139 research outputs found

    Evolutionary Granular Kernel Machines

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    Kernel machines such as Support Vector Machines (SVMs) have been widely used in various data mining applications with good generalization properties. Performance of SVMs for solving nonlinear problems is highly affected by kernel functions. The complexity of SVMs training is mainly related to the size of a training dataset. How to design a powerful kernel, how to speed up SVMs training and how to train SVMs with millions of examples are still challenging problems in the SVMs research. For these important problems, powerful and flexible kernel trees called Evolutionary Granular Kernel Trees (EGKTs) are designed to incorporate prior domain knowledge. Granular Kernel Tree Structure Evolving System (GKTSES) is developed to evolve the structures of Granular Kernel Trees (GKTs) without prior knowledge. A voting scheme is also proposed to reduce the prediction deviation of GKTSES. To speed up EGKTs optimization, a master-slave parallel model is implemented. To help SVMs challenge large-scale data mining, a Minimum Enclosing Ball (MEB) based data reduction method is presented, and a new MEB-SVM algorithm is designed. All these kernel methods are designed based on Granular Computing (GrC). In general, Evolutionary Granular Kernel Machines (EGKMs) are investigated to optimize kernels effectively, speed up training greatly and mine huge amounts of data efficiently

    Investigation of Rice Bran Derived Anti-cancer Pentapeptide for Mechanistic Potency in Breast Cancer Cell Models

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    Bioactive peptides derived from food sources with anti-proliferative properties against cancer have drawn more attention in recent years. A pentapeptide derived from rice bran has shown anti-proliferative propertiesagainst human breast cancer cells. The objective of this study was to investigate the mechanistic action of the pentapeptide-induced apoptosis in breast cancer cell models (MCF-7 and MDA-MB-231). The growth inhibition activity of the pentapeptide was evaluated by MTS[3-(4,5-dimethylthiazol-2-yl)-5-(3- arboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt] assayand trypan blue assay in a dose- and time-dependent manner. The apoptotic properties of pentapeptide-induced apoptosis on cancerous breast cells were evaluated by morphological changes, DNA fragmentation, and caspases-3/7, -8,and -9 activities. The levels of molecular targets (p53, COX-2, TNF-α, Fas, Bax, Bcl-2, and ErbB-2) were evaluated by enzyme-linked immunosorbent assay (ELISA) kits. Pentapeptide showed growth inhibition activities on MCF-7 and MDA-MB-231 cells. Apoptotic features including morphological changes,DNA fragmentation, and caspases activation were observed in both cells lines after pentapeptide treatment. Decreased levels of COX-2, Bcl-2, and ErbB-2 and increased levels of p53, TNF-α, Fas, and Bax expression were detected after cells exposed to pentapeptide from 72 to 96 hr. The results suggest that the pentapeptide inhibits growth of human breast cancer cells by introducing apoptosis through a caspase-dependent pathway. The pentapeptide amplifies the death signal by down-regulating the expression of ErbB-2 in both cell lines and COX-2 in ER (Estrogen Receptor)-positive MCF-7 cells. This study provides insight on the molecular mechanism of action of the pentapeptide against breast cancer cells. After further animal and human clinic trial, the pentapeptide has the potentiality to be analternative strategy to current anti-cancer drugs

    13th Annual Pacific Research Day Abstracts

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    Endometriosis

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    This book provides an insight into the emerging trends in pathogenesis, diagnosis and management of endometriosis. Key features of the book include overviews of endometriosis; endometrial angiogenesis, stem cells involvement, immunological and hormonal aspects related to the disease pathogenesis; recent research reports on infertility, endometrial receptivity, ovarian cancer and altered gene expression associated with endometriosis; various predictive markers, and imaging modalities including MRI and ultrasound for efficient diagnosis; as well as current non-hormonal and hormonal treatment strategies This book is expected to be a valuable resource for clinicians, scientists and students who would like to have an improved understanding of endometriosis and also appreciate recent research trends associated with this disease

    Fragment Based Protein Active Site Analysis Using Markov Random Field Combinations of Stereochemical Feature-Based Classifications

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    Recent improvements in structural genomics efforts have greatly increased the number of hypothetical proteins in the Protein Data Bank. Several computational methodologies have been developed to determine the function of these proteins but none of these methods have been able to account successfully for the diversity in the sequence and structural conformations observed in proteins that have the same function. An additional complication is the flexibility in both the protein active site and the ligand. In this dissertation, novel approaches to deal with both the ligand flexibility and the diversity in stereochemistry have been proposed. The active site analysis problem is formalized as a classification problem in which, for a given test protein, the goal is to predict the class of ligand most likely to bind the active site based on its stereochemical nature and thereby define its function. Traditional methods that have adapted a similar methodology have struggled to account for the flexibility observed in large ligands. Therefore, I propose a novel fragment-based approach to dealing with larger ligands. The advantage of the fragment-based methodology is that considering the protein-ligand interactions in a piecewise manner does not affect the active site patterns, and it also provides for a way to account for the problems associated with flexible ligands. I also propose two feature-based methodologies to account for the diversity observed in sequences and structural conformations among proteins with the same function. The feature-based methodologies provide detailed descriptions of the active site stereochemistry and are capable of identifying stereochemical patterns within the active site despite the diversity. Finally, I propose a Markov Random Field approach to combine the individual ligand fragment classifications (based on the stereochemical descriptors) into a single multi-fragment ligand class. This probabilistic framework combines the information provided by stereochemical features with the information regarding geometric constraints between ligand fragments to make a final ligand class prediction. The feature-based fragment identification methodology had an accuracy of 84% across a diverse set of ligand fragments and the mrf analysis was able to succesfully combine the various ligand fragments (identified by feature-based analysis) into one final ligand based on statistical models of ligand fragment distances. This novel approach to protein active site analysis was additionally tested on 3 proteins with very low sequence and structural similarity to other proteins in the PDB (a challenge for traditional methods) and in each of these cases, this approach successfully identified the cognate ligand. This approach addresses the two main issues that affect the accuracy of current automated methodologies in protein function assignment

    Biointormatics of Targeted Therapeutics And Applications in Drug Discovery

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    Ph.DDOCTOR OF PHILOSOPH

    Director\u27s report of research in Kansas 2009

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    This report contains the title, author, and publication information for manuscripts published by station scientists

    Role of the Neuropeptide Substance P in Burn-Induced Distant Organ Damage

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    Ph.DDOCTOR OF PHILOSOPH

    Machine Learning Approaches to Predict Recurrence of Aggressive Tumors

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    Cancer recurrence is the major cause of cancer mortality. Despite tremendous research efforts, there is a dearth of biomarkers that reliably predict risk of cancer recurrence. Currently available biomarkers and tools in the clinic have limited usefulness to accurately identify patients with a higher risk of recurrence. Consequently, cancer patients suffer either from under- or over- treatment. Recent advances in machine learning and image analysis have facilitated development of techniques that translate digital images of tumors into rich source of new data. Leveraging these computational advances, my work addresses the unmet need to find risk-predictive biomarkers for Triple Negative Breast Cancer (TNBC), Ductal Carcinoma in-situ (DCIS), and Pancreatic Neuroendocrine Tumors (PanNETs). I have developed unique, clinically facile, models that determine the risk of recurrence, either local, invasive, or metastatic in these tumors. All models employ hematoxylin and eosin (H&E) stained digitized images of patient tumor samples as the primary source of data. The TNBC (n=322) models identified unique signatures from a panel of 133 protein biomarkers, relevant to breast cancer, to predict site of metastasis (brain, lung, liver, or bone) for TNBC patients. Even our least significant model (bone metastasis) offered superior prognostic value than clinopathological variables (Hazard Ratio [HR] of 5.123 vs. 1.397 p\u3c0.05). A second model predicted 10-year recurrence risk, in women with DCIS treated with breast conserving surgery, by identifying prognostically relevant features of tumor architecture from digitized H&E slides (n=344), using a novel two-step classification approach. In the validation cohort, our DCIS model provided a significantly higher HR (6.39) versus any clinopathological marker (p\u3c0.05). The third model is a deep-learning based, multi-label (annotation followed by metastasis association), whole slide image analysis pipeline (n=90) that identified a PanNET high risk group with over an 8x higher risk of metastasis (versus the low risk group p\u3c0.05), regardless of cofounding clinical variables. These machine-learning based models may guide treatment decisions and demonstrate proof-of-principle that computational pathology has tremendous clinical utility

    An overview of Neem (Azadirachta indica) and its potential impact on health

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    Global health and medical practice seek to merge alternative medicine with evidence-based medicine for a better understanding of the metabolic process and its effects in the human body. An example is the use of complementary medicine like phytotherapy. Azadirachta indica (Neem), a tree originally from India and Myanmar, called by many “The village pharmacy” or “Divine tree” because of its many health properties. In recent times, Neem-derived extracts have been shown to work from anywhere from insect repellent, to supplements to lower inflammation, diabetic control, and even to combat cancer. Herein, we state the health benefits found in diverse compounds and extracts derived from Neem, highlighting the mechanisms and pathways in which Neem compounds produce their effects, while warning that the improper and unstandardized conditions to produce extracts can lead to health issues, particularly certain compounds might have damaging effects on the liver and kidneys
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