Clinical Significance of Elevated S100A8 Expression in Breast Cancer Patients

Breast cancer is the leading cause of female cancer-related death; however, novel biomarkers for predicting cancer recurrence still need to be explored. Aberrant expression of S100A8 has been reported to be related to tumor progression in various cancer types. This study aims to evaluate the clinical significance of S100A8 expression in breast cancer patients. In this study, data from 140 breast cancer patients were retrospectively collected to examine the association between S100A8 expression and clinical prognosis. Increased S100A8 expression was detected in breast cancer patients with relapse. The patients with increased S100A8 levels had significantly shorter disease-free survival (DFS) and overall survival (OS). In a multivariate survival analysis, a high histological grade and an elevated S100A8 level were independent factors associated with poor DFS and OS. Moreover, S100A8 expression was correlated with clinical subtype in breast cancer patients. The results showed that ER-negative and triple-negative breast cancer (TNBC) patients had significantly higher expression of S100A8 than patients with other subtypes. In conclusion, this study identified S100A8 as a potential biomarker for relapse in breast cancer patients

A Vector Grouping Learning Brain Storm Optimization Algorithm for Global Optimization Problems

The original brain storm optimization (BSO) method does not rationally compromise global exploration and local exploitation capability, which results in the premature convergence when solving complicated optimization problems like the shifted or shifted rotated functions. To address this problem, the paper develops a vector grouping learning BSO (VGLBSO) method. In VGLBSO, the individuals’ creation based on vector grouping learning (IC-VGL) scheme is first developed to improve the population diversity and compromise the global exploration and local exploitation capability. Moreover, a hybrid individuals’ update (H-IU) scheme is established by reasonably combing two different individuals’ update schemes, which further compromises the global exploration and local exploitation capability. Finally, the random grouping (RG) scheme, instead of K-means grouping is allowed to shrink the computational cost and maintain the diversity of the information exchange between different individuals. Twenty-eight popular benchmark functions are used to compare VGLBSO with 12 BSO and nine swarm intelligence methods. Experimental results present that VGLBSO achieves the best overall performance including the global search ability, convergence speed, and scalability amongst all the compared algorithms

A highly sensitive silicon nanowire array sensor for joint detection of tumor markers CEA and AFP

Liver cancer is one of the malignant tumors with the highest fatality rate and increasing incidence, which has no effective treatment plan. Early diagnosis and early treatment of liver cancer play a vital role in prolonging the survival period of patients and improving the cure rate. Carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP) are two crucial tumor markers for liver cancer diagnosis. In this work, we firstly proposed a wafer-level, highly controlled silicon nanowire (SiNW) field-effect transistor (FET) joint detection sensor for highly sensitive and selective detection of CEA and AFP. The SiNWs-FET joint detection sensor possesses 4 sensing regions. Each sensing region consists of 120 SiNWs arranged in a 15 × 8 array. The SiNW sensor was developed by using a wafer-level and highly controllable top-down manufacturing technology to achieve the repeatability and controllability of device preparation. To identify and detect CEA/AFP, we modified the corresponding CEA antibodies/AFP antibodies to the sensing region surface after a series of surface modification processes, including O2 plasma treatment, soaking in 3-aminopropyltriethoxysilane (APTES) solution, and soaking in glutaraldehyde (GA) solution. The experimental results showed that the SiNW array sensor has superior sensitivity with a real-time ultralow detection limit of 0.1 fg ml−1 (AFP in 0.1× PBS) and 1 fg ml−1 (CEA in 0.1× PBS). Also, the logarithms of the concentration of CEA (from 1 fg ml−1 to 10 pg ml−1) and AFP (from 0.1 fg ml−1 to 100 pg ml−1) achieved conspicuously linear relationships with normalized current changes. The R2 of AFP in 0.1× PBS and R2 of CEA in 0.1× PBS were 0.99885 and 0.99677, respectively. Furthermore, the sensor could distinguish CEA/AFP from interferents at high concentrations. Importantly, even in serum samples, our sensor could successfully detect CEA/AFP. This demonstrates the promising clinical development of our sensor

A supersensitive silicon nanowire array biosensor for quantitating tumor marker ctDNA

Cancer has become one of the major diseases threatening human health and life. Circulating tumor DNA (ctDNA) testing, as a practical liquid biopsy technique, is a promising method for cancer diagnosis, targeted therapy and prognosis. Here, for the first time, a field effect transistor (FET) biosensor based on uniformly sized high-response silicon nanowire (SiNW) array was studied for real-time, label-free, super-sensitive detection of PIK3CA E542K ctDNA. High-response 120-SiNWs array was fabricated on a (111) silicon-on-insulator (SOI) by the complementary metal oxide semiconductor (CMOS)-compatible microfabrication technology. To detecting ctDNA, we modified the DNA probe on the SiNWs array through silanization. The experimental results demonstrated that the as-fabricated biosensor had significant superiority in ctDNA detection, which achieved ultralow detection limit of 10 aM and had a good linearity under the ctDNA concentration range from 0.1 fM to 100 pM. This biosensor can recognize complementary target ctDNA from one/two/full-base mismatched DNA with high selectivity. Furthermore, the fabricated SiNW-array FET biosensor successfully detected target ctDNA in human serum samples, indicating a good potential in clinical applications in the future

