dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Parashkev Nachev ,  Robert Gray ,  Sami Hamdan , and  Cembre Zor  for insightful comments on a previous version of this manuscript. The authors declare no conflict of interest. We thank the  UK Biobank  participants for their voluntary commitment and the  UK Biobank team  for their work in collecting, processing and disseminating these data for analysis. This research was conducted using the  UK Biobank Resource  under approved project  23827. DB was funded by the  Deutsche Forschungsgemeinschaft (DFG ,  BZ2/2-1 ,  BZ2/3-1 , and  BZ2/4-1 ;  International Research Training Group   IRTG2150 ),  Amazon   AWS Research Grant  ( 2016  and  2017 ), as well as the  START-Program  of the  Faculty of Medicine  ( 126/16 ) and  Exploratory Research Space  ( OPSF449 ),  RWTH Aachen.
Chromosomal aberrations of cancers could also be detected by cell-free DNA analysis. Interestingly, recent studies revealed that the fragmentation patterns of cellfree DNA are reflective of the chromatin structure of the cell-of-origin. The analysis of such patterns, termed fragmentomics, could potentially provide a tissue map of the organs and cells contributing cell-free DNA and possibly the detection of diseases associated with those organs. (supported by the Hong Kong RGC Theme-based Research Scheme (T12-403/15N) and (T12-401/16-W) and a collaborative Research Grant from GRAIL). Non-invasive prenatal testing (NIPT) is an efficient means to detect fetal aneuploidies. In our center, we use a genome-wide approach. Given that 90% of cell-free DNA is of maternal origin this also results in a genome-wide screening of the pregnant mothers for maternal copy number variations (CNV's).
O10 Patterns of acquired uniparental disomy reveal biallelic inactivation of tumor suppresssor genes in gastrointestinal cancers and in colorectal advanced adenomas Eva Hernández-Illán 1 , Pau Erola 2 , Keyvan Torabi 3 , Maria Isabel Alvarez-Mora 4 , Marcos Díaz-Gay 1 , Queralt Ferrer 1 , Antoni Castells 1 , Sergi Castellví-Bel 1 , Montserrat Milà 4 , Immaculada Ponsa 3 , Juanjo Lozano 2 , Rosa Miró 3 , Jordi Camps 1,3  events in solid tumors and have been associated with cancer-related genes. Here, we aimed at integrating aUPD profiles with wholeexome sequencing mutational data in a tumor-type specific manner. Using TCGA datasets for 1,032 gastrointestinal cancers, including colon (COAD), rectum (READ), stomach (STAD), esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), we show that gastrointestinal cancers show tumor-type specific profiles of aUPD.
Here we present our data on individuals with a terminal 6q deletion collected in a successful collaboration with the Chromosome 6 Facebook group. Families signed up for the study via the secured project's website by uploading the proband's array report. Phenotype data was collected directly from the individuals or parents via an online multilingual questionnaire. Literature case reports were added to the database using the same questionnaire. We collected data on 30 and 55 individuals with a terminal 6q deletion via our website and literature, respectively. The terminal 6q26q27 region encompasses thirty-nine protein coding genes of which seven are predicted to exhibit a haploinsufficiency effect (MAP3K7, PARK2, QKI, PDE10A, MLLT4, DLL1, TBP). We analysed the clinical data of the total group of individuals and of the seven subgroups based on the number of deleted haploinsufficiency genes.
Neuropsychological assessment, including intellectual (WASI; VABS-II) and behavioral (CBCL) measures, showed overall and severe cognitive, language and adaptive functions impairments. The 19p deleted region of our patient encompasses, besides NFIX and CACNA1A, 20 other genes. Among them, CC2D1A and NACC1 are associated with mental retardation, epilepsy and delayed brain myelination, which could contribute to the severe cognitive and adaptive functions impairments found in our patient. Apart from postnatal overgrowth and psychomotor developmental delay, the most consistent physical features among all patients with deletions encompassing the NFIX gene are hypotonia, skeletal anomalies, mainly scoliosis, and facial dysmorphisms that are consistent with the phenotypic findings in our patient. These data emphasize the importance of detailed cytogenomic and clinical analyses for an accurate diagnosis, prognosis, and genetic counseling and provide an opportunity to improve genotype-phenotype correlations of Malan syndrome patients.
Interpretation of clinical importance of CNVs was based on patient's medical records and ACMG Standards and Guidelines for constitutional cytogenomic microarray analysis. The CMA was performed for 844 patients with epilepsy. Pathogenic CNVs described as a syndrome in the OMIM database were found in 8. 6% (73 patients). Pathogenic deletions (25 cases) and duplications (9 cases) of a single chromosome not annotated in OMIM were found in 4% (34 patients). Chromosomal abnormality of two non-homologous chromosomes were detected in 2. 1% (18 patients). Contiguous AOH loci were found in 3. 4% (29 patients). Further evaluation was recommended for 8 of these patients, because they have epilepsy-associated genes in AOH region. 76 of 132 patients with disease-causing CNVs have well described clinical history.
Mutations and hemizygous deletions of the MID1 gene have been previously reported in the literature and international databases in male patients with X-linked Opitz G/BBB syndrome. But the variety of described mutations and the lack of a strict genotype-phenotype correlation suggested us to report the case. Mb, the phenotype is highly variable among individuals. About 180 clinical manifestations have been described in the syndrome, such as congenital cardiac malformations, characteristic facial features, immune deficiency, and psychiatric disorders. Although the dosesensitive genes mapped in the deleted region are the main candidates for the phenotype, it is suggested that more complex molecular mechanisms are involved in the etiology of the phenotypic variability of the syndrome. The presence of single nucleotide variants (SNVs) on the remaining allele of 22q11.
We report on the results of genomic investigations of 27 AML patients. Material and methods: Cytogenetic analysis was done on diagnostic bone marrow samples. FLT3 gene internal tandem duplication testing was performed by PCR followed by fragment analysis as previously described. CGH+SNP microarray 4x180k cancer design platforms (Agilent Technologies) were used for genomic profiling. Targeted NGS with a panel covering 19 genes (AML Research Panel) was performed on Ion PGM (ThermoFisher Scientific). Results and discussion: The cytogenetic risk category was intermediate for 25 patients and adverse for 2 patients. Genomic imbalances and copy-neutral loss of heterozygosity were detected in 7 patients each. The mutational screening revealed a total of 61 mutations, with 51 driver mutations and 10 mutations rarely or unreported in cancer databases.
Therefore, accumulation of repetitive sequences and transposable elements probably led to quail genome increased size. Besides telomeric (TTAGGG)n repeat, only PO41 and BglII 41 bp tandem repeats were described in the Japanese quail genome to date. In this work, we performed a search of tandem repeats in unassembled short raw read datasets of the Japanese quail genome deep sequencing. We found 23 tandem repeats representing at least 4. 8% of the Japanese quail genome. FISH with specific oligonucleotide probes revealed some of them contribute to heterochromatin of short arms of CJA3, CJA4 and microchromosomes. Repeat CjapSAT was found in pericentromeric heterochromatin on CJA 1-6 and three pairs of microchromosomes, as well as in p-and q-arms of CJAW. The description of satellite repeats is a necessary step to understand the significance of satellite DNA for the genome functioning and genome changes during the evolutionary process.
Using cloning technique we isolated and characterized r(18)HESM01 cell line. Thus two isogenic hESC cell lines with the only differences in ring chromosome 18 were obtained. These cell line and their differentiated derivatives in various lineages were used for proteomic analysis. The proteomes of these cell lines and their differentiated derivatives in various lineages were analyzed using shotgun proteomichigh performance liquid chromatography connected with tandem mass spectrometry (LC-MS/MS). Mass spectra in raw format were processed using Progenesis LC-MS software ("Nonlinear Dynamics Ltd. "). Mascot engine (www. matrixscience. com) was used to perform a database search using a target decoy approach with FDR ≤ 1%. The Mascot data in xml format was re-imported into Progenesis LC-MS software for protein/peptide quantification.
Acknowledgement ERA-NET NEURON SYNSCHIZ project 6/2018; Projects of Excellence Funding in RDI, Contract No. 7PFE/16. 10. 2018 1. P16 Gonosomal findings in three different germ layers in patients with Turner syndrome phenotype. Haplotyping of X chromosome Katerina Adamova 1 , Dita Vrbicka 1 , Vrtel Radek 1 , Petr Vrtěl 1 , Martin Prochazka 1 , Eva Klaskova 2 Hospital And Palacky University Olomouc, Department Of Medical Genetics, Olomouc-Czech Republic; 2 University Hospital and Palacky University Olomouc, Department of Paediatrics, Olomouc-Czech Republic Correspondence: Katerina Adamova (dita. vrbica@email. cz) Molecular Cytogenetics 2019, 12(Suppl 1):1. P16.
Acknowledgments the research was supported by Project  PON1_486-GENOBU  Acknowledgments Partially supported by  Ministry of Research and Innovation in Romania , under Program 1 -The Improvement of the National System of Research and Development,  Subprogram 1. 2 -Institutional Excellence -Projects of Excellence Funding in RDI , Contract No. 7PFE/16. 10. 2018  and Grants  PN19. 29. 01. 03  and  PN18. 21. 01. 03. Acknowledgments This work was supported by  Ph. D. grant  SFRH/BD/98122/2013  from the  Science and Technology Foundation (FCT)  from  Portugal. This work was funded by the  BioISI  project  UID/MULTI/04046/2019 Research Unit grant  from  FCT , Portugal. Acknowledgements This study was supported by the  ASAS Foundation  Project  167/2014-2018. 4. P9   Gradual chromosome elimination during gametogenesis of interspecies hybrids from Pelophylax esculentus complex Dmitrij Dedukh 1 , Sergey Rymin 1 , Magdalena Chmielewska 2 , Beata Rozenblut-Kościsty 2 , Krzysztof Kolenda 2 , Mikołaj Kazmierczak 2  ,  Maria Ogielska 2 , Alla Krasikova 1 Acknowledgements Financial  and technical support: RFBR # 18-04-01276-а ,  Research Resource Centre Chromas and Research Resource Centre Molecular and Cell Technologies of Saint Petersburg State University.
Introduction Association studies have been extensively used to identify genes or molecular markers associated with disease states, exposure levels or phenotypic traits. Given a large number of molecular markers, the objective of those studies is to test whether any of the markers exhibits significant correlation with a primary variable of interest. Among those methods, gene-environment association (GEA) studies propose to test for correlation with ecological gradients in order to detect genomic signatures of local adaptation  (Savolainen et al. 2013). Although they bring useful information on the molecular targets of selection, GEA studies suffer from the problem of confounding. This problem arises when there exist unobserved variables that correlate both with the primary variables and genomic data  (Wang et al.
Open Access epigenome-wide association studies (EWAS) measure DNA methylation levels in different individuals to derive associations between epigenetic variation and exposure levels or phenotypes  (Rakyan et al. 2011; Teschendorff and Relton 2018). Here, we extend the definition of the LFMM data matrix to DNA methylation profiles and other molecular markers within a unified framework  (Leek and Storey 2007; Carvalho et al. 2008). We present our new LFMM method in the next section. Then we demonstrate that our new method is several orders faster than its previous Bayesian version without loss of power or precision. In a GEA study of individuals from the 1000 Genomes Project, the new program detects genes linked to climate in humans. In an EWAS of patients with rheumatoid arthritis (RA), it identifies a set of genes for which DNA methylation potentially mediates the effect of tobacco consumption on the disease phenotype.
New Approach GEA methods evaluate associations between the elements of a response matrix, Y, and some variables of interest, called "environmental" or "primary" variables, X, measured for n individuals. The response matrix records data for the n individuals, which often correspond to genotypes measured from p genetic markers. Here we extend the definition of Y to DNA methylation profiles (beta-normalized values) or geneexpression data. Nuisance variables such as observed confounders can be included in the X matrix, which dimension is then n Â d, where d represents the total number of primary and nuisance variables. LFMMs are regression models combining fixed and latent effects as follows Y ¼ XB T þ W þ E: (1) The fixed effect sizes are recorded in the B matrix, which has dimension p Â d. The E matrix represents residual errors, and it has the same dimensions as the response matrix.
LMMs incorporate "fixed and random effects" whereas LFMMs incorporate "fixed and latent effects. " Thus an LMM can use estimates computed by an LFMM. ). In practice, we used the simple linear models which revealed themselves computationally efficient and performed well in simulations. Our two-step approach is similar to other methods for confounder adjustment in association studies  (Price et al. 2006). It differs from the other approaches through the latent scores estimates, Û, that, in our case, capture the part of response variation not explained by the primary variable. The methods presented in this study and their extensions are implemented in the R package lfmm.
