dataesr/scientific-paragraphs-categorization huggingface dataset:
Several filtering steps led to a final dataset with 95 VTUs, representing 96% of the virus-like reads. The taxonomic diversity was relatively large, including 25 family-like clusters, 16 orders and all the five phyla of RNA viruses (Fig 2 and S2A). Around three quarters of the VTUs had percent identities at the amino-acid level below 90% with the virus species of their best hit. This observation strongly suggested the discovery of new virus species as usually observed in mosquito viromes (S3A Fig). The read distribution of VTUs and clusters was highly skewed within each library, with most reads provided by a few taxa, as previously observed (16,48,49) (Fig 2 and S4). For example, four out of the 25 clusters represented more than 50% of the virus-like reads (Fig 2). This observation was found in the viromes of the two Culex species, although with different dominant taxa (Fig 2 and S4).
Fig). Shared taxa between the two mosquitoes represented 92% of the clusters and 75% of the VTUs (S5 Fig). We then analyzed the potential influence of host and habitat on virome diversity and abundance both at the cluster and the VTU level. For the sake of conciseness, only the results from the analysis of the cluster dataset are presented in the main text. The results for the VTUs followed those obtained with the clusters (available in the Supplementary Material, S6 Fig). As expected from the high diversity overlap, taxon richness was not significantly different for host and habitat (Mann Whitney Wilcoxon tests for host, pvalue = 0. 80; Kruskal Wallis test for habitat, p-value = 0. 183) (Fig 3A). Shannon and Simpson indices were significantly different between mosquito species but not between habitats (Mann Whitney Wilcoxon tests for host, p-value = 0.
5 (mean Bray-Curtis dissimilarity = 0. 69) (Fig 3C). These observations suggested that differences in relative abundance of shared clusters dominated over differences in taxonomic diversity. The analysis of the VTU dataset suggested a similar pattern, although differences in diversity were more important (means of Sorensen and Bray-Curtis dissimilarities = 0. 49 and 0. 86, respectively; S6 Fig).
METHODS This was a retrospective, multicenter, registrybased analysis approved by the Chronic Malignancies Working Party of the EBMT. The EBMT is a nonprofit scientific society representing more than 600 transplant centers located mainly in Europe. Data are entered, managed, and maintained in a central database with internet access; each EBMT center is represented in this database. EBMT centers commit to obtain informed consent according to the local regulations applicable at the time for reporting pseudonymized data to the EBMT. Patient selection was performed by identifying CML patients undergoing a first allo-HCT between 2012 and 2019 from a UD using PTCy as GVHD prophylaxis (PTCy UD), allo-HCT from UD using a non-PTCy GVHD prophylaxis (non-PTCy UD) and allo-HCT from MMRD with PTCy as GVHD prophylaxis (PTCy MMRD).
The resulting 50 datasets were analyzed separately, and results were combined using Rubin's rules  [28]. For aGVHD/death with aGVHD, outcomes were artificially censored at 100 days after allo-HCT; all other outcomes were artificially censored at 36 months after allo-HCT. The following variables were considered as potential confounders: patient age at allo-HCT, donor age, patient sex, graft source, number of different types of TKI before allo-HCT (imatinib, dasatinib, nilotinib, bosutinib, ponatinib, and asciminib), type of conditioning (myeloablative conditioning [MAC] versus reduced-intensity conditioning), type of GVHD prophylaxis, TCD, total body irradiation, disease status (CP1 versus others) at allo-HCT, Karnofsky Performance Status (KPS), Hematopoietic Cell Transplantation Specific-Comorbidity Index (HCT-CI), donor/ recipient cytomegalovirus serostatus (-/-, other), year of allo-HCT, and the time from diagnosis to allo-HCT.
RESULTS A total of 1341 CML patients treated in 257 EBMT centers were selected from the registry. A total of 1094 patients (82%) underwent non-PTCy UD, 113 patients (8%) underwent PTCy UD allo-HCT, and 134 patients (10%) underwent PTCy MMRD allo-HCT. Table  1  displays patient-and transplantation-specific characteristics across the 3 donor/GVHD prophylaxis cohorts. The median age at allo-HCT in the entire population was 47 years (interquartile range [IQR], 37 to 56 years). The majority of the non-PTCy UD cohort received calcineurin inhibitor-based GVHD prophylaxis (88%) (Table  2 ). TCD was used in 83% of the non-PTCy UD patients, 19% of the PTCy UD patients, and 9% of the PTCy MMRD patients. Supplementary Table  S1  provides details on TCD. In the 990 patients with available data on the type of TKI treatment, 650 (65.
Phase-shift theory with straight-line trajectories: applications The general considerations presented above are supported below by calculations for some test systems. To choose them, we took into account the availability of experimental data up to high temperatures (see Table  1 ) and representativity of various leading interactions. NO and OH with the permanent dipole moments differing by an order of magnitude (0. 158 D  [71]  and 1. 668 D  [72] , respectively) were chosen as active molecules whereas Ar and N 2 -a rare-gas atom and a nonpolar molecule with the quadrupole as its leading multipole -were taken as perturbers. With these combinations we probe the behaviour of the active molecule with weak and strong dipoles and the role of dispersion/induction and electrostatic interactions. Inter-molecule potential energy surfaces (PES) of the complexes NO-Ar, NO-N 2 , OH-Ar and OH-N 2 were computed ab initio using the MOLPRO quantum chemistry package  [73]  at the coupled-cluster level of theory CCSD(T): RCCSD(T)/aug-cc-pV(X+d)z (spin-restricted) or UCCSD(T)/aug-cc-pV(X+d)z (spin-unrestricted), where X = T,Q.
NO-Ar (Fit I) NO-Ar (Fit II)   difference to the RHF wavefunctions computed prior, would often diverge to values > 100 after only 2-3 iterations. Attempts were also made using a RMP2 (second-order restricted Møller-Plesset) perturbation theory, but correct convergence in the excited state energy was not possible. Tests were made using the aug-cc-pVXz (X = T,Q,5,6,T+d,Q+d,5+d,6+d) basis sets  [84, 85]  for both UCCSD(T) and RMP2 calculations. Finally, icMRCI calculations using molecular orbitals obtained from state-averaged CASSCF calculations were tried for the system, but extremely long convergence times were seen, which is not practical for the number of geometries we need to compute. We leave the OH-N 2 system for a later study, where we aim to use the CFOUR software  [86]  to employ further coupled cluster methods on this system.
Acknowledgements This work was supported by the  European Research Council  under Advanced Investigator Project  883830  and by  UK STFC  under grant  ST/R000476/1. The authors acknowledge the use of the  UCL Myriad High Performance Computing Facility  and associated support services in the completion of this work. JB acknowledges the mobility grant provided by the  Region Bourgogne-Franche-Comté  for her visit to UCL.
Statistical analysis The results are presented as mean ± standard errors. The Mann-Whitney test was used for biodistribution study to compare unpaired datasets with unequal variance between 5-and 45-min post injection. Kruskal-Wallis non-parametric one-way ANOVA was used to compare RCP at different time point in comparison to the radiolabeling. Differences were considered signi cant at a p level of less than 0. 05. The analyses were performed using GraphPad 9. 0.
Acknowledgements Constantin Bouras  and  Enikö Kövari  from the  Department of Psychiatry, University Hospitals, Geneva, Switzerland , are acknowledged for their contribution in obtaining human brain samples. This work was partly supported by  France Life Imaging  grant  ANR 11-INBS-0006. The NanoBio ICMG platforms ( FR 2607 ) are acknowledged for providing facilities for AFM analyses ( H. Bonnet ).
001). After adjustment for confounding factors, the seven-day survival rate [95% con dence interval (CI)] was respectively 88% [80-96], 80% [72-88] and 75% [56-94] in the subphenotypes A, B and C. With subphenotype A as reference, the adjusted hazard-ratio [95%CI] for seven-day mortality was 1. 53 [0. 76-3. 08] (p=0. 24) in subphenotype B and 5. 90 [2. 04-17. 07] (p=0. 001) in subphenotype C. Conclusions: By combining conventional kidney function variables with urine measurements of a cellcycle arrest biomarker, we identi ed three distinct SA-AKI subphenotypes with different short-term courses and survival rates. This approach might help to better stratify patients in the early phases of septic shock but its value must be con rmed in a larger, independent cohort. Trial registration: the AKI-CHECK study was registered at ClinicalTrials.
The [TIMP-2]*[IGFBP7] level is the best-established AKI biomarker and was recently use to guide a therapeutic intervention for preventing AKI progression  [32, 33]. Nevertheless, sepsis is a complex condition, and kidney-function-limited subphenotypes might not wholly re ect all the various patterns of organ dysfunction. Analyses of large databases (using much the same methods as in the present study) identi ed several sepsis subphenotypes, which differed with regard to the course of disease and the treatment response  [34, 35]. Our study had several limitations. Firstly, it was an unplanned analysis of previously collected data. Secondly, our study population was relatively small, which might have limited the study's statistical power. Thirdly, the fact that the study population presented AKI within 6 h of the diagnosis of septic shock (i.
Materials and methods We performed a single-center, descriptive, retrospective study. All consecutive patients who consulted at the Hospital of Cayenne, the main city of French Guiana, for suspicion of LB between January 1st, 2010 and December 31, 2021 were included. The inclusion criteria were those at least 15 years old (y. o. ), consulting for suspected LB, who were symptomatic, and having at least one serological test for B. burgdorferi. The exclusion criteria included those younger than 15 y. o. or who refused to participate. Patients were recruited using different strategies. First, hospitalized patients with a diagnosis code of "Lyme disease" in the International Classification of Diseases 10 (ICD10) were extracted from the databases of the hospital. Second, patients consulting for suspicion of LB were recruited by interviewing treating physicians.
98 and 1. 21, which corresponds to no cooperativity  [41] , meaning that the binding of one ligand molecule does not affect the binding of adjacent molecules. In this case, the protein still might have one or multiple non-cooperative ligand-binding sites, which cannot be deduced from the data obtained  [42, 43]. Finally, while QCM results suggest that protein-NPs binding is mainly electrostatically driven, this is not so clear from the analysis of the binding constants. Focusing on the data at 25 °C (the same temperature as the QCM experiment), the + NPs have the strongest interaction with Tf and Ft. Contrarily, BSA seemed to have the highest interaction with + NPs in QCM experiments. The difference in the results might be related to the technique since QCM required the previous fixation of protein onto the sensor, while fluorescence emission was measured after the protein established an equilibrium with NPs in solution.
Overall discussion This study aimed to investigate the interactions between different proteins (albumin, transferrin, hemoglobin, and fetuin) and NPs of different surface charges and compare the data obtained with the protein corona data obtained from an in vivo study performed with similar NPs  [17]. QCM and fluorescence quenching explored the binding affinity, while potential changes in the secondary structure were investigated by CD. BSA and transferrin were previously identified as part of the top 10 most abundant proteins adsorbed in vivo  [17]  independently of their charge. This can be related to the affinity of these proteins to the NP surface and their high abundance in the blood  [5]. Fetuin-A, identified as one of the most abundant constituents of the hard corona  [44, 52, 53] , was correlated with the increase in the circulation time of [0] NPs and -NPs.
