dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis between groups was performed by unpaired unequal variance Welch's t-test. Genes were considered to be statistically significant at p≤0. 05. The false discovery rate (FDR), used to correct for errors in multiple testing  [27, 28] , was estimated for each of these tests, using a single type I error cutoff (α = 0. 05), as described previously  [29] [30] [31]. Data from other microarray studies: Gene expression profiles in samples from the frontal cortex following simulated stress  [32] , depression  [33] , alcohol dependence  [33] , stroke  [35] , and Alzheimer's disease  [36]  were collected directly from the original authors or downloaded from Gene Expression Omnibus (GEO) Datasets. All studies used adult rats while the Alzheimer's disease model used transgenic mice.
Finding overlapping genes across pathobiological models: To test the possibility that gene expression profiles associated with brain trauma share some common patterns with other pathobiological models, we performed an overlap analysis of genes, comparing the present datasets with those of rodent models of stress, depression, alcohol dependence, stroke, and Alzheimer's disease. We are aware that comparing across experimental platforms and analytical methods may introduce some variability. To counteract these caveats, we focused on a list of significant genes (at p≤0. 05) across reported datasets rather than magnitudes of expression changes. Data sets from selected mentioned microarray studies were imported into Excel. To identify overlap genes a Venn diagram was generated based on Gene Symbol as primary field.
Statistical analysis was performed using a binomial test  [37]. Functional analysis of significant genes: Significant genes were classified into functional related gene groups using the Web-based gene ontology tool from the Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6. 7 (http://david. abcc. ncifcrf. gov) [37] [38] [39]. This analysis was performed by using the two Functional Annotation tools, the Chart and the Clustering tool. The Chart report lists annotation terms and their associated genes and the Clustering tool group redundant annotation terms in clusters. Only Biological Process (BP) related functions were considered and default software settings were applied. A cut-off value of Enrichment Score of clusters was set at 2. To reduce redundancy, only a single significant BP (p≤0.
The qRT-PCR assays were conducted according to the manufacturer's instructions (Ab Applied, France) using a LightCycler 480 Instrument with software version 1. 5. 0 (Roche, Meylan, France). Each reaction was run in duplicate in a final volume of 25 µl containing 12. 5 µl of SYBR Green 2 X Master Mix (Roche Diagnostics, Mannheim, Germany), 300 nmol/l of each primer, and 5 µl of cDNA. PCR programmes were carried out as follows: one cycle at 95°C for 5 min and 45 cycles of amplification (95°C for 10 s, 60°C for 30 s, and 72°C for 15 s). The crossing point (Ct) of each set of primers was determined and compared to the Ct of the peptidylprolyl isomerase A (PpiA; NM_017101. 1) mRNA used as endogenous controls. Subsequently data was imported into Excel for log2 transformation and statistical analyses.
Comparison to other pathobiological models Complete microarray datasets are available for rat brain studies of depression (GSE20388)  [33]  and alcohol dependence (GSE4494)  [34]. Although three studies examined gene expression changes in rat cortex following stress  [32] , stroke  [33] , and Alzheimer's disease  [34]  only lists of significant genes, rather than complete data sets, are available for these studies. Data are reported in Figure  2  and Table  5. Following lfp-TBI, stroke represented the pathology showing the most overlap of genes, with 25% of homology, i. e. , of the 1425 genes identified 1 month post lfp-TBI, 357 were identical to those reported in cortex following stroke (p<0. 05; binomial test). In particular, these common genes were involved in biological activities related to immune/inflammation response and matrix organization as well as being subjected to network construction.
Acknowledgment This research was supported by the  French Ministry for Education and Research funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author contributions are as follow: conceived and designed the experiments: VP, CC; data analysis: VP; reagents/ materials contribution: MO, JD, NF; paper writing: VP; manuscript supervision: MR, JD, BAL. We are grateful to  Dr. E. M. Blalock  for microarray analysis advice and to  Dr. C. Benatti ,  Dr. K. Bosser , and  Dr. M. W. Kimpel  for providing microarrays data sheets. There is no conflict of interest.
The trapped sample was then reversely eluted onto the analytical column at a 300 nL min -1 flow rate. Both the trapping column (150 μm i. d. x 4 cm long) and analytical column (75 μm i. d. x 70 cm long) were packed in-house using Jupiter C18 media (Phenomenex) particles (5 μm for the trapping column and 3 μm for the analytical column) into 360 μm o. d. fused silica (Polymicro Technologies Inc. ) with 1 cm sol-gel frits for media retention  [41]. The gradient began with mobile phase A (0. 1% formic acid in water) and switched to mobile phase B (0. 1% formic acid in acetonitrile) with the following gradient profile (min, %B): 0, 1; 2, 8; 20, 12; 75, 30; 97, 45; 100, 95; 110, 95; 115, 1; 150, 1. LC-MS/MS data were acquired using a LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific) with a custom nano-ESI interface.
The ESI emitters were custom-made by chemically etching fused silica (150 μm o. d. x 20 μm i. d)  [42]. The heated capillary temperature was set to 350°C with a spray voltage of 2. 2 kV. Data were acquired for a total of 100 min after a 15 min delay from sample injection. Full MS spectra were acquired for 300<m/z<1800 at a resolution of 30K. Higher-energy collisional dissociation (HCD) MS/MS with a normalized collision energy setting of 45% (a resolution of 7. 5K) was performed in the data-dependent mode using the ten most abundant parent ions, excluding single charge states. Data analysis. Tandem mass spectra were deconvoluted into ASCII text files using the DeconMSn tool  [43]. Spectra from all 4 fungi were then searched as a combined dataset with MSGF+  [44]  using a customized protein database containing translated protein sequences from the genomes of all 4 fungal species.
The protein database was amended with common contaminants (e. g. , trypsin and human keratin sequences). The peptide identifications were filtered to 1% FDR (using MGSF+'s reported Q-value 0. 01, which is derived using the standard decoy approach)  [45]. Because this 1% FDR criterion carefully controls for false discovery events, we chose the minimum number of peptides for unambiguous protein identification to be 1. Identified proteins based on 1 peptide comprised 24-29% of the total number of proteins identified in the secretomes of each species in this study, but they represented merely 3-5% of the total number of identified carbon-degrading enzymes (i. e. , carbohydrate-active enzymes, peptidases, and lipases) per species. Thus, 95-97% of carbon-degrading enzymes in each species were identified with 2 or more peptides.
For the database search, the iTRAQ 8-plex reporter ion conjugate mass (+304. 205353 Da on Lysine residues and N-termini) was set as the static modification, and the oxidation on methionine was set as the dynamic modification. Other settings included parent mass tolerance of +/-20 ppm, partially tryptic cleavages included, +/-1 Da parent corrections (to account for incorrect monoisotopic mass determination), MS level data centroiding, and decoy search mode enabled. Because peptide sequences from all 4 fungi were searched against all 4 genomes simultaneously, multiple protein matches for some peptides were identified among the 4 genomes. For biological interpretation of the data, only the first protein reference identified for each peptide was used, and the function of this protein was manually checked against other protein matches for that peptide to ensure that all protein functions for a particular peptide were similar.
Functional information was derived by searching a combined database of NCBI and Uni-Prot fungal protein sequences for proteins identified in this study using BLAST analysis. For proteins for which the highest-scoring (lowest E-value) match was hypothetical or uncharacterized, the highest-scoring non-hypothetical match was reported. Proteins with no matches (hypothetical or otherwise) having an E-value below 10 -9 (for proteins with 1 identified peptide) or 10 -6 (for proteins with 2+ identified peptides) were reported as hypothetical. Functional information obtained via BLAST analysis was manually checked against automatically assigned annotations in the genomes (sequenced by the Joint Genome Institute (JGI)). To facilitate this, proteins identified in this study were mapped to JGI protein IDs, which were then used to retrieve and check genome annotations.
As automatically-assigned annotations often contain errors  [46] , emphasis was placed on functional information from the BLAST analysis, as has been done by others  [8]. All proteins were then functionally categorized according to the Carbohydrate-Active Enzymes Database (CAZy) (www. cazy. org) [47]  and MEROPS Peptidase Database (http://merops. sanger. ac. uk/)  [48]  classification systems, using UniProt family and domain designations to aid in categorization.
Fungal genome sequence availability The genome sequences and annotations for the 4 fungi in this study are available at the JGI fungal genomics resource MycoCosm  [56]  at http://genome. jgi. doe. gov/programs/fungi/index. jsf; genomes are identified in MycoCosm as Altal1 (A. alternata), Parsp1 (P. sporulosum), Pyrsp1 (Pyrenochaeta sp. ), and Stasp1 (Stagonospora sp. ). Genome assemblies and annotations were also deposited at DDBJ/ENA/GenBank under the following accession numbers: LXPP00000000 (A. alternata), LXPO00000000 (P. sporulosum), LXSZ00000000 (Pyrenochaeta sp. ), and LXTA000 00000 (Stagonospora sp. ). The versions described in this paper are LXPP01000000, LXPO010 00000, LXSZ01000000, and LXTA01000000, respectively.
The size of the full predicted secretomes (i. e. , based on genomic data only) of each fungus was comparable to that of the experimental secretomes, ranging from 1,352 proteins in the A. alternata predicted secretome to 1,604 proteins in the P. sporulosum predicted secretome (Fig  1C ). Within the predicted secretomes, 478 (in A. alternata) to 535 (in P. sporulosum) proteins were classified within CAZy and MEROPS functional groups.
Secretome diversity across broad functional groups Categorizing the proteins in each experimental secretome based on broad functional groups according to the CAZy and MEROPS databases reveals a striking similarity in secretome functional diversity among the four organisms (Fig  1A ). The total proportion of CAZymes identified in the secretomes ranged from 22% in P. sporulosum to 28% in A. alternata and consisted predominantly of glycoside hydrolases (GHs; 12-15%) and redox-active auxiliary activities (AAs; 5-6%) in all four fungi. Peptidases comprised 12-13% of identified proteins in each of the fungal secretomes, while lipases comprised only 1%. Identified proteins that are not considered by the CAZy or MERPOS databases were categorized as "other" and constituted a large portion of the secretome for each fungus, ranging from 47% in A.
Moreover, it is noteworthy that no GH families containing more than 3 identified proteins were represented exclusively by shared sequences; thus, species-specific versions of functionally similar enzymes (i. e. , potential isozymes) were an inherent characteristic of these fungal secretomes. The example shown here for GH families was chosen for its rich diversity, but the patterns identified herein were representative of other CAZy and MEROPS protein families (data not shown). Among proteins experimentally identified in the secretomes, proteins unique to each fungus spanned the full range of broad CAZy and MEROPS functional groups that were identified in the full secretomes (Fig  5A ). However, the proportion of CAZymes was considerably lower among unique proteins (ranging from 15% in Stagonospora sp.
Hypothetical proteins and proteins with limited functional information. The role of hypothetical proteins in the fungal secretomes remains ambiguous due to either the lack of functional information assigned to these proteins or the lack of a protein match with an acceptably low E value in the NCBI and UniProt databases. Hypothetical proteins comprise a nonnegligible proportion (up to 14%) of identified proteins in the experimental secretomes and include up to 194 individual proteins (Fig  1 ). As such, at least some of these proteins likely play important roles in extracellular carbon transformations. Interestingly, the predicted secretomes contain nearly three times as many hypothetical proteins (up to 580 per species) than the experimentally observed secretomes (Fig  1 ). It is possible that the genes that encode these proteins are rarely expressed under laboratory conditions and thus have received little or no characterization in previous studies.
As secretome plasticity is dependent upon carbon source and growth conditions, these hypothetical proteins may represent uncharacterized functional diversity in these species. Another challenge in working with large proteomic datasets, particularly for environmental isolates of fungal species for which relatively little proteomic information is available for closely-related organisms, is the incompleteness of functional information in existing fungal databases, even for non-hypothetical proteins. For instance, many of the proteins we identified in this study were mapped to proteins for which only family-level annotations were available (e. g, "glycoside hydrolase family 5" or "M18 metallopeptidase"), rather than a specific enzyme name, thus limiting our understanding of which enzymes are present and which reactions they may be catalyzing in the secretome.
