cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24570361.v2
title: Fast Matrix-Free Methods for Model-Based Personalized Synthetic MR Imaging

description: Synthetic Magnetic Resonance (MR) imaging predicts images at new design parameter settings from a few observed MR scans. Model-based methods, that use both the physical and statistical properties underlying the MR signal and its acquisition, can predict images at any setting from as few as three scans, allowing it to be used in individualized patient- and anatomy-specific contexts. However, the estimation problem in model-based synthetic MR imaging is ill-posed and so regularization, in the form of correlated Gaussian markov random fields, is imposed on the voxel-wise spin-lattice relaxation time, spin-spin relaxation time and the proton density underlying the MR image. We develop theoretically sound but computationally practical matrix-free estimation methods for synthetic MR imaging. Our evaluations demonstrate superior performance of our methods in currently-used clinical settings when compared to existing model-based and deep learning methods. Moreover, unlike deep learning approaches, our fast methodology can synthesize needed images during patient visits, with good estimation and prediction accuracy and consistency. An added strength of our model-based approach, also developed and illustrated here, is the accurate estimation of standard errors of regional contrasts in the synthesized images. A R package symr implements our methodology. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24712600
title: A Geometric Algorithm for Contrastive Principal Component Analysis in High Dimension

description: Principal component analysis (PCA) has been widely used in exploratory data analysis. Contrastive PCA (Abid et al.), a generalized method of PCA, is a new tool used to capture features of a target dataset relative to a background dataset while preserving the maximum amount of information contained in the data. With high dimensional data, contrastive PCA becomes impractical due to its high computational requirement of forming the contrastive covariance matrix and associated eigenvalue decomposition for extracting leading components. In this article, we propose a geometric curvilinear-search method to solve this problem and provide a convergence analysis. Our approach offers significant computational efficiencies. Specifically, it reduces the time complexity from O((n∨m)p2) to a more manageable O((n∨m)pr), where <i>n</i>, <i>m</i> are the sample sizes of the target data and background data, respectively, <i>p</i> is the data dimension and <i>r</i> is the number of leading components. Additionally, we streamline the space complexity from O(p2), necessary for storing the contrastive covariance matrix, to a more economical O((n∨m)p), sufficient for storing the data alone. Numerical examples are presented to show the merits of the proposed algorithm. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24712600.v1
title: A Geometric Algorithm for Contrastive Principal Component Analysis in High Dimension

description: Principal component analysis (PCA) has been widely used in exploratory data analysis. Contrastive PCA (Abid et al.), a generalized method of PCA, is a new tool used to capture features of a target dataset relative to a background dataset while preserving the maximum amount of information contained in the data. With high dimensional data, contrastive PCA becomes impractical due to its high computational requirement of forming the contrastive covariance matrix and associated eigenvalue decomposition for extracting leading components. In this article, we propose a geometric curvilinear-search method to solve this problem and provide a convergence analysis. Our approach offers significant computational efficiencies. Specifically, it reduces the time complexity from O((n∨m)p2) to a more manageable O((n∨m)pr), where <i>n</i>, <i>m</i> are the sample sizes of the target data and background data, respectively, <i>p</i> is the data dimension and <i>r</i> is the number of leading components. Additionally, we streamline the space complexity from O(p2), necessary for storing the contrastive covariance matrix, to a more economical O((n∨m)p), sufficient for storing the data alone. Numerical examples are presented to show the merits of the proposed algorithm. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24570361.v1
title: Fast matrix-free methods for model-based personalized synthetic MR imaging

description: Synthetic Magnetic Resonance (MR) imaging predicts images at new design parameter settings from a few observed MR scans. Model-based methods, that use both the physical and statistical properties underlying the MR signal and its acquisition, can predict images at any setting from as few as three scans, allowing it to be used in individualized patient- and anatomy-specific contexts. However, the estimation problem in model-based synthetic MR imaging is ill-posed and so regularization, in the form of correlated Gaussian Markov Random Fields, is imposed on the voxel-wise spin-lattice relaxation time, spin-spin relaxation time and the proton density underlying the MR image. We develop theoretically sound but computationally practical matrix-free estimation methods for synthetic MR imaging. Our evaluations demonstrate superior performance of our methods in currently-used clinical settings when compared to existing model-based and deep learning methods. Moreover, unlike deep learning approaches, our fast methodology can synthesize needed images during patient visits, with good estimation and prediction accuracy and consistency. An added strength of our model-based approach, also developed and illustrated here, is the accurate estimation of standard errors of regional contrasts in the synthesized images. A R package symr implements our methodology.
DOI: 10.6084/m9.figshare.24724772
title: Smooth Multi-Period Forecasting With Application to Prediction of COVID-19 Cases

