968 research outputs found
sj-docx-1-pie-10.1177_09544089231215227 - Supplemental material for Process modeling and optimization of titanium alloy Ti-6Al-7Nb during WEDM using regression and ANN
No full textSupplemental material, sj-docx-1-pie-10.1177_09544089231215227 for Process modeling and optimization of titanium alloy Ti-6Al-7Nb during WEDM using regression and ANN by Vikas Sharma, Joy Prakash Misra and Sandeep Singhal in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p
Multidimensional Range Query and Load Balancing in Wireless Ad Hoc and Sensor Networks
No full textSensor networks are usually composed by spatially distributed devices able to monitor physical or environmental conditions (pressure, temperature, motion, etc.). This kind of networks were originally built with the idea of transmitting elementary information to external sinks for further processing and querying. Nowadays, the growth of sensors memory and computational capability together with the significant reduction of energy consumptions have changing the potential of sensor networks allowing in-network storage and processing. The W-Grid infrastructure follows a Data Centric approach that indexes data according to any number of attributes so that it is possible to query events of interest through multi-dimensional range queries. Differently from existing Data Centric solutions W-Grid does not use either sensors physical position (i.e. GPS) nor estimation of their positions. For this reason W-Grid can be applied to a wider number of scenarios than existing solutions, as it works both indoor and outdoor, and can be easily suitable to other kind of ad-hoc networks, such as mesh networks and wireless community networks. In this paper we describe how W-Grid is able to efficiently managing and querying data in wireless sensor networks and we report, by means of an extensive number of simulations, several performance measures of its efficiency in comparison with a well-know competitor solution in literature
Machine Learning And Data Mining To Validate The Prognostics And Predictive Breast Cancer Biomarkers On A Large Racially Diverse Population
Get PDFBreast cancer biomarkers have great potential in providing clinicians more individualized information about the composition and outcomes of a patient’s breast cancer. However, many breast cancer biomarkers have not been evaluated on a large scale or in groups of patients with diverse characteristics, leading to difficulty in their translation to having an impact on patients. In this study, we compile a large, pooled breast cancer patient data cohort and evaluate breast cancer biomarkers on patients with diverse characteristics. Biomarkers are found to have varying expression patterns within the different breast cancer subtypes, validating the need to evaluate biomarkers on patient populations with diverse backgrounds, subtypes, and other breast cancer characteristics. As expected, ESR1, an estrogen receptor biomarker, showed significant increased expression in the Luminal A and Luminal B subtypes for this dataset. The large, pooled cohort developed in this study has future potential in many areas of breast cancer research
Proceedings of ASME Turbo Expo 2013: Power for Land, Sea and Air, Volume 1A: Combustion, Fuels and Emissions
No full textShahrokh Etemad (with Sandeep Alavandi and Benjamin Baird) is a contributing author, Fuel Flexible Rich Catalytic Lean Burn System for Low Btu Fuels
Developing A Multiomic Association Between Ionizing Radiation Exposure And Biological Aging Processes For Space Exploration Purpose
Get PDFIf humans continue to push to be a space exploring species, time and radiation pose the most significant barriers. While not a new idea, approaching a rad-age association with today’s technology offers a fresh look at treating and monitoring both as a similar challenge. To get to that point, starting from square one helps shape the project and avoid common pitfalls. Each chapter is an incremental step to developing this association using multi-omics by building theoretical knowledge of the relationship between radioactive damage and aging processes, building technical knowledge by developing bioinformatic pipelines to integrate different -omes, using genetics data to create a new foundation for associative studies, then integrating epigenetic data to identify potential control mechanisms and corroborate a rad-age assessment. Results of each chapter include (1) a better understanding of how to develop an association and a comprehensive table of biological age indicators, (2) three submodules on processing genetic data, epigenetic data, and integrating the two, (3) using human genetics data to define a 29-year-old threshold between young and old patients to then identify 664 genes from various statistical analyses of age, radiation, sex, and dependent interactions of age and radiation, and (4) integrating human DNA methylation data with previous gene expression findings to narrow the list of genes of interest to 17 statistically significant genes with regard to p-value \u3c 0.05 and |fold-change|\u3e2. Functional analysis emphasize pathways dealing with DNA repair, mitochondrial function, immune response, and metabolism with diseases including cardiovascular diseases, cognitive disfunction, and a multitude of cancers. These 17 genes could serve as future starting points for dedicated studies on controlling rad-age outcomes and benefit outlooks on the radiation workforce, cancer radiotherapy, geriatrics, and general aerospace medicine
Acute Ethanol Administration Rapidly Increases Phosphorylation of Conventional Protein Kinase C in Specific Mammalian Brain Regions in Vivo
Get PDFBackground
Protein kinase C (PKC) is a family of isoenzymes that regulate a variety of functions in the central nervous system including neurotransmitter release, ion channel activity, and cell differentiation. Growing evidence suggests that specific isoforms of PKC influence a variety of behavioral, biochemical, and physiological effects of ethanol in mammals. The purpose of this study was to determine whether acute ethanol exposure alters phosphorylation of conventional PKC isoforms at a threonine 674 (p-cPKC) site in the hydrophobic domain of the kinase, which is required for its catalytic activity.
Methods
Male rats were administered a dose range of ethanol (0, 0.5, 1, or 2 g/kg, intragastric) and brain tissue was removed 10 minutes later for evaluation of changes in p-cPKC expression using immunohistochemistry and Western blot methods.
Results
Immunohistochemical data show that the highest dose of ethanol (2 g/kg) rapidly increases p-cPKC immunoreactivity specifically in the nucleus accumbens (core and shell), lateral septum, and hippocampus (CA3 and dentate gyrus). Western blot analysis further showed that ethanol (2 g/kg) increased p-cPKC expression in the P2 membrane fraction of tissue from the nucleus accumbens and hippocampus. Although p-cPKC was expressed in numerous other brain regions, including the caudate nucleus, amygdala, and cortex, no changes were observed in response to acute ethanol. Total PKC? immunoreactivity was surveyed throughout the brain and showed no change following acute ethanol injection
Characterizing collagen mimetic peptides for orthogonal self-assembly
No full textA computational design of collagen mimetic peptides (CMPs) that self-assemble orthogonally (mutually exclusively), in the presence of other pre-existing collagen trimer mixtures, in vitro, has been proposed. The orthogonality in self-assembly was brought about by orthogonal patterning of ionic salt bridges and residues, along the collagen trimers’ axial length. Through the aid of circular dichroism spectroscopy alone, a novel experimental protocol was set-up to rapidly assess the level of cross-talk that may arise in such designed ‘heterogeneous monomer to trimer folding’ mixture environments. It is shown that the designed collagen mimetic peptides are stable and hetero-specific within their composite 3 chain peptide ecosystem. We experimentally demonstrate the extent to which loss in specificity could possibly occur, upon moving to a higher order ‘more than 3 monomers in solution’ peptide ensemble. Although the desired level of multi-state orthogonality was not achieved in the current design, the experimental results obtained were used to estimate the stability and specificity barrier threshold that one might run into, if one were to instead design orthogonal systems where-in specificity is incorporated during the computational design stage itself a priori. A Pareto frontier plot indicating the specificity versus stability trade-off is plotted. We conclude that a bottom-up design approach, incorporating design of specificity during the sequence design stage, would be a better way forward for achieving self-assembling orthogonality. In contrast to the complex chaperone assisted protein folding systems existing in nature, our method is a simplistic first step towards the complementary approach of modular synthetic collagen molecule design.Ph.D.Includes bibliographical referencesby Sandeep Vishwanath Belur
Resin and steel-reinforced resin used as injection materials in bolted connections
No full textInjection bolts are bolts in which the cavity produced by the clearance between the bolt and the wall of the hole is completely filled up with a two-component resin. Filling of the clearance is carried out through a small hole in the head of the bolt. After injection and complete curing, the connection is slip resistant. Recently the injection material, typically an epoxy resin, was modified at TU Delft by adding steel shots (spherical particles) to mitigate the effects of resin compliance in the shear connection of reusable composite (steel-concrete) structures. Experimental compressive material tests on unconfined/confined resin and steel-reinforced resin are evaluated in this chapter. The uniaxial model which combines damage mechanics and the Ramberg-Osgood relationship is proposed to describe the uniaxial compressive behavior of resin and steel-reinforced resin. First-order numerical homogenization is employed as a high-fidelity model, where a combined nonlinear isotropic/kinematic cyclic hardening model is employed to define the steel plasticity, the linear Drucker-Prager plastic criterion was used to simulate resin damage, and the cohesive surfaces reflecting the relationship between traction and displacement at the interface. The linear Drucker-Prager plastic model is used as a low-fidelity model.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Steel & Composite Structure
Analysis of gene expression and methylation data for the identification of novel biomarkers in breast cancer
No full textAbstract<p>Context: Breast cancer treatment has experienced several changes in the last decades due to the innovation of specific prognostic and predictive biomarkers that facilitate the application of more personalized therapies to different molecular sub-groups. Presently, more women are be- ing treated with neoadjuvant (preoperative) therapy which involves chemotherapy or endocrine agents before surgery, for earlier-stage operable breast carcinoma. Following this mode of pre- operative systemic treatment could improve the surgical option and make inoperable tumors operable. It can also increase the breast conservation rate. Another key benefit of neoadjuvant therapy is monitoring response to the treatment. The good response to neoadjuvant therapy with complete pathological response (pCR) is a surrogate marker for overall survival.<p>Objective: 1) To investigate the association between early changes in several gene expression signatures, recapitulating several biological processes, and neoadjuvant letrozole (endocrine therapy), and to compare those to Ki67 values. 2) To interrogate the association between chemotherapy response (Pathological complete response (pCR) in this case) and gene expres- sion modules, recapitulating important biological processes such as the gene expression grade index (GGI) and ”druggable”oncogenic pathways in different breast cancer subtypes.<p>Data Sources: We collected publicly available gene expression data based on the review of selected literature on breast carcinoma after neoadjuvant therapy with the clinical and patho- logic characteristics.<p>Results: In this work we have shown, 1) Residual proliferation after short-term endocrine therapy can be used as an early surrogate marker of clinical to response to endocrine therapy in this population. 2) Different processes and pathways are associated with pCR in different BC subtypes.<p>Conclusions: Our analysis has several limitations such as: 1) Lacks of statistical power due to small dataset for endocrine treated patients, 2) We have included only anthracycline-based neoadjuvant chemotherapy regimens; therefore, it is not known if the associations between gene modules and pCR are anthracycline specific or indicate general chemosensitivity. More- over, patients with HER2-positive tumors did not receive preoperative trastuzumab, and it is not known how this could modulate the identified associations. But our results generate sev- eral hypotheses that should be tested in BC subtype - focused trials of targeted agents like IGF1, PARP inhibitors, and agents modulating immune response. If results are confirmed by additional validation studies, this may lead to a paradigm shift in early breast cancer treatment.Doctorat en Sciences biomédicales et pharmaceutiquesinfo:eu-repo/semantics/nonPublishe
Anomaly-Based DNN Model for Intrusion Detection in IoT and Model Explanation: Explainable Artificial Intelligence
No full textIoT has gained immense popularity recently with advancements in technologies and big data. IoT network is dynamically increasing with the addition of devices, and the big data is generated within the network, making the network vulnerable to attacks. Thus, network security is essential, and an intrusion detection system is needed. In this paper, we proposed a deep learning-based model for detecting intrusions or attacks in IoT networks. We constructed a DNN model, applied a filter method for feature reduction, and tuned the model with different parameters. We also compared the performance of DNN with other machine learning techniques in terms of accuracy, and the proposed DNN model with weight decay of 0.0001 and dropout rate of 0.01 achieved an accuracy of 0.993, and the reduced loss on the NSL-KDD dataset having five classes. DL models are a black box and hard to understand, so we explained the model predictions using LIME.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit
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