cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e112877v1-43687
title: Replication data for: CoVaR

description: CoVaR, defined as the change in the value at risk of the financial system conditional on an institution being under distress relative to its median state. Our estimates show that characteristics such as leverage, size, maturity mismatch, and asset price booms significantly predict CoVaR. We also provide out-of-sample forecasts of a countercyclical, forward-looking measure of systemic risk, and show that the 2006:IV value of this measure would have predicted more than one-third of realized CoVaR during the 2007-2009 financial crisis.
DOI: 10.6084/m9.figshare.11671407
title: Supplementary Material for: Recovery of Dementia Syndrome following Treatment of Brain Inflammation

description: <b><i>Aim/Background:</i></b> This research aims to prevent progression from mild cognitive impairment (MCI) to Alzheimer’s disease. A Japanese study of leprosy patients revealed that the incidence of dementia in leprosy patients was lower than that in patients taking dapsone who had never been treated. But a similar study the following year refuted the finding of less dementia in leprosy patients taking dapsone. According to conflicting reports, <i>Mycobacterium leprae</i> was a factor in reducing the incidence of Alzheimer’s disease. Thus, we formed a hypothesis that if dapsone is administered to patients without leprosy but with MCI and the prophylactic effect of dementia syndrome is observed over a long period of time, we can determine whether dapsone can prevent the progression of MCI to dementia syndrome. If dementia does not occur after treating inflammation in brain cells while dementia develops after a certain long-term period (usually within 2–3 years), brain cell inflammation can be demonstrated as the cause of dementia. <b><i>Methods:</i></b> This is a prospective cohort research. We report on an elderly patient diagnosed with MCI from February 2008 to January 2019. The patient took dapsone 100 mg once a day from 2010 to 2015 for the treatment of MCI. Since 2016, the production of dapsone has ceased in Korea. In June 2018, the patient was diagnosed with Alzheimer’s disease. The patient took Aricept for the treatment of Alzheimer’s disease but complained of serious side effects. And dapsone was re-administered to the patient from November 2018. <b><i>Results:</i></b> The patient recovered to MCI and improved her daily life owing to the treatment with dapsone. The drug controls the inflammatory response in the brain, irrespective of whether proteins are deposited in neurons. <b><i>Conclusions:</i></b>This finding means that dementia syndrome is an inflammatory disease. This research suggests that diagnostic criteria for Alzheimer’s disease should be based on the presence or absence of inflammation in neurons. Because inflammation in neurons can occur in middle age due to various causes, we can treat inflammation in neurons and prevent and treat dementia syndrome, including Alzheimer’s disease.
DOI: 10.6084/m9.figshare.11670603
title: CMIP jet latitude data

description: Data used to produce Figures 1-4 in the paper titled "Role of the Mean State for the Southern Hemispheric Jet Stream Response to CO2 Forcing in CMIP6 models".<br>The CMIP data files are in the format experiment_collection_jet_latitude.csv where "collection" refers to CMIP5 or CMIP6.<br>The CAM4 data is in the file named CAM4_Jet_Latitudes.csv.<br>The ERA5 jet latitude values are in the file amip_cmip5_jet_latitude.csv.
DOI: 10.6084/m9.figshare.11669727
title: Raw data

description: <b>Identification of Genetic Biomarkers for Diagnosis of Myocardial Infarction(MI) compared with angina patients</b><b></b>
DOI: 10.6084/m9.figshare.11670603.v1
title: CMIP jet latitude data

description: Data used to produce Figures 1-4 in the paper titled "Role of the Mean State for the Southern Hemispheric Jet Stream Response to CO2 Forcing in CMIP6 models".<br>The filenames are in the format __.csv where "collection" refers to CMIP5 or CMIP6.
DOI: 10.6084/m9.figshare.11670603.v2
title: CMIP jet latitude data

description: Data used to produce Figures 1-4 in the paper titled "Role of the Mean State for the Southern Hemispheric Jet Stream Response to CO2 Forcing in CMIP6 models".<br>The CMIP data files are in the format experiment_collection_jet_latitude.csv where "collection" refers to CMIP5 or CMIP6.<br>The CAM4 data is in the file named "CAM4_Jet_Latitudes.csv".
DOI: 10.6084/m9.figshare.11670603.v4
title: CMIP jet latitude data

