This article presents an improved version of Fractional Order Darwinian PSO (IFODPSO) for segmenting 3D histogram-based color images at multiple levels of Berkley Segmentation Dataset (BSDS500). The success of convergence and accuracy rate of FODPSO algorithm depends on the value of fractional coefficient. This concept may provide drawback to the algorithm specially for multilevel problems of large dataset. So, to overcome the full dependency on fractional coefficient, delta potential model of quantum mechanics has been incorporated with FODPSO for updating the particle’s present as well as global position by destroying the worst particles (solutions), formulated using the introduction of the context parameter. Multi-level Massi Entropy (MME), of current interest, has been chosen here as the objective function for finding the threshold values in combination with IFODPSO. Further, the small segmented regions have been removed or merged into bigger regions for showing the better discrimination between different segmented objects. The effectiveness of the proposed MME-IFODPSO algorithm has been extensively investigated in terms of statistically and qualitatively in terms of the fidelity parameters with the state-of-art approaches and it has been found that the proposed method has improved at least 2–5% to the conventional methods in terms of accuracy.

IFODPSO-based multi-level image segmentation scheme aided with Masi entropy

MicroRNAs (miRNAs) are small non-coding RNAs regulating the expression of several target genes. miRNAs play a significant role in cancer biology, as they can downregulate their corresponding target genes by impeding the translation of mRNA (at the mRNA level) as well as degrading mRNAs by binding to the 3'-untranslated (UTR) regions (at the protein level). miRNAs may be employed as cancer biomarkers. Therefore, miRNAs are widely investigated for early detection of cancers which can lead to improved survival rates and quality of life. This is particularly important in the case of gastrointestinal cancers, where early detection of the disease could substantially impact patients' survival. MicroRNA-21 (miR-21 or miRNA-21) is one of the most frequently researched miRNAs, where it is involved in the pathophysiology of cancer and the downregulation of several tumor suppressor genes. In gastrointestinal cancers, miR-21 regulates phosphatase and tensin homolog (PTEN), programmed cell death 4 (PDCD4), mothers against decapentaplegic homolog 7 (SMAD7), phosphatidylinositol 3-kinase /protein kinase B (PI3K/AKT), matrix metalloproteinases (MMPs), β-catenin, tropomyosin 1, maspin, and ras homolog gene family member B (RHOB). In this review, we investigate the functions of miR-21 in pathogenesis and its applications as a diagnostic and prognostic cancer biomarker in four different gastrointestinal cancers, including colorectal cancer (CRC), pancreatic cancer (PC), gastric cancer (GC), and esophageal cancer (EC). 

The role of microRNA-21 (miR-21) in pathogenesis, diagnosis, and prognosis of gastrointestinal cancers: A review.

Image segmentation is an important part of image processing. For the disadvantages of image segmentation under multiple thresholds such as long time and poor quality, an improved cuckoo search (ICS) is proposed for multithreshold image segmentation strategy. Firstly, the image segmentation model based on the maximum entropy threshold is described, and secondly, the cuckoo algorithm is improved by using chaotic initialization population to improve the diversity of solutions, optimizing the step size factor to improve the possibility of obtaining the optimal solution, and using probability to reduce the complexity of the algorithm; finally, the maximum entropy threshold function in image segmentation is used as the individual fitness function of the cuckoo search algorithm for solving. The simulation experiments show that the algorithm has a good segmentation effect under four different thresholding conditions.

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An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

Satellite images help in monitoring change detection as they are the big repository of information. An imperative task from the prospects of land development monitoring, disaster management, resource management, and environment evaluation is change detection. For change detection, segmentation of an image is being performed for locating the areas of interest. Nature-inspired particle swarm optimization is a metaheuristic algorithm that is simple, robust, and makes a fewer number of assumptions for the problem considered. This paper implements a particle swarm optimization (PSO) algorithm in MATLAB environment as edge detection segmentation technique for satellite images, which are being acquired from Google Earth. For qualitative analysis, the results are compared with the conventional edge detector operators such as Sobel, Canny, and Prewitt with the help of entropy values. It has been observed that PSO outperforms the conventional edge detection image segmentation methods, thereby giving better edges and clarity in images for change detection.

