Sharif Digital Repository

Minimally invasive surgery is highly valued mainly due to the reduction of the patient’s recovery period. Catheters are among the most important tools in minimally invasive surgeries. Catheter is a flexible tool that has the ability to pass through difficult paths. In common localization methods, fluoroscopy is used to determine the position of the catheter’s tip. One main disadvantage of this method is that it is very dangerous for therapists who are exposed to X-ray radiation for long periods of time. A magnetic catheter is created by adding a magnet to the end of the catheter. The possibility of guiding magnetic catheter by an external magnetic field, controlling the applied force and... 

As a result of extraction from underground oil and gas reservoirs, the pore pressure in the reservoir decreases and the effective stress increases accordingly. Although the gradual consolidation of underground reservoirs and their compaction due to the extraction can improve the production process (Compaction Drive) and facilitate the release of hydrocarbon fluid, it may cause some problems. Field measurements in the past two decades have shown that in addition to the change of effective stress, the total horizontal and vertical stress field can also change in and around the reservoir. As a result of the settlement that occurs at the upper part of the reservoir due to the consolidation... 

Motion dynamics of cat species has always been attractive to be studied. Flexibility in motion due to specific skeleton and complex muscle-skeleton mechanism, control concepts, special way of running, high speed direction change while moving, ability of twisting the body during free fall, and landing on four limbs were widely investigated in literature and the results have been used in different branches such as control, robotics and aerospace.In this project, kinematic and dynamic equations of cat species falling maneuver are derived using quaternions for a simple two-link robot, a three-link robot with tail, and a more complete eight-link model with the addition of legs which is designed... 

In this thesis an algorithm has been implemented for automatic landing of a quadrotor under the ground effect. In this regard the six degrees of freedom equations of motion using the Newton-Euler method has been designed. Then, the ground effect has been modeled by inspiring from the similar available models in the literature. The proposed models and proportional-integral-derivative attitude control loops have been simulated in MATLAB/Simulink environment. Also, two control strategies, a classical proportional-integral-derivative controller and a sliding mode controller have been utilized for height control loop.Since sliding mode controller requires all state variables to generate control... 

Sight is probably our most important sense. Every day, humans are exposed to many visual stimuli in their surroundings. The human brain is able to identify and prioritize important and valuable stimuli and memorize them. Identifying and remembering these valuable stimuli is vital to meeting the needs and maintaining survival. The aim of the proposed research is to find the effect of reward learning on the coding of visual objects in the human brain. Previous results have shown that long-term reward-object association make valuable objects more recognizable behaviorally. Studies have also shown that visual stimuli and the pattern of activity of primary visual cortex neurons are closely... 

Triple Negative Breast Cancer (TNBC) is an invasive subtype of breast cancer. Finding prognostic biomarkers is helpful in choosing the appropriate treatment procedure for patients of this cancer. In recent years, the role of microRNAs in various biological processes, including cancer, has been identified, and their accessibility and stability have made these types of molecules an ideal biomarker. In the first phase of this study, with the aim of overcoming the limitations of previous studies, a new bioinformatics protocol has been proposed to investigate the prognostic miRNAs of triple negative breast cancer. First, using survival analysis, 56 prognostic miRNAs which had a significant... 

Unsupervised neural spike sorting is a crucial tool in studying neural systems in the resolution of a neural cell. In extracellular recording from neural cells, the voltage of media is captured by the electrodes. The situation is possible that an electrode record activity of multiple neurons at the same time. The spike sorting goal is assigning each spike (extracellular recorded neural action potential) to a neural cell that generates it. Conventionally, more than one electrode is used to recording media voltages. The electrodes are placed in a small space as a single device called a multi-electrode array. After the spike sorting procedure, the occurrence time of activity of several cells is... 

Traditional techniques use invasive histology techniques to diagnose cancer. Cancer tissue is sampled directly in this method, which is very painful for the patient. In recent years, scientists have discovered that the cell world is released into the blood plasma after cell death, obtaining useful cancer information. Since methylation changes in cancer cells are very significant and the death rate of cancer cells is high, the methylation of each tissue is different from the other. Furthermore, they were diagnosing the type of cancer.On the other hand, due to the different patterns in methylated DNA with normal DNA and the use of bisulfite treatment technique to detect the degree of... 

The advent and advance of single-cell technologies have enabled us to measure the cell function and identity by using different assays and viewing it by different technologies. Nowadays, we are able to measure multiple feature vectors from same- single cells from multiple abstract molecular levels (genome, transcriptome, proteome, ...) simultaneously. Hence, the analysts can view the cell from different yet correlated angles and study their behaviours. Such progress in joint single-cell assessments plus the development and spread of more common single-cell assays - that measure one feature vector per cell - caused the growing need for computational tools to integrate these datasets in order... 

With the growing use of machine learning algorithms, especially in deep neural networks, the need for annotated data for supervised learning has also increased. In many cases, it is possible to collect data widely, but annotating all of these data is usually very time-consuming, expensive, and even impossible in some cases. The goal of active learning algorithms is to maximize the model’s performance with the least annotated data. Active learning algorithms are iterative algorithms that train the model in each iteration with the current annotated data. Then, using the results of the model on the remaining data without annotation, select some new data to annotate. This process usually... 

The Major Histocompatibility Complex (MHC) Class I molecules play a crucial role in the immune system. These molecules present peptides derived from intracellular proteins on the cell surface to be recognized by T cells. This process is vital for identifying and eliminating cancerous or infected cells. In cancer therapy, particularly in the development of personalized vaccines, accurately selecting peptides that can effectively bind to MHC Class I and stimulate a strong immune response is a significant challenge. This research introduces an innovative neural network model that utilizes a cross-encoder architecture and leverages a pre-trained model to simultaneously process peptide and MHC... 

DNA methylation in one of the most important epigenetic variations, which causes significant variations in gene expressions of mammalians. Our current knowledge about DNA methylation is based on measurments from samples of bulk data which cause ambiguity in intracellular differences and analysis of rare cell samples. For this reason, the ability to measure DNA methylation in single-cells has the potential to play an important role in understanding many biological processes including embryonic developement, disease progression including cancer, aging, chromosome instability, X chromosome inactivation, cell differentiation and genes regulation. Recent technological advances have enabled... 

The human leukocyte antigen protein, commonly known as HLA, has the ability to present small protein fragments called peptides on the surface of cells, whether they originate from within the cell or externally. The binding of these peptides to HLA receptors is a crucial step that triggers an immune response. By estimating the affinity between peptides and HLA class I, we can identify novel antigens that have the potential to be targeted by cancer therapeutic vaccines. Computational methods that predict the binding affinity between peptides and HLA receptors have the potential to expedite the design process of cancer vaccines. Currently, most computational methods exclusively rely on... 

This research investigates the competitive behavior of producers in an oligopolistic market structure and presents new approaches to contrive appropriate bidding strategies using game theory. As a case of oligopolistic competition, we have considered a simplified model of electricity markets, and turned our focus to related economic models of competition. In particular, the supply function model has been adopted because of its realistic simulation of the bidding structure in electricity markets, for which, a new method is proposed to provides closed-form expressions in computing Nash strategies. This method is not only capable of computing all Nash equilibriums of the model, but also the... 

In this thesis, first we simulate the needle penetration in the soft tissues via finite element and adaptive finite elemet methods. Then, we design a proper controller for the model of tissue-needle interaction model considering some uncertain parameters and conditions in the needle-tisuue model. Needle penetration speed into the soft tissue and the applied force to needle are supposed to be the system’s inputs in the model. Deformations of tissue particles are modeled based on the tissue particles’ velocities through a set of equations. To solve these equations, numerical methods are used which are conceptually based on finite element methods. In the control step, the needle’s velocity and... 

In recent years, monoclonal antibodies have gained attention as highly effective drugs for treating diseases, especially cancer. The high binding affinity between an antibody and its corresponding antigen is one of the key factors in triggering an effective immune response. Modeling binding affinity using machine learning is considered a promising and cost-effective computational approach; however, due to the lack of training data, the performance of these models is often poor and limited. In contrast, recent advances in geometric learning have demonstrated that incorporating the three-dimensional geometry of protein structures in the learning process can significantly impact 3D... 

Genome mutations whose effects are not specified pose one of the challenges in identifying genetic diseases. Utilizing wet lab tests to detect the pathogenicity of variants can be time-consuming and fiscally expensive. A rapid and cost-effective solution to this problem is the use of machine learning-based variant effect predictors, which have the ability to determine whether a mutation is pathogenic or not. The objective of this research is to predict the pathogenicity of genome variations. The proposed model exclusively utilizes the protein sequence as its input feature and does not have access to other protein features. The data used to construct the model comprises mutations with...