Researchers have previously adopted the double stimulus paradigm to study refractoriness in human neuromotor control. Currently, refractoriness, such as the Psychological Refractory Period (PRP) has only been quantified in discrete movement conditions. Whether refractoriness and the associated serial ballistic hypothesis generalises to sustained control tasks has remained open for more than sixty years. Recently, a method of analysis has been presented that quantifies refractoriness in sustained control tasks and discriminates intermittent (serial ballistic) from continuous control. Following our recent demonstration that continuous control of an unstable second order system (i.e. balancing a 'virtual' inverted pendulum through a joystick interface) is unnecessary, we ask whether refractoriness of substantial duration (~200 ms) is evident in sustained visual-manual control of external systems. We ask whether the refractory duration (i) is physiologically intrinsic, (ii) depends upon system properties like the order (0, 1(st), and 2(nd)) or stability, (iii) depends upon target jump direction (reversal, same direction). Thirteen participants used discrete movements (zero order system) as well as more sustained control activity (1(st) and 2(nd) order systems) to track unpredictable step-sequence targets. Results show a substantial refractory duration that depends upon system order (250, 350 and 550 ms for 0, 1(st) and 2(nd) order respectively, n=13, p<0.05), but not stability. In sustained control refractoriness was only found when the target reverses direction. In the presence of time varying actuators, systems and constraints, we propose that central refractoriness is an appropriate control mechanism for accommodating online optimization delays within the neural circuitry including the more variable processing times of higher order (complex) input-output relations.

It is unclear whether the new anti-catabolic agent denosumab represents a viable alternative to the widely used anti-catabolic agent pamidronate in the treatment of Multiple Myeloma (MM)-induced bone disease. This lack of clarity primarily stems from the lack of sufficient clinical investigations, which are costly and time consuming. However, in silico investigations require less time and expense, suggesting that they may be a useful complement to traditional clinical investigations. In this paper, we aim to (i) develop integrated computational models that are suitable for investigating the effects of pamidronate and denosumab on MM-induced bone disease and (ii) evaluate the responses to pamidronate and denosumab treatments using these integrated models. To achieve these goals, pharmacokinetic models of pamidronate and denosumab are first developed and then calibrated and validated using different clinical datasets. Next, the integrated computational models are developed by incorporating the simulated transient concentrations of pamidronate and denosumab and simulations of their actions on the MM-bone compartment into the previously proposed MM-bone model. These integrated models are further calibrated and validated by different clinical datasets so that they are suitable to be applied to investigate the responses to the pamidronate and denosumab treatments. Finally, these responses are evaluated by quantifying the bone volume, bone turnover, and MM-cell density. This evaluation identifies four denosumab regimes that potentially produce an overall improved bone-related response compared with the recommended pamidronate regime. This in silico investigation supports the idea that denosumab represents an appropriate alternative to pamidronate in the treatment of MM-induced bone disease.

BACKGROUND: Trauma systems should consistently monitor a given trauma population over a period of time. The Abbreviated Injury Scale (AIS) and derived scores such as the Injury Severity Score (ISS) are commonly used to quantify injury severities in trauma registries. To reflect contemporary trauma management and treatment, the most recent version of the AIS (AIS08) contains many codes which differ in severity from their equivalents in the earlier 1998 version (AIS98). Consequently, the adoption of AIS08 may impede comparisons between data coded using different AIS versions. It may also affect the number of patients classified as major trauma. METHODS: The entire AIS98-coded injury dataset of a large population based trauma registry was retrieved and mapped to AIS08 using the currently available AIS98-AIS08 dictionary map. The percentage of codes which had increased or decreased in severity, or could not be mapped, was examined in conjunction with the effect of these changes to the calculated ISS. The potential for free text information accompanying AIS coding to improve the quality of AIS mapping was explored. RESULTS: A total of 128280 AIS98-coded injuries were evaluated in 32134 patients, 15471 patients of whom were classified as major trauma. Although only 4.5% of dictionary codes decreased in severity from AIS98 to AIS08, this represented almost 13% of injuries in the registry. In 4.9% of patients, no injuries could be mapped. ISS was potentially unreliable in one-third of patients, as they had at least one AIS98 code which could not be mapped. Using AIS08, the number of patients classified as major trauma decreased by between 17.3% and 30.3%. Evaluation of free text descriptions for some injuries demonstrated the potential to improve mapping between AIS versions. CONCLUSIONS: Converting AIS98-coded data to AIS08 results in a significant decrease in the number of patients classified as major trauma. Many AIS98 codes are missing from the existing AIS map, and across a trauma population the AIS08 dataset estimates which it produces are of insufficient quality to be used in practice. However, it may be possible to improve AIS98 to AIS08 mapping to the point where it is useful to established registries.

INTRODUCTION: Many trauma registries have used the Abbreviated Injury Scale 1990 Revision Update 98 (AIS98) to classify injuries. In the current AIS version (Abbreviated Injury Scale 2005 Update 2008 - AIS08), injury classification and specificity differ substantially from AIS98, and the mapping tools provided in the AIS08 dictionary are incomplete. As a result, data from different AIS versions cannot currently be compared. The aim of this study was to develop an additional AIS98 to AIS08 mapping tool to complement the current AIS dictionary map, and then to evaluate the completed map (produced by combining these two maps) using double-coded data. The value of additional information provided by free text descriptions accompanying assigned codes was also assessed. METHODS: Using a modified Delphi process, a panel of expert AIS coders established plausible AIS08 equivalents for the 153 AIS98 codes which currently have no AIS08 map. A series of major trauma patients whose injuries had been double-coded in AIS98 and AIS08 was used to assess the maps; both of the AIS datasets had already been mapped to another AIS version using the AIS dictionary maps. Following application of the completed (enhanced) map with or without free text evaluation, up to six AIS codes were available for each injury. Datasets were assessed for agreement in injury severity measures, and the relative performances of the maps in accurately describing the trauma population were evaluated. RESULTS: The double-coded injuries sustained by 109 patients were used to assess the maps. For data conversion from AIS98, both the enhanced map and the enhanced map with free text description resulted in higher levels of accuracy and agreement with directly coded AIS08 data than the currently available dictionary map. Paired comparisons demonstrated significant differences between direct coding and the dictionary maps, but not with either of the enhanced maps. CONCLUSIONS: The newly-developed AIS98 to AIS08 complementary map enabled transformation of the trauma population description given by AIS98 into an AIS08 estimate which was statistically indistinguishable from directly coded AIS08 data. It is recommended that the enhanced map should be adopted for dataset conversion, using free text descriptions if available.

Artificial intelligence has been depicted in film for decades. It embodies the tension in our societies between interest, enthusiasm, scepticism and anxiety about the technologies we have created and which surround us every day. Hollywood imagery exists alongside productions in all types of genres, from sci-fi to melodrama, that depict machines controlling the most intimate aspects of our lives.

Since the inception of global gene expression profiling platforms in the mid-1990s, there has been a significant increase in publications of differentially expressed genes in the process of epileptogenesis. In particular for mesial temporal lobe epilepsy, the presence of a latency period between the first manifestation of seizures to chronic epilepsy provides the opportunity for therapeutic interventions at the molecular biology level. Using global expression profiling techniques, approximately 2000 genes have been published demonstrating differential expression in mesial temporal epilepsy. The majority of these changes, however, are specific to laboratory or experimental conditions with only 53 genes demonstrating changes in more than two publications. To this end, we review the current status of gene expression profiling in epileptogenesis and suggest standard guidelines to be followed for greater accuracy and reproducibility of results.

Objective: To investigate the effect of cathodal transcranial direct-current stimulation (c-tDCS) on seizure frequency in patients with drug-resistant temporal lobe epilepsy (TLE). Method: Twenty-nine patients with drug-resistant TLE participated in this study. They were randomized to experimental or sham group. Twenty participants (experimental group) received within-session repeated c-tDCS intervention over the affected temporal lobe, and nine (sham group) received sham tDCS. Paired-pulse transcranial magnetic stimulation was used to assess short interval intracortical inhibition (SICI) in primary motor cortex ipsilateral to the affected temporal lobe. SICI was measured from motor evoked potentials recorded from the contralateral first dorsal interosseous muscle. Adverse effects were monitored during and after each intervention in both groups. A seizure diary was given to each participant to complete for 4 weeks following the tDCS intervention. The mean response ratio was calculated from their seizure rates before and after the tDCS intervention. Results: The experimental group showed a significant increase in SICI compared to the sham group (F = 10.3, p = 0.005). None of the participants reported side effects of moderate or severe degree. The mean response ratio in seizure frequency was -42.14% (standard deviation [SD] 35.93) for the experimental group and -16.98% (SD 52.41) for the sham group. Significance: Results from this pilot study suggest that tDCS may be a safe and efficacious nonpharmacologic intervention for patients with drug-resistant TLE. Further evaluation in larger double-blind randomized controlled trials is warranted.

The main aim in ensemble learning is using multiple classifiers rather than one classifier to aggregate classifiers' outputs for more accurate classification. Generating an ensemble classifier generally is composed of three steps: selecting a base classifier, applying a sampling strategy to generate different simple classifiers and aggregating the classifiers' outputs. This paper focuses on the classifiers' outputs aggregation step in ensemble learning and presents a new interval-based aggregation approach using Bagging and Interval Agreement Approach (IAA). Bagging is an ensemble learning approach to generate ensembles of classifiers by manipulation of the training data set and IAA is an aggregation approach in decision making which was introduced to combine decision makers' opinions when they present their opinions by intervals. In this paper, we use Bagging approach to generate uncertainty intervals for simple classifiers in ensemble learning and implement IAA to aggregate the intervals with the aim of capturing uncertainty. In fact, we design some experiments to encourage researchers to use interval modeling in ensemble learning because it preserves more uncertainty and leads to more accurate classification. We compare the results of implementing the proposed method to the majority vote, as the most commonly used aggregation function in ensemble learning, for 10 medical data sets. The results show the better performance of the proposed interval-based aggregation approach in binary classification when it comes to ensemble learning. The Bayesian signed-rank test confirms the competency of our proposed approach in this research.

It is timely that Engineering should devote a special issue to the topic of clean energy. The authors of the research articles and the views and comments cover much of what is a very diverse and controversial field. Responses to this topic cover a spectrum ranging from those that argue for emergency action to prevent the extinction of the human race to those that deny the existence of climate change. Before dismissing any group, it is informative for engineers and technologists to note that there is a fairly even distribution across this spectrum

Cognitive deficits are prevalent in schizophrenia, and these deficits represent a disabling aspect of the illness for which there are no current effective treatments. Recent work has shown that sex hormone levels correlate with brain activity and cognitive abilities differentially in patients with schizophrenia relative to healthy control groups. There is emerging evidence suggesting that oestrogen-based therapies may be useful in reversing the cognitive deficits associated with schizophrenia. To date, the results from clinical trials using oestrogen-based therapies to reverse cognitive impairment in schizophrenia have shown that the selective oestrogen receptor modulator raloxifene may be useful to improve attention, memory, learning and the associated brain activity in chronically ill men and women with schizophrenia or schizoaffective disorder. While these findings of cognitive enhancement with a selective oestrogen receptor modulator in people with schizophrenia are encouraging, additional studies will be required to replicate the initial results, assess the time frame of treatment effects, identify biomarkers in subsets of patients who may be more likely to optimally respond to treatment, and identify a more precise mechanism of action, which may include anti-inflammatory effects of oestrogen-based treatments.

Genetic generalized epilepsy (GGE) consists of several syndromes diagnosed and classified on the basis of clinical features and electroencephalographic (EEG) abnormalities. The main EEG feature of GGE is bilateral, synchronous, symmetric, and generalized spike-wave complex. Other classic EEG abnormalities are polyspikes, epileptiform K-complexes and sleep spindles, polyspike-wave discharges, occipital intermittent rhythmic delta activity, eye-closure sensitivity, fixation-off sensitivity, and photoparoxysmal response. However, admixed with typical changes, atypical epileptiform discharges are also commonly seen in GGE. There are circadian variations of generalized epileptiform discharges. Sleep, sleep deprivation, hyperventilation, intermittent photic stimulation, eye closure, and fixation-off are often used as activation techniques to increase the diagnostic yield of EEG recordings. Reflex seizure-related EEG abnormalities can be elicited by the use of triggers such as cognitive tasks and pattern stimulation during the EEG recording in selected patients. Distinct electrographic abnormalities to help classification can be identified among different electroclinical syndromes.