A novel role for retromer in the control of epithelial cell polarity

The establishment and maintenance of epithelial cell polarity is essential throughout the development and adult life of all multicellular organisms. A key player in maintaining epithelial polarity is Crumbs (Crb), an evolutionarily conserved type-I transmembrane protein initially identified in Drosophila. Correct Crb levels and apical localization are imperative for its function. However, as is the case for many polarized proteins, the mechanisms of its trafficking and strict apical localization are poorly understood. To address these questions, we developed a liposome-based assay to identify trafficking coats and interaction partners of Crb in a native-like environment. Thereby, we demonstrated that Crb is a cargo for Retromer, a trafficking complex required for transport from endosomes to the trans-Golgi-network. The functional importance of this interaction was revealed by studies in Drosophila epithelia, which established Retromer as a novel regulator of epithelial cell polarity and verified the vast potential of this technique.

Retromer controls epithelial cell polarity by trafficking the apical determinant crumbs

The evolutionarily conserved apical determinant Crumbs (Crb) is essential for maintaining apicobasal polarity and integrity of many epithelial tissues [1]. Crb levels are crucial for cell polarity and homeostasis, yet strikingly little is known about its trafficking or the mechanism of its apical localization. Using a newly established, liposome-based system described here, we determined Crb to be an interaction partner and cargo of the retromer complex. Retromer is essential for the retrograde transport of numerous transmembrane proteins from endosomes to the trans-Golgi network (TGN) and is conserved between plants, fungi, and animals [2]. We show that loss of retromer function results in a substantial reduction of Crb in Drosophila larvae, wing discs, and the follicle epithelium. Moreover, loss of retromer phenocopies loss of crb by preventing apical localization of key polarity molecules, such as atypical protein kinase C (aPKC) and Par6 in the follicular epithelium, an effect that can be rescued by overexpression of Crb. Additionally, loss of retromer results in multilayering of the follicular epithelium, indicating that epithelial integrity is severely compromised. Our data reveal a mechanism for Crb trafficking by retromer that is vital for maintaining Crb levels and localization. We also show a novel function for retromer in maintaining epithelial cell polarity.

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.

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.

Automatically extracting polarity-bearing topics for cross-domain sentiment classification

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning.

PhonItalia:a phonological lexicon for Italian

In this article, we present the first open-access lexical database that provides phonological representations for 120,000 Italian word forms. Each of these also includes syllable boundaries and stress markings and a comprehensive range of lexical statistics. Using data derived from this lexicon, we have also generated a set of derived databases and provided estimates of positional frequency use for Italian phonemes, syllables, syllable onsets and codas, and character and phoneme bigrams. These databases are freely available from phonitalia.org. This article describes the methods, content, and summarizing statistics for these databases. In a first application of this database, we also demonstrate how the distribution of phonological substitution errors made by Italian aphasic patients is related to phoneme frequency. © 2013 Psychonomic Society, Inc.

Hepatitis C virus infection reduces hepatocellular polarity in a vascular endothelial growth factor-dependent manner

Background & Aims - Hepatitis C virus (HCV) infection leads to progressive liver disease, frequently culminating in fibrosis and hepatocellular carcinoma. The mechanisms underlying liver injury in chronic hepatitis C are poorly understood. This study evaluated the role of vascular endothelial growth factor (VEGF) in hepatocyte polarity and HCV infection. Methods - We used polarized hepatoma cell lines and the recently described infectious HCV Japanese fulminant hepatitis (JFH)-1 cell culture system to study the role of VEGF in regulating hepatoma permeability and HCV infection. Results - VEGF negatively regulates hepatocellular tight junction integrity and cell polarity by a novel VEGF receptor 2–dependent pathway. VEGF reduced hepatoma tight junction integrity, induced a re-organization of occludin, and promoted HCV entry. Conversely, inhibition of hepatoma expressed VEGF with the receptor kinase inhibitor sorafenib or with neutralizing anti-VEGF antibodies promoted polarization and inhibited HCV entry, showing an autocrine pathway. HCV infection of primary hepatocytes or hepatoma cell lines promoted VEGF expression and reduced their polarity. Importantly, treatment of HCV-infected cells with VEGF inhibitors restored their ability to polarize, showing a VEGF-dependent pathway. Conclusions - Hepatic polarity is critical to normal liver physiology. HCV infection promotes VEGF expression that depolarizes hepatoma cells, promoting viral transmission and lymphocyte migration into the parenchyma that may promote hepatocyte injury.

Morphological-compound dysgraphia in an aphasic patient:"A wild write through the lexicon"

We describe the case of a dysgraphic aphasic individual-S.G.W.-who, in writing to dictation, produced high rates of formally related errors consisting of both lexical substitutions and what we call morphological-compound errors involving legal or illegal combinations of morphemes. These errors were produced in the context of a minimal number of semantic errors. We could exclude problems with phonological discrimination and phonological short-term memory. We also excluded rapid decay of lexical information and/or weak activation of word forms and letter representations since S.G.W.'s spelling showed no effect of delay and no consistent length effects, but, instead, paradoxical complexity effects with segmental, lexical, and morphological errors that were more complex than the target. The case of S.G.W. strongly resembles that of another dysgraphic individual reported in the literature-D.W.-suggesting that this pattern of errors can be replicated across patients. In particular, both patients show unusual errors resulting in the production of neologistic compounds (e.g., "bed button" in response to "bed"). These patterns can be explained if we accept two claims: (a) Brain damage can produce both a reduction and an increase in lexical activation; and (b) there are direct connections between phonological and orthographic lexical representations (a third spelling route). We suggest that both patients are suffering from a difficulty of lexical selection resulting from excessive activation of formally related lexical representations. This hypothesis is strongly supported by S.G.W.'s worse performance in spelling to dictation than in written naming, which shows that a phonological input, activating a cohort of formally related lexical representations, increases selection difficulties. © 2014 Taylor & Francis.

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.

AP-2 complex-mediated endocytosis of Drosophila Crumbs regulates polarity via antagonizing Stardust

Maintenance of epithelial polarity depends on the correct localization and levels of polarity determinants. The evolutionarily conserved transmembrane protein Crumbs is crucial for the size and identity of the apical membrane, yet little is known about the molecular mechanisms controlling the amount of Crumbs at the surface. Here, we show that Crumbs levels on the apical membrane depend on a well-balanced state of endocytosis and stabilization. The adaptor protein 2 (AP-2) complex binds to a motif in the cytoplasmic tail of Crumbs that overlaps with the binding site of Stardust, a protein known to stabilize Crumbs on the surface. Preventing endocytosis by mutations in AP-2 causes expansion of the Crumbs-positive plasma membrane and polarity defects, which can be partially rescued by removing one copy of crumbs. Strikingly, knocking-down both AP-2 and Stardust retains Crumbs on the membrane. This study provides evidence for a molecular mechanism, based on stabilization and endocytosis, to adjust surface levels of Crumbs, which are essential for maintaining epithelial polarity.

Insights into the influence of solvent polarity on the crystallization of poly(ethylene oxide) spin-coated thin films via in situ grazing incidence wide-angle X-ray scattering

Controlling polymer thin-film morphology and crystallinity is crucial for a wide range of applications, particularly in thin-film organic electronic devices. In this work, the crystallization behavior of a model polymer, poly(ethylene oxide) (PEO), during spin-coating is studied. PEO films were spun-cast from solvents possessing different polarities (chloroform, THF, and methanol) and probed via in situ grazing incidence wide-angle X-ray scattering. The crystallization behavior was found to follow the solvent polarity order (where chloroform < THF < methanol) rather than the solubility order (where THF > chloroform > methanol). When spun-cast from nonpolar chloroform, crystallization largely followed Avrami kinetics, resulting in the formation of morphologies comprising large spherulites. PEO solutions cast from more polar solvents (THF and methanol) do not form well-defined highly crystalline morphologies and are largely amorphous with the presence of small crystalline regions. The difference in morphological development of PEO spun-cast from polar solvents is attributed to clustering phenomena that inhibit polymer crystallization. This work highlights the importance of considering individual components of polymer solubility, rather than simple total solubility, when designing processing routes for the generation of morphologies with optimum crystallinities or morphologies.