dataesr/scientific-paragraphs-categorization huggingface dataset:
02 TeV, the EKRT model can describe both the shape and the overall magnitude of multiplicity on centrality. In general, almost all models reproduce the steep rise versus N part while EPOS-LHC, ASW and KLN show a saturation behaviour. The predictions show a similar trend as for the Pb-Pb case  [8]  and altogether a flatter distribution with respect to data. In Fig. 7 , the models are compared to the pseudorapidity dependence of the dN ch /dη for the top 5% central collisions. HIJING 2. 1 reproduces the pseudorapidity dependence at mid-rapidity well, but overestimates the data at forward rapidity, due to the large value of the shadowing parameter used. AMPT and PYTHIA/Angantyr describe the data fairly well, with a slight overestimate at forward rapidities. EPOS LHC reproduces the shape well, but under-predicts the multiplicity overall.
Every procedure lasted 90 minutes or less to minimize complications. At the end, 24 cm and 7 Fr silicone DJS was used according to the operator. The stent was removed 1 to 4 weeks after the intervention, in consultation under local anesthesia, or during the next procedure. Each patient received non-steroidal anti-inflammatory drug (NSAID) after the intervention for 3 days. Thirteen surgeons performed URS and were categorized between two groups, junior or senior. Seniors were titular surgeons and junior, one or two years after graduation. Data protection commission and ethic comity approved the study. Prestenting was used in case of emergency or if renal access was not possible in a prior intervention. Pain was evaluated using verbal rating scale (VRS). A telephone interview was conducted on day one for outpatient surgery; otherwise, the nurse used VRS during the hospitalization.
Our primary outcomes were post-operative pain and post-operative complications. Patient's data included age, gender, body mass index (BMI) and American Society of Anesthesiologists (ASA) score. Surgical data included presence of preoperative stent, use of ureteral access sheath, stone free status based on preoperative view or renal ultrasound or CT scan at 3 month, stone location, surgeon's experience, length of hospitalization and outpatient surgery status. Stone dimensions were measured with a CT scan; stone burden was evaluated with the biggest stone size and the cumulative size. Complications were recorded according to Clavien Dindo classification. 9 Both groups were compared on the basis of these parameters. We also made a multivariate regression analysis to highlight complications factors.
Statistical Analysis Patients characteristics were summarized as counts (frequencies) for qualitative variables and with a mean ± standard deviation or median -[Inter-Quartile Range (IQR)], as appropriate, for continuous variables. Patients characteristics were compared using the Fisher or chi square test for categorical variables and with Mann-Whitney for continuous variables, as appropriate. A logistic regression was performed to find complications factors and validated, with manual backward variable selection process. This analysis is conducted to identify potential risk factors. All tests were bilateral, with a type I error rate of 5%. The statistical analysis was performed using Graphpad Prism 6. 0 and R software version 3. 4. 1.
Density-Functional Calculations: The relaxed YPc 2 molecular structure on the MgO/Ag(100) and Ag(100) substrates was calculated via density functional theory within the Kohn-Sham formalism  [60]  and with use of QuickStep module  [61]  in the CP2K code (rB86-vdW-DF2 approximation  [62]  ). More details are given in the Supporting Information. Spin-Hamiltonian Calculations: The equilibrium magnetization M(H) shown in Figure  5  was calculated using a spin-Hamiltonian model implemented in a MATLAB code operating on the lowest manifolds www. advancedscience. com J = 15/2 for Dy 3+ and J = 6 for Tb 3+. The ligand field was taken into account using Stevens operators, with the Stevens parameters obtained from ref. [44] after conversion by α, β, and γ factors. [48] The Hamiltonian was solved by full diagonalization and magnetization was calculated considering thermodynamical population of the energy levels.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  701647. M. S. and J. D. gratefully acknowledge funding by the  Swiss National Science Foundation  (Grant No. 200021_165774/1 ). K. D. acknowledges support from the 'EPFL Fellows' fellowship programme co-funded by  Marie Curie ,  FP7  Grant agreement no. 291771. The authors thank  Stefan Zeugin  for technical assistance during the measurements. A. P. S. acknowledges super-computing resources at the  Centro Svizzero di Calcolo Scientifico (CSCS) ,  Lugano (TI) , under the project  uzh11. A. S. and F. D. acknowledge support from the  Institute of Basic Science, Korea , through the project  IBS-R027-D1.
8 mm ID, 1. 3 mm OD, and 10 kDa Mw cut-off, corresponding to an average pore diameter of 5 nm. The HF5 channels used in the experiment were a standard cartridge containing a 17 cm long fiber. The OpenLab CDS ChemStation (Agilent Technologies) data system for Agilent instrumentation allows the control of the instrumentation and analytical analysis. The software package Wyatt Eclipse @ ChemStation (Wyatt Technology Europe) was used to set and control the flow rate and to move the focus position during the sample focus/concentration. An 18-angle multiangle light scattering detector model DAWNMD HELEOS (Wyatt Technology Corporation, Santa Barbara, CA, USA) operating at a wavelength of 658 nm, was used to measure the radius of particles in solution. A RID 1260 (Agilent technologies) differential refractive index (dRI) detector operating at a wavelength of 280 nm was used on occasion as a concentration detector, when the capabilities of the UV detector were overcome by the complexity of An HF5 method is composed of the following steps: focus, focus-injection, elution and elution-injection.
Acknowledgements The ARRONAX cyclotron is a project promoted by the  Regional Council of Pays de la Loire  financed by local authorities, the  French government  and the  European Union. This work has been, in part, supported by a grant from the  French National Agency for Research called "Investissements d'Avenir", Equipex Arronax-Plus  no  ANR-11-EQPX-0004  and  Labex  no  ANR-11-LABX-0018-01.
Extracts were then boiled for 10 min at 100°C, sonicated to reduce viscosity and centrifuged 10 min at 15000 rpm. The supernatant was harvested and stored at -80°C. Protein concentration was determined using the Pierce BCA reducing agent compatible kit (ref 23252). The sample extracts were then deposited onto nitrocellulose covered slides (Supernova, Grace Biolabs) using a dedicated arrayer (2470 arrayer, Aushon Biosystems). Four serial dilutions, ranging from 2000 to 250 µg/ml, and two technical replicates per dilution were printed for each sample. Arrays were labeled with each of the 20 specific antibodies or without primary antibody (negative control), using an Autostainer Plus (Dako) as detailed in our previous papers  [20, 22, 26]. After protein quantification by RPPA, the raw data were normalized using Normacurve following the procedure described by  [28] , which normalizes for fluorescent background per spot, a total protein stain and potential spatial bias on the slide.
Statistical analysis The statistical analyses were performed using SAS statistical software (SAS 9. 1, SAS Institute INC, Cary, NC, USA) and XLSTAT 2017. 19. 4 (AddinSoft, Paris, France). Before analysis, raw data means were scrutinized for data entry errors and outliers. Normal distribution and homogeneity of the dataset was first tested by the Shapiro-Wilk test (P> 0. 05). The PROC GLM procedure of SAS was then used to study the muscle type (5 muscles), gender (cows vs. steers) and interactions effects on the relative abundances of the proteins. Significant differences among muscles were performed using Tukey's test at a significance level of P< 0. 05. Similarly, the protein abundances were further compared between the two genders within each muscle separately and the effect of rearing practices on the abundances of the 20 proteins was analyzed for both cows and steers.
Z-scores represent the deviation of each observation relative to the mean of the corresponding individual in each rearing practice and were calculated using PROC STANDARD of SAS that standardizes data to a mean of 0 and standard deviation of 1. These normalized data were used to build PCAs to depict the relationships between the rearing practices of the 86 PDO Maine-Anjou cows with i) animal, rearing factors and carcass characteristics, and with ii) the 20 protein biomarkers from the 5 muscles quantified by RPPA technique within the rearing factors. The Kaiser-Meyer-Olkin (KMO) measure, known also as Kaiser's Measure of Sampling Adequacy (MSA) was applied to test the validity of the sampling  [29]. Subsequently, unsupervised hierarchical clustering heatmap was generated using the same data to assess the differences among the 5 muscles based on the normalized data for each rearing practice.
For the 15 steers, only two rearing practices were identified (grass (n = 5) and haylage (n = 10)) and were considered in the analyses in same manner than cows. Finally, the PROC CORR of SAS after Z-scores calculation was used to compute the Pearson's correlations of coefficients between the 20 proteins and the animal, rearing factors and carcass characteristics of the whole data of the 86 cows. Correlation coefficients were considered significant at P< 0. 05.
Acknowledgements The authors thank  Albéric Valais ,  Ghislain Aminot  and  Marlène Pécot  from  SICA Rouge des Prés  for muscle sampling and data on animal, rearing factors and carcass properties. They thank  Leanne De Koning ,  Aurélie Cartier  and  Bérengere Ouine  from  Institut Curie, RPPA Plateform, Paris France , for the quantification of the biomarkers.
Whole body composition assessment A dual-energy X-ray absorptiometry (DXA) scanner (Hologic Discovery A, Hologic Inc. , Bedford, MA, USA) was used to measure BMD (g/cm 2 ) at the whole body (WB), lumbar spine (LS), total hip (TH) and femoral neck (FN). The coefficient of variation for our device during measurement on a standard phantom was less than 1% for FN, WB and LS BMD. We further assessed total body fat mass (FM, kg), total lean mass (LM, kg) and the percentage of fat mass according to manufacturer-recommended procedures. Analysis of scans was performed using software (Hologic Discover, version 13. 4. 2) and the regions of interest for all sites were placed manually by trained study staff according to a standard analysis protocol. All analyses were checked by one researcher for consistency (TS).
Descriptive characteristics of the population One hundred men with a mean age of 24. 4±2. 8 years and mean BMI of 23. 4±2. 8 kg/cm 2 were included in the analysis. None of the participants had a chronic disease or medication known to affect bone. Detailed participant characteristics are shown in Table  1. The percentage of overweight participants (BMI � 25kg/m 2 ) was 28%. Among these participants 22% were students, 77% worked in various professions (medical doctors, mechanics, engineers, technicians) and 1% was unemployed. The Orleans population census undertaken in 2014 showed that the population of young men in this region was distributed as follows: 71%, 26% and 3% were respectively workers, students and unemployed people. There were 11% of smokers in our population. We found significantly lower values of BMD at all sites in smokers versus non-smokers (data not shown).
Low grade inflammation, associated with increased inflammatory cytokines in the case of excess adipose tissue, produces a negative impact on bone metabolism  [39]. In addition the skeletal muscle metabolism might be altered in obese subjects due to the increase in lipotoxicity and insulin resistance  [40]. Thus, in the present study, we specifically assessed visceral adipose tissue by DXA and looked for its association with bone parameters but we did not find any correlation either in univariate or multivariate analyses (data not shown). Conversely, visceral fat measured by computerized tomography in 100 healthy young women had a negative effect on the femoral bone  [41]. Due to the weak age range of the subjects, we did not find any association between ALMI and this variable. Our study also highlighted that handgrip strength was significantly but moderately correlated with BMD at all sites.
BVS Segmentation and Creation of Cloud Points and Surface Meshes A binary image was created from the original Region of interest (ROI) from CT images by applying a gray-level thresholding method using ITK-Snap software  [13]. In the case of pre-ViV scans, calcifications and artifacts were removed manually. A surface mesh was then created based on the edge points of the binary image. The surface mesh was created for visualization purposes. Its vertices were considered as cloud points, used in the registration method and in the comparison metric.
RESULTS The proposed evaluation was performed on two datasets (Table  1 ). The first group involved data of 15 degenerated tissue valves scanned from patients undergoing a ViV procedure. The second involved 4 deteriorated tissue valves extracted surgically during redo surgical aortic valve replacement. The dataset of all analyzed valves is summarized in table 1. Three measures have been used to evaluate and visualize the result of the alignment of the meshes: local surface differences, volume-to-volume overlap measure and point-to-surface distance.
Hence, the modified Haussdorff distance, was used, described for matching two objects based on their edge points. Finally, computer assistance in ViV procedures may rely on the 3D/2D registration of surface meshes extracted from the segmentation of pre-operative CT-scans onto the fluoroscopic image  [11]. This semi-automated step can however be challenging in the case of imaging artifacts, poor-quality or non ECG-gated CT-scans, and in the case of tissue valves with limited radiopaque landmarks, such as the Medtronic Mosaic or the Saint Jude Trifecta valves. Our study suggests therefore that this initial step can be avoided by using surface meshes extracted from undeteriorated corresponding valves. Such valves can in fact easily be scanned, allowing the creation of a database of surface meshes for computer-assisted ViV procedures.
Introduction Eleven cysteine cathepsins (B, C, F, H, L, S, K (a. k. a. O 2 ), O, V, X (a. k. a. Z/P) and W) that are related to papain (clan CA, family C1, subfamily C1A; see MEROPS: the peptidase database (Release 12. 1): http:// merops. sanger. ac. uk) have been identified and characterized to date in humans  [1] [2] [3]. They all are lysosomal proteinases, which not only act as house-keeping proteinases involved in bulk protein degradation, but also process other endogenous proteins in a relatively specific manner  [1, 2]. Cathepsin C (CatC), also known as dipeptidyl peptidase I (EC 3. 4. 14. 1), is a unique member of the cathepsin family in view of its elaborate biosynthesis and homo-tetrameric structure  [4, 5]. CatC is initially synthesized as a ~60 kDa single chain glycosylated monomer ( 1 Asp-Leu 439 ), that associates to form the proCatC homodimer  [4].
Structural Modeling of Human Wt-proCatC and ProCatC(Leu172Pro) The model was based on the crystal structure of human CatC (Protein Data Bank (PDB) code 3PDF,  [31] ) and the crystal structure of trypanosoma brucei proCatB (PDB code 4HWY,  [21] ). The dimer was constructed by the application of the symmetry operator present in the PDB file. The middle fragment of propeptide was modeled using SWISS-MODEL server (https://swissmodel. expasy. org/). All missing loops, connecting the propeptide with the other chains, were then added using Yasara program (www. yasara. org). The obtained model was refined by coarse-grained replica exchange molecular dynamics simulation with a UNRES force field  [32]. During the simulation, secondary structure and positional (applied for Cα atoms) restraints (based on two 3PDF and 4HWY), were applied.
Finally, to obtain a low energy, all-atom structure, the model was optimized using minimization and short, low temperature molecular dynamics with Generalized Born implicit solvent in the AMBER package (http://ambermd. org/). This procedure was repeated in cycles until all close contacts were optimized. To keep the model structure as close as possible to the experimental structures, the positional restraints for carbon α atoms of 2 kcal/mol. Å 2 were used during all-atom simulations. All models were analyzed using RasMol AB software  [33]. All molecular dynamics simulations were performed for neutral pH using AMBER and UNRES force fields, since the force field parametrization was made to imitate the behavior of amino acids at pH around 7.
The recent development of cancer precision medicine is associated with the emergence of 'Molecular Tumour Boards' (MTBs). Attended by a heterogenous set of practitioners, MTBs link genomic platforms to clinical practices by establishing 'actionable' connections between drugs and molecular alterations. Their activities rely on a number of evidential resourcese. g. databases, clinical trial results, basic knowledge about mutations and pathways -that need to be associated with the clinical trajectory of individual patients. Experts from various domains are required to master and align diverse kinds of information. However, rather than examining MTBs as an institution interfacing different kinds of expertise embedded in individual experts, we argue that expertise is the emergent outcome of MTBs, which can be conceptualized as networks or 'agencements' of humans and devices.
2 Equipping and engendering expertise: expertise as a network The performance of MTB activities requires the mobilization of a number of devices. In addition to reports and visual inscriptions derived from the high-throughput analysis of patient material, they include generally available databases and, more specifically, 'knowledgebases' that provide access to highly curated functional information about variants  (Cambrosio et al. forthcoming) , various kinds of software, but also convention-based tools such as Levels of Evidence tables (e. g. Mateo et al. 2018)  designed to obviate the lack of standards by helping MTB members assess the trustworthiness of drug-biomarker matches. MTBs will also implement local tools such as algorithmic decision trees that, while open to revisions, are included in trial protocols.
