MODELING OF TRANSFER PATH FOR DETERMINATION OF COMBUSTION AND NOISE METRICS ON DIESEL ENGINES

Determination of combustion metrics for a diesel engine has the potential of providing feedback for closed-loop combustion phasing control to meet current and upcoming emission and fuel consumption regulations. This thesis focused on the estimation of combustion metrics including start of combustion (SOC), crank angle location of 50% cumulative heat release (CA50), peak pressure crank angle location (PPCL), and peak pressure amplitude (PPA), peak apparent heat release rate crank angle location (PACL), mean absolute pressure error (MAPE), and peak apparent heat release rate amplitude (PAA). In-cylinder pressure has been used in the laboratory as the primary mechanism for characterization of combustion rates and more recently in-cylinder pressure has been used in series production vehicles for feedback control. However, the intrusive measurement with the in-cylinder pressure sensor is expensive and requires special mounting process and engine structure modification. As an alternative method, this work investigated block mounted accelerometers to estimate combustion metrics in a 9L I6 diesel engine. So the transfer path between the accelerometer signal and the in-cylinder pressure signal needs to be modeled. Depending on the transfer path, the in-cylinder pressure signal and the combustion metrics can be accurately estimated - recovered from accelerometer signals. The method and applicability for determining the transfer path is critical in utilizing an accelerometer(s) for feedback. Single-input single-output (SISO) frequency response function (FRF) is the most common transfer path model; however, it is shown here to have low robustness for varying engine operating conditions. This thesis examines mechanisms to improve the robustness of FRF for combustion metrics estimation. First, an adaptation process based on the particle swarm optimization algorithm was developed and added to the single-input single-output model. Second, a multiple-input single-output (MISO) FRF model coupled with principal component analysis and an offset compensation process was investigated and applied. Improvement of the FRF robustness was achieved based on these two approaches. Furthermore a neural network as a nonlinear model of the transfer path between the accelerometer signal and the apparent heat release rate was also investigated. Transfer path between the acoustical emissions and the in-cylinder pressure signal was also investigated in this dissertation on a high pressure common rail (HPCR) 1.9L TDI diesel engine. The acoustical emissions are an important factor in the powertrain development process. In this part of the research a transfer path was developed between the two and then used to predict the engine noise level with the measured in-cylinder pressure as the input. Three methods for transfer path modeling were applied and the method based on the cepstral smoothing technique led to the most accurate results with averaged estimation errors of 2 dBA and a root mean square error of 1.5dBA. Finally, a linear model for engine noise level estimation was proposed with the in-cylinder pressure signal and the engine speed as components

Review of sensing methodologies for estimation of combustion metrics

For reduction of engine-out emissions and improvement of fuel economy, closed-loop control of the combustion process has been explored and documented by many researchers. In the closed-loop control, the engine control parameters are optimized according to the estimated instantaneous combustion metrics provided by the combustion sensing process. Combustion sensing process is primarily composed of two aspects: combustion response signal acquisition and response signal processing. As a number of different signals have been employed as the response signal and the signal processing techniques can be different, this paper did a review work concerning the two aspects: combustion response signals and signal processing techniques. In-cylinder pressure signal was not investigated as one of the response signals in this paper since it has been studied and documented in many publications and also due to its high cost and inconvenience in the application

Frequency Tuning of Work Modes in Z

Frequency tuning of work modes in the silicon vibratory gyroscope is studied by the theoretical, numerical, and experimental methods in this paper. First, the schematic structure and simplified kinematics model of the gyroscope were presented for deducing the natural frequencies. Then, the width and length of support beams were optimized to tune work frequencies at their designed value. Besides, the frequency difference was experimentally tested and manually tuned by varying the voltage applied on the tuning capacitors. The test on a prototype showed that the difference could be localized between −55.8 Hz and 160.2 Hz when the tuning voltage limit is 20 V. Finally, a frequency control loop was developed to automatically tune the sense frequency toward the drive frequency. Both the theoretical analysis and numeric simulation show that the difference is stabilized at 0.8 Hz when no Coriolis force or quadrature coupling force is applied. It is proved that the frequency difference is successfully tuned by modifying the size of support beams before fabrication as well as the voltage applied on the tuning capacitors after fabrication. The automatic tuning loop, used to match the work modes, is beneficial to enhance the performance of the gyroscope as well as its resistance to environment disturbances

An enhanced genetic mutation-based model for predicting the efficacy of immune checkpoint inhibitors in patients with melanoma

BackgroundProgrammed death ligand 1 (PD-L1) and tumor mutation burden (TMB) have been developed as biomarkers for the treatment of immune checkpoint inhibitors (ICIs). However, some patients who are TMB-high or PD-L1-high remained resistant to ICIs therapy. Therefore, a more clinically applicable and effective model for predicting the efficacy of ICIs is urgently needed.MethodsIn this study, genomic data for 466 patients with melanoma treated with ICIs from seven independent cohorts were collected and used as training and validation cohorts (training cohort n = 300, validation cohort1 n = 61, validation cohort2 n = 105). Ten machine learning classifiers, including Random Forest classifier, Stochastic Gradient Descent (SGD) classifier and Linear Support Vector Classifier (SVC), were subsequently evaluated. ResultsThe Linear SVC with a 186-gene mutation-based set was screened to construct the durable clinical benefit (DCB) model. Patients predicted to have DCB (pDCB) were associated with a better response to the treatment of ICIs in the validation cohort1 (AUC=0.838) and cohort2 (AUC=0.993). Compared with TMB and other reported genetic mutation-based signatures, the DCB model showed greater predictive power. Furthermore, we explored the genomic features in determining the benefits of ICIs treatment and found that patients with pDCB were associated with higher tumor immunogenicity. ConclusionThe DCB model constructed in this study can effectively predict the efficacy of ICIs treatment in patients with melanoma, which will be helpful for clinical decision-making

Proceedings of the I ndo‐ U.S. bilateral workshop on accelerating botanicals/biologics agent development research for cancer chemoprevention, treatment, and survival

With the evolving evidence of the promise of botanicals/biologics for cancer chemoprevention and treatment, an Indo‐U.S. collaborative Workshop focusing on “Accelerating Botanicals Agent Development Research for Cancer Chemoprevention and Treatment” was conducted at the Moffitt Cancer Center, 29–31 May 2012. Funded by the Indo‐U.S. Science and Technology Forum, a joint initiative of Governments of India and the United States of America and the Moffitt Cancer Center, the overall goals of this workshop were to enhance the knowledge (agents, molecular targets, biomarkers, approaches, target populations, regulatory standards, priorities, resources) of a multinational, multidisciplinary team of researcher's to systematically accelerate the design, to conduct a successful clinical trials to evaluate botanicals/biologics for cancer chemoprevention and treatment, and to achieve efficient translation of these discoveries into the standards for clinical practice that will ultimately impact cancer morbidity and mortality. Expert panelists were drawn from a diverse group of stakeholders, representing the leadership from the National Cancer Institute's Office of Cancer Complementary and Alternative Medicine (OCCAM), NCI Experimental Therapeutics (NExT), Food and Drug Administration, national scientific leadership from India, and a distinguished group of population, basic and clinical scientists from the two countries, including leaders in bioinformatics, social sciences, and biostatisticians. At the end of the workshop, we established four Indo‐U.S. working research collaborative teams focused on identifying and prioritizing agents targeting four cancers that are of priority to both countries. Presented are some of the key proceedings and future goals discussed in the proceedings of this workshop. With the evolving evidence of the promise of botanicals/biologics for cancer chemoprevention and treatment, the proceedings of the Indo‐U.S. collaborative Workshop represent one of the most contemporary issues in Cancer Medicine .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96353/1/cam442.pd

A pH-Responsive Cluster Metal-Organic Framework Nanoparticle for Enhanced Tumor Accumulation and Antitumor Effect

As a result of the deficient tumor-specific antigens, potential off-target effect, and influence of protein corona, metal–organic framework nanoparticles have inadequate accumulation in tumor tissues, limiting their therapeutic effects. In this work, a pH-responsive linker (L) is prepared by covalently modifying oleylamine (OA) with 3-(bromomethyl)-4-methyl-2,5-furandione (MMfu) and poly(ethylene glycol) (PEG). Then, the L is embedded into a solid lipid nanoshell to coat apilimod (Ap)-loaded zeolitic imidazolate framework (Ap-ZIF) to form Ap-ZIF@SLN#L. Under the tumor microenvironment, the hydrophilic PEG and MMfu are removed, exposing the hydrophobic OA on Ap-ZIF@SLN#L, increasing their uptake in cancer cells and accumulation in the tumor. The ZIF@SLN#L nanoparticle induces reactive oxygen species (ROS). Ap released from Ap-ZIF@SLN#L significantly promotes intracellular ROS and lactate dehydrogenase generation. Ap-ZIF@SLN#L inhibits tumor growth, increases the survival rate in mice, activates the tumor microenvironment, and improves the infiltration of macrophages and T cells in the tumor, as demonstrated in two different tumor-bearing mice after injections with Ap-ZIF@SLN#TL. Furthermore, mice show normal tissue structure of the main organs and the normal serum level in alanine aminotransferase and aspartate aminotransferase after treatment with the nanoparticles. Overall, this pH-responsive targeting strategy improves nanoparticle accumulation in tumors with enhanced therapeutic effects.</p

Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones

The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology

Integrative annotation of 21,037 human genes validated by full-length cDNA clones.

publication en ligne. Article dans revue scientifique avec comité de lecture. nationale.National audienceThe human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology