dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work is part of the  INSPEC  project ( Integrating Sensor Based Process Monitoring and Advanced Process Control ) managed by the  Institute for Sustainable Process Technology (ISPT)  and co-funded by  TKI-E & I  with the supplementary grant ' TKI-Toeslag' for Topconsortia for Knowledge and Innovation (TKI's)  of the  Ministry of Economic Affairs and Climate Policy.
The supervised training program is detailed in the Supplementary Materials. At the initial visit, the following data were collected: age, ethnicity, number of football seasons played at the university level, and height. Weight, body fat percentage, heart rate, blood pressure, and personal medical history (past and recent) were collected at both visits. Resting heart rate and blood pressure were measured after 10 min of quiet rest in a supine position. Blood pressure was averaged on duplicate successive measurements assessed using an automated monitor (OMRON Healthcare, Hoofddorp, The Netherlands) with an appropriate-sized arm cuff. Body mass index (BMI) was calculated as follows: (weight (kg)/height (m) 2 ). The body surface area (BSA) was calculated with the Mosteller formula  [16]. Body fat percentage was measured using a tetrapolar bioelectrical impedance analysis (Tanita Body Composition Analyzer BF-350, Tanita Corporation, US).
Echocardiographic Acquisitions and Image Processing The echocardiographic functional and morphological assessment was carried out according to the current guidelines  [17]  using a commercially available echocardiographic system EPIQ 7 equipped with an X5-1 xMATRIX-array transducer (Philips Healthcare, Eindhoven, The Netherlands). Participants were scanned at the pre-inter-seasonal medical assessment and within one week of completing the inter-seasonal training program. Cardiac 3D volume acquisitions were performed during end-expiratory apnea (4 to 6 cardiac cycles) in two separate sequences for the LV and RV. All data were stored digitally on a computer system. Offline data analysis was processed, semi-automatically by a single operator blinded to study time points, using commercially available clinically validated software (4D RV-Function and 4D LV-Function, TOMTEC-Arena TTA2, TOMTEC Imaging Systems GMBH, Unterschleißheim, Germany), from which left and right ventricular volumes and RV free wall global longitudinal strain were obtained.
RV 3D volume segmentation sequences corresponded to a full cardiac cycle and resulted in a mesh for each time step consisting of 938 nodes and 1872 triangles of the endocardium, and the coordinates of each node were exported for our in-house regional analysis. Two-dimensional linear dimensions for both ventricles and atria were measured manually following the recommendations  [17]. To assess the intra-and inter-observer reproducibility, quantifications were blindly repeated in 10 randomly selected players by the same operator who originally quantified the whole dataset and a second independent operator.
Statistical Analysis Statistical analysis was performed in Python 3. 6 (Anaconda Inc, Austin, TX, USA), with packages statsmodels (v0. 10. 0) and scipy (v1. 2. 1). Categorical variables are presented as proportions and continuous variables as mean ± standard deviation. The normality of distribution was verified using the Shapiro-Wilk test. Paired data were compared using Student's paired t-test or McNemar's exact test, while unpaired data were compared using the Student's independent sample t-test or Fisher's exact test. Multiple hypothesis testing was corrected using the Holm-Sidak procedure. A mixed-effect linear model with a random effect controlling inter-individual variability was used to predict the relationship between pre-/post-season, field position, and the interaction between the temporal variable and field position (introduced as fixed effects in the model) and the imaging parameters (the dependent variable in the model).
Although our 3D RV segmentation does not correspond to the exact anatomical definition of the RV three sub-units, this method is based on a mathematical model to guarantee the stability of the segmentation in the absence of anatomical landmarks in the mid-ventricle. This fully automated tool for RV regional 3D quantification uses information that can be easily exported using commercial software with no extra manual processing. Our team has shown that this technique is sensitive to detect synthetic regional remodeling  [14] , and it could be easily applied to clinical datasets to explore geometrical and functional changes that might occur in physiological states and pathological situations. Distinguish RV adaptive remodeling induced by physical exercise from pathological changes such as those observed in arrhythmogenic right ventricular cardiomyopathy (ARVC) which is responsible for juvenile sudden death, is essential in clinical practice when dealing with athletes.
Statistical Analysis The statistical analysis was performed with the software Sigma Plot v. 14. Data was checked for normality (Shapiro-Wilk test) and homogeneity (the equal variance test Brown-Forsythe). Two-way analysis of variance (ANOVA) was then performed to assess statistically significant differences among the cellular density, the specific growth rates, the chlorophyll and the nutrients concentration within the time and the effluent concentration. The Holm-Sidak multiple comparison t-test was used after the rejection of the two-way ANOVA null hypothesis.
Acknowledgments: The authors acknowledge the company  Águas da Figueira S. A  for the supply of the effluent samples. This work was also supported by  FCT  and the  University of Coimbra  through the Decree-law 57/2016 contract of Tiago Verdelhos, subsidised by the  European Social Fund and Portuguese Ministry of Science, Technology and Higher Education (MCTES)  through the  Human Potential Operational Programme (POPH).
ERP Analysis ERP analysis was performed using the BrainVision Analyzer 2. 1 software (Brain Products GmbH, Gilching, Germany; https://www. brainproducts. com/). ERPs were epoched from 21500 to 1500 ms around stimulus onset (Go or NoGo trials) for the N2 and around response onset (Go trials) for the Frontal Theta and Intraindividual Reaction Time Variability prestimulus slow wave. ERPs were low-pass filtered at 30 Hz and baseline corrected from 2200 to 0 ms before stimulus presentation (Go or NoGo trials). All EEG data were visually inspected for artifacts and then current source density transformed  (36). The stimulus-locked N2 was averaged separately for Go trials and NoGo trials followed by a correct withhold. The N2 was defined as the most negative peak in a window from 200 to 400 ms after stimulus onset.
Statistical Analyses Behavioral data were subjected to analysis of variance (ANOVA) including the within-subject factor period (low/high RT variability) and the between-subject factor group (SPs/ HCs). To evaluate reactive mode, ANOVAs were performed on the frontal midline theta and the N2 amplitude including the between-subject factor group (SPs/HCs), the within-factor current trial (Go/NoGo, Figure  3A ), and the within-subject factor period (low/high RT variability). To evaluate the proactive mode, statistics were performed on the frontal lateral theta and the prestimulus slow wave. The ANOVA performed on frontal lateral theta included the between-subject factor group (SPs/HCs), the within-subject factor trial-by-trial (Go N21/Go N11) (Figure  4A ), and the within-factor period (low/high RT variability).
ACKNOWLEDGMENTS AND DISCLOSURES This research was supported by the  French Ministry of Higher Education and Research  grant (to MC). We thank the staff of the Unit 1114 of the  French Institute of Health and Medical Research  and of the  Strasbourg University Hospital  and especially  Marc Hoonakker  for his assistance with data collection. This is the first publication from this dataset. The authors report no biomedical financial interests or potential conflicts of interest.
Analyses For Phase II focus groups conducted face-to-face (i. e. those in the US), feedback from participants was captured in real time using Linguistic Resonance Dial Testing, a proprietary technology for implementing instant-response methodology (Fig. 2 )  [25]. This technology enables instant word-by-word measurement of emotional responses to a wide variety of language stimuli, to select and refine the phrases that resonate best with the target audience. During each session, participants reacted to the language stimuli on a moment-to-moment basis, using a dial with a rating of 0 (don't like) to 100 (like) to continuously rate each message based on their 'gut feelings'. For each message, the dial was centered at 50 (i. e. neutral), and participants were encouraged to keep their hand on the dial at all times and to react immediately to each word, phrase, or sentence rather than wait to give it a score at the end, using the whole range of the dial to show how they were feeling throughout the delivery of the message.
For messages that participants liked (made sense, was clear, was compelling), they turned the dial up towards 100; for something they did not like for any reason, they turned the dial down towards 0. Perception Analyzer 9. 0 software was used for collection and analysis of dial responses and was critical to understanding the most promising language to carry into Phase III  [25] ; this software performs quantitative analyses of the graphical outputs from Linguistic Resonance Dial Testing, with positive lines indicating promising language to use, and negative lines indicating language to avoid. The level and slope of these lines provide a semiquantitative measure of the strength of emotion. Data from the dial testing exercises were collected anonymously. The quantitative analysis was supplemented by qualitative group discussion to explore the reasoning behind their reactions to particular words, phrases, ideas, or images.
Participants also completed written exercises asking them to compare and rank messages and choose the language that was most resonant to them. Back-room viewing of the focus group activities and outputs enabled the assessment team to observe, capture, and analyze responses unseen. European Phase II focus groups were conducted in native/local language by local teams, with logistics managed remotely by the US-based research team. For this reason, it was not possible to utilize the Linguistic Resonance Dial Testing technology in these sessions and an online focus group format was more practical, using the interactive platform InterVu provided by FocusVision  [26]. Similar qualitative discussions and written exercises were performed as those used in the US groups. The European group sessions were conducted in their local languages, with simultaneous translation available so that the assessment team could follow proceedings in real time.
Acknowledgements This study would not have been possible without the participation of global hemophilia community members. The authors and study sponsor thank all participating patients, patient advocates, caregivers, nurses, and physicians who contributed to this research. The study was designed and performed by Maslansky + Partners, LLC, a market research organization specializing in language strategy, on behalf of and funded by  BioMarin Pharmaceutical Inc. Support for medical writing and preparation of figures was provided by  Joanne Fitz-Gerald  and  J A C Lee  of  FourWave Medical Communications , funded by  BioMarin Pharmaceutical Inc. Kendra Bolt at BioMarin Pharmaceutical Inc. assisted with content development.
Nevertheless, the diversity of the clinical molecular profile of lung cancers and the rarity of cfDNA amounts per sample requires broader oncogene coverage than can be provided by ddPCR. Here we present clinical validation for a recently-introduced sequencing error-reduction method based on molecular amplification pools (MAPs) that has shown improved analytical accuracy  14. This methodology tracks variants present in large collections of molecules, as opposed to single molecule UMIs. Used in conjunction with the ERASE-Seq variant caller (Fluxion Biosciences, Alameda, CA, USA), MAPs provide a widely-applicable approach to ultrasensitive NGS test development. Sensitivity and specificity were measured with respect to ddPCR testing performed as part of standard clinical work-up. This retrospective study reports data from 356 lung cancer patients receiving plasma testing as part of routine clinical management and utilizing the MAPs approach.
cfDNA from each sample was separated into two separate pools, and analyzed via NGS on a 56-gene panel as described in the analysis flow chart, Fig. 1. The NGS assay and ddPCR give complementary information for a complete overview of the molecular profile of the patients at all moments of the disease. With ddPCR BEAMing, we have a focused view but usually with higher sensitivity as we demonstrated it in clinical setting for RAS alterations in colon cancer cohort  1. The results here present data from a large, orthogonally validated liquid biopsy lung cancer dataset. The advantages in terms of additional actionable variant discovery by NGS as compared to both ddPCR and tissue sequencing were quantified.
Tissue mutational data was obtained from physician testing records. The sequencing panel contained 263 amplicons across 56 genes covering pan-cancer mutations of literature-cited clinical relevance (Supplementary Table  S1 ). The panel contained all of the variants tested by ddPCR. The ddPCR test method was chosen for comparison based on the fact that it has high sensitivity, covers the EGFR alterations of high clinical utility (Exon 18; 19; 20; 21), and has a low cost in comparison with commercial NGS tests such as Guardant360 (Guardant Health) and FoundationOne Liquid (Foundation Medicine). Additionally, the ddPCR test is an orthogonal method and thus less likely to mask inherent biases that might not be revealed by comparison between NGS-based tests. Gene-level mutational status for all patient samples with at least one mutation detected by liquid biopsy NGS is plotted in Fig.
6b ) shows concordance of AF values for sequencing versus ddPCR (Y = 1. 3), with an R 2 coefficient of 0. 85. Clinically-actionable mutations. Clinically-actionable mutations were defined as mutations with targeted therapies either approved or used as part of a clinical trial or a compassionate use basis (Supplementary Table  S3 )  13. These include EGFR mutations governing TKI treatment sensitivity and resistance and other mutations identified in Supplementary Table  S3. For each EGFR-positive sample, individual EGFR mutations are identified as a green (sensitizing) or red (resistance) marker in Fig. 7A. We detected all of the clinicallyactionable alteration types previously identified in tissue across EGFR exons 18-21 (Fig. 7b ). Prevalence of the EGFR mutations at initial diagnosis and progression was found to be within the expected range as reported in the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) database  17.
Variant detection performance. Orthogonal blood-to-blood concordance testing is ideal because it removes questions of tumor heterogeneity and time-dependent variability and tests sensitivity/specificity in a real-world setting. The clinically-approved ddPCR testing was performed independently of sequencing results, as part of the standard of care. Sequencing results were generated in a blinded manner and compared to ddPCR. We obtained excellent sensitivity (98. 5%) and specificity (98. 9%) when comparing the MAP/ERASE-Seq approach to ddPCR results (Table  1 ). It should be noted that while ddPCR was considered a truth table, ERASE-Seq and ddPCR perform similarly: Of 7 discordant calls, 4 calls present in sequencing data only were concordant with tissue data, indicating that real-life PPV is 97%, versus 91.
Many earlier liquid biopsy validation reports have focused on closely monitoring response to treatment in a relatively small number of patients  9, 18, 22  , or data collated from multiple cancer types  4, 8, 10. This data set has focused on a larger clinical data set in lung cancer cfDNA. In addition to the prevalence of commonly mutated genes covered by our 56 genes panel-TP53, EGFR and KRAS (Fig. 3 ), we also detected clinicallyimportant mutations of lower prevalence. A large dataset also enables a comparison of our liquid biopsy results to the somatic molecular signature of solid tumors with the same tissue of origin. The question asked was: is this a pan-cancer mutational profile, or is it specific to adenocarcinoma of the lung (over 80% of the patient population tested)? To that end, we mined the TCGA database to assemble similar unique mutation counts per gene for lung as well as melanoma, colon and bladder cancer.
g. mixed histology, never smokers) might also have been included in this screening upon approval by their local multidisciplinary tumor board. All samples and medical data used in the CIRCAN study were anonymized. Sample collection and processing protocols were approved by the regional ethics committee Lyon Sud Est IV (CPP L15-188 11/04/2015; amended by L16-160 09/21/2016) and French National committee in Informatics (CNIL 15-131 01. 12. 2015). Written informed consent for total blood sampling was obtained from all patients included in the study. All methods were carried out in accordance with relevant guidelines and regulations. cfDNA sample collection. Blood samples (3 × 10 mL) were collected in K2 EDTA tubes (BD, 367,525, 18 mg) within the framework of the CIRCAN routine patient management at Lyon University Hospital.
As a consequence, some clinical data are missing, since the justification of the prescription is not an obligation for the clinicians. Plasma samples from NSCLC patients were collected for the detection of somatic alterations in cfDNA in the setting of routine patient management at diagnosis (in cases where FFPE tissue was unavailable or biopsy was not contributive) or during disease progression. In France, EGFR T790M mutation detection in blood samples is the preferred method for tumor resistance genotyping in this setting. Blood sampling was performed (1) at diagnosis and (2) during progression alongside of regular follow-up CT-scan (usually performed quarterly). Extracted cfDNA was collected in one tube (60 µL), split for ddPCR, beaming and NGS assays. All extracted cfDNA was used. DNA was quantified by Qubit (Thermo Fisher Scientific).
Tissue data. Where available, tissue sequencing data was obtained from the treating physician. FFPE tumor samples were micro-dissected to select areas of the sample with the highest percentage of tumor cells and the smallest amount of normal tissue. Hence, samples were constituted of at least 15%-20% tumor cells. These samples were then analyzed using a customized AmpliSeq library and next-generation sequencing (PGM, Life Technologies, Carlsbad, CA, USA). Gene-level alteration presence/absence for the target mutations overlapping the ddPCR assays was reported and compared to cfDNA NGS and ddPCR data. At diagnosis, all tumor cases were histologically or cytologically confirmed on FFPE biopsy specimens and EGFR sensitizing mutation detection was performed either on FFPE tumor samples or using cfDNA in case of tumor tissue genotyping failure as part of routine practice.
No AL-56248). Sample loading was tuned to obtain a minimum of 5000 × read median depth per MAP. Fastq files, obtained by the demultiplexing of base-call files, were analyzed using the ERASE-Seq pipeline. Bioinformatics. All samples consisted of 2 MAPs, which were indexed as separate samples and sequenced at read depth above 5000 ×. The resulting fastq files generated by HCL laboratory were analyzed using the Fluxion Biosciences ERASE-Seq pipeline modified for a two-MAP data input and to maximize sensitivity/specificity for clinical use in lung cancer samples. Implementation and performance details for the general ERASE-Seq variant caller have been previously published  14. Briefly, residual adapters and primers were trimmed using Trimmomatic  34  and Cutadapt  35. Cleaned reads were aligned to hg19 (GRCh37) using the MEM algorithm of BWA  36.
For indel calling, base indels were realigned using GATK  37. LoFreq 38 was used to identify all possible variants for the two sample MAPs and background reference sample MAPs. Custom Perl scripts were then used to parse pile-up data into a matrix containing read counts for each sample and control replicate at each panel variant, and the data matrix was then processed in R, using a negative binomial test to quantify the significance of enrichment between variant count observations in sample and control replicates  14. ERASE-Seq uses this model to assign a confidence score (p-value) to each possible sample variant call. For each possible variant, if the multiple testing-corrected p-value is above a cutoff/threshold α (here, 0. 05), then the null hypothesis cannot be rejected, suggesting no mutation present in the sample as compared to control runs.
Cancer-specific prevalence was determined using the total number of verified somatic variant observations in the Catalogue of Somatic Mutations in Cancer (COSMIC) database  39. Ultralow calls at 0. 1% AF and above were made for variants with over 100 verified somatic observations (258 SNVs); these included the 3 variants covered by the orthogonal ddPCR tests (DelEx19, L858R, and T790M). High local background for T790M mutations in our training set prompted an increase to 0. 13% AF for this variant only. Calls at 0. 2% AF and above were made for variants with over 5 COSMIC observations (1,186 SNVs). The remainder of approximately 100,000 variants were called if an AF above 1. 0% was observed. In addition to the detection of clinically-actionable variants, this approach covered other possible variants with associated clinical value at high sensitivity.
The same pipeline parameters were then fixed and applied in a blinded manner to the full 356-sample set. Sample inclusion criteria were defined as all samples meeting a minimum 5000 × per MAP read depth, and were included in the concordance analysis via ddPCR. For tissue concordance, some previous studies report overall concordance  6, 7  , while others exclude samples based on the absence of ctDNA as defined by no alterations being detected  8. This can lead to disparate conclusions from the same data set. We report both metrics when describing tissue concordance. The assay limits of detection (LOD) are defined as the lowest variant detected and may be much lower than the high > 95% confidence allele frequency (AF) range. Comparison to ddPCR data was performed within the high-confidence AF range for that specific variant, generally above 0.
Cells were treated for 4 h with rapamycin, LNC-rapa at 1 µM (IC50 LNC-rapa at 21% O 2 corresponding to a 1/1,000 dilution from initial suspension) and with empty LNCs at the same dilution than LNC-rapa. 8Gy radiation was performed 6 h after the treatment by Faxitron CP-160. Ten days after treatment, colonies were colorized by crystal violet and their number was evaluated with ImageJ Software version 1. 43. Depending on the condition considered, the cells were placed throughout the experiment in a conventional 21% O 2 incubator at 37 • C/5% CO 2 (normoxia) or 0. 4% O 2 (hypoxia) at 37 • C/5% CO 2 in an InVivO2 400 SCI-tive hypoxia workstation (Ruskinn); they are only placed in isolated flasks for the duration of the irradiations.