dataesr/scientific-paragraphs-categorization huggingface dataset:
There exist a multitude of different signal processing and inverse methods which are known to vary the degree of accuracy for localizing ectopic activity  (Svehlikova et al. , 2022). While evaluation of these different methods was not feasible in this study, the experimental data used will be made available through EDGAR  (Aras et al. , 2015)  and can be accessed freely by other groups wishing to test their methods. In particular, it may be of interest to incorporate information about the pathological regions into the forward model such as regional reduction in conduction velocity in the zone of hyperkalemia. Validation was performed using a torso tank experimental setup where inhomogeneous extra-cardiac electrical properties and respiration induced cardiac movement are not present. Previous studies have demonstrated the inclusion of inhomogeneous structures produces no systematic improvement in pacing site localization with ECGI  (Bear et al.
Funding This work was supported by the  French National Research Agency  ( ANR-10-IAHU04-LIRYC ), the  PHC Bosphore program  (Project  INVERSE ), the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement  N°715093 ), and the  Scientific and Technological Research Council of Türkiye  ( 221N175 ). This work was also supported by the  NIH NIGMS Center for Integrative Biomedical Computing  (www. sci. utah. edu/cibc)funded by the  National Institute of General Medical Sciences  of the  National Institutes of Health  under grant numbers  P41 GM103545  and.
However, the cardboard does contain appreciable K and Ca, but the fluorescent X-rays of these elements have a very low energy and normally do not penetrate through the herbarium specimen to the detector and are hence invisible in the elemental map of the specimen. In addition, leaves of Macadamia integrifolia (from a tree growing at St Lucia, Brisbane) were scanned in fresh hydrated state and following on immersed in methylated spirit (95% ethanol) for 24 hrs and then scanned again. The XRF spectra on the UQ microXRF facility (Fig. 1 ) were acquired in mapping mode using the instrument control package, Iridium (IXRF systems), and then imported into the GeoPIXE software package version 7. 5 s (beta). The XRF spectra were subsequently fitted using the Dynamic Analysis method  (Reeves and Baker 2000; Ryan and Jamieson 1993; Ryan et al.
Discussion The use of laboratory μ-XRF analysis of herbarium specimens can form the starting point for further studies using specimens properly prepared for micro-analytical investigations. The examples illustrated here provide a case in point on how this approach may assist in investigating abnormal metal accumulation in plants and rule out extraneous contamination. Of course, the use of this method relies on the (local) availability of this instrumentation. However, costs are comparable to that of routinely used analytical infrastructure, such as ICP-MS, it is non-destructive and it is significantly cheaper than SEM-EDS, and likely to become widely accessible in the near future. ments d'avenir" (ANR-10-LABX-21 -RESSOURCES21.
1, are encountered at λ L ¼ 200%, 100%,66:67%,50% and 40%. To study the effects of sampling of the mechanical wavefield, two levels of spatial resolution where considered, N ¼ 64 and N ¼ 8, where N ¼ 64 ensures over 25 sampling points for one wave at the shortest wavelength, λ L ¼ 40%, while N ¼ 8 provides 3 measurements per period for these shortest waves. In parallel, the damping ratio, ξ was varied from 0% to 10%, a range indicative of biological tissue. 30 For the Monte-Carlo study, 2000 identifications for the storage and loss modulus indicated in Equation (  2 ) were performed, with μ and ξ then defined by Equation (7) and Equation (8). These identifications were performed based on the minimization of F via the Matlab function fmincon (version 2021b, The MathWorks, Inc. ; Natick MA, USA).
Especially, numerical simulations are performed with the finite element software COMSOL Multiphysics. [32] Section 2 presents the numerical results obtained in linear elastodynamics, embedding a simplified modeling of the mechanical loading. Section 3 shows results obtained with a weakly coupled eddy current-linear elastodynamic analysis. Such coupling enables a more accurate computation of the mechanical pulse resulting from the generated Lorentz forces. Finally, a comparison between both modelings allows a discussion on the importance of accounting for such coupling in simulations about the definition of the optimal conditions for disassembling the laminate.
