Maximum transition run codes for generalized partial responsechannels

A new twins constraint for maximum transition run (MTR) codes is introduced to eliminate quasi-catastrophic error propagation in sequence detectors for generalized partial response channels with spectral nulls both at dc and at the Nyquist frequency. Two variants of the twins constraint that depend on whether the generalized partial response detector trellis is unconstrained or j-constrained are studied. Deterministic finite-state transition diagrams that present the twins constraint are specified, and the capacity of the new class of MTR constraints is computed. The connection between (G,I) constraints and MTR(j) constraints is clarified. Code design methodologies that are based on look-ahead coding in combination with violation detection/substitution as well as on state splitting are used to obtain several specific constructions of high-rate MTR codes     

Optimisation of ultraclean air. The role of instrument preparation

The use of ultraclean air (UCA) in operating theatres reduces the infection rate after joint replacement but some cases of infection still occur. We investigated one possible source of contamination, namely the setting up of instruments in a conventional plenum-ventilated preparation room. We measured bacterial fallout using agar settle plates and compared instruments set up in the preparation room with those set up in the UCA theatre, assessed the effect of covering instruments after preparation and compared fallout during their preparation with total fallout throughout the operation. Our findings showed that covering the instruments reduced total bacterial fallout fourfold by reducing the exposure time, particularly during periods of increased activity and bacterial dispersal. Preparation in the UCA theatre and subsequent covering of the instruments reduced total fallout 28-fold. All measurable bacterial fallout occurred during the setting up and not during surgery.     

Microwave‐Assisted Synthesis of Pyrazino[2,1‐b]quinazolines and 3‐Indolyl‐2(1H)‐pyrazinones Employing a Chemoselective Silver(I)‐ and Gold(I)‐Catalyzed Reaction

Novel microwave‐assisted syntheses of pyrazino[2,1‐b]quinazolines and 3‐indolyl‐2(1H)‐pyrazinones employing silver(I)‐ or gold(I)‐catalyzed protocols have been elaborated. The scope and limitations of these processes have been investigated.     

A learning law for density estimation

Probability density functions are estimated by an exponential family of densities based on multilayer feedforward networks. The role of the multilayer feedforward networks, in the proposed estimator, is to approximate the logarithm of the probability density functions. The method of maximum likelihood is used, as the main contribution, to derive an unsupervised backpropagation learning law to estimate the probability density functions. Computer simulation results demonstrating the use of the derived learning law are presented.     

Feature Weighting in k-Means Clustering

Data sets with multiple, heterogeneous feature spaces occur frequently. We present an abstract framework for integrating multiple feature spaces in the k-means clustering algorithm. Our main ideas are (i) to represent each data object as a tuple of multiple feature vectors, (ii) to assign a suitable (and possibly different) distortion measure to each feature space, (iii) to combine distortions on different feature spaces, in a convex fashion, by assigning (possibly) different relative weights to each, (iv) for a fixed weighting, to cluster using the proposed convex k-means algorithm, and (v) to determine the optimal feature weighting to be the one that yields the clustering that simultaneously minimizes the average within-cluster dispersion and maximizes the average between-cluster dispersion along all the feature spaces. Using precision/recall evaluations and known ground truth classifications, we empirically demonstrate the effectiveness of feature weighting in clustering on several different application domains.     

Interaction of E1B 19K with Bax is required to block Bax-induced loss of mitochondrial membrane potential and apoptosis

The Bcl-2 homologous region 3 (BH3) is sufficient for interaction of pro-apoptotic with anti-apoptotic Bcl-2 family members, and functional antagonism may determine whether cell survival or death is the outcome of this protein-protein interaction. To address the biological role of BH3, two Bax-Bcl2 chimeras were generated in which 13 amino acids encompassing BH3 was swapped between anti-apoptotic Bcl-2 and pro-apoptotic Bax, thereby generating Bax with BH3 of Bcl-2 (Bax-BH3Bcl2), and Bcl-2 with BH3 of Bax (Bcl2-BH3Bax). Function and binding of the chimeras was then assessed utilizing the adenoviral Bcl-2 homologue, E1B 19K, which blocks apoptosis, and interacts with Bax, but not with Bcl-2. E1B 19K did not interact with Bax-BH3Bcl2 but did interact with Bcl2-BH3Bax. Bax-BH3Bcl2 retained pro-apoptotic function, while Bcl2-BH3Bax did not exhibit either pro- or anti-apoptotic activity. Thus, BH3 of Bcl-2 encodes binding specificity but not the apoptotic propensity. E1B 19K could not block Bax-BH3Bcl2-induced apoptosis, suggesting that E1B 19K may act to antagonize pro-apoptotic proteins rather than as an effector of survival. Furthermore, Bax expression disrupted the mitochondrial membrane potential, which could be rescued by E1B 19K expression. Thus, BH3 controls the binding specificity among Bcl-2 family members, and direct interaction between pro-apoptotic and anti-apoptotic proteins is a mechanism to regulate mitochondrial membrane potential and apoptosis.     

Metabolic alterations in organs of Meriones unguiculatus infected with Echinococcus multilocularis

1. 1H-NMR spectra of liver, spleen and kidney extracts from Meriones unguiculatus infected with Echinococcus multilocularis showed decreased levels of glucose. In addition, the liver extracts were severely glycogen-depleted. 2. Both livers and spleens contained less glycine, taurine and acetate. Spleens also had fewer cholines and less lactate but more betaine and alanine. 3. In the kidneys, elevated concentrations of succinate, acetate and lactate were found.     

Concept Decompositions for Large Sparse Text Data Using Clustering

Unlabeled document collections are becoming increasingly common and available; mining such data sets represents a major contemporary challenge. Using words as features, text documents are often represented as high-dimensional and sparse vectors–a few thousand dimensions and a sparsity of 95 to 99% is typical. In this paper, we study a certain spherical k-means algorithm for clustering such document vectors. The algorithm outputs k disjoint clusters each with a concept vector that is the centroid of the cluster normalized to have unit Euclidean norm. As our first contribution, we empirically demonstrate that, owing to the high-dimensionality and sparsity of the text data, the clusters produced by the algorithm have a certain fractal-like and self-similar behavior. As our second contribution, we introduce concept decompositions to approximate the matrix of document vectors; these decompositions are obtained by taking the least-squares approximation onto the linear subspace spanned by all the concept vectors. We empirically establish that the approximation errors of the concept decompositions are close to the best possible, namely, to truncated singular value decompositions. As our third contribution, we show that the concept vectors are localized in the word space, are sparse, and tend towards orthonormality. In contrast, the singular vectors are global in the word space and are dense. Nonetheless, we observe the surprising fact that the linear subspaces spanned by the concept vectors and the leading singular vectors are quite close in the sense of small principal angles between them. In conclusion, the concept vectors produced by the spherical k-means algorithm constitute a powerful sparse and localized basis for text data sets.     

Prequential and Cross-Validated Regression Estimation

Prequential model selection and delete-one cross-validation are data-driven methodologies for choosing between rival models on the basis of their predictive abilities. For a given set of observations, the predictive ability of a model is measured by the model's accumulated prediction error and by the model's average-out-of-sample prediction error, respectively, for prequential model selection and for cross-validation. In this paper, given i.i.d. observations, we propose nonparametric regression estimators—based on neural networks—that select the number of hidden units (or neurons) using either prequential model selection or delete-one cross-validation. As our main contributions: (i) we establish rates of convergence for the integrated mean-squared errors in estimating the regression function using off-line or batch versions of the proposed estimators and (ii) we establish rates of convergence for the time-averaged expected prediction errors in using on-line versions of the proposed estimators. We also present computer simulations (i) empirically validating the proposed estimators and (ii) empirically comparing the proposed estimators with certain novel prequential and cross-validated mixture regression estimators.     

Outperforming LRU with an adaptive replacement cache algorithm

The self-tuning, low-overhead, scan-resistant adaptive replacement cache algorithm outperforms the least-recently-used algorithm by dynamically responding to changing access patterns and continually balancing between workload recency and frequency features. Caching, a fundamental metaphor in modern computing, finds wide application in storage systems, databases, Web servers, middleware, processors, file systems, disk drives, redundant array of independent disks controllers, operating systems, and other applications such as data compression and list updating. In a two-level memory hierarchy, a cache performs faster than auxiliary storage, but it is more expensive. Cost concerns thus usually limit cache size to a fraction of the auxiliary memory's size.