cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12827763
title: 1-PdLRattinessData.csv from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
DOI: 10.6084/m9.figshare.12827778
title: 4-Prediction.R from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
DOI: 10.6084/m9.figshare.18070655.v1
title: Additional file 18 of Dynamic genetic differentiation drives the widespread structural and functional convergent evolution of snake venom proteinaceous toxins

description: Additional file 18. lectin tree.tre.
DOI: 10.6084/m9.figshare.12827754
title: 3-FitFullModel.R from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
DOI: 10.6084/m9.figshare.12827754.v1
title: 3-FitFullModel.R from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
DOI: 10.6084/m9.figshare.18070652
title: Additional file 17 of Dynamic genetic differentiation drives the widespread structural and functional convergent evolution of snake venom proteinaceous toxins

description: Additional file 17. lectin alignment.fasta.
DOI: 10.6084/m9.figshare.18070652.v1
title: Additional file 17 of Dynamic genetic differentiation drives the widespread structural and functional convergent evolution of snake venom proteinaceous toxins

description: Additional file 17. lectin alignment.fasta.
DOI: 10.6084/m9.figshare.12827757
title: 2-LRTTestSpatialCorr.R from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
DOI: 10.6084/m9.figshare.12827757.v1
title: 2-LRTTestSpatialCorr.R from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
DOI: 10.6084/m9.figshare.12827772
title: PredictionExcPlates.R from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
DOI: 10.6084/m9.figshare.12827772.v1
title: PredictionExcPlates.R from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
DOI: 10.6084/m9.figshare.18070661.v1
title: Additional file 1 of Dynamic genetic differentiation drives the widespread structural and functional convergent evolution of snake venom proteinaceous toxins

description: Additional file 1. 3ftx alignment.fasta.
DOI: 10.6084/m9.figshare.12827742.v1
title: PredictionExcSigns.R from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
DOI: 10.6084/m9.figshare.19643442
title: Additional file 1 of BIRC5 regulates inflammatory tumor microenvironment-induced aggravation of penile cancer development in vitro and in vivo

description: Additional file 1: Fig. S1. Silencing BIRC5 inhibited cell viability, migration, invasion of PC cells under IFN-γ treatment (20 ng/mL, 24 h).PC cellswere transfected with shRNA lentiviral vectors. (A and B) Cell viability was studied by CCK-8 assay. (C and D) Wound healing assay and Transwell assay were performed to detect cell migration and invasion. The columns were presented as the mean ± SEM (n ≥ 3). * and**stood forp 0.05 and p 0.01. Fig. S2. Silencing BIRC5 depressed the aggravated tumor growth and survival of PC xenograft mice induced by IFN-γ.Penl1 cells in different groups (IFN-γ; IFN-γ + BIRC5 shRNAb) and untreated Penl1 cells (model) were subcutaneously inoculated at the right axilla (100 µL containing 1 × 106 cells) after mice were anesthetized. (A) Image of tumor growth in living mice. (B, C and D) Tumor volume and tumor weight were measured and survival rate was calculated. The columns were presented as the mean ± SEM. ** stands for p 0.01 compared with model group. ## stands for p 0.01 compared with IFN-γ group. n= 5 in every group.
DOI: 10.6084/m9.figshare.12827775
title: outline1_points.csv from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
DOI: 10.6084/m9.figshare.12827766
title: PredictionExcTraps.R from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
DOI: 10.6084/m9.figshare.19643442.v1
title: Additional file 1 of BIRC5 regulates inflammatory tumor microenvironment-induced aggravation of penile cancer development in vitro and in vivo

description: Additional file 1: Fig. S1. Silencing BIRC5 inhibited cell viability, migration, invasion of PC cells under IFN-γ treatment (20 ng/mL, 24 h).PC cellswere transfected with shRNA lentiviral vectors. (A and B) Cell viability was studied by CCK-8 assay. (C and D) Wound healing assay and Transwell assay were performed to detect cell migration and invasion. The columns were presented as the mean ± SEM (n ≥ 3). * and**stood forp 0.05 and p 0.01. Fig. S2. Silencing BIRC5 depressed the aggravated tumor growth and survival of PC xenograft mice induced by IFN-γ.Penl1 cells in different groups (IFN-γ; IFN-γ + BIRC5 shRNAb) and untreated Penl1 cells (model) were subcutaneously inoculated at the right axilla (100 µL containing 1 × 106 cells) after mice were anesthetized. (A) Image of tumor growth in living mice. (B, C and D) Tumor volume and tumor weight were measured and survival rate was calculated. The columns were presented as the mean ± SEM. ** stands for p 0.01 compared with model group. ## stands for p 0.01 compared with IFN-γ group. n= 5 in every group.
DOI: 10.6084/m9.figshare.12827748
title: FitModelExcTraps.R from A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of <i>rattiness</i> in a low-income urban Brazilian community

description: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, <i>rattiness</i>, that acts as a proxy for abundance. We show how to explore the association between <i>rattiness</i> and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify <i>rattiness</i> hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.