dataesr/scientific-paragraphs-categorization huggingface dataset:
Sociodemographic data and reasons for refusing to participate will be documented for patients who refuse to participate. Data on patient withdrawal or death will be documented in the case report form. Reasons for study withdrawal should also be mented. All data from this study will be transcribed in an electronic case report form (Clinsight). The cancer-specific EORTC-QLQ-C30 questionnaire and its BC-specific QLQ-BR 23 module are available in French and are validated tools to assess HRQoL in cancer and more specifically in BC  [29]. The EORTC-QLQ-C30 questionnaire makes it possible to assess one dimension of HRQoL/overall health, five functional dimensions (physical, current activities, cognitive, emotional, and social), eight dimensions of symptoms (fatigue, nausea and vomiting, pain, dyspnea, insomnia, loss of appetite, constipation, and diarrhea), and a scale of financial difficulties.
CHES For this study, a French version of the CHES web-based solution will be available. Each patient will be represented by an identifier to guarantee data confidentiality and anonymous exploitation of the database for statistical purposes. CHES offers a cross-sectional presentation (presentation of scores for each dimension) and a presentation of the longitudinal course of HRQoL for each dimension of the questionnaires. CHES also minimizes input errors, reduces the number of missing data related to data collection, and avoids ambiguities in the responses. This software also allows remote access via a platform that will let patients (outside the consultations) give an input on their HRQoL in the event of a deterioration or improvement perceived at the clinical level.
Statistical analyses 2. 6. 1. Descriptive analysis A descriptive analysis of patients' clinical and sociodemographic characteristics at inclusion will be performed for each arm. Data will be expressed as number and percentage for categorical variables and as mean ± standard deviation or as median and interquartile range for continuous variables, as appropriate. The number of missing data will be specified. The number of patients with available data will also be specified for quantitative variables. Normality will be tested by using the Shapiro-Wilk test. According to data distribution, independent Student t tests or non-parametric Mann-Whitney tests will be used to compare results between groups. The basic categorical characteristics will be compared using tests of chi-squared or Freeman Halton according to the number of the variable categories.
HRQoL analysis All HRQoL scores will be calculated according to FACT-G guidelines and described according to the arm (interventional arm or control arm). A logistic regression model will be used to assess the capacity of HRQoL to predict 12-month compliance with ET. The modulatory potential of social support on compliance will be assessed using an interaction term between the availability/satisfaction of social support that patients receive and HRQoL in a logistic regression model. The modulatory potential of psychological distress on compliance will be assessed using an interaction term between patient anxiety/depression and HRQoL in a logistic regression model. An analysis of missing HRQoL data profiles will also be performed. If a missing not at random (MNAR) profile is demonstrated/suspected, multiple imputation of missing data can be performed in sensitivity analysis, taking into account the variables associated with the occurrence of missing data.
Ethical considerations As a specific research intervention is carried out, the protocol falls within the scope of interventional biomedical research and was authorized by the French Health Agency (ANSM-Agence Nationale de la Sécurité du Médicaments et des produits de santé", IDRCB number: 2019-A01323-54) in June 2019 and by the French Ethical Research Committee (CPP-"Comité de Protection des Personnes") in October 2019. The clinical trial has been registered at ClinicalTrials. gov with the identifier NCT04176809. This study will be carried out in accordance with the ethical principles of the Helsinki Declaration and the Good Clinical Practice of the International Harmonization Conference. Participants must provide informed consent. Subjects will be informed of the objectives the project and the risks and benefits of the explorations to be carried out.
Acknowledgments We thank  Isabel Grégoire , PhD Medical Writer CGFL, for carefully reading the manuscript and  Fiona Caulfield  for English language editing. 7  Burgundy Franche-Comté University, 7 Boulevard Jeanne d'Arc, Dijon 21000, France. 8Centre for Research in Epidemiology and  Population Health (CESP) ,  INSERM U1018 ,  University Paris-Sud, UVSQ Gustave Roussy, 12 Avenue Paul Vaillant Couturier, Villejuif 94800, France. 9Medical Oncology Unit, Centre Georges-François Leclerc Centre -UNICANCER , 1 rue du  Professeur Marion , Dijon 21000, France.
Background: Tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND) have unique, individual patterns that pose significant challenges for diagnosis, psycho-education, and intervention planning. A recent study suggested that it may be feasible to use TAND Checklist data and data-driven methods to generate natural TAND clusters. However, the study had a small sample size and data from only two countries. Here, we investigated the replicability of identifying natural TAND clusters from a larger and more diverse sample from the TOSCA study. Methods: As part of the TOSCA international TSC registry study, this embedded research project collected TAND Checklist data from individuals with TSC. Correlation coefficients were calculated for TAND variables to generate a correlation matrix.
Design The detailed methodology of the overall TOSCA clinical study has been published previously  [15]. In brief, TOSCA was a non-interventional, multicenter, natural history registry of individuals with TSC. The study was designed with a "core" section and six research projects, each focusing on a specific area of TSC-subependymal giant cell astrocytoma, renal angiomyolipoma, genetics, epilepsy, quality of life, and TAND. Here, we present data on the research project focusing on TAND.
Data analysis In contrast to "hypothesis-testing" statistical approaches where data are analyzed in relation to an a priori prediction, unsupervised learning or data-driven methods searches for previously undetected patterns or groupings in a dataset without any a priori rules, predictions, or labels to data. In this study, we used cluster analysis and factor analysis, two unsupervised learning/data-driven statistical methods, to help understand the complex TAND data. The objectives of cluster and factor analysis methods are, however, different. Cluster analysis aims to group observations (e. g. , a sample of subjects or variables) into distinct groups in a way that objects in that group are more similar to each other than to those in other clusters or groups. Many different methods are used for cluster analysis.
Discussion Identification of natural TAND clusters through datadriven methods has been proposed as a potential solution for the "treatment paralysis" seen in TSC, given the highly variable and apparently unique nature of TAND profiles in individuals. In a proof-of-principle study, Leclezio, Gardner, and de Vries showed the feasibility of using data reduction methods in TAND and identified six putative natural clusters  [14]. However, the sample size of the Leclezio study was very small, and individuals were recruited from only two countries. Given these limitations and the highly heterogeneous nature of TSC, we set out to replicate the feasibility findings in a larger sample of 85 individuals, including children, from seven countries. We observed six natural TAND clusters (scholastic, ASD-like, dysregulated behavior, neuropsychological, overactive/ impulsive, and mood/anxiety).
All these proposals will require further evaluation in larger-scale studies. For the purposes of this early-phase replication study, we wanted to see if, first, we were able to identify robust methodologies and whether they would replicate in an independent sample, and second, whether natural clusters could be identified even in the absence of age and intellectual ability data. The association between age and intellectual ability on TAND clusters, however, raises interesting conceptual and empirical questions. It is likely that TAND cluster profiles may emerge or change over time. For instance, the scholastic cluster is likely not to be relevant in the first few years of life. Similarly, intellectual ability may be a very strong marker of the likelihood of TAND clusters. These important questions will require larger-scale and longitudinal datasets.
These extra steps will extend the investigation of the psychometric properties and robustness of the putative natural TAND clusters. We also acknowledge that the natural clusters were generated using only the TAND Checklist data. There may therefore be other natural clusters that could be identified using different kinds of fine-grain data. However, the purpose of the TAND Checklist was to provide a simple and easy-to-use tool for clinical practice. For this reason, we set out to examine the potential of the TAND Checklist data to generate natural TAND Clusters, given that such a strategy has a far greater potential for larger-scale implementation.
Conclusion In spite of the highly heterogeneous nature of TAND manifestations, the data-driven strategies used here in search of natural TAND clusters were able to replicate the findings from the feasibility study in a larger sample of children and adults with the pen-and-paper TAND Checklist data collected across seven countries. The study not only identified several similarities between the findings from the two data sets but also identified key aspects and next steps that will require larger-scale data, replication, and expansion. If these steps could replicate and extend the natural TAND clusters suggested in these preliminary studies, the natural TAND clusters may have the potential to help develop novel approaches to identification and treatment of TAND and may suggest novel data-driven strategies to subgroup individuals with TSC for clinical and research purposes.
RN received grant support, paid to her institution, from  Eisai  and lecture fees from  Nutricia ,  Eisai ,  Advicenne , and  GW Pharma. YT received personal fees from  Novartis  for lecture and for copyright of referential figures from the journals and grant from the  Japanese government  for intractable epilepsy research. SJ was partly financed by the  EC Seventh Framework Programme  (FP7/2007-2013; EPISTOP, grant agreement no. 602391 ), the  Polish Ministerial  funds for science (years  2013-2018 ) for the implementation of international co-financed project, and the grant EPIMARKER of the  Polish National Center for Research and Development No. STRATEGMED3/306306/4/2016. JCK, PC, CH, JAL, and JQ received research grants from  Novartis. RM and SS are employees of  Novartis. LD'A was an employee of Novartis at the time of manuscript concept approval.
In Section 5, we construct the fibrant replacement as a quotient inductive type (QIT) in ITT. Section 6 provides the complete definition of a model category. Then, we describe internally in ITT the (AC,F) weak factorization system (Section 7) and two variants of the (C,AF) weak factorization system (Section 9), one which makes use of the notion of mapping cylinders (Section 8), and one which makes us of the notion of partial elements. Finally, we provide a complete description of the model structure on U in ITT (Section 10) and discuss the formalization (Section 11). Formalization. Following Orton and Pitts approach, all the results of our article have been formalized using an axiomatic presentation of ITT. The formalization as been carried in Coq (while Orton and Pitts used Agda) and can be found in the following repository: https://gitlab.
Acknowledgements. We would like to warmly thanks the reviewers and editors for their insightful comments. https://www. cambridge. org/core/terms. https://doi. org/10. 1017/S0960129520000213Downloaded from https://www. cambridge. org/core. IP address:  193. 48. 96. 13 , on 14 Oct 2020 at 09:52:14, subject to the Cambridge Core terms of use, available at.
03 mg/L. The response factor between internal standard and analytes was assumed as one. All analyses were carried out in triplicate and the results were expressed as µg/L or the equivalent of IS. Volatile compounds were identified with Shimadzu QP2010 Plus gas chromatography coupled with a mass spectrometer (GC/MS). GC conditions were the same as those used for the GC-FID chromatographic analysis with polar column. Helium was used as a carrier gas at a flow rate of 2 mL/min. MS was operated in electron ionization (EI) mode at 70 eV, scanning from m/z 35 to 350. The volatile compounds were tentatively identified by comparing their mass spectra to commercial spectra databases (NIST 14), and by comparing their experimental retention index (RI) with the literature.
5Å and higher). In fact, less than 5% of deposited models in the Protein Data Bank (PDB) were obtained from crystals diffracting X-rays to (sub-)atomic resolution, and even in the structures with resolution better than 0. 5 Å, a third of the expected hydrogens could still not be experimentally identified  [2]. The positions of the more mobile or labile hydrogen atoms, which are typically the most biologically relevant, had to be inferred. Neutron crystallography therefore provides an excellent alternative to determine the position of hydrogen atoms in a molecular structure. It is even able to identify highly polarized H atoms or protons (H + ), which cannot be observed by X-rays as they do not have any electrons. This neutron specificity arises from the coherent scattering length of the two stable isotopes of hydrogen ( 1 H and 2 H (also delineated deuterium, D)) being of similar magnitude to that of other atoms which compose a biological macromolecule (Figure  1 ).
Water molecule orientations can be directly inferred from neutron diffraction data. Indeed, waters are often present in protein active sites and form an integral component of the catalytic reaction  [16, 17]. A significant advantage of neutrons is that, since their scattering is non-destructive, data collection can be performed at room temperature. Obtaining a structure free of damage is of great interest for biological systems that are highly sensitive to X-ray radiation damage  [18]. For example, redox centres of metallo-proteins can be damaged by X-rays even at temperatures as low 100K  [19, 20]. Neutron diffraction has also been used to identify the chemical nature of catalytic intermediates of heme-peroxidases  [7, 8] , while dozens of X-ray datasets could not converge onto a unique structural model due to radiation damage of the active site.
In return, each crystal will concurrently contribute diffraction data, yielding a diffraction pattern of poor quality. The crystal size needed for a neutron diffraction experiment not only depends on the biological system itself, and its unit-cell parameters, but also depends on the neutron diffractometer performances (linked to the available neutron flux). Hydrogenated crystals should be in the mm 3 range, while one order of magnitude in volume can be gained with a fully perdeuterated crystal. Furthermore, the crystallization buffer surrounding the crystal at the time of data collection needs to be deuterated. Two possibilities, i) the crystal is directly grown in a deuterated buffer or ii) the crystallization solution is hydrogenated and the crystal is subsequently transferred into a deuterated buffer (this can be directly done in the quartz capillaries).
Data reduction For any crystallography experiment, processing of all recorded diffraction patterns yields, for each measured point of the reciprocal lattice (of Miller index hkl), an averaged intensity (<I>) and its corresponding error (s(I)), which are then modified into an averaged structure factor amplitude (<F>) and its corresponding error (s(F)). Although often appropriately scaled, the structure factor amplitudes are the square root of the measured intensities. Such data reduction requires a precise description of both the crystalline system (unit cell parameters and symmetry) and the instrument (detector geometry, crystal-detector distance, wavelength spectrum, etc…). Depending on the neutron source and the diffraction technique used for a given neutron protein diffractometer, different processing packages have been developed.
For monochromatic instruments, traditional software developed for X-ray crystallography can be directly used or slight modifications are needed. For example, the Biodiff instrument readily uses the hkl2000 software  [45] , while XDS  [46]  is being adapted for the D19 diffractometer of ILL. Quasi-Laue instruments generally utilize the LAUEGEN suite, which was originally developed to reduce X-ray Laue diffraction data  [47]. The particularity of the quasi-Laue neutron diffraction data is their propensity to be overlapped. Dealing with such is non-trivial as the data reduction software needs to extract a single set of (<I>, s(I)) from two or more overlapped reflections. Another level of complexity arises from the multitude of wavelengths which can stimulate the same reciprocal lattice point with different crystal orientations.
The recorded diffracted intensities have to be normalized with respect to the incident stimulating wavelength. Two factors come into play here: i) the diffracted intensity is proportional to l 4 (see Eq. (  4 ) above) and ii) the incident neutron flux is dependent of the wavelength. This wavelength normalization procedure and the scaling of all reflections is performed with the software LSCALE  [48]. In the quasi-Laue method, a proper estimation of the individual intensities of overlapped reflections is extremely difficult, and in this last step of data reduction, multiple intensity measurements of the same hkl index from different diffraction patterns or from symmetry equivalent reflections are compared, and, if they deviate too much, the software is unable to provide an accurate estimation of the intensity so those reflections are discarded from the data.
Software developments is still ongoing with new procedures to integrate peaks from these diffractometers via neural networks for MaNDi  [49] , or the addition of profile fitting at iBIX  [50]. The resolution of the diffraction dataset is therefore based on several data quality indicators  [51]. The Rmerge, and subsequently the Rpim, have conventionally provided an estimate of the precision of the individual measurements. The difference between the two is that the latter is weighted to take into account the redundancy in the data. The Rmerge drastically rises with increased data multiplicity, and therefore does not provide accurate insights into data quality. Data measured multiple times should become more accurate. The I/s ratio provides another indication about which scattering data are reasonable to include in the dataset.
However, the previous strict I/ s ~2 ratio which defined the highest resolution has been relaxed, so as to include as much data as possible in the dataset (I/s >1) (detectors are much more accurate nowadays…). A more rigorous statistical approach is to use the correlation coefficient between half data sets. Typically, CC1/2 is ~ 1. 0 at low resolution, and drops toward 0 with increasing resolution, but also decreasing signal-to-noise ratio. The resolution with CC1/2 cut-offs between 0. 2 and 0. 5 have been found to be acceptable.
The molecular model; the pdb format The aim of crystallography is to produce a 3-dimensional atomic model of the repeating asymmetric unit of the biological crystal. This asymmetric unit may contain one or more molecules. The Protein Data Bank archive (PDB, http://www. rcsb. org) serves as a world-wide repository of information about the structures of biological macromolecules, like protein and nucleic acids. High-resolution structures were determined by X-ray and neutron crystallography, electron microscopy and NMR. The repository also includes pseudo-atomic models derived from lower resolution structural techniques. Importantly, prior to publication, coordinates, and associated experimental information (including diffraction structure factors, and NMR constraints) must now be deposited to the PDB.
The accuracy of the model is obviously limited by the quality of the experimental data used to build the model. For example, at low resolution, multiple chemically correct models can be built that fit the available experimental data. Atoms that are not accounted for by the diffraction data are typically omitted from the model, or placed in chemical acceptable positions, but with zero occupancy. In X-ray structures, due to resolution, the model therefore does not often list hydrogen positions, while in neutron structures, observed hydrogens and deuteriums are detailed. The deposited structure is the best model that fits the diffraction data and the chemistry. A priori biological knowledge may also have provided a guiding hand in model building. The precision that atoms can be positioned in the model depend on the regularity of the crystal  [56] , and a well-ordered crystal will likely yield atomic positions with better precision.
A structure determined to 1-Angstrom resolution would allow individual atoms to be directly positioned in the structural model. At resolutions better than 3-Angstrom, the shape of the amino acid can be clearly seen, but here, as resolution gets worse, the chemistry of the model plays an increasingly important role to limit the possible positions of the atoms can have in the structure. In particular, the ability to distinguish between oxygens, nitrogens and carbons is compromised, which raises issues with the orientation of Asparagine, Glutamate and Histidine side chains. In the case that diffraction is worse than 4-Angstrom resolution, secondary structures elements become difficult to identify. An interesting series of movies has been realized by James Holton (https://bl831. als. lbl. gov/~jamesh/movies/) which stress out the importance of various parameters of dataset on the resulting density maps, in which the crystallographer has to build his molecular model (here done with X-rays, so electron density).
Breast cancer (BC) is the leading cause of cancer in sub-Saharan Africa (SSA) with rapidly increasing incidence rates reported in Uganda and Zimbabwe. However, the magnitude of these rising trends in premenopausal and postmenopausal women is unknown in most African countries. We used data from the African Cancer Registry Network on incident breast cancers in women from 11 population-based cancer registries in 10 countries representing each of the four SSA regions. We explored incidence changes among women before and after age 50 by calendar period and, where possible, generational effects in this unique sub-Saharan African cohort. Temporal trends revealed increasing incidence rates in all registries during the study period, except in Nairobi where rates stabilised during 2010 to 2014 after rapidly increasing from 2003 to 2010 (APC = 8.
11 There are currently 32 population-based member registries within the network, although for this incidence trend analyses, we included only those with at least 10 years of continuous data on breast cancer incidence. We grouped them according to the United Nations geoscheme for Africa. 12 om the Eastern African region, we included data from Blantyre, Malawi (1994-2011), Bulawayo, Zimbabwe (1963-1972    13  are used by all the registries. We included data on invasive primary tumours of the breast occurring in black African females aged 15 and above. Country-specific population censuses were obtained from their respective National Statistics Office reports. To obtain the population-at-risk for each year, intercensal interpolations were made by sex, and within 5-year age groups assuming a constant logarithmic increase.