dataesr/scientific-paragraphs-categorization huggingface dataset:
Background Living organisms are stable systems even though the molecules within organisms constantly change in a myriad of chemical reactions. Based on the Greek word "metabole" (English: change), "metabolism" is used to describe all the chemical reactions that change molecules in living organisms. Key metabolic discoveries by the early biochemists are the discovery and characterization of glycolysis by Pasteur, Embden, Meyerhof, and Parnas; the discovery of enzymes; the mapping of metabolic pathways; and discoveries linked to biochemical genetics  [1, 2]. Our current knowledge of human metabolism is summarized in genome-scale metabolic reconstructions such as the Virtual Metabolic Human database, containing 17730 reactions and 5180 metabolites  [3]. Metabolites are the molecules that change or react in metabolic reactions of a living being.
This information can therefore be used to identify the detected metabolites through matching against databases of known metabolites  [4]. While nuclear magnetic resonance spectroscopy-based metabolomics methods are also available and have specific advantages  [15] , mass spectrometry-based metabolomics methods dominate. Further variations of metabolomics are untargeted or global metabolomics which measures all detectable metabolites in a sample versus targeted metabolomics where a specific subset of metabolites is measured  [4]. Collectively, the improvements in metabolomics methods allow researchers to detect more and more metabolites in human body fluids  [16]. While exercise physiologists have traditionally focused on measuring individual metabolites such as lactate  [17] , they have since 2009 used metabolomics methods to obtain a global view of how exercise changes metabolite concentrations  [18].
Search Strategy To identify publications that use a metabolomics approach to measure metabolite changes after a bout of exercise, we carried out a systematic review following the PRISMA guidelines  [19]. We searched four different literature databases using the PICO (Population, Intervention, Comparison, Outcome) strategy  [20]. This search strategy combines the parameters of the research question into one search string to find relevant studies (Fig. 1 ). For the literature search, we only used the parameters "intervention" and "comparison" of the PICO search strategy in the search terms because the eligible studies did not need to have a control group. To get a first overview, we searched PubMed using the search term ("metabolomics" AND "exercise") and repeated this search in PubReMiner to extract additional, relevant search terms.
We ended up with the search terms (("metabolomics" [MeSH Terms] OR "metabolome" The earliest study matching our search criteria was published in 2010. We included studies from that date onwards. From the abstracts retrieved, we included articles from peer-reviewed journals, written in English that investigated metabolic changes in humans in response to a bout of exercise. Our eligibility criteria were as follows: -  per week, intervention of the study (intervention sessions, intensity (%VO 2max ), duration, kind of exercise, nutrition during intervention), samples for metabolomics analysis (number of samples, tissue sampled, nutritional protocol before sampling, interval between exercise and post-exercise sample, fasting/no fasting before sampling, outcomes (number of metabolites detected, quantitative change of significantly altered metabolites, analyses method, database comparison)), and remarks.
Discussion In this review, we summarize how metabolite concentrations change in human blood and other biofluids within 24 h after a bout of exercise. Our analysis provides the first overview of results across metabolomics studies that use different human subjects, endurance, and resistance exercise; analyze different body fluids; utilize several analysis methods; and collect samples at different time points after exercise. Even though there are many differences in-between studies, the concentrations of many metabolites such as fatty acids or acylcarnitines often change similarly after exercise. There are, however, exceptions where metabolite concentrations change in different directions after exercise. This combined dataset illustrates such differences and may help researchers to identify the causes.
Limitations This systematic review has limitations. First, the studies summarized in this review combine many different exercise modes with variable intensity and duration and further vary in their nutrition and sampling times. Moreover, subjects are men and women, differing in their sex hormone concentrations and in the concentration of roughly one-third of all metabolites  [73]. Furthermore, subjects were of different ages, differentially trained, and may have varied in their health and body composition. This is a key source of variation in this dataset. Thus, if metabolites such as fatty acids (Fig. 6 ) all increase their average concentration in response to different exercise modes and in different subjects then this suggests that the increase of fatty acid concentrations is a robust response to exercise.
Conclusion and Outlook Across different exercise modes and in different subjects, exercise often consistently changes the average concentrations of metabolites involved in energy metabolism and other branches of metabolism. This dataset should therefore be a useful resource for those that wish to study human exercise metabolism. For the future, one important focus should be to use metabolomics to investigate whether individual metabolite concentrations or "metabolite fingerprints" (i. e. , combinations of metabolites) are biomarkers for disease, metabolic function, trainability, or other "hard to measure" traits such as muscle fiber percentages. Here, it may be essential, similar to cardiovascular stress tests  [76] , to activate a system by exercise, as the capacity and function of many systems can only be assessed when the system is active and under stress.
Methods: The study is based on retrospective register data and includes 180 individuals with a history of illicit drug use, who died from opioid intoxication in Skåne, Sweden, between 1 January 2012 to 31 December 2013 and 1 July 2014 to 30 June 2016. Intoxications caused by heroin, methadone, buprenorphine and fentanyl were included. Data were collected from the National Board of Forensic Medicine, regional health care services, municipal social services and the Prison and Probation Service. Statistical testing was performed using Pearson's chi-square test, Fisher's exact test and the Mann-Whitney U test to analyse group differences. Results: A total of 89% of the deceased individuals had been in contact with one or more of the care-providing authorities during the year prior to death; 75% had been in contact with health care, 69% with the social services, 28% with the Prison and Probation Service, and 23% had been enrolled in opioid substitution treatment at some point during their final year of life.
Study inclusion In this study, we analyse deaths where the deceased individuals had a history of illicit drug use and the cause of death was intoxication, drawn from the larger data set described above. Intoxications caused by heroin, methadone, buprenorphine and fentanyl were included in the study. Other opioids were present in too few cases per substance to be included in comparisons. 3  The classification regarding the history of illicit drug use in forensic records has been based on whether information on illicit drug problems was mentioned in the autopsy report or police investigation, whether the presence of injection marks had been noted in the autopsy report or whether illegal drugs (heroin/6-MAM, amphetamine, cocaine or THC) were detected in forensic analysis. To determine whether a death was caused by intoxication, we have consulted the death certificates of the deceased individuals.
Data sources and variables Data were collected from the Swedish National Board of Forensic Medicine, regional health care services (including OST), municipal social services and the national Prison and Probation Service. Information from the different authorities was collected through manual journal review (the National Board of Forensic Medicine, social services and partially health care) and from searches based on personal identity numbers in databases (the Prison and Probation Service and partially health care). The manual searches were performed by the first author (LA) and a medical student. The health care services, social services and the Prison and Probation Service will hereafter be referred to as "care-providing authorities". All data sources were linked via the personal identification number of the deceased, which is unique to every Swedish resident  [41].
Statistical analysis The data were analysed by means of frequency calculations, cross-tabulations and comparisons of medians. Significance testing of group differences has been carried out using Pearson's chi-square and Fisher's exact test. The Mann-Whitney U test for independent samples was used to compare medians between groups. Methadone, the most common opioid among the deceased, is the reference value for pairwise comparisons between the substance groups. All analyses were performed using IBM SPSS Statistics version 24 for Windows. A p value < 0. 05 was considered statistically significant.
048) and contact with addiction or financial assistance units (p = 0. 02). The data from the Prison and Probation Service indicate that 28% of the deceased had been under supervision during the year prior to death and 8% had served a prison sentence. There were no significant differences between the substance groups. Table  4  also shows that 89% of the deceased had been in contact with at least one of the three main care-providing authorities for individuals with addiction problems-health care, social services or the Prison and Probation Serviceduring the year prior to death.
Strengths and limitations Potential strengths of the current study include the fact that it covers a complete regional population, with no missing cases, and that the data are register based and thus linked at an individual level between the different data sources. The study also includes data obtained from social services, which has largely been lacking in previous research. Among the limitations of the study can be mentioned the fact that additional data from other authorities could have been obtained in order to provide further in-depth knowledge of the studied population. This might, for example, have included demographic data concerning income and employment, relationship status and residential area, as well as additional information from the health care services regarding primary care contact, diagnoses and prescription history.
Results: We carried out a forward genetic screen and identified 40 independent Arabidopsis mutants with defects in systemic spreading of RNA interference (RNAi), or more specifically in root-to-shoot transmission of post-transcriptional gene silencing (rtp). To classify the mutants as either representing a new allele of a known or candidate gene versus carrying a mutation in an undiscovered gene, bulk genomic DNA from up to 23 independent mutants was used as template to amplify a collection of 47 known or candidate genes. These amplified sequences were combined into Can-Seq libraries and deep sequenced. Subsequently, mutations in the known and candidate genes were identified using a custom Snakemake script (https ://githu b. com/Carro ll-Lab/can_seq), and PCR zygosity tests were then designed and used to identify the individual mutants carrying each mutation.
2  The Can-Seq workflow. Bulk DNA is prepared from leaf tissue of up to 23 independent mutants. Candidate gene PCR amplicons generated from this template are then combined in equimolar ratios and deep sequenced. Bioinformatic analysis using the Can-Seq script (https ://githu b. com/Carro ll-Lab/can_seq) allows for identification of C to T and G to A substitutions at frequencies above an arbitrarily set threshold of 0. 75%; the expected frequency for a homozygous candidate mutation in a bulk of 23 independent mutants is 1 in 23 or ~ 4%. The individual mutant containing the candidate mutation is identified via allele-specific PCR assays. Complementation tests involving crosses between independent mutants carrying candidate mutations in the same gene can be used to resolve whether the EMS-induced nucleotide variant detected by Can-Seq is the causative mutation is very unlikely that this non-zero variant in THO6 is a homozygous candidate mutation in a mutant represented in the Can-Seq libraries, which would be expected to occur at a frequency of ~ 4%.
Optimisation of Can-Seq bioinformatics workflow The Can-Seq bioinformatics workflow (https ://githu b. com/Carro ll-Lab/can_seq) was designed to be as simple and user-friendly as possible, and can be installed and operated on a consumer-grade PC. A single command instigates the workflow, which processes Can-Seq raw read files and GenBank-format reference files, carries out alignments, identifies variant nucleotides above the threshold frequency and the consequent amino acid changes, and outputs these data as both CSV and annotated GenBank files. To date, we have produced Can-Seq libraries based on up to 23 mutants in a bulk DNA template for up to 32 candidate genes. In view of the potential risk of false positives, we feel we have approached the limit for the number of independent mutants that can be included in a single Can-Seq library.
Bioinformatics analysis A workflow for identification of candidate gene mutations and annotation of GenBank format sequence files was developed as a custom Snakemake script (https ://githu b. com/Carro ll-Lab/can_seq). The process was implemented as follows: Trim-galore was used to remove adapter and low-quality portions of each read (  [23] ; https ://www. bioin forma tics. babra ham. ac. uk/proje cts/trim_ galor e/). GenBank format reference files for each gene were converted to FASTA format using the SeqIO Biopython module (https ://biopy thon. org/wiki/Biopy thon). Alignment of reads to reference sequences was carried out using Bowtie 2  [24] , with variant positions identified using SAMtools mpileup output piped to VarScan  [25, 26]. CSV files produced by VarScan were in turn parsed and used to generate a new annotated version of the original GenBank file for each gene, along with an additional simplified CSV output.
Funding JC was supported by the  China Scholarship Council. NRG was supported by a  GRDC postgraduate research scholarship  ( GRS114 ). CT was supported by a post-doctoral fellowship from  INRA  and from BJC grants. Research in the Vaucheret laboratory is supported by grants from the  French Agence Nationale pour la Recherche  ( ANR-10-LABX-40  and  ANR-11-BSV6-007 ) and the  Fondation Louis D. de l'Institut de France. BJC is grateful for support from  Australian Research Council (ARC)  grants ( DP0988294 ,  DP120103966  and  DP150104048 ).
ST has a maximum depth of 800 m versus 1,100 m for Jøkelbugten Fjord and a minimum depth of 350 m at two sills (Fig. 3B ). Oceanographic Data. In the summer of 2019, NASA's OMG mission deployed Airborne Expendable CTD (AXCTD) ocean probes in front of ZI  (29). Additional AXCTD data were collected on a NASA Gulfstream III aircraft in September to October 2016, C-130 Hercules in October 2017, a Basler DC-3 Turbo Prop in September 2018, and a Basler BT-67 in September 2019. AXCTD accuracy is ±0. 1 • C for temperature and ±2% for depth. The probe in front of ZI reached 627 m depth, with a bottom temperature of 1. 4 • C (Fig. 3 ). The probe in the deepest part of 79N reached 453 m depth, with a bottom temperature of 1. 36 • C. At AXCTD 1 to 5, the temperature/salinity relationship indicates a mixture of warm AIW at 1.
S4 ). We calculate ice thinning averaged across the glacier width, bed slope, β, and surface slope, α, at the glacier center over several ice thicknesses to convert the rate of ice thinning, dh/dt, into a rate of flotation retreat, qs = dh/dt / ([1ρw /ρ i ] βα)  (45)  in meters per year. The cumulative thinning-induced retreat, Qs, totals 7. 6 km for ZI and 2. 1 km for 79N from 1979 to 2019. DEM have a 1-to 2-m noise level over 80-m peak thinning, or 2%. Average thinning is 10 m/y, hence 15%. The noise in bed and surface slopes is 10%, yielding an error in Qs of 18% for ZI and 25% for 79N. Data Availability. All study data are included in the article and SI Appendix. The airborne gravity data and AXCTD used for this study are publicly available at California Institute of Technology's Jet Propulsion Laboratory (omg.
jpl. nasa. gov). Radar profiles are available at the Center for Remote Sensing of Ice Sheets, the University of Kansas (data. cresis. ku. edu). Ice velocity data from the MEaSUREs are available at the National Snow and Ice Data Center (NSIDC) in Boulder, CO. BedMachine Greenland is available at NSIDC. Other data (ice front, ocean gate, CTD) are available in Dryad at https://doi. org/10. 7280/D19987. ACKNOWLEDGMENTS. This work was performed in the Department of Earth System Science, University of California, Irvine, and at California Institute of Technology's Jet Propulsion Laboratory under a contract with NASA.
In the frame of the research project IOGA 4 MET, a new ground-based GNSS network is being deployed in the Southwest Indian Ocean (SWIO) since November 2017. This new network opens up for new research opportunities in geodesics and atmospheric sciences in this area regularly exposed to natural hazards, and significantly expands the International GNSS System (IGS) network, which only includes eight stations so far in the SWIO. After a description of the current status of IOGA 4 MET, several examples of regional applications in meteorology (climatology and numerical modeling) and geodesy (monitoring of ground deformations) are presented using data collected in northern Madagascar and small neighboring islands in 2018-2020. The observed water vapor cycle is shown to exhibit different characteristics and important variability at all locations.
Observations are sent to the French "Institut National de l'Information Géographique et Forestière" (IGN) data center and integrated into IGN's permanent GNSS network (RGP), allowing for data reception and formatting, routine GNSS calculations and wide dissemination of products such as times series of geographic coordinate and zenithal tropospheric delays. GNSS data files are processed upon reception with the Bernese GNSS software, which is a scientific, high-precision, multi-GNSS data processing tool developed at the Astronomical Institute of the University of Bern  (Dach et al. , 2015) , with hourly, daily, and weekly updates. Tropospheric GNSS data are also deposited on global meteorological databases, allowing for potential data assimilation in main NWP systems.
, 2020). Note that the assimilation of GNSS data is nevertheless taken into account indirectly through IFS, which assimilates GNSS-derived zenithal delay observations collected at IGS stations (see blue dots in Figure  1  for locations). -Hourly IWV reanalysis data extracted from the new, highresolution, ECMWF 5 th generation reanalysis (ERA5), which replaced the previous ERA-Interim reanalysis on 31 August 2019. ERA5 provides hourly estimates of a large number of atmospheric, land and oceanic climate variables over the Earth on a 0. 25 × 0. 25-degree horizontal grid (31 km) using 137 levels from the surface up to 80 km  (Hersbach et al. , 2020).
Examples of Meteorological Applications GNSS tropospheric data such as atmospheric integrated water vapor (IWV) contents have become one of the most reliable datasets for monitoring global warming and assessing mediumto-long-term trends in space-time distribution of atmospheric moisture around the world (e. g. ,  Wang and Zhang, 2009; Chen and Liu, 2016; Parracho et al. , 2018). Due to the limited number of observations available in the SWIO, very little is however known yet about the distribution of water vapor in this area. IOGA 4 MET therefore represents a great opportunity to investigate further the properties of the water cycle in this part of the world as well as its representation in NWP systems. The mountainous island of Madagascar separates the SWIO basin into two parts: the Mozambique Channel to the west, and the open ocean to the East.
m -2 , 25 • S). Highest absolute (hourly) minimum IWV values are also observed in Aldabra with ∼20 kg. m -2 , against ∼15 kg. m -2 elsewhere. GNSS-derived IWV data can also be used as an independent source to evaluate and monitor the water vapor distribution in model reanalyses (e. g. ,  Bock et al. , 2005; Bock and Parracho, 2019; Lees et al. , 2020)  as well as NWP and climate model forecasts  (Guerova et al. , 2016). In the following, GNSS-derived IWV observations collected from January 1-December 31, 2019 are compared against IWV contents analyzed daily at 00, 06, 12, and 18 UTC with the AROME-IO NWP system over the same period of time. The evolution of IWV contents analyzed four times daily by this model at Aldabra, Grande Glorieuse, Diego-Suarez and Sainte-Marie are shown by the red lines in Figure  2.
Positioning data were obtained daily at 23 UTC over 645 days at MAYG, 580 days at DSUA and 320 days at GLOR and were corrected from plate velocity -Note that positioning data used in this figure were processed independently by IPGP using the GIPSy-X software suite  (Bertiger et al. , 2020). GNSS observations collected in Mayotte (Figure  4A ) show that this island has been subjected to strong and regular ground deformation over this period. Ground motion began to be recorded in July 2018, approximately 2 months after the beginning of the unusual seismic activity and increased progressively in a more or less linear way. Since then, the MAYG station recorded a cumulated subsidence of approximately 17 cm and drifted nearly 20 cm to the east and 2 cm to the south. As mentioned earlier, data collected at nearby locations can be used to constrain and monitor the spatial extension of the ground deformation associated with the submarine eruption, which is key to anticipate potentially devastating impacts of this seismic-volcanic activity on neighboring territories.
DISCUSSION The deployment of 10 new permanent GNSS stations in the SWIO considerably increases atmospheric and geodesic observation capabilities in this poorly instrumented area. Although datasets collected to date by these new stations are still limited -most GNSS stations were installed less than a year agothe analysis of data collected by the oldest ones already allows to assess the potential of these new observations for applications in geodesy and atmospheric sciences. For instance, the analysis of tropospheric GNSS data collected during year 2019 in northern Madagascar (Diego-Suarez) and three small neighboring islands (Aldabra, Sainte-Marie, and Grande Glorieuse) has shown that these new observations represent a great opportunity to describe more accurately the water cycle at various time and space scales in this little studied area.
The analysis of positioning data collected at and nearby Mayotte also shows that these new datasets can be particularly useful to monitor ground-based displacements associated with the unique off-shore volcanic activity currently taking place in this area. In the short-term additional GNSS stations will be deployed to further increase the density of the network, especially in and around Madagascar (Figure  1 ). In 2020, five additional stations will thus be installed at Majunga (MAMA), Morondava (MAMO), Tulear (MATU) and Manakara (MAMK), while a second station will be installed at Aldabra (ALBR2) to monitor more precisely potential ground displacements on this emblematic and unique atoll. The installation of a couple of additional GNSS stations in Seychelles (Praslin) and Comoros (Mohéli) is also currently investigated.