Pectic Galactan Polysaccharide-Based Gene Delivery System for Targeting Neuroinflammation

Treating neuroinflammation-related injuries and disorders through manipulation of neuroinflammation functions is being heralded as a new therapeutic strategy. In this study, a novel pectic galactan (PG) polysaccharide based gene therapy approach is developed for targeting reactive gliosis in neuroinflammation. Galectin-3 (Gal-3) is a cell protein with a high affinity to β-galactoside sugars and is highly expressed in reactive gliosis. Since PG carries galactans, it can target reactive gliosis via specific carbohydrate interaction between galactan and Gal-3 on the cell membrane, and therefore can be utilized as a carrier for delivering genes to these cells. The carrier is synthesized by modifying quaternary ammonium groups on the PG. The resulting quaternized PG (QPG) is found to form complexes with plasmid DNA with a mean diameter of 100 nm and have the characteristics required for targeted gene therapy. The complexes efficiently condense large amounts of plasmid per particle and successfully bind to Gal-3. The in vivo study shows that the complexes are biocompatible and safe for administration and can selectively transfect reactive glial cells of an induced cortical lesion. The results confirm that this PG-based delivery system is a promising platform for targeting Gal-3 overexpressing neuroinflammation cells for treating neuroinflammation-related injuries and neurodegenerative diseases.     

Mixture model for face-color modeling and segmentation

In this paper, we propose a general methodology for face-color modeling and segmentation. One of the major difficulties in face detection and retrieval is partial face extraction due to highlights, shadows and lighting variations. We show that a mixture-of-Gaussians modeling of the color space, provides a robust representation that can accommodate large color variations, as well as highlights and shadows. Our method enables to segment within-face regions, and associate semantic meaning to them, and provides statistical analysis and evaluation of the dominant variability within a given archive.     

Adaptation logic for HTTP dynamic adaptive streaming using geo-predictive crowdsourcing for mobile users

The increasing demand for video streaming services with a high Quality of Experience (QoE) has prompted considerable research on client-side adaptation logic approaches. However, most algorithms use the client’s previous download experience and do not use a crowd knowledge database generated by users of a professional service. We propose a new crowd algorithm that maximizes the QoE. We evaluate our algorithm against state-of-the-art algorithms on large, real-life, crowdsourcing datasets. There are six datasets, each of which contains samples of a single operator (T-Mobile, AT&T or Verizon) from a single road (I100 or I405). All measurements were from Android cellphones. The datasets were provided by WeFi LTD and are public for academic users. Our new algorithm outperforms all other methods in terms of QoE (eMOS).     

When We Stop Talking Politics: The Maintenance and Closing of Conversation in Contentious Times

Despite the democratic significance of citizen talk about politics, the field of communication has not considered how that talk is weathering stresses facing our civic culture. We examine political talk during an archetypal case of political contentiousness: the recall of Governor Scott Walker of Wisconsin in 2012. Pairing qualitative and quantitative methods, we show that a fracturing of civic culture took place in which many citizens found it impossible to continue political discussion. Individuals at fault lines of contention, by nature of occupation, geographic location, or other personal circumstance, were most prone to this breakdown. Our results call into question the ability of talk to bridge political and social differences in periods of polarization and fragmentation, with implications for democratic functioning.     

Temperature Dependence of Friction at the Nanoscale: When the Unexpected Turns Normal

The dynamics of frictional motion have been studied for hundreds of years, yet many key aspects of these important processes are not understood. The main challenge in predicting frictional response is the complexity of highly non-equilibrium processes going on in any tribological contact. This includes the continuous detachment and reattachment of multiple microscopic junctions at the sliding interface, the kinetics of which are controlled by the interface temperature. Our experiments reveal a non-monotonic enhancement of dry nanoscale friction at cryogenic temperatures for different material classes. We propose a model that reproduces the experimental observations and shows that the peak in temperature dependence of friction emerges from two competing processes acting at the interface: the thermally activated formation as well as the rupturing of an ensemble of atomic contacts. Our experiments and simulations provide a direct link between the temperature and velocity dependencies of friction, thus offering a new conceptual framework to describe the dynamics of dry nanoscale friction.     

Binarization, character extraction, and writer identification of historical Hebrew calligraphy documents

We present our work on the paleographic analysis and recognition system intended for processing of historical Hebrew calligraphy documents. The main goal is to analyze documents of different writing styles in order to identify the locations, dates, and writers of test documents. Using interactive software tools, a data base of extracted characters has been established. It now contains about 20,000 characters of 34 different writers, and will be distinctly expanded in the near future. Preliminary results of automatic extraction of pre-specified letters using the erosion operator are presented. We further propose and test topological features for handwriting style classification based on a selected subset of the Hebrew alphabet. A writer identification experiment using 34 writers yielded 100% correct classification.