dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Collection and response assessment We extracted data from the medical records, including: age and Eastern Cooperative Oncology Group Performance Status (ECOG PS) at the beginning of first line chemotherapy and before the start of second line therapy, gender, smoking history, histology, molecular profiling, details of first and second line therapy including start and end date, best response according to RECIST 1. 1, and date of death or last follow-up). We included patients who had begun first line treatment between March 2010 and November 2017 (Chemotherapy: March 2010-May 2017; ICI: May 2014-November 2017). Finally, we collected post second line treatment when it was done.
Study objectives and outcomes Each center locally assessed response to systemic therapies using RECIST v1. 1 criteria, defined as a complete or partial response (CR/PR), stable disease (SD) or PD during therapy. We collected data of progression; Progression Free Survival (PFS) and Overall Survival, date of death (or last follow-up) and patients' characteristics. ORR was defined as CR or PR. We divided the PFS on both 2nd line treatments into 3 groups: Progression within the first 2 months corresponding approximately to hyperprogressive disease description, progression between 2 and 6 months and progression after 6 months. The 2 months cutoff for PFS was used corresponding to the first evaluation in daily practice of the three major ICI drugs. 6 months being approximately the time we observed long responders PFS curve stabilisation on ICI landmark study.
Statistical methods Demographic and clinical data were presented according to treatment modality (chemotherapy vs. ICI). Qualitative variables were summarized using counts, percent, number of missing data. Comparisons between groups were performed through chi-square or Fisher's exact test. Association between variables and early progression (< 2 months) on ICI treatment were quantified by the Odds Ratio (ORs) and their corresponding confidence intervals (CIs) in univariable and multivariable analysis using logistic regression models. All survival times were calculated from the initiation of second line treatment and estimated by the Kaplan-Meier method with 95% confidence intervals (CI), using the following first event definitions: progression or death from any cause for PFS and death from any cause for OS.
Data extraction Data were extracted from the included studies by two authors (E. E. and F. D. ), independently, using a preestablished standard assessment. The assessment included: author, year, country, cohort name, type of study (study design), settings, rate of participation, demographic characteristics, size of the sample, details of the gerontological assessment, and any or all of the following assessment results: cognitive, functional, nutritional, and physical status. For longitudinal studies, we used baseline data. Some studies did not provide adequate details on sex of the participants, in which case we provided aggregated results for men and women.
Study characteristics (see Additional file 2) Studies were conducted in 15 countries across four continents: Europe (n = 20)  [20, 22, 23, 27-32, 37-40, 42-44, 47-49, 51] , North America (n = 9)  [18-20, 24-26, 41, 45, 46] , Asia (n = 5)  [33] [34] [35] [36] 52] , and Oceania (n = 1)  [46]. Studies included individuals over 90 (n = 22)  [18-25, 28, 30, 34, 35, 37, 38, 43-47, 49-51] , individuals under and over 90 (only data from individuals over 90 were included) (n = 8)  [29, 31, 32, 39, 40, 42, 48, 52] , and centenarians (n = 5)  [26, 30, 33, 36, 41]. The participation rate ranged between 47%  [24]  and 89%  [18] ; it was over 70% in more than 40% of studies. There were 27 longitudinal studies  [18, 20-23, 25, 28, 29, 31, 34-49, 51, 52]  and eight cross-sectional studies  [19, 24, 26, 27, 30, 32, 33, 50]  (three using databases from electronic medical files or administrative databases  [29, 32, 50] ).
Strengths and limitations Our study is the first to synthetize data relative to global descriptions of the oldest old. The studies included in this review are representative of the target population due to the decision to include older people living at home and in nursing homes, including those with loss of mobility. Data collection was generally performed in participants' homes. This permitted the inclusion of individuals with mobility difficulties, and thus made samples more representative of the target population. Our findings should be interpreted in light of several limitations. First, the studies included were from Western countries, with only four Asian studies and none from Africa, limiting the generalizability of findings. Second, when applicable, we used data from the base line and not from longitudinal surveys.
