cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19940753.v1
title: Clinical profile of dengue in the elderly using surveillance data from two epidemics

description: ABSTRACT Background: Population aging and mobility have increased the exposure of elderly individuals to dengue. This study evaluated the clinical features of dengue in the elderly during the epidemic (2008 and 2012) and interepidemic (2009 and 2010) periods. Methods: This cross-sectional study was based on dengue surveillance data from Rio de Janeiro, Brazil: 2008 (n=31,210), 2009‒2010 (n=2,884), and 2012 (n=30,773). The analysis was stratified by age group (&lt;60 and ≥60 years). Results: Case-fatality rates were higher in the elderly. In 2008, elderly individuals were found to be more prone to hematuria and thrombocytopenia. Conclusions: These results can improve the understanding of dengue in elderly individuals who live in or travel to tropical regions.
DOI: 10.6084/m9.figshare.19940752.v1
title: Secondary infection profile after snakebite treated at a tertiary referral center in the Brazilian Amazon

description: ABSTRACT Background: Bothrops envenomations can often lead to complications, such as secondary infections. Methods: This cross-sectional study analyzed the medical records of all patients diagnosed with snakebite. Results: A total of 127 patients were included. Clindamycin was the most commonly prescribed antibiotic, with 105 patients (82.7%) receiving it as the primary antibiotic regimen. In 31 (24.4%) individuals, the first-choice antibiotic did not cease the infection. Conclusions: Secondary infection is an important complication resulting from snakebites, and evidence-based management of this complication can contribute to better clinical outcomes.
DOI: 10.6084/m9.figshare.19940782
title: Chagas disease mortality during the coronavirus disease 2019 pandemic: A Brazilian referral center experience

description: ABSTRACT Background We investigated the mortality rates of patients with Chagas disease (CD) during the coronavirus disease 2019 (COVID-19) pandemic and assessed the association between this mortality and CD clinical presentation and comorbidities. Methods: This was an observational retrospective study with clinical data retrieved from medical records. Results: Comorbidities were more prevalent among patients who died from COVID-19 than those who died from other causes. The proportion of patients according to CD clinical presentation was similar between the two groups. Conclusions: The prevalence of comorbidities seems to be related to a poorer prognosis in CD and COVID-19.
DOI: 10.6084/m9.figshare.19940782.v1
title: Chagas disease mortality during the coronavirus disease 2019 pandemic: A Brazilian referral center experience

description: ABSTRACT Background We investigated the mortality rates of patients with Chagas disease (CD) during the coronavirus disease 2019 (COVID-19) pandemic and assessed the association between this mortality and CD clinical presentation and comorbidities. Methods: This was an observational retrospective study with clinical data retrieved from medical records. Results: Comorbidities were more prevalent among patients who died from COVID-19 than those who died from other causes. The proportion of patients according to CD clinical presentation was similar between the two groups. Conclusions: The prevalence of comorbidities seems to be related to a poorer prognosis in CD and COVID-19.
DOI: 10.6084/m9.figshare.19940792.v1
title: Factors associated with COVID-19 mortality in municipalities in the state of São Paulo (Brazil): an ecological study

description: ABSTRACT Background: The mortality rate of coronavirus disease (COVID-19) in the state of São Paulo is highly heterogeneous. This study investigated geographic, economic, social, and health-related factors associated with this discrepancy. Methods: An ecological study compared COVID-19 mortality rates according to geographic, economic, social, and health-related variables during initial infection of 2.5% of the population in municipalities with more than 30,000 inhabitants. Results: Mortality was positively associated with demographic density and social inequality (Gini index), and inversely associated with HDI income and longevity of these municipalities, accounting for 33.2% of the variation in mortality. Conclusions: Social determinants influenced COVID-19 outcomes.
DOI: 10.6084/m9.figshare.19940792
title: Factors associated with COVID-19 mortality in municipalities in the state of São Paulo (Brazil): an ecological study

description: ABSTRACT Background: The mortality rate of coronavirus disease (COVID-19) in the state of São Paulo is highly heterogeneous. This study investigated geographic, economic, social, and health-related factors associated with this discrepancy. Methods: An ecological study compared COVID-19 mortality rates according to geographic, economic, social, and health-related variables during initial infection of 2.5% of the population in municipalities with more than 30,000 inhabitants. Results: Mortality was positively associated with demographic density and social inequality (Gini index), and inversely associated with HDI income and longevity of these municipalities, accounting for 33.2% of the variation in mortality. Conclusions: Social determinants influenced COVID-19 outcomes.
DOI: 10.6084/m9.figshare.19940803
title: Seroprevalence of Trypanosoma cruzi infection in blood donors in the extreme South of Brazil

description: ABSTRACT Background: We aimed to evaluate the seroprevalence of Chagas disease among blood donors in southern Rio Grande do Sul, Brazil. Methods: The study was conducted from 2010 to 2019 based on data registered by the Hemocentro Regional de Pelotas, Rio Grande do Sul. Results: There were 106,320 blood donations, and the discard rate of blood bags, either due to positive reactions to anti-T. cruzi antibodies or inconclusive results was 0.27% (283 bags). Conclusions: The usage of methods that enable the safe identification of donors with positive serology for Chagas disease is fundamental to ensure transfusional safety.
DOI: 10.6084/m9.figshare.19940803.v1
title: Seroprevalence of Trypanosoma cruzi infection in blood donors in the extreme South of Brazil

description: ABSTRACT Background: We aimed to evaluate the seroprevalence of Chagas disease among blood donors in southern Rio Grande do Sul, Brazil. Methods: The study was conducted from 2010 to 2019 based on data registered by the Hemocentro Regional de Pelotas, Rio Grande do Sul. Results: There were 106,320 blood donations, and the discard rate of blood bags, either due to positive reactions to anti-T. cruzi antibodies or inconclusive results was 0.27% (283 bags). Conclusions: The usage of methods that enable the safe identification of donors with positive serology for Chagas disease is fundamental to ensure transfusional safety.
DOI: 10.6084/m9.figshare.17257306
title: Developmental relations between mathematics anxiety, symbolic numerical magnitude processing and arithmetic skills from first to second grade

description: We investigated the levels of and changes in mathematics anxiety (MA), symbolic numerical magnitude processing (SNMP) and arithmetic skills, and how those changes are linked to each other. Children’s (<i>n</i> = 264) MA, SNMP and arithmetic skills were measured in Grade 1, and again in Grade 2, also including a mathematics performance test. All three constructs correlated significantly within each time point, and the rank-order stability over time was high, particularly in SNMP and arithmetic skills. By means of latent change score modelling, we found overall increases in SNMP and arithmetic skills over time, but not in MA. Most interestingly, changes in arithmetic skills and MA were correlated (i.e. steeper increase in arithmetic skills was linked with less steep increase in MA), as were changes in SNMP and arithmetic skills (i.e. improvement in SNMP was associated with improvement in arithmetic skills). Only the initial level of arithmetic skills and change in it predicted mathematics performance. The only gender difference, in favour of boys, was found in SNMP skills. The differential effects associated with MA (developmentally only linked with arithmetic skills) and gender (predicting only changes in SNMP) call for further longitudinal research on the different domains of mathematical skills.
DOI: 10.6084/m9.figshare.17257306.v1
title: Developmental relations between mathematics anxiety, symbolic numerical magnitude processing and arithmetic skills from first to second grade

