Population Dynamics of RNA viruses

Between 50 and 100 million people are infected with dengue viruses each year and more than 100,000 of these die. Dr Choudhury has demonstrated that populations of dengue viruses in individual patients are genetically and functionally very diverse and that this diversity changes significantly at the time of major outbreaks of disease. The results of his studies may inform strategies which will make dengue vaccines far more effective.

Host selection in phytophagous insects: a new explanation for learning in adults

Insect learning can change the preferences an egg laying female displays towards different host plant species. Current hypotheses propose that learning may be advantageous in adult host selection behaviour through improved recognition, accuracy or selectivity in foraging. In this paper, we present a hypothesis for when learning can be advantageous without such improvements in adult host foraging. Specifically, that learning can be an advantageous strategy for egg laying females when larvae must feed on more than one plant in order to complete development, if the fitness of larvae is reduced when they switch to a different host species. Here, larvae benefit from developing on the most abundant host species, which is the most likely choice of host for an adult insect which increases its preference for a host species through learning. The hypothesis is formalised with a mathematical model and we provide evidence from studies on the behavioural ecology of a number of insect species which demonstrate that the assumptions of this hypothesis may frequently be fulfilled in nature. We discuss how multiple mechanisms may convey advantages in insect learning and that benefits to larval development, which have so far been overlooked, should be considered in explanations for the widespread occurrence of learning.

How host plant variability influences the advantages to learning : a theoretical model for oviposition behaviour in Lepidoptera

Learning can allow individuals to increase their fitness in particular environments. The advantage to learning depends on the predictability of the environment and the extent to which animals can adjust their behaviour. Earlier general models have investigated when environmental predictability might favour the evolution of learning in foraging animals. Here, we construct a theoretical model that predicts the advantages to learning using a specific biological example: oviposition in the Lepidoptera. Our model includes environmental and behavioural complexities relevant to host selection in these insects and tests whether the predictions of the general models still hold. Our results demonstrate how the advantage of learning is maximised when within-generation variability is minimised (the local environment consists mainly of a single host plant species) and between-generation variability is maximised (different host plant species are the most common in different generations). We discuss how our results: (a) can be applied to recent empirical work in different lepidopteran species and (b) predict an important role of learning in lepidopteran agricultural pests.

Increased plant volatile production affects oviposition, but not larval development, in the moth Helicoverpa armigera

It is well established that herbivorous insects respond to changes in plant odour production, but little attention has been given to whether these responses relate to direct fitness costs of plant volatile production on insect growth and survival. Here, we use transgenic Nicotiana tabacum (tobacco) plants that produce relatively large amounts of the volatile (S)-linalool to study whether the responses of egg-laying herbivorous insects to linalool production relate directly to the growth and survival of offspring. In choice tests, fewer eggs were laid on transgenic plants compared with non-transformed controls, indicating that increased linalool emissions have a deterrent effect on Helicoverpa armigera oviposition. Larval survival and larval mass after feeding on transgenic leaves, however, was comparable to non-transformed controls. (S)-linalool, whether in volatile or sequestered form, does not appear to have a direct effect on offspring fitness in this moth. We discuss how the ecology of this polyphagous moth species may necessitate a high tolerance for certain volatiles and their related non-volatile compounds, and suggest that responses by adult female H. armigera moths towards increased linalool production may be context specific and relate to other indirect effects on fitness.

Can mechanism help explain insect host choice?

Evolutionary theory predicts that herbivorous insects should lay eggs on plants in a way that reflects the suitability of each plant species for larval development. Empirical studies, however, often fail to find any relationship between an adult insect’s choice of host–plant and offspring fitness, and in such cases, it is generally assumed that other ‘missing’ factors (e.g. predation, host–plant abundance, learning and adult feeding sites) must be contributing to overall host suitability. Here, I consider an alternative theory – that a fitness cost inherent in the olfactory mechanism could constrain the evolution of insect host selection. I begin by reviewing current knowledge of odour processing in the insect antennal lobe with the aid of a simple schematic: the aim being to explain the workings of this mechanism to scientists who do not have prior knowledge in this field. I then use the schematic to explore how an insect’s perception of host and non-host odours is governed by a set of processing rules, or algorithm. Under the assumptions of this mechanistic view, the perception of every plant odour is interrelated, and seemingly bad host choices can still arise as part of an overall adaptive behavioural strategy. I discuss how an understanding of mechanism can improve the interpretation of theoretical and empirical studies in insect behaviour and evolution.

Enhancing critical analysis and problem-solving skills in undergraduate psychology : An evaluation of a collaborative learning and problem-based learning approach

Critical analysis and problem-solving skills are two graduate attributes that are important in ensuring that graduates are well equipped in working across research and practice settings within the discipline of psychology. Despite the importance of these skills, few psychology undergraduate programmes have undertaken any systematic development, implementation, and evaluation of curriculum activities to foster these graduate skills. The current study reports on the development and implementation of a tutorial programme designed to enhance the critical analysis and problem-solving skills of undergraduate psychology students. Underpinned by collaborative learning and problem-based learning, the tutorial programme was administered to 273 third year undergraduate students in psychology. Latent Growth Curve Modelling revealed that students demonstrated a significant linear increase in self-reported critical analysis and problem-solving skills across the tutorial programme. The findings suggest that the development of inquiry-based curriculum offers important opportunities for psychology undergraduates to develop critical analysis and problem-solving skills.

Determining process model precision and generalization with weighted artificial negative events

Process mining encompasses the research area which is concerned with knowledge discovery from event logs. One common process mining task focuses on conformance checking, comparing discovered or designed process models with actual real-life behavior as captured in event logs in order to assess the “goodness” of the process model. This paper introduces a novel conformance checking method to measure how well a process model performs in terms of precision and generalization with respect to the actual executions of a process as recorded in an event log. Our approach differs from related work in the sense that we apply the concept of so-called weighted artificial negative events towards conformance checking, leading to more robust results, especially when dealing with less complete event logs that only contain a subset of all possible process execution behavior. In addition, our technique offers a novel way to estimate a process model’s ability to generalize. Existing literature has focused mainly on the fitness (recall) and precision (appropriateness) of process models, whereas generalization has been much more difficult to estimate. The described algorithms are implemented in a number of ProM plugins, and a Petri net conformance checking tool was developed to inspect process model conformance in a visual manner.

Improving business process models using observed behavior

Process-aware information systems (PAISs) can be configured using a reference process model, which is typically obtained via expert interviews. Over time, however, contextual factors and system requirements may cause the operational process to start deviating from this reference model. While a reference model should ideally be updated to remain aligned with such changes, this is a costly and often neglected activity. We present a new process mining technique that automatically improves the reference model on the basis of the observed behavior as recorded in the event logs of a PAIS. We discuss how to balance the four basic quality dimensions for process mining (fitness, precision, simplicity and generalization) and a new dimension, namely the structural similarity between the reference model and the discovered model. We demonstrate the applicability of this technique using a real-life scenario from a Dutch municipality.

Information quality in social media : a conceptual model

Social Media (SM) is increasingly being integrated with business information in decision making. Unique characteristics of social media (e.g. wide accessibility, permanence, global audience, recentness, and ease of use) raise new issues with information quality (IQ); quite different from traditional considerations of IQ in information systems (IS) evaluation. This paper presents a preliminary conceptual model of information quality in social media (IQnSM) derived through directed content analysis and employing characteristics of analytic theory in the study protocol. Based in the notion of ‘fitness for use’, IQnSM is highly use and user centric and is defined as “the degree to which information is suitable for doing a specified task by a specific user, in a certain context”. IQnSM is operationalised as hierarchical, formed by the three dimensions (18 measures): intrinsic quality, contextual quality and representational quality. A research plan for empirically validating the model is proposed.

The role of intensity of interval training on fat oxidation and eating behaviour in overweight/obese men