Availability of data and materials Additional file 1 provides the search strategies applied to Medline, Cochrane Library, Pascal, and Web of Science; additional file 2 provides the characteristics of included studies; additional file 3 provides a comparison between individuals aged 65 and over, frail individuals aged 65 and over, and individuals aged 90 and over regarding cognitive, functional, nutritional and physical status. The datasets generated during this study (i. e. quality score assessment) are available on reasonable request.
VF Population This study included 110 patients (89 male, 47 ± 18 years) enrolled for VF ablation. As in  Haïssaguerre et al. (2018) , SHDs were screened by echocardiography and delayed gadoliniumenhanced magnetic resonance imaging in 32 patients, whereas the remaining subjects (78) presented no structural alterations (NSHD). Pharmacological testing was performed by injecting ajmaline or catecholamine (adrenaline and isoprenaline) to diagnose patients with Brugada syndrome, long QT, and catecholaminergic polymorphic VT, respectively. This study was carried out in accordance with the recommendations of the protocol CARRY, ID-RCB: 2015-A00401-48, Comité de Protection des Personnes Sud-Ouest et Outre Mer III (ClinicalTrial. gov number: NCT02647749). It was approved by the Institutional Clinical Research and Ethics Committee and all subjects gave written informed consent in accordance with the Declaration of Helsinki.
Specifically, numerical thresholds for these transitions were computed as the cutoff values, which simultaneously maximized the sensitivity (Sens: rate of correct predictions of "Disorganized" VF) and the specificity (Spec: rate of correct predictions of "Organized" VF) of the classifier, thus providing the best discrimination between "Organized" and "Disorganized" stages of VF. This approach allowed us to automatically determine a unique threshold optimized over the entire database and cope with interpatient and intrapatient variability of signal indices. Classifiers' performance was quantified by the values of sensitivity and specificity associated with the optimal cutoff, as well as the area under the ROC curve (AUC).
BSPM-Based Analysis of Electrical Complexity Dynamics An overall increase in VF complexity over time is underlined by all BSPM markers (p < 0. 0001). At early stage, organized VF is measured by low NDI (VF start 1. 9 ± 1. 4% vs. VF end 3. 4 ± 2. 4%), amplitude voltage A BSPM is high (0. 5 ± 0. 3 vs. 0. 3 ± 0. 2 mV), and slow activities are measured by long BsCL BSPM (166 ± 21 vs. 154 ± 22 ms) in the entire dataset. An unexpected, albeit significant, trend of ShEn BSPM is observed in the entire dataset, with a decrease in time from 6. 6 ± 0. 5 to 6. 2 ± 0. 6, in contrast with our initial hypothesis. As shown in Figure  1A , at the onset of VF, none of these indices can significantly distinguish between sustained and self-terminating VF, while at the end of DCC-shocked episodes complexity is significantly higher, and amplitude and entropy appear reduced, with faster oscillations than in case of spontaneous termination (p < 0.
0001). Furthermore, time evolution of sustained VF is characterized by more complex and variable waveforms, which are significantly captured by all BSPM markers. By contrast, self-terminating VF results in more regular and simple patterns and a stable trend in signal features. After removing outliers identified by the Grubb's test (with significance taken for p < 0. 05), a modest increasing correlation between the length to self-termination from VF onset and NDI BSPM fold change from "VF start" to "VF end" is observed in our database (Pearson' correlation coefficient R = 0. 56, p = 0. 003), while the other BSPM features do not exhibit any significant correlation. Temporal increase in body surface complexity is also corroborated by a decrease in intracardiac CL (Organized 197 ± 24 ms vs. Disorganized 169 ± 20 ms; p < 0.
Relation Between Electrical Complexity of VF From BSPMs and Structural Alterations of the Myocardium As underlined by the statistical analysis of BSPM metrics in Figure  3A , according to NDI, in SHD patients VF is initially more complex than in NSHD patients (p = 0. 0007). Similarly, f-wave amplitude is slightly lower in patients with structural alterations (p < 0. 0001), with moderately slower cycles (p = 0. 06). Entropy is also lower at early stage of VF in these patients (p < 0. 0001). In both populations, there is a strong increase in electrical complexity with progression of VF (p < 0. 0001). At the end of the episode, marked intergroup differences in amplitude and entropy are still preserved (p < 0. 0001). In contrast, body surface CL (p = 0. 06) and NDI (p = 0. 54) are comparable at the end of VF in both datasets.
In the NSHD group, ECG markers of VF organization highlight similar patterns to those obtained by BSPM analysis. According to all ECG parameters, differences in body surface complexity between sustained and non-sustained episodes clearly appear only later in time ("VF end"), whereas at the onset of VF ("VF start") ECG features are comparable in both datasets. As for BSPM indices, a strong reduction in electrical organization (i. e. , with an increase in NDI, Figure  4E.
Temporal Evolution of Electrocardiographic Markers of VF Organization As in  Tovar and Jones (2000) ,  Haïssaguerre et al. (2018), and Robson et al. (2018) , overall we observed a sharp transition from early organization to late disorganization of electrical patterns of VF, due to the progressive and rapid metabolic deterioration of myocardial tissue, which drastically reduces the probability of survival to cardiac arrest and defibrillation success  (Brown et al. , 1989; Winkle et al. , 1990). In our database, these changes could be captured from body surface signal analysis within a few seconds after the onset of VF (usually less than four), and they were validated with simultaneous measure of endocardial CL in a subset of patients. Furthermore, the dynamics of VF could be tracked in high detail, and the contribution of signals from multiple electrodes could be effectively summarized into one curve per parameter, thus easing the inspection of the fibrillatory patterns.
In  Meo et al. (2020) , we have tried (at least partially) to address this question, and we have shown that in explanted porcine hearts NDI can identify more heterogeneous repolarization substrates, with uneven spatial distribution of epicardial CL and reentrant drivers from ventricular sites with different repolarization timings. Further investigation is needed to translate these findings from our experimental model to clinical patients, and to test additional hypotheses to explain all the pathologies encountered across our dataset. No specific gender-related differences in VF dynamics were found in our dataset based on surface signal analysis. Overall, we observed that VF progression was comparable both in male and female patients according to all parameters, independently of the lead configuration.
Further investigation is needed to elucidate the mechanisms underlying potential inter-gender differences in subpopulations from our dataset and link them to other risk factors. Our patients' cohort has been split into two macro-categories (i. e. , SHD vs. NSHD), although each group includes patients with different pathologies and electrocardiographic manifestations. This choice is motivated not only by the will to investigate the impact of structural alterations in a more general framework, but also by the reduced number of VF recordings in certain categories (even one or two for certain diseases), which hampers the generalization of certain findings. The inclusion of additional signal recordings in those categories in a future study may allow for the assessment of more specific patient-related electrical signatures during VF.
Global score is obtained by the sum of these factors. The BIS-11 showed adequate reliability and construct validity in both USA  (35)  and Italian  (36)  samples. The MOAS is a clinician-rated scale consisting of four subscales for different types of aggression (verbal aggression, aggression against objects, aggression against others, and selfaggression). The subscales are rated on a 5-point scale (score 0-4). Higher scores for each subscale reflect the higher severity of a subject's aggressiveness  (32, 37). The SHI is a brief, self-reporting instrument that provides informative data about clinically-relevant self-harm behaviors. Although not considered in the scoring, many of the SHI items provide additional information regarding the number of times a patient engages in a self-destructive act, as well as how recently he or she has done so.
Statistics Statistical analysis was performed with one-way analysis of variance (ANOVA) or chi-square test to compare baseline characteristics of the two treatment groups (age, gender, level of education, and clinical and functional rating scales scores). Comparison of score changes at the end of the trial between the two groups was calculated for each rating scale with the analysis of variance for repeated measures. The software IBM SPSS Statistics version 26 (IBM Corp, 1989, 2019) was used for the analyses. P values were considered significant when ≤ 0. 05.
Sample and data collection Data came from four sources: 1) documentation from organizations and MH teams in each selected network; and questionnaires completed by 2) team managers, 3) MHP, and 4) service users. Documents consulted between November 2012 and March 2013 provided data on population demographics, government financial support for MH, and service provider characteristics. Questionnaires were completed as follows: team managers (October 2013 to June 2014), MHP (May to November 2013), and service users (June 2013 to August 2014). An advisory committee consisting of key decision makers from the MHSN was established to help with recruitment, validate instruments, and support data collection. The committee identified all MH managers from the three networks, who in turn identified professionals from network MH teams.
Conceptual framework and instruments The conceptual framework adapted from the Donabedian model guided data collection and analysis (Figure  1 ). Regarding structures, variables extracted from documentation included government financial support for MH, and proportions of network inhabitants with low income. The manager questionnaire provided data on high emergency room (ER) users (≥4 visits/yearly)  [40] ; use of standardized clinical procedures and tools, clinical approaches, integration strategies, and interactions with other teams or services, with all variables measured on five-point Likert scales and calculated as global scores. An additional variable, organizational culture, was assessed with the Organizational Culture Assessment Instrument (OCAI)  [41]. This measure consists of six questions, with responses based on distributions of 100 points among four possible choices.
, schizophrenia, and severity of needs) from service user medical records (with consent). Data were screened for missing values and outliers; missing values were replaced using multiple imputation methods (<5%). Statistical analyses included univariate and cluster analyses. Univariate analyses consisted of frequency distributions for categorical variables (numbers and percentages), and central tendency distributions for continuous variables (mean values and standard deviations). For the cluster analyses, participant typologies were calculated using two-step cluster analysis with SPSS version 24. The choice of variables for inclusion was driven by their importance based on the Donabedian model  [10] , and by significant differences in scores between variables for each of the three MHSN. Participant network (i.
F. Channel Modeling Three different channel models are considered, and are analyzed analytically, and by using simulations and experiments. 1) Analytical Model. In order to assess the performance of the considered backscattering system, we introduce a model for the polarization-based dipole antenna in a LOS propagation environment. This is discussed in Section III. 2) Numerical Electromagnetic Code (NEC) Simulator. The dipole antennas are studied by using the 4NEC2 simulation software. 4NEC2 is a software based on the method of moments. By using 4NEC2, it is possible to model and configure the dipole antennas, the source, the tag, the reader, the N SC scatterers, the ground plane, the load impedances, and the physical characteristics of the dipole antennas and the conductive lines. In particular, this tool allows us to (i) configure the state of the tag (transparent or backscattering), by changing the load impedance; and (ii) to measure the resulting voltage at the reader port.
| Variant interpretation Variant interpretation was aided using ALAMUT (Interactive Bio-Software), which includes splice site analysis tools (SpliceSiteFinder, MaxEntScan), protein-function prediction tools (SIFT, Polyphen 2. 0), and links to relevant databases (ClinVar, Leiden Open Variation Database [LOVD], other syndrome-specific databases). Variants were classified according to the American College of Medical Genetics (ACMG) recommendations,  5  aided by the French National Database of variants (Groupe Génétique et Cancer). Only pathogenic or probably pathogenic variants (classes 4 and 5) were considered in this study.
| Complementary analyses All class 5 (pathogenic) or 4 (likely pathogenic) variants were confirmed on a second patient sample using techniques appropriate for the variant. Sanger sequencing was performed using a 3500xl instrument and BigDye terminator kit 3. 1 (Applied Biosystems). Interpretation was performed using Seqman software (DNASTAR). copy number variation (CNV) were confirmed using QMPSF (quantitative multiplex PCR of short fragments). At least one probe within the CNV and if possible at least one probe in the same gene but outside the CNV, as well as control probes on other chromosomes were used in each multiplex mix. Interpretation was per- RNA was reverse-transcribed using High Capacity RNA to cDNA kit (Applied Biosystems) and amplified for the region of interest. PCR products were gel-purified if appropriate before sequencing.
| DISCUSSION In the present study, 1530 consecutive patients for whom hereditary predisposition to cancer was suspected were analyzed by capture and sequencing of relevant gene regions. Regardless of personal and familial history, similar proportions of patients accepted testing of an EP in HBOC and digestive indications. Thus, if the choice is offered to them, most patients with these indications agree to an extensive analysis of actionable genes, for which medical management can be proposed in the event of the identification of a pathogenic variant, even if these variants are incidental. This proportion was stable over the recruitment period, but was somewhat dependent on the prescribing physician (data not shown), underlining the importance of the presentation by the physicians of the various choices proposed and their individual and familial consequences.
formed using GeneMapper software (Applied Biosystems). If the breakpoints of large deletions were identified in the NGS data, allelespecific PCR and Sanger sequencing were used to confirm the CNV. Variants likely to affect splicing were confirmed by RT-PCR unless the classes 4 and 5 character of the variant was already well established in the literature or national databases. Blood drawn on PaxGene tubes was used for RNA extraction using PAXgene Blood RNA Kit (Qiagen).
13, 14 However, the most commonly used techniques, such as DNase-seq or histone-ChIP, require relatively high amount of cells, which excludes many tissues, including heart valve, from such analyses. On the other hand, expression quantitative trait loci (eQTL) in disease-relevant tissues are widely used to narrow down causal variants and potential target genes at GWAS loci,  15  but such datasets are not available for the mitral valve. Here we aimed to fill in the existing gap in functional annotation datasets for human mitral valve using the assay for transposase accessible chromatin using high-throughput sequencing (ATAC-Seq) approach, a genomic technique that provides a high-resolution map of open chromatin from samples with low cell content, as is the case of mitral valve tissue. Using this genomic annotation technique, we provide valuable information about active gene expression and main regulatory elements within human mitral valve cells.
As a complementary approach, and in the absence of eQTL datasets for mitral valve, we performed circular chromatin conformation capture analyzed by high-throughput sequencing (4C-Seq) to provide evidence for physical proximity between MVP-associated variants and promoters as an indication of possible regulatory event between genetic variants and target genes. 16, 17 Finally, we provide two successful examples of the application of the genomic annotation, which we have generated, combined with chromatin architecture and gene reporter enhancer assay in vitro to propose candidate genetic variants for causality and their potential target genes at MVP GWAS loci.
Sequencing of ATAC-Seq libraries and peak calling ATAC-seq libraries were sequenced using 42 paired-end sequencing cycles on an Illumina NextSeq500 system at the high throughput sequencing core facility of Institute for Integrative Biology of the Cell (CNRS, France). Reads were demultiplexed using bcl2fastq2-2. 18. 12, and 31 to 95 million reads (fragments) were obtained per sample. Adapter sequences were trimmed using CutAdapt v1. 15. For heart samples from ENCODE, raw reads from ATAC-Seq experiments on human heart left ventricle (ENCSR117PYB, ENCSR851EBF) and human right atrium auricular region (ENCSR062SVK) were downloaded from ENCODE webserver. Read length was adjusted to 42bp using Cutadapt. Further analyses were performed on the Galaxy webserver. 20 Reads were mapped on GRCh38 (hg38) genome using Bowtie2 v2.
24 Identified GOBP terms were clustered using REVIGO webserver (http://revigo. irb. hr/), with "medium" settings. 25 We analyzed ATAC-seq peaks for enrichment in putative TF binding motifs using MEME-ChIP tool on MEME webserver (http://meme-suite. org/)  26 set with HOCOMOCO Human v11 motif database. We used Integrated Genome Browser (IGB) to visualize read density profiles and peak positions in the context of human genome. 27.