A Bayesian Multivariate Functional Dynamic Linear Model

We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data--functional, time dependent, and multivariate components--we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained optimization framework. The proposed methods identify a time-invariant functional basis for the functional observations, which is smooth and interpretable, and can be made common across multivariate observations for additional information sharing. The Bayesian framework permits joint estimation of the model parameters, provides exact inference (up to MCMC error) on specific parameters, and allows generalized dependence structures. Sampling from the posterior distribution is accomplished with an efficient Gibbs sampling algorithm. We illustrate the proposed framework with two applications: (1) multi-economy yield curve data from the recent global recession, and (2) local field potential brain signals in rats, for which we develop a multivariate functional time series approach for multivariate time-frequency analysis. Supplementary materials, including R code and the multi-economy yield curve data, are available online

Integrated care in German mental health services as benefit for relatives – a qualitative study

Background: As mental health services undergo the process of deinstitutionalization, this is resulting in a higher burden of care for relatives. Evidence suggests that interventions for carers have a beneficial impact on their psychological health. A reduction of responsibility for relatives is linked with a significantly improved outcome for the severely mentally ill. The aim of the study was to explore the relatives’ experiences with severely mentally ill patients in different integrated care service providers. Methods: Semi-structured focus groups and interviews were conducted with 24 relatives of patients receiving community based integrated care for severe mental illness. The collected data was transcribed and evaluated using qualitative content analysis. A deductive-inductive approach was used in generating thematic categories. Results: Four main categories were found related to the structural aspects of the integrated care services and for the experiences of the relatives within these services. Relatives reported that the services offered significant relief and substantial support in daily life. In addition, relatives felt a reduced burden of carer responsibility and therefore that they were provided with more protection and stability. This resulted in a sense of encouragement and not feeling left alone to face challenges. Conclusion: Relatives are a critical resource for patients suffering from mental health problems and benefit from formal structures and interventions to support them in carer role. An important need is to ensure continuity of care for patients and the bridging of gaps concerning information and support needs for relatives when providing integrated mental health services in the community

Analysing domain suitability of a sentiment lexicon by identifying distributionally bipolar words

Abstract Contemporary sentiment analysis approaches rely heavily on lexicon based methods. This is mainly due to their simplicity, although the best empirical results can be achieved by more complex techniques. We introduce a method to assess suitability of generic sentiment lexicons for a given domain, namely to identify frequent bigrams where a polar word switches polarity. Our bigrams are scored using Lexicographers Mutual Information and leveraging large automatically obtained corpora. Our score matches human perception of polarity and demonstrates improvements in classification results using our enhanced contextaware method. Our method enhances the assessment of lexicon based sentiment detection algorithms and can be further used to quantify ambiguous words

Exploring Supervised Techniques for Automated Recognition of Intention Classes from Portuguese Free Texts on Agriculture

Technical and scientific knowledge is vast and complex, particularly in interdisciplinary fields such as sustainable agriculture, which is available in several interrelated, geographically dispersed and interdisciplinary online textual information sources. In this context, it is essential to support people with computational mechanisms that allow them to retrieve and interpret information in an appropriate way, as communication in these software systems is typically asynchronous and textual. User’s intention recognition and analysis in textual documents results in benefits for better information retrieval. However, intentions are expressed implicitly in texts in natural language and the specificities of the domain and cultural aspects of language make it difficult to process and analyze the text by computer systems. This requires the study of methods for the automatic recognition of intention classes in text. In this article, we conduct extensive experimental analyses on techniques based on language models and machine learning to detect instances of intention classes in texts about sustainable agriculture written in Portuguese. In our methodology, we perform a morphological analysis of the sentences and evaluate four Word Embeddings techniques (Word2Vec, Wang2Vec, FastText and Glove) combined with four machine learning techniques (Support Vector Machine, Artificial Neural Network, Random Forest and Transfer Learning). The results obtained by applying the techniques proposed in a database with textual information on sustainable agriculture indicate promising possibilities in the recognition of intentions in free texts  in  Portuguese language on sustainable agriculture

Comparative proteome analysis of lung tissue from patients with idiopathic pulmonary fibrosis (IPF), non-specific interstitial pneumonia (NSIP) and organ donors

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Functional inhibition related to structure of a highly potent insulin-specific CD8 T cell clone using altered peptide ligands

Insulin-reactive CD8 T cells are amongst the earliest islet-infiltrating CD8 T cells in NOD mice. Cloned insulin B15–23-reactive cells (designated G9C8), restricted by H-2Kd, are highly diabetogenic. We used altered peptide ligands (APL) substituted at TCR contact sites, positions (p)6 and 8, to investigate G9C8 T cell function and correlated this with structure. Cytotoxicity and IFN-γ production assays revealed that p6G and p8R could not be replaced by any naturally occurring amino acid without abrogating recognition and functional response by the G9C8 clone. When tested for antagonist activity with APL differing from the native peptide at either of these positions, the peptide variants, G6H and R8L showed the capacity to reduce the agonist response to the native peptide. The antagonist activity in cytotoxicity and IFN-γ production assays can be correlated with conformational changes induced by different structures of the MHC-peptide complexes, shown by molecular modeling. We conclude that p6 and p8 of the insulin B15–23 peptide are very important for TCR stimulation of this clone and no substitutions are tolerated at these positions in the peptide. This is important in considering the therapeutic use of peptides as APL that encompass both CD4 and CD8 epitopes of insulin

Bayesian hierarchical age-period-cohort models with time-structured effects: An application to religious voting in the US, 1972–2008

To examine dynamics of political processes using repeated cross-section data, effects of age, cohort, and time period have to be disentangled. I propose a Bayesian dynamic hierarchical model with cohort and period effects modeled as random walk through time. It includes smoothly time-varying effects of covariates, allowing researchers to study changing effects of individual characteristics on political behavior. It provides a flexible functional form estimate of age by integrating a semi-parametric approach in the hierarchical model. I employ this approach to examine religious voting in the United States using repeated cross-sectional surveys from 1972 to 2008. I find starkly differing nonlinear trends of de- and re-alignment among different religious denominations