dataesr/scientific-paragraphs-categorization huggingface dataset:
1% TFA 0. 5% acetic acid. Peptide samples were analyzed twice. First, 18mL of each peptide sample was analyzed by LC-MS/MS on a Q Exactive mass spectrometer (Thermo Scientific) coupled to an EASY-nLC 1000 liquid chromatography system (Thermo Scientific) via an EASY-Spray ion source (Thermo Scientific). Peptides were fractionated on a 75 mm x 500 mm EASY-Spray column (Thermo Scientific) over a 240 minute gradient. For all runs precursor ion full scan spectra were acquired over (m/z 300 to 1,800) with a resolution of 70,000 at m/z 400 (target value of 1,000,000 ions, maximum injection time 20 ms). Up to fifteen data dependent MS2 spectra were acquired with a resolution of 35,000 at m/z 400 (target value of 500,000 ions, maximum injection time 120 ms). Ions with unassigned charge state, and singly or highly (> 8) charged ions were rejected.
Intensity threshold was set to 2. 1 3 10 4 units. Peptide match was set to preferred, and dynamic exclusion option was enabled (exclusion duration 15 s). The second MS runs used 90minute fractionation gradients with a top 10 method, 40 s dynamic exclusion period and loaded 16 mL peptide solution per slice. All other parameters were the same as the first run. For both runs samples of recombinant NEDP1 tryptic peptides were also analyzed at the end of each run batch. This was in an attempt to identify peptides in the SILAC samples using spectral matching in MaxQuant, although this was unsuccessful. The 66 raw MS data files were processed using MaxQuant software (version 1. 6. 1. 0) (Cox and Mann, 2008) , and searched against UniProtKB human proteome (canonical and isoform sequences; downloaded in April 2019), plus a fasta file containing the sequence of the induced NEDP1 nanobody-RING construct: Most settings were left at default but briefly: The appropriate SILAC labels were selected and enzyme specificity was set to Trypsin (three missed cleavages).
5 Da for MS2 peaks. For the first MS run files slices were numbered 1 to 16 in the ''Fraction'' column of the experimental design template file, and from 101-116 for the second run, so spectral matching did not occur between MS runs. All slices from the same SILAC mix were given the same 'Experiment' name to separate the ratio data into the two mixes (A & B) and aggregate data from both MS runs. The proteinGroups. txt file was filtered for entries from the decoy database, those identified by modified peptide(s) only, potential contaminants, and those with SILAC ratio variability > 140% in either MixA or MixB. This left 5635 proteins, of which 4907 had SILAC ratios reported in both mixes. Outliers in each mix were defined using SigB in Perseus, (v 1. 6. 1. 1) (Tyanova et al. , 2016)  using the 'both sides' method, truncated using a Benjamini-Hochberg FDR threshold of 5%.
The 7 proteins ultimately defined as having significantly affected protein abundance by Dox treatment (Figures  5C  and 5D ) were that that met the SigB cutoff in both SILAC mixes and whose increase or decrease in response to Dox consistently in both. This left four proteins. Summary of these data can be found in Table  S1 , worksheet ''Summary_by_mix. '' The slice-by-slice comparison to monitor protein changes throughout the gel used just the 240 minute MS run data. In MaxQuant, every raw file was given a unique 'Experiment' name so protein data was separated by slice in the final output files. In this instance 'requantify' was turned off to ensure detection in any slice was not made by matching across files. All other MaxQuant setting were left as default. Summary of these data can be found in Table  S1 , worksheet ''Summary_by_slice.
'' Four peptides derived from NEDD8 itself were assigned by MaxQuant to the fusion protein NEDD8-MDP1 [UniProtKB -E9PL57 (E9PL57_HUMAN)]. One NEDD8 peptide not shared with this construct was assigned to NEDD8 [UniProtKB -Q15843 (NEDD8_HU-MAN)]. For the slice-by-slice analysis, to extract protein level data for NEDD8 only, the five individual NEDD8 peptides intensity data were summed and these values entered into the proteinGroups table under the protein name ''NEDD8 (MHT curated). '' This included data for the peptides (TLTGKEIEIDIEPTDKVER, EIEIDIEPTDKVER, IKERVEEKEGIPPQQQR, VEEKEGIPPQQQR, and ILGGSVLHLV-LALR). The original entries for NEDD8-MDP1 and NEDD8 were deleted. In the non slice-by-slice (total protein change) analysis, the original entries were left as reported by MaxQuant due to there being no evidence of abundance change upon Dox treatment.
Discovery experimental design is described in Table  S2 , worksheet ''Discovery experimental design. '' iRT peptides were spiked into all samples (Biognosys Cat# Ki-3002-2), and both the iRT and control peptides were added to the inclusion list. MS runs were acquired over identical 90 minute gradients (Table  S2 , worksheet ''QE methods'') with flow rate 20 ul/min, buffer A HPLC-grade water 0. 1% formic acid, and buffer B mass spectrometry-grade acetonitrile 0. 1% formic acid. DDA methods consisted of precursor ion full scan acquired over m/z range of from 300 to 1,800 with a resolution of 70,000 at m/z 200, a target value of 1,000,000 ions, and maximum injection times of 20 ms. Up to 4 data dependent MS2 spectra were acquired with a resolution of 70,000 at m/z 200, a target value of 1,000,000 ions, and a maximum injection time 300 ms.
txt data file for PRM. The inclusion list combined 24 NEDP1 peptides and 11 iRT peptides, as well as 63 high scoring human protein peptides for use as sample loading controls. PRM was performed on 12 ul (approximately one third) of each of the 18 cellular peptide samples described above, each spiked with iRT control peptides, using the same 90 min elution gradients as the DDA runs. PRM methods included precursor full scans acquired over a scan range of 300-1800 m/z with chromatogram peak widths of 30 s, resolution 70,000 at 200 m/z, a target value of 1,000,000, and a maximum injection time of 100 ms. The inclusion list generated from the DDA data was imported. Up to 12 data dependent MS2 spectra were acquired with a resolution of 70,000 at m/z 200, a target value of 200,000 ions, a maximum injection time of 247 ms, NCE 28, and spectrum data type was set to centroid.
The beautiful idea in this study was to track down information about this dramatic slimming in the accelerometer data. Indeed, the accelerometer was attached on the side of the fish (close to the dorsal fin). A change in its angle with the vertical plane could be correlated with the change in roundness, the angle declining with the female thinning. Accelerometers have already been used to record body posture  [6]  but, in the present study, the novelty was to monitor the change in body shape. Unfortunately, the data by Tentelier et al. are inconclusive so far. Broadly speaking, the accelerometer angle recorded declined through the spawning season, indicating an average slimming of the females, but there was no correlation between the change in angle and the mass loss at the individual level.
Reviewed by Aidan Jonathan Mark Hewison, 2020-08-26 16:02 This manuscript presents empirical data issued from accelerometers born by female Allis shad to detect spawning events and infer associated energetic expenditure. The authors seek to link behaviour to energy expenditure and loss of body condition over the reproductive season. The empirical data indicated that female shad exhausted their energetic stock faster than their egg stock. The authors conclude that this approach using accelerometers is promising for monitoring behaviour-dependent energy expenditure in the wild. This is something of a pilot study that investigates how high technology sensors can be employed in the wild to measure important physiological parameters such as energy expenditure and infer behaviours that are otherwise impossible to observe.
This latter section should be carefully edited. In its present form, it contains many details which are more related with experimental procedure than statistical analysis. Additional requirements of the managing board: As indicated in the 'How does it work?' section and in the code of conduct, please make sure that: -Data are available to readers, either in the text or through an open data repository such as Zenodo (free), Dryad or some other institutional repository. Data must be reusable, thus metadata or accompanying text must carefully describe the data. -Details on quantitative analyses (e. g. , data treatment and statistical scripts in R, bioinformatic pipeline scripts, etc. ) and details concerning simulations (scripts, codes) are available to readers in the text, as appendices, or through an open data repository, such as Zenodo, Dryad or some other institutional repository.
The scripts or codes must be carefully described so that they can be reused. -Details on experimental procedures are available to readers in the text or as appendices. -Authors have no financial conflict of interest relating to the article. The article must contain a "Conflict of interest disclosure" paragraph before the reference section containing this sentence: "The authors of this preprint declare that they have no financial conflict of interest with the content of this article. " If appropriate, this disclosure may be completed by a sentence indicating that some of the authors are PCI recommenders: "XXX is one of the PCI XXX recommenders. " Preprint DOI: https://www. biorxiv. org/content/10. 1101/436295v3.
Reviewed by Aidan Jonathan Mark Hewison, 2020-06-22 17:13 The dynamics of spawning acts by a semelparous fish and its associated energetic expenses This manuscript presents empirical data issued from accelerometers born by female Allis shad to detect spawning events and infer associated energetic expenditure. The authors seek to link behaviour to energy expenditure and loss of body condition over the reproductive season. The empirical data indicated that female shad exhausted their energetic stock faster than their egg stock. The authors conclude that this approach using accelerometers is promising for monitoring behaviour-dependent energy expenditure in the wild. This is something of a pilot study that investigates how high technology sensors can be employed in the wild to measure important physiological parameters such as energy expenditure and infer behaviours that are otherwise impossible to observe.
24 mass spectra per strain were acquired on a Microflex LT mass spectrometer (Bruker Daltonics, Bremen, Germany). Re-analysis of the spectra was performed for the purpose of this work. Spectra were first preprocessed by applying smoothing and baseline subtraction with FlexAnalysis software using default parameters, exported as text files from the Brucker system and then imported and analyzed in a dedicated BioNumerics v7. 6. 3 (Applied-Maths, Belgium) database following the protocol described by Rodrigues et al. [23]. To allocate proteins to the specific peaks detected, we extracted all the molecular weights from the genomes of the type strains (NTCT11397 T , FRC0043 T and FRC0190 T ) using a Biopython script (https:// biopython. org/DIST/docs/api/Bio. SeqUtils-module. html) and performed sequence alignments with ClustalW for the candidate proteins.
Genomic sequencing was performed from Nextera XT libraries using a NextSeq-500 instrument (Illumina, San Diego, USA) with a 2 Â 150 nt paired-end protocol. Contig sequences were assembled using SPAdes v3. 12. 0  [24]  (Table  S1 ). JSpeciesWS  [25]  was used to calculate the BLAST-based average nucleotide identity (ANIb). BLASTN was used to extract 16S rRNA and rpoB sequences from genome assemblies and to determine the presence or absence of the narIJHGK nitrate reduction gene cluster using as query the cluster of strain NCTC 13129 (RefSeq accession number: DIP_RS13820 to DIP_RS13845)  [26]. rpoB and 16S rRNA gene sequences of atypical C. belfantii strains from cats  [12]  were included for comparison. For genome-based phylogenetic analysis, the pairwise p-distance (i. e. , proportion of aligned nucleotide differences) between each pair of genomes was estimated based on Mash  [27]  using a multiple hit correction  [28]  with JolyTree (https:// gitlab.
Results and discussion Six isolates were isolated from five cutaneous lesions and one ascitic fluid sample (Table  1 ). Strikingly, human cutaneous lesions were all ulcerations due to underlying chronic arteritis. Ascitic fluid was sampled on a patient with a suspicion of spontaneous peritonitis. The dog was investigated in the context of purulent orbital cellulitis. The six isolates were tox negative (Table  S1 ); more specifically, they were negative for amplification of the expected 910-bp PCR product encompassing fragments A and B of the toxin gene  [19]  and also negative for the amplification of a 117-bp region of diphtheria toxin fragment A  [31]  by multiplex qPCR  [20]. We also confirmed by BLASTN that the tox gene sequence (query: tox gene sequence from strain NCTC 13129, RefSeq accession number: DIP_RS12515) was absent from the genomic assemblies.
0. 003174; January 2019), the latter subspecies appears to be a later heterotypic synonym of C. belfantii. Based on the MALDI Biotyper Compass database version 4. 1. 90 (Bruker Daltonics, Bremen, Germany), the six isolates were identified as C. diphtheriae. However, detailed analysis of their spectra led to the identification of six pairs of biomarkers (12 peaks corresponding to the same proteins, with either single and doublecharged ion forms) corresponding to three different proteins within the range 3255e9495 m/z, which were associated either with the group of six isolates or with C. diphtheriae and C. belfantii (Fig. S4 , Table  S4 ). We presumptively identified the specific biomarkers as two ribosomal proteins, L30 and S20, and one putative stress response protein (CsbD). Consistently, their amino-acid sequences differed between the C.
rouxii on the one hand, and C. diphtheriae/C. belfantii on the other hand (Fig. S5 ). Based on the current dataset, the specificity and sensitivity of peak distribution among the three species ranged between 95e100% and 76e100%, respectively (Table  S4 ). MALDI-TOF MS thus allows the discrimination between C. rouxii and C. diphtheriae/C. belfantii. These results warrant future updates of reference MALDI-TOF databases to incorporate the novel taxon. Based on the above results, the isolates of the novel clade represent a novel species, which we propose to name Corynebacterium rouxii. Description of C. rouxii sp. nov (rou'. xi. i. N. L. gen. n. rouxii, of Roux, a French scientist and former director of Institut Pasteur who made critical contributions to diphtheria toxin discovery and antitoxin treatment).
C. rouxii conforms biochemically to the description of C. diphtheriae strains belonging to biovar Belfanti  [2, 21] , except that strains are negative for maltose fermentation (API Coryne), being nearly negative or weakly positive with the Rosco Diagnostica maltose test. Key characteristics that distinguish C. rouxii from other members of the C. diphtheriae complex are specific MALDI-TOF MS biomarkers as described herein. The G þ C content of C. rouxii genomes ranges from 53. 2% to 53. 3%, with a value of 53. 3% for the type strain. So far, strains were isolated from 5 humans and a dog in France, as well as from two related cats in the USA. The type strain is FRC0190 T (¼ CIP 111752 T ¼ DSM 110354 T ), isolated in 2013 from a foot ulceration reported in Cahors, France. The genome accession number of strain FRC0190 T is ERS3795540.
a A trailing T after the strain identifier denotes that the strain is the type strain of its corresponding taxon. project number PRJEB22103. The EMBL (GenBank/DDBJ) accession numbers of the genomic sequences released in this study are ERS3795539 to ERS3795544. The annotated genomic sequence of strain FRC0190 T was deposited in the European Nucleotide Archive and is available under accession number ERZ1195831. rpoB and 16S rRNA gene sequences were also submitted individually under accession numbers MN542347 to MN542352 and MN535982 to MN535987, respectively.
174) Since those statements were written, systematic data on UK medical school differences have begun to emerge and will continue to do so in the future as the UKMED database develops  [6]. Different students have long been known to apply to different medical schools for different reasons  [7]. Comparing outcomes at graduation from different UK medical schools is difficult, since schools currently have no common final assessment (and indeed may set different standards  [8] ), although the UK Medical Licensing Assessment (UKMLA) should change that  [9]. In recent years, it has become clear that graduates from different medical schools vary substantially in their performance on postgraduate assessments, including MRCP (UK)  [10, 11] , MRCGP  [11] [12] [13] [14] , MRCOG  [15]  and FRCA  [16] , direct comparison being possible as the exams are identical for graduates of all medical schools.
The present study, Medical School Differences (Med-Difs), which evaluates the nature of medical school differences, addresses three specific questions and one general question about how UK medical schools differ, using a database containing fifty different descriptive measures of medical schools. 1. Preparedness. Do medical schools differ in the preparedness of their graduates for Foundation training, and do differences in preparedness matter? 2. Problem-based learning (PBL) schools. Do graduates from PBL schools differ in their outcomes compared with non-PBL graduates? 3. Specialty teaching and specialty choice. Does more undergraduate teaching of particular specialties, such as General Practice or Psychiatry, result in more graduates choosing careers in General Practice or Psychiatry? 4.
Specialty teaching and specialty choice The GMC's report has shown that there is little certainty about most issues concerning medical school differences, with empirical data being limited and seldom cited. In contrast, there are plenty of clear opinions about why medical schools might differ. Concerns about a shortage of GPs and psychiatrists have driven a recent discourse in medical education which concludes that it is differences between medical schools in their teaching which drive differences in outcomes. Professor Ian Cumming, the chief executive of Health Education England (HEE), put the position clearly when he said: It's not rocket science. If the curriculum is steeped in teaching of mental health and general practice you get a much higher percentage of graduates who work in that area in future.
Medical school histories A problem with research on medical schools is that medical schools evolve and mutate. Descriptions of different UK medical schools show relative stability until about 2000, when for most databases there are 19 medical schools (Aberdeen, Birmingham, Bristol, Cambridge, Cardiff, Dundee, Edinburgh, Glasgow, Leeds, Leicester, Liverpool, Manchester, Newcastle, Nottingham, Oxford, Queen's, Sheffield and Southampton). The various London schools underwent successive mergers in the 1980s and 1990s, but were then grouped by the GMC under a single awarding body, the University of London. A group of six "new" medical schools was formed in the 2000s (Brighton and Sussex (2002), Norwich (2000), Hull York (2003),  Keele (2003)  and Peninsula (2000))  1  of which Warwick was a 4-year graduate-entry school only.
2  The five London medical schools (Barts, Imperial, King's, St. George's and UCL) only became distinguishable within the GMC's List of Registered Medical Practitioners (LRMP), as with many other databases, from about 2008 (Imperial), 2010 (King's and UCL), 2014 (Barts) and 2016 (St. George's). Peninsula medical school began to split into Exeter and Plymouth from 2013, but for most purposes here can be treated as one medical school, with averages used for the few recent datasets referring to them separately. By 2016, there are therefore 18 of the 19 older medical schools (excluding London), plus five new ones, and five London schools, making 28 schools, with Exeter and Plymouth treated as one. In terms of data records, there are 29 schools, London being included both as a single entity for data up to about 2000, and for data after about that as the five separate entities which emerged out of the University of London.
Values for the University of London for Our analysis of teaching considered only schools with 5year courses ("standard entry medicine"; UCAS A100 codes or equivalent)  [56] , and therefore, schools which are entirely graduate entry only, such as Warwick and Swansea, were excluded. Graduates, in the sense of mature entrants, can enter either standard entry courses or graduate courses, but where schools offer several types of course, entrants to graduate courses are usually only a minority of entrants, although they do show some systematic differences  [57]. For most datasets, it is rare to find separate information for 5-year and graduate entry or other courses, most analyses not differentiating graduates by courses (and indeed the LRMP only records the Primary Medical Qualification, and not the type of course).
Missing values Data were missing for various reasons: some medical schools only coming into existence relatively recently, some not existing when historical measures were being collected and some medical schools not responding to requests for data in previous studies  [22, 23]. A particular issue is with data based on "University of London", which exist in earlier datasets whereas later datasets have the five separate London medical schools. We have therefore used imputation to replace the missing variables, in order to keep N as high as possible, and to make statistical analysis more practical. A constraint on imputation is that the number of cases (medical schools; n = 29) is less than the number of measures (n = 50), making conventional multiple imputation difficult. Missing values were therefore imputed via a single hotdeck imputation  [59]  based on the k nearest neighbours function kNN() in R.
Correlations between the measures For all analyses, the imputed data matrix was used, for which a correlogram is shown in Fig. 2. Of the 1225 correlations, 395 (32. 2%) reached a simple p < 0. 05 criterion, and 201 (16. 4%) reached the Tukeyadjusted criterion of 0. 0025, making clear that there are many relationships between the measures which require exploration and explanation. As a contrast, a dataset of comparable size but filled with random numbers, whose correlogram is shown in Supplementary File 1 Fig. S1 , had just 55 correlations significant at the 0. 05 level (4. 5%) and only two correlations (0. 16%) reached the Tukey-adjusted criterion. The correlogram and equivalent descriptive statistics for the raw, non-imputed, data are available in Supplementary File 1 Fig. S2. Although there is much of interest in the entire set of correlations in Fig.
Open naming of medical schools The present study is only possible because the names of medical schools are published, as otherwise it would not have been possible to link data across sources. There is however a reluctance of many medical schools to have information published about themselves, to the extent that Freedom of Information (FoI) requests were therefore used in AToMS, as has also been the case for in another UK study which needed apparently straightforward statistics  [32]. We have incorporated some data that were provided to us from other studies on the basis of non-naming of schools, and we have respected such requests, despite recognising that it potentially makes problems for future researchers. However, the data for all of the main measures used here are available in Supplementary File 2 for secondary analysis by other researchers.