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Raw flow cytometry data files (fcs files) part 4
<p>These FCS files are raw data for Figure 3H of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 1
<p>These FCS files are raw data for Figure 1C and S2A-C of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 2
<p>These FCS files are raw data for Figure 2D, 2E, 3B, 3D and 3E of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 5
<p>These FCS files are raw data for Figure 5E-I, 5M and 5N of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 9
<p> </p> <p>These FCS files are raw data for Figure 6B of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Enhancing Distributed Summary Synthesis with Data-Flow Analysis
<p>With increasing software complexity, scalable and precise verification is essential, especially in safety-critical areas. Distributed Summary Synthesis (DSS) supports scalability by enabling parallel processing of program segments (blocks). However, it faces limitations in achieving early-stage abstraction due to the inherent laziness of Predicate Analysis, which only refines abstractions when errors are detected. This thesis addresses this by integrating Data-Flow Analysis (DFA) into DSS, enhancing the initial information shared among program blocks to potentially accelerate and improve verification. Implemented in CPAchecker, DFA runs in parallel with Predicate Analysis, providing coarse summaries that strengthen the preconditions for successor blocks. Experimental evaluation using SV-COMP 2024 benchmarks, however, indicated that while DFA integration occasionally improved verification coverage, it also introduced additional resource demands. This increase in CPU time, wall time and memory usage, due to message handling and serialization and deserialization overhead, limited the number of programs that could be verified compared to the DSS implementation with only predicate analysis. This trade-off suggests that additional optimizations are needed to reduce performance costs and better harness the potential of DFA for scalable and effective verification.<br><br></p>
Estimates of daily river flows for 190 catchments in Great Britain from the GR6J model using CRCM5 large ensemble
<p>Simulated river flows for 200 catchments using the GR6J hydrological model driven by the CRCM5 50-member large ensemble</p>
Bilateral asthenospheric flow fed by the Caroline plume
<p>This dataset is an ASCII-format data file of S-wave velocity and radial anisotropy models for the study area.</p>
[Supplementary Data] PowerModel-AI: A First On-the-fly Machine-Learning Predictor for AC Power Flow Solutions.
<h1><strong>Abstract</strong></h1> <p>The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to autonomously build models while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce <em>PowerModel-AI</em>, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures the load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model's performance is sub-optimal, based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using <em>PowerModel.jl</em>, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.</p> <div> <h1><strong>Overview</strong></h1> <p>This dataset, provided as supplementary material for the above-referenced study, includes a comprehensive collection of images (plots) from the study’s analyses, along with Jupyter notebooks containing Python scripts used for the training, validation, and testing phases of PowerModel-AI. Additionally, it includes all training and external test data used in this work, generated via LANL-based open-source power flow solver, PowerModels.jl.</p> <p>The primary objective of this dataset is to ensure full reproducibility of the study’s analyses and facilitate critical examination by the scientific community, thereby maximizing the overall impact of the work.</p> <h1>Directory Structure</h1> <p>The hierarchy of folders and file organization of the dataset is illustrated in the chart below. The directory contains a README.md file which contains the information provided here and a requirements.txt that contains all libraries necessary to run the python scripts or Jupyter notebooks in this directory. A brief description of the folders and what they contain are provided below: </p> </div> <div> <strong>.</strong></div> <div>├── <strong>Models/</strong></div> <div>│ ├── _PM_AI_Models/</div> <div>│ │ ├── Type1/</div> <div>│ │ ├── Type2/</div> <div>│ │ └── Type3/</div> <div>│ │</div> <div>│ ├── _PM_AI_Module/</div> <div>│ │ ├── __init__.py</div> <div>│ │ └── PM_Methods.py</div> <div>│ │</div> <div>│ ├── _PM_JL_Data/</div> <div>│ ├── C1_Model/</div> <div>│ ├── C2_Model/</div> <div>│ ├── C3_Model/</div> <div>│ ├── M1_Model/</div> <div>│ ├── M2_Model/ </div> <div>│ └── M3_Model/</div> <div>│</div> <div>├── <strong>NodeSensitivityAnalysis/</strong></div> <div>│ ├── Sensitivity_Plots/</div> <div>│ ├── Sensitivity_PM_JL_Data/</div> <div>│ ├── get_sensitivity_PMJL_data.py</div> <div>│ └── NodeSensitivityAnalysis.ipynb</div> <div>│</div> <div>├── <strong>PlotsForOnTheFlyAnalysis/</strong></div> <div>│ ├── BaseModel_A/</div> <div>│ ├── BaseModel_B/ </div> <div>│ └── BaseModel_C/</div> <div>│ </div> <div>├── <strong>README.md</strong></div> <div>└── <strong>requirements.txt</strong></div> <p> </p> <h2>Files Description </h2> <h3><strong>1. </strong><strong>Models/</strong></h3> <p><strong><em>_PM_AI_Models/</em></strong> contains the ML models discussed in the manuscript. Type1, Type2, and Type3 refers to the C and M models with numbers "1", "2," and "3". Each Type folder contains individual subfolders for the synthetic grids discussed.</p> <p><strong> <em>_PM_AI_Module/</em></strong> contains a python script that has all the functions used in model training and analysis. It is imported in the Jupyter notebooks in the C and M subfolders in this directory.</p> <p><strong><em>_PM_JL_Data/</em> </strong>contains the following subfolders:</p> <p>a) <strong> </strong><em>_</em><strong><em><strong>G</strong>eneratePowerModelData/</em></strong> has a python script (<u>get_PowerModelJLData.py)</u> that is used to parse <u>PowerModels.jl</u> to compute AC power flow solutions for different power demand configurations. It also contains a subfolder, <strong><em>BusData_MATLAB/</em></strong>, that has all the synthetic grids used in the study and in MATLAB format.</p> <p>b) It also contains other subfolders (not shown in the chart above) that contains AC power flow solutions generated using LANL’s PowerModels.jl, for each synthetic grids and other grid-related data.</p> <p>The folders starting with C and M are the control and candidate models (more details in manuscript). Each folder contains Jupyter notebooks (for each power grid) that has python algorithms used in model training and testing, as well as functions to analyze and plot the prediction performance of the models. It accesses (if already trained) or stores (if newly trained) the models in the <strong><em>_PM_AI_Models/</em></strong> directory. Additionally, they contain 2 subfolders (not shown in the chart above): <strong>AbsoluteErrorPlots/</strong> and <strong>PredictionPlots/</strong> were the results (plots) from the analyses are stored. Some of these results are shown in the manuscript (<em>Figures 4</em>,<em> 5</em>, <em>6</em>, and <em>7</em>).</p> <h3><strong>2. </strong><strong>NodeSensitivityAnalysis/</strong></h3> <p>This folder contains the tools used for the node dependency analysis in Section 3.1.1 of the manuscript.</p> <p>It contains a python script called get_sensitivity_PMJL_data.py, which has similar operation like the <u>get_PowerModelJLData.py</u> script but only compute changes for one bus at a time (see details in manuscript). There is also a Jupyter notebook called <u>NodeSensitivityAnalysis.ipynb </u>that analyzes the bus node dependencies and produces the plots that are shown in Figure 3 in the manuscript. It contains 2 additional sub-folders:</p> <p>a) <strong> <em>Sensitivity_PM_JL_Data/</em></strong> where PowerModels.jl generated AC power flow solution data are stored, and</p> <p>b) <em><strong>Sensitivity_Plots/</strong> </em>where the results from <u>NodeSensitivityAnalysis.ipynb</u> are stored.</p> <h3><strong>3. </strong><strong>PlotsForOnTheFlyAnalysis/</strong></h3> <p>This directory contains only the results for the discussion in Section 3.2 in the manuscript, which is the on-the-fly implementation of PowerModel-AI. The on-the-fly algorithm will be provided and distributed separately in the PowerModel-AI package, which will be publicly available through LANL’s <a href="https://github.com/lanl-ansi" target="_blank" rel="noopener">The Advanced Network Science Initiative</a> (Github). It contains 3 subfolders with similar names but ends with "A", "B," and "C" which correspond to the designations shown and discussed in <em>Figure 8</em> of the manuscript.</p> <h1>Summary</h1> <h3><strong> </strong><strong>Models/</strong></h3> <p><strong>1. _PM_AI_Models/: </strong></p> <p>Contains the machine learning models discussed in the manuscript. The Type1, Type2, and Type3 folders refer to C and M models labeled "1", "2," and "3". Each type folder includes subfolders for the corresponding synthetic grids analyzed.</p> <div><strong>2. _PM_AI_Module/: </strong> </div> <div>Contains Python scripts for model training and analysis. PM_Methods.py is the script that defines functions for model training and analysis, which are imported in the Jupyter notebooks in the C and M subfolders. </div> <div> </div> <div><strong>3. _PM_JL_Data/: </strong></div> <div>-_GeneratePowerModelData/: Contains a Python script (get_PowerModelJLData.py) used to parse PowerModels.jl and compute AC power flow solutions for various power demand configurations. This folder also includes BusData_MATLAB/, which holds the synthetic grid data in MATLAB format. </div> <div>- Other Subfolders: Contain PowerModels.jl AC power flow solutions for each synthetic grid, as well as related data.</div> <div> </div> <div><strong>4. C1_Model/, C2_Model/, C3_Model/, M1_Model/, M2_Model/, M3_Model/: </strong></div> <div>These folders contain the control and candidate models (refer to manuscript details). Each folder includes pre-run Jupyter notebooks for model training and analysis, as well as two subfolders: </div> <div> - <em>AbsoluteErrorPlots/</em>: Contains saved analysis results for each grid.</div> <div> <strong> </strong>- <em>PredictionPlots/</em>: Stores model prediction results. </div> <div>Some results are shown in <em>Figures 4, 5, 6,</em> and <em>7</em> of the manuscript and can be reproduced using the notebooks.</div> <h3>NodeSensitivityAnalysis/</h3> <div><strong>1. Node Dependency Analysis Tools: </strong> </div> <div> This folder contains scripts and data used for the node dependency analysis in Section 3.1.1 of the manuscript.</div> <div> </div> <div><strong>2. Scripts: </strong></div> <div> - get_sensitivity_PMJL_data.py: Computes power flow changes for individual buses (refer to the manuscript for details). </div> <div> - NodeSensitivityAnalysis.ipynb: Analyzes dependencies and generates plots for Figure 3 in the manuscript. </div> <div> </div> <div><strong>3. Subfolders:</strong> </div> <div> - Sensitivity_PM_JL_Data/: Stores PowerModels.jl data for sensitivity analysis. </div> <div> - Sensitivity_Plots/: Contains the generated results from the Jupyter notebook.</div> <h3>PlotsForOnTheFlyAnalysis/</h3> <div><strong>On-the-Fly Learning Results: </strong>Contains the results for the on-the-fly learning analysis discussed in Section 3.2 of the manuscript. The subfolders BaseModel_A/, BaseModel_B/, and BaseModel_C/ correspond to the designations in <em>Figure 8</em> of the manuscript. These subfolders contain plots generated during analysis.</div> <h3>Additional Files</h3> <div>- README.md: This file.</div> <div>- requirements.txt: Contains a list of necessary Python libraries required to run the scripts and Jupyter notebooks.</div> <h3>Notes:</h3> <div>- The PowerModel-AI code is publicly available on GitHub.</div> <div>- The supplementary materials include additional Jupyter notebooks and results for each power grid analysis. </div> <div>- The generated results and predictions in this repository are consistent with those discussed in the manuscript.</div>
Head-on quenching of turbulent premixed NH3/H2/air flames in the channel flow with friction Reynolds number 300
Open the record for dataset details and reuse information.
Unstable gas flow in a flat channel under the influence of a transverse force field: self-oscillations of the jet
<p>For a description of the task, see the original paper.</p> <div> <div> <div> <div>For each video, there are the meaning of force (F), Section and Subsection of the article where this video is mentioned, as well as the number of Figure from the article corresponding to this calculation. The Mach number is 0.29 for all the calculations.</div> </div> </div> </div> <p>(1) "Video-1.avi": </p> <p>F = 2.5, </p> <p>Section "Main modeling results"</p> <p>Subsection "Weak force field"</p> <p>Figure 2</p> <p> </p> <p>(2) "Video-2.avi": </p> <p>F = 4, </p> <p>Section "Main modeling results"</p> <p>Subsection "Strong force field"</p> <p>Figure 3</p> <p> </p> <p>(3) "Video-3.avi": </p> <p>F = 3, </p> <p>Section "Main modeling results"</p> <p>Subsection "Average force field"</p> <p>Figure 4</p> <p> </p> <p>(4) "Video-4.avi": </p> <p>F = 7 (continuous force field, see Figure 5), </p> <p>Section "Main modeling results"</p> <p>Subsection "Continuous force field"</p> <p>Figure 6</p>
Experimental investigation on dynamics and flow resistance of granular-fluid flows
<p><span>Dataset related to the manuscript “Experimental investigation on dynamics and flow resistance of granular-fluid flows”, submitted to the </span><em><span>Journal of Geophysical Research: Earth Surface</span></em><span>.</span></p>
Data from: Extensive gene flow over Europe and possible speciation over Eurasia in the ectomycorrhizal basidiomycete Laccaria amethystina complex.
Biogeographic patterns and large-scale genetic structure have been little studied in ectomycorrhizal fungi, despite the ecological and economic importance of ectomycorrhizal symbioses. We coupled population genetics and phylogenetic approaches to understand spatial structure in fungal populations on a continental scale. Using 9 microsatellite markers, we characterised gene flow among 16 populations of the widespread ectomycorrhizal basidiomycete Laccaria amethystina over Europe (over 2900km). We also widened our scope to two additional populations from Japan (104 km away), and compared them with European populations through microsatellite markers and multi-locus phylogenies, using 3 nuclear genes (NAR, G6PD and ribosomal DNA) and two mitochondrial ribosomal genes. European L. amethystina populations displayed limited differentiation (average FST=0.041) and very weak isolation by distance. This panmictic European pattern may result from effective aerial dispersal of spores, high genetic diversity in populations, and mutualistic interactions with multiple hosts that all facilitate migration. The multi-locus phylogeny based on nuclear genes confirmed that Japanese and European specimens were closely related but clustered on a geographical basis. By using microsatellite markers, we found that Japanese populations were strongly differentiated from the European populations (FST=0.416), more than expected by extrapolating the European pattern of isolation by distance. Population structure analyses clearly separated the populations into two clusters, European and Japanese clusters. We discuss the possibility of isolation by distance in a continuous population (considering some evidence for a ring species over the Northern Hemisphere) versus an allopatric speciation over Eurasia, making L. amethystina a promising model of intercontinental species for future studies.
Data from: Scale-dependent effects of landscape variables on gene flow and population structure in bats
Aim: A common pattern in biogeography is the scale-dependent effect of environmental variables on the spatial distribution of species. We tested the role of climatic and land cover variables in structuring the distribution of genetic variation in the grey long-eared bat, Plecotus austriacus, across spatial scales. Although landscape genetics has been widely used to describe spatial patterns of gene flow in a variety of taxa, volant animals have generally been neglected because of their perceived high dispersal potential.Location: England and Europe. Methods: We used a multiscale integrated approach, combining population genetics with species distribution modelling and geographical information under a causal modelling framework, to identify landscape barriers to gene flow and their effect on population structure and conservation status. Genotyping involved 23 polymorphic microsatellites and 259 samples from across the species' range. Results: We identified distinct population structure shaped by geographical barriers and evidence of population fragmentation at the northern edge of the range. Habitat suitability (as captured by species distribution models, SDMs) was the most important landscape variable affecting genetic connectivity at the broad spatial scale, while at the fine scale, lowland unimproved grasslands, the main foraging habitat of P. austriacus, played a pivotal role in promoting genetic connectivity. Main conclusions: The importance of lowland unimproved grasslands in determining the biogeography and genetic connectivity in P. austriacus highlights the importance of their conservation as part of a wider landscape management for fragmented edge populations. This study illustrates the value of using SDMs in landscape genetics and highlights the need for multiscale approaches when studying genetic connectivity in volant animals or taxa with similar dispersal abilities.
Data from: Adaptive differentiation in floral traits in the presence of high gene flow in scarlet gilia (Ipomopsis aggregata)
Plant-pollinator interactions are thought to be major drivers of floral trait diversity. However, the relative importance of divergent pollinator-mediated selection versus neutral processes in floral character evolution has rarely been explored. We tested for adaptive floral trait evolution by comparing differentiation at neutral genetic loci to differentiation at quantitative floral traits in a putative Ipomopsis aggregata hybrid zone. Typical I. aggregata subsp. candida displays slender white tubular flowers that are typical of flowers pollinated by hawkmoths and subsp. collina displays robust red tubular flowers typical of flowers pollinated by hummingbirds; yet hybrid flower morphs are abundant across the East Slope of the Colorado Rockies. We estimated genetic differentiation (FST) for nuclear and chloroplast microsatellite loci and used a half-sib design to calculate quantitative trait divergence (QST) from collection sites across the morphological hybrid zone. We found little evidence for population structure and estimated mean FST to be 0.032. QST values for several floral traits including corolla tube length and width, color, and nectar volume were large and significantly greater than mean FST. We performed multivariate comparisons of neutral loci to genetic correlations within and between populations and found a strong signal for divergent selection, suggesting that specific combinations of floral display and reward traits may be the targets of selection. Our results show little support for historical subspecies categories, yet floral traits are more diverged than expected due to drift alone. Non-neutral divergence for multivariate quantitative traits suggests that selection by pollinators is maintaining a correlation between display and reward traits.
Data from: The geography of divergence-with-gene-flow facilitates multi-trait adaptation and the evolution of pollinator isolation in Mimulus aurantiacus
Ecological adaptation is the driving force during divergence-with-gene-flow and generates reproductive isolation early in speciation. Although gene flow opposes divergence, local adaptation can be facilitated by factors that prevent the breakup of favorable allelic combinations. We investigated how selection, genetic architecture, and geography have contributed to the maintenance of floral trait divergence and pollinator isolation between parapatric ecotypes of Mimulus aurantiacus. Combining greenhouse, field, and genomic studies, we show that sharp clines in floral traits are maintained by spatially-varying selection. Although adaptation breaks down where the ecotypes co-occur, leading to the formation of a hybrid zone, the largely non-overlapping distributions of the ecotypes shield them from immigrant genes, facilitating divergence across most of the range. In contrast to the sharp genetic discontinuities observed across most hybrid zones, we observed a gradual cline in genome-wide divergence and a pattern of isolation-by-distance across the landscape. Thus, contrary to a long period of allopatry followed by recent re-contact, our data suggest that floral trait divergence in M. aurantiacus may have evolved with locally-restricted, but ongoing gene flow Therefore, our study reveals how the geographic distribution of an organism can contribute to the evolution of premating isolation in the early stages of divergence-with-gene-flow.
Data from: Contrasting patterns of population structure and gene flow facilitate exploration of connectivity in two widely distributed temperate octocorals
Connectivity is an important component of metapopulation dynamics in marine systems and can influence population persistence, migration rates and conservation decisions associated with Marine Protected Areas (MPAs). In this study, we compared the genetic diversity, gene flow and population structure of two octocoral species, Eunicella verrucosa and Alcyonium digitatum, in the northeast Atlantic (ranging from the northwest of Ireland and the southern North Sea, to southern Portugal), using two panels of 13 and 8 microsatellite loci, respectively. Our results identified regional genetic structure in E. verrucosa partitioned between populations from southern Portugal, northwest Ireland and Britain/France; subsequent hierarchical analysis of population structure also indicated reduced gene flow between southwest Britain and northwest France. However, over a similar geographical area, A. digitatum showed little evidence of population structure, suggesting high gene flow and/or a large effective population size; indeed, the only significant genetic differentiation detected in A. digitatum occurred between North Sea samples and those from the English Channel/northeast Atlantic. In both species the vast majority of gene flow originated from sample sites within regions, with populations in southwest Britain being the predominant source of contemporary exogenous genetic variants for the populations studied. Overall, historical patterns of gene flow appeared more complex, though again southwest Britain appeared to be an important source of genetic variation for both species. Our findings have major conservation implications, particularly for E. verrucosa, a protected species in UK waters and listed by the IUCN as 'Vulnerable', and for the designation and management of European MPAs.
Data from: Combined analyses of kinship and FST suggest potential drivers of chaotic genetic patchiness in high gene flow populations
We combine kinship estimates with traditional F-statistics to explain contemporary drivers of population genetic differentiation despite high gene flow. We investigate range-wide population genetic structure of the California spiny (or red rock) lobster (Panulirus interruptus) and find slight, but significant global population differentiation in mtDNA (ΦST = 0.006, P = 0.001; Dest_Chao = 0.025) and seven nuclear microsatellites (FST = 0.004, P < 0.001; Dest_Chao = 0.03), despite the species' 240- to 330-day pelagic larval duration. Significant population structure does not correlate with distance between sampling locations, and pairwise FST between adjacent sites often exceeds that among geographically distant locations. This result would typically be interpreted as unexplainable, chaotic genetic patchiness. However, kinship levels differ significantly among sites (pseudo-F16,988 = 1.39, P = 0.001), and ten of 17 sample sites have significantly greater numbers of kin than expected by chance (P < 0.05). Moreover, a higher proportion of kin within sites strongly correlates with greater genetic differentiation among sites (Dest_Chao, R2 = 0.66, P < 0.005). Sites with elevated mean kinship were geographically proximate to regions of high upwelling intensity (R2 = 0.41, P = 0.0009). These results indicate that P. interruptus does not maintain a single homogenous population, despite extreme dispersal potential. Instead, these lobsters appear to either have substantial localized recruitment or maintain planktonic larval cohesiveness whereby siblings more likely settle together than disperse across sites. More broadly, our results contribute to a growing number of studies showing that low FST and high family structure across populations can coexist, illuminating the foundations of cryptic genetic patterns and the nature of marine dispersal.
Data from: Complex histories of repeated gene flow in Cameroon crater lake cichlids cast doubt on one of the clearest examples of sympatric speciation
One of the most celebrated examples of sympatric speciation in nature are monophyletic radiations of cichlid fishes endemic to Cameroon crater lakes. However, phylogenetic inference of monophyly may not detect complex colonization histories involving some allopatric isolation, such as double invasions obscured by genome-wide gene flow. Population genomic approaches are better suited to test hypotheses of sympatric speciation in these cases. Here we use comprehensive sampling from all four sympatric crater lake cichlid radiations in Cameroon and outgroups across Africa combined with next-generation sequencing to genotype tens of thousands of SNPs. We find considerable evidence of gene flow between all four radiations and neighboring riverine populations after initial colonization. In a few cases, some sympatric species are more closely related to outgroups than others, consistent with secondary gene flow facilitating their speciation. Our results do not rule out sympatric speciation in Cameroon cichlids, but rather reveal a complex history of speciation with gene flow, including allopatric and sympatric phases, resulting in both reproductively isolated species and incipient species complexes. The best remaining non-cichlid examples of sympatric speciation all involve assortative mating within microhabitats. We speculate that this feature may be necessary to complete the process of sympatric speciation in nature.
Data from: Landscape genomics of Populus trichocarpa: the role of hybridization, limited gene flow and natural selection in shaping patterns of population structure
Populus trichocarpa is an ecologically important tree across western North America. We used a large population sample of 498 accessions over a wide geographical area genotyped with a 34K Populus SNP array to quantify geographical patterns of genetic variation in this species (landscape genomics). We present evidence that three processes contribute to the observed patterns: (1) introgression from the sister species P. balsamifera (2) isolation-by-distance and (3) natural selection. Introgression was detected only at the margins of the species' distribution. Isolation-by-distance was significant across the sampled area as a whole, but no evidence of restricted gene flow was detected in a core of drainages from southern British Columbia. We identified a large number of FST outliers. GO analyses revealed that FST outliers are overrepresented in genes involved in circadian rhythm and response to red/far-red light when the entire dataset is considered, while in southern British Columbia heat response genes are overrepresented. We also identified strong correlations between geoclimate variables and allele frequencies at FST outlier loci that provide clues regarding the selective pressures acting at these loci.
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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.