Solving the Bin-Packing Problem by Means of Tissue P System with 2-Division

The ability of tissue P systems with 2-division for solving NP problems in polynomial time is well-known and many solutions can be found in the literature to several of such problems. Nonetheless, there are very few papers devoted to the Bin-packing problem. The reason may be the difficulties for dealing with different number of bins, capacity and number of objects by using exclusively division rules that produce two offsprings in each application. In this paper we present the design of a family of tissue P systems with 2 division which solves the Bin-packing problem in polynomial time by combining design techniques which can be useful for further research

Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action

Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or “golden rules,” for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice

Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval

Texture is one of the visual contents of an image used in content-based image retrieval (CBIR) to represent and index the image. Statistical textural representation methods characterize texture by the statistical distribution of the image intensity. This paper proposes a gray level statistical matrix from which four statistical texture features are estimated for the retrieval of mammograms from mammographic image analysis society (MIAS) database. The mammograms comprising architectural distortion, asymmetry, calcification, circumscribed, ill-defined, spiculated and normal classes are used in the experimentation. Precision, recall, retrieval rate, normalized average rank, average matching fraction, storage requirement and retrieval time are the performance measures used for the evaluation of retrieval performance. Using the proposed method, the highest mean precision rate obtained is 85.1 %. The results show that the proposed method outperforms the state-of-the-art texture feature extraction methods in mammogram retrieval problem. © 2013 Springer Science+Business Media New York

Analysis of the Measurement Matrix in Directional Predictive Coding for Compressive Sensing of Medical Images

Compressive sensing of 2D signals involves three fundamental steps: sparse representation, linear measurement matrix, and recovery of the signal. This paper focuses on analyzing the efficiency of various measurement matrices for compressive sensing of medical images based on theoretical predictive coding. During encoding, the prediction is efficiently chosen by four directional predictive modes for block-based compressive sensing measurements. In this work, Gaussian, Bernoulli, Laplace, Logistic, and Cauchy random matrices are used as the measurement matrices. While decoding, the same optimal prediction is de-quantized. Peak-signal-to-noise ratio and sparsity are used for evaluating the performance of measurement matrices. The experimental result shows that the spatially directional predictive coding (SDPC) with Laplace measurement matrices performs better compared to scalar quantization (SQ) and differential pulse code modulation (DPCM) methods. The results indicate that the Laplace measurement matrix is the most suitable in compressive sensing of medical images

Analysis of the Measurement Matrix in Directional Predictive Coding for Compressive Sensing of Medical Images

Compressive sensing of 2D signals involves three fundamental steps: sparse representation, linear measurement matrix, and recovery of the signal. This paper focuses on analyzing the efficiency of various measurement matrices for compressive sensing of medical images based on theoretical predictive coding. During encoding, the prediction is efficiently chosen by four directional predictive modes for block-based compressive sensing measurements. In this work, Gaussian, Bernoulli, Laplace, Logistic, and Cauchy random matrices are used as the measurement matrices. While decoding, the same optimal prediction is de-quantized. Peak-signal-to-noise ratio and sparsity are used for evaluating the performance of measurement matrices. The experimental result shows that the spatially directional predictive coding (SDPC) with Laplace measurement matrices performs better compared to scalar quantization (SQ) and differential pulse code modulation (DPCM) methods. The results indicate that the Laplace measurement matrix is the most suitable in compressive sensing of medical images

Hepatitis E produces severe decompensation in patients with chronic liver disease

Background and Aims: The adverse effect of acute hepatitis A in chronic liver disease is well known. The outcome of acute hepatitis E in chronic liver disease has not been extensively studied. The present study aimed to examine the clinical profile and outcome of patients with chronic liver disease and hepatitis E virus (HEV) superinfection, and the seroprevalence of hepatitis A and E infections in patients with chronic liver disease and controls in India. Methods: A retrospective study of patients with chronic liver disease and acute icteric hepatitis E was performed. Acute hepatitis E was diagnosed by immunoglobulin (Ig)M ELISA. Seroprevalence studies were carried out using IgG ELISA in 100 patients with chronic liver disease and 79 age- and sex-matched controls. Results: From June 2001 to December 2002, nine patients with chronic liver disease were found to have superinfection with HEV. Out of these, six patients died of advanced liver failure. The etiology of liver disease was Wilson's disease in six, hepatitis B virus in one, autoimmune in one and cryptogenic in one case. The seroprevalence of hepatitis A was 99 and 100% and 56 and 21% for HEV in cases and controls, respectively. Conclusions: Acute HEV in patients with chronic liver disease has a grave prognosis. Wilson's disease was the most common cause of chronic liver disease complicated by acute HEV. Seroprevalence studies showed that 44% of patients with chronic liver disease were at risk of developing hepatitis E. Hepatitis E vaccine, when available, is indicated for use in this group