cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8135199
title: Artifact of paper "Transforming Test Suites Into Croissants"

description: This is the artifact of paper "Transforming Test Suites Into Croissants".
DOI: 10.5281/zenodo.7977104
title: Artifact of paper "Transforming Test Suites Into Croissants"

description: This is the artifact of paper "Transforming Test Suites Into Croissants".
DOI: 10.6084/m9.figshare.21397158.v21
title: South African LNCS papers 1973-2022

description: This study investigated the research participation of South African (RSA) authors in LNCS for the period 1973 to 2022.
DOI: 10.25919/9kms-tp98
title: Parkes observations for project P1185 semester 2023APRS_11

description: Ultra-long period neutron stars (ULPNSs) are an emerging class of objects that have sparked interest and curiosity in the fast transient community. Along with standard magnetars, they have been theorised to be potential progenitors of the enigmatic fast radio bursts (FRBs). The MeerTRAP project at the MeerKAT radio telescope is uncovering several magnetar and ultra-long period neutron star candidates, an example of which is PSR J0901-4046 with a period of 76s. Continued long-term monitoring of PSR J0901-4046 has shown it to be potentially transitioning into radio quiescence. The proposed observations will enable us to continue to monitor and characterise the decline in flux density. We have also discovered two new candidates MTP0027 and MTP0068 with periods of ~10 seconds and ~5 seconds respectively. Both sources exhibit magnetar-like pulse morphology and have only been detected in one epoch with MeerKAT. We propose to continue monitoring these sources to re-detect pulses and study their spectro-temporal-polarimetric properties.
DOI: 10.6084/m9.figshare.21397158.v22
title: South African LNCS papers 1973-2022

description: This study investigated the research participation of South African (RSA) authors in LNCS for the period 1973 to 2022.
DOI: 10.6084/m9.figshare.23663979
title: Pre-trained models

description: Pre-trained models for the deep-fus neural network implementation.
DOI: 10.6084/m9.figshare.23663979.v1
title: Pre-trained models

description: Pre-trained models for the deep-fus neural network implementation.
DOI: 10.5281/zenodo.8122076
title: StrainIQ software mamuscript- Supplementary data

description: This is supplemental material presented in the study, "StrainIQ: A novel <em>n</em>-gram based method for taxonomic profiling of human microbiota at the strain-level". Please find the new link for this data at https://zenodo.org/record/8132164
DOI: 10.5281/zenodo.7790029
title: StrainIQ software mamuscript- Supplementary data

description: This is supplemental material presented in the study, "StrainIQ: A novel <em>n</em>-gram based method for taxonomic profiling of human microbiota at the strain-level". Please find the new link for this data at https://zenodo.org/record/8132164
DOI: 10.5281/zenodo.7789718
title: Validation of StarinIQ software performance using metagenomic data from the ATCC gut microbiome genomic mix

description: 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. We tested our method using simulated (GI tract reference genomes) and mock metagenomic datasets (from ATCC microbial genomic mix) and compared its performance with existing methods. The gut_even.zip file contains the metagenomic sequence data from the ATCC Gut Microbiome Genomic Mix (MSA-1006). From this original sequencing sequence (nearly 120X), we created additional datasets with 90X, 60X, 30X, 5X, 3X, and 1X coverage and tested StrainIQ performance on those sequencing datasets with varying coverage. Please find the new link for this data at https://zenodo.org/record/8132164
DOI: 10.5281/zenodo.7789717
title: Validation of StarinIQ software performance using metagenomic data from the ATCC gut microbiome genomic mix

description: 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. We tested our method using simulated (GI tract reference genomes) and mock metagenomic datasets (from ATCC microbial genomic mix) and compared its performance with existing methods. The gut_even.zip file contains the metagenomic sequence data from the ATCC Gut Microbiome Genomic Mix (MSA-1006). From this original sequencing sequence (nearly 120X), we created additional datasets with 90X, 60X, 30X, 5X, 3X, and 1X coverage and tested StrainIQ performance on those sequencing datasets with varying coverage. Please find the new link for this data at https://zenodo.org/record/8132164
DOI: 10.5281/zenodo.7789971
title: Validation of StrainIQ software performance using metagenomic data from ATCC staggered mix genomes

description: 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. We tested our method using simulated (GI tract reference genomes) and mock metagenomic datasets (from ATCC microbial genomic mix) and compared its performance with existing methods. Here, the gut_mix.zip file contains the metagenomic sequence data from the ATCC 20 strain staggered genomic mix (MSA-1003). From this original sequencing sequence (nearly 120X), we created additional datasets with 90X, 60X, 30X, 5X, 3X, and 1X coverage and tested StrainIQ performance on those varying sequencing datasets. Please find the new link for this data at https://zenodo.org/record/8132164
DOI: 10.5281/zenodo.7789970
title: Validation of StrainIQ software performance using metagenomic data from ATCC staggered mix genomes

description: 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. We tested our method using simulated (GI tract reference genomes) and mock metagenomic datasets (from ATCC microbial genomic mix) and compared its performance with existing methods. Here, the gut_mix.zip file contains the metagenomic sequence data from the ATCC 20 strain staggered genomic mix (MSA-1003). From this original sequencing sequence (nearly 120X), we created additional datasets with 90X, 60X, 30X, 5X, 3X, and 1X coverage and tested StrainIQ performance on those varying sequencing datasets. Please find the new link for this data at https://zenodo.org/record/8132164
DOI: 10.5281/zenodo.7786804
title: Simulated datasets used for assessing the performance of StrainIQ software

description: 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. We used ten simulated dataset ( from GI tract reference genomes) to measure the sensitivity and specificity of StrainIQ software in strain identification. Please find the new link for this data at https://zenodo.org/record/8132164
DOI: 10.5281/zenodo.7786803
title: Simulated datasets used for assessing the performance of StrainIQ software

description: 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. We used ten simulated dataset ( from GI tract reference genomes) to measure the sensitivity and specificity of StrainIQ software in strain identification. Please find the new link for this data at https://zenodo.org/record/8132164
DOI: 10.6084/m9.figshare.21397158.v23
title: South African LNCS papers 1973-2022

description: This study investigated the research participation of South African (RSA) authors in LNCS for the period 1973 to 2022.
DOI: 10.5281/zenodo.7787134
title: Simulated metagenomic datasets used for determining the threshold score cutoff for taxa prediction using StrainIQ software

description: 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. Please find the new link for this data at https://zenodo.org/record/8132164
DOI: 10.5281/zenodo.7787133
title: Simulated metagenomic datasets used for determining the threshold score cutoff for taxa prediction using StrainIQ software

description: 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. Please find the new link for this data at https://zenodo.org/record/8132164
DOI: 10.6084/m9.figshare.21397158.v24
title: South African LNCS papers 1973-2022

description: This study investigated the research participation of South African (RSA) authors in LNCS for the period 1973 to 2022.
DOI: 10.6084/m9.figshare.23638352
title: S2 from Crossmodal association between visual and acoustic cues in a tortoise (<i>Testudo hermanni</i>)

description: Dataset
DOI: 10.6084/m9.figshare.23638352.v2
title: S2 from Crossmodal association between visual and acoustic cues in a tortoise (<i>Testudo hermanni</i>)

description: Dataset
DOI: 10.6084/m9.figshare.23638352.v1
title: S2 from Crossmodal association between visual and acoustic cues in a tortoise (<i>Testudo hermanni</i>)

description: Dataset
DOI: 10.6084/m9.figshare.21397158.v25
title: South African LNCS papers 1973-2022

description: This study investigated the research participation of South African (RSA) authors in LNCS for the period 1973 to 2022.
DOI: 10.17909/pjvt-h370
title: Data from Schreyer et al. 2023

description: All STIS/G140M Lyα data of GJ 436 to date.
DOI: 10.34740/kaggle/dsv/6119594
title: Balochi Language to English Translation Dataset
DOI: 10.6084/m9.figshare.21598610
title: Kava (<i>Piper methysticum</i>) in the United States: the quiet rise of a substance with often subtle effects

description: <i>Background:</i> Piper methysticum, commonly called kava, has long been consumed in beverage form in the Pacific Islands. Kava use in the US has slowly increased since the 1990s, but is not assessed in major epidemiological surveys. <i>Objectives:</i> To analyze social-media posts about kava from current, past, and prospective users, for motivations, patterns of co-use, and effects. <i>Methods:</i> Text from Reddit posts, and accompanying metadata, were collected and thematically coded by two independent raters. <i>Results:</i> 423 posts were collected, spanning January 2006 through December 2021. Of the 1,211 thematic codes applied, 1,098 (90. 7%) were concordant. Motivations for use bifurcated into self-treatment (for psychiatric or physical health conditions) and recreation; these were not mutually exclusive. Kava was rarely considered strongly euphoriant, but was valued as an anxiolytic. Kava was frequently used with other substances, most commonly kratom. Kava was used at lower doses for self-treatment than for other purposes (pseudo-R<sup>2</sup> = 0.11). Undesirable effects (gastrointestinal upset, fatigue) were mentioned, though less often than benefits. Hepatotoxicity, reported elsewhere as a rare, non-dose-related risk, was disputed on the basis of its not having been experienced by those posting. <i>Conclusion:</i> Kava appears to be conceptualized among Reddit posters as an anxiolytic with few risks or adverse effects. As it grows in popularity, especially among people who use other drugs that are more liable to misuse or addiction, it should be assessed in probability samples (i.e. in the major national drug surveys) and clinical practice for its risks, potential benefits, and possible drug-drug interactions.
DOI: 10.6084/m9.figshare.21598610.v1
title: Kava (<i>Piper methysticum</i>) in the United States: the quiet rise of a substance with often subtle effects

description: <i>Background:</i> Piper methysticum, commonly called kava, has long been consumed in beverage form in the Pacific Islands. Kava use in the US has slowly increased since the 1990s, but is not assessed in major epidemiological surveys. <i>Objectives:</i> To analyze social-media posts about kava from current, past, and prospective users, for motivations, patterns of co-use, and effects. <i>Methods:</i> Text from Reddit posts, and accompanying metadata, were collected and thematically coded by two independent raters. <i>Results:</i> 423 posts were collected, spanning January 2006 through December 2021. Of the 1,211 thematic codes applied, 1,098 (90. 7%) were concordant. Motivations for use bifurcated into self-treatment (for psychiatric or physical health conditions) and recreation; these were not mutually exclusive. Kava was rarely considered strongly euphoriant, but was valued as an anxiolytic. Kava was frequently used with other substances, most commonly kratom. Kava was used at lower doses for self-treatment than for other purposes (pseudo-R<sup>2</sup> = 0.11). Undesirable effects (gastrointestinal upset, fatigue) were mentioned, though less often than benefits. Hepatotoxicity, reported elsewhere as a rare, non-dose-related risk, was disputed on the basis of its not having been experienced by those posting. <i>Conclusion:</i> Kava appears to be conceptualized among Reddit posters as an anxiolytic with few risks or adverse effects. As it grows in popularity, especially among people who use other drugs that are more liable to misuse or addiction, it should be assessed in probability samples (i.e. in the major national drug surveys) and clinical practice for its risks, potential benefits, and possible drug-drug interactions.
DOI: 10.6084/m9.figshare.21867542
title: Sublingual dexmedetomidine (BXCL501) reduces opioid withdrawal symptoms: findings from a multi-site, phase 1b/2, randomized, double-blind, placebo-controlled trial

description: <i>Background:</i> Like other alpha-2-adrenergic receptor agonists, dexmedetomidine may reduce the severity of opioid withdrawal but with fewer adverse cardiovascular effects. <i>Objective:</i> This study assessed the safety of sublingual dexmedetomidine (BXCL501) and its preliminary efficacy in treating opioid withdrawal (ClinicalTrials.gov: NCT04470050). <i>Methods:</i> Withdrawal was induced among individuals with physiological dependence on opioids via discontinuation of oral morphine (Days 1–5). Participants were randomized to receive placebo or active BXCL501: 30, 60, 90, 120, 180, and 240 μg twice daily (Days 6–12). Treatment-emergent adverse events (TEAEs) were the primary outcome measure. Secondary outcomes included the Clinical and Subjective Opiate Withdrawal Scales (COWS and SOWS-Gossop, respectively), and the Agitation and Calmness Evaluation Scale (ACES). <i>Results:</i> Of 225 participants enrolled, 90 discontinued during morphine stabilization. Post-BXCL501 randomization (Day 6) data were available from 135 participants (73% male), with 33% completing thru Day 12. In total, 36 subjects reported 1 or more TEAE. Higher doses of BXCL501 (i.e. 180 and 240 µg, twice daily) increased the frequency of: hypotension, orthostatic hypotension, and somnolence. TEAEs related to BXCL501 were mild or moderate in severity, except for one participant in the 120 µg condition whose orthostatic hypotension and bradycardia were classified as severe. Higher BXCL501 dose conditions (120, 180, and 240 µg) resulted in statistically significant reductions in COWS & SOWS scores. Mean ratings on the ACES were between 3 (mild), 4 (normal), and 5 (mild calmness), with few significant differences as a function of dose. <i>Conclusions:</i> These findings support the continued development of BXCL501 for the management of opioid withdrawal.
DOI: 10.6084/m9.figshare.21867542.v1
title: Sublingual dexmedetomidine (BXCL501) reduces opioid withdrawal symptoms: findings from a multi-site, phase 1b/2, randomized, double-blind, placebo-controlled trial

description: <i>Background:</i> Like other alpha-2-adrenergic receptor agonists, dexmedetomidine may reduce the severity of opioid withdrawal but with fewer adverse cardiovascular effects. <i>Objective:</i> This study assessed the safety of sublingual dexmedetomidine (BXCL501) and its preliminary efficacy in treating opioid withdrawal (ClinicalTrials.gov: NCT04470050). <i>Methods:</i> Withdrawal was induced among individuals with physiological dependence on opioids via discontinuation of oral morphine (Days 1–5). Participants were randomized to receive placebo or active BXCL501: 30, 60, 90, 120, 180, and 240 μg twice daily (Days 6–12). Treatment-emergent adverse events (TEAEs) were the primary outcome measure. Secondary outcomes included the Clinical and Subjective Opiate Withdrawal Scales (COWS and SOWS-Gossop, respectively), and the Agitation and Calmness Evaluation Scale (ACES). <i>Results:</i> Of 225 participants enrolled, 90 discontinued during morphine stabilization. Post-BXCL501 randomization (Day 6) data were available from 135 participants (73% male), with 33% completing thru Day 12. In total, 36 subjects reported 1 or more TEAE. Higher doses of BXCL501 (i.e. 180 and 240 µg, twice daily) increased the frequency of: hypotension, orthostatic hypotension, and somnolence. TEAEs related to BXCL501 were mild or moderate in severity, except for one participant in the 120 µg condition whose orthostatic hypotension and bradycardia were classified as severe. Higher BXCL501 dose conditions (120, 180, and 240 µg) resulted in statistically significant reductions in COWS & SOWS scores. Mean ratings on the ACES were between 3 (mild), 4 (normal), and 5 (mild calmness), with few significant differences as a function of dose. <i>Conclusions:</i> These findings support the continued development of BXCL501 for the management of opioid withdrawal.
DOI: 10.6084/m9.figshare.22143976
title: Concordance between substance use self-report and hair analysis in community-based adolescents

description: <i>Background:</i> Accurate drug use identification through subjective self-report and toxicological biosample (hair) analysis are necessary to determine substance use sequelae in youth. Yet consistency between self-reported substance use and robust, toxicological analysis in a large sample of youth is understudied. <i>Objectives:</i> We aim to assess concordance between self-reported substance use and hair toxicological analysis in community-based adolescents. <i>Methods:</i> Hair results by LC-MS/MS and GC-MS/MS and self-reported past-year substance use from an Adolescent Brain Cognitive Development (ABCD) Study subsample (<i>N</i> = 1,390; ages 9–13; 48% female) were compared. The participants were selected for hair selection through two methods: high scores on a substance risk algorithm selected 93%; 7% were low-risk, randomly selected participants. Kappa coefficients the examined concordance between self-report and hair results. <i>Results:</i> 10% of youth self-reported any past-year substance use (e.g. alcohol, cannabis, nicotine, and opiates), while a mostly non-overlapping 10% had hair results indicating recent substance use (cannabis, alcohol, non-prescription amphetamines, cocaine, nicotine, opiates, and fentanyl). In randomly selected low-risk cases, 7% were confirmed positive in hair. Combining methods, 19% of the sample self-reported substance use and/or had a positive hair sample. Kappa coefficient of concordance between self-report and hair results was low (kappa = 0.07; <i>p</i> = .007). <i>Conclusions:</i> Hair toxicology identified substance use in high-risk and low-risk ABCD cohort subsamples. Given low concordance between hair results and self-report, reliance on either method alone would incorrectly categorize 9% as non-users. Multiple methods for characterizing substance use history in youth improves accuracy. Larger representative samples are needed to assess the prevalence of substance use in youth.
DOI: 10.6084/m9.figshare.22143976.v1
title: Concordance between substance use self-report and hair analysis in community-based adolescents

description: <i>Background:</i> Accurate drug use identification through subjective self-report and toxicological biosample (hair) analysis are necessary to determine substance use sequelae in youth. Yet consistency between self-reported substance use and robust, toxicological analysis in a large sample of youth is understudied. <i>Objectives:</i> We aim to assess concordance between self-reported substance use and hair toxicological analysis in community-based adolescents. <i>Methods:</i> Hair results by LC-MS/MS and GC-MS/MS and self-reported past-year substance use from an Adolescent Brain Cognitive Development (ABCD) Study subsample (<i>N</i> = 1,390; ages 9–13; 48% female) were compared. The participants were selected for hair selection through two methods: high scores on a substance risk algorithm selected 93%; 7% were low-risk, randomly selected participants. Kappa coefficients the examined concordance between self-report and hair results. <i>Results:</i> 10% of youth self-reported any past-year substance use (e.g. alcohol, cannabis, nicotine, and opiates), while a mostly non-overlapping 10% had hair results indicating recent substance use (cannabis, alcohol, non-prescription amphetamines, cocaine, nicotine, opiates, and fentanyl). In randomly selected low-risk cases, 7% were confirmed positive in hair. Combining methods, 19% of the sample self-reported substance use and/or had a positive hair sample. Kappa coefficient of concordance between self-report and hair results was low (kappa = 0.07; <i>p</i> = .007). <i>Conclusions:</i> Hair toxicology identified substance use in high-risk and low-risk ABCD cohort subsamples. Given low concordance between hair results and self-report, reliance on either method alone would incorrectly categorize 9% as non-users. Multiple methods for characterizing substance use history in youth improves accuracy. Larger representative samples are needed to assess the prevalence of substance use in youth.