dataesr/scientific-paragraphs-categorization huggingface dataset:
Imaging of KRAS-transfected cells w/o rocaglamide treatment on nanobar arrays was performed with laser scanning confocal microscopy (Zeiss LSM 800 with Airyscan) at 100x/1. 4 oil objective. Each image had a resolution of 512×512 pixels, with a pixel size of 124 nm and a bit depth of 16. To quantify the curved nanobar-end preferred distribution of each protein with or without drug treatment, the background intensity of each image was subtracted by a rolling ball algorithm at 3. 5-pixel radius in Fiji NIH  (Schindelin et al. , 2012)  and the intensity ratio of nanobar-end to nanobar-cencer was measured and calculated using a customwritten Matlab code derived from previously reported work  (Zhao et al. , 2017).
Acknowledgement: KR would like to thank the excellent technical support of  Stefanie Wenzel. We thank  Prof. Frank McCormick  for critical advice. Ulf Rapp  for providing cell lines and  Dr. Schonfeld  for critical reading of the manuscript. Part of this work is supported through a  DFG grant   RA1739/8-1  to KR and  CRC1292  ( TP05 ). KR is a Heisenberg professor of the DFG and a GFK fellow. Financial support to W. Z. NTU Start- Up-Grant  ( M4082114 )  NTU-NNI Joint Grant  ( M4082292 ). Technical support to nanobar assay:  Centre for Disruptive Photonic Technologies (CDPT)  and and  Nanyang NanoFabrication Centre  ( N2FC ) of  Nanyang Technological University.
Study design and patients This case series is part of an overall French clinical cohort assessing patients with COVID-19 (NCT04262921). All patients at French hospitals who were diagnosed with COVID-19 according to the French National Health Agency criteria were enrolled in this cohort from Jan 24 to Jan 29, 2020. 5 ogether with the French National Regulatory authorities and French Ministry of Health, on Jan 25, 2020, we determined the indication criteria for compassionate use of an investigational antiviral treatment (remdesivir) as signs of severe illness at diagnosis or secondary clinical aggravation (respiratory symptoms or general signs) based on WHO criteria for severe pneumonia caused by SARS-CoV-2  10  (appendix p 2). On the basis of expert opinion and available data in January, 2020, we considered that remdesivir might be the best potential drug for the treatment of COVID-19, although restricted to patients with severe disease (intravenous route, a loading dose of 200 mg, then maintenance daily dose of 100 mg for a total duration of 10 days).
26 emdesivir treatment improved disease outcomes and reduced viral loads in SARS-CoV-infected mice  27  and is inhibitory for SARS-CoV-2 in vitro. 28 A phase 3 clinical trial assessed this drug for the treatment of Ebola virus infection; therefore, data exist for the safety of use in humans. 29 Hence, on the basis of expert opinion, we considered remdesivir use in the three patients with severe disease patterns. To the best of our knowledge, only one case of remdesivir use in COVID-19 has been reported so far. 20 Two randomised controlled trials are enrolling patients in China to assess the clinical benefit of this treatment (NCT04257656; NCT04252664). On the basis of our data, we cannot draw any conclusions on the potential efficacy of remdesivir on COVID-19 infections. In two patients, the drug was initiated at the time of disease worsening, when the virus was already barely detectable in the clinical specimens.
Acknowledgments This study was funded by  REACTing  (Research & Action Emerging Infectious Diseases),  INSERM, Paris, France. We dedicate this article to  Mr Z , who died in our hospital in Paris on Feb 14, 2020, and to his daughter. We gratefully acknowledge the authors, the originating and submitting laboratories for their sequence and metadata shared through Global Initiative on Sharing All Influenza Data, on which this research is based. We acknowledge  Gilead Science  for providing remdesivir. We are also grateful to the following people and teams (collaborators): all the infectious disease staff, and more specially  Laurène Deconinck ,  Jade Gohsn ,  Sophie Ismaël ,  Veronique Joly ,  Anne-Claire Lehur ,  Nora Poey ,  Annabelle Pourbaix ,  Christophe Rioux ,  Bérénice Souhail , and  Simon Valayer  at  AP-HP Bichat-Claude Bernard Hospital (Paris, France) ; all the intensive care unit staff, and more specially  Juliette Patrier ,  Etienne de Montmollin ,  Pierre Jacquet ,  Medhi Marzouk ,  Sophie Jacques ,  Delphine Saint Leandre , and  Fattia Essardy , at  AP-HP Bichat-Claude Bernard Hospital ;  Cédric Laouénan ,  Isabelle Hoffmann ,  Minerva Cervantes ,  Theo Trioux ,  Guillaume Lingas  at the  Department of Epidemiology, Biostatistics and Clinical Research and Center for Clinical Investigation, AP-HP, Bichat-Claude Bernard Hospital ;  Charlotte Charpentier ,  Gilles Collin ,  Florence Damond ,  Valentine Ferre ,  Houria Ichou ,  Lucile Larrouy ,  Vincent Mackiewicz ,  Benoit Visseaux ,  Alexandre Storto ,  Badia Phin ,  Mélanie Bertine ,  Samuel Lebourgeois , and  Manuela Onambele-Guindi  at the  Department of Virology, AP-HP, Bichat-Claude Bernard Hospital ; all the technical staff at The  Institut Pasteur (Paris) ;  Mélanie Albert ,  Marion Barbet ,  Angela Brisebarre , and staff of the National Reference Center for  Respiratory Viruses  (The  Institut Pasteur) ;  Maud Vanpeene ,  Méline Bizard , and staff of the P2M platform;  Florence Morfin ,  Vanessa Escuret ,  Laurence Josset ,  Geneviève Billaud , and  Emilie Frobert at the Lyon National Reference Center for Respiratory Viruses (France) ;  Gisele Bendjelloul ,  Isabelle Lolom  at the  AP-HP, Bichat-Claude Bernard Hospital, Infection Control Unit ;  Jean-Luc Diehl  at the  AP-HP Georges Pompidou European Hospital intensive care unit ;  Thierry Pistone ,  Arnaud Desclaux ,  Alexandre Duvignaud ,  Isabelle Guarrigue ,  Didier Gruson ,  Alexandre Boyer ,  Benjamin Clouzeau ,  Jean-Michel Dindart ,  Eric Tentillier ,  Xavier Combes ,  Pauline Perreau ,  Pantxika Bellecave , and  Camille Ciccone  at the  University Hospital of Bordeaux , Bordeaux GeoSentinel site;  Agnès Lepoutre  at the  Santé publique France -Ile-de-France Regional Offices ;  Christine Campese ,  Sibylle Bernard-Stoecklin ,  Daniel Levy-Bruhl , and  Bruno Coignard  at the  Santé publique France -Infectious Diseases Direction ;  Julien Poissy  at the  Intensive Care Unit, Lille Hospital, France ; and  Céline Féger  ( EMIBiotech, Paris, France ) for her editorial support.
ebi. ac. uk/ena) or the NCBI assembly (https://www. ncbi. nlm. nih. gov/assembly) website. SNP prediction was performed with Snippy (https://github. com/tseemann/snippy) and indications of putative horizontal gene transfer events were defined as a unique SNPs cluster (≥3) and purged from the alignment. For functional studies (MICs, fitness, virulence), isolates were selected at various temporal steps of their inferred population dynamics. For the CA-MRSA USA300 lineages ancestors, 2 clinical isolates were included (Table  1  & Fig. 2 ): (i) one ancestral strain of the ST8 lineage, susceptible to methicillin and lacking ACME or COMER (Ancestral ST8 MSSA), (ii) one strain corresponding to the most recent common ancestor of the USA300-NA and LV clones, being resistant to methicillin but lacking the ACME or COMER sequence (Basal USA300 MRSA).
It is made The copyright holder for this preprint (which this version posted July 1, 2020. ; https://doi. org/10. 1101/2020. 07. 01. 150961 doi: bioRxiv preprint estimate to each haplotype (isolate) in a collection. Such density estimates where shown both empirically and in simulations to correlate with the epidemic success in the ancestry of the isolate  (17, 23). THD computations were conducted using R 3. 6. 1 with the thd package, available at github. com/rasigadelab/thd. The matrix of pairwise SNP distances was used as input; the THD parameters were an effective genome size (number of DNA base pairs effectively used in distance computations) of 3x10 6 bp; a per-nucleotide mutation rate of 1x10 -6 per year; and a timescale of 20 years, consistent with the recent expansion of the USA300 lineage.
Crude doubling time. Isolates growth curves were determined from Brain Heart Infusion broth (BHI) cultures incubated in 96-well flat bottom plates (Greiner) for 24 h at 37 °C with continuous optical density monitoring at 600 nm (Tecan Infinite® 200 PRO). Each strain was inoculated in three independent wells (technical replicate), and the experiment was repeated on three different days (biological replicate). Doubling times were calculated by graphical method with the logtransformed optical density data of the exponential growth phase. Competitive fitness. Each strain to be tested in a competitive pair was adjusted to an OD600 nm of 1, then three mL of a 1/100 th dilution in BHI of each strain was mixed in a glass tube. For experiments involving HgCl2, the later was added at final concentrations corresponding to 1/10 th to 1/100 th of the susceptible strain's MIC.
Data deposition: The data reported in this paper have been deposited in the European Nucleotide Archive (accession no. PRJEB38071). CC-BY-ND 4. 0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted July 1, 2020. ; https://doi. org/10. 1101/2020. 07. 01. 150961 doi: bioRxiv preprint.
025004-2 rarefaction wave  [16]. This plasma will be constituted of thermally distributed heavy ions with a temperature of the order of the keV (gold) plus a component of faster lighter ions from surface contaminants, with an average energy per nucleon of tens of keV  [14, 15]. Hydrodynamic simulations (carried out with the HYADES code  [17] ) indicate that this dense plasma expands into a more rarefied background plasma induced by the full ionization of the nitrogen gas by secondary x-ray emission from the solid target  [18] , with an average electron temperature and density of 650 eV and 10 16 cm À3 , respectively. Such an experimental configuration resembles the one already adopted in Ref. [7], although with a much higher ambient plasma density. The plasma evolution was monitored by temporally and spatially resolved proton radiographs with a resolution of approximately a picosecond and a few microns, respectively  [10].
29 https://gitlab. com/arm-hpc/packages/wikis/packages/EPOCH 30 https://github. com/ALaDyn/piccante 31 www. maisondelasimulation. fr/smilei 32 https://github. com/ntoles/tristan-mp-pitp 33 http://ipag-old. osug. fr/ ~ceruttbe/Zeltron/index. html 56 A. Marcowith 1 et al.
Acknowledgements The authors thank  Fabien Casse  and  Laurent Gremillet  for several fruitful discussions. RW thanks  Mickaël Melzani  who contributed a lot to his knowledge of magnetic reconnection and  Doris Folini  for profound discussions and proof-reading. AM and MG thank  Anna Grassi  for her important contributions to Secs. 2. 6 and 3. 1-3. 4. MG also thanks  Simon Bolanos  for his input on magnetic reconnection experiments (Sec. 2. 6. 4), Francesco Califano ,  Daniele Del Sarto  and  Alain Ghizzo  for various discussions on Vlasov codes (Sec. 3. 4) and the SMILEI dev-team for various discussions on the PIC method. This work is supported by the  ANR-14-CE33-0019   MACH  project. We acknowledge support by the  Programme National de Haute Énergie (PNHE).
We point towards the corresponding code websites: Dragon: https://github. com/cosmicrays, Galprop: https://galprop. stanford. edu/code. php, Picard: http:// astro-staff. uibk. ac. at/ ~kissmrbu/Picard. html Let us also mention the semi-analytical tool USINE, https://dmaurin. gitlab. io/USINE/, where the FPE is solved using a path integral method.
