Performance and Emission Characteristics of Diesel Fuelled Homogeneous Charge Compression Ignition (HCCI) Engine

ABSTRACT: An experimentally investigation is carried out to analysis the performance and emission characteristics of Homogeneous charge compression ignition engine (HCCI). Over the past decades many researcher have been discussed about working of HCCI engine. The HCCI engine is a suitable replacement for compression ignition engine (CI). This paper experimentally investigate the performance and emission characteristics of a HCCI engine at different load with constant speed and compare with convention CI engine. In this research, the HCCI mode engine uses the port fuel injection (PFI) for preparing the homogeneous air-fuel mixture. The results show that the specific fuel consumption is decreased for diesel fuelled HCCI mode engine compared to DI-CI engine. The brake thermal efficiency of HCCI mode engine is even as same or slightly increased. From the result observed that the value of oxides of nitrogen (NOx) and particulate matter (PM) emissions are very low than the DI diesel engine. The rate of reduction of NOx and PM are about 20% and 5% respectively. But the exhaust emission of un burnt hydrocarbon (UHC) and carbon monoxide (CO) are higher than DI mode diesel engine

Influence of Growth Time on Zinc Oxide Nano Rods Prepared By Dip Coating Method

ABSTRACT: The Dip coating method was used for the preparation of ZnO nano rods and their structural, morphological, optical and photoluminescence properties were taken for study. ZnO seed layer thin films were prepared by dip coating method on well cleaned glass substrates. ZnO seed-coated glass substrates were immersed in aqueous solution of zinc nitrate and hexamethylenetetramine (HMT) at three different growth time of 3, 4 and 5 hours at low temperature of 90°C. 0.02 mol of Zinc nitrate and 0.2 mol of Hexamethylenetetramine (HMT) on 1:10 molar concentration were used for the growth of Zinc oxide nano rods. The growth time influence on the surface morphology of the films was examined. The structure of the ZnO nano rod was studied with X-ray diffraction. The surface morphology was studied with Scanning Electron Microscope. The absorption and transmittance was studied with UVVis spectrophotometer. The excitation studies were examined with photoluminescence spectroscopy. Experimental results have shown that prepared ZnO nano rods by this method have increase in c-axis orientation due to increase in growth time

UV photodecomposition of zinc acetate for the growth of ZnO nanowires

The thermal annealing of zinc precursors to form suitable seed layers for the growth of ZnO nanowires is common. However, the process is relatively long and involves high temperatures which limit substrate choice. In this study the use of a low temperature, ultra-violet (UV) exposure is demonstrated for photodecomposition of zinc acetate precursors to form suitable seed layers. Comparisons are made between ZnO nanowire growth performed on seed layers produced through thermal annealing and exposure to UV. The dependence of growth density and nanowire diameter on UV exposure time is investigated. Growth quality is confirmed with energy dispersive x-ray (EDX) and x-ray diffraction analyses. The chemical composition of the exposed layers is investigated with EDX and x-ray photoelectron spectroscopy (XPS). Atomic force microscopy (AFM) is utilized to investigate morphological changes with respect to UV exposure. The diameter and density of the resultant growth was found to be strongly dependent on the UV exposure time. UV exposure times of only 25–30 s led to maximum density of growth and minimum diameter, significantly faster than thermal annealing. EDX, XPS and AFM analyses of the seed layers confirmed decomposition of the zinc precursor and morphological changes which influenced the growth

Targeting the interaction between RNA-binding protein HuR and FOXQ1 suppresses breast cancer invasion and metastasis

This work is licensed under a Creative Commons Attribution 4.0 International License.Patients diagnosed with metastatic breast cancer have a dismal 5-year survival rate of only 24%. The RNA-binding protein Hu antigen R (HuR) is upregulated in breast cancer, and elevated cytoplasmic HuR correlates with high-grade tumors and poor clinical outcome of breast cancer. HuR promotes tumorigenesis by regulating numerous proto-oncogenes, growth factors, and cytokines that support major tumor hallmarks including invasion and metastasis. Here, we report a HuR inhibitor KH-3, which potently suppresses breast cancer cell growth and invasion. Furthermore, KH-3 inhibits breast cancer experimental lung metastasis, improves mouse survival, and reduces orthotopic tumor growth. Mechanistically, we identify FOXQ1 as a direct target of HuR. KH-3 disrupts HuR–FOXQ1 mRNA interaction, leading to inhibition of breast cancer invasion. Our study suggests that inhibiting HuR is a promising therapeutic strategy for lethal metastatic breast cancer

Immune epitope database analysis resource (IEDB-AR)

We present a new release of the immune epitope database analysis resource (IEDB-AR, http://tools.immuneepitope.org), a repository of web-based tools for the prediction and analysis of immune epitopes. New functionalities have been added to most of the previously implemented tools, and a total of eight new tools were added, including two B-cell epitope prediction tools, four T-cell epitope prediction tools and two analysis tools

On Evaluating MHC-II Binding Peptide Prediction Methods

Choice of one method over another for MHC-II binding peptide prediction is typically based on published reports of their estimated performance on standard benchmark datasets. We show that several standard benchmark datasets of unique peptides used in such studies contain a substantial number of peptides that share a high degree of sequence identity with one or more other peptide sequences in the same dataset. Thus, in a standard cross-validation setup, the test set and the training set are likely to contain sequences that share a high degree of sequence identity with each other, leading to overly optimistic estimates of performance. Hence, to more rigorously assess the relative performance of different prediction methods, we explore the use of similarity-reduced datasets. We introduce three similarity-reduced MHC-II benchmark datasets derived from MHCPEP, MHCBN, and IEDB databases. The results of our comparison of the performance of three MHC-II binding peptide prediction methods estimated using datasets of unique peptides with that obtained using their similarity-reduced counterparts shows that the former can be rather optimistic relative to the performance of the same methods on similarity-reduced counterparts of the same datasets. Furthermore, our results demonstrate that conclusions regarding the superiority of one method over another drawn on the basis of performance estimates obtained using commonly used datasets of unique peptides are often contradicted by the observed performance of the methods on the similarity-reduced versions of the same datasets. These results underscore the importance of using similarity-reduced datasets in rigorously comparing the performance of alternative MHC-II peptide prediction methods

GeneDB--an annotation database for pathogens.

GeneDB (http://www.genedb.org) is a genome database for prokaryotic and eukaryotic pathogens and closely related organisms. The resource provides a portal to genome sequence and annotation data, which is primarily generated by the Pathogen Genomics group at the Wellcome Trust Sanger Institute. It combines data from completed and ongoing genome projects with curated annotation, which is readily accessible from a web based resource. The development of the database in recent years has focused on providing database-driven annotation tools and pipelines, as well as catering for increasingly frequent assembly updates. The website has been significantly redesigned to take advantage of current web technologies, and improve usability. The current release stores 41 data sets, of which 17 are manually curated and maintained by biologists, who review and incorporate data from the scientific literature, as well as other sources. GeneDB is primarily a production and annotation database for the genomes of predominantly pathogenic organisms

PeptX: Using Genetic Algorithms to optimize peptides for MHC binding

<p>Abstract</p> <p>Background</p> <p>The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different <it>in silico </it>techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain <it>in silico </it>scoring functions?</p> <p>Results</p> <p>Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders.</p> <p>Conclusion</p> <p>We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.</p

Natural product derivative Gossypolone inhibits Musashi family of RNA-binding proteins

Background: The Musashi (MSI) family of RNA-binding proteins is best known for the role in post-transcriptional regulation of target mRNAs. Elevated MSI1 levels in a variety of human cancer are associated with up-regulation of Notch/Wnt signaling. MSI1 binds to and negatively regulates translation of Numb and APC (adenomatous polyposis coli), negative regