer trained species model

description: Trained models of our MetaTransformer with size 13 k-mer embedding trained on species microbial data.
DOI: 10.5281/zenodo.7594286
title: MetaTransformer trained genus model

description: Trained models of our MetaTransformer with size 13 k-mer embedding trained on genus microbial data.
DOI: 10.5281/zenodo.7594285
title: MetaTransformer trained genus model

description: Trained models of our MetaTransformer with size 13 k-mer embedding trained on genus microbial data.
DOI: 10.6084/m9.figshare.19750079
title: Three-Way Mixed Effect ANOVA to Estimate MRMC Limits of Agreement

description: When evaluating the clinical performance of a medical imaging device, a multi-reader multi-case (MRMC) analysis is usually applied to account for both case and reader variability. For a clinical task that equates to a quantitative measurement, an agreement analysis such as a limits of agreement (LOA) method can be used to compare different measurement methods. In this work, we introduce four types of comparisons; these types differ depending on whether the measurements are within or between readers and within or between modalities. A three-way mixed effect ANOVA model is applied to estimate the variances of individual differences, which is an essential step for estimating LOA. To verify the estimates of LOA, we propose a hierarchical model to simulate quantitative MRMC data. Two simulation studies were conducted to validate both the simulation and the LOA variance estimates. From the simulation results, we can conclude that our estimate of variance is unbiased, and the uncertainty of the estimation drops as the number of readers and cases increases and rises as the value of true variance increases.
DOI: 10.6084/m9.figshare.19750079.v1
title: Three-Way Mixed Effect ANOVA to Estimate MRMC Limits of Agreement

description: When evaluating the clinical performance of a medical imaging device, a multi-reader multi-case (MRMC) analysis is usually applied to account for both case and reader variability. For a clinical task that equates to a quantitative measurement, an agreement analysis such as a limits of agreement (LOA) method can be used to compare different measurement methods. In this work, we introduce four types of comparisons; these types differ depending on whether the measurements are within or between readers and within or between modalities. A three-way mixed effect ANOVA model is applied to estimate the variances of individual differences, which is an essential step for estimating LOA. To verify the estimates of LOA, we propose a hierarchical model to simulate quantitative MRMC data. Two simulation studies were conducted to validate both the simulation and the LOA variance estimates. From the simulation results, we can conclude that our estimate of variance is unbiased, and the uncertainty of the estimation drops as the number of readers and cases increases and rises as the value of true variance increases.
DOI: 10.6084/m9.figshare.14394412
title: Teasing Out the Overall Survival Benefit With Adjustment for Treatment Switching to Multiple Treatments

description: In oncology clinical trials, characterizing the long-term overall survival (OS) benefit for an experimental treatment is often unobservable if some patients in the control group switch to drugs in the experimental group and/or other cancer treatments after disease progression. A key question often raised by payers and reimbursement agencies is how to estimate the true benefit of the experimental treatment on OS that would have been estimated if there were no treatment switches. Several commonly used statistical methods are available to estimate OS benefit while adjusting for treatment switching, ranging from naive exclusion or censoring approaches to more advanced methods including inverse probability of censoring weighting (IPCW), two-stage model (2-stage), iterative parameter estimation (IPE) algorithm or rank-preserving structural failure time models (RPSFTM). However, many clinical trials now have patients in the control group switching to different treatment regimens other than the experimental treatments. While some existing methods including RPSFTM and IPE cannot handle complicated scenarios such as multilevel switching, other methods such as IPCW and two-stage methods are restricted with strong assumptions. To address this challenge, we propose two additional methods: stratified RPSFTM and random-forest-based prediction. A simulation study is conducted to assess the performance of the existing methods along with the two newly proposed approaches.
DOI: 10.6084/m9.figshare.14394412.v2
title: Teasing Out the Overall Survival Benefit With Adjustment for Treatment Switching to Multiple Treatments

description: In oncology clinical trials, characterizing the long-term overall survival (OS) benefit for an experimental treatment is often unobservable if some patients in the control group switch to drugs in the experimental group and/or other cancer treatments after disease progression. A key question often raised by payers and reimbursement agencies is how to estimate the true benefit of the experimental treatment on OS that would have been estimated if there were no treatment switches. Several commonly used statistical methods are available to estimate OS benefit while adjusting for treatment switching, ranging from naive exclusion or censoring approaches to more advanced methods including inverse probability of censoring weighting (IPCW), two-stage model (2-stage), iterative parameter estimation (IPE) algorithm or rank-preserving structural failure time models (RPSFTM). However, many clinical trials now have patients in the control group switching to different treatment regimens other than the experimental treatments. While some existing methods including RPSFTM and IPE cannot handle complicated scenarios such as multilevel switching, other methods such as IPCW and two-stage methods are restricted with strong assumptions. To address this challenge, we propose two additional methods: stratified RPSFTM and random-forest-based prediction. A simulation study is conducted to assess the performance of the existing methods along with the two newly proposed approaches.
DOI: 10.6084/m9.figshare.20272392.v2
title: Similarity-Principle-Based Machine Learning Method for Clinical Trials and Beyond

description: With recent success in <i>supervised learning</i>, artificial intelligence (AI) and machine learning (ML) can play a vital role in precision medicine. Deep learning neural networks have been used in drug discovery when <i>larger data</i> is available. However, applications of machine learning in clinical trials with small sample size (around <i>a few hundreds</i>) are limited. We propose a Similarity-Principle-Based Machine Learning (SBML) method, which is applicable for small and large sample size problems. In SBML, the attribute-scaling factors are introduced to objectively determine the relative importance of each attribute (predictor). The gradient method is used in learning (training), that is, updating the attribute-scaling factors. We evaluate SBML when the sample size is small and investigate the effects of tuning parameters. Simulations show that SBML achieves better predictions in terms of mean squared errors for various complicated nonlinear situations than full linear models, optimal and ridge regressions, mixed effect models, support vector machine and decision tree methods.
DOI: 10.6084/m9.figshare.20272392.v1
title: Similarity‐Principle-Based Machine Learning Method for Clinical Trials and Beyond

description: With recent success in <i>supervised learning</i>, artificial intelligence (AI) and machine learning (ML) can play a vital role in precision medicine. Deep learning neural networks have been used in drug discovery when larger data is available. However, applications of machine learning in clinical trials with small sample size (around a few hundreds) are limited. We propose a Similarity-Principle-Based Machine Learning (SBML) method, which is applicable for small and large sample size problems. In SBML, the attribute-scaling factors are introduced to objectively determine the relative importance of each attribute (predictor). The gradient method is used in learning (training), i.e., updating the attribute-scaling factors. We evaluate SBML when the sample size is small and investigate the effects of tuning parameters. Simulations show that SBML achieves better predictions in terms of mean squared errors for various complicated nonlinear situations than full linear models, optimal and ridge regressions, mixed effect models, support vector machine and decision tree methods.
DOI: 10.6084/m9.figshare.14596900.v2
title: Exact Properties of Some Heteroscedastic TOST Alternatives for Bioequivalence

description: The two one-sided tests (TOST) procedure is the common method for assessing bioequivalence between two different drugs or formulations of the same drug. Several solutions to Behrens-Fisher problem have been extended to establish statistical equivalence under variance heterogeneity. Unlike other existing studies that rely exclusively on simulation results, the analytical formulas of power and Type I error rate are derived. Exact numerical investigations were conducted to evaluate the behavior of the Welch–Satterthwaite, Cochran–Cox, and Banerjee–McCullough TOST methods. The results show that the Type I error rates of the Cochran–Cox and Banerjee–McCullough tests never exceed the nominal significance level. The Banerjee–McCullough technique is actually more conservative than the Cochran–Cox procedure. The extended Welch–Satterthwaite approach maintains closest to the preassigned significance level and is almost equal to the target error probability. The exact power levels of the Cochran–Cox and Banerjee–McCullough tests are completely or nearly identical, and are always less than that of the Welch–Satterthwaite method. On the basis of excellent control of Type I error and greater power performance, the extended Welch–Satterthwaite procedure is recommended over the other two heteroscedastic TOST methods. These findings provide an update of previous studies that endorse the somehow inferior Banerjee–McCullough method.
DOI: 10.6084/m9.figshare.20272392
title: Similarity-Principle-Based Machine Learning Method for Clinical Trials and Beyond

description: With recent success in <i>supervised learning</i>, artificial intelligence (AI) and machine learning (ML) can play a vital role in precision medicine. Deep learning neural networks have been used in drug discovery when <i>larger data</i> is available. However, applications of machine learning in clinical trials with small sample size (around <i>a few hundreds</i>) are limited. We propose a Similarity-Principle-Based Machine Learning (SBML) method, which is applicable for small and large sample size problems. In SBML, the attribute-scaling factors are introduced to objectively determine the relative importance of each attribute (predictor). The gradient method is used in learning (training), that is, updating the attribute-scaling factors. We evaluate SBML when the sample size is small and investigate the effects of tuning parameters. Simulations show that SBML achieves better predictions in terms of mean squared errors for various complicated nonlinear situations than full linear models, optimal and ridge regressions, mixed effect models, support vector machine and decision tree methods.
DOI: 10.6084/m9.figshare.14394412.v1
title: Teasing Out the Overall Survival Benefit with Adjustment for Treatment Switching to Multiple Treatments

description: In oncology clinical trials, characterizing the long-term overall survival (OS) benefit for an experimental treatment is often unobservable if some patients in the control group switch to drugs in the experimental group and/or other cancer treatments after disease progression. A key question often raised by payers and reimbursement agencies is how to estimate the true benefit of the experimental treatment on overall survival that would have been estimated if there were no treatment switches. Several commonly used statistical methods are available to estimate overall survival benefit while adjusting for treatment switching, ranging from naive exclusion or censoring approaches to more advanced methods including inverse probability of censoring weighting (IPCW), two-stage model (2-stage), iterative parameter estimation (IPE) algorithm or rank-preserving structural failure time models (RPSFTM). However, many clinical trials now have patients in the control group switching to different treatment regimens other than the experimental treatments. While some existing methods including RPSFTM and IPE cannot handle complicated scenarios such as multi-level switching, other methods such as IPCW and two-stage methods are restricted with strong assumptions. To address this challenge, we propose two additional methods: stratified RPSFTM and random-forest-based prediction. A simulation study is conducted to assess the performance of the existing methods along with the two newly proposed approaches.
DOI: 10.6084/m9.figshare.14596900
title: Exact Properties of Some Heteroscedastic TOST Alternatives for Bioequivalence

description: The two one-sided tests (TOST) procedure is the common method for assessing bioequivalence between two different drugs or formulations of the same drug. Several solutions to Behrens-Fisher problem have been extended to establish statistical equivalence under variance heterogeneity. Unlike other existing studies that rely exclusively on simulation results, the analytical formulas of power and Type I error rate are derived. Exact numerical investigations were conducted to evaluate the behavior of the Welch–Satterthwaite, Cochran–Cox, and Banerjee–McCullough TOST methods. The results show that the Type I error rates of the Cochran–Cox and Banerjee–McCullough tests never exceed the nominal significance level. The Banerjee–McCullough technique is actually more conservative than the Cochran–Cox procedure. The extended Welch–Satterthwaite approach maintains closest to the preassigned significance level and is almost equal to the target error probability. The exact power levels of the Cochran–Cox and Banerjee–McCullough tests are completely or nearly identical, and are always less than that of the Welch–Satterthwaite method. On the basis of excellent control of Type I error and greater power performance, the extended Welch–Satterthwaite procedure is recommended over the other two heteroscedastic TOST methods. These findings provide an update of previous studies that endorse the somehow inferior Banerjee–McCullough method.
DOI: 10.6084/m9.figshare.14596900.v1
title: Exact properties of some heteroscedastic TOST alternatives for bioequivalence

description: The two one-sided tests (TOST) procedure is the common method for assessing bioequivalence between two different drugs or formulations of the same drug. Several solutions to Behrens-Fisher Problem have been extended to establish statistical equivalence under variance heterogeneity. Unlike other existing studies that rely exclusively on simulation results, the analytical formulas of power and Type I error rate are derived. Exact numerical investigations were conducted to evaluate the behavior of the Welch-Satterthwaite, Cochran-Cox, and Banerjee-McCullough TOST methods. The results show that the Type I error rates of the Cochran-Cox and Banerjee-McCullough tests never exceed the nominal significance level. The Banerjee-McCullough technique is actually more conservative than the Cochran-Cox procedure. The extended Welch-Satterthwaite approach maintains closest to the preassigned significance level and is almost equal to the target error probability. The exact power levels of the Cochran-Cox and Banerjee-McCullough tests are completely or nearly identical, and are always less than that of the Welch-Satterthwaite method. On the basis of excellent control of Type I error and greater power performance, the extended Welch-Satterthwaite procedure is recommended over the other two heteroscedastic TOST methods. These findings provide an update of previous studies that endorse the somehow inferior Banerjee-McCullough method.
DOI: 10.34740/kaggle/dsv/4932022
title: Egypt Gold Prices | Daily Updated
DOI: 10.14278/rodare.2136
title: Data publication: Analysis of Cadmium Retention Mechanisms by a Smectite Clay in the Presence of Carbonates

description: Cadmium (Cd) is a toxic heavy metal with very low permissible exposure limits and is, thus, a very dangerous pollutant for the environment and public health and is considered by the World Health Organisation as one of the ten chemicals of major public concern. Adsorption onto solid phases and (co)precipitation processes are the most powerful mechanisms to retain pollutants and limit their migration; thus, the understanding of these processes is fundamental for assessing the risks of their presence in the environment. In this study, the immobilisation of Cd by smectite clay has been investigated by batch sorption tests, and the experimental data were interpreted with a thermodynamic model, including cation exchange and surface complexation processes. The model can describe the adsorption of Cd in smectite under a wide range of experimental conditions (pH, ionic strength, and Cd concentration). Under the conditions analysed in this study, the precipitation of otavite (CdCO₃) is shown to have a limited contribution to Cd immobilisation.
DOI: 10.14278/rodare.2125
title: Data publication: Analysis of Cadmium Retention Mechanisms by a Smectite Clay in the Presence of Carbonates

description: Cadmium (Cd) is a toxic heavy metal with very low permissible exposure limits and is, thus, a very dangerous pollutant for the environment and public health and is considered by the World Health Organisation as one of the ten chemicals of major public concern. Adsorption onto solid phases and (co)precipitation processes are the most powerful mechanisms to retain pollutants and limit their migration; thus, the understanding of these processes is fundamental for assessing the risks of their presence in the environment. In this study, the immobilisation of Cd by smectite clay has been investigated by batch sorption tests, and the experimental data were interpreted with a thermodynamic model, including cation exchange and surface complexation processes. The model can describe the adsorption of Cd in smectite under a wide range of experimental conditions (pH, ionic strength, and Cd concentration). Under the conditions analysed in this study, the precipitation of otavite (CdCO₃) is shown to have a limited contribution to Cd immobilisation.
DOI: 10.6084/m9.figshare.21989768
title: Special Brazilian Jiu-Jitsu Fitness Test (SBJJFT) for the Analysis of Anaerobic Capacity in Juniors and Adult Fighters

description: SBJJFT development study database.