cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4529009.v1
title: Portal Forecasts 1/6/2017

description: This is a forecast made for the upcoming January 6 - 8 rodent trapping at the long-term Portal research site in southern Arizona. Four models were used to achieve forecasts with 90% prediction intervals. There are two forecast tables. One is for the site as a whole (total means across all plots, all treatment types), named "All". The other is for the 4 long-term Control plots only ("Controls").<br>Two naive models using the defaults in the forecast package in R were used to create forecasts for the total granivore captures (species="Total") over the next 12 trapping periods, the first being tomorrow.<br>The "Forecast" modelforecast(abundances$total,h=12,level=0.95,BoxCox.lambda(0),allow.multiplicative.trend=T)<br>The "AutoArima" modelforecast(auto.arima(abundances$total,lambda = 0),h=12,level=0.95,fan=T)<br>Species-level predictions, as well as another total captures prediction, were made using the tscount package in R.<br>The "NegBinom Time Series" model, by species (species codes are the first letter of the genus and species)<br>model=tsglm(abundances[[s]],model=list(past_obs=1,past_mean=11),distr="nbinom")<br>predict(model,12,level=0.9) <br>The "Poisson Environmental" model, by species (species codes are the first letter of the genus and species), using monthly minimum temperature, maximum tempurature, mean temperature, total precipitation from our on-site weather station, and NDVI from a compiled LANDSAT MODIS estimate. Model selection was based on AIC and performed for each species.<br>model=tsglm(abundances[[s]],model=list(past_obs=1,past_mean=11),distr="poisson",xreg=weather)<br>predict(model,12,level=0.9,newdata=weatherforecast)
DOI: 10.6084/m9.figshare.4529033.v1
title: duodenum tumor1.xls

description: raw data for duodenal GISTs
DOI: 10.6084/m9.figshare.4529033.v3
title: duodenum tumor1.xls

description: raw data for duodenal GISTs
DOI: 10.6084/m9.figshare.4525082
title: A data set with kinematic and ground reaction forces of human balance

description: This data set comprises data with the three-dimensional kinematics of the whole body and the ground reaction forces (with a dual force platform setup) of subjects standing still for 60 s in different conditions where vision and the standing surface were manipulated. Twenty-seven young subjects and 22 old subjects were evaluated.The data set comprises a file with meta data plus 1813 files with the ground reaction force and kinematics data for the 49 subjects (3 files for each of the 12 trials plus one file for each subject). The file with meta data has information about the subject’s sociocultural, demographic, and health characteristics.A companion Jupyter Notebook (available at https://github.com/demotu/datasets/tree/master/PDS) presents the programming code to generate such analyses and other examples.<br>
DOI: 10.6084/m9.figshare.4529033.v2
title: duodenum tumor1.xls

description: raw data for duodenal GISTs
DOI: 10.6084/m9.figshare.12445067
title: Ms: Trail-pheromone modulates subjective reward evaluation in Argentine ants

description: Row Data accompanying the manuscript. <b>Trail-pheromone modulates subjective reward evaluation in Argentine ants<i></i></b> <b>Natacha Rossi, Muriel Pereyra, Mariel Alejandra Moauro, Martin Giurfa, Patrizia d’Ettorre, Roxana Josens</b><br>
DOI: 10.6084/m9.figshare.4525076
title: Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset

description: Harris TD, Graham JL. 2017. Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset. Lake Reserve Manage. 33:32-48. Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations &lt;60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.
DOI: 10.6084/m9.figshare.4525079
title: Precision Matrix Estimation With ROPE

description: It is known that the accuracy of the maximum likelihood-based covariance and precision matrix estimates can be improved by penalized log-likelihood estimation. In this article, we propose a ridge-type operator for the precision matrix estimation, ROPE for short, to maximize a penalized likelihood function where the Frobenius norm is used as the penalty function. We show that there is an explicit closed form representation of a shrinkage estimator for the precision matrix when using a penalized log-likelihood, which is analogous to ridge regression in a regression context. The performance of the proposed method is illustrated by a simulation study and real data applications. Computer code used in the example analyses as well as other supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4525073
title: SupplTable4_v1.5.xlsx

description: Supplementary table of T cell epitopes<br>
DOI: 10.6084/m9.figshare.4525076.v1
title: Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset

description: Harris TD, Graham JL. 2017. Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset. Lake Reserve Manage. 33:32-48. Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations &lt;60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.
DOI: 10.6084/m9.figshare.4528016.v1
title: A Cross-Entropy Approach to the Estimation of Generalised Linear Multilevel Models

