dataesr/scientific-paragraphs-categorization huggingface dataset:
The quantile normalized data were log transformed for further analyses. To rule out batch effects, we examined the normalized expression values for a set of 40 housekeeping genes expected to have comparable expression value distributions among the three datasets (WCM UTUC, BCM-MDA UTUC, TCGA UCB) (Supplementary Fig. 1 ). Differential gene expression (DGE) between UTUC and UCB cohorts was performed on the counts data using the Bioconductor package DESeq2. The threshold to select differentially regulated genes was determined at a fold change of >1 for upregulated and <-1 for downregulated genes and results were deemed significant at an adjusted p-value of 0. 05 (Benjamini-Hochberg correction). Hierarchical clustering to infer UTUC subtypes. The subtypes in the UTUC cohorts, namely WCM and BCM-MDA, were inferred using previously published subtype-specific gene signatures together with previously reported 2 subtype classifications for TCGA UCB samples.
To this end, first, the normalized expression values corresponding to the BASE47-signature, MDACC-signature, and TCGAsignature genes were extracted from the three integrated data sets (namely WCM UTUC, BCM-MDA UTUC, and TCGA UCB). For each signature gene set, the subtype for each of the WCM UTUC and BCM-MDA UTUC samples was inferred as follows: (1) The samples were clustered based on Pearson correlation and average linkage, and the cophenetic distances among them were calculated, (2) TCGA samples with minimum cophenetic distances to each UTUC sample were identified, and corresponding TCGA subtype labels were assigned to the specific UTUC samples. Subsequently, the results corresponding to each signature gene set were visualized in heatmaps, scaled across signature genes, and grouped based on inferred subtypes.
PCA was then performed on the coefficients obtained from NMF to visualize the separation of the three components. Outlier analysis. Genes expression outliers Z-scores were calculated for a list 74 cancer-related genes generated from the intersection of Sanger database and Drugbank (https://www. drugbank. ca/ https://www. sanger. ac. uk/science/tools/gdsc-genomics-drug-sensitivity-cancer). Z-scores were calculated across the WCM UTUC and BCM-MDA UTUC cohorts for these 74 cancer-related genes. For each sample, the quantiles were calculated and then used to compute the lower and upper bound to define an outlier. A cutoff of Z-score > 1 and FPKMS > 50 was reported for the UTUC outlier genes after comparison with TCGA UCB samples. Identification of T-cell-inflamed and T-cell-depleted subtypes. The top 5000 genes with the most variable normalized expression levels across TCGA UCB and UTUC samples (WCM and BCM-MDA) were selected based on their median absolute deviations.
These genes data were then median centered and used as an input for hierarchal clustering and Euclidean distance (linkage = ward. D2, k = 20). A 170-gene cluster containing CD8A and other key immune genes was identified. A k-means consensus clustering of these 170 genes across both UTUC and UCB cohorts revealed the presence of two prominent subclusters that we labeled as Tcell inflamed (with higher expression of cluster genes) and T-cell depleted (with lower expression of cluster genes). Differential expression analysis of FGFR3 shRNA dataset. To study the role of FGFR3 in up-regulation of the interferon response, we obtained the publicly available Affymetrix microarray dataset from the RT-112 bladder cancer cell line, with or without shRNA-mediated knockdown of FGFR3  17, 18. The dataset comprised of 12 samples transduced with doxycycline-inducible shRNAs, which were either a shRNA-targeting EGFP (control) or one of three distinct shRNAs-targeting FGFR3 (FGFR3-shRNA); each condition had three biological replicates.
This data was downloaded as raw signal intensity values for 54,675 probesets (Affymetrix Human Genome U133 Plus 2. 0 Array). We used robust multiarray average (RMA) for background correction, normalization, and probe level intensity calculation from Affy Bioconductor Package (Version 1. 52), in R statistical environment  44. The normalized expression profiles were then used to identify differentially expressed probes between FGFR3-shRNA versus control samples using the limma package (version 3. 30. 13)  45. Probes were collapsed to gene level after taking the median fold change of the probes, utilizing hgu133-plus2 annotation data  46. Genes that were differentially expressed after doxycycline induction in all three FGFR3-depleted cell lines but not in the control cell line were considered putative FGFR3-regulated genes.
We identified 58 upregulated genes (log-fold change > 1 and adjusted P-value < 0. 05) and 45 downregulated genes. The log-fold change values of >1 or <-1 were used as thresholds to select for up-regulated or down-regulated genes, respectively (adjusted P-value < 0. 05). Gene set enrichment analysis. A pre-ranked gene set enrichment analysis (GSEA) was applied to the differentially expressed genes, ordered based on their log-fold change values, to identify the cellular pathways significantly altered after shRNA-mediated knockdown of FGFR3  47. Gene sets available through the Gene Ontology Biological Pathways collection in the Molecular Signatures Database  48  were used for the GSEA analysis. Network analysis. Gene sets found to have a statistically significant enrichment using GSEA were visualized using network-based enrichment maps in Cytoscape  49  , where each node in the network was representative of an individual gene set  50.
Total RNA was isolated with the RNeasy Plus Mini Kit (Qiagen #74134) according to the manufacturer's protocol. Total RNA concentration was measured by NanoDrop (Thermo Fisher Scientific). cDNA was synthesized using SuperScript TM III First-Strand Synthesis system (Invitrogen #18080051). Real-time PCR was performed by LightCycler 480 (Roche) using Power SYBR Green Master Mix (Applied Biosystems #4367659). All calculations were collected and analyzed with LightCycer 480 software (Roche) using the delta-delta Ct method. The reaction components, conditions, and primers used are listed in Supplementary Table  4. All data were normalized to the expression of the housekeeping gene beta-actin (β-actin) and then compared to the expression in the DMSO-treated group. Two independent experiments were performed, each with two technical replicates.
Acknowledgements The work conducted at WCM was supported by the  Conquer Cancer Foundation  and the  John and Elizabeth Leonard Family Foundation Young Investigator Award  (B. M. F. ). This work was also supported by a  Charles, Lilian and Betty Neuwirth Foundation   Fellowship in Oncology Award  (P. J. V. ), by the  Translational Research Program  at  WCM Department of Pathology and Laboratory Medicine  (B. D. R. , J. M. M. ) and by the  Englander Institute for Precision Medicine at WCM  (O. E. , M. A. R. ). The part of this work that was conducted at BCM-MDA was funded in part by the  Robert J. Kleberg Jr. and Helen C. Kleberg Foundation , the  Khalifa Bin Zayed Al Nahyan Foundation , and the  Eleanor and Scott Petty Fund for UTUC Research, University of Texas MD Anderson Cancer Center. This work was also funded in part by the  Partnership for Bladder Cancer Research, Scott Department of Urology, Dan L.
Data availability The genomic data that support the findings of this study are available in the database of Genotypes and Phenotypes (dbGaP) and on cBioPortal for Cancer Genomics with the identifier https://www. cbioportal. org/study?id=utuc_cornell_baylor_mdacc_2019. The source data underlying Figs. 1a-d , 2a-e, 3a, b, and 4a-f and Supplementary Figs. 1, 2, 3 and 4 are provided as a Source Data file.
Statistical analyses For statistical analyses (SPSS, IBM, New York, USA, v. 23), group characteristics were compared using t-tests. Repeated measures ANOVAs were used to analyse gait speed and fluidity (2 groups Â 3 locomotor conditions Â 4 cognitive conditions), obstacle clearance including only the two obstacle conditions (2 Â 2Â4), and cognitive variables including only the three cognitive conditions (2 Â 3Â3). Only group effects and interactions were examined. In case of significance (p 0. 05), post hoc analyses were conducted. In the case of a significant Box test evaluating homogeneity of variance between groups, Levene's test results were used. A Huynh-Feldt correction was applied when the Mauchly test for within-group homogeneity was significant. For the second objective, for the mTBI group, Pearson's correlations were computed between number of prior head injuries, including the current one, and gait speed, DTC for gait speed, response time, answers/time ratios and DTC for answers/time ratio.
Performance in both groups was affected similarly when cognitive tasks were added (Figure  1 ). However, when we controlled for baseline gait speed, the difference between groups    on identifying which dependent variables are most sensitive to reveal alterations in DTW after mTBI. Further research is also needed to understand how personal factors might influence DTW performance after concussion, for example age or training received to develop particular skills. To date, most DTW studies have focused on motor performance. The few studies that have explored cognitive performance have not found any significant group differences. 13, 14, 16 The present study made an effort to rigorously document cognitive performance. When data from all three cognitive tasks were combined, we found average response times to be slower in the mTBI group.
Research in context Evidence before this study The current clinically used diagnostic biomarkers for gastric cancer (GC) such as carcinoembryonic antigen (CEA), cancer antigen 19-9 (CA19-9), and cancer antigen 72-4 (CA72-4) are neither sufficiently sensitive nor specific. The identification of novel reliable markers and development of new technologies are still urgent for GC detection and hence improving the survival of patients. Large-scale proteome screening in non-invasive biological specimens is a strategy that provides the possibility of discovering new biomarkers. We performed a PubMed database search for studies focused on GC biomarker discovery published until May 2018 using the following terms: "biomarker" AND "proteomic" AND ("gastric cancer" OR "stomach cancer"). The criteria for inclusion were GC diagnostic biomarker studies using any human materials such as serum, plasma, tissue, gastric fluid, urine, circulating cells, or exosomes.
Study cohort This retrospective study was followed the Standards for the Reporting of Diagnosis Accuracy studies (STARD) statement  [14]. 100 patients with primary gastric cancer were undergone surgery at the Department of General Surgery and Surgical Oncology in Siena University Hospital, Italy, between July 1990 and February 2010. The subjects were chosen inconsecutively from the hospital surgical database. Sera samples were taken from the 100 patients upon admission to the hospital and one to two weeks after surgery. Fifty control serum samples were collected at the same time from cancer free patients treated in the same surgery department with other gastrointestinal diseases but not related to stomach. Serum samples were aliquoted immediately after centrifugation and stored at -80 °C until tests.
Protein abundance measurement Samples from different groups were evenly distributed within a plate but randomly among plates. Assays were done at Uppsala University before accessing patient's clinical information. Protein levels were measured by multiplex PEA (Olink Proteomics™, Sweden) using three commercial panels (Oncology, Inflammation, Cell regulation), and two experimental panels (Cancer, Cellular pathway). Each panel is able to measure 92 proteins in 90 samples simultaneously in as little as 1 μl sample aliquots  [9]. Each sample is spiked in four controls to monitor PEA process. Each panel also includes three positive controls and three negative controls, which is used for data normalization and determination of limit of detection (LOD), respectively. Assays were performed following the manufacturer's instructions.
Statistics Data analyses were performed using R software (www. r-project. org)  [19]. NPX values were used for analysis since the values tended towards a normal distribution. To minimize the inter-plate variation, samples from different disease groups were evenly distributed throughout the plates, and the inter-plate variation was further normalized for each protein in each plate by adding the Z-score factor calculated as follows: factor = (actual valuemedian of all samples)/standard deviation. For comparison between GC and controls, NPX values were adjusted if a significant effect (corrected p value b0•05) of age or gender on the protein levels was found by linear regression in both the control and cancer groups. Therefore, 13 proteins measured in serum (CDCP1, CTSV, CXCL9, EPHA1, KIT, OPG, RET, RSPO3, TGFBR2, TNFRSF10B, TRANCE, VEGFR2, WFDC2) were adjusted for age, while no protein was found significantly associated with gender in either group.
Protein detection Full names and corresponding UniProtKB accession numbers of all the measured proteins are listed in Supplementary Table  2. Apart from overlapping proteins and proteins with antibody cross activity, proteins levels with NPX values below the LOD in N60% of samples in each disease group were excluded for further analysis (Fig. 1 , Supplementary Table  2 ). This led to 245 proteins for tissue sample analysis and 316 for serum sample analysis while 232 proteins are common between serum and tissue (Fig. 2A ). Among the proteins measured in both serum and tissue samples, 13 of those detectable in tissue but not in serum are more cytoplasmic and involved in inflammation, whereas 14 detectable in serum but not in tissue tend to be extracellular proteins and involved in immune-response, as revealed by the functional enrichment analysis tool (FunRich) (Fig.
3A  and B ). Therefore, the generated diagnostic signature was found to be: y ¼ -16 Á 64 þ 1 Á 02 Â MMP1 þ 1 Á 38 Â IL7 þ 0 Á 68 Â CA9 þ 1 Á 23 Â CDCP1 þ 0 Á 78 Â ZBTB17 þ 0 Á 86 Â DDAH1 þ 1 Á 57 Â FLI1 þ 0 Á 94 Â MSLN þ 0 Á 73 Â CEACAM5 þ 0 Á 5 Â KAZALD1 þ 0 Á 46 Â CCL20 þ 1 Á 15 Â SCF-1 Á 14 Â PPIA-0 Á 98 Â TGF alpha-0 Á 78 Â HMOX1-0 Á 7 Â MMP10 þ 0 Á 6 Â APBB1IP þ 0 Á 58 Â IGF1-0 Á 42 Â ADAMTS15 The sensitivity and specificity of the combined model for distinguishing GC patients from controls were 93% and 100%, respectively, with an AUC of 0. 99 (95% CI: 0•98-1). The diagnostic performance of the combined signature is clearly greater than that of each of the 19 proteins and also the clinical used biomarkers CA19-9 and CA72-4 (Table  3 ). Querying the STRING database confirmed the absence of experimental evidence of protein-protein interactions among those selected 19 proteins.
(3) PPIA, has been reported to be overexpressed in several types of human cancers including gastric cancer  [51, 55]. (4) HMOX1 is considered to be the main protein for cytoprotection against various cell stresses, and increased expression of this protein was observed in several types of cancers  [56, 57]. However, there are no differences in the levels of PPIA and HOMX1 between GC and controls in serum or in tissue in the present study. Furthermore, we identified six novel proteins associated with GC including (1) IL7, (2) ZBTB17, (3) FLI1, (4) KAZALD1, (5) APBB1IP, and (6) ADAMTS15. (1) A recent bioinformatic analysis based on genomewide association studies (GWAS) data identified IL7 pathway as one of the three pathways associated with GC risk  [58]. (2) ZBTA17, also known as MIZ1, is a transcription factor that forms a complex with Myc and mediates Myc-dependent tumorigenesis  [59].
(3) FLI11 is also a transcription factor, and its high level expression as well as translocations were observed in hematopoietic and solid tumours  [60]. (4) KAZALD1 is a member of the insulin growth factor-binding protein (IGFBP) superfamily, and hypomethylation of the gene was found in cancer. "The Human Protein Atlas (HPA)" database (https://www. proteinatlas. org) shows a moderate staining (6/10) of KAZALD1 protein in stomach cancer tissues. In the present cohort, the levels of IL7, ZBTB17, FLI1, and KAZALD1 were all significantly increased in serum from GC patients as compared with controls, indicating that they may play roles in GC tumorigenesis and progress. in the signal transduction from Ras activation to actin cytoskeletal remodeling and mediates Rap1-induced adhesion. (6) ADAMTS15 is one of the extracellular metalloproteinases, which functions as a putative tumour suppressor, and has been linked to a number of different cancers such as prostatic, breast and colorectal cancers  [61].
The HPA database displays a weak staining for APBB1IP and moderate for ADAMTS15 in 10 GC tissue sections. In our study, ADAMTS15 was also significantly downregulated in GC tissues compared to surrounding normal tissues. Many of the 19 identified proteins are inflammation related, such as IL7, MMP1, MMP10, CCL20, CDCP1, SCF, and TGFA. As one of the consequences of long-term gastric inflammation is malignances, these markers may play important roles in GC development. GCNT1 is a glycosyltransferase that adds beta1,6 GlcNAc arm to core 1 O-glycans and forms core 2 O-glycans. Core 2 O-glycans are known to be a particularly good scaffold for sialyl Lewis antigens, which can be recognized by selectin family members and thereby mediate leukocyte rolling and cancer cell metastasis  [62]. GCNT1 was significantly increased in sera of GC patients compared to controls in univariate analysis, and was the most significantly increased serum protein in GC patients at TNM I-II stage as well as in high MSI patients.
Rapid-response/automated transient triggering VOEvents are the standard for broadcasting alerts related to transient events set out by the International Virtual Observatory Alliance (IVOA;  [2] ). Many multi-messenger facilities disseminate VOEvents of newly discovered transients including the Swift Burst Alert Telescope (BAT), the Fermi Gamma-ray Burst Monitor (GBM), Advanced LIGO and Virgo (aLIGO/Virgo), the Antares and IceCube Neutrino Observatories, and the All-Sky Automated Survey for Supernovae (ASASSN), just to name a few. Several software packages have been written to.
PoS(Asterics2019)002 enable the parsing and filtering of VOEvents that can then allow for the direct automation of transient follow-up. The most utilised of these software packages include the '4 Pi Sky VOEvent' broker  [3]  and the Comet VOEvent client  [4] , which are used by facilities such as the Murchison Widefield Array (MWA), the Low-Frequency Array (LOFAR), the Australia Telescope Compact Array (ATCA), Antares, IceCube, the High Energy Stereoscopic System (H. E. S. S. ), the Zwicky Transient Facility (ZTF), along with future plans for the Cherenkov Telescope Array (CTA) and the next generation neutrino telescope KM3Net. This is an excellent demonstration of how a useful and well documented tool can become widely used by the community.
Automated temporal and positional correlation There are several programs set to automate the temporal and positional correlation of transient alert streams. The correlation of subthreshold multi-messenger events via AMON has already been described in Section 1. Another example includes the Alert Management, Photometry and Evaluation of Lightcurves (AMPEL) transient processing and analysis package  [5] , which is currently run on IceCube neutrino data with the transient alert stream generated by ZTF, with the aim of identifying multimessenger matches. AMPEL offers the capability of contributing additional transient input channels for correlation with the ZTF alert streams.
Standardisation of communication protocols for transient and multi-messenger events The standardisation of transient message format is essential. This has been well facilitated by the IVAO VOEvents and many facilities make use of software designed to interface with VOEvent streams (see Section 2. 1). While communication protocols should be standardised, enforcing a triggering software standard across all triggering facilities is not realistic given the diversity in software and hardware platforms, along with their different operational modes and science priorities. In addition, such software portability would require large efforts to implement and potentially prevent the evolution of individual facility platforms.
Flow Cytometry Analysis After thawing, PBMCs were stained with a combination of fluorochrome-labeled monoclonal antibodies for γδ T and iNKT cells. Expression of surface receptors was measured using a fourcolor FACSCalibur II flow cytometer (BD FACSCalibur Flow Cytometry System, RRID:SCR_000401). Data acquisition was performed by BD CellQuest Pro (BD CellQuest Pro, RRID:SCR_014489) and analyzed using FlowJo Version 7. 6. 5 (FlowJo, Tree Star, Inc. , RRID:SCR_000410). Gamma-delta (γδ) and δ2 pos γδ pos T cells were defined as pan-γδTCR pos and δ2 pos γδ pos by staining with anti-pan γδTCR-PC5 (Beckman Coulter Cat# IM2662, RRID:AB_131175) and anti-TCRVδ2-FITC (Beckman Coulter Cat# IM1464, RRID:AB_131019) monoclonal antibodies (mAbs). δ2 neg T cells, including γδ1 pos and γδ3 pos T cell subsets, was defined by gating δ2 neg population in the total γδ pos T cell.
Statistical Analyses Statistical analysis was performed using GraphPad Prism software version 6. 0e (GraphPad Software In. , San Diego, CA, USA, RRID:SCR_002798). Phenotypic parameters obtained at baseline were first compared across the five groups by the Kruskal-Wallis test. If the test showed a significant difference, pair-wise comparisons were conducted. Phenotypic markers of IRS patients were compared to age, gender, CD4 T cells count and viral load matched non-IRIS patients using non-parametric (Mann Whitney U test). A p-value of <0. 05 was considered statistically significant.
ACKNOWLEDGMENTS The authors thank the  ANRS (French National Agency for HIV/AIDS and Viral Hepatitis Research)  as the promoter of  CAPRI-NK project  ( ANRS No. 12153 ). We thank all the patients who participated in the study and the medical staff of  Khmer Soviet Friendship Hospital in Phnom Penh, Cambodia ;  Takeo Referral Hospital in Takeo province, Cambodia, Preah Kossamak Hospital in Phnom Penh, Cambodia, and Svay Rieng Referal Hospital in Svay Rieng Province, Cambodia. The authors thank to  Dr. Erik Karlsson  for critically reading and English writing correction of this manuscript.
Numerical scheme The numerical integration of the set of equations Eq. (  11 ) is done using the library scipy. integrate from Python2. 7, more precisely the method odeint 2. Since the method does not provide an event handle to detect if a lumen is empty, the numerical solving is done by restarting the integration for small time frames, checking the conditions and starting it again. Supplementary Text Figure  4 : A simple network evolution. Lumen 2 becomes bridge 0, then lumen 4 disappears but the link between bridge 0 and lumen 5 still exists. However, because of the dynamics, the disappearance of lumens will change the network quickly at small times, and more slowly at larger times. Moreover, the dynamics at large times is much slower than at small times, because of the competition between larger lumens is slower.