cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7272/q6hq3x3m
title: A Systems Biology approach to determine cell-specific gene regulatory effects of genetic associations in multiple sclerosis

description: We have conducted a cell-specific pathway analysis of the latest GWAS in
 multiple sclerosis (MS); which analyzed a total of 47,351 cases and 68,284
 healthy controls and found more than 200 non-MHC genome-wide associations.
 Our approach makes extensive use of gene regulatory data generated by the
 ENCODE and REP projects to build data-driven models of the predicted
 regulatory effects (PRE) of each associated variant and their flanking
 correlated variation over a wide range of linkage disequilibrium
 thresholds. The detailed mapping of regulatory information for each SNP
 suggests that if PRE are computed for a given cell type in a single
 individual based on the carriage of relevant risk alleles, these values
 should capture a non-negligible proportion of the variance in gene
 expression in that cell type. To test this hypothesis, we interrogated the
 expression of the entire transcriptome of FACS-sorted CD4+ T
 cells, and CD14+ monocytes from 25 MS patients by RNAseq and then
 assessed the correlation of their genotype dependent PRE and their actual
 gene expression in each cell type separately. Our results showed that the
 correlation observed was in all cases significantly higher than what would
 be expected by chance if these metrics were independent. Furthermore, the
 computed correlations were always higher for the matching cell type
 (CD4/CD8 expression with T cells PRE and CD14 expression with monocytes
 PRE).  The average correlation between RNA expression and PRE
 within the same cell type was 0.331 (CD4 vs. T
 cells, p&lt;10-300), 0.324 (CD8 vs. T
 cells, p&lt;10-300), and 0.246 (CD14 vs.
 monocytes, p&lt;10-300), representing a significantly higher
 than expected value for each cell type. Correlations between PRE and RNA
 expression of mismatched cell types were significantly lower. These
 results suggest that the computation of PRE can be applied to single
 patients and individual scores can be generated for each of them.
description: 3’mRNA-Seq library for each cell subsets (CD4+, CD8+, and CD14+
 cells) was prepared from 100 ng total RNA using QuantSeq kit (Lexogen)
 according to the manufacturer’s instructions and sequenced 50-bp
 single-end on the HiSeq 4000 (Illumina).
DOI: 10.15146/r3c39d
title: Mortality risk across life stages and habitats of an intertidal mussel (Mytilus californianus)

description: These data are associated with the manuscript "Mortality risk across
 life stages and habitats of an intertidal mussel (Mytilus
 californianus)". Code for statsitical analyses can be found on L.
 Pandori's GitHub (https://github.com/llmpandori/Mussel_Habitat_Risk).
 Feel free to conatct L. Pandori with any requests regarding this
 dataset.
DOI: 10.6078/d1239b
title: Software and Calibration for Photothermal Threshold Quantum Yield

description: This is code used to process and apply calibrations to photothermal
 threshold quantum yields taken, with our specific spectrometer. The
 calibrations included will not work for any other optics path or
 components, but this serves as a guide to calibrate one built in any other
 lab. This code is written in Matlab2018b. 
description: for more information about
 calibrations included please see:  www.sciencemag.org/content/363/6432/1199/suppl/DC1   Error Analysis & Propagation The uncertainty was computed by quantifying as many different sources of uncertainty in the measurement as possible and performing a bootstrap error analysis on each data set. A random Gaussian-distributed perturbation for each source is applied to the data at the beginning of the analysis, and then the entire analysis is carried out. This process is repeated many times to collect adequate statistics. Typically 1,000 random perturbations is sufficient for a good average and standard deviation, and 10,000 perturbations allows a smooth plot of the distribution of resulting PTQY values. This is preferable to simpler error propagation analysis because it captures non-linearities and co-variances in the uncertainties. The sources of error analyzed are: (1) the detection spectrograph wavelength accuracy, (2) the detection spectrograph spectral sensitivity, (3) the average excitation uncertainty, measured by the detection spectrograph, and (4) the statistical uncertainty due to finite averaging of the photoluminescence and photothermal deflection signals. Finite averaging of photoluminescence and photothermal deflection signals, per energy step, depending on the sampling conditions. Our software was set to increase N until standard errors of from were achieved per data point for both photothermal deflection magnitude and E<sub>PL</sub>. Measurements were done at a variety of chopping frequencies to ensure measurement fidelity. Energy steps 10-50meVwere used to extract a photothermal threshold quantum yield over excitation energies where integrating sphere PLQY was measured to be constant The total measurement uncertainty budget is calculated by running this bootstrap error analysis on a data set with all four sources of uncertainty enabled. Subsequently, by each source of uncertainty one at a time and then reperforming the bootstrap error analysis, the amount of uncertainty associated with it individually can be determined.  
description: We have included two datasets and one set of calibrations to use
 our PTQY analysis scripts to be adapted for your own use in the
 future.  Source script: 
 RUNME_PTQY_Processing_main_script.m    
 Example 1-BAK_99perc_2Hz_1s     Example 2-Good Core Bad
 Shell
DOI: 10.7280/d1ft10
title: Association of Serum Paraoxonase/Arylesterase Activity With All-Cause Mortality in Maintenance Hemodialysis Patients

description: Context: In end-stage renal disease (ESRD), serum high-density lipoprotein
 cholesterol (HDL-C) level is not an accurate predictor of mortality,
 partly because it does not necessarily correlate with indices of HDL
 function. Paraoxonase (PON) is a major enzyme constituent of HDL and key
 component of HDL antioxidant activity. Apolipoprotein (Apo) A-I is the
 core HDL structural protein that plays a major role in various
 aspects of HDL function. Objective: We sought to examine PON activity and
 Apo A-I levels in patients with ESRD vs healthy controls. Design and
 Setting: PON/Arylesterase activity was measured in 499 patients with
 maintenance hemodialysis (MHD) and 24 healthy controls with similar
 distributions of age, sex and race/ethnicity. Serum acrolein-modified Apo
 A-I was measured in 30 patients with MHD and 10 healthy controls. Main
 Outcome Measures: Multilevel Cox models were used to assess associations
 among PON activity, Apo A-I, and HDL-C levels with 12-month all-cause
 mortality. Results: PON activity was significantly lower in patients with
 MHD vs controls. Furthermore, acrolein-modified Apo A-I levels
 were higher in patients with MHD vs controls. In fully adjusted
 models, high PON activity was associated with lower 12-month mortality,
 whereas no difference of mortality risk was observed across HDL-C levels.
 The combination of high PON and low Apo A-I compared with low PON
 and low Apo A-I was associated with lower mortality risk. Conclusions: In
 patients with MHD, PON activity had a stronger association with 12-month
 mortality than HDL-C. Future studies are needed to examine the role of
 these markers as potential diagnostic and therapeutic tools in ESRD.
DOI: 10.15146/r32q3j
title: Menin Associates with the Mitotic Spindle and is Important for Cell Division

description: Menin is the protein mutated in patients with multiple endocrine neoplasia
 type 1 (MEN1) syndrome and their corresponding sporadic tumor
 counterparts. Here, we have uncovered a novel function for menin in
 promoting proper cell division. We show that menin localizes to the
 mitotic spindle poles and the mitotic spindle during early mitosis and to
 the intercellular bridge microtubules during cytokinesis in HeLa cells.
 Menin depletion led to defects in spindle assembly and chromosome
 congression during early mitosis, lagging chromosomes during anaphase,
 defective cytokinesis, multinucleated interphase cells, and cell death.
 Additionally, pharmacological inhibition of the menin-MLL1 interaction
 also led to similar cell division defects. These results indicate that
 menin and the menin-MLL1 interaction are important for proper cell
 division. These results highlight a novel function for menin in cell
 division and aid our understanding of how mutation and misregulation of
 menin promotes tumorigenesis.
DOI: 10.15146/r3xx07
title: Personal thermal comfort models with wearable sensors

description: A personal comfort model is an approach to thermal comfort modeling, for
 thermal environmental design and control, that predicts an individual’s
 thermal comfort response, instead of the average response of a large
 population. We developed personal thermal comfort models using lab grade
 wearable in normal daily activities. We collected physiological signals
 (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male
 adults) and environmental parameters (e.g., air temperature, relative
 humidity) for 2-4 weeks (at least 20 hours per day). Then we trained 14
 models for each subject with different machine-learning algorithms to
 predict their thermal preference. The results show that the median
 prediction power could be up to 24%/78%/0.79 (Cohen’s kappa/accuracy/AUC)
 with all features considered. The median prediction power reaches
 21%/71%/0.7 after 200 subjective votes. We explored the importance of
 different features on the prediction performance by considering all
 subjects in one dataset. When all features included for the entire
 dataset, personal comfort models can generate the highest performance of
 35%/76%/0.80 by the most predictive algorithm. Personal comfort models
 display the highest prediction power when occupants’ thermal sensations is
 outside thermal neutrality. Skin temperature measured at the ankle is more
 predictive than measured at the wrist. Smart shoes would be a better
 choice than wristband concerning intrusiveness and prediction accuracy. We
 suggest that Cohen’s kappa or AUC should be employed to assess the
 performance of personal thermal comfort models due to the capacity to
 exclude random success.
DOI: 10.15146/r3s68s
title: Personal thermal comfort models with wearable sensors