2009 (Cocquet et al. , 2012)). To determine the consequences of Sly knockdown on SLX/ SLXL1 genomic distribution we performed, in parallel, SLX/ SLXL1 ChIP-Seq in WT and Sly-KD spermatids in three independent experiments (see supplementary table S2, Supplementary Material online). Despite differences in the number of obtained peaks (probably due to modifications in the experimental procedure, see supplementary table  S2 , Supplementary Material online), overlaps between the three sets of experiments FIG. 1. SLX/SLXL1 proteins are enriched at the TSS of the same spermatid-expressed genes as SLY, and correlate with active epigenetic marks, similarly to SLY. (A) Heatmap comparison of SLX/SLXL1 ChIP-Seq with SLY, Kcr, H3K4me3, H3K79me2, H3K9ac, H4ac, H3K27ac, H3K27me3, and H3K9me3 ChIP-Seq data sets, all performed in WT round spermatids.
SLX/SLXL1 versus SLY Competition Drives the Expression and Evolution of Spermatid-Specific Multicopy Genes Located on the Sex Chromosomes and Autosomes Our data show that SLX/SLXL1 versus SLY competition-via their interaction with SSTY-drives the expression of hundreds of genes (thousands, if one considers one copy equals one gene), many of which are multicopy. Among them are sex chromosome-encoded spermatid-specific genes such as Slx/Sly themselves and Ssty, Mgclh (Btbd35f), and H2al gene families. All of them have been reported to have coamplified during mouse evolution  (Ellis et al. 2011; Yogo et al. 2012; Morgan and Pardo-Manuel de Villena 2017; Molaro et al. 2018). Here, we show that appearance and massive amplification of the autosome-encoded gene family Speer/Takusan in rodents (>200 Speer/Takusan on M.
SLX/SLXL1/SLY/SSTY Target Genes Are Potential Transmission Distorters As said above, many of SLX/SLXL1/SLY/SSTY target genes are spermatid-expressed multicopy genes, whether sex chromosome-encoded or autosomal. This can be viewed as a way to globally control the expression of potential TDs, which would act via increasing their own expression and gene dosage. Identification of Smok genes among SLX/ SLXL1/SLY/SSTY target genes adds further evidence to the hypothesis that Slx/Sly system could be intertwined with other transmission distortion systems. Interestingly, a recent analysis of spermatid-expressed transcripts at the single-cell level has identified thousands of genes which are not equally shared among spermatids, called GIMs for GenoInformative Markers  (Bhutani et al. 2019). Comparisons with our data reveal that more GIMs than non-GIMs are found among SLX/SLXL1/SLY/SSTY targetsthis is true for autosomal genes (P < 2.
2 Â 10 À16 ) as well as Xencoded genes (P ¼ 2. 8 Â 10 À4 ). Of note, multicopy genes are not specifically addressed in GIM data set, yet one can find an enrichment of Speer/Takusan (31 confident GIMs vs. 83 nonconfident/remaining, P ¼ 2. 9 Â 10 À9 ) and Smok genes (8 confident GIMs vs. 0 nonconfident/remaining genes, P ¼ 2. 7 Â 10 À7 ) among GIMs versus non-GIMs. Therefore, SLX/ SLXL1/SLY/SSTY control the expression of several sex chromosome-encoded and autosome-encoded genes, products of which are not equally shared among spermatids, and could therefore be transmission distorters and/or responders. It is worth mentioning that we did not find the X-encoded Toll-like receptor Tlr7 and Tlr8, which have been recently implicated in sperm motility and sex ratio distortion in the mouse  (Umehara et al.
Our data also indicate that other TDs, whether sex chromosome-encoded or autosomal, are among SLX/SLXL1/SLY/SSTY target genes, in particular genes of the t-haplotype system. Further studies will be needed to understand the involvement of these genes, specifically of Ssty and Speer/Takusan, in the conflict, and the evolutionary impact they may have had on rodent species other than mice. Finally, X versus Y antagonism has been previously described in other species (mainly Drosophila), and competition at the molecular level often appears to be driven by RNA interference  (Bachtrog et al. 2019; Courret et al. 2019 ). In the model described here, X versus Y antagonism is mediated at the protein level, but the picture is likely more complex and one cannot exclude contribution of noncoding RNA. FIG.
ChIP-Seq ChIP-Seq was performed to assess genomic location of SLX/ SLXL1, SSTY1, and TBL1XR1 (see parameters in supplementary tables S1-S3 and S6, Supplementary Material online). The data sets generated and/or analyzed during the current study are available in the SRA repository (https://www. ncbi. nlm. nih. gov/sra/ ; last accessed April 30, 2020) with the following project numbers: PRJNA627396, PRJNA275694, PRJNA62757, and PRJNA643726. To compare SLX/SLXL1-and SLY-binding sites, SLX/SLXL1 ChIP-Seq was performed on the same WT spermatid chromatin samples and under the same conditions as previously reported for SLY ChIP-Seq  (Moretti et al. 2017). In brief, $10 million of FACS-sorted enriched fractions of WT round spermatids (with a purity >90%) were fixed with 1% formaldehyde for 15 min then quenched with 125 mM glycine.
The subsequent stages were performed as described by the manufacturer (Novolink polymer detection, Leica), and the sections were counterstained with Mayer's hematoxylin solution for 2 min (Merck, Millipore). Sections were dehydrated in a graded series of alcohol bath and a final xylene bath. Cochin Institute Mouse House Facility, Matthieu Benard, Amandine Janvier and Otilia de Oliveira, Antoine Gueraud for DNA extraction, Francois Guillonneau from the 3P5 Proteomic Facility, as well as the Histology, Immunostaining and Laser Microdissection Facility, the Cellular Imaging Facility, and the Cytometry and Immunobiology Facility of the Cochin Institute (INSERM, CNRS, Universit e de Paris). This work was supported by INSERM (Institut National de la Sante et de la Recherche Medicale); the Agence Nationale de la Recherche (ANR-12-JSV2-0005-01 and ANR-17-CE12-0004-01 to J.
Background: Traditional bortezomib, thalidomide, and dexamethasone (VTd) regimens for patients with newly diagnosed multiple myeloma (NDMM) include doses of thalidomide up to 200 mg/day (VTd-label). Clinical practice has evolved to use a lower dose (100 mg/day) to reduce toxicity (VTd-mod), which was evaluated in the phase III CAS-SIOPEIA study, without or with daratumumab (D-VTd; an anti-CD38 monoclonal antibody). We used propensity score matching to compare efficacy and safety for VTd-mod and D-VTd with VTd-label. Methods: Patient-level data for VTd-mod and D-VTd from CASSIOPEIA (NCT02541383) and data for VTd-label from the PETHEMA/GEM study (NCT00461747) were analyzed. Propensity scores were estimated using logistic regression, and nearest-neighbor matching procedure was used. Outcomes included overall survival (OS), progression-free survival (PFS), time to progression (TTP),.
Data sources Data for the PSM were from two phase III clinical trials, PETHEMA/GEM (ClinicalTrials. gov, NCT00461747), and CASSIOPEIA (ClinicalTrials. gov, NCT02541383). Data for the VTd-label group were taken from the PETHEMA/GEM study, in which patients were randomized to one of three regimens: the alternating chemotherapy regimens vincristine/carmustine/melphalan/cyclophosphamide/prednisone and vincristine/carmustine/doxorubicin/dexamethasone, followed by bortezomib; vs thalidomide/dexamethasone; vs VTd  [10, 11].
