Cerebral dominance and asynchrony between bimanual two-dimensional movements.

The asynchrony of bimanual movements was investigated. Right- and left-handers traced simple geometrical patterns (ellipses) continuously with both hands. All combinations of the direction of rotation in each hand were executed at different rhythms. Geometrically, performances were largely independent of manual dominance. However, by comparing the passage times at homologous positions, the authors found that the dominant hand led the nondominant one by about 25 msec. The asynchrony was affected by neither movement type nor rhythm. The variability of the asynchrony varied along the trajectory, with well-defined maxima and minima. The variability profiles for movements that engaged homologous muscles differed markedly from those that engaged nonhomologous muscles. The authors discuss the hypotheses that bimanual periodic movements are timed by a lateralized functional module and that asynchrony is due to the necessity of transmitting time-keeping information to the other hemisphere. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications - In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble...     

High-Fat Diet Leads to Reduced Protein O-GlcNAcylation and Mitochondrial Defects Promoting the Development of Alzheimer’s Disease Signatures

The disturbance of protein O-GlcNAcylation is emerging as a possible link between altered brain metabolism and the progression of neurodegeneration. As observed in brains with Alzheimer’s disease (AD), flaws of the cerebral glucose uptake translate into reduced protein O-GlcNAcylation, which promote the formation of pathological hallmarks. A high-fat diet (HFD) is known to foster metabolic dysregulation and insulin resistance in the brain and such effects have been associated with the reduction of cognitive performances. Remarkably, a significant role in HFD-related cognitive decline might be played by aberrant protein O-GlcNAcylation by triggering the development of AD signature and mitochondrial impairment. Our data support the impairment of total protein O-GlcNAcylation profile both in the brain of mice subjected to a 6-week high-fat-diet (HFD) and in our in vitro transposition on SH-SY5Y cells. The reduction of protein O-GlcNAcylation was associated with the development of insulin resistance, induced by overfeeding (i.e., defective insulin signaling and reduced mitochondrial activity), which promoted the dysregulation of the hexosamine biosynthetic pathway (HBP) flux, through the AMPK-driven reduction of GFAT1 activation. Further, we observed that a HFD induced the selective impairment of O-GlcNAcylated-tau and of O-GlcNAcylated-Complex I subunit NDUFB8, thus resulting in tau toxicity and reduced respiratory chain functionality respectively, highlighting the involvement of this posttranslational modification in the neurodegenerative process.     

Robust event discovery from photo collections using Signature Image Bases (SIBs)

Analyzing personal photo albums for understanding the related events is an emerging trend. A reliable event recognition tool could suggest appropriate annotation of pictures, provide the context for single image classification and tagging, achieve automatic selection and summarization, ease organization and sharing of media among users. In this paper, a novel method for fast and reliable event-type classification of personal photo albums is presented. Differently from previous approaches, the proposed method does not process photos individually but as a whole, exploiting three main features, namely Saliency, Gist, and Time, to extract an event signature, which is characteristic for a specific event type. A highly challenging database containing more than 40.000 photos belonging to 19 diverse event-types was crawled from photo-sharing websites for the purpose of modeling and performance evaluation. Experimental results showed that the proposed approach meets superior classification accuracy with limited computational complexity.     

When a Tritrophic Interaction Goes Wrong to the Third Level: Xanthoxylin From Trees Causes the Honeybee Larval Mortality in Colonies Affected by the River Disease

The “River Disease” (RD), a disorder impacting honeybee colonies located close to waterways with abundant riparian vegetation (including Sebastiania schottiana, Euphorbiaceae), kills newly hatched larvae. Forager bees from RD-affected colonies collect honeydew excretions from Epormenis cestri (Hemiptera: Flatidae), a planthopper feeding on trees of S. schottiana. First-instar honeybee larvae fed with this honeydew died. Thus, we postulated that the nectars of RD-affected colonies had a natural toxin coming from either E. cestri or S. schottiana. An untargeted metabolomics characterization of fresh nectars extracts from colonies with and without RD allowed to pinpoint xanthoxylin as one of the chemicals present in higher amounts in nectar from RD-affected colonies than in nectars from healthy colonies. Besides, xanthoxylin was also found in the aerial parts of S. schottiana and the honeydew excreted by E. cestri feeding on this tree. A larva feeding assay where xanthoxylin-enriched diets were offered to 1^st instar larvae showed that larvae died in the same proportion as larvae did when offered enriched diets with nectars from RD-colonies. These findings demonstrate that a xenobiotic can mimic the RD syndrome in honeybee larvae and provide evidence of an interspecific flow of xanthoxylin among three trophic levels. Further, our results give information that can be considered when implementing measures to control this honeybee disease.

     

Design of a decision support system for machine tool selection based on machine characteristics and performance tests

Economic globalization, together with heightened market competition and increasingly short product life cycles are motivating companies to use advanced manufacturing technologies. Use of high speed machining is increasingly widespread; however, as the technology is relatively new, it lacks a deep-rooted knowledge base which would facilitate implementation. One of the most frequent problems facing companies wishing to adopt this technology is selecting the most appropriate machine tool for the product in question and own enterprise characteristics. This paper presents a decision support system for high speed milling machine tool selection based on machine characteristics and performance tests. Profile machining tests are designed and conducted in participating machining centers. The decision support system is based on product dimension accuracy, process parameters such as feed rate and interpolation scheme used by CNC and machine characteristics such as machine accuracy and cost. Experimental data for process error and cycle operation time are obtained from profile machining tests with different geometrical feature zones that are often used in manufacturing of discrete parts or die/moulds. All those input parameters have direct impact on productivity and manufacturing cost. Artificial neural network models are utilized for decision support system with reasonable prediction capability.     

Data challenges of time domain astronomy

Astronomy has been at the forefront of the development of the techniques and methodologies of data intensive science for over a decade with large sky surveys and distributed efforts such as the Virtual Observatory. However, it faces a new data deluge with the next generation of synoptic sky surveys which are opening up the time domain for discovery and exploration. This brings both new scientific opportunities and fresh challenges, in terms of data rates from robotic telescopes and exponential complexity in linked data, but also for data mining algorithms used in classification and decision making. In this paper, we describe how an informatics-based approach—part of the so-called “fourth paradigm” of scientific discovery—is emerging to deal with these. We review our experiences with the Palomar-Quest and Catalina Real-Time Transient Sky Surveys; in particular, addressing the issue of the heterogeneity of data associated with transient astronomical events (and other sensor networks) and how to manage and analyze it.     

Output feedback control based on a high-order sliding manifold approach

In this note, we address the problem of output feedback control of multiple-input-multiple-output plants. The proposed approach is based on a high-order sliding manifold strategy. The resulting controller exhibits strong robustness properties, similar to high-gain control laws, but avoids peaking phenomena, thanks to the adoption of a time-varying sliding surface. Moreover, the dynamic control law is continuous and differentiable, thus avoiding chattering problems.     

MOS-junction-based nanostructures by thermal oxidation of silicon wires for hydrogen detection

Heavily p-doped monocrystalline silicon wires have been fabricated by employing isotropic Si wet etch and thermal oxidation to achieve a nanometric cross section-a gate-oxide growth and a final palladium evaporation made up the MOS junction able to detect hydrogen concentration in air. Several types of wire dimensions have been designed and fabricated: length ranges from 5 to 70 /spl mu/m; the smallest widths obtained are around 250-300 nm, while the biggest are up to 7 /spl mu/m. Preliminary experimental results show a high signal/noise ratio sensor response to 100 ppm concentration of H/sub 2/ at room temperature, 1-atm air.