Evaluation and characterisation of Citrullus colocynthis (L.) Schrad seed oil: comparison with Helianthus annuus (sunflower) seed oil.

The physicochemical properties, fatty acid, tocopherol, thermal properties, 1H NMR, FTIR and profiles of non-conventional oil extracted from Citrullus colocynthis (L.) Schrad seeds were evaluated and compared with conventional sunflower seed oil. In addition, the antioxidant properties of C. colocynthis seed oil were also evaluated. The oil content of the C. colocynthis seeds was 23.16%. The main fatty acids in the oil were linoleic acid (66.73%) followed by oleic acid (14.78%), palmitic acid (9.74%), and stearic acid (7.37%). The tocopherol content was 121.85 mg/100 g with γ-tocopherol as the major one (95.49%). The thermogravimetric analysis showed that the oil was thermally stable up to 286.57 °C, and then began to decompose in four stages namely at 377.4 °C, 408.4 °C, 434.9 °C and 559.2 °C. The present study showed that this non-conventional C. colocynthis seed oil can be used for food and non-food applications to supplement or replace some of the conventional oils

A study of caesar cipher and transposition cipher in jawi messages

Cryptography known as art and science which is used to hide the messages that contain a few policy terminologies. These terminologies in cryptography are plaintext/ messages, ciphertext, encryption, decryption and key. Encryption is a proses to transform the plaintext together with key into ciphertext. Decryption is the reverse process of encryption. Caesar cipher and transposition cipher are two historical ciphers in cryptography. Caesar cipher is a monoalphabetic cipher. It is a substitution cipher which replace each letter in plaintext with another letter to form the ciphertext. Transposition cipher uses a technique which rearrangement letters in plaintext with a keyword and produce the ciphertext. Caesar cipher and Tansposition cipher both are commonly used to encrypt the English letters. The output of encrypted of English letters are known as ciphertext. The attacker can easily cryptanalysed the Caesar cipher by observing the frequency distribution English letters and ciphertext. For Transposition cipher, the cipher can be cracked by knowing the keyword. To date, there is no any research encrypt Jawi letters using Caesar cipher and Transposition cipher. Hence, in this paper encryption and decryption by using Caesar cipher and Transposition cipher in Jawi messages are proposed. Next, the security level of Caesar cipher and Transposition cipher in Jawi messages are compared. The result has shown that both ciphers are still not secure to protect the confidentiality of the Jawi messages

Assessment of algorithms for mitosis detection in breast cancer histopathology images

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists

Effects of Big Data Analytics on Sustainable Manufacturing: A Comparative Study Analysis

Application of big data analytics (BDA) is seen in various disciplines within an organization to predict trends, explore opportunities and monitor performance. Among all the industries, BDA presents immense value in sustainable manufacturing (SM) given that it is an industry that consumes a high amount of energy, emits high amounts of waste and carbon emissions and requires a large amount of manpower. This paper aims at illustrating the effects of BDA in supporting SM by studying the Indian manufacturing firms which have unfavorable labor laws compared to other developing countries. With an extensive literature review, this paper discusses the relationship between BDA and sustainability, the capabilities of BDA, the concept of SM, the BDA framework for SM, the relationship between Industry 4.0 and SM and the challenges of implementing BDA. Using qualitative meta-analysis research methodology, the paper examines the nine common critical success factors that enable SM through BDA implementation by comparing 15 primary studies. Finally, the paper concludes the research findings and outlines future research directions. The study provides theoretical and practical contributions to BDA implementation in achieving effective SM practices in emerging economies

A systematic review of randomised controlled trials on the effectiveness of exercise programs on lumbo pelvic pain among postnatal women

