Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression

The neural networks that putatively modulate aspects of normal emotional behavior have been implicated in the pathophysiology of mood disorders by converging evidence from neuroimaging, neuropathological and lesion analysis studies. These networks involve the medial prefrontal cortex (MPFC) and closely related areas in the medial and caudolateral orbital cortex (medial prefrontal network), amygdala, hippocampus, and ventromedial parts of the basal ganglia, where alterations in grey matter volume and neurophysiological activity are found in cases with recurrent depressive episodes. Such findings hold major implications for models of the neurocircuits that underlie depression. In particular evidence from lesion analysis studies suggests that the MPFC and related limbic and striato-pallido-thalamic structures organize emotional expression. The MPFC is part of a larger “default system” of cortical areas that include the dorsal PFC, mid- and posterior cingulate cortex, anterior temporal cortex, and entorhinal and parahippocampal cortex, which has been implicated in self-referential functions. Dysfunction within and between structures in this circuit may induce disturbances in emotional behavior and other cognitive aspects of depressive syndromes in humans. Further, because the MPFC and related limbic structures provide forebrain modulation over visceral control structures in the hypothalamus and brainstem, their dysfunction can account for the disturbances in autonomic regulation and neuroendocrine responses that are associated with mood disorders. This paper discusses these systems together with the neurochemical systems that impinge on them and form the basis for most pharmacological therapies

Warm water pathways in the northeastern North Atlantic ACCE RAFOS float data report November 1996 - November 1999

This is the final data report of all acoustically tracked RAFOS float data collected by the Woods Hole Oceanographic Institution in 1996-1999 during the Atlantic Climate Change Experiment (ACCE). The RAFOS float component of ACCE, entitled "Warm Water Pathways and Intergyre Exchange in the Northeastern North Atlantic," was designed to measure the warm water currents entering the northeastern North Atlantic which become the source of intermediate and deep waters in the subpolar region. The experiment was comprised of three RAFOS float deployments on the R/V Knorr: the first in fall 1996 along the continental slope seaward of Porcupine Bank, the second in spring 1997 along the mid-Atlantic Ridge, and the final deployment in fall 1997 along both the Ridge and the Bank. Seventy floats were deployed, 13 RAFOS and 2 ALFOS in fall 1996, 14 RAFOS in spring 1997, and 41 RAFOS in fall 1997. The isobaric ALFOS floats were ballasted for 800 decibars and were launched to monitor the regions' sound sources during the experiment. The RAFOS floats were isopycnal and ballasted for the 27.5 sigma-t surface to target the intermediate-depth North Atlantic and Poleward Eastern Boundary Currents. The objectives of the Lagrangian float study were (1) to provide a quantitative description of the bifurcation of the North Atlantic Current east of the Mid-Atlantic Ridge, (2) to assess the importance of meridional eddy fluxes, compared to large-scale advection, in the northward flux of heat and salt in the northeastern North Atlantic, and (3) to establish the degree of continuity of the Poleward Eastern Boundary Current as it flows to the entrance of the Norwegian Sea and the fate of the Mediterranean Outflow Water carried by this current.Funding was provided by the National Science Foundation under Grant Number OCE-9831877

Feature Extraction Methods for Neural Networks in the Classification of Structural Health Anomalies

Failure of large complex structures such as buildings and bridges can have monumental repercussions such as human mortality, environmental destruction and economic consequences. It is therefore paramount that detection of structural damage or anomalies are identified and managed early. This highlights the need to develop automated Structural Health Monitoring (SHM) systems that can continuously allow the safety status of structures to be determined, even in the worst and most isolated conditions, to ultimately help prevent destruction and save lives. Signal processing is a crucial step to detecting structural anomalies and recent work demonstrates the opportunities for neural networks, however the encoding of data for SHM requires the extraction of features due to often, noisy data. This paper focuses on feature extraction methods for artificial neural networks (ANNs) and spiking neural networks (SNNs) and aims to identify bespoke features which enable SNNs to encode data and perform the classification of anomalies. Results show that extraction of particular features in large real-world applications improve the classification accuracy of SNNs

Selective Effects of Cholinergic Modulation on Task Performance during Selective Attention

