Differential patterns of activity and functional connectivity in emotion processing neural circuitry to angry and happy faces in adolescents with and without suicide attempt

Background - Neural substrates of emotion dysregulation in adolescent suicide attempters remain unexamined. Method - We used functional magnetic resonance imaging to measure neural activity to neutral, mild or intense (i.e. 0%, 50% or 100% intensity) emotion face morphs in two separate emotion-processing runs (angry and happy) in three adolescent groups: (1) history of suicide attempt and depression (ATT, n = 14); (2) history of depression alone (NAT, n = 15); and (3) healthy controls (HC, n = 15). Post-hoc analyses were conducted on interactions from 3 group × 3 condition (intensities) whole-brain analyses (p < 0.05, corrected) for each emotion run. Results - To 50% intensity angry faces, ATT showed significantly greater activity than NAT in anterior cingulate gyral–dorsolateral prefrontal cortical attentional control circuitry, primary sensory and temporal cortices; and significantly greater activity than HC in the primary sensory cortex, while NAT had significantly lower activity than HC in the anterior cingulate gyrus and ventromedial prefrontal cortex. To neutral faces during the angry emotion-processing run, ATT had significantly lower activity than NAT in the fusiform gyrus. ATT also showed significantly lower activity than HC to 100% intensity happy faces in the primary sensory cortex, and to neutral faces in the happy run in the anterior cingulate and left medial frontal gyri (all p < 0.006,corrected). Psychophysiological interaction analyses revealed significantly reduced anterior cingulate gyral–insula functional connectivity to 50% intensity angry faces in ATT v. NAT or HC. Conclusions - Elevated activity in attention control circuitry, and reduced anterior cingulate gyral–insula functional connectivity, to 50% intensity angry faces in ATT than other groups suggest that ATT may show inefficient recruitment of attentional control neural circuitry when regulating attention to mild intensity angry faces, which may represent a potential biological marker for suicide risk

Structure of Extreme Correlated Equilibria: a Zero-Sum Example and its Implications

We exhibit the rich structure of the set of correlated equilibria by analyzing the simplest of polynomial games: the mixed extension of matching pennies. We show that while the correlated equilibrium set is convex and compact, the structure of its extreme points can be quite complicated. In finite games the ratio of extreme correlated to extreme Nash equilibria can be greater than exponential in the size of the strategy spaces. In polynomial games there can exist extreme correlated equilibria which are not finitely supported; we construct a large family of examples using techniques from ergodic theory. We show that in general the set of correlated equilibrium distributions of a polynomial game cannot be described by conditions on finitely many moments (means, covariances, etc.), in marked contrast to the set of Nash equilibria which is always expressible in terms of finitely many moments

The effect of aggression management training programmes for nursing staff and students working in an acute hospital setting. A narrative review of current literature

BACKGROUND: Patient aggression is a longstanding problem in general hospital nursing. Staff training is recommended to tackle workplace aggression originating from patients or visitors, yet evidence on training effects is scarce. AIMS: To review and collate current research evidence on the effect of aggression management training for nurses and nursing students working in general hospitals, and to derive recommendations for further research. DESIGN: Systematic, narrative review. DATA SOURCES: Embase, MEDLINE, the Cochrane library, CINAHL, PsycINFO, pubmed, psycArticles, Psychology and Behavioural Sciences Collection were searched for articles evaluating training programs for staff and students in acute hospital adult nursing in a 'before/after' design. Studies published between January 2000 and September 2011 in English, French or German were eligible of inclusion. REVIEW METHODS: The methodological quality of included studies was assessed with the 'Quality Assessment Tool for Quantitative Studies'. Main outcomes i.e. attitudes, confidence, skills and knowledge were collated. RESULTS: Nine studies were included. Two had a weak, six a moderate, and one a strong study design. All studies reported increased confidence, improved attitude, skills, and knowledge about risk factors post training. There was no significant change in incidence of patient aggression. CONCLUSION: Our findings corroborate findings of reviews on training in mental health care, which point to a lack of high quality research. Training does not reduce the incidence of aggressive acts. Aggression needs to be tackled at an organizational level

Interpreting wde-band neural activity using convolutional neural networks

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity

On the Asymptotic Performance of IDA*

Since best-first search algorithms such as A* require large amounts of memory, they sometimes cannot run to completion, even on problem instances of moderate size. This problem has led to the development of limited-memory search algorithms, of which the best known is IDA*. This paper presents the following results about IDA and related algorithms: The analysis of asymptotic optimality for IDA* in [10] is incorrect. There are trees satisfying the asymptotic optimality conditions given in [10] for which IDA* is not asymptotically optimal. To correct the above problem, we state and prove necessary and sufficient conditions for asymptotic optimality of IDA* on trees. On trees not satisfying our conditions, we show that no best-first limited-memory search algorithm can be asymptotically optimal. On graphs, IDA* can perform quite poorly. In particular, there are graphs on which IDA* does node expansions where N is the number of nodes expanded by A'. (Also cross-referenced as UMIACS-TR-95-22

Quadrupole correlations and inertial properties of rotating nuclei

The contribution of quantum shape fluctuations to inertial properties of rotating nuclei has been analyzed for QQ-nuclear interaction using the random phase approximation (RPA). The different recipes to treat the cranking mean field plus RPA problem are considered. The effects of the dN=2 quadrupole matrix elements and the role of the volume conservation condition are discussed.Comment: 14 pages, 7 figures, To be published in J. Phys. G: Nucl. Phy

Integrating conversational case retrieval with generative planning

Advances in Case-Based Reasoning Research and Development: Proceedings of the 5th European Workshop on Case-Based Reasoning, EWCBR 2000, pp. 210-221.Some problem-solving tasks are amenable to integrated case retrieval and generative planning techniques. This is certainly true for some decision support tasks, in which a user controls the problem-solving process but cannot provide a complete domain theory. Unfortunately, existing integrations are either non-interactive or require a complete domain theory and/or complete world state to produce acceptable plans preventing them from being easily used in these situations. We describe a novel integrated algorithm, named SiN, that is interactive and does not require a complete domain theory or complete world state. SiN users leverage a conversational case retriever to focus both partial world state acquisition and plan generation. We highlight the benefits of SiN (e.g. quadratically fewer cases needed) in an experimental study using a new travel planning domain