dataesr/scientific-paragraphs-categorization huggingface dataset:
The Jain Foundation has provided an estimated US$4 million to fund this study. MRC funding of the John Walton Muscular Dystrophy Centre also supports this work. (Grant number MR/LK000608/1). disclaimer The views expressed in this submission are the authors' own and not the official position of their institutions or funding bodies. competing interests None declared. ethics approval NReS Committee North east-Newcastle and North Tyneside 2. The trial was also registered at ClinicalTrials. gov (NCT01676077). Provenance and peer review Not commissioned; externally peer reviewed. open access This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4. 0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited.
The vorticity produced by the blood and the systole and diastole movements can produce disturbances in the electrical activity measurements, making it difficult to determine diseased areas on the surface of the heart, especially if the criterion of a static threshold is considered as a reference for decision making. In the present work, data exploration was carried out with the purpose of constructing contact models based on the force signal, as a criterion to determine if the catheter is in contact with the tissue. We scrutinize patterns such as pulsatility or trends in the force signals recorded during the electrophysiological examination of a patient, by considering a time window around the EGM maximum peak used for the static force threshold. The Principal Component Analysis (PCA) techniques are used to provide us with a multivariate decomposition of the most characteristic force signal profiles, and linear classification is used to determine their relevance.
The patient database used for this purpose consists of 1391 force signals and their corresponding EGM, which were recorded during 2 and 8 s, respectively, from 17 patients (82 ± 60 points per patient) at electrophysiological study. The outline of the document maintains the following structure. In Section 2, the characteristics of the patient base, the PCA fundamentals, and the used linear classifiers are summarized. Section 3 describes the experimental methods carried out and the obtained results. Section 4 deals with the discussion, future work, and conclusions of our findings.
Database and Patients The patient data used in this work comes from a previous clinical study  [13] , carried out at the Arrhythmia Unit in University Hospital Virgen de la Arrixaca (Murcia, Spain). That study was approved by the Ethics Committee of the hospital, and recorded in the clinicaltrials. gov register of clinical trials under identification number NCT01639365. It involved the acquisition in each patient of a left ventricular voltage map, during sinus rhythm and using the Carto R 3 navigation system and the Navistar Thermocool catheter (Biosense-Webster, Diamond Bar, CA, United States). The population consisted of 17 patients (8 men, mean age 62, range 38-80 years) with ventricular arrhythmias due to ischemic cardiomyopathy or dilated cardiomyopathy, who underwent ventricular tachycardia ablation by clinical indications, and their clinical description is included in Table  1.
For each patient, a voltage map was first generated without accounting for the force signal. Then, the map was corrected by discarding those points with force below threshold, which yielded more compact voltage maps allowing to better identify arrhythmia channels and ablation targets. The same set of patients and signals was used in this work. The Carto R 3 System records the electrophysiological examination data in plain text format. Extract, Transformation, and Load techniques were used on these files  [18, 19] , allowing the construction of a database in which anonymized patient identification, case identification, EMG data, and force sensor data were stored. This database facilitated the processing of the acquired signals with a custom-developed computer tool, as well as a subsequent manual data classification, called labeling, where the doctor-issued criteria were followed.
The developed computer tool consists of a dashboard with 3 panels, as shown in Figure  1. The first panel plots two signals on a shared axis, namely, the 2 s of EGM signal (in blue), and the peak heartbeat selected within a window of ±80 ms (in red). The second panel represents the force sensor signal, which lasts 8 s, and it is synchronized with the EGM signal after 6 s, as seen in the figure. A dashed line represents the 5-gr threshold to be used as a reference by the observer. The third panel provides an additional view of the force signal after filtering and rectification, aiming to better support the labeling in some doubtful cases. Control buttons allowed the label selection, as well as the step forward (») and step back («) to review the cases. The result of this process was stored in a new label field in the database, which was specific to each case.
The amount of information conveyed in a component is measured by its variance, which can be used as an indicator to sort them in terms of their relevance, this is, the larger the variance associated to a component, the larger the amount of information in that component. In statistics, PCA is often used to reduce the dimensionality of a data set, which makes it possible in our case to distinguish among the most relevant dynamic changes in the system, the redundant information, and the noise in the signal  [20]. This analysis defines a new coordinate space where the variance is maximized and the correlation among the variables is minimized. In our case, we measure a force signal during 8 s at a given anatomical location of the heart, which is represented by a signal vector x m ∈ R M , where M is the total number of signal samples in 8 s.
