cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/izmynj
title: BEDERE_2023_ASREMLScript_bivariate_3x3strata.pvc

description: :unav
DOI: 10.6073/pasta/edb14581fa6dae179e559e25a81ea5a9
title: Bayesian Analysis of Tree Distributions Across Space and Time in Eastern North America 2010-2011

description: The distributions of many organisms are spatially autocorrelated, but it is unclear whether including spatial terms in species distribution models (SDMs) improves projections of future species distributions. We provide the first comparative test of a purely spatial SDM, a purely non-spatial SDM, and an SDM that combines spatial and environmental information. Spatial SDMs provided better fits to the calibration data, more accurate predictions of a hold-out validation data set of modern trees, and lower false positive rates at all time periods than non-spatial SDMs. Hindcasted projection of spatial SDMs had higher variance than those of non-spatial SDMs. Overall predictive performance of non-spatial and spatial SDMs varied temporally and as a function of niche overlap. Ecological modelers should include spatial terms in SDMs used for projecting future distributions of species.
DOI: 10.6073/pasta/c7d96f6671c2a65eaa5088acc340986d
title: Modeling Range Expansion of Hemlock Woolly Adelgid in Eastern North America 1951-2009

description: Range expansion by native and non-native species will continue to be a major component of global change. Anticipating the potential effects of changes in species distributions requires models capable of forecasting population spread across realistic, heterogeneous landscapes and subject to spatiotemporal variability in habitat suitability. Several decades of theory and model development, as well as increased computing power and availability of fine-resolution GIS data, now make such models possible. This R code allows simulation of the spread of the hemlock woolly adelgid (HWA, Adelges tsugae) under climatic conditions experienced from December of 1951 until March of 2009. The code describes a spatially explicit stochastic model that combines dynamic dispersal and population processes with fine-resolution maps characterizing spatiotemporal heterogeneity in winter temperature and hemlock abundance to model range expansion of HWA. The model is parameterized using multi-year datasets describing population and dispersal dynamics of HWA and is applied to eastern North America.
DOI: 10.17632/9zfkrt8zbg.1
title: Efficacy and Tolerability of Systemic Therapies in Oral Lichen Planus – A Retrospective Cohort Study

description: Supplemental material.
DOI: 10.6073/pasta/5b5fcc0396264899417391c54e1af025
title: Simulating Intrastand Movement of Hemlock Woolly Adelgid at Harvard Forest 2009

description: The hemlock woolly adelgid (HWA), Adelges tsugae Annand (Hemiptera: Adelgidae), has spread rapidly across the eastern USA since its introduction from Japan 60 years ago, causing widespread mortality of both eastern hemlock [Tsuga canadensis (L.) Carriere] and Carolina hemlock [Tsuga caroliniana Engelm. (Pinaceae)]. Although HWA spread patterns have been repeatedly analyzed at regional scales, comparatively little is known about its dispersal potential within and between hemlock stands. As the small size and clonal nature of HWA make it nearly impossible to identify the source populations of dispersing individuals, we simulated intrastand HWA movement in the field by monitoring the movement of clumps of fluorescent powder that are slightly larger than HWA, but much easier to detect in the forest understory. Using three hemlock trees with three colors of fluorescent powder as source populations, we detected dispersal events at the farthest distances within our trapping array (400 m). However, more than 90% of dispersal events were less than 25 m. Dispersal patterns were similar from all three source trees and the distribution of dispersal distances in all cases could be described by lognormal probability density functions with mean dispersal distance of 12-14 m, suggesting that dispersal was relatively independent of location of source trees. In general, we documented tens of thousands of passive dispersal events in the forest understory despite the presence of a dense forest canopy. Thus, even under relatively light-wind conditions, particles of similar dimensions to HWA are capable of intra-stand movement, suggesting that a large population of HWA could rapidly infest other trees within several hundred meter radius, or beyond.
DOI: 10.5281/zenodo.10279446
title: github_1000k_developer_info
DOI: 10.5281/zenodo.10279447
title: github_1000k_developer_info
DOI: 10.5281/zenodo.10160428
title: Individual .ttl files for plate VGF147 samples

description: Individual .ttl files for plate VGF147 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10215788
title: Individual .ttl files for plate VGF147 samples

description: Individual .ttl files for plate VGF147 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10284405
title: Individual .ttl files for plate VGF147 samples

description: Individual .ttl files for plate VGF147 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027096
title: Individual .ttl files for plate VGF147 samples

description: Individual .ttl files for plate VGF147 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027097
title: Individual .ttl files for plate VGF147 samples

description: Individual .ttl files for plate VGF147 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10160429
title: Individual .ttl files for plate VGF146 samples

description: Individual .ttl files for plate VGF146 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10284408
title: Individual .ttl files for plate VGF146 samples

description: Individual .ttl files for plate VGF146 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027083
title: Individual .ttl files for plate VGF146 samples

description: Individual .ttl files for plate VGF146 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10215762
title: Individual .ttl files for plate VGF146 samples

description: Individual .ttl files for plate VGF146 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027082
title: Individual .ttl files for plate VGF146 samples

description: Individual .ttl files for plate VGF146 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10160430
title: Individual .ttl files for plate VGF145 samples

description: Individual .ttl files for plate VGF145 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10215761
title: Individual .ttl files for plate VGF145 samples

description: Individual .ttl files for plate VGF145 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10284409
title: Individual .ttl files for plate VGF145 samples

description: Individual .ttl files for plate VGF145 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027075
title: Individual .ttl files for plate VGF145 samples

description: Individual .ttl files for plate VGF145 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027076
title: Individual .ttl files for plate VGF145 samples

description: Individual .ttl files for plate VGF145 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10160431
title: Individual .ttl files for plate VGF144 samples

description: Individual .ttl files for plate VGF144 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10215758
title: Individual .ttl files for plate VGF144 samples

description: Individual .ttl files for plate VGF144 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10284412
title: Individual .ttl files for plate VGF144 samples

description: Individual .ttl files for plate VGF144 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027070
title: Individual .ttl files for plate VGF144 samples

description: Individual .ttl files for plate VGF144 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027071
title: Individual .ttl files for plate VGF144 samples

description: Individual .ttl files for plate VGF144 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10160432
title: Individual .ttl files for plate VGF143 samples

description: Individual .ttl files for plate VGF143 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10215753
title: Individual .ttl files for plate VGF143 samples

description: Individual .ttl files for plate VGF143 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10284413
title: Individual .ttl files for plate VGF143 samples

description: Individual .ttl files for plate VGF143 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027062
title: Individual .ttl files for plate VGF143 samples

description: Individual .ttl files for plate VGF143 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027063
title: Individual .ttl files for plate VGF143 samples

description: Individual .ttl files for plate VGF143 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027060
title: Individual .ttl files for plate VGF142 samples

description: Individual .ttl files for plate VGF142 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10160433
title: Individual .ttl files for plate VGF142 samples

description: Individual .ttl files for plate VGF142 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10215752
title: Individual .ttl files for plate VGF142 samples

description: Individual .ttl files for plate VGF142 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10284416
title: Individual .ttl files for plate VGF142 samples

description: Individual .ttl files for plate VGF142 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027059
title: Individual .ttl files for plate VGF142 samples

description: Individual .ttl files for plate VGF142 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10160434
title: Individual .ttl files for plate VGF141 samples

description: Individual .ttl files for plate VGF141 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027056
title: Individual .ttl files for plate VGF141 samples

description: Individual .ttl files for plate VGF141 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10215749
title: Individual .ttl files for plate VGF141 samples

description: Individual .ttl files for plate VGF141 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10284417
title: Individual .ttl files for plate VGF141 samples

description: Individual .ttl files for plate VGF141 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027055
title: Individual .ttl files for plate VGF141 samples

description: Individual .ttl files for plate VGF141 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027048
title: Individual .ttl files for plate VGF140 samples

description: Individual .ttl files for plate VGF140 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10160435
title: Individual .ttl files for plate VGF140 samples

description: Individual .ttl files for plate VGF140 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10215748
title: Individual .ttl files for plate VGF140 samples

description: Individual .ttl files for plate VGF140 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10284422
title: Individual .ttl files for plate VGF140 samples

description: Individual .ttl files for plate VGF140 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027047
title: Individual .ttl files for plate VGF140 samples

description: Individual .ttl files for plate VGF140 samples processed using the ENPKG framework
DOI: 10.6084/m9.figshare.24763872
title: Data_AI_ATP22

description: Data of ATP tennis players from season 2022.
DOI: 10.5281/zenodo.10160436
title: Individual .ttl files for plate VGF139 samples

description: Individual .ttl files for plate VGF139 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10215747
title: Individual .ttl files for plate VGF139 samples

description: Individual .ttl files for plate VGF139 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10284425
title: Individual .ttl files for plate VGF139 samples

description: Individual .ttl files for plate VGF139 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027043
title: Individual .ttl files for plate VGF139 samples

description: Individual .ttl files for plate VGF139 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027044
title: Individual .ttl files for plate VGF139 samples

description: Individual .ttl files for plate VGF139 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10160437
title: Individual .ttl files for plate VGF138 samples

description: Individual .ttl files for plate VGF138 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10215740
title: Individual .ttl files for plate VGF138 samples

description: Individual .ttl files for plate VGF138 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.10284426
title: Individual .ttl files for plate VGF138 samples

description: Individual .ttl files for plate VGF138 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027031
title: Individual .ttl files for plate VGF138 samples

description: Individual .ttl files for plate VGF138 samples processed using the ENPKG framework
DOI: 10.5281/zenodo.8027032
title: Individual .ttl files for plate VGF138 samples

description: Individual .ttl files for plate VGF138 samples processed using the ENPKG framework
DOI: 10.6073/pasta/e8733e1029356a70e145c5c4207d2070
title: Thresholds and Tipping Points in a Sarracenia Microecosystem at Harvard Forest 2012-2013

description: The primary goal of this project is to determine experimentally the amount of lead time required to prevent a state change. To achieve this goal, we will (1) experimentally induce state changes in a natural aquatic ecosystem - the Sarracenia microecosystem; (2) use proteomic analysis to identify potential indicators of states and state changes; and (3) test whether we can forestall state changes by experimentally intervening in the system. This work uses state-of-the art molecular tools to identify early warning indicators in the field of aerobic to anaerobic state changes driven by nutrient enrichment in an aquatic ecosystem. The study tests two general hypotheses: (1) proteomic biomarkers can function as reliable indicators of impending state changes and may give early warning before increasing variances and statistical flickering of monitored variables; and (2) well-timed intervention based on proteomic biomarkers can avert future state changes in ecological systems.
DOI: 10.6084/m9.figshare.24763878
title: Source Code for Optical Superresolution Microscopy

description: Conventional optical microscopes generally provide blurry and indistinguishable images for subwavelength nanostructures. However, a wealth of intensity and phase information is hidden in the corresponding diffraction-limited optical patterns and can be used for the recognition of structural features, such as size, shape, and spatial arrangement. Here, we apply a deep-learning framework to improve the spatial resolution of optical imaging for metal nanostructures with regular shapes yet varied arrangement. A convolutional neural network (CNN) is constructed and pre-trained by the optical images of randomly distributed gold nanoparticles as input and the corresponding scanning-electron microscopy images as ground truth. The CNN is then learned to recover reversely the non-diffracted super-resolution images of both regularly arranged nanoparticle dimers and randomly clustered nanoparticle multimers from their blurry optical images. The profiles and orientations of these structures can also be reconstructed accurately. Moreover, the same network is extended to deblur the optical images of randomly cross-linked silver nanowires. Most sections of these intricate nanowire nets are recovered well with a slight discrepancy near their intersections. This deep-learning augmented microscopy can be applied to significantly enhance the resolving capability of low-magnification scanning-electron microscopy.
DOI: 10.6084/m9.figshare.24763878.v1
title: Source Code for Optical Superresolution Microscopy

description: Conventional optical microscopes generally provide blurry and indistinguishable images for subwavelength nanostructures. However, a wealth of intensity and phase information is hidden in the corresponding diffraction-limited optical patterns and can be used for the recognition of structural features, such as size, shape, and spatial arrangement. Here, we apply a deep-learning framework to improve the spatial resolution of optical imaging for metal nanostructures with regular shapes yet varied arrangement. A convolutional neural network (CNN) is constructed and pre-trained by the optical images of randomly distributed gold nanoparticles as input and the corresponding scanning-electron microscopy images as ground truth. The CNN is then learned to recover reversely the non-diffracted super-resolution images of both regularly arranged nanoparticle dimers and randomly clustered nanoparticle multimers from their blurry optical images. The profiles and orientations of these structures can also be reconstructed accurately. Moreover, the same network is extended to deblur the optical images of randomly cross-linked silver nanowires. Most sections of these intricate nanowire nets are recovered well with a slight discrepancy near their intersections. This deep-learning augmented microscopy can be applied to significantly enhance the resolving capability of low-magnification scanning-electron microscopy.
DOI: 10.6084/m9.figshare.24571675
title: Supplementary Material for: Characteristics and prognostic factors of SARS-CoV-2 Omicron variant infection in hemodialysis patients: a single-center study in China

description: Introduction: This study aimed to evaluate the characteristics and prognostic factors for coronavirus disease 2019 (COVID-19) patients on maintenance hemodialysis (HD).
Methods: All admitted HD patients who were infected with severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) from December 1, 2022 to January 31, 2023 were included. Patients with pneumonia were further classified into the mild, moderate, severe and critical illness. Clinical symptoms, laboratory results, radiologic findings, treatment and clinical outcomes were collected. Independent risk factors for progression to critical disease, and in-hospital mortality were determined by the multivariate regression analysis. The receiver operating characteristic (ROC) analysis with the area under the curve (AUC) was used to evaluate the predictive performance of developing critical status and in-hospital mortality.
Results: A total of 182 COVID-19 patients with HD were included, with an average age of the 61.55 years. Out of the total, 84 (46.1%) patients did not have pneumonia and 98 (53.8%) patients had pneumonia. Among patients with pneumonia, 48 (49.0%) had moderate illness, 26 (26.5%) severe illness, and 24 (24.5%) critical illness, respectively. Elder age [HR (95% CI): 1.07 (1.01-1.13), P&lt;0.01], increased levels of lactate dehydrogenase (LDH) [1.01 (1.003-1.01), P&lt;0.01] and C-reactive protein (CRP) [1.01 (1.00-1.01), P=0.04] were risk factors for developing critical illness. Elder age [1.11 (1.03-1.19), P=0.01], increased procalcitonin (PCT) [1.07 (1.02-1.12), P=0.01], and LDH level [1.004 (1-1.01), P=0.03] were factors associated with increased risk of in-hospital mortality. 
Conclusions: Age, CRP, PCT and LDH can be used to predict negative clinical outcomes for HD patients with COVID-19 pneumonia.
DOI: 10.25345/c5g737f42
title: MassIVE MSV000093597 - Suspension Cell Culture of Panax vietnamensis as a biotechnological source of ginsenosides: Growth, Cytology, and Ginsenoside Profile Assessment
title: Raw data of UPLC-qOrbitrap profiling results of extract from Cell Culture of Panax vietnamensis and standart compounds mixtures. In case of extract profiling, target list based DDA was conducted based on previous peak picking results.
DOI: 10.5281/zenodo.10281420
title: Data accompanying The preoptic area and dorsal habenula jointly support homeostatic navigation in larval zebrafish

description: "The preoptic area and dorsal habenula jointly support homeostatic navigation in larval zebrafish"
This repository contains the data presented in Palieri et al, 2023 (preprint https://www.biorxiv.org/content/10.1101/2023.05.18.541289v1 ).
The data is organized into the following subfolders:

Behavior: This folder contains all the data related to freely swimming experiments, including both spatial (spatial_gradient subfolder) and temporal (temporal_gradient subfolder) temperature gradients. The former also includes data from chemogenetic and 2-photon mediated ablations.
Imaging: Within this folder, you will find data obtained through confocal microscopy (in the confocal subfolder) and lightsheet microscopy (in the lightsheet subfolder). Confocal imaging was used to verify proper habenula ablation (dHb_preablation and dHb_postablation). Lightsheet imaging was performed for functional calcium imaging experiments during temperature and salinity gradients. The lightsheet subfolder is further subdivided into long and short experiments, with each fish having its own dedicated folder. Inside each fish's folder, you can access imaging data (traces, local coordinates, coordinates in reference space, along with relevant indexes such as the reliability index) and behavioral data (acquired using the Stytra software).
Please contact Ruben Portugues for further information.
DOI: 10.5281/zenodo.10281421
title: Data accompanying The preoptic area and dorsal habenula jointly support homeostatic navigation in larval zebrafish

description: "The preoptic area and dorsal habenula jointly support homeostatic navigation in larval zebrafish"
This repository contains the data presented in Palieri et al, 2023 (preprint https://www.biorxiv.org/content/10.1101/2023.05.18.541289v1 ).
The data is organized into the following subfolders:

Behavior: This folder contains all the data related to freely swimming experiments, including both spatial (spatial_gradient subfolder) and temporal (temporal_gradient subfolder) temperature gradients. The former also includes data from chemogenetic and 2-photon mediated ablations.
Imaging: Within this folder, you will find data obtained through confocal microscopy (in the confocal subfolder) and lightsheet microscopy (in the lightsheet subfolder). Confocal imaging was used to verify proper habenula ablation (dHb_preablation and dHb_postablation). Lightsheet imaging was performed for functional calcium imaging experiments during temperature and salinity gradients. The lightsheet subfolder is further subdivided into long and short experiments, with each fish having its own dedicated folder. Inside each fish's folder, you can access imaging data (traces, local coordinates, coordinates in reference space, along with relevant indexes such as the reliability index) and behavioral data (acquired using the Stytra software).
Please contact Ruben Portugues for further information.
DOI: 10.25934/pr00004746.1
title: Available datapackage for study 'Daratumumab (HuMax®-CD38) Safety Study in Multiple Myeloma - Open Label, Dose-escalation Followed by Open Label, Single-arm Study'

description: Establishment of safety profile of HuMax-CD38 when given as monotherapy in participants with multiple myeloma relapsed from or refractory to at least 2 different cytoreductive therapies and without further established treatment options.