Simulation Study In a first series of computer experiments, we compared the runtimes of the new version of LFMM to the former version used with its default parameter settings (LFMM 1. 5,  Frichot and Francois 2015). Several values of the number of individuals (n), markers (p), and number of factors (K) were simulated. The user runtimes for the Markov chain Monte Carlo algorithm implemented in LFMM 1. 5 ranged between 8 min (n ¼ 100, p ¼ 1,000, K ¼ 2) and 32. 5 h (n ¼ 1,000, p ¼ 20,000, K ¼ 15). Note that the results for LFMM 1. 5 were obtained for a single CPU, and that the multi-threated version of the program runs significantly faster. With the same data sets and a single CPU, the user runtimes for LFMM 2. 0 ranged between 0. 5 s (n ¼ 100, p ¼ 1,000, K ¼ 2) and 12. 5 s (n ¼ 1,000, p ¼ 20,000, K ¼ 15).
Humans and Climate To detect genomic signatures of adaptation to climate in humans, we performed a GEA study using 5,397,214 SNPs for 1,409 individuals from the 1000 Genomes Project (2015), and bioclimatic data from the WorldClim database  (Fick and Hijmans 2017). The size of the data sets represents one of the largest GEA study conducted so far. Nine confounders were estimated by a cross-validation approach. This estimated number was confirmed by the visual inspection of factor 9 showing more noise than information (fig. 3A , Supplementary figs. S3 and S4, Supplementary Material online). The factors mainly described correlation between population structure and climate in the sample, and differed from principal components of the genomic data. PCA, CATE, and two variants of LFMM 2. 0 led to a list of 1,335 SNPs after pooling the list of candidates from the four methods (expected FDR ¼ 5%, fig.
2015). Our new implementation of LFMM disconnects the testing steps from the latent factor estimation steps. This disconnection facilitates the implementation of approaches that alleviate the above issues. For example, the human SNP data were pruned for LD by taking the most informative SNPs in genomic windows before estimating latent factors. Although potential improvements such as sparse modeling, random effects, logistic or robust regressions and stepwise conditional tests were not included in our results, those options are available with the lfmm program, and they may provide additional power to detect true associations in GEA studies.
Other Algorithms In the R programing environment, we considered the following methods and software: 1) We implemented a standard approach that estimates confounders from a PCA of the response matrix Y, and uses linear regression to perform the tests. Principal components were estimated with the svd function of the RSpectra package. Distinct scalings of the response matrix were used in simulations or in EWAS analysis and in the GEA analysis. For the GEA analysis, we used the scaling procedure implemented in the EIGENSTRAT method  (Price et al. 2006) , whereas in EWAS, we scaled with division by standard deviations. 2) We implemented the CATE method  (Wang et al. 2017) , which uses a linear transformation of the response matrix such that the first axis of this transformation is colinear to X and the other axes are orthogonal to X.
One property of CATE is to use robust regression models to perform statistical testing. CATE was used without negative controls and with a test recalibration option similar to genomic control  (Devlin and Roeder 1999). The CATE method was implemented in the R package cate. 3) We implemented a variant of LFMM 2. 0 using an L 1 norm regularizer instead of the L 2 norm (Lasso estimates). All programs contained several options and many algorithmic variants. Unless specified, we used the default options of programs.
GEA Study We performed a GEA study using whole genome sequencing data and bioclimatic variables to detect genomic signatures of adaptation to climate in humans. The data are publicly available, and they were downloaded from the 1000 Genomes Project (2015) and from the WorldClim database  (Fick and Hijmans 2017). The genomic data included 84. 4 millions of genetic variants genotyped for 2,506 individuals from 26 world-wide human populations. Nineteen bioclimatic data were downloaded for each individual geographic location, considering capital cities of their country of origin. The bioclimatic data were summarized by projection on their first principal component axis. The genotype matrix was preprocessed so that SNPs with minor allele frequency <5% and individuals with relatedness >8% were removed from the matrix.
Admixed individuals from Afro-American and Afro-Caribbean populations were also removed from the data set. After those filtering steps, the response matrix contained 1,409 individuals and 5,397,214 SNPs. We performed LD pruning to retain SNPs with the highest frequency in windows of one hundred SNPs, and identified a subset of 296,948 informative SNPs. Four GEA methods were applied to the 1000 Genomes Project data set: PCA, CATE, and two LFMM estimation algorithms. For all methods the latent factors were estimated from the pruned genotypes, and association tests were performed for all 5,397,214 loci. Because the results were highly concordant, the significance values were combined by using the Fisher method. The results obtained from clumps with an expected FDR level of 1% were analyzed using the variant effect predictor (VEP) program  (McLaren et al.
RA Data Set The RA data are publicly available and were downloaded from the GEO database under the accession number GSE42861  (Liu et al. 2013). For this study, beta-normalized methylation levels at 485,577 probed CpG sites were measured for 354 cases and 335 controls  (Liu et al. 2013). Following  (Zou et al. 2014) , probed CpG sites having a methylation level < 0. 1 or >0. 9 were filtered out. Two primary variables were included in the model, tobacco consumption and the health outcome. Ex-smokers were removed from the analysis, and all filtering steps resulted in 345,067 CpGs and 234 cases and 225 controls. Tobacco consumption was encoded as an ordinal variable with three levels (nonsmokers, occasional smokers, and regular smokers), and the health outcome was encoded as a dichotomous variable.
Age and gender were included as nuisance variables. The goal of the study was to identify CpG sites with joint association with tobacco smoking and RA. The data were centered and scaled for a standard deviation of one. Since the cell composition of blood in RA patients typically differs from that in the general population, there is a risk for false discoveries that stem from unaccounted-for cell-type heterogeneity  (Jaffe and Irizarry 2014). Since cell-type heterogeneity was not measured, we used latent factors to model it  (Zou et al. 2014). Three methods were applied to the RA data set: PCA, CATE, and our new version of LFMM. The number of factors was set to K ¼ 5 according to the screeplot of the PCA eigenvalues. For each method, significance values for the association with smoking and RA were combined using a squared-max transform that guaranteed that the resulting P-values follow a uniform distribution under the null hypothesis.
Acknowledgments The authors are grateful to two anonymous reviewers for their constructive comments. They thank  M. G. B Blum , all organizers and participants of the SSMPG 2017 workshop held in Aussois for their feedback on the program. They also thank  Katie Lotterhos  and  Matthieu Gautier  for fruitful discussions during this workshop. This work was supported by a grant from  LabEx PERSYVAL Lab ,  ANR-11-LABX-0025-01 , and by a grant from  French National Research Agency (Agence Nationale pour la Recherche) ETAPE ,  ANR-18-CE36-0005. This article was developed in the framework of the Grenoble Alpes Data Institute, supported by the  French National Research Agency  under the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ).
Profiled side-channel attacks based on deep learning, and more precisely Convolutional Neural Networks, is a paradigm showing significant potential. The results, although scarce for now, suggest that such techniques are even able to break cryptographic implementations protected with countermeasures. In this paper, we start by proposing a new Convolutional Neural Network instance that is able to reach high performance for a number of considered datasets. Additionally, for a dataset protected with the random delay countermeasure, our neural network is able to break the implementation by using only 2 traces in the attack phase. We compare our neural network with the one designed for a particular dataset with masking countermeasure and we show how both are good designs but also how neither can be considered as a superior to the other one.
Next, we address how the addition of artificial noise to the input signal can be actually beneficial to the performance of the neural network. Such noise addition is equivalent to the regularization term in the objective function. By using this technique, we are able to improve the number of measurement needed to reveal the secret key by orders of magnitude in certain scenarios for both neural networks. To strengthen our experimental results, we experiment with a number of datasets which differ in the levels of noise (and type of countermeasure) where we show the viability of our approaches.
Our Contributions This paper investigates what are the limits of CNNs' performance when considering side-channel attacks. To that end, we explore 4 datasets that we believe encompass the main types of measurements as detailed in Section 4. Our main contributions are: 1. a new CNN instance. We introduce a new CNN neural network instance and we experimentally show that it is able to reach high performance over considered datasets. 2. investigation how additional noise can be beneficial for the performance of CNNs in SCA. More precisely, we show that, although maybe counterintuitive, the addition of noise can help neural networks to avoid overfitting and consequently reach better performance. 3. we show how random splitting of training data results in a drastically different behavior for neural networks.