cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16627231
title: Supplementary Material for: Efficacy and Safety of Molidustat for Anemia in ESA-Naive Nondialysis Patients: A Randomized, Phase 3 Trial

description: <b><i>Introduction:</i></b> Molidustat is an oral hypoxia-inducible factor prolyl hydroxylase inhibitor that predominantly induces renal production of erythropoietin (EPO). Molidustat was evaluated for the treatment of anemia associated with chronic kidney disease (CKD) in the “Molidustat Once Daily Improves Renal Anemia by Inducing EPO” (MIYABI) program, which comprises 5 phase 3 clinical trials. The present MIYABI Non-Dialysis Correction (ND-C) study investigated the efficacy and safety of molidustat in Japanese patients with renal anemia who were not undergoing dialysis and were not receiving erythropoiesis-stimulating agent (ESA) treatment. <b><i>Methods:</i></b> This was a 52-week, randomized (1:1), open-label, active-control, parallel-group, multicenter, phase 3 study in Japanese patients with renal anemia associated with CKD (stages 3–5). Molidustat or the ESA darbepoetin alfa (hereinafter referred to as darbepoetin) were initiated at 25 mg once daily or 30 μg every 2 weeks, respectively, and doses were regularly titrated to correct and to maintain hemoglobin (Hb) levels in the target range of ≥11.0 g/dL and &lt;13.0 g/dL. The primary efficacy outcome was the mean Hb level and its change from baseline during the evaluation period (weeks 30–36). The safety outcomes included evaluation of all adverse events. <b><i>Results:</i></b> In total, 162 patients were randomized to receive molidustat (<i>n</i> = 82) or darbepoetin (<i>n</i> = 80). Baseline characteristics were generally well balanced between treatment groups. The mean (standard deviation) Hb levels at baseline were 9.84 (0.64) g/dL for molidustat and 10.00 (0.61) g/dL for darbepoetin. The mean (95% confidence interval [CI]) for mean Hb levels during the evaluation period for molidustat (11.28 [11.07, 11.50] g/dL) and darbepoetin (11.70 [11.50, 11.90] g/dL) was within the target range. Based on a noninferiority margin of 1.0 g/dL, molidustat was noninferior to darbepoetin in the change in mean Hb level during the evaluation period from baseline; the least-squares mean (95% CI) difference (molidustat-darbepoetin) was −0.38 (−0.67, −0.08) g/dL. The proportion of patients who reported at least 1 treatment-emergent adverse event (TEAE) was 93.9% for molidustat and 93.7% for darbepoetin. Most TEAEs were mild (54.9% for molidustat and 63.3% for darbepoetin) or moderate (22.0% for molidustat and 22.8% for darbepoetin) in intensity. There were 3 deaths in the molidustat group and 1 in the darbepoetin group. <b><i>Discussion/Conclusion:</i></b> In the MIYABI ND-C study, molidustat appeared to be an efficacious and generally well-tolerated alternative to darbepoetin for the treatment of renal anemia in Japanese patients who were not undergoing dialysis and were not receiving ESA treatment.
DOI: 10.6084/m9.figshare.16627204
title: Supplementary Material for: Molidustat for Renal Anemia in Nondialysis Patients Previously Treated with Erythropoiesis-Stimulating Agents: A Randomized, Open-Label, Phase 3 Study

description: <b><i>Introduction:</i></b> Erythropoiesis-stimulating agents (ESAs) are the current standard of care for anemia due to chronic kidney disease (CKD) in patients not undergoing dialysis. Molidustat, an oral hypoxia-inducible factor prolyl hydroxylase inhibitor, is being investigated as an alternative treatment for renal anemia. Molidustat was evaluated in five phase 3 studies, the molidustat once daily improves renal anemia by inducing erythropoietin (MIYABI) program. The present study investigated the safety and efficacy of molidustat in Japanese patients with renal anemia not undergoing dialysis and previously treated with ESAs. <b><i>Methods:</i></b> This was a 52-week, active-controlled, randomized (1:1), open-label, parallel-group, multicenter, phase 3 study in Japanese patients with anemia due to CKD (stages 3–5). Molidustat was initiated at 25 mg or 50 mg once daily according to previous ESA dose. The ESA darbepoetin alfa (darbepoetin) was initiated at a starting dose in accordance with the previous ESA dose and injected subcutaneously once every 2 or 4 weeks. Doses were regularly titrated to maintain hemoglobin (Hb) levels in the target range of 11.0–13.0 g/dL. The primary efficacy outcome was the mean Hb level and its change from baseline during the evaluation period (weeks 30–36). The safety outcomes included evaluation of all adverse events. <b><i>Results:</i></b> In total, 164 patients were randomized to receive molidustat (<i>n</i> = 82) or darbepoetin (<i>n</i> = 82). Baseline characteristics were well balanced. Mean (standard deviation) Hb levels at baseline were 11.31 (0.68) g/dL for molidustat and 11.27 (0.64) g/dL for darbepoetin. The mean (95% confidence interval [CI]) for mean Hb levels during the evaluation period for molidustat (11.67 [11.48–11.85] g/dL) and darbepoetin (11.53 [11.31–11.74] g/dL) was within the target range. Based on a noninferiority margin of 1.0 g/dL, molidustat was noninferior to darbepoetin regarding the change in mean Hb level during the evaluation period from baseline, with a least squares mean (95% CI) difference (molidustat-darbepoetin) of 0.13 (−0.15, 0.40) g/dL. The proportion of patients who reported at least 1 treatment-emergent adverse event (TEAE) was 92.7% for molidustat and 96.3% for darbepoetin. TEAEs leading to death were reported in 2 patients (2.4%) in the molidustat group and none in the darbepoetin group; serious TEAEs were reported in 32.9% and 26.8% of patients, respectively. <b><i>Discussion/Conclusion:</i></b> Molidustat was noninferior to darbepoetin and maintained Hb levels in the prespecified target range in patients with renal anemia not undergoing dialysis and previously treated with ESA. Molidustat was well tolerated, and no new safety signal was observed.
DOI: 10.5522/04/14566635
title: Satellite-derived estimates of ammonia emissions in the UK

description: UK monthly mean ammonia emissions estimated using satellite observations and the GEOS-Chem chemical transport model for March to September at ~10 km spatial resolution.
DOI: 10.5522/04/14566635.v1
title: Satellite-derived estimates of ammonia emissions in the UK

description: UK monthly mean ammonia emissions estimated using satellite observations and the GEOS-Chem chemical transport model for March to September at ~10 km spatial resolution.
DOI: 10.5281/zenodo.5511063
title: goosynthpart7

description: goosynthpart7
DOI: 10.5281/zenodo.5511062
title: goosynthpart7

description: goosynthpart7
DOI: 10.5281/zenodo.5511340
title: Subjective human thresholds over computer generated images

description: Realistic image computation mimics the natural process of acquiring pictures by simulating the physical interactions of light between all the objects, lights and cameras lying within a modelled 3D scene. This process is known as global illumination and was formalised by Kajiya with the following rendering Equation:<br> \(\begin{equation} \label{eq:rendering_equation} L_o(x, \omega_o) = {L_e(x, \omega_o)} + \int_{\Omega}^{} {L_i(x, \omega_i)} \cdot f_r(x, \omega_i \rightarrow \omega_o) \cdot \cos \theta_i d\omega_i \end{equation}\) where: \(L_o(x, \omega_o)\) is the luminance traveling from point \(x\) in direction \(\omega_o\); \(L_e(x, \omega_o)\) is point \(x\) emitted luminance (it is null if point x does not lie on a ligth source surface); the integral represents the set of luminances \(L_i\)incident in \(x \) from the hemisphere of the directions \(\Omega\) and reflected in the direction \(\omega_o\). The reflected luminances are weighted by the materials reflecting properties (bidirectionnal reflectance function \(f_r(x, \omega_i \rightarrow \omega_o)\)) and the cosinus of the incident angle. This equation cannot be analytically solved and Monte Carlo approaches are generally used to estimate the value of the pixels of the final image. This proposed dataset is composed of 80 points of view of photo realistics images with different level of samples (following the Monte Carlo approach) for each. Each image is 800 x 800 pixels in size. The most noisy image is of 20 samples and the reference one (the most converged image obtained) is of 10000 samples. The pbrt rendering engine (version 3) was used to generate these images. By exploiting these levels of samples obtained and therefore of noise perceptible in the images, average subjective human thresholds were collected. For this purpose, the images were divided into 16 areas of 200 x 200 pixels in size for each point of view. The proposed image database is composed of the following files: <strong>human-thresholds.csv</strong> : the set of human subjective thresholds obtained on 40 points of view. A line is composed of the name of the point of view followed by all the thresholds obtained for each of the 16 zones; <strong>SIN3D_dataset.tar.gz</strong> : is an archive containing all the images from 20 to 10000 samples in steps of 20 samples for each point of view (i.e. 500 images per point of view). Each folder in the archive corresponds to a point of view. <em>This image database has been exploited in order to propose an objective model for noise detection in photo-realistic computer-generated images (article referenced to this image database).</em> <strong>Note:</strong> Some of the proposed scenes come from: https://pbrt.org/scenes-v3 https://benedikt-bitterli.me/resources/ <strong>Funding:</strong> This research was funded by ANR support: project ANR-17-CE38-0009.
description: {"references": ["Buisine, J\u00e9r\u00f4me, et al. \"Stopping Criterion during Rendering of Computer-Generated Images Based on SVD-Entropy.\" Entropy 23.1 (2021): 75."]}
DOI: 10.5281/zenodo.5171402
title: STEP4WIND PhD - ESR1

description: Repository of the PhD research on hydrodynamic modelling of floating offshore wind turbine carried out by the STEP4WIND ESR1.
DOI: 10.5281/zenodo.5171401
title: STEP4WIND PhD - ESR1

description: Repository of the PhD research on hydrodynamic modelling of floating offshore wind turbine carried out by the STEP4WIND ESR1.
DOI: 10.5258/soton/d1593
title: Dataset for: Energy-aware HW/SW Co-modeling of Batteryless Wireless Sensor Nodes

description: Dataset supporting the paper: Samuel C.B. Wong, Sivert T. Sliper, William Wang, Alex S. Weddell, Stephanie Gauthier, Geoff V. Merrett. "Energy-aware HW/SW Co-modeling of Batteryless Wireless Sensor Nodes". The 8th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems. Energy harvesting wireless sensor nodes are sensitive to spatial and temporal fluctuations in energy availability. This issue is especially prevalent in batteryless systems, where devices are directly connected to power sources with little or no buffering. The strong coupling of energy supply and demand introduces a new dimension to the problem of designing robust networked sensing systems. We propose a modeling framework for this class of batteryless systems with an emphasis on the interactions between energy and function. The tool models energy harvesters, power management circuitry, energy storage, microcontrollers, sensors, radio modules, environmental models, and is fully extensible. The microcontroller model is based on cycle-accurate instruction set simulators from \emph{Fused}, with various peripheral extensions to enable board-level functionality, such as SPI, DMA, hardware multiplier etc. The tool enables virtual prototyping of self-powered wireless sensor nodes, but is especially useful for studying intermittent operation and developing application specific software, hardware, or combined solutions.The simulator is capable of executing real workloads under realistic conditions and this is demonstrated through a case study where the same compiled binary is executed on a virtual prototype and its corresponding physical wireless sensor system to yield matching digital traces and current profiles.
DOI: 10.6084/m9.figshare.16627398
title: Supplementary information on Ninteract windows from Mitonuclear interactions and introgression genomics of macaque monkeys (<i>Macaca</i>) highlight the influence of behaviour on genome evolution

description: Additional information on Ninteract genes including gene acronym, genomic location, and polymorphism statistics.
DOI: 10.6084/m9.figshare.14445315
title: Supplementary Table 1.txt

description: Enhancer regions
DOI: 10.6084/m9.figshare.15035853
title: Scripts.zip

description: Scripts
DOI: 10.6084/m9.figshare.15035853.v1
title: Scripts.zip

description: Scripts
DOI: 10.6084/m9.figshare.15034977
title: pCR_Raw.Counts.Regions.bed

description: pCR_Raw.Counts.Regions
DOI: 10.6084/m9.figshare.15034977.v1
title: pCR_Raw.Counts.Regions.bed

description: pCR_Raw.Counts.Regions
DOI: 10.5281/zenodo.4633704
title: ONE DATA Data Sience Workflows

description: The ONE DATA data science workflow dataset ODDS-full comprises 815 unique workflows in temporally ordered versions. <br> A version of a workflow describes its evolution over time, so whenever a workflow is altered meaningfully, a new version of this respective workflow is persisted. <br> Overall, 16035 versions are available. The ODDS-full workflows represent machine learning workflows expressed as node-heterogeneous DAGs with 156 different node types.<br> These node types represent various kinds of processing steps of a general machine learning workflow and are grouped into 5 categories, which are listed below. <strong>Load</strong> Processors for loading or generating data (e.g. via a random number generator). <strong>Save</strong> Processors for persisting data (possible in various data formats, via external connections or as a contained result within the ONE DATA platform) or for providing data to other places as a service. <strong>Transformation</strong> Processors for altering and adapting data. This includes e.g. database-like operations such as renaming columns or joining tables as well as fully fledged dataset queries. <strong>Quantitative Methods</strong> Various aggregation or correlation analysis, bucketing, and simple forecasting. <strong>Advanced Methods</strong> Advanced machine learning algorithms such as BNN or Linear Regression. Also includes special meta processors that for example allow the execution of external workflows within the original workflow. Any metadata beyond the structure and node types of a workflow has been removed for anonymization purposes ODDS, a filtered variant, which enforces weak connectedness and only contains workflows with at least 5 different versions and 5 nodes, is available as the default version for supervised and unsupvervised learning. Workflows are served as JSON node-link graphs via <em>networkx.</em> They can be loaded into python as follows: <pre><code class="language-python">import pandas as pd import networkx as nx import json with open('ODDS.json', 'r') as f: graphs = pd.Series(list(map(nx.node_link_graph, json.load(f)['graphs'])))</code></pre>
DOI: 10.5281/zenodo.4633703
title: ONE DATA Data Sience Workflows

description: The ONE DATA data science workflow dataset ODDS-full comprises 815 unique workflows in temporally ordered versions. <br> A version of a workflow describes its evolution over time, so whenever a workflow is altered meaningfully, a new version of this respective workflow is persisted. <br> Overall, 16035 versions are available. The ODDS-full workflows represent machine learning workflows expressed as node-heterogeneous DAGs with 156 different node types.<br> These node types represent various kinds of processing steps of a general machine learning workflow and are grouped into 5 categories, which are listed below. <strong>Load</strong> Processors for loading or generating data (e.g. via a random number generator). <strong>Save</strong> Processors for persisting data (possible in various data formats, via external connections or as a contained result within the ONE DATA platform) or for providing data to other places as a service. <strong>Transformation</strong> Processors for altering and adapting data. This includes e.g. database-like operations such as renaming columns or joining tables as well as fully fledged dataset queries. <strong>Quantitative Methods</strong> Various aggregation or correlation analysis, bucketing, and simple forecasting. <strong>Advanced Methods</strong> Advanced machine learning algorithms such as BNN or Linear Regression. Also includes special meta processors that for example allow the execution of external workflows within the original workflow. Any metadata beyond the structure and node types of a workflow has been removed for anonymization purposes ODDS, a filtered variant, which enforces weak connectedness and only contains workflows with at least 5 different versions and 5 nodes, is available as the default version for supervised and unsupvervised learning. Workflows are served as JSON node-link graphs via <em>networkx.</em> They can be loaded into python as follows: <pre><code class="language-python">import pandas as pd import networkx as nx import json with open('ODDS.json', 'r') as f: graphs = pd.Series(list(map(nx.node_link_graph, json.load(f)['graphs'])))</code></pre>
DOI: 10.6084/m9.figshare.15034980
title: pCR_Fetal_Raw_filtered.bed

description: pCR_Fetal_Raw_filtered
DOI: 10.6084/m9.figshare.15034980.v1
title: pCR_Fetal_Raw_filtered.bed

description: pCR_Fetal_Raw_filtered
DOI: 10.6084/m9.figshare.15034983
title: nDAE_ncER_10bin.bed

description: nDAE_ncER_10bin
DOI: 10.6084/m9.figshare.15034983.v1
title: nDAE_ncER_10bin.bed

description: nDAE_ncER_10bin
DOI: 10.6084/m9.figshare.15035010
title: nDAE_epigenome_10bins.bed

description: nDAE_epigenome_10bins
DOI: 10.6084/m9.figshare.15035010.v1
title: nDAE_epigenome_10bins.bed

description: nDAE_epigenome_10bins
DOI: 10.6084/m9.figshare.15035022
title: DAE_ncER_10bin.bed

description: DAE_ncER_10bin
DOI: 10.6084/m9.figshare.15035022.v1
title: DAE_ncER_10bin.bed

description: DAE_ncER_10bin
DOI: 10.6084/m9.figshare.15035025.v1
title: DAE_epigenome_10bins.bed

description: DAE_epigenome_10bins
DOI: 10.6084/m9.figshare.15035025
title: DAE_epigenome_10bins.bed

description: DAE_epigenome_10bins
DOI: 10.6084/m9.figshare.16627491
title: R code from Mast seeding promotes evolution of scatter-hoarding

description: Many plant species worldwide are dispersed by scatter-hoarding granivores: animals that hide seeds in numerous, small caches for future consumption. Yet, the evolution of scatter-hoarding is difficult to explain because undefended caches are at high risk of pilferage. Previous models have attempted to solve this problem by giving cache owners large advantages in cache recovery, by kin selection, or by introducing reciprocal pilferage of ‘shared’ seed resources. However, the role of environmental variability has been so far overlooked in this context. One important form of such variability is masting, which is displayed by many plant species dispersed by scatterhoarders. We use a mathematical model to investigate the influence of masting on the evolution of scatter-hoarding. The model accounts for periodically varying annual seed fall, caching and pilfering behaviour, and the demography of scatterhoarders. The parameter values are based mostly on research on European beech (<i>Fagus sylvatica</i>) and yellow-necked mice (<i>Apodemus flavicollis</i>). Starvation of scatterhoarders between mast years decreases the population density that enters masting events, which leads to reduced seed pilferage. Satiation of scatterhoarders during mast events lowers the reproductive cost of caching (i.e. the cost of caching for the future rather than using seeds for current reproduction). These reductions promote the evolution of scatter-hoarding behaviour especially when interannual variation in seed fall and the period between masting events are large.This article is part of the theme issue ‘The ecology and evolution of synchronized seed production in plants’.
DOI: 10.6084/m9.figshare.16627491.v1
title: R code from Mast seeding promotes evolution of scatter-hoarding

description: Many plant species worldwide are dispersed by scatter-hoarding granivores: animals that hide seeds in numerous, small caches for future consumption. Yet, the evolution of scatter-hoarding is difficult to explain because undefended caches are at high risk of pilferage. Previous models have attempted to solve this problem by giving cache owners large advantages in cache recovery, by kin selection, or by introducing reciprocal pilferage of ‘shared’ seed resources. However, the role of environmental variability has been so far overlooked in this context. One important form of such variability is masting, which is displayed by many plant species dispersed by scatterhoarders. We use a mathematical model to investigate the influence of masting on the evolution of scatter-hoarding. The model accounts for periodically varying annual seed fall, caching and pilfering behaviour, and the demography of scatterhoarders. The parameter values are based mostly on research on European beech (<i>Fagus sylvatica</i>) and yellow-necked mice (<i>Apodemus flavicollis</i>). Starvation of scatterhoarders between mast years decreases the population density that enters masting events, which leads to reduced seed pilferage. Satiation of scatterhoarders during mast events lowers the reproductive cost of caching (i.e. the cost of caching for the future rather than using seeds for current reproduction). These reductions promote the evolution of scatter-hoarding behaviour especially when interannual variation in seed fall and the period between masting events are large.This article is part of the theme issue ‘The ecology and evolution of synchronized seed production in plants’.
DOI: 10.5517/ccdc.csd.cc285j33
title: CCDC 2090674: Experimental Crystal Structure Determination

description: Related Article: Renzhi Chen, Dongsheng Qiu, Xin Lei, Yujie Niu, Yuhui Hua, Huayu Peng, Tao Zeng, Yandong Zhang|2021|Org.Lett.|23|6972|doi:10.1021/acs.orglett.1c02599
DOI: 10.5517/ccdc.csd.cc285j44
title: CCDC 2090675: Experimental Crystal Structure Determination

description: Related Article: Renzhi Chen, Dongsheng Qiu, Xin Lei, Yujie Niu, Yuhui Hua, Huayu Peng, Tao Zeng, Yandong Zhang|2021|Org.Lett.|23|6972|doi:10.1021/acs.orglett.1c02599
DOI: 10.5517/ccdc.csd.cc285j55
title: CCDC 2090676: Experimental Crystal Structure Determination

description: Related Article: Renzhi Chen, Dongsheng Qiu, Xin Lei, Yujie Niu, Yuhui Hua, Huayu Peng, Tao Zeng, Yandong Zhang|2021|Org.Lett.|23|6972|doi:10.1021/acs.orglett.1c02599
DOI: 10.5517/ccdc.csd.cc2871sc
title: CCDC 2092152: Experimental Crystal Structure Determination

description: Related Article: Wen-Chao Yang, Ming-Ming Zhang, Yu Sun, Cai-Yun Chen, Lei Wang|2021|Org.Lett.|23|6691|doi:10.1021/acs.orglett.1c02260
DOI: 10.5517/ccdc.csd.cc28b2xm
title: CCDC 2095070: Experimental Crystal Structure Determination

description: Related Article: Dae Young Bae, Gunhee Lee, Eunsung Lee|2021|Inorg.Chem.|60|12813|doi:10.1021/acs.inorgchem.1c01050
DOI: 10.5517/ccdc.csd.cc28c1rg
title: CCDC 2095995: Experimental Crystal Structure Determination

description: Related Article: Irene Reviejo, Joan Vinueza, Javier Ramos, Marta E. G. Mosquera, Vanessa Tabernero, Gerardo Jiménez|2021|Organometallics|40|3076|doi:10.1021/acs.organomet.1c00421
DOI: 10.5517/ccdc.csd.cc27nd1c
title: CCDC 2075172: Experimental Crystal Structure Determination

description: Related Article: Sunhee Lee, Sunmi Kim, Seok Hyun Yoon, Anuradha Dagar, Ikyon Kim|2021|J.Org.Chem.|86|12367|doi:10.1021/acs.joc.1c01315
DOI: 10.5517/ccdc.csd.cc27n503
title: CCDC 2074954: Experimental Crystal Structure Determination

description: Related Article: Xing Chen, Mengke Li, Zhipeng Liu, Can Yang, Haisheng Xie, Xinwei Hu, Shi-Jian Su, Huanfeng Jiang, Wei Zeng|2021|Org.Lett.|23|6724|doi:10.1021/acs.orglett.1c02283