description: In this paper we use the cross-entropy method for noisy optimisation for fitting generalised linear multilevel models through maximum likelihood. We propose specifications of the instrumental distributions for positive and bounded parameters that improve the computational performance. We also introduce a new stopping criterion, which has the advantage of being problem-independent. In a second step we find, by means of extensive Monte Carlo experiments, the most suitable values of the input parameters of the algorithm. Finally, we compare the method to the benchmark estimation technique based on numerical integration. The cross-entropy approach turns out to be preferable from both the statistical and the computational point of view. In the last part of the paper, the method is used to model the probability of firm exits in the healthcare industry in Italy. Supplemental materials are available online.
DOI: 10.6084/m9.figshare.4525073.v1
title: SupplTable4_v1.5.xlsx

description: Supplementary table of T cell epitopes<br>
DOI: 10.6084/m9.figshare.4525079.v1
title: Precision Matrix Estimation with ROPE

description: It is known that the accuracy of the maximum likelihood based covariance and precision matrix estimates can be improved by penalized log-likelihood estimation. In this article we propose a Ridge-type Operator for the Precision matrix Estimation, ROPE for short, to maximize a penalized likelihood function where the Frobenius norm is used as the penalty function. We show that there is an explicit closed form representation of a shrinkage estimator for the precision matrix when using a penalized log-likelihood, which is analogous to ridge regression in a regression context. The performance of the proposed method is illustrated by a simulation study and real data applications. Computer codes used in the example analyses as well as other supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4525079.v3
title: Precision Matrix Estimation With ROPE

description: It is known that the accuracy of the maximum likelihood-based covariance and precision matrix estimates can be improved by penalized log-likelihood estimation. In this article, we propose a ridge-type operator for the precision matrix estimation, ROPE for short, to maximize a penalized likelihood function where the Frobenius norm is used as the penalty function. We show that there is an explicit closed form representation of a shrinkage estimator for the precision matrix when using a penalized log-likelihood, which is analogous to ridge regression in a regression context. The performance of the proposed method is illustrated by a simulation study and real data applications. Computer code used in the example analyses as well as other supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4525079.v2
title: Precision Matrix Estimation With ROPE

description: It is known that the accuracy of the maximum likelihood-based covariance and precision matrix estimates can be improved by penalized log-likelihood estimation. In this article, we propose a ridge-type operator for the precision matrix estimation, ROPE for short, to maximize a penalized likelihood function where the Frobenius norm is used as the penalty function. We show that there is an explicit closed form representation of a shrinkage estimator for the precision matrix when using a penalized log-likelihood, which is analogous to ridge regression in a regression context. The performance of the proposed method is illustrated by a simulation study and real data applications. Computer code used in the example analyses as well as other supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4524914
title: Functional Autoregression for Sparsely Sampled Data

description: We develop a hierarchical Gaussian process model for forecasting and inference of functional time series data. Unlike existing methods, our approach is especially suited for sparsely or irregularly sampled curves and for curves sampled with nonnegligible measurement error. The latent process is dynamically modeled as a functional autoregression (FAR) with Gaussian process innovations. We propose a fully nonparametric dynamic functional factor model for the dynamic innovation process, with broader applicability and improved computational efficiency over standard Gaussian process models. We prove finite-sample forecasting and interpolation optimality properties of the proposed model, which remain valid with the Gaussian assumption relaxed. An efficient Gibbs sampling algorithm is developed for estimation, inference, and forecasting, with extensions for FAR(<i>p</i>) models with model averaging over the lag <i>p</i>. Extensive simulations demonstrate substantial improvements in forecasting performance and recovery of the autoregressive surface over competing methods, especially under sparse designs. We apply the proposed methods to forecast nominal and real yield curves using daily U.S. data. Real yields are observed more sparsely than nominal yields, yet the proposed methods are highly competitive in both settings. Supplementary materials, including R code and the yield curve data, are available online.
DOI: 10.5281/zenodo.4012578
title: kg-covid-19 graph in KGX TSV format, built on Sep 1, 2020, with no CORD-19 data

description: KG-COVID-19 graph in KGX TSV format, built on Sep 1, 2020, with no CORD-19 data
DOI: 10.6084/m9.figshare.4524914.v1
title: Functional Autoregression for Sparsely Sampled Data

description: We develop a hierarchical Gaussian process model for forecasting and inference of functional time series data. Unlike existing methods, our approach is especially suited for sparsely or irregularly sampled curves and for curves sampled with nonnegligible measurement error. The latent process is dynamically modeled as a functional autoregression (FAR) with Gaussian process innovations. We propose a fully nonparametric dynamic functional factor model for the dynamic innovation process, with broader applicability and improved computational efficiency over standard Gaussian process models. We prove finite-sample forecasting and interpolation optimality properties of the proposed model, which remain valid with the Gaussian assumption relaxed. An efficient Gibbs sampling algorithm is developed for estimation, inference, and forecasting, with extensions for FAR(<i>p</i>) models with model averaging over the lag <i>p</i>. Extensive simulations demonstrate substantial improvements in forecasting performance and recovery of the autoregressive surface over competing methods, especially under sparse designs. We apply the proposed methods to forecast nominal and real yield curves using daily U.S. data. Real yields are observed more sparsely than nominal yields, yet the proposed methods are highly competitive in both settings. Supplementary materials, including R code and the yield curve data, are available online.
DOI: 10.5281/zenodo.4012577
title: kg-covid-19 graph in KGX TSV format, built on Sep 1, 2020, with no CORD-19 data

description: KG-COVID-19 graph in KGX TSV format, built on Sep 1, 2020, with no CORD-19 data
DOI: 10.6084/m9.figshare.4524878
title: <i>MIR7–3HG</i>, a MYC-dependent modulator of cell proliferation, inhibits autophagy by a regulatory loop involving AMBRA1

description: Macroautophagy/autophagy is a tightly regulated intracellular catabolic pathway involving the lysosomal degradation of cytoplasmic organelles and proteins to be recycled into metabolic precursors. AMBRA1 (autophagy and Beclin 1 regulator 1) has a central role in the autophagy signaling network; it acts upstream of MTORC1-dependent autophagy by stabilizing the kinase ULK1 (unc-51 like autophagy activating kinase 1) and by favoring autophagosome core complex formation. AMBRA1 also regulates the cell cycle by modulating the activity of the phosphatase PPP2/PP2A (protein phosphatase 2) and degradation of MYC. Of note, post-transcriptional regulation mediated by noncoding microRNAs (MIRNAs) contributes significantly to control autophagy. Here we describe a new role for the microRNA <i>MIR7–3HG/MIR-7</i> as a potent autophagy inhibitor. Indeed, <i>MIR7–3HG</i> targets the 3′ untranslated region (UTR) of <i>AMBRA1</i> mRNA, inducing a decrease of both <i>AMBRA1</i> mRNA and protein levels, and thus causing a block in autophagy. Furthermore, <i>MIR7–3HG</i>, through AMBRA1 downregulation, prevents MYC dephosphorylation, establishing a positive feedback for its own transcription. These data suggest a new and interesting role of <i>MIR7–3HG</i> as an anti-autophagic MIRNA that may affect oncogenesis through the regulation of the tumor suppressor AMBRA1.
DOI: 10.6084/m9.figshare.4524878.v2
title: <i>MIR7–3HG</i>, a MYC-dependent modulator of cell proliferation, inhibits autophagy by a regulatory loop involving AMBRA1

description: Macroautophagy/autophagy is a tightly regulated intracellular catabolic pathway involving the lysosomal degradation of cytoplasmic organelles and proteins to be recycled into metabolic precursors. AMBRA1 (autophagy and Beclin 1 regulator 1) has a central role in the autophagy signaling network; it acts upstream of MTORC1-dependent autophagy by stabilizing the kinase ULK1 (unc-51 like autophagy activating kinase 1) and by favoring autophagosome core complex formation. AMBRA1 also regulates the cell cycle by modulating the activity of the phosphatase PPP2/PP2A (protein phosphatase 2) and degradation of MYC. Of note, post-transcriptional regulation mediated by noncoding microRNAs (MIRNAs) contributes significantly to control autophagy. Here we describe a new role for the microRNA <i>MIR7–3HG/MIR-7</i> as a potent autophagy inhibitor. Indeed, <i>MIR7–3HG</i> targets the 3′ untranslated region (UTR) of <i>AMBRA1</i> mRNA, inducing a decrease of both <i>AMBRA1</i> mRNA and protein levels, and thus causing a block in autophagy. Furthermore, <i>MIR7–3HG</i>, through AMBRA1 downregulation, prevents MYC dephosphorylation, establishing a positive feedback for its own transcription. These data suggest a new and interesting role of <i>MIR7–3HG</i> as an anti-autophagic MIRNA that may affect oncogenesis through the regulation of the tumor suppressor AMBRA1.
DOI: 10.6084/m9.figshare.4524878.v1
title: <i>MIR7-3HG</i>, a MYC-dependent modulator of cell proliferation, inhibits autophagy by a regulatory loop involving AMBRA1

description: Macroautophagy/autophagy is a tightly regulated intracellular catabolic pathway involving the lysosomal degradation of cytoplasmic organelles and proteins to be recycled into metabolic precursors. AMBRA1 (autophagy and beclin 1 regulator 1) has a central role in the autophagy signaling network; it acts upstream of MTORC1-dependent autophagy by stabilizing the kinase ULK1 (unc-51 like autophagy activating kinase 1) and by favoring autophagosome core complex formation. AMBRA1 also regulates the cell cycle by modulating the activity of the phosphatase PPP2/PP2A (protein phosphatase 2) and degradation of MYC. Of note, post-transcriptional regulation mediated by noncoding microRNAs (MIRNAs) contributes significantly to control autophagy. Here we describe a new role for the microRNA <i>MIR7–3HG/MIR-7</i> as a potent autophagy inhibitor. Indeed, <i>MIR7–3HG</i> targets the 3′ untranslated region (UTR) of <i>AMBRA1</i> mRNA, inducing a decrease of both <i>AMBRA1</i> mRNA and protein levels, and thus causing a block in autophagy. Furthermore, <i>MIR7–3HG</i>, through AMBRA1 downregulation, prevents MYC dephosphorylation, establishing a positive feedback for its own transcription. These data suggest a new and interesting role of <i>MIR7–3HG</i> as an anti-autophagic MIRNA that may affect oncogenesis through the regulation of the tumor suppressor AMBRA1.
DOI: 10.6084/m9.figshare.4524848
title: Metabolic effects of fasting on human and mouse blood in vivo

description: Starvation is a strong physiological stimulus of macroautophagy/autophagy. In this study, we addressed the question as to whether it would be possible to measure autophagy in blood cells after nutrient deprivation. Fasting of mice for 48 h (which causes ∼20% weight loss) or starvation of human volunteers for up to 4 d (which causes &lt;2% weight loss) provokes major changes in the plasma metabolome, yet induces only relatively minor alterations in the intracellular metabolome of circulating leukocytes. White blood cells from mice and human volunteers responded to fasting with a marked reduction in protein lysine acetylation, affecting both nuclear and cytoplasmic compartments. In circulating leukocytes from mice that underwent 48-h fasting, an increase in LC3B lipidation (as assessed by immunoblotting and immunofluorescence) only became detectable if the protease inhibitor leupeptin was injected 2 h before drawing blood. Consistently, measurement of an enhanced autophagic flux was only possible if white blood cells from starved human volunteers were cultured in the presence or absence of leupeptin. Whereas all murine leukocyte subpopulations significantly increased the number of LC3B<sup>+</sup> puncta per cell in response to nutrient deprivation, only neutrophils from starved volunteers showed signs of activated autophagy (as determined by a combination of multi-color immunofluorescence, cytofluorometry and image analysis). Altogether, these results suggest that white blood cells are suitable for monitoring autophagic flux. In addition, we propose that the evaluation of protein acetylation in circulating leukocytes can be adopted as a biochemical marker of organismal energetic status.
DOI: 10.6084/m9.figshare.4524869
title: Metabolic interactions between cysteamine and epigallocatechin gallate

description: Phase II clinical trials indicate that the combination of cysteamine plus epigallocatechin gallate (EGCG) is effective against cystic fibrosis in patients bearing the most frequent etiological mutation (CFTRΔF508). Here, we investigated the interaction between both agents on cultured respiratory epithelia cells from normal and CFTRΔF508-mutated donors. We observed that the combination of both agents affected metabolic circuits (and in particular the tricarboxylic acid cycle) in a unique way and that cysteamine plus EGCG reduced cytoplasmic protein acetylation more than each of the 2 components alone. In a cell-free system, protein cross-linking activity of EGCG was suppressed by cysteamine. Finally, EGCG was able to enhance the conversion of cysteamine into taurine in metabolic flux experiments. Altogether, these results indicate that multiple pharmacological interactions occur between cysteamine and EGCG, suggesting that they contribute to the unique synergy of both agents in restoring the function of mutated CFTRΔF508.