dataesr/scientific-paragraphs-categorization huggingface dataset:
Query performance We then estimated the abundance of some test sequences in the 2585 datasets using the REINDEER index. Queried sequences were randomly sampled from the RefSeq human transcripts. Batches of 10, 100, 1000 and 10 000 sequences were created to study query scalability. The mean sequence size over the batches was 3341 nucleotides. Each batch type was repeated 10 times with random sampling, and results show the mean, max and min query wallclock times for each batch type (using 20 threads). Results are shown in Table  5. The query wallclock time can be divided into a first step in which REINDEER loads the index into memory, and a second step in which k-mers from each query are queried in the REINDEER structure. Final results are computed and written to an output file. We used the same parameters as for the index construction ðk ¼ 21; m ¼ 10; ÀS ¼ 75%Þ, with ÀS being the minimum percentage of found k-mers required to return a hit in a dataset.
One can take an array of genes/transcripts, and collect an abundance signature across thousands of datasets, and then perform unsupervised clustering to look for patterns. One can pick a transcript of interest (e. g. from an oncogene) and compare the abundance of that and a control transcript, across all datasets, or maybe compare this query across tumor and normal samples (assuming appropriate metadata is available). As a proof of concept we selected four oncogenes well known for their role in breast cancer (ERBB2, FOXM, MYC and PIK3CA), as well as three tumor suppressor genes (BRCA1, PTEN and TP53)  (Perera and Bardeesy, 2012; Song et al. , 2017). We launched REINDEER on the longest transcript of those genes. The transcript was split in 100 bp long sequences, to be able to take into account matches on a few exons.
We required at least 78% of the k-mers to be found in the split sequences. We averaged the counts returned by REINDEER in each split sequence and kept the maximal value among them. The distribution in each dataset, for which counts are reported, are displayed in Figure  4. We recall that our protocol is only a demonstration of what could be done with REINDEER. Obtaining robust quantifications, in a similar way as e. g. Kallisto  (Bray et al. , 2016)  but on thousands of datasets in a single query, is an important direction left for future work. Even with crude un-normalized counts, we observe that the counts in oncogenes are higher in the 'cancer-related' datasets compared to the 'non cancer-related'. This is particularly true for the ERBB2 gene where counts higher than 1000 are only present in the cancer-related samples.
Conclusion REINDEER is the first and currently only tool that enables scalable indexing of k-mer counts in a collection of sequencing datasets. As a byproduct it allows to query the abundances of arbitrary sequences in these datasets. REINDEER relies on the new concept of monotigs, on run-length encoding of count-vectors, optional lower-precision encoding of k-mer counts, and on the de-duplication of rows within the count-vectors matrix. To the best of our knowledge and despite their apparent simplicity, neither monotigs nor the greedy construction algorithm described here have previously appeared in the literature. A related idea of breaking unitigs at color changes was used in Kallisto  (Bray et al. , 2016). Note that REINDEER does not encode k-mer counts as they exactly appear in a dataset, but instead relies on averaged counts per unitig as an approximation; thus it is not an index that records exact raw counts.
REINDEER is able to answer presence/absence queries, although it is not particularly optimized to this task: other techniques such as HowDeSBT, Mantis and SeqOthello, produce indices that occupy smaller space. Several exciting future features can be envisioned for REINDEER. We plan on exploring index partitioning (i. e. constructing several REINDEER indices on subsets and merging query results) to accelerate REINDEER's construction and aim toward indexing SRA-scale collections of datasets. To reduce memory usage at the expense of query speed, we are also investigating the possibility of performing on-disk queries. Dataset quality control is also crucial for some applications such as RNA-seq to draw correct biological conclusions. Removing low abundance k-mers before indexing appears to be necessary, as numerous artefactual k-mers are created due to sequencing errors.
Acknowledgements Part of the experiments presented in this work were made possible thanks to the EBI computing resources and the  Institut Pasteur cluster. The authors would like to thank  Antoine Limasset  and  Mae ¨l Kerbiriou  for their immense help on BLight software, technical advice. They also thank the whole  Transipedia group , and in particular  Haoliang Xue  and  Benoit Guibert  for their tests on REINDEER; and finally  Paul Medvedev  for many helpful comments on the manuscript.
The systemic insulin resistance, mimicking type 2 diabetes phenotype, was associated with hyperglycemia and insulin resistance triggered in skeletal muscle by doxorubicin treatment  [31, 41]. This observation would require further investigation, focusing on combining doxorubicin and blood glucose normalizing components such as metformin or SGL2 inhibitors to assess whether normalized blood glucose impacts treatment responses. In fact, a clinical trial combining doxorubicin and metformin was initiated in breast cancer patients (NCT02506777/Active-Recruiting Aug 2020) to further elucidate whether such a combination would improve patient outcome. The metabolic alterations in ascorbate and glutathione metabolism could reflect on the antioxidative capacity of the tumors, enabling the oxidative stress to be overcome, triggered by doxorubicin, and thus the development of tumor masses.
The accelerated glutathione metabolism, supported by glutamate, potentially delivered from multiple pathways including glutaminolysis, glycolysis, and BCAA catabolism, could be considered as crucial factors to cancer survival under the treatment and tumor formation. Previous studies have suggested a significant role of glutathione metabolism in cancer resistance to chemotherapeutics  [11, 34]. Thus, strategies aiming to suppress the antioxidative potential of the tumor could improve the treatment. The intervention in glutamate metabolism by glutaminase inhibitors was shown to impact the glutathione level  [15, 39]  and thus, reduce the antioxidative tumor capacity. Indeed, strategies investigating combination of glutaminase inhibitor, CB-838, with chemotherapeutics such as paclitaxel and azacitidine are already the subjects of clinical trials (NCT03057600 and NCT03047993).
Statistical Data Analysis We used run-day median-scaled metabolite values as provided by Metabolon. Data analysis was performed using R studio (R version 3. 5. 3) and our in-house developed tool Autonomics (https: //github. com/bhagwataditya/autonomics). Using an Autonomics pipeline, data were log-transformed and fitted to the "~0 + subgroup" model using the limma package. We extracted the metabolites with a significant differential effect (p < 0. 05) for the contrast "Vehicle treated-Doxorubicin treated", and box plots were generated. Principal component analysis (PCA) was performed with SIMCA version 15 (Umetrics, Umea, Sweden).
Uncertainty quantification in the design of IPT systems Three simplified but realistic configurations of wireless power transfer operating at 85 kHz are studied in this section. The first one was built in Politecnico di Torino  [6, 98] , the second one in GeePs  [99]  and the third one has been investigated in the framework of the European project MICEV  [100, 101]. All the calculations in this chapter are performed by the UQLab module in Matlab  [102]. The first structure considered in this section contains two rectangular coils (the transmitter and the receiver), and two ferrite plates. This test case corresponds to an existing inductive power system that has been built in Politecnico di Torino, Italy. Preliminary results have been published in  [98]. The design includes a steel plate that represents the chassis of the electric vehicle (Figure  23 ).
Despite the important roles of sphingolipid metabolism in cancer biology, only very few agents targeting this pathway have reached clinical trials relating to cancer. These include safingol (L-threo-dihydrosphingosine), a PKC inhibitor (Phase 1; NCT01553071 and NCT00084812); ABC294640, a SPHK2 inhibitor (Phase I/II; NCT01488513, NCT02229981, NCT02757326, NCT02939807, NCT03377179, and NCT03414489); sonepcizumab (ASONEP), an S1P-specific monoclonal antibody (Phase I/II; NCT00661414 and NCT01762033); and Fingolimod, an S1P receptor antagonist (Phase I; NCT02490930). Given the synergistic effects of PF543 and doxorubicin on breast CSCs and non-CSCs in our study, it was proposed that neoadjuvant treatment with SPHK1 inhibitors with doxorubicin would reduce both CSCs and non-CSCs and thereby potentially improve therapeutic response in breast cancer patients.
Optical means of measurement Full-field optical techniques enable obtaining a large amount of data in one single experiment. The kinematic field can be ob-155 tained on the sample outer surface using digital images correlation (DIC) for instance. The thermal field can be either obtained using infrared (IR) thermography or near-infrared thermography (NIRT) techniques. The richness of these optical methods enables heterogeneous tests to be performed with complex 160 loading conditions. Under very high thermal transients a tiny eccentricity between the specimen and the induction coil leads to a loss of axisymmetry of the thermal loading. Therefore it was chosen to force this eccentricity so that the hottest point on the sample.
