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FIGURE 2 in Improved modelling of compositional heterogeneity reconciles phylogenomic conflicts among lacewings
FIGURE 2. Congruent interfamilial relationships of Neuropterida inferred from transcriptomic (left) and AHE (right) amino acid data respectively under the site-heterogeneous CAT-GTR+G4 model. All branches have a strong support (BPP> 0.93), except for the monophyly of Myrmeleontidae (only weakly supported in AHE nucleotide data under the CAT-GTR+G4 model). Note that the transcriptomic dataset has fewer and sparser familial sampling than the AHE data.
Data for: Link between glucose metabolism and EMT drives triple negative breast cancer migratory heterogeneity
<p>Intracellular and environmental cues result in heterogeneous cancer cell populations with different metabolic and migratory behaviors. While glucose metabolism and EMT have previously been linked, we aim to understand how this relationship fuels cancer cell migration. We show that while glycolysis drives single-cell migration in confining microtracks, fast and slow cells display different migratory sensitivities to glycolysis and oxidative phosphorylation inhibition. Phenotypic sorting of highly and weakly migratory subpopulations (MDA<sup>+</sup>, MDA<sup>-</sup>) reveals that more mesenchymal, highly migratory MDA<sup>+</sup> preferentially use glycolysis while more epithelial, weakly migratory MDA<sup>-</sup> utilize mitochondrial respiration. These phenotypes are plastic and MDA<sup>+</sup> can be made less glycolytic, mesenchymal, and migratory and MDA<sup>-</sup> can be made more glycolytic, mesenchymal, and migratory via modulation of glucose metabolism or EMT. These findings reveal an intrinsic link between EMT and glucose metabolism that controls migration. Identifying mechanisms fueling phenotypic heterogeneity is essential to develop targeted metastatic therapeutics.</p>
Data and Code for "Behavioral factors and SARS-CoV-2 transmission heterogeneity within a household cohort in Costa Rica"
<p>Data and Code for "Behavioral factors and SARS-CoV-2 transmission heterogeneity within a household cohort in Costa Rica"</p>
Exploring functional flow heterogeneity in regulated flow regime: fish species turnover along hydraulic gradients in an artificial waterway network
<p><span>Humans have altered river flows and lateral aquatic habitats. The expansion of agriculture in floodplains has resulted in landscapes dominated by irrigated farmland. A key challenge in water management is to conserve existing ecological communities and habitat heterogeneity, while simultaneously maintaining engineered infrastructure for agriculture. In this study, we focused on an artificial channel network for irrigation with a regulated flow regime and its function as habitat for various fish species. Differences of hydraulic conditions among channels and compositional changes in fish species were examined to clarify functional flow heterogeneity. Species turnover was analyzed using pairwise Simpson dissimilarity among sampling reaches. Species turnover was positively associated with Froude number (flow intensity) differences at intermediate discharges, and with differences in cross-sectional areas (flow magnitude) at low discharges. Drastic changes in inflows should be considered for the effective conservation of flow heterogeneity, even under a regulated flow regime. Improved engineering design to manage the hydraulic environment is one option for maintaining the ecological value of lateral waterbodies in human-dominated landscapes. Our findings provide insights into the importance of functional flow heterogeneity to conserve fish species diversity.</span></p>
Molybdenum isotope heterogeneity of metal sulfides from magmatic hydrothermal systems
<p>Exploration of the application of Mo isotope in the field of mineralogy with precise knowledge of the geochemical behaviors of Mo in the PCDs is of considerable concerns. This study puts critical constraints on the Mo isotope geochemistry in magmatic-hydrothermal systems based on analysis of Mo isotope of the metal sulfides from different mineralization stages (i.e., epidote-chlorite, chlorite-illite, and quartz-illite stages) in Pulang porphyry Cu deposit, Yunnan, Southwest China. We observed that pyrites, chalcopyrites and pyrrhotites with highly variableMo abundances and heavier δ<sup>98</sup>Mo (-0.34 ± 0.04‰ to 3.01 ± 0.03‰) in comparison to molybdenites (δ<sup>98</sup>Mo: -0.90 ± 0.04‰ to 0.08 ± 0.04‰), whole-rock ore-forming porphyries (δ<sup>98</sup>Mo: -0.18 ± 0.03‰ to -0.08 ± 0.03‰) and surrounding rocks (-0.41± 0.05‰ to -0.14± 0.03‰).The variability of δ<sup>98</sup>Mo signatures of these metal sulfides is independent of lithology of originally magma sources, which instead constrained by the evolutions of ore-forming fluids, changes of ore-forming temperature or metallogenic settings, as well as differential geochemical behaviors of Mo species.</p> <p>In the magmatic hydrothermal metallogenic system, there is a progressive partitioning of Mo into exsolved metallic minerals, with molybdenites preferentially partitioning lighter δ<sup>98</sup>Mo while pyrites, chalcopyrites and pyrrhotites are enriched in heavier δ<sup>98</sup>Mo. Additionally, these metallic sulfides have a preference for relatively heavier δ<sup>98</sup>Mo enrichment in the early metallogenic stage, thereby leaving residual metallogenic fluids with progressively homogenized lighter δ<sup>98</sup>Mo, and consequently relatively homogeneous but lighter isotopes of the metallic minerals in the later stages. The extensively maintained Mo isotope fractionation in PCDs is indicative of a maldistribution of ore-forming components in the sulfides, of which it is this disequilibrium effect that points to the enrichment of metal elements as well as the significant process of magmatic hydrothermal metallogenesis. Mo isotope systematics provide a robust geochemical proxy to interrogate PCDs genetic mechanisms and metal precipitation stages, allowing to consider it as a subservience indicator for the complex metal enrichment that can be extrapolated to other porphyry-type deposits.</p>
FIG. 3 in Spatial heterogeneity of fruit bats in a primary tropical lowland evergreen rainforest in northeastern Luzon, Philippines
FIG. 3. Capture rates of eight fruit bat species (A–H) within the understory and canopy in a tropical lowland evergreen rainforest in northeastern Luzon, Philippines. Capture rates are expressed as the number of individuals captured per mist-net hour (m2·h). Bars indicate standard errors. Statistics refer to Kruskal-Wallis tests. Note the different y-axis scale for (C) E. robusta
FIG. 5 in Spatial heterogeneity of fruit bats in a primary tropical lowland evergreen rainforest in northeastern Luzon, Philippines
FIG. 5. Wing loading and aspect ratio of eight fruit bat species captured in a primary tropical lowland evergreen rainforest in northeastern Luzon, Philippines. Mean values of each flight index are shown with 95% confidence intervals. Sample size for all species is n = 10 (five adult males and five non-pregnant adult females), except for E. spelaea (n = 5, all adult males)
FIG. 2 in Spatial heterogeneity of fruit bats in a primary tropical lowland evergreen rainforest in northeastern Luzon, Philippines
FIG. 2. Accumulation curves of species in (A) the understory and canopy, and in (B) ridges, streams, and forest edges in a primary tropical lowland evergreen rainforest in northeastern Luzon, Philippines. Curves are constructed based on averaging from random permutations of the weighted data
FIG. 1 in Spatial heterogeneity of fruit bats in a primary tropical lowland evergreen rainforest in northeastern Luzon, Philippines
FIG. 1. Location of the study area in the Northern Sierra Madre Natural Park, province of Isabela, northeastern Luzon, Philippines. Dark grey indicates forested areas, light grey indicates water bodies, and white indicates open areas. Map adapted from NAMRIA basemap downloaded from http://www.geoportal.gov.ph/viewer
FIG. 4 in Spatial heterogeneity of fruit bats in a primary tropical lowland evergreen rainforest in northeastern Luzon, Philippines
FIG. 4. Capture rates of eight fruit bat species (A–H) in ridges, streams, and forest edges in a tropical lowland evergreen rainforest in northeastern Luzon, Philippines. Capture rates are expressed as the number of individuals captured per mist-net hour (m2·h). Bars indicate standard errors. Statistics refer to Kruskal-Wallis tests. Note the different y-axis scale for (C) E. robusta and (H) R. amplexicaudatus
Trait-based and multi-scale approach provides insight on responses of freshwater mussels to environmental heterogeneity
<p>Our understanding of the factors driving the distribution of metacommunities at different scales can be obscured by high variation in species composition between sites and a lack of fine-scale distribution data. Trait-based approaches have long been used to better identify and examine ecological patterns. Most recent studies of riverine metacommunities examining trait-based patterns have focused on shorter-lived organisms. Here we focused on a group of longer-lived, sedentary riverine organisms, unionid freshwater mussels. The objective of this study was to examine how (1) the distribution of mussels with different life history strategies (trait-based approach) and (2) the relative importance of environmental and spatial factors (as a proxy for dispersal) would differ with spatial scale and position in the river; and to (3) further compare this with patterns derived from a taxonomic approach. Fine-scale distribution data of mussels and environmental factors were collected every 100 m in spatially extensive surveys in an up- and downstream segment (200 sites/20 km-segment) of a semi-arid river, making them some of the most spatially intensive surveys documented to date. A combination of redundancy analysis, asymmetric eigenvector mapping, and variation partitioning analyses revealed that more variation was explained by environmental factors where more environmental differences occur between sites. Where environmental heterogeneity was lower the amount of variation explained by smaller-scale spatial factors was higher, likely mostly associated with stochastic rather than dispersal processes. A higher amount of unexplained variation at the taxonomic level suggests that stochasticity may also play an important role in determining species composition. In contrast, different life history groups had a highly predictable distribution pattern driven by environmental heterogeneity, especially between river segments and mesohabitat, which was associated with different flow conditions. The role we predict for environmental heterogeneity and stochasticity in shaping the distribution of mussels in our study river likely also applies to other taxa and ecosystems at a spatial scale at which neither dispersal limitation nor mass effects occur. Thus, understanding the magnitude and extent of dispersal relative to the amount of environmental heterogeneity may be key for predicting metacommunity structure and dynamics for different organisms.</p>
Figure 3 in The heterogeneity of Caatinga biome: an overview of the bat fauna
Figure 3: Cluster analyses based on the Euclidean distance for the bat species composition in the eight Caatinga ecoregions (A) and for the main references in the best surveyed ecore- gions for the bat fauna. (B) * Fazenda Arara/Caatinga Arbustiva ** Reserva Particular do Patrimônio Natural – Fazenda Bituri/ Floresta Montana. Chapada Diamantina Complex (CDC), São Francisco Dunes (SFD), Raso da Catarina (RAC), Campo Maior Complex (CMC), Borborema Highlands (BOH), Southern Sertaneja Depression (SSD), Ibiapaba-Araripe Complex (IAC), and Northern Sertaneja Depression (NSD).
Figure 2 in The heterogeneity of Caatinga biome: an overview of the bat fauna
Figure 2: Scatter plot showing the relationship between the number of bat species known to each Caatinga ecoregion and (A) the ecoregion area and (B) the number of studies that included the sampling of chiropterans in each ecoregion.
Figure 1 in The heterogeneity of Caatinga biome: an overview of the bat fauna
Figure 1: Map of Caatinga ecoregions and municipalities surveyed for chiropteran fauna. Inset shows the South American continent and Brazil, more specifically northeastern Brazil (light gray) and the area covered by the Caatinga biome (dark gray). The numbers indicate the names of municipalities, as follows. Superscript numbers in brackets indicate the studies that cite the municipality, as listed in Supplementary Table S1. 1 – Batalha – PI (39); 2 – José de Freitas – PI (36); 3 – Teresina – PI (28;35;54); 4 – Monsenhor Gil – PI (25); 5 – Arajara – CE (23,64); 6 – Crateús – CE (4;21;52); 7 – Crato – CE (23;28;55;60;61); 8 – Ipu – CE (57); 9 – Juazeiro do Norte – CE (2); 10 – Nova Olinda – CE (53;28); 11 – Parambú – CE (24); 12 – Ubajara – CE (20;50); 13 – Cocal – PI (25); 14 – Guaribas – PI (19); 15 – São Raimundo Nonato – PI (28); 16 – Valença do Piauí – PI (28;61); 17 – Canindé – CE (7;8); 18 – Fortaleza – CE (7;27;63); 19 – Guaramiranga – CE (7;63); 20 – Jaguaruana – CE (7; 8); 21 – Limoeiro do Norte – CE (7;8;32); 22 – Morada Nova – CE (7); 23 – Palhano – CE (7); 24 – Pereiro – CE (7;8); 25 – Quixadá – CE (2;7;8;32;63); 26 – Russas – CE (7;8;32); 27 – Brejo dos Santos – PB (10); 28 – Maturéia – PB (32); 29 – Santa Terezinha – PB (10;32); 30 – São José dos Cordeiros – PB (10;13;32); 31 – Pombal – PB (32); 32 – Exu – PE (2;18;23;28;53;60;61); 33 – Apodi – RN (12); 34 – Baraúna – RN(12); 35 – Felipe Guerra – RN (12); 36 – Martins – RN (12); 37 – Mossoró – RN (5); 38 – Nova Cruz – RN (11;32); 39 – Pedra Grande – RN (12); 40 – Alagoinha – PB (32); 41 – Araruna – PB (4;9; 10;32); 42 – Areia – PB (32); 43 – Boqueirão – PB (32); 44 – Cabaceiras – PB (10;32); 45 – Cacimba de Dentro – PB (32); 46 – Guarabira – PB (32); 47 – Juazeirinho – PB(2); 48 – Soledade – PB (18); 49 – Umbuzeiro – PB (10); 50 – Arcoverde – PE (2;22); 51 – Belo Jardim – PE (2; 23); 52 – Brejo da Madre de Deus – PE (2;23;32;47;49); 53 – Buíque – PE (2; 23); 54 – Caruaru – PE (2;23); 55 – Panelas – PE (23); 56 – Pedra – PE (2); 57 – Sanharó – PE (23); 58 – Toritama – PE (2; 23); 59 – Vertentes – PE (32);60 – Penedo – AL(25;58);61 – Anagé – BA (32); 62 – Aporá – BA (15); 63 – Bom Jesus da Lapa – BA (43); 64 – Contendas do Sincorá – BA (41;42); 65 – Euclides da Cunha – BA (18); 66 – Glória – BA (2); 67 – Irecê – BA (17); 68 – Iuiú – BA (43); 69 – Juazeiro – BA (28,32); 70 – Malhada – BA (43); 71 – Palmas de Monte Alto – BA (43); 72 – Paratinga – BA (43); 73 – São Gonçalo dos Campos – BA (32); 74 – São José – BA (32); 75 – Senhor do Bonfim – BA (14;28); 76- Itacarambi – MG (32;40;56); 77 – Jaíba – MG (33;34;37;56); 78 – Januária – MG (56); 79 – Manga – MG (56); 80 – Mocambinho – MG (32); 81 – São João das Missões – MG (40); 82 – Betânia – PE (4); 83 – Bodocó – PE (2;23); 84 – Floresta – PE (2;4); 85 – Orocó – PE (2;23;51); 86 – Petrolândia – PE (2;23); 87 – Salgueiro – PE (2;23); 88 – São Caetano do Navio – PE (32); 89 – São José do Egito – PE (48); 90 – Serra Talhada – PE(3;2;23;28); 91 – Sertânia – PE (2;23); 92 – Areia Branca – SE (31); 93 – Canindé do São Francisco – SE (1; 2;23); 94 – Itabaiana – SE (29;30;31); 95 – Canarana – BA (44); 96 – Itaeté – BA (16;55); 97 – Iraquara – BA (44); 98- Itaberaba – BA (38); 99 – Gentio do Ouro – BA (32); 100 – Jacobina – BA (32); 101 – João Dourado – BA (44); 102 – Jussiape – BA(38); 103 – Lençóis – BA (38;46); 104 – Morro do Chapéu – BA (32;38;44); 105 – Mucugê – BA (38); 106 – Ourolândia – BA (44); 107 – Palmeiras – BA (44); 108 – Rio de Contas – BA (38); 109 – Utinga – BA (44;45); 110 – Barra – BA (26); 111 – Sobradinho – BA (2); 112 – Pilão Arcado – BA (32); 113 – Canudos – BA (18); 114 – Rodelas – BA (2); 115 – Floresta do Navio – PE (23); 116 – Ibimirim – PE (2;23); 117 – Inajá – PE (23;32); 118 – Jardim – CE (64); 119- São João do Piauí – PI (65). PI, Piauí; CE, Ceará; PB, Paraíba; RN, Rio Grande do Norte; PE, Pernambuco; AL, Alagoas; BA, Bahia; MG, Minas Gerais; SE, Sergipe.
SCM data for "Representing the subgrid surface heterogeneity of precipitation in a general circulation model"
<p>Single Column Model output from simulations described in the paper, "Representing the subgrid surface heterogeneity of precipitation in a general circulation model," by Nathan Arnold, Randal Koster, and Atanas Trayanov. </p>
Cell cycle heterogeneity in pancreatic ductal adenocarcinoma
<p>Multiplexed, single-cell imaging data for PDAC cell lines.</p>
The heterogeneous driving forces behind carbon emissions change in 30 selective emerging economies
<p>The CO<sub>2</sub> emission inventories included 30 emerging economies, spanning from 2010 to 2018. We matched 17 sectors based on indicators such as energy consumption and economic statistics.</p>
Supplemental Material for Manuscript: The Heterogeneity of Social Network and Institutional Covariance in the American Southeast
<p>Supplemental Material for Manuscript: The Heterogeneity of Social Network and Institutional Covariance in the American Southeast</p>
A Deep Learning Approach to Afternoon Convection Prediction Using Heterogeneous Weather Data: Taking the Important Areas in Taiwan as an Example
<p>The following partial dataset is provided by the authors for the Afternoon Convection Prediction method presented in the paper.</p> <p>afternoon_rainfall_label is labeled as afternoon convection data</p> <p>weather_image is weather images</p>
Data for: Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model
<p>Epidemics, such as COVID-19, have caused significant harm to human society worldwide. A better understanding of epidemic transmission dynamics can contribute to more efficient prevention and control measures. Compartmental models, which assume homogeneous mixing of the population, have been widely used in the study of epidemic transmission dynamics, while agent-based models rely on a network definition for individuals. In this study, we developed a real-scale contact-dependent dynamic (CDD) model and combined it with the traditional susceptible-exposed-infectious-recovered (SEIR) compartment model. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.