dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The present work was carried out in the framework of the  European Space Agency  research project AO-1999-110: EVAPO-RATION. We thank all of the  ARLES Science Team Members  for their contribution in making possible the  Sounding Rocket Experiment  in the framework of the ESA MASER-14 Campaign : Science Team Coordinator:  Dr. C. S. Iorio ,  Université Libre de Bruxelles, University of Pisa, University of Alberta, University of Darmstadt, Université de Liège, Chinese Academy of Science, University of Edinburgh, University of Loughborough, Trinity Collegue Dublin ,  Siberian Branch Russian Academy of Science. The authors would also like to thank  Dr. D. Mangini ,  Dr. B. Toth , and  Dr. A. Verga  for their interest in and support of the activities linked to ARLES and for the fruitful discussions.
The research team of  Prof. A. Ferrari  of the  University of Cambridge  is also gratefully acknowledged for the enormous support in preparing the fluids with nanoparticle suspensions tested both on the ground and in microgravity conditions. The authors are grateful to  A. I. Garivalis  from  University of Pisa  for his contribution in EF calculations and analysis. The authors would also like to thank the  Swedish Space Corporation , with particular gratitude to the project manager,  Mr. M. Lundin. We would also like to acknowledge the financial support of the  French National Space Agency  ( Centre national d'études spatiales: CNES ) research grant for the DROPS experiment on the FLUIDES space mission.
Association of Biomolecular Research Facilities (ABRF) Metabolomics Research Group (MRG) 2016 Study Design The Metabolomics Research Group (MRG) designed an interlaboratory data analysis study to recapitulate a typical metabolomics experiment where the participants would use any data analysis package of their choice to report back the top 50 most statistically significant perturbed spectral features between two study groups. In untargeted metabolomics, the raw MS data is routinely preprocessed using either vendor-based software (depending on the MS type) or using open source software (e. g. , MZmine or XCMS). For the latter, the raw data files have to be converted to a universal reading format like mzxml or netCDF. The preprocessed data files (typically in csv format) are then used for statistical/bioinformatics analysis wherein normalized intensities for the spectral features are compared between and across study groups to select features that contribute significantly to group differences using a statistical test cutoff value (i.
e. , p-value < 0. 05), followed by false discovery rate (FDR) correction. The shortlisted features are then subjected to accurate mass-based database searches for putative annotations. Several publicly available databases including Metlin  [4] , HMDB (Human Metabolome Database)  [5] , MMCD (Madison Metabolomics Consortium database)  [6] , and CEU Mass Mediator  [7]  enable an automated batch search for a given list of features. Once the features are putatively annotated, their identities need to be validated using MS/MS fragmentation and retention time confirmation and/or using pure reference standard compounds. While these general steps are traditionally followed by most laboratories, the software and data analysis criteria used vary among the laboratories. To do this, we used an untargeted, comparative metabolomics approach using urine samples from C57BL6 mice exposed to 3 Gy of external beam gamma rays and those exposed to sham irradiation (control group) (Figure  1 ).
We used five biological replicates for each group. A total of 11 urine samples including 5 controls, 5 irradiated samples, and 1 pooled urine sample (used as quality control; QC) were used in this study. The pooled QC was prepared by combining 5 µL of each sample extract from the two groups. The urine samples were processed for mass spectrometry analysis by extraction with a solvent containing 30% acetonitrile, 40% methanol, and 30% water and removal of precipitated proteins by centrifugation (Supplementary Figure  S1 ). The urine extracts were resolved on an H-class Acquity Ultra-performance liquid chromatography (UPLC) using reversed-phase chromatography (see Supplementary Figure  S2  for gradient details). Data were acquired on a quadrupole time-of-flight mass spectrometer operating in positive or negative ionization modes.
org/sites/default/files/2016_mrg_study_outline_060716. pdf), contacting core laboratory directors, and advertising the study at the ABRF and American Society of Mass Spectrometry annual meetings. We asked the participants to analyze the data, available for download on Bioshare (https://bioshare. bioinformatics. ucdavis. edu/bioshare/view/tthvputgo2am4yo/), employing methods that are routinely used in their laboratory. The MRG also collected fragmentation data using MSE (elevated collision energy) with the QC samples and these files were available upon request for metabolite identification based on fragmentation patterns. For this purpose, we made the raw data as well as netCDF files available to provide more flexibility for data preprocessing. However, we expected that only those participants who use Waters software (Mass Lynx v4.
5 years in the metabolomics field. The majority of the participants also offered proteomics services, which seems to be a common trend in biomolecular core facilities at academic institutions (Figure  2 , Panel C). We invited metabolomics researchers by announcing the study on the MRG homepage (https: //abrf. org/sites/default/files/2016_mrg_study_outline_060716. pdf), contacting core laboratory directors, and advertising the study at the ABRF and American Society of Mass Spectrometry annual meetings. We asked the participants to analyze the data, available for download on Bioshare (https://bioshare. bioinformatics. ucdavis. edu/bioshare/view/tthvputgo2am4yo/), employing methods that are routinely used in their laboratory. The MRG also collected fragmentation data using MSE (elevated collision energy) with the QC samples and these files were available upon request for metabolite identification based on fragmentation patterns.
For this purpose, we made the raw data as well as netCDF files available to provide more flexibility for data preprocessing. However, we expected that only those participants who use Waters software (Mass Lynx v4. 0, Waters Corporation, Milford, MA, USA) would be able to take advantage of raw data. We asked the participants to report on the top 50 spectral features that show statistically significant differences in their relative abundances in the irradiated group compared to the sham group. The overarching goal was to inform the scientific community about the current status of metabolomics data analysis strategies. Specifically, we wanted to find out the possible causes of variability in data preprocessing routines, data postprocessing software, parameters used, choice of data filtering and imputation, feature selection, and metabolite identification.
Assign putative identification to these urinary spectral features using various online databases. As shown in Figure  3 , the majority of the participants either used XCMS or other software for peak picking (Panel A), different statistical tests for feature selection including PCA, student's t-test, ANOVA, and so forth (Panel B), technical replicates in software-based data analysis (Panel C), and normalized data to the internal standard (Panel D). In addition, the majority of the participants did not use retention time, but stringent database search criteria for metabolite annotation (Figure  3B ). The databases used included Human Metabolome Database (HMDB), KEGG, and CEU Mass Mediator. The results were submitted online or via email in an anonymous format. In addition to the deliverables above, we requested from those participants who used the raw data that they submit intermediate files containing data-processing parameters and small molecule identifications and/or quantitative values (i.
MRG 2016 Study: Lessons Learned and Future Directions Global metabolomic and lipidomic profiling offer promise as discovery tools for precision medicine approaches. Because this field is still emerging, there is an urgent need for standardization at every step of the workflow to allow for robust, reliable, and reproducible data interpretations. The goal of the MRG 2016 study was to determine the impact of data pre-and postprocessing methodologies on study outcome. We chose a very simplistic study design where mouse urine metabolomic profiles were generated using an ultra-performance liquid chromatographic system online with a quadrupole time-of-flight mass spectrometer. For analysis of untargeted data, the raw data files are typically converted to a universal file format that can then be processed by a peak detection software like XCMS (Scripps, La Jolla, CA, USA)  [8]  or MZmine  [9].
The extracted features are used for difference detection using a combination of data filtering and normalization techniques followed by statistical analyses  [10]. The most significant features are subject to accurate mass-based database search for assignment of putative annotations. Surprisingly, there was little overlap across laboratories with respect to the reported top 50 statistically significant urinary spectral features between the sham and 5 Gy irradiated mice. On the other hand, there was a reasonable overlap of the reported total number of raw extracted features among the participant laboratories. Because we had asked the participants to use their preferred data analysis methodologies routinely used in their laboratories, the differences in their results suggest that the software/ peak detection algorithm can generate varied results for the same data set.
