Network Approaches to the Study of Genomic Variation in Cancer

Advances in genomic sequencing technologies opened the door for a wider study of cancer etiology. By analyzing datasets with thousands of exomes (or genomes), researchers gained a better understanding of the genomic alterations that confer a selective advantage towards cancerous growth. A predominant narrative in the field has been based on a dichotomy of alterations that confer a strong selective advantage, called cancer drivers, and the bulk of other alterations assumed to have a neutral effect, called passengers. Yet, a series of studies questioned this narrative and assigned potential roles to passengers, be it in terms of facilitating tumorigenesis or countering the effect of drivers. Consequently, the passenger mutational landscape received a higher level of attention in attempt to prioritize the possible effects of its alterations and to identify new therapeutic targets. In this dissertation, we introduce interpretable network approaches to the study of genomic variation in cancer. We rely on two types of networks, namely functional biological networks and artificial neural nets. In the first chapter, we describe a propagation method that prioritizes 230 infrequently mutated genes with respect to their potential contribution to cancer development. In the second chapter, we further transcend the driver-passenger dichotomy and demonstrate a gradient of cancer relevance across human genes. In the last two chapters, we present methods that simplify neural network models to render them more interpretable with a focus on functional genomic applications in cancer and beyond

Exploring Students Mental Behaviors Using Unsupervised Learning Algorithms and Graph Theory

With the high amount of mentally seeking behaviors among students nationwide according to Healthy minds network survey (2019) and lack of related studies to students mental health in the Gulf region, a gap is created Fatima Al-Darmaki et al. (2015) this project is executed to fill the gap by utilizing data analytics techniques to generate insights that can be used to support future decisions. Students who suffer from mental disorders are most likely to disengage from university which can be translated into a dropout. Many organizations are conducting periodically to capture as many insights to tackle those behaviors. This project is aiming to tackle this problem and gather insights from a web-based survey that is answered anonymously by the target group of students. Answers will be analyzed and preprocessed by several statistical techniques, transformed and finally modeled to generate insights. Gathered insights can lead to new hypotheses to be formulated. In addition, unsupervised learning algorithms have been introduced such as Apriori and FP-growth to generate the frequent itemsets which use association rules mining to associate the different mental health factors that are related to this research. such as the association between participant sex related to mental health concern, and related sexual orientation that has more likely to visit mental health consultant services, and the relation between sexual orientation to mental disorder has been investigated to find no significant relationship. On the other hand combining Social network analysis approach with frequent pattern mining found interesting patterns in building relations between demographic features of who are more likely to consult a mental health specialist. Results and further research can be used to support decisions of future mental related behaviors and support future decisions for related problems

Influence of Okra Extract Supplementation on Some Haematological Parameters of Male Mice Exposed to Aflatoxin

This research was directed to determine the influence of an alcoholic extract of okra on the lessening of the destructive impact of the aflatoxin produced by Aspergillus fumigatus in white mice and its influence on some physiological blood parameters. Different food samples, (grains and fruits) such as  (wheat, barley, corn, rice, citrus, strawberries, and apples) were selected for the isolation of a variety of fungi. The results showed  that Aspergillus flavus 15(18.7%), Aspergillus niger12(15%), Penicillium spp 7(8.7%), Aspergillus terreus 7 (8.7%), Aspergillus fumigatus7(8.7%), Alternaria spp. 10 (12.5%), Aspergillus parasiticus 6 (7.5%) Fusarium 6 (7.5%), Penicillium chrysogenum5(6.3), Mucor spp.2(2.5%),and Rhizopus stoloinfier 3(5.5%).The identified fungi were tested for aflatoxin production, and the results revealed  that Aspergillus fumigatus produced the most aflatoxin. Okra alcoholic extract was tested in vivo against  the negative impact of aflatoxins using different concentrations. The findings revealed that alcoholic extracts showed reasonable influence on some blood parameters, and the results are promising. &nbsp

Development of machine learning-based algorithm to determine the condition in transformer oil

One very popular and useful electric device in daily life is a transformer, and it is one of the greatest components of the power network system. The main fault of these transformers can purpose considerable damage. This not only disrupts other functions of the power supply, rather than caused very large losses. The interpretation of dissolved gas analysis (DGA) is used to detect incipient faults in transformer oil. This paper aims to develop a model for taking into consideration the results obtained from DGA to investigate the condition of transformer oil fault. Machine Learning (ML) algorithm have been utilized to detect the fault more accurate. Classification learning app used to train DGA data divided into three categories fault, Not determined (N/D) and stray gassing. Three different types of ML algorithm have achieved high accuracy of 93.0%, 95.4% and 97.7% support vector machine (SVM), Naïve Bayes algorithm (NB), K-nearest neighbour (KNN) respectively. Graphical User Interface (GUI) has overall the system by testing and verified with many different user data and performed a correct classification

Mutations in Exon 4 of ESR1 Gene in Iraqi Women with Breast Cancer

This study was aimed to determine the mutations and single nucleotide polymorphisms (SNPs) in exon 4 in women with breast cancer from Iraq. Different samples (blood, fresh tissue with blood from same patient, and formalin fixed paraffin embedded, FFPE). Molecular analysis of exon 4 has been studied by using PCR. It was found that exon 4 appeared as a single band with size 370. Single nucleotide polymorphisms (SNPs) were determined in exon 4 ESR1 using DNA sequence. Then, nucleotide sequences of this exon were aligned with control group (healthy women) and with NCBI. It was detected five polymorphisms (AAA, TTT, AAA, CCG, AAA, and AAC) in exon 4 of ESR1; all of them were novel SNPs, all types of polymorphism in exon 4 of ESR1were substitution. Keywords: SNPs in ESR1, ESR1 gene mutations, Breast cancer mutation

Area contraction effect on shock tube performance, numerical and experimental study

The paper presents numerical and experimental study on the effect of area contraction in shock tube facility. The shock tube is the main component of short duration test facility at The Universiti Tenaga Nasional (UNITEN), Malaysia. In the shock tube, a small area contraction in form of a removable bush was facilitated adjacent to the diaphragm section. The flow process was simulated using a two-dimensional time-accurate Navier-Stokes solver. The solver uses second order accurate cell-vertex finite volume spatial discretization and fourth orders accurate Runge-Kutta temporal integration. In this study, the solver was programmed based on the dimensions and configuration of UNITEN’s shock tube facility. The numerical results were validated with experimental data from the ground based test facility. Numerical pressure histories were found to be in accordance with the experimental data. For further investigations, simulations were conducted for different operating conditions. The results showed that shock tube performance in term of producing shock wave and steady gas flow is highly influenced by area contraction in the diaphragm section. The shock wave strength and speed decreased by 18% and 8% respectively

The Effects of Area Contraction on the Performance of UNITEN\u27s Shock Tube: Numerical Study

Numerical study into the effects of area contraction on shock tube performance has been reported in this paper. The shock tube is an important component of high speed fluid flow test facility was designed and built at the Universiti Tenaga Nasional (UNITEN). In the above mentioned facility, a small area contraction, in form of a bush, was placed adjacent to the diaphragm section to facilitate the diaphragm rupturing process when the pressure ratio across the diaphragm increases to a certain value. To investigate the effects of the small area contraction on facility performance, numerical simulations were conducted at different operating conditions (diaphragm pressure ratios P4/P1 of 10, 15, and 20). A two-dimensional time-accurate Navier-Stokes CFD solver was used to simulate the transient flow in the facility with and without area contraction. The numerical results show that the facility performance is influenced by area contraction in the diaphragm section. For instance, when operating the facility with area contraction using diaphragm pressure ratio (P4/P1) of 10, the shock wave strength and shock wave speed decrease by 18% and 8% respectively. 1

Exploring the Attitude of ESP Learners towards Using Mind Mapping in Learning Vocabulary

The current study aims at exploring the attitude of ESP students towards using mind mapping in learning vocabulary. The research design is quasi-experimental. The sample of the study consisted of 29 students from the college of engineering at the Arab Academy for Science, Technology and Maritime Transport, Egypt. A questionnaire was used to investigate the attitude of students towards using mind maps in learning ESP vocabulary. The results of the study revealed that students were interested in using the mind mapping strategy in learning ESP vocabulary and the significant features of mind map; namely: colours, branches and pictures. Based on the findings, it is recommended that researchers delve deep into studying the mind mapping strategy and its relation to students’ learning preferences and that teachers apply them when teaching vocabulary items. The researcher also recommends a model to evaluate vocabulary learning techniques that can be used in future studies

The Impact of the Cold War on the Gensis and Development of the Central Intelligence Agency(CIA) until 1955

فرضت ظروف الحرب الباردة على إدارة واشنطن عام 1947 انشاء هيأة استخبارية جديدة بمستوى تحديات تلك الحرب التي بدى انها ستتجاوز استخدام الأسلحة العسكري التقليدية الى أسلحة أخرى سياسية واقتصادية وإعلامية- نفسية قائمة أساسا على قدرة الدولة استخباراتيا، الامر الذي ادركته إدارة البيت الأبيض عندما اكتشفت انها متأخرة بالحصول على المعلومات اللازمة لصقل قراراتها السياسية، كما ادركت انها عاجزة استخباريا عن مواكبة ليس الاتحاد السوفيتي فحسب، وانما حتى بريطانيا حليفتها، فصدر الامر الرئاسي بتأسيس وكالة تجاري الاستخبارات السوفيتية والبريطانية. أسست وكالة سي آي ايه بشكل مستقل عن كل الوزارات ومرتبطة بالرئيس مباشرة، فسبب ذلك الخلافات بينها وبين الوزارات السيادية لاسيما الخارجية والدفاع مما أعاق عمل الوكالة بادئ الامر، ولم تتخلص من ذلك المعوق الا في عهد الرئيس آيزنهاور الذي امتلك خبرة إدارية واستخباراتية اهلته للتغلب على ذلك المعوق بتعيين الشقيقين دالاس في وزارة الخارجية ورئاسة الوكالة بغية التعاون بينهما. لم يكن دور الوكالة في سياسة الرئيس ترومان مؤثرا، اذ كانت في مكانة ثانوية بعد الوزارات السيادية، بفعل ان الوكالة لم تنال ثقة الرئيس في رسم وتحديد الأسس السياسية، وانما (حسب رؤيته) هي مسؤولة عن توفير المعلومات فقط، لذلك لم تنجح الوكالة في معظم عملها باستثناء تنفيذ خطة مارشال. انعكس الحال في عهد آيزنهاور وباتت الوكالة الجهة الرئيسة في رسم وتحديد الأسس السياسية فضلا عن التنفيذ، فكان لها دورا بارزا في سياسة التحرر وصياغة اساليبها، ومن ثم تنفيذها ليتجاوز عملها البعد المعلوماتي الى ابعاد أخرى نظرية وعملية.      The circumstances of the Cold War forced Washington's administration in 1947 to set up a new intelligence body at the level of the challenges of that war, which would appear to go beyond the use of conventional military weapons to other political, economic and media-psychological weapons. These weaponsare primarily based on the state's intelligence capability, which the White House administration realized when it discovered that it was too late to obtain the necessary information to refine its political decisions, and realized that it was incapable of knowing not only the Soviet Union, but also its ally Britain. The presidential order was issued due to the creation of a Soviet-British intelligence agency. The CIA was established independently of all ministries and directly related to the president. This was the reason for the differences between them and the sovereign ministries, especially foreign affairs and defence, which hindered the Agency's work initially. The Agency could not get rid of that differences, but it did during the reign of President Eisenhower, who had administrative and intelligence experience made him overcome the barrier by appointing the two brothers Dallas in the Ministry of Foreign Affairs and the presidency of the Agency in order to cooperate.The role of the agency in President Truman's policy was not impressive, but it was unimpressive to the sovereign ministries, because the agency did not get the president's confidence in drawing and defining the political bases, but (as he sees it)the Agency was only to supply information; therefore, the Agency did not succeed in most of its work except for the implementation of the Marshall Plan.In the Eisenhower era, the agency became the main actor in drawing and defining the political foundations as well as the implementation. It played a prominent role in the policy of emancipation and the formulation of its methods, and then implemented it beyond the work of the information dimension to other theoretical and practical dimensions