description: A personal comfort model is an approach to thermal comfort modeling, for
 thermal environmental design and control, that predicts an individual’s
 thermal comfort response, instead of the average response of a large
 population. We developed personal thermal comfort models using lab grade
 wearable in normal daily activities. We collected physiological signals
 (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male
 adults) and environmental parameters (e.g., air temperature, relative
 humidity) for 2-4 weeks (at least 20 hours per day). Then we trained 14
 models for each subject with different machine-learning algorithms to
 predict their thermal preference. The results show that the median
 prediction power could be up to 24%/78%/0.79 (Cohen’s kappa/accuracy/AUC)
 with all features considered. The median prediction power reaches
 21%/71%/0.7 after 200 subjective votes. We explored the importance of
 different features on the prediction performance by considering all
 subjects in one dataset. When all features included for the entire
 dataset, personal comfort models can generate the highest performance of
 35%/76%/0.80 by the most predictive algorithm. Personal comfort models
 display the highest prediction power when occupants’ thermal sensations is
 outside thermal neutrality. Skin temperature measured at the ankle is more
 predictive than measured at the wrist. Smart shoes would be a better
 choice than wristband concerning intrusiveness and prediction accuracy. We
 suggest that Cohen’s kappa or AUC should be employed to assess the
 performance of personal thermal comfort models due to the capacity to
 exclude random success.
description: We collected physiological signals (e.g., skin temperature, heart
 rate) of 14 subjects (6 female and 8 male adults) and environmental
 parameters (e.g., air temperature, relative humidity) for 2-4 weeks (at
 least 20 hours per day).
DOI: 10.5281/zenodo.18393418
title: THE IMPACT OF FEAR AND STRESS ON THE TREATMENT PROCESS IN PATIENTS

description: Fear and stress are significant psychological factors that can influence the treatment process in patients [1, 2, 3]. High levels of anxiety, worry, and stress can negatively affect patients' adherence to medical recommendations, delay recovery, and reduce overall treatment efficacy [4, 5]. Physiologically, stress activates the hypothalamic-pituitary-adrenal (HPA) axis and increases cortisol levels, which may impair immune function and exacerbate underlying health conditions [6, 7, 8, 9]. Psychologically, fear can lead to avoidance behavior, non-compliance with therapy, and decreased motivation to engage in treatment [3, 4]. This study aims to analyze the relationship between fear, stress, and patient outcomes, highlighting strategies for healthcare professionals to manage these factors effectively [5, 10]. Understanding the impact of psychological stressors on treatment is crucial for improving patient care, optimizing therapeutic interventions, and enhancing recovery [1, 8, 10].
DOI: 10.5281/zenodo.18393417
title: THE IMPACT OF FEAR AND STRESS ON THE TREATMENT PROCESS IN PATIENTS

description: Fear and stress are significant psychological factors that can influence the treatment process in patients [1, 2, 3]. High levels of anxiety, worry, and stress can negatively affect patients' adherence to medical recommendations, delay recovery, and reduce overall treatment efficacy [4, 5]. Physiologically, stress activates the hypothalamic-pituitary-adrenal (HPA) axis and increases cortisol levels, which may impair immune function and exacerbate underlying health conditions [6, 7, 8, 9]. Psychologically, fear can lead to avoidance behavior, non-compliance with therapy, and decreased motivation to engage in treatment [3, 4]. This study aims to analyze the relationship between fear, stress, and patient outcomes, highlighting strategies for healthcare professionals to manage these factors effectively [5, 10]. Understanding the impact of psychological stressors on treatment is crucial for improving patient care, optimizing therapeutic interventions, and enhancing recovery [1, 8, 10].
DOI: 10.7272/q6765chg
title: Noise Modulation in Mammalian Cells

description: The noise rheostat is dope.
DOI: 10.5281/zenodo.18393428
title: SUN'IY INTELEKT YORDAMIDA TARMOQ TRAFIGIDA ANOMALIYALARNI ANIQLASH ALGORITMLARINI TADQIQ QILISH

