Varenicline decreases ethanol intake and increases dopamine release via neuronal nicotinic acetylcholine receptors in the nucleus accumbens

BACKGROUND AND PURPOSE Varenicline, a neuronal nicotinic acetylcholine receptor (nAChR) modulator, decreases ethanol consumption in rodents and humans. The proposed mechanism of action for varenicline to reduce ethanol consumption has been through modulation of dopamine (DA) release in the nucleus accumbens (NAc) via α4*-containing nAChRs in the ventral tegmental area (VTA). However, presynaptic nAChRs on dopaminergic terminals in the NAc have been shown to directly modulate dopaminergic signalling independently of neuronal activity from the VTA. In this study, we determined whether nAChRs in the NAc play a role in varenicline’s effects on ethanol consumption. EXPERIMENTAL APPROACH Rats were trained to consume ethanol using the intermittent-access two-bottle choice protocol for 10 weeks. Ethanol intake was measured after varenicline or vehicle was microinfused into the NAc (core, shell or core-shell border) or the VTA (anterior or posterior). The effect of varenicline treatment on DA release in the NAc was measured using both in vivo microdialysis and in vitro fast-scan cyclic voltammetry (FSCV). KEY RESULTS Microinfusion of varenicline into the NAc core and core-shell border, but not into the NAc shell or VTA, reduced ethanol intake following long-term ethanol consumption. During microdialysis, a significant enhancement in accumbal DA release occurred following systemic administration of varenicline and FSCV showed that varenicline also altered the evoked release of DA in the NAc. CONCLUSION AND IMPLICATIONS Following long-term ethanol consumption, varenicline in the NAc reduces ethanol intake, suggesting that presynaptic nAChRs in the NAc are important for mediating varenicline’s effects on ethanol consumption.

Modeling conditional correlations of asset returns : a smooth transition approach

In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to a predetermined or exogenous transition variable. An LM–test is derived to test the constancy of correlations and LM- and Wald tests to test the hypothesis of partially constant correlations. Analytical expressions for the test statistics and the required derivatives are provided to make computations feasible. An empirical example based on daily return series of five frequently traded stocks in the S&P 500 stock index completes the paper.

Factors influencing robustness and effectiveness of conditional random fields in active learning frameworks

Active learning approaches reduce the annotation cost required by traditional supervised approaches to reach the same effectiveness by actively selecting informative instances during the learning phase. However, effectiveness and robustness of the learnt models are influenced by a number of factors. In this paper we investigate the factors that affect the effectiveness, more specifically in terms of stability and robustness, of active learning models built using conditional random fields (CRFs) for information extraction applications. Stability, defined as a small variation of performance when small variation of the training data or a small variation of the parameters occur, is a major issue for machine learning models, but even more so in the active learning framework which aims to minimise the amount of training data required. The factors we investigate are a) the choice of incremental vs. standard active learning, b) the feature set used as a representation of the text (i.e., morphological features, syntactic features, or semantic features) and c) Gaussian prior variance as one of the important CRFs parameters. Our empirical findings show that incremental learning and the Gaussian prior variance lead to more stable and robust models across iterations. Our study also demonstrates that orthographical, morphological and contextual features as a group of basic features play an important role in learning effective models across all iterations.

An efficient DSE using conditional multivariate complex Gaussian distribution

This paper presents an efficient noniterative method for distribution state estimation using conditional multivariate complex Gaussian distribution (CMCGD). In the proposed method, the mean and standard deviation (SD) of the state variables is obtained in one step considering load uncertainties, measurement errors, and load correlations. In this method, first the bus voltages, branch currents, and injection currents are represented by MCGD using direct load flow and a linear transformation. Then, the mean and SD of bus voltages, or other states, are calculated using CMCGD and estimation of variance method. The mean and SD of pseudo measurements, as well as spatial correlations between pseudo measurements, are modeled based on the historical data for different levels of load duration curve. The proposed method can handle load uncertainties without using time-consuming approaches such as Monte Carlo. Simulation results of two case studies, six-bus, and a realistic 747-bus distribution network show the effectiveness of the proposed method in terms of speed, accuracy, and quality against the conventional approach.

Are there generalizable trends in the release of airborne synthetic clay nanoparticles from a jet milling process?

Despite recent efforts to assess the release of nanoparticles to the workplace during different nanotechnology activities, the existence of a generalizable trend in the particle release has yet to be identified. This study aimed to characterize the release of synthetic clay nanoparticles from a laboratory-based jet milling process by quantifying the variations arising from primary particle size and surface treatment of the material used, as well as the feed rate of the machine. A broad range of materials were used in this study, and the emitted particles mass (PM2.5) and number concentrations (PNC) were measured at the release source. Analysis of variance, followed by linear mixed-effects modeling, was applied to quantify the variations in PM2.5 and PNC of the released particles caused by the abovementioned factors. The results confirmed that using materials of different primary size and surface treatment affects the release of the particles from the same process by causing statistically-significant variations in PM2.5 and PNC. The interaction of these two factors should also be taken into account as it resulted in variations in the measured particles release properties. Furthermore, the feed rate of the milling machine was confirmed to be another influencing parameter. Although this research does not identify a specific pattern in the release of synthetic clay nanoparticles from the jet milling process generalizable to other similar settings, it emphasizes that each tested case should be handled individually in terms of exposure considerations.

Strain- and host species-specific inflammasome activation, IL-1β release, and cell death in macrophages infected with uropathogenic Escherichia coli

Uropathogenic Escherichia coli (UPEC) is the main etiological agent of urinary tract infections (UTIs). Little is known about interactions between UPEC and the inflammasome, a key innate immune pathway. Here we show that UPEC strains CFT073 and UTI89 trigger inflammasome activation and lytic cell death in human macrophages. Several other UPEC strains, including two multidrug-resistant ST131 isolates, did not kill macrophages. In mouse macrophages, UTI89 triggered cell death only at a high multiplicity of infection, and CFT073-mediated inflammasome responses were completely NLRP3-dependent. Surprisingly, CFT073- and UTI89-mediated responses only partially depended on NLRP3 in human macrophages. In these cells, NLRP3 was required for interleukin-1β (IL-1β) maturation, but contributed only marginally to cell death. Similarly, caspase-1 inhibition did not block cell death in human macrophages. In keeping with such differences, the pore-forming toxin α-hemolysin mediated a substantial proportion of CFT073-triggered IL-1β secretion in mouse but not human macrophages. There was also a more substantial α-hemolysin-independent cell death response in human vs. mouse macrophages. Thus, in mouse macrophages, CFT073-triggered inflammasome responses are completely NLRP3-dependent, and largely α-hemolysin-dependent. In contrast, UPEC activates an NLRP3-independent cell death pathway and an α-hemolysin-independent IL-1β secretion pathway in human macrophages. This has important implications for understanding UTI in humans.

Aspect-based opinion mining from product reviews using conditional random fields

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Natural language data is typically very sparse; the most common words are those that do not carry a lot of semantic content, and occurrences of any particular content-bearing word are rare, while co-occurrences of these words are rarer. Mining product aspects, along with corresponding opinions, is essential for Aspect-Based Opinion Mining (ABOM) as a result of the e-commerce revolution. Therefore, the need for automatic mining of reviews has reached a peak. In this work, we deal with ABOM as sequence labelling problem and propose a supervised extraction method to identify product aspects and corresponding opinions. We use Conditional Random Fields (CRFs) to solve the extraction problem and propose a feature function to enhance accuracy. The proposed method is evaluated using two different datasets. We also evaluate the effectiveness of feature function and the optimisation through multiple experiments.

Clinoenstatite coatings have high bonding strength, bioactive ion release, and osteoimmunomodulatory effects that enhance in vivo osseointegration

A number of coating materials have been developed over past two decades seeking to improve the osseointegration of orthopedic metal implants. Despite the many candidate materials trialed, their low rate of translation into clinical applications suggests there is room for improving the current strategies for their development. We therefore propose that the ideal coating material(s) should possess the following three properties: (i) high bonding strength, (ii) release of functional ions, and (iii) favourable osteoimmunomodulatory effects. To test this proposal, we developed clinoenstatite (CLT, MgSiO3), which as a coating material has high bonding strength, cytocompability and immunomodulatory effects that are favourable for in vivo osteogenesis. The bonding strength of CLT coatings was 50.1 ± 3.2 MPa, more than twice that of hydroxyapatite (HA) coatings, at 23.5 ± 3.5 MPa. CLT coatings released Mg and Si ions, and compared to HA coatings, induced an immunomodulation more conducive for osseointegration, demonstrated by downregurelation of pro-inflammatory cytokines, enhancement of osteogenesis, and inhibition of osteoclastogenesis. In vivo studies demonstrated that CLT coatings improved osseointegration with host bone, as shown by the enhanced biomechanical strength and increased de novo bone formation, when compared with HA coatings. These results support the notion that coating materials with the proposed properties can induce an in vivo environment better suited for osseointegration. These properties could, therefore, be fundamental when developing high-performance coating materials.

Preparation and in vitro release of drug-loaded microparticles for oral delivery using wholegrain sorghum kafirin protein

Kafirin microparticles have been proposed as an oral nutraceutical and drug delivery system. This study investigates microparticles formed with kafirin extracted from white and raw versus cooked red sorghum grains as an oral delivery system. Targeted delivery to the colon would be beneficial for medication such as prednisolone, which is used in the management of inflammatory bowel disease. Therefore, prednisolone was loaded into microparticles of kafirin from the different sources using phase separation. Differences were observed in the protein content, in vitro protein digestibility, and protein electrophoretic profile of the various sources of sorghum grains, kafirin extracts, and kafirin microparticles. For all of the formulations, the majority of the loaded prednisolone was not released in in vitro conditions simulating the upper gastrointestinal tract, indicating that most of the encapsulated drug could reach the target area of the lower gastrointestinal tract. This suggests that these kafirin microparticles may have potential as a colon-targeted nutraceutical and drug delivery system.

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.

The gradual release of responsibility: A case study of teaching science inquiry skills

This thesis investigated how a year-4 teacher used a pedagogical approach referred to as the Gradual Release of Responsibility (GRR) model of instruction for teaching Science Inquiry Skills in a primary classroom. Through scaffolding her students' learning using the GRR, the teacher guided her students towards developing an understanding about Scientific Inquiry leading to the foundations of scientific literacy. A learning environment was established in which students engaged in rich conversations, designed and conducted experiments using fair testing procedures, analysed and offered justifications for results, and negotiated knowledge claims in ways similar to some of those in the scientific community.

Occupational release of engineered nanoparticles: A review

Characterising the release of different types of Engineered Nanoparticles (ENPs) from various processes is of critical importance for the assessment of human exposure, as well as understanding the possible health effects of these particles. Therefore, the main aim of this chapter is to present a comprehensive review of studies which report on the release of airborne ENPs in different nanotechnology workplaces. The chapter will cover topics of relevance to the occupational characterisation of ENP emissions, ranging from the identification of different particle release sources and scenarios, to measurement methods and working towards a more uniform approach to characterisation. Furthermore, a brief review of ENP exposure control strategies, together with the application of mathematical modelling as an effective tool for the characterisation of emissions at nanotechnology workplaces is included.

Effect of deletion of Ghrelin-O-Acyltransferase on the pulsatile release of growth hormone in mice

Ghrelin, a gut hormone originating from the post-translational cleavage of preproghrelin, is the endogenous ligand of growth hormone secretagogue receptor 1a (GHS-R1a). Within the growth hormone (GH) axis, the biological activity of ghrelin requires octanoylation by ghrelin-O-acyltransferase (GOAT), conferring selective binding to the GHS-R1a receptor via acylated ghrelin. Complete loss of preproghrelin-derived signalling (through deletion of the Ghrl gene) contributes to a decline in peak GH release; however, the selective contribution of endogenous acyl-ghrelin to pulsatile GH release remains to be established. We assessed the pulsatile release of GH in ad lib. fed male germline goat−/− mice, extending measures to include mRNA for key hypothalamic regulators of GH release, and peripheral factors that are modulated relative to GH release. The amount of GH released was reduced in young goat−/− mice compared to age-matched wild-type mice, whereas pulse frequency and irregularity increased. Altered GH release did not coincide with alterations in hypothalamic Ghrh, Srif, Npy or Ghsr mRNA expression, or pituitary GH content, suggesting that loss of Goat does not compromise canonical mechanisms that contribute to pituitary GH production and release. Although loss of Goat resulted in an irregular pattern of GH release (characterised by an increase in the number of GH pulses observed during extended secretory events), this did not contribute to a change in the expression of sexually dimorphic GH-dependent liver genes. Of interest, circulating levels of insulin-like growth factor (IGF)-1 were elevated in goat−/− mice. This rise in circulating levels of IGF-1 was correlated with an increase in GH pulse frequency, suggesting that sustained or increased IGF-1 release in goat−/− mice may occur in response to altered GH release patterning. Our observations demonstrate that germline loss of Goat alters GH release and patterning. Although the biological relevance of altered GH secretory patterning remains unclear, we propose that this may contribute to sustained IGF-1 release and growth in goat−/− mice.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.