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opengpl-3.0-or-laterNov 2023View details →

Multiomics analyses reveal a Type III-associated immune response in immunotherapy-induced toxicity in melanoma and identifies potential new therapeutic targets

Immune checkpoint inhibitors (ICIs) are standard-of-care for the treatment of advanced melanoma, but their use is limited by immune-related adverse events (irAEs). Proteomic analyses and multiplex cytokine/chemokine assays from serum at baseline and at irAEs onset in melanoma patients indicated aberrant T-cell activity with differential expression of Type I and III immune signatures. This was in line with an increase in the proportions of monocytes and decrease of IL-17A-producing CD4+ T-cells in the peripheral blood using single cell RNA sequencing. Multiplex immunohistochemistry and spatial transcriptomics on ICI-induced skin rash and colitis showed an increase in the proportion of CD4+ T-cells with IL-17A expression.   Anti-IL-17A mAbs were administered in two patients with myocarditis, colitis and skin rash with resolution of the irAE. This study highlights the potential role of Type III CD4+ T-cells in irAEs development and provides proof-of-principle evidence for a corresponding clinical trial using anti-IL17A for treating irAEs. Code to generate the figures are located at https://github.com/pcheng84/AE_analysis/

Strategic Environmental Messaging: Identifying the Most Impactful Communication Characteristics Through Cluster Analysis

Datasets for study 1 & 2 for the manuscript Strategic Environmental Messaging: Identifying the Most Impactful Communication Characteristics Through Cluster Analysis

Identifying biomarkers for chronic obstructive pulmonary disease in the salivary metabolome.

Background: The lack of specificity in spirometry and accuracy in ‘pre-disease states’, and elderly indicates the need for other accurate, inexpensive and non-invasive tests for diagnosis, triage severity and particularly for choosing and monitoring response to treatments in chronic obstructive pulmonary disease (COPD). We compared salivary metabolomic signatures from patients with COPD and controls across a range of severity of airflow obstruction. <a name="_61k4y1plsv5"></a>Methods 45 people with COPD (mean age 66.1 ± 23 years, forced expiratory volume [FEV1] 47+15% predicted) and 48 healthy controls (mean age 56.0 +16.2 years, FEV1 90+23 % predicted) provided saliva that was assessed by flow infusion electrospray mass spectrometry (FIE-MS). Spectra were interrogated using an online library package. Data (patient data and metabolomic outputs) are available from this site.  <a name="_j99pcjszp0an"></a>Results Four potential biomarkers identified the presence of COPD with a sensitivity of 73%, specificity of 72%.  Six metabolites predicted FEV1 % in the COPD cohort (P < 0.001, R2 > 0.3, AUC > 0.7) whilst a range of multivariate approaches targeted six metabolites linked to GOLD stage (P < 0.001, AUC > 0.7). Identification of the metabolites suggested changes in pterin biosynthesis, lipid processing, nucleotide metabolism and melatonin in COPD patients. <a name="_2n4ucvxs4l1n"></a>Conclusions Metabolic fingerprinting of saliva samples could differentiate COPD from age matched controls and inform COPD severity of airflow obstruction. Potential biomarkers are suggested which could inform the diagnosis and monitoring of COPD.   <h2> </h2>

Set of 4 597 baits designed in collaboration with RapidGenomics (Gainesville, Florida, USA) to capture the identified low‐ to single‐copy nuclear genes (LSCN).

This dataset presents a set of 4,597 baits designed in collaboration with RapidGenomics (Gainesville, Florida, USA) to capture the identified low- to single-copy nuclear genes (LSCN) in our study. These baits were instrumental in our research on the evolutionary relationships of the Neotropical magnolias based on plastome and nuclear phylogenomics. The data provided are crucial for understanding the methodology and results of our study.

dataset for identifying opinion leader

This dataset is created for identifying opinion leaders in social network.

Supplementary material: Machine learning and phylogenetic models identify predictors of genetic variation in Neotropical amphibians

Aim: Intraspecific genetic variation is key for adaptation and survival in changing environments and is known to be influenced by many factors, including population size, dispersal, and life history traits. We investigated genetic variation within Neotropical amphibian species to provide insights into how natural history traits, phylogenetic relatedness, climatic, and geographic characteristics can explain intraspecific genetic diversity. Location: Neotropics. Taxon: Amphibians. Methods: We assembled datasets using open-access databases for natural history traits, genetic sequences, phylogenetic trees, climatic, and geographic data. For each species, we calculated overall nucleotide diversity (π) and tested for isolation by distance (IBD) and isolation by environment (IBE). We then identified predictors of π, IBD, and IBE using Random Forest (RF) regression or RF classification. We also fitted phylogenetic generalized linear mixed models (PGLMMs) to predict π, IBD, and IBE. Results: We compiled 4,052 mitochondrial DNA sequences from 256 amphibian species (230 frogs and 26 salamanders), georeferencing 2,477 sequences from 176 species that were not linked to occurrence data. RF regressions and PGLMMs were congruent in identifying range size and precipitation (σ) as the most important predictors of π, influencing it positively. RF classification and PGLMMs identified minimum elevation as an important predictor of IBD; most species without IBD tended to occur at higher elevations. Maximum latitude and precipitation (σ) were the best predictors of IBE, and most species without IBE occur at lower latitudes and in areas with more variable precipitation. Main conclusions: This study identified predictors of genetic variation in Neotropical amphibians using both machine learning and phylogenetic methods. This approach was valuable to determine which predictors were congruent between methods. We found that species with small ranges or living in zones with less variable precipitation tended to have low genetic diversity. We also showed that Western Mesoamerica, Andes, and Atlantic Forest biogeographic units harbor high diversity across many species that should be prioritized for protection. These results could play a key role in the development of conservation strategies for Neotropical amphibians.

Data from: mapping endemic freshwater fish richness to identify high priority areas for conservation: an ecoregion approach

Freshwater ecosystems are experiencing accelerating global biodiversity loss. Thus, knowing where these unique ecosystems' species richness reaches a peak can facilitate their conservation planning. By hosting more than 290 freshwater fishes, Iran is a major freshwater fish hotspot in the Middle East. Considering the accelerating rate of biodiversity loss, there is an urgent need to identify species rich areas and understanding of the mechanisms driving biodiversity distribution. In this study, we gathered distribution records of all endemic freshwater fishes of Iran (85 species) to develop their richness map and determine the most critical drivers of their richness patterns from an ecoregion approach. We performed a generalized linear model (GLM) with quasi-Poisson distribution to identify contemporary and historical determinants of endemic freshwater fish richness. We also quantified endemic fish similarity among the 15 freshwater ecoregions of Iran. Results showed that endemic freshwater fish richness is highest in the Zagros Mountains while moderate level of richness was observed between Zagros and Alborz Mountains. High, moderate and low richness of endemic freshwater fish match with Upper Tigris & Euphrates, Namak, and Kavir & Lut Deserts ecoregions respectively. Kura - South Caspian Drainages and Caspian Highlands were the most similar ecoregions and Orumiyeh was the most unique ecoregion according to endemic fish presence. Precipitation and precipitation change velocity since the Last Glacial Maximum were the most important predictors of endemic freshwater fish richness. Areas identified to have the highest species richness have high priority for the conservation of freshwater fish in Iran, therefore, should be considered in future protected areas development.

opencc-zeroMar 2023View details →

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Pamela Puppo, <a href="https://orcid.org/0000-0002-5038-0238">https://orcid.org/0000-0002-5038-0238</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

opencc-zeroJan 2021View details →

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Joscelyn Wooden, <a href="https://orcid.org/0009-0007-2982-1987">https://orcid.org/0009-0007-2982-1987</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Piper Preuss, <a href="https://orcid.org/0009-0004-4438-6176">https://orcid.org/0009-0004-4438-6176</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

opencc-zeroSep 2023View details →

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Leodivina Dodoo, <a href="https://orcid.org/0009-0003-1693-7340">https://orcid.org/0009-0003-1693-7340</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

Data from: Male mouse recombination maps for each autosome identified by chromosome painting

Linkage maps constructed from genetic analysis of gene order and crossover frequency provide few clues to the basis of the genomewide distribution of meiotic recombination, such as chromosome structure, that influences meiotic recombination. To bridge this gap, we have generated the first cytological recombination map that identifies individual autosomes in the male mouse. We prepared meiotic chromosome (synaptonemal complex [SC]) spreads from 110 mouse spermatocytes, identified each autosome by multicolor fluorescence in situ hybridization of chromosome- specific DNA libraries, and mapped 12,000 sites of recombination along individual autosomes, using immunolocalization of MLH1, a mismatch repair protein that marks crossover sites. We show that SC length is strongly correlated with crossover frequency and distribution. Although the length of most SCs corresponds to that predicted from their mitotic chromosome length rank, several SCs are longer or shorter than expected, with corresponding increases and decreases in MLH1 frequency. Although all bivalents share certain general recombination features, such as few crossovers near the centromeres and a high rate of distal recombination, individual bivalents have unique patterns of crossover distribution along their length. In addition to SC length, other, as-yet-unidentified, factors influence crossover distribution leading to hot regions on individual chromosomes, with recombination frequencies as much as six times higher than average, as well as cold spots with no recombination. By reprobing the SC spreads with genetically mapped BACs, we demonstrate a robust strategy for integrating genetic linkage and physical contig maps with mitotic and meiotic chromosome structure.

opencc-by-4.0Jan 2024View details →

Spatial mapping of the hepatocellular carcinoma landscape identifies unique intratumoural perivascular-immune neighbourhoods

The uploaded data includes results from imaging mass cytometry (IMC) data collected from hepatocellular carcinoma patients. The associated publication can be found <a href="https://journals.lww.com/hepcomm/fulltext/2024/11010/spatial_mapping_of_the_hcc_landscape_identifies.11.aspx">here</a> (Marsh-Wakefield et al., 2024, Hepatology Communications). The CSV file contains segmented cells from IMC data. This includes the Patient, ROI, and Group each cell is assigned. Marker signal intensities underwent arcsine transformation and were rescaled. The “simprof_cluster” column contains the final iteration of clustering following initial X-shift clustering. Notes on additional columns: <ul> <li>“Sample” is barcoded such that the first three digits are the ablation number, followed by the region on the TMA, the group, and the patient. I.e., “[ablation.number]_[TMA.location]_[group]_[patient]”.</li> <li>“x” and “y” refer to the coordinates of samples.</li> <li>“Group” refers to the tissue type. Included non-tumour (NT), invasive margin (IM), and tumour (T) regions.</li> <li>The area for each ROI has been calculated (µm^2 and mm^2).</li> <li>“Batch” refers to TMA.</li> <li>In most cases each area from each patient has three ablation sites. Three samples have an extra ablation site due to technical difficulties during the ablation, and hence have a “split” sample.</li> </ul> The PDF file contains patient information associated with the IMC data. DOI of dataset: 10.5281/zenodo.10622397 Any further questions can be addressed to Felix Marsh-Wakefield felix.marsh-wakefield@sydney.edu.au

opencc-by-4.0Feb 2024View details →

Survey and focus group results to identify European and Regional needs, opportunities and expectations to bio-based education/training model.

This data was used to produce the “Report on European and regional analysis of the needs, opportunities and expectations to bio-based education/training model” in the framework of BioBEC project (101023381-H2020-BBI-JTI-2020). The data provide information about the needs and expectations of the main stakeholders (Academics / Researchers / Consultants / Educational Administrators / Non profit foundations / Clusters) regarding the design, development and implementation of Bio Based Education Centres across different European Regions. The survey used asked the informants to provide their opinion regarding different statements in a scale from 1 (strongly disagree) to 5 (strongly agree). The focus group includ open questions focused on the topics described.

opencc-zeroFeb 2024View details →

Data from: A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images

Accurate species identification is a prerequisite to assess the medical relevance of a mosquito specimens. In monitoring or surveillance programs, mosquitoes are typically identified based on morphological characters, which can be supported by molecular biological assays. Both methods require intensive experience of the observers and well-equipped laboratories. The use of convolutional neural networks (CNNs) to identify species based on images may be a cost-effective and reliable alternative. In this proof-of-concept study, we developed a CNN to identify seven Aedes species by wing images, only. While previous studies used images of the whole mosquito body, the nearly two-dimensional wings may facilitate standardized image capture and thereby reduce the complexity of the CNN implementation. Mosquitoes were sampled from different sites in Germany. Their wings were mounted and photographed with a professional stereomicroscope. The data set consisted of 1,155 wing images from seven Aedes species, including the exotic species Aedes albopictus und six native Aedes species, as well as 554 wings from different non-Aedes mosquitoes. The wing images were used to train a CNN to differentiate between Aedes and non-Aedes mosquitoes and to classify the seven Aedes species. The training was conducted separately for grayscale and RGB images. Image processing, data augmentation, training, validation and testing were conducted in python using deep-learning framework PyTorch. For both input images, i.e. grayscale and RGB images, our best-performing CNN configuration achieved an accuracy of 100% to discriminate Aedes from non-Aedes mosquito species. The accuracy to predict the Aedes species reached 93% for grayscale images and 96% for RGB images. Aedes albopictus could be identified with an accuracy of 100%. In conclusion, wing images are sufficient to identify mosquito species by CNN based image classification. Thus, wing images can represent a useful complement for CNN-based image classification, e.g. for damaged mosquito specimens. Larger training data sets with further mosquito species and a greater variety of images are required to improve and test broad applicability.

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Ryan Whitehouse, <a href="https://orcid.org/0000-0001-9149-3127">https://orcid.org/0000-0001-9149-3127</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

Data from: Digital PCR quantification of ultrahigh ERBB2 copy number identifies poor breast cancer survival after trastuzumab

HER2/ERBB2 evaluation is necessary for treatment decision-making in breast cancer (BC), however current methods have limitations and considerable variability exists. DNA copy number (CN) evaluation by droplet digital PCR (ddPCR) has complementary advantages for HER2/ERBB2 diagnostics. In this study, we developed a single-reaction multiplex ddPCR assay for determination of ERBB2 CN in reference to two control regions, CEP17 and a copy-number-stable region of chr. 2p13.1, validated CN estimations to clinical in situ hybridization (ISH) HER2 status, and investigated the association of ERBB2 CN with clinical outcomes. 909 primary BC tissues were evaluated and the area under the curve for concordance to HER2 status was 0.93 and 0.96 for ERBB2 CN using either CEP17 or 2p13.1 as reference, respectively. The accuracy of ddPCR ERBB2 CN was 93.7% and 94.1% in the training and validation groups, respectively. Positive and negative predictive value for the classic HER2 amplification and non-amplification groups was 97.2% and 94.8%, respectively. An identified biological "ultrahigh" ERBB2 ddPCR CN group had significantly worse survival within patients treated with adjuvant trastuzumab for both recurrence-free survival (hazard ratio, HR: 3.3; 95% CI 1.1–9.6; p = 0.031, multivariable Cox regression) and overall survival (HR: 3.6; 95% CI 1.1–12.6; p = 0.041). For validation using RNA-seq data as a surrogate, in a population-based SCAN-B cohort (NCT02306096) of 682 consecutive patients receiving adjuvant trastuzumab, the ultrahigh-ERBB2 mRNA group had significantly worse survival. Multiplex ddPCR is useful for ERBB2 CN estimation and ultrahigh ERBB2 may be a predictive factor for decreased long-term survival after trastuzumab treatment.

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Juliana Webber, <a href="https://orcid.org/0000-0003-1933-3182">https://orcid.org/0000-0003-1933-3182</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Thomas Stoughton, <a href="https://orcid.org/0000-0002-8764-6626">https://orcid.org/0000-0002-8764-6626</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Jelle Devalez, <a href="https://orcid.org/0000-0003-1110-0640">https://orcid.org/0000-0003-1110-0640</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.