Propensity score matching PSM was used to correct for differences in baseline characteristics in the CASSIOPEIA and PETHEMA/GEM trials. The National Institute for Health and Care Excellence [NICE] decision tree was used to determine which propensity score methodology best suited the data for this analysis  [12]. As there was some imbalance in baseline characteristics before matching, and good balance was possible to achieve after matching, analysis on matched samples was deemed more appropriate in both the primary and sensitivity analyses. In an exploratory analysis, several types of matching methods were applied to pick the best performing method. For each method, the distribution of propensity scores before and after matching and the postmatch balance between treatment groups (VTd-mod vs VTd-label or D-VTd vs VTd-label) was assessed.
To determine how adequately PSM balanced the covariates, pre-and postmatch balance between treatment groups (VTd-mod vs VTd-label or D-VTd vs VTd-label) was assessed using standardized mean differences for the included covari-ates (described below), with values >0. 1 suggesting potentially important imbalances  [13]. Additionally, chi-square tests were performed to assess the statistical significance of differences in covariates between treatment groups before and after matching. This assessment determined that the best performing PSM method was nearest-neighbor matching (without replacement). A 2:1 ratio was used (number of VTdmod or D-VTd patients matched to each VTd-label patient). Propensity scores were estimated using logistic regression, and matching was carried out using the Matchit R package  [14].
These results are in agreement with those of the CASSIOPEIA trial, which demonstrated the clinical benefit of adding daratumumab to VTd in transplant-eligible NDMM patients  [7]. However, OS results for VTd-mod and D-VTd should be interpreted with the caveat that CASSIOPEIA is ongoing, so OS data remain immature (medians have not yet been reached). The current PSM analysis also found D-VTd to be noninferior to VTd-label confounding). If any important variables were omitted, then the groups may remain unbalanced and study results can be seriously biased  [21]. However, both CASSIOPEIA and PETHEMA/GEM are RCTs, with data from most of the clinically relevant baseline variables collected and included in this analysis; therefore, the risk of unobserved confounding may be minimized. Second, PSM requires large samples because matching reduces the sample size, negatively affecting the precision of the estimates.
PSM also cannot correct for selection bias, and regional differences in local standard-of-care regimens may have further contributed to differences in the CASSIOPEIA and PETHEMA/GEM studies. Lastly, a substantial number of patients without cytogenetic risk data in the PETHEMA/GEM study were excluded from the primary analysis, thereby reducing the power of the sensitivity analysis. In addition to limitations of the PSM methodology, longer median follow-up and differences in maintenance treatments between the CASSIOPEIA and PETHEMA/GEM studies may have biased the results in favor of the VTd-label arm, particularly for long-term survival. Median follow-up in PETHEMA/GEM for VTd-label was 35. 2 months  [10, 11]  compared with 18. 8 months in CASSIOPEIA  [7]. Thus, more patients in the VTd-label arm of the analysis were exposed to maintenance therapies compared with VTd-mod/D-VTd.
ACKNOWLEDGMENTS The authors thank the patients who participated in this study, the staff members at the study sites, the data and safety monitoring committee, and the staff members involved in data collection and analyses. The authors are grateful to  Charlene Lee, BScPhm ,  MSc, PharmD , for her review of the manuscript. Medical writing support was provided by  Linda Wychowski, PhD , CMPP, of  Eloquent Scientific Solutions , and was funded by  Janssen Pharmaceuticals. This study was supported by  Janssen Research & Development  in collaboration with the  Intergroupe Francophone du Myélome and Dutch-Belgian Cooperative Trial Group for Hematology Oncology.
6% vs 78. 4%; rate difference, 13. 2 [95% CI: 5. 21-21. 19]; P =. 0003) (Table 4). However, D-VTd was signifi- cantly inferior to VTd-label for posttransplant ≥CR after matching (22. 4% vs 46. 4%; rate difference, -24. 0 [95% CI: -34. 16 to -13. 84]; P <. 0001). Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; IgG, immunoglobulin G; ISS, Multiple Myeloma International Staging System; NA, not applicable; VTd, bortezomib, thalidomide, and dexamethasone. a In the sensitivity analysis, patients with no cytogenetic test done were excluded from the dataset.