Moreover, it can be shown that it does not influence the cross-sectional wave-propagation pattern, see Remark 2 for more details on this point. IðtÞ ¼ I 0 e Àt=τ I sinðωtÞ The electromagnetic analysis is performed with an eddycurrent formulation,  [34]  i. e. , neglecting displacements currents, and more precisely using a formulation in potentials involving the electric scalar potential V and the magnetic vector potential A. Numerical simulation was performed with the finite element software COMSOL Multiphysics. [32] The inductor and the laminate shown in Figure  6  are plunged into a large air box, with a vanishing magnetic vector potential at its boundary, except for the symmetry plane for which its normal gradient component vanishes. From the solution of the eddy-current analysis, Lorentz forces are computed from induced current flowing on the top face of the laminate.
Methods Summary genetic association data were obtained from three cancer-specific genome-wide association study (GWAS) consortia. Summary genetic association estimates for overall and oestrogen receptor (ER)-stratified breast cancer risk in up to 122,977 cases and 105,974 controls were obtained from the Breast Cancer Association Consortium (BCAC)  [29]. Summary genetic association estimates for overall and site-specific (i. e. [31]. These analyses were restricted to participants of European ancestry. Overall (i. e. site-combined) cancer risk data in 27,483 incident cases and 372,016 controls were also obtained from a GWAS performed in the UK Biobank cohort study  [32]. Briefly, cancer cases were classified according to ICD-9 (http:// www. icd9d ata. com/ 2007/ Volum e1/ defau lt. htm) and ICD-10 (http:// apps.
who. int/ class ifica tions/ icd10/ browse/ 2016/ en) with data completed to April 2019 and controls were defined as individuals who did not have any cancer code (ICD9 or ICD10) and did not self-report a cancer diagnosis. GWAS were performed using a linear mixed model as implemented in BOLT-LMM (v2. 3) (to account for relatedness and population stratification) and adjusted for age, sex and genotyping array  [33]. Further information on imputation and quality control measures have been reported elsewhere  [33]. Further information on statistical analysis, imputation, and quality control measures for summary genetic association data obtained from cancer consortia is available in the original publications. All studies contributing data to these analyses had the relevant institutional review board approval from each country, in accordance with the Declaration of Helsinki, and all participants provided informed consent.
Instrument construction To generate genetic instruments to proxy glucose-lowering drug target perturbation, summary genetic association data were obtained from a GWAS of type 2 diabetes in the Million Veteran Program (148,726 cases; 965,732 controls of European ancestry)  [34]. Analyses were adjusted for age, sex and ten principal components of genetic ancestry. Instruments were constructed in PLINK by obtaining SNPs associated with type 2 diabetes at genomewide significance (p<5×10 -8 ) that were in or within ±500 kb from the gene encoding each respective target (PPARG, Chr3: 12328867-12475855; ABCC8, Chr11: 17414432-17498449; GLP1R, Chr6: 39016574-39055519) using the 1000 Genomes Phase 3 reference panel  [35, 36]. We were unable to identify genome-wide significant SNPs within 500 kb windows from SLC5A2 and DPP4 (i.
Co-localisation analysis was performed using the coloc (version 2. 0) R package (https:// cran. r-proje ct. org/ web/ packa Fig. 4  Regional Manhattan plot of associations of SNPs with type 2 diabetes ±500 kb from the ABCC8 locus. rs5219 (purple dot) represents the sentinel SNP associated with genetic liability to type 2 diabetes in the ABCC8 locus ges/ coloc/ index. html), which uses approximate Bayes factor computation to generate posterior probabilities that associations between two traits represent each of the following configurations: (1) neither trait has a genetic association in the region (H 0 ); (2) only the first trait has a genetic association in the region (H 1 ); (3) only the second trait has a genetic association in the region (H 2 ); (4) both traits are associated but have different causal variants (H 3 ); and (5) both traits are associated and share a single causal variant (H 4 )  [44].
Co-localisation analysis was performed by generating ±500 kb windows around the gene encoding each respective drug target. We used a posterior probability of >50% to indicate support for a configuration tested. Where there was not support for H 4 , we then examined the possibility of co-localisation across other secondary conditionally independent signals for either genetic liability to type 2 diabetes or positive controls within drug target loci by performing pairwise conditional and co-localisation analysis on all conditionally independent association signals using GCTA-COJO and the coloc package as implemented in pwCoCo  [45]. We employed default priors for p1 (i. e. prior probability that a SNP is associated with type 2 diabetes liability within a drug target locus, 1×10 -4 ), p2 (i. e.
Acknowledgements The authors would like to thank the participants of the individual studies contributing to the BCAC and the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for their participation in these studies, along with the principal investigators of these consortia for generating the data used for this analysis and for making these data available in the public domain. Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO)-specific acknowledgements include French Association STudy Evaluating RISK for Sporadic Colorectal Cancer (ASTERISK); we are very grateful to B. Buecher without whom this project would not have existed. We also thank all participants in this study, as well as all the physicians, technicians and students. The CCFR graciously thanks the generous contributions of their study participants and acknowledge the dedication of study staff and the financial support from the US National Cancer Institute, without which this important registry would not exist.
251553 and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. Multiethnic Cohort (MEC) was supported by NIH grants (R37 CA54281, P01 CA033619 and R01 CA063464). The work of Molecular Epidemiology of Colorectal Cancer (MECC) was supported by the NIH, US Department of Health and Human Services (R01 CA081488, R01 CA197350, U19 CA148107, R01 CA242218) and a generous gift from Daniel and Maryann Fong. The work of Memorial Sloan Kettering Cancer Center (MSKCC) at Sloan Kettering in New York was supported by the Robert and Kate Niehaus Center for Inherited Cancer Genomics and the Romeo Milio Foundation.
The authors wish to acknowledge the contribution of A. Belisle and the genotyping team of the McGill University and Génome Québec Innovation Centre, Montréal, Canada, for genotyping the Sequenom panel in the NFCCR samples. Funding was provided to M. O. Woods by the Canadian Cancer Society Research Institute. The Northern Swedish Health and Disease Study (NSHDS) research was supported by Biobank Sweden through funding from the Swedish Research Council (VR 2017-00650, VR 2017-01737), the Swedish Cancer Society (CAN 2017/581), Region Västerbotten (VLL-841671, VLL-833291), Knut and Alice Wallenberg Foundation (VLL-765961), and the Lion's Cancer Research Foundation (several grants) and Insamlingsstiftelsen, both at Umeå University. Ohio State University Medical Center (OSUMC) Ohio Colorectal Cancer Prevention Initiative (OCCPI) funding was provided by Pelotonia and HNPCC funding was provided by the NCI (CA16058 and CA67941).
The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and the Intramural Research Program of the Division of Cancer Epidemiology and Genetics were supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. Funding was provided by NIH, Genes, Environment and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446 and NIH GEI U01 HG 004438. For SEARCH, the University of Cambridge has received salary support in respect of PDDP from the NHS in the East of England through the Clinical Academic Reserve. Cancer Research UK (C490/A16561) and the UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge also provided support. SE-LECT research reported in this publication was supported in part by the National Cancer Institute of the NIH under Award Numbers U10 CA37429 (C.
the Early Detection Research Network (EDRN) resource at University of Pittsburgh School of Medicine, Department of Gastroenterology, Department of Pathology, Hepatology and Nutrition and Biomedical Informatics. For European Prospective Investigation into Cancer and Nutrition (EPIC), where authors are identified as personnel of the International Agency for Research on Cancer/WHO, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/WHO. We are sincerely grateful to contra el Cáncer (GCB13131592CAST), Beca Grupo de Trabajo 'Oncología' AEG (Asociación Española de Gastroenterología), Fundación Privada Olga Torres, FP7 CHIBCHA Consortium, Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya, 2014SGR135, 2014SGR255, 2017SGR21, 2017SGR653), Catalan Tumour Bank Network (Pla Director d'Oncologia, Generalitat de Catalunya), PERIS (SLT002/16/00398, Generalitat de Catalunya), CERCA Programme (Generalitat de Catalunya) and COST Action BM1206 and CA17118; CIBERehd is funded by the Instituto de Salud Carlos III.
The Hawaii Adenoma Study is supported by NCI grant R01 CA72520. The Hwasun Cancer Epidemiology Study-Colon and Rectum Cancer (HCES-CRC) is supported by grants from Chonnam National University Hwasun Hospital, HCRI15011-1. In Kentucky, work was supported by a Clinical Investigator Award from Damon Runyon Cancer Research Foundation (CI-8) and grant NCI R01CA136726. The Leeds Colorectal Cancer Study (LCCS) was funded by the Food Standards Agency and Cancer Research UK Programme Award (C588/A19167). Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 509348, 209057,.
Department of Health and Human Services , nor does mention of trade names, commercial products or organisations imply endorsement by the  US government. Cancer data from Maryland were provided by the  Maryland Cancer Registry, Center for Cancer Prevention and Control, Maryland Department of Health , with funding from the  State of Maryland  and the  Maryland Cigarette Restitution Fund. The collection and availability of cancer registry data are also supported by the  Cooperative Agreement   NU58DP006333 , funded by the  Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the  Centers for Disease Control and Prevention  or the  Department of Health and Human Services. The work of ColoCare was supported by the  NIH  (grant numbers  R01  [ C.
Casey. The  CCFR Set-3 (Affymetrix Axiom CORECT Set  array) was supported by  NIH  award  U19 CA148107  and  R01 CA81488  (to S. B. Gruber). The CCFR Set-4 (Illumina OncoArray  600K  SNP array) was supported by  NIH  award  U19 CA148107  (to S. B. Gruber) and by the  Center for Inherited Disease Research (CIDR) , which is funded by the  NIH  to the  Johns Hopkins University , contract number  HHSN268201200008I. Additional funding for the OFCCR/ARCTIC was through award  GL201-043  from the  Ontario Research Fund  (to B. W. Zanke), award 112746 from the  Canadian Institutes of Health Research  (to T. J. Hudson ), through a  Cancer Risk Evaluation (CaRE) Program  grant from the  Canadian Cancer Society  (to S. Gallinger), and through generous support from the  Ontario Ministry of Research and Innovation.
The NQplus study is sponsored as follows: by a  ZonMW investment grant  ( 98-10030 ); by the project  PREVention of diabetes through lifestyle intervention and population studies in Europe and around the World (PRE-VIEW)  project, which received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) under grant no. 312057 ; by funds from  TI Food and Nutrition  (cardiovascular health theme), a public-private partnership on precompetitive research in food and nutrition; and by the  Food Biomarker Alliance (FOODBALL) , a project from  JPI Healthy Diet for a Healthy Life. The  CORECT Study  was supported by the  National Cancer Institute ,  NIH  ( NCI/NIH ),  US Department of Health and Human Services  (grant numbers  U19 CA148107 ,  R01 CA081488 ,  P30 CA014089 ,  R01 CA197350 ;  P01 CA196569 ;  R01 CA201407 ;  R01 CA242218 ),  National Institutes of Environmental Health Sciences, NIH  (grant number  T32 ES013678 ) and a generous gift from Daniel and  Maryann Fong.
The  Colorectal Cancer Study of Austria (CORSA) study  was funded by  Austrian Research Funding Agency (FFG) BRIDGE  (grant  829675 , to  A. Gsur ), the  'Herzfelder'sche Familienstiftung ' (grant to  A. Gsur ) and was supported by  COST Action   BM1206. The  American Cancer Society  funds the creation, maintenance, and updating of the  Cancer Prevention Study-II (CPS-II ) cohort. This study was conducted with institutional review board approval. Colorectal Cancer Genetics & Genomics, Spanish study (CRCGEN)  was supported by  Instituto de Salud Carlos III , co-funded by  FEDER  funds -a way to build Europe-(grants  PI14-613  and  PI09-1286 ),  Agency for Management of University and Research Grants (AGAUR) of the Catalan Government  (grant  2017SGR723 ),  Junta de Castilla y León  (grant  LE22A10-2 ), the  Spanish Association Against Cancer (AECC) Scientific Foundation  grant  GCTRA18022MORE  and the  Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP) , action Genrisk.
The  Swedish Low-risk Colorectal Cancer Study  was supported by grants from the  Swedish Research Council  ( K2015-55X-22674-01-4 ,  K2008-55X-20157-03-3 ,  K2006-72X-20157-01-2 ) and the  Stockholm County Council  ( ALF  project). The Swedish Mammography Cohort and Cohort of Swedish Menwas supported by the  Swedish Research Council /Infrastructure  grant, the  Swedish Cancer Foundation , and the  Karolinska Institute´s Distinguished Professor Award  to  A. Wolk. UK Biobank  research has been conducted using the UK Biobank Resource under Application Number  8614. VITamin D and OmegA-3 TriaL (VITAL) was supported by  NIH  ( K05 CA154337 ). The Women's  Health Initiative (WHI) program  is funded by the  National Heart, Lung and Blood Institute ,  NIH ,  US Department of Health and Human Services  through contracts  HHSN268201100046C ,  HH-SN268201100001C ,  HHSN268201100002C ,  HHSN268201100003C ,  HHSN268201100004C  and  HHSN271201100004C.
The Hawaii Adenoma Study is supported by NCI grant R01 CA72520. The Hwasun Cancer Epidemiology Study-Colon and Rectum Cancer (HCES-CRC) is supported by grants from Chonnam National University Hwasun Hospital, HCRI15011-1. In Kentucky, work was supported by a Clinical Investigator Award from Damon Runyon Cancer Research Foundation (CI-8) and grant NCI R01CA136726. The Leeds Colorectal Cancer Study (LCCS) was funded by the Food Standards Agency and Cancer Research UK Programme Award (C588/A19167). Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 509348, 209057, The work of Epidemiological investigations of the chances of prevent- ing, recognizing early and optimally treating chronic diseases in an elderly population (ESTHER)/VERDI was supported by grants from the Baden-Württemberg Ministry of Science, Research and Arts and the German Cancer Aid.
The importance of gene amplifications in evolution is more and more recognized. Yet, tools to study multi-copy gene families are still scarce, and many such families are overlooked using common sequencing methods. Haplotype reconstruction is even harder for polymorphic multi-copy gene families. Here, we show that all variants (or haplotypes) of a multi-copy gene family present in a single genome, can be obtained using Oxford Nanopore Technologies sequencing of PCR products, followed by steps of mapping, SNP calling and haplotyping. As a proof of concept, we acquired the sequences of highly similar variants of the cidA and cidB genes present in the genome of the Wolbachia wPip, a bacterium infecting Culex pipiens mosquitoes. Our method relies on a wide database of cid genes, previously acquired by cloning and Sanger sequencing.
Lately, PacBio long reads have successfully enabled to identify new isoforms from RNAseq data, thanks to new tools  [13] ; improvement of the Nanopore basecallers with Nanopolish  [14] , Nanocaller  [15]  or the recent Bonito basecalling (https://github. com/nanoporetech/bonito) made possible the use of Nanopore sequencing for identification of inter-individual SNPs and variations in gene copy numbers  [16]. Error correction enabled using Nanopore technology for reference-free transcriptome analysis  [17]. Nanopore sequencing has also recently been used for multiplex amplicon sequencing, decreasing the financial cost by 200x as compared to Sanger sequencing  [18]. A persistent problem for identification of genetic variants is the nonrandom distribution of long read sequencing errors, these errors being more frequent in homopolymer regions (representing approximately half of sequencing errors) and in GC-rich regions  [12] , making it hard to discriminate true mutations from sequencing errors even when increasing the depth of sequencing coverage.
This can be mitigated by a previous knowledge of the within-gene polymorphism distribution, e. g. with a good pre-existing database. A further issue arises after having identified SNPs, when haplotype phasing is required to obtain the haplotypes. Indeed, common haplotype phasing tools (e. g. GATK, WhatsHap) require a previous knowledge on the expected number of different gene copies (copy number for multi-copy gene families, ploidy in most of the cases). This is an issue for multi-copy gene families, in which among individual CNVs are frequent. Here, we studied the cidA and cidB genes, present in tandem in the genome of the endosymbiotic bacteria wPip infecting Culex pipiens mosquitoes. cid genes are respectively 1475 bp (cidA) and 3524 bp long (cidB). These genes are amplified and diversified in wPip with up to 6 different copies, named variants, described within a single Wolbachia genome  [19].