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9,302 results for “DETECT”
Assessment of four in vitro phenotypic biofilm detection methods in relation to antimicrobial resistance in aerobic clinical bacterial isolates
<p><strong>Introduction</strong>: The lack of standardized methods for detecting biofilms continues to pose a challenge to microbiological diagnostics since biofilm-mediated infections induce persistent and recurrent infections in humans that often defy treatment with common antibiotics. This study aimed to evaluate diagnostic parameters of four in vitro phenotypic biofilm detection assays in relation to antimicrobial resistance in aerobic clinical bacterial isolates.</p> <p><strong>Methods</strong>: In this cross-sectional study, bacterial strains from clinical samples were isolated and identified following the standard microbiological guidelines. The antibiotic resistance profile was assessed through the Kirby-Bauer disc diffusion method. Biofilm formation was detected by gold standard tissue culture plate method (TCPM), tube method (TM), Congo red agar (CRA), and modified Congo red agar (MCRA). Statistical analyses were performed using SPSS version 17.0, with a significant association considered at p<0.05.</p> <p><strong>Result</strong>: Among the total isolates (n=226), TCPM detected 140 (61.95%) biofilm producers, with CoNS (9/9) (p<0.001) as the predominant biofilm former. When compared to TCPM, TM (n=119) (p<0.001) showed 90.8% sensitivity and 70.1% specificity, CRA (n=88) (p=0.123) showed 68.2% sensitivity and 42% specificity, and MCRA (n=86) (p=0.442) showed 65.1% sensitivity and 40% specificity. Juxtaposed to CRA, colonies formed on MCRA developed more intense black pigmentation from 24 to 96 hours. There were 77 multi-drug-resistant (MDR)-biofilm formers and 39 extensively drug-resistant (XDR)-biofilm formers, with 100% resistance to ampicillin and ceftazidime, respectively.</p> <p><strong>Conclusion</strong>: It is suggested that TM be used for biofilm detection. Unlike MCRA, black pigmentation in colonies formed on CRA declined with time. MDR- and XDR-biofilm formers were frequent among the clinical isolates.</p>
Proteomic detection of COX-2 pathway-related factors in patients with adenomyosis
<p>Peptide, protein, protein_merge_info and psm are part of mass spectrometry. HE and LE, only LE and only HE are part of bioinformatics analysis.</p>
Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data
<p>Source data for the paper "Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data"</p>
Fig. 2 in Detection of candidate proteins in the indican biosynthetic pathway of Persicaria tinctoria (Polygonum tinctorium) using protein-protein interactions and transcriptome analyses
Fig. 2. Analysis of the protein–protein interaction using co-immunoprecipitation. Cytosolic (a) and microsomal fractions (b) were incubated with an anti-PtIGS antibody bound to the AminoLink Plus Coupling Resin. After elution with an acidic buffer, proteins were separated by SDS-PAGE using a 12.5% acrylamide gel for (a) and 10% for (b). After the samples corresponding to (a) 0.205 and (b) 0.057 g wet weight leaves were analyzed, total proteins were visualized with silver staining. As the control, PtIGS in the samples corresponding to 0.041 and 0.057 g wet weight leaves was also detected with western blotting using the anti-PtIGS antibody. C1–C6 and M1–M10 show the regions that were cut from gels and subjected to MS/MS analysis. The symbols "+" and "-" represent the use of AminoLink Plus Coupling Resins bound with anti-PtIGS antibody and without antibody, respectively.
Fig. 1 in Detection of candidate proteins in the indican biosynthetic pathway of Persicaria tinctoria (Polygonum tinctorium) using protein-protein interactions and transcriptome analyses
Fig. 1. Chemical crosslinking assay in vitro and in vivo. Cytosolic (a) and microsomal (b) fractions and the protoplasts (c) were treated with chemical crosslinkers (BS3 and DSS, respectively). Treated samples were then subjected to SDS-PAGE using a 10% acrylamide gel for (a) and (b) and 7.5% for (c). Proteins that reacted against the anti-PtIGS antibody were detected by western blotting. "I" indicates PtIGS monomer. "II" denotes molecules larger than the monomer.
Fig. 5 in Detection of candidate proteins in the indican biosynthetic pathway of Persicaria tinctoria (Polygonum tinctorium) using protein-protein interactions and transcriptome analyses
Fig. 5. The validation of the expression amount of candidates, which might relate to the indican biosynthesis pathway, by qRT-PCR. a, Indican synthase (PtIGS) and the degradation enzyme (PtBGL); b, indole synthesis-related proteins; c, proteins that might relate to the indole oxidation; d, UDP-glucose biosynthesis enzymes and transport proteins that might relate to the indican biosynthesis pathway. Each error bar shows the standard error. The detailed values of (a) and (b) are indicated in Supplementary Table 4S. Those values of (c) and (d) are showed in Tables 4 and 5, respectively.
Fig. 2. Chemical structures for terpenes detected from branch tip samples. Numbering follows Table 1 in Headspace GC-MS analysis of differences in intra- and interspecific Terpene profiles of Picea pungens Engelm. and P. abies (L.) Karst
Fig. 2. Chemical structures for terpenes detected from branch tip samples. Numbering follows Table 1 (elution order).
2023 IEEE SPS Video and Image Processing (VIP) Cup: Ophthalmic Biomarker Detection Phase 2
<p>Ophthalmic clinical trials that study treatment efficacy of eye diseases are performed with a specific purpose and a set of procedures that are predetermined before trial initiation. Hence, they result in a controlled data collection process with gradual changes in the state of a diseased eye. In general, these data include 1D clinical measurements and 3D optical coherence tomography (OCT) imagery. Physicians interpret structural biomarkers for every patient using the 3D OCT images and clinical measurements to make personalized decisions for every patient.</p> <p>Two main challenges in medical image processing has been <em>generalization</em> and <em>personalization</em>.</p> <p>Generalization aims to develop algorithms that work well across diverse patients and scenarios, providing standardized and widely applicable solutions. Personalization, in contrast, tailors algorithms to individual patients based on their unique characteristics, optimizing diagnosis and treatment planning. Generalization offers broad applicability but may overlook individual variations. Personalization provides tailored solutions but requires patient-specific data. While deep learning has shown an affinity towards generalization, it is lacking in personalization.</p> <p>The presence and absence of biomarkers is a personalization challenge rather than a generalization challenge. The variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Morphological, texture, statistical and fuzzy image processing techniques through adaptive thresholds and preprocessing may prove substantial to overcome these fine-grained challenges. This challenge provides the data and application to address personalization.</p> <p> </p> <p>These files constitute the second phase of the VIP CUP 2023 Challenge at ICIP 2023. This test set has a more general patient base than the first one and as such is a better indicator of the performance of models. This test set was created by taking a subset of the data from a publicly available OCT dataset and then asking our medical partners to provide fine-grained biomarker labels for the competition. We provide the citation for the source of these images below: </p> <p>Kermany D, Goldbaum M, Cai W et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018; 172(5):1122-1131. doi:10.1016/j.cell.2018.02.010.</p> <p> </p> <p>This zenodo repository contains the images and submission template file needed for the second phase of the competition.</p>
CitrusUAT: A Dataset of Orange Citrus sinensis Leaves for Abnormality Detection Using Image Analysis Techniques
<p>This dataset provides a collection of color images taken from the orange leaves of <em>Citrus sinensis</em> (L.) Osbeck species with diseases, nutritional deficiencies, and pest symptoms, proper to develop abnormality detection algorithms based on digital image analysis techniques. The dataset comprises 953 color images divided into 12 classes of orange leaves: Healthy, Huanglongbing (HLB), Greasy spot, Iron deficiency, Magnesium deficiency, Manganese deficiency, Nitrogen deficiency, Zinc deficiency, Texas citrus mite, Red scale, Red scale sequelae, and Citrus leafminer. Each color image was segmented by a thresholding method to obtain a binary mask of the leaf region. Samples were analyzed by the quantitative real-time polymerase chain reaction (qPCR) diagnostic test to detect the <em>Ca.</em> L. asiaticus bacterium that causes HLB disease.</p>
Machine learning-based detection of weather fronts with DL-FRONT in CESM1.3
<p>These data are the results of detecting weather fronts with the machine learning algorithm DL-FRONT (see <a href="https://doi.org/10.5194/ascmo-5-147-2019">Biard and Kunkel 2019</a>) in simulations with the Community Earth System Model, version 1.3 (CESM1.3, see <a href="http://doi.org/10.1029/2019GL084057">Meehl et al. 2019</a>). The specific CESM1.3 simulations used here include: a historical climate simulation from 2000 to 2005, a simulation with Representative Concentration Pathway 2.6 (RCP2.6) forcing from 2006 to 2015, and a simulation with Representative Concentration Pathway 8.5 (RCP8.5) forcing from 2086–2100. See <a href="http://doi.org/10.1029/2022JD037038">Dagon et al. 2022</a> for a publication analyzing these machine learning detected weather fronts and associated extreme precipitation in historical and future climates.</p> <p>At each 3-hourly time step of simulation output over a North American spatial domain (10-77ºN, 171-31°W), DL-FRONT produced a set of spatial grids at 1° spatial resolution, for each of the five categories: cold front, warm front, stationary front, occluded front, and no front. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>The dataset contains three sets of files. The first set contains the original front probability maps. The second set contains "one hot" versions of the front probability maps. In the one hot version the five front-type probabilities for a spatial grid cell for a given time step are replaced by the value 1 for the largest front-type probability, and by 0 for the others. The third set contains front crossing rates (monthly, seasonal, annual) and climatologies/anomalies/standard deviations (monthly, seasonal) for each front type.</p> <p>The front probability files have names that follow the form cesm_fronts_<start_year>_<end_year>.nc for each simulation period. The one hot files have names that follow the form cesm_fronts__<start_year>_<end_year>_MaskedNetCDF_customgrid.nc. The rates files have names that follow the form cesm_fronts_<start_year>_<end_year>_frontRates_viaPolylines_customgrid.nc. For the one-hot and rates files, the historical and RCP2.6 simulation output have been combined into a single file.</p>
The dataset of Helmet Detection
<p>The Helmet Detection dataset.</p> <p>A link to the original dataset: <em>https://www.kaggle.com/datasets/andrewmvd/helmet-detection</em></p> <p>We randomly divided it into new training set, Validation set and test set in a ratio close to 7:2:1.</p>
Anomaly Detection dataset for the fuselage of an aircraft
<p>If you use the dataset, please cite:</p> <p><em>Siddhant Shete, Dennis Mronga</em></p> <p><strong>"Adaptive Online Anomaly Detection using Transfer Learning"</strong></p> <p>About the dataset: The dataset is basically used for anomaly detection in the fuselage of an aircraft manufacturing company. We captured the data on the mockup of the fuselage with several iterations at different distances away from the mockup. The dataset is basically the scans of mockup from top to bottom with and without anomalies. The dataset has been segregated into two panels.</p> <p>Contents of <em><strong> AircraftFuselageMockupDataset.zip </strong></em></p> <ol> <li>Nomal_panel1 </li> <li>Nomal_panel2</li> <li>Anomaly_panel1</li> <li>Anomaly_panel2</li> </ol> <p>Every folder has data at 3 distances 15cm, 25cm, 35cm.</p> <p> </p> <p><em>This dataset is provided by the Robotics Innivation Center, DFKI GmbH.</em></p> <p><em>The grant was provided by Federal Ministry for Economic Affairs and Climate Action </em></p> <p><em>Grant number: 20W1922F</em></p>
Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from MMS Point Cloud using OBB Collision Detection Techniques
<p>This video demonstrates the application of our method in a software tool for constructing road boundaries and lane marking data.</p>
Anomaly Detection dataset for the ISS Panel mockup
<p>If you use the dataset, please cite:</p> <p><em>Siddhant Shete, Dennis Mronga</em></p> <p><strong>"Adaptive Online Anomaly Detection using Transfer Learning"</strong></p> <p>About the dataset: The dataset is basically used for anomaly detection in the ISS(International Space Station) panel. This dataset was captured from the mockup used for experiments at the institute. The panel replicates the curcuit and control boards at ISS. The dataset is segregated in two parts Normal data and Anomalous data.</p> <p>Contents of <em><strong> ISSPanelDataset.zip </strong></em></p> <ol> <li>Nomal</li> <li>Anomaly</li> </ol> <p>The Anomaly folder has data with different scenarios where the led lights are on, the fan panel cover is missing or some parts are missaligned.</p> <p> </p> <p><em>This dataset is provided by the Robotics Innivation Center, DFKI GmbH.</em></p> <p><em>The grant was provided by Federal Ministry for Economic Affairs and Climate Action </em></p> <p><em>Grant number: 20W1922F</em></p>
Insect Detect - insect classification dataset v2
<p>The <strong>Insect Detect - insect classification dataset v2</strong> contains mainly images of various insects sitting on or flying above an artificial flower platform. All images were automatically recorded with the <a href="https://maxsitt.github.io/insect-detect-docs/">Insect Detect</a> DIY camera trap, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring (<a href="https://doi.org/10.1101/2023.12.05.570242">bioRxiv preprint</a>).</p><p>Most of the images were captured by camera traps deployed at different sites in 2023. For some classes (e.g. ant, bee_bombus, beetle_cocci, bug, bug_grapho, hfly_eristal, hfly_myathr, hfly_syrphus) additional images were captured with a lab setup of the camera trap. For some classes (e.g. bee_apis, fly, hfly_episyr, wasp) images from the <a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_classification">first</a> dataset version were transferred to this dataset.</p><p>This dataset is also available on <a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_classification_v2">Roboflow Universe</a>. The images in the dataset from Roboflow are automatically compressed, which decreases model accuracy when used for training. Therefore it is recommended to use this uncompressed Zenodo version and split the dataset into train/val/test subsets in the provided <a href="https://colab.research.google.com/github/maxsitt/insect-detect-ml/blob/main/notebooks/YOLOv5_classification_training.ipynb">training notebook</a>.</p><p> </p><h2>Classes</h2><p>This dataset contains the following 27 classes:</p><ul><li><strong>ant</strong> (<a href="https://en.wikipedia.org/wiki/Ant">Formicidae</a>)</li><li><strong>bee</strong> (<a href="https://en.wikipedia.org/wiki/Bee">Anthophila</a> excluding <i>Apis mellifera</i> and <i>Bombus</i> sp.)</li><li><strong>bee_apis</strong> (<a href="https://en.wikipedia.org/wiki/Western_honey_bee"><i>Apis mellifera</i></a>)</li><li><strong>bee_bombus</strong> (<a href="https://en.wikipedia.org/wiki/Bumblebee"><i>Bombus</i> sp.</a>)</li><li><strong>beetle</strong> (<a href="https://en.wikipedia.org/wiki/Beetle">Coleoptera</a> excluding Coccinellidae and some Oedemeridae)</li><li><strong>beetle_cocci</strong> (<a href="https://en.wikipedia.org/wiki/Coccinellidae">Coccinellidae</a>)</li><li><strong>beetle_oedem</strong> (visually distinct <a href="https://en.wikipedia.org/wiki/Oedemeridae">Oedemeridae</a>)</li><li><strong>bug</strong> (<a href="https://en.wikipedia.org/wiki/Heteroptera">Heteroptera</a> excluding <i>Graphosoma italicum</i>)</li><li><strong>bug_grapho</strong> (<a href="https://en.wikipedia.org/wiki/Graphosoma_italicum"><i>Graphosoma italicum</i></a>)</li><li><strong>fly</strong> (<a href="https://en.wikipedia.org/wiki/Brachycera">Brachycera</a> excluding Empididae, Sarcophagidae, Syrphidae and small Brachycera)</li><li><strong>fly_empi</strong> (<a href="https://en.wikipedia.org/wiki/Empididae">Empididae</a>)</li><li><strong>fly_sarco</strong> (visually distinct <a href="https://en.wikipedia.org/wiki/Flesh_fly">Sarcophagidae</a>)</li><li><strong>fly_small</strong> (small <a href="https://en.wikipedia.org/wiki/Brachycera">Brachycera</a>)</li><li><strong>hfly_episyr</strong> (hoverfly <a href="https://en.wikipedia.org/wiki/Episyrphus_balteatus"><i>Episyrphus balteatus</i></a>)</li><li><strong>hfly_eristal</strong> (hoverfly <a href="https://en.wikipedia.org/wiki/Eristalis"><i>Eristalis</i></a> sp., mainly <a href="https://en.wikipedia.org/wiki/Eristalis_tenax"><i>Eristalis tenax</i></a><i>)</i></li><li><strong>hfly_eupeo</strong> (mainly hoverfly <a href="https://en.wikipedia.org/wiki/Eupeodes_corollae"><i>Eupeodes corollae</i></a> and <a href="https://en.wikipedia.org/wiki/Scaeva_pyrastri"><i>Scaeva pyrastri</i></a><i>)</i></li><li><strong>hfly_myathr</strong> (hoverfly <a href="https://en.wikipedia.org/wiki/Myathropa_florea"><i>Myathropa florea</i></a>)</li><li><strong>hfly_sphaero</strong> (hoverfly <a href="https://en.wikipedia.org/wiki/Sphaerophoria"><i>Sphaerophoria</i> sp.</a>, mainly <a href="https://en.wikipedia.org/wiki/Sphaerophoria_scripta"><i>Sphaerophoria scripta</i></a><i>)</i></li><li><strong>hfly_syrphus</strong> (mainly hoverfly <a href="https://en.wikipedia.org/wiki/Syrphus"><i>Syrphus</i> sp.</a>)</li><li><strong>lepi</strong> (<a href="https://en.wikipedia.org/wiki/Lepidoptera">Lepidoptera</a>)</li><li><strong>none_bg</strong> (images with no insect - background (platform))</li><li><strong>none_bird</strong> (images with no insect - bird sitting on platform)</li><li><strong>none_dirt</strong> (images with no insect - leaves and other plant material, bird droppings)</li><li><strong>none_shadow</strong> (images with no insect - shadows of insects or surrounding plants)</li><li><strong>other</strong> (other Arthropods, including various <a href="https://en.wikipedia.org/wiki/Hymenoptera">Hymenoptera</a> and <a href="https://en.wikipedia.org/wiki/Sawfly">Symphyta</a>, <a href="https://en.wikipedia.org/wiki/Fly">Diptera</a>, <a href="https://en.wikipedia.org/wiki/Orthoptera">Orthoptera</a>, <a href="https://en.wikipedia.org/wiki/Auchenorrhyncha">Auchenorrhyncha</a>, <a href="https://en.wikipedia.org/wiki/Neuroptera">Neuroptera</a>, <a href="https://en.wikipedia.org/wiki/Spider">Araneae</a>)</li><li><strong>scorpionfly</strong> (<a href="https://en.wikipedia.org/wiki/Panorpa"><i>Panorpa</i> sp.</a>)</li><li><strong>wasp</strong> (mainly <a href="https://en.wikipedia.org/wiki/Vespula"><i>Vespula</i> sp.</a> and <a href="https://en.wikipedia.org/wiki/European_paper_wasp"><i>Polistes dominula</i></a><i>)</i></li></ul><p>For the classes <strong>hfly_eupeo</strong> and <strong>hfly_syrphus</strong> a precise taxonomic distinction is not possible with images only, due to a potentially high variability in the appearance of the respective species. While most specimens will show the visual features that are important for a classification into one of these classes, some specimens of <i>Syrphus</i> sp. might look more like <i>Eupeodes</i> sp. and vice versa.</p><p>The images were sorted to the respective class by considering taxonomic and visual distinctions. However, this dataset is still rather small regarding the visually extremely diverse <a href="https://en.wikipedia.org/wiki/Insect">Insecta</a>. Insects that are not included in this dataset can therefore be classified to the wrong class. All results should always be manually validated and false classifications can be used to extend this basic dataset and <a href="https://maxsitt.github.io/insect-detect-docs/modeltraining/train_classification/">retrain</a> your custom classification model.