dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population and samples The source population for this nested study was the cohort of 1273 HIV-infected mothers who participated in a multicenter randomized controlled trial (PROMISE-PEP/ANRS 12174, NCT00640263) conducted in Burkina Faso, South Africa, Uganda, and Zambia. [20, 21] Women included were not eligible for ART based on World Health Organization (WHO) recommendations at the time (enrollment between 2009 and 2012). Mothers were advised to exclusively breastfeed their infants during the first 6 months postpartum and were thereafter advised to introduce complementary foods and stop breastfeeding by week 49. The main trial protocol and results have been described elsewhere. [21] Substudies from this trial including the one presented herein have been approved by the scientific PROM-ISE-PEP committee in April 2014.
The described model was chosen as it computed the regression coefficients assuming potential nonindependence between left and right breast milk samples. Maternal age, parity, breast milk HIV-1 RNA, and blood CD4 T cell counts were included in the analysis to evaluate the independency of associations. Factors having P values more than 0. 2 in crude estimates were not further evaluated in adjusted estimates. Receiver operating characteristic (ROC) curves were plotted to assess the diagnostic potential of human cell-free DNA (b globin) and IL-8 as markers of SCM. Except the GEE model, where both breast milk variables were combined per mother, in all other analyses breast milk data are analyzed separately. All viral loads were log transformed and undetectable viral loads were transformed to logarithmic zeros for analyses.
EBV in breast milk: free or encapsidated DNA? DNase exposure prior to DNA extraction was used to assess whether EBV DNA was encapsidated and therefore protected from enzymatic digestion. Beta globin DNA quantification was used as a human cell-free DNA control. DNase I treatment resulted in a dramatic decrease in b globin DNA with a median (IQR) change from 14,500 GE/mL (10,850-25,075) before DNase treatment to 0. 0 GE/mL (0. 0-971. 3) after DNase treatment. Among 12 EBV positive breast milk samples 4 (33%) showed a lack or negligible decrease of EBV DNA after DNase exposure with median (IQR) change from 2. 78 log 10 copies/mL (2. 68-2. 98) before DNase exposure to 2. 48 log 10 copies/mL (2. 44-2. 54) after DNase exposure suggesting that EBV DNA was protected from DNase of samples (data not shown).
[32, 33]  Breast milk cell count and secretory leukocyte protease inhibitor activity can also be used to detect SCM. [8, 12]  As cellular DNA is released during apoptosis and necrosis  [34]  we measured human cell-free DNA in breast milk to explore lysed cells. Our data show that the quantification of human cell-free DNA in breast milk may serve as a surrogate marker for SCM. Moreover, SCM has also been associated with increased production of proinflammatory mediators such as IL-8, RANTES, and lactoferrin  [9, 10, 35, 36]  and in our study as well, high IL-8 levels strongly correlated with this condition. EBV is frequently detected in the breast milk of women with chronic EBV infection,  [36]  irrespective of HIV infection. Junker et al  [37]  reported an overall 46% prevalence of EBV DNA in the cellular fraction of breast milk in the general population.
Acknowledgments We are grateful to the participants. We thank to  Naomi Taylor  and  Valerie Zimmermann  for critical evaluation of the manuscript and codirection of the A. Sanosyan's doctoral study, and to the ANRS 12174 trial group: University of Montpellier 1 (France):  Roselyne Vallo ,  Valerie Marechal ,  Dorine Neveu ,  Vincent Foulongne ,  Michel Segondy ;  University of Paris V (France) :  Stephane Blanche ,  Jean-Marc Treluyer ,  Deborah Hirt ;  Makerere University (Uganda ):  James K. Tumwine ,  Grace Ndeezi ,  Charles Karamagi ,  Philippa Musoke ,  Proscovia M. Mugaba ,  Mary Kwagala ,  Joan Murungi ,  Hawa Nabuuma Muweesi ,  Evelyn Ninsiima ,  Simon Baryeija ,  Frederic Juma ,  Caleb Bwengye Kata ,  Stuart Katushabe ;  University of Ouagadougou (Burkina Faso ):  Nicolas Meda ,  Rasmata Ouedraogo ,  Diarra Ye ,  Eric Some ,  Hugues A.
625 μg/μl. To avoid imbalances within the fi rst six bases that are used for base calibration, particularly when few different viewpoints or barcodes are used, we advise mixing in 25 % PhiX balancer DNA. Samples can be sequenced on the Illumina HiSeq system using at least 75 bp read length without further processing. Due to the wide length distribution of the PCR amplifi ed material, we load the fl ow cell at moderately reduced cluster density (around 50-75 %). After Illumina sequencing, bioinformatics tools should be used to generate a density profi le of (averaged) DNA contacts for each viewpoint. In recent years, several 4C-seq data analysis pipelines have been developed. Publicly available examples are indicated in Subheading 2. 4. All abovementioned pipelines essentially follow the same data fl ow, as briefl y outlined below (discussed in more detail in ref.
Acknowledgements We thank  Céline Hernandez  for comments on the manuscript. Work in the D. N. laboratory is supported by funds from the  Fondation pour la Recherche Médicale  (FRM-Amorçage de jeunes équipes 2014, grant  AJE20140630069 ), the  Biologie Intégrative des Génomes  project funded by the  Inititiative d'Excellence Paris-Saclay  ( ANR-11-IDEX-0003-02 ) and the  Centre National de la Recherche Scientifi que (CNRS).
e. , history constraints and method postconditions. Finally, we provide an elegant and flexible mechanism to specify restrictions on specifications in subtypes. Thus behavioural subtyping can be enforced, yet it still allows for other specification paradigms. We provide the semantics for model methods by giving a translation into a first order logic and according proof obligations. We fully implemented this framework in the KeY program verifier and successfully verified relevant examples. We have also implemented an extension to KeY to support permission-based verification of concurrent Java programs. In this context model methods provide a modular specification method to treat code synchronisation through API methods.
In this paper, we propose a universal solution to make dynamic dispatch for specifications possible by employing multi-state abstract functions/predicates through Java Modelling Language (JML) model methods. Model methods are like usual Java methods subject to dynamic dispatch. Since they resemble normal methods in syntax and semantics, this is a most natural extension to the DbC paradigm and, hence, should be easily adoptable by programmers. Although (one-state) model methods are already part of the JML syntax definition  [11, 28] , they lack a clear semantics and are not fully and soundly implemented in any JML-based tool. We provide a precise semantics by giving an explicit logical encoding of overridable model methods in a first-order verification logic. This encoding is used in the implementation of our approach within the KeY program verifier  [2, 4].
In KeY Java programs and their JML specifications are translated to proof obligations and then proved correct by the KeY verification engine that provides a high degree of automation, and allows for user guided proof interactions where necessary. The work we present here builds on top of previous work done with KeY to support abstract specifications  [45, 47]. Other verification systems have similar support for specification abstraction (see, e. g. ,  [24, 29] ), but none of them allow the specifier to refer to two or more program states in one function, i. e. , functions refer to one state of the program only. In turn it is, e. g. , impossible to define functions that would fully encapsulate a non-trivial relation between the pre and post state of the method. We enrich the concept of abstract predicates by allowing them to refer to more than one program state.
Motivating Example We motivate and explain our specification approach by means of a small Java example. Though small, it captures a typical and intricate situation which occurs symptomatically when object-oriented programs are extended by classes overriding methods with additional unforeseen features. Traditionally, such situations would require that the specification of the original code be re-adjusted to accommodate the new behaviour. Using model methods with dynamic dispatch, such re-adjustment is not needed. The challenge, shown in Fig. 1 , has originally been proposed in  [43]  and has been dealt with in  [5]  using a higher order separation logic. This first figure shows the program annotated with traditional specification means. Cell objects encapsulate integer values which can be set using a method set and be retrieved using get.
set(4); Client. callSet(rc, 5); rc. undo(); assert rc. get() == 4; While this program would not fail its assertion, the proof for that would not succeed as the abstraction of callSet by its contract neglects the additional postcondition oval == \old(val) introduced in Recell and only ensures the weaker postcondition of Cell. This could be amended by introducing case distinctions on the type of the argument in the postcondition of Cell. set. An additional clause c instanceof Recell ==> ((Recell)c). oval == \old(c. val) would achieve this. However, it has significant limitations regarding the modularity of the specification: (1) Details on the implementation of Recell are revealed where it is not necessary and should be kept under the hood and, more severely, (2) the implementation of Recell might not yet be known at the time that Cell is implemented or specified.
Translation into Java Dynamic Logic To verify a Java program, we translate the program and its specification into proof obligations in Java Dynamic Logic (JavaDL from now on), the logic of the KeY theorem prover  [2]. JavaDL is a hierarchically typed first order logic in which the type system contains the reference types of the Java language (java. lang. Object and its subtypes) together with the types Int 1  , Bool, Heap, LocSet and Field. Every type is subtype of the top type Any. We write f : T 1 ×. × T n → T to denote a function symbol mapping n elements of types T 1 ,. ,T n to an element of type T. We write s : T if s is constant symbol or a logical variable symbol. For an n-ary predicate symbol p, we write p : T 1 ×. × T n to denote that it represents a relation on these types. JavaDL uses the standard first-order operators ¬, →, ∧, ∨, ∀, ∃,a n d↔ for, respectively, negation, implication, conjunction, disjunction, universal and existential quantification, and equivalence.
On the level of the prover engine, the previously existing support for model fields and parameter-less observer symbols was extended to support model methods. In particular, the KeY data structures were changed to allow for the observer symbols to take additional heap arguments as well as formal parameter arguments. Consequently, the generation of the corresponding proof rules and proof obligations changed accordingly. The only really new thing in terms of the implementation are the contract rules that allow the use of model method specifications as lemmas. That is, we had to implement generation of proof rules representing formula (2) for every model method specified with a contract rule. The implementation of all the other formulas was an extension and adoption of existing rules for model fields.
These footprints are again defined with model methods. Figure  5  shows an excerpt of the specification for leftSubTree. The reminder of the solution is to maintain the validity of the leftSubTree predicate while the tree is being iterated by the algorithm to find the node to be removed. By the specification of leftSubTree this removal is guaranteed to only change a small part of the tree and keep the overall binary search tree structure intact. Because of the use of several model methods that are recursive and mutually dependent, this challenge is only provable interactively by the current version of KeY, nevertheless, model methods were the enabling factor to solve the challenge at all. Apart from increased expressiveness, model methods also allow one to optimise the verification effort in terms of the size of the underlying proof, especially in the context of an evolving specification when earlier proof attempts can be reused to prove subsequent versions of the program correct.
However, subtyping between predicates is not considered. Darvas  [14, 15]  covers the issue of queries in specifications in a logical setting similar to ours. In particular, welldefinedness and wellfoundedness are treated in depth. We employ techniques from his thesis to conduct termination proofs in our approach. The approach does not distinguish between contracts (postconditions) and definitions (method bodies) and needs a satisfiability check for the specifications. An advantage of our approach is that satisfiability of the model method description does not need to be shown as the return statement always gives an explicit witness to the value of the method. Inheritance and two-state predicates are not considered by  Darvas. The first occurrence of calculus rules for dynamic dispatch of regular method invocations in program verification is by Soundarajan  [46] , the concept has since been introduced into many verification tools for languages with polymorphism.
Acknowledgements. We are very grateful to  Peter H. Schmitt  for his constructive suggestions and valuable comments which helped improve this paper and to  Jesper Bengtson  for pointing out this problem to us and ensuing discussions. The work of  Wojciech Mostowski  is supported by  ERC  grant  258405  for the  VerCors  project and by the  Swedish Knowledge Foundation  grant for the project  AUTO-CAAS. The work of  Mattias Ulbrich  is supported by the  DFG (German Research Foundation ) under the  Priority Programme SPP1593: Design For Future -Managed Software Evolution. Open Access. This chapter is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/ 4. 0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.
Datasets We compared miRNAFold with Mirinho on several sequences, including three artificial sequences: (i) The artificial sequence considered in (5) and composed of 101 human pre-miRNAs and 100 human pseudo pre-miRNAs (Artificial1). (ii) A sequence composed of 863 human pre-miRNAs and 7422 human pseudo pre-miRNAs (dataset from (  10 )) (Artificial2). (iii) A sequence composed of 1677 cross-species pre-miRNAs and 8266 cross-species pseudo pre-miRNAs (dataset from (  10 )) (Artificial3). For the two first sequences, which are composed of human data, we consider both All genomes model and Homo-Sapiens model (see above). For the third sequence, composed of cross species sequences, we consider only the All genomes model.
Methodology validation A sizeable discrepancy was found in the ∆ p T 34 and ∆φ 34 distributions between the data and AHJ (See Section 9 for details), suggesting that there are more sub-leading jets (jets 3 and 4) that are back-to-back in AHJ than in the data. In order to test that the discrepancies are not from mis-modelling of SPS in AHJ, the ∆ p T 34 and ∆φ 34 distributions in the SPS sample extracted from AHJ were compared to the distributions in the SPS sample generated in Sherpa. Good agreement between the shapes of the distributions was observed for both variables. This and further studies indicate that the excess of events with jets 3 and 4 in the back-to-back topology is due to an excess of DPS events in the AHJ sample compared to the data. In order to verify that the topologies of cDPS and sDPS events can be reproduced by overlaying two events, the overlay samples are compared to the cDPS and sDPS samples extracted from AHJ.
adjusted symmetry factor is 1 1 + δ AB -→ 1 - 1 2 σ B 2j σ A 2j = 0. 9353 ± 0. 0003 (stat. ) , (22) as determined from the measured dijet cross-sections. This factor was also determined using Pythia6 and good agreement was observed between the two values. The relative difference in the value of σ eff obtained by using the symmetry factors extracted from the data and from Pythia6 was of the order of 0. 2%, a negligible difference compared to the statistical uncertainty of σ eff. An additional correction of +4% is applied to the measured DPS cross-section due to the probability of jets from the secondary interaction overlapping with jets from the primary interaction. In this configuration, the anti-k t algorithm merges the two overlapping jets into one, and hence the event cannot pass the four-jet requirement.
Normalized differential cross-sections To allow the results of this study to be used in future comparisons with MPI models, the distributions of the variables used as input to the NN were corrected for detector effects. The corrections were derived using an iterative unfolding, producing an unfolding matrix for each observable, relating the particle-level and reconstructed-level quantities. These matrices were derived using samples of four-jet events selected from the AHJ and Pythia6 samples by imposing the cuts detailed in Eq. (  11 ) on particle jets. The AHJ sample generated with the AUET1 tune was used to derive the unfolding matrix. The distributions were unfolded with the Bayesian unfolding algorithm, implemented in the RooUnfold package  [74] , using two iterations. The unfolding matrices derived from AHJ were taken as the nominal matrices and the differences observed when using the matrices derived from Pythia6 were used as an additional systematic uncertainty, typically of the order of a few percent in each bin.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF, I-CORE  and  Benoziyo Center , Israel; INFN, Italy;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  RCN, Norway ;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
In addition, individual groups and members have received support from  BCKDF , the  Canada Council, CANARIE ,  CRC ,  Compute Canada, FQRNT , and the  Ontario Innovation Trust ,  Canada ;  EPLANET ,  ERC ,  FP7 ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex and Idex ,  ANR ,  Région Auvergne  and  Fondation Partager le Savoir, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF; BSF ,.
Bioinformatic and functional characterization of new loci. We used several techniques to prioritize and characterize genes underlying the identified associations, uncover connections between associated regions, detect relevant tissues and assign functional annotations to associated variants. These included expression quantitative trait loci (eQTL) analyses, pathway analyses, DNAse I hypersensitivity site (DHS) mapping, chromatin mapping, manual curation of genes in each region and zebrafish knockdown. eQTL analysis. We performed eQTL analysis using publically available eQTL databases (see Methods). These analyses connected novel SNPs to transcript abundance of SYPL2, SDCCAG8, MANBA, KBTBD2, PTPRO and SPATA33 (C16orf55), thereby supporting these as potential candidate genes in the respective associated regions (Supplementary Table  11 ).
zFor random-effect estimate, see Supplementary Table  4. #P value of the meta-analysis of the twice GC-corrected discovery meta-analysis results and replication studies. **Between-study heterogeneity, as assessed by the I 2. Q statistic P value 40. 05 for all SNPs, except rs11666497 (SIPA1L3, P value ¼ 0. 04). wwFor this SNP, the conditions for replication were not all met (q-value 40. 05 in the replication stage). DNase I hypersensitivity and H3K4m3 chromatin mark analyses. To evaluate whether eGFRcrea-associated SNPs map into gene regulatory regions and to thereby gain insight into their potential function, we evaluated the overlap of independent eGFRcreaassociated SNPs with P values o10 À 4 (or their proxies) with DHSs using publicly available data from the Epigenomics Roadmap Project and ENCODE for 123 cell types (see Methods).
5 million SNPs, based on European-ancestry haplotype reference samples (HapMap II CEU). Four studies based their imputation on the 1000 Genomes Project data. Imputed genotypes were coded as the estimated number of copies of a specified allele (allelic dosage). Genome-wide association analysis. By following a centralized analysis plan, each study regressed sex-and age-adjusted residuals of the logarithm of eGFRcrea or eGFRcys on SNP dosage levels. Logistic regression of CKD status was performed on SNP dosage levels adjusting for sex and age. For all traits, adjustment for appropriate study-specific features, including study site and genetic principal components was included in the regression and family-based studies appropriately accounted for relatedness. Stage 1 discovery meta-analysis. GWAS of eGFRcrea were contributed by 48 studies (total sample size, N ¼ 133,413); 45 studies contributed GWAS data for the non-diabetes subgroup (N ¼ 118,448) and 39 for the diabetes group (N ¼ 11,522).
GWAS of CKD were comprised by 43 studies, for a total sample size of 117,165, including 12,385 CKD cases. GWAS of eGFRcys were comprised by 16 studies for a total sample size of 32,834. All GWAS files underwent quality control using the GWAtoolbox package  32  in R, before including them into the meta-analysis. Genome-wide meta-analysis was performed with the software METAL  33  , assuming fixed effects and using inverse-variance weighting. The genomic inflation factor l was estimated for each study as the ratio between the median of all observed test statistics (b/s. e. )  2  and the expected median of a w 2 with 1 degree of freedom, with b and s. e. representing the effect of each SNP on the phenotype and its standard error, respectively  34. Genomic-control (GC) correction was applied to P values and s.
Summarizing all in silico and de novo replication studies (Supplementary Table  1 ), replication data for eGFRcrea were contributed by 14 studies (total sample size ¼ 42,166), which also contributed eGFRcrea results from non-diabetes (13 studies, N ¼ 36,433) and diabetes samples (13 studies, N ¼ 4,955). Thirteen studies contributed replication data on CKD (N ¼ 33,972; 4,245 CKD cases; studies with o50 CKD cases were excluded) and five on eGFRcys (N ¼ 14,930). Association between eGFRcrea, CKD and eGFRcys and each of the 48 SNPs in the replication studies was assessed using the same analysis protocol detailed for the discovery studies above. Quality control of the replication files was performed with the same software as described above. We performed a combined fixed-effect meta-analysis of the double-GC corrected results from the discovery meta-analysis and the replication studies, based on inverse-variance weighting.