dataesr/scientific-paragraphs-categorization huggingface dataset:
Derivation of a conserved core human microglial signature Observing the variability across published studies, we set out to define a human microglia gene signature from human tissue and cell data using a GCN (Figure  2A , Table  S1 ). For this meta-analysis, tissue datasets including the GTEx project and ABA data were chosen, which cover a broad spectrum of sampling, across numerous CNS regions and donors  (Hawrylycz et al. , 2012; Lonsdale et al. , 2013; Shen, Overly, & Jones, 2012). Additionally, Cell transcriptomic data from  Zhang et al. reporting the top 20 marker genes for different brain cell types, was also included in the analysis (Y. Zhang et al. , 2016). Post QC, the data amounted to a total of 5,020. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under The copyright holder for this preprint (which was not this version posted April 26, 2018. ; https://doi. org/10. 1101/308908 doi: bioRxiv preprint samples isolated from 197 donors and 440 anatomical regions of the brain. To extract a microglial cluster from individual datasets, each was analyzed independently using a GCN (Tom C  Freeman et al. , 2007; Theocharidis, Van Dongen, Enright, & Freeman, 2009). This method exploits the inherent variability amongst samples due to variation in sampling, donors and cellular diversity across different CNS regions. In this case, genes expressed specifically by microglia in the context of the CNS, will vary in expression according to the regional abundance of these cells and therefore correlate in their expression, e. g. the poorly populated cerebellum presents a low expression of these genes relative to other regions.
For constructing GCNs, genes are represented by nodes, and connected by an edge based on the similarity between their expression profiles, as quantified by the Pearson correlation coefficient (Figure  S1 ). In this network, correlated genes form highly connected cliques within the overall topology of the GCN, and are defined as clusters using the MCL algorithm  (Enright, Van Dongen, & Ouzounis, 2002; B. B. Shih et al. , 2017). Using this approach on individual datasets, a microglial cluster containing the known marker genes CX3CR1, AIF1 and CSF1R, was identified for each dataset (Table  S2 )  (Elmore et al. , 2014; Mittelbronn et al. , 2001). The final high confidence microglia gene signature was defined by 249 genes, which were present in three or more dataset-derived clusters, so as to avoid biases towards individual datasets.
Validation and description of the core human microglial signature To validate the microglial signature genes, various lines of evidence were examined. First, a comparison of the average expression of core signature genes across cell types revealed a significantly higher (P < 0. 001) expression in myeloid cells relative to other immune (most of which are scarce within non-neuropathologic brain tissue) and CNS cell types (Figure  2B )  (Ginhoux et al. , 2010). Second, the average expression of core genes across brain regions in the GTEx and ABA datasets correlated with regional microglial densities as measured in the mouse (Figure  2C )  (Lawson et al. , 1990). Third, where data was available, the IHC staining of proteins encoded by signature genes was examined in the CNS. This confirmed the microglial expression of known markers e.
A majority of the core signature genes (64%, 142 genes) were identified in two or more human studies, whilst 99 genes overlapped solely with the Galatro et al. signature. To further validate the specificity of the current microglial signature, the coexpression of these genes was compared with that of the Galatro et al. signature (1,236 genes), which included the majority of genes in other signatures. On constructing a GCN from the GTEx RNA-Seq dataset, genes of the current signature were strongly coexpressed with one another within the network graph (Figure  4A ). Many Galatro et al. signature genes were similarly coexpressed, however, many others were scattered across the network, indicative of an overall poor correlation between them in comparison to the current signature (Figure  4B ). Cluster analysis showed a contrast in the expression profiles of clusters enriched in Galatro et al.
Microglia in Alzheimer's disease We next used the 249 gene signature to assess the human microglial profile in aging and Alzheimer's through analysis of a transcriptomics dataset derived from cortical and hippocampal regions of Alzheimer's patients and non-neuropathic controls  (Berchtold et al. , 2013) (Table  S1 ). As a preliminary analysis, the average expression of signature genes was used as a proxy measure of microglial number and calculated for all 20 yr age groups across regions (Figure  5A ). Apart from the entorhinal cortex, a significant increase in expression of core genes was observed with aging. For example, in the hippocampus, a 1. 6 fold change (FC) in expression (FDR < 0. 01) was observed between the oldest and youngest control age groups. The lack of significance for the entorhinal cortex is likely attributed to the significant variation between samples across the different age groups.
, 2014; Mittelbronn et al. , 2001). To identify a conserved human microglial signature, we used an unbiased correlation network analysis, harnessing the power of cell and tissue transcriptomics data including two large studies; namely the GTEx and ABA datasets. Together they provide the largest publicly available transcriptomic datasets covering a comprehensive range of brain regions, collected from numerous donors  (Hawrylycz et al. , 2012; Lonsdale et al. , 2013). GCNs were constructed to identify groups of genes with similar expression profiles, corresponding to cells or pathways, as has been shown possible using this approach (Tom C. Freeman et al. , 2012; Mabbott et al. , 2013; B. B. Shih et al. , 2017). From each dataset, a microglia cluster was identified, based on the presence of canonical marker genes for these cells.
The consensus from these dataset derived signatures, provided 249 genes representative of human microglia across datasets. To our knowledge, this is the first study to deconvolute a microglia signature from the current GTEx and ABA tissue data. The derived signature included all known markers of microglia, including TMEM119, P2RY12, and CD68  (Bennett et al. , 2016; Perego, Fumagalli, & De Simoni, 2011; Wes, Holtman, Boddeke, Möller, & Eggen, 2016)  and many other genes known to be associated with microglial/macrophage biology. This includes representatives of the TLR, complement, and MHC class 2 antigen-presenting immune pathways. Validation of the signature, included an examination of HPA immunostaining of proteins encoded by the signature genes, demonstrating that they were significantly expressed in myeloid cell types relative to other neuronal and immune cells, and comparison with published microglial signatures.
This final step revealed approximately half of the core genes as conserved between species, reaffirming the widely accepted idea that many constitutively expressed microglial genes are conserved between mouse and human. Although their response to processes like aging may diverge  (Bennett et al. , 2016; Galatro et al. , 2017). To examine the specificity of the core signature, a comparison was made with the Galatro et al. (Galatro et al. , 2017)  signature, derived by comparing the fold expression of microglia isolated from the parietal cortex of post-mortems, relative to whole tissue. The signature provides an insight into human microglial functionality under homeostasis and has a high degree of overlap with current signatures, although being significantly larger. Using the GTEx brain atlas data the coexpression and regional expression of both signatures were investigated.
, 2016). In contrast, our core signature exhibited a well-defined and condensed coexpression pattern corresponding to the known regional CNS variation in microglial abundance. Therefore, while isolated cells have provided fundamental insight into microglial identity, coexpression analysis of the employed datasets aids in defining the microglial specific profile in the CNS. Evidence for the central role microglia play in the pathogenesis of neurodegenerative disease continues to grow, however, the cellular and molecular changes that occur in human brain pathologies are poorly understood. Furthermore, using the core signature we conducted various analysis, to discern the influence of cell number and activation state in both healthy aging and Alzheimer's, across a number of brain regions using the dataset generated by  Berchtold et al.
, 2014; Velayudhan et al. , 2013). In contrast, the postcentral gyrus, a region shown to maintain its grey matter content and functional connectivity with other regions in late-onset Alzheimer's, showed little change in Alzheimer's versus controls  (Adriaanse et al. , 2014; Thompson et al. , 2003). Strikingly, these findings are consistent with regional Alzheimer's progression based on tau burden, neuroinflammation and neuronal loss, which are prominent in the entorhinal cortex and hippocampus  (Cope et al. , 2017; Freer et al. , 2016; Kreisl et al. , 2016). Overall, these data demonstrate the utility of the signature in assessing quantitative differences in microglial numbers from tissue-level expression datasets. To gain insight into molecular pathways specifically affected in Alzheimer's, qualitative changes in the profile of microglia were examined.
Acknowledgments T. C. F. and B. W. M. are funded by an  Institute Strategic Programme  Grant funding from the  Biotechnology and Biological Sciences Research Council  [ BB/J004227/1 ]. B. W. M. receives funding from the  UK Dementia Research Institute and Medical Research Council  [ MR/L003384/1 ]. B. S. is supported by  Experimental Medicine Challenge  Grant funding from the  Medical Research Council  [ MR/M003833/1 ].
Conflict of Interest Statement The authors have no competing financial interests. diagram showing steps in the derivation of the core microglial signature. From each dataset (upper panel) a microglial-specific cluster was identified using coexpression network analysis (blue sectors). In comparing these gene clusters, 395 overlapped across more than one dataset (green sector). From this set of overlapping genes, green lines connect a specific gene to all datasets in which it was identified. Of the overlapping genes, those co-occurring in three or more datasets were taken to represent the core microglial signature (red sector). (B) Average expression of core signature genes in various neuronal and immune cell types selected from an expression atlas of primary cells  (Mabbott et al. , 2013). (C) Comparison of the average expression across tissue transcriptomics data and microglial cell numbers in mouse, for regions common to the respective studies  (Lawson et al.
Therefore, the numerical analysis of mechanisms of blast energy dissipation may be modeled using the Lagrangian approach. The finite element code Ls-Dyna ver. R. 9. 0. 1 is applied to simulate the deformation of the honeycomb observed in the performed blast experiment using the explicit time integration. The main application of Ls-Dyna is to model crash and dynamic events, so typical calculations are performed by means of the explicit solver, in particular, the central-difference technique. The quasi-static compression of the honeycomb is also modeled in Ls-Dyna but in the implicit formulation, also available in the software.
Methods In this work, the prediction of forces exerted by muscles during dynamic contraction is obtained based on direct dynamics approach. This approach consists of three main steps: signal process, dynamic activation and dynamic contraction. First, the EMG signals of the muscles and the motion capture data were used as inputs of this approach. The first step is the signal process consisting to transform raw signal to filtered excitation signal. Then, the second step is the dynamic activation, where neural signal is transformed to muscle activation. In the third step, musculotendon forces are calculated using the dynamics contraction based in musculoskeletal model. _ F MT ¼ @F MT @t ¼ K T V MT À V M cos ðaÞ ! (1) To calculate musculotendon forces, two nonlinear differential equations were used.
Patients All consecutive patients treated for a locally advanced lower or mid rectal cancer (T3/4 and/or N+) with neoadjuvant radiation therapy between January 2000 and December 2016 in two university hospitals have been identified and studied. T and N status were assessed as recommended by MRI and/or ultrasound (echo-endoscopy)  (11, 15). If reported stages differed between the two evaluation modalities, the patient was assigned to the worse one. Tumors were staged according to the AJCC 6 th or 7 th editions. Lower rectum was defined from the end of anal canal until 2 cm above it and mid rectum from 2 cm above the anal canal until 7 cm above it. All patients with available MMR status have been included in this retrospective study. Patients had previously given their consent for their data to be used in retrospective studies in accordance with French laws.
Methylation status of the proximal part of the promoter region of MLH1 was assessed by methylationspecific PCR (MSP). All DNA samples were treated chemically with sodium bisulphite according to the Zymo Research DNA-methylation Kit. Subsequent methylation-specific PCRs were carried out as described by Capel et al. (22)  Two PCRs were performed for each chemically modified DNA sample, one with primers annealing specifically to the methylated gene and the other to unmethylated DNA, with a fluorescent primer in each case. PCR products were run on an ABI 3730 DNA sequencer (Applied Biosystems, Foster City, CA). The resulting data were analyzed with the GeneMapper software, version 4. 0 (Applied Biosystems). All PCRs were performed with positive controls of both methylated and unmethylated DNA.
Germ-Line Mutation Analysis DNA was isolated from peripheral blood after obtaining informed consent for MMR genetic analysis, as requested by French laws. Human genomic DNA was prepared from whole blood samples with a semiautomated Extragene extractor (Genomic Industry, Archamps, France) and a DNA extraction kit (Promega France). All hMLH1, hMSH2, and hMSH6 exons were amplified by PCR with primers designed with primer3 software (http://Frodo. wi. mit. edu/cgi-bin/primer3/primer3_www. cgi). Amplification was performed on a GeneAmp PCR system 9700 (Applied Biosystems). PCR products were cleaned up on a MultiScreen PCR 96-well plate (Millipore, Billerica, MA) and sequencing reactions were carried out with the BigDye Terminator v1. 1 cycle sequencing kit (Applied Biosystems). The products of the sequencing reactions were cleaned up with the Sephadex G-50 (GE Healthcare, Life Sciences) in a MultiScreen-HV 96-well filter plate (Millipore), then run up on an ABI 3730 DNA sequencer (Applied Biosystems).
The resulting sequence data were analyzed by SeqScape software, version 2. 5 (Applied Biosystems) in comparison with the reference sequences of MLH1 and MSH2 genes (accession numbers, respectively: NM_000249 and NM_000251). The SALSA MLPA P003 MLH1/MSH2 probe mix and SALSA MLPA P008 kits (MRC-Holland, Holland, The Netherlands) were used according to the manufacturer's instructions to screen rearrangements of one or more exons of the hMLH1, hMSH2, and hMSH6 genes. Fragment analysis of multiplex PCR was carried out on the ABI 3730 DNA analyzer (Applied Biosystems) and results were analyzed by GeneMapper software, version 4. 0 (Applied Biosystems). Normalization of samples was performed against the mean control ratios. DNA samples from unaffected individuals were used as controls in each experiment  (20).
Endpoints Endpoints included tumor downstaging (defined as a lower pT or pN staging than cT or cN with neither having risen), resection margins (R0 or R1), recurrence free survival (overall, local, and metastatic), and overall survival. Survival endpoints were defined from the date of diagnosis (i. e. endoscopic biopsy). Recurrences were defined as local if located within the pelvis or involving the inguinal nodes and metastatic otherwise. Overall survival (OS) was calculated from the date of diagnosis to the date of death from any cause. Survival data were censored at the last follow-up. Progression-free survival (PFS) was calculated from the date of diagnosis to the date of progression or death from any cause, or the date of the last follow-up, at which point data were censored. Recurrence-free survival (RFS) was calculated from the date of diagnosis to the date of progression, or the date of the last follow-up or death from any cause, at which point data were censored.
Patients' characteristics We identified a total of 349 patients treated with neoadjuvant chemoradiation therapy followed by TME. Fifty-three had to be excluded from the analysis because of lack of data on MMR status (including 49 patients with pathological complete response). Among the 296 included patients, 273 were pMMR (92. 2%) and 23 dMMR (7. 8%). For the whole population, median age at diagnosis was 60. 6 years (IQR: 50. 3-70. 2). Median follow-up was 43. 0 months (IQR: 27. 9-66. 7). hMSH2 activity was lost in eight patients, hMLH1 in six, hMSH6 in three, and PMS2 in one. In one patient, the defective protein could not conclusively be identified due to lack of material, genetic testing then identified the mutation as being MSH6, bringing the total of MSH6 mutated patients to four. In four patients, two proteins were defective (hMLH1 and hMLH2 in two and hMSH2 and hMSH6 in two).
One could wonder whether some of the patients with pCR in Hasan's study who were reported as pMMR were so by default as the technique and timing of MMR status analysis was not reported as it was a database study. Their pCR rate (8. 6%) was also rather low as compared to that of prospective studies such as GRECCAR-6 (15% at seven weeks), despite a higher dose of radiation (including 11% above 54 Gy)  (28). Additionally, inclusion of pCR patients in our worst-case scenario analysis did not change the downstaging results, with it remaining more frequent in the dMMR group. In a descriptive report about 62 dMMR patients, De Rosa et al. identified 29 patients treated with neoadjuvant chemoradiotherapy followed by surgery  (29). As in our study, most patients had a tumor downstaging on the surgical specimen.
METHODS The protocol of this retrospective study was approved by the review board of Numazu City Hospital and Juntendo Shizuoka Hospital on May 1 st , 2019, and all examinations were conducted in accordance with the standards of good clinical practice and the Declaration of Helsinki. The corresponding protocol/approval number is  [298]. Electronic medical charts from January 2011 to March 2019 were retrospectively reviewed for all patients with the main disease of 'femur shaft fracture' who had been transported to Numazu City Hospital, and these patients were included in the present study. Femoral shaft fracture was defined as a fracture of the middle portion of the femur, which extends from the lesser trochanter to the femoral condyle. We collected the data on each patient's sex, age, mechanism of injury, an open or closed fracture, vital signs on arrival (Glasgow Coma Scale, systolic blood pressure, heart rate), value of fibrin degradation product on arrival, fracture type based on fracture and dislocation classification compendium-2018 [7], surgical operation status, methods of operation, operation day, transfusion status, post-operative infection, duration of admission, final outcome (survival discharge or not), and non-or mal-union after discharge at Numazu City Hospital.
The Department of Orthopedic Surgery at Numazu City Hospital did not measure the vitamin D level of admitted patients routinely. Patients were then divided into two groups based on the mechanism of injury: a Low-Energy (LE) group, which included patients who had been injured by falling down or spontaneously injured; and a High-Energy (HE) group, which included patients who had been injured by traffic accidents or free fall from a high place (over 3 m). The variables mentioned above were compared between the two groups. Statistical analyses were performed using a non-paired Student's t-test, a chi-square test, and a contingency table analysis as appropriate. A P value less than 0. 05 was considered to indicate a statistically significant difference. All of the data are presented as the mean ± standard deviation or median with quartile.
Pupal volume and growth rate measurements Larval growth rate was measured by sorting larvae at the transition of L2 into L3 and transferred to standard cornmeal food. At 80, 97 and 100 hours AEL, images of the larvae were captured under bright light by using the Leica M205 FA. Similar, to obtain pupal volume, pupae were aligned in a petridish and captured under bright light. Pupal or larval length (L) and width (W) were obtained with ImageJ by measuring the medial line between anterior-posterior, and by measuring along the axial line, respectively. Pupal or larval volume was calculated in Microsoft Excel by using the formula for prolate spheroid: (π/6)W 2 *L. Values were normalized to one control condition to give the "∆ pupal volume". Data obtained was plotted in GraphPad to obtain bar graphs.
Ecdysone titers Biological replicates of 4-15 larvae were homogenized twice in methanol and cleared by centrifugation to give a final combined volume of ∼450 µl. Duplicate samples were dried and resuspended in 50 µl EIA buffer (spi bio 20-HE ELISA kit -A05120). Spi Bio Kit for 20-HE ELISA were used as per manufacturer's recommendation. The standard curve was determined using GraphPad Prism software, non-linear regression curve fit, asymmetric sigmoidal, 5PL, X is log (conc). Per animal, E titers were adjusted for the volume used in the assay.
Quantitative RT-PCR Larvae were washed in PBS and frozen in liquid nitrogen. Total RNA was extracted by using RNeasy lipid tissue Minikit (QIAGEN) according to the manufacturer's protocol. RNA samples were treated with DNase and then reserve transcribed to cDNA using the SuperScript II reverse transcriptase (Invitrogen). cDNA samples were then used for RT-qPCR (StepOne Plus, Applied Biosystems) upon using PowerSYBRGreen PCR mastermix (Applied Biosystems). dilp2 and ptth transcript levels were obtained by ΔΔCT method using rpl49 as an internal control. In Microsoft Excel, unpaired Student's T tests were used to compare two sets of data and to obtain statistical values. Primers were designed by using the NCBI/Primer-BLAST.
Acknowledgements We thank  Gisele Jarretou ,  Thomas Phil  and  Alessandra Mauri  for technical assistance. We thank all members of the laboratory and the fly community for insightful discussions and comments on the manuscript. We thank the  Vienna Drosophila RNAi Center , the  Drosophila Genetics Resource Center , the  Bloomington stock Center and Kyoto Stock Center  for providing Drosophila lines. This work was supported by the  CNRS, INSERM (Junior Researcher  grant  2015/6  to N. R. ),  European Research Council  (Advanced grant no. 268813  to P. L. ),  ARC  (grant n°  PGA120150202355  to P. L. ), and the  Labex Signalife program  (grant  ANR-11-LABX-0028-01  to P. L. ). F. A. M. is a recipient of a RyC-2014-14961 contract and supported by  BFU2014-54346-JIN.