The Motivation System in a Governmental Organization

AbstractThe managers and management researchers believe that without constant commitment of the members of organizations, achieving the organizational objectives seems to be unattainable. Motivation is considered as a human mental characteristic which indicates the individual's level of organizational commitment. Paying attention to the employees’ motivation plays a vital role in the way the organizations offer their services. In this study, after comprehensive evaluation of various theories and models regarding motivation, the model of Frederick Herzberg has been used. This paper aims to investigate the relationships between hygiene and motivational factors and motivation among chief operating officers and employees. The statistical population of this study includes all the employees of the mentioned government organizations with the occupational group of 12 to 16 who have answered the questionnaire. After investigating the face and content of validity of the questionnaire, the Cronbach's alpha was conducted to investigate its reliability coefficient which was equal to 0.87. The results of the study revealed that Herzberg's hygiene factors have a significant impact on improving employees and chief operating officers’ motivation than motivational factors. Moreover, the findings indicated that the impact of Herzberg's motivational factors on middle managers’ motivation raising is more than hygiene factors

Study of the immunogenicity of outer membrane protein A (ompA) gene from Acinetobacter baumannii as DNA vaccine candidate in vivo

Objective(s): Acinetobacter baumannii is one the most dangerous opportunistic pathogens in hospitalized infections. This bacterium is resistant to 90% of commercial antibiotics. Therefore, developing new strategies to cure A. baumannii-infections is urgent. The DNA vaccines new approach in which the immunogen can be directly expressed inside the target cells through cloning of immunogen into an expression vector. The outer membrane protein A(OmpA) is one the critical factors in pathogenicity of A. baumannii which has been repeatedly described as a powerful immunogen to trigger the immune responses. As the pure form of the OmpA is insoluble, vaccine delivery is very hard. Materials and Methods: We previously cloned the ompA gene from A. baumannii into the eukaryotic expression vector pBudCE4.1 and observed that the OmpA protein has been considerably expressed in eukaryotic cell model. In current study, the immunogenic potential of pBudCE4.1-ompA has been evaluated in mice model of experimental. The serum levels of IgM, IgG, IL-2, IL-4, IL-12 and INF-γ were measured by enzyme-linked immunosorbent assay (ELISA) after immunization with ompA-vaccine. The protective efficiency of the designed-DNA vaccine was evaluated following intranasal administration of mice with toxic dose of A. baumannii.Results: Obtained data showed the elevated levels of IgM, IgG, IL-2, IL-4, IL-12 and INF-γ in serum following the vaccine administration and mice who immunized with recombinant vector were survived more than control group.Conclusion: These findings indicate ompA-DNA vaccine is potent to trigger humoral and cellular immunity responses although further experiments are needed

Tissue Specific Expression Levels of Apoptosis Involved Genes Have Correlations with Codon and Amino Acid Usage

Different mechanisms, including transcriptional and post transcriptional processes, regulate tissue specific expression of genes. In this study, we report differences in gene/protein compositional features between apoptosis involved genes selectively expressed in human tissues. We found some correlations between codon/amino acid usage and tissue specific expression level of genes. The findings can be significant for understanding the translational selection on these features. The selection may play an important role in the differentiation of human tissues and can be considered for future studies in diagnosis of some diseases such as cancer

Effect of peppermint water on prevention of nipple cracks in lactating primiparous women: a randomized controlled trial

BACKGROUND: Nipple pain and damage in breastfeeding mothers are common causes of premature breastfeeding cessation. Peppermint water is popularly used for the prevention of nipple cracks in the North West of Iran. The aim of this study was to determine the effectiveness of peppermint water in the prevention of nipple cracks during breastfeeding in comparison with the application of expressed breast milk (EBM). METHODS: One hundred and ninety-six primiparous breastfeeding women who gave birth between February and May 2005 in a teaching hospital in Tabriz, Iran, were randomized to receive either peppermint water or EBM. Each woman was followed for up to three visits or telephone calls within 14 days and then by telephone call at week six postpartum. RESULTS: Women who were randomized to receive peppermint water were less likely to experience nipple and areola cracks (9%) compared to women using EBM (27%; p < 0.01). Women who used the peppermint water on a daily basis were less likely to have a cracked nipple than women who did not use peppermint water (relative risk 3.6, 95%CI: 2.9, 4.3). Nipple pain in the peppermint water group was lower than the expressed breast milk group (OR 5.6, 95% CI: 2.2, 14.6; p < 0.005). CONCLUSION: This study suggests that peppermint water is effective in the prevention of nipple pain and damage. Further studies are needed to assess the usefulness of peppermint water in conjunction with correct breastfeeding techniques. Trial registration number: NCT0045640

Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?

BACKGROUND: In here, we constructed personalized models for predicting breast cancer (BC) recurrence according to timing of recurrence (as early and late recurrence). METHODS: An efficient algorithm called group LASSO was used for simultaneous variable selection and risk factor prediction in a logistic regression model. RESULTS: For recurrence  5 years, stage 2 cancer (OR 1.67, 95% CI = 1.31-2.14) and radiotherapy+mastectomy (OR 2.45, 95% CI = 1.81-3.32) were significant predictors; furthermore, relative to mastectomy without radiotherapy (as reference for comparison), quadranectomy without radiotherapy had a noticeably higher odds ratio compared to quadranectomy with radiotherapy for recurrence > 5 years (OR 7.62, 95% CI = 1.52-38.15 vs. OR 1.75, 95% CI = 1.32-2.32). Accuracy, sensitivity, and specificity of the model were 71%, 78.8%, and 55.8%, respectively. CONCLUSION: For the first time, we constructed models for estimating recurrence based on timing of recurrence which are among the most applicable models with excellent accuracy (> 80%)

Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens

Background: Nowadays, there is a growing global concern over rapidly increasing screen time (smartphones, tablets, and computers). An accumulating body of evidence indicates that prolonged exposure to short-wavelength visible light (blue component) emitted from digital screens may cause cancer. The application of machine learning (ML) methods has significantly improved the accuracy of predictions in fields such as cancer susceptibility, recurrence, and survival. Objective: To develop an ML model for predicting the risk of breast cancer in women via several parameters related to exposure to ionizing and non-ionizing radiation.Material and Methods: In this analytical study, three ML models Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron Neural Network (MLPNN) were used to analyze data collected from 603 cases, including 309 breast cancer cases and 294 gender and age-matched controls. Standard face-to-face interviews were performed using a standard questionnaire for data collection. Results: The examined models RF, SVM, and MLPNN performed well for correctly classifying cases with breast cancer and the healthy ones (mean sensitivity> 97.2%, mean specificity >96.4%, and average accuracy >97.1%).  Conclusion: Machine learning models can be used to effectively predict the risk of breast cancer via the history of exposure to ionizing and non-ionizing radiation (including blue light and screen time issues) parameters. The performance of the developed methods is encouraging; nevertheless, further investigation is required to confirm that machine learning techniques can diagnose breast cancer with relatively high accuracies automatically

An Approximate Solution for Boundary Value Problems in Structural Engineering and Fluid Mechanics

Variational iteration method (VIM) is applied to solve linear and nonlinear boundary value problems with particular significance in structural engineering and fluid mechanics. These problems are used as mathematical models in viscoelastic and inelastic flows, deformation of beams, and plate deflection theory. Comparison is made between the exact solutions and the results of the variational iteration method (VIM). The results reveal that this method is very effective and simple, and that it yields the exact solutions. It was shown that this method can be used effectively for solving linear and nonlinear boundary value problems

Simple Sequence Repeats amplification: a tool to survey the genetic background of olive oils

A reliable DNA extraction method for use on extra virgin olive oil based on a commercial kit was defined, and the possibility of using this DNA for fingerprinting the original cultivar was demonstrated. The genetic traceability of single-cultivar virgin olive oil from two cultivars (Carolea and Frantoio) was achieved by identifying the varieties from which they were produced. This involved the analysis of DNA sequences using a panel of seven simple sequence repeats (SSRs) to provide genotype-specific allelic profiles. The amplified SSR fragments and the DNA profiles from the monovarietal oil corresponded to the profiles from the leaves of the same cultivar. The most reliable SSR in providing correct allele sizing in distinguishing either single-cultivar olive oil samples or the different ratios of their blends are DCA3, DCA4, DCA16, DCA17, and GAPU101, while DCA9, GAPU59 produced less concordance against data obtained by the genetic analysis of leaf samples. To have reproducible results, PCR product purification and selection of a set of markers with a highly robust amplification pattern is suggested