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FIGURE 9 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 9. Non-stationary associations between ecological diversity (ED) and standard deviation in altitude (ALTstd). The maps show the spatial variation in local beta coefficients (b) for ALTstd as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equalarea projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 5 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 5. Non-stationary associations between ecological diversity (ED) and net primary productivity (NPP). The maps show the spatial variation in local beta coefficients (b) for NPP as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 4 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 4. Non-stationary associations between ecological diversity (ED) and mean annual temperature (TEMP). The maps show the spatial variation in local beta coefficients (b) for TEMP as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of tropics in the Northern and Southern Hemispheres.
FIGURE 8 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 8. Non-stationary associations between ecological diversity (ED) and coefficient of variation in annual precipitation (PRECcv). The maps show the spatial variation in local beta coefficients (b) for PRECcv as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 7 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 7. Non-stationary associations between ecological diversity (ED) and annual range in temperature (TEMPr). The maps show the spatial variation in local beta coefficients (b) for TEMPr as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equalarea projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 1 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 1. Spatial patterns of variation in the ecological diversity (ED) of different mammal groups over the Americas. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 14 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 14. Non-stationary associations between phylogenetic diversity (AvPD) and coefficient of variation in annual precipitation (PRECcv). The maps show the spatial variation in local beta coefficients (b) for PRECcv as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 6 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 6. Non-stationary associations between ecological diversity (ED) and annual precipitation (PREC). The maps show the spatial variation in local beta coefficients (b) for PREC as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of tropics in the Northern and Southern Hemispheres.
FIGURE 13 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 13. Non-stationary associations between phylogenetic diversity (AvPD) and annual range in temperature (TEMPr). The maps show the spatial variation in local beta coefficients (b) for TEMPr as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 12 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 12. Non-stationary associations between phylogenetic diversity (AvPD) and annual precipitation (PREC). The maps show the spatial variation in local beta coefficients (b) for PREC as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 10 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 10. Non-stationary associations between phylogenetic diversity (AvPD) and mean annual temperature (TEMP). The maps show the spatial variation in local beta coefficients (b) for TEMP as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 2 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 2. Spatial patterns of variation in the phylogenetic diversity (AvPD) of different mammal groups over the Americas. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 3 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 3. Non-stationary associations between ecological diversity (ED) and phylogenetic diversity (AvPD). The maps show the local beta coefficients (b) for AvPD as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Following (Matthews & Yang 2012) non-significant values (p> 0.05) are excluded from the maps to optimize the visualization of patterns. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
Data from: Clinical correlation of multiple sclerosis immunopathological subtypes
<p><b>Objective:</b> To compare clinical characteristics across immunopathological subtypes of patients with multiple sclerosis.</p> <p><b>Methods:</b> Immunopathological subtyping was performed on specimens from 547 patients with biopsy and/or autopsy confirmed CNS demyelination.</p> <p><b>Results: </b>The frequency of immunopathological subtypes were pattern I (23%), II (56%), and III (22%). Immunopatterns were similar in terms of age at autopsy/biopsy (median age 41 years, range 4-83 years, p=0.16) and proportion female (54%, p=0.71). Median follow-up after symptom onset was 2.3 years (range 0-38y). In addition to being overrepresented among autopsy cases (45% vs. 19% in biopsy cohort, p<0.001), index attack-related disability was higher in pattern III vs. pattern II (median EDSS 4 vs. 3, p=0.02). Monophasic clinical course was more common in patients with pattern III than pattern I or II (59% vs. 33% vs. 32%, p<0.001). Similarly, patients with pattern III pathology were likely to have progressive disease compared to patients with patterns I or II, when followed for ≥5 years (24% overall, p=0.49), with no differences in long-term survival, despite a more fulminant attack presentation.</p> <p><b>Conclusion:</b> All three immunopatterns can be detected in active lesions, although they are found less frequently later into the disease due to the lower number of active lesions. Pattern III is associated with a more fulminant initial attack than either pattern I or II. Biopsied patients appear to have similar long-term outcomes irrespective of their immunopatterns. Progressive disease is less associated with the initial immunopattern and suggests convergence into a final common pathway related to the chronically denuded axon.</p>
FIGURES 6–9 in Character correlation and its use for identification
FIGURES 6–9. Dichotomous cladogram, ordered correlations, and output. 6, dichotomous cladogram that results in zero correlation between characters 0 and 1; 7, one correlation graph showing the maximal possible correlation between two additive characters; 8, correlation with incomplete ordering; 9, output to determine which state-combinations are compatible only with state 1 of the third character (combinations that predict only 2:1 in bold).
FIGURES 2–5. Pectinate cladogram and unordered correlations. 2 in Character correlation and its use for identification
FIGURES 2–5. Pectinate cladogram and unordered correlations. 2, pectinate tree, optimized for characters 0 and 1; 3, correlation of characters 0 (x) and 1 (y) on pectinate tree ("·": observed state combination; 0: zero extra steps needed; 1: one extra step needed); 4, pectinate tree with optimization for characters 2 and 3, showing ambiguity; 5, correlation of characters 2 (x) and 3 (y) on pectinate tree.
A Preliminary Study on the Acoustic Correlates of Pseudoregister in Pacoh
<p>Register is a suprasegmental contrast relevant in many languages of Southeast Asia. Conventionally, register develops under conditioning from historical onset voicing, but there are several languages with ostensible register contrasts that are unrelated to onset voicing. I propose the term “pseudoregister” for such languages. For a pseudoregister language, Pacoh (< Katuic < Austroasiatic) employs a peculiar combination of phonetic cues. It is demonstrated here that F1 difference is the primary acoustic correlate of pseudoregister in Pacoh with secondary reinforcement from F0 and spectral tilt. Implications for a general model of pseudoregister formation are discussed.</p>
Correlating single-molecule rupture mechanics with cell population adhesion by yeast display
<p>Data underlying the figures in the publication “Correlating single-molecule rupture mechanics with cell population adhesion by yeast display”, published in <em>Biophysical Reports, </em><em><strong>2022</strong></em><em>, Volume 2, Issue 1, 100035, ISSN 2667-0747.</em></p> <p><em><a href="https://doi.org/10.1016/j.bpr.2021.100035">https://doi.org/10.1016/j.bpr.2021.100035</a></em></p> <p>Table of contents:</p> <p><strong>1. Fig2_SMFS.xlsx</strong>: Contour Length vs probability density values for <em>Figure 2D</em>. Rupture Force and Loading rate values for all SMFS curves analysed for <em>Figure 2E</em> and <em>2F</em>.</p> <p><strong>2. Fig3_SDA.xlsx</strong>: Shear Stress <em>vs</em> Cell Density values of all replicates and speeds for F<em>igures 3D</em> and <em>3E</em>.</p> <p><strong>3. FigS1_SPR curves.xlsx</strong>: SPR curves reported in Supplementary <em>Figure 1</em>.</p> <p><strong>4. FigS2_one step FLN.xlsx</strong>: Force <em>vs</em> Extension trace and Contour Length Histogram values for one step FLN unfolding shown in <em>Supplementary Figure 2</em>.</p> <p><strong>5. FigS3_SMFS_internal control.xlsx</strong>: Rupture Force and Loading rate values for N-S25H reported in <em>Supplementary Figure 3</em>.</p> <p><strong>6. FigS5_SDA_internal control.xlsx</strong>: Shear Stress <em>vs</em> Cell Density values of N-WT shown in <em>Supplementary figure 5B</em>.</p> <p><strong>7. FigS6_SDA_induction timepoints.xlsx</strong>: Shear Stress <em>vs</em> Cell Density values and median of expression detected by flow cytometry reported in <em>Supplementary Figure 6</em>.</p>
Pyramidal Cell-Interneuron Coupling with Positive Correlation
<p>The firing-rate maps of the pyramidal cells and interneurons had a pronounced spatial-overlap with the positive correlation in the rectangular-shaped track at higher firing rates.</p>
Correlations Images
<p>COMMSENV-20-0370B.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.