A Novel Adaptive LBP-Based Descriptor for Color Image Retrieval

In this paper, we present two approaches to extract discriminative features for color image retrieval. The proposed local texture descriptors, based on Radial Mean Local Binary Pattern (RMLBP), are called Color RMCLBP (CRMCLBP) and Prototype Data Model (PDM). RMLBP is a robust to noise descriptor which has been proposed to extract texture features of gray scale images for texture classification. For the first descriptor, the Radial Mean Completed Local Binary Pattern is applied to channels of the color space, independently. Then, the final descriptor is achieved by concatenating the histogram of the CRMCLBP_S/M/C component of each channel. Moreover, to enhance the performance of the proposed method, the Particle Swarm Optimization (PSO) algorithm is used for feature weighting. The second proposed descriptor, PDM, uses the three outputs of CRMCLBP (CRMCLBP_S, CRMCLBP_M, CRMCLBP_C) as discriminative features for each pixel of a color image. Then, a set of representative feature vectors are selected from each image by applying k-means clustering algorithm. This set of selected prototypes are compared by means of a new similarity measure to find the most relevant images. Finally, the weighted versions of PDM is constructed using PSO algorithm. Our proposed methods are tested on Wang, Corel-5k, Corel-10k and Holidays datasets. The results show that our proposed methods makes an admissible tradeoff between speed and retrieval accuracy. The first descriptor enhances the state-of-the-art color texture descriptors in both aspects. The second one is a very fast retrieval algorithm which extracts discriminative features

A Structural Based Feature Extraction for Detecting the Relation of Hidden Substructures in Coral Reef Images

In this paper, we present an efficient approach to extract local structural color texture features for classifying coral reef images. Two local texture descriptors are derived from this approach. The first one, based on Median Robust Extended Local Binary Pattern (MRELBP), is called Color MRELBP (CMRELBP). CMRELBP is very accurate and can capture the structural information from color texture images. To reduce the dimensionality of the feature vector, the second descriptor, co-occurrence CMRELBP (CCMRELBP) is introduced. It is constructed by applying the Integrative Co-occurrence Matrix (ICM) on the Color MRELBP images. This way we can detect and extract the relative relations between structural texture patterns. Moreover, we propose a multiscale LBP based approach with these two schemes to capture microstructure and macrostructure texture information. The experimental results on coral reef (EILAT, EILAT2, RSMAS, and MLC) and four well-known texture datasets (OUTEX, KTH-TIPS, CURET, and UIUCTEX) show that the proposed scheme is quite effective in designing an accurate, robust to noise, rotation and illumination invariant texture classification system. Moreover, it makes an admissible tradeoff between accuracy and number of features

An eye-tracking study on how the popularity and gender of the endorsers affected the audience’s attention on the advertisement

Funding Information: This work was funded by the Ferdowsi University of Mashhad. Publisher Copyright: © 2023, The Author(s).Nowadays, advertising is regarded as one of the vital elements of marketing tools’ promotional strategies. Therefore, advertising is very important to businesses’ marketing strategies and policies. In light of this, the purpose of the current study was to ascertain the influence of endorsers’ gender and the level of attention given to various aspects of advertising. A quasi-experimental study was used for the current investigation. All students at Mashhad’s Ferdowsi University made up the study’s statistical population. 80 students were chosen as the research sample out of the entire student body. Eye motions were captured using an eye tracking gadget. According to research findings, the number and length of fixes on advertisement items were significantly influenced by the popularity of their endorsers. However, there was no statistically significant difference between the genders in terms of the quantity and length of fixations. These conclusions suggest that the popularity of the endorser is a key factor in commercials, but that the endorser’s gender has little effect.publishersversionpublishe

Mechanisms of tumor cell resistance to the current targeted-therapy agents

Abstract Resistance to chemotherapy agents is a major challenge infront of cancer patient treatment and researchers. It is known that several factors, such as multidrug resistance proteins and ATP-binding cassette families, are cell membrane transporters that can efflux several substrates such as chemotherapy agents from the cell cytoplasm. To reduce the adverse effects of chemotherapy agents, various targeted-based cancer therapy (TBCT) agents have been developed. TBCT has revolutionized cancer treatment, and several agents have shown more specific effects on tumor cells than chemotherapies. Small molecule inhibitors and monoclonal antibodies are specific agents that mostly target tumor cells but have low side effects on normal cells. Although these agents have been very useful for cancer treatment, however, the presence of natural and acquired resistance has blunted the advantages of targeted therapies. Therefore, development of new options might be necessary. A better understanding of tumor cell resistance mechanisms to current treatment agents may provide an appropriate platform for developing and improving new treatment modalities. Therefore, in this review, different mechanisms of tumor cell resistance to chemotherapy drugs and current targeted therapies have been described

Lifestyle and occupational risks assessment of bladder cancer using machine learning‐based prediction models

Background: Bladder cancer, one of the most prevalent cancers globally, can be regarded as considerable morbidity and mortality for patients. The bladder is an organ that comes in constant exposure to the environment and other risk factors such as inflammation. Aims: In the current study, we used machine learning (ML) methods and developed risk prediction models for bladder cancer. Methods: This population‐based case–control study is focused on 692 cases of bladder cancer and 692 healthy people. The ML, including Neural Network (NN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Gradient Boosting (GB), and Logistic Regression (LR), were applied, and the model performance was evaluated. Results: The RF (AUC = .86, precision = 79%) had the best performance, and the RT (AUC = .78, precision = 73%) was in the next rank. Based on variable importance analysis in RF, recurrent infection, bladder stone history, neurogenic bladder, smoking and opium use, chronic renal failure, spinal cord paralysis, analgesic, family history of bladder cancer, diabetic mellitus, low dietary intake of fruit and vegetable, high dietary intake of ham, sausage, can and pickles were respectively the most important factors, which effect on the probability of bladder cancer. Conclusion: Machine learning approaches can predict the probability of bladder cancer according to medical history, occupational risk factors, and dietary and demographical characteristics

The worldwide prevalence of intestinal helminthic parasites among food handlers: A systematic review and meta-analysis

Food handlers have a major role in transmission of food-borne parasitic infections including intestinal helminths. The objective of the present study was to assess the global prevalence of intestinal helminthic parasites among food handlers. Multiple databases (PubMed, Scopus, ProQuest, Web of Science, Science Direct and Google Scholar) were searched for literature published from 1990 to 2022. Pooled prevalence was estimated using the meta-package in R (version 3.6.1). One hundred twenty seven articles, including 220,705 individuals, were considered in this study. The global pooled prevalence (95% confidence interval) was 0.115% (0.091% - 0.141%). The highest pooled prevalence was reported from Africa (0.160%, 0.124%–0.210%). The most prevalent helminth was Ascaris lumbricoides (0.062%, 0.047%–0.079%). Moreover, among different countries, Ghana had the highest pooled prevalence (0.496%, 0–1.000%). This study revealed a high prevalence of intestinal helminths among food handlers. Routine parasitological investigation, food safety and personal sanitation training are recommended to prevent intestinal helminths transmitted by food handlers

Ibrutinib-A double-edge sword in cancer and autoimmune disorders

Targeted therapies have appeared as new treatment options for several disease types, including cancer and autoimmune disorders. Of several targets, tyrosine kinases (TKs) are among the most promising. Overexpression of TKs provides a target for novel therapeutic agents, including small molecule inhibitors of tyrosine kinases (TKI). Ibrutinib (PCI-32765) is a TKI of Bruton’s tyrosine kinase (Btk), a key kinase of the B-cell receptor signaling pathway that plays a significant role in the proliferation, differentiation and survival of B cells. In addition to inhibitory effects, recent studies have shown that ibrutinib has multiple immunomodulatory effects. It binds covalently to IL-2 inducible tyrosine kinase (Itk) in T lymphocytes and suppresses the survival of T-helper (Th) 2 cells. This changes the balance of Th1/Th2 cells toward Th1 subset, which are the main immune cells targeting tumor cells. The dual activity of ibrutinib has paid a great attention and several studies are evaluating the anti-tumor and immunomodulatory effects in cancer, autoimmune disorders and infectious diseases. In this article we review the inhibitory and immunomodulatory effects of ibrutinib in B-cell malignancies, autoimmune diseases and infections, as well as the communication between the Ror1 receptor tyrosine kinase and BCR and effects of ibrutinib on this crosstalk.CLL Global Research FoundationManuscrip

Relation of Type 2 Diabetes Mellitus with Gender, Education, and Marital Status in an Iranian Urban Population

Background: Type 2 diabetes mellitus is one of the most important cardiovascular risk factors. Objectives: This study was performed to assess the relationship of diabetes with gender, education, and marital status in an Iranian urban population. Methods: A total of 892 men and women aged 30-85 were recruited using a cluster-stratified sampling method from an urban population. Using a questionnaire, demographical data including gender, education, and marital status were collected. A blood sample after fasting for at least eight hours was collected from each subject. Associations of type 2 diabetes mellitus and studied variables were tested for significance. Results: The prevalence of diabetes mellitus was 11.6%; 11.1% in men and 12.1% in women with no significant difference between them. Diabetes mellitus was most prevalent in the oldest age (age more than 60 years, 22.9%) and low education groups (17.9%, P < 0.001). Marital status was not significantly related to diabetes mellitus (P= 0.37). Conclusion: The prevalence of diabetes mellitus is related to education within the Iranian population. Thus preventive strategies should be based on the affective factors

Effect of Neurofeedback Interactions and Mental Imagery on the Elderly’s Balance

Objectives Balance maintenance is one of the indices of determining independence in older people. Identifying other factors that have considerable impact on the independence of older peoples is an interesting research topic. The present study aims at determining the effect of Neurofeedback and Mental Motor Imagery practices on balance in the elderly. Methods & Materials The population of this study consisted of elderly people of Mashhad, a city in northeast Iran. A total of 24 elderly people&nbsp; with age ranging from 60-82 years old volunteered to participate in the study and were randomly assigned to three groups (with eight participants in each group). The participants of experimental groups were involved in the special training (neurofeedback training and mental imagery practice) for eight weeks (with three sessions each week)while the control group were not involved in any practice. Stork Balance Stand Test and Timed Up and Go (TUG) tests were used to assess the static and dynamic balance of participants respectively, before and after the training sessions. The Shapiro&ndash;Wilk test of normality was used to check normality of data. Additionally,&nbsp; Analysis of Covariance (ANCOVA) was used to determine the effect of training with pre-test scores used as covariate. Statistical analysis was performed using SPSS Statistics 21 at a=0.05. Results The results of Analysis of Covariance revealed that there was a significant effect of neurofeedback and mental imagery on the static and dynamic balance of elderly people (P<0.05). Furthermore, neurofeedback had superior effect in both of the balance conditions (P<0.05). Conclusion The study recommends neurofeedback and mental motor imagery practices&nbsp; to prevent balance loosing and improving balance ability in elderly people