description: Data used to produce Figures 1-4 in the paper titled "Role of the Mean State for the Southern Hemispheric Jet Stream Response to CO2 Forcing in CMIP6 models".<br>The CMIP data files are in the format experiment_collection_jet_latitude.csv where "collection" refers to CMIP5 or CMIP6.<br>The CAM4 data is in the file named CAM4_Jet_Latitudes.csv.<br>The ERA5 jet latitude values are in the file amip_cmip5_jet_latitude.csv.
DOI: 10.6084/m9.figshare.11670603.v3
title: CMIP jet latitude data

description: Data used to produce Figures 1-4 in the paper titled "Role of the Mean State for the Southern Hemispheric Jet Stream Response to CO2 Forcing in CMIP6 models".<br>The CMIP data files are in the format experiment_collection_jet_latitude.csv where "collection" refers to CMIP5 or CMIP6.<br>The CAM4 data is in the file named CAM4_Jet_Latitudes.csv.<br>The ERA5 jet latitude values are in the file amip_cmip5_jet_latitude.csv.
DOI: 10.6084/m9.figshare.11669280
title: alignments Carabidae

description: Combining with the published mitogenomes of Carabidae and five outgroup species from Trachypachidae, Gyrinidae and Dytiscidae, we performed phylogenetic estimates under maximum likelihood and Bayesian inference criteria to investigate the phylogenetic relationships of carabid beetles. The results showed that the family Carabidae was a non-monophyletic assemblage. The subfamilies Cicindelinae, Elaphrinae, Carabinae, Trechinae and Harpalinae were recovered as monophyletic groups. Moreover, the clade (Trechinae + (Brachininae + Harpalinae)) was consistently recovered in all analyses.
DOI: 10.6084/m9.figshare.11669280.v1
title: alignments Carabidae

description: Combining with the published mitogenomes of Carabidae and five outgroup species from Trachypachidae, Gyrinidae and Dytiscidae, we performed phylogenetic estimates under maximum likelihood and Bayesian inference criteria to investigate the phylogenetic relationships of carabid beetles. The results showed that the family Carabidae was a non-monophyletic assemblage. The subfamilies Cicindelinae, Elaphrinae, Carabinae, Trechinae and Harpalinae were recovered as monophyletic groups. Moreover, the clade (Trechinae + (Brachininae + Harpalinae)) was consistently recovered in all analyses.
DOI: 10.6084/m9.figshare.11666460
title: Raw results both pcr and mtt.xlsx

description: SHEDs proliferation assessment via Real time PCR and MTT assay
DOI: 10.6084/m9.figshare.11666460.v1
title: Raw results both pcr and mtt.xlsx

description: SHEDs proliferation assessment via Real time PCR and MTT assay
DOI: 10.6084/m9.figshare.11665593
title: Supplementary Material from Mindreading in the balance: adults' mediolateral leaning and anticipatory looking foretell others’ action preparation in a false-belief interactive task

description: Dataset
DOI: 10.6084/m9.figshare.11665593.v1
title: Supplementary Material from Mindreading in the balance: adults' mediolateral leaning and anticipatory looking foretell others’ action preparation in a false-belief interactive task

description: Dataset
DOI: 10.6084/m9.figshare.11665578
title: ResultsFeaSelR1 from Constraining classifiers in molecular analysis: invariance and robustness

description: Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of this data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by <i>ad hoc</i> simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that <i>a priori</i> chosen invariant models can replace <i>ad hoc</i> robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.
DOI: 10.6084/m9.figshare.11665575
title: Results Accuracies from Constraining classifiers in molecular analysis: invariance and robustness

description: Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of this data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by <i>ad hoc</i> simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that <i>a priori</i> chosen invariant models can replace <i>ad hoc</i> robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.
DOI: 10.6084/m9.figshare.11665575.v1
title: Results Accuracies from Constraining classifiers in molecular analysis: invariance and robustness

description: Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of this data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by <i>ad hoc</i> simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that <i>a priori</i> chosen invariant models can replace <i>ad hoc</i> robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.
DOI: 10.6084/m9.figshare.11665578.v1
title: ResultsFeaSelR1 from Constraining classifiers in molecular analysis: invariance and robustness

description: Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of this data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by <i>ad hoc</i> simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that <i>a priori</i> chosen invariant models can replace <i>ad hoc</i> robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.
DOI: 10.6084/m9.figshare.11665569
title: Read Me from Constraining classifiers in molecular analysis: invariance and robustness

description: Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of this data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by <i>ad hoc</i> simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that <i>a priori</i> chosen invariant models can replace <i>ad hoc</i> robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.
DOI: 10.6084/m9.figshare.11665572
title: foldlists.tar from Constraining classifiers in molecular analysis: invariance and robustness

description: Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of this data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by <i>ad hoc</i> simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that <i>a priori</i> chosen invariant models can replace <i>ad hoc</i> robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.
DOI: 10.6084/m9.figshare.11665569.v1
title: Read Me from Constraining classifiers in molecular analysis: invariance and robustness

description: Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of this data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by <i>ad hoc</i> simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that <i>a priori</i> chosen invariant models can replace <i>ad hoc</i> robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.
DOI: 10.6084/m9.figshare.11665572.v1
title: foldlists.tar from Constraining classifiers in molecular analysis: invariance and robustness

description: Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of this data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by <i>ad hoc</i> simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that <i>a priori</i> chosen invariant models can replace <i>ad hoc</i> robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.
DOI: 10.6084/m9.figshare.11665563
title: R script for connecting LIM-MCMC results to the enaR library from Shifting levels of ecological network's analysis reveals different system properties

description: Network analyses applied to models of complex systems generally contain at least three levels of analyses. Whole-network metrics summarize general organizational features (properties or relationships) of the entire network, while node-level metrics summarize similar organization features but considering individual nodes at the time. The network- and node-level metrics build upon the primary pairwise relationships in the model. As with many analyses, sometimes there are interesting differences at one level that disappear in the summary at another level of analysis. We illustrate this phenomenon with ecosystem network models, where nodes are trophic compartments and pairwise relationships are flows of organic carbon, such as when a predator eats a prey. For this demonstration, we analysed a time-series of 16 models of a lake planktonic food web that describes carbon exchanges within an autumn cyanobacteria bloom and compared the ecological conclusions drawn from the three levels of analysis based on inter-time-step comparisons. A general pattern in our analyses was that the closer the levels are in hierarchy (node versus network, or flow versus node level), the more they tend to align in their conclusions. Our analyses suggest that selecting the appropriate level of analysis, and above all regularly using multiple levels, may be a critical analytical decision.This article is part of the special issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.
DOI: 10.6084/m9.figshare.11665566
title: invariantSVM_1.0.tar from Constraining classifiers in molecular analysis: invariance and robustness

description: Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of this data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by <i>ad hoc</i> simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that <i>a priori</i> chosen invariant models can replace <i>ad hoc</i> robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.
DOI: 10.6084/m9.figshare.11665560
title: Table A1. Table of values of the flows in the new balanced LIM from Shifting levels of ecological network's analysis reveals different system properties

description: Network analyses applied to models of complex systems generally contain at least three levels of analyses. Whole-network metrics summarize general organizational features (properties or relationships) of the entire network, while node-level metrics summarize similar organization features but considering individual nodes at the time. The network- and node-level metrics build upon the primary pairwise relationships in the model. As with many analyses, sometimes there are interesting differences at one level that disappear in the summary at another level of analysis. We illustrate this phenomenon with ecosystem network models, where nodes are trophic compartments and pairwise relationships are flows of organic carbon, such as when a predator eats a prey. For this demonstration, we analysed a time-series of 16 models of a lake planktonic food web that describes carbon exchanges within an autumn cyanobacteria bloom and compared the ecological conclusions drawn from the three levels of analysis based on inter-time-step comparisons. A general pattern in our analyses was that the closer the levels are in hierarchy (node versus network, or flow versus node level), the more they tend to align in their conclusions. Our analyses suggest that selecting the appropriate level of analysis, and above all regularly using multiple levels, may be a critical analytical decision.This article is part of the special issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.