Constrained multi-objective particle swarm optimization with application in power generation

This thesis is devoted to the study of metaheuristic optimization algorithms and their application in power generation. The study focuses on constrained multi-objective optimization using Particle Swarm Optimization algorithm. A multi-objective constraint-handling method incorporating a dynamic neighbourhood PSO algorithm is proposed for tackling single objective constrained optimization problems. The benchmark simulation results demonstrate the proposed approach is effective and efficient in finding the consistent quality solutions. Compared with the recent research results, the proposed approach is able to provide better or similar good results in most benchmark functions. The proposed performance-based dynamic neighbourhood topology has proved to be able to help make convergence faster than the static neighbourhood topology. The thesis also presents a modified PSO algorithm for solving multi-objective constrained optimization problems. Based on the constraint dominance concept, the proposed approach defines two sets of rules for determining the cognitive and social components of the PSO algorithm. Simulation results for the four numerical optimization problems demonstrate the proposed approach is effective. The proposed approach has a number of advantages such as being applicable to any number of objective functions and computationally inexpensive. As applications, three engineering design optimization problems and the power generation loading optimization problem are investigated. The simulation results for the engineering design optimization problems and the power generation loading optimization problem reveal the capability, effectiveness and efficiency of the proposed approaches. The methodology can be readily applicable to a broad range of applications

A novel method of curve fitting based on optimized extreme learning machine

Li, DL ORCiD: 0000-0002-2220-2389; Li, M ORCiD: 0000-0002-6019-1035In this article, we present a new method based on extreme learning machine (ELM) algorithm for solving nonlinear curve fitting problems. Curve fitting is a computational problem in which we seek an underlying target function with a set of data points given. We proposed that the unknown target function is realized by an ELM with introducing an additional linear neuron to correct the localized behavior caused by Gaussian type neurons. The number of hidden layer neurons of ELM is a crucial factor to achieve a good performance. An evolutionary computation algorithm–particle swarm optimization (PSO) technique is applied to determine the optimal number of hidden nodes. Several numerical experiments with benchmark datasets, simulated spectral data and measured data from high energy physics experiments have been conducted to test the proposed method. Accurate fitting has been accomplished for various tough curve fitting tasks. Comparing with the results of other methods, the proposed method outperforms the traditional numerical-based technique. This work clearly demonstrates that the classical numerical analysis problem-curve fitting can be satisfactorily resolved via the approach of artificial intelligence. © 2020, © 2020 Taylor & Francis

A novel method of curve fitting based on optimized extreme learning machine

In this article, we present a new method based on extreme learning machine (ELM) algorithm for solving nonlinear curve fitting problems. Curve fitting is a computational problem in which we seek an underlying target function with a set of data points given. We proposed that the unknown target function is realized by an ELM with introducing an additional linear neuron to correct the localized behavior caused by Gaussian type neurons. The number of hidden layer neurons of ELM is a crucial factor to achieve a good performance. An evolutionary computation algorithm–particle swarm optimization (PSO) technique is applied to determine the optimal number of hidden nodes. Several numerical experiments with benchmark datasets, simulated spectral data and measured data from high energy physics experiments have been conducted to test the proposed method. Accurate fitting has been accomplished for various tough curve fitting tasks. Comparing with the results of other methods, the proposed method outperforms the traditional numerical-based technique. This work clearly demonstrates that the classical numerical analysis problem-curve fitting can be satisfactorily resolved via the approach of artificial intelligence. © 2020, © 2020 Taylor & Francis

Considerations for online course delivery from educators' perspective

With the rapid development of information technology, and market demands, distance education is becoming increasingly popular for both students and educators because of its flexibility and convenience. The Internet plays a key role for delivering online courses. Operation of online courses involves many players such as administrators, software facilitators, students and instructors. However, what should an academic educator consider when offering an online course? In what forms can the communication between instructors and students most effectively take place? What kinds of assessment are better suited for online course? Based on the authors’ experiences with online course delivery, this paper explores key issues regarding the above questions from an educator’s point of view. It briefly points out the characteristics of online education. Considerations for online course delivery are particularly discussed. It describes what an educator should consider during the four stages known as planning, designing, developing and delivery for an online course. Suggestions are provided as to considerations for online course delivery