Efficacy and safety of second-generation antipsychotic long-acting injections (SGA LAIs) in maintenance treatment of bipolar disorder:protocol for a systematic review and meta-analysis

INTRODUCTION: Bipolar disorder requires long-term treatment but non-adherence is a common problem. Antipsychotic long-acting injections (LAIs) have been suggested to improve adherence but none are licensed in the UK for bipolar. However, the use of second-generation antipsychotics (SGA) LAIs in bipolar is not uncommon albeit there is a lack of systematic review in this area. This study aims to systematically review safety and efficacy of SGA LAIs in the maintenance treatment of bipolar disorder. METHODS AND ANALYSIS: The protocol is based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) and will include only randomised controlled trials comparing SGA LAIs in bipolar. PubMed, EMBASE, CINAHL, Cochrane Library (CENTRAL), PsychINFO, LiLACS, http://www.clinicaltrials.gov will be searched, with no language restriction, from 2000 to January 2016 as first SGA LAIs came to the market after 2000. Manufacturers of SGA LAIs will also be contacted. Primary efficacy outcome is relapse rate or delayed time to relapse or reduction in hospitalisation and primary safety outcomes are drop-out rates, all-cause discontinuation and discontinuation due to adverse events. Qualitative reporting of evidence will be based on 21 items listed on standards for reporting qualitative research (SRQR) focusing on study quality (assessed using the Jadad score, allocation concealment and data analysis), risk of bias and effect size. Publication bias will be assessed using funnel plots. If sufficient data are available meta-analysis will be performed with primary effect size as relative risk presented with 95% CI. Sensitivity analysis, conditional on number of studies and sample size, will be carried out on manic versus depressive symptoms and monotherapy versus adjunctive therapy.

Conversational interaction in the scanner:mentalizing during language processing as revealed by MEG

Humans are especially good at taking another's perspective-representing what others might be thinking or experiencing. This "mentalizing" capacity is apparent in everyday human interactions and conversations. We investigated its neural basis using magnetoencephalography. We focused on whether mentalizing was engaged spontaneously and routinely to understand an utterance's meaning or largely on-demand, to restore "common ground" when expectations were violated. Participants conversed with 1 of 2 confederate speakers and established tacit agreements about objects' names. In a subsequent "test" phase, some of these agreements were violated by either the same or a different speaker. Our analysis of the neural processing of test phase utterances revealed recruitment of neural circuits associated with language (temporal cortex), episodic memory (e.g., medial temporal lobe), and mentalizing (temporo-parietal junction and ventromedial prefrontal cortex). Theta oscillations (3-7 Hz) were modulated most prominently, and we observed phase coupling between functionally distinct neural circuits. The episodic memory and language circuits were recruited in anticipation of upcoming referring expressions, suggesting that context-sensitive predictions were spontaneously generated. In contrast, the mentalizing areas were recruited on-demand, as a means for detecting and resolving perceived pragmatic anomalies, with little evidence they were activated to make partner-specific predictions about upcoming linguistic utterances.

Genome-wide screening for DNA variants associated with reading and language traits

Reading and language abilities are heritable traits that are likely to share some genetic influences with each other. To identify pleiotropic genetic variants affecting these traits, we first performed a genome-wide association scan (GWAS) meta-analysis using three richly characterized datasets comprising individuals with histories of reading or language problems, and their siblings. GWAS was performed in a total of 1862 participants using the first principal component computed from several quantitative measures of reading- and language-related abilities, both before and after adjustment for performance IQ. We identified novel suggestive associations at the SNPs rs59197085 and rs5995177 (uncorrected P≈10 for each SNP), located respectively at the CCDC136/FLNC and RBFOX2 genes. Each of these SNPs then showed evidence for effects across multiple reading and language traits in univariate association testing against the individual traits. FLNC encodes a structural protein involved in cytoskeleton remodelling, while RBFOX2 is an important regulator of alternative splicing in neurons. The CCDC136/FLNC locus showed association with a comparable reading/language measure in an independent sample of 6434 participants from the general population, although involving distinct alleles of the associated SNP. Our datasets will form an important part of on-going international efforts to identify genes contributing to reading and language skills. Genome-wide association scan meta-analysis for reading and language ability. © 2014 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

A computational model of intonation for yorùbá text-to-speech synthesis:design and analysis

In this paper we present the design and analysis of an intonation model for text-to-speech (TTS) synthesis applications using a combination of Relational Tree (RT) and Fuzzy Logic (FL) technologies. The model is demonstrated using the Standard Yorùbá (SY) language. In the proposed intonation model, phonological information extracted from text is converted into an RT. RT is a sophisticated data structure that represents the peaks and valleys as well as the spatial structure of a waveform symbolically in the form of trees. An initial approximation to the RT, called Skeletal Tree (ST), is first generated algorithmically. The exact numerical values of the peaks and valleys on the ST is then computed using FL. Quantitative analysis of the result gives RMSE of 0.56 and 0.71 for peak and valley respectively. Mean Opinion Scores (MOS) of 9.5 and 6.8, on a scale of 1 - -10, was obtained for intelligibility and naturalness respectively.

Bridging the gap between stylistic and cognitive approaches to authorship analysis using systemic functional linguistics and multidimensional analysis

In the present state of the art of authorship attribution there seems to be an opposition between two approaches: cognitive and stylistic methodologies. It is proposed in this article that these two approaches are complementary and that the apparent gap between them can be bridged using Systemic Functional Linguistics (SFL) and in particular some of its theoretical constructions, such as codal variation. This article deals with the theoretical explanation of why such a theory would solve the debate between the two approaches and shows how these two views of authorship attribution are indeed complementary. Although the article is fundamentally theoretical, two example experimental trials are reported to show how this theory can be developed into a workable methodology of doing authorship attribution. In Trial 1, a SFL analysis was carried out on a small dataset consisting of three 300-word texts collected from three different authors whose socio-demographic background matched across a number of parameters. This trial led to some conclusions about developing a methodology based on SFL and suggested the development of another trial, which might hint at a more accurate and useful methodology. In Trial 2, Biber's (1988) multidimensional framework is employed, and a final methodology of authorship analysis based on this kind of analysis is proposed for future research. © 2013, EQUINOX PUBLISHING.

A statistical analysis of regional variation in adverb position in a corpus of written Standard American English

This paper investigates whether the position of adverb phrases in sentences is regionally patterned in written Standard American English, based on an analysis of a 25 million word corpus of letters to the editor representing the language of 200 cities from across the United States. Seven measures of adverb position were tested for regional patterns using the global spatial autocorrelation statistic Moran’s I and the local spatial autocorrelation statistic Getis-Ord Gi*. Three of these seven measures were indentified as exhibiting significant levels of spatial autocorrelation, contrasting the language of the Northeast with language of the Southeast and the South Central states. These results demonstrate that continuous regional grammatical variation exists in American English and that regional linguistic variation exists in written Standard English.

Management of Information on Program Flow Analysis

The article proposes the model of management of information about program flow analysis for conducting computer experiments with program transformations. It considers the architecture and context of the flow analysis subsystem within the framework of Specialized Knowledge Bank on Program Transformations and describes the language for presenting flow analysis methods in the knowledge bank.

Knowledge-Based Approach to Document Analysis

The paper presents an approach to extraction of facts from texts of documents. This approach is based on using knowledge about the subject domain, specialized dictionary and the schemes of facts that describe fact structures taking into consideration both semantic and syntactic compatibility of elements of facts. Actually extracted facts combine into one structure the dictionary lexical objects found in the text and match them against concepts of subject domain ontology.

From icon to naturalised icon:a linguistic analysis of media representations of the BP Deepwater Horizon crisis

This research explores how news media reports construct representations of a business crisis through language. In an innovative approach to dealing with the vast pool of potentially relevant texts, media texts concerning the BP Deepwater Horizon oil spill are gathered from three different time points: immediately after the explosion in 2010, one year later in 2011 and again in 2012. The three sets of 'BP texts' are investigated using discourse analysis and semi-quantitative methods within a semiotic framework that gives an account of language at the semiotic levels of sign, code, mythical meaning and ideology. The research finds in the texts three discourses of representation concerning the crisis that show a movement from the ostensibly representational to the symbolic and conventional: a discourse of 'objective factuality', a discourse of 'positioning' and a discourse of 'redeployment'. This progression can be shown to have useful parallels with Peirce's sign classes of Icon, Index and Symbol, with their implied movement from a clear motivation by the Object (in this case the disaster events), to an arbitrary, socially-agreed connection. However, the naturalisation of signs, whereby ideologies are encoded in ways of speaking and writing that present them as 'taken for granted' is at its most complete when it is least discernible. The findings suggest that media coverage is likely to move on from symbolic representation to a new kind of iconicity, through a fourth discourse of 'naturalisation'. Here the representation turns back towards ostensible factuality or iconicity, to become the 'naturalised icon'. This work adds to the study of media representation a heuristic for understanding how the meaning-making of a news story progresses. It offers a detailed account of what the stages of this progression 'look like' linguistically, and suggests scope for future research into both language characteristics of phases and different news-reported phenomena.

Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis

In recent years, learning word vector representations has attracted much interest in Natural Language Processing. Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics. In this paper we go beyond the vector representations at the word level and propose a novel framework that learns higher-level feature representations of n-grams, phrases and sentences using a deep neural network built from stacked Convolutional Restricted Boltzmann Machines (CRBMs). These representations have been shown to map syntactically and semantically related n-grams to closeby locations in the hidden feature space. We have experimented to additionally incorporate these higher-level features into supervised classifier training for two sentiment analysis tasks: subjectivity classification and sentiment classification. Our results have demonstrated the success of our proposed framework with 4% improvement in accuracy observed for subjectivity classification and improved the results achieved for sentiment classification over models trained without our higher level features.

Analysis and Coordination of Expert Statements in the Problems of Intellectual Information Search

* This work was financially supported by RFBF-04-01-00858.

Applied Problems of Functional Homonymy Resolution for Russian Language

Applied problems of functional homonymy resolution for Russian language are investigated in the work. The results obtained while using the method of functional homonymy resolution based on contextual rules are presented. Structural characteristics of minimal contextual rules for different types of functional homonymy are researched. Particular attention is paid to studying the control structure of the rules, which allows for the homonymy resolution accuracy not less than 95%. The contextual rules constructed have been realized in the system of technical text analysis.

Systems Analysis: the Structure-and-Purpose Approach Based on Logic-linguistic Formalisation

Systems analysis (SA) is widely used in complex and vague problem solving. Initial stages of SA are analysis of problems and purposes to obtain problems/purposes of smaller complexity and vagueness that are combined into hierarchical structures of problems(SP)/purposes(PS). Managers have to be sure the PS and the purpose realizing system (PRS) that can achieve the PS-purposes are adequate to the problem to be solved. However, usually SP/PS are not substantiated well enough, because their development is based on a collective expertise in which logic of natural language and expert estimation methods are used. That is why scientific foundations of SA are not supposed to have been completely formed. The structure-and-purpose approach to SA based on a logic-and-linguistic simulation of problems/purposes analysis is a step towards formalization of the initial stages of SA to improve adequacy of their results, and also towards increasing quality of SA as a whole. Managers of industrial organizing systems using the approach eliminate logical errors in SP/PS at early stages of planning and so they will be able to find better decisions of complex and vague problems.

Computer-aided System of Semantic Text Analysis of a Technical Specification

The given work is devoted to development of the computer-aided system of semantic text analysis of a technical specification. The purpose of this work is to increase efficiency of software engineering based on automation of semantic text analysis of a technical specification. In work it is offered and investigated the model of the analysis of the text of the technical project is submitted, the attribute grammar of a technical specification, intended for formalization of limited Russian is constructed with the purpose of analysis of offers of text of a technical specification, style features of the technical project as class of documents are considered, recommendations on preparation of text of a technical specification for the automated processing are formulated. The computer-aided system of semantic text analysis of a technical specification is considered. This system consists of the following subsystems: preliminary text processing, the syntactic and semantic analysis and construction of software models, storage of documents and interface.