Independent sets of some graphs associated to commutative rings

Let $G=(V,E)$ be a simple graph. A set $S\subseteq V$ is independent set of $G$, if no two vertices of $S$ are adjacent. The independence number $\alpha(G)$ is the size of a maximum independent set in the graph. %An independent set with cardinality Let $R$ be a commutative ring with nonzero identity and $I$ an ideal of $R$. The zero-divisor graph of $R$, denoted by $\Gamma(R)$, is an undirected graph whose vertices are the nonzero zero-divisors of $R$ and two distinct vertices $x$ and $y$ are adjacent if and only if $xy = 0$. Also the ideal-based zero-divisor graph of $R$, denoted by $\Gamma_I(R)$, is the graph which vertices are the set {x\in R\backslash I | xy\in I \quad for some \quad y\in R\backslash I\} and two distinct vertices $x$ and $y$ are adjacent if and only if $xy \in I$. In this paper we study the independent sets and the independence number of $\Gamma(R)$ and $\Gamma_I(R)$.Comment: 27 pages. 22 figure

Motion compensation for image compression: pel-recursive motion estimation algorithm

In motion pictures there is a certain amount of redundancy between consecutive frames. These redundancies can be exploited by using interframe prediction techniques. To further enhance the efficiency of interframe prediction, motion estimation and compensation, various motion compensation techniques can be used. There are two distinct techniques for motion estimation block matching and pel-recursive block matching has been widely used as it produces a better signal-to-noise ratio or a lower bit rate for transmission than the pel-recursive method. In this thesis, various pel-recursive motion estimation techniques such as steepest descent gradient algorithm have been considered and simulated. [Continues.

Genetic Programming Method in Urban Water Consumption Prediction (Case Study: Najafabad City)

Improving the lifestyle of city residents is conditional on benefiting from high-quality urban infrastructure to satisfy daily demands. The urban water supply network is one of the most basic urban infrastructures, and its optimal design and service are essential during the planning period. Therefore, it is important to determine the actual amount of consumption and predict it for the future. For this purpose, in this research, a method based on artificial intelligence, i.e., genetic programming (GP), as well as Pearson's correlation coefficient data mining method, is proposed. The data mining method is applied here for the database, including daily data on temperature, precipitation, humidity, and the amount of daily water produced in Najafabad city (presenting the total water consumption) from the beginning of 2014 to the end of 2018, and the best set of input data vectors is selected. The selected data are used as input data vectors for the proposed. The obtained results are compared with the results of models based on artificial neural network (ANN). To investigate the performance of the models, R², RMSE, and NSE statistical indices are calculated. A comparison of the results indicates the acceptable performance of the proposed models based on the GP. In other words, the values of RMSE, NSE, R², and MAPE statistical indices for training data in the best GP model are equal to 3262.59 MCM, 0.80, 0.80, and 5.38%, respectively, and for test data equal to 3507.68 MCM, 0.78, 0.78, and 6.67%

Prevention, inhibition, and degradation effects of melittin alone and in combination with vancomycin and rifampin against strong biofilm producer strains of methicillin-resistant Staphylococcus epidermidis

Methicillin-resistant Staphylococcus epidermidis (MRSE) bacteria are being recognized as true pathogens as they are able to resist methicillin and commonly form biofilms. Recent studies have shown that antimicrobial peptides (AMPs) are promising agents against biofilm-associated bacterial infections. In this study, we aimed to explore the antibiofilm activity of melittin, either alone or in combination with vancomycin and rifampin, against biofilm-producing MRSE strains. Minimum biofilm preventive concentration (MBPC), minimum biofilm inhibition concentration (MBIC), and minimum biofilm eradication concentration (MBEC), as well as fractional biofilm preventive-, inhibitory-, and eradication concentrations (FBPCi, FBICi, and FBECi), were determined for the antimicrobial agents tested. Cytotoxicity and hemolytic activity of melittin at its synergistic concentration were examined on human embryonic kidney cells (HEK-293) and Red Blood Cells (RBCs), respectively. The effect of melittin on the downregulation of biofilm-associated genes was explored using Real-Time PCR. MBPC, MBIC, and MBEC values for melittin were in the range of 0.625–20, 0.625–20, and 10–40 μg/μL, respectively. Melittin showed high synergy (FBPCi, FBICi and FBECi < 0.5). The synergism resulted in a 64–512-fold, 2–16 and 2–8-fold reduction in melittin, rifampicin and vancomycin concentrations, respectively. The synergistic melittin concentration found to be effective did not manifest either cytotoxicity on HEK-293 or hemolytic activity on RBCs. Results showed that melittin downregulated the expression of biofilm-associated icaA, aap, and psm genes in all isolates tested, ranging from 0.04-folds to 2.11-folds for icaA and from 0.05 to 3.76-folds for aap and psm. The preventive and therapeutic indexes of melittin were improved 8-fold when combined with vancomycin and rifampin. Based on these findings, the combination of melittin with conventional antibiotics could be proposed for treating or preventing biofilm-associated MRSE infections

Bacterial biofilm in colorectal cancer: What is the real mechanism of action?

Human colorectal cancer is the third most common cancer around the world. Colorectal cancer has various risk factors, but current works have bolded a significant activity for the microbiota of the human colon in the development of this disease. Bacterial biofilm has been mediated to non-malignant pathologies like inflammatory bowel disease but has not been fully documented in the setting of colorectal cancer. The investigation has currently found that bacterial biofilm is mediated to colon cancer in the human and linked to the location of human cancer, with almost all right-sided adenomas of colon cancers possessing bacterial biofilm, whilst left-sided cancer is rarely biofilm positive. The profound comprehension of the changes in colorectal cancer can provide interesting novel concepts for anticancer treatments. In this review, we will summarize and examine the new knowledge about the links between colorectal cancer and bacterial biofilm. © 202

Identification and determination of the urinary metabolite of iodotyrosine in vivo

Background: Congenital hypothyroidism screening traditionally relies on detecting elevated thyroid-stimulating hormone levels, yet this approach may not detect a specific type of congenital hypothyroidism caused by iodotyrosine dehalogenase-1 (Dehal1) deficiency. The deficiency of this enzyme prevents the deiodination of mono-iodotyrosine (MIT) and di-iodotyrosine (DIT) in the process of iodine recycling. This underscores the potential use of iodotyrosine or its metabolites as non-invasive urinary biomarkers for early diagnosis of congenital hypothyroidism. However, the urinary metabolites of MIT/DIT have not yet been discovered. Thus, this study aimed to identify the urinary metabolites of iodotyrosine in experimental models. Method: Gas chromatography mass spectrometry was used to identify the urinary metabolites of iodotyrosine following intraperitoneal injection of MIT in rats. An isotope dilution mass spectrometric assay was developed for assessment of identified metabolites. Urine samples from Dehal1 knockout mice were used to confirm the results. Results: We identified novel iodotyrosine metabolites, 3-iodo-4-hydroxyphenylacetic acid (IHPA), and 3,5-diiodo4-hydroxyphenylacetic acid (Di-IHPA) as the primary urinary metabolites of MIT and DIT respectively. The concentrations of urinary IHPA and Di-IHPA were significantly higher in Dehal1 knockout mice. Conclusion: Our findings suggest that IHPA is detected in larger quantities and may hold more clinical significance than previously identified biomarkers like MIT and DIT, making it a promising candidate for diagnosing congenital hypothyroidism or other conditions associated with iodine recycling inhibition