description: We investigated the levels of and changes in mathematics anxiety (MA), symbolic numerical magnitude processing (SNMP) and arithmetic skills, and how those changes are linked to each other. Children’s (<i>n</i> = 264) MA, SNMP and arithmetic skills were measured in Grade 1, and again in Grade 2, also including a mathematics performance test. All three constructs correlated significantly within each time point, and the rank-order stability over time was high, particularly in SNMP and arithmetic skills. By means of latent change score modelling, we found overall increases in SNMP and arithmetic skills over time, but not in MA. Most interestingly, changes in arithmetic skills and MA were correlated (i.e. steeper increase in arithmetic skills was linked with less steep increase in MA), as were changes in SNMP and arithmetic skills (i.e. improvement in SNMP was associated with improvement in arithmetic skills). Only the initial level of arithmetic skills and change in it predicted mathematics performance. The only gender difference, in favour of boys, was found in SNMP skills. The differential effects associated with MA (developmentally only linked with arithmetic skills) and gender (predicting only changes in SNMP) call for further longitudinal research on the different domains of mathematical skills.
DOI: 10.6084/m9.figshare.19940912
title: The increasing burden of pancreatic cancer in Brazil from 2000 to 2019: estimates from the Global Burden of Disease Study 2019.

description: Abstract INTRODUCTION: Pancreatic cancer is increasing worldwide. The burden of pancreatic cancer in Brazil and its states was analyzed and compared with that from the USA and China. METHODS: This is a descriptive study of the incidence and mortality estimates from the Global Burden of Disease 2019 study, from 2000 to 2019. The Brazilian states presenting the highest and lowest socio-demographic index (SDI) were selected from each of the five regions. The SDI consists of the per capita income, education, and fertility rate of each population. RESULTS: A significant increase was found in age-standardized incidence and mortality of pancreatic cancer in all three countries, with differences in magnitude and annual increases. In Brazil, this incidence rose from 5.33 [95% Uncertainty Interval (UI): 5.06- 5.51] to 6.16 (95% UI: 5.68- 6.53) per 100,000 inhabitants. China and the Brazilian states with the lowest SDI, such as Pará and Maranhão, showed lower incidence and mortality rates, although presenting the highest annual increases. No difference was found between the sexes. A higher mortality rate was observed for those individuals of 70+ years, which was three to four times higher than those aged 50 to 69 years. CONCLUSIONS: The increasing burden of pancreatic cancer in the studied countries, and the higher estimates for the elderly in a fast-aging country such as Brazil, indicates that more resources and health policies will be necessary. The greatest increase in the states with lower SDI reflects inequalities in the access to diagnosis and registries of this cancer.
DOI: 10.6084/m9.figshare.19940912.v1
title: The increasing burden of pancreatic cancer in Brazil from 2000 to 2019: estimates from the Global Burden of Disease Study 2019.

description: Abstract INTRODUCTION: Pancreatic cancer is increasing worldwide. The burden of pancreatic cancer in Brazil and its states was analyzed and compared with that from the USA and China. METHODS: This is a descriptive study of the incidence and mortality estimates from the Global Burden of Disease 2019 study, from 2000 to 2019. The Brazilian states presenting the highest and lowest socio-demographic index (SDI) were selected from each of the five regions. The SDI consists of the per capita income, education, and fertility rate of each population. RESULTS: A significant increase was found in age-standardized incidence and mortality of pancreatic cancer in all three countries, with differences in magnitude and annual increases. In Brazil, this incidence rose from 5.33 [95% Uncertainty Interval (UI): 5.06- 5.51] to 6.16 (95% UI: 5.68- 6.53) per 100,000 inhabitants. China and the Brazilian states with the lowest SDI, such as Pará and Maranhão, showed lower incidence and mortality rates, although presenting the highest annual increases. No difference was found between the sexes. A higher mortality rate was observed for those individuals of 70+ years, which was three to four times higher than those aged 50 to 69 years. CONCLUSIONS: The increasing burden of pancreatic cancer in the studied countries, and the higher estimates for the elderly in a fast-aging country such as Brazil, indicates that more resources and health policies will be necessary. The greatest increase in the states with lower SDI reflects inequalities in the access to diagnosis and registries of this cancer.
DOI: 10.6084/m9.figshare.22317694
title: Sequencing results of vectors contains containing phototropin CDSs for expression in insect cells and bacteria

description: Results of sequencing of vectors with phototropin CDS: You can view all sequencing results as assembled contigous sequence of mutants of phototropin1 or phototropin2 at Gene Snap Viewer (www.snapgene.com/snapgene-viewer). Phototropins sequences represented by: phot1 CDS, phot2 CDS phot1 CDS – 2991 nucleotides phot2 CDS – 2748 nucleotides phot2LOV1LOV2 - N-terminal PHOT2 fragment, containing LOV1 and LOV2 – 1161 nucleotides <strong>Mutants:</strong> Phot1 1_pFASTphot1D806N – mutation of 1 nucleotide at 2416 position, 2_pFASTphot1C234A – mutation of 2 nucleotides at 700 and 701 positions, 3_pFASTphot1C512A – mutation of 2 nucleotides at 1534 and 1535 positions, 4_pFASTphot1C234AC512A – mutation of 2 nucleotides at 700 and 701 as well as at 1534 and 1535 positions, Phot2 5_pFASTphot2D720A – mutation of 1 nucleotide at 2158 position, 6_pFASTphot2C170A – mutation of 2 nucleotides at 508 and 509 positions, 7_pFASTphot2C426A – mutation of 2 nucleotides at 1276 and 1277 positions, 8_pFASTphot2C170AC426A – mutations of 2 nucleotides at 508 and 509 positions as well as at 1276 and 1277, phot2LOV1LOV2 fragment mutants 9_pDONR221phot2LOV1LOV2V392T – mutation of 2 nucleotides at 799 and 800 positions, 10_pDONR221phot2LOV1LOV2V392L – mutation of 1 nucleotide at 799 position, Phot2 without stop codon (2745 nucleotides) 11_pDONR221phot2V392T – mutation of 2 nucleotides at 1174 and 1175 positions, 12_pDONR221phot2V392L – working on Phot2 with stop codon (2748 nucleotides) 13_pDONR221phot2V392Tstop – mutation of 2 nucleotides at 1174 and 1175 positions, 14_pDONR221phot2V392Lstop – mutation of 1 nucleotide at 1174 position. Phot2 574aa stop (1722 nucleotides) 15_pDONR221phot2 574aaastop – wild type, 16_pDONR221phot2 574aaastop V392T – mutation of 2 nucleotides at 1174 and 1175 positions, 17_pDONR221phot2 574aaastop V392L – mutation of 1 nucleotide at 1174 position.
DOI: 10.5517/ccdc.csd.cc28ykph
title: CCDC 2113787: Experimental Crystal Structure Determination

description: Related Article: Kouki Matsubara, Kanta Tomomatsu, Ayame Tajiri, Arisa Watanabe, Yuji Koga, Ryuta Ishikawa, Yuji Yamada|2022|Eur.J.Inorg.Chem.|2022|e202100870|doi:10.1002/ejic.202100870
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc299n2c
title: CCDC 2124432: Experimental Crystal Structure Determination

description: Related Article: Hongxia Zhao, Chao Qi, Xuewu Yan, Jinyan Ji, Zhifang Chai, Shuao Wang, Tao Zheng|2022|ACS Applied Materials and Interfaces|14|14380|doi:10.1021/acsami.2c01671
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.22320481
title: surficialDL: A geomorphology deep learning dataset of alluvium and thick glacial till derived form 1:24,000 scale surficial geology data for the western portion of Massachusetts, USA

description: <strong>surficialDL: A geomorpholgy deep learning dataset of alluvium and thick glacial till derived form 1:24,000 scale surficial geology data for the western portion of Massachusetts, USA</strong> <br> <strong>scripts.zip</strong> <br> <strong>arcgisTools.atbx:</strong> <strong>terrainDerivatives</strong>: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). <strong>rasterizeFeatures</strong>: convert vector polygons to raster masks (1 = feature, 0 = background). <br> <strong>makeChips.R</strong>: R function to break terrain derivatives and chips into image chips of a defined size. <strong>makeTerrainDerivatives.R</strong>: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). <strong>merge_logs.R</strong>: R script to merge training logs into a single file. <strong>predictToExtents.ipynb</strong>: Python notebook to use trained model to predict to new data. <strong>trainExperiments.ipynb</strong>: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. <strong>assessmentExperiments.ipynb</strong>: Python code to generate assessment metrics using PyTorch and the torchmetrics library. <strong>graphs_results.R</strong>: R code to make graphs with ggplot2 to summarize results. <strong>makeChipsList.R</strong>: R code to generate lists of chips in a directory. <strong>makeMasks.R</strong>: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool). <br> <strong>surficialDL</strong> The digital terrain model associated with these data/project is available here: https://s3.us-east-1.amazonaws.com/download.massgis.digital.mass.gov/lidar/LIDAR_DEM_32BIT_FP.gdb.zip. <strong>alluvDL</strong>: polygons (vectors folder) and extents (extents folder) for alluvium features separated into training, validation, and testing partitions. These data were derived from the 1:24,000 scale Massachusetts Surficial Geology dataset: https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology. <strong>tillDL</strong>: polygons (vector folder) and extents (extents folder) for thick till features separated into training, validation, and testing partitions. These data were derived from the 1:24,000 scale Massachusetts Surficial Geology dataset: https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology.
DOI: 10.6084/m9.figshare.22141181.v1
title: Network Inference Using the Hub Model and Variants

description: Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the groups formed by individual subjects. In these applications, the network is itself a parameter of a statistical model. Zhao and Weko (2019) propose a model-based approach, called the hub model, to infer implicit networks from grouping behavior. The hub model assumes that each member of the group is brought together by a member of the group called the hub. The set of members which can serve as a hub is called the hub set. The hub model belongs to the family of Bernoulli mixture models. Identifiability of Bernoulli mixture model parameters is a notoriously difficult problem. This paper proves identifiability of the hub model parameters and estimation consistency under mild conditions. Furthermore, this paper generalizes the hub model by introducing a model component that allows hubless groups in which individual nodes spontaneously appear independent of any other individual. We refer to this additional component as the null component. The new model bridges the gap between the hub model and the degenerate case of the mixture model – the Bernoulli product. Identifiability and consistency are also proved for the new model. In addition, a penalized likelihood approach is proposed to estimate the hub set when it is unknown.
DOI: 10.6084/m9.figshare.22320373
title: terraceDL: A geomorphology deep learning dataset of agricultural terraces in Iowa, USA

description: <strong>scripts.zip</strong> <br> <strong>arcgisTools.atbx:</strong> <strong>terrainDerivatives</strong>: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). <strong>rasterizeFeatures</strong>: convert vector polygons to raster masks (1 = feature, 0 = background). <br> <strong>makeChips.R</strong>: R function to break terrain derivatives and chips into image chips of a defined size. <strong>makeTerrainDerivatives.R</strong>: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). <strong>merge_logs.R</strong>: R script to merge training logs into a single file. <strong>predictToExtents.ipynb</strong>: Python notebook to use trained model to predict to new data. <strong>trainExperiments.ipynb</strong>: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. <strong>assessmentExperiments.ipynb</strong>: Python code to generate assessment metrics using PyTorch and the torchmetrics library. <strong>graphs_results.R</strong>: R code to make graphs with ggplot2 to summarize results. <strong>makeChipsList.R</strong>: R code to generate lists of chips in a directory. <strong>makeMasks.R</strong>: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool). <br> <strong>terraceDL.zip</strong> <br> <strong>dems</strong>: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. <strong>extents</strong>: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. <strong>vectors</strong>: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs<strong>. </strong>