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13,621 results for “Biologics”
A machine learning framework for extracting information from biological pathway images in the literature
<p><strong>466 target chemicals_selected chemicals:</strong></p> <p>Target chemicals satisfying the criteria for biochemical reactions not covered by MetaNetX and KEGG.</p> <p> </p> <p><strong>466 target chemicals_statistics:</strong><br>Numbers of MetaCyc reactions, papers, and pathway images collected for 466 target chemicals from the bio-based chemicals map (Jang et al., Trends in Biotechnology, 2023).</p> <p> </p> <p><strong>arrow detection_bounding box labels:</strong><br>Bounding box labels for 6,471 images in the training and validation datasets and 100 images in the test dataset. The corresponding images are available in "arrow detection_training and validation datasets.zip".</p> <p> </p> <p><strong>arrow detection_test dataset:</strong></p> <p>Test dataset for arrow detection using Faster R-CNN model. A total of 100 images have been prepared from 89 papers searched through PubMed Central (PMC).</p> <p> </p> <p><strong>arrow detection_training and validation datasets:</strong></p> <p>Training and validation datasets for arrow detection using Faster R-CNN model. A total of 6,471 images have been prepared, including 2,332 images from five different sources and 4,139 augmented images.</p> <p> </p> <p><strong>EBPI outputs:</strong></p> <p>Reaction information extracted using EBPI from 49,846 biological pathway images across 466 target chemicals.</p> <p> </p> <p><strong>text classification_training, validation and test datasets:</strong></p> <p>Dataset for text classification using BioBERT. A total of 59,370 terms have been prepared, including 15,101 “gene” terms, 21,417 “protein” terms, and 22,852 “others” terms by combining the data from MetaCyc and the PaddleOCR results from the papers.</p>
Fig. 2 in Correction of the holotype citations of three vascular plants at the herbarium of the National Institute of Biological Resources, Korea
Fig. 2. Holotype of Isoetes coreana Y.H. Chung & H.K. Choi.
Fig. 3 in Correction of the holotype citations of three vascular plants at the herbarium of the National Institute of Biological Resources, Korea
Fig. 3. Holotype of Huperzia jejuensis B.Y. Sun & J. Lim.
Processed Data for Short Gianotti et al., "Two Sub-Annual Time-Scales and Coupling Modes for Terrestrial Water and Carbon Cycles" (2024), Global Change Biology.
<p>These files include all data used to create Figures in Short Gianotti et al., "Two Sub-Annual Time-Scales and Coupling Modes for Terrestrial Water and Carbon Cycles" (2024), Global Change Biology. Raw data provenances and methodological processing are cited in the published manuscript.</p> <p>See README file for metadata information.</p>
Figure 2. Energy dispersive X in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 2. Energy dispersive X-ray spectroscopy of α - and γ-Al2O3NPs.
Figure 1 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 1. Field emission scanning electron microscope (FESEM) of α- and γ-Al2O3NPs.
Figure 3 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 3. UV-Vis and direct optical band gap spectra of (A) α-Al2O3NPs and (B) γ-Al2O3NPs.
Data and code for figures: Photonic chip-based soliton frequency combs covering the biological imaging window
<p>This dataset contains the data presented in the figures of the paper Photonic chip-based soliton frequency combs covering the biological imaging window.</p> <p>The data for figure X (X = 1,2,3,4,S5) is gathered in one Matlab dataset file Fig_X.mat, which contains structure variable figX whose fields are the panels of the figure in the manuscript (a,b,c,...). In each of the panel field, you find subfields containing the data arrays for all the lines presented in the panel.</p> <p>A minimal script znd_Fig.m is provided for each figure in order to plot all the panels.</p> <p>The datasets and scripts were generated and tested using Matlab 2016b or 2014b.</p>
Biological and technical complications of tilted implants in comparison to straight implants supporting fixed dental prostheses: A systematic review and meta-analysis
<p>Dataset for all meta-analyses performed in the paper, which is submitted in Clin Oral Implants Res.<br> <br> For each outcome, the main dataset is to be used for all main analyses/subgroup analyses/sensitivity analyses. A separate version of each outcome dataset exists for the subgroup analysis according to jaw (as it included separate trial arms for maxilla and mandible).</p>
Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics
<p>Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics</p>
An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer
<p>Supplemental data for the paper titled "An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer"</p>
Computational biology and how this field supports new drug discovery
<p>PechaKucha 20x20 is a simple presentation format where you show 20 images, each for 20 seconds. The images advance automatically and this presentation will explore the use of computational modeling supports the early stage of drug discovery. https://www.pechakucha.org/presentations/drug-discovery-and-computational-biology</p>
MADFORWATER: WP2: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task 2.3–Agro-industrial wastewater treatment: Subtask 2.3.1. –Treatment of olive mill wastewater (OMWW): Aerobic biological treatment in sequenced batch reactors (SBRs): Subset 1
<p>This dataset contains the data underlying the following publication: Fatma AROUS, Chadlia HAMDI, Souhir KMIHA, Nadia KHAMMASSI, Amani AYARI, Mohamed NEIFAR, Tahar MECHICHI, Atef JAOUANI (2018). Treatment of olive mill wastewater through employing sequencing batch reactor: Performance and microbial diversity assessment. 3 Biotech 2018, 8, 481. https://doi.org/10.1007/s13205-018-1486-6.</p>
Genomes assemblies presented in Zhou et al., 2018, Current Biology 28, 2420–2428
<p>Snapshot of genome assemblies published as part of Zhou et al., 2018, Current Biology 28, 2420–2428. Please see <a href="http://dx.doi.org/10.1016/j.cub.2018.05.058">the paper</a> for full description and list of authors.</p>
Images used for protein quantification in Hayes et al (2019) Current Biology: Figure 3
<p>Images used for quantification of PIF4-HA and PIF5-HA protein stability in Fig 3. Hayes et. al 2018.</p> <p><em>35S:PIF4-HA </em>and <em>35S:PIF5-HA</em> (L<em>er</em>) were germinated on ½ MS plates in white light (16:8h photoperiod) for 3 days, before being transferred to plates with or without 75mM NaCl. Plates were then grown in red light or a further 2 days. On day 5, at Zt 3, half the plates were moved to red+ far-red light. Tissues were harvested at Zt 4. 10 seedlings were homogenized and proteins were extracted in 70 µl Cracking Buffer (125mM Tris pH 7.4, 2% SDS, 10% Glycerol, 6M Urea, 5% βME) and 15 µl of the extract was run on a 10% polyacrylamide gel. Blots were probed with anti-GFP-HRP (1:1000). Membranes were developed with 50/50 ‘pico’ and ‘femto’ chemiluminescence substrate (Thermo) on a ChemiDoc (Biorad).</p> <p> </p> <p>Experiment was performed on 4 dates (in duplicate).</p> <p>R= red light</p> <p>FR= red +far-red light</p> <p>RN= red light +NaCl</p> <p>FRN= red +far-red light +NaCl</p> <p> </p> <p>Sample order is as follows:</p> <p> </p> <p>01-08-18:</p> <p>PIF4-HA: R1, R2, FR1, FR2, RN1, RN2, FRN1, FRN2</p> <p>PIF5-HA: R1, R2, FR1, FR2, RN1, RN2, FRN1, FRN2</p> <p> </p> <p>08-08-18:</p> <p>PIF4-HA: R3, R4, FR3, FR4, RN3, RN4, FRN3, FRN4</p> <p>PIF5-HA: R3, R4, FR3, FR4, RN3, RN4, FRN3, FRN4</p> <p> </p> <p>15-08-18:</p> <p>PIF4-HA: R5, FR5, RN5, FRN5, R6, FR6, RN6, FRN6</p> <p>PIF5-HA: R5, FR5, RN5, FRN5, R6, FR6, RN6, FRN6</p> <p> </p> <p>10-10-18:</p> <p>PIF4-HA: R7, FR7, RN7, FRN7, R8, FR8, RN8, FRN8</p> <p>PIF5-HA: R7, FR7, RN7, FRN7, R8, FR8, RN8, FRN8</p>
Image used for GUS staining quantification in Hayes et al (2019) Current Biology: Figure 3
<p><em>DR5v2:GUS</em> plants in the Col or <em>pif4-101</em>/<em>pif5</em> background were germinated for 3 days on soil (white light- 16:8h photoperiod), before being transferred to soil with or without 25mM NaCl. Plants were then grown in Wl for a further day. On day 4, at Zt 4.5, half the plants were moved to white light + far-red light. Tissues were fixed at Zt 4.5 on day 6 by 20 minutes in 90% acetone at -20°C. Tissues were washed twice under a vacuum in GUS wash solution (0.1M Phospho-PI pH 7.0, 10 mM EDTA, 2 mM K3Fe(CN)6) and then stained overnight at 37°C in GUS staining solution (0.1M Phospho-PI pH 7.0, 10 mM EDTA, 1 mM K3Fe(CN)6, 1mM K4Fe(CN)6 * 3H2O, 0.5 mg/ ml X-Gluc). The GUS reaction was inhibited by treatment with 3:1 ethanol: acetic acid mix for 1 hour at 37°C. Tissues were cleared with 70% ethanol over several days and then mounted in 10% chloral hydrate, 30% glycerol. Slides were scanned and the images used for quantification.</p> <p> </p> <p>Sample order is as follows:</p> <p> </p> <p>pif4pif5 Wl Col Wl</p> <p>pif4pif5 Wl Col Wl</p> <p>pif4pif5 Wl+ NaCl Col Wl+ NaCl</p> <p>pif4pif5 Wl+ NaCl Col Wl+ NaCl</p> <p>pif4pif5 Wl +FR Col Wl +FR </p> <p>pif4pif5 Wl +FR Col Wl +FR </p> <p>pif4pif5 Wl +NaCl +FR Col Wl +NaCl +FR</p> <p>pif4pif5 Wl +NaCl +FR Col Wl +NaCl +FR</p>
Images used for protein quantification in Hayes et al (2019) Current Biology: Figure 4
<p>Images used for quantification of BES1-GFP protein stability in Fig 4. Hayes et. al 2019.</p> <p><em>35S:BES1-GFP</em> plants were germinated on ½ MS plates in white light (16:8h photoperiod) for 3 days, before being transferred to plates with or without 75mM NaCl. Plates were then grown in red light or a further 2 days. On day 5, at Zt 3, half the plates were moved to red+ far-red light. Tissues were harvested at Zt 4. 10 seedlings were homogenized and proteins were extracted in 70 µl Cracking Buffer (125mM Tris pH 7.4, 2% SDS, 10% Glycerol, 6M Urea, 5% βME) and 15 µl of the extract was run on a 10% polyacrylamide gel. Blots were probed with anti-GFP-HRP (1:1000). Membranes were developed with 50/50 ‘pico’ and ‘femto’ chemiluminescence substrate (Thermo) on a ChemiDoc (Biorad).</p> <p> </p> <p>Experiment was performed on 3 dates (in duplicate).</p> <p>R= red light</p> <p>FR= red +far-red light</p> <p>RN= red light +NaCl</p> <p>FRN= red +far-red light +NaCl</p> <p> </p> <p>Sample order is as follows:</p> <p> </p> <p>01-08-18:</p> <p>BES1-GFP: R1, FR1, RN1, FRN1, R2, FR2, RN2, FRN2</p> <p> </p> <p>08-08-18:</p> <p>BES1-GFP: R3, FR3, RN3, FRN3, R4, FR4, RN4, FRN4</p> <p> </p> <p>15-08-18:</p> <p>BES1-GFP: R5, FR5, RN5, FRN5, R6, FR6, RN6, FRN6</p>
The effect of particle board industry waste tar on the physical and biological durability of wood
<p>This manuscript investigated the effect of waste tar from particle board factories on some physical and biological resistance properties of Scots pine (<em>Pinus sylvestris </em>L.) and beech (<em>Fagus orientalis </em>L.) woods. Solutions were prepared by dissolving waste tar in ethanol:toluene (1v:1v) in concentrations of 5%, 10%, 15% and 20% and treated under vacuum and pressure. In addition, surface coating (SC) was applied by spreading 96% waste tar on the wood surfaces after treatment. Deep-treated and surface-coated (DT+SC) wood samples were exposed to the wood-decay fungi (<em>Corillous versicolor</em> L. and <em>Neolentinus lepideus</em> Fr.) and wood destroying house borer (<em>Hylotrupes bajulus </em>L.) larvaes. Total phenolic content, water uptake, water-repellent efficiency and surface contact angle were tested. The highest mean weight loss (45.23%) was found in the beech wood control samples exposed to <em>C. versicolor</em>. Beech samples deep-treated with a 20% concentration and surface treatment (DT+SC) yielded a mass loss of 14.03%. The <em>H. bajulus</em> larvae mortality rate was found to be 80% in the Scots pine wood samples deep-treated with 20% waste tar. The deep treatment with the waste tar solution significantly increased the surface contact angle values of the Scots pine wood, thereby significantly reducing the wettability of the wood compared to the untreated control samples. </p>
Biological and mechanical complications of angulated abutments connected to fixed dental prostheses. A systematic review with meta-analysis
<p>Dataset for all analyses in the study</p>
Biological Process Summary (shared genes)
<p><strong>Compiled data for Gene Ontology analysis done using TOPPFUN for significant genes from pairwise Prakriti comparisons (PK,VK,VP)</strong></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.