Genome-wide analysis identifies a general requirement for polarity proteins in endocytic traffic

In a genome-wide RNA-mediated interference screen for genes required in membrane traffic - including endocytic uptake, recycling from endosomes to the plasma membrane, and secretion - we identified 168 candidate endocytosis regulators and 100 candidate secretion regulators. Many of these candidates are highly conserved among metazoans but have not been previously implicated in these processes. Among the positives from the screen, we identified PAR-3, PAR-6, PKC-3 and CDC-42, proteins that are well known for their importance in the generation of embryonic and epithelial-cell polarity. Further analysis showed that endocytic transport in Caenorhabditis elegans coelomocytes and human HeLa cells was also compromised after perturbation of CDC-42/Cdc42 or PAR-6/Par6 function, indicating a general requirement for these proteins in regulating endocytic traffic. Consistent with these results, we found that tagged CDC-42/Cdc42 is enriched on recycling endosomes in C. elegans and mammalian cells, suggesting a direct function in the regulation of transport.

Comparing sentence-level features for authorship analysis in Portuguese

In this paper we compare the robustness of several types of stylistic markers to help discriminate authorship at sentence level. We train a SVM-based classifier using each set of features separately and perform sentence-level authorship analysis over corpus of editorials published in a Portuguese quality newspaper. Results show that features based on POS information, punctuation and word / sentence length contribute to a more robust sentence-level authorship analysis. © Springer-Verlag Berlin Heidelberg 2010.

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.

From filters to features:Scale-space analysis of edge and blur coding in human vision

To make vision possible, the visual nervous system must represent the most informative features in the light pattern captured by the eye. Here we use Gaussian scale-space theory to derive a multiscale model for edge analysis and we test it in perceptual experiments. At all scales there are two stages of spatial filtering. An odd-symmetric, Gaussian first derivative filter provides the input to a Gaussian second derivative filter. Crucially, the output at each stage is half-wave rectified before feeding forward to the next. This creates nonlinear channels selectively responsive to one edge polarity while suppressing spurious or "phantom" edges. The two stages have properties analogous to simple and complex cells in the visual cortex. Edges are found as peaks in a scale-space response map that is the output of the second stage. The position and scale of the peak response identify the location and blur of the edge. The model predicts remarkably accurately our results on human perception of edge location and blur for a wide range of luminance profiles, including the surprising finding that blurred edges look sharper when their length is made shorter. The model enhances our understanding of early vision by integrating computational, physiological, and psychophysical approaches. © ARVO.

Chronotopographical analysis of the pattern onset visual evoked magnetic response (VEMR):Implications for waveform peak identification

The topography of the visual evoked magnetic response (VEMR) to a pattern onset stimulus was studied in five normal subjects using a single channel BTi magnetometer. Topographic distributions were analysed at regular intervals following stimulus onset (chronotopograpby). Two distinct field distributions were observed with half field stimulation: (1) activity corresponding to the C11 m which remains stable for an average of 34 msec and (2) activity corresponding to the C111 m which remains stable for about 50 msec. However, the full field topography of the largest peak within the first 130 msec does not have a predictable latency or topography in different subjects. The data suggest that the appearance of this peak is dependent on the amplitude, latency and duration of the half field C11 m peaks and the efficiency of half field summation. Hence, topographic mapping is essential to correctly identify the C11 m peak in a full field response as waveform morphology, peak latency and polarity are not reliable indicators. © 1993.

Comparing German and British political culture through values : and analysis of the values of health care reform

This research sets out to compare the values in British and German political discourse, especially the discourse of social policy, and to analyse their relationship to political culture through an analysis of the values of health care reform. The work proceeds from the hypothesis that the known differences in political culture between the two countries will be reflected in the values of political discourse, and takes a comparison of two major recent legislative debates on health care reform as a case study. The starting point in the first chapter is a brief comparative survey of the post-war political cultures of the two countries, including a brief account of the historical background to their development and an overview of explanatory theoretical models. From this are developed the expected contrasts in values in accordance with the hypothesis. The second chapter explains the basis for selecting the corpus texts and the contextual information which needs to be recorded to make a comparative analysis, including the context and content of the reform proposals which comprise the case study. It examines any contextual factors which may need to be taken into account in the analysis. The third and fourth chapters explain the analytical method, which is centred on the use of definition-based taxonomies of value items and value appeal methods to identify, on a sentence-by-sentence basis, the value items in the corpus texts and the methods used to make appeals to those value items. The third chapter is concerned with the classification and analysis of values, the fourth with the classification and analysis of value appeal methods. The fifth chapter will present and explain the results of the analysis, and the sixth will summarize the conclusions and make suggestions for further research.

Self-training from labeled features for sentiment analysis

Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents.

Incorporating sentiment prior knowledge for weakly supervised sentiment analysis

This article presents two novel approaches for incorporating sentiment prior knowledge into the topic model for weakly supervised sentiment analysis where sentiment labels are considered as topics. One is by modifying the Dirichlet prior for topic-word distribution (LDA-DP), the other is by augmenting the model objective function through adding terms that express preferences on expectations of sentiment labels of the lexicon words using generalized expectation criteria (LDA-GE). We conducted extensive experiments on English movie review data and multi-domain sentiment dataset as well as Chinese product reviews about mobile phones, digital cameras, MP3 players, and monitors. The results show that while both LDA-DP and LDAGE perform comparably to existing weakly supervised sentiment classification algorithms, they are much simpler and computationally efficient, rendering themmore suitable for online and real-time sentiment classification on the Web. We observed that LDA-GE is more effective than LDA-DP, suggesting that it should be preferred when considering employing the topic model for sentiment analysis. Moreover, both models are able to extract highly domain-salient polarity words from text.

Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.

Strategic development and physicochemical analysis of oral preparations for unstable drugs

Oral liquid formulations are ideal dosage forms for paediatric, geriatric and patient with dysphagia. Dysphagia is prominent among patients suffering from stroke, motor neurone disease, advanced Alzheimer’s and Parkinson’s disease. However oral liquid preparations are particularly difficult to formulate for hydrophobic and unstable drugs. Therefore current methods employed in solving this issue include the use of ‘specials’ or extemporaneous preparations. In order to challenge this, the government has encouraged research into the field of oral liquid formulations, with the EMEA and MHRA publishing list of drugs of interest. The current work investigates strategic formulation development and characterisation of select API’s (captopril, gliclazide, melatonin, L-arginine and lansoprazole), each with unique obstacles to overcome during solubilisation, stabilisation and when developing a palatable dosage from. By preparing a validated calibration protocol for each of the drug candidates, the oral liquid formulations were assessed for stability, according to the ICH guidelines along with thorough physiochemical characterisation. The results showed that pH and polarity of the solvent had the greatest influence on the extent of drug solubilisation, with inclusion of antioxidants and molecular steric hindrance influencing the extent of drug stability. Captopril, a hydrophilic ACE inhibitor (160 mg.mL-1), undergoes dimerisation with another captopril molecule. It was found that with the addition of EDTA and HP-β-CD, the drug molecule was stabilised and prevented from initiating a thiol induced first order free radical oxidation. The cyclodextrin provided further steric hindrance (1:1 molar ratio) resulting in complete reduction of the intensity of sulphur like smell associated with captopril. Palatability is a crucial factor in patient compliance, particularly when developing a dosage form targeted towards paediatrics. L-arginine is extremely bitter in solution (148.7 g.L-1). The addition of tartaric acid into the 100 mg.mL-1 formulation was sufficient to mask the bitterness associated with its guanidium ions. The hydrophobicity of gliclazide (55 mg.L-1) was strategically challenged using a binary system of a co-solvent and surfactant to reduce the polarity of the medium and ultimately increase the solubility of the drug. A second simpler method was developed using pH modification with L-arginine. Melatonin has two major obstacles in formulation: solubility (100 μg.mL-1) and photosensitivity, which were both overcome by lowering the dielectric constant of the medium and by reversibly binding the drug within the cyclodextrin cup (1:1 ratio). The cyclodextrin acts by preventing UV rays from reaching the drug molecule and initiated the degradation pathway. Lansoprazole is an acid labile drug that could only be delivered orally via a delivery vehicle. In oral liquid preparations this involved nanoparticulate vesicles. The extent of drug loading was found to be influenced by the type of polymer, concentration of polymer, and the molecular weight. All of the formulations achieved relatively long shelf-lives with good preservative efficacy.

Influence of birth cohort on age of onset cluster analysis in bipolar I disorder

PURPOSE: Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database. METHODS: The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared. RESULTS: There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups. CONCLUSION: These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research.

Peptide binding to the HLA-DRB1 supertype:a proteochemometrics analysis

A proteochemometrics approach was applied to a set of 2666 peptides binding to 12 HLA-DRB1 proteins. Sequences of both peptide and protein were described using three z-descriptors. Cross terms accounting for adjacent positions and for every second position in the peptides were included in the models, as well as cross terms for peptide/protein interactions. Models were derived based on combinations of different blocks of variables. These models had moderate goodness of fit, as expressed by r2, which ranged from 0.685 to 0.732; and good cross-validated predictive ability, as expressed by q2, which varied from 0.678 to 0.719. The external predictive ability was tested using a set of 356 HLA-DRB1 binders, which showed an r2(pred) in the range 0.364-0.530. Peptide and protein positions involved in the interactions were analyzed in terms of hydrophobicity, steric bulk and polarity.

Contextual semantics for sentiment analysis of Twitter

Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets.