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7,518 results for “screenings”
Micelle size screening - Magaining 2 simulation - 45 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data.</p>
Micelle size screening - eYqjD simulation - 45 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data.</p>
Micelle size screening - Magaining 2 simulation - 40 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data.</p>
Resources and datasets for automated high-throughput droplet screening by iSort
<p>Contains the design files and source data linked to the following publication in Cell Reports Methods (2023, https://doi.org/10.1016/j.crmeth.2023.100478)</p> <p><strong><em>iSort </em></strong><strong>enables automated complex microfluidic droplet sorting in an effort to democratize technology.</strong></p> <p><strong>Abstract: </strong><br> Fluorescence activated droplet sorting (FADS) is a widely used microfluidic technique for high throughput screening. However, it requires highly trained specialists to determine optimal sorting parameters, resulting in a large combinatorial space that is challenging to optimize systematically. Additionally, there is currently no way to track every single droplet within a screen, leading to compromised sorting and “hidden” false positive events. To overcome all these limitations, we have developed a setup in which the droplet frequency, spacing and trajectory at the sorting junction are monitored in real time using impedance analysis. The resulting data is used to continuously optimize all parameters automatically and to counteract perturbations, resulting in higher throughput, higher reproducibility, increased robustness and a beginner-friendly character. We believe this provides a missing piece for the spreading of phenotypic single-cell analysis methods, similar to what we have seen for single-cell genomics platforms.</p> <p><strong>To cite:</strong> https://doi.org/10.1016/j.crmeth.2023.100478</p>
A genome-wide genetic screen uncovers novel determinants of human pigmentation
<p>1717.gwas.imputed_v3.both_sexes.variants.nolowconf.maf01.clumped.txt - GWAS results for LD-clumped genome-wide significant variants from White British skin color GWAS<br> 1717.gwas.imputed_v3.both_sexes.variants.nolowconf.maf01.pbsfilt.100kb.tsv - GWAS results for LD-pruned variants within 100kb of pro-melanin genes<br> eqtl.gwas.minpergene.txt - melanocyte eQTL and White British skin color GWAS results for SNPs near pro-melanin gene. Columns 1-12 are eQTL info, 13 onwards are GWAS info. Select rows with "closest" = TRUE and both p-values < 0.01 to recreate figure in the paper.<br> csv_and_r_script_files.tar- csv and R script files to recreate the figures in the paper. <br> PBS_Pigmentation.tar- txt and R script files to recreate PBS figures in the Figure <br> </p>
Positive selection screen in yeast for inhibitors of SARS-CoV-2 Main Protease. Dataset from docking studies.
<p>Best docked poses from XP docking and covalent docking (Glide; Schrödinger 2021-2) towards the SARS-CoV-2 MPro protein (PDB ID 7CB7). List of ligands is also included. </p>
Receptor cavity-based screening reveals potential allosteric modulators of gonadotropin receptors in carp (Cyprinus carpio)
<p>The dataset include phase database consisting of prepared ligand used in screening of potential allosteric modulators for carp FSHR and LHR. the original dataset were sourced from The compound libraries from <a href="https://enamine.net/compound-libraries">https://enamine.net/compound-libraries</a> and are free to access and downloaded and used as per the mentioned sites terms and conditions the Datasets given here are processed databases constructed using the Phase module (Phase, Schrödinger, LLC, New York, NY, 2021.).</p>
Tailored machine learning models for functional RNA detection in genome-wide screens
<p>The prediction of noncoding RNA and protein coding genetic loci has received<br> considerable attention in comparative genomics aiming in particular<br> at the identification of properties of nucleotide sequences that are<br> informative of their biological role in the cell. We present here a<br> software framework for the alignment-based training, evaluation and<br> application of machine learning models with user-defined<br> parameters. Instead of focusing on the one-size-fits-all approach of<br> pervasive \is annotation pipelines, we offer a framework for the<br> structured generation and evaluation of models based on arbitrary<br> features and input data, focusing on stable and explainable results.<br> Furthermore, we showcase the usage of our software package in a<br> full-genome screen of Drosophila melanogaster and evaluate<br> our results against the well-known but much less flexible program<br> RNAz.</p> <p> </p>
Characterization and Catalytic Datasets for the Multi-Batch Screening Photoreactor
<p>Datasets obtained during characterization of the Multi-Batch_Screening_Photoreactor</p> <p>The folder structure is listed below.</p> <p>The documentation, software and all required files for manufacturing are available at GitHub: <br> https://github.com/photonZfeed/modularPhotoreactor<br> https://github.com/photonZfeed/Py4ModPhotoreactor</p>
Fig. 1 in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system
Fig. 1. Rapid screening of plant glycosyltransferases using the LET-based-TX-TL system. We can either use long primers which contain a promoter, a ribosome binding site, and a terminator to generate expressible linear DNAs or use short primers to amplify the targeted gene fragments and then ligate them with a promoter, a ribosome binding site, a terminator, and a backbone; then another pair of primers is used to generate expressible linear DNAs. Afterward, combine TX-TL extracts, buffers, and expressible linear DNAs to start protein expression. Then this TX-TL mixture is directly added with substrates (such as quercetin) to start glycosylation reactions. Finally, UPLC-MS is used to analyze the reaction mixture to examine whether targeted products (such as isoquercitrin) are generated.
Fig. 2 in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system
Fig. 2. UPLC-MS analysis of isoquercitrin converted from quercetin by AtUGTs expressed in TX-TL. (A) A glycosylation reaction catalyzed by a UDP-glucose glycosyltransferase. (B) The chromatogram of the blank sample, which only has 50% methanol solvent. (C) The chromatogram of the negative group, which has TX-TL, quercetin, UDPglucose but no additional DNA. (D) The chromatogram of the quercetin standard. (E) The chromatogram of the isoquercitrin standard. (F–O) Chromatograms of products from the catalysis of quercetin by different AtUGTs (the final concentrations of the linear DNAs used for each AtUGTs are listed below in brackets): (F) AT1G07250 (32.25 nM), (G) AT1G07260 (29.24 nM), (H) AT2G36790 (20.64 nM), (I) AT2G15480 (29.98 nM), (J) AT2G15490 (29.50 nM), (K) AT3G16520 (25.03 nM), (L) AT3G21750 (23.7 nM), (M) AT3G46660 (24.14 nM), (N) AT4G15280 (24.47 nM), and (O) AT4G34138 (22.52 nM), see Supplementary Fig. S4 for the duplicate group. The y axis indicates ion signal abundance relative to the highest signal in each chromatogram (%). The numbers in the top right of each chromatogram (for instance 3.38e7 in [E]) indicate the ion counts represented by "100" relative abundance. The retention time of the isoquercitrin standard is 4.0 min, and the red arrow in each figure indicates the retention time of the product isoquercitrin. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system
Fig. 4. UPLC-MS analysis of isoquercitrin converted from quercetin by ArUGTs using heterologous expression. (A) The SDS-PAGE gel of heterologous expression of ArUGTs AR14572, AR11662, and AR43718 protein. The theoretical molecular weights of AR14572 protein, AR11662 protein, and AR43718 protein are 80.1 kDa, 79.8 kDa, and 80.4 kDa, respectively. (B) The chromatogram of isoquercitrin standard. (C–E) Chromatograms of products from the catalysis of quercetin by different ArUGTs using heterologous expression: (C) AR14572, (D) AR11662, and (E) AR43718, see Supplementary Fig. S6 for the duplicate group. The y axis indicates ion signal abundance relative to the highest signal in each chromatogram (%). The numbers in the top right of each chromatogram (for instance 4.56e6 in [B]) indicate the ion counts represented by "100" relative abundance. The retention time of the isoquercitrin standard is 3.93 min, and the red arrow in each figure indicates the retention time of the product isoquercitrin. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5. A in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system
Fig. 5. A phylogenetic tree based on protein sequences of six ArUGTs: AR06047, AR06981, AR07558, AR11662, AR14572, and AR43718. The phylogenetic tree is constructed using MEGA. Numbers at the forks are bootstrap values from 100 replicates.
