dataesr/scientific-paragraphs-categorization huggingface dataset:
Circulating monocyte HLA-DR measurements (mHLA-DR) The quantification of the expression of HLA-DR on monocytes was assessed using the number of antibodies per cell (AB/C) by flow cytometry (FACS Canto II instrument, FACS Diva software, Becton Dickinson, San Jose, CA USA) as previously described  [7]  (see detailed protocol in the e-method section of Additional file 1). In our center, the median and IQ range of mHLA-DR expression in healthy people for the measurements at the same period (n = 13) was a median log mHLA-DR value of 40,134 (IQR: 36,315-44,353). The first blood sample and measurement of mHLA-DR were performed within the first 3 days after admission. The 2nd measurement of mHLA-DR was obtained on fixed days (Monday or Thursday) until the patient's discharge or death. Since we used a survival model, only measurements sampled before the event were considered for analyses, and data were blinded at the time of the event (death or IAI).
Statistical analysis The data were described as number and percentage for categorical variables and median (interquartile range (IQR)) for continuous variables. Comparisons relied on the Fisher exact test or χ 2 test for categorical data and the Kruskal-Wallis or Wilcoxon test for continuous data. Because of non-linearity, all the mHLA-DR values were log-transformed. Age and SOFA scores were categorized based on the median value. A p-value of less than 0. 05 was considered statistically significant. Standard survival analyses are affected by the time of onset of the event of interest. Patients who have not experienced the event at the end of follow-up were censored. To determine the risk of an event occurring at a certain time-point, a fundamental assumption is that such censoring is not associated with an altered chance of the event occurring at any given moment.
Results The short isoform of the MAP3K ZAK is activated by osmotic shock To identify proteins controlling p38 activation upon osmotic shock, we designed a genetic screen in the human haploid cell line HAP1  (Brockmann et al, 2017). This unbiased approach was based on the random insertion of gene-traps, FACS-assisted sorting of cells with high and low levels of phospho-p38 and deep sequencing to map insertions  (Fig EV1A). Next to p38 and MAP2K6, the MAP3K genes TAK1 (also shown by  (Huangfu et al, 2006; Inagaki et al, 2008) ) and ZAK appeared as the only hits in the MAPK signaling pathway  (Fig 1A; Dataset EV1). The result for the latter could be validated both in ZAK-deleted HAP1 and U2OS cells, where p38 activation by the ribotoxic stress-inducing agent anisomycin was completely abolished as previously shown  (Vind et al, 2020b) , and sorbitol-induced p38 activation was clearly reduced  (Figs 1B and EV1B).
J C2C12 myoblasts and myotubes (6 d differentiation) were pretreated with ZAK inhibitor (ZAKi, 0. 5 h) and treated with 500 mM sorbitol (1 h) as indicated. Lysates were analyzed by immunoblotting with the indicated antibodies. Source data are available online for this figure. Stretch-induced signaling has clear physiological counterparts and has been linked to essential processes such as growth regulation and differentiation in the skin, heart physiology, and urinary function in the bladder  (Le et al, 2016; Aragona et al, 2020; Marshall et al, 2020; Jiang et al, 2021). In contrast, cell compression has been much less studied. In search of a potential in vivo role for compression-induced ZAKb activation, we noticed that skeletal muscle has the highest expression of the ZAK gene among human tissues (data from gtexportal.
Cell culture and reagents Human U2OS osteosarcoma (ATCC, HTB-96), human Hela malignant cervical epithelial cells (ATCC, CCL-2), human HEK293 embryonic kidney cells (ATCC, CRL-1573) and murine C2C12 myoblast (ATCC, CRL-1772) were cultured in Dulbecco's modified eagle's medium (DMEM, Biowest) supplemented with 10% fetal bovine serum (FBS, Biowest), L-Glutamine, penicillin, and streptomycin (P/S). Human HAP1 haploid cells (EMBL-EBI, RRID: CVCL_5G07) were cultured in Iscove Modified Dulbecco Medium (IMDM, Gibco) supplemented with 10% FBS and P/S. All cell lines were regularly tested for mycoplasma infection. Cells were maintained at 37°C in a humidified 5% CO 2 cell incubator. Stable cell lines were created by transfecting cells with a plasmid carrying the gene of interest and subsequently cultured for weeks with appropriate antibiotic selection (Zeocin (0.
Muscle fibers For fiber typing of soleus muscle sections, relative numbers of type I and type IIa fibers were determined manually by counting the total of fibers expressing each myosin isoform. The fiber crosssectional area was calculated as an average of three individual muscles. To avoid regional fiber size variation in the TA muscle to confound comparisons, a scale factor (average area of electroporated: average area nonelectroporated) was obtained from at least three electroporated regions within the muscle, which were then averaged to produce a single data point per muscle. For morphological analyses of H&E stained sections, 10x magnification images were captured using the Leica brightfield microscope (DM2500), camera and imaging software (SPOT Insight FireWire; Diagnostic instruments).
Centralized nuclei were counted manually. The number of fibers displaying centralized nuclei was expressed as a percentage of the total number of myofibers to create one single data point per muscle. An average of the percentage of fibers with centralized nuclei was calculated across three mice per genotype. Data were compared using a one-way ANOVA test followed by a Tukey's HSD post hoc test. P values less than 0. 05 were deemed significant.
RNA sequencing Muscles were snap-frozen in liquid nitrogen and homogenized twice for 2 min at 30 Hz using Tissuelyser II (Qiagen). Total RNA was isolated using the TRIzol reagent as above. Total RNA was sent to BGI Europe for RNA sequencing. Here, a polyA-selected mRNA library was prepared from the total RNA and subjected to PE100 sequencing with 20 M pair reads using the DNBSEQ platform. Low-quality reads were filtered with SOAPnuke 1. 5. 2 and the remaining reads were mapped to version GCF_000001635. 26_GRCm38. p6 of the Mus musculus genome. After alignment using Bowtie2 2. 2. 5, the expression level of each gene was calculated by RSEM 1. 2. 12, and differential expression analysis was performed using DESeq2 1. 4. 5 with the parameters fold change ≥2 and adjusted P value ≤ 0. 001. The sequencing data analysis, including principal component analysis (PCA) and Venn diagram creation, was performed using BGI Dr.
Raw data processing Raw files were searched in Spectronaut (v14) using a library-free approach (directDIA). Carbamylation of cysteines was set as a fixed modification, whereas oxidation of methionines, acetylation of protein N-termini, and in phosphor-proteomics samples phosphorylation of serine, threonine, and tyrosine were set as possible variable modifications. Mus musculus FASTA database (UniprotKB/Swissprot 22888 entries) and a common contaminants database were used. For phospho-proteomics samples, PTM localization cutoff was set as 0. 75. Cross-run normalization was on. Phospho-peptide quantification data were exported and collapsed to site information using the Perseus plugin described in Bekker-Jensen et al  (Bekker-Jensen et al, 2020b).
Data analysis Phospho-site and proteome datasets were processed using R (v3. 6. 2). Data were log2 transformed and two valid values in at least one experimental group were required to preserve the phospho-site for further analysis. Imputation of missing values was performed using the data analysis pipeline of Prostar (v 1. 18. 4)  (Wieczorek et al, 2017). First, partially observed values (i. e. , values missing within a condition in which there are valid quantitative values) were imputed using the function KNN (10 neighbors) and labeled with blue in Table  S2 ; second. Values missing in an entire condition were imputed using the detQuant function (quantile = 1, factor = 1) and labeled with orange in Table  S2. Next, data were exported into Perseus (v1. 6. 5. 0)  (Tyanova et al, 2016)  for differential expression analysis using ANOVA (5% FDR) followed by a post hoc test to evaluate significant pairs.
Gene Ontology enrichment annotation was performed in Perseus (v1. 6. 5. 0), using GO BP, MF, and CC names and slim names. Significance of the enrichment was calculated using the Fisher´s exact test (two-sided) with the Benjamini-Hochberg FDR correction. Principal component analysis was performed in Perseus (v1. 6. 5. 0) using as input the data obtained after imputation.
À/À mice to sequencing-based transcriptome analysis (Fig 5F). At the global level, principal component analysis (Fig 5G), correlation analysis (Fig EV5B), and comparison of the number of differentially expressed genes (DEG) (Fig EV5C) clearly indicated a large separation of pathological soleus (but not nonpathological TA) dependent on genotype. Additionally, the few DEGs in the unaffected TA were largely identified among the many likely pathology-associated DEGs found in soleus (Figs 5H andEV5D and E; Dataset EV3). These data suggest that tonically active slow-twitch muscles are more susceptible to ZAKb deficiency than fast muscles, likely because they are permanently stimulated to generate tension to support body weight against gravity. Despite the clear soleus pathology, but perhaps because other fast-twitch muscles appear largely unaffected, ZAK À/ À mice behaved similarly to their WT counterparts in an open field assay, both with respect to the distance covered, speed of movement, and time spent exploring the surroundings(Fig EV5F and G).
Acknowledgements We thank  Dr. Andres Lopez-Contreras  ( University of Copenhagen, Denmark ) for support with mouse husbandry, and  Drs. Xiaoyun Lu ,  Ke Ding  (both  Jinan University, China ), and  Paul Pryor  ( University of York, UK ) for providing reagents. We thank  Drs. Atul Deshmukh  and  Alba Gonzalez-Franquesa  (both  University of Copenhagen, Denmark ) for help with C2C12 culture. For providing access to confocal and time-lapse microscopy we acknowledge the Danstem and  NNF-CPR imaging platform (University of Copenhagen, Denmark). and  Michele M. Nava  ( University of Helsinki ) for help with mechanical manipulations. The  Genotype-Tissue Expression (GTEx) Project  was supported by the  Common Fund  of the Office of the Director of the  National Institutes of Health , and by  NCI ,  NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS.
Work in the Bekker-Jensen lab  was supported by grants from the  Lundbeck Foundation  ( R190-2014-4037 ), The  Danish Medical Research Council  ( 9039-00007B ), The  Novo Nordisk Foundation  ( NNF21OC0071475 ), The  NEYE Foundation , The  Nordea Foundation , and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement  863911 -PHYRIST ). Anna Constance Vind is supported by the  BRIDGE-Translational Excellence Program  funded by the  Novo Nordisk Foundation  ( NNF20SA0064340 ). Christoffer Clemmensen receives funding from the  Lundbeck Foundation  (Fellowship  R238-2016-2859 ) and the  Novo Nordisk Foundation  (grant number  NNF17OC0026114 ). Novo Nordisk Foundation Center for Basic Metabolic Research  is an independent Research Center, based on the  University of Copenhagen, Denmark , and partially funded by an unconditional donation from the  Novo Nordisk Foundation  (www.
cbmr. ku. dk) (Grant number  NNF18CC0034900 ). Work in the Wojtaszewski lab was supported by a grant from the  Danish Council for Independent Research -Medical Sciences  ( FSS 8020-00288 ) and a research grant to  Rasmus Kjøbsted  from the  Danish Diabetes Academy , which is funded by the  Novo Nordisk Foundation  ( NFF 17SA0031406 ). Work in  Jesper Olsen 's lab at The  Novo Nordisk Foundation Center for Protein Research (CPR)  is funded in part by a generous donation from the  Novo Nordisk Foundation  (Grant number  NNF14CC0001 ).
Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD028548 (https://www. ebi. ac. uk/pride/ archive/projects/PXD028548). Fastq RNA sequencing files from this study have been deposited in the NCBI BioProject database under accession code PRJNA816072 (https://www. ncbi. nlm. nih. gov/ bioproject/?term=PRJNA816072). All other data supporting the findings of this study are available within the article and its supplementary data. Expanded View for this article is available online.
Statistics Categorical and continuous variables are reported as numbers and percentages, or median interquartiles, respectively. Association between MDS diagnosis and categorical and continuous parameters was evaluated using chi-square test and Mann-Whitney test, respectively. All tests were two-sided at a significance level of p < 0. 05. All analyses were performed using R statistical software V. 3. 3. 2 (R Foundation for Statistical Computing, Vienna, Austria). We further applied machine-learning techniques to visualize and investigate the pattern between MDS diagnosis and several variables. Contribution of each biological variable, including CPD, to the diagnosis of MDS was investigated with random forest models using the randomForestSRC package. The importance of each variable was evaluated with the Variable Importance (VIMP) estimates.
The relative contribution of each variable was evaluated by summing the VIMP of all covariates. The CARET (Classification And REgression Training) package was then used to evaluate, using resampling, the effect of model tuning parameters on performance and choose the "optimal" model across these parameters. Random forest models and CART (Classification And Regression Trees) were built and Receiver Operating Characteristic (ROC) curves were used to illustrate the diagnostic ability of classification trees in the machine learning methodology. All statistical analyses were performed with R 4. 0. 3 (http:// www. Rproje ct. org/).
Machine learning identification of the most contributive parameters for MDS diagnosis Using Breiman's random forests (RF) classification, we identified Ne-WX and IPF as the two most discriminatory predictors for MDS diagnosis (Fig. 2A ), explaining 37 and 33% of diagnoses respectively. Comparatively, ANC and MCV only contributed to 18 and 6% of diagnoses. We then used the CARET (Classification And REgression Training) package to evaluate the effect of model tuning parameters on performance and choose the "optimal" model across these parameters. A bootstrapping approach was used, splitting the cohort in five sub-cohorts, to cross-validate these models. We started with the three parameters from the MDS-CBC score and added the other parameters one by one to random forest (RF) models, considering parameters with the highest VIMP first.
From artificial intelligence to routine practice: a two-step approach We then used the CARET package to design CART (Classification And Regression Trees). To obtain "simplified" trees, which could be easily used with the laboratory middleware, we only introduced MDS-CBC score and IPF into the model. Two CART were proposed with similar performances, one with three levels of decision (data not shown) and one with a two-step algorithm (Fig. 3A ). Strikingly, the MDS-CBC score threshold proposed by these two algorithms for classification was 0. 23, very close to the published threshold of 0. 2 by Boutault et al. In the two-step algorithm, if MDS-CBC score was inferior or equal to 0. 23, no additional testing or slide review was requested (311 patients including thirteen MDS patients). When MDS-CBC score was superior to 0.
Barometric plethysmography The respiratory function of mice was evaluated by whole-body plethysmography using an EMKA Technologies plethysmograph, essentially as described by TREAT-NMD. Briefly, unrestrained conscious mice were placed in calibrated animal chambers and the pressure difference between the reference and animal chambers was measured using a pressure transducer. Mice were allowed to acclimate in the chambers for 45 min at stable temperature and humidity. Data were then collected every 5 s using the iox2 software (version 2. 8. 0. 19; EMKA Technologies). The inspiration time (TI) was defined as the time from the start of inspiration to the end of inspiration, and the expiration time (TE) was defined as the time from the start of expiration to the end of expiration. The relaxation time (RT) was defined as the time from the start of expiration to the time when 65% of the total expiratory pressure occurred.
Flow cytometry analysis Mice were euthanized using carbon dioxide, and the heart was excised following perfusion with ice-cold HBSS (Gibco). Heart tissue was then cut into 6-8 smaller pieces and transferred to MACS C-tube (Miltenyi Biotec, USA) containing 5 ml collagenase solution containing 500 U/mL collagenase type II (Worthington Biochemical Corporation) in HBSS. The C-tube was then transferred to gentleMACS Dissociator (Miltenyi Biotec, USA), and program 37_multiE_01 was used to prepare the single-cell suspension. After adding 10 ml HBSS, the homogenate was passed through 70-µm cell strainer and centrifuged at 350 g for 10 min at 4°C. The debris were removed using debris removal solution following the manufacturer's instructions (Miltenyi Biotec, USA). Following debris removal, the RBCs were eliminated using ACK lysis buffer (Gibco).
The cells were then stained with live/dead stain as instructed in the kit (Molecular Probes). Before staining cells with specific antibodies, they were incubated with Fc receptor (FcR) blocking reagent (Miltenyi Biotec, USA) for 10 min at 4°C. Finally, the cells were stained with C38-PE (Miltenyi Biotec, 1/66), CD31-VioBright 515 (Miltenyi Biotec, 1/50), and CD45-BV785 (Biolegend, 1/66) for 1 h at 4°C. The cells were then centrifuged at 350 g for 10 min at 4°C. The cells were fixed using 2% paraformaldehyde for 15 min at room temperature. After centrifugation, the cells were resuspended in cell staining buffer (Biolegend, USA) and flow cytometry was run on LSRII (BD Biosciences). The analysis was performed using FlowJo software (FlowJo LLC). These experiments were performed in blind.
For SR Ca 2+ load estimation, cardiomyocytes were rapidly perfused with 10 mM caffeine immediately after field stimulation. The amplitude of the caffeine-evoked [Ca 2+ ]i transients was used to assess the SR Ca 2+ load. Fractional SR release was measured by normalizing the steady state of [Ca 2+ ]i transients (peak F/F0) to that of caffeine-evoked [Ca 2+ ]i transients. Post-rest potentiation was calculated by normalizing the first [Ca 2+ ]i transients (peak F/F0) after a period of rest to reach the steady-state [Ca 2+ ]i transients. [Ca 2+ ] i transient properties and the amplitude (maximum F/F0) were calculated by using the IDL software with a homemade program.
Acknowledgements This research has been supported by grants from  CNRS ,  INSERM , and  Paris-Saclay University. Funding for this work was provided by  AFM-telethon grants (JMC, SDP , and AZ). AZ is a recipient of a PhD fellowship from the  Paris-Sud University. This work was also partly funded by the  Fondation de France (JMC) ,  ANRs  ( NAD-Heart # 17-CE17-0015-01  to MM; and  ANR-19-CE14-0031-01  and  ANR-13-BSV1-0023-01  to AMG), and  NIH   AG58812  to ENC. INSERM U1180 is a member of the  Laboratory of Excellence LERMIT  supported by a grant from  ANR  ( ANR-10-LABX-33 ) under the program " Investissements d'Avenir "  ANR-11-IDEX-0003-01. The authors thank the radiology department of the  Raymond Poincar e Hospital. The authors thank  Sanofi  for providing isatuximab. The authors thank  Frances Lund  for providing CD38 À/À mice and the platform for immortalization of human cells "MyoLine" from the  Institut of Myology  for providing the DMD and healthy subject cell lines.
Statistical Analysis Continuous variables are reported as mean or median (IQR). Counts with percentages are reported for categorical variables. The χ 2 test was used to compare proportions and the Wilcoxon rank-sum test was used for comparisons of non-normally distributed continuous variables. We used the Kaplan-Meier method and log rank test to analyze progression-free survival. Statistical inferences were based on a two-sided significance level of 0. 05 without correction for multiplicity. Analyses were performed with SAS version 9. 4 and R version 3. 6. 3.
Strengths and Weaknesses of Study This study describes the impact of palliative chemotherapy on symptoms and HRQL in a large international real-world patient sample using a dataset of patient-rated outcome measures with excellent (≥84%) completion rates. Our study demonstrates the importance of regular assessment of symptoms during chemotherapy and underscores the critical importance of supportive care and symptom management beyond the administration of chemotherapy alone. One limitation of our study was that our patientreported outcome data were collected for research and not relayed to the treating clinicians in real time.
Acknowledgements The  Gynecologic Cancer Intergroup (GCIG) Symptom Benefit study  was led by the  Australia New Zealand Gynaecological Oncology Group (ANZGOG)  in collaboration with the  NHMRC Clinical Trials Centre, University of Sydney ,  Cancer Research UK  and  UCL Cancer Trials Centre. The authors thank all of the patients and their families, physicians, and nurses who participated in the study. Contributors YCL, MTK, RO, and  MRS  contributed to the research proposal, concept development, data analysis, interpretation of results, and drafted and revised the manuscript. AD and FTR contributed to data analysis, interpretation of results and revision of the manuscript. AL, FJ, FH, AO, JB, PD, AMO, EA-L, JSB, JL, DB, JS, AF, and M-CK contributed to patient recruitment to the study, data analysis, interpretation of results and revisions of the manuscript.
Funding In Australia the study was supported by  NHMRC  grants ( 1063012  and  570893 ). In the UK this was a  National Institute for Health Research (NIHR)  study jointly funded by  Target Ovarian Cancer  ( UCL-P001AL ) and the  Cancer Research UK  and  UCL Cancer Trials Centre  ( Programme Grant   C444/A15953 ). MTK is supported by the  Australian Government through Cancer Australia. MLF is supported by an  NHMRC   Program grant. AL was assisted by the  National Institute for Health Research (NIHR) UCLH/UCL Biomedical Research Centre  which is supported by the  Department of Health.
RESULTS H 2 O 2 is a suitable candidate to act as a metabolic hub for several reasons. First, multiple H 2 O 2responsive transcription factors and target promoters have been identified, providing an appropriate space for exploring and optimizing an H 2 O 2 transcriptional response system. Second, unlike many other metabolites (e. g. amino acids) or cofactors (e. g. NAD, Coenzyme A), H 2 O 2 is not part of the cell-free buffer, limiting interference with the sensor. Finally, H 2 O 2 is a central metabolite and a byproduct of many enzymatic reactions, which enables connection with many metabolites. This is demonstrated by the Rhea database  14  that documents more than 350 different enzymatic reactions producing H 2 O 2. To map the space of molecules potentially detectable through enzymatic reactions producing H 2 O 2, we developed the BioSensor Galaxy workflow (Figure  1A , Supplementary Figure  S1 ), combining the RetroPath2.