Development of novel transition metal-catalyzed cross-coupling reactions and applications thereof

Thesis (Ph. D. in Organic Chemistry)--Massachusetts Institute of Technology, Dept. of Chemistry, 2013.Cataloged from PDF version of thesis.Includes bibliographical references.Chapter 1 The first example of Pd(0)/(II) catalyzed fluorination of aryl bromides is reported herein. Based on these data, an analogous method was developed for the fluorination of aryl triflates. The reaction proceeds under mild conditions and represents the first report of reductive elimination from a Pd(II) center of a C-F bond. Chapter 2 Herein we report the first example of a Pd-catalyzed synthesis of aryl trifluoromethyl sulfides. A wide range of aryl bromides are converted to their corresponding trifluoromethyl sulfides in good to excellent yields. Furthermore, we were successful in synthesizing an intermediate in the synthesis of Toltrazuril in two steps from commercially available starting materials. Chapter 3 The development of a novel precatalyst for Ni-catalyzed C-N bond formation is described herein. Furthermore, the substrate scope of the reaction has been expanded to include a wide range of nucleophiles and electrophiles. Finally, we report the first use of weak base in the Nicatalyzed arylation of anilines. Chapter 4 The development of a novel triptycene-based hole-transport material is reported. Computational as well as preliminary photophysical and voltammetric data suggests that this class of compounds could serve as an excellent host material for blue triplet emitters.by Georgiy Teverovskiy.Ph.D.in Organic Chemistr

Pd-Catalyzed Synthesis of Ar-SCF3 Compounds under Mild Conditions

Good to excellent yields of aryl trifluoromethyl sulfides, which are an important class of compounds in both the pharmaceutical and agrochemical areas, can be achieved under mild conditions by the Pd-catalyzed reaction of aryl bromides with a trifluoromethylthiolate nucleophile (see scheme).National Institutes of Health (U.S.) (GM-58160)American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship (32 CFR 168a)National Science Foundation (U.S.) (CHE-980861

Semi-automatic identification of punching areas for tissue microarray building: the tubular breast cancer pilot study

Background: Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously. It allows faster analysis, considerably reducing costs incurred in staining. A time consuming phase of the methodology is the selection of tissue areas within paraffin blocks: no utilities have been developed for the identification of areas to be punched from the donor block and assembled in the recipient block.Results: The presented work supports, in the specific case of a primary subtype of breast cancer (tubular breast cancer), the semi-automatic discrimination and localization between normal and pathological regions within the tissues. The diagnosis is performed by analysing specific morphological features of the sample such as the absence of a double layer of cells around the lumen and the decay of a regular glands-and-lobules structure. These features are analysed using an algorithm which performs the extraction of morphological parameters from images and compares them to experimentally validated threshold values. Results are satisfactory since in most of the cases the automatic diagnosis matches the response of the pathologists. In particular, on a total of 1296 sub-images showing normal and pathological areas of breast specimens, algorithm accuracy, sensitivity and specificity are respectively 89%, 84% and 94%.Conclusions: The proposed work is a first attempt to demonstrate that automation in the Tissue MicroArray field is feasible and it can represent an important tool for scientists to cope with this high-throughput technique

Automatic Tumor-Stroma Separation in Fluorescence TMAs Enables the Quantitative High-Throughput Analysis of Multiple Cancer Biomarkers

The upcoming quantification and automation in biomarker based histological tumor evaluation will require computational methods capable of automatically identifying tumor areas and differentiating them from the stroma. As no single generally applicable tumor biomarker is available, pathology routinely uses morphological criteria as a spatial reference system. We here present and evaluate a method capable of performing the classification in immunofluorescence histological slides solely using a DAPI background stain. Due to the restriction to a single color channel this is inherently challenging. We formed cell graphs based on the topological distribution of the tissue cell nuclei and extracted the corresponding graph features. By using topological, morphological and intensity based features we could systematically quantify and compare the discrimination capability individual features contribute to the overall algorithm. We here show that when classifying fluorescence tissue slides in the DAPI channel, morphological and intensity based features clearly outpace topological ones which have been used exclusively in related previous approaches. We assembled the 15 best features to train a support vector machine based on Keratin stained tumor areas. On a test set of TMAs with 210 cores of triple negative breast cancers our classifier was able to distinguish between tumor and stroma tissue with a total overall accuracy of 88%. Our method yields first results on the discrimination capability of features groups which is essential for an automated tumor diagnostics. Also, it provides an objective spatial reference system for the multiplex analysis of biomarkers in fluorescence immunohistochemistry

TRULY 3D MIDSAGITTAL PLANE EXTRACTION FOR ROBUST NEUROIMAGE REGISTRATION

can be removed by defining an ideal midsagittal plane (iMSP) as a virtual geometric plane about which the three-dimensional anatomi-This paper describes a robust algorithm for reliable ideal Midsagitcal structure captured in the given neuroimage exhibits maximum bital Plane extraction (iMSP) from 3D neuroimages. The algorithm lateral symmetry [10]. Fully automatic extraction of iMSP presents makes no assumptions about initial orientation of a given 3D brain a number of challenges posed by various extrinsic and intrinsic fac-image and works reliably on neuroimages of normal brains as well tors. These factors include initial orientation of the neuroimage, the as brains with significant pathologies. Presented technique is truly amount of noise present and the degree of asymmetry of the input three-dimensional since we treat each neuroimage as a three-dimensional neuroimage. In this paper we propose a simple algorithm that ca

Learning-based Neuroimage Registration

Neuroimage registration has been a crucial area of research in medical image analysis for many years. Aligning brain images of different subjects in such a way that same anatomical structures correspond spatially is required in many different applications, including neuroimage classification, computer aided diagnosis, statistical quantification of human brains and neuroimage segmentation. We combine statistical learning, computer vision and medical image analysis to propose a multiresolution framework for learning-based neuroimage registration. Our approach has four distinct characteristics not present in other registration methods. First, instead of subjectively choosing which features to use for registration, we employ feature selection at different image scales to learn an appropriate subset of features for registering a specific pair of neuroimages. Second, we use interesting-voxel selection to identify image voxels that have the most distinct image feature vectors. These voxels are then used to estimate the deformation field for registration. Third, we iteratively improve our choice of features and interesting voxels during registration process. Fourth, we create and take advantage of a statistical model containing information on image feature distributions in each anatomical location

Truly 3D Midsagittal Plane Extraction for Robust Neuroimage Registration ∗

This paper describes a robust algorithm for reliable ideal Midsagittal Plane extraction (iMSP) from 3D neuroimages. The algorithm makes no assumptions about initial orientation of a given 3D brain image and works reliably on neuroimages of normal brains as well as brains with significant pathologies. Presented technique is truly three-dimensional since we treat each neuroimage as a three-dimensional volume rather than a set of two-dimensional slices. We use an edge-based approach which employs cross-correlation to extract iMSP. This work also includes quantitative evaluation of the performance of the proposed algorithm when applied to a wide variety of real neuroimages. We find that our algorithm is able to extract iMSP from neuroimages with arbitrary initial orientations, large asymmetries, and low signal to noise ratio. We also demonstrate how presented algorithm can increase robustness of existing neuroimage registration algorithms, be it rigid, affine or less restricted deformable registration. Our algorithm was implemented using Insight Toolkit(ITK)