The cholinergic neurotransmitter system is critically linked to cognitive functions including attention. The current studies were designed to evaluate the effect of a cholinergic agonist and an antagonist on performance during a selective visual attention task where the inherent salience of attended/unattended stimuli was modulated. Two randomized, placebo-controlled, crossover studies were performed, one (n=9) with the anticholinesterase physostigmine (1.0 mg/h), and the other (n=30) with the anticholinergic scopolamine (0.4 mc/kg). During the task, two double-exposure pictures of faces and houses were presented side by side. Subjects were cued to attend to either the face or the house component of the stimuli, and were instructed to perform a matching task with the two exemplars from the attended category. The cue changed every 4–7 trials to instruct subjects to shift attention from one stimulus component to the other. During placebo in both studies, reaction time (RT) associated with the first trial following a cued shift in attention was longer than RT associated with later trials (p<0.05); RT also was significantly longer when attending to houses than to faces (p<0.05). Physostigmine decreased RT relative to placebo preferentially during trials greater than one (p<0.05), with no change during trial one; and decreased RT preferentially during the attention to houses condition (p<0.05) vs attention to faces. Scopolamine increased RT relative to placebo selectively during trials greater than one (p<0.05), and preferentially increased RT during the attention to faces condition (p<0.05). The results suggest that enhancement or impairment of cholinergic activity preferentially influences the maintenance of selective attention (ie trials greater than 1). Moreover, effects of cholinergic manipulation depend on the selective attention condition (ie faces vs houses), which may suggest that cholinergic activity interacts with stimulus salience. The findings are discussed within the context of the role of acetylcholine both in stimulus processing and stimulus salience, and in establishing attention biases through top-down and bottom-up mechanisms of attention

Altered interaction with environmental reinforcers in major depressive disorder: Relationship to anhedonia

Anhedonia—defined as loss of interest or pleasure—is one of two core symptoms of major depressive disorder (MDD). Anhedonia may involve decreased enjoyment of potentially rewarding activities and decreased motivation to engage in such activities. Increased engagement with reinforcers—activities with the potential to be positive experiences—is a frequent target of cognitive-behavioral therapies. Nevertheless, how environmental reinforcers are perceived, and how decisions to approach or avoid them are made by individuals with MDD, is largely unknown. We developed an experimental Behavioral Approach Motivation Paradigm to study how activities are evaluated and approached in MDD. Twenty-one MDD participants and 23 healthy controls performed an experimental task that rated activity words for their hedonic value, then engaged in an approach-avoidance joystick task with each individual’s unique set of ‘liked’ and ‘disliked’ activity words. A negative correlation was observed between anhedonia and the number of ‘liked’ activities across participants. No significant difference between approach and avoidance behavior was found in direct comparisons between healthy controls and MDD participants; however, weaker avoidance and greater approach toward ‘disliked’ activities was found in MDD participants. This suggests negative bias in selecting environmental opportunities, potentially further compromising access to hedonic experiences in MDD

Chromatin accessibility reveals insights into androgen receptor activation and transcriptional specificity

BACKGROUND: Epigenetic mechanisms such as chromatin accessibility impact transcription factor binding to DNA and transcriptional specificity. The androgen receptor (AR), a master regulator of the male phenotype and prostate cancer pathogenesis, acts primarily through ligand-activated transcription of target genes. Although several determinants of AR transcriptional specificity have been elucidated, our understanding of the interplay between chromatin accessibility and AR function remains incomplete. RESULTS: We used deep sequencing to assess chromatin structure via DNase I hypersensitivity and mRNA abundance, and paired these datasets with three independent AR ChIP-seq datasets. Our analysis revealed qualitative and quantitative differences in chromatin accessibility that corresponded to both AR binding and an enrichment of motifs for potential collaborating factors, one of which was identified as SP1. These quantitative differences were significantly associated with AR-regulated mRNA transcription across the genome. Base-pair resolution of the DNase I cleavage profile revealed three distinct footprinting patterns associated with the AR-DNA interaction, suggesting multiple modes of AR interaction with the genome. CONCLUSIONS: In contrast with other DNA-binding factors, AR binding to the genome does not only target regions that are accessible to DNase I cleavage prior to hormone induction. AR binding is invariably associated with an increase in chromatin accessibility and, consequently, changes in gene expression. Furthermore, we present the first in vivo evidence that a significant fraction of AR binds only to half of the full AR DNA motif. These findings indicate a dynamic quantitative relationship between chromatin structure and AR-DNA binding that impacts AR transcriptional specificity

Resilience in groundwater supply systems: integrating resource based approaches with agency, behaviour and choice

Access to safe and reliable water supplies is a key goal for households and governments across most of Africa. Groundwater reserves can play a critical role in achieving this, yet risks of contamination and over-abstraction threaten to undermine the resilience of this supply. A rapidly rising trend for privately-developed wells and boreholes raises additional concerns about the vulnerability of water supplies to natural or man-made environmental shocks. The potential scale of the situation is particularly marked in Nigeria where the use of boreholes has increased exponentially since 1999 (from 10% of the population to 38% in 2015), with most other forms of water supply, notably piped tap water, falling. Developing effective groundwater management approaches that build the resilience of communities is challenging, not least given the range of different actors involved, their competing interests and demands, and variations in the hydrogeological environment. Insights from resilience studies in social science emphasise how the resilience of ecological resources to shocks and change is critically linked to the adaptive capacity of social systems and their agents. Choices made now have long-lasting effects, yet these choices are little understood. Understanding the choices made by consumers, drillers and policy actors requires a strong interdisciplinary dimension and argues for new perspectives as to how the resilience of communities and societies might be built. The project brings together a unique interdisciplinary collaboration between academics from the UK and Nigeria working in the fields of economic geography, psychology, hydrogeology and journalism studies

Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers