cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11763591.v1
title: Non-coding RNAs as cell wall regulators in <i>Saccharomyces cerevisiae</i>

description: The cell wall of <i>Saccharomyces cerevisiae</i> is an extracellular organelle crucial for preserving its cellular integrity and detecting environmental cues. The cell wall is composed of mannoproteins attached to a polysaccharide network and is continuously remodelled as cells undergo cell division, mating, gametogenesis or adapt to stressors. This makes yeast an excellent model to study the regulation of genes important for cell wall formation and maintenance. Given that certain yeast strains are pathogenic, a better understanding of their life cycle is of clinical relevance. This is why transcriptional regulatory mechanisms governing genes involved in cell wall biogenesis or maintenance have been the focus of numerous studies. However, little is known about the roles of long non-coding RNAs (lncRNAs), a class of transcripts that are thought to possess little or no protein coding potential, in controlling the expression of cell wall-related genes. This review outlines currently known mechanisms of lncRNA-mediated regulation of gene expression in <i>S. cerevisiae</i> and describes examples of lncRNA-regulated genes encoding cell wall proteins. We suggest that the association of currently annotated lncRNAs with the coding sequences and/or promoters of cell wall-related genes highlights a potential role for lncRNAs as important regulators of the yeast cell wall structure.
DOI: 10.6084/m9.figshare.11763609.v2
title: Chlorophyll <i>a</i> relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions

description: Model-derived relationships between chlorophyll <i>a</i> (Chl-<i>a</i>) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl<i>-a</i> model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl<i>-a</i>; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl<i>-a</i> was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl<i>-a</i> and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl<i>-a</i>–nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl<i>-a</i> predictions. Compared to Chl<i>-a</i> observations, Chl<i>-a</i> predictions of the best performing approach for mountain lakes (oligotrophic–eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.
DOI: 10.6084/m9.figshare.11763609.v1
title: Chlorophyll <i>a</i> relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions

description: Model-derived relationships between chlorophyll <i>a</i> (Chl-<i>a</i>) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl<i>-a</i> model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl<i>-a</i>; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl<i>-a</i> was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl<i>-a</i> and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl<i>-a</i>–nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl<i>-a</i> predictions. Compared to Chl<i>-a</i> observations, Chl<i>-a</i> predictions of the best performing approach for mountain lakes (oligotrophic–eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.
DOI: 10.6084/m9.figshare.11763519
title: df_phackathon.Rda

description: An R data frame from a "p-hack-athon" where we encouraged users to analyze a dataset with the goal of producing the smallest p-value.<br>This data frame contains 3 columns:<br><b>id:</b> Numeric. the id of the participant<b>analysis_job:</b> Character. "Yes" or "No" indicating whether the participant performs data analyses as part of their job<b>expr</b>: List. A list containing all R functions run by the participant, captured by the <b>matahari</b> R package
DOI: 10.6084/m9.figshare.11763519.v1
title: df_phackathon.Rda

description: An R data frame from a "p-hack-athon" where we encouraged users to analyze a dataset with the goal of producing the smallest p-value.<br>This data frame contains 3 columns:<br><b>id:</b> Numeric. the id of the participant<b>analysis_job:</b> Character. "Yes" or "No" indicating whether the participant performs data analyses as part of their job<b>expr</b>: List. A list containing all R functions run by the participant, captured by the <b>matahari</b> R package
DOI: 10.6084/m9.figshare.11763465
title: Unveiling <i>Taenia solium</i> kinome profile and its potential for new therapeutic targets

description: <b>Background</b>: Helminth infections cause widespread morbidity and are a significant global disease burden. One among them is Neurocysticercosis, a central nervous system infection caused by the larvae <i>Taenia solium</i>, leading to epilepsy. Helminths are strong immune modulators and can survive for a long time in adverse host environments. Kinases are molecular switches and are essential to initiate/propagate signaling cascades and are detrimental to the regulation of homeostasis. They have been implicated in the progression of many diseases and are potentially lucrative drug targets. <b>Objective</b>: To identify kinases in <i>T. solium</i> proteome and prioritize them as drug targets. <b>Methodology</b>: A Hidden Markov Model (HMM) was used to curate and classify kinases into families based on sequence homology to model organisms followed by phylogenetic analysis of each family. To predict potential drug targets, kinases were identified based on a homologically lethal relationship to <i>C. elegans</i> but non-lethal to humans. Kinases thus selected were searched for matching ligands in SARFkinase and DrugBank databases. <b>Result and conclusion</b>: <i>T. solium</i> kinases make up 1.8% of its proteome, CMGC is the largest kinase family and RGC is the smallest and catalytically inactive family. We predict 23-potential kinases to be drug targets for <i>T. solium</i>.
DOI: 10.6084/m9.figshare.11763465.v1
title: Unveiling <i>Taenia solium</i> kinome profile and its potential for new therapeutic targets

description: <b>Background</b>: Helminth infections cause widespread morbidity and are a significant global disease burden. One among them is Neurocysticercosis, a central nervous system infection caused by the larvae <i>Taenia solium</i>, leading to epilepsy. Helminths are strong immune modulators and can survive for a long time in adverse host environments. Kinases are molecular switches and are essential to initiate/propagate signaling cascades and are detrimental to the regulation of homeostasis. They have been implicated in the progression of many diseases and are potentially lucrative drug targets. <b>Objective</b>: To identify kinases in <i>T. solium</i> proteome and prioritize them as drug targets. <b>Methodology</b>: A Hidden Markov Model (HMM) was used to curate and classify kinases into families based on sequence homology to model organisms followed by phylogenetic analysis of each family. To predict potential drug targets, kinases were identified based on a homologically lethal relationship to <i>C. elegans</i> but non-lethal to humans. Kinases thus selected were searched for matching ligands in SARFkinase and DrugBank databases. <b>Result and conclusion</b>: <i>T. solium</i> kinases make up 1.8% of its proteome, CMGC is the largest kinase family and RGC is the smallest and catalytically inactive family. We predict 23-potential kinases to be drug targets for <i>T. solium</i>.
DOI: 10.6084/m9.figshare.11763447
title: Seafloor morphology and sediment magnetic fabric in a putative 1771 Meiwa Tsunami source region, in the southern Ryukyu Islands, southwest Japan

description: Supplementary data 1: Depth variation of L* data (Lightness in CIE-L*a*b* method) in PC13.
DOI: 10.6084/m9.figshare.11763447.v1
title: Seafloor morphology and sediment magnetic fabric in a putative 1771 Meiwa Tsunami source region, in the southern Ryukyu Islands, southwest Japan

description: Supplementary data 1: Depth variation of L* data (Lightness in CIE-L*a*b* method) in PC13.
DOI: 10.6084/m9.figshare.11763441
title: Seafloor morphology and sediment magnetic fabric in a putative 1771 Meiwa Tsunami source region, in the southern Ryukyu Islands, southwest Japan

description: Supplementary data 4: Anisotropy of magnetic susceptibility data of PC013. For the full caption see the accompanying supplementary data 5.
DOI: 10.6084/m9.figshare.11763441.v1
title: Seafloor morphology and sediment magnetic fabric in a putative 1771 Meiwa Tsunami source region, in the southern Ryukyu Islands, southwest Japan

description: Supplementary data 4: Anisotropy of magnetic susceptibility data of PC013. For the full caption see the accompanying supplementary data 5.
DOI: 10.6084/m9.figshare.11763435
title: Seafloor morphology and sediment magnetic fabric in a putative 1771 Meiwa Tsunami source region, in the southern Ryukyu Islands, southwest Japan

description: Supplementary data 3: Anisotropy of magnetic susceptibility data of PC011. For the full caption see the accompanying supplementary data 5.
DOI: 10.6084/m9.figshare.11763438
title: Seafloor morphology and sediment magnetic fabric in a putative 1771 Meiwa Tsunami source region, in the southern Ryukyu Islands, southwest Japan

description: Supplementary data 2: Anisotropy of magnetic susceptibility data of PC03. For the full caption see the accompanying supplementary data 5.
DOI: 10.6084/m9.figshare.11763435.v1
title: Seafloor morphology and sediment magnetic fabric in a putative 1771 Meiwa Tsunami source region, in the southern Ryukyu Islands, southwest Japan

description: Supplementary data 3: Anisotropy of magnetic susceptibility data of PC011. For the full caption see the accompanying supplementary data 5.
DOI: 10.6084/m9.figshare.11763438.v1
title: Seafloor morphology and sediment magnetic fabric in a putative 1771 Meiwa Tsunami source region, in the southern Ryukyu Islands, southwest Japan

description: Supplementary data 2: Anisotropy of magnetic susceptibility data of PC03. For the full caption see the accompanying supplementary data 5.
DOI: 10.6084/m9.figshare.11763192
title: Appendix AeB.pdf

description: This appendix describes the numerical methodology of the new method of implementing viscoelastic modeling in the time domain - DIM (Direct Insertion Method). This appendix complements the text of the article and provides more details of the method.
DOI: 10.6084/m9.figshare.11763204
title: Figure 1 raw data

description: Raw fluorescence intensities and stochastic simulation outputs, which were used to generate data, presented in Figure 1.<br><b><br></b><b>Figure 1.</b> Rab5:GDI activation on phospholipid membranes is ultrasensitive and stochastic. (A) Schematic of Rab5 activation reconstitution assay on a supported lipid bilayer (SLB). (B) Top panel: addition of Rabex5:Rabaptin5 triggers nucleotide exchange by CF488A-Rab5, which can be followed by an increase of fluorescence intensity on the membrane surface. Solid line is mean normalized intensity, shaded area corresponds to SD (N = 4). Bottom: micrographs of CF488A-Rab5 binding to the SLB after addition of 200 nM GEF complex and corresponding kymograph (below) taken along the yellow line. Scale bar = 5 µm. (C) Rab5 intensity traces obtained at increasing Rabex5:Rabaptin5 concentrations. (D) Rab5:GDI activation response diagram with Rabex5:Rabaptin5. The fold change was calculated by dividing the fluorescence intensity at steady state with the average signal 10 min before GEF addition. (E) Activation delay Ti decreases with higher Rabex5:Rabaptin5 concentration. Where no detectable activation was observed within 150 min, the Tis are denoted as &gt;150 min and shown in orange. Error bars are SD. (F) Relative maximum rates kmax against the GEF complex concentration reveal cooperativity of Rab5 activation. Without detectable activation within 150 min, the activation rate was determined to be 0 and the corresponding points are depicted in orange. Error bars are means ± SD. (G) Schematic representation of modeled molecular interactions. We constructed a model of the minimal Rab5 activation network based on the known literature (9–11, 21, 53). The ODEs were derived from mass action kinetics. (H) Stochastic model simulations of Rab5 activation at increasing Rabex5:Rabaptin5 particle numbers. Shown are average curves from 50 individual runs in bold and 10 random traces per condition. The effective simulation time was scaled to align with experimental results. (I-K) Signal fold change, temporal delays and relative maximum rates from the stochastic simulations in H. We ran 50 individual stochastic simulations per condition.
DOI: 10.6084/m9.figshare.11763204.v1
title: Figure 1 raw data

description: Raw fluorescence intensities and stochastic simulation outputs, which were used to generate data, presented in Figure 1.<br><b><br></b><b>Figure 1.</b> Rab5:GDI activation on phospholipid membranes is ultrasensitive and stochastic. (A) Schematic of Rab5 activation reconstitution assay on a supported lipid bilayer (SLB). (B) Top panel: addition of Rabex5:Rabaptin5 triggers nucleotide exchange by CF488A-Rab5, which can be followed by an increase of fluorescence intensity on the membrane surface. Solid line is mean normalized intensity, shaded area corresponds to SD (N = 4). Bottom: micrographs of CF488A-Rab5 binding to the SLB after addition of 200 nM GEF complex and corresponding kymograph (below) taken along the yellow line. Scale bar = 5 µm. (C) Rab5 intensity traces obtained at increasing Rabex5:Rabaptin5 concentrations. (D) Rab5:GDI activation response diagram with Rabex5:Rabaptin5. The fold change was calculated by dividing the fluorescence intensity at steady state with the average signal 10 min before GEF addition. (E) Activation delay Ti decreases with higher Rabex5:Rabaptin5 concentration. Where no detectable activation was observed within 150 min, the Tis are denoted as &gt;150 min and shown in orange. Error bars are SD. (F) Relative maximum rates kmax against the GEF complex concentration reveal cooperativity of Rab5 activation. Without detectable activation within 150 min, the activation rate was determined to be 0 and the corresponding points are depicted in orange. Error bars are means ± SD. (G) Schematic representation of modeled molecular interactions. We constructed a model of the minimal Rab5 activation network based on the known literature (9–11, 21, 53). The ODEs were derived from mass action kinetics. (H) Stochastic model simulations of Rab5 activation at increasing Rabex5:Rabaptin5 particle numbers. Shown are average curves from 50 individual runs in bold and 10 random traces per condition. The effective simulation time was scaled to align with experimental results. (I-K) Signal fold change, temporal delays and relative maximum rates from the stochastic simulations in H. We ran 50 individual stochastic simulations per condition.
DOI: 10.6084/m9.figshare.11763192.v1
title: Appendix AeB.pdf

description: This appendix describes the numerical methodology of the new method of implementing viscoelastic modeling in the time domain - DIM (Direct Insertion Method). This appendix complements the text of the article and provides more details of the method.
DOI: 10.6084/m9.figshare.11763192.v3
title: Appendix AeB.pdf

description: This appendix describes the numerical methodology of the new method of implementing viscoelastic modeling in the time domain - DIM (Direct Insertion Method). This appendix complements the text of the article and provides more details of the method.
DOI: 10.6084/m9.figshare.11763186
title: Catalogue of Transparent Lobbying - Czech Rep (data to 31.12.2018).xlsx

description: Catalogue of Transparent Lobbying - data for the Czech Republic, data updated to 31.12.2018
DOI: 10.6084/m9.figshare.11763192.v2
title: Appendix AeB.pdf

description: This appendix describes the numerical methodology of the new method of implementing viscoelastic modeling in the time domain - DIM (Direct Insertion Method). This appendix complements the text of the article and provides more details of the method.
DOI: 10.6084/m9.figshare.11763186.v1
title: Catalogue of Transparent Lobbying - Czech Rep (data to 31.12:2018).xlsx

description: Catalogue of Transparent Lobbying - data for the Czech Republic, data updated to 31.12.2018
DOI: 10.6084/m9.figshare.11763075
title: The first archaeointensity records from New Zealand: evidence for a fifteenth century AD archaeomagnetic "spike" in the SW Pacific region?

description: All specimen-level palaeointensity data, quality parameters, checks and site averages. Columns give specimen ID (specimens marked with a * are those for which a high blocking temperature component was subtracted before the palaeointensity was estimated); palaeointensity estimate; uncertainty <i>σ</i><sub>B</sub>); T<sub>min</sub> and T<sub>max</sub>, the blocking temperature range over which the palaeointensity was calculated; <i>N</i>, the number of temperature steps in this range; <i>β</i> is the fractional uncertainty – the ratio of <i>σ</i><sub>B</sub> to the palaeointensity; <i>f</i> is the fraction of the NRM, (or low blocking temperature component) used in the calculation; <i>g</i> is the gap factor and <i>q</i> a quality factor (= <i>fg/β</i>) (both from Coe <i>et al</i>., 1978); <i>w</i> (=<i>q</i>/√<i>N</i>−2) is the weighting factor of Prevot <i>et al</i>. (1985); <i>δ</i><sub>CK</sub> and <i>δ</i><sub>TR</sub> are measures of the maximum differences found in pTRM checks and tail checks respectively; MAD<i><sub>free</sub></i> is the maximum angular deviation of the principal component analysis fit to the remanence data over the selected temperature interval calculated without anchoring the fit to the origin of the vector component plot; TT* class is the overall quality classification of Leonhardt <i>et al</i>., 2004, modified by Paterson <i>et al</i>. (2014). The criteria for TT*A and TT*B classes are given at the end of the appendix. For each site a simple average and standard deviation is given, an inverse variance weighted average, and an average and standard deviation of data weighted according to the parameter <i>w</i> of Prevot <i>et al</i>., (1985). There is insignificant difference between the results of these different methods of averaging, and the <i>w</i>-weighted average was adopted.
DOI: 10.6084/m9.figshare.11763075.v1
title: The first archaeointensity records from New Zealand: evidence for a fifteenth century AD archaeomagnetic "spike" in the SW Pacific region?

description: All specimen-level palaeointensity data, quality parameters, checks and site averages. Columns give specimen ID (specimens marked with a * are those for which a high blocking temperature component was subtracted before the palaeointensity was estimated); palaeointensity estimate; uncertainty <i>σ</i><sub>B</sub>); T<sub>min</sub> and T<sub>max</sub>, the blocking temperature range over which the palaeointensity was calculated; <i>N</i>, the number of temperature steps in this range; <i>β</i> is the fractional uncertainty – the ratio of <i>σ</i><sub>B</sub> to the palaeointensity; <i>f</i> is the fraction of the NRM, (or low blocking temperature component) used in the calculation; <i>g</i> is the gap factor and <i>q</i> a quality factor (= <i>fg/β</i>) (both from Coe <i>et al</i>., 1978); <i>w</i> (=<i>q</i>/√<i>N</i>−2) is the weighting factor of Prevot <i>et al</i>. (1985); <i>δ</i><sub>CK</sub> and <i>δ</i><sub>TR</sub> are measures of the maximum differences found in pTRM checks and tail checks respectively; MAD<i><sub>free</sub></i> is the maximum angular deviation of the principal component analysis fit to the remanence data over the selected temperature interval calculated without anchoring the fit to the origin of the vector component plot; TT* class is the overall quality classification of Leonhardt <i>et al</i>., 2004, modified by Paterson <i>et al</i>. (2014). The criteria for TT*A and TT*B classes are given at the end of the appendix. For each site a simple average and standard deviation is given, an inverse variance weighted average, and an average and standard deviation of data weighted according to the parameter <i>w</i> of Prevot <i>et al</i>., (1985). There is insignificant difference between the results of these different methods of averaging, and the <i>w</i>-weighted average was adopted.
DOI: 10.5281/zenodo.3998875
title: Annotree - GTDB bacteria R95 database

description: GTDB bacteria R95 database from AnnoTree contains all necessary information for visualizing the bacterial tree (tree structure, kegg hits, pfam hits, tigrfam hits)
DOI: 10.5281/zenodo.3998874
title: Annotree - GTDB bacteria R95 database

description: GTDB bacteria R95 database from AnnoTree contains all necessary information for visualizing the bacterial tree (tree structure, kegg hits, pfam hits, tigrfam hits)
DOI: 10.6084/m9.figshare.11762886
title: Comprehensive characterization of driver genes in diffuse large B-cell lymphoma

description: Somatic mutations, expression data and clinical features of 48 lymphoma cases
DOI: 10.6084/m9.figshare.11762886.v1
title: Comprehensive characterization of driver genes in diffuse large B-cell lymphoma

description: Somatic mutations, expression data and clinical features of 48 lymphoma cases