</p><p> </p><h2>Deployment</h2><p>You can use this dataset as starting point to train your own insect classification models with the provided Google Colab <a href="https://colab.research.google.com/github/maxsitt/insect-detect-ml/blob/main/notebooks/YOLOv5_classification_training.ipynb">training notebook</a>. Read the model training <a href="https://maxsitt.github.io/insect-detect-docs/modeltraining/train_classification/">instructions</a> for more information.</p><p>A insect classification model trained on this dataset is available in the <a href="https://github.com/maxsitt/insect-detect-ml#classification-model">insect-detect-ml</a> GitHub repo. To deploy the model on your PC (ONNX format for fast CPU inference), follow the provided step-by-step <a href="https://maxsitt.github.io/insect-detect-docs/deployment/classification/">instructions</a>.</p><p> </p><h3>License</h3><p>This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>).</p><p> </p>
SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection
<p><strong>Paper Accept by WACV 2024</strong></p> <p>[paper, supp] [<a href="https://arxiv.org/abs/2309.01907">arXiv</a>]</p> <p><strong>Overview</strong></p> <p>Synthetic datasets, recognized for their cost effectiveness, play a pivotal role in advancing computer vision tasks and techniques. However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models. This complexity is compounded by the difficulties associated with real remote sensing datasets, including limited data acquisition and high annotation costs, which amplifies the need for high-quality synthetic alternatives. To address this, we present SyntheWorld, a synthetic dataset unparalleled in quality, diversity, and scale. It includes 40,000 images with submeter-level pixels and fine-grained land cover annotations of eight categories, and it also provides 40,000 pairs of bitemporal image pairs with building change annotations for building change detection task. We conduct experiments on multiple benchmark remote sensing datasets to verify the effectiveness of SyntheWorld and to investigate the conditions under which our synthetic data yield advantages.</p> <pre><strong>Description</strong> ------------ This dataset has been designed for land cover mapping and building change detection tasks. File Structure and Content: --------------------------- 1. **1024.zip**: - Contains images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m. - `images` and `ss_mask` folders: Used for the land cover mapping task. - `images` folder: Post-event images for building change detection. - `small-pre-images`: Images with a minor off-nadir angle difference compared to post-event images. - `big-pre-images`: Images with a large off-nadir angle difference compared to post-event images. - `cd_mask`: Ground truth for the building change detection task. 2. **512-1.zip**, **512-2.zip**, **512-3.zip**: - Contains images of size 512x512 with a GSD of 0.3-0.6m. - `images` and `ss_mask` folders: Used for the land cover mapping task. - `images` folder: Post-event images for building change detection. - `pre-event` folder: Images for the pre-event phase. - `cd-mask`: Ground truth for building change detection. Land Cover Mapping Class Grep Map: ---------------------------------- class_grey = { "Bareland": 1, "Rangeland": 2, "Developed Space": 3, "Road": 4, "Tree": 5, "Water": 6, "Agriculture land": 7, "Building": 8, }</pre> <p><strong>Reference</strong></p> <p>@misc{song2023syntheworld,<br> title={SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection}, <br> author={Jian Song and Hongruixuan Chen and Naoto Yokoya},<br> year={2023},<br> eprint={2309.01907},<br> archivePrefix={arXiv},<br> primaryClass={cs.CV}<br> }</p> <p> </p>
TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery
<p>TrendyGenes Literature Mining</p> <p>This repository contains the files and code to build the TrendyGenes pipeline described in the paper "TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery" (Serrano Nájera et al. 2021).</p> <p>Contents</p> <p>The folder contains the following files:</p> <ul> <li>PubMed_*.csv.gz: CSV files containing PubMed metadata (titles, abstracts etc.) split into multiple files</li> <li>CoCitations*.csv.gz: CSV files containing co-citation networks computed from PubMed</li> <li>MeSH2PMID.csv.gz: Map of MeSH terms to PMIDs</li> <li>Authorship_Neo4J_complete.csv.gz: Authorship information for PubMed papers</li> <li>Disease2PMID_Neo4J_complete.csv.gz: Map of disease terms to PMIDs after disambiguation</li> <li>Genes_Neo4J_complete_CCPU.csv.gz: Map of genes to PMIDs after disambiguation</li> <li>genes.csv.gz: List of human genes</li> <li>diseases.csv.gz: List of MeSH disease terms</li> <li>import_command*.txt: Commands to import data into Neo4j graph database</li> </ul> <p>Building the Knowledge Graph</p> <p>The various CSV files can be imported into a Neo4j graph database to build the knowledge graph containing publications, authors, genes, diseases etc. and their connections as described in the paper.</p> <p>The import_command*.txt files contain the Neo4J bulk import syntax needed to import the data into Neo4j:<br> https://neo4j.com/developer/guide-import-csv/</p> <p>Citation</p> <p>Serrano Nájera G, Narganes Carlón D, Crowther DJ. TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery. Scientific Reports. 2021 Aug 3;11(1):15747.</p> <p>License</p> <p>[MIT]</p> <p>This summarizes the key files provided and briefly explains how they can be used to build the knowledge graph database for the TrendyGenes pipeline. The citation provides a reference to the original paper.</p>
Monitoring the birds and the bees: Environmental DNA metabarcoding of flowers detects plant–animal interactions
<p>Animal pollinators are vital for the reproduction of ~90% of flowering plants. However, many of these pollinating species are experiencing declines globally, making effective pollinator monitoring methods more important than ever before. Pollinators can leave DNA on the flowers they visit, and metabarcoding of these environmental DNA (eDNA) traces provides an opportunity to detect the presence of flower visitors. Our study, collecting flowers from seven plant species with diverse floral morphologies, for eDNA metabarcoding analysis, illustrated the value of this novel survey tool. eDNA metabarcoding using three assays, including one developed in this study to target common bush birds, recorded more animal species visiting flowers than visual surveys conducted concurrently, including birds, bees, and other species. We also recorded the presence of a flower visit from a western pygmy possum; to our knowledge, this is the first eDNA metabarcoding study to simultaneously identify the interaction of insect, mammal, and bird species with flowers. The highest diversity of taxa was detected on large inflorescence flower types found on <em>Banksia arborea </em>and <em>Grevillea georgeana</em>. The study demonstrates that the ease of sample collection and the robustness of the metabarcoding methodology have profound implications for future management of biodiversity, allowing us to monitor both plants and their attendant cohort of potential pollinators. This opens avenues for rapid and efficient comparison of biodiversity and ecosystem health between different sites and may provide insights into surrogate pollinators in the event of pollinator declines.</p>
Dark Pattern Detection Tool Capabilities and Limitations
<p>It concludes the details information on Dark pattern tools and their coverage of detecting dark patterns.</p>
Electrical Alignment Signatures of Ice Particles before Intracloud Lightning Activity Detected by Dual-polarized Phased Array Weather Radar
<p>lightning data from LIDEN system and the data of the Dual-polarized Phased Array Weather Radar (DP-PAWR) on August 20, 2019. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.