Adjuvant Systemic Therapy in Stage II and III Colon Cancer

The prognosis of colon cancer is primarily determined through staging of the disease. After curative surgery, clinically occult micrometastases are thought to be the major source of disease recurrence. The main aim of postoperative systemic treatment is to eradicate micrometastases, thereby improving outcomes with an increased cure rate. Adjuvant systemic chemotherapy is indicated for patients with stage III colon cancer, as well as for patients with high-risk stage II colon cancer. Prognostic and predictive markers that identify heterogeneous groups are needed to implement tailored therapeutic strategies. Due to the lack of evidence of predictive value of multigene assays in terms of potential value of adjuvant chemotherapy, multigene assays should not be used to determine adjuvant therapy. The standard treatment for most patients with stage III disease is a combination of oxaliplatin with infusional and bolus 5-fluorouracil (5-FU) or with an oral agent such as capecitabine, which has equivalent results. Adjuvant therapy should not be administered to all patients with stage II colon cancer. High-risk stage II patients may be considered as an eligible group for adjuvant therapy after a complete discussion. There is no high level of evidence to use irinotecan-based combination chemotherapies in the adjuvant setting. The antiangiogenic agent bevacizumab in combination with standard adjuvant chemotherapy regimens also failed to improve outcomes, as did the EGFR agent cetuximab

Uncertain linear equations

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 72-79.In this thesis, new theoretical and practical results on linear equations with various types of uncertainties and their applications are presented. In the first part, the case in which there are more equations than unknowns (overdetermined case) is considered. A novel approach is proposed to provide robust and accurate estimates of the solution of the linear equations when both the measurement vector and the coefficient matrix are subject to uncertainty. A new analytic formulation is developed in terms of the gradient flow to analyze and provide estimates to the solution. The presented analysis enables us to study and compare existing methods in literature. We derive theoretical bounds for the performance of our estimator and show that if the signal-to-noise ratio is low than a treshold, a significant improvement is made compared to the conventional estimator. Numerical results in applications such as blind identification, multiple frequency estimation and deconvolution show that the proposed technique outperforms alternative methods in mean-squared error for a significant range of signal-to-noise ratio values. The second type of uncertainty analyzed in the overdetermined case is where uncertainty is sparse in some basis. We show that this type of uncertainty on the coefficient matrix can be recovered exactly for a large class of structures, if we have sufficiently many equations. We propose and solve an optimization criterion and its convex relaxation to recover the uncertainty and the solution to the linear system. We derive sufficiency conditions for exact and stable recovery. Then we demonstrate with numerical examples that the proposed method is able to recover unknowns exactly with high probability. The performance of the proposed technique is compared in estimation and tracking of sparse multipath wireless channels. The second part of the thesis deals with the case where there are more unknowns than equations (underdetermined case). We extend the theory of polarization of Arikan for random variables with continuous distributions. We show that the Hadamard Transform and the Discrete Fourier Transform, polarizes the information content of independent identically distributed copies of compressible random variables, where compressibility is measured by Shannon’s differential entropy. Using these results we show that, the solution of the linear system can be recovered even if there are more unknowns than equations if the number of equations is sufficient to capture the entropy of the uncertainty. This approach is applied to sampling compressible signals below the Nyquist rate and coined ”Polar Sampling”. This result generalizes and unifies the sparse recovery theory of Compressed Sensing by extending it to general low entropy signals with an information theoretical analysis. We demonstrate the effectiveness of Polar Sampling approach on a numerical sub-Nyquist sampling example.Pilancı, MertM.S

Hizalanmış graf tabanları öğrenerek graflar üzerinde alan uyarlama.

In this thesis, the domain adaptation problem is studied and a method for domain adaptation on graphs is proposed. Given sufficiently many observations of the label function on a source graph, we study the problem of transferring the label information from the source graph to a target graph for estimating the target label function. Our assumption about the relation between the two domains is that the frequency content of the label function, regarded as a graph signal, has similar characteristics over the source and the target graphs. We propose a method to learn a pair of coherent bases on the two graphs, such that the corresponding source and target graph basis vectors have similar spectral content, while “aligning” the two graphs at the same time so that the reconstructed source and target label functions have similar coefficients over the bases. We formulate the basis learning problem as the learning of a linear transformation between the source and target graph Fourier bases so that each source Fourier basis vector is mapped to a new basis vector in the target graph obtained as a linear combination of the target Fourier basis vectors. One synthetic dataset, two image datasets and one book review dataset are used to test the performance of the proposed algorithm. Besides, baseline machine learning methods and recent domain adaptation algorithms are utilized to compare the performance of the proposed algorithm with the methods in the literature. Experiments on several types of data sets suggest that the proposed method compares quite favorably to reference domain adaptation methods. To the best of our knowledge, our treatment is the first to study the domain adaptation problem in a purely graph-based setting with no need for embedding the data in an ambient space. This feature is particularly convenient for many problems of interest concerning learning on graphs or networks.M.S. - Master of Scienc

Domain Adaptation on Graphs by Learning Aligned Graph Bases

A common assumption in semi-supervised learning is that the class label function has a slow variation on the data graph, while in many problems, the label function may vary abruptly in certain graph regions, resulting in high-frequency components. Although semi-supervised learning is an ill-posed problem, it is often possible to find a source graph on which the label function has similar frequency content to the target graph where the actual classification problem is defined. In this paper, we propose a method for domain adaptation on graphs based on learning the spectrum of the label function in a source graph with many labels, and transferring the spectrum information to the target graph. When transferring the frequency content, it is not easy to share graph Fourier coefficients directly between the two independently constructed graphs, since no match exists between their Fourier bases. We solve this by learning a transformation between the Fourier bases of the two graphs that flexibly “aligns” them. The unknown class label function on the target graph is then reconstructed from the learnt spectrum while retaining consistency with the available labels. Comparative experiments suggest that the proposed algorithm often outperforms recent domain adaptation methods in various data classification applications

Domain Adaptation on Graphs via Frequency Analysis

Classical machine learning algorithms assume the training and test data to be sampled from the same distribution, while this assumption may be violated in practice. Domain adaptation methods aim to exploit the information available in a source domain in order to improve the performance of classification in a target domain. In this work, we focus on the problem of domain adaptation in graph settings. We consider a source graph with many labeled nodes and aim to estimate the class labels on a target graph with few labeled nodes. Our main assumption about the relation between the two graphs is that the frequency content of the label function has similar characteristics. Building on the recent advances in frequency analysis on graphs, we propose a novel graph domain adaptation algorithm. Experiments on image data sets show that the proposed method performs successfully

Türkiye'de 1970-1990 yılları arasında izciliğin tarihi

Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2018.This work is a student project of the Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.The History of Turkey course (HIST200) is a requirement for all Bilkent undergraduates. It is designed to encourage students to work in groups on projects concerning any topic of their choice that relates to the history of Turkey. It is designed as an interactive course with an emphasis on research and the objective of investigating events, chronologically short historical periods, as well as historic representations. Students from all departments prepare and present final projects for examination by a committee, with 10 projects chosen to receive awards.Includes bibliographical references (page 15).by Kudret Emiroğlu

Non-Gynecologic and Gynecologic Laparoscopic Surgery During Pregnancy

With the popularity of minimally invasive procedures in the last two decades, laparoscopy has been performed for gynecologic and non-gynecologic conditions during pregnancy. In spite of the fact that laparoscopic approach provides some advantages such as less post operative discomfort; many potantial risks including uterine and fetal injury, decreased uterine blood flow due to increased intraabdominal pressure and carbon dioxide absorption can take place during laparoscopy. This review evaluates the literature for laparoscopic surgery during pregnancy

Crossover replantation of the foot after bilateral traumatic lower extremity amputation

Background: Bilateral traumatic amputation and limb-threatening injury of the lower extremities is more challenging than the unilateral amputation. Successful replantation of both lower extremities has been reported previously. However, orthotopic implantations may not be possible when amputation of both lower limbs with different levels of section and degrees of damage to surrounding tissues occurs. It was reported that the crossover replanted foot in combination with prosthetic limb is better than 2 artificial limbs. Hence, crossover replantation should be considered when anatomic replantation of both lower extremities is not possible as a result of bilateral total or subtotal amputation. To our knowledge, there are few reports about the crossover replantation of the lower extremity in the literature

Assessment of tissue perfusion following conventional liposuction of perforator-based abdominal flaps

BackgroundThe effect of liposuction on the perforators of the lower abdominal wall has been investigated in several studies. There are controversial results in the literature that have primarily demonstrated the number and patency of the perforators. The aim of this study was to determine the effect of liposuction on the perfusion of perforator-based abdominal flaps using a combined laser–Doppler spectrophotometer (O2C, Oxygen to See, LEA Medizintechnik).MethodsNine female patients undergoing classical abdominoplasty were included in the study. Perforators and the perfusion zones of the deep inferior epigastric artery flap were marked on the patient's abdominal wall. Flap perfusion was quantitatively assessed by measuring blood flow, velocity, capillary oxygen saturation, and relative amount of hemoglobin for each zone preoperatively, after tumescent solution infiltration, following elevation of the flap on a single perforator, and after deep and superficial liposuction, respectively.ResultsThe measurements taken after elevation of the flap were not significantly different than measurements taken after the liposuction procedures.ConclusionsThe liposuction procedure does not significantly alter the perfusion of perforator-based abdominal flaps in the early period. The abdominal tissue discarded in a classic abdominoplasty operation can be raised as a perforator flap and has been demonstrated to be a unique model for clinical research