The increasing prevalence of obesity in society has been associated with a number of atherogenic risk factors such as insulin resistance. Aerobic training is often recommended as a strategy to induce weight loss, with a greater impact of high-intensity levels on cardiovascular function and insulin sensitivity, and a greater impact of moderate-intensity levels on fat oxidation. Anaerobic high-intensity (supramaximal) interval training has been advocated to improve cardiovascular function, insulin sensitivity and fat oxidation. However, obese individuals tend to have a lower tolerance of high-intensity exercise due to discomfort. Furthermore, some obese individuals may compensate for the increased energy expenditure by eating more and/or becoming less active. Recently, both moderate- and high-intensity aerobic interval training have been advocated as alternative approaches. However, it is still uncertain as to which approach is more effective in terms of increasing fat oxidation given the issues with levels of fitness and motivation, and compensatory behaviours. Accordingly, the objectives of this thesis were to compare the influence of moderate- and high-intensity interval training on fat oxidation and eating behaviour in overweight/obese men. Two exercise interventions were undertaken by 10-12 overweight/obese men to compare their responses to study variables, including fat oxidation and eating behaviour during moderate- and high-intensity interval training (MIIT and HIIT). The acute training intervention was a methodological study designed to examine the validity of using exercise intensity from the graded exercise test (GXT) - which measured the intensity that elicits maximal fat oxidation (FATmax) - to prescribe interval training during 30-min MIIT. The 30-min MIIT session involved 5-min repetitions of workloads 20% below and 20% above the FATmax. The acute intervention was extended to involve HIIT in a cross-over design to compare the influence of MIIT and HIIT on eating behaviour using subjective appetite sensation and food preference through the liking and wanting test. The HIIT consisted of 15-sec interval training at 85 %VO2peak interspersed by 15-sec unloaded recovery, with a total mechanical work equal to MIIT. The medium term training intervention was a cross-over 4-week (12 sessions) MIIT and HIIT exercise training with a 6-week detraining washout period. The MIIT sessions consisted of 5-min cycling stages at ±20% of mechanical work at 45 %VO2peak, and the HIIT sessions consisted of repetitive 30-sec work at 90 %VO2peak and 30-sec interval rests, during identical exercise sessions of between 30 and 45 mins. Assessments included a constant-load test (45 %VO2peak for 45 mins) followed by 60-min recovery at baseline and the end of 4-week training, to determine fat oxidation rate. Participants’ responses to exercise were measured using blood lactate (BLa), heart rate (HR) and rating of perceived exertion (RPE) and were measured during the constant-load test and in the first intervention training session of every week during training. Eating behaviour responses were assessed by measuring subjective appetite sensations, liking and wanting and ad libitum energy intake. Results of the acute intervention showed that FATmax is a valid method to estimate VO2 and BLa, but is not valid to estimate HR and RPE in the MIIT session. While the average rate of fat oxidation during 30-min MIIT was comparable with the rate of fat oxidation at FATmax (0.16 ±0.09 and 0.14 ±0.08 g/min, respectively), fat oxidation was significantly higher at minute 25 of MIIT (P≤0.01). In addition, there was no significant difference between MIIT and HIIT in the rate of appetite sensations after exercise, but there was a tendency towards a lower rate of hunger after HIIT. Different intensities of interval exercise also did not affect explicit liking or implicit wanting. Results of the medium-term intervention indicated that current interval training levels did not affect body composition, fasting insulin and fasting glucose. Maximal aerobic capacity significantly increased (P≤0.01) (2.8 and 7.0% after MIIT and HIIT respectively) during GXT, and fat oxidation significantly increased (P≤0.01) (96 and 43% after MIIT and HIIT respectively) during the acute constant-load exercise test. RPE significantly decreased after HIIT greater than MIIT (P≤0.05), and the decrease in BLa was greater during the constant-load test after HIIT than MIIT, but this difference did not reach statistical significance (P=0.09). In addition, following constant-load exercise, exercise-induced hunger and desire to eat decreased after HIIT greater than MIIT but were not significant (p value for desire to eat was 0.07). Exercise-induced liking of high-fat sweet (HFSW) and high-fat non-sweet (HFNS) foods increased after MIIT and decreased after HIIT (p value for HFNS was 0.09). The intervention explained 12.4% of the change in fat intake (p = 0.07). This research is significant in that it confirmed two points in the acute study. While the rate of fat oxidation increased during MIIT, the average rate of fat oxidation during 30-min MIIT was comparable with the rate of fat oxidation at FATmax. In addition, manipulating the intensity of acute interval exercise did not affect appetite sensations and liking and wanting. In the medium-term intervention, constant-load exercise-induced fat oxidation significantly increased after interval training, independent of exercise intensity. In addition, desire to eat, explicit liking for HFNS and fat intake collectively confirmed that MIIT is accompanied by a greater compensation of eating behaviour than HIIT. Findings from this research will assist in developing exercise strategies to provide obese men with various training options. In addition, the finding that overweight/obese men expressed a lower RPE and decreased BLa after HIIT compared with MIIT is contrary to the view that obese individuals may not tolerate high-intensity interval training. Therefore, high-intensity interval training can be advocated among the obese adult male population. Future studies may extend this work by using a longer-term intervention.

Evolution of lure response in tephritid fruit flies : phytochemicals as drivers of sexual selection

The males of many Bactrocera species (Diptera: Tephritidae) respond strongly and positively to a small number of plant-derived chemicals (=male lures). Males that have imbibed the lures commonly have a mating advantage over unfed males, but no female benefits have been demonstrated for females mating with lure-fed males. It has been hypothesized that the strong lure response is a case of runaway selection, where males receive direct benefits and females receive indirect benefits via 'sexy sons', or a case of sensory bias where females have a lower threshold response to lures. To test these hypotheses we studied the effects of lure feeding on male mating, remating and longevity; while for females that had mated with lure-fed males we recorded mating refractoriness, fecundity, egg viability and longevity. We used Bactrocera tryoni as our test animal and as lures the naturally occurring zingerone and chemically related, but synthetic chemical cuelure. Feeding on lures provided direct male benefits in greater mating success and increased multiple mating. For the first time, we recorded direct female effects: increased fecundity and reduced remating receptivity. Egg viability did not differ in females mated with lure-fed or unfed males. The life span of males and females exposed to lures was reduced. These results reveal direct, current-generation fitness benefits for both males and females, although the male benefits appear greater. We discuss that while lure response is indeed likely to be a sexual selection trait, there is no need to invoke runaway selection to explain its evolution.

Tools of the trade: quick measures and validated tools for assessing health and wellness. How you can better help your clients/employees help themselves

Worksite wellness efforts can generate enormous health-care savings. Many of the methods available to obtain health and wellness measures can be confusing and lack clarity; for example it can be difficult to understand if measures are appropriate for individuals or population health. Come along and enjoy a hands-on learning experience about measures and better understanding health and wellness outcomes from baseline, midway and beyond.

Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

Cardiac parasympathetic reactivation following exercise : implications for training prescription

The objective of exercise training is to initiate desirable physiological adaptations that ultimately enhance physical work capacity. Optimal training prescription requires an individualized approach, with an appropriate balance of training stimulus and recovery and optimal periodization. Recovery from exercise involves integrated physiological responses. The cardiovascular system plays a fundamental role in facilitating many of these responses, including thermoregulation and delivery/removal of nutrients and waste products. As a marker of cardiovascular recovery, cardiac parasympathetic reactivation following a training session is highly individualized. It appears to parallel the acute/intermediate recovery of the thermoregulatory and vascular systems, as described by the supercompensation theory. The physiological mechanisms underlying cardiac parasympathetic reactivation are not completely understood. However, changes in cardiac autonomic activity may provide a proxy measure of the changes in autonomic input into organs and (by default) the blood flow requirements to restore homeostasis. Metaboreflex stimulation (e.g. muscle and blood acidosis) is likely a key determinant of parasympathetic reactivation in the short term (0–90 min post-exercise), whereas baroreflex stimulation (e.g. exercise-induced changes in plasma volume) probably mediates parasympathetic reactivation in the intermediate term (1–48 h post-exercise). Cardiac parasympathetic reactivation does not appear to coincide with the recovery of all physiological systems (e.g. energy stores or the neuromuscular system). However, this may reflect the limited data currently available on parasympathetic reactivation following strength/resistance-based exercise of variable intensity. In this review, we quantitatively analyse post-exercise cardiac parasympathetic reactivation in athletes and healthy individuals following aerobic exercise, with respect to exercise intensity and duration, and fitness/training status. Our results demonstrate that the time required for complete cardiac autonomic recovery after a single aerobic-based training session is up to 24 h following low-intensity exercise, 24–48 h following threshold-intensity exercise and at least 48 h following high-intensity exercise. Based on limited data, exercise duration is unlikely to be the greatest determinant of cardiac parasympathetic reactivation. Cardiac autonomic recovery occurs more rapidly in individuals with greater aerobic fitness. Our data lend support to the concept that in conjunction with daily training logs, data on cardiac parasympathetic activity are useful for individualizing training programmes. In the final sections of this review, we provide recommendations for structuring training microcycles with reference to cardiac parasympathetic recovery kinetics. Ultimately, coaches should structure training programmes tailored to the unique recovery kinetics of each individual.