Stop-signal response inhibition in schizophrenia : Behavioural, event-related potential and functional neuroimaging data

Inhibitory control deficits are well documented in schizophrenia, supported by impairment in an established measure of response inhibition, the stop-signal reaction time (SSRT). We investigated the neural basis of this impairment by comparing schizophrenia patients and controls matched for age, sex and education on behavioural, functional magnetic resonance imaging (fMRI) and event-related potential (ERP) indices of stop-signal task performance. Compared to controls, patients exhibited slower SSRT and reduced right inferior frontal gyrus (rIFG) activation, but rIFG activation correlated with SSRT in both groups. Go stimulus and stop-signal ERP components (N1/P3) were smaller in patients, but the peak latencies of stop-signal N1 and P3 were also delayed in patients, indicating impairment early in stop-signal processing. Additionally, response-locked lateralised readiness potentials indicated response preparation was prolonged in patients. An inability to engage rIFG may predicate slowed inhibition in patients, however multiple spatiotemporal irregularities in the networks underpinning stop-signal task performance may contribute to this deficit.

Twitter data: what do they represent?

Scholarly research into the uses of social media has become a major area of growth in recent years, as the adoption of social media for public communication itself has continued apace. While social media platforms provide ready avenues for data access through their Application Programming interfaces, it is increasingly important to think through exactly what these data represent, and what conclusions about the role of social media in society the research which is based on such data therefore enables. This article explores these issues especially for one of the currently leading social media platforms: Twitter.

Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1–28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.

Meta-analysis of microarray data identifies GAS6 expression as an independent predictor of poor survival in ovarian cancer

Seeking new biomarkers for epithelial ovarian cancer, the fifth most common cause of death from all cancers in women and the leading cause of death from gynaecological malignancies, we performed a meta-analysis of three independent studies and compared the results in regard to clinicopathological parameters. This analysis revealed that GAS6 was highly expressed in ovarian cancer and therefore was selected as our candidate of choice. GAS6 encodes a secreted protein involved in physiological processes including cell proliferation, chemotaxis, and cell survival. We performed immunohistochemistry on various ovarian cancer tissues and found that GAS6 expression was elevated in tumour tissue samples compared to healthy control samples (P < 0.0001). In addition, GAS6 expression was also higher in tumours from patients with residual disease compared to those without. Our data propose GAS6 as an independent predictor of poor survival, suggesting GAS6, both on the mRNA and on the protein level, as a potential biomarker for ovarian cancer. In clinical practice, the staining of a tumour biopsy for GAS6 may be useful to assess cancer prognosis and/or to monitor disease progression.

Improving national hospice/palliative care service symptom outcomes systematically through point-of-care data collection, structured feedback and benchmarking

PURPOSE Every health care sector including hospice/palliative care needs to systematically improve services using patient-defined outcomes. Data from the national Australian Palliative Care Outcomes Collaboration aims to define whether hospice/palliative care patients' outcomes and the consistency of these outcomes have improved in the last 3 years. METHODS Data were analysed by clinical phase (stable, unstable, deteriorating, terminal). Patient-level data included the Symptom Assessment Scale and the Palliative Care Problem Severity Score. Nationally collected point-of-care data were anchored for the period July-December 2008 and subsequently compared to this baseline in six 6-month reporting cycles for all services that submitted data in every time period (n = 30) using individual longitudinal multi-level random coefficient models. RESULTS Data were analysed for 19,747 patients (46 % female; 85 % cancer; 27,928 episodes of care; 65,463 phases). There were significant improvements across all domains (symptom control, family care, psychological and spiritual care) except pain. Simultaneously, the interquartile ranges decreased, jointly indicating that better and more consistent patient outcomes were being achieved. CONCLUSION These are the first national hospice/palliative care symptom control performance data to demonstrate improvements in clinical outcomes at a service level as a result of routine data collection and systematic feedback.

MMP-14 is expressed in preeclamptic placentas and mediates release of soluble endoglin

Soluble endoglin is an anti-angiogenic protein that is released from the placenta and contributes to both maternal endothelial dysfunction and the clinical features of severe preeclampsia. The mechanism through which soluble endoglin is released from the placenta is currently unknown; however, recent work in colorectal cancer identified matrix metalloproteinase 14 (MMP-14) as the cleavage protease of endoglin. To determine whether this is also the mechanism responsible for soluble endoglin release in preeclampsia, we investigated the expression of MMP-14 within the placenta and the effects of its inhibition on soluble endoglin release. Placentas were obtained from severe, early onset preeclamptic pregnancies (n = 8) and gestationally matched preterm controls (n = 8). MMP-14 was predominately localized to the syncytiotrophoblast. Results from a proximity ligation assay showed protein interactions between endogenous MMP-14 and endoglin within the preeclamptic placenta. To demonstrate that this interaction produces soluble endoglin, we treated trophoblastic BeWo cells with either a broad-spectrum MMP inhibitor (GM6001) or MMP-14 siRNA. Both treatments produced a decrease in soluble endoglin (P ≤ 0.05). Treatment of mice bearing BeWo xenografts with GM6001 decreased circulating soluble endoglin levels in mouse serum (P ≤ 0.05). These findings indicate that MMP-14 is the likely cleavage protease of endoglin in the setting of preeclampsia. This approach provides a novel method for the development of potential therapeutics to reduce circulating soluble endoglin and ameliorate the clinical features of severe preeclampsia.

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.

Separating resource rents from intra-marginal rents in fisheries economics survey data

Economic surveys of fisheries are undertaken in several countries as a means of assessing the economic performance of their fisheries. The level of economic profits accruing in the fishery can be estimated from the average economic profits of the boats surveyed. Economic profits consist of two components—resource rent and intra-marginal rent. From a fisheries management perspective, the key indicator of performance is the level of resource rent being generated in the fishery. Consequently, these different components need to be separated out. In this paper, a means of separating out the rent components is identified for a heterogeneous fishery. This is applied to the multi-purpose fleet operating in the English Channel. The paper demonstrates that failing to separate out these two components may result in a misrepresentation of the economic performance of the fishery.

Simulation-based density estimation for time series using covariate data

This paper proposes a simulation-based density estimation technique for time series that exploits information found in covariate data. The method can be paired with a large range of parametric models used in time series estimation. We derive asymptotic properties of the estimator and illustrate attractive finite sample properties for a range of well-known econometric and financial applications.

Large-scale analysis of soccer matches using spatiotemporal tracking data

Although the collection of player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of such spatiotemporal data has yet to surface. In this paper, given an entire season's worth of player and ball tracking data from a professional soccer league (approx 400,000,000 data points), we present a method which can conduct both individual player and team analysis. Due to the dynamic, continuous and multi-player nature of team sports like soccer, a major issue is aligning player positions over time. We present a "role-based" representation that dynamically updates each player's relative role at each frame and demonstrate how this captures the short-term context to enable both individual player and team analysis. We discover role directly from data by utilizing a minimum entropy data partitioning method and show how this can be used to accurately detect and visualize formations, as well as analyze individual player behavior.