dataesr/scientific-paragraphs-categorization huggingface dataset:
In situ observations For 6 weeks, between January 17th and February 28th, 2018, the principal investigator (PI) (NB) attended all weekly meetings at the MTB. There were two types of weekly meeting: 1) an MTB session where team members and referring oncologists presented and discussed therapeutic proposals, and a 2) "preparatory meeting", restricted to MTB team members only. During this latter meeting, the team discussed genomic findings and clinical data for patients. Between the study dates, the PI observed five preparatory meetings and four MTB sessions. At each session, the PI recorded elements such as location, duration, purpose of exchanges, the number of people and the materials available (circulating documents). For preparatory meetings, attention was focused on the interpretation of genetic results.
Molecular analyses and results validation: an interdisciplinary collaboration Results analyses and validation often went back and forth (depending on case complexity) between teams, pathology teams and MTB members. After histopathological biopsy analysis and cell selection for sequencing, sequencing analysis was performed in two steps. The first step assessed the validity of the sequencing results, while the second determined its functional and potential clinical significance. Questions underlying this analytical process were summarized by an expert: "When you have a mutation, you wonder what is its functionality, what is its pathogenicity? But the first question we ask ourselves, is it a sequencing error? "(BIO). Data analyses were complex and depended on who performed them. This was why data interpretation underwent peer review by laboratory members.
A second person, a biologist or pathologist then processed these data to validate them. This analytical process included further clinical annotations. Laboratory pathologists and biologists reviewed the scientific literature and used relevant databases listing pathogenic variants  [24] [25] [26]. These steps were recorded and documented in an internal laboratory sequencing database which, in addition to constituting the central database of the laboratory, included a notification system which automatically informed an individual working on the analysis of any changes made to them. This feature improved sequencing annotation quality and reduced error rates. The number of variants identified in each sequencing sample varied greatly, depending on the quality and quantity of tumor cells in the sample, but also on tumor type.
According to one expert, the NGS400 panel detected small but variable numbers of significant mutations: "There may be zero, there may be sixty, there may be three. but generally speaking, we don't have many, at most about ten. "(BIO). In general, approximately ten mutations were identified, of which 5-8 were selected by the laboratory team. Information on pathogenic variants were integrated into the laboratory's internal database, and into integrative reports made available to MTB members via electronic records.
Importantly, sequencing results were examined in a broader context, accounting for a patient's clinical history. Test results, as a list of variants, were reviewed by the group and if genetic findings were considered sufficiently informative, they were presented at the next MTB session. A final step in this process was the transformation of biological data into therapeutic proposals. This step, carried out by an oncologist, consisted of researching the literature and databases to formulate proposals based on the therapeutic resources available. The description of the treatment process showed that all decisions were based on clinical expertise and interpretation of the scientific literature. Thus, from the 5-8 genetic variants selected by the pathology laboratory, then examined by the MTB team, the oncologist focused the analysis on one or two variants per patient case.
Integrating cutting-edge research data into clinical decisions: browsing the grey zone Validation work was initiated in the molecular pathology laboratory which controlled the quality of genetic results, validated genomic alteration analyses, and proposed a list of variants, classified according to pathogenicity. Thus, 5-8 variants were selected by the laboratory and discussed by the MTB team, allowing the MTB oncologists to formulate therapeutic proposals. A thorough selection allowed the oncologist to focus on one or two variants at most. Accordingly, the oncologists' analyses were based on the scientific literature and their own expertise. First, actionable variant sorting was performed by consulting large comprehensive databases  [24] [25] [26] , and digesting clinical and genomic research articles  [27].
Results Sequencing of GRN gene revealed the c. 19T>C, p. Trp7Arg in the exon 2 at heterozygous state (NM_002087. 2) in the proband (II. 1). Two asymptomatic first-degree relatives (II. 2 and II. 3), investigated in a research setting, carried the same heterozygous GRN mutation. DNA of the affected parent and of the unaffected sibling II. 4 was not available. This mutation was previously identified in an unrelated French FTD family (Le  Ber et al. , 2008)  and is absent from public databases of controls (GnomAD, dbSNP, EVS, ESP, ExAC). The p. Trp7Arg substitution is located in the signal peptide sequence of the protein and is thus expected to affect the translocation of the protein in the lumen of endoplasmic reticulum (ER). A whole gene deletion was excluded by MLPA in the proband. The plasma progranulin level was low in all the three mutation carriers compared to reference levels in the normal population  (Ghidoni et al.
, 2018). The pathogenicity of p. Val5Leu and p. Trp7Arg variants remains elusive  (Cruts et al. , 2012). Notably, the p. Trp7Arg mutation was identified in a French patient affected by bvFTD with a family history of dementia. However, no formal evidence for a clear pathogenic role could be provided in this case (Le  Ber et al. , 2008)  and no other families carrying this variant have been identified so far to confirm its implication in FTD. So far, the p. Trp7Arg has therefore been considered as a variant of unknown significance (VUS) and its pathogenic nature has been unclear in genetic FTD databases  (Cruts et al. , 2012). In this study, we put forward compelling arguments in favor of the pathogenicity of the p. Trp7Arg mutation. First, the discovery of the mutation in a second family, unrelated to the previous one, and its absence from public databases of controls support an association with the disease.
Acknowledgements. This study was funded by  PHRC FTLD-exome (promotion AP-HP , to I. L. B. ),  PHRC Predict-PGRN (promotion AP-HP , to I. L. B. ) and by " Investissements d'avenir "  ANR-10-IAIHU-06. Leila Sellami  received a grant from the  Société Française de Neurologie -Revue Neurologique. The authors wish to thank  Alexis Guyot  for his support in the final editing of the manuscript. All the authors declare that they have no actual or potential conflicts of interest with this work.
a b s t r a c t Background: Evidence on how weight loss correlates to health-related quality-of-life (HRQOL) among obese breast cancer (BC) patients is limited. We aimed to evaluate associations between weight changes and HRQOL. Methods: We included 993 obese women with stage I-II-III BC from CANTO, a multicenter, prospective cohort collecting longitudinal, objectively-assessed anthropometric measures and HRQOL data (NCT01993498). Associations between weight changes (±5% between diagnosis and post-treatment [shortly after completion of surgery, adjuvant chemo-or radiation-therapy]) and patient-reported HRQOL (EORTC QLQ-C30/B23) were comprehensively evaluated. Changes in HRQOL and odds of severely impaired HRQOL were assessed using multivariable generalized estimating equations and logistic regression, respectively.
Methods CANTO (CANcer TOxicities, ClinicalTrials. gov/NCT01993498) enrolled patients with stage I-II-III breast cancer  [16]  across 26 French centers. For this sub study, we used data collected at diagnosis (baseline) and during the first visit after primary treatment completion, defined as completion of definitive breast surgery, adjuvant chemotherapy or radiotherapy, as appropriate (adjuvant endocrine therapy and anti-human epidermal growth-factor receptor (HER)-2 therapy were allowed to be ongoing). Study protocol was approved by a central ethical committee for human subjects. Informed consent was obtained at patient inclusion. Details about the CANTO study procedures were previously published  [15]. We accessed information from 5801 women diagnosed from 2012 to 2015. We excluded patients who were not obese at diagnosis (Body Mass Index [BMI]<30.
Results Our analytic cohort included 993 obese patients. Excluded patients did not differ from those included in the analytic cohort (data not shown). Median time from diagnosis to post-treatment was 10. 5 months (interquartile range [IQR] 7. 8e12. 5). Mean age at diagnosis was 59. 1 years (Standard Deviation ¼ 10. 5). All patients in this cohort received breast cancer surgery, 91. 6% received radiation therapy, and 53. 6% received adjuvant chemotherapy. Complete cohort characteristics are displayed in Table  1. Mean baseline BMI was 34. 5 kg/m 2 (range, 30. 0e59. 0), mean baseline weight was 89. 4 Kg (range, 61. 0e153. 0). The majority of women, 67. 3%, remained stable, 14. 1% gained !5%, and 18. 6% lost !5% of baseline weight. Women who lost weight were more likely to be older, postmenopausal, and never smokers (Table  1 ).
Funding This research was supported by a  Clinical Research Fellowship  from the  European Society for Medical Oncology (ESMO)  to  Antonio Di Meglio , a  Career Catalyst Research grant  from  Susan G. Komen  ( CCR17483507 ) to  Ines Vaz-Luis , funding from  Odyssea  to  Ines Vaz-Luis  and from  Foundation Gustave Roussy  to  Ines Vaz-Luis. The CANTO study is supported by the  French Government  under the " Investment for the Future" program  managed by the  National Research Agency (ANR) , grant n  ANR-10-COHO-0004. Lee W Jones is supported by grants from  AKTIV Against Cancer, Kavli Trust , and the  Memorial Sloan Kettering Cancer Center   Support Grant/Core Grant  ( P30 CA008748 ).
Patients and tissue In this retrospective study, we collected data of postmortem genetic analyses performed at a single molecular laboratory from patients with (i) SCD or other cardiovascular cause of death (such as acute heart failure), (ii) suspected cardiomyopathy, based on either autopsy or clinical data (if cardiac examination was performed before death) and (iii) appropriate quality of DNA extraction. A cohort of 35 patients (from six hospitals in France) with such criteria was retrieved from our database from 2007 to 2014 (samples were sent during this period, just after death, to the Molecular Cardiogenetics Unit at Pitié-Salpêtrière Hospital and sequenced according to the practice of genetic diagnosis at that moment). It was before the use of highthroughput sequencing in diagnostic routine, so molecular screening was limited to focused analyses with Sanger sequencing.
The entire coding sequences and intronic adjacent sequences of genes were systematically analysed in all patients. Chromatographs were analysed with the SeqScape software (AB®) and the variants were numbered according to their RefSeq transcripts (details in Section 5). Alamut (Interactive Biosoftware, North Seattle, WA, USA) was used for variants' numbering and to determine the pathogenicity of the variants via its links to prediction software.
Interpretation of variants Pathogenicity of variants was determined according to the current American College of Medical Genetics (ACMG) guidelines  [22] , which recommend classifying variants into five categories: pathogenic (class 5), likely pathogenic (class 4), uncertain significance (class 3), likely benign (class 2) or benign (class 1). The identified variants were first checked for their allele frequency as reported in the databases about populations of the same ethnic origin (http://gnomad. broadinstitute. org/). We evaluated the putative pathogenicity of each missense variant by using our knowledge of genes and proteins (the location of the variant in the gene and its impact on the resulting corresponding protein), in silico prediction tools (Polyphen-2, SIFT and Mutation taster) and SpliceSiteFinder like ® , Max-EntScan ® , NNSPLICE ® , GeneSplicer ® and Human Splicing Finder ® for putative splicing variants.
We then looked at the score for pathogenicity (combined annotation-dependent depletion [CADD] score) calculated on the basis of multiple algorithms (Phred-like score). This was followed by a careful review of the literature, available published segregation analyses and functional studies. Finally, we searched our local database to check whether the variant was already found in familial forms of cardiomyopathies and/or arrhythmias and validated as pathogenic with segregation analysis. In practice, we considered as "pathogenic" (class 5) a variant with confirmed pathogenicity criteria and already proved to be responsible for cardiomyopathies or a novel nonsense variant with a frequency below 0. 01%. We considered as "likely pathogenic" (class 4) the unpublished variants with a frequency below 0.
01% and unknown in our database, located in a functional domain of the protein and with pathogenicity prediction tools in favour of a strong effect with a CADD score of >24. Only variants of classes 5 and 4 (certainly pathogenic or probably pathogenic) were reported in this study and were referred to as "causal variants". Unfortunately, except in few families, no informative segregation analyses could be done because there was no additional relative with a cardiac phenotype already expressed.
