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38 research outputs found

Determination of Optimal Double Sampling Plan using Genetic Algorithm

Author: S P Deepa
Sampath Sundaram
Publication venue: 'Pakistan Journal of Statistics and Operation Research'
Publication date: 01/03/2012
Field of study

Designing double sampling plan requires identification of sample sizes and acceptance numbers. In this paper a genetic algorithm has been designed for the selection of optimal acceptance numbers and sample sizes for the specified producer’s risk and consumer’s risk. Implementation of the algorithm has been illustrated numerically for different choices of quantities involved in a double sampling plan    <script type="text/javascript"><!--\ud var Transliterate = {\ud loaded: false,\ud // HRef\ud is_local:false,\ud is_write:false,\ud is_newtab:false,\ud getHead: function(){\ud var head = document.getElementsByTagName('head')[0];\ud if (!head) {\ud return document.body;\ud }\ud return head;\ud },\ud addGlobalStyle: function(css) {\ud var head = Transliterate.getHead();\ud var style = document.createElement('style');\ud style.type = 'text/css';\ud style.innerHTML = css;\ud head.appendChild(style);\ud },\ud \ud addScript: function(scriptSrc, callback){\ud var head = Transliterate.getHead();\ud var script = document.createElement('script');\ud script.language = "JavaScript";\ud script.src = scriptSrc;\ud script.type = 'text/javascript';\ud if(callback){\ud script.onload = function(){\ud callback();\ud script.onload = null;\ud };\ud }\ud head.appendChild(script);\ud },\ud \ud addStyles: function(){\ud Transliterate.addGlobalStyle('.goog-transliterate-indic-suggestion-menu {position: absolute;background-color: #EFEFEF;border: 1px outset #7F7F7F;cursor: default;font: small arial, helvetica, sans-serif;margin: 0px;padding: 0px;outline: none;z-index: 20000;}');\ud //Transliterate.addGlobalStyle('.goog-transliterate-indic-suggestion-menuitem-highlight {background-color: #C3D9FF;border-color: #70a0b0;}');\ud Transliterate.addGlobalStyle('.goog-transliterate-indic-suggestion-menuitem {position: relative;padding: 1px 1em;margin: 0px;list-style: none;}');\ud Transliterate.addGlobalStyle('.fnts, .combo119922{font-family:arial,sans-serif;font-size:10px !important;}.lab{cursor: pointer;}');\ud //Transliterate.addGlobalStyle('.cbut{background: #C3D9FF;border: 1px solid #5F9DFF;}');\ud // HRef\ud Transliterate.addGlobalStyle('.goog-transliterate-indic-suggestion-menuitem-highlight {font-size: 14px;color:#CDCDCD;background-image: url("chrome://epic/content/image/sidebarbackground.png");background-repeat: repeat-x;border-color: #70a0b0;}');\ud Transliterate.addGlobalStyle('.opt0, .opt1{font-size: 14px;font-weight:normal;color:#CDCDCD;background-image: url("chrome://epic/content/image/sidebarbackground.png");background-repeat: repeat-x;}');\ud Transliterate.addGlobalStyle('.opt0:hover, .opt1:hover{background:#FF6D00;}');\ud if ((this.is_local) || (this.is_write)) {\ud Transliterate.addGlobalStyle('.cbut{background: #CDCDCD;border: 1px solid #000000;}');\ud Transliterate.addGlobalStyle('.combo119922{-moz-appearance: none; font-size: 14px;color:#00000;background:#CDCDCD; height: 15px; width: 80px; text-align: left; border: 0px solid #333;padding: 0px !important; margin: 0px !important;}');\ud } else {\ud Transliterate.addGlobalStyle('.cbut{background: #CDCDCD;border: 1px solid #8A8384;}');\ud Transliterate.addGlobalStyle('.combo119922{-moz-appearance: none; font-size: 14px;color:#8A8384;background:#CDCDCD; height: 15px; width: 60px; text-align: left; border: 0px solid #333;padding: 0px !important; margin: 0px !important;}');\ud }\ud //Transliterate.addGlobalStyle('.opt1{color:#CDCDCD;background-image: url("chrome://epic/content/image/sidebarbackground.png");background-repeat: repeat-x;}');\ud //Transliterate.addGlobalStyle('.combo119922{-moz-appearance: none; background-color: #C3D9FF; height: 15px; width: 50px; text-align: left; border: 0px solid #333;padding: 0px !important; margin: 0px !important;}');\ud Transliterate.addGlobalStyle('.check1{-moz-appearance: none;border: 1px solid #a82; margin-left: 3px; color: #13c;}');\ud Transliterate.addGlobalStyle('.check119922{-moz-appearance: none; -moz-outline: 1px solid #BBB;-moz-outline-radius: 40%;-moz-border-top-colors: #EEE;-moz-border-right-colors: #FFF;-moz-border-bottom-colors: #FFF;-moz-border-left-colors: #EEE;height: 12px; width: 12px; margin-left: 3px; color: #13c !important;background: none;}');\ud },\ud \ud transliterationControl: undefined,\ud initialized: false,\ud defaultLanguage: 'HINDI',\ud currentLanguage: null,\ud supportTextBoxes: false,\ud enabledElements: [],\ud \ud init: function() {\ud if(Transliterate.initialized){\ud return;\ud }\ud var lang = Transliterate.getCookie('Transliterate_LANG');\ud lang = (la

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Directory of Open Access Journals

Pakistan Journal of Statistics and Operation Research

Determination of Optimal Tightened Normal Tightened Plan Using a Genetic Algorithm

Author: Parthasarathy Deepa S
Sundaram Sampath
Publication venue: DigitalCommons@WayneState
Publication date: 01/05/2016
Field of study

Designing a tightened normal tightened sampling plan requires sample sizes and acceptance number with switching criterion. An evolutionary algorithm, the genetic algorithm, is designed to identify optimal sample sizes and acceptance number of a tightened normal tightened sampling plan for a specified consumer’s risk, producer’s risk, and switching criterion. Optimal sample sizes and acceptance number are obtained by implementing the genetic algorithm. Tables are reported for various choices of switching criterion, consumer’s quality level, and producer’s quality level

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Digital Commons@Wayne State University

The long noncoding RNA, treRNA, decreases DNA damage and is associated with poor response to chemotherapy in chronic lymphocytic leukemia.

Author: Blachly James S
Chen Timothy L
Coombes Kevin R
Flinn Ian W
Fobare Sydney
Grever Michael R
Hertlein Erin K
Kipps Thomas J
Lehman Amy
Liu Chaomei
Lucas David M
Miller Cecelia R
Rassenti Laura Z
Ruppert Amy S
Sampath Deepa
Tallman Martin S
Zhang Xiaoli
Publication venue: eScholarship, University of California
Publication date: 16/02/2017
Field of study

The study of long noncoding RNAs (lncRNAs) is an emerging area of cancer research, in part due to their ability to serve as disease biomarkers. However, few studies have investigated lncRNAs in chronic lymphocytic leukemia (CLL). We have identified one particular lncRNA, treRNA, which is overexpressed in CLL B-cells. We measured transcript expression in 144 CLL patient samples and separated samples into high or low expression of treRNA relative to the overall median. We found that high expression of treRNA is significantly associated with shorter time to treatment. High treRNA also correlates with poor prognostic indicators such as unmutated IGHV and high ZAP70 protein expression. We validated these initial findings in samples collected in a clinical trial comparing the nucleoside analog fludarabine alone or in combination with the alkylating agent cyclophosphamide in untreated CLL samples collected prior to starting therapy (E2997). High expression of treRNA was independently prognostic for shorter progression free survival in patients receiving fludarabine plus cyclophosphamide. Given these results, in order to study the role of treRNA in DNA damage response we generated a model cell line system where treRNA was over-expressed in the human B-CLL cell line OSU-CLL. Relative to the vector control line, there was less cell death in OSU-CLL over-expressing treRNA after exposure to fludarabine and mafosfamide, due in part to a reduction in DNA damage. Therefore, we suggest that treRNA is a novel biomarker in CLL associated with aggressive disease and poor response to chemotherapy through enhanced protection against cytotoxic mediated DNA damage

Crossref

eScholarship - University of California

Determination of Optimal Double Sampling Plan using Genetic Algorithm

Author: Deepa S P
Sundaram Sampath
Publication venue: 'Pakistan Journal of Statistics and Operation Research'
Publication date: 01/03/2012
Field of study

Designing double sampling plan requires identification of sample sizes and acceptance numbers. In this paper a genetic algorithm has been designed for the selection of optimal acceptance numbers and sample sizes for the specified producer’s risk and consumer’s risk. Implementation of the algorithm has been illustrated numerically for different choices of quantities involved in a double sampling plan    <!-- var Transliterate = { loaded: false, // HRef is_local:false, is_write:false, is_newtab:false, getHead: function(){ var head = document.getEle

Directory of Open Access Journals

Pakistan Journal of Statistics and Operation Research

ATM and the Mre11-Rad50-Nbs1 Complex Respond to Nucleoside Analogue–Induced Stalled Replication Forks and Contribute to Drug Resistance

Author: Brett Ewald
Deepa Sampath
William Plunkett
Publication venue: 'American Association for Cancer Research (AACR)'
Publication date
Field of study

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Therapy for older patients with acute myeloblastic leukemia: a problem in search of a solution

Author: Deepa Sampath
Deschler B
Guillermo Garcia-Manero
Publication venue: 'Informa UK Limited'
Publication date
Field of study

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Fludarabine increases oxaliplatin cytotoxicity in normal and chronic lymphocytic leukemia lymphocytes by suppressing interstrand DNA crosslink removal

Author: Keating Michael J.
Moufarij Mazin A.
Plunkett William
Sampath Deepa
Publication venue: The American Society of Hematology
Publication date
Field of study

Oxaliplatin and fludarabine have different but potentially complementary mechanisms of action. Previous studies have shown that DNA repair is a major target for fludarabine. We postulate that potentiation of oxaliplatin toxicity by fludarabine may be due to the inhibition by fludarabine of the activity of the DNA excision repair pathways activated by oxaliplatin adducts. To test this, we investigated the cytotoxic interactions between the 2 drugs in normal and chronic lymphocytic leukemia (CLL) lymphocytes. In each population, the combination resulted in greater than additive killing. Analysis of oxaliplatin damage revealed that fludarabine enhanced accumulation of interstrand crosslinks (ICLs) in specific regions of the genome in both populations, but to a lesser extent in normal lymphocytes. The action of fludarabine on the removal of oxaliplatin ICLs was explored to investigate the mechanism by which oxaliplatin toxicity was increased by fludarabine. Lymphocytes from patients with CLL have a greater capacity for ICL unhooking compared with normal lymphocytes. In the presence of fludarabine the extent of repair was significantly reduced in both populations, more so in CLL. Our findings support a role of fludarabine-mediated DNA repair inhibition as a mechanism critical for the cytotoxic synergy of the 2 drugs

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PubMed Central

Genetic and Epigenetic Profiling Reveals EZH2-mediated Down Regulation of OCT-4 Involves NR2F2 during Cardiac Differentiation of Human Embryonic Stem Cells

Author: Deepa Bhartiya
Mohsin Bashir
Prabha Sampath
Prasad Pethe
Varsha Pursani
Vivek Tanavde
Publication venue: 'Springer Science and Business Media LLC'
Publication date: 01/01/2017
Field of study

Abstract Human embryonic (hES) stem cells are widely used as an in vitro model to understand global genetic and epigenetic changes that occur during early embryonic development. In-house derived hES cells (KIND1) were subjected to directed differentiation into cardiovascular progenitors (D12) and beating cardiomyocytes (D20). Transcriptome profiling of undifferentiated (D0) and differentiated (D12 and 20) cells was undertaken by microarray analysis. ChIP and sequential ChIP were employed to study role of transcription factor NR2F2 during hES cells differentiation. Microarray profiling showed that an alteration of about 1400 and 1900 transcripts occurred on D12 and D20 respectively compared to D0 whereas only 19 genes were altered between D12 and D20. This was found associated with corresponding expression pattern of chromatin remodelers, histone modifiers, miRNAs and lncRNAs marking the formation of progenitors and cardiomyocytes on D12 and D20 respectively. ChIP sequencing and sequential ChIP revealed the binding of NR2F2 with polycomb group member EZH2 and pluripotent factor OCT4 indicating its crucial involvement in cardiac differentiation. The study provides a detailed insight into genetic and epigenetic changes associated with hES cells differentiation into cardiac cells and a role for NR2F2 is deciphered for the first time to down-regulate OCT-4 via EZH2 during cardiac differentiation

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