dataesr/scientific-paragraphs-categorization huggingface dataset:
8, PMOD Technologies Ltd. , Zurich, Switzerland). Brain PET images were co-registered with an MRI template of the mouse brain to define selected regions of interest, consistent with the spatial resolution of the scanner  [66]. Regional time-activity curves (TACs) were generated and expressed as percent of injected dose per volume (ID%/cm 3 ). Specific binding of [ 11 C]flumazenil to GABA A Rs in tested brain regions was estimated using the Logan-reference kinetic model. The regional binding-potential (BP ND ) was estimated using the brain stem, a region with low expression of GABA A Rs, as a previously described reference region  [34]. Parametric images describing [ 11 C]flumazenil BP ND were then generated for each animal. The parametric images obtained in each group (n = 5 WT and 6 mdx mice) were then compared using a statistical parametric mapping (SPM) and a voxel-to-voxel analysis (SPM8 software, London, UK).
All samples of a given structure from both genotypes were deposited on the same gel in order to obtain a reliable quantification. Antibody signals were visualized using the Odyssey CLx system (Li-Cor, Homburg, Germany) and quantification was done using the Empiria Studio software (Li-Cor, Homburg, Germany). The specificity of the primary antibody was checked by omitting the primary antibody, which did not reveal unspecific bands at the expected molecular weight of the target proteins, and/or by using incubation with a control peptide to confirm the specific bands to be quantified (for the antibodies directed against the α1, α3, and α5 subunits). As described by the manufacturer (Synaptic system, Gottingen, Germany), the α2, β1, and β3 antibodies reveal several relatively close bands which are specific of the analyzed subunits, as previously validated using specific KO mice (α2) and/or control peptides (β1 and β3).
Quantification of GABA A R clusters was performed from a single confocal image taken in the middle of the stack. The minimal cluster size was arbitrarily set to 0. 02 µm 2 , corresponding to two adjacent pixels at the magnification used. A threshold segmentation algorithm was used for automatic detection of clusters. The mean number and size of the clusters, as well as the distribution of cluster sizes, were analyzed within a total tissue surface of 10,000 µm 2 per animal in each brain structure or subregion. Clusters double-labeled for the α1 and α2 GABA A R subunits were identified using all images of the stacks with the 3D colocalization analysis tool of the Imaris image analysis software (version 9, Bitplane, Zurich, Switzerland). Data presented as means ± SEM were statistically analyzed using the Mann-Whitney U test, while the distributions of the cluster sizes were compared using the Kolmogorov-Smirnov test with a significance threshold set at p < 0.
29 0] [41]  But although an enormous amount of experimental data is accessible in online databases,  [42] [43] [44]  via web-based interfaces (https://mlos. leloir. org. ar),  45 there is no mechanistic understanding of how IDP molecular architecture controls the structure of their dense phase. 7] [48] [49]  Exquisite detail on residue-residue interactions  50  and the conformational dynamics of IDPs within condensates is revealed by Atomistic Molecular dynamics (aaMD) simulations,  23, 51  but these are limited to near-molecular system sizes and short times. ] [54] [55] [56] [57]  A minimal model of the phase separation of multivalent proteins is the patchy particle scheme in which proteins are treated as spheres with attractive sites on their surface. 58, 59 hese models, however, ignore conformational fluctuations that may be important for flexible proteins.
Dissipative particle dynamics simulation technique We use the dissipative particle dynamics simulation technique (DPD) to study the phase behaviour of a series of model IDPs. The source code for the simulations is available on Github. 81 PD is a coarse-grained, explicit-solvent simulation technique invented to study the hydrodynamic behaviour of complex fluids. 68, 69 It has since been applied to many soft matter systems including amphiphilic membranes,  [82] [83] [84]  vesicle fusion,  85, 86  and domain formation in vesicles,  87  among many others. 88 It is highly suited to simulations of (uncharged) IDPs because the interactions are weak, and it has been shown that polymers in DPD exhibit self-avoiding walk scaling. 89 DPD is able to follow the evolution of fluid systems over large length and time scales by grouping atoms or atomic groups into beads, and replacing complex, interatomic potentials by effective forces that are softer and shortranged.
Acknowledgements This study was supported by funding to the  Blue Brain Project , a research centre of the E ´cole polytechnique federale de Lausanne (EPFL), from the  Swiss government's ETH Board of the Swiss Federal Institutes of Technology , and the  80PRIME MITI CNRS programme. The authors gratefully acknowledge computer time provided by the  Blue Brain Project  and  Swiss National Supercomputing Centre  and the Abacus 2. 0 Supercomputing cluster at the  University of Southern Denmark. The Dissipative Particle Dynamics simulation source code used in this work is available on GitHub. 81Custom python code written for the data analysis includes algorithms from the open-source libraries scikit-learn (https://scikit-learn. org/stable/)and networkx (https://networkx. org). The graph analysis code is available on GitHub (https://github.
Background: Human papilloma virus infection is known to influence oropharyngeal cancer (OPC) risk, likely via sexual transmission. However, sexual behaviour has been correlated with other risk factors including smoking and alcohol, meaning independent effects are difficult to establish. We aimed to evaluate the causal effect of sexual behaviour on the risk of OPC using Mendelian randomization (MR). Methods: Genetic variants robustly associated with age at first sex (AFS) and the number of sexual partners (NSP) were used to perform both univariable and multivariable MR analyses with summary data on 2641 OPC cases and 6585 controls, obtained from the largest available genome-wide association studies (GWAS). Given the potential for genetic pleiotropy, we performed a number of sensitivity analyses: (i) MR methods to account for horizontal pleiotropy, (ii) MR of sexual behaviours on positive (cervical cancer and seropositivity for Chlamydia trachomatis) and negative control outcomes (lung and oral cancer), (iii) Causal Analysis Using Summary Effect estimates (CAUSE), to account for correlated and uncorrelated horizontal pleiotropic effects, (iv) multivariable MR analysis to account for the effects of smoking, alcohol, risk tolerance and educational attainment.
MR makes three key assumptions in that the genetic instrument (i) is robustly associated with the risk factor (i. e. 'relevance'), (ii) does not share a common cause with the outcome (i. e. 'exchangeability') and (iii) affects the outcome only through the risk factor (i. e. 'exclusion restriction principle') to check for genetic pleiotropy  [24, 25]. Here, we applied two-sample Mendelian randomization (MR) using summary-level genetic data from the largest available GWAS for each sexual behaviour (sample 1) and OPC (sample 2). We first conducted univariable MR analysis to assess the effects of NSP and AFS on OPC risk. We next performed univariable MR analysis to explore the effect of sexual behaviours on HPV seropositivity. Genetic proxies for complex human behaviours are more likely to have broad pleiotropic effects and may influence multiple upstream pathways that indirectly impact on sexual behaviour.
Summary-level data for sexual behaviours Summary statistics for AFS were obtained from a GWAS conducted in the UK Biobank (n = 397,338)  [30]   [28]. AFS was treated as a continuous variable, with individuals considered as eligible if they had given a valid answer to the question "What was your age when you first had sexual intercourse? (Sexual intercourse includes vaginal, oral or anal intercourse)" and ages < 12 years old were excluded. Since AFS had a non-normal distribution, a within-sex inverse rank normal transformation was applied  [28]. Where possible, the full 272 SNP AFS instrument was used, except in the primary analysis of OPC, whereby only 139 SNPs could be extracted from head and neck cancer data (Additional file 1). We obtained summary statistics for the NSP instrument (117 SNPs) from a GWAS conducted in UK Biobank  [26]  (n = 370,711) (Additional file 1).
NSP was treated as a continuous variable based on responses to the question: "About how many sexual partners have you had in your lifetime?". Respondents who reported > 99 lifetime sexual partners were asked to confirm their responses and a value of zero was assigned to participants who reported having never had sex, which was normalised separately for both males and females with an inverse rank normal transformation  [26]. Both AFS and NSP GWAS adjusted for the top 10 principal components (accounting for population stratification), sex and birth year. For AFS, those participants with family data were controlled with non-independence of family members or else one family member was included in the analysis  [28].
Summary-level data for oropharyngeal cancer The largest available GWAS for OPC was performed on 2641 OPC cases and 6585 matched controls from 12 studies which were part of the Genetic Associations and Mechanisms in Oncology (GAME-ON) Network  [31]. Cancer cases comprised the following ICD-10 codes: oropharynx (C01. 9, C02. 4 and C09. 0-C10. 9). Stratification was conducted by geographical region to evaluate potential heterogeneity in any effects given potential differences in the distribution of genetic variants for specific traits within populations. As GAME-ON included participants from Europe (45. 3%), North America (43. 9%) and South America (10. 8%), this study was restricted to individuals of predominantly European ancestry to avoid the effect of population structure. Details of the studies included as well as the genotyping and imputation performed have been described previously  [31, 32].
Univariable Mendelian randomization To assess effects of NSP and AFS, we used SNPs which reached genome-wide significance (p < 5 × 10 -8 ) and were determined to be independent in their respective GWAS  [26, 28]  using pairwise r 2 < 0. 1 (with 250-kb linkage disequilibrium (LD) windows). Further repeated analysis using a more stringent clumping threshold r 2 < 0. 001 was also conducted. Two-sample MR analyses were conducted using the "TwoSampleMR" package (version 0. 5. 5) in R (version 4. 0. 2) to extract the SNPs instrumenting the risk factor from the OPC GWAS. Harmonization of the direction of effects between exposure and outcome associations was performed, and palindromic SNPs were aligned when minor allele frequencies (MAFs) were less than 0. 3 or were otherwise excluded. SNP-specific Wald estimates were calculated (SNP-outcome estimate divided by SNPexposure estimate) and an inverse variance weighted (IVW) method applied to meta-analyse these in order to obtain an effect estimate of the risk factor on OPC risk.
Summary-level genetic data for HPV16 and HPV18 serological measures were obtained from UK Biobank. We performed individual GWAS for each measure using a similar approach as described by Kachuri et al. [34]  using GWAS was performed using PLINK 2. 0 (July 27, 2020, version)  [35]. Details on how these GWAS were conducted can be found in Additional file 2: Supplementary information  [12, 33, [36] [37] [38] [39] [40].
Positive and negative control analyses To further assess the specificity and sensitivity of the genetic instruments identified in relation to sexual behaviour, we conducted additional positive and negative control MR analyses. These were selected based on current evidence and aimed to appraise the role of AFS and NSP on (a) cervical cancer and C. trachomatis seropositivity, as positive control outcomes where evidence of an effect would support the aetiological link via HPV; and (b) lung cancer and oral cancer as negative controls, where a direct causal effect of sexual behaviour is unlikely and so where any evidence of an effect would indicate potential violation of the MR assumptions due to pleiotropy, population stratification or selection bias  [46]. Details on the GWAS summary data used to conduct positive and negative control outcomes can be found in Additional file 2: Supplementary information  [47, 48].
2. 0) and MendelianRandomization  [57]  (version 0. 5. 0) packages in R (version 4. 0. 2). To further clarify the direction of causal effect between AFS, NSP and other risk factors (including smoking initiation, the comprehensive smoking index, alcohol drinks per week, risk tolerance and educational attainment), bidirectional MR was conducted. Causal Analysis using Summary Effect Estimates, LD Score Regression and multivariable Mendelian randomization approaches all require full GWAS summary data for the proposed risk factors of interested. Full data were available for the GWAS of NSP  [26] , but these have yet to be published for the GWAS of AFS. Therefore, for these approaches, we used another GWAS for AFS, also conducted using UK Biobank data (n = 406,457), for which full summary data are publicly available (https://gwas.
Multivariable MR was also used to directly model the potential indirect effects of the genetic variants via other traits (smoking, alcohol, risk tolerance and educational attainment) and supported the conclusions of the CAUSE method. Finally, we could not distinguish between HPV-positive and HPV-negative oropharyngeal tumours in the GAME-ON summary data, which would require further analysis at an independent level or a GWAS of OPC stratified by HPV status. The GWAS-bysubtraction approach  [81]  could be useful to account for latent factors of other behavioural traits to identify more specific genetic instruments for sexual behaviour, if valid instruments for these traits exist. More serological data may become available in the UK Biobank and other clinical genetic studies, which could enhance power to evaluate potential the extent to which any effect of sexual behaviour on cancer risk is mediated by HPV.
Results Of the twenty selected subjects, two were excluded for reasons unrelated to the protocol used in the study. Thus, a total of 18 healthy men were included in the present study and were randomly divided into the Control and Cuffs groups (n = 9/group). Since our previous work on the same subject did not show any significant difference in atrophy and muscle strength loss between CTL and Cuffs groups  [31] , we arbitrarily chose to pool the data of all participants (n = 18) to increase the statistical power. However, due to limited biopsy materials, the number of subjects for each analysis is indicated in the figure legend.
Magnetic Resonance Imaging (MRI) Siemens-Avanto device was used in CHU Rangueil in Toulouse, France, to perform images. Subjects were transferred to an MRI room keeping -6 • head-down bed rest position to simulate microgravity in the best way. Imaging was recorded 4 days before immersion and directly after. Briefly, the mark was located exactly halfway between the left anterior superior iliac spine and the upper part of the ipsilateral (left) patella. The measurements were taken on the left side because of the biopsy on the right side. This condition was chosen to avoid inducing heterogenic signals in MRI analysis of the biopsy site. The fat content was calculated by OsiriX MD software (v. 7. 0. 1. 64-bit software, managed by Rosset A. , Genève, Switzerland). Three-point Dixon MRI was used to measure intramuscular fat content (IFC) as a percentage inside the anterior muscle compartment from the images.
Acknowledgments: We would thank the team of the  Space clinic of the Institute of Space Medicine and Physiology (Medes-IMPS ) in Toulouse for organizing and implementing the DI protocol. We would also like to thank  Biocampus , the  'Réseau d'Histologie Expérimentale de Montpellier' (RHEM) , ' Montpellier resource imagerie ' ( MRI ) for histology and microscopy (fluorescent and polarized slide scan) and ' Réseau des Animaleries de Montpellier'  ( RAM-Metamus ) for the development of methods. We particularly acknowledge the participants in this project. This work was supported by the  "Centre National d'Etudes Spatiales" (CNES)  and the  Canadian Space Agency.
Institutional Review Board Statement: All subject gave their informed consent to the experimental procedures that were approved by the local ethics committee (CPP Est III: October 2, 2018, n • ID RCB  2018-A01470-55 ) and  French Health Authorities  ( ANSM: August 13 ,  2018 ), and were in accordance with the  Declaration of Helsinki. This study was registered at ClinicalTrials. gov(Identifier:  NCT03915457 ). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
A. Numerical calculation of effective fields and scaling factors All numerical calculations were implemented in MAT-LAB (The MathWorks Inc) and follow the treatment outlined in the appendix. An example script that calculates the effective fields and scaling factors can be found in the SM. First, the trajectory of the electron spin under a given irradiation scheme is calculated using quaternions, which directly yield the overall flip angle and the effective field. Then, the trajectory is then transformed into a cyclic frame, where it is Fourier transformed to generate the coefficients (see appendix). One of the Fourier coefficients corresponds to the scaling factor a ±. The same procedure can be applied to cases when an offset is present. MATLAB functions from EasySpin 32 were used for quaternion calculations.
D. Instrumentation and EPR/NMR spectroscopy All experimental data were acquired on a home-built X-band spectrometer which is based on the design described by Doll et al. 36. Notable differences compared to the earlier design are that a 1. 8 GSa/s digitizer (SP Devices ADQ412) was used and that the temperature of 80 K was achieved with a cryogen-free cryostat (Cryogenic Limited). Mw pulses were generated with an arbitrary waveform generator (AWG) model M8190A (Keysight) and amplified with a 1 kW traveling wave tube (TWT) amplifier (Applied Systems Engineering). A standard Bruker EN4118A-MD4 ENDOR resonator was used, with an external rf tuning and matching circuit. NMR experiments were performed using a Stelar PC-NMR spectrometer. An Arduino board was used to count TWT gate triggers of the EPR spectrometer, each corresponding to an h increment (Fig.
1 ). The Arduino board triggers the NMR acquisition after h loops. FT EPR spectra were acquired by a chirp echo sequence with linear chirp pulses spanning 300 MHz, a duration of 200 ns (π/2) and 100 ns (π) and an inter-pulse delay of 2 µs. All EPR and NMR signals were processed in MATLAB. All experimental results presented in this work were acquired within a single session, i. e. , the sample was not moved between different DNP experiments.
V. DISCUSSION We have demonstrated a general theoretical treatment applicable to periodic mw DNP sequences in static samples. This is realized by analyzing the resonance conditions and determining the scaling factors, which encode the details in a sequence -Rabi fields, phases, amplitudes, and mw offsets. We showed here an example of how theory can help to improve existing sequences, such as XiX-DNP, and an example of how to design a broadband sequence that is robust against mw offsets. We show that adiabatic BEAM has outperformed RA-NOVEL by obtaining ε max of ≈ 361 and ε max • T 1,n /T B ≈ 701 on trityl radicals at a temperature of 80 K, a field of 0. 35 T, and in static conditions. While the adiabatic solid effect can achieve similar maximal enhancements of 360,  23  it has a slightly longer buildup time T B (and thus a lower sensitivity enhancement of ε max • T 1,n /T B ≈ 629).
ACKNOWLEDGMENTS This article is dedicated to  Dr. Anton Ashuiev , stranded in Ukraine at the time of writing. Author contributions: NW, KOT, and GJ designed the research. NW performed all measurements with help from LAV and LS. NW, ABN and  NCN  developed the theory. All simulation scripts were written by  NW , with general help and input from KOT, ABN and LAV. Further acknowledgements:  Dr. Daniel Klose  and  René Tschaggelar  are acknowledged for building the EPR spectrometer used for this work. We thank  Prof. Matthias Ernst  and  Prof. Robert G. Griffin  for extensive and numerous discussions. Prof. Jan Henrik Ardankjaer-Larsen  generously provided  OX063  for an earlier project. Funding: KOT acknowledges the funding support from the  French National Research Agency  ( ANR-20-ERC9-0008  and  HFPulsedDNP  ) and the  Respore program  (petit équipement projet n°339299).
NW and GJ acknowledge funding by  ETH Zürich  grant  ETH-48 16-1. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. MATLAB code to compute effective fields and scaling factors can be found at https: //github. com /JeschkeLab/DNPtheory.
Acknowledgements We thank the platform for immortalization of human cells from the  Institut de Myologie ,  GH Pitié-Salpétrière  for providing immortalized myoblasts. We thank  Dr. Franca Abbruzzese  ( Università Campus Bio-Medico di Roma ) for her valuable technical support on confocal microscopy acquisition. The research has been carried out thanks to PRIN 2017:  201742SBXA_004  founded by  Italian Ministry of University and Research  to C. G.
The NEU-SFL was obtained on the Sysmex TM XN-10 R analyzer and values for survivors and non-survivors were compared. The intra-patient NEU-SFL variations between the hospital entry and the last day of hospitalization were also analyzed (IRB 22. 06. 01, NCT 05413824). Results: Non-survivors presented higher NEU-SFL values. NEU-SFL values above the 4th quartile were independently associated with a 2. 88-fold risk of death. Furthermore, the difference of NEU-SFL values between the first and the last available data during hospitalization revealed that a decrease in NEU-SFL was associated to survivors and vice versa.
Study design and participants We made a retrospective cohort study using the national "Programme de médicalisation des systèmes d'information" (program for the medicalization of information systems = PMSI) database, designed to include discharge summaries of all patients admitted to hospitals in France. For the COVID-19 cohort in this study, all adult patients admitted to Nîmes University Hospital for COVID-19 from January 1 to December 31, 2021, were included. Hospital admissions for COVID-19 were identified by primary diagnoses, related diagnoses, or associated diagnoses, with ICD-10 codes U07. 10, U07. 11, U07. 14, or U07. 15. The study was approved by the Institutional Review Board (IRB 22. 06. 01) and ethics committee at Nîmes University Hospital. This clinical investigation was performed in accordance with the Helsinki declaration of 1975 as revised in 1996.
Statistical analysis Data were analyzed with version 5. 01 of the GraphPad R statistical software program (San Diego, United States) using Mann-Whitney, Wilcoxon, Fisher's and chi-square tests for two group comparisons as appropriate. ROC curve and logistics regression were made using Stat View R (Abacus concepts, Berkeley, CA, USA). The associations between NEU-SFL values and the patient outcome were explored by means of logistic regression analysis and estimated from odds ratios (OR) with a 95% confidence interval (CI). For these analyses, patients were divided into quartiles for NEU-SFL values with the lowest quartile (≤25th percentile) used as the reference. The potential confounding influence of simple demographic, laboratory risk factors for death due to COVID-19 (i. e. , age, gender, blood cell count, and inflammation parameters) and comorbidities on the associations between NEU-SFL and patient outcomes were evaluated using multivariate logistic regression models.