Acknowledgments We would like to thank all the participants including in this study. We would like to thank  BIOGEN  and IONIS which funded the  NCT04288128  study and  INSERM , which sponsored the  NCT04288128  study (to A. D. ). This work was supported by the  International Max Planck Research School for Intelligent Systems (IMPRS-IS)  (to J. S. ) and the  Else Kröner-Fresenius-Stiftung   Medical Scientist programme  ' ClinbrAIn ' (to W. I. ). as well as the  Else Kröner -Fresenius Stiftung Clinician Scientist programme  "PRECISE. net" (to M. S. ). Work on this project was supported, in part, by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) No  441409627 , as part of the  PROSPAX  consortium under the frame of EJP RD, the  European Joint Programme on Rare Diseases , under the  EJP   RD COFUND-EJP N° 825575  (to M.
Estimated time from onset was defined as the di erence between current age and estimated age at onset 19  , with estimated disease onset calculated based on the individual's CAG repeats, as described in 20. Negative values denote estimated disease onset in the future, positive values denote estimated disease onset in the past. Healthy controls (n=33) consisted of expansion-negative first-degree relatives of SCA2 carriers and unrelated healthy individuals, all without signs of neurodegenerative disease upon clinical examination. SARA assessments were performed by expert neurologists (Paris: GC, Tübingen: MS). The study was approved by the local institutional review boards of both participating centres (NCT04288128, IRCB 2018-A02563-52, CPP 19081-60311 for Paris and 598/2011BO1 for Tübingen).
Statistical Analysis All statistical analyses were conducted using SAS v. 9. 4 (SAS Institute Inc. , Cary, NC). Before analysis, data were assessed for normality and transformed using optimum Box-Cox transformations where necessary (TRANSREG). Data relating to oocyte collection data, blastocyst percentage, and blastocyst number were analyzed using generalized linear mixed models (GLIMMIX) with repeated measures. Several models were constructed to compare the oocyte collection and embryo production between donors and embryo production between sires used. Due to parity structure differences between elite dairy donors and elite beef donors, data were analyzed separately for each type of donor. In each model assessing oocyte collection, the model fixed effects included donor, parity of donor (nulliparous vs.
092) following AI compared with ET (AI: 47. 0%  [40. 4, 53. 7]  vs. ET: 40. 6% [36. 8, 44. 6]) and did not differ between ET-DAIRY and ET-BEEF: (42. 1% [35. 7, 48. 8] vs. 39. 7% [34. 8, 44. 9] for Dairy versus Beef, respectively; P = 0. 85). Overall, P/S was greater (P < 0. 001) on d 62 following transfer of fresh embryos (48. 4%; 43. 3, 53. 4) compared with frozen embryos (32. 9%; 28. 3, 37. 8). The magnitude of the difference between fresh embryos and frozen embryos was larger for ET-DAIRY (53. 3% vs. 31. 3%; P = 0. 0009) than for ET-BEEF (45. 7% vs. 35. 3%; P = 0. 122; Figure  6A ). To compare the efficiency of AI (i. e. , all synchronized cows inseminated) versus ET (9. 6% of synchronized cows deemed unsuitable for ET), data were analyzed to determine P/Sync for each treatment. The predicted probability of P/Sync at d 32 was not different be- tween AI and ET (47.
ACKNOWLEDGMENTS The authors' work is funded by the Teagasc Walsh Scholarships Program, The Department of Agriculture, Food and the Marine (Dublin, Ireland) Research Stimulus Fund (Grant 2021R665), Science Foundation Ireland (Grant 16/IA/4474), as well as funding from a research grant from Science Foundation Ireland and the Department of Agriculture, Food and Marine (Grant 16/RC/3835; VistaMilk). Funding from the Dairy Levy Trust (Dublin, Ireland) and FBD Trust (Dublin, Ireland) is gratefully acknowledged. M. B. Rabaglino was supported by an Horizon 2020 Marie Skłodowska-Curie Actions Individual Fellowship (Proposal 101021311; Brussels, Belgium). The authors thank the staff and students at Teagasc Moorepark (Fermoy, Co. Cork, Ireland) for help in sample collection. Ceva Santé Animale (Bordeaux, France) provided the pharmaceuticals used in estrous synchronization.
