Does a combination of virtual reality, neuromodulation and neuroimaging provide a comprehensive platform for neurorehabilitation? - A narrative review of the literature

In the last decade, virtual reality (VR) training has been used extensively in video games and military training to provide a sense of realism and environmental interaction to its users. More recently, VR training has been explored as a possible adjunct therapy for people with motor and mental health dysfunctions. The concept underlying VR therapy as a treatment for motor and cognitive dysfunction is to improve neuroplasticity of the brain by engaging users in multisensory training. In this review, we discuss the theoretical framework underlying the use of VR as a therapeutic intervention for neurorehabilitation and provide evidence for its use in treating motor and mental disorders such as cerebral palsy, Parkinson’s disease, stroke, schizophrenia, anxiety disorders, and other related clinical areas. While this review provides some insights into the efficacy of VR in clinical rehabilitation and its complimentary use with neuroimaging (e.g., fNIRS and EEG) and neuromodulation (e.g., tDCS and rTMS), more research is needed to understand how different clinical conditions are affected by VR therapies (e.g., stimulus presentation, interactivity, control and types of VR). Future studies should consider large, longitudinal randomized controlled trials to determine the true potential of VR therapies in various clinical populations.

Hydro-acoustic remote sensing of benthic biological communities on the shallow South East Australian continental shelf

Information regarding the composition and extent of benthic habitats on the South East Australian continental shelf is limited. In this habitat mapping study, multibeam echosounder (MBES) data are integrated with precisely geo-referenced video ground-truth data to quantify benthic biotic communities at Cape Nelson, Victoria, Australia. Using an automated decision tree classification approach, 5 representative biotic groups defined from video analysis were related to hydro-acoustically derived variables in the Cape Nelson survey area. Using a combination of multibeam bathymetry, backscatter and derivative products produced highest overall accuracy (87%) and kappa statistic (0.83). This study demonstrates that decision tree classifiers are capable of integrating variable data types for mapping distributions of benthic biological assemblages, which are important in maintaining biodiversity and other system services in the marine environment.

An application of novel clustering technique for information security

This article presents experimental results devoted to a new application of the novel clustering technique introduced by the authors recently. Our aim is to facilitate the application of robust and stable consensus functions in information security, where it is often necessary to process large data sets and monitor outcomes in real time, as it is required, for example, for intrusion detection. Here we concentrate on the particular case of application to profiling of phishing websites. First, we apply several independent clustering algorithms to a randomized sample of data to obtain independent initial clusterings. Silhouette index is used to determine the number of clusters. Second, we use a consensus function to combine these independent clusterings into one consensus clustering . Feature ranking is used to select a subset of features for the consensus function. Third, we train fast supervised classification algorithms on the resulting consensus clustering in order to enable them to process the whole large data set as well as new data. The precision and recall of classifiers at the final stage of this scheme are critical for effectiveness of the whole procedure. We investigated various combinations of three consensus functions, Cluster-Based Graph Formulation (CBGF), Hybrid Bipartite Graph Formulation (HBGF), and Instance-Based Graph Formulation (IBGF) and a variety of supervised classification algorithms. The best precision and recall have been obtained by the combination of the HBGF consensus function and the SMO classifier with the polynomial kernel.

Multicentre phase I/II study of PI-88, a heparanase inhibitor in combination with docetaxel in patients with metastatic castrate-resistant prostate cancer

Background: Docetaxel (Taxotere) improve survival and prostate-specific antigen (PSA) response rates in patients with metastatic castrate-resistant prostate cancer (CRPC). We studied the combination of PI-88, an inhibitor of angiogenesis and heparanase activity, and docetaxel in chemotherapy-naive CRPC.

Patients and methods: We conducted a multicentre open-label phase I/II trial of PI-88 in combination with docetaxel. The primary end point was PSA response. Secondary end points included toxicity, radiologic response and overall survival. Doses of PI-88 were escalated to the maximum tolerated dose; whereas docetaxel was given at a fixed 75 mg/m2 dose every three weeks

Results: Twenty-one patients were enrolled in the dose-escalation component. A further 35 patients were randomly allocated to the study to evaluate the two schedules in phase II trial. The trial was stopped early by the Safety Data Review Board due to a higher-than-expected febrile neutropenia of 27%. In the pooled population, the PSA response (50% reduction) was 70%, median survival was 61 weeks (6–99 weeks) and 1-year survival was 71%.

Conclusions: The regimen of docetaxel and PI-88 is active in CRPC but associated with significant haematologic toxicity. Further evaluation of different scheduling and dosing of PI-88 and docetaxel may be warranted to optimise efficacy with a more manageable safety profile.

Performance evaluation of multi-tier ensemble classifiers for phishing websites

This article is devoted to large multi-tier ensemble classifiers generated as ensembles of ensembles and applied to phishing websites. Our new ensemble construction is a special case of the general and productive multi-tier approach well known in information security. Many efficient multi-tier classifiers have been considered in the literature. Our new contribution is in generating new large systems as ensembles of ensembles by linking a top-tier ensemble to another middletier ensemble instead of a base classifier so that the top~ tier ensemble can generate the whole system. This automatic generation capability includes many large ensemble classifiers in two tiers simultaneously and automatically combines them into one hierarchical unified system so that one ensemble is an integral part of another one. This new construction makes it easy to set up and run such large systems. The present article concentrates on the investigation of performance of these new multi~tier ensembles for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of multi~level ensembles with three tiers. The results presented here demonstrate that new three-tier ensemble classifiers performed better than the base classifiers and standard ensembles included in the system. This example of application to the classification of phishing websites shows that the new method of combining diverse ensemble techniques into a unified hierarchical three-tier ensemble can be applied to increase the performance of classifiers in situations where data can be processed on a large computer.

Automatic generation of meta classifiers with large levels for distributed computing and networking

This paper is devoted to a case study of a new construction of classifiers. These classifiers are called automatically generated multi-level meta classifiers, AGMLMC. The construction combines diverse meta classifiers in a new way to create a unified system. This original construction can be generated automatically producing classifiers with large levels. Different meta classifiers are incorporated as low-level integral parts of another meta classifier at the top level. It is intended for the distributed computing and networking. The AGMLMC classifiers are unified classifiers with many parts that can operate in parallel. This make it easy to adopt them in distributed applications. This paper introduces new construction of classifiers and undertakes an experimental study of their performance. We look at a case study of their effectiveness in the special case of the detection and filtering of phishing emails. This is a possible important application area for such large and distributed classification systems. Our experiments investigate the effectiveness of combining diverse meta classifiers into one AGMLMC classifier in the case study of detection and filtering of phishing emails. The results show that new classifiers with large levels achieved better performance compared to the base classifiers and simple meta classifiers classifiers. This demonstrates that the new technique can be applied to increase the performance if diverse meta classifiers are included in the system.

Progesterone and testosterone in combination act in the hypothalamus of castrated rams to regulate the secretion of LH

We tested the hypotheses that progesterone enhances the negative feedback actions of testosterone in rams and that this occurs through actions at the hypothalamus. In the first part of this study, blood samples were collected every 10 min for 12 h before and after 7 days of treatment (i.m.) of castrated Romney Marsh rams (n=5 per group) with vehicle, progesterone (4 mg/12 h), testosterone (4 mg/12 h) or a combination of progesterone (4 mg/12 h) and testosterone (4 mg/12 h). In the second part of this study the brains of four gonad-intact Romney Marsh rams were collected, the hypothalamus was sectioned and in situ hybridisation of mRNA for progesterone receptors conducted. After 7 days of treatment with vehicle or progesterone or testosterone alone, there were no changes in the secretion of LH. In contrast, treatment with a combination of progesterone and testosterone resulted in a significant (P<0.01, repeated measures ANOVA) decrease in mean plasma concentrations of LH, the number of LH pulses per hour and the pre-LH pulse nadir and a significant (P<0.01) increase in the inter-LH pulse interval. We found cells containing mRNA for progesterone receptors throughout the hypothalamus, including the preoptic area (where most GnRH neurons are located in sheep), the periventricular, ventromedial and arcuate nuclei and the bed nucleus of the stria terminalis. This study shows that progesterone is capable of acting centrally with testosterone to suppress the secretion of LH in castrated rams and that cells containing mRNA for progesterone receptors are located in the hypothalamus of rams in the vicinity of GnRH neurons.