dataesr/scientific-paragraphs-categorization huggingface dataset:
4D-6D beam-beam  [12]  Wire [13] Electron lens  [14]  Fringe fields  [15, 16, 17]  Table  1  shows the different types of beam-line elements implemented in SixTrack. Thin multipoles are used in conjunction with the MAKETHIN and SIXTRACK commands in MAD-X  [18]  to implement symplectic integrators of thick maps. Thin multipoles include the effect of the curvature, when present, up to second order. Recently, a model of s-dependent fringe fields has been merged and is being used to study the impact on the dynamic aperture of the field quality of the new triplet quadrupoles foreseen in the HL-LHC  [17]. SixTrack allows setting turn-varying functions for most of the beam elements and simulation parameters (see DYNK module  [19]  and references therein), including multipoles, RF amplitude and phase, reference energy, and beam-beam elements.
Scattering SixTrack is widely used for simulating the performance of a collimation system in circular machines  [25, 26, 27, 28] , which considers the physics of scattering events undergone by particles in beam-intercepting devices on a turn-by-turn basis. The K2 scattering engine  [29, 30]  was initially embedded in SixTrack  [31]  and used for the design of existing cleaning systems  [32, 33]. In the coupling  [34]  between FLUKA  [35]  and SixTrack, the two codes exchange particles at run-time. Tracked particles are handled by either of the codes depending on whether the tracking takes place in the accelerator lattice or in a beam-intercepting device. Contrary to K2, FLUKA is a full Monte Carlo code for particle-matter interactions. It allows dealing with any hadron, lepton, and ion species interacting with any elemental or composite material with arbitrarily complicated geometries, greatly increasing the accuracy of results and the range of potential applications.
In addition, the same collimator geometries and material definitions used for subsequent energy-deposition calculations can be deployed, thus increasing the degree of consistency of results. The SixTrack scatter module was added recently to generate scattering events at the interaction points for medium-and long-term tracking simulations. The scattering point is inserted as a marker in the lattice, and elastic or diffractive events are generated on the fly  [36]. The scattering probability can be scaled on a turn-by-turn basis via the DYNK module. At present, one internal event generator is provided by SixTrack. This generator provides elastic events by Monte Carlo sampling of a fit to data published by the TOTEM Collaboration  [37]. Pythia8 [38]  is also available as an event generator, but must be included via a compiler flag when SixTrack is built.
Tracking of arbitrary charge-to-mass ratio particles Previous work on tracking of ions  [39] , and by extension, any generic particle with any chargeto-mass ratio, has been implemented in SixTrack. This allows tracking of particles other than protons, such as heavy ions, and even electrons (synchrotron radiation effects are yet to be included). In conjunction with the updates to the scattering code described in the previous section, this allows the tracking of all charged particle types exiting a collimator as a result of the interaction of a primary charged particle with the collimator jaw. These secondary particles can interact further with downstream collimators. In order to account for a potentially large number of secondary particles generated by a collimator interaction, all SixTrack internal particle arrays are dynamically allocated, and will be expanded automatically as required when the number of tracked particles changes within a simulation run.
The latter provides additional computing power for CPU-intensive applications with small data sets on as many operating systems, architectures, and CPU instruction sets as possible, with important requirements on code structure, build system and test suite. LHC@home is capable of handling 1 × 10 5 tasks on average, with peaks of 3. 5 × 10 5 tasks simultaneously running on 2. 4 × 10 4 hosts observed during SixTrack massive simulation campaigns. Task redundancy is deployed to eliminate random host errors and minimise the impact of a failing host. The LHC@Home capacity available for SixTrack can be compared to the average of 2. 5×10 5 running tasks on 1. 4×10 The SixDeskDB post-processing tool collects data from SixDesk, performs post-processing analysis, and prepares reports and plots. It also offers a Python application programming interface (API) for interactive analysis.
Label-free proteomics of human brain extracts For proteomics analysis, two AD samples, two non-AD samples, and one corresponding to a patient with preamyloid diffuse plaques but without evidence of AD were used (all samples were enriched as described in the previous section). The protein extracts (150 μg per sample) were concentrated on gel and analyzed by reverse phase liquid chromatography-tandem mass spectrometry (RP-LC-MS/MS) using an Easy-nLC II system coupled to a linear ion trap mass spectrometer model LTQ (Thermo Scientific) as previously described  [30]. The MS/MS raw files were searched against the Uniprot-Human proteome database (70,931 entries in January 2019) (http://www. uniprot. org) using the SEQUEST algorithm (Proteome Discoverer 1. 4, Thermo Scientific). The following constraints were used for the searches: tryptic cleavage after Arg and Lys, up to two missed cleavage sites, and tolerances of 1 Da for precursor ions and 0.
e. ,  [32] ). As a background, we used the most recent database of a global quantitative analysis of the human brain proteome in Alzheimer's disease, extracted from  [33]  (16,559 recognized proteins in GOrilla; database updated June 2019). We set p value to 10 -6 in order to avoid unreliable data. Gene ontology was investigated at three levels: biological process, biological function, and biological component.
Blocking consisted of 5% NDS+ 0. 3% Triton X-100 in PBS. Sections were incubated overnight at 4 °C with primary antibodies containing 0. 3% Triton X-100 and 5% normal serum in phosphate-buffered saline. Controls included omitting primary or secondary antibodies. Sections were counterstained using DAPI (Santa Cruz Biotechnology; Inc. ; Sc-3598) or Nissl and coverslipped with PVA-DABCO or DPX after dehydrating. Human tissue autofluorescence was not reduced. Images were captured using an LSM 800 confocal microscope and analyzed using ZEN software.
Proteomic characterization of Aβ-enriched extracts Next, the enriched fraction containing insoluble Aβ was characterized in order to identify possible protein components bound to the Aβ plaque that had not been observed in the non-AD extracts. To that end, proteomic analysis on enriched extracts of two AD and two non-AD samples was performed. Specifically, 149 and 133 proteins were identified in AD-enriched samples, and 144 and 131 in non-AD samples (Additional file 4: Table  S3. ) In AD samples, six of these proteins were overrepresented and three proteins were underrepresented (Table  1 ). Proteins that were exclusively detected in AD or non-AD extracts were also examined (Table  2 ; 1% FDR, target-decoy database approach). A total of 34 proteins were identified at least in one of the two AD samples, while 12 proteins were identified in the non-AD extracts, which probably correspond to proteins that decrease in AD-enriched extracts.
This is especially relevant, and we are Table  1  Proteins overrepresented in Aβ-enriched fraction and proteins overrepresented in non-AD samples. The MS/MS raw files were examined against the universal protein database UniProt (70,931 entries in January 2019) (http://www. uniprot. org), using the SEQUEST algorithm (Proteome Discoverer 1. 4, Thermo Fisher Scientific). Fold of change; P value < 0. 05. PSM peptide-spectrum matches, x ̄average, ND-AD not detected in Alzheimer extracts, ND-non-AD not detected in control extracts  currently undergoing further experiments to identify the nature of the incorporated proteins. In addition, our study also found that cultured endothelial cells, as the primary components of the blood-brain barrier, uptake Aβ extracts from Aβ-enriched fractions in what may constitute the pathway for Aβ to reach the bloodstream.
An important simplifying circumstance in this case is the refusal to take into account the dissipative processes and the reverse influence of the generated quasiparticles on the active field. It allows us to solve a system of equations  (5)  for each point of the momentum space independently and work with the grid points in parallel. In software, this is implemented using MPI procedures. MPI was selected because it is necessary to have the opportunity to simulate on massive parallel systems without limit of scaling level. We solve the system of ODE using the GSL library  [18]. The choice of GSL is determined by the presence of different levels objects for the organization of procedure for solving the Cauchy problem of the ODE system and the support of various methods for the numerical solution of such problem.
