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2,129 results for “scores”
Figure S1. Fusion map of propensity score matching for high-risk and very high-risk patient.
<p>Figure S1. Fusion map of propensity score matching for high-risk and very high-risk patient.</p>
A Comparative Evaluation of Different Scores in Predicting Severity and Outcome in Acute Pancreatitis
<p>This dataset is regarding my study which I conducted on 110 subjects.</p>
Simulated metagenomic datasets used for determining the threshold score cutoff for taxa prediction using StrainIQ software
<p>StrainIQ (Strain Identification and Quantification) is a novel tool that implements a new <em>n</em>-gram based algorithm for predicting and quantifying strain-level taxa from whole genome metagenomic sequencing data. To avoid the false positive prediction of by StrainIQ algorithm, we determined cutoff score for each site specific n-gram model called DSEM (DNA Signature Element Model) using positive and negative metagenomic datasets. The Figure2-datasets.zip has positive (from GI tract genomes) and negative (from non-GI tract genomes) simulated metagenomic datasets that were used for determining optimal cut-off based on GI tract DSEM.</p> <p>Please find the new link for this data at https://zenodo.org/record/8132164</p>
A cholesterol-lowering diet score correlates with serum LDL-cholesterol: Results from the DEDiCa trial
<p>A cholesterol-lowering diet score, previously developped for epidemiological studies on seven dietary indicators, was tested to reflect serum cholesterol levels using data from DEDiCa clinical trial. Breast cancer patients with the highest adherence to the cholesterol-lowering diet reported lower LDL-cholesterol than those scarcely adherent (107 and 122 mg/dL, respectively; <em>p</em>=0.04). The difference was significant after adjustment for age, obesity, and physical activity. This provides support for the use of this score to identfy dietary patterns that reduce serum cholesterol levels.</p>
Ode to My Frayed Nerves (installation, score, and rehearsal footage)
<p>This piece is an exploration of the sensations of the nerve damage in my left hand from a physical injury I sustained from a utility knife in 2020. (Don't tidy up while exhausted, you will make dumb mistakes). This piece was written for my PhD thesis - due to be completed in 2023. An excerpt was first shown at the Australasian Computer Music Conference 2021, the video was created December 2022. It was created using my voice and MiMu gloves. Contained in this link are the video score and some rehearsal footage taken using a GoPro Hero Max 8</p>
Assessing Tumor-Infiltrating Lymphocytes in Breast Cancer: A Proposal for Combining Immunohistochemistry and Gene Expression Analysis to Refine Scoring
<p>Whole tissue scans of histochemistry (H&E) and immunohistochemistry (CD3, CD4, CD8 andCD45) images that have been used to calculate TIL scores.</p> <p>All stainings are numbers for each patient..</p>
THE EFFECT OF BODY SIZE AND COMPOSITION ON LUMBAR SPINE TRABECULAR BONE SCORE IN MORPHOLOGICALLY DIVERSE SUBJECTS
<p><strong>Aim:</strong> The trabecular bone score (TBS) is a tool for assessing bone quality and health. Current TBS algorithm corrects for body mass index (BMI), as a proxy of regional tissue thickness. However, this approach fails to consider BMI inaccuracies due to individual differences in body stature, composition and somatotype. This study investigated the relationship between TBS and body size and composition in subjects with a normal BMI, but with large morphological diversity in body fatness and height.</p> <p><strong>Methods:</strong> Young male subjects (n=97; age 17.2±1.0 years), including ski jumpers (n=25), volleyball players (n=48) and non-athletes (controls n=39), were recruited. The TBS was determined from L1-L4 dual-energy X-ray absorptiometry (DXA) scans using TBSiNsight software.</p> <p><strong>Results:</strong> TBS correlated negatively with height and tissue thickness in the L1-L4 area in ski jumpers (r= -0.516 and r= -0.529), volleyball players (r= -0.525 and r= -0.436), and the total group (r=-0.559 and r=-0.463), respectively. Multiple regression analyses revealed that height, L1-L4 soft tissue thickness, fat mass and muscle mass were significant determinants of TBS (R<sup>2</sup>= 0.587, p<0.001). L1-L4 soft tissue thickness explained 27% and height 14% of the TBS variance.</p> <p><strong>Conclusion: </strong>The negative association of TBS and both features suggests that a very low L1-L4 tissue thickness may lead to overestimation of the TBS, while tall stature may have the opposite effect. It seems that the utility of the TBS as a skeletal assessment tool in lean and/or tall young male subjects could be improved if tissues thickness in the lumbar spine area and stature instead of BMI were considered in the algorithm.</p>
LD Scores for the Admixed American, African, East Asian, European and South-East Asian ancestries derived from 1000 G
<p>LD Scores for the Admixed American, African, East Asian, European and South-East Asian ancestries derived from 1000 G.</p> <p>These files are needed to run the <a href="https://github.com/bulik/ldsc">LD Score regression</a> method embeded in the <a href="https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline">JASS pipeline</a></p> <p>Full detail on how these datasets have been derived are available here: https://www.biorxiv.org/content/10.1101/2023.06.23.546248v1</p> <p> </p>
Bird plumage brightness scores and blood parasite prevalence values of Central European passerine species
<p>Dataset with bird plumage brightness scores and blood parasite prevalence values for 113 Central European passerine host species. One file contains the data table. One file contains a table with descriptions of the columns in the data table.</p> <p>Note: These data were reconstructed from files used in Read & Harvey 1989 (<a href="https://doi.org/10.1038/339618a0">https://doi.org/10.1038/339618a0</a>) with column headings inferred with the help of Read 1991 (<a href="https://doi.org/10.1086/285225">https://doi.org/10.1086/285225</a>).</p>
European LD scores from 1000 Genomes
<p>This is a gzipped copy of the European LD scores from 1000 Genomes provided by the Alkes Group, originally available at https://data.broadinstitute.org/alkesgroup/LDSCORE/eur_w_ld_chr.tar.bz2.</p>
Fig. 6. Overlapped top-scored poses for ligands 19 in Monoterpene indole alkaloids from Vinca minor L. (Apocynaceae): Identification of new structural scaffold for treatment of Alzheimer's disease
Fig. 6. Overlapped top-scored poses for ligands 19 (blue) and crystal structure of tacrine (green) in the active site of hBuChE (PDB ID: 4BDS). Amino acid residues involved in the interactions with ligands are depicted as either grey or yellow (catalytic triad) lines. The rest of the receptor is displayed in light-grey cartoon conformation. The Figure was created with The PyMOL Molecular Graphics System, Version 2.4.1, Schr¨odinger, LLC. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3. PCA scores scatter plots and HCA dendrograms for S in A chemometric assessment of essential oil variation of three Salvia species indigenous to South Africa
Fig. 3. PCA scores scatter plots and HCA dendrograms for S. africana-lutea (A and B), S. lanceolata (C and D) and S. chamelaeagnea (E and F) samples from various localities. The inserts are the PCA scores plots coloured according to the HCA dendrograms.
Fig. 5 in Streamlined targeting of Amaryllidaceae alkaloids from the bulbs of Crinum scillifolium using spectrometric and taxonomically-informed scoring metabolite annotations
Fig. 5. Comparison of the experimental ECD spectra of 2 and calculated ECD spectra for stereoisomer of 2 shown in Fig. 2.
Fig. 1. Overall molecular network obtained from MZmine2 in Streamlined targeting of Amaryllidaceae alkaloids from the bulbs of Crinum scillifolium using spectrometric and taxonomically-informed scoring metabolite annotations
Fig. 1. Overall molecular network obtained from MZmine2-preprocessed HPLC-MS2 data of C. scillifolium bulbs crude alkaloid extract and first chromatographic fractions. Triangle-shaped nodes are tentatively-tagged against ISDB and benefit from a taxonomical re-ranking of the tentative candidates (their structures are provided in Fig. S1, Supporting Information). Nodes highlighted in orange correspond to the targeted structures (1–4) in the phytochemical workflow. (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 Streamlined targeting of Amaryllidaceae alkaloids from the bulbs of Crinum scillifolium using spectrometric and taxonomically-informed scoring metabolite annotations
Fig. 4. Comparison of the experimental ECD spectrum of 1 and calculated ECD spectrum for the (3S, 4aS, 10bS, 11S) enantiomer.
Most damaging CADD scores for hg19 human genome build (CADD scores generated with bStatistic removed)
<p>Analyses of genetic variation in many taxa have established that neutral genetic diversity is shaped by natural selection at linked sites. Whether the mode of selection is primarily the fixation of strongly beneficial alleles (selective sweeps) or purifying selection on deleterious mutations (background selection) remains unknown, however. We address this question in humans by fitting a model of the joint effects of selective sweeps and background selection to autosomal polymorphism data from the 1000 Genomes Project. After controlling for variation in mutation rates along the genome, a model of background selection alone explains ~60% of the variance in diversity levels at the megabase scale. Adding the effects of selective sweeps driven by adaptive substitutions to the model does not improve the fit, and when both modes of selection are considered jointly, selective sweeps are estimated to have had little or no effect on linked neutral diversity. The regions under purifying selection are best predicted by phylogenetic conservation, with ~80% of the deleterious mutations affecting neutral diversity occurring in non-exonic regions. Thus, background selection is the dominant mode of linked selection in humans, with marked effects on diversity levels throughout autosomes.</p>
Hip and Knee Scores
ClinicalTrials.gov study NCT04057651. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Improving Outpatient Colonoscopy Bowel Prep with the Orton Score
ClinicalTrials.gov study NCT04093557. IPD Sharing: NO. Countries: 1. Publications: 16.
Influence of Diabetic Control on the Degree of Liver Fibrosis Assessed by Non-invasive Scores in Patients Followed in Diabetology
ClinicalTrials.gov study NCT05605821. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Prospective Evaluation of a Scoring System in Patients Newly Diagnosed With Crohn's Disease
ClinicalTrials.gov study NCT02193048. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.