A novel deep mining model for effective knowledge discovery from omics data

Knowledge discovery from omics data has become a common goal of current approaches to personalised cancer medicine and understanding cancer genotype and phenotype. However, high-throughput biomedical datasets are characterised by high dimensionality and relatively small sample sizes with small signal-to-noise ratios. Extracting and interpreting relevant knowledge from such complex datasets therefore remains a significant challenge for the fields of machine learning and data mining. In this paper, we exploit recent advances in deep learning to mitigate against these limitations on the basis of automatically capturing enough of the meaningful abstractions latent with the available biological samples. Our deep feature learning model is proposed based on a set of non-linear sparse Auto-Encoders that are deliberately constructed in an under-complete manner to detect a small proportion of molecules that can recover a large proportion of variations underlying the data. However, since multiple projections are applied to the input signals, it is hard to interpret which phenotypes were responsible for deriving such predictions. Therefore, we also introduce a novel weight interpretation technique that helps to deconstruct the internal state of such deep learning models to reveal key determinants underlying its latent representations. The outcomes of our experiment provide strong evidence that the proposed deep mining model is able to discover robust biomarkers that are positively and negatively associated with cancers of interest. Since our deep mining model is problem-independent and data-driven, it provides further potential for this research to extend beyond its cognate disciplines

The Effect of Continuous Suspension Constraint on the Free Vibration and Buckling of a Beam

In this paper, the free vibration and the buckling of a continuously suspended simply-supported beam are analyzed. A semi-analytical approach is used to calculate the natural circular frequencies and the critical forces of the beam. The length of the suspension is used as a parameter, and the natural circular frequencies and the critical forces are presented in a frequency map or a buckling map. The maps are analyzed in view of the trivial solutions, and the frequency map is compared to the map of discrete cable-stayed beams. Finally, for the validation of the results a numerical, finite element analysis is performed

Breast cancer diagnosis using a hybrid genetic algorithm for feature selection based on mutual information

Feature Selection is the process of selecting a subset of relevant features (i.e. predictors) for use in the construction of predictive models. This paper proposes a hybrid feature selection approach to breast cancer diagnosis which combines a Genetic Algorithm (GA) with Mutual Information (MI) for selecting the best combination of cancer predictors, with maximal discriminative capability. The selected features are then input into a classifier to predict whether a patient has breast cancer. Using a publicly available breast cancer dataset, experiments were performed to evaluate the performance of the Genetic Algorithm based on the Mutual Information approach with two different machine learning classifiers, namely the k-Nearest Neighbor (KNN), and Support vector machine (SVM), each tuned using different distance measures and kernel functions, respectively. The results revealed that the proposed hybrid approach is highly accurate for predicting breast cancer, and it is very promising for predicting other cancers using clinical data

Cutaneous complications of insulin therapy in patients with Type1 diabetes mellitus

Background: Common complications of subcutaneous insulin injection include lipoatrophy and lipohypertrophy which may lead to erratic absorption of the insulin with the potential for poor glycemic control and unpredictable hypoglycemia. Other cutaneous complications are local and systemic insulin allergy. Patients and methods: The study included 150 patients with type1 diabetes mellitus attending thediabetic clinic of Children Welfare Teaching Hospital who were assessed for cutaneous omplications of insulin therapy especially at the sites of the injections. Data collected evaluated using chi square and Pvalue. Results: Out of 150 patients, the male to female ratio was 1:1.3, with mean age of 11.34 years ± 4.461SD.The cutaneous complications of insulin therapy present in (56.7%) of patients, (94.1%) of them had lipohypertrophy and (5.9%) had allergy to insulin while lipoatrophy and other cutaneous complications were not reported.The cutaneous complications were associated with increase in the incidence of other complications of diabetes mellitus (72.9%).Most of cutaneous complications developed in the upper arms (69.4%) especially in those who did not change the sites and those with wrong technique of injections. Conclusions: The cutaneous complications developed because of poor education about the proper use of insulin or failure to follow the instructions, as the school achievement of the person who injects the insulin for the patient had no role. So I recommend proper education about the sites and the technique of insulin injection and the maintenance of this education

The Effect of Twisted Wire Configuration on the Stability of External Fixator: A Biomechanical Study

The Ilizarov fixator is a type of external fixator that is used to treat patients who have suffered injuries from accidents, bone shortening, or nonunion of the bone. The principle behind the Ilizarov fixator is that thin wires (called Kirschner wires) are used to support the bones and connect them to framed rings. Before being fastened to the rings, the wires are tensioned and drilled through the bones. This study suggests using a new parallel wires configuration at the same level on the same ring and two revised versions, which are divergent and convergent models, and compare them with standard wires, 60 angle wires model. All models were designed using SolidWorks, a computer-aided design (CAD) software, and then analyzed in four conditions (axial compression, medial bending, posterior bending, and torsion) with Finite Element Analysis (FEA) using Ansys Workbench 2020 R2. Mechanical testing was conducted to validate the FEA results, A simple model consisting of a single ring, two K-wires, and polylactic acid (PLA) cylinders was utilized in a tensile test. It has been concluded from the results that the parallel model and its improvement have higher stiffness to axial compression, medial bending, and torsion, but a lower posterior bending stiffness, except the divergent model with 8-hole separation which has a relatively acceptable stiffness for posterior bending

Combining Islamic Equity Portfolios and Digital Currencies: Evidence from Portfolio Diversification

Digital currencies are unregulated and potentially have a destabilizing effect coupled with increased concerns over capital gains and losses in a high volatility environment. When added to a portfolio, this currency may have certain driving factors in terms of return and risks in the case of portfolio diversification. In this study, from the Sharia angle, we follow the position of Monzer Kahf (Fatwa on Bitcoin (by Monzer Kahf). http://lightuponlight.com/blog/fatwa-on-bitcoin-by-monzer-kahf/. Accessed 03 Feb 2020, 2017) who explained that Bitcoin is considered “Like any other currency”. It should be used under the “same conditions of exchanging currencies”. Therefore, we explore the effects of adding digital currencies to an Islamic portfolio by relying on a mean-variance efficient frontier and comparing the risk-return of portfolios with and without digital currencies for different scenarios. The results show that by adding digital currencies to Shariah-compliant portfolios, its performance improves; but this depends more or less on the increase in returns than in the reduction of total risk. Specifically, digital currencies may have a big role in bringing high risks with speculative effect in portfolio diversification. Therefore, we provide some recommendations to investors and regulators to secure these currencies in Islamic capital markets

Glomerulogenesis and Histomorphometeric in Mus musculus Embryo

In mammals, the kidneys originate in an embryo from the mesoderm through three excretory organs, namely: Pronephros, Mesonephros, and Metanephros. After the formation of Metanephros is completed, the kidneys begin to form nephrogenesis through mesenchymal cells located at the tip of the ureteric bud, that contribute in the formation of glomerulus and Renal tubules. The stages of glomerulus formation in the embryo of albino mice at the age of 14 to 19 days of gestation were studied. It was obtained after the sacrifice of the expectant mother and the kidneys were excised from the embryos and fixed using Aqueous Bouin's solution, Microscopic slices with a thickness of 6 microns were then made in a paraffin method and were photographed by a camera for microscopic imaging. Histological measurements were performed on them using the program IMAGE J program and analyzed statistically using the SPSS program.  Results of the current study showed the presence of five stages of glomerulus formation, namely, the renal vesicle, which represents the first stage in the formation of the nephron and glomerulus. This is followed by the Comma shape stage, S-shaped stage, Capillary loop stage and finally the Mature glomerulus. This is surrounded by a capsule, known as Bowman’s capsule being part of the Malpighian or Renal corpuscle. The statistical analysis showed that there were significant differences between the average diameters of the glomerular development stages, and that the mature glomerulus was larger in diameter than the rest of the stages. The study aims to determine the stages of glomerulus formation and histologically measure its diameter in the embryo of a Mus musculus.