Depolarisation and suppression of burst firing activity in the mouse subthalamic nucleus by dopamine D1/D5 receptor activation of a cyclic-nucleotide gated non-specific cation conductance

Neuronal burst firing in the subthalamic nucleus (STN) is one of the hallmarks of dopamine depletion in Parkinson's disease. Here, we have determined the postsynaptic effects of dopamine in the STN and the functional consequences of dopamine receptor modulation on burst firing in vitro. STN cells displayed regular spiking activity at a rate of 7.9 +/- 0.5 Hz. Application of dopamine (30 mu M) induced membrane depolarisations accompanied by an increase in firing rate of mean 12.0 +/- 0.6 Hz in all 69 cells. The dopamine effect was mimicked by the dopamine D1/D5 receptor agonist SKF38393 (10 mu M, 17 cells) and the dopamine D2-like receptor agonist quinpirole (10 mu M, 35 cells), partly reduced by D1/D5 antagonist SCH23390 (2 mu M, seven cells), but unaffected by the D2 antagonists sulpiride (10 mu M, seven cells) or eticlopride (10 mu M, six cells). Using voltage ramps, dopamine induced an inward current of 69 +/- 9.4 pA at a holding potential of -60 mV (n = 17). This current was accompanied by an increase in input conductance of 1.55 +/- 0.35 nS which reversed at -30.6 +/- 2.3 mV, an effect mimicked by SKF38393 (10 AM, nine cells). Similar responses were observed when measuring instantaneous current evoked by voltage steps and in the presence of the I-h blocker, ZD7288, indicating effects independent of I-h. The increase in conductance was blocked by SCH23390 (2 mu M, n = 4), mimicked by the activator of adenylyl cyclase forskolin (10 mu M, n = 7) and blocked by H-89, an inhibitor of cyclic AMP dependent protein kinase A (10 PM, n = 6). These results indicate that the dopamine depolarisation is in part mediated by D1/D5 receptor mediated activation of a cyclic-nucleotide gated (CNG) non-specific cation conductance. This conductance contributes to the membrane depolarisation that changes STN neuronal bursting to more regular activity by significantly increasing burst duration and number of spikes per burst.

Gingipains from Porphyromonas gingivalis increase the chemotactic and respiratory burst-priming properties of the 77-amino-acid interleukin-8 variant

Porphyromonas gingivalis, a gram-negative anaerobe which is implicated in the etiology of active periodontitis, secretes degradative enzymes (gingipains) and sheds proinflammatory mediators (e.g., lipopolysaccharides [LPS]). LPS triggers the secretion of interleukin-8 (IL-8) from immune (72-amino-acid [aa] variant [IL-8(72aa)]) and nonimmune (IL-8(77aa)) cells. IL-8(77aa) has low chemotactic and respiratory burst-inducing activity but is susceptible to cleavage by gingipains. This study shows that both R- and K-gingipain treatments of IL-8(77aa) significantly enhance burst activation by fMLP and chemotactic activity (P < 0.05) but decrease burst activation and chemotactic activity of IL-8(72aa) toward neutrophil-like HL60 cells and primary neutrophils (P < 0.05). Using tandem mass spectrometry, we have demonstrated that R-gingipain cleaves 5- and 11-aa peptides from the N-terminal portion of IL-8(77aa) and the resultant peptides are biologically active, while K-gingipain removes an 8-aa N-terminal peptide yielding a 69-aa isoform of IL-8 that shows enhanced biological activity. During periodontitis, secreted gingipains may differentially affect neutrophil chemotaxis and activation in response to IL-8 according to the cellular source of the chemokine.