Acknowledgements We thank  Dr. Xiaoxue Wang  for the P. aeruginosa strains used in this study,  Dr. Renjie Jiao  for the S. marcescens and Listeria monocytogenes strain,  Drs. Danielle Garsin  and  Michael Lorentz  for E. faecalis strain,  Dr. Chengshu Wang  for the M. robertsii strain, the late  Dr. Hans-Michael Muller  for the PO antibody. We are grateful to  Dr. Bruno Lemaitre  for the kind gifts of Sp7, PPO1 Δ , PPO2 Δ , PPO1 Δ -PPO2 Δ fly strains. We thank  Dr. Chuping Cai  for help with the design of schemes for figures. This work was supported by the  China Postdoctoral Science Foundation  ( 2017M612634 ),  Research Capability Enhancement Project  of the  Guangzhou Medical University , and  Natural Science Foundation of Guangdong Province  ( 2018A030310180 ) to JC and by grants from the  111 Project  (# D18010 ;  China ) and the  Incubation Project for Innovative Teams of the Guangzhou Medical University  to DF.
ACKNOWLEDGEMENTS The authors would especially like to thank  Ulrike Herian ,  Rahel Klein ,  Kai Volz ,  Lena Wendler ,  Jennifer Schmitt ,  Danijela Heide ,  Christine Schmitt , for excellent technical assistance. We thank  Rani Burm ,  Bettina Fleischmann-Mundt  and  Peter Malin  for their help with the mice experiments and  Veronika Eckel  for her assistance with scanning immunohistochemistry images. We are grateful to  Takaji Wakita  for the JFH-1 isolate,  Janos Botyanszki  for compound PI4KA-F1,  Richard Harbottle  for the pS/MARt vectors,.
the author/funder. This project was funded by grants from the  Deutsche Forschungsgemeinschaft  Project Number  314905040-SFB/TRR209  to Vo. L. , R. B. , F. K. , Pe. S. , D. F. T. , K. B. , M. H. ; Project Number  278191845  to Vo. L. ; Project Number  272983813  to Vo. L. and R. B. D. F. T. is supported by an  ERC Starting Grant   'CrispSCNAs'  (grant number:  948172 ) by the  European Research Council. P. M. was supported by grants from the  Ghent University Special Research Fund (UGent BOF)  as well as an  Excellence of Science grant  ( VirEOS2. 0 ) from the  Research Foundation -Flanders (FWO-Vlaanderen)  and  FNRS. F. K. was supported by  Deutsche Krebshilfe   70113873.
The percent cumulative release of NRG and DOX was calculated at each time point, and the release data were fitted to various kinetic release models, namely zero order, first order, Higuchi, and Korsmeyer-Peppas to assess the best-fit release kinetics model based on the linearity of the release curves. In addition, the possible release mechanism was predicted by the value of "n" of the Korsmeyer-Peppas equation  (Pai et al. , 2016; Chen et al. , 2021).
Oversight A study Steering Group was set up, chaired by the chief editor of the WCT (I. C. ). Oxford (R. C. ) led on running the study, drafted the surveys and complete initial analyses on the results. The Steering Group finalised survey questions, reviewed, and analyzed the data after each round and made final executive decisions on the hierarchy's structure. The final hierarchy was agreed upon by a central panel, including the Steering Group and the wider WCT EVI MAP project team, during the 3-day hybrid meeting in Lyon, France. The external WCT EVI MAP Advisory Board (below) provided input and feedback midway through this meeting. Thus, the final hierarchy represents a consensus between the WCT authors, the WCT EVI MAP expert project group, and input from external advisors and stakeholders.