Nevertheless, it is noteworthy that we were able to assign functional information to over 85% of peptides generated by these environmental isolates (i. e. , non-model organisms) for which a protein match could be found in the sequenced genomes. This is particularly encouraging in light of the fact that species-level determination of two of our isolates, Stagonospora sp. and Pyrenochaeta sp. , remains elusive. As research on fungal genomics and proteomics is rapidly increasing, we look forward to delving more deeply into datasets such as these as more information becomes available.
alternata secretome, α-glucosidases in the GH31 family were identified in all four organisms, as were proteins in families GH38, GH47, GH76, and GH92, all of which contain α-mannonsidases. Notably, the predominance of GH92 family α-mannonsidases in the Stagonorpora sp. secretome is similarly offset by the presence of these functionally related families. The acid trehalase in the GH65 family and the α-glucoronidase in the GH67 family, both identified only in the A. alternata secretome, may share functionality with trehalases in the GH37 family and proteins in the GH115 family, respectively, both of which were identified in all four fungi. The GH23 and GH25 families, uniquely identified in the P. sporulosum secretome, are described as lysozymes, peptidoglycan lyases, and chitinases in the CAZy database; these families share similar functionality with GH18 family proteins, which were identified in the secretomes of all four organisms.
Acknowledgments This research was performed under the  Facilities Integrating Collaborations for User Science (FICUS)  exploratory effort and used resources at the  DOE Joint Genome Institute  and the  Environmental Molecular Sciences Laboratory , which are  DOE Office of Science User Facilities. Both facilities are sponsored by the  Office of Biological and Environmental Research  and operated under Contract Nos. DE-AC02-05CH11231 (JGI)  and  DE-AC05-76RL01830  (EMSL). Part of this research was performed at the  Bauer Core Facility of the FAS Center for Systems Biology at Harvard University. A portion of the bioinformatics analysis was performed at  Harvard 's  FAS Research Computing facility. We thank  Joshua Aldrich  (EMSL) and  Michele Clamp  ( Harvard ) for bioinformatic analyses at various stages of this project, and  Therese R.
Methods Item 5. Whether data collection was planned before the index test and reference standard were performed ( prospective study) or after (retrospective study) Example. 'We reviewed our database of patients who underwent needle localization and surgical excision with digital breast tomosynthesis guidance from April 2011 through January 2013. […] The patients' medical records and images of the 36 identified lesions were then reviewed retrospectively by an author with more than 5 years of breast imaging experience after a breast imaging fellowship'. 23 xplanation. There is great variability in the way the terms 'prospective' and 'retrospective' are defined and used in the literature. We believe it is therefore necessary to describe clearly whether data collection was planned before the index test and reference standard were performed, or afterwards.
Since the same set of eligibility criteria applies to all study participants, this is an example of a single-gate study. In the second example, the authors used different eligibility criteria for participants with and without the target condition: one group consisted of patients with a confirmed diagnosis of Clostridium difficile, and one group consisted of healthy controls. This is an example of a multiple-gate study. Extreme contrasts between severe cases and healthy controls can lead to inflated estimates of accuracy. 6 29 em 7. On what basis potentially eligible participants were identified (such as symptoms, results from previous tests, inclusion in registry) Example. 'We reviewed our database of patients who underwent needle localization and surgical excision with digital breast tomosynthesis guidance from April 2011 through January 2013'.
23 xplanation. The eligibility criteria specify who can participate in the study, but they do not describe how the study authors identified eligible participants. This can be performed in various ways. 30 A general practitioner may evaluate every patient for eligibility that he sees during office hours. Researchers can go through registries in an emergency department, to identify potentially eligible patients. In other studies, patients are only identified after having been subjected to the index test. Still other studies start with patients in whom the reference standard was performed. Many retrospective studies include participants based on searching hospital databases for patients that underwent the index test and the reference standard. 31 ifferences in methods for identifying eligible patients can affect the spectrum and prevalence of the target condition in the study group, as well as the range and relative frequency of alternative conditions in patients without the target condition.
32 These differences can influence the estimates of diagnostic accuracy. In the example, participants were identified through searching a patient database and were included if they underwent the index test and the reference standard. Item 8. Where and when potentially eligible participants were identified (setting, location and dates) Example. 'The study was conducted at the Emergency Department of a university-affiliated children's hospital between January 21, 1996, and April 30, 1996'. 33 xplanation. The results of a diagnostic accuracy study reflect the performance of a test in a particular clinical context and setting. A medical test may perform differently in a primary, secondary or tertiary care setting, for example. Authors should therefore report the actual setting in which the study was performed, as well as the exact locations: names of the participating centres, city and country.
Other information Item 28. Registration number and name of registry Example. 'The study was registered at http://www. clinicaltrials. org (NCT00916864)'. 107 xplanation. Registering study protocols before their initiation in a clinical trial registry, such as ClinicalTrials. 9] [110] [111] [112]  This has many advantages, including avoiding overlapping or redundant studies, and allowing colleagues and potential participants to contact the study coordinators. Additional benefits of study registration are the prospective definition of study objectives, outcome measures, eligibility criteria and data to be collected, allowing editors, reviewers and readers to identify deviations in the final study report. Trial registration also allows reviewers to identify studies that have been completed but were not yet reported.
Many journals require registration of clinical trials. A low but increasing number of diagnostic accuracy studies are also being registered. In a recent evaluation of 351 test accuracy studies published in high-impact journals in 2012, 15% had been registered. 113 ncluding a registration number in the study report facilitates identification of the trial in the corresponding registry. It can also be regarded as a sign of quality, if the trial was registered before its initiation. In the example, the authors reported that the study was registered at ClinicalTrials. gov. The registration number was also provided, so that the registered record could be easily retrieved. Item 29. Where the full study protocol can be accessed Example. 'The design and rationale of the OPTIMAP study have been previously published in more detail [with reference to study protocol]'.
A re-examination of specimens often necessitates loans or visits to the corresponding collections. Archaeologists focusing on carved, inscribed, or even painted artefacts have traditionally faced similar difficulties. Photographic and computing techniques referred to as reflectance transformation imaging (RTI) have been developed in this field to solve these issues  (Earl et al. , 2010, and references therein). In short, these approaches compute, from a set of photographs, a single "interactive specimen" whose illumination can be modified at will. The data can be enhanced in various ways. This approach has already been applied to various types of fossil material  (Hammer et al. , 2002). Herein we report on our testing in documenting fossil insect imprints. We selected the holotype of Protelytron permianum Tillyard, 1931 from the Artinskian Elmo locality (Early Permian; KS, USA) for its particular importance as putative age calibration point.
The size of processed photographs ranges from ca. 13 to 18 MB. Photographs of a set were then compiled into an RTI file using the RTIbuilder software v. 2. 0. 2 (freely available under GNU license; using the HSH fitter). For each set, four computations were carried out: a pair for each hemisphere, and a pair for the second and third fitting orders. The latter order proved to produce slightly sharper data and was then selected. A complete RTI computation took about an hour with a computer equipped with a 3. 3 GHz processor and 8 GB of RAM. We provide an online Dryad dataset, cited below as "  Béthoux et al. (2016) ", in which the RTI files along with viewer software and instructions for use are available. Photographs reproduced herein were extracted from the RTI files (and are bookmarked in the corresponding files).
Acknowledgements. We are grateful to  V. Blagoderov ,  R. G. Beutel  and  J. T. Haug , and the editorial board of the journal for constructive comments; to  V. Rouchon  ( CRCC, Paris ) for introducing the  RTI  process to the senior author; to  Susan Butts  ( YPM, New Haven ) for allowing the loan of the specimen  YPM IP 001019  and the preparation of the block containing it; and to  Ricardo Perez-de la Fuente  ( MCZ, Harvard ) for providing photographs of specimen MCZ 3366. Edited by: D. Korn Reviewed by:  J. Haug ,  R. Beutel , and  V. Blagoderov.