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In cases with sensory processing dysfunctions like autism spectrum disorder (ASD) and cognitive decline like Alzheimer's disease (AD), optimistic neurological improvements have been recorded after systematic sensorimotor enrichment stimulation and art therapeutic procedures. While art therapy may offer a cognitive-behavioral breakthrough, several latest studies have explored the effectiveness of brain signals representations through audio and visual transformations of electroencephalography (EEG) measurements and the necessity of neurological evaluation of sensory integration therapies. In this technical report, new software based on the JAVA and processing programming language is presented, that detects, displays and analyzes EEG signals for individual or a group of patients. This neuroinformatics software offers a digital painting environment and a real time transformation of EEG signals into adjustable music volume and octave configuration per electrode, for the real time observation and evaluation of therapeutic procedures. The EEG acquisition is wireless, therefore, brain data can also be collected from the application of other sensory or sensorimotor therapeutic sessions as well. The Neurocognitive Assessment Software for Enrichment Sensory Environments (NASESE) includes two main functionalities, organized in five different modules. The first functionality includes recording, filtering and visualization of the EEG signals exported to a rotating 3D brain model and a real-time transformation of brain activity to sound sculptures, while the second functionality generates statistical tests and coherence calculation in a fully customizable computerized environment.

Background: The investigation of the cellular components, their interactions and related functions constitute the major conditions in order to understand the cell as an integrated system. More specifically, the Protein-Protein Interactions and the obtained networks are very important in the majority of biological functions and processes, while most of the proteins appear to activate their functionalities through their interaction. Methods: Our in depth review analysis, include Sixty-five peer-reviewed research and review studies from several bibliographic databases. The most significant components were fully described, filtered, combined and analyzed in order to provide documented proofs on the Protein-Protein Interaction Network' applications in biomedicine. Results: The Protein-Protein Interaction Network' alignment and mapping give the opportunity of further knowledge extraction concerning the evolutionary relationships between the species through conserved pathways and protein complexes. Additionally, Protein-Protein Interaction Network information has been demonstrated to be able to predict functionally orthologous proteins within sequence homology clusters. Our review analysis concluded that, while Protein-Protein Interaction was used to be characterized just by their large and plain interacting surfaces, they were considered inapplicable for drug discovery studies for a long time. Conclusion: The present review explores multiple technologies implicated in Protein-Protein Interaction Networks, implicating their potential role in drug discovery mechanisms.

The diagnosis of inherited peripheral neuropathies can be a challenging issue in several ways. Current research is focused on a multidisciplinary approach, developing new therapeutic strategies mainly involving online databases and repositories for sharing data and models used in some clinical trials. In this paper authors introduce the general architecture of an automated neural network based model for simulating the prediction procedure of Charcot-Marie-Tooth disease, according to the latest clinical studies.

Protein to protein interactions (PPI) have been studied in great detail during the last decade. The informations derived from PPI analysis can be very helpful for large scale construction of different networks and the disambiguation of functional orthologs within of homologous sequence complexes. Several experimental techniques offered great tools for high throughput network mapping applications, although as long as the data increases massively, new computational methods arise to overcome the processing requirements for time and cost. Computational PPI network alignment tools have been used on comparative studies between various species to detect evolutionary pathways and investigate the topological features of the proteins’ networks, a crucial information that can give the opportunity to develop new therapeutic strategies for several diseases in the future. In this manuscript, we have analyzed different methods and techniques that are currently being used for the identification, construction and alignment of the PPI networks, and have explored their great potential in drug discovery purposes.

Trains that tilt on curves can go faster, but passengers complain of motion sickness. We studied the control signals and tilts to determine why this occurs and how to maintain speed while eliminating motion sickness. Accelerometers and gyros monitored train and passenger yaw and roll, and a survey evaluated motion sickness. The experimental train had 3 control configurations: an untilted mode, a reactive mode that detected curves from sensors on the front wheel set, and a predictive mode that determined curves from the train's position on the tracks. No motion sickness was induced in the untilted mode, but the train ran 21% slower than when it tilted 8° in either the reactive or predictive modes (113 vs. 137 km/h). Roll velocities rose and fell faster in the predictive than the reactive mode when entering and leaving turns (0.4 vs. 0.8 s for a 4°/s roll tilt, P