Stereo-correlation (3D-DIC) The 3D-surface displacements field was obtained using the 185 global stereo-correlation software UFreckles developed by J. Réthoré  [28] ,  [29]. A finite elements basis of quadrilateral elements with linear shape functions was used to perform the images analysis with an element size of 35x35px 2 (1 px 9µm). An example of a correlation mesh is given in Figure  4. b. 190 In order to avoid chromatic aberrations at high temperatures the calibration of the optical system was performed at low tem- perature by lighting the sample in the near infrared domain using a LED ring at the wavelength λ = 860nm. It is worth noting that because of the device geometry a "standard" calibration using calibration grids couldn't be performed. The calibration step was then achieved using the well-known geometry of the sample.
Summary and conclusions A creep ballooning device was set-up to reproduce simulated RIA post-DNB conditions on fuel rods sections. More than twenty ballooning tests were performed at regulation tempera-550 tures ranging from 840 • C to 1020 • C with heating rates around 1200 • C/s. Two different levels of pressure of 7 and 11 bar were investigated. The use of full-field optical methods enabled heterogeneous tests to be performed from which a large amount of data could be recorded and further processed. These tests evidenced three domains of behavior that appear to be very-well correlated with the phase fraction present within the material obtained at such heating rates: • The first domain is defined from 840 • C to 910 • C ( f 1200 • C/s β 10%). In this temperature range the behavior may be seen as "standard" with a creep rate increasing with the temperature.
Objectives: Kidney transplant recipients are at high-risk for donor-derived infections in the early posttransplant period. Transplant preservation fluid (PF) samples are collected for microbiological analysis. In case of positive PF cultures, the risk for the recipient is unknown and there is no consensus for prescribing prophylactic antibiotics. This nationwide observational study aimed to determine the epidemiology of bacterial and fungal agents in kidney transplant PF cultures and identify risk factors associated with positive PF cultures. Methods: We performed a retrospective observational study on the following data collected from a national database between October 2015 and December 2016: characteristics of donor, recipient, transplantation, infection in donor and PF microbiological data.
Data source Exhaustive clinical information on all kidney transplant procedures carried out in France are routinely and prospectively collected in national databases, which are maintained by the ABM. In this study, data were retrieved from these national databases of kidney transplantations, and retrospectively analysed. All kidney transplant procedures performed in France between 1 October 2015 and 31 December 2016 were included. We double-checked that we comply with the STROBE reporting guidelines for observational studies  [26].
Data collection Kidney transplantations are performed in 42 university hospital centres in France including 28 adult centres, 9 paediatric centres, and 5 that are both adult and paediatric centres  [27]. Furthermore, 157 health-care institutions across France are authorized to perform kidney procurement procedures  [28]. Allocation and distribution of organs are coordinated by the ABM. All data prospectively collected by the ABM are recorded in real time; clinical data are recorded by organ procurement and transplant teams, and microbiological data are recorded by the ABM. Since 2008, the ABM has recommended withdrawing 2 Â 25 mL of PF at the opening of the graft container (25 mL for mycological analysis and 25 mL for bacteriological analysis). After direct examination, the PF is cultured on solid media, and in an aerobic blood-culture bottle, an anaerobic blood-culture bottle and a mycological blood-culture bottle  [16].
Each microorganism is identified and antibiograms are performed following the standards of the French Microbiology Society  [29]. Data reporting is mandatory, guaranteeing the exhaustive nature of the information. Data anonymization, consistency and completion are controlled by the data manager of the ABM. The following data are routinely collected: (a) donor: clinical, biological and imaging parameters; cause of death and hospitalization process in intensive care when appropriate; (b) microbiological records of kidney transplant PF cultures; and (c) recipient: pre-transplant information, mandatory for the file creation process and registration on the national kidney transplant waiting list, as well as post-transplant information on serious events occurring within the first 30 days after transplantation, such as recipient death and graft non-function (GNF).
Variables selection All variables of interest and the statistical analysis plan were a priori selected by the interdisciplinary scientific committee of the study (kidney transplant specialists, infectious disease physician, epidemiologist and medical expert of the ABM) based on literature data (see Supplementary material, Data S1)  [17, 19, 21, 23, 25, 30, 31]. Microbiological analyses of PF cultures included positivity of bacterial and fungal cultures, and if appropriate, isolated agent(s) (only species because susceptibility testing results were unavailable). These analyses were performed in kidney graft PF and contralateral kidney PF when it was also transplanted. Microbiological species were grouped as follows: coagulase-negative staphylococci, Staphylococcus aureus, streptococci other than Streptococcus pneumoniae, enterococci, Enterobacteriaceae, nonfermenting Gram-negative bacilli, Candida, commensal bacteria of skin, naso-laryngeal sphere and gastrointestinal tract flora (such as Actinomyces, Propionibacterium, Clostridium, Haemophilus, Corynebacteriae, Moraxella, Lactobacillus, Bacteroides, Bacillus, Neisseria, Streptococcus pneumoniae) and others.
Statistical analyses Analysis of characteristics for donors, recipients, procurement and transplantation was first descriptive. Data are expressed as frequency and percentages for categorical variables and as mean and standard deviation for continuous variables. Microbiological analyses (frequency, %) were performed for all positive kidney transplant PF cultures, and across subpopulations (living-donor graft, deceased-donor graft, en bloc and positive contralateral kidney PF cultures). To explore the process leading to the positivity of PF cultures, microbiological concordance was calculated according to the positivity or negativity of cultures: (a) between kidney transplant and contralateral kidney PF cultures, (b) between kidney transplant PF cultures and donor's samples (blood cultures and urine cultures).
Two distinct analyses were conducted according to the clinical status of the donor (living versus deceased) because of disparity in donor selection criteria, surgical procedures and graft distribution. Variables found to be significant with a p value < 0. 10 in the bivariate logistic regression were entered into the multivariable logistic regression model for which a p value < 0. 05 (bilateral) was considered statistically significant. As missing data were observed for only four independent variables and their percentage was always <5%, missing values were not imputed. So, the analyses were performed on PF cultures for which there were no missing data on these variables. Odds ratios (crude and adjusted) with 95% CI and p values were calculated. No collinearity was observed between variables, and the HosmereLemeshow goodness-of-fit p value was at 0.
Characteristics of kidney recipients and donors Over the 15-month study period, 4487 kidney transplant procedures were performed in France, 725 from living donors (16. 2%, 725/4487). Characteristics of kidney transplant recipients, organ donors, procurement and transplantation are detailed in Table  1. Most donors (76. 9%, 3449/4487) had neither bacteraemia (negative blood cultures), nor bacteriuria (negative urine cultures) at the time of procurement. No living donor had bacteraemia or bacteriuria at time of procurement. Among the 64. 9% (2423/3736) of donors who received antibiotic treatment before procurement, 76. 6% (1856/ 2423) of them received monotherapy, the most prescribed medications were b-lactams, most commonly amoxicillin-clavulanic acid (68%, 1641/2423, data not shown).
In case of cultures positive for Candida or of intestinal perforation, a systematic fungal treatment is recommended in kidney transplant recipients because of the risk for mycotic pseudoaneurysm  [16, 17]. There is no evidence of complications related to PF cultures positive for bacteria. In contrast, the risk for MDR bacteria emergence in case of prophylactic antibiotic therapy is well-proven for the latter situation [7e9,  11, 32]. These findings lead us to question the need to systematically share results of bacteriological (not mycological) analysis of PF cultures with transplant teams, because it often leads to an antibiotic prescription (Le Berre et al. unpublished material). This study brings original findings and included a large number of observations (national exhaustive data over a 1-year period) from data available for transplant teams in routine clinical practice.
These results functionally demonstrate that ISG15 protects Tregs from IFNa-induced contraction in vitro. Based on our previous publication,  16  on existing literature,  20  and on the above data, we hypothesise that reduced activation, more than increased cell death, concur to such contraction and thus could be antagonised by ISG15. Human ISG15 deficiency is characterised by systemic type I-IFN-mediated inflammation. 12 To investigate the hypothesis that ISG15 also plays a role in IFN response by Tregs in vivo, we first analysed the frequencies of circulating Tregs in these patients. CyTOF analyses revealed no appreciable difference in Treg frequencies when compared to healthy controls at steady state (Supplementary figure  3 ). To assess transcriptional  differences of Tregs in patients with ISG15 deficiency, we analysed a previously published single-cell RNA-sequencing (scRNA-seq) data set of PBMCs.
In line with published data,  24  we found increased frequency of circulating Tregs (identified as FOXP3 + CD127 lo cells) in SLE patients with active disease (SLEDAI > 4) compared to inactive disease (SLEDAI ≤ 4; Figure  7a ). Patients with active disease also displayed a trend for higher expression of ISG15, PKR and MXA mRNAs in PBMCs (not shown), being the three genes generally coregulated, in line with data in the literature. 4 More importantly, the SLEDAI score positively correlated with the expression of ISG15 protein in Tregs (Figure  7b ): this suggested that, in active disease, the ISG15 increase that probably follows IFN exposure may favor Treg accumulation. We hypothesised that such effect might be due to ISG15-mediated resistance to the IFN signal and STAT1 phosphorylation. To test this hypothesis, we assessed pSTAT1 by intracellular flow cytometry in Tregs and Tconvs from SLE patients, stratified according to their ISG15 mRNA expression in PBMCs.
Samples were analysed directly ex vivo or following a 48-h culture in complete medium at 37°C, in the presence of anti-CD3 (1 lg mL À1 ), with or without 10 4 IU mL À1 recombinant IFNa (rhIFN-a2b, Miltenyi Biotec, Bergisch Gladbach, Germany). For ex vivo analysis of ISG15 and pSTAT1 induction, cells were treated with recombinant IFNa in different conditions: 10 2 IU mL À1 for 15 min or 10 4 IU mL À1 for 25 min; after incubation at 37°C, cells were fixed without washing. Samples were acquired on the BD LSRFortessa cell analyzer (BD Biosciences) and analysed with FlowJo software, version 10. 0. 8r1 (Treestar, Ashland, OR, USA).
Mass cytometry Peripheral blood mononuclear cells from an ISG15 deficient patient and eight healthy controls were stained and analysed by mass cytometry (CyTOF) at the Human Immune Monitoring Center of the Icahn School of Medicine at Mt. Sinai. Cells were barcoded, combined and then stained by incubation with antibodies against selected surface markers for 30 min on ice. Cells were then washed and fixed, resuspended in diH2O containing EQ Four Element Calibration Beads (Fluidigm, San Francisco, CA, USA) and acquired on a CyTOF2 mass cytometer (Fluidigm). Data files were normalised with a bead-based normalisation algorithm (CyTOF software; Fluidigm) and debarcoded with CD45 gating.
Dimensional reduction of the integrated data set was performed by principal component analysis (PCA) and first 25 principal components were used for unsupervised graph-based clusterings (resolution: 1. 2) and visualised by Uniform manifold approximation and projection (UMAP; parameters: n. dims: 25, n. neighbors: 30, metric: cosine). Regulatory Tcell gene signature scores were computed at single-cell resolution using Seurat's AddModuleScore function. T regulatory marker genes included in input set were CD4, IL2RA (CD25), FOXP3, CD5, ENTPD1, ITGAE, LAG3, CTLA4, TNFRSF4 (OX40) and TNFRSF18 (GITR). Differential gene expression was conducted using Seurat's FindMarkers function using a log 2 FC threshold of 0. 1 and an adjusted Pvalue of 0. 05.
ACKNOWLEDGMENTS This work was supported by the following grants to SP:  Associazione Italiana per la Ricerca sul Cancro  Grant  IG-2017 19784 ;  Ministry of Education, University and Research (MIUR) Progetti di Ricerca di Interesse Nazionale (PRIN)  Grant  2017 Prot. 2017K7FSYB ;  Istituto Pasteur Italia-Fondazione Cenci Bolognetti Call 2019  under 45; and by the following grant to VB:  Associazione Italiana per la Ricerca sul Cancro  Grant  IG-2017 19784. This research was supported by  National Institute of Allergy and Infectious Diseases  grants  R01 AI127372 ,  R01 AI148963  awarded to DB. We thank  John Hiscott ,  Sandra Pellegrini  and  Frederique Michel  for helpful discussion. We also acknowledge  Daniele Accapezzato ,  Gabriella D'Ettorre  and  Giancarlo Labbadia  for clinical information about CHC patients and  Agnese Po  for helping with Nucleofector.