Partial digestion of neurophysins with proteolytic enzymes: unusual interactions between bovine neurophysin II and chymotrypsin

Bovine neurophysin II was partially digested by chymotrypsin and by chymotrypsin followed by carboxy-peptidase B to produce large fragments collectively representing deletions of residues 1-5 and 91-95. All such fragments were capable of binding peptides to the principal hormone-binding site of neurophysin with normal or near-normal affinity, indicating that residues 1-5 and 91-95 do not directly participate in binding. In addition, preliminary results with thermolysin-derived fragments suggested that residue 6 does not participate in peptide binding. During the course of chymotrypsin studies, it was demonstrated that bovine neurophysin II behaves as a transient competitive inhibitor of chymotrypsin; for neurophysin-peptide complexes, Ki congruent to 8 x 10(-6) M. This inhibition is dependent on neurophysin conformation and is relieved by the anomalous preferential splitting by chymotrypsin of Arg-Arg and Phe-Pro bonds near the carboxyl terminus of neurophysin II. It is suggested that this phenomenon might reflect the interaction of neurophysin II with a chymotrypsin-related enzyme in the pituitary. One approach used in the study of binding properties of proteolytically modified neurophysin was affinity chromatography; the preparation and properties of a conveniently prepared affinity column for neurophysin are described.     

A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem

The sequencing of products for mixed-model assembly line in Just-in-Time manufacturing systems is sometimes based on multiple criteria. In this paper, three major goals are to be simultaneously minimized: total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Due to the NP-hardness of the problem, a new multi-objective particle swarm (MOPS) is designed to search locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms (MOGAs), i.e. PS-NC GA, NSGA-II, and SPEA-II. Comparison shows that MOPS provides superior results to MOGAs.     

Computer‐aided diagnosis software for vulvovaginal candidiasis detection from Pap smear images

Vulvovaginal candidiasis (VVC) is a common gynecologic infection and it occurs when there is overgrowth of the yeast called Candida. VVC diagnosis is usually done by observing a Pap smear sample under a microscope and searching for the conidium and mycelium components of Candida. This manual method is time consuming, subjective and tedious. Any diagnosis tools that detect VVC, semi‐ or full‐automatically, can be very helpful to pathologists. This article presents a computer aided diagnosis (CAD) software to improve human diagnosis of VVC from Pap smear samples. The proposed software is designed based on phenotypic and morphology features of the Candida in Pap smear sample images. This software provide a user‐friendly interface which consists of a set of image processing tools and analytical results that helps to detect Candida and determine severity of illness. The software was evaluated on 200 Pap smear sample images and obtained specificity of 91.04% and sensitivity of 92.48% to detect VVC. As a result, the use of the proposed software reduces diagnostic time and can be employed as a second objective opinion for pathologists.     

A learning‐based approach for virtual machine placement in cloud data centers

In recent years, the increasing use of cloud services has led to the growth and importance of developing cloud data centers. One of the challenging issues in the cloud environments is high energy consumption in data centers, which has been ignored in the corporate competition for developing cloud data centers. The most important problems of using large cloud data centers are high energy costs and greenhouse gas emission. So, researchers are now struggling to find an effective approach to decreasing energy consumption in cloud data centers. One of the preferred techniques for reducing energy consumption is the virtual machines (VMs) placement. In this paper, we present a VM allocation algorithm to reduce energy consumption and Service Level Agreement Violation (SLAV). The proposed algorithm is based on best‐fit decreasing algorithm, which uses learning automata theory, correlation coefficient, and ensemble prediction algorithm to make better decisions in VM allocation. The experimental results indicated improvement regarding energy consumption and SLAV, compared with well‐familiar baseline VM allocation algorithms.     

Design and fabrication of optimized magnetic roller for permanent roll magnetic separator (PRMS): Finite element method magnetics (FEMM) approach

In the present work, an attempt has been made to develop a PRMS in a cost effective and environmental friendly way through FEMM analysis of magnetic roller (active part of PRMS). The FEMM analysis indicates that, the optimized magnetic roller having magnet-to-steel disk thickness ratio of 5 mm: 2.5 mm was proved to be gainful in beneficiating paramagnetic minerals due to the best magnetic field value from the roller surface that is, 0.89 to 2.59 T. Prediction analysis was performed on FEMM data using artificial neural network (ANN) modelling technique. Further, the design calculations of lab scale PRMS in terms of power requirements and belt tensions were addressed. The fabricated PRMS was tested on paramagnetic mineral (hematite ore) assayed 51.24% of Fe, 10.20% of SiO₂, and 2.98% of Al₂O₃ for different roller speeds and the belt thickness. The result showed that, at 0.5 mm belt thickness with 180 rpm roller speed the fabricated lab scale PRMS works well in terms of improvement in the Fe content up to 59.5% at the concentrate along with the Fe recovery of 71.41%. The obtained results suggest that, the FEMM analysis is more suitable to optimize the effective magnetic roller for the PRMS.     

Catalytic Friedel–Crafts Acylation and Benzoylation of Aromatic Compounds Using Activated Hematite as a Novel Heterogeneous Catalyst

Catalytic Friedel–Crafts acylation of benzene and unactivated benzenes such as chlorobenzene and nitrobenzene have been successfully carried out using activated hematite (α‐Fe₂O₃) as a new, heterogeneous and green catalyst. Sonication of neat α‐Fe₂O₃ in a water bath under air atmosphere at room temperature followed by heating at 200 °C, dramatically increase the activity of α‐Fe₂O₃. With the catalyst loading as low as 5.0 mol%, a wide variety of benzene derivatives were easily converted into the corresponding acylated products in a clean and high‐yielding acylation reaction. It was found that the activated α‐Fe₂O₃ could be efficiently recycled and reused several times by simple washing with ethyl acetate, this cannot be attained with most of the traditional catalysts.     

Distribution and circumstances of injuries in squatter settlements of Karachi, Pakistan

OBJECTIVES: This research was conducted to study incidence, distribution, type, causes, severity and circumstances of injuries among people living in squatter settlements in Karachi, Pakistan. METHODS: Trained interviewers sought a 2 weeks recall for minor injuries for which no health care was sought, 2 months recall for major injuries for which a health provider was consulted and 1 year recall for hospitalization, disability and death from every third household in five squatter settlements in Karachi between May and August 1995. For understanding the injury circumstances 250 in-depth interviews of the injured or the close relatives were also conducted. RESULTS: Among 1182 households, having 9891 residents, 84 minor, 42 major injury episodes, 7 hospitalizations, 0.6 permanent disabilities and 0.3 deaths per 1000 person years were reported. Including all injury episodes, the common types of injury were cutting or piercing (n = 532), falls (n = 382) and burns (n = 235) estimating to 54, 38 and 23 injury episodes per 1000 person years. Injury mainly resulted from a piece of glass (n = 367), falling from height (n = 98) and knife (n = 97) estimating to 37, 10 and 10 injury episodes per 1000 person years, respectively. Fall (n = 32, 3.2/1000 person years) was the major reason for hospitalizations and all permanent disabilities resulted from closed injuries. Medically trained providers were mostly contacted (n = 339, 34/1000 person years), but traditional healers were preferred in bone injury. CONCLUSION: Injuries are a major cause of morbidity and mortality in Pakistan and the national health and development agenda should include assessment and prevention of injuries.     

Speckle noise reduction of medical ultrasound images in complex wavelet domain using mixture priors

Speckle noise is an inherent nature of ultrasound images, which may have negative effect on image interpretation and diagnostic tasks. In this paper, we propose several multiscale nonlinear thresholding methods for ultrasound speckle suppression. The wavelet coefficients of the logarithm of image are modeled as the sum of a noise-free component plus an independent noise. Assuming that the noise-free component has some local mixture distribution (MD), and the noise is either Gaussian or Rayleigh, we derive the minimum mean squared error (MMSE) and the averaged maximum ${a quad posteriori}$ (AMAP) estimators for noise reduction. We use Gaussian and Laplacian MD for each noise-free wavelet coefficient to characterize their heavy-tailed property. Since we estimate the parameters of the MD using the expectation maximization (EM) algorithm and local neighbors, the proposed MD incorporates some information about the intrascale dependency of the wavelet coefficients. To evaluate our spatially adaptive despeckling methods, we use both real medical ultrasound and synthetically introduced speckle images for speckle suppression. The simulation results show that our method outperforms several recently and the state-of-the-art techniques qualitatively and quantitatively.