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Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</p>
Calich lagoon multiparametric probe profile 07/06/2023
<p>This is a file containing the data collected with a multiparametric probe during a field exercise on 7 June 2023 with the students of the Planning and policies for the city, environment and landscape (CAP) course (LM–48) of the Department of Architecture, Design and Urban Planning of the University of Sassari.</p>
Profiling phage-host interactions between Skunavirus receptor binding proteins and lactococcal cell wall polysaccharide structures
Open the record for dataset details and reuse information.
Elution profiles and protein interaction data accompanying "Ancient eukaryotic protein interactions illuminate modern genetic disorders"
<div> <p> </p> <table> <tbody> <tr> <td> <h2><strong>DESCRIPTION</strong></h2> </td> <td> <h2><strong>FILENAME</strong></h2> </td> <td> <h2><strong>LOCATION</strong></h2> </td> </tr> <tr> <td> <p>LECA 10K OG set</p> </td> <td> <p>leca_ogs_annotated.xlsx</p> </td> <td> <p>Paper, Table S1</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Summary of biological resources</p> </td> <td> <p>resource_summary.xlsx</p> </td> <td> <p>Paper, Table S2</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>LECA interactome (complexes)</p> </td> <td> <p>leca_ppis_fdr10_clustered_annotated.xlsx</p> </td> <td> <p>Paper, Table S3</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - ref proteomes</p> </td> <td> <p>cfms_ref_proteomes.xlsx</p> </td> <td> <p>Paper, Table S4</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>ML - top algorithms</p> </td> <td> <p>tpot_top_algorithms.xlsx</p> </td> <td> <p>Paper, Table S5</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>LECA interactome (pairwise)</p> </td> <td> <p>leca_ppis_fdr10_pairwise.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>UniProt Subcellular Localization IDs</p> </td> <td> <p>uniprot_localization_codes.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Dollo parsimony - ref proteomes</p> </td> <td> <p>dollo_parsimony_ref_proteomes.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Dollo parsimony - input trait matrix</p> </td> <td> <p>dollo_parsimony_count_matrix.tsv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - raw elution profiles</p> </td> <td> <p>amorphea_raw_elution_vectors.csv</p> <p>excavata_raw_elution_vectors.csv</p> <p>tsar_raw_elution_vectors.csv</p> <p>archaeplastida_raw_elution_vectors.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - normalized elution profiles</p> </td> <td> <p>amorphea_norm_elution_vectors.csv</p> <p>excavata_norm_elution_vectors.csv</p> <p>tsar_norm_elution_vectors.csv</p> <p>archaeplastida_norm_elution_vectors.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS/APMS - complete feature matrix</p> </td> <td> <p>feature_matrix.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>ML - top features</p> </td> <td> <p>linearsvc_top_100_features.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>OMIM disease propagation, statistics</p> </td> <td> <p>omim_disease_propagation_stats.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>OMIM disease propagation, top 20 hits per disease</p> </td> <td> <p>omim_disease_propagation_top20hits_per_disease.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Curated OMIM gene-disease relationships for LECA OGs</p> </td> <td> <p>omim_disease_network.tsv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td>Curated OMIM gene-disease relationships for human UniProt IDs</td> <td>omim_disease_groups.csv</td> <td>Zenodo</td> </tr> </tbody> </table> </div> <p> </p>
Figure 1 in Hematology and blood biochemistry profile of the freshwater stingray Potamotrygon magdalenae as a tool for population assessment in artificial environments
Figure 1. Different cell types found in peripheral blood of captive adults of Potamotrygon magdalenae.
Figure 2 in Hematology and blood biochemistry profile of the freshwater stingray Potamotrygon magdalenae as a tool for population assessment in artificial environments
Figure 2. Correlation's matrix test among body condition and physiological parameters in captive adults of Potamotrygon magdalenae. RBC:Erythrocyte count; Hb: Hemoglobin; Ht: Hematocrit; Leu: Leukocte count; Thr: Thrombocyte count; He%: Heterophils proportion; Eos%: Eosinophils proportion; Ly%: Lymphocytes proportion; Mo%: Monocytes proportion; Ure: Urea; Tri: Triglycerides; Pp: Plasma proteins; Sp: Serum proteins; Glu: Glucose; Col: Cholesterol. Ellipses show the type of correlation between each variable, slope shows whether the correlation is positive or negative, color intensity shows the correlation's strength, and amplitude of the ellipse corresponds to the data dispersion. The upper diagonal shows the p values for each correlation.
Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer
<p>Wallen ZD, Ko H, Nesline MK, Hastings SB, Strickland KC, Previs RA, Zhang S, Pabla S, Conroy J, Jackson JB, Saini KS, Jensen TJ, Eisenberg M, Caveney B, Sathyan P, Severson EA, Ramkissoon SH. <strong>Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer.</strong> <em>Front Immunol.</em> 2024 Jun 21;15:1413956. doi: <a href="https://doi.org/10.3389/fimmu.2024.1413956">10.3389/fimmu.2024.1413956</a>. PMID: <a href="https://pubmed.ncbi.nlm.nih.gov/38975340/">38975340</a>; PMCID: <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC11224431/">PMC11224431</a>.</p> <p><strong>ABSTRACT</strong></p> <p>Younger patients with non-small cell lung cancer (NSCLC) (<50 years) represent a significant patient population with distinct clinicopathological features and enriched targetable genomic alterations compared to older patients. However, previous studies of younger NSCLC suffer from inconsistent findings, few studies have incorporated sex into their analyses, and studies targeting age-related differences in the tumor immune microenvironment are lacking. We performed a retrospective analysis of 8,230 patients with NSCLC, comparing genomic alterations and immunogenic markers of younger and older patients while also considering differences between male and female patients. We defined older patients as those ≥65 years and used a 5-year sliding threshold from <45 to <65 years to define various groups of younger patients. Additionally, in an independent cohort of patients with NSCLC, we use our observations to inform testing of the combinatorial effect of age and sex on survival of patients given immunotherapy with or without chemotherapy. We observed distinct genomic and immune microenvironment profiles for tumors of younger patients compared to tumors of older patients. Younger patient tumors were enriched in clinically relevant genomic alterations and had gene expression patterns indicative of reduced immune system activation, which was most evident when analyzing male patients. Further, we found younger male patients treated with immunotherapy alone had significantly worse survival compared to male patients ≥65 years, while the addition of chemotherapy reduced this disparity. Contrarily, we found younger female patients had significantly better survival compared to female patients ≥65 years when treated with immunotherapy plus chemotherapy, while treatment with immunotherapy alone resulted in similar outcomes. These results show the value of comprehensive genomic and immune profiling (CGIP) for informing clinical treatment of younger patients with NSCLC and provides support for broader coverage of CGIP for younger patients with advanced NSCLC.</p> <p><strong>DATA AVAILABILITY: </strong></p> <p>De-identified, individual-level patient data, genomic variants, and individual immune gene expression data used in the manuscript can be found in this repository (https://zenodo.org/record/11396552). An R markdown file with R code used to perform the analyses and generate figures is also provided in the repository along with the data. All versions of software used are provided in the Methods section of the manuscript. Raw sequencing data were derived from routine clinical testing of real-world patients and cannot be shared publicly. Data for immune gene expression signatures are not publicly available due to a non‑provisional patent filing covering the methods used to generate and analyze these data but are available from the corresponding author on reasonable request.</p>
Figure 3 in Analysis of the toxicological and pharmacokinetic profile of Kaempferol-3-O-β-D-(6"-E-p-coumaryl) glucopyranoside - Tiliroside: in silico, in vitro and ex vivo assay
Figure 3. Photomicrography of exfoliated oral mucosa cells with: (A) karyorrhexis; (B) karyolysis; (C) micronucleus; (D) binucleation; and (E) macronucleus. Magnification X1000.
Figure 2 in Analysis of the toxicological and pharmacokinetic profile of Kaempferol-3-O-β-D-(6"-E-p-coumaryl) glucopyranoside - Tiliroside: in silico, in vitro and ex vivo assay
Figure 2. Cytotoxic effect of tiliroside (H. velutina) against RBC; (C-) Negative control (erythrocytes 0.5%), (C+) Positive control (1% Triton X-100). P <0.05 (*), P <0.01(**) and P <0.001 (***) versus positive control.
Supporting data for "Chromatin conformation and histone modification profiling across human kidney anatomic regions"
<p>Here we deposit supporting data for our manuscript entitled "Chromatin conformation and histone modification profiling across human kidney anatomic regions".</p> <ul> <li>donor_info.pdf: Additional clinical information of the donor involved in the study</li> <li>Large zip files with names starting as "hic": Juicer Hi-C outputs aligned with hg38 genome <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>hg19_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg19 genome</li> <li>hg38_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg38 genome</li> <li>CUTRUN_peak_hg19.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg19 alignment</li> <li>CUTRUN_peak_hg38.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg38 alignment</li> <li>CUTRUN_bigwig_hg38.tar.gz: CUT&RUN bigwig outputs generated by hg38 alignment <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>CUTRUN_overlapped_peaks.xlsx: Overlaps between H3K27me3 and H3K4me3 in each anatomical region. Peaks were loaded from ‘.peaks.stringent.bed’ files in R and converted to GRanges objects using package ‘GenomicRanges’. R function ‘intersect’ was used to calculate the overlap between two GRanges objects, represented as each row in the table. <ul> <li>Here we list the details of overlaps between H3K27me3 and H3K4me3 in each anatomical region, as well as overlaps for each of the histone markers across anatomical regions.</li> </ul> </li> </ul>
FIG. 25 in Fungal Biodiversity Profiles 91-100
FIG. 25. — Russula castanopsidis Hongo field habit (coll. B. Buyck & X.H. Wang, no. 4524 [HKAS 104824, PC0125085]). Photos: B. Buyck.
FIG. 23 in Fungal Biodiversity Profiles 91-100
FIG. 23. — Part of the most likely tree obtained by ML analysis of a 168 specimens/5 locus dataset (-ln 57423.49377). For details on vouchers, see Buyck et al. (2018); only subgenus Russula is here depicted; other subgenera are as shown in Buyck et al. (2018). Branches significantly supported are in bold and bootstrap values indicated along the branches. Newly sequenced taxa are in blue font and the new section is indicated as a green rectangle.
FIG. 13 in Fungal Biodiversity Profiles 91-100
FIG. 13. — Maximum Likelihood (ML) phylogram inferred from raxmlGUI (Silvestro & Michalak 2012) based on nrITS sequences of Ramaria Fr. ex Bonord. with Gomphus Pers. chosen as outgroup. One thousand bootstrap replicates were analyzed to obtain nodal support values. Bootstrap support values (>50%) obtained from ML analysis are shown above or below the branches at nodes. Two collections of the novel Indian species are shown in red and the holotype in bold in the phylogram.
FIG. 11 in Fungal Biodiversity Profiles 91-100
FIG. 11. — Hymenochaete boddingii Hembrom, A. Parihar, K. Das & A. Ghosh, sp. nov. (from holotype): A-D, fresh basidiomata in the forest and basecamp; E, transverse section of the basidiomata showing compactly interwoven hyphae and hymeneal region; F, transverse section through hymenium; G, hymenial setae; H, thin- to distinctly thick-walled generative hyphae; I-K, basidia; L, cystidioles; M, Hymenophore showing setae, cystidioles, hyphidia, basidioles and basidia; N, basidiospores. Scale bars: E-F, 50 µm; G-N, 10 µm.
FIG. 22 in Fungal Biodiversity Profiles 91-100
FIG. 22. — Russula castanopsidis Hongo, the type species of subsect. Castanopsidum Buyck & X. H. Wang, subsect. nov.; microscopic features of the type collection. A, vertical section of the pileipellis; B, hyphal terminations of the pileipellis; C, basidia; D, basidiola; E, hymenial gloeocystidia on gill sides; F, hymenial gloeocystidia on gill edge; G, marginal cells, H, spores in Melzer's reagent. Drawings: B. Buyck. Scale bars: A-H, 10 µm.
FIG. 9. — Gomphidius pseudoglutinosus K in Fungal Biodiversity Profiles 91-100
FIG. 9. — Gomphidius pseudoglutinosus K. Das, Hembrom, A. Parihar & Vizzini, sp. nov. (from holotype): A, basidiospores; B, cheilocystidia; C, pleurocystidia; D, basidia and basidioles; E, pileipellis. Scale bars: A-D, 10 µm; E, 25 µm.
FIG. 2. — Corethromyces marshallii W in Fungal Biodiversity Profiles 91-100
FIG. 2. — Corethromyces marshallii W. Rossi & Santam., sp. nov.: A, B, two thalli from the type slide; C, short thallus from the wing of the host insect, paratype WR3531. Scale bars: 50 µm.
FIG. 18 in Fungal Biodiversity Profiles 91-100
FIG. 18. — Russula antsikana Buyck & Randrianjohany, sp. nov.: A, pileocystidia arising from subpellis or from beneath the surface; B, terminal pileocystidia at the pileus surface; C, hyphal extremities of the pileipellis; D, incrustations of the hyphal walls shown in section. Drawings: B. Buyck. Scale bar: 10 µm.
FIG. 8. — Gomphidius pseudoglutinosus K in Fungal Biodiversity Profiles 91-100
FIG. 8. — Gomphidius pseudoglutinosus K. Das, Hembrom, A. Parihar & Vizzini, sp. nov. (from holotype): A, B, fresh basidiomata in the forest and basecamp; C-E, basidiomata showing yellowish context towards stipe base; F, transverse section through lamella showing cheilocystidia; G, transverse section through pileipellis; H-I, cheilocystidia; J, basidium; K, basidiospores. Scale bars: F-G, 20 µm; H-K, 10 µm.
FIG. 1. — Corethromyces gibbosus W in Fungal Biodiversity Profiles 91-100
FIG. 1. — Corethromyces gibbosus W. Rossi & Santam., sp. nov.: A, B, two thalli from the type slide. Scale bars: 50 µm.
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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.
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