Change Detection in Remote-Sensed Data by Particle Swarm Optimized Edge Detection Image Segmentation Technique

Prostate cancer (PCa) is known as one of the most prevalent malignancies globally and is not yet curable owing to its progressive nature. It has been well documented that Genetic and epigenetic alterations maintain mandatory roles in PCa development. Apoptosis, a form of programmed cell death, has been shown to be involved in a number of physiological processes. Apoptosis disruption is considered as one of the main mechanism involved in lots of pathological conditions, especially malignancy. There is ample of evidence in support of the fact that microRNAs (miRNAs) have crucial roles in several cellular biological processes, including apoptosis. Escaping from apoptosis is a common event in malignancy progression. Emerging evidence revealed miRNAs capabilities to act as apoptotic or anti-apoptotic factors by altering the expression levels of tumor inhibitor or oncogene genes. In the present narrative review, we described in detail how apoptosis dysfunction could be involved in PCa processes and additionally, the mechanisms behind miRNAs affect the apoptosis pathways in PCa. Identifying the mechanisms behind the effects of miRNAs and their targets on apoptosis can provide scientists new targets for PCa treatment. 

Prostate cancer and microRNAs: New insights into apoptosis.

Gastric cancer is one of the most common malignant tumours worldwide. Genetic and epigenetic alterations are key factors in gastric carcinogenesis and drug resistance to chemotherapy. Competing endogenous RNA (ceRNA) regulation models have defined circRNA/lncRNA as miRNA sponges that indirectly regulate miRNA downstream target genes. The ceRNA regulatory network is related to the malignant biological behaviour of gastric cancer. The circRNA/lncRNA-miRNA-mRNA axis may be a marker for the early diagnosis and prognosis of gastric cancer and a potential therapeutic target for gastric cancer. Exosomal ncRNAs play an important role in gastric cancer and are expected to be ideal biomarkers for the diagnosis, prognosis, and treatment of gastric cancer. This review summarizes the specific ceRNA regulatory network (circRNA/lncRNA-miRNA-mRNA) discovered in gastric cancer in recent years, which may provide new ideas or strategies for early clinical diagnosis, further development, and application. 

Research progress on the circRNA/lncRNA-miRNA-mRNA axis in gastric cancer.

A method for image segmentation based on improved hybrid particle swarm optimisation (PSO) is proposed. In view of the shortcoming that the traditional PSO algorithm is easy to fall into local opti...

Improved hybrid particle swarm optimisation for image segmentation

Calreticulin as a marker and therapeutic target for cancer.

There are many raw materials for the production of rice wine, rice flour and glutinous rice products, It is one of the problems to be solved at present to select the appropriate proportion of raw materials for the production of these three products. In order to solve this problem, this paper finds out the specialized raw material index standard of the above three products through data analysis method and multi-objective optimization under the condition of meeting the various characteristic demands of the three products, so as to guide the production of enterprises. Based on the index data of three raw materials and products obtained from experimental tests, firstly, the data was standardized, and the noise of sample data was eliminated through residual analysis. Secondly, the optimal prediction equation was found by comparing various prediction methods, so as to make the formulation of raw materials more effective. Finally, a multi-objective optimization model is established. According to the contribution rate of the raw material index of the three products, the information entropy method is used to calculate the weight of the raw material index corresponding to the three products, so that the final solution of the specific raw material index range is more accurate, effective and reasonable.

Jiangrong Liu

Papers

Research progress on the circRNA/lncRNA-miRNA-mRNA axis in gastric cancer.

Improved hybrid particle swarm optimisation for image segmentation

Calreticulin as a marker and therapeutic target for cancer.

Research and Application of the Standard Formulation Technology for Three Kinds of Specialized Raw Materials

Energy metabolism: a new target for gastric cancer treatment.