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3,329 results for “heterogeneity”
Droplet-based Microfluidics Reveals Insights into Cross-Coupling Mechanisms over Single-Atom Heterogeneous Catalysts
<p>Data set supporting the publication of : "Droplet-based Microfluidics Reveals Insights into Cross-Coupling Mechanisms over Single-Atom Heterogeneous Catalysts" (<a href="https://doi.org/10.1002/anie.202401056">https://doi.org/10.1002/anie.202401056</a>) by T. Moragues, G. Giannakakis, A. Ruiz-Ferrando, C. N. Borca, T. Huthwelker, A. Bugaev, A. J. deMello, J. Pérez-Ramírez and S. Mitchell.</p>
Machine learning predicts earthquakes in the continuum model of a rate-and-state fault with frictional heterogeneities
<p>Numerical data used to make Figures in the manuscript entitled "Machine learning predicts earthquakes in the continuum model of a rate-and-state fault with frictional heterogeneities". We provide the data to create Figures 1 to 4 from the main text and Figures S1 to S9 from the supplementary information. We also provide Python scripts to plot them.</p>
Figures datasets for "Wave momentum shaping for moving objects in heterogeneous and dynamic media"
<p>Source data for Figures used in the manuscript "Wave momentum shaping for moving objects in heterogeneous and dynamic media".</p>
scRNA-seq data for article: Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis
<p>Single-cell RNA-seq dataset from sorted CD11bInt, F4/80Hi, CD64+ mouse liver cells in naive or Leishmania infantum-infected animals at 42 d.p.i.. Data analyses and results are described in manuscript: "Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis". Data files are Seurat objects in RDS format. Filtered-out potential doublets, low quality cells and dying cells (excluded cells with <1000 genes detected, cells with >6000 genes detected, cells with mitochondrial gene expression > 10% and cells with <5000 transcript molecules). Data normalization, scaling and integration performed using Seurat.</p> <p>Filtered dataset containing all KCs and macrophages is in the "pessenda_KC_Macro_seurat" file.</p> <p>Our data were then mapped onto a reference dataset published by Remmerie et al. (DOI: 10.1016/j.immuni.2020.08.004) for annotation consistent with the literature. The reference mapped object can be found in the "pessenda_refmap_KC_Macro_seurat" file.</p> <p>Dataset containing the additional analysis of CLEC4F-TIM4+ FACS-sorted KCs can be found in the "pessenda_refmap_KCTimPos_seurat" file.</p>
Supplementary material for "High semi-natural vegetation cover and heterogeneity of field sizes promote bird beta-diversity at larger scales in Ethiopian Highlands"
<p><strong>Abstract</strong></p> <ol> <li>The intensification of farming practices exerts detrimental effects on biodiversity. Most research has focused on declines in species richness at local scales (alpha-diversity) although species loss is exacerbated by biotic homogenization that operates at larger scales (i.e., affecting beta-diversity). The majority of studies have been conducted in temperate, industrialized countries while tropical areas remain poorly studied. Agricultural landscapes of sub-Saharan Africa are still largely dominated by small-scale subsistence farming, but strenuous efforts to intensify farming practices are currently spreading to meet a growing food demand. It is therefore crucial to understand how these intensified practices affect biodiversity to mitigate their negative impacts. </li> <li>We investigated how farming system (small- vs large-scale farming) and landscape complexity (semi-natural vegetation cover) drive bird species composition, community turnover, and beta-diversity patterns in Ethiopian Highlands’ agroecosystems. We evaluated the following hypotheses: (1) large-scale farming homogenizes bird communities, (2) community turnover is higher in small-scale farms, (3) interactive effects between landscape complexity and farming systems shape avian communities, (4) heterogeneity of field sizes increases community turnover at larger scales. </li> <li>Bird communities underwent greater compositional changes along the landscape complexity than along the agricultural intensity gradient. Contrary to our expectations, beta-diversity was not significantly lower within large-scale farms (no biotic homogenization), and complex landscapes that still offer a high amount of semi-natural vegetation promoted community turnover in both farming systems. </li> <li>Semi-natural vegetation cover mediated how avian communities responded to agricultural intensification: the compositional differences between small- and large-scale farms increased with vegetation cover, further promoting avian community heterogeneity at the landscape level.</li> <li>The heterogeneity in field sizes also enhanced bird community turnover, suggesting that a combination of both small- and large-scale farming systems within a given landscape unit would promote beta-diversity at larger scales, provided large-scale farms do not become dominant.</li> <li>Synthesis and applications: Landscape complexity shaped avian communities to a stronger degree than farming intensity, emphasizing the importance of semi-natural vegetation and landscape heterogeneity for the maintenance of diverse bird communities and for achieving multifunctional landscapes promoting biodiversity and associated ecosystem services on the High Ethiopian plateaus. <br> </li> </ol>
Synthetic Data for Uplift Modeling and Heterogenous Treatment Effect with Known Counterfactuals and ITE
<p>This dataset is designed and simulated for evaluating uplift modeling. The data generation process is based on a logistic regression model - no real data is included or used for generating this dataset.</p> <p>This dataset has several signatures:</p> <ul> <li>It generates features with various patterns associated with the outcome variable and the causal effect (or treatment effect). Thus it is suitable for evaluating feature importance and model interpretation for uplift modeling.</li> <li>The true counterfactual outcomes under control and treatment are known for each user, as well as the true ITE (Individual treatment effect).</li> </ul> <p>This dataset consists of 50 trials (replicates with different random seeds), each trial with 20,000 samples and 36 features. The outcome variable is binary, which makes this dataset for classification problems. The samples are equally split for the control and treatment groups (10,000 samples in each group in each trial).</p> <p>The generated data has three types of features: (1) uplift features influencing the treatment effect on the conversion probability; (2) classification features affecting the conversion probability but independent of the treatment effect; and (3) irrelevant features that are independent of both conversion probability and the treatment effect.</p> <p>To simulate the relationship between uplift features and the treatment effect and classification features and outcome probability, we implement six types of association patterns in the data generation process: linear, quadratic, cubic, ReLU (Rectified Linear Unit), trigonometric function sine, and cosine.</p> <p>In this data set, there are 36 features in total, including 10 classification features, 6 uplift features, and 20 irrelevant features.</p> <p>Column names:</p> <p> Trial ID: 'trial_id'<br> Experiment group label: 'treatment_group_key'<br> Outcome variable (classification label): 'conversion'<br> Feature names: ['x1_informative',<br> 'x2_informative',<br> 'x3_informative',<br> 'x4_informative',<br> 'x5_informative',<br> 'x6_informative',<br> 'x7_informative',<br> 'x8_informative',<br> 'x9_informative',<br> 'x10_informative',<br> 'x11_irrelevant',<br> 'x12_irrelevant',<br> 'x13_irrelevant',<br> 'x14_irrelevant',<br> 'x15_irrelevant',<br> 'x16_irrelevant',<br> 'x17_irrelevant',<br> 'x18_irrelevant',<br> 'x19_irrelevant',<br> 'x20_irrelevant',<br> 'x21_irrelevant',<br> 'x22_irrelevant',<br> 'x23_irrelevant',<br> 'x24_irrelevant',<br> 'x25_irrelevant',<br> 'x26_irrelevant',<br> 'x27_irrelevant',<br> 'x28_irrelevant',<br> 'x29_irrelevant',<br> 'x30_irrelevant',<br> 'x31_uplift_increase',<br> 'x32_uplift_increase',<br> 'x33_uplift_increase',<br> 'x34_uplift_increase',<br> 'x35_uplift_increase',<br> 'x36_uplift_increase']<br> True underlying control conversion probability: 'control_conversion_prob'<br> True underlying treatment conversion probability: 'treatment1_conversion_prob'<br> True treatment effect: 'treatment1_true_effect'</p>
Data Set for the Journal Article "Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis"
<p>This dataset includes the XYZ structures of the centroids of all compounds found. Charge and multiplicity are given in the comment line of each XYZ file.</p>
PANACEA dataset - Heterogeneous COVID-19 Claims
<p>The peer-reviewed publication for this dataset has been presented in the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), and can be accessed here: https://arxiv.org/abs/2205.02596. Please cite this when using the dataset.</p> <p> </p> <p>This dataset contains a heterogeneous set of True and False COVID claims and online sources of information for each claim.</p> <p> </p> <p>The claims have been obtained from online fact-checking sources, existing datasets and research challenges. It combines different data sources with different foci, thus enabling a comprehensive approach that combines different media (Twitter, Facebook, general websites, academia), information domains (health, scholar, media), information types (news, claims) and applications (information retrieval, veracity evaluation).</p> <p> </p> <p>The processing of the claims included an extensive de-duplication process eliminating repeated or very similar claims. The dataset is presented in a LARGE and a SMALL version, accounting for different degrees of similarity between the remaining claims (excluding respectively claims with a 90% and 99% probability of being similar, as obtained through the MonoT5 model). The similarity of claims was analysed using BM25 (Robertson et al., 1995; Crestani et al., 1998; Robertson and Zaragoza, 2009) with MonoT5 re-ranking (Nogueira et al., 2020), and BERTScore (Zhang et al., 2019).</p> <p> </p> <p>The processing of the content also involved removing claims making only a direct reference to existing content in other media (audio, video, photos); automatically obtained content not representing claims; and entries with claims or fact-checking sources in languages other than English.</p> <p> </p> <p>The claims were analysed to identify types of claims that may be of particular interest, either for inclusion or exclusion depending on the type of analysis. The following types were identified: (1) Multimodal; (2) Social media references; (3) Claims including questions; (4) Claims including numerical content; (5) Named entities, including: PERSON − People, including fictional; ORGANIZATION − Companies, agencies, institutions, etc.; GPE − Countries, cities, states; FACILITY − Buildings, highways, etc. These entities have been detected using a RoBERTa base English model (Liu et al., 2019) trained on the OntoNotes Release 5.0 dataset (Weischedel et al., 2013) using Spacy.</p> <p> </p> <p>The original labels for the claims have been reviewed and homogenised from the different criteria used by each original fact-checker into the final True and False labels.</p> <p> </p> <p>The data sources used are:</p> <p>- The CoronaVirusFacts/DatosCoronaVirus Alliance Database. https://www.poynter.org/ifcn-covid-19-misinformation/</p> <p>- CoAID dataset (Cui and Lee, 2020) https://github.com/cuilimeng/CoAID</p> <p>- MM-COVID (Li et al., 2020) https://github.com/bigheiniu/MM-COVID</p> <p>- CovidLies (Hossain et al., 2020) https://github.com/ucinlp/covid19-data</p> <p>- TREC Health Misinformation track https://trec-health-misinfo.github.io/</p> <p>- TREC COVID challenge (Voorhees et al., 2021; Roberts et al., 2020) https://ir.nist.gov/covidSubmit/data.html</p> <p> </p> <p>The LARGE dataset contains 5,143 claims (1,810 False and 3,333 True), and the SMALL version 1,709 claims (477 False and 1,232 True).</p> <p> </p> <p>The entries in the dataset contain the following information:</p> <p>- Claim. Text of the claim.</p> <p>- Claim label. The labels are: False, and True.</p> <p>- Claim source. The sources include mostly fact-checking websites, health information websites, health clinics, public institutions sites, and peer-reviewed scientific journals.</p> <p>- Original information source. Information about which general information source was used to obtain the claim.</p> <p>- Claim type. The different types, previously explained, are: Multimodal, Social Media, Questions, Numerical, and Named Entities.</p> <p> </p> <p>Funding. This work was supported by the UK Engineering and Physical Sciences Research Council (grant no. EP/V048597/1, EP/T017112/1). ML and YH are supported by Turing AI Fellowships funded by the UK Research and Innovation (grant no. EP/V030302/1, EP/V020579/1).</p> <p> </p> <p>References</p> <p>- Arana-Catania M., Kochkina E., Zubiaga A., Liakata M., Procter R., He Y.. Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims. NAACL 2022 https://arxiv.org/abs/2205.02596</p> <p>- Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al. 1995. Okapi at trec-3. Nist Special Publication Sp,109:109.</p> <p>- Fabio Crestani, Mounia Lalmas, Cornelis J Van Rijsbergen, and Iain Campbell. 1998. “is this document relevant?. . . probably” a survey of probabilistic models in information retrieval. ACM Computing Surveys (CSUR), 30(4):528–552.</p> <p>- Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc.</p> <p>- Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document ranking with a pre-trained sequence-to-sequence model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 708–718.</p> <p>- Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations.</p> <p>- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.</p> <p>- Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013. Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia, PA, 23.</p> <p>- Limeng Cui and Dongwon Lee. 2020. Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885.</p> <p>- Yichuan Li, Bohan Jiang, Kai Shu, and Huan Liu. 2020. Mm-covid: A multilingual and multimodal data repository for combating covid-19 disinformation.</p> <p>- Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 misinformation on social media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.</p> <p>- Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021. Trec-covid: constructing a pandemic information retrieval test collection. In ACM SIGIR Forum, volume 54, pages 1–12. ACM New York, NY, USA.</p>
Spectral induced polarization of non-consolidated heterogeneous clay mixtures
<p>We present a spectral induced polarization dataset on heterogeneous mixtures of illite and red montmorillonite, with two longitudinal, and one transversal arrangement. Additionally, there is a 50-50% in volume content homogeneous mixture of illite and red montmorillonite.</p> <p>Each file has its header, describing each column. The ReadMe file also explains the content and format of each dataset.</p>
Experimental characterization of transversal-heterogeneous clay mixtures by the spectral induced polarization method
<p>In this folder you will find multiple datasets (*.txt) from SIP measurements of transversal-heterogeneous clay mixtures using spectral induced polarization acquired between April and May 2022. Additionally, we include two python codes to read and process the data.</p> <p>SIP_Plot_ReWrite.py is a python program aimed to process a .res file from a SIP Fuchs III.<br> It gives a .txt file with the frequency, the resistivity, the phase and the associated errors.<br> In order for the program to give the resistivity, you will need to enter the geometric factor of the studied sample.</p> <p><br> The six text files in the folder (excluding README.txt) were created using SIP_Plot_ReWrite.py.</p> <p>IL_1by1.txt and IL_1by1_V2.txt are from two different homogeneous mixtures of illite and water with a concentration of initially 0.01 mol/L of NaCl.<br> MtR_1by1.txt is from a homogeneous mixture of red montmorillonite and water with a concentration of initially 0.01 mol/L of NaCl.</p> <p>IL_MtR_1by2.txt, IL_MtR_1by4.txt and IL_MtR_1by8.txt are from three transversal-heterogeneous mixtures of illite and red montmorillonite with water containing a concentration of initially 0.01 mol/L of NaCl.</p> <p><br> For IL_MtR_1by2.txt, there was one portion of each clay types, occupying a half of the cylindrical container each.</p> <p><br> For IL_MtR_1by4.txt, there was two portions of each clay types, occupying a quarter of the cylindrical container each.</p> <p><br> These two samples were made using the same mixtures as for IL_1by1.txt and MtR_1by1.txt.</p> <p><br> For IL_MtR_1by8.txt, there was four portions of each clay types, occupying an eighth of the cylindrical container each.<br> This sample was made using the same mixtures as for IL_1by1_V2.txt and MtR_1by1.txt.</p> <p><br> TestDoubleColeColeFit.py is a python program which optimizes a double Cole-Cole model by multiplication on SIP data.<br> This program needs a file with the same structure as the .txt file made by SIP_Plot_ReWrite.py.</p>
Understanding the heterogeneous rheologic structure across the Longmenshan fault from ten-year postseismic GPS observations
<p>The two datasets are the 10-year cumulative displacements following the 2008 Wenchuan earthquake, GPS time-series observations for all sites, GPS time-series simulation for all sites and the secular velocity corresponding to the interseismic tectonic response, respectively.</p>
Dataset of "PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing"
<p>This is the underlying dataset of "PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing"</p>
Row sequcenes data for assessing the risks of potential pathogens and antibiotic resistance genes among heterogeneous habitats in a temperate estuary wetland
<p>The study included 118 usable samples within three different habitats (water, soil, and sediment) across the Liaohe River basin to the Red Beach wetland collected from seven papers, and all of the sequence files were uploaded for availability.</p>
Dataset of "Single-Cell RNA-Seq Reveals Transcriptional Heterogeneity in Latent and Reactivated HIV-infected Cells"
<p><strong>Detailed quantitative analysis of GFP expression in SAHA and TCR-treated cells & Computational analysis of bulk and single-cell RNA-Seq data.</strong></p> <p> </p> <p><em><strong>Detailed quantitative analysis of GFP expression in SAHA and TCR-treated cells.</strong></em></p> <p>Cells were prepared for single cell analysis at the Genome Technology Facility (GTF) of the University of Lausanne. Cells were loaded on Fluidigm C1 IFC plates (5-10 μm), with run ID smart33, smart34 and smart35, corresponding to untreated, SAHA- and TCR-treated conditions respectively. After single cell capture on the Fluidigm C1 IFC plate, each chamber was inspected visually by microscopy and pictures were captured with a Zeiss Axiovert 200 M fluorescence microscope equipped with a Roper Scientific CoolSnap HQ camera using a Plan-Neofluar 10X lens (smart34 run) or 20X lens (for smart35 run). For each capture chamber, pictures in bright field and FITC channel were taken with the MetaMorph 6.3 software. Picture analysis was then performed using ImageJ 1.50b software (open access software: website). Brightness and contrast were adjusted for qualitative assessment of the pictures.</p> <p><em><strong>Computational analysis of bulk and single-cell RNA-Seq data.</strong></em></p> <p>Upon bulk or single cell isolation, RNA extraction and library preparation was performed according to Illumina protocols. Bulk and single-cell RNA-Seq data analysis are detailed here.</p> <p> </p> <p>Linked to the paper published in Cell Reports (doi:10.1016/j.celrep.2018.03.102): </p> <p><strong>Single-Cell RNA-Seq Reveals Transcriptional Heterogeneity in Latent and Reactivated HIV-infected Cells</strong></p> <p>Despite effective treatment, HIV can persist in latent reservoirs, which represent a major obstacle towards HIV eradication. Targeting and reactivating latent cells is challenging due to the heterogeneous nature of HIV infected cells. Here, we used a primary model of HIV latency and single-cell RNA sequencing to characterize transcriptional heterogeneity during HIV latency and reactivation. Our analysis identified transcriptional programs leading to successful reactivation of HIV expression.</p> <p> </p> <p> </p>
Resource heterogeneity leads to unjust effort distribution in climate change mitigation
<p>Climate change mitigation is a shared global challenge that involves the collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together toward a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer - sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two "generous clusters'" and the richest into a "greedy cluster''. Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences.</p> <p>Vicens J, Bueno-Guerra N, Gutiérrez-Roig M, Gracia-Lázaro C, Gómez-Gardeñes J, Perelló J, et al. (2018) Resource heterogeneity leads to unjust effort distribution in climate change mitigation. PLoS ONE 13(10): e0204369. https://doi.org/10.1371/journal.pone.0204369</p>
Data for "SeaMoon: from protein language models to continuous structural heterogeneity"
<p>Datasets used for development of SeaMoon: <br><a href="https://github.com/PhyloSofS-Team/seamoon">https://github.com/PhyloSofS-Team/seamoon</a>.</p> <p>This upload contains the following data:</p> <ul> <li><strong>precomputed_emb.tar.gz</strong> is a compressed archive containing the precomputed data used for training and testing the models of the SeaMoon method, in Torch <strong>.pt </strong>format. <br>The file prefixes consist of two IDs, "ID1_ID2_", identifying the <a href="https://github.com/PhyloSofS-Team/DANCE">DANCE</a> [1] protein conformational collection used for its generation. "ID1" represents the first member of the collection in alphabetical order, while "ID2" is the reference conformation for the structural alignment. The "ESM_data" or "ProstT5_data" suffixes designate the type of embeddings, generated by either ESM2 [2] or ProstT5 [3].<br>The dictionnary contains the following keys: <ul> <li><strong>emb:</strong> The per-residue embedding.</li> <li><strong>data: </strong>A tuple containing "ID2" (the reference), the amino acid sequence, and the coverage of the positions in the original DANCE collection.</li> <li><strong>eigvect:</strong> The eigenvectors of the covariance matrix of the "ID1_ID2" collection, centered on reference conformaton "D2".</li> <li><strong>eigval: </strong>The associated eigenvalues.</li> <li><strong>ref:</strong> The coordinates of the C-alpha atoms of the reference conformaton "ID2".</li> </ul> </li> <li><strong>train_list.txt, train_list_5ref.txt, val_list.txt </strong>and<strong> test_list.txt</strong> contain the identifiers of the samples used for training and evaluating the SeaMoon models. In the "5ref" setting, we used up to 5 reference conformations per collection. </li> </ul> <p>For details on SeaMoon see:</p> <div> <div>SeaMoon: Prediction of molecular motions based on language models</div> </div> <div>Valentin Lombard, Dan Timsit, Sergei Grudinin, Elodie Laine</div> <div>bioRxiv 2024.09.23.614585; doi: https://doi.org/10.1101/2024.09.23.614585</div> <div> </div> <div>For more information on data usage and generation please see <a href="https://github.com/PhyloSofS-Team/seamoon">https://github.com/PhyloSofS-Team/seamoon</a>.</div> <div> </div> <div>Abstract:</div> <p>How protein move and deform determines their interactions with the environment and is thus of utmost importance for cellular functioning. Following the revolution in single protein 3D structure prediction, researchers have focused on repurposing or developing deep learning models for sampling alternative protein conformations. In this work, we explored whether continuous compact representations of protein motions could be predicted directly from protein sequences, without exploiting nor sampling protein structures. Our approach, called SeaMoon, leverages protein Language Model (pLM) embeddings as input to a lightweight (~1M trainable parameters) convolutional neural network. SeaMoon achieves a success rate of up to 40% when assessed against ~1,000 collections of experimental conformations exhibiting a wide range of motions. SeaMoon capture motions not accessible to the normal mode analysis, an unsupervised physics-based method relying solely on a protein structure's 3D geometry, and generalises to proteins that do not have any detectable sequence similarity to the training set. SeaMoon is easily retrainable with novel or updated pLMs. </p> <p> </p> <p>[1] Lombard, V.; Grudinin, S.; Laine, E. Explaining Conformational Diversity in Protein Families through Molecular Motions. Scientific Data 2024, 11, 752.</p> <p>[2] Lin, Z.; Akin, H.; Rao, R.; Hie, B.; Zhu, Z.; Lu, W.; Smetanin, N.; Verkuil, R.; Kabeli, O.; Shmueli, Y.; Dos Santos Costa, A.; Fazel-Zarandi, M.; Sercu, T.; Candido, S.; Rives, A. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 2023, 379, 1123–1130.</p> <p>[3] Heinzinger, M.; Weissenow, K.; Sanchez, J. G.; Henkel, A.; Steinegger, M.; Rost, B. ProstT5: Bilingual language model for protein sequence and structure. bioRxiv 2023, 2023–07.</p>
SHIFT: A DEM-Based Spatial Heterogeneity Improved Mapping of Global Geomorphic Floodplains
<h2>Description</h2> <p><strong>SHIFT</strong> (Spatial Heterogeneity Improved Floodplain by Terrain analysis) is a 90-m resolution global geomorphic floodplain map based on terrain analysis. It takes MERIT-Hydro as the terrain input and Floodplain Hydraulic Geometry (FHG) as the thresholding scheme, with the scaling parameters estimated by a stepwise framework that both respects the power law and approximates the spatial extent of hydrodynamic modeling. SHIFT effectively captures the global patterns of the geomorphic floodplains, with better regional details than existing data.</p> <h2>Data Structure</h2> <p>We provide 2 resolutions of data for different needs.</p> <ul> <li><strong>SHIFT_v3_90m</strong>: The original SHIFT data derived from MERIT-Hydro, with lakes and reservoirs removed. The resolution is 0.000833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 90 meters at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> <li><strong>SHIFT_v3_1km</strong>: The resampled SHIFT data with lakes and reservoirs marked. The resolution is 0.00833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 1 km at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> </ul> <p>Also, we provide our derived spatially-varying parameters in all Level-3 basins to support future studies. Parameters are provided in a shapefile, with 'a' denotes the proportional parameter and 'b' denotes the exponent. We aggregated MERIT-Basins based on its spatial relationship with basins from Level-3 HydroBASINS, ensuring that the centroid of a MERIT-Basin falls within the corresponding boundary. This approach accounts for slight differences in boundaries due to the use of different terrain data, preventing confusion in hydrological representation.</p> <p>For more details, please refer to:</p> <ul> <li>Zheng, K., Lin, P., and Yin, Z.: SHIFT: a spatial-heterogeneity improvement in DEM-based mapping of global geomorphic floodplains, Earth Syst. Sci. Data, 16, 3873–3891, <a href="https://doi.org/10.5194/essd-16-3873-2024" rel="noopener">https://doi.org/10.5194/essd-16-3873-2024</a>, 2024.</li> </ul> <h2>Development Log</h2> <ol> <li><strong>Changes in v3 compared to v2:</strong> <ol> <li> <p><strong>Inclusion of Missing Level-3 Basin:</strong> We have added a previously missing Level-3 basin (PFAF ID: 242) that covers an area in Eastern Europe, specifically from Warsaw to Minsk. This omission was due to a technical problem that has now been resolved. Data are now still available in two resolutions: 90-meter and 1-kilometer.</p> </li> <li> <p><strong>Updated Parameters</strong>: Along with the new boundaries, updated parameters are provided in the shapefile.</p> </li> <li> <p><strong>Re-estimated Global Floodplain Area</strong>: Based on the new data, we have re-estimated the global total floodplain area from 9.9 × 10^6 km² to 9.92 × 10^6 km². This area still represents approximately 6.6% of the total land mass.</p> </li> </ol> </li> <li><strong>Changes in v2 compared to v1:</strong> <ol> <li><strong>Parameter 'b' Estimation:</strong> We modified the technical details of parameter 'b' estimation, specifically the binning parameter, adding a constraining mechanism to handle data noise. This resulted in stabler estimates for large basins and a clearer pattern of global residual uncertainty.</li> <li><strong>Target Function for Parameter 'a':</strong> We changed our target function to balance information from both datasets, using Fleiss’s Kappa (FK) and a penalty term to reduce bias.</li> </ol> </li> </ol> <h2>Contacts</h2> <ul> <li>Kaihao Zheng, <a href="mailto:Mostaly@pku.edu.cn" target="_blank" rel="noopener">Mostaly@pku.edu.cn</a></li> <li>Peirong Lin, <a href="mailto:peironglinlin@pku.edu.cn" target="_blank" rel="noopener">peironglinlin@pku.edu.cn</a></li> </ul> <p> </p>
Dataset and code for "Adjoint-aided homogenization for flows through heterogeneous membranes"
<p>This dataset and code support the manuscript 'Adjoint-aided homogenization for flows through heterogeneous membranes' by Kevin Wittkowski, Edouard Boujo, François Gallaire and Giuseppe A. Zampogna, under revision in the <em>Journal of Fluid Mechanics</em>, 2024. The data are Stokes flow simulations around an elliptical solid inclusion with periodic boundary conditions. Direct computations of the average flow quantities for several geometry parameters are compared with shape-sensitivity-based predictions obtained by solving a set of direct and adjoint equations around a circular solid inclusion. Please refer to the manuscript for further details.</p> <p><strong>Contents:</strong></p> <ul> <li><em>comsol_code/Mnn.mph, Mtt.mph:</em> COMSOL Multiphysics 6.0 codes used to generate the datasets;</li> <li><em>README.txt</em>: a .txt file describing the code usage;</li> <li><em>Mnn.txt,Mtt.txt</em>: dataset of the direct evaluations of these two average microscopic quantities, input of the .m files;</li> <li><em>S1_Mnn.txt, S2_Mnn.txt, S1_Mtt.txt, S2_Mtt.txt</em>: dataset containing the first and second order shape sensitivities computed on the reference circular geometry, input of the .m files;</li> <li><em>Mnn.m,Mtt.m:</em> MatLab code used to compare the direct evaluations and adjoint predictions of the microscopic quantities;</li> <li><em>ellipse.m</em>: Matlab function to generate the elliptical deformation;</li> <li><em>.fig</em> files: Matlab figures, output of the .m files. They are used to compare the direct evaluation with the prediction based on the adjoint computations.</li> </ul>
Tissue heterogeneity is prevalent in gene expression studies
<p>This archive contains results associated with the publication</p> <p><em>Tissue heterogeneity is prevalent in gene expression studies. Gregor Sturm, Markus List and Jitao David Zhang.</em></p> <p> </p> <ul> <li>expr.tissuemark.affy.roche.symbols.gmt: The tissue signatures from the BioQC publication used in this study</li> <li>gtex_v6_gini_solid.gmt: The cross-platform cross-species validated tissue signatures produced in this study</li> <li>heterogeneity_results.tsv.gz: Signature scores and heterogeneity calls for each tested signature</li> <li>heterogeneity_fractions.tsv: Fraction of heterogeneous and severely heterogeneous samples per tissue</li> </ul>
Recovery of Lithium Carbonate from Dilute Li-Rich Brine via Homogenous and Heterogeneous Precipitation
<p>An extensive experimental campaign on Li recovery<br> from relatively dilute LiCl solutions (i.e., Li+ ∼ 4000 ppm) is<br> presented to identify the best operating conditions for a Li2CO3<br> crystallization unit. Lithium is currently mainly produced via solar<br> evaporation, purification, and precipitation from highly concentrated<br> Li brines located in a few world areas. The process requires<br> large surfaces and long times (18−24 months) to concentrate Li+<br> up to 20,000 ppm. The present work investigates two separation<br> routes to extract Li+ from synthetic solutions, mimicking those<br> obtained from low-content Li+ sources through selective Li+<br> separation and further concentration steps: (i) addition of<br> Na2CO3 solution and (ii) addition of NaOH solution + CO2<br> insufflation. A Li recovery up to 80% and purities up to 99% at 80<br> °C and with high-ionic strength solutions was achieved employing NaOH solution + CO2 insufflation and an ethanol washing step.</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.