Glucose administration and cognitive function: differential effects of age and effort during a dual task paradigm in younger and older adults

RATIONALE: Current research suggests that glucose facilitates performance on cognitive tasks which possess an episodic memory component and a relatively high level of cognitive demand. However, the extent to which this glucose facilitation effect is uniform across the lifespan is uncertain. METHODS: This study was a repeated measures, randomised, placebo-controlled, cross-over trial designed to assess the cognitive effects of glucose in younger and older adults under single and dual task conditions. Participants were 24 healthy younger (average age 20.6 years) and 24 healthy older adults (average age 72.5 years). They completed a recognition memory task after consuming drinks containing 25 g glucose and a placebo drink, both in the presence and absence of a secondary tracking task. RESULTS AND CONCLUSIONS: Glucose enhanced recognition memory response time and tracking precision during the secondary task, in older adults only. These findings do not support preferential targeting of hippocampal function by glucose, rather they suggest that glucose administration differentially increases the availability of attentional resources in older individuals.

Comparing kinematic changes between a finger-tapping task and unconstrained finger flexion-extension task in patients with Parkinson's disease.

Repetitive finger tapping is a well-established clinical test for the evaluation of parkinsonian bradykinesia, but few studies have investigated other finger movement modalities. We compared the kinematic changes (movement rate and amplitude) and response to levodopa during a conventional index finger-thumb-tapping task and an unconstrained index finger flexion-extension task performed at maximal voluntary rate (MVR) for 20 s in 11 individuals with levodopa-responsive Parkinson's disease (OFF and ON) and 10 healthy age-matched controls. Between-task comparisons showed that for all conditions, the initial movement rate was greater for the unconstrained flexion-extension task than the tapping task. Movement rate in the OFF state was slower than in controls for both tasks and normalized in the ON state. The movement amplitude was also reduced for both tasks in OFF and increased in the ON state but did not reach control levels. The rate and amplitude of movement declined significantly for both tasks under all conditions (OFF/ON and controls). The time course of rate decline was comparable for both tasks and was similar in OFF/ON and controls, whereas the tapping task was associated with a greater decline in MA, both in controls and ON, but not OFF. The findings indicate that both finger movement tasks show similar kinematic changes during a 20-s sustained MVR, but that movement amplitude is less well sustained during the tapping task than the unconstrained finger movement task. Both movement rate and amplitude improved with levodopa; however, movement rate was more levodopa responsive than amplitude.

Promoting reasoning through the magic V task

Reasoning in mathematics plays a critical role in developing mathematical understandings. In this article, Bragg, Loong, Widjaja, Vale and Herbert explore an adaptation of the Magic V Task and how was used in several classrooms to promote and develop reasoning skills.

Effectiveness of dual-task functional power training for preventing falls in older people: study protocol for a cluster randomised controlled trial

BACKGROUND: Falls are a major public health concern with at least one third of people aged 65 years and over falling at least once per year, and half of these will fall repeatedly, which can lead to injury, pain, loss of function and independence, reduced quality of life and even death. Although the causes of falls are varied and complex, the age-related loss in muscle power has emerged as a useful predictor of disability and falls in older people. In this population, the requirements to produce explosive and rapid movements often occurs whilst simultaneously performing other attention-demanding cognitive or motor tasks, such as walking while talking or carrying an object. The primary aim of this study is to determine whether dual-task functional power training (DT-FPT) can reduce the rate of falls in community-dwelling older people. METHODS/DESIGN: The study design is an 18-month cluster randomised controlled trial in which 280 adults aged ≥65 years residing in retirement villages, who are at increased risk of falling, will be randomly allocated to: 1) an exercise programme involving DT-FPT, or 2) a usual care control group. The intervention is divided into 3 distinct phases: 6 months of supervised DT-FPT, a 6-month 'step down' maintenance programme, and a 6-month follow-up. The primary outcome will be the number of falls after 6, 12 and 18 months. Secondary outcomes will include: lower extremity muscle power and strength, grip strength, functional assessments of gait, reaction time and dynamic balance under single- and dual-task conditions, activities of daily living, quality of life, cognitive function and falls-related self-efficacy. We will also evaluate the cost-effectiveness of the programme for preventing falls. DISCUSSION: The study offers a novel approach that may guide the development and implementation of future community-based falls prevention programmes that specifically focus on optimising muscle power and dual-task performance to reduce falls risk under 'real life' conditions in older adults. In addition, the 'step down' programme will provide new information about the efficacy of a less intensive maintenance programme for reducing the risk of falls over an extended period. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12613001161718 . Date registered 23 October 2013.

A shadow-like task migration model based on context semantics for mobile and pervasive environments

Pervasive computing is a user-centric mobile computing paradigm, in which tasks should be migrated over different platforms in a shadow-like way when users move around. In this paper, we propose a context-sensitive task migration model that recovers program states and rebinds resources for task migrations based on context semantics through inserting resource description and state description sections in source programs. Based on our model, we design and develop a task migration framework xMozart which extends the Mozart platform in terms of context awareness. Our approach can recover task states and rebind resources in the context-aware way, as well as support multi- modality I/O interactions. The extensive experiments demonstrate that our approach can migrate tasks by resuming them from the last broken points like shadows moving along with the users.

Fighting fires and fatigue: sleep, physical activity, and physical task performance

 During bushfires, firefighters often work long shifts with little sleep. Grace’s research showed that although firefighters’ sleep is restricted, it does not adversely affect their ability to perform physical tasks. This contrasts the cognitive impairments associated with sleep loss and could prompt revision of fatigue management guidelines in physically-demanding occupations.

Studying mathematics teacher education: analysing the process of task variation on learning

Self-study of variations to task design offers a way of analysing how learning takes place. Over several years, variations were made to improve an assessment task completed by final-year teacher candidates in a primary mathematics teacher education subject. This article describes how alterations to a task informed on-going developments in self-study of one assessment task employed in an online subject. Analysis of my journal, notes from conversations with colleagues, teacher candidates’ work on the task and responses to online forums, and survey data inspired variations focused on better exploration of key concepts involved in the task, raising of focal awareness, developing a stronger professional eye in the students and the author, adaptations for multiple curriculum levels, and explorations of dual teacher–student perspectives. The overall challenge has been to support teacher candidates to learn to design effective open-ended tasks with a critical professional eye. Descriptions of the changes made to the task and the development of my own professional eye as a consequence of the application of self-study are included. Data show that variations to the task increased teacher candidates’ understanding of mathematics problem posing and generated pedagogical insights for task design.

Oxytocin enhances the appropriate use of human social cues by the domestic dog (Canis familiaris) in an object choice task

It has been postulated that the neuropeptide, oxytocin, is involved in human-dog bonding. This may explain why dogs, compared to wolves, are such good performers on object choice tasks, which test their ability to attend to, and use, human social cues in order to find hidden food treats. The objective of this study was to investigate the effect of intranasal oxytocin administration, which is known to increase social cognition in humans, on domestic dogs' ability to perform such a task. We hypothesised that dogs would perform better on the task after an intranasal treatment of oxytocin. Sixty-two (31 males and 31 females) pet dogs completed the experiment over two different testing sessions, 5-15 days apart. Intranasal oxytocin or a saline control was administered 45 min before each session. All dogs received both treatments in a pseudo-randomised, counterbalanced order. Data were collected as scores out of ten for each of the four blocks of trials in each session. Two blocks of trials were conducted using a momentary distal pointing cue and two using a gazing cue, given by the experimenter. Oxytocin enhanced performance using momentary distal pointing cues, and this enhanced level of performance was maintained over 5-15 days time in the absence of oxytocin. Oxytocin also decreased aversion to gazing cues, in that performance was below chance levels after saline administration but at chance levels after oxytocin administration.

Cultural intelligence (CQ) in virtual, cross-cultural interactions: generalizability of measure and links to personality dimensions and task performance

Previous studies have focused on investigating CQ in face-to-face contexts but very few have assessed CQ in virtual, cross-cultural interactions. This study highlights the relevance of cultural intelligence (CQ) as an intercultural capability in cross-cultural communications that are virtual. This two-study research (study 1: n = 274; study 2: n = 223) conducted in call centers in the Philippines (a) assesses the generalizability of the four-factor CQ model (i.e., cognitive, metacognitive, motivational and behavioral CQ) as applied in the virtual context and (b) tests the relationship between CQ, personality dimensions (i.e., openness to experience and extraversion) and supervisor’s ratings of task performance. Study 1 results show that the structural validity of the four-factor CQ model was supported with minor issues in some ofthe items indicating the need to modify the CQ measure when utilized in the virtual context. Study 2 results show that CQ is positively and significantly related to openness to experience and extraversion. In addition, results show that CQ predicts task performance highlighting the importance of developing CQ among call center representatives and other working professionals who virtually engage and interact with clients and customers from culturally diverse backgrounds.

The trajectory of cognitive functioning following first episode mania: a 12-month follow-up study

OBJECTIVES: Cognitive deficits are apparent in the early stages of bipolar disorder; however, the timing and trajectory of cognitive functioning following a first episode of mania remains unclear. The aim of this study was to assess the trajectory of cognitive functioning in people following a first episode of mania over a 12-month period, relative to healthy controls. METHOD: The cohort included 61 participants who had recently stabilised from a first treated manic episode, and 21 demographically similar healthy controls. These groups were compared on changes observed over time using an extensive cognitive battery, over a 12-month follow-up period. RESULTS: A significant group by time interaction was observed in one measure of processing speed (Trail Making Test - part A,) and immediate verbal memory (Rey Auditory Verbal Learning Test - trial 1), with an improved performance in people following a first episode of mania relative to healthy controls. On the contrary, there was a significant group by time interaction observed on another processing speed task pertaining to focussed reaction time (Go/No-Go, missed go responses), with first episode of mania participants performing significantly slower in comparison with healthy controls. Furthermore, a significant group by time interaction was observed in inhibitory effortful control (Stroop effect), in which healthy controls showed an improvement over time relative to first episode of mania participants. There were no other significant interactions of group by time related to other measures of cognition over the 12-month period. CONCLUSION: Our findings revealed cognitive change in processing speed, immediate memory and one measure of executive functioning over a 12-month period in first episode of mania participants relative to healthy controls. There was no evidence of change over time for all other cognitive domains. Further studies focussed on the at-risk period, subgroup analysis, and the effects of medication on the cognitive trajectory following first episode of mania are needed.

Energy minimization in multi-task software-defined sensor networks

Abstract—
After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies makes it practical to realize the software-defined sensor networks (SDSNs), which are able to adapt to various application requirements and to fully explore the resources of WSNs. A sensor node in SDSN is able to conduct multiple tasks with different sensing targets simultaneously. A given sensing task usually involves multiple sensors to achieve a certain quality-of-sensing, e.g., coverage ratio. It is significant to design an energy-efficient sensor scheduling and management strategy with guaranteed quality-of-sensing for all tasks. To this end, three issues are investigated in this paper: 1) the subset of sensor nodes that shall be activated, i.e., sensor activation, 2) the task that each sensor node shall be assigned, i.e., task mapping, and 3) the sampling rate on a sensor for a target, i.e., sensing scheduling. They are jointly considered and formulated as a mixed-integer with quadratic constraints programming (MIQP) problem, which is then reformulated into a mixed-integer linear programming (MILP) formulation with low computation complexity via linearization. To deal with dynamic events such as sensor node participation and departure, during SDSN operations, an efficient online algorithm using local optimization is developed. Simulation results show that our proposed online algorithm approaches the globally optimized network energy efficiency with much lower rescheduling time and control overhead.

Multiple task transfer learning with small sample sizes

Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. The degree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning.

Prosthetic motor imaginary task classification using single channel of electroencephalography

Brain Computer Interface (BCI) is playing a very important role in human machine communications. Recent communication systems depend on the brain signals for communication. In these systems, users clearly manipulate their brain activity rather than using motor movements in order to generate signals that could be used to give commands and control any communication devices, robots or computers. In this paper, the aim was to estimate the performance of a brain computer interface (BCI) system by detecting the prosthetic motor imaginary tasks by using only a single channel of electroencephalography (EEG). The participant is asked to imagine moving his arm up or down and our system detects the movement based on the participant brain signal. Some features are extracted from the brain signal using Mel-Frequency Cepstrum Coefficient and based on these feature a Hidden Markov model is used to help in knowing if the participant imagined moving up or down. The major advantage in our method is that only one channel is needed to take the decision. Moreover, the method is online which means that it can give the decision as soon as the signal is given to the system. Hundred signals were used for testing, on average 89 % of the up down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial prosthetic limbs and wheelchairs due to it's high speed and accuracy.

Prosthetic motor imaginary task classification based on EEG quality assessment features

Brain Computer Interface (BCI) plays an important role in the communication between human and machines. This communication is based on the human brain signals. In these systems, users use their brain instead of the limbs or body movements to do tasks. The brain signals are analyzed and translated into commands to control any communication devices, robots or computers. In this paper, the aim was to enhance the performance of a brain computer interface (BCI) systems through better prosthetic motor imaginary tasks classification. The challenging part is to use only a single channel of electroencephalography (EEG). Arm movement imagination is the task of the user, where (s)he was asked to imagine moving his arm up or down. Our system detected the imagination based on the input brain signal. Some EEG quality features were extracted from the brain signal, and the Decision Tree was used to classify the participant's imagination based on the extracted features. Our system is online which means that it can give the decision as soon as the signal is given to the system (takes only 20 ms). Also, only one EEG channel is used for classification which reduces the complexity of the system which leads to fast performance. Hundred signals were used for testing, on average 97.4% of the up-down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial limbs and wheelchairs due to it's high speed and accuracy.

Collaborating differently on different topics: a multi-relational approach to multi-task learning

Multi-task learning offers a way to benefit from synergy of multiple related prediction tasks via their joint modeling. Current multi-task techniques model related tasks jointly, assuming that the tasks share the same relationship across features uniformly. This assumption is seldom true as tasks may be related across some features but not others. Addressing this problem, we propose a new multi-task learning model that learns separate task relationships along different features. This added flexibility allows our model to have a finer and differential level of control in joint modeling of tasks along different features. We formulate the model as an optimization problem and provide an efficient, iterative solution. We illustrate the behavior of the proposed model using a synthetic dataset where we induce varied feature-dependent task relationships: positive relationship, negative relationship, no relationship. Using four real datasets, we evaluate the effectiveness of the proposed model for many multi-task regression and classification problems, and demonstrate its superiority over other state-of-the-art multi-task learning models