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opencc-by-4.0Jul 2024View details →

Spatial association and modelling of vaccination coverage in Thailand, 2021 - 2022

Information on COVID-19 vaccine services from MOPH immunization Center (MOPH IC) (Department of Disease Control, 2023) Information on COVID-19 patients (per 1,000 population), COVID-19 deaths (per 100,000 population), people with chronic diseases (%), pregnant women (per 1,000 population), medical personnel (per 1,000 population), hospitals (per 100,000 population), subdistrict health promotion hospitals (per 100,000 population), and village health volunteers (per 1,000 population) from the Ministry of Public Health. (Department of Disease Control, 2023; Department Of Health Service Support, 2023; HDC, 2023; Ministry of Public Health, 2022) Information on population density (sq. km.), proportion of population in municipal areas (%), proportion of elderly people (%), proportion of working age (%), business establishments (per 1,000 population), average monthly household income (baht), proportion of population that has a mobile phone (%), and proportion of population internet access (%) from the National Statistical Office. (National Statistical Office, 2023) Information on nighttime light from The Earth Observation Group (EOG., 2023) Information on public transport vehicles (per 1,000 population) and private vehicles (per 1,000 population) from the Ministry of Transport. (Department of Land Transport, 2023) Information on the proportion of treatment rights (%) includes universal coverage scheme rights, social security scheme (SSS), and government rights (OFC) from the National Health Security Office. (National Health Security Office, 2023)   a_pop : population density (sq. km.) in 2021 a_pop65 : population density (sq. km.) in 2022 urban% : proportion of population in municipal areas (%) in 2021 Urban%65 : proportion of population in municipal areas (%) in 2022 Older_21 : proportion of elderly people (%) in 2021 Older_22 : proportion of elderly people (%) in 2022 7CD_21 : people with chronic diseases (%) in 2021 7CD_22 : people with chronic diseases (%) in 2022 Preg_21 : pregnant women (per 1,000 population) in 2021 Preg_22 : pregnant women (per 1,000 population) in 2022 Work_21 : proportion of working age (%) in 2021 Work_22 : proportion of working age (%) in 2022 NTL64 : nighttime light in 2021 NTL65 : nighttime light in 2022 BSN_21 : business establishments (per 1,000 population) in 2021  BSN_22 : business establishments (per 1,000 population) in 2022 P-car_21 : public transport vehicles (per 1,000 population) in 2021 P-car_22 : public transport vehicles (per 1,000 population) in 2022 I-car_21 : private vehicles (per 1,000 population) in 2021 I-car_22 : private vehicles (per 1,000 population) in 2022 Phone_21 : population that has a mobile phone (%) in 2021 Phone_22 : population that has a mobile phone (%) in 2022 Internet_21 : proportion of population internet access (%) in 2021 Intermet_22 : proportion of population internet access (%) in 2022 Income : average monthly household income (baht) PH-P_21 : medical personnel (per 1,000 population) in 2021 PH-P_22 : medical personnel (per 1,000 population) in 2022 VHV_21 : village health volunteers (per 1,000 population) in 2021 VHV_22 : village health volunteers (per 1,000 population) in 2022 Hos-P_21 : hospitals (per 100,000 population) in 2021 Hos-P_22 : hospitals (per 100,000 population) in 2022 Local-p_21 : subdistrict health promotion hospitals (per 100,000 population) in 2021 Local-p_22 : subdistrict health promotion hospitals (per 100,000 population) in 2022 UC_21 : universal coverage scheme rights (%) UC_22 : universal coverage scheme rights (%) SSS_21 : social security scheme (%) SSS_22 : social security scheme (%) OFC_21 : government rights (%) OFC_22 : government rights (%) Covid-p_21 : COVID-19 patients (per 1,000 population) in 2021 Covid-p_22 : COVID-19 patients (per 1,000 population) in 2022 Dcovid-p_21 : COVID-19 deaths (per 100,000 population) in 2021 Dcovid-p_22 : COVID-19 deaths (per 100,000 population) in 2022 Vcovid21_1 : COVID-19 vaccine coverage 1 dose (%) in 2021 Vcovid21_2 : COVID-19 vaccine coverage 2 dose (%) in 2021 Vcovid21_3 : COVID-19 vaccine coverage 3 dose (%) in 2021 Vcovid22_1 : COVID-19 vaccine coverage 1 dose (%) in 2022 Vcovid22_2 : COVID-19 vaccine coverage 2 dose (%) in 2022 Vcovid22_3 : COVID-19 vaccine coverage 3 dose (%) in 2022

opennotspecifiedMay 2019View details →

FIGURE 17 in First records of Leucania rawlinsi Adams and L. senescens M̂schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution

FIGURE 17. Leucania larvae collected in conventional and transgenic maize, Nortelândia, Mato Grosso state, Brazil.

Fig. 7 in Ecological and spatial patterns associated with diversification of South American Physaria (Brassicaceae) through the general concept of species

Fig. 7 Climatic niche comparisons along the environmental space using hypervolumes for delimited lineages of South American Physamia. (a–c) Hypervolumes (point density and alpha-hull contour boundary) for delimited lineages of South American Physamia representing their climatic niches, and estimated using the values extracted from the components of the PCA-env (first three components). (a) PCenv1 vs PCenv2. (b) PCenv1 vs PCenv3. (c) PCenv2 vs PCenv3. (d) Phylomorphospace plot showing niche position between delimited lineages obtained using centroid distances between each pair of hypervolumes and multidimensional scaling

Fig. 4 in Ecological and spatial patterns associated with diversification of South American Physaria (Brassicaceae) through the general concept of species

Fig. 4 Phylogenetic placement of sampled specimens of South American Physamia. (a–c) Maximum clade credibility (MCC) tree generated by Bayesian inference with BEAST 1.8.4. (a) nrITS dataset. (b) cpDNA dataset (tmnL-F/tmnH-psbA/tmnG intron/tmnS-tmnG). (c) Concatenated ITS + cpDNA datasets. (d) MCC tree estimated from ITS and cpDNA datasets using the multispecies coalescent method implemented in *BEAST v.1.8.4. The small circles on nodes indicate posterior probability (pp): black circles pp≥0.9, gray circles 0.9>pp≥0.7, white circles

Fig. 1 in Ecological and spatial patterns associated with diversification of South American Physaria (Brassicaceae) through the general concept of species

Fig. 1 Representatives of South American Physamia. a–c P. cmassistigma. a Plant with flowers. b Plant with fruits. c Detail of fruits. d–e P. latemalis. d Plant with flowers and fruits. e Detail of fruits. f–g P. mendocina. f Plant with flowers. g Plant with fruits. h–i P. pygmaea. h Plant with flowers and fruits. i Details of fruits. j–l P. umbaniana. j Plant with flowers. k Plant with fruits. l Detail of fruits. a–c from Salamiato et al.

Fig. 6 in Ecological and spatial patterns associated with diversification of South American Physaria (Brassicaceae) through the general concept of species

Fig. 6 Geographic and climatic niche distribution for delimited lineages of South American Physamia. (a) Maximum clade credibility (MCC) species tree estimated from ITS and cpDNA datasets using the multispecies coalescent method implemented in *BEAST 1.8.4 and the hypothesis of six independently evolving lineages. Numbers on branches correspond to posterior probability. (b) Geographic distribution of lineages: green,

Fig. 5 in Ecological and spatial patterns associated with diversification of South American Physaria (Brassicaceae) through the general concept of species

Fig. 5 Results of species delimitation analyses. (a) Results from GMYC (discovery approach), BPP, and BFD (validation approaches) plotted onto the MCC tree obtained with the concatenated ITS+cpDNA dataset. GMYC analyses were conducted using MCC trees obtained with nrITS, cpDNA, concatenated nrITS+cpDNA, and coalescence nrITS–cpDNA analyses. BPP analyses were conducted using six different combinations

Fig. 3 in Ecological and spatial patterns associated with diversification of South American Physaria (Brassicaceae) through the general concept of species

Fig. 3 Median-joining networks of a, nrITS dataset; b, cpDNA dataset (tmnLF, tmnH-psbA, tmnG intron, tmnS-tmnG spacer). Six morphologically defined species are distinguished by different colors: blue, P. cmassistigma; pink, P. latemalis; red, P. mendocina; black, P. okanensis; yellow, P. pygmaea; green: P. umbaniana. Intermediate (unobserved) haplotypes are distinguished by small gray circles. Circle sizes correspond to relative numbers of in- dividuals sharing a particular haplotype

opencc-zeroDec 2017View details →

Data from: How do similarities in spatial distributions and interspecific associations affect the coexistence of Quercus species in the Baotianman National Nature Reserve, Henan, China

Congeneric species often have similar ecological characteristics and use similar resources. These similarities may make it easier for them to co-occur in a similar habitat but may also lead to strong competitions that limit their coexistence. Hence, how do similarities in congeneric species affect their coexistence exactly? This study mainly used spatial point pattern analysis in two 1 hm2 plots in the Baotianman National Nature Reserve, Henan, China, to compare the similarities in spatial distributions and interspecific associations of Quercus species. Results revealed that Quercus species were all aggregated under the complete spatial randomness null model, and aggregations were weaker under the heterogeneous Poisson process null model in each plot. The interspecific associations of Quercus species to non-Quercus species were very similar in Plot 1. However, they can be either positive or negative in different plots between the co-occurring Quercus species. The spatial distributions of congeneric species, interspecific associations with non-Quercus species, neighborhood richness around species, and species diversity were all different between the two plots. We found that congeneric species did have some similarities, and the closely related congeneric species can positive or negative associate with each other in different plots. The co-occurring congeneric species may have different survival strategies in different habitats. On one hand, competition among congenerics may lead to differentiation in resource utilization. On the other hand, their similar interspecific associations can strengthen their competitive ability and promote local exclusion to non-congeneric species to obtain more living space. Our results provide new knowledge for us to better understand the coexistence mechanisms of species.

opencc-by-4.0Mar 2021View details →

Single-cell spatial architectures associated with clinical outcome in head and neck squamous cell carcinoma

Data supporting the findings of the paper "<a href="https://doi.org/10.1038/s41698-022-00253-z">Single-cell spatial architectures associated with clinical outcome in head and neck squamous cell carcinoma</a>." Files include output of multiplex immunohistochemistry computational image processing workflow for each tumor region and survival data for each patient. The code used to produce the results of this study is available at: <a href="https://github.com/kblise/HNSCC_mIHC_paper">https://github.com/kblise/HNSCC_mIHC_paper</a>. Notes about data files: <ul> <li>clinicalData.csv = Contains the following columns for each tumor region: <ul> <li>sample = tumor region ID</li> <li>dtr = Progression free survival (days to recurrence)</li> <li>tnm = TNM stage</li> <li>anatomy = anatomic site of resection</li> <li>tx = therapy administered</li> <li>area = area in mm2 of tissue region</li> </ul> </li> <li>pt .csv files = Matrix of single cells (rows) and marker expression (columns). One file per tumor region (n=47). Other columns include: <ul> <li>class = Cell phenotype assigned via hierarchical gating strategy (see below for classes)</li> <li>Location_Center_X and Location_Center_Y = Cartesian coordinates of cell center</li> <li>Cellsp_PD1p = PD-1 expression; 1 = PD-1+, 0 = PD-1-</li> <li>Cellsp_PDL1p = PD-L1 expression; 1 = PD-L1+, 0 = PD-L1-</li> <li>Cellsp_KI67p = Ki-67 expression; 1 = Ki-67+, 0 = Ki-67-</li> </ul> </li> </ul> Classes, corresponding cell phenotype, and gating strategy used: <ul> <li>A = CD8+ T Cell (CD45+ CD20- CD3+ CD8+)</li> <li>B = CD4+ T Helper (CD45+ CD20- CD3+ CD8- FOXP3-)</li> <li>C = B Cell (CD45+ CD20+)</li> <li>D = Macrophage (CD45+ CD20- CD3- CD66B- CD68+)</li> <li>E = Other Immune (CD45+ CD20- CD3- CD66B- CD68- MHCII- CD8- FOXP3-)</li> <li>F = Other Non-Immune (CD45- PANCK- αSMA-) - excluded from analysis</li> <li>G = Noise - excluded from analysis</li> <li>H = Neoplastic Tumor (CD45- PANCK+)</li> <li>J = Granulocyte (CD45+ CD20- CD3- CD66B+)</li> <li>K = CD4+ Regulatory T Cell (CD45+ CD20- CD3+ CD8- FOXP3+)</li> <li>N = αSMA+ Mesenchymal (CD45- PANCK- αSMA+)</li> <li>X = Antigen Presenting Cell (CD45+ CD20- CD3- CD66B- CD68- MHCII+)</li> </ul>

opencc-zeroOct 2021View details →

Data from: Fine-scale spatial associations between functional traits and tree growth

This data set relates to a 40x60 m2 stem-mapped plot that was established in a temperate rainforest of southern Chile. It contains data for each individual stem located within the plot. Each individual is characterized by a species code, diameter at breast height (dbh, 1.35 m), diameter at coring height (dch, ca. 30 cm), x and y coordinates, basal area index of the last 10 years (bai, cm2), and growth efficiency (ge, cm2/cm2).

opencc-zeroDec 2021View details →

Data and code for: Human density modulates spatial associations among tropical forest terrestrial mammal species

The spatial aggregation of species pairs often increases with the ecological similarity of the species involved. However, the way in which environmental conditions and anthropogenic activity affect the relationship between spatial aggregation and ecological similarity remains unknown despite the potential for spatial associations to affect species interactions, ecosystem function, and extinction risk. Given that human disturbance has been shown to both increase and decrease spatial associations among species pairs, ecological similarity may have a role in mediating these patterns. Here, we test the influences of habitat diversity, primary productivity, human population density, and species' ecological similarity based on functional traits (i.e., functional trait similarity) on spatial associations among tropical forest mammals. Large mammals are highly sensitive to anthropogenic change and therefore susceptible to changes in interspecific spatial associations. Using two-species occupancy models and camera trap data, we quantified the spatial overlap of 1,216 species pairs from 13 tropical forest-protected areas around the world. We found that the association between ecological similarity and interspecific species associations depended upon surrounding human density. Specifically, aggregation of ecologically similar species was more than an order of magnitude stronger in landscapes with the highest human density compared to those with the lowest human density, even though all populations occurred within protected areas. Human-induced changes in interspecific spatial associations have been shown to alter top-down control by predators, increase disease transmission and increase local extinction rates. Our results indicate that anthropogenic effects on the distribution of wildlife within protected areas are already occurring and that impacts on species interactions, ecosystem functions, and extinction risk warrant further investigation.