Lipopolysaccharide isolated from Porphyromonas gingivalis grown in hemin-limited chemostat conditions has a reduced capacity for human neutrophil priming

One way prokaryotes respond to environmental stresses is by modifying selected outer membrane components. Iron, in the form of hemin, has been shown to be a significant regulator of Porphyromonas gingivalis growth and virulence and of the expression of outer membrane proteins and lipopoly saccharide. Since lipopoly saccharide has profound effects on host immune cells, this study compared the effect of hemin-restricted and hemin-normal P. gingivalis growth conditions on lipopolysaccharide priming of N-formylmethionyl-leucyl-phenylalanine-induced superoxide generation by human neutrophils. P. gingivalis was grown in a chemostat under normal (5 micrograms hemin/ml) and hemin-restricted (0.08 microgram hemin/ml) conditions. Purified lipopolysaccharide from both P. gingivalis normal and hemin-limited environments increased N-formylmethionyl-leucyl-phenylalanine-induced superoxide release by neutrophils in a dose-dependent manner. Lipopolysaccharide isolated from the hemin-normal conditions was a significantly more potent neutrophil priming agent than the lipopolysaccharide isolated from hemin-restricted conditions. Addition of normal human serum enhanced the priming effect of both lipopolysaccharide preparations; this effect, however, was more evident with the hemin-normal lipopolysaccharide. Further, this enhancing effect of serum was partly reduced in the presence of antibodies raised against the serum lipopolysaccharide-binding protein. The differences in the biological activity of the two lipopolysaccharide preparations could be associated with structural differences detected by sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis. These results indicate that hemin availability affects regulation of an aspect of P. gingivalis virulence, lipopolysaccharide-human neutrophils priming. The reduced capacity for neutrophil priming by hemin-restricted lipopolysaccharide appears to be related to lipopolysaccharide-neutrophil interactions and not to serum factors Targeting bacterial cell-surface components involved in hemin transport might be effective therapy for P. gingivalis-associated periodontal diseases.     

Shape blending using the star-skeleton representation

Shape blending, the metamorphosis of one shape into another, is a central problem in two-dimensional computer animation. In spite of impressive uses of morphing in film and video productions, the problem is far from solved. In particular, shape blending still requires considerable manual effort. By decomposing two polygons into equivalent star-shaped pieces and a connecting skeleton, the paper presents a blending method which can represent polygon interiors, not just boundaries, and generate high-quality results with minimal user intervention     

Recognition of handwritten musical notes by a modified Neocognitron

A neural network for recognition of handwritten musical notes, based on the well-known Neocognitron model, is described. The Neocognitron has been used for the what pathway (symbol recognition), while contextual knowledge has been applied for the where (symbol placement). This way, we benefit from dividing the process for dealing with this complicated recognition task. Also, different degrees of intrusiveness in learning have been incorporated in the same network: More intrusive supervised learning has been implemented in the lower neuron layers and less intrusive in the upper one. This way, the network adapts itself to the handwriting of the user. The network consists of a 13×49 input layer and three pairs of simple and complex neuron layers. It has been trained to recognize 20 symbols of unconnected notes on a musical staff and was tested with a set of unlearned input notes. Its recognition rate for the individual unseen notes was up to 93%, averaging 80% for all categories. These preliminary results indicate that a modified Neocognitron could be a good candidate for identification of handwritten musical notes.     

FSCOAL—Parallel simultaneous fuzzy co‐clustering and learning

A model‐based co‐clustering divides the data based on two main axes and simultaneously trains a supervised model for each co‐cluster using all other input features. For example, in the rating prediction task of recommender system, the main two axes are items and users. In each co‐cluster, we train a regression model for predicting the rating based on other features such as user's characteristics (e.g., gender), item's characteristics (e.g., genre), contextual features (e.g., location), and so on. In reality, users and items do not necessarily belong to a single co‐cluster, but rather can be associated with several co‐clusters. We extend the model‐based co‐clustering to support fuzzy co‐clustering. In this setting, each item–user pair is associated to every co‐cluster with some membership grade. This grade indicates the level of relevance of the item–user pair to the co‐cluster. Furthermore, we propose a distributed algorithm, based on a map‐reduce approach, to handle big datasets. Evaluating the fuzzy co‐clustering algorithm on three datasets shows a significant improvement comparing with a regular co‐clustering algorithm. In addition, a map‐reduce version of the fuzzy co‐clustering algorithm significantly reduces the runtime.     

When standard RANSAC is not enough: cross-media visual matching with hypothesis relevancy

The same scene can be depicted by multiple visual media. For example, the same event can be captured by a comic image or a movie frame; the same object can be represented by a photograph or by a 3D computer graphics model. In order to extract the visual analogies that are at the heart of cross-media analysis, spatial matching is required. This matching is commonly achieved by extracting key points and scoring multiple, randomly generated mapping hypotheses. The more consensus a hypothesis can draw, the higher its score. In this paper, we go beyond the conventional set-size measure for the quality of a match and present a more general hypothesis score that attempts to reflect how likely is each hypothesized transformation to be the correct one for the matching task at hand. This is achieved by considering additional, contextual cues for the relevance of a hypothesized transformation. This context changes from one matching task to another and reflects different properties of the match, beyond the size of a consensus set. We demonstrate that by learning how to correctly score each hypothesis based on these features we are able to deal much more robustly with the challenges required to allow cross-media analysis, leading to correct matches where conventional methods fail.     

ConfDTree： A Statistical Method for Improving Decision Trees

Decision trees have three main disadvantages： reduced performance when the training set is small; rigid decision criteria; and the fact that a single ＂uncharacteristic＂ attribute might ＂derail＂ the classification process. In this paper we present ConfDTree （Confidence-Based Decision Tree） -- a post-processing method that enables decision trees to better classify outlier instances. This method, which can be applied to any decision tree algorithm, uses easy-to-implement statistical methods （confidence intervals and two-proportion tests） in order to identify hard-to-classify instances and to propose alternative routes. The experimental study indicates that the proposed post-processing method consistently and significantly improves the predictive performance of decision trees, particularly for small, imbalanced or multi-class datasets in which an average improvement of 5%-9% in the AUC performance is reported.     

Vitamin B12 deficiency in patients with chronic-tinnitus and noise-induced hearing loss

The high rate of comorbidity between major depressive disorder and alcohol abuse is noted and the possible causes of the association are examined. Three reasons for the high degree of comorbidity are discarded--underlying dependent personality disorder and/or childhood deprivation, a common hereditary defect, and self-medication with alcohol for primary depression. Alternative explanations are favored, namely, the chance simultaneous occurrence and subsequent oversampling of two common disorders, and the fact that alcohol abuse is often the "horse" to the "cart" of major depressive disorder. The treatment implications of such a view are discussed.     

All-Pairs Bottleneck Paths in Vertex Weighted Graphs

Let G=(V,E,w) be a directed graph, where w:V→ℝ is a weight function defined on its vertices. The bottleneck weight, or the capacity, of a path is the smallest weight of a vertex on the path. For two vertices u,v the capacity from u to v, denoted by c(u,v), is the maximum bottleneck weight of a path from u to v. In the All-Pairs Bottleneck Paths (APBP) problem the task is to find the capacities for all ordered pairs of vertices. Our main result is an O(n ^2.575) time algorithm for APBP. The exponent is derived from the exponent of fast matrix multiplication.