Innovative Hybridisation of Genetic Algorithms and Neural Networks in Detecting Marker Genes for Leukaemia Cancer

Methods for extracting marker genes that trigger the growth of cancerous cells from a high level of complexity microarrays are of much interest from the computing community. Through the identified genes, the pathology of cancerous cells can be revealed and early precaution can be taken to prevent further proliferation of cancerous cells. In this paper, we propose an innovative hybridised gene identification framework based on genetic algorithms and neural networks to identify marker genes for leukaemia disease. Our approach confirms that high classification accuracy does not ensure the optimal set of genes have been identified and our model delivers a more promising set of genes even with a lower classification accurac

Perceptual underwater image enhancement with deep learning and physical priors

Underwater image enhancement, as a pre-processing step to support the following object detection task, has drawn considerable attention in the field of underwater navigation and ocean exploration. However, most of the existing underwater image enhancement strategies tend to consider enhancement and detection as two fully independent modules with no interaction, and the practice of separate optimisation does not always help the following object detection task. In this paper, we propose two perceptual enhancement models, each of which uses a deep enhancement model with a detection perceptor. The detection perceptor provides feedback information in the form of gradients to guide the enhancement model to generate patch level visually pleasing or detection favourable images. In addition, due to the lack of training data, a hybrid underwater image synthesis model, which fuses physical priors and data-driven cues, is proposed to synthesise training data and generalise our enhancement model for real-world underwater images. Experimental results show the superiority of our proposed method over several state-of-the-art methods on both real-world and synthetic underwater datasets

MECHANICAL PERFORMANCE OF PLLA STENT

Stent implantation is widely used to treat blocked lumen. Stents were meshed structure made of polymers and metal alloys, including stainless steel, cobalt chrome and nitinol [1]. Clinical studies had demonstrated that stents helped to scaffold the diseased lesion up to one year when tissue adapted to the stented environment [2]. However, the permanently implanted stents inside artery were associated with complications such as stent fracture, tissue inflammation, in-stent restenosis and thrombosis [3]. Currently, biodegradable stents are attracting more attention due to its potential long-term efficacy in treating blocked lumens. The detailed characterizations of biodegradable stents are essential for the desired clinical outcomes. In this work, the mechanical performance of Absorb GTI™ Bioresorbable stent made of PLLA (Poly-L-Lactide Acid) was studied using finite element method (FEM). Both the stent crimping and deployment were quantified towards the optimization of its scaffolding capacity in a limited time

Stabilizing forces acting on ZnO polar surfaces: STM, LEED, and DFT

published_or_final_versio

Meta-Regression on the Heterogenous Factors Contributing to the Prevalence of Mental Health Symptoms During the COVID-19 Crisis Among Healthcare Workers.

Objective: This paper used meta-regression to analyze the heterogenous factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) in China under the COVID-19 crisis. Method: We systematically searched PubMed, Embase, Web of Science, and Medrxiv and pooled data using random-effects meta-analyses to estimate the prevalence rates, and ran meta-regression to tease out the key sources of the heterogeneity. Results: The meta-regression results uncovered several predictors of the heterogeneity in prevalence rates among published studies, including severity (e.g., above severe vs. above moderate, p < 0.01; above moderate vs. above mild, p < 0.01), type of mental symptoms (PTSD vs. anxiety, p = 0.04), population (frontline vs. general HCWs, p < 0.01), sampling location (Wuhan vs. Non-Wuhan, p = 0.04), and study quality (p = 0.04). Conclusion: The meta-regression findings provide evidence on the factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) to guide future research and evidence-based medicine in several specific directions. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=220592, identifier: CRD42020220592

Hierarchical Liouville-space approach for accurate and universal characterization of quantum impurity systems

A hierarchical equations of motion (HEOM) based numerical approach is developed for accurate and efficient evaluation of dynamical observables of strongly correlated quantum impurity systems. This approach is capable of describing quantitatively Kondo resonance and Fermi liquid characteristics, achieving the accuracy of latest high-level numerical renormalization group approach, as demonstrated on single-impurity Anderson model systems. Its application to a two-impurity Anderson model results in differential conductance versus external bias, which correctly reproduces the continuous transition from Kondo states of individual impurity to singlet spin-states formed between two impurities. The outstanding performance on characterizing both equilibrium and nonequilibrium properties of quantum impurity systems makes the HEOM approach potentially useful for addressing strongly correlated lattice systems in the frame work of dynamical mean field theory.Comment: 5 pages, 4 figures, to appear in PR