Acknowledgments: The authors would like to acknowledge the  Metabolomics Shared Resource in Georgetown University (Washington, DC, USA ) which is partially supported by  NIH/NCI/CCSG  grant  P30-CA051008. Where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the  International Agency for Research on Cancer/World Health Organization. Finally, we want to thank the anonymous reviewers whose constructive suggestions have helped elevate the quality of the manuscript.
MRI data acquisition MRI data were acquired on the same 3. 0-Tesla MRI scanner (MAGNETOM Trio Tim system, Siemens, Erlangen, Germany) at the University Hospital of Angers, France, using a standard protocol. Acquisition parameters for sequences including a high-resolution 3D T1-weighted volume using a magnetization-prepared rapid acquisition gradient-echo sequence, covering the whole brain (cerebellum included) were: 176 slices, repetition time (TR) = 2300 msec, echo time (TE) = 4. 18 msec, slice thickness = 1 mm, field of view (FOV) = 256 9 256, flip angle = 9, voxel size: 1 9 1 9 1 mm 3.
Voxel-based morphometry Voxel-based morphometry across the entire brain were conducted with the Computational Anatomy Toolbox 12 (CAT12) (http://www. neuro. unijena. de/cat) implemented in the Statistical Parametric Mapping 12 (SPM12) software (https://www. fil. ion. ucl. ac. uk/spm/software/spm12/) using a 2017a Matlab platform (The MathWorks, Natick, MA, USA), following the standard procedure. First 3D T1 volumes were segmented in native-space to obtain three tissues classes: GM, white matter, (WM) and cerebrospinal fluid (CSF). 8 Then, nonlinear modulation GM and WM volumes were aligned using the default DARTEL (diffeomorphic anatomical registration using exponential Lie algebra) template and were spatially normalized into the Montreal Neurological Institute space (ICBM). 9 inally, images were smoothed by an isotropic Gaussian kernel of 8mm full-width at half-maximum (FWHM).
Statistics Statistical analyses were performed using Tanagra 2. 0 software (http://eric. univ-lyon2. fr/~ricco/tanagra/fr/tanagra. html), Excel (Microsoft office Professional Plus 2013) and CAT12. For neuroimaging, smoothed, modulated and normalized data were used. With CAT12, morphometric data from participants were associated with a linear regression model with 25OHD concentration. We used threshold-free cluster enhancement (TFCE) for a combined nonparametric analysis about height and size of the effects (http://www. neuro. uni-jena. de/tfce), and applied FWE (family wise error) corrected threshold of P < 0. 05 to control for multiple comparisons. Anatomy toolbox 2. 1 was used for anatomical localizations. 10.
We minimized these limitations by using SPM12 software that performs better than previous versions. In conclusion, we found that lower serum 25OHD concentration was associated in older adults with GM atrophy in the calcarine sulcus, part of the primary visual cortex. This finding highlights for the first time that specific brain areas are altered with hypovitaminosis D, and helps understanding the effects of vitamin D on brain health and function in older adults, notably the brain treatment of visual stimuli. Further prospective observational cohorts and randomized clinical trials, preferentially on a variety of adult populations, are needed to clarify whether those with hypovitaminosis D are more likely to experience loss of GM in the visual cortex, and whether vitamin D supplementation could prevent or limit this change.
Genomic Information and Bioinformatic Analyses The genome sequencing information of ALL, AHG, and BSS strains was obtained from the "1002 yeast genomes project"  (Peter et al. , 2018). The overlapping DNA contigs of regions A, B, and C were assembled using the de novo assembler tool from the Geneious 8. 1. 8 software (Biomatters, New Zealand). The ORFs inside regions A, B, and C were manually annotated using information from the EC1118 genome and the "1002 yeast genomes project"  (Novo et al. , 2009; Peter et al. , 2018). Then, the ORFs were translated and analyzed using blastp from the blast suit of the NCBI 1  and SGD databases 2  for the identification of orthologous genes in the S228c genome (Table  1 ). The Codon Adaptation Index (CAI) was calculated for each ORF within the three horizontally acquired regions using the CAI calculator  (Puigbò et al.
, 2008). This was performed using the codon usage of S. cerevisiae as acceptor species and the codon usage of Z. bailii (region B) and T. delbrueckii (region A and C) as donor species. We used the codon usage information of T. delbrueckii because it was the only one available for a yeast of the Torulaspora genus in the Codon Usage Database  (Nakamura et al. , 2000). The promoter region of each gene inside regions A, B, and C was scanned for transcription factor binding sites, using the YeTFaSCo database  (De Boer and Hughes, 2012). We considered as a promoter the 700 bp upstream of the start codon (ATG) of each ORF and used the whole intergenic region if the distance to the ORF upstream was inferior to 700 bp. We retrieved the information for transcription factor binding sites with nondubious motifs from YeTFaSCo (Supplementary Table  3 ).
SC, YNB, SM300, and SM60 culture medias supplemented with 0. 1 mM luciferin were used  (Salinas et al. , 2016). The experiments were conducted in a Synergy HTX plate reader (BioTek, United States) for the simultaneous measurement of OD 600 nm and luminescence (Lum) every 30 min. The raw luminescence and OD 600 data (Supplementary Figure  1 ) of each strain was normalized, dividing the luminescence by the OD 600 of the cell culture (Lum/OD 600 nm ). Then, the area under the normalized curves was calculated using the GraphPad Prism 6 software (GraphPad, United States). The total area values were employed as a measurement of the global transcriptional activity for the horizontally acquired genes in a period of 24 h (Supplementary Figure  2 ;  Kessi-Perez et al. , 2019b). The TDH3 ORF was replaced by the luciferase reporter gene in the ALL, AHG, and BSS strains and used as a positive control in the luciferase expression experiments.
Levels of mCherry fluorescent protein were assayed in microcultivation experiments using 96-well plates with 300 µL culture medium (SC, YNB, SM300, and SM60) at 30 • C. The experiments were conducted in a Synergy HTX plate reader (BioTek, United States), for the simultaneous measurement of OD 600 nm and fluorescence (Fluo) every 30 min. Fluorescence data were normalized by the OD 600 data (Fluo/OD 600 nm ), and the normalized values were corrected by the auto-fluorescence of the wild type strains. Since the mCherry fluorescent protein accumulates in the cells over the time course of the experiments, the last time point (24 h) was used to compare the protein levels between horizontally acquired genes. The stop codon of the TDH3 gene was replaced by the mCherry fluorescent protein in strains ALL, AHG, and BSS and used as a positive control in the fluorescence experiments.
Growth Curves and Fermentation Experiments The growth curves of the strains carrying the luciferase reporter gene (deletion strains) were performed in microcultivation experiments using 96-well plates with 300 µL culture medium (SC, YNB, SM300, or SM60) at 30 • C. The OD 600 nm of the cell cultures was measured every 30 min using a Synergy HTX plate reader (Biotek, United States). The kinetic parameters were automatically extracted from the growth curves using the Gompertz equation  (Zwietering et al. , 1990; Yin et al. , 2003) , which was incorporated as a function in the GraphPad Prism 6 software (GraphPad, United States). The kinetic parameters analyzed for each strain were the latency time (Lag), growth rate (Rate), and growth efficiency (Efficiency)  (Warringer and Blomberg, 2003). These phenotypes were normalized, dividing the phenotypes of the strains carrying gene deletions by the phenotypes of their respective wild type strain (ALL, AHG, or BSS), and represented as a heat map using the Heatmapper web server  (Babicki et al.
, 2009). The putative biological function of the ORFs within each region was extracted from the EC1118 strain genome and the "1002 yeast genomes project" information (Table  1 ). In addition, we translated each ORF in silico and performed a blastp alignment against both the NCBI and the fungal SGD databases, allowing us to identify orthologous genes in the reference genome (S288C strain) (Table  1 ). As expected, most of the genes within the three regions have putative functions in the transport and metabolism of carbon and nitrogen sources (Table  1 ). Altogether, the observed genetic structure and hypothetical biological functions for the genes encoded in regions A, B, and C in their respective strains (ALL, AHG, and BSS) do not show differences compared to the regions in the EC1118 strains.
, , 2016)). Similarly, we were not able to detect transcriptional activity for the FSY1 gene (C53) in the assayed culture conditions (Supplementary Figure  3 ), consistent with the described repression of this gene under high levels of glucose  (Galeote et al. , 2010). Overall, we cannot discard transcriptional activity for the other ORFs present in the analyzed regions, whose promoter regions could have transcriptional activity below the detection level of our destabilized reporter gene. Alternatively, these promoters may have a cryptic behavior or be inactive in the culture conditions assayed. To explore this idea, we scanned the promoter region of each ORF using the YeTFasCo database for yeast transcription factors binding sites, confirming that all ORFs contain multiple and different sites for yeast transcription factor binding in their promoter regions (Supplementary Table  3 ), suggesting that all the ORFs should be transcriptionally active under specific environmental conditions.
However, all ORFs that displayed a luciferase signal also produced the mCherry fluorescent protein signal (Supplementary Figure  2 ). Interestingly, the protein levels of six ORFs inside region C (C43, C44, C45, C48, C49, and C53) increased in culture conditions with limited amino acid availability (YNB and SM60) (Figure  3  and Supplementary Figure  4 ), with the rise being more pronounced in a nitrogen-limited fermentative condition (SM60) (Figure  3  and Supplementary Figure  4 ). These data confirm the importance of region C for the adaptation of yeast to this environmental condition. Finally, we assessed whether the protein levels measured are influenced by the codon usage differences between the acceptor (S. cerevisiae) and donor (Z. bailii or Torulaspora sp. ) species. On calculating the Codon Adaptation Index (CAI) for each ORF of the horizontally acquired regions, we observed a CAI superior to 0.
In our case the system is composed by 21 nodes and we want to simulate the temperature evolutions in the ball bearing in steady state regime depending on the radial load, and the rotational velocity in different configurations. All thermal resistance values are calculated by given formulae developed before. All heat dissipation sources are Q 1 corresponding to the node T3 (Inner ring/ball contacts), Q 2 corresponding to the node T5 (outer ring/ball contacts) and Q 3 defect temperature. This considered values are considered as inputs and calculated by the use of formulae developed in last section. A computer program on Matlab software language was written to solve Equation (36) based on the theoretical model in steady state regime described in the last section. The temperature rise in the nodes and the heat generations under different operating parameters is calculated.
On the other the structural residues will leave a measurable signal since all the artificial sequences fold into the target. We artificially design three single domain proteins, chosen as representative of PDZ domain, FKBP-type peptidyl prolyl cis-trans isomerase, and Response reg. We selected the structures corresponding to the PDB IDs: 1WI2, 2PPN  [63]  and 1NXW  [58]  respectively. It is important to notice that we opted for the crystal structure 1NXW, where the Response reg is in complex with an acetate molecule, rather than the native 1NXO, because of the disordered nature of some residues in the latter structure  [58]. Firstly, we relax the crystallographic structure to the caterpillar protein model by discarding the side chain atoms. As fully described in Ref. s  [61, 62] , the Caterpillar model, in combination with Virtual Move Parallel Tempering (VMPT), that includes adaptive umbrella sampling technique  [64, 65] , is capable of producing a large number of sequences that should fold into realistic protein target structures (see SI for details).
Results We consider three protein domains, and for each of them we select a pool of natural sequences and a single domain representative protein structure: PDZ domain, 38522 sequences from the Pfam  [10]  protein family PF00595, structure from PDB 1WI2; FKBP domain, 19610 sequences corresponding to the family PF00254, structure PDB 2PPN; and Response reg domain, 1000 sequences taken from a BLAST  [77]  alignment on the sequence of the protein 1NXW from PDB (in order to reduce the enormous variability of functions and biological pathways in which Response reg are involved) also taken as a reference for the structure. To identify residues essential for functionality and stability and further categorise them as S, F or OFSR, we need to generate an artificial protein family that, by construction, contains only structural information.