Polyphosphates and pyrophosphates of hexopyranoses as allosteric effectors of human hemoglobin: synthesis, molecular recognition, and effect on oxygen release

Polyphosphorylated and perphosphorylated hexopyranose monosaccharides and disaccharides were synthesized from parent or partially protected carbohydrates as potential allosteric effectors of hemoglobin. A study toward the construction of seven- and eight-membered cyclic pyrophosphates was also performed on the sugars which had the proper orientation, protection, and number of phosphates. All final compounds were tested for their efficiency on oxygen release from human hemoglobin. Several compounds presented higher potency than myo-inositol hexakisphosphate, which is the most efficient of the known allosteric effectors of hemoglobin. Structure-activity relationships were analyzed. The affinity and efficiency depend on the number of phosphates attached to the carbohydrate skeleton and are related primarily to the number of negative charges present. Other effects operate, but play a lesser role.     

Small High-Density Lipoprotein (HDL) Subclasses are Increased with Decreased Activity of HDL-Associated Phospholipase A2 in Subjects with Prediabetes

Alterations in high-density lipoprotein (HDL) subclass distribution, as well as in the activities of HDL-associated enzymes, have been associated with increased cardiovascular disease (CVD) risk. HDL subclass distribution and the activities of HDL-associated enzymes remain unknown in prediabetic patients, a condition also associated with increased CVD risk. The aim of the present study was to assess any differences in HDL subclass distribution (using polyacrylamide gel electrophoresis) and in activities of HDL-associated enzymes between prediabetic (impaired fasting glucose, IFG, n = 80) and non-prediabetic subjects (n = 105). Subjects with prediabetes had significantly increased waist circumference, blood pressure and triacylglycerol (TAG) levels compared with subjects with fasting glucose levels <100 mg/dL (all p < 0.05). The proportion of small HDL3 over HDL cholesterol (HDL-C) was significantly increased in prediabetic subjects compared with their controls (p < 0.05). The activity of the anti-atherogenic HDL-associated lipoprotein-associated phospholipase A₂ (HDL-LpPLA₂) was significantly lower in subjects with prediabetes (p < 0.05), whereas the activity of paraoxonase 1 (using both paraoxon and phenyl acetate as substrates) did not significantly differ between subjects with or without prediabetes. In a stepwise linear regression analysis, the proportion of small HDL3 over HDL-C concentration was independently associated with the presence of prediabetes and with total cholesterol and TAG concentration (positively), as well as with HDL-C levels (negatively). We also observed a trend of increased small dense low-density lipoprotein cholesterol levels in prediabetic subjects compared with their controls. Subjects with IFG exhibit increased proportion of small HDL3 particles combined with decreased activity of the anti-atherogenic HDL-LpPLA₂.     

The role of non-covalent interactions and matrix viscosity on the dispersion and properties of LLDPE/MWCNT nanocomposites

Linear low density polyethylene (LLDPE)/multi-walled carbon nanotube (MWCNT) composites were prepared by melt compounding, following two different compatibilization strategies that involved non-covalent interactions between the matrix and the filler. The first approach involved grafting pyridine aromatic moieties on the maleated polyolefin backbone, which are able to interact by π–π stacking with the surface of the nanotubes. The second method implemented non-covalent/non-specific surface functionalization of the MWCNTs with a hyperbranched polyethylene (HBPE). The enhanced interfacial interactions established in the composites containing LLDPE functionalized with pyridine grafts improved the dispersion of the nanotubes within the polymer matrix. Dispersion was also favoured by higher matrix viscosity. Composites containing finely dispersed MWCNTs exhibited an increase in the rheological and electrical percolation thresholds, and a significant improvement in mechanical properties. On the contrary the composites based on the low viscosity matrix contained large amounts of aggregates, which promoted lower percolation thresholds. Manipulation of matrix viscosity and compatibilization resulted in composites with good mechanical properties, and low percolation thresholds.     

A comparative study of the local heat transfer distributions around various surface mounted obstacles

In many engineering applications, heat transfer enhancement techniques are of vital importance in order to ensure reliable thermal designs of convective heat transfer applications. This study examines experimentally the heat transfer characteristics on the base plate around various surface mounted obstacles. Local convection coefficients are evaluated in the vicinity of each individual protruding body with great spatial resolution using the transient liquid crystal technique. Five different obstacles of constant height-to-hydraulic diameter ratio （-1.3） are consid- ered. These include： a cylinder, a square, a triangle, a diamond and a vortex generator of delta wing shape design. The experiments were carried out over a range of freestream Reynolds numbers, based on the hydraulic diameter of each obstacle, varying from 4,000 to 13,000. The results indicate a negligible effect of the flow speed on the heat transfer topological structure and a considerable effect of the obstacle geometry on the level and distribution of heat transfer enhancement.     

Quantifiable data mining using ratio rules

Association Rule Mining algorithms operate on a data matrix (e.g., customers products) to derive association rules [AIS93b, SA96]. We propose a new paradigm, namely, Ratio Rules, which are quantifiable in that we can measure the “goodness” of a set of discovered rules. We also propose the “guessing error” as a measure of the “goodness”, that is, the root-mean-square error of the reconstructed values of the cells of the given matrix, when we pretend that they are unknown. Another contribution is a novel method to guess missing/hidden values from the Ratio Rules that our method derives. For example, if somebody bought $10 of milk and $3 of bread, our rules can “guess” the amount spent on butter. Thus, unlike association rules, Ratio Rules can perform a variety of important tasks such as forecasting, answering “what-if” scenarios, detecting outliers, and visualizing the data. Moreover, we show that we can compute Ratio Rules in a single pass over the data set with small memory requirements (a few small matrices), in contrast to association rule mining methods which require multiple passes and/or large memory. Experiments on several real data sets (e.g., basketball and baseball statistics, biological data) demonstrate that the proposed method: (a) leads to rules that make sense; (b) can find large itemsets in binary matrices, even in the presence of noise; and (c) consistently achieves a “guessing error” of up to 5 times less than using straightforward column averages. Received: March 15, 1999 / Accepted: November 1, 1999     

Dynamic action recognition based on dynemes and Extreme Learning Machine

In this paper, we propose a novel method that performs dynamic action classification by exploiting the effectiveness of the Extreme Learning Machine (ELM) algorithm for single hidden layer feedforward neural networks training. It involves data grouping and ELM based data projection in multiple levels. Given a test action instance, a neural network is trained by using labeled action instances forming the groups that reside to the test sample’s neighborhood. The action instances involved in this procedure are, subsequently, mapped to a new feature space, determined by the trained network outputs. This procedure is performed multiple times, which are determined by the test action instance at hand, until only a single class is retained. Experimental results denote the effectiveness of the dynamic classification approach, compared to the static one, as well as the effectiveness of the ELM in the proposed dynamic classification setting.     

Silhouette-based human action recognition using sequences of key poses

In this paper, a human action recognition method is presented in which pose representation is based on the contour points of the human silhouette and actions are learned by making use of sequences of multi-view key poses. Our contribution is twofold. Firstly, our approach achieves state-of-the-art success rates without compromising the speed of the recognition process and therefore showing suitability for online recognition and real-time scenarios. Secondly, dissimilarities among different actors performing the same action are handled by taking into account variations in shape (shifting the test data to the known domain of key poses) and speed (considering inconsistent time scales in the classification). Experimental results on the publicly available Weizmann, MuHAVi and IXMAS datasets return high and stable success rates, achieving, to the best of our knowledge, the best rate so far on the MuHAVi Novel Actor test.     

Hand dexterity assessment based on mouse pointer trajectory measurements in children with learning disabilities

Universal Access in the Information Society - We present an investigation of the systematic differences in upper limb motor skills between children with learning disabilities (LDs) and children...     

Suitability,efficiency and microbiological safety of novel physical technologies for the processing of ready‐to‐eat meals,meats and pumpable products

Consumer studies and market reports show an increase in consumption of ready‐to‐eat (RTE) foods. Although conventional processing technologies can in most cases produce safe products, they can also lead to the degradation of nutritional compounds and negatively affect quality characteristics. Consumers strongly prefer food that is minimally processed with the maximum amount of health‐promoting substances. Novel processing technologies as pre‐ or post‐treatment decontamination methods or as substitutes of conventional technologies have the potential to produce foods that are safe, rich in nutrient content and with superior organoleptic properties. Combining novel with conventional processes can eliminate potential drawbacks of novel technologies. This review examines available scientific information and critically evaluates the suitability and efficiency of various novel thermal and nonthermal technologies in terms of microbial safety, quality as well as nutrient content on the production of RTE meals, meats and pumpable products.