description: Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this article we consider the problem of multi-period forecasting that aims to predict several horizons at once. We propose a novel approach that forces the prediction to be “smooth” across horizons and apply it to two tasks: point estimation via regression and interval prediction via quantile regression. This methodology was developed for real-time distributed COVID-19 forecasting. We illustrate the proposed technique with the COVIDcast dataset as well as a small simulation example. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24724772.v2
title: Smooth Multi-Period Forecasting With Application to Prediction of COVID-19 Cases

description: Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this article we consider the problem of multi-period forecasting that aims to predict several horizons at once. We propose a novel approach that forces the prediction to be “smooth” across horizons and apply it to two tasks: point estimation via regression and interval prediction via quantile regression. This methodology was developed for real-time distributed COVID-19 forecasting. We illustrate the proposed technique with the COVIDcast dataset as well as a small simulation example. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24724772.v1
title: Smooth multi-period forecasting with application to prediction of COVID-19 cases

description: Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this paper we consider the problem of multi-period forecasting that aims to predict several horizons at once. We propose a novel approach that forces the prediction to be ”smooth” across horizons and apply it to two tasks: point estimation via regression and interval prediction via quantile regression. This methodology was developed for real-time distributed COVID-19 forecasting. We illustrate the proposed technique with the CovidCast dataset as well as a small simulation example.
DOI: 10.6073/pasta/b594cf6f5054db6457e2caded4f0f3f3
title: PIE LTER 15-minute meteorological data from the Marshview Farm weather station located in Newbury, MA, year 2023

description: Meteorological measurements for 2023 at MBL Marshview Farm, Newbury, MA. Sensors conduct measurements every 5 seconds and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
DOI: 10.6073/pasta/fe7eb105f7764848afd3eb7fb63a132b
title: PIE LTER 15-minute OTT RLS measurements of water column depth at 15 minute intervals in the lower Plum Island Sound off the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2023.

description: Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2023. An OTT radar level sensor (RLS) installed September 20, 2011 out of the water under the concrete pad on the Ipswich Bay Yacht Club pier, Ipswich, MA. The RLS was mounted so that continuous year round measurements can be conducted without the concern of ice flows damaging the sensor.
DOI: 10.5066/p955iq3v
title: Data release of monazite and xenotime geochronology for Proterozoic rocks in central Colorado and monazite and zircon geochronology and whole-rock isotope geochemistry fo a Proterozoic drill core sample from western Colorado

description: This U.S. Geological Survey (USGS) data release provides monazite and xenotime geochronology from Proterozoic rocks collected from outcrop in central Colorado and whole rock isotope geochemistry, and zircon and monazite geochronology from a Proterozoic rock sampled from a drill core from western Colorado. The drill core sample was collected from the 1 Paradox Valley injection core near Bedrock, CO at a depth of 4,755-4,765 m below the surface. The samples were in order to constrain the age, provenance, metamorphic history, and geochemical character of basement terranes in Colorado.
DOI: 10.5281/zenodo.10471551
title: MetaCompare: A computational pipeline for prioritizing environmental resistome risk
DOI: 10.5281/zenodo.10471550
title: MetaCompare: A computational pipeline for prioritizing environmental resistome risk
DOI: 10.6073/pasta/74e0fc1153f226508a4a52a4a08f08b1
title: PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2023.

description: Wind speed and direction measurements for 2023 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
DOI: 10.5281/zenodo.10432516
title: Usability Evaluation of OFLAT Platform

description: Data, weekly challenges and questionnaires used in Usability Evaluation of OFLAT Platform Dissertation.
DOI: 10.5281/zenodo.10432517
title: Usability Evaluation of OFLAT Platform

description: Data, weekly challenges and questionnaires used in Usability Evaluation of OFLAT Platform Dissertation.
DOI: 10.5281/zenodo.10458699
title: Usability Evaluation of OFLAT Platform

description: Data, weekly challenges and questionnaires used in Usability Evaluation of OFLAT Platform Dissertation.
DOI: 10.5281/zenodo.10471751
title: Usability Evaluation of OFLAT Platform

description: Data, weekly challenges and questionnaires used in Usability Evaluation of OFLAT Platform Dissertation.
DOI: 10.34740/kaggle/dsv/7364524
title: All India Airport Traffic Data (1995-2013)
DOI: 10.6073/pasta/446c0afb9676bd2381c79c0a776b4cb7
title: Prospect Hill Hydrological Stations at Harvard Forest since 2005

description: To better understand the critical role of headwater streams and wetlands in our forest ecosystem, long-term measurements were initiated in 2005 on two small watersheds in the Prospect Hill Tract of the Harvard Forest. On Nelson Brook, weirs were installed on outlet streams of an 11-ha spruce-hemlock wetland (watershed area = 44 ha). On Bigelow Brook, pipes were installed to measure flow above (watershed = 24 ha) and below (watershed = 65 ha) a 3-ha shrub-dominated beaver swamp. The gaged watersheds, though adjacent and comparable in size, differ significantly in topography, soils, wetlands, stream chemistry, stream biota, land-use history, and forest vegetation, and provide an extraordinary opportunity to study the impacts of these factors on small watershed hydrology and ecology.
 Weekly manual measurements were initiated in April 2005. Continuous automated measurements were initiated in December 2007. Data for the current month are available online, updated every 15 minutes, with out-of-range values replaced by NA but values not otherwise checked. Earlier data are checked and archived with missing, questionable, and estimated values flagged, following methods of the LTER ClimbDB project. A log of events affecting station measurements is also posted.
 For current data, please see: https://harvardforest.fas.harvard.edu/meteorological-hydrological-stations. For a complete set of archived data, please see: https://harvardforest.fas.harvard.edu/exist/apps/datasets/showData.html?id=HF070.
DOI: 10.5281/zenodo.10471743
title: Late Cenozoic sediments and foraminifera from the western continental shelf of South Africa

description: Foraminifera are useful in tracking environmental change over time and providing the biostratigraphy of a certain area. On the continental shelf of South Africa, combined with isotopic data, foraminifera provide the required age constraints for the sediments from the margin. This dataset comprises sedimentary data as well as foraminifera counts to document late Cenozoic marine environmental change, the stratigraphy and the sedimentary classification of the shelf.
DOI: 10.5281/zenodo.10471744
title: Late Cenozoic sediments and foraminifera from the western continental shelf of South Africa

description: Foraminifera are useful in tracking environmental change over time and providing the biostratigraphy of a certain area. On the continental shelf of South Africa, combined with isotopic data, foraminifera provide the required age constraints for the sediments from the margin. This dataset comprises sedimentary data as well as foraminifera counts to document late Cenozoic marine environmental change, the stratigraphy and the sedimentary classification of the shelf.
DOI: 10.25380/iastate.24944529
title: Prairie Strip Soil Data

description: This data was collected on July 1, 2019 and July 1, 2020 at Neal Smith National Wildlife Refuge in Jasper county Iowa USA. Samples were collected along transects that bisected contour prairie strips and `ghost` prairie strips in control watersheds. Samples were collected from 3 m upslope to 9 m downslope in order to quantify the prairie strips effects on the surrounding crops and soil. Soil samples were collected to a depth of 15 cm and analyzed for microbial biomass, carbon, nitrogen, plant available nutrients and enzyme activity.
DOI: 10.25380/iastate.24944529.v1
title: Prairie Strip Soil Data

description: This data was collected on July 1, 2019 and July 1, 2020 at Neal Smith National Wildlife Refuge in Jasper county Iowa USA. Samples were collected along transects that bisected contour prairie strips and `ghost` prairie strips in control watersheds. Samples were collected from 3 m upslope to 9 m downslope in order to quantify the prairie strips effects on the surrounding crops and soil. Soil samples were collected to a depth of 15 cm and analyzed for microbial biomass, carbon, nitrogen, plant available nutrients and enzyme activity.
DOI: 10.6073/pasta/e86689f0059c912236e61e657b8547f0
title: Harvard Forest Snow Pillow since 2009

description: Water storage in snowpack is a key factor in the hydrological cycle of central New England. The Harvard Forest Snow Pillow provides continuous long-term measurements of the water content of snowpack (snow water equivalent) during the winter months. The snow pillow is located in a mature mixed hardwood stand with scattered conifers about 50 m north of the Nelson Brook Big Weir. For current data, please see: https://harvardforest.fas.harvard.edu/meteorological-hydrological-stations. For a complete set of archived data, please see: https://harvardforest.fas.harvard.edu/exist/apps/datasets/showData.html?id=HF155.
DOI: 10.7294/24808245.v1
title: <b>Cross-Sectional Survey of Cybercriminology - September 2021</b>

description: This survey is part of a series of cross-sectional online polls. The series begins in 2019. Questions focus on three main areas, cyber offending, cyber victimization, and criminology. Demographic and other computer-use questions are also included.<br><br>
DOI: 10.6084/m9.figshare.24640118.v1
title: Comparison of Fungal and Bacterial Microbiomes of Bats and Their Cave Roosting Environments at El Malpais National Monument, New Mexico, USA

description: The arrival in North America of white-nose syndrome (WNS), a devastating fungal disease in bats, has emphasized the necessity of a comprehensive understanding of the bats’ external skin microbiota. Here, we investigated the composition of the natural bat microbiota pre-WNS and how they are acquired. The fur surfaces of 12 roosting bats, adjacent cave walls, and cave chamber air were sampled in two New Mexican lava caves. Bacterial and fungal diversity were assessed using Illumina MiSeq sequencing. Although many taxa were shared among the sample types, there were significant differences in alpha and beta diversity within and among communities. Bacterial phyla Actinobacteriota (39.1%) and Proteobacteria (27.9%) comprised two-thirds of the sequences. Fungal communities were dominated by Ascomycota (78.9%), followed by Basidiomycota (14.7%), and Mucoromycota (4.3%). Results for bacterial communities suggested that cave walls and cave air influence the bat microbiome and that the bat microbiome can have a reciprocal influence on the microbiome of cave walls. Fungal microbiomes of cave walls, air, and bats appear to have very low impact on each other. Our results begin to elucidate how cave environments may provide natural microbial defenses for bats, one facet in predicting the effect of WNS on western bats.
DOI: 10.6084/m9.figshare.24640118
title: Comparison of Fungal and Bacterial Microbiomes of Bats and Their Cave Roosting Environments at El Malpais National Monument, New Mexico, USA

description: The arrival in North America of white-nose syndrome (WNS), a devastating fungal disease in bats, has emphasized the necessity of a comprehensive understanding of the bats’ external skin microbiota. Here, we investigated the composition of the natural bat microbiota pre-WNS and how they are acquired. The fur surfaces of 12 roosting bats, adjacent cave walls, and cave chamber air were sampled in two New Mexican lava caves. Bacterial and fungal diversity were assessed using Illumina MiSeq sequencing. Although many taxa were shared among the sample types, there were significant differences in alpha and beta diversity within and among communities. Bacterial phyla Actinobacteriota (39.1%) and Proteobacteria (27.9%) comprised two-thirds of the sequences. Fungal communities were dominated by Ascomycota (78.9%), followed by Basidiomycota (14.7%), and Mucoromycota (4.3%). Results for bacterial communities suggested that cave walls and cave air influence the bat microbiome and that the bat microbiome can have a reciprocal influence on the microbiome of cave walls. Fungal microbiomes of cave walls, air, and bats appear to have very low impact on each other. Our results begin to elucidate how cave environments may provide natural microbial defenses for bats, one facet in predicting the effect of WNS on western bats.
DOI: 10.6084/m9.figshare.24962586
title: Supplementary tables: Ataluren delays loss of ambulation and respiratory decline in nonsense mutation Duchenne muscular dystrophy patients

description: <b>These are peer-reviewed supplementary tables for the article 'Ataluren delays loss of ambulation and respiratory decline in nonsense mutation Duchenne muscular dystrophy patients' published in the </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>. </b><b>Supplementary Table 1. </b>Baseline demographics and disease characteristics for Study 019 patients with nmDMD who received ataluren (40 mg/kg/day) plus SoC in at least Study 019 (N = 59) and for propensity-score matched patients with DMD receiving SoC alone in the CINRG DNHS (N = 59), using age at diagnosis for both datasets as the fourth covariate for matching, for the sensitivity analysis of age at LoA. <b>Supplementary Table 2. </b>Baseline demographics and disease characteristics for non-ambulatory Study 019 patients with nmDMD who received ataluren 40 mg/kg/day plus SoC in at least this study (N = 45) and propensity-score matched patients with DMD who received SoC alone in the CINRG DNHS (N = 45), using age at diagnosis for both datasets as the fourth covariate for matching, for the sensitivity analysis of age at decline in respiratory function. <b>Supplementary Table 3. </b>Treatment-emergent adverse events† experienced by patients in the as-treated population of Study 019 (N = 94). <b>Supplementary Table 4. </b>Lipid profile of patients in the as-treated population of Study 019 (N = 94) at baseline and Week 240. <b>Supplementary Table 5.</b> Hypertensive status of patients in the as-treated population of Study 019 (N = 94) from baseline to Week 240. <b>Supplementary Table 6. </b>ECG results of patients of patients in the as-treated population of Study 019 (N = 94) from screening to post-treatment. <b>Supplementary Table 7. </b>Hepatic and renal abnormalities in patients in the as-treated population of Study 019 (N = 94) from screening to post-treatment. <b>Summary: Aim:</b> We investigated the effect of ataluren plus standard of care (SoC) on age at loss of ambulation (LoA) and respiratory decline in patients with nonsense mutation Duchenne muscular dystrophy (nmDMD) versus patients with DMD on SoC alone. <b>Patients & methods: </b>Study 019 was a long-term Phase III study of ataluren safety in nmDMD patients with a history of ataluren exposure. Propensity score matching identified Study 019 and CINRG DNHS patients similar in disease progression predictors. <b>Results & conclusion: </b>Ataluren plus SoC was associated with a 2.2-year delay in age at LoA (p = 0.0006), and a 3.0-year delay in decline of predicted forced vital capacity to &lt;60% in nonambulatory patients (p = 0.0004), versus SoC. Ataluren plus SoC delays disease progression and benefits ambulatory and nonambulatory patients with nmDMD. ClinicalTrials.gov registration: NCT01557400.