In other words, in order to carry out its work, MTBs need to be properly equipped, although we should quickly add that equipment itself becomes a topic of investigation when it provides uncertain or conflicting information, as it often happens. To claim that MTB expertise is a property of a (temporarily stable) network amounts to arguing that, while knowledge and know-how can be said to be embodied in persons, they "cannot function as 'expertise' unless they become part of a network without which these skills could not be expressed or activated"  (Cambrosio et al. 1992: 345). The following excerpt exemplifies this dynamic: Biologist (BIO) 1 explains that the NGS results [of a breast cancer patient] show a TSC2 missense mutation that "is non-pathogenic according to SHIFT [a functional prediction software] and there is no associated LOH [loss of the second allele], … plus it's a missense on a suppressor gene".
He adds that "there is a mutation on NOTCH4, which can be found in databases of SNP polymorphisms, but 2% only, which is bizarre because if germline it should be 25%, and in the CGH  [Comparative Genomic Hybridization]  analysis NOTCH4 is normal, so this NOTCH4 mutation is kind of ambiguous". Concerning KDR [another gene], "there is indeed a mutation around 10%, but it needs to be double-checked because it's out of the kinase domain, and here again, it's described as non-pathogenic in the PolyPhen [database]… so this is not very theragnostic". To sum up, "with NGS we have nothing of a real theragnostic interest". BIO2 then describes additional results of the CGH analysis, stating that it "found a clear amplification of FGFR1". The MTB discusses which alteration to choose given that according to rules, mutations have priority over amplifications.
The MTB decides, however, to opt for the amplification on the basis of information provided by databases and prediction algorithms for that specific kind of TSC2 mutations. BIO1 sums up the situation: "TSC2, there is no loss of the second allele, and it's a suppressor gene, it's not a truncating mutation, it's a missense mutation and the algorithms suggest that the missense mutation is not pathogenic, so…". BIO2 concurs, arguing that "above all, with CGH we have a FGFR1 amplification, 1. 7 megabases, and log-wise above 3, we could retain that one". The following short exchange ensues: Oncologist (ONCO) 1: OK, so obviously this one instead of TSC2. ONCO2: We thus opt for Drug X [that targets the amplification]. ONCO1: It's clear-cut and logical. This excerpt clearly illustrates the role of databases and knowledgebases as socio-technical prostheses that allow the MTB to reach temporary closure without fully determining it.
The collective, sequential form of reasoning displayed by MTB members (including biologists who, far from merely providing technical information, are full-fledged reasoning partners) rests decisively on the triangulation of results provided by different kinds of equipment (NGS, CGH) with the information about pathogenic variants derived from dedicated knowledgebases or, is some cases, elicited from ad hoc searches in publication databases such as PubMed. The previous excerpt, in other words, shows both the necessary role played by generally available data repositories (in most cases freely accessible), and the fact that the information provided by these repositories needs to be aligned with the bio-clinical results derived from individual patient tumours. Also notice how a somewhat meandering sequence of arguments (even in our simplified summary) transmogrifies in the end into a "clear-cut and logical" conclusion.
Conclusion Tumour sequencing leads to the identification of tumour-driving events that need to be summarized, interpreted, and prioritized in the context of current literature, drug-gene interaction databases, relevant clinical trials and known clinical actionability from knowledgebases  (Good et al. 2014). These events also need to be related to clinical data from the case history of individual patients  (Van Allen et al. 2013) , for their main purpose is not to produce scientific facts, but to lead to clinical interventions, although practitioners insist on the dual purpose of bio-clinical studies in genomic medicine: to treat patients and to learn about the biopathology of cancer. As noted by  Ghazani et al. (2017: 787) , "assigning clinical meaning to each somatic and germ-line variant, including the therapeutic, prognostic, and diagnostic implications for individual patients, poses considerable difficulties in light of the inconsistent state of genome biological annotation".
Confronted with this task, molecular oncologists have developed MTBs as sui generis institutions for the production of 'informed' data interpretation, both in the context of clinical trials and within routine, albeit increasingly experimental clinical settings. In this respect, readers might wonder whether MTB activities concern patients or research subjects, but as illustrated by some of the passages discussed in this paper and argued in more detail in another paper  (Cambrosio et al. 2018) , the fading of the boundary between research and care is one of the main characteristics of precision oncology. MTBs mobilize a diverse set of human actors on both sides of the traditional clinical/scientific divide as well as a heterogeneous set of evidential resources, ranging from databases and knowledgebases to clinical trial results, previous clinical experience, and basic knowledge about mutations and pathways.