Protein sequences classification based on weighting scheme

We present a new technique to recognize remote protein homologies that rely on combining probabilistic modeling and supervised learning in high-dimensional feature spaces. The main novelty of our technique is the method of constructing feature vectors using Hidden Markov Model and the combination of this representation with a classifier capable of learning in very sparse high-dimensional spaces. Each feature vector records the sensitivity of each protein domain to a previously learned set of sub-sequences (strings). Unlike other previous methods, our method takes in consideration the conserved and non-conserved regions. The system subsequently utilizes Support Vector Machines (SVM) classifiers to learn the boundaries between structural protein classes. Experiments show that this method, which we call the String Weighting Scheme-SVM (SWS-SVM) method, significantly improves on previous methods for the classification of protein domains based on remote homologies. Our method is then compared to five existing homology detection methods

The quality of mental health status in pregnancy and it's contributing factors on women visiting the health care centers of Shahrekord,(2001-2002)

Abstract There are many psychological and physiological changes during pregnancy and postpartum periods that are sometimes they become pathologic. Thus, it is necessary for a medical team to identify those patients and their families who have a predisposition to mental disorders and to guide them through this period. Aimed at assessing the prevalence and predisposing factors of mental disorders during pregnancy, an analytical-descriptive and cross-sectional study was performed on 267 pregnant women. The data were

90th percentile of body mass index (BMI) and some obesity risk factors among 7-12 years old school children, Chaharmahal & Bakhtiary, 2002

چاقی رایج ترین مشکل تغذیه ای جهان صنعتی به شمار می رود که می تواند اثرات منفی روانی و فیزیکی بر کودک داشته باشد. این مطالعه با هدف تعیین صدک نودم و برخی فاکتورهای خطر آفرین چاقی در کودکان دبستانی استان چهارمحال و بختیاری انجام شده است. پژوهش حاضر مطالعه ای دو مرحله ای بود که مرحله اول آن یک مطالعه مقطعی گروهی و مرحله دوم آن مورد- شاهدی است. در مرحله اول با استفاده از قد و وزن 2772 دانش آموزان دختر و پسر، صدک نودم BMI (Body Mass Index) به دست آمد. در مرحله دوم از 188 کودک چاق و 282 کودک لاغر به عنوان گروه های مورد و شاهد، پرسشنامه فاکتورهای خطر آفرین تکمیل گردید. سپس اطلاعات مورد تجزیه و تحلیل آماری قرار گرفت. نتایج مطالعه نشان داد که کودکان دارای 26/18 BMI< به عنوان غیر چاق محسوب می گردند. هم چنین شیوع چاقی در زمان مطالعه 9/9 بودو فاکتورهایی نظیر وجود چاقی در والدین و بستگان درجه یک، مصرف برخی مواد غذایی، میانگین وزن زمان تولد (فقط در میان دختران) با میزان چاقی کودک ارتباط معنی دار داشت ولی میزان فعالیت، وضعیت اقتصادی - اجتماعی خانواده، سطح تحصیلات والدین ابتلا به بیماری ها با میزان چاقی کودک ارتباط معنی داری نداشت. با توجه به نتایج به دست آمده در این پژوهش به نظر می رسد با تشویق والدین به حذف این فاکتورهای خطر آفرین می توان از چاقی کودکان پیشگیری نمود و بدینوسیله سلامت آنان را در بزرگسالی بیمه کرد

Pressure Rise Due to a Fire in an Enclosure

Can temperature and pressure rise resulting from a fast developing fire in an enclosure be predicted? Toward that end, the authors developed equations, the results of which were compared with the few available experimental data and with the Apollo accident pressure record

Extending the decomposition algorithm for support vector machines training

The Support Vector Machine (SVM) is found to de a capable learning machine. It has the ability to handle difficult pattern recognition tasks such as speech recognition, and has demonstrated reasonable performance. The formulation in a SVM is elegant in that it is simplified to a convex Quadratic IProgramming (QP) problem. Theoretically the training is guaranteed to converge to a global optimal. The training of SVM is not as straightforward as it seems. Numerical problems will cause the training to give non- optimal decision boundaries. Using a conventional optimizer to train SVM is not the ideal solution. One can design a dedicated optimizer that will take full advantage of the specific nature of the QP problem in SVM training. The decomposition algorithm developed by Osuna et al. (1997a) reduces the training cost to an acceptable level. In this paper we have analyzed and developed an extension to Osuna's method in order 110 achieve better performance. The modified method can be used to solve the training of practical SVMs, in which the training might not otherwise converge

Effect of initiation time of oral hydration on the return of bowel function and woman's satisfaction after elective caesarean section in primiparous women

Background and Objective: Abdominal operations as gynaecological procedures result in gastrointestinal dysmotility. Early feeding and ambulation are nonpharmacologic interventions which can be useful in re-initiation of bowel function. This study was done to evaluate the effect of early oral hydration on the return of bowel function and woman's satisfaction after elective caesarean section in primiparous women. Materials and Methods: In this randomized clinical trial, 120 primiparous women undergone elective cesarean section were assigned to control and intervention groups in Hajar hospital, Shahrekord, Iran during 2007. In the interventional group, oral hydration with liquids was started 4 hours after surgery regardless of presence of bowel sounds and solid food was started after bowel sounds appeared. The control group recieved liquid diet 12 hours after the operation if it was tolerated, they were given soft diet and regular food at the next meal. The return of bowel activity, time of ambulating, satisfaction, discharge from the hospital and complications were compared in two groups. The data were analyzed using SPSS-15, Chi-Square, T and one way ANOVA tests. Results: The mean postoperative time interval to first hearing of normal intestinal sounds in interventional versus control groups were (9.5±1.38 and 12.5±2.5 hours) the first passage of flatus (15.7±3.61 vs.22.4±4.1 hours), time to first sensation of bowel movement (10.8±1.99 versus 15.7±3.4 hours) and defecation (18.9±3.65 versus 23.4±4.85 hours). These differences were significant (P<0.05). Also discharge from the hospital (0.96±0.18 versus 1.1±34 days) were significantly shorter in interventional group (P<0.05). The women in the early feeding group got out of bed (patient mobilisation) earlier than their interventional group (14.1 hours versus 18.8 hours (P<0.05). Maternal satisfaction was significantly higher among the early fed women (P<0.05). Conclusion: Early oral hydration after elective cesarean section associated with rapid resumption of intestinal motility and increased woman’s satisfaction

The effect of occupational therapy on some aspects of quality of life in schizophrenic patients

چکیده: زمینه و هدف: بیماری اسکیزوفرنیا شدیدترین و مزمن شونده ترین بیماری روانپزشکی است که با اختلال در تواناییهای اجتماعی و شغلی همراه است. کار درمانی باعث افزایش اعتماد به نفس، خودسازی و تقویت رفتارهای کاری در بیمار می شود. این پژوهش با هدف تعیین تاثیر کاردرمانی بر ابعاد مختلف کیفیت زندگی بیماران اسکیزوفرنیک مزمن بستری در بیمارستان سینا انجام شده است. روش بررسی: این پژوهش یک مطالعه کار آزمایی بالینی است که ابتدا بیماران اسکیزوفرن مزمن بستری در بیمارستان سینای فارسان در استان چهارمحال و بختیاری بصورت سرشماری انتخاب و کیفیت زندگی آنان بوسیله پرسشنامه کیفیت زندگی بررسی و سپس بیماران بصورت تصادفی به دو گروه مورد (32 نفر) و شاهد (30 نفر) تقسیم گردیدند. کاردرمانی به مدت 20 ساعت در هفته در طی 6 ماه برای گروه مورد اجرا شد. بعد از اجرای کاردرمانی مجدداً کیفیت زندگی بیماران بررسی و اطلاعات با استفاده از آمار توصیفی و استنباطی (t مستقل) تجزیه و تحلیل شد. یافته ها: نتایج نشان داد در بدو مطالعه، تفاوت معنی داری بین میانگین نمره کیفیت زندگی گروه مورد و شاهد، وجود نداشت، بعد از مطالعه این تفاوت در حیطه انگیزه و انرژی و نمره کل کیفیت زندگی بین گروه مورد و شاهد معنی دار بود (001/0

Applicability and usability of predefined natural language boilerplates in documenting requirements

Natural language is frequently applied to document the stakeholders’ statements during requirement elicitation activities. Nevertheless, the use of generic natural language has potential for the issues of unclear and inconsistent requirements. These issues may result from the diverse interpretations by the stakeholders or other various sources of documents and artefacts. The main objective of this paper was to discuss the definition and application of predefined boilerplates to specify the requirements in the form of natural language statements. The proposed boilerplates were defined and classified based on two main types of requirements, namely functional and non-functional (performance, constraints, and specific quality). Two methods have been applied to evaluate the research results; the applicability of the predefined boilerplates was demonstrated using two different case studies, and the usability aspect is evaluated through synthetic environment experimentation using human respondents. As a summary, the predefined boilerplates were found helpful, especially among novice requirement engineers to express and specify their requirements in a consistent manner and a standardized way, relatively able to improve the quality of the natural language statements

Improved support vector machine using multiple SVM-RFE for cancer classification

Support Vector Machine (SVM) is a machine learning method and widely used in the area of cancer studies especially in microarray data. A common problem related to the microarray data is that the size of genes is essentially larger than the number of samples. Although SVM is capable of handling a large number of genes, better accuracy of classification can be obtained using a small number of gene subset. This research proposed Multiple Support Vector Machine- Recursive Feature Elimination (MSVMRFE) as a gene selection to identify the small number of informative genes. This method is implemented in order to improve the performance of SVM during classification. The effectiveness of the proposed method has been tested on two different datasets of gene expression which are leukemia and lung cancer. In order to see the effectiveness of the proposed method, some methods such as Random Forest and C4.5 Decision Tree are compared in this paper. The result shows that this MSVM-RFE is effective in reducing the number of genes in both datasets thus providing a better accuracy for SVM in cancer classification