dataesr/scientific-paragraphs-categorization huggingface dataset:
0. 5 (A. F. A. Smit, R. Hubley, and P. Green, RepeatMasker Open-4. 0 2013-2015; http://www. repeatmasker. org) for synteny analyses and gene model predictions. RNA-seq-assisted gene model prediction. The accuracy of gene prediction is significantly enhanced by incorporating evidence for intron splice sites and gene models from a closely related organism  (66). The transcriptome of 3D7 was previously characterized using transcriptome sequencing (RNA-seq)  (41). We used RNA-seq data covering six time points throughout the infection cycle of 3D7 on wheat seedlings (3 to 56 days postinfection  [dpi] ). We also used additional deep RNA-seq data generated for time points of 7, 14, and 56 dpi. All raw data were accessed from the records deposited in the NCBI Short Read Archive under accession numbers SRX1116288 and SRX1116289.
RNA-seq reads were quality trimmed using Trimmomatic version 0. 33  (67)  and aligned to the 3D7 genome using TopHat version 2. 0. 14  (68). Intron splice site hints were generated using bam2hints, included in the AUGUSTUS version 3. 2. 1 software distribution  (69). Due to the very high RNA-sequencing depth available, intron splice hints were filtered for a minimum coverage of 20 reads to avoid an impact of spurious splice signals on gene prediction. In addition to splice site hints, we mapped predicted protein sequences of the fully sequenced reference genome of Z. tritici isolate IPO323 to the genome of 3D7 using exonerate (protein2genome model; minimum identity 95%)  (70). We retrieved protein sequences based on the RNA-seq-assisted and significantly improved gene models by Grandaubert et al.
Furthermore, we screened protein sequences for the presence of secretion signal, transmembrane, cytoplasmic, and extracellular domains using a combination of SignalP version 4. 1  (73) , Phobius version 1. 01  (74) , and TMHMM version 2. 0  (75). Differential gene expression analyses during infection. We assessed the transcription profiles of all genes using RNA-seq data generated from the 3D7 isolate  (41). Plants of the wheat cultivar Drifter were infected, and leaves were collected between 3 and 56 days postinfection (dpi). Time points between 3 and 14 dpi cover both the early asymptomatic and late symptomatic infection phases  (41). Time points from 21 to 56 dpi were considered to be the saprotrophic growth phase of the fungus. We mapped raw reads retrieved from the data deposited in the NCBI Short Read Archive under accession number SRX1116288.
We quality trimmed reads using Trimmomatic version 0. 33  (67) , using the settings "ILLUMI-NACLIP:TruSeq3-PE. fa:2:30:10 LEADING:10 TRAILING:10 SLIDING-WINDOW:5:10 MINLEN:50. " We used TopHat version 2. 0. 14 to align reads to the 3D7 genome and included the genome annotation as a guide  (68). Mapped reads overlapping gene models were counted using HTSeq-count, setting the matching mode to "union" and filtering out reads with an alignment quality below 10 (76). Reads per kilobase of transcript per million mapped reads (RPKM) were calculated using the R package edgeR  (77). The maximum upregulation of genes during infection was calculated by identifying the maximum and minimum RPKM among the time points covering the infection  (3, 7, 11, and 14 dpi). Prediction of the secretome and identification of candidate effectors.
(43)  for details on the effector gene library used for training. Orphan genes and chromosomal synteny analyses. The predicted genes in 3D7 were mapped on the genome of IPO323 using BLASTn version 2. 4. 0  (78). The orphan genes were defined as having no blast hit in the IPO323 genome. Synteny analyses between the 3D7 and IPO323 genomes were performed using BLASTn on repeat masked genomic sequences. As the synteny analyses were performed for intraspecific genome comparisons, blast hits were stringently filtered for a minimum alignment length of 500 bp, a minimum identity of 90%, and an E value of 0. Syntenic regions were visualized jointly with the location of genes and transposable elements using the R package genoPlotR  (79). Accession number(s). The genome assembly and annotation of the 3D7 genome were submitted to the European Nucleotide Archive (http://www.
ACKNOWLEDGMENTS We are grateful for advice on PacBio sequencing from  Andrea Patrignani  and  Gerrit Kuhn. The sequencing was performed at the  Functional Genomics Center Zurich (FGCZ). Marcello Zala  provided advice on DNA extraction methods and assistance in the laboratory. Robert King  and  Guy Leonard  provided advice on data submission. Simone Fouché ,  Fanny Hartmann ,  Bruce A. McDonald , and  Thierry Rouxel  provided critical input on a previous version of the manuscript. C. P. was supported by an  INRA   Young Scientist grant.
7. 5a)  45. BAM files were subjected to PCR duplicate removal (Picard v1. 119), followed by realignment around indels and base recalibration using GATK tools (v3. 2-2). Single nucleotide and Indel variant calling was performed using samtools mpileup (v1. 2) and VarScan (v2. 3. 7)  46 on the on-target BAM files obtained for each patient using the following parameters: samtools-minimum mapping quality ¼ 1, coefficient for downgrading mapping quality ¼ 50, minimum number gapped reads for indel candidates ¼ 3 and minimum fraction of gapped reads ¼ 0. 0002; VarScan somatic algorithm-minimum variant allele frequency threshold ¼ 0. 08, tumour purity 0. 9, minimum read depth at a position to make a call ¼ 10 and Somatic P value ¼ 0. 1 (Fisher's Exact Test). Raw variant calls were annotated for presence in the dbSNP and COSMIC databases as well as mutation effect on gene transcript by Annovar (v.
Raw counts were imported in R, log transformed and smoothed by computing the median using bin of 100 kb. DNAcopy (v1. 40) and cghMCR (v1. 24) packages were used to identify genomic regions with altered copy number. This was obtained by smoothing the data to remove single point outliers before applying a circular binary segmentation algorithm, which tests the significance of the alteration by using a hybrid approach  50. Segments with ratio over 0. 25 or under À 0. 25 and more than eight supporting markers were considered as 'gain' or 'loss', respectively. Except when indicated, all analysis was carried out using either an assortment of R system software (http://www. R-project. org, v3. 1. 2) packages including those of Bioconductor (v3. 1) or original R code. Evaluation of mutation effects on SETD2.
Among the additional 16 cases, paired normal/tumour DNAs were sequenced in 10 (two EATL-II and eight EATL-I) with matching normal tissue available, and only tumour DNA was sequenced in eight EATL-II without matching normal tissue available. The Ion AmpliSeq designer software was used to generate three highly specific panels consisting of 281 amplicons (amplicon range 125-175 bp), covering the whole coding sequence of SETD2, CREBB and JAK1 and the most recurrent mutation hotspots in JAK3 and STAT5B and other JAK and STAT family members described in hematopoietic or lymphoid tumours (COSMIC) and in our EATL-II cohort (Supplementary Table  3 ). DNA libraries were prepared using the Ion AmpliSeq Library Kit v2 and the Ion OneTouch automated systems according to the manufacturer's instructions.
1 (Fisher's Exact Test). Furthermore, we kept only variants that showed an allele frequency 415% in the tumour counterpart while having allele frequency o10% in the normal as well as having at least 18 reads supporting the reference sequence and more than five reads supporting the variant. In addition, the differences in frequency between tumour and normal were set to be Z15%. Discrepancies with exome sequencing were resolved by visual inspection with IGV software to confirm or refuse the candidate alteration. Immunohistochemistry. IHC staining was done on 4 mm FFPE sections from native blocks and from a tissue microarray comprising cores representative of a subset of EATL-II cases. Reactive tonsils were used as external controls for all staining. Briefly, after deparaffinization, antigen retrieval was performed in Tris/EDTA pH 9.
0 in a pressure cooker respectively 1. 30 min for Histone H3, H3K36me3 and H3K36me2 and 5 min for SETD2. Slides were incubated with Histone H3 (Abcam, ab1791, dilution 1:1,000), H3K36me3 (Abcam, ab9050, dilution 1:2,000) or H3K36me2 (Abcam, ab 9049, 1: 4,000) or SETD2 (Sigma, HPA042451, 1:400) antibodies for 1 h at room temperature. Antibody detection was performed with diaminobenzidine detection system (Dako), according to the manufacturer's instructions. For SETD2 we used a C-terminal SETD2 antibody that would not detect SETD2 affected by protein truncations distal to the reported mutations. Two pathologists independently performed quantification of H3K36me3, H3K36me2 and SETD2, blinded to the SETD2 genotype. The individual scores were overall concordant and the cases were reviewed for consensus final scoring.
FISH assays. To validate CNV data from WES, we developed original FISH probes designed to identify deletions of the SETD2 locus at 3p21. 31. Bacterial artificial chromosome (BAC) clones were chosen using the UCSC (University of California Santa Cruz) Genome Browser website (http://genome. ucsc. edu, February 2009 assembly) and purchased from BACPAC Resources. We selected two BAC clones hybridizing to the SETD2 locus (target probes at 3p21. 31: RP11-425J9 and RP11-650F17, GenBank references AC094020. 2 and AQ463932. 1/AQ511510. 1, respectively) and two BAC clones hybridizing to a preserved region on chromosome 3 (control probe at 3p25: RP11-266J6 and RP11-485N3, GenBank references AC011610. 11/AC022382. 4 and AQ633871. 1/AQ633873. 1, respectively), as illustrated in Supplementary Fig. 3. Following overnight culture, BAC DNA was extracted (Qiagen Plasmid Maxi Kit, Qiagen) and fluorescently labelled by nick translation (Nick Translation Kit, Abbott Molecular).
Acknowledgements This work was supported by grants from the  MEDIC Foundation , the  Ligue du Cancer (Switzerland) , and the  Plan Cancer (Belgium). We thank  Catherine Chapuis ,  Patricia Hornitschek  and  Susana Leuba  ( Pathology Institute, Lausanne )  Keith Harshmann  ( Sequencing Platform Facility, University of Lausanne ), and  Virginie Fataccioli  (Tenomic Coordinator).
Data availability. The VCF files containing the somatic variants of the samples analysed by whole-exome sequence have been deposited in the European Genome Archive (EGA) under accession code EGAS00001001879. SET and AWS domain structures (PDB-ID 4FMU) and the SRI domain structure (PDB-ID 2A7O) were obtained from PDB. All other data are available in the paper and Supplementary Information files, or available from the author upon request.
Familial aggregation analysis is the first fundamental step to perform when assessing the extent of genetic background of a disease. However, there is a lack of software to analyze the familial clustering of complex phenotypes in very large pedigrees. Such pedigrees can be utilized to calculate measures that express trait aggregation on both the family and individual level, providing valuable directions in choosing families for detailed follow-up studies. We developed FamAgg, an open source R package that contains both established and novel methods to investigate familial aggregation of traits in large pedigrees. We demonstrate its use and interpretation by analyzing a publicly available cancer dataset with more than 20 000 participants distributed across approximately 400 families.
Besides basic pedigree analysis, sub-setting and plotting methods, it implements the previously published methods based on average kinship and disease incidence rates as well as two novel approaches to detect familial aggregation employing statistics based on kinship coefficients combined with Monte Carlo simulation techniques. The FamAgg package implements five family aggregation detection methods that can be run on a single family or sets of families and allow stratification according to different conditions such as gender, age and generation. The kinship sum (KS) test assesses whether an affected subject is more closely related to other affected rather than unaffected cases in the pedigree. Let A be the set of affected subjects and N the number of simulation steps. The kinship sum of subject i to all other affected cases is S i ¼ P j2A; j6 ¼i U ij , whose null distribution S is obtained by N-time random sampling of #(A) affected cases from the complete pedigree without replacement.
This measure has been refined by weighting the individual's contribution and time spent in the study by the kinship coefficient U ij to arrive at a measure of familial incidence rate FR i for any individual i. Finally, we provide a convenience interface to compute the exact probability of familial clustering (PFC) of phenotypes as provided in the gap R package  (Yu et al. , 2002). It contrasts the number of affected cases against family sizes in a contingency table but the estimation of an exact p-value is possible only for families of limited size, due to the high computational demand. The method is based on the exact test for multinomial distributions, and therefore its application to large pedigrees is possible only with the aid of pedigree splitting software such as for example Jenti  (Falchi et al.
, 2008). With the exception of the GIF method, which identifies aggregation of a trait in the full pedigree all kinship-based methods are applied at the level of individuals and thus allow to identify either individuals in families with significant aggregation (KS test), or groups of highly clustered affected individuals within families (KG test), or assess the risk for individuals given their relation to affected individuals in the pedigree (FIR). In addition to these familial aggregation methods, FamAgg provides functions to sub-set pedigrees, to identify common ancestors for any given list of individuals, to identify matched controls within pedigrees and to convert pedigrees into graphs, which opens the whole world of graph-theory methods to pedigree analyses. It uses the kin-ship2 R package  (Sinnwell et al.
Applications We applied the KS, GIF, KG and FIR tests from the FamAgg package to the publicly available Minnesota Breast Cancer dataset  (Sellers et al. , 1999) , which contains genealogical information from 426 unrelated affected founders whose families entered a longitudinal study on cancer in the state of Minnesota (USA) in 1944. There are 1376 cases spread over these 426 families with a median family size of 53 members, the largest family comprises of 382 individuals in six generations. The performed tests did not utilize sampling stratification and the null distributions were calculated with N ¼ 50 000 sampling steps. Runtimes on a single 2. 4 GHz processor of a MacBook Pro with 16 GB of memory are as follows: FIR test, 2 s; GIF test, 7 min; KS test, 23 min; and KG test, 3 h. At a significance level of 0.
Preface This book presents the scientific results achieved within the framework of the European Cooperation in Science and Technology (COST) Action IC1004 "Cooperative Communications for Green and Smart Environments" (see www. ic1004. org for details). COST Actions have succeeded in providing one of the best networking instruments of FP6 and FP7, and currently in H2020. In COST the research excellence of universities meets the technological leadership and applied view of the Industry, increasing the synergies between both, and getting mutual benefit from joint discussions. COST Actions base their success in the networking effects produced within its framework, which are not relying on any partnership contract or specific funded competitive objectives, but on the bottom-up approach of sharing results, and the technical discussions among its participants.
COST actions are better understood as a networking instrument, where results from projects funded by many different sources are discussed, and a natural coordination between the participating researchers' interests is reached. A total of 600 individuals from more than 140 institutions have participated to IC1004, both from Academia and Industries, coming from 34 countries in Europe, America and Asia. The scientific focus of COST IC1004 has been the evolution of Mobile and Wireless technologies towards cooperative networking strategies, in all the new scenarios of application and challenges that came up after the exponential increase of broadband data service demand. COST IC1004 run in parallel with the deployment of the 4G (LTE) networks and its evolution, as well as with the initial steps in the definition of 5G concepts, being many of the green and smart scenarios identified at the beginning of the Action those currently included in the ITU and 3GPP roadmaps.
In RAN deployment, it is by now obvious that installing smaller cells in areas where the infrastructure can provide the required backhaul connectivity is the current natural evolution of the RAN infrastructure. The cost of its deployment versus the amount of data it can handle is beginning to be competitive compared to other solutions. But these small cells will also require new techniques for configuration, management and optimisation. Self-configuration of such elements of access networks is expected, as well as coordinated behaviour among groups of small cells, together with neighbouring network sites. The deployment of new radio networks is based on facilitating access to devices by installing small cells or access nodes, which are interconnected in groups by high-speed backhaul networks, and cooperate within the group to manage the resources in a joint cooperative area.
Assuming that the amount of data traffic is to grow by two orders of magnitude in the coming decade, no matter what will be the type of traffic and the "killer" applications or services in these future networks, the need for more resources to deal with even more than 100 times more mobile data traffic in the 2020s is apparent. These "resources" could of course come from additional spectrum, although this is already scarce below 3 GHz and less feasible after the second digital dividend, but obviously still available in higher frequency bands for short-range broadband connections. While UHF remains being the main allocation for mobile services, with many thousands of moving terminals per square kilometre, new concepts for efficient spectrum usage will have to appear. Among other approaches, opportunistic access to certain frequency bands, CR, co-primary usage in shared bands, and massive and distributed MIMO are already under deployment.
Scope of the Book This book is structured into twelve chapters. Chapter 2 provides an understanding of the propagation phenomena in the diverse urban environments, to enable the design of efficient wireless networks in future bands and scenarios. The chapter includes results of studies related to 4G and future 5G radio systems for both outdoor and outdoor to indoor, while scenarios include rural and highway, base station to pedestrian users, vehicular-to-vehicular, V2i, container terminals, vegetation and high-speed mobility such as trains. Chapter 3 addresses aspects of radio link and system design in indoor scenarios for supporting ubiquitous indoor data connectivity and location and tracking services. In particular, the chapter deals with modelling of the complex indoor radio channels for effective antenna deployment, evaluation of mmW radios for supporting higher data rates, and indoor localisation and tracking techniques.
Scope of the Book 15 Chapter 11 gives first an insight on general underlying concepts of the state of the art of MIMO over-the-air (testing) (OTA) technologies, and then describes four specific methods for MIMO terminals testing proposed by COST IC1004 to standardisation bodies: multi-probe anechoic, reverberation, two-stage, and decomposition. Finally, Chapter 12 attempts to risk a vision on the future challenges and developments in mobile and wireless communications, on a time scale of about 5 years. This is based on the results obtained by the COST IC1004 network of scientific experts, and on the three main "pillars" of the success story of follow-up Radio communication COST Actions, which are antennas and channels, signal processing, and device networking. The increase in demand for high-data rates on the move in the complex urban environment requires either the allocation of new spectrum such as available contiguous spectrum in the mm-wave band or the use of novel configurations such as the application of massive multiple-input multiple-output (MIMO) technology.
The information of spread in angle domain and time domain cannot be predicted with the conventional empirical propagation models. Instead, deterministic prediction models become more interesting to predict the propagation channels for the 4G networks. The RT model is one of the popular deterministic models nowadays. It uses physical models of radio propagation mechanisms, such as reflection and diffraction, and detailed information of the environment to provide deep insight into the propagation channels. Most RT models with a detailed building database in urban scenarios results in excessive computational complexity, which limits the use by the mobile system operators. Most of the current research in the area of deterministic propagation modelling deals with reducing the computational complexity without losing the prediction accuracy, modelling effects like diffuse scattering or the use of efficient computational techniques to reduce time.
Pedestrian users Pedestrian users are generated by using a simulation approach stated in the study by  Hahn et al. [HRSK15]. The resulting traces are representing individual users, which follow an aim-oriented behaviour. Besides paths that are provided as sidewalks in the geographical database, automatically generated short cuts a pedestrian might also take when leaving the path are considered. The contained traces simulate users on their way moving from an entry point of a building to another one, the route in between is determined by a routing algorithm.
8). Figure  3. 20 shows an example of the multipath-based tracking of an agent position using the EKF with data association (EKF-DA). Here only one anchor was used. The SINRs were estimated at 60 measurement positions indicated by grey crosses. Also the position error bound (PEB) is shown over the entire floor plan. It has been computed using the estimated SINRs, providing a prediction of the spatial distribution of the achievable performance. We consider these maps as an indicator for the robustness of the localisation system. A real-time implementation of this multipath-assisted tracking method has been described in  [MLLW14]  to demonstrate the practicality of this approach. This work presents an in-depth analysis of position-related information estimation and agent tracking for different environments.
For the ray-tracing models, there are a number of ways to develop a detailed and accurate topographic database including all objects on the surface depending upon the type of channel under investigation. One way is to use public-domain OpenStreetMap (OSM) database for the purpose of outdoors deterministic vehicular channel modelling. In Nuckelt et al. [NRJK13] , a guideline to make use of OSM data is presented. It is also investigated that whether or not the available building data is capable to provide a satisfying accuracy required for an adequate channel modelling. Furthermore, in Nuckelt et al. [NAT + 13], a ray-tracing approach is used for V2V channel characterisation in an urban intersection scenario. For the simulations, relevant building data has been obtained from the OSM database as described in Nuckelt et al.