Background: A substantial number of women tend to be affected by Lumbo Pelvic Pain (LPP) following child birth. Physical exercise is indicated as a beneficial method to relieve LPP, but individual studies appear to suggest mixed findings about its effectiveness. This systematic review aimed to synthesise evidence from randomised controlled trials on the effectiveness of exercise on LPP among postnatal women to inform policy, practice and future research. Methods: A systematic review was conducted of all randomised controlled trials published between January 1990 and July 2014, identified through a comprehensive search of following databases: PubMed, PEDro, Embase, Cinahl, Medline, SPORTDiscus, Cochrane Pregnancy and Childbirth Group’s Trials Register, and electronic libraries of authors’institutions. Randomised controlled trials were eligible for inclusion if the intervention comprised of postnatal exercise for women with LPP onset during pregnancy or within 3 months after delivery and the outcome measures included changes in LPP. Selected articles were assessed using the PEDro Scale for methodological quality and findings were synthesised narratively as meta-analysis was found to be inappropriate due to heterogeneity among included studies. Results: Four randomised controlled trials were included, involving 251 postnatal women. Three trials were rated as of ‘good’ methodological quality. All trials, except one, were at low risk of bias. The trials included physical exercise programs with varying components, differing modes of delivery, follow up times and outcome measures. Intervention in one trial, involving physical therapy with specific stabilising exercises, proved to be effective in reducing LPP intensity. An improvement in gluteal pain on the right side was reported in another trial and a significant difference in pain frequency in another. Conclusion: Our review indicates that only few randomised controlled trials have evaluated the effectiveness of exercise on LPP among postnatal women. There is also a great amount of variability across existing trials in the components of exercise programs, modes of delivery, follow up times and outcome measures. While there is some evidence to indicate the effectiveness of exercise for relieving LPP, further good quality trials are needed to ascertain the most effective elements of postnatal exercise programs suited for LPP treatment

Strong signature of natural selection within an FHIT intron implicated in prostate cancer risk

Previously, a candidate gene linkage approach on brother pairs affected with prostate cancer identified a locus of prostate cancer susceptibility at D3S1234 within the fragile histidine triad gene (FHIT), a tumor suppressor that induces apoptosis. Subsequent association tests on 16 SNPs spanning approximately 381 kb surrounding D3S1234 in Americans of European descent revealed significant evidence of association for a single SNP within intron 5 of FHIT. In the current study, resequencing and genotyping within a 28.5 kb region surrounding this SNP further delineated the association with prostate cancer risk to a 15 kb region. Multiple SNPs in sequences under evolutionary constraint within intron 5 of FHIT defined several related haplotypes with an increased risk of prostate cancer in European-Americans. Strong associations were detected for a risk haplotype defined by SNPs 138543, 142413, and 152494 in all cases (Pearson's χ2 = 12.34, df 1, P = 0.00045) and for the homozygous risk haplotype defined by SNPs 144716, 142413, and 148444 in cases that shared 2 alleles identical by descent with their affected brothers (Pearson's χ2 = 11.50, df 1, P = 0.00070). In addition to highly conserved sequences encompassing SNPs 148444 and 152413, population studies revealed strong signatures of natural selection for a 1 kb window covering the SNP 144716 in two human populations, the European American (π = 0.0072, Tajima's D= 3.31, 14 SNPs) and the Japanese (π = 0.0049, Fay & Wu's H = 8.05, 14 SNPs), as well as in chimpanzees (Fay & Wu's H = 8.62, 12 SNPs). These results strongly support the involvement of the FHIT intronic region in an increased risk of prostate cancer. © 2008 Ding et al

A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised fuzzy c-means

Previously, a semi-manual method was used to identify six novel and clinically useful classes in the Nottingham Tenovus Breast Cancer dataset. 663 out of 1,076 patients were classified. The objectives of our work is three folds. Firstly, our primary objective is to use one single automatic method (post-initialisation) to reproduce the six classes for the 663 patients and to classify the remaining 413 patients. Secondly, we explore using semi-supervised fuzzy c-means with various distance metrics and initialisation techniques to achieve this. Thirdly, the clinical characteristics of the 413 patients are examined by comparing with the 663 patients. Our experiments use various amount of labelled data and 10-fold cross validation to reproduce and evaluate the classification. ssFCM with Euclidean distance and initialisation technique by Katsavounidis et al. produced the best results. It is then used to classify the 413 patients. Visual evaluation of the 413 patients’ classifications revealed common characteristics as those previously reported. Examination of clinical characteristics indicates significant associations between classification and clinical parameters. More importantly, association between classification and survival based on the survival curves is shown

Electromagnetic Wave Theory and Applications

Contains reports on twelve research projects.Joint Services Electronics Program (Contract DAALO3-86-K-0002)National Science Foundation (Grant ECS 85-04381)National Aeronautics and Space Administration/Goddard Space Flight Center (Contract NAG5-270)National Aeronautics and Space Administration/Goddard Space Flight Center (Contract NAG5-725)U.S. Navy - Office of Naval Research (Contract N00014-83-K-0258)U.S. Navy - Office of Naval Research (Contract N00014-86-K-0533)U.S. Army - Research Office Durham (Contract DAAG29-85-K-0079)International Business Machines, Inc.National Aeronautics and Space Administration/Goddard Space Flight Center (Contract NAG5-269)Simulation TechnologiesSchlumberger-Doll Researc

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network\u27s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine