dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion and conclusions Multiple features, which are at least in part interdependent, affect what can be considered as 'quality' of a long-range chromatin interaction assay. It is obvious that several experimental steps and procedures can be performed under more or less optimal conditions and that this will influence the final dataset. Some of the variables are purely experimental (crosslinking, restriction digest, end repair and biotin labelling in HiC; crosslinking, sonication and IP/antibody quality in ChIA-PET; generation of the sequencing library as well as sequencing coverage); others are bioinformatic (read alignment stringency). In this context, previous studies suggested that quality assessment in chromatin interaction assays could be performed by evaluating the alignment statistics, the frequency of dangling-end or self-circle PETs to reveal potential experimental problems during sample preparation, the levels of duplicated PETs as indicator of library complexity and PCR amplification bias, the fraction of intra over inter-chromosomal interactions and the frequency of long-range versus short-range intra-chromosomal interactions (see also  [13] ).
The use of random subsampling of mapped PETs follows the same principle as for mapped reads from ChIP-seq assays. Specifically, this methodology is based on the concept of a "sequencing saturation point", beyond which no new enrichments can be identified  [7, 14]. This concept has been initially evaluated in a retrospective manner in ChIP-sequencing assays by assessing the number of significant binding sites retrieved when only a subset of the original sequenced reads is used for profile reconstruction (read random sub-sampling approach;  [15] ). In a similar manner we have shown empirically that in ChIP-sequencing assays genomic regions with high intensity levels followed a proportional decrease after mapped read subsampling  [6]. LOGIQA is an independent tool that complements the NGS-QC database with quality score information associated to long-range chromatin interaction assays.
In fact, the study of chromatin interactomes is rapidly gaining popularity in scientific community, as revealed by >170 publications indexed in Medline (November 2015) and >500 datasets deposited in GEO. While these numbers are small compared to several thousand ChIP-seq and related datasets, there is an obvious need of establishing quality standards for both types of datasets. Since our first release of the NGS-QC Generator tool in 2013, we have processed more than 30,000 public datasets and we expect to cover virtually all ChIP-seq datasets by 2016. Similarly, LOGIQA will be expanded to cover all available HiC datasets and other type of datasets, like ChIA-PET. Ultimately, we will provide to users a cross-visualisation platform that displays datasets processed by the NGS-QC Generator together with those retrieved by LOGIQA such that users can explore long-range chromatin interaction maps in the context of available ChIP-seq and related datasets.
Acknowledgements NGS-QC Generator tool/database and LOGIQA were developed in the laboratory of  Hinrich Gronemeyer , which is supported by the  AVIESAN-ITMO Cancer , the  Ligue National Contre le Cancer  ( HG; Equipe Labellisée ) and the  Institut National du Cancer (INCa). The support of the  Fondation pour la Recherche Médicale (FRM)  for the position of a bioinformatics engineer is acknowledged. Author details 1  Equipe Labellisée Ligue Contre le Cancer, Illkirch, France. 2 Department of Functional Genomics and Cancer, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) ,  Illkirch, France. 3  Centre National de la Recherche Scientifique   UMR 7104 , Illkirch, France. 4  Institut National de la Santé et de la Recherche Médicale   U964 , Illkirch, France. 5  University of Strasbourg, Illkirch, France.
The genus Pan is the closest genus to our own and it includes two species, Pan paniscus (bonobos) and Pan troglodytes (chimpanzees). The later is constituted by four subspecies, all highly endangered. The study of the Pan genera has been incessantly complicated by the intricate relationship among subspecies and the statistical limitations imposed by the reduced number of samples or genomic markers analyzed. Here, we present a new method to reconstruct complete mitochondrial genomes (mitogenomes) from whole genome shotgun (WGS) datasets, mtArchitect, showing that its reconstructions are highly accurate and consistent with long-range PCR mitogenomes. We used this approach to build the mitochondrial genomes of 20 newly sequenced samples which, together with available genomes, allowed us to analyze the hitherto most complete Pan mitochondrial genome dataset including 156 chimpanzee and 44 bonobo individuals, with a proportional contribution from all chimpanzee subspecies.
t. schweinfurthii, also known as the eastern chimpanzee, is located in Tanzania, Burundi, Rwanda, Uganda, and the Democratic Republic of the Congo  (Stone et al. 2010) ; and (iv) P. t. ellioti, is the Nigeria-Cameroon chimpanzee, and although its establishment has been very controversial  (Becquet et al. 2007 ), the latest nuclear genome  (Bowden et al. 2012; Prado-Martinez et al. 2013 ) as well as Y chromosome studies  (Hallast et al. 2016 ) have found clear evidence supporting its distinct identity. In contrast, bonobos are not classified in subspecies but three clades have been described based on mitochondrial DNA analysis  (Zsurka et al. 2010 ) and they are found exclusively in the Democratic Republic of the Congo, separated from chimpanzees by the Congo River. Mitochondrial DNA has been widely used in evolutionary studies due to its high mutation rate  (Lynch et al.
Recent technological developments have made it much simpler to generate full genome datasets in humans (The 1000 Genomes Project Consortium 2015) and closely related species  (Prado-Martinez et al. 2013 , Carbone et al. 2014 ). This has motivated the development of methods to reconstruct mitochondrial sequences from WGS data, with MITObim  (Hahn et al. 2013)  being the most powerful and recently published methodology. This software uses modules of the MIRA sequence assembler  (Chevreux et al. 1999)  to map the reads to a reference sequence based on conserved regions. The resulting contigs are subsequently used to retrieve overlapping reads and incorporate them in an iterative way to extend and complete the sequence. Although this approach proved useful and has a good accuracy rate (more than 99.
5% for publication data and 99. 38% for our data), it has limitations such as the inclusion of nuclear mitochondrial regions (NUMTs) into the final sequence or lower mapping efficiency in the start and end of the linearized mitogenome. In order to overcome these limitations, we developed a different approach, which we called mtArchitect (http://www. biologiaevolutiva. org/tmar ques/mtarchitect/). Considering both published and new mtArchitect reconstructed mitogenomes, this work gathers the largest dataset of complete mitochondrial sequences in the Pan genus, including 200 samples. We analyzed and compared mitochondrial and nuclear genomes, trying to infer the evolutionary forces and demographic changes that shaped the mitochondrial genome of the genus Pan.
Dataset A new set of 20 complete chimpanzee genomes was sequenced to a mean coverage of 25Â from peripheral blood on an Illumina HiSeq 2000 sequencing platform (supplementary table 1, Supplementary Material online) and their mitogenome was reconstructed with our new computational approach, mtArchitect (Genbank access numbers KU308529-KU308548) (fig. 2 ). In addition, we added de novo mitochondrial genome reconstructions from whole genome data of 32 individuals (Prado-Martinez et al. 2013) (Genbank access nos. KX211928-KX211959) and we merged data from previously published articles  (Stone et al. 2010; Zsurka et al. 2010; Bjork et al. 2011; Fisher et al. 2011; Hvilsom et al. 2014). To avoid the presence of redundant sequences from relatives in our dataset, we looked for matrilineally related individuals in the studbooks  (Carlsen and Jongh 2007; Ross 2009 ) and eliminated the sample with the lowest FIG.
(7) The final sequence is constructed from the consensus of the 40 assemblies. quality of each corresponding pair. Because many samples did not have any family information, we aligned each subset against the rest using BLAST  (Camacho et al. 2009)   1 , Supplementary Material online)) was aligned to the chimpanzee NCBI reference sequence (NC001643) using MAFFT 7. 130b  (Katoh and Standley 2013). Neighbor joining trees of whole mitochondrial genomes, coding regions and D-loops were constructed using MAFFT with 1,000 bootstrap repetitions and were edited with FigTree v1. 4. 0 (http://tree. bio. ed. ac. uk/software/figtree/). The similarity of the trees was assessed by calculating the linear correlation coefficient (r) between the distance matrices obtained for each alignment with ClustalW2  (Larkin et al.
Genetic Diversity Genetic diversity analyses were performed using our full dataset and were compared with a similar dataset of Homo sapiens mitochondrial sequences (200 African and 200 European individuals) randomly obtained from MitoTool  (Fan and Yao 2011). Intraspecific and interspecific nucleotide pairwise distances (nucleotide diversity, , Nei 1987) of complete mitogenomes were calculated using MEGA 6. 06  (Tamura et al. 2013). To compare mitochondrial with nuclear variation, chromosome 21 pairwise distances were also calculated with data from Prado-Martinez et al. (  2013 ), using R (R Development Core Team 2014). The mitochondrial multifasta alignment was divided in gene sequences using Jalview 2  (Waterhouse et al. 2009 ), maintaining the reading frame. Interpopulational fst values, the number of segregating sites (S), Tajima's D  (Tajima 1989) , and Fu's F s  (Fu 1997)  were obtained for each gene and for the whole mitogenome of each subset using Arlequin  (Excoffier and Lischer 2010)  For this analysis, the control regions of the 198 samples were used, removing missing data, gaps and length polymorphisms to focus on potentially neutrally evolving sites.
They were ranked based on the resulting P value and the first 10 summary statistics overlapping among the 10 random subsets were chosen. To calculate each model's posterior probability we used a weighted multinomial logistic regression (LR) (Beaumont 2008) using R scripts from https://code. google. com/archive/p/popabc/, modified by S. G. and A. B. To evaluate the stability of the posterior probabilities, we fixed different thresholds of retained simulations (supplementary fig. 1 , Supplementary Material online). Type I Error and the rate of true and false positives were estimated by generating 1,000 pseudo-observed datasets according to each model analyzed, with parameter values randomly chosen from the corresponding prior distributions. Further, we inferred the demographic history by Bayesian Markov Chain Monte Carlo (MCMC) with BEAST 2 v.
Acknowledgments Centre de RegulacioGenomica/Centro Nacional de Analisis Genomico (CRG-CNAG) ,  Baldiri Reixach  4-8, 08023  Barcelona, Spain/SGR AGAUR/Maria de Maeztu. The authors thank  Luca Betti  for drawing the map of the Chimpanzees distribution,  Stefania Rubbini  for helping with the dataset management, T. M-B is supported by  ICREA  (www. icrea. cat), EMBO YIP  (www. embo. org)2013, MINECO  BFU2014-55090-P (FEDER) ,  BFU2015-7116-ERC  and  BFU2015-6215-ERCU01 MH106874  grant,  Fundacio Zoo Barcelona  and  Secretaria d Universitats i Recerca del Departament d Economia i Coneixement de la Generalitat de Catalunya. S. T. was supported by the  Young Researcher Fellowship  ( 5Â1000 Year 2010  and Unicredit 2013 Funds) from the  University of Ferrara.
The literature also speaks of other approaches which have treated dialogues as planning operators. In  [23]  the authors introduce a new language called MAPL (Multi Agent Programming Language) which represents speech acts as operators with qualitative and quantitative temporal relations between them. Other works use AP to generate natural language sentences for communication  [24]  or treating utterances in the form of actions  [25]. Contrary to the previous works, our problem is centered on the learning of the action model which serves as a prerequisite to be fed to a planner prior to it being utilized in the forms mentioned as in the aforedescribed works.
Annotation and generalization In this phase, dialogues in the dialogue corpus are first annotated to actions. These dialogues are taken from the Vernissage corpus which is a multimodal HRI dataset  [4] , and has a total of 10 conversation instances between Nao and human participants. In the scenario, the robot explains paintings in a room and then performs a quiz with the participants. We first annotate with predefined predicates the initial state of the world before the beginning of each interaction. We then annotate each robot dialogue within each trace as an action drawn from the following set of actions: (sayHello, farewell, inform, say, ask, claim, deny, advise, thank, autoFeedback (feedback on last heard dialogue e. g. "oh", "great" etc. )). The participant responses are encoded in the form of (speech, silence, laughter).
Data encoding for labelling of operator sequences We use the sequence labelling capabilities of LSTM to identify the most likely operator that succeeds a given one. The input to our LSTM system is a large corpus comprising vector representations of each operator of each trace. Each trace is taken one by one, and the comprising operators are sequentially encoded into input and output vectors; thus producing a large corpus of vectors. Each operator of each trace is represented by two distinct vectors: an input vector which encodes the operator, and an output vector which classifies the successive operator. These vec- tors serve as the input and output respectively to the LSTM cells, the encoding of which represents the core of this section. This corpus of vector representations is divided into a training and validation set to eventually train the LSTM on the training set and gauge its performance on the validation set.
The rules with a highest value of confidence and support (empirically determined) are retained for inter-operator constraint generation (see Table  2 ). Finally we encode all the intra and inter-operator constraints in the form of a weighted MAX-SAT problem. The weights of the CNF clauses representing the constraints are determined differently for the inter and intra-operator cases. While the weights of the intra-operator clauses are empirically determined, the weights of the inter-operator clauses are equal to the support of the association rules. This problem can be stated as: Given a collection C of m clauses, (C 1 ,. , Cm) involving n logical variables with clause weights w i , find a truth assignment that maximizes the total weight of the satisfied clauses in C  [15]. We use 2 SAT solvers: the MaxSatSolver  [30]  and the MaxWalkSat  [31].
Different theorem provers tend to produce proof objects in different formats and this is especially the case for modal logics, where several deductive formalisms (and provers based on them) have been presented. This work falls within the general project of establishing a common specification language in order to certify proofs given in a wide range of deductive formalisms. In particular, by using a translation from the modal language into a first-order polarized language and a checker whose small kernel is based on a classical focused sequent calculus, we are able to certify modal proofs given in labeled sequent calculi, prefixed tableaux and free-variable prefixed tableaux. We describe the general method for the logic K, present its implementation in a Prolog-like language, provide some examples and discuss how to extend the approach to other normal modal logics.
Describing a format in terms of an FPC allows software to check proofs in this format, much like a context-free grammar allows a parser to check the syntactical correctness of a program. The parser in this case would be a kernel: a small and trusted component that checks a proof evidence with respect to an FPC specification. Checkers  [8]  is a generic proof certifier based on the ProofCert ideas. It allows for the certification of arbitrary proof evidences using various trusted kernels. The certification is carried out by using dedicated FPC specifications which guide the construction of proofs in the target kernels. A particularly trusted and low-level kernel is the focused classical sequent calculus LKF  [13]. In  [17] , a translation from the language of the labeled sequent system G3K  [18]  for propositional modal logic into the language of LKF was described.
$. /prover. sh ftab1 where the argument is the name of the λ Prolog module denoting the proof evidence one wishes to check. In this example, the name is the module of the detailed proof (to be used with the fittings-tableaux  FPC specification) of the first problem, which can be found in Fig. 9. The module for the "essential" one (to be used with the simpfit-tableaux FPC specification) can be found in Fig. 10. More examples can be found in the following folder (shipped together with Checkers): /src/test/tableaux.
Cloud based healthcare services with Internet of healthcare things is a model for healthcare delivery that utilizes digital communications and the IoHT devices to provide flexible opportunities for quality healthcare outside of the traditional hospital setting, connecting patients and providers anywhere and anytime, and patient experience. Cloud-based healthcare services are perpetually being deployed, where an increasing volume of personal health data is being processed in return for personally tailored health insights for wellness and preventive services that will help the end-users to perform personalized life-style that will improve their health. This personalization task is performed by a health recommender service, which might be running as part of the healthcare web service or as a third party service.
As for privacy legislation, it refers to data protection legislation restricting the gathering and usage of private health data by data processors in order to define the best practices for the protection of personal health information. Five examples for such privacy guidelines are the EU Directives 95/46/EC  [14]  and 2002/58/EC  [10] , UK's Data Protection Act, The health insurance portability and accountability act (HIPAA)  [49] , and OECD privacy principles  [11]. Despite the fact that several nations have developed privacy protection laws and regulations to guard against the secret use of personal health information, the present laws and their conceptual foundations have become outdated because of the continuous changes in technology  [8]. As a result, these personal health data reside on databases of service providers, largely beyond the control of existing privacy laws, leading to a potential privacy invasion on a scale never before possible.
The M-psychiatry system consists of three primary components: a body sensor network that is composed of different types of sensors, which are varying from patient to patient, a network gateway that connects the sensors network to a wide area network and query stations which are back-end servers used by healthcare professionals to alter and query the sensed data stored in the m-psychiatry's data store. The m-psychiatry system allows the registration and substitution of a wide variety of sensors. Additionally, the system allows the patients to control the monitoring process and gives them the ability to stop it whenever they want. The collection of data via m-psychiatry system is preferable to paper-based collection for a number of reasons, such as responsiveness, time, labor and cost savings, clinician data entry reduction, prevention of data entry errors, reduction of missing information, and standardized data collection.
A similar system that offers the same functionality was proposed in  [4]. The C-SMART is a mobile health platform for continuous real time remote monitoring. The platform covers the communication between a set of sensors on patient's body, a mobile phone, and a centralized healthcare server. The main advantage of C-SMART is that it is implemented on application layer and thus can be compatible to different existing telemedicine and medical database standards with minimal overhead. The C-SMART is working in a plug and play manner but yet to give the user maximum control on the system operation. This property facilitates interfacing to a wide variety of vendors' devices and sensors and it is attained by forming a dedicated remote control and installation center and by using an operation menu on the mobile phone.
The authors in  [1]  presented a completely distributed approach to help in medical diagnosis of disease propagation using wireless body area networks. Additionally, they discussed the basic building blocks of mobile wireless body area network platform. The architecture of this platform usually consists of three-tier as follows: • 1st Tier is client (Sensor Node), which is wearable, or implantable sensor device placed on patient body. • 2nd Tier is middleware (Personal Server), which is the personal server, with its computer software could be installed on a personal computer or smartphone. • Finally, the 3rd Tier is back-end server (Database Server), which is the database server located in the medical institution. With this architecture, the healthcare monitoring system can detect some diseases indoor and outdoor, and also investigate people who may have a disease, who may propagate epidemic diseases and who move in an urban region.
In  [7]  a privacy preserving approach is proposed based on peer to peer techniques using users' communities, where the community will have an aggregate user profile representing the group as a whole but not individual users. Personal information will be encrypted and the communication will be between individual users but not servers. Thus, the recommendations will be generated at the client side. In  [28]  a theoretical framework is proposed to preserve the privacy of customers and the commercial interests of merchants. Their system is a hybrid recommender system that uses secure two party protocols and public key infrastructure to achieve the desired goals. In  [54]  and  [55]  another method is suggested for privacy preserving on centralized recommender systems by adding uncertainty to the data using a randomized perturbation tech-nique while attempting to make sure that necessary statistical aggregates such as the mean don't get disturbed much.
Moreover, the end-users are responsible for making their data public or private by employing privacy preferences languages to specify rules or levels for releasing their data such that a conscious automatic choice can be made about which group gets to see what. Also, catering to the second boundary, it gives the end-users the choice to join a recommendation request, using an anonymous network or leaving the recommendation process, where the end-users can join recommendation requests only with a trusted cloud based service providers. The temporal boundary was partially addressed in this paper, where the fog node has the choice to share outdated measurements and results with external fog nodes, in order to compute the trust level with the cloud based service provider. However, we plan to fully address the temporal boundary in future research.