The International Society for Bipolar Disorders (ISBD) Task Force report on the nomenclature of course and outcome in bipolar disorders

Objectives:  Via an international panel of experts, this paper attempts to document, review, interpret, and propose operational definitions used to describe the course of bipolar disorders for worldwide use, and to disseminate consensus opinion, supported by the existing literature, in order to better predict course and treatment outcomes. Methods:  Under the auspices of the International Society for Bipolar Disorders, a task force was convened to examine, report, discuss, and integrate findings from the scientific literature related to observational and clinical trial studies in order to reach consensus and propose terminology describing course and outcome in bipolar disorders. Results:  Consensus opinion was reached regarding the definition of nine terms (response, remission, recovery, relapse, recurrence, subsyndromal states, predominant polarity, switch, and functional outcome) commonly used to describe course and outcomes in bipolar disorders. Further studies are needed to validate the proposed definitions. Conclusion:  Determination and dissemination of a consensus nomenclature serve as the first step toward producing a validated and standardized system to define course and outcome in bipolar disorders in order to identify predictors of outcome and effects of treatment. The task force acknowledges that there is limited validity to the proposed terms, as for the most part they represent a consensus opinion. These definitions need to be validated in existing databases and in future studies, and the primary goals of the task force are to stimulate research on the validity of proposed concepts and further standardize the technical nomenclature.

Factorial multi-task learning : a Bayesian nonparametric approach

Multi-task learning is a paradigm shown to improve the performance of related tasks through their joint learning. However, for real-world data, it is usually difficult to assess the task relatedness and joint learning with unrelated tasks may lead to serious performance degradations. To this end, we propose a framework that groups the tasks based on their relatedness in a subspace and allows a varying degree of relatedness among tasks by sharing the subspace bases across the groups. This provides the flexibility of no sharing when two sets of tasks are unrelated and partial/total sharing when the tasks are related. Importantly, the number of task-groups and the subspace dimensionality are automatically inferred from the data. To realize our framework, we introduce a novel Bayesian nonparametric prior that extends the traditional hierarchical beta process prior using a Dirichlet process to permit potentially infinite number of child beta processes. We apply our model for multi-task regression and classification applications. Experimental results using several synthetic and real datasets show the superiority of our model to other recent multi-task learning methods. Copyright 2013 by the author(s).

Staging systems in bipolar disorder: an International Society for Bipolar Disorders Task Force report

We discuss the rationale behind staging systems described specifically for bipolar disorders. Current applications, future directions and research gaps in clinical staging models for bipolar disorders are outlined.

The influence of protective and risk factors in individual, peer and school domains on Thai adolescents' alcohol and illicit drug use: a survey

This study investigates risk and protective factors for substance abuse in a sample of 1778 students attending technical colleges in Bangkok and Nakhon Ratchasima provinces of Thailand using a self-report questionnaire modified from the Communities That Care youth survey. Low school commitment was strongly associated with illicit drug use, with adjusted odds ratios ranging from 2.84 (glue sniffing) to 10.06 (ecstasy). Having friends using drugs, and friends with delinquent behaviors increased the risk of using alcohol and illegal drugs, with adjusted odds ratios of 6.84 and 6.72 respectively for marijuana use. For protective factors, approximately 40-60% of students with high levels of moral belief, participation in religious activities, and social skills were less likely to use alcohol. It is concluded that peer influence is a significant contributor to Thai adolescents' participation in substance abuse and that engaging in religiosity may assist adolescents to internalize negative aspects of harmful drugs into positive perceptions and encourage them to avoid alcohol and illegal drugs.

Sleep restriction during simulated wildfire suppression: effect on physical task performance

OBJECTIVES: To examine the effects of sleep restriction on firefighters' physical task performance during simulated wildfire suppression. METHODS: Thirty-five firefighters were matched and randomly allocated to either a control condition (8-hour sleep opportunity, n = 18) or a sleep restricted condition (4-hour sleep opportunity, n = 17). Performance on physical work tasks was evaluated across three days. In addition, heart rate, core temperature, and worker activity were measured continuously. Rate of perceived and exertion and effort sensation were evaluated during the physical work periods. RESULTS: There were no differences between the sleep-restricted and control groups in firefighters' task performance, heart rate, core temperature, or perceptual responses during self-paced simulated firefighting work tasks. However, the sleep-restricted group were less active during periods of non-physical work compared to the control group. CONCLUSIONS: Under self-paced work conditions, 4 h of sleep restriction did not adversely affect firefighters' performance on physical work tasks. However, the sleep-restricted group were less physically active throughout the simulation. This may indicate that sleep-restricted participants adapted their behaviour to conserve effort during rest periods, to subsequently ensure they were able to maintain performance during the firefighter work tasks. This work contributes new knowledge to inform fire agencies of firefighters' operational capabilities when their sleep is restricted during multi-day wildfire events. The work also highlights the need for further research to explore how sleep restriction affects physical performance during tasks of varying duration, intensity, and complexity.

Task-specific effects of modular body armor.

Eleven recreationally active males performed 11 circuits of military work, wearing torso armor on one occasion, and full armor on another. Performance was measured by the time taken to complete individual tasks, and the overall time to completion (TTC) for each circuit. Heart rate, intestinal temperature, ratings of perceived exertion (RPE), and thermal sensation were recorded after each circuit. Participants' circuit TTC was no different between conditions; however, specific tasks were differentially impeded by the two armor configurations. Vaulting and crawling were significantly slower (0.28 ± 0.06 and 0.55 ± 0.26 seconds) in full armor; however, box lifting and shooting were significantly slower (0.36 ± 0.18 and 0.86 ± 0.23 seconds) when wearing torso armor. Heart rate and core temperature were significantly higher during the full armor trial (5 ± 1 beats · min(-1) and 0.22 ± 0.03 °C). Similarly, RPE and thermal sensation were significantly higher (1 ± 0 and 0.5 ± 0.0) during the full armor condition. Military tasks were differentially impaired by the armor configurations used, which suggests a need to explore role-specific armor for military personnel. Physiological and perceptual responses were elevated in full armor, which could be exacerbated during longer periods of work or in hot conditions.

Predicting the likely response of data-poor ecosystems to climate change using space-for-time substitution across domains

Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly applicable mechanistic models can be developed. This limits predictions to qualitative assessments or simplistic rules of thumb in data-poor regions, making management of the relevant systems difficult. We demonstrate a method for developing quantitative predictions of ecological response in data-poor ecosystems based on a space-for-time substitution, using distant, well-studied systems across an inherent climatic gradient to predict ecological response. Changes in biophysical data across the spatial gradient are used to generate quantitative hypotheses of temporal ecological responses that are then tested in a target region. Transferability of predictions among distant locations, the novel outcome of this method, is demonstrated via simple quantitative relationships that identify direct and indirect impacts of climate change on physical, chemical and ecological variables using commonly available data sources. Based on a limited subset of data, these relationships were demonstrably plausible in similar yet distant (>2000 km) ecosystems. Quantitative forecasts of ecological change based on climate-ecosystem relationships from distant regions provides a basis for research planning and informed management decisions, especially in the many ecosystems for which there are few data. This application of gradient studies across domains - to investigate ecological response to climate change - allows for the quantification of effects on potentially numerous, interacting and complex ecosystem components and how they may vary, especially over long time periods (e.g. decades). These quantitative and integrated long-term predictions will be of significant value to natural resource practitioners attempting to manage data-poor ecosystems to prevent or limit the loss of ecological value. The method is likely to be applicable to many ecosystem types, providing a robust scientific basis for estimating likely impacts of future climate change in ecosystems where no such method currently exists.

Type-aware task placement in geo-distributed data centers with low OPEX using data center resizing

With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the type-aware task placement problem over geo-distributed data centers. With the consideration of the diversity of user requests and server clusters in modern data centers, we formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e., the expected response time of tasks. Furthermore, an efficient solution is designed for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the greedy algorithm and much approaches the optimal results. © 2014 IEEE.