Critical requirements for the study include clear case definitions for GBS and the collection of data on the clinical and electrophysiological phenotype, as this can be associated with a specific infectious agent and may provide evidence of an association. To study the impact for patients, outcome of at least 6-12 months with validated outcome measures should be recorded. Such a protocol would be supported by a network of neurologists, such as the Inflammatory Neuropathy Consortium of the Peripheral Nerve Society, and can be based on the protocol of the International GBS Outcome Study, that is running in 19 countries and is also used by other research groups. 5 6 Existing networks such as The Global Health Network could help to make the existence of such a protocol widely known. 7 Inspiration can be drawn from large international research consortia on infectious diseases, such as the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) and the Platform for European Preparedness Against (Re-)emerging Epidemics (PREPARE) that assure and prepare an agile research response to outbreak-prone infectious diseases (https://www.
http://bmjopen. bmj. com/cgi/content/full/bmjopen-2012-001893. Briefly dementia. The protocol has been described in detail elsewhere [27]. participants were recruited through the dementia register database, the Joint Dementia Research website and information about the study on the Internet and in study presentations. The present study included 378 participants from West London, Oxford and Cambridge for whom base-line data was available and validated on all variables and of whom 203 have a parent with diagnosed AD, vascular or mixed dementia. Persons with a parent with diagnosed dementia were considered to be at increased risk of future dementia and current pre-clinical brain changes. The following hypotheses are examined: (i) middle aged persons who have experienced childhood trauma will have higher rates of clinical depression than those who have not (ii) middle-aged persons exposed to childhood trauma will have lower hippocampal volume (iii) childhood trauma will be associated with current poorer cognitive function (iv) cognitive performance will be poorest in persons with both a history of childhood trauma and a high dementia risk METHODS Subjects were recruited from the PREVENT Dementia study; a prospective population study enriched for dementia risk, with around half of the participants having a family history of dementia designed to detect mid-life pre-clinical changes in persons at risk of later-life.
Patients Overall, 104 patients were enrolled in the study (DLT and expansion phases) between February 27, 2017, and April 11, 2019. The primary analysis set included 100 patients who were treated in the first-line setting (4 patients from the DLT phase were excluded because of prior sorafenib treatment). At the data cutoff date (October 31, 2019), all patients in the primary analysis set had an opportunity for a minimum follow-up period of $ 6 months; 37 patients (37%) were still receiving treatment (both study drugs, n 5 34; lenvatinib only, n 5 3); 63 patients (63%) had discontinued treatment; and 26 of the 63 patients remained in survival follow-up (Appendix Table  A1 , online only). The primary reasons for treatment discontinuation are listed in Appendix Table  A1. Data on subsequent anticancer medications during survival follow-up are summarized in Appendix Table  A2  (online only).
The LMD-MGCM model and MCS dataset used here are described in Sections 3 and 4, respectively. The impact of our non-orographic GW scheme on the simulated wind and temperature is described in Section 5. The method implemented here to identify the set of "best-fit" GW parameters is explained in Section 6, together with the comparison with MCS results. A number of sensitivity tests is discussed in Section 7 and concluding remarks are given in the Section 8.
GW parameters setup In this section we describe the main tunable parameters used in the non-orographic GW scheme implemented here. The characteristics of every wave launched in the GCM are selected randomly with a prescribed box-shaped probability distribution, whose boundaries are key model parameters. These are chosen on the basis of observational constraints (whenever available) and theoretical considerations, and tuned using MCS data comparison. A total of 26 runs for the full Martian year (MY) 29 have been performed to cover a large range of possible combinations of the GW characteristics, based on those constraints. One of the advantages of the GW scheme used here is that, contrarily to other GW parameterizations, each parameter has a physical meaning, as described below. Source height and duration First, we assumed that the non-orographic GW source is placed above typical convective cells (i.
6 µm and 11. 9 µm), nominally from the surface to about 0. 1 Pa with 5 km vertical resolution. MCS data  [McCleese et al. , 2010]  currently provide the only systematic measurements of temperature in Mars' mesosphere up to about 80 km. For several technical reasons explained in the above-cited papers, though, a large number of profiles stop well before reaching the surface. Although the nominal vertical resolution of the instrument is 5 km (corresponding to the separation of the peaks of the weighting functions), the profiles are over-sampled using information from more than one weighting function. Both temperature and aerosol extinction opacity profiles are standard MCS products. Most recently,  Kleinböhl et al. [2017]  developed a new scheme for the retrievals of these quantities, based on a two-dimensional rather than one-dimensional radiative transfer.
MCS Data set description In this paper, we use these latest retrievals (version 5. 2). Temperature uncertainties are typically around 0. 5 K and only increase at low altitudes (below about 5 km altitude), where the atmosphere starts to become opaque, and at altitudes above about 60 km, where the instrument signal-to-noise ratio starts to decrease. Since we are interested in comparing gridded GCM temperature fields with data, we have carried out binning of MCS observed temperatures in 4-D bins (latitude, longitude, solar longitude, and local time). We separate observations into dayside (06:00 h < LT ≤ 18:00 h) and nightside (00:00 h < LT ≤ 06:00 h and 18:00 h < LT ≤ 24:00 h) bins. As mentioned above, most MCS observations at latitudes equatorward of |80 • | are provided around 03:00 h in the nightside and 15:00 h in the dayside.
Results: comparison with MCS data Since the characteristics of GW spectrum are not well known on Mars, the strategy adopted in this work was to identify a set of reference tunable GW parameters that reduced model data biases by the greatest amount by comparing the LMD-MGCM and MCS thermal structure, specifically using diurnal tides as diagnostics. We run 26 simulations of a full Martian Year (MY 29) with non-orographic GW parameterization included. The subset of "bestfit" GW characteristics listed in Table  1  was selected with the help of sensitivity tests (see Section 7). Both MCS data and LMD-MGCM simulations were binned in boxes of 3. 75 • latitude x 5. 625 • longitude. In this section we will focus only on the LMD-MGCM simulations performed with the non-orographic GW scheme activated using the wave characteristics as in Table  1.
Impact of non-orographic GW drag on LMD-MGCM thermal structure Examples of the impact of our GW scheme on the thermal structure of the Martian mesosphere predicted by the LMD-MGCM (up to about 100 km) are given in Figures  9 10 11 12 13 14 15 16 17 18. Figures 9-13 compare zonal mean day-night temperature averages T mean from MCS data (top panels) with model results from the LMD-MGCM with and without the non-orographic GW parameterization (middle panels). Differences between the model runs with and without the parameterization are also given. T mean stands for (T am + T pm )/2, where T am and T pm are the temperature at 03:00 h and 15:00 h local time, respectively. As discussed in Section 4, latitudes larger than 80 • (North and South) are excluded from this study to avoid comparison at non-homogeneous local time.
2), which partly controls the high latitude warming around northern winter solstice  [Wilson and Hamilton, 1996]. Data-model comparison improves mostly in NH winter, with biases at pressure range 10-1 Pa reduced up to 20 K, especially between 40 • N and 80 • N latitude (see Figure  12 ). There are still differences between MGCM and MCS results: at similar pressure levels and around the equator simulations are warmer than MCS data by 5 K, thus increasing model biases. Possible causes for those remaining discrepancies are related to: i) the uncertainties on the dust distribution in the model during the dust season, ii) the radiative effects of water-ice clouds on the thermal structure during the NH summer, and/or iii) the vertical resolution in the water ice cloud structure, during the northern summer season.
Acknowledgments This work was supported by the  European Space Agency  contract  4000122721/17/NL/LF/as  and the  Fundação para a Ciência e a Tecnologia (FCT, Portugal)  through national funds and from  FEDER  through  COMPETE2020  by grants  UID/FIS/04434/2013 ,  POCI-01-0145-FEDER-007672 ,  PTDC/FIS-AST/1526/2014 ,  POCI-01-0145-FEDER-016886 , and  P-TUGA PTDC/FIS-AST/29942/2017. GG also received funding from the  European Union  s  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  796923. This work was granted access to the HPC resources of CINES (Grant  A0040110391 ). MGCM and MCS data used in the paper are available at https://doi. org/10. 14768/20181126001. 1.