dataesr/scientific-paragraphs-categorization huggingface dataset:
Eligibility Eligible patients were aged 18 years or older and had a LB and a FibroScan examination performed within 6 months. Additionally, a single blood collection with all the required biological parameters was available within 6 months of the LB and 1 month of the FibroScan examination. Patients who met the following criteria were excluded: non-metabolic comorbidities that could have induced liver disease such as viral hepatitis, drug-induced liver injury, excessive alcohol consumption, or HIV; less than eight valid measurements for LSM by VCTE; 7 missing data for the variables needed in the developed scores and for the fibrosis stage. Furthermore, in case of patients assessed with both M and XL probes (8. 5% of patients from the TS and 8. 1% of patients from the internal VS), the FibroScan examination corresponding to the XL probe was only considered when the patients' BMI was greater or equal to 35 kg/m 2.
Sensitivity analyses AUROCs of both the scores for patients with BMI > -30 kg/m 2 vs. BMI <30 kg/m 2 , with steatosis severity S0/S1 vs. S> -2, with vs. without diabetes and with LSM measured with M vs. XL probes were compared to evaluate the impact of obesity, steatosis, diabetes, and probe on Agile 4 and Agile 3+. Since the predictive values depend on the target prevalence, a sensitivity analysis was carried out in order to assess the impact of prevalence on the predictive values at given sensitivity and specificity and therefore at a fixed cut-off. Prevalence of AF varied from 0. 05 to 0. 55 and that of cirrhosis from 0. 02 to 0. 25. Statistical analyses were performed using the R software version 3. 6 and subsequent  20  Packages pROC,  21  glmnet  22  and mfp  23  were used to develop and study the performances of the models.
Patient characteristics The internal dataset consisted of 2,134 patients (flowchart in Fig. S1 ), of whom 1,434 were in the TS (to construct the scores) and 700 were in the internal VS. As expected, the TS and the internal VS had similar characteristics in terms of collected parameters and distribution of fibrosis stages (Table  1 ). 5] [26]  For external validation, the NASH CRN cohort comprised 585 patients, of whom 13% had cirrhosis and 37% had AF. The French NAFLD cohort comprised 1,042 patients and was very similar to the NASH CRN cohort: 13% had cirrhosis and 38% had AF. Both NASH CRN and French NAFLD cohorts correspond to the intended use population, so for the TS and the internal VS, PPV and NPV were adjusted using a prevalence of 13% for cirrhosis and 37% for AF. As reported in Table  1 , the TS and the internal VS had broadly similar demographic, metabolic, and serological characteristics to the external VS.
Moreover, the reduction in the numbers of cases with indeterminate results with Agile 4 in all datasets was substantial compared to those achieved using FIB-4 or LSM. Finally, an improvement in the identification of patients with cirrhosis using Agile 4 was observed. The sensitivity in the rulein zone was higher than that achieved with FIB-4 or LSM in the TS, the internal VS and the NASH CRN cohort. Moreover, the PPV for Agile 4 increased in all datasets. Results of the performances of high specificity (99%) cut-off values for the diagnosis of cirrhosis are presented in the supplementary information (Fig. S14 , Table  S12 ).
Overall diagnostic performances As for Agile 4, for all datasets, the calibration lines of Agile 3+ (Fig. S8-10 ) were also close to the ideal calibration, which indicates an excellent goodness of fit of predicted probabilities of AF. Excellent discrimination of Agile 3+ was observed with AUROCs around 0. 9, significantly different from those of LSM and FIB-4 in the TS, the internal VS and the NASH CRN cohort (Table  2 , Table  3  and Fig. S11 ). Furthermore, decision curves (Fig. 1C ,D and Fig. S12 ) suggest that Agile 3+ is a better option compared to FIB-4, LSM alone (except for the French NAFLD cohort) or even treating all patients as having AF since it has the highest net benefit across the range of threshold probabilities (0. 0; 0. 5). On the French NAFLD cohort (Fig. 1D ), Agile 3+ has the highest net benefit across the range of threshold probabilities between 0.
Dual cut-off approach It was decided to select a rule-out cut-off that achieved sensitivity of > -85% and a rule-in cut-off that achieved specificity of > -90% for the diagnosis of F > -3 (Table  2 ). Thus, the cutoff values of Agile 3+ were 0. 451 and 0. 679 for rule-out and rule-in, respectively, characteristics detailed in Table  2 , Table  3  and Fig. 4. No more than 18% of cases had indeterminate results in all datasets with Agile 3+. Moreover, an improvement of the proportion of patients correctly/accurately ruled out with Agile 3+ with high specificities compared to FIB-4 and LSM was observed in the TS and in the internal VS. However, in both external VSs, this increase was confirmed only when comparing Agile 3+ to FIB-4. Finally, a small improvement of the identification of patients with AF was observed.
The sensitivity in the rule-in zone was indeed higher than those of FIB-4 and LSM in all datasets and the PPV slightly increased in the TS and the internal VS. Nevertheless, in both external VSs, even if an improvement of the PPV compared to FIB-4 was observed, the PPV of LSM was higher or equal to that of Agile 3+. Results of the performances of FIB-4 and LSM using published cut-off values  18, 19  vs. Agile 3+ for the diagnosis of AF are presented in Table  S13.
This study has the following strengths. Firstly, the scores were derived from a large cohort of 1,434 patients recruited in secondary/tertiary liver clinics from North America, Europe and Asia. Secondly, the study was able to validate the scores in three other large cohorts: (i) an internal VS made from the remaining third of the initial global pool of patients not used for the TS, (ii) a large subset of patients from the NAFLD Adult Database 2 of the NASH CRN conducted in eight expert centers in the USA and (iii) a large cohort of patients from three expert centers in France. This contributed to limit the overfitting. Moreover, the shrinkage factor used to determine the sample size was a priori defined at 0. 9 (close to 1), high enough to minimize potential model overfitting. Thirdly, these scores were developed using widely available routine biomarkers.
By doing so and making the score formula public and available through an app and a website, we aim to make the scores easily and readily accessible without additional cost, at the same time as LSM by VCTE is obtained. Nevertheless, we also compared in all the datasets, the performances of two scenarios: (i) Agile scores calculated for all patients, or (ii) patients first undergo LSM by VCTE then Agile is performed only on patients who are either ruled-in or indeterminate with LSM (Figs S15 -17). The results show that, compared to Agile scores alone, sequential use of LSM by Agile scores slightly increases the number of patients ruled out and slightly decreases the number of cases with indeterminate results while improving PPV. However, there are some limitations to this study. LSM by VCTE, for which access is limited across the globe, is needed for the computation of the scores.
74]/ 0. 90 0. 90 0. 90 [ 0. 90 0. 90 0. 91 [0. 88-0. 92] [0. 88-0. 92] 0. 88-0. 92] [0. 87-0. 93] [0. 87-0. 93] [0. 88-0. 94] PPV 0. 76* 0. 78* 0. 81* 0. 77* 0. 77* 0. 81* FIB-4, fibrosis-4 index; LSM, liver stiffness measurement; NAFLD, non-alcoholic fatty liver disease; NPV, negative predictive value; PPV, positive predictive value; Se, sensitivity; Sp, specificity. AUROC comparisons were performed using the Delong test (at a two-sided 5% significance level). *Due to the high prevalence of cirrhosis and AF in the TS and the internal VS, PPV and NPV for these datasets were adjusted on the prevalence of external validation, i. e. F4 = 13% and F > -3 = 37%.
The combination of these strategies can reduce discomfort levels by up to 50% in temperate climates, 31% in semi-arid climates and 6% in desert climates. A dynamic simulation, using TRNSYS software, was carried out on a building in Longwy (Lorraine, North-East France) by Mnasri et al. [55]  to evaluate the cooling and heating needs. Various insulation scenarios were investigated. They showed that insulation significantly reduces the total energy demand, but it does not result in heat removal during the summer season, so they concluded that a natural ventilation system is recommended to increase the building's energy efficiency. According to Li et al. [58] , wood-cement composite materials can contribute to improve indoor indoor thermal comfort and are useful in the building of low-energy buildings.
3% decrease in the building's annual energy consumption as compared to traditional masonry blocks. DPA content in masonry blocks, according to these writers, is a promising choice for reducing thermal stress within the house, as shown in Fig. 9. Mokhtari et al. [78]  investigated the impact of using a new natural durable insulation material available semi-hydrated gypsum/date palm fibre (HG-DPF) for exterior walls on the interior thermal behaviour under the arid climatic conditions of the city of Bechar using TRNSYS simulation software under stable periodic conditions (southern Algeria). Several configurations of exterior walls are proposed. These configurations are based on the alteration of two aspects: wall Fig. 9  Average monthly variation of indoor temperature for various wall options  [77]  building materials using stabilised earth blocks (SEB) and clay bricks, and insulation materials using polystyrene and a new referenced biocomposite insulation material (HG-DPF).
Bicuspid aortic valve (BAV), the most common congenital heart defect, is a major cause of aortic valve disease requiring valve interventions and thoracic aortic aneurysms predisposing to acute aortic dissections. The spectrum of BAV ranges from early onset valve and aortic complications (EBAV) to sporadic late onset disease. Rare genomic copy number variants (CNVs) have previously been implicated in the development of BAV and thoracic aortic aneurysms. We determined the frequency and gene content of rare CNVs in EBAV probands (n = 272) using genome-wide SNP microarray analysis and three complementary CNV detection algorithms (cnvPartition, PennCNV, and QuantiSNP). Unselected control genotypes from the Database of Genotypes and Phenotypes were analyzed using identical methods. We filtered the data to select large genic CNVs that were detected by multiple algorithms.
Findings were replicated in cohorts with late onset sporadic disease (n = 5040). We identified 34 large and rare (< 1:1000 in controls) CNVs in EBAV probands. The burden of CNVs intersecting with genes known to cause BAV when mutated was increased in case-control analysis. CNVs intersecting with GATA4 and DSCAM were enriched in cases, recurrent in other datasets, and segregated with disease in families. In total, we identified potentially pathogenic CNVs in 8% of EBAV cases, implicating alterations of candidate genes at these loci in the pathogenesis of BAV.
Samples were collected and genotyped similar to our previous study  [21]. For comparison, we analyzed a cohort of older individuals of European ancestry with sporadic BAV disease selected from the International BAV Consortium (Table  1 )  [22]. Phenotypes were derived from record review with confirmation of image data whenever possible  [23] [24]. The computational pipeline for CNV analysis of Illumina single nucleotide polymorphism (SNP) array data included three independent CNV detection algorithms (Fig  1). SNP, single nucleotide polymorphism. QC, Quality control. CNV, copy number variant. The software and algorithms used for the analysis are provided in boxes to the left of the corresponding steps. Illumina raw signal intensity data was trimmed and exported using GenomeStudio. The intensity data was then analyzed with three different CNV calling algorithms (PennCNV  [25] , cnvPartition, and QuantiSNP  [26] ) to generate initial CNV calls and sample-level statistics.
We identified 7 additional CNV regions that are enriched in BAVGWAS cases but not in EBAV and are rare or absent in controls (Table  S5 ). NANOG and NIBPL are essential for early heart development, and mutation of NIBPL causes Cornelia-de Lange syndrome with a spectrum of congenital heart malformations including BAV. We also identified 21 very large genomic CNVs more than 5 Mb in length in the BAVGWAS dataset. Analysis of GenomeStudio data showed that most of these were mosaic loss of heterozygosity regions or duplications. Nine were large germline chromosome-scale aberrations, including two cases of trisomy 21 (Table  S8 ). We did not identify any large X chromosome copy variants that may be consistent with Turner syndrome. There were no megabase-scale copy number variants in the EBAV dataset.
Discussion We identified large, rare, and likely pathogenic CNVs in almost 10% of EBAV probands that are enriched in genes that cause BAV when mutated. The percentage of EBAV cases with likely pathogenic CNVs is similar to our previous observations in a cohort with early onset TAD  [30]. Enrichment of CNVs involving GATA4 and DSCAM in EBAV cases replicated in two additional BAV datasets and thousands of unselected control genotypes. This analysis provides compelling evidence that rare CNVs collectively cause more BAV cases than any single mutated gene. GATA-Binding Protein 4 is a transcription factor that is required for cardiac and neuronal differentiation during embryogenesis  [31]. Mutations of GATA4 and its homologs GATA5 and GATA6 cause congenital heart lesions  [32]. Mutations in the GATA4 gene have been linked to a range of congenital heart diseases in humans, such as cardiac septal defects, tetralogy of Fallot, amd patent ductus arteriosus  [33].
These results are consistent with our observation that pathogenic CNVs involving candidate BAV genes are also enriched in EBAV compared to BAVGWAS cases. Our data suggests that pathogenic CNVs at these loci may predict accelerated disease onset or more severe complications. We also identified recurrent rare CNVs of specific dosage-sensitive regions that affect cardiac developmental genes and are implicated in non-syndromic CHD. Recurrent 1q21. 1 distal deletions encompassing GJA5, the gene encoding Connexin-40, are associated with CHD lesions including BAV. A study of 807 TOF cases showed significant enrichment of small duplications spanning the GJA5 gene, providing compelling evidence that it acted as the primary candidate gene, supporting the association of GJA5 and CHD  [31]. Additionally, cardiac abnormalities have been documented in mice with a targeted GJA5 deletion, implying that haploinsufficiency of GJA5 might contribute to cardiac defects in individuals affected by 1q21.
Our combinatorial analysis method eliminated many CNVs that were detected by single algorithms or did not meet quality control benchmarks. Therefore, our analysis likely underestimated the contribution of rare pathogenic CNVs to BAV. We also recognize that cardiac development involves the complex interaction of many genes. We selectively validated individual CNVs at loci of interest but may have underrepresented CNVs that had no a priori relationship with CHD. The apparent penetrance of some CNVs may be less than expected due to missing phenotypic information. The available clinical data was not sufficiently detailed to permit genotype-phenotype correlations with specific CHD clinical features. In conclusion, we identified large rare CNVs in a significant proportion of BAV cases, including a subset of CNVs that may predict early onset complications of BAV disease.
Acknowledgments We thank  Joana Castillo  and  Jacqueline Jennings  for sample preparation, and  Gladys Zapata ,  Nitesh Mehta , and the  Laboratory for Translational Genomics at Baylor College of Medicine  for microarray genotyping. This study was supported in part by  R01HL137028  (SP). Fig 1 created with  BioRender. com. The EBAV Investigators are:  Shaine A. Morris ,  Rita Milewski ,  Giuseppe Limongelli ,  Allesandro Della Corte ,  Laura Perrone ,  Yuli Y. Kim ,  Hector Michelena ,  Maria G. Andreassi ,  Arturo Evangelista ,  Denver Sallee ,  Anji Yetman ,  Kim McBride ,  Eduardo Bossone ,  Rodolfo Citro ,  Dawn S. Hui ,  Malenka M. Bissell ,  Andrea Ballotti ,  Ilenia Foffa ,  Margot De Marco ,  Anthony Caffarelli ,  Rita Weise ,  Julie DeBacker ,  Laura Muino Mosquera ,  Robbin Cohen ,  Laura Dos Subira ,  Justin T.
OR: odds ratio; 95% CI, 95% confidence interval for respective odds ratio. Next, we attempted to replicate our observations by identifying CNVs in the BAVGWAS dataset that overlapped with rare EBAV CNVs. We found that large duplications involving SOX7 and GATA4 in 8p23 and the VCFS region in 22q11. 2 were also significantly enriched in BAVGWAS cases compared to HRS controls (Table 6 , Table S6 and S7 ).
S3 Table. Large, Rare Copy Number Variants Identified in the EBAV Cohort. Cohort, name of control cohort. Study, study from which genotypes were obtained. Samples, number of control samples in each dataset. Accession, Database of Genotypes and Phenotypes accession number. Microarray, Illumina microarray used for genotyping. Chr. , Chromosome on which 561 CNV is located. Start BP, start basepair of CNV. Stop BP, stop basepair of CNV. Type, denotes if a CNV is a 562 duplication (DUP) or deletion (DEL) event. All CNVs were validated in GenomeStudio. 8 2319555 2585105 DUP 8 4201652 4493979 DUP Cohort WLS Study Wisconsin Longitudinal 8 10111571 10721128 DUP Samples Accession Study on Aging 8969 Phs001157. v1. pl 8 11103895 11856864 DUP 8 9368431 9745798 DUP Microarray Illumina HumanOmniExpress- 8 11448529 11732454 DUP 24 v1.
, LOC728989, NBPF13P, PRKAB2, PDIA3P, FM05, CHD1L, LINC00624, BCL9, ACP6, and GJA5 568 569 TTN, AX746670, TTN-AS1, and MIR548N* GATA4, C8orf49, NEIL2, FDFT1, and CTSB GATA4, C8orf49, NEIL2, FDFT1, and CTSB GATA4, C8orf49, NEIL2, FDFT1, and CTSB GATA4, C8orf49, NEIL2, FDFT1, and CTSB PARD3. * Indicates the call was from an unaffected family member. 2 179395466 179517632 DUP 8 11506208 DUP 8 11103895 DUP 8 11448529 DUP 8 11448529 DUP 10 NANOG and NANOGNB 12 7918339 8109412 DUP Gene(s) Chr Start BP Stop BP NANOG and NANOGNB 12 7942473 8109412 DUP Type LOC100507334 2 NANOG and NANOGNB 12 7942945 8123777 DUP 110852875 111406073 DUP LOC100507334 2 NANOG and NANOGNB 12 7942945 8105015 DUP 110982530 112007875 DUP MIR128-2 3 NANOG and NANOGNB 12 7945559 8101326 DUP 35775249 35938795 DUP MIR128-2 3 NANOG and NANOGNB 12 7945559 8105015 DUP 35775249 35938795 DUP MIR128-2 3 NANOG and NANOGNB 12 7945559 8105015 DUP 35785608 35936616 DUP TMPRSS11E, UGT2B17, 4 NANOG and NANOGNB 12 7945559 8109412 DUP 69599357 69712995 DUP NANOG and NANOGNB 12 7945559 8130958 DUP UGT2B15, UGT2B10 35107733 DUP PARD3 10 35107733 DUP AHRR, C5orf55, EXOC3, FLJ00157, AK023178, PP7080, 5 323965 889536 LOC146481, and LOC100130700 DUP UBE2MP1, LOC283914, 16 34355747 34740580 DUP KLHL1 and ATXN8OS BC013821, LOC100996325, and LOC283914 and LOC146481 13 16 70578273 DUP 34428972 34723621 DUP KLHL1 and ATXN8OS* CEP72 LOC283914 13 16 70589082 DUP 34433468 34663346 DUP KLHL1 and ATXN8OS* AHRR, C5orf55, EXOC3, FAM101B, VPS53, and FAM57A 17 13 5 70730307 DEL 287907 602256 DUP 1389 641023 DUP 565 567 566 564 1 1 2 16 17 16 21 5 17 21 5 17 21 17 21 5 21 19 22 22 19 5 19 19 19 41349732 41508557 DUP 146326373 147340734 DUP 146326373 147229299 DUP 114426115 115208197 DUP 225778 906268 DEL 83302526 DUP 83303915 DUP 310925 548342 DUP 41278694 DUP 225778 649766 DUP 41268738 DUP 426109 673408 DUP 238906 650372 DUP 41278694 DUP 284614 831667 DUP 41278694 DUP 589727 701920 DUP 38683266 39116961 DUP 41268738 DUP 19580050 DUP 18877787 DUP 38976659 39116961 DUP 589727 701920 38993142 39116961 DUP DUP 38993142 39116961 DUP 5 19 41350509 41600054 DUP 36764235 37046626 DUP 5 36805679 37046626 DUP 5 19 41354458 41588347 DUP 36898424 37046626 DUP 5 36911625 37052624 DUP 19 41386035 41522338 DUP 2 8 8064756 11143272 DUP 114614021 114732241 DUP 8 8064756 11882065 DUP 19 41386814 41531705 DUP 2 8 8064756 8655355 DUP 114458921 115208197 DUP 2 114458921 115208197 DUP 2 S4 Table.
Rare CNVs Enriched in EBAV Cohort. Gene(s), genes intersected by CNV. Chr, chromosome on HYDIN2, NBPF12, LOC728989, NBPF13P, PRKAB2, PDIA3P, FM05, CHD1L, LINC00624, BCL9, ACP6, and GJA5* MIR4782, SLC35F5, ACTR3, NECAB2 FLJ00157, AK023178, PP7080, FAM101B, VPS53, FAM57A, NECAB2* and BC013821 GEMIN4, DQ581337, and PCP4, DSCAM, MIR4760, and AHRR, C5orf55, EXOC3, DBIL5P FLJ00157, AK023178, PP7080, FAM101B, VPS53, FAM57A, DSCAM-AS1 PCP4, DSCAM, MIR4760, and and BC013821 and GEMIN4 AHRR, C5orf55, EXOC3, FAM101B, VPS53, FAM57A, DSCAM-AS1 PCP4, DSCAM, MIR4760, and FLJ00157, AK023178, PP7080, and GEMIN4 BC013821, LOC100996325, and FAM101B, VPS53, FAM57A, DSCAM-AS1 PCP4, DSCAM, MIR4760, and CEP72 GEMIN4, DQ581337, and AHRR, C5orf55, EXOC3, DBIL5P DSCAM-AS1 PCP4, DSCAM, MIR4760, and DSCAM-AS1 FLJ00157, AK023178, PP7080, RYR1, MAP4K1, and EIF3K TBX1, GNB1L, C22orf29, TXNRD2, COMT, MIR4761, ARVCF, TANGO2, MIR185, DGCR8, MIR3618, MIR1306, TRMT2A, RANBP1, ZDHHC8, LOC388849, LOC284865, and LINC00896 TBX1, GNB1L, C22orf29, TXNRD2, COMT, MIR4761, BC013821, LOC100996325, and RYR1, MAP4K1, and EIF3K CEP72 AHRR, C5orf55, EXOC3, RYR1, MAP4K1, and EIF3K RYR1, MAP4K1, and EIF3K FLJ00157, AK023178, PP7080, CYP2A7, CYP2G1P, CYP2B7P1, BC013821, LOC100996325, and and CYP2B6 CEP72 NIPBL CYP2A7, CYP2G1P, CYP2B7P1, NIPBL and CYP2B6 NIPBL CYP2A7, CYP2G1P, CYP2B7P1, NIPBL and CYP2B6 CYP2A7, CYP2G1P, CYP2B7P1, LOC100499194, and LOC440900* MIR4782, SLC35F5, ACTR3, ARVCF, TANGO2, MIR185, SGK223, CLDN23, and MFHAS1 and CYP2B6 DGCR8, MIR3618, MIR1306, SGK223, CLDN23, and MFHAS1 CYP2A7, CYP2G1P, CYP2B7P1, LOC100499194, and LOC440900* MIR4782, SLC35F5, ACTR3, LOC100499194, and LOC440900* TRMT2A, RANBP1, ZDHHC8, SGK223, CLDN23, and MFHAS1 and CYP2B6 MIR4782, SLC35F5, ACTR3, LOC100499194, and LOC440900 TTN, AX746670, TTN-AS1, and MIR548N which each CNV is on.
Participants listened to a four-minute audio recording of a series of letters (B, D, P, and T) announced randomly. The objective for participants was to count the number of occurrences of a predetermined letter (one of the four letters) in the recording. Participants provided their response (i. e. , how many times they heard the predetermined letter) after the four-minute recording. This task had two levels of difficulty based on the inter-stimulus interval (ISI): difficult with a 2-second ISI (2s modality) and easy with a 5-second ISI (5s modality). The letters were recorded once by a speech therapist, and then MATLAB recordings were generated using a program. Participants listened to the recording using a SoundLink II wireless headset (Bose, Framingham, MA, USA). During the task, participants were not allowed to count on their fingers, maximizing cognitive effort.
Additionally, this auditory task prevented verbal communication between the two humans while they were working together. The success rate for each condition was evaluated. Additionally, for each trial, the absolute error between the participants' response and the correct response was calculated (i. e. , how much they were off by). They did not receive feedback on their response. Video coding to describe interactions between participants and co-worker. Each fourminute trial between participants and co-worker was filmed with a QFHD Pro Series camera at 15Hz (Dahua, Hangzhou, China) mounted on the wall. Then, the video recordings were exported from Smart PSS 1. 13. 1 software. These videos were imported into Captiv-L7000 2. 3. 18 software (TEA, Vandoeuvre-lès-Nancy, France). Once imported, a coding was created in order to quantify the co-workers' and participants' actions.