Fig. 3 in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system
Fig. 3. UPLC-MS analysis of isoquercitrin converted from quercetin by ArUGTs expressed in TX-TL. (A) A glycosylation reaction catalyzed by a UDP-glucose glycosyltransferase. (B) The chromatogram of the blank sample is 50% methanol solvent. (C) The chromatogram of the negative group, which has TXTL, quercetin, UDP-glucose but no additional DNA. (D) The chromatogram of quercetin standard. (E) The chromatogram of isoquercitrin standard. (F–K) Chromatograms of products from the catalysis of quercetin by different ArUGTs (the final concentrations of the linear DNAs used for each ArUGTs are 30 nM): (F) AR14572, (G) AR11662, (H) AR43718, (I) AR06047, (J) AR06981, and (K) AR07558, see Supplementary Fig. S5 for the duplicate group. The y axis indicates ion signal abundance relative to the highest signal in each chromatogram (%). The numbers in the top right of each chromatogram (for instance 2.21e7 in [E]) indicate the ion counts represented by "100" relative abundance. The retention time of the isoquercitrin standard is 4.08 min, and the red arrow in each figure indicates the retention time of the product isoquercitrin. The numbers under the protein names are the peak intensities of the product. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Screening of in vitro and in silico α-amylase, α-glucosidase, and lipase inhibitory activity of oxyprenylated natural compounds and semisynthetic derivatives
Fig. 5. Docking of compounds 3, 9, 11 and 15 in human lipase. The structure of lipase (green surface) with the residues lining the binding pocket (sticks with white carbons) and the catalytic resides (S169, D193 and H280 in yellow). The docking results in lipase with compounds 3 (pink, A), 9 (cyan, B), 11 (magenta, C) and 15 (green, D) are shown with their main interacting residues labelled, hydrogen bonds and binding energies. (E) Zoom in on the superimposition of the docked compounds 3, 9, 11 and 15 (coloured as in A-D) with the position of the aromatic rings encircled and the residues, which interact with all compounds of them, are labelled. (F) The original structure of lipase (1LPB) contains the inhibitor methoxy-undecylphosphinic acid (MUP in purple). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Screening of in vitro and in silico α-amylase, α-glucosidase, and lipase inhibitory activity of oxyprenylated natural compounds and semisynthetic derivatives
Fig. 2. In vitro inhibition activities.α-Amylase inhibition (A) andα-glucosidase inhibition (B) by acarbose and the pure active compounds. (C) Pancreatic lipase inhibition by orlistat and the pure active compounds. In each test, data are expressed as IC50 values in μM and values with the same letter (a–h) are not significantly different at p ≤ 0.05 level, according to a one-way analysis of variance (ANOVA).
Fig. 4 in Screening of in vitro and in silico α-amylase, α-glucosidase, and lipase inhibitory activity of oxyprenylated natural compounds and semisynthetic derivatives
Fig. 4. Docking of compounds 3, 9, 11, and 15 in human lipase enzyme. The original structure of the lipase enzyme (1LPB) contains the inhibitor methoxyundecylphosphinic acid (MUP, carbons coloured purple (A)). The docking results in lipase enzyme (B-E, green surface) with compounds 3 (pink, B), 9 (cyan, C), 11 (magenta, D), and 15 (green, E) are shown with their respective binding energies. The carbons of the residues lining the binding pocket are coloured in white, with the active site residues in yellow (S169, D193, and H280). Residues making hydrogen bonds with the ligands are indicated (B–E). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Screening of in vitro and in silico α-amylase, α-glucosidase, and lipase inhibitory activity of oxyprenylated natural compounds and semisynthetic derivatives
Fig. 3. Docking of compounds 3, 9, 11, and 15 in human α-amylase and α-glucosidase enzymes. The original structures of α-amylase (1XD0) and α-glucosidase (3TOP) enzymes contain the inhibitor acarbose (carbons coloured in orange and hexoses numbered in A and F, respectively). The docking results in α-amylase (B-E, light grey surface) and α-glucosidase (G-J, beige) enzymes with compounds 3 (pink, B and G), 9 (cyan, C and H), 11 (magenta, D and I), and 15 (green, E and J) are shown with their respective binding energies. The carbons of the residues lining the binding pocket are coloured in white, with the active site residues in yellow (R210, D212, E248, and R352 in α-amylase; D1420, E1423, and D1526 in α-glucosidase enzyme). Residues making hydrogen bonds with the ligands are indicated (B- E and G-J). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Design, synthesis and screening of a drug discovery library based on an Eremophila-derived serrulatane scaffold
Fig. 1. Chemical structures of the targeted NP diterpenoid scaffolds (1–2), methylated products (3–4) and the amide library (5–16).
Fig. 4 in Design, synthesis and screening of a drug discovery library based on an Eremophila-derived serrulatane scaffold
Fig. 4. Representative flow cytometry examples of GFP expression (i.e., HIV production) in J-Lat 10.6 cells in the absence of stimulation (A), treatment with 50 nM PMA (B) and PMA plus 100 μM compound 2 (C). Dose response profiles (D) of compounds on GFP expression induced by 50 nM PMA in J-Lat 10.6 cells. Data denote mean ± s.d. from three independent experiments.
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.