Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported

A Simple fMRI Compatible Robotic Stimulator to Study the Neural Mechanisms of Touch and Pain.

This paper presents a simple device for the investigation of the human somatosensory system with functional magnetic imaging (fMRI). PC-controlled pneumatic actuation is employed to produce innocuous or noxious mechanical stimulation of the skin. Stimulation patterns are synchronized with fMRI and other relevant physiological measurements like electroencephalographic activity and vital physiological parameters. The system allows adjustable regulation of stimulation parameters and provides consistent patterns of stimulation. A validation experiment demonstrates that the system safely and reliably identifies clusters of functional activity in brain regions involved in the processing of pain. This new device is inexpensive, portable, easy-to-assemble and customizable to suit different experimental requirements. It provides robust and consistent somatosensory stimulation, which is of crucial importance to investigating the mechanisms of pain and its strong connection with the sense of touch

Trabectedin triggers direct and NK-mediated cytotoxicity in multiple myeloma

Background: Genomic instability is a feature of multiple myeloma (MM), and impairment in DNA damaging response (DDR) has an established role in disease pathobiology. Indeed, a deregulation of DNA repair pathways may contribute to genomic instability, to the establishment of drug resistance to genotoxic agents, and to the escape from immune surveillance. On these bases, we evaluated the role of different DDR pathways in MM and investigated, for the first time, the direct and immune-mediated anti-MM activity of the nucleotide excision repair (NER)-dependent agent trabectedin. Methods: Gene-expression profiling (GEP) was carried out with HTA2.0 Affymetrix array. Evaluation of apoptosis, cell cycle, and changes in cytokine production and release have been performed in 2D and 3D Matrigel-spheroid models through flow cytometry on MM cell lines and patients-derived primary MM cells exposed to increasing nanomolar concentrations of trabectedin. DNA-damage response has been evaluated through Western blot, immunofluorescence, and DNA fragmentation assay. Trabectedin-induced activation of NK has been assessed by CD107a degranulation. miRNAs quantification has been done through RT-PCR. Results: By comparing GEP meta-analysis of normal and MM plasma cells (PCs), we observed an enrichment in DNA NER genes in poor prognosis MM. Trabectedin triggered apoptosis in primary MM cells and MM cell lines in both 2D and 3D in vitro assays. Moreover, trabectedin induced DDR activation, cellular stress with ROS production, and cell cycle arrest. Additionally, a significant reduction of MCP1 cytokine and VEGF-A in U266-monocytes co-cultures was observed, confirming the impairment of MM-promoting milieu. Drug-induced cell stress in MM cells led to upregulation of NK activating receptors ligands (i.e., NKG2D), which translated into increased NK activation and degranulation. Mechanistically, this effect was linked to trabectedin-induced inhibition of NKG2D-ligands negative regulators IRF4 and IKZF1, as well as to miR-17 family downregulation in MM cells. Conclusions: Taken together, our findings indicate a pleiotropic activity of NER-targeting agent trabectedin, which appears a promising candidate for novel anti-MM therapeutic strategies

INFEZIONE DA HIV2 E MANAGEMENT CLINICO-DIAGNOSTICO-TERAPEUTICO

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Comparability of web and telephone survey modes for the measurement of subjective well-being

We compare subjective well-being measures collected with a web and telephone surveys to test whether survey mode affects people's evaluations of their well-being. We use unique, nationally representative data from Luxembourg which contains five measures of subjective well-being collected through web and telephone surveys. Oaxaca decomposition and multinomial logit with Coarsened Exact Matching indicate that the survey mode affects peoples' well-being scores. Web respondents are more likely to report low well-being and less likely to report the neutral category. However, the consequences for statistical inference are negligible. Our results support the view that web and telephone surveys are comparable tools for collecting subjective data, such as people's well-being

Optimization of EMG-based hand gesture recognition: supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

tWe propose a methodological study for the optimization of surface EMG (sEMG)-based hand gestureclassification, effective to implement a human–computer interaction device for both healthy subjectsand transradial amputees. The widely commonly used unsupervised Principal Component Analysis (PCA)approach was compared to the promising supervised common spatial pattern (CSP) methodology toidentify the best classification strategy and the related tuning parameters. A low density array of sEMGsensors was built to record the muscular activity of the forearm and classify five different hand gestures.Twenty healthy subjects were recruited to compute optimized parameters for (“within” analysis) and tocompare between (“between” analysis) the two strategies. The system was also tested on a transradialamputee subject, in order to assess the robustness of the optimization in recognizing disabled users’gestures.Results show that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach (accu-racy 88.81 ± 6.58%), whereas M-RMS-WA/ANN is the best pair for the CSP methodology (accuracy of89.35 ± 6.16%). Statistical analysis on classification results shows no significant differences between thetwo strategies. Moreover we found out that the optimization computed for healthy subjects was provento be sufficiently robust to be used on the amputee subject. This motivates further investigation of theproposed methodology on a larger sample of amputees. Our results are useful to boost EMG-based handgesture recognition and constitute a step toward the definition of an efficient EMG-controlled system foramputees

Optimization of EMG-based hand gesture recognition: supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

tWe propose a methodological study for the optimization of surface EMG (sEMG)-based hand gestureclassification, effective to implement a human–computer interaction device for both healthy subjectsand transradial amputees. The widely commonly used unsupervised Principal Component Analysis (PCA)approach was compared to the promising supervised common spatial pattern (CSP) methodology toidentify the best classification strategy and the related tuning parameters. A low density array of sEMGsensors was built to record the muscular activity of the forearm and classify five different hand gestures.Twenty healthy subjects were recruited to compute optimized parameters for (“within” analysis) and tocompare between (“between” analysis) the two strategies. The system was also tested on a transradialamputee subject, in order to assess the robustness of the optimization in recognizing disabled users’gestures.Results show that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach (accu-racy 88.81 ± 6.58%), whereas M-RMS-WA/ANN is the best pair for the CSP methodology (accuracy of89.35 ± 6.16%). Statistical analysis on classification results shows no significant differences between thetwo strategies. Moreover we found out that the optimization computed for healthy subjects was provento be sufficiently robust to be used on the amputee subject. This motivates further investigation of theproposed methodology on a larger sample of amputees. Our results are useful to boost EMG-based handgesture recognition and constitute a step toward the definition of an efficient EMG-controlled system foramputees

Technological Uncertainty and Standardization Strategies: A Coopetition Framework

Standards may be arrived at through various coordination mechanisms, including cooperation, coopetition, or competition. This article explores how technological uncertainty affects the coordination mechanism for standardization. The article is based on the Community Innovation Survey, a sizeable firm-level survey representative of the Luxembourgish economy. The econometric analysis finds evidence that firms facing technological uncertainty will choose for standardization through competition and coopetition.Economics of Technology and Innovatio