dataesr/scientific-paragraphs-categorization huggingface dataset:
Searching for plausible genes Information about the mutated genes is also crucial in selecting variations and filtering out others. There are at least three ways by which a mutated gene can be selected as a candidate disease-causing gene in a single patient. First, the gene may encode a protein that belongs to a pathway already implicated in patients with the same phenotype. The relationship between these genes may be distant and indirect, and tools such as the human gene connectome can be helpful in revealing their connectivity  (Itan et al. , 2013). In other words, there can be physiological homogeneity behind genetic heterogeneity. Second, there is increasingly detailed information in various databases regarding the expression of human genes in a wide array of human cell lines, cell types, tissues, and organs  (GTEx Consortium, 2013; Rung and Brazma, 2013).
The impact of missense mutations, which form more than 90% of rare and common nonsynonymous variations (and a smaller proportion of pathogenic mutations;  Tennessen et al. , 2012) , is less easily predictable and has therefore received much attention. Some missense mutations are intrinsically more disruptive than others. Moreover, a missense mutation at a residue or in a domain that is highly conserved throughout evolution is probably damaging. Several software programs, such as Sorting Intolerant From Tolerant (SIFT;  Kumar et al. , 2009)  and PolyPhen2  (Adzhubei et al. , 2010) , have been developed to predict the pathogenicity of missense mutations based on a combination of biochemical and evolutionary data  (Li et al. , 2013). Although mutations predicted to be loss-of-expression and/or loss-of-function are more readily convincing, amino acid substitutions may be more interesting because they can provide insights into function of a particular domain or the effects of decreased but not absent protein function, or even into the impact of a gain of function.
These metabolic models are based on optimization principles and need one or more optimization objectives to function. Optimization objectives in CB metabolic models can be multiple and describe what the organism A scaffold metabolic network is constructed from an annotated genome and completed after a rigorous survey of organism specific databases and literature. This metabolic network represents all the different possibilities for metabolites to travel through the network (metabolic states). After this network has been constructed, a stoichiometric matrix is created that encompasses the stoichiometry of all metabolic reactions under steady state conditions. Constraints on uptake and/or secretion rates are subsequently set, and the optimization of one or multiple objectives leads to the prediction of a metabolic state.
Topological and elemental balancing inconsistencies In a CB metabolic model all reactions must be stoichiometrically balanced so that there is no net internal production of any metabolite. Software tools, such as the COBRA Toolbox  [27]  include functionalities to inspect the model and detect unbalanced reactions. These tools require all metabolites in the model to be annotated with their chemical formula, which is the case for iNJ661 but not for GSMN-TB 1. 1. Moreover, GSMN-TB 1. 1 does not explicitly contain water or protons (apart from transport reactions and respiration), making it impossible to verify whether the reactions are elementally balanced. Futile cycles are metabolic routes with no net gain. The existence of futile cycles in a metabolic network expands the solution space and complicates flux predictions.
1: a curated and extended version of GSMN-TB  [24] , sMtb: in silico Mycobacterium tuberculosis. cycle, which is critical for intracellular growth of Mtb  [31]. iNJ661 has a more detailed annotation containing chemical formulas for each metabolite (except for some groups of metabolites that are lumped together and protein-metabolite complexes) and it is topologically more consistent, since it contains no duplicated reactions or metabolites. In 2009, Colijn and colleagues  [32]  metabolically interpreted gene expression data to predict the impact of 75 different drugs, combinations of drugs and media compositions on the mycolic acid synthesis capacity of Mtb. The mycolic acid synthesis pathway is described with greater detail within MAP than in GSMN-TB. Therefore, all mycolic acid reactions in GSMN-TB were replaced with the mycolic acid reactions from MAP creating a more comprehensive model (indicated by MMF-RmwBo in Fig.
The joining of both models by MetaMerge is an automated process, therefore manual curation is still required to select correct reactions from highly similar reactions derived from both models and to identify metabolites that could not automatically be assigned to a database identifier, or whose chemical formula could not be determined. In 2013, an improved and extended version of GSMN-TB, GSMN-TB 1. 1 appeared  [24]. GSMN-TB 1. 1 contains the cholesterol degradation pathway and additional corrections to the original GSMN-TB model.
A consensus metabolic model of Mtb (sMtb) The mere existence of eight different genome-scale metabolic models of Mtb, of which most are extensions of previous ones, reflects the importance of keeping CB metabolic models up to date. Two major independently created CB models of Mtb metabolism have thus far not been merged and manually curated. These two models: GSMN-TB 1. 1 and iNJ661 differ in size and cover partly overlapping parts of Mtb metabolism. Metabolites are annotated differently for both models. Model iNJ661 contains for the metabolites: abbreviations, full names, chemical formulas and charges, whereas GSMN-TB 1. 1 only contains abbreviations and full names. Both models use different abbreviations and few metabolite names appear the same in both models. Neither model contains references to persistent chemical databases, such as ChEBI  [41] , PubChem  [42]  or KeGG  [43]  or database-independent identifiers, such as SMILES  [44].
We have used iNJ661, GSMN-TB 1. 1 and sMtb to predict genes that upon in silico deletion would result in a decrease of the specific growth rate by 95% or more (see supplementary methods). Those genes were said to be essential and compared to an in vitro gene essentiality dataset generated via deep sequencing  [5]. It can be seen in Table  2  that sMtb performs best in predicting in vitro gene essentiality, with an accuracy of 80% as compared to 75% for GSMN-TB 1. 1 and 64% for iNJ661. However, as the chosen threshold changes, so do the sensitivity (also called true positive rate) and the false positive rate (1specificity). The relationship between the false positive rate and the true positive rate for the gene essentiality predictions by the various models for different threshold values is given in a Receiver Operating Characteristic (ROC) curve (Supplementary Fig.
2 ). The corresponding Area Under the Curve (AUC) represents the chance that a randomly chosen experimentally observed essential gene is predicted as such and is commonly used for model comparison. For iNJ661, GSMN-TB 1. 1 and sMtb this chance equals 0. 65, 0. 78 and 0. 80 respectively. In all three cases the p-values (all lower than 10 -5 ) associated with the AUC show that these areas are significantly different from 0. 5, which would correspond to a random prediction. 3. 2. Central carbon metabolic flux predictions compared to 13 C data To validate CB metabolic models, ideally the predicted metabolic states would be compared to measured metabolic states. Although metabolic states cannot directly be measured, they can be inferred by isotopic labeling experiments. Flux distributions obtained from Mtb CB genome-scale metabolic models have thus far not been compared to in vitro 13 C inferred fluxes as has been done for other organisms, such as E.
Mtb infection is a complex interplay between the pathogen and its host that involves cellular changes in both organisms  [57]. Therefore, modeling both host and pathogen metabolism simultaneously is required for an accurate representation of infection. While CB metabolic models unfortunately cannot directly predict which molecules are effective drugs, they can predict which metabolic enzymes make for suitable drug targets. Whether or not such enzymes can be effectively inhibited depends on the characteristics of the enzyme itself. Databases such as TuberQ  [58]  can provide a druggability analysis for an enzyme predicted to be a suitable drug target, thereby verifying if the enzyme can effectively be targeted. An approach to select suitable drug targets will be more effective if essentiality analysis is combined with additional systems level information such as information on the accumulation of stable toxic intermediates.
This algorithm was tested using two models, MAP and MMF-RmwBo. The Boshoff Mtb gene expression compendium  [66]  contains over 400 microarray experiments measuring the transcriptional adaptations of Mtb to 75 different drugs, drug combinations and growth conditions. E-Flux was used to predict the impact of each of these conditions and drugs on the biosynthesis of mycolic acids. This approach correctly predicted the specificity of seven of the eight known inhibitors of mycolic acid biosynthesis included in the data compendium. Additionally, it was also able to identify a small number of non-specific potential inhibitors and enhancers of mycolic acid biosynthesis. While E-Flux uses transcript data to improve the predictions of metabolic fluxes, Fang and colleagues  [67]  proposed an in silico approach to create state-specific models by integrating gene expression data.
Their method relies on comparing gene expression levels between a metabolically well-characterized reference state and the perturbed state of interest. This method uses the flux distribution in the reference state and imposes soft constraints on the fluxes according to the observed changes in gene expression to characterize the perturbed metabolic state. Changes in gene expression data for wild type Mtb H37Rv, as well as for the dosR deletion mutant, associated with the transfer from normoxic to hypoxic conditions were combined with iNJ661v to produce condition specific models for both strains. These models correctly  predicted the essentiality of dosR for the adaptation to hypoxia. Additionally, the model also predicted the altered biomass composition of Mtb in hypoxic conditions (linked to the increased production of cell-wall metabolites) and the critical contribution of the reductive side of the tricarboxylic acid cycle to the adaptation to low oxygen environments.
Acknowledgements We thank  Anne Lemassu  and  Mamadou Daffé  ( Centre National de la Rescherche Scientifique ) for their help on the dimycocerosate ester biosynthesis, mycolic acid biosynthesis and starch and sucrose pathways. We also thank  Brett G. Olivier  ( VU Amsterdam ) for his help in converting sMtb in an SBML format. This work has been supported by  Framework Program  7 of the  European Research Council, SysteMTb Collaborative Project  ( HEALTH-2009-2. 1. 1-1-241587 )and by the  Netherlands Consortium for Systems Biology (NCSB)  which is part of the  Netherlands Genomics Initiative/Netherlands Organization for Scientific Research.
This paper proposes a novel fully distributed and collaborative k-anonymity protocol (LPAF) to protect users' location information and ensure better privacy while forwarding queries/replies to/from un-trusted Location-based Service (LBS) over opportunistic mobile networks (OppMNet). We utilize a lightweight multi-hop Markov-based stochastic model for location prediction to guide queries towards the LBS's location as well as to reduce required resources in terms of retransmission overheads. We develop a formal analytical model and present theoretical analysis and simulation of the proposed protocol performance. We further validate our results by performing extensive simulation experiments over pseudo realistic city-map using map-based mobility models and using real-world data trace to compare LPAF to existing location privacy and benchmark protocols.
B. Why encryption is not the solution to location privacy This paper is not concerned with security issues in OppMNet, and for that reason we aim to explain briefly the challenges and limitation of using Encryption to address location-privacy. Encryption is widely used to ensure confidentiality, integrity and authenticity of the information transferred between two endpoints. Over traditional networks, data encryption is used during transmission over un-trusted communication medium or when data is saved to storage devices to prevent unauthorized access or modification  [17] ,  [18]. Security of any such encryption schema must not rely on the obfuscation of their algorithm, but only on the secrecy of the decryption key. Hence, distribution and management of keys among all nodes in OppMNet is critical to any such schema.
V. PERFORMANCE EVALUATION We compare LPAF performance against No Privacy, Oracle-based k-Anonymity  [39]  (OBK), Social-Only  [39]  (SLPD) and ALAR  [40]  protocols. We implement all privacy-preserving protocols as extensions to ONE simulator  [41]. We track the performance across three dimensions: 1) Quality of service expressed as success ratio (SR) and end-to-end delay, 2) Quality of anonymization reflected in the achieved number of obfuscation hops (k), and 3) Energy efficiency tracked through the retransmission overhead. In addition to using an urban scenario, we perform evaluation using real-world data trace of Bluetooth and Wi-Fi (UIUC) collected at the University of Illinois. For ALAR, we set the number of the alternative disjoint path (k=2), and the number of splits (S=2); similar to the settings used in  [40].
Running experiments over Helsinki scenario allows us to evaluate different random topology conditions and observes variations in results. By repeating these experiments many times, we study the statistical significance of any performance results. For the aforementioned reasons, we are complementing the evaluation using pseudo-simulation with UIUC real-world data trace scenario. This work is not concerned with vehicular technology, and it focuses only on social opportunistic network. We utilize a set of heterogeneous mobile nodes (Pedestrians, Cars, and Trams) positioned on top of the Helsinki-city map, where cars and trams are used to ferry data between pedestrians when the possibility occurs. Communication between pedestrians is performed over OppMNet using Bluetooth, while communication between Trams, Cars is over Wi-Fi.
We then generate a user-to-user (pair-wise) total encounter frequency. As participants in this dataset are faculty, staff, and students; more socially connected nodes meet inside as well as outside the university campus. We     We adopt the definition of contact time and inter-any-contact time from  [46] , where contact time refers to a pairwise measure of the period when two users are within the communication area of each other, and can exchange data if they wish to; and inter-any-contact-time is a user metric that shows the average duration between any contact with other nodes (i. e. , isolation periods). Figure  10c  shows the contact time distribution between users' pairs. The figure shows that the contact time follows the power-law distribution as observed in  [46]. The figure shows that only 5% of the users have a contact time greater than 5 seconds, and ≈ 1% of the nodes have contact time above 100 seconds.
These are typical connectivity characteristics of opportunistic communication in DTN. Figure  10d  showing the inter-any-contact time which indicates periods of complete user isolation. We observe that 50% of the users have isolation periods of about 300 seconds, and ≈ 20% of all users have isolation periods above 1000 seconds. The inter-any-contact time is useful to analysis, as it shows that users have long isolation periods at which no data exchange can be performed which is not a privacy-related constraint but due to network topology formed through users' movement (or lack of). The dataset duration is split into three equal simulation duration of one week (7 days), which represents an adequate window for enough events occurring between nodes to allow data dissemination in the OppMNet. All MACs for external devices were discarded, i.
2) Simulation Results in UIUC This section shows the success ratio and delay comparing protocols that offer location-privacy in the OppMNet. We examine the performance of LPAF under two social scenarios (i. e. , low and moderate social network). This is in order to validate the impact of the social group size, with results obtained from the analytical model evaluation in section IV-B. Figure  11a  shows a set of experiments comparing location-privacy protocols performance using UIUC realworld data trace. The social network between nodes is the one presented in figure  10b  with moderate social group size (|S| ≈ 50%). We can see that LPAF has better performance; with a higher -better-(≈ 70%) median success ratio, and higher -better-min/max success ratios; compared to SLPD and ALAR. We can also see that the range between the minimum and maximum success ratios is big, represented by the lower/upper whiskers respectively; due to the difference in the network topology across the three weeks, and the change in participants movements and quality of collected contacts during the trace collection.
g. , anonymizing server, or RSUs) to offer obfuscation. Our proposed protocol is based on k-anonymity  [55]  which is a widely used approach for anonymity in many fields such as communication networks and databases. In traditional networks, k-anonymity is used in privacy solutions such as Tor  [56]  for Internet, P2P VoIP  [57] , where the anonymized user is indistinguishable from at least k-1 other users called the anonymity set. One popular way of offering location k-anonymity is through cloaking. Cloaking is the technique by which the geo-spatial dimension is divided into areas called cloaks, and the user is k-anonymized if the formed cloaking region is occupied by at least k -1 other users, so the user's location is hidden among k anonymity set. Spatial and temporal cloaking introduced by Gruteser and Grunwald  [58]  are using a centralized location broker service to form the cloaking region.
Objective: The aim of this study was to propose a practical search engine, called Multilingual PubMed, which will permit access to a PubMed subset in 1 language and to evaluate the precision and coverage for the French version (Multilingual PubMed-French). Methods: To create this tool, translations of MeSH were enriched (eg, adding synonyms and translations in French) and integrated into a terminology portal. PubMed subsets in several European languages were also added to our database using a dedicated parser. The response time for the generic semantic search engine was evaluated for simple queries. BabelMeSH, Multilingual PubMed-French, and 3 different PubMed strategies were compared by searching for literature in French. Precision and coverage were measured for 20 randomly selected queries.
Introduction MEDLINE, created by the US National Library of Medicine (NLM), is the most used medical bibliographic database in the world. Currently (as of September 3, 2014), it contains 21,515,657 citations [1] from 5650 indexed journals from 81 countries around the world. Each MEDLINE record is indexed with the NLM's controlled vocabulary, Medical Subject Headings (MeSH)  [2]. MEDLINE is the largest component of PubMed  [3] , the freely accessible online database of biomedical journal citations and abstracts. In addition to MEDLINE citations, PubMed also contains  [4] : 1. Citations not yet indexed with MeSH and added to MEDLINE (in-process citations or when supplied electronically by the publisher); 2. Some "old MEDLINE" citations that have not yet been updated with the current vocabulary and converted to MEDLINE status; 3.
These 3 tools rely on MeSH translation in multiple languages to ease querying  [12]  and also rely on some metadata available in existing user languages to ease the results of browsing. Nevertheless, these 3 tools lack most PubMed functionalities (eg, advanced query builder, filters, citation sensor); in truth, they just lack functionalities. A multilingual search engine to access the PubMed/MEDLINE subset in any non-English language (eg, French, German, Spanish, or Norwegian) with advanced functionalities would be of great interest for any user who is not comfortable in English. The goal of this paper was to present such a tool (Multilingual PubMed) and evaluate the potential increase in information retrieval task performance by using the French version.
Architecture The CISMeF generic information system is divided into 5 layers (Figure  1 ): 1. A relational database layer, which ensures data persistence and Structured Query Language query processing; execution plans are optimized by dynamic partitioning and local indexes. 2. A cache layer, which is devoted to clustering data distribution. It is based on random access memory and is distributed between several servers; therefore, it is very fast. Moreover, it avoids data loss after crashes. 3. A business components layer mainly composed of CISMeF Java libraries dealing with business logic. 4. A service layer hosting Web services that rely on business components and are, in turn, used by each client application of the information system. 5. A presentation layer that includes the gateways, Web-based user interfaces, rich Internet apps, and any client application of the service layer.
Technical Feasibility Table  2  shows the number of PubMed citations for the main European languages (excluding English). As a proof of concept, PubMed citations of different languages were included in the CISMeF information system: French (n=665,359), German (n=7102), Portuguese (n=4497), Spanish (n=4297), and Norwegian (n=3764). The end user was able to choose 1 language and then perform a query in the same language. Thanks to our colleagues mentioned in the Acknowledgments, the Multilingual PubMed interface was translated into French, German, Portuguese, Spanish, and Norwegian. The same main metadata displayed by default in the PubMed database was similarly displayed in the Multilingual PubMed search engine, as well as the indexing MeSH descriptors and qualifiers (Figure  2 ). Direct links to the full-text article in the same language were also presented to the end user (Figure  2 ), often via Digital Object Identifier  [22].
The objective of creating a bibliographic database extracted from PubMed in several languages and available in 1 specific language was then completed as a proof of concept. In the previous CISMeF gateway, the objective for response time was the following: all queries with 1 or 2 terms should be less than 2 seconds. Since 2001, this objective has been fulfilled for the 101,000 Web resources included in the CISMeF information system. The number of PubMed citations in French versus CISMeF Web resources is approximately 6 times more. All response times for all queries were below the limit of 2 seconds (Table  3 ). As expected, there was a strong correlation between the response time and the number of citations retrieved (r=. 73, 95% CI. 42-. 89). According to linear regression coefficient, this would lead to an unacceptable response time for queries that collect more than 20,000 citations.
This evaluation of response time is an important step in determining the feasibility of this Multilingual PubMed-French search engine to create a bibliographic database in French for health professionals. The current version of Multilingual PubMed-French is available on the Internet  [23]. The discrepancy between the number of French citations in PubMed and the number of French citations inserted in our information system comes primarily from some doubtful classifications in PubMed (ie, articles mislabeled as French).