Application of Cosine Similarity in Bioinformatics

Finding similar sequences to an input query sequence (DNA or proteins) from a sequence data set is an important problem in bioinformatics. It provides researchers an intuition of what could be related or how the search space can be reduced for further tasks. An exact brute-force nearest-neighbor algorithm used for this task has complexity O(m * n) where n is the database size and m is the query size. Such an algorithm faces time-complexity issues as the database and query sizes increase. Furthermore, the use of alignment-based similarity measures such as minimum edit distance adds an additional complexity to the exact algorithm. In this thesis, an alignment-free method based similarity measures such as cosine similarity and squared euclidean distance by representing sequences as vectors was investigated. The cosine-similarity based locality-sensitive hashing technique was used to reduce the number of pairwise comparisons while finding similar sequences to an input query. We evaluated our algorithm on a proteins dataset of size 100,000 sequences and found that our cosine-similarity based algorithm is 28 times faster than the exact algorithm and 13 times faster than the BLASTP[3] algorithm for finding similar sequences with percent identity greater than 90%. It also has 99.5% accuracy. We also developed a greedy incremental clustering algorithm based on our cosine-similarity nearest neighbor algorithm for removing redundant sequences in a protein dataset. We compared our clustering algorithm with a popular clustering algorithm CD-HIT. The clustering results on protein dataset of size 100000 show that our clustering algorithm generated clusters with accuracy almost equal to the CD-HIT algorithm accuracy. We further demonstrated two bioinformatics application where our cosine-similarity based algorithm can be used: an analysis of assembly data of various assemblers and a clustering of a protein dataset. Using our algorithm, we successfully compared the quality of assembly data of multiple de novo and genome-guided assemblers. Adviser: Jitender Deogu

Autoimmune Parkinsonism: A Newer Manifestation of Contactin-Associated Protein-Like 2 Autoimmunity: A Case Report

Contactin-associated protein-like 2 (CASPR2) antibodies are part of an expanding spectrum of disorders. Although they were initially associated with Morvan’s syndrome and peripheral nerve hyperexcitability, their clinical manifestations are more varied than previously recognized. In this report, we present a rare case of a middle-aged woman who experienced gait disturbances, sleep disturbances, behavioral changes, and postural abnormalities over a period of five months. A thorough examination revealed a Parkinsonian phenotype. Considering the timeline and symptomatology, an autoimmune work-up was conducted, which detected CASPR2 antibodies in the patient’s serum. Treatment with high-dose intravenous Methylprednisolone followed by rituximab effectively reversed her clinical manifestations without residual neurological deficits

The United Nations Peacekeeping and Rule of Law Reform in Post-Conflict Societies

The United Nations frequently engages in peacekeeping activities in post-conflict societies in order to build and sustain peace. A crucial component of the durability of peace is the rule of law. While there is extensive empirical literature on the effect of UN peacekeeping on the durability of peace in a post-conflict setting, there is little literature that empirically examines whether UN peacekeeping can improve the rule of law in a post-conflict state. This study uses data to determine whether UN peacekeeping has a positive effect on the quality of rule of law in post-conflict settings. Using regression and matching analysis, this study does not find evidence that peacekeeping influences the rule of law in a post-conflict setting in either a positive or negative direction. Even so, the "non-findings" of this study have implications for policy makers who wish to have post-conflict peace building efforts in order to improve the rule of law

Leveraging Image Analysis to Compute 3D Plant Phenotypes Based on Voxel-Grid Plant Reconstruction

High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions

Bronchoscopic extraction of multiple endobronchial broncholiths in a patient with active pulmonary tuberculosis

Broncholithiasis is an unusual clinical condition characterized by the presence of calcified or ossified material within the airways. Multiple endobronchial broncholithiasis during active infection with tuberculosis is an extremely uncommon presentation. Bron-choscopy plays an important role in the diagnosis and management of broncholithiasis. Currently, there are no specific guidelines for the management of broncholithiasis. Here, we present a case report where multiple broncholiths were successfully removed in a staged manner via rigid bronchoscopy