K+ontrol rapidly and efficiently reduces potassium in donor blood during ex vivo circulation.

BACKGROUND: Patients with kidney failure are at risk for lethal complications from hyperkalemia. Resuscitation, medications, and hemodialysis are used to mitigate increased potassium (K+) levels in circulating blood; however, these approaches may not always be readily available or effective, especially in a resource limited environment. We tested a sorbent cartridge (KC, K+ontrol CytoSorbents Medical Inc., Monmouth Junction, New Jersey) which contains a resin adsorber for K+. The objective of this study was to test the utility of KC in an ex vivo circulation system. We hypothesized that KC reduces K+ levels in extracorporeal circulation of donor swine whole blood infused with KCl. METHODS: A six-hour circulation study was carried out using KC, a NxStage (NxStage Medical, Inc., Lawrence, MA) membrane, blood bag containing heparinized whole blood with KCl infusion, 3/16-inch ID tubing, a peristaltic pump, and flow sensors. The NxStage permeate line was connected back to the main circuit in the Control group (n = 6), creating a recirculation loop. For KC group (n = 6), KC was added to the recirculation loop, and a continuous infusion of KCl at 10 mEq/hour was administered for two hours. Blood samples were acquired at baseline and every hour for 6 h. RESULTS: In the control group, K+ levels remained at ∼9 mmol/L; 9.1 ± 0.4 mmol/L at 6 h. In the KC group, significant decreases in K+ at hour 1 (4.3 ± 0.3 mmol/L) and were sustained for the experiment duration equilibrating at 4.6 ± 0.4 mmol/L after 6 h (p = 0.042). Main loop blood flow was maintained under 400 mL/min; recirculation loop flow varied between 60 and 70 mL/min in the control group and 45-55 mL/min in the KC group. Decreases in recirculation loop flow in KC group required 7% increase of pump RPM. CONCLUSIONS: During ex-vivo extracorporeal circulation using donor swine blood, KC removed approximately 50% of K+, normalizing circulating levels.

Cognitive Behavior Therapy for Anxious and Depressed Youth: Improving Homework Adherence Through Mobile Technology.

BACKGROUND: Anxiety and mood disorders are the most common mental illnesses, peaking during adolescence and affecting approximately 25% of Canadians aged 14-17 years. If not successfully treated at this age, they often persist into adulthood, exerting a great social and economic toll. Given the long-term impact, finding ways to increase the success and cost-effectiveness of mental health care is a pressing need. Cognitive behavior therapy (CBT) is an evidence-based treatment for mood and anxiety disorders throughout the lifespan. Mental health technologies can be used to make such treatments more successful by delivering them in a format that increases utilization. Young people embrace technologies, and many want to actively manage their mental health. Mobile software apps have the potential to improve youth adherence to CBT and, in turn, improve outcomes of treatment. OBJECTIVE: The purpose of this project is to improve homework adherence in CBT for youth anxiety and/or depression. The objectives are to (1) design and optimize the usability of a mobile app for delivering the homework component of CBT for youth with anxiety and/or depression, (2) assess the app's impact on homework completion, and (3) implement the app in CBT programs. We hypothesize that homework adherence will be greater in the app group than in the no-app group. METHODS: Phase 1: exploratory interviews will be conducted with adolescents and therapists familiar with CBT to obtain views and perspectives on the requirements and features of a usable app and the challenges involved in implementation. The information obtained will guide the design of a prototype. The prototype will be optimized via think-aloud procedures involving an iterative process of evaluation, modification, and re-evaluation, culminating in a fully functional version of the prototype that is ready for optimization in a clinical context. Phase 2: a usability study will be conducted to optimize the prototype in the context of treatment at clinics that provide CBT treatment for youth with anxiety and/or depression. This phase will result in a usable app that is ready to be tested for its effectiveness in increasing homework adherence. Phase 3: a pragmatic clinical trial will be conducted at several clinics to evaluate the impact of the app on homework adherence. Participants in the app group are expected to show greater homework completion than those in the no-app group. RESULTS: Phase 3 will be completed by September 2019. CONCLUSIONS: The app will be a unique adjunct to treatment for adolescents in CBT, focusing on both anxiety and depression, developed in partnership with end users at every stage from design to implementation, customizable for different cognitive profiles, and designed with depression symptom tracking measures for youth made interoperable with electronic medical records.

TreeVector: scalable, interactive, phylogenetic trees for the web.

BACKGROUND: Phylogenetic trees are complex data forms that need to be graphically displayed to be human-readable. Traditional techniques of plotting phylogenetic trees focus on rendering a single static image, but increases in the production of biological data and large-scale analyses demand scalable, browsable, and interactive trees. METHODOLOGY/PRINCIPAL FINDINGS: We introduce TreeVector, a Scalable Vector Graphics-and Java-based method that allows trees to be integrated and viewed seamlessly in standard web browsers with no extra software required, and can be modified and linked using standard web technologies. There are now many bioinformatics servers and databases with a range of dynamic processes and updates to cope with the increasing volume of data. TreeVector is designed as a framework to integrate with these processes and produce user-customized phylogenies automatically. We also address the strengths of phylogenetic trees as part of a linked-in browsing process rather than an end graphic for print. CONCLUSIONS/SIGNIFICANCE: TreeVector is fast and easy to use and is available to download precompiled, but is also open source. It can also be run from the web server listed below or the user's own web server. It has already been deployed on two recognized and widely used database Web sites.

On optimal decision-making in brains and social insect colonies.

The problem of how to compromise between speed and accuracy in decision-making faces organisms at many levels of biological complexity. Striking parallels are evident between decision-making in primate brains and collective decision-making in social insect colonies: in both systems, separate populations accumulate evidence for alternative choices; when one population reaches a threshold, a decision is made for the corresponding alternative, and this threshold may be varied to compromise between the speed and the accuracy of decision-making. In primate decision-making, simple models of these processes have been shown, under certain parametrizations, to implement the statistically optimal procedure that minimizes decision time for any given error rate. In this paper, we adapt these same analysis techniques and apply them to new models of collective decision-making in social insect colonies. We show that social insect colonies may also be able to achieve statistically optimal collective decision-making in a very similar way to primate brains, via direct competition between evidence-accumulating populations. This optimality result makes testable predictions for how collective decision-making in social insects should be organized. Our approach also represents the first attempt to identify a common theoretical framework for the study of decision-making in diverse biological systems.

Noise, cost and speed-accuracy trade-offs: decision-making in a decentralized system.

Many natural and artificial decision-making systems face decision problems where there is an inherent compromise between two or more objectives. One such common compromise is between the speed and accuracy of a decision. The ability to exploit the characteristics of a decision problem in order to vary between the extremes of making maximally rapid, or maximally accurate decisions, is a useful property of such systems. Colonies of the ant Temnothorax albipennis (formerly Leptothorax albipennis) are a paradigmatic decentralized decision-making system, and have been shown flexibly to compromise accuracy for speed when making decisions during house-hunting. During emigration, a colony must typically evaluate and choose between several possible alternative new nest sites of differing quality. In this paper, we examine this speed-accuracy trade-off through modelling, and conclude that noise and time-cost of assessing alternative choices are likely to be significant for T. albipennis. Noise and cost of such assessments are likely to mean that T. albipennis' decision-making mechanism is Pareto-optimal in one crucial regard; increasing the willingness of individuals to change their decisions cannot improve collective accuracy overall without impairing speed. We propose that a decentralized control algorithm based on this emigration behaviour may be derived for applications in engineering domains and specify the characteristics of the problems to which it should be suited, based on our new results.

A study of structural and parametric learning in XCS.

The performance of a learning classifier system is due to its two main components. First, it evolves new structures by generating new rules in a genetic process; second, it adjusts parameters of existing rules, for example rule prediction and accuracy, in an evaluation step, which is not only important for applying the rules, but also for the genetic process. The two components interleave and in the case of XCS drive the population toward a minimal, fit, non-overlapping population. In this work we attempt to gain new insights as to the relative contributions of the two components. We find that the genetic component has an additional role when using the train/test approach which is not present in online learning. We compare XCS to a system in which the rule set is restricted to the initial random population (XCS-NGA, that is, XCS No Genetic Algorithm). For small Boolean functions we can give XCS-NGA all possible rules of a particular condition length. In online learning, XCS-NGA can, given sufficiently many rules, achieve a surprisingly high classification accuracy, comparable to that of XCS. In a train/test approach, however, XCS generalises better than XCS-NGA and there seem to be limitations of XCS-NGA which cannot be overcome simply by increasing the population size. This illustrates that the requirements of a function approximator tend to differ between reinforcement learning (which is typically online) and concept learning (which is typically train/test).

Rule fitness and pathology in learning classifier systems.

It has long been known that in some relatively simple reinforcement learning tasks traditional strength-based classifier systems will adapt poorly and show poor generalisation. In contrast, the more recent accuracy-based XCS, appears both to adapt and generalise well. In this work, we attribute the difference to what we call strong over general and fit over general rules. We begin by developing a taxonomy of rule types and considering the conditions under which they may occur. In order to do so an extreme simplification of the classifier system is made, which forces us toward qualitative rather than quantitative analysis. We begin with the basics, considering definitions for correct and incorrect actions, and then correct, incorrect, and overgeneral rules for both strength and accuracy-based fitness. The concept of strong overgeneral rules, which we claim are the Achilles' heel of strength-based classifier systems, are then analysed. It is shown that strong overgenerals depend on what we call biases in the reward function (or, in sequential tasks, the value function). We distinguish between strong and fit overgeneral rules, and show that although strong overgenerals are fit in a strength-based system called SB-XCS, they are not in XCS. Next we show how to design fit overgeneral rules for XCS (but not SB-XCS), by introducing biases in the variance of the reward function, and thus that each system has its own weakness. Finally, we give some consideration to the prevalence of reward and variance function bias, and note that non-trivial sequential tasks have highly biased value functions.

Medical considerations in the use of helicopters in mountain rescue.

The outcome of patient care can be dramatically improved by bringing rapid rescue and medical care to the mountain rescue scene and by rapid transport to a medical facility. The use of a helicopter for these purposes is common. It is necessary when it has clear advantages for victims in comparison with ground rescue and transport. Helicopters should work within the existing emergency medical system and must be staffed by appropriate mountain rescue and medically trained personnel. Activation time should be as short as possible. Activation of a helicopter for a mountain rescue should primarily include indication and assessment of flight and safety conditions. No other mediators or delaying factors should be permitted. The main safety criteria are appropriate mountain rescue and flight training, competence of air and ground crews, radio communication between the air and ground crews, and mission briefing before the rescue. Criteria for a helicopter used for mountain rescue are proper medical and rescue equipment, load capacity, adequate space, and others. There are two main groups of indications for use of a helicopter for mountain rescue: the patient's condition and the circumstances at the site of the accident. All persons responsible for the activation of the helicopter rescue operation should be aware of specific problems in the mountains or wilderness.