Can sesame consumption improve blood pressure? A systematic review and meta‐analysis of controlled trials

Hypertension is a major risk factor for cardiovascular disease, myocardial infarction, stroke and renal failure. Sesame consumption may benefit blood pressure (BP ) owing to its high polyunsaturated fatty acid, fibre, phytosterol and lignan contents. To clarify this, a systematic review and meta‐analysis of controlled trials was conducted. The PubMed (MEDLINE ), Cumulative Index to Nursing and Allied Health Literature (CINAHL ) and Cochrane Library (Central) databases were systematically searched until August 2016. Eight controlled trials with a total of 843 participants met the eligibility criteria. A random effect meta‐analysis showed that sesame consumption can reduce systolic BP (?7.83 mmHg , 95% CI : ?14.12, ?1.54; P < 0.05, I ² = 99%) and diastolic BP (?5.83 mmHg, 95% CI: ?9.58, ?2.08; P < 0.01, I ² = 98%). To reduce the heterogeneity, the meta‐analysis was limited to high methodology quality trials (n = 4), which resulted in a significant reduction in systolic BP (?3.23 mmHg, 95% CI: ?5.67, ?0.79; I ² = 33%) and a non‐significant reduction in diastolic BP (?2.08 mmHg, 95% CI: ?4.85, 0.69; I ² = 62%). This study concluded that sesame consumption can reduce systolic and diastolic BP. However, further investigations with larger sample sizes and better methodology quality are required to confirm the BP‐lowering effect of sesame consumption. © 2017 Society of Chemical Industry     

Toward software quality enhancement by Customer Knowledge Management in software companies

Customer Knowledge Management (CKM) plays an important role in the production of high quality software products. As CKM in Enterprise Software (ES) development is still immature, this raises questions on how CKM enablers can be used to help ES development companies improve their software quality. In this study, Human, Organizational and Technological CKM enablers were identified from the literature. The weights of these factors were determined by experts from the ES development companies. Based on the most important factors, a theoretical model was developed. The proposed model was evaluated by distributing a survey questionnaire to decision-makers in ES development companies. The results showed that “Customer Involvement” together with “Trust” were the most influential factors, followed by “CRM Technology Infrastructure” and “Cross-Functional Cooperation”. In addition, there was no impact from “Organizational Training”, “Customer Knowledge Map”, and “CKM Strategy Development”. The results also revealed that the impact of CKM on software quality is significant. The proposed model in this study can be used as a guideline for the successful application of CKM in ES development companies to improve the software quality.     

Microstructural characteristics and mechanical properties of Al-2024 alloy processed via a rheocasting route

This article reports the effects of stirring speed and T6 heat treatment on the microstructure and mechanical properties of Al-2024 alloy synthesized by a rheocasting process. There was a decrease in g...     

Learning compact Markov logic networks with decision trees

Statistical-relational learning combines logical syntax with probabilistic methods. Markov Logic Networks (MLNs) are a prominent model class that generalizes both first-order logic and undirected graphical models (Markov networks). The qualitative component of an MLN is a set of clauses and the quantitative component is a set of clause weights. Generative MLNs model the joint distribution of relationships and attributes. A state-of-the-art structure learning method is the moralization approach: learn a set of directed Horn clauses, then convert them to conjunctions to obtain MLN clauses. The directed clauses are learned using Bayes net methods. The moralization approach takes advantage of the high-quality inference algorithms for MLNs and their ability to handle cyclic dependencies. A?weakness of moralization is that it leads to an unnecessarily large number of clauses. In this paper we show that using decision trees to represent conditional probabilities in the Bayes net is an effective remedy that leads to much more compact MLN structures. In experiments on benchmark datasets, the decision trees reduce the number of clauses in the moralized MLN by a factor of 5?C25, depending on the dataset. The accuracy of predictions is competitive with the models obtained by standard moralization, and in many cases superior.     

Balancing and Trajectory Tracking of Two-Wheeled Mobile Robot Using Backstepping Sliding Mode Control: Design and Experiments

The key attributes of Two Wheeled Balancing Mobile Robots (TWBMRs) are nonholonomic constraints and inherent instability. This paper deals with the problem of balancing and trajectory tracking of TWBMR using backstepping Sliding Mode Controller (SMC). First, the mathematical representation of TWBMR is derived using Lagrangian method by incorporating the dynamics of DC motors. Then, a decoupling approach is applied for simplifying the dynamic equations. The backstepping SMC technique is finally adopted to achieve the balancing and trajectory tracking of the TWBMR, whereas both model uncertainties and exogenous disturbance are taken into account in the controller design methodology. In order to determine the velocity, the trajectory tracking is achieved by the kinematic control, which is a common backstepping controller. For the velocity convergence of TWBMR to the generated desired value, two SMCs are designed, in which the motors voltage are directly controlled as the control laws. Simplicity in practical implementation and control law, ability to overcome uncertainties and appropriate performance are the main advantages of the proposed controller. The effectiveness of the proposed controller is verified through simulation and experimental results.     

BBN conjugated GNPs: a new targeting contrast agent for imaging of breast cancer in radiology

Using of targeted contrast agents in X‐ray imaging of breast cancer can improve the accuracy of diagnosis, staging, and treatment planning by providing early detection and superior definition of tumour volume. This study demonstrates a new class of X‐ray contrast agents based on gold nanoparticles (GNPs) and bombesin (BBN) for imaging of breast cancer in radiology. GNPs were synthesised in spherical shape in the size range of 15 ± 2 nm and conjugated with BBN followed by coating with polyethyleneglycol (PEG). The in vitro and in vivo behaviour of PEG‐coated GNPs‐BBN conjugate was investigated performing cytotoxicity, binding, and internalisation assays as well as biodistribution and X‐ray imaging studies in mouse bearing breast tumour. Cytotoxicity study showed biocompatibility of the prepared bioconjugate. The binding and internalisation studies using T47D cell line approved the targeting ability of new agent. The biodistribution study showed the considerable accumulation of prepared conjugate in breast tumour in mouse model. The breast tumour was clearly visualised in X‐ray images taken from the mouse model. The results showed the potential of PEG‐coated GNPs‐BBN conjugate as a contrast agent in X‐ray imaging of breast tumour in humans that need further investigations.Inspec keywords: diagnostic radiography, cancer, tumours, radiology, gold, nanoparticles, nanomedicine, polymers, coatings, toxicology, cellular biophysicsOther keywords: bombesin conjugated gold nanoparticles, breast cancer, radiology, targeted contrast agents, X‐ray imaging, X‐ray contrast agents, spherical shape, polyethyleneglycol, coating, in vitro behaviour, in vivo behaviour, cytotoxicity, internalisation assays, biodistribution, mouse bearing breast tumour, biocompatibility, bioconjugate, T47D cell line, Au     

Application of Organoclay Nanoparticle in Low‐Density Polyethylene Films for Packaging of UF Cheese

Polymer/clay nanocomposites are a group of hybrid materials made from layered silicates of clay dispersed in the polymer matrix with a layer thickness in nanometre. The ability of organoclay nanoparticle including cloisite 15A, cloisite 20A and cloisite 30B incorporated into a low‐density polyethylene (LDPE) polymeric matrix was investigated in order to inhibit the growth of coliform bacteria in ultra‐filtrated cheese. The number of surviving cells of coliforms was decreased by 2.21 log cfu/g after 4 weeks storage at 4 ± 0.5°C, while pure LDPE showed a reduction of only 0.759 log cfu/g. LDPE film composed of 3% cloisite 15A, 3% cloisite 20A, with no cloisite 30B nano‐filler was found as optimum point by combined design approach in design‐expert analysis. The resulting film showed coliform load decreased by 2.05 log cfu/g in the optimum point, which was in line with Japanese industrial standard (JIS Z 2801: 2000) about antibacterial effect on coliform bacteria. A suitable microbial model for bacteria growth reduction was also suggested. Copyright © 2016 John Wiley & Sons, Ltd.