Mechanisms contributing to the enhanced respiratory burst of neutrophils observed in periodontitis

The aim of this thesis is to investigate possible mechanisms that may contribute to neutrophil hyperactivity and hyper-reactivity. One possibility is the presence of a neutrophil priming factors within the peripheral circulation of periodontitis patients. To examine this possibility differentiated HL-60 cells and primary neutrophils were studied in the presence and absence of plasma from periodontitis patients. In independent experiments, plasma was depleted of IL-8, GM-CSF, interferon-a, immunoglobulins and albumin. This work demonstrated that plasma factors such as IL-8, GM-CSF, and interferon-a present during periodontitis may contribute towards the reported hyperactive neutrophil phenotype. Furthermore, this work demonstrated that products from Pg may regulate neutrophil accumulation at infected periodontal sites by promoting gingipain-dependent modification of IL-8-77 into a more biologically active chemokine. To elucidate whether the oxidatively stressed environment that neutrophils are exposed to in periodontitis could influence hyperactivity and hyper-reactivity, neutrophils were depleted of glutathione. This work showed that during oxidative stress, where cellular redox-levels have been altered, neutrophils exhibit an increased respiratory burst. In conclusion, this work highlights the multiple mechanisms that may contribute to neutrophil hyperactivity and hyperreactivity including gingipain-modulated activity of IL-8 variants, the effect of host factors such as IL-8, GM-CSF, interferon-a on neutrophils priming and activation, and the shift of neutrophil GSH:GSSG ratio in favour of a more oxidised environment as observed in periodontitis.

Activation of the neutrophil respiratory burst by plasma from periodontitis patients is mediated by pro-inflammatory cytokines

Aim: To determine the effect of periodontitis patients' plasma on the neutrophil oxidative burst and the role of albumin, immunoglobulins (Igs) and cytokines. Materials and Methods: Plasma was collected from chronic periodontitis patients (n=11) and periodontally healthy controls (n=11) and used with/without depletion of albumin and Ig or antibody neutralization of IL-8, GM-CSF or IFN-a to prime/stimulate peripheral blood neutrophils, isolated from healthy volunteers. The respiratory burst was measured by lucigenin-dependent chemiluminescence. Plasma cytokine levels were determined by ELISA. Results: Plasmas from patients were significantly more effective in both directly stimulating neutrophil superoxide production and priming for subsequent formyl-met-leu-phe (fMLP)-stimulated superoxide production than plasmas from healthy controls (p<0.05). This difference was maintained after depletion of albumin and Ig. Plasma from patients contained higher mean levels of IL-8, GM-CSF and IFN-a. Individual neutralizing antibodies against IL-8, GM-CSF or IFN-a inhibited the direct stimulatory effect of patients' plasma, whereas the ability to prime for fMLP-stimulated superoxide production was only inhibited by neutralization of IFN-a. The stimulating and priming effects of control plasma were unaffected by antibody neutralization. Conclusions: This study demonstrates that plasma cytokines may have a role in inducing the hyperactive (IL-8, GM-CSF, IFN-a) and hyper-reactive (IFN-a) neutrophil phenotype seen in periodontitis patients.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.

Weakly supervised joint sentiment-topic detection from text

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 called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

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.

A comparative study of bayesian models for unsupervised sentiment detection

This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentiment-topic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection.

Learning sentiment classification model from labeled features

We propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon. Preferences on expectations of sentiment labels of those lexicon words are 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 exiting weakly-supervised sentiment classification methods despite using no labeled documents.

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.

Semantic sentiment analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

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.

Optical burst-switched SSB-OFDM using a fast switching SG-DBR laser

The application of orthogonal frequency-division multiplexing (OFDM) in an optical burst-switched system employing a single fast switching sample grating-distributed Bragg reflector (SG-DBR) laser is demonstrated experimentally. The effect of filter profiles compatible with 50, 25, and 12.5 GHz wavelength-division multiplexing grids on the system is investigated with system performance examined in terms of error vector magnitude per subcarrier for OFDM burst data beginning at various times after a switching event. Additionally the placement of the OFDM training sequence within the data burst and its effect on the system is investigated.

The signal transduction pathways involved in the respiratory burst of murine peritoneal macrophages

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