description: Mazkur maqolada sun’iy intellekt texnologiyalaridan foydalanish orqali tarmoq trafigida anomaliyalarni aniqlash algoritmlarini tadqiq etish masalalari yoritilgan. Tadqiqot doirasida tarmoq trafigini tahlil qilishning an’anaviy va intellektual yondashuvlari taqqoslanib, mashinaviy hamda chuqur o‘rganish algoritmlarining nazariy asoslari va amaliy qo‘llanish imkoniyatlari tahlil qilindi. Shuningdek, xususiyatlar muhandisligi, ma’lumotlarni oldindan qayta ishlash, gibrid va ansambl modellar, real vaqt rejimidagi aniqlash mexanizmlarining ahamiyati asoslab berildi. Tadqiqot natijalari sun’iy intellektga asoslangan anomaliya aniqlash tizimlari tarmoq xavfsizligini ta’minlashda yuqori samaradorlikka ega ekanligini ko‘rsatadi.
DOI: 10.5281/zenodo.18393427
title: SUN'IY INTELEKT YORDAMIDA TARMOQ TRAFIGIDA ANOMALIYALARNI ANIQLASH ALGORITMLARINI TADQIQ QILISH

description: Mazkur maqolada sun’iy intellekt texnologiyalaridan foydalanish orqali tarmoq trafigida anomaliyalarni aniqlash algoritmlarini tadqiq etish masalalari yoritilgan. Tadqiqot doirasida tarmoq trafigini tahlil qilishning an’anaviy va intellektual yondashuvlari taqqoslanib, mashinaviy hamda chuqur o‘rganish algoritmlarining nazariy asoslari va amaliy qo‘llanish imkoniyatlari tahlil qilindi. Shuningdek, xususiyatlar muhandisligi, ma’lumotlarni oldindan qayta ishlash, gibrid va ansambl modellar, real vaqt rejimidagi aniqlash mexanizmlarining ahamiyati asoslab berildi. Tadqiqot natijalari sun’iy intellektga asoslangan anomaliya aniqlash tizimlari tarmoq xavfsizligini ta’minlashda yuqori samaradorlikka ega ekanligini ko‘rsatadi.
DOI: 10.7272/q63f4msr
title: AlleleAnalyzer supporting data

description: The CRISPR/Cas system is a highly specific genome editing tool capable of
 distinguishing alleles differing by even a single base pair. Target sites
 might carry genetic variations that are not distinguishable by sgRNA
 designing tools based on one reference genome. AlleleAnalyzer is
 open-source software that incorporates single nucleotide variants and
 short insertions and deletions to design sgRNAs for precisely editing one
 or multiple haplotypes of a sequenced genome, currently supporting eleven
 Cas proteins.  It also leverages patterns of shared genetic
 variation to optimize sgRNA design for different human populations.
 AlleleAnalyzer is available
 at https://github.com/keoughkath/AlleleAnalyzer.
description: Please see readme and manuscript. 
description: Please see readme and manuscript.
DOI: 10.7272/q6zp449v
title: Map PAINS substructures according to PAINS class and find the highest PAINS violation

description: Many scientists are unaware that PAINS substructures are classified by hit
 frequency; this has led to a tendency to irresponsibly use PAINS filters
 during compound triage by filtering out potential leads based on the
 presence of a PAINS substructure instead of the presence of a type of
 PAINS substructure. Baell suggested in his review, Seven Year Itch:
 Pan-Assay Interference Compounds (PAINS) in 2017 - Utility and
 Limitations, that scientists should focus on class A, maybe class B, and
 not class C. Because the original paper only reported hit frequency based
 on a singular assay technology, compounds should not be necessarily
 excluded for containing a class A substructure. This protocol splits the
 different classes of PAINS into distinct substructure mapping subprotocols
 to produce a more informed PAINS report. The SMARTS patterns can be
 obtained here. This protocol is being made available in conjunction with
 an upcoming publication.
description: Please refer to <strong>Materials and Methods
 </strong>and <strong>Supplementary Material</strong> of
 the associated manuscript, <em>Small molecule inhibitors of the
 human recombination-associated ATPase, RAD54</em>. 
description: Requires access to Pipeline Pilot Core Set and Pipeline Pilot
 Chemistry Collection.
DOI: 10.7272/q6v122z9
title: Score compounds using 275 frequent hitter rules published in the Lilly-Medchem-Rules

description: Robert Bruns and Ian Watson published 275 rules to identify frequent
 hitters based on 18-years of experiments at Eli-Lilly. This protocol
 requires the Lilly-Medchem-Rules to be installed on the server and
 accessible to Pipeline Pilot. Reference: Rules for Identifying Potentially
 Reactive or Promiscuous Compounds GitHub Repository: Lilly-Medchem-Rules
 This protocol is being made available in conjunction with an upcoming
 publication.
description: Please refer to <strong>Materials and
 Methods </strong>and <strong>Supplementary</strong><strong> Material</strong> of the associated manuscript, <em>Small molecule inhibitors of the human recombination-associated ATPase, RAD54</em>. 
description: Requires access to Pipeline Pilot Core Set, Pipeline Pilot
 Chemistry Collection, and the Eli-Lilly Lilly-Medchem-Rules
DOI: 10.7272/q6q81b8f
title: OpenEye PAINS Mapping Python Script

description: OpenEye advertises an additional 170 PAINS rules (versus the original
 480) to accomodate data loss when converting the substructure
 patterns from SLN to SMARTS format. However, it was found that many of
 these additional rules mapped to rules that had been blocked out by Baell
 as part of an expanded set. These rules have not necessarily been
 validated as PAINS substructures. The naming pattern for the OpenEye PAINS
 classification is: "pains_{PAINS class}_{PAINS registration
 ID}". If the rule has come from the expanded set, the rule is
 annotated as class "pre", otherwise it is designated with the
 traditional A, B, or C.  This script was written based on the
 openly accessible OpenEye template scripts.  This protocol is
 being made available in conjunction with an upcoming publication.
description: Please refer to <strong>Materials and Methods
 </strong>and <strong>Supplementary Material</strong> of
 the associated manuscript, <em>Small molecule inhibitors of the
 human recombination-associated ATPase, RAD54</em>. 
description: Requires acces to a server with Python 3 and OpenEye
 installed.  To run the script: <pre>
 ./oepains_table.py file.sdf &gt; out.txt</pre>
DOI: 10.7280/d11h3x
title: Annual Ice Velocity of the Greenland Ice Sheet (2010-2017)

description: We derive surface ice velocity using data from 16 satellite sensors
 deployed by 6 different space agencies. The list of sensors is given in
 the Table S1. The SAR data are processed from raw to single look complex
 using the GAMMA processor (www.gamma-rs.ch). All measurements rely on
 consecutive images where the ice displacement is estimated from tracking
 or interferometry (Joughin et al. 1998, Michel and Rignot 1999, Mouginot
 et al. 2012). Surface ice motion is detected using a speckle tracking
 algorithm for SAR instruments and feature tracking for Landsat. The
 cross-correlation program for both SAR and optical images is ampcor from
 the JPL/Caltech repeat orbit interferometry package (ROI_PAC). We assemble
 a composite ice velocity mosaic at 150 m posting using our entire speed
 database as described in Mouginot et al. 2017 (Fig. 1A). The ice velocity
 maps are also mosaicked in annual maps at 150 m posting, covering July,
 1st to June, 30th of the following year, i.e. centered on January, 1st
 (12) because a majority of historic data were acquired in winter season,
 hence spanning two calendar years. We use Landsat-1&2/MSS images
 between 1972 and 1976 and combine image pairs up to 2 years apart to
 measure the displacement of surface features between images as described
 in Dehecq et al., 2015 or Mouginot et al. 2017. We use the 1978 2-m
 orthorectified aerial images to correct the geolocation of Landsat-1 and
 -2 images (Korsgaard et al., 2016). Between 1984 and 1991, we process
 Landsat-4&5/TM image pairs acquired up to 1-year apart. Only few
 Landsat-4 and -5 images (~3%) needed geocoding refinement using the same
 1978 reference as used previously. Between 1991 and 1998, we process radar
 images from the European ERS-1/2, with a repeat cycle varying from 3 to 36
 days depending on the mission phase. Between 1999 and 2013, we used
 Landsat-7, ASTER, RADARSAT-1/2, ALOS/PALSAR, ENVISAT/ASAR to determine
 surface velocity (Joughin et al., 2010; Howat, I. 2017; Rignot and
 Mouginot, 2012). After 2013, we use Landsat-8, Sentinel-1a/b and
 RADARSAT-2 (Mouginot et al., 2017). All synthetic aperture radar (SAR)
 datasets are processed assuming surface parallel flow using the digital
 elevation model (DEM) from the Greenland Mapping Project (GIMP; Howat et
 al., 2014) and calibrated as described in Mouginot et al., 2012, 2017.
 Data were provided by the European Space Agency (ESA), the EU Copernicus
 program (through ESA), the Canadian Space Agency (CSA), the Japan
 Aerospace Exploration Agency (JAXA), the Agenzia Spaziale Italiana (ASI),
 the Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) and the National
 Aeronautics and Space Administration (NASA). SAR data acquisitions were
 coordinated by the Polar Space Task Group (PSTG). Errors are estimated
 based on sensor resolution and time lapse between consecutive images as
 described in Mouginot et al. 2017.