Given a set of training data that contains N observation vectors (corresponding to the aggregation of all the cases for every patient in the database), the array X ∈ R N×M is constructed with the observed vectors by rows, which represents the original data matrix. The covariance matrix C XX ∈ R M×M can be calculated from X in the following way, C XX = 1 N-1 X X. By decomposing this matrix in its eigenvalues and eigenvectors, the correlation structure of the variables is obtained, C XX = V ΛV , (1) where Λ ∈ R M×M is a diagonal matrix which contains the non-negative real eigenvalues of C XX , this is, λ m , m = 1, 2, • • • , M, and they are sorted in decreasing order; and matrix V ∈ R M×M conveys the sorted eigenvectors in columns. By holding only the a eigenvectors (a < M) corresponding to the a largest eigenvalues, we can build the so-called reduced principal component matrix P ∈ R M×a , which projects the observation vector x ∈ R M into a smaller space R a , and it can be denoted by t a = xP.
Experiments and Results As previously described, our database consisted of 17 patients with a total of 1391 cases (82 ± 60 points per patient). The points in each case were tagged by the expert cardiologists. This database was randomly divided, in such a way that 80% (20%) of the data were in the training (test) set. The PCA was applied to the training group, and the projected vectors resulting from this analysis were used to train the Fisher and SVM classifiers. The trained classifiers were subsequently tested with the test group. Each process is specifically described below.
In addition, a new type of pattern can be seen, which consists of pulsatility that is modulated with its amplitude increasing to the end of the time interval (eigendirections 1 and 4). Again, the signal is estimated with acceptable quality by using 8 eigenvectors. A moderate signal-to-noise can be observed now in the force signals in this group. The eigenvalue profile in the likely non-contact group is also similar to the high-confidence non-contact group, though the first eigenvalue (corresponding to the group average) is in this case larger. Now, the first relevant eigendirections also include mixed patterns of trends with some presence of pulsatility (e. g. , as in eigendirections 3 and 4). After analyzing the patterns of the different contact and non-contact groups separately, we examined the patters of the complete database, as seen in Figure  4.
On the one hand, the likely-contact group (all cases together) shows an eigenvalue profile which is similar to the previous ones (and more similar to the contact subgroups). Current relevant patters in this complete set include a mixture of purely pulsatile waveforms (eigendirection 5), pulsatility with increasing amplitude (eigendirections 1 and 4), trends with some pulsatility (eigendirections 3 and 6), and just trends (eigendirection 2). On the other hand, in the high-confidence contact group (cases with labels 0 and 3), eigendirections 1 and 2 are very similar to the ones in the complete dataset, and the patterns include purely pulsatile waveforms (eigendirections 3 and 5), pulsatility with increasing amplitude (eigendirection 1), trends with small pulsatility or pulsatility at the end of the signal (eigendirections 2, 4, and 6).
In addition, in general they are visibly smaller and distributed around zero in the high-confidence non-contact cases. With respect to the likely-contact cases (lower panel), the first weight is again prominent in the contact cases, but it is also noticeably higher than in the non-contact cases. The other weights (2 to 10) consist both on small values together with some larger positive and negative weights, but the number of larger weights seems to be higher in the contact cases. Accordingly, differences exist in the weights of contact and non-contact cases, but they cannot be immediately used quantitatively in order to discriminate if the catheter is in contact or not. Moreover, it is desirable to quantify how relevant each of the eigendirections are in the database. Representation of the weights given by the projection of each force signal case on the first 10 eigenvectors, for the contact (blue) and non-contact (red) cases, for a = 10, in the high-confidence (up) groups and on the complete database (down).
The oval emphasizes that the differences are more visually patent on the first four weights, in such a way that larger variance can be observed on them. Nevertheless, the other components also exhibit statistical differences, which can be less present to the naked eye, but can be exploited from statistical data processing techniques. For this purpose, we used the projected vectors of the signal force cases (again with a = 10) to build several linear classifiers, which were trained and tested with 80% and 20% of the database cases, respectively. Note that only the training samples were used to perform the PCA projection matrices. This procedure was repeated 10 times, which allowed us to scrutinize the distribution of the estimations for each classifier weight. The classification results for this experiment are shown in Table  2 , where the Area Under the Curve (AUC), is shown for the two classifiers applied to the different contact groups.
Figure5. Representation of the weights given by the projection of each force signal case on the first 10 eigenvectors, for the contact (blue) and non-contact (red) cases, for a = 10, in the high-confidence (up) groups and on the complete database (down). The oval emphasizes that the differences are more visually patent on the first four weights, in such a way that larger variance can be observed on them. Nevertheless, the other components also exhibit statistical differences, which can be less present to the naked eye, but can be exploited from statistical data processing techniques.
Funding: This work has been partly supported by  Research Grants PRINCIPIAS and FINALE  ( TEC2013-48439-C4-1-R  and  TEC2016-75161-C2-1-R ) from  Spanish Government ,  Research Grant PRICAM  ( S2013/ICE-2933 ) from  Comunidad de Madrid , supported in part by the  Universidad de las Fuerzas Armadas ESPE  under Research Grant  2015-PIC-004.
Material and Methods 10 patients treated for breast cancer with lymph node involvement were selected. Prescription dose was 63 Gy to the tumor bed, 51. 8 Gy to the whole breast and 50. 4 Gy to the lymph nodes in 28 fractions. HT_FW_5, VMAT and PT plans were designed and compared to the clinically approved plan (HT_FW_2. 5) using dosimetric indices for OAR (Dmax for the spinal cord, Dmean for the heart, both lungs and contralateral breast) and PTV (D95%, D2% and Homogeneity Index). A paired Student's t-test (α=0. 05) was used to cross-check all plans. For all plans, a homemade dosimetric score was computed (SOAR and SPTV). Based on previous data gathered through the years, OAR results were divided into four quartiles: Q1 (SOAR=1), Q2 (SOAR=2), Q3 (SOAR=3) and Q4 (SOAR=4). PTV scores are based on the D95%.
INTRODUCTION Genotype imputation enables the integration of genome-wide data for consortia research, improves genotype density for finemapping, and can result in considerable cost-savings. But does this translate into imputation of disease-relevant haplotypes? Cystic fibrosis (CF) (MIM: 219700) is caused by dysfunction of the CF transmembrane conductance regulator (CFTR [MIM: 602421]). CFTR displays substantial allelic heterogeneity with greater than 300 variants reported to be disease-causing  1  with some relatively frequent (e. g. the p. Phe508del 3 bp in-frame deletion; 70% of European CF chromosomes) but most spanning the rarer frequency spectrum (e. g. p. Gly551Asp; 2. 11% of CF chromosomes according to the Clinical and Functional Translation of CFTR database or CFTR2; https://www. cftr2.
Motivation We initially used the 1000 Genomes (1KG; 2504 individuals) 3 as reference to impute chromosome 7 for 1995 CF participants from the International CF Gene Modifier Consortium. 4  The most common CF-causing allele p. Phe508del (g. 117199646-117199648delCTT; hg19) was imputed with good accuracy (r 2 = 0. 77; r 2 is a predicted correlation measure between the imputed and true genotypes). However, all other CF-relevant variants with allele frequencies >1% in CF populations based on the CFTR2 database, 2 such as p. Gly551Asp (g. 117227860 G > A), p. Trp1282Ter (g. 117282620 G > A), p. Gly542Ter (g. 117227832 G > T), p. Asn1303Lys (g. 117292931 C > G), and p. Arg117His (g. 117171029 G > A) were not present in the 1KG reference panel; consequently they were not imputed and their corresponding CF-relevant haplotypes would be excluded in the genetic association analysis.
Failure to impute population-specific genomic variation has previously been highlighted,  5, 6  and the use of study-specific reference panels has recently been shown to improve imputation accuracy. 7, 8 We considered imputation of chromosome 7 using two alternative reference panels: (1) the Haplotype Reference Consortium (HRC)  9  resource of 32,470 individuals, to improve the probability of having as many population haplotypes as possible including CF causing haplotypes; and (2) a hybrid reference that combines the 2504 individuals from the 1KG with 101 Canadian individuals with CF sequenced at high read depth (30×), to provide enrichment with disease-specific haplotypes. The 101 Canadians with CF chosen for sequencing had severe CFTR genotypes that was representative of the Canadian CF population, and had comprehensive clinical data spanning several decades.
Phasing and imputation using the HRC The chromosome 7 VCF file generated after genotyping quality control was strand-aligned using the suggested HRC-1000G-check-bim. pl script with the -n option to turn off the removal of variants due to MAF differences between the reference panel and the CF genotyped sample. For the full chromosome 7, only 89 variants were removed due to no matches found in the HRC reference, and this file was submitted to the Michigan Imputation Server 10 (https://imputationserver. sph. umich. edu; submission date December 20th, 2017) for imputation using the HRC r1. 1  9  reference panel.
ACKNOWLEDGEMENTS Genome-wide genotyping of subjects in North America were provided by the  US and Canadian CF  foundations. We thank the  US CF Foundation  for the use of  CF Foundation Patient Registry data. We thank the patients, care providers and clinic coordinators at CF Centers throughout the  US and Canada  for their contributions to the  CF Foundation Patient Registry  and  Canadian Gene Modifier Study. We thank the  Canadian Genome Sequencing Resource  for CF;  M. Corey ,  R. Dorfman ,  A. Sandford ,  P. Pare ,  Y. Berthiaume  ( Canadian Gene Modifier  recruitment); and  The Centre for Applied Genomics, The Hospital for Sick Children. The funding was provided by:  Canadian Institutes of Health Research (CIHR ;  MOP-258916  to LJS); the  Natural Sciences and Engineering Research Council of Canada (NSERC ;  371399-2009  to LJS);  Cystic Fibrosis Canada  # 2626  (to LJS).
HRC reference panel Sensitivity(%), Specificity(%) CFTR variant a Frequency in European Frequency in CF c Number of alleles in 101 d Number of alleles in 1995 HRC reference Hybrid reference HRC b /1KG clinical records clinical records (Minimac r 2 ) (Minimac r 2 ) rs113993960 (p. Phe508del) NA / 0. 40% 69. 86% 159 2255 -98. 5, 88. 2 (0. 914) rs75527207 (p. Gly551Asp) 0. 028% / NA 2. 11% 2 106 70. 8, 99. 93 (0. 892) 94. 3, 99. 7 (0. 927) rs77010898 (p. Trp1282Ter) 0. 010% / NA 1. 22% 6 60 28. 3, 100 (0. 730) 96. 7, 99. 8 (0. 964) rs113993959 (p. Gly542Ter) 0. 025% / NA 2. 54% 7 116 20. 7, 100 (0. 738) 70. 7, 99. 93 (0. 874) rs80034486 (p. Asn1303Lys) NA / NA 1. 57% 2 48 -4. 2, 100 (0. 380) rs78655421 (p. Arg117His) 0. 224% / NA 1. 31% 0 18 100, 99. 97 (0. 929) - a The CFTR variants chosen for comparison are reported as having a frequency of at least 1% in the Clinical and Functional Translation of CFTR database (CFTR2; https://www.
The lipid profile of type 2 DM is characterized by reduced high-density lipoprotein cholesterol (HDL-C), high triglycerides, and an increase in the proportion of LDL-C particles that are small and dense, with or without high levels of LDL-C  [12, 13]. In the ODYSSEY DM-INSULIN study (NCT02585778), alirocumab 75 mg every 2 weeks (Q2W; with possible dose increase to 150 mg Q2W) significantly reduced LDL-C levels and other lipids in individuals with type 1 DM (n = 76) or type 2 DM (n = 441) treated with insulin  [14]. Furthermore, in individuals with type 2 DM and mixed dyslipidemia, the same alirocumab dosing regimen also resulted in significant reductions in non-HDL-C, LDL-C, and other lipids in ODYSSEY DM-DYSLIPIDEMIA (NCT02642159)  [15]. However, the efficacy and safety of alirocumab 75 mg Q2W (with possible dose adjustment to 150 mg Q2W) and 150 mg Q2W (without dose adjustment) have not been compared in a larger pool of individuals with and without DM treated for longer duration.
This subgroup analysis of five placebo-controlled phase 3 studies (LONG TERM [NCT01507831]  [16] , HIGH FH [NCT01617655]  [17] , COMBO I [NCT01644175]  [18] , FH I [NCT01623115]  [19] , and FH II [NCT01709500]  [19] ) aimed to compare the efficacy and safety of alirocumab in a large group of individuals with and without DM at baseline, with the primary efficacy endpoint being LDL-C reduction from baseline to week 24.
Trial Participants and Study Designs The current analysis used patient-level data from study participants according to alirocumab dosing regimen who enrolled for five double-blind, randomized, placebo-controlled ODYSSEY phase 3 studies with 52-78 weeks' treatment duration. Methods for each of the individual studies have been previously reported  [16] [17] [18] [19]. In summary, all participants, with or without DM, had hypercholesterolemia at study entry and were on maximally tolerated, stable, background statin therapy with or without other lipid-lowering agents. The FH I and FH II studies recruited participants with heterozygous familial hypercholesterolemia  (HeFH)   All study protocols were approved by the appropriate institutional review boards, and all participants provided informed, written consent.
Changes from baseline for the main LDL-C efficacy endpoint and other lipid values were statistically evaluated with an intention-to-treat (ITT) approach, which included lipid data from all randomized persons regardless of adherence to treatment. The analysis utilized a mixed-effect model with repeated measures to account for missing data, as previously described  [20]. Data on changes over time were presented according to ITT analysis, and on-treatment analysis using a modified ITT approach, which included only lipid data collected while the individual was receiving study treatment. Other lipid parameters were analyzed either in the same way as the main efficacy endpoint or, in the case of lipoprotein (a) [Lp(a)] and triglyceride (TG) percentage changes, and LDL-C goal achievement proportions, analysis involved a multiple imputation approach then robust regression (for Lp(a) and TG percentage changes) or logistic regression (for LDL-C goal achievement), in the ITT population.
Safety Analysis Safety data are reported in subgroups of individuals assigned to alirocumab or placebo, regardless of alirocumab dose, according to baseline DM status. All adverse events, regardless of seriousness and irrespective of potential relationship to alirocumab, were recorded by the investigators up to the last visit planned in the protocol. Treatment-emergent adverse events (TEAEs) were defined as events that developed, worsened, or became serious between the first and last dose of study treatment plus 70 days, classified according to the Medical Dictionary for Regulatory Activities. Adverse events of special interest included injection-site reactions, general allergic events, neurocognitive disorders, and adjudicated cardiovascular events. Statistical analysis of the safety population included all randomized individuals who received at least one dose or part of a dose of study drug, and safety data were analyzed by descriptive statistics.
As a consequence, safety results should be considered in the context of the overall ODYSSEY program. Fewer injection-site reactions were reported in individuals with DM, a difference that may in part be due to a greater familiarity and tolerance associated with glucometer and/or injectable medication use in the DM population (27. 8% and 72. 2% of persons with DM were receiving antihyperglycemic injectable and non-injectable medication at baseline, respectively). However, as previously reported, administration by injection does not deter persons from self-administration of alirocumab  [23]. The ODYSSEY OUTCOMES study (NCT01663402) will help to further establish if DM status has an influence on the frequency of injection-site reactions  [24]. Mean HbA1c and FPG measurements in individuals with DM were comparable between alirocumab and placebo for up to 78 weeks of treatment.
ACKNOWLEDGEMENTS The authors would like to thank study individuals and investigators, and the following persons from sponsors for their contributions to data collection and analysis, assistance with statistical analysis, or critical review of the manuscript:  Regeneron Pharmaceuticals Inc. :  Eva-Lynne Greene ,  Carol Hudson ,  Rita Samuel , and  Desmond Thompson ; Sanofi:  Corinne Hanotin ,  Michael Howard ,  L. Veronica Lee ,  Guillaume Lecorps ,  Laurence Merlet ,  Christelle Lorenzato , and  Jonas Mandel.