Study design and patients BARIASPERM was a French multicentre observational and controlled prospective study. Male candidates for bariatric surgery (GB and SG) were recruited in five Universitary Hospital centres; none had a history of infertility. Sperm characteristics were studied before and after surgery. The protocol was approved by the local ethics committee (CPP2012-08-01) and all participants gave written informed consent. This trial was registered with clinicaltrials. gov (NCT01842295). Inclusion criteria were men of reproductive age (18 -50 years) with a body mass index (BMI) ≥ 40 kg/m 2 or 35 kg/m 2 and, at least one obesity comorbidity, as recommended by French guidelines for candidates for bariatric surgery  (16). All participants were included after approval of the bariatric surgery project.
Statistical analysis A paired t test, with adjustment on the mean-centered baseline value to enhance power, was used to compare the mean of quantitative outcomes between baseline and after surgery  (18). McNemar's test (or Bowker's test for symmetry, when appropriate) was used to compare qualitative paired data. For secondary outcomes, Hochberg adjustment for multiple comparisons was used to maintain the overall type 1 error rate at 5%. An adjusted p <0. 05 was considered statistically different. Statistical analysis was performed using SAS software, version 9. 3 (SAS Institute Inc. , Cary, N. C. ) and R software (version 3. 4. 1).
Acknowledgements The study was funded by a grant from  Programme Hospitalier de Recherche Clinique -PHRC 2011 (Ministère de la Santé ,  N° AOM 11193 ). The sponsor was  Assistance Publique -Hôpitaux de Paris  ( Délégation à la Recherche Clinique  et à l'innovation). We are indebted to  Pr Jean-Marc Treluyer  and the staff of the  Clinical Research Department (Unité de Recherche Clinique "Paris Cochin")  for their valuable technical and logistical support. We would like to thanks  Myriam Benarroch  and  Celine Faure  for their logistical support during the study.
| Design For this pilot study, we used a mixed-method triangulation design, consisting of pre-post surveys and post-intervention interviews in five outpatient clinics (Departments) of the Radboud University Medical Center (Departments of Cardiology, Radiotherapy, Breast Cancer, Nephrology and Psychiatry) and one inpatient clinic (General Internal Medicine). The qualitative interviews were used to better understand the quantitative data as obtained from the surveys, using a convergent, sequential model in which qualitative and quantitative findings are analysed and interpreted separately and then combined to complement each other. 19.
| Data collection methods and measurement instruments Data collection took place between November 2014 and July 2015, and consisted of quantitative surveys and qualitative interviews. o the SDMQ9 consists of 9 questions measured on a 5-point Likert scale (Cronbach's α in our study was. 96), which were recoded to an overall SDM score ranging from 0 to 100. A higher score indicates more SDM. 15 the CollaboRATE consists of three items measured on a 10point scale ranging from 0 to 9. We calculated top scores and average scores per item and for all three items together. To calculate CollaboRATE top scores, scores from 0 to 8 were interpreted as 'absence of SDM' whereas a '9' was interpreted as 'presence of SDM'. To calculate CollaboRATE mean scores, we calculated average scores. [16] [17] [18] [19]  • Qualitative, semi-structured individual interviews with patients, health-care professionals (physicians and nurses) and the project manager, post-implementation, based on the model of behavioural change.
| Quantitative surveys All data were entered in SPSS. We performed descriptive statistics on all data (calculation of n, %, means, SD, medians, ranges). No statistical tests were performed other than bivariate correlations between asking the 3GV, SDMQ9 and CollaboRATE. Due to fewer participants in 4 out of 6 departments, data were analysed and presented for all departments together, instead of per department. For the breast cancer department, we used data from interviews instead of post-questionnaire results with regard to their experience with 3GV.
| Qualitative interviews Interviews were audio-recorded and transcribed verbatim. Any identifying information was removed from the interview transcripts. Two independent researchers performed a framework analysis  29  starting with open content coding and subsequent thematic categorization of the codes into one of six determinants of behaviour change (ie beliefs and motivations, attitude, subjective norm, perceived behavioural control, intention, behaviour). 27, 29 This enabled us to systematically assess the process of implementation, its barriers and facilitators. The analyses were supported by Atlas. ti software for qualitative analysis, to support coding and structuring the data. The quotes in the results section were translated from Dutch.
Third, one of our SDM measures (collaboRATE) was measured only post-implementation because the Dutch translation was not ready at the time of the pre-survey. Therefore, we were not able to assess how CollaboRATE scores actually changed after implementation of 3GV. We did have pre-and post-data from the SDMQ9 and did not find pre-post differences. However, at the latest International Shared Decision-Making conference (July 7-10, 2019, Quebec City, Quebec, Canada) some concerns were raised about the sensitivity of the SDMQ9 in picking up a patient's SDM experiences. Fourth, although the implementation strategies used were as much as possible adapted to and embedded in standard care and current improvement initiatives in order to facilitate implementation, this also meant that different implementation strategies were used and that we cannot determine which strategy is best.
Methods This was retrospective and descriptive study on cases of breast cancer in which the hormone receptor status and expression of the Her2 oncogene have been reported. These patients were followed at the Surgical Oncology Unit (SOU) of the Donka National Hospital,   1 ). The index ki-67, if available in the patient's file was used. The molecular subtype was considered indeterminate if any of the three elements (ER, PR, Her2) were not reported. We reviewed the indications of hormone therapy and targeted therapy (trastuzumab) according to the hormonal status and expression of the oncogene Her2. Statistical analysis was performed using SPSS software (SPSS Inc. , Chicago, IL). Categorical variables were shown as the frequency and percentage (%) and continuous variables were presented as the mean and standard deviation (±SD).
| Patients Plasma of patient B1 was obtained from the blood plasma bank at Radboud University Medical Center in Nijmegen, the Netherlands. From B2 and B3, venous blood plasma samples were collected after overnight fast from patients and their parents at the Medical University of Vienna, Austria. Plasma samples of 11 age-and gender-matched, healthy, unaffected children from our own plasma databank were used as controls (unaffected siblings of children with familial hypercholesterolemia, proven to carry no mutations in known lipid genes). Plasma from a patient with CETP deficiency due to a mutation in CETP-also from our plasma biobank-was used as a positive control. The study was performed in accordance with the Declaration of Helsinki of the World Medical Association and informed consent was obtained from the parents.
| Statistical analysis Data were compared between groups with a two-tailed Students t test and presented as means ± SD or, for nonparametric parameters, tested with a Mann-Whitney U test and presented as medians with interquartile ranges. Categorical variables were tested with a Chisquare test. All statistical analyses were done using SPSS software (version 22. 0, SPSS Inc. , Chicago, Illinois). Error bars indicate standard deviations. Probability values <. 05 were considered statistically significant.
ACKNOWLEDGMENTS We would like to thank all patients, families and other subjects for participating in this study. This work was financially supported by the  Dutch Organization for Scientific Research  ( Veni grant  for A. G. H. ; Vidi grant for D. J. L. ), the  Amsterdam UMC Fellowship and Gilead Research Scholar  (to A. G. H. ) and the  AMC Graduate School  (PhD Scholarship to M. vdB. ).
Auditability and GDPR compliance Auditability is a key component in data security which in turns can help to achieve GDPR compliance. Given the traceability feature of the system, it is possible to examine and systematically review the data management. Even more, given the design of the network, it is possible to achieve the desired granularity in the retrievable process, with the possibility of knowing specifically where each data set has gone. Due to the immutability principle of the blockchain, it is possible to audit the data trails and data management with assurance that the database has not been tampered. One of the interesting aspects about this design, is that the "right to access"  [13]  enabled by the GDPR could be accomplish by the model. If a given user wants to carry out its own right of knowing where its data has been and to whom it was shared with, a positive answer should be delivered to him.
Network nodes We define a network node by the following components: • A blockchain peer, eventually with more than one peer running in the same node for performance purposes. • A driver (backend application) which implements the main business logic. It process requests from the central web server and communicates with the local peers to execute and process transactions. • A local mapping database that is used to keep track of the references that every data item has in the blockchain. It links a data item with every data request in which it was used. • A certificate authority (CA) responsible of issuing certificates within the organization. • An authentication API which communicates internally with the CA and externally with the central web server. The idea behind this is that the authentication API connects to the CA to generate a user and store it locally.
Once data is indexed it needs to be registered so it is available to the network. To register the data, the driver invokes ExchangeFlow. registerData which in turns creates an asset attesting the registration of the data in the blockchain. This works as a proof of existence and allows the driver to later prove that it had available data with a given consent by the time of the registration. Note that in order to register data the consent needs to be specified but most importantly the consent will need to be checked before sharing data. In this sense, the consent management can be seen as the process of checking that these two steps are being done correctly (i. e a consent is defined and properly checked for every data item). In order to register the data the local mapping database is used. The content of this database are pairs (key,value) where the key is a unique reference for every data item and value is a set of references from that data item to the blockchain.
Introduction The ability to predict disease risk accurately is a principal aim of modern precision medicine. As more populationscale genetic datasets become available, polygenic risk scores (PRS) are expected to become more accurate and clinically relevant. The most commonly used method for computing polygenic scores is clumping and thresholding (CþT), also known as pruning and thresholding (PþT). 2] [3] [4] [5] [6]  More precisely, the variants are first clumped (C) so that only variants that are weakly correlated with one another are retained. Clumping selects the most significant variant iteratively, computes correlation between this index variant and nearby variants within some genetic distance w c , and removes all the nearby variants that are correlated with this index variant beyond a particular value, r 2 c.
1 (default of PRSice), 0. 2 or 0. 5 (default of PLINK), and w c of 250 kb (default of PRSice and PLINK) or 500 kb, and test several values for p T ranging from 1 to 10 À8. 2, 4, 5, 7 Moreover, to compute the PRS, the target sample genotypes are usually imputed to some degree of precision in order to match the variants of summary statistics. The inclusion of imputed variants with relatively low imputation quality is common, assuming that using more variants in the model yields better prediction. Here, we explore the validity of this approach and suggest an additional INFO T threshold on the quality of imputation (often called the INFO score) as a fourth parameter of the CþT method. We implement an efficient way to compute CþT scores for many different parameters (LD, window size, p value, and INFO score) in R package bigsnpr.
Overall, we compute 22 3 4 3 7 3 4 3 50 ¼ 123,200 vectors of polygenic scores. Then, we stack all these polygenic scores (for individuals in the training set) by using these scores as explanatory variables and the phenotype as the outcome in penalized regression. 9 In other words, we fit weights for each CþT scores using an efficient penalized logistic regression available in R package bigstatsr. 12 This results in a linear combination of CþT scores, where CþT scores are linear combinations of variants, so that we can derive a single vector of variant effect sizes to be used for prediction in the test set. We refer to this method as SCT (stacked clumping and thresholding) in the rest of the paper. From this grid of 123,200 vectors of polygenic scores, we also derive two CþT scores for comparison.
Simulations We use variants from the UK Biobank (UKBB) imputed dataset that have a minor allele frequency larger than 1% and an imputation INFO score larger than 0. 3. There are almost 10M such variants, of which we randomly choose 1M in two different ways. First, we randomly sample 1M from these 10M variants, so we use variants that are mainly well imputed (Figure  S1A ). Second, we sample variants according to the inverse of INFO score density, so we use variants that are globally not as well imputed as before (Figure  S1B ). To limit population structure and family structure, we restrict individuals to the ones identified by the UK Biobank as British with only subtle structure and exclude all second individuals in each pair reported by the UK Biobank as being related. 10 his results in a total of 335,609 individuals that we split into three sets: a set of 315,609 individuals for computing summary statistics (GWAS), a set of 10,000 individuals for training hyperparameters, and a test set of 10,000 individuals for evaluating models.