The types of work considered in this study and the decisions in the subsequent Delphi rounds regarding their inclusion or exclusion from the evidence hierarchy. The terminology of the listed types of work evolved during the Delphi study, and the Steering Group added and merged some categories at various points in the process to deal with areas where agreement could not be reached. Some terms change later in the study, see the glossary (Supplementary File 2) for full and final definitions. Richard Colling et al. / Mod Pathol 37 (2024) 100357 Table Evaluation of various evidence types during Delphi rounds Round Include in hierarchy (counts as evidence) Exclude from hierarchy (does not count as evidence) 1 Case-control studies Conference abstract Case series Conference posters Consensus studies Editorials Cross-sectional/observational studies Narrative/nonsystematic reviews Cohort studies Opinion pieces Diagnostic test studies Oral platform abstracts Evidence synthesis papers Patent descriptors Laboratory in vitro studies Personal/author/expert opinion written in text Randomized controlled trials Study protocols Systematic reviews "Unpublished data" as quote 2 Cancer registry data Clinical trial protocols Clinical laboratory test studies Patient interview studies Genome database entries Preprint versions of papers Surgical technique papers 3 Animal studies Commentaries Case reports Guidelines a Molecular biology databases Letters Noncontrolled or nonrandomized trials Surveys a Unless systematically developed, then counted as a systematic review.
Evaluation of the color during the reduction To monitor the color change during the reduction, 2 cm of Agalginate fibers were put in contact with 2 mL of the reducing solutions (see the composition in Table  1 ) in plastic cuvettes, and photos were taken at regular intervals for 30 min. A white-balance operation was carried out on the images using a graphic editor software, according to a white reference present in all photos. The color variation in time was quantified by converting the photos in 8-bit (i. e. , conversion in grayscale), and applying an auto-contrast operation using the software ImageJ. The gray values of the fibers at half of their height were sampled, and plotted for each sample as a function of time.
Thermogravimetric analysis (TGA) Thermogravimetric analysis was performed using Discovery SDT650 (TA Instruments). A heating ramp from room temperature (RT) to 1000 • C, with a rate of 10 • C/min, was applied to all the samples previously equilibrated at RH95. For the water content determination in the air dried samples, a heating ramp from RT to 200 • C (rate of 10 • C/min) was applied. Measurements were performed under N 2 flux (100 mL/ min), using alumina pan. Data were analyzed with Trios v5. 1. 1. 46572 software (TA Instruments). To determine the silver content of the fibers, a heating ramp from RT to 1000 • C, with a rate of 20 • C/min, was applied to all the samples under air flux (100 mL/min). Measurements were performed in duplicate. Data were analyzed with Trios v5. 1. 1. 46572 software (TA Instruments).
Differential scanning calorimetry (DSC) DSC analysis was carried out by means of Discovery DSC 2500 (TA Instruments). Tzero Aluminum Hermetic pan (TA Instruments) were used for all the measurements. Samples were analyzed by applying a heating ramp from 0 • C to 120 • C, with an heating rate of 20 • C/min. Measurements were performed in duplicate. Data were analyzed with Trios v5. 1. 1. 46572 software (TA Instruments).
Mechanical tests A Discovery DHR-3 rheometer (TA Instruments) was used to evaluate the mechanical properties of the fibers. The latter were fixed to a paper support using bicomponent glue and clamped  (Alves Fidelis et al. , 2013; Tordi et al. , 2023) , applying a force equal to 30 cN⋅m (see Fig. S2  for details). The traction test was performed with a 10 μm/s rate. Measurements were performed in quadruplicate and the data were analyzed using Trios v5. 1. 1. 46572 software (TA Instruments).
Acknowledgments CSGI ( Consorzio Interuniversitario per lo Sviluppo dei Sistemi a Grande Interfase ) and  MUR-Italy  (" Progetto Dipartimenti di Eccellenza 2023-2027 " allocated to  Department of Chemistry "Ugo Schiff ") are acknowledged for the financial support. Prof. Mirko Severi  is acknowledged for the ICP-OES measurements.