Intelligent agents are considered as significant means towards realizing the semantic web vision. On the Semantic Web, integrating ontologies and rules enables software agents to interoperate between them, however, this leads to a problem, that no studies have focused on effective distributed reasoning for integrating ontologies and rules in multiple knowledge-bases. The methods that have been presented for distributed reasoning not only get a lot of times and memory, but also do not lead to a complete and sound reasoning. In this paper, to solve this problem, we present a distributed reasoning system that deals with the representation of the knowledge-base of order sorted logic. This logic is able to describe the hierarchy of predicates and inheritance of expressions that there are in our natural language. To have a distributed reasoning, our proposed method uses the expansion of rigid and valid-non-rigid properties between knowledge-bases. Furthermore, with considering time and the situation of properties for reasoning, the non-rigid properties have not been ignored, in fact, in their valid time and situation, they are used. With this method, we achieve a complete reasoning and, moreover, the extracted knowledge is completely considered in the knowledge-bases and we have a distributed reasoning with high efficiency and sound without missing any information.

Background and Aim: Laboratories, on average, allocated for about 4 percent of Hospital's budget and are often considered the main focus of health care spending. There is a wide range of laboratory automation options available today that Designed to improve the quality and efficiency of laboratory tests. The aim of this study was to investigate The effect of Implementation of Automatic Stop Order program on the management of the request and the cost of frequent and costly tests in Shahid Rajaei Educational and medical center. Research Methods: This research is analytical and comparative. In terms of time, the work is cross-sectional-longitudinal. After of data collection of before and after of Implementation of Automatic Stop Order program, analysis was carried out with the SPSS 19&Excel softwares. Results: In order to establish the Auto Stop Order program to control the amount of requests and the cost of frequent tests, first the number of requests and the total cost of tests in Shahid Rajaei Medical Training Center were examined and all the laboratory services requested in the hospital, 27 laboratory services were selected to apply in the Auto Stop Order program. The total number of requests after the implementation of the Auto Stop Order program decreased by 11% and 8100 laboratory services. Also, after the implementation of the Auto Stop Order program on 27 costly tests, the cost of the tests was reduced by 16% and the amount was 445, 004, 725 Rials. Conclusion: Using the Auto Stop Order program can lead to rational prescribing of tests according to clinical guidelines, reducing the cost of patients' and resource control, and saving hospital laboratory costs. Therefore, managers and policy makers of the health system should create a suitable platform to use this program as a way to reduce costs in the laboratories of public hospitals in the country.

To investigate the response of corn to combined application of chemical fertilizers with rhizobacteria plant growth promoting, an experiment was conducted in 2017 at Research farm of Agricultural and Natural Resources Campus of Tehran University, Karaj, Iran, in a randomized complete block design with four replications. Four nutritional treatments including T1 (Control treatment without applying fertilizer), T2 (Just PGPRs), T3 (Use chemical fertilizers based on soil test) and T4 (T3 + PGPRs) were considered. According to the results, the highest total dry weight (3.9 kg/m2), crop growth rate (79.8 g.m-2.day-1), net assimilation rate (15.3 g.m-2.day-1) and grain yield (18.2 ton.ha-1) were observed in T4 treatment and T2 treatment produced the highest  leaf area index (5.3), leaf area duration (205.2) and specific leaf weight (78.5 g.m-2) . Also, the lowest value of all traits was observed in in T1 (control) treatment. The results showed that the presence of rhizobacteria plant growth promotioninduction in the corn nutrition program increased the growth and growth indices of the plant. Combined application of chemical fertilizers with rhizobacteria plant growth promoting resulted in the highest growth and final grain yield of corn.

Background: Breast cancer is a multifactorial potentially lethal disease that is triggered by genetic factors as well as numerous environmental factors. The present research aimed to examine the expression of Vascular endothelial growth factor messenger RNA (VEGF mRNA), cytokeratin-19 mRNA (CK19 mRNA), and vascular endothelial growth factor (VEGF) protein biomarker in the peripheral blood of patients with breast cancer.Methods: 40 patients with breast cancer were compared to 40 healthy individuals. The real-time reverse transcriptase polymerase chain reaction (real-time RT-PCR) method was used to determine the expressions of the CK19 mRNA and VEGF mRNA biomarkers in the peripheral blood samples of the healthy participants and the patients. The VEGF protein was also compared using the (ELISA) method.Findings: The positive VEGF mRNA biomarker was observed in 30 of the 40 patients with breast cancer; thus the sensitivity of this marker was 75%. In the healthy participants group, 6 of the 40 participants showed a positive VEGF mRNA biomarker expression. The CK19 mRNA marker was positive in 25 of the 40 patients, which indicated a sensitivity of 62.5%. In the healthy participants group, the positive expression of the CK19 mRNA biomarker was observed in 7 of the 40 participants. VEGF was positive in 27 of the 40 patients. In the control group, 5 of the 40 participants showed the positive expression of this biomarker.Conclusion: In sum, based on the results of this research, the assessed breast cancer tumor markers can be used as screening tests for the early diagnosis of patients. To further prove the findings of this study, more extensive studies with larger sample sizes are recommended.

Introduction: The discovery of hidden patterns in pharmaceutical data can contribute to improving the performance of hospital pharmacies. One of the applications of advanced data analysis techniques is the identification of drug interactions. Method: This study was conducted using data mining techniques with the FP-growth algorithm in the RapidMiner Studio® 10.1 environment to extract association rules and frequent pharmaceutical patterns. Data preprocessing and modeling were performed based on the CRISP-DM model. The type and level of drug interactions were determined based on the algorithm's results and by referencing the database at www.drugs.com. Results: The results included 17 association rules and 126 prescribing patterns, ranging from single-drug to four-drug combinations. Of the 64 two-drug prescribing patterns, 56 had no interaction, 6 had moderate interactions, 1 had a minor interaction, and 1 had a major interaction. Additionally, of the 19 three-drug patterns, 18 had no interaction, and only 1 had a moderate interaction. No interactions were observed in the four-drug prescribing pattern. Conclusion: The findings of this study can assist stakeholders in improving the pharmaceutical supply chain, optimizing prescriptions, reducing drug interactions, and lowering costs. The discovered patterns may also be used as part of a clinical decision support system. Although no significant drug interactions were observed in this study, the discovery of even one major interaction highlights its importance and further underscores the practical role of computer applications in medicine.

Selection based on molecular markers is one of the new methods that may improve progress and accuracy of selection in animal breeding programs. The GHRH gene (Growth Hormone-releasing Hormone) is a candidate gene for marker-assisted selection strategies. Polymorphs of GHRH gene are reported to be significantly associated with milk production and constituent traits. In order to study the polymorphism of GHRH gene, blood samples were collected from 112 Sarabi cows. Genomic DNA was extracted and a fragment of 297 bp in size was amplified using polymerase chain reaction. The amplified fragments were subjected to restriction digestion with HaeIII endonuclease enzyme and the resultant digested products were run on 2% Agarose gel. The results revealed the existence of two alleles of GHRHA and GHRHB for the examined locus with frequencies of 0.19 and 0.81 respectively. Three different genotypic variants including GHRHA GHRHA, GHRHA GHRHB and GHRHB GHRHB were identified with genotypic frequencies of 0.0357, 0.3037 and 0.6607 respectively. The  c2 test showed that population is in Hardy-Weinberg equilibrium (P<0.05). These data provide evidence that sarabi cattle breed have a genetic variability, which opens interesting prospects for future selection programs, especially marker-assistant selection.