Ovidiu Daescu

Bio: Ovidiu Daescu is an academic researcher from University of Texas at Dallas. The author has contributed to research in topics: Approximation algorithm & Shortest path problem. The author has an hindex of 18, co-authored 146 publications receiving 1197 citations. Previous affiliations of Ovidiu Daescu include University of Notre Dame.

The human red blood cell proteome and interactome.

Abstract: The red blood cell or erythrocyte is easily purified, readily available, and has a relatively simple structure. Therefore, it has become a very well studied cell in terms of protein composition and function. RBC proteomic studies performed over the last five years, by several laboratories, have identified 751 proteins within the human erythrocyte. As RBCs contain few internal structures, the proteome will contain far fewer proteins than nucleated cells. In this minireview, we summarize the current knowledge of the RBC proteome, discuss alterations in this partial proteome in varied human disease states, and demonstrate how in silico studies of the RBC interactome can lead to considerable insight into disease diagnosis, severity, and drug or gene therapy response. To make these latter points we focus on what is known concerning changes in the RBC proteome in Sickle Cell Disease.

Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models

Abstract: Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma, employing advances in histopathology digitization and automated learning. We selected 40 digitized whole slide images representing the heterogeneity of osteosarcoma and chemotherapy response. With the goal of labeling the diverse regions of the digitized tissue into viable tumor, necrotic tumor, and non-tumor, we trained 13 machine-learning models and selected the top performing one (a Support Vector Machine) based on reported accuracy. We also developed a deep-learning architecture and trained it on the same data set. We computed the receiver-operator characteristic for discrimination of non-tumor from tumor followed by conditional discrimination of necrotic from viable tumor and found our models performing exceptionally well. We then used the trained models to identify regions of interest on image-tiles generated from test whole slide images. The classification output is visualized as a tumor-prediction map, displaying the extent of viable and necrotic tumor in the slide image. Thus, we lay the foundation for a complete tumor assessment pipeline from original histology images to tumor-prediction map generation. The proposed pipeline can also be adopted for other types of tumor.

Convolutional Neural Network for Histopathological Analysis of Osteosarcoma

Abstract: Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% ...

The proteomics and interactomics of human erythrocytes.

Abstract: In this minireview, we focus on advances in our knowledge of the human erythrocyte proteome and interactome that have occurred since our seminal review on the topic published in 2007. As will be explained, the number of unique proteins has grown from 751 in 2007 to 2289 as of today. We describe how proteomics and interactomics tools have been used to probe critical protein changes in disorders impacting the blood. The primary example used is the work done on sickle cell disease where biomarkers of severity have been identified, protein changes in the erythrocyte membranes identified, pharmacoproteomic impact of hydroxyurea studied and interactomics used to identify erythrocyte protein changes that are predicted to have the greatest impact on protein interaction networks.

Efficiently approximating polygonal paths in three and higher dimensions

Abstract: We present efficient algorithms for solving polygonal-path approximation problems in three and higher dimensions. Given an n -vertex polygonal curve P in \R d , d \geq 3 , we approximate P by another poly- gonal curve P' of m ≤ n vertices in \R d such that the vertex sequence of P' is an ordered subsequence of the vertices of P . The goal is either to minimize the size m of P' for a given error tolerance \eps (called the min-\# problem), or to minimize the deviation error \eps between P and P' for a given size m of P' (called the min- \eps problem). Our techniques enable us to develop efficient near-quadratic-time algorithms in three dimensions and subcubic-time algorithms in four dimensions for solving the min-\# and min-\eps problems. We discuss extensions of our solutions to d -dimensional space, where d > 4 , and for the L 1 and L ∈ fty metrics.

Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.

Abstract: Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them—STK11, EGFR, FAT1, SETBP1, KRAS and TP53—can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .

Triangulating a simple polygon in linear time

Abstract: We give a deterministic algorithm for triangulating a simple polygon in linear time. The basic strategy is to build a coarse approximation of a triangulation in a bottom-up phase and then use the information computed along the way to refine the triangulation in a top-down phase. The main tools used are the polygon-cutting theorem, which provides us with a balancing scheme, and the planar separator theorem, whose role is essential in the discovery of new diagonals. Only elementary data structures are required by the algorithm. In particular, no dynamic search trees, of our algorithm.

Platelet-rich plasma: a milieu of bioactive factors.

Abstract: Platelet concentrates such as platelet-rich plasma (PRP) have gained popularity in sports medicine and orthopaedics to promote accelerated physiologic healing and return to function. Each PRP product varies depending on patient factors and the system used to generate it. Blood from some patients may fail to make PRP, and most clinicians use PRP without performing cell counts on either the blood or the preparation to confirm that the solution is truly PRP. Components in this milieu have bioactive functions that affect musculoskeletal tissue regeneration and healing. Platelets are activated by collagen or other molecules and release growth factors from alpha granules. Additional substances are released from dense bodies and lysosomes. Soluble proteins also present in PRP function in hemostasis, whereas others serve as biomarkers of musculoskeletal injury. Electrolytes and soluble plasma hormones are required for cellular signaling and regulation. Leukocytes and erythrocytes are present in PRP and function in inflammation, immunity, and additional cellular signaling pathways. This article supports the emerging paradigm that more than just platelets are playing a role in clinical responses to PRP. Depending on the specific constituents of a PRP preparation, the clinical use can theoretically be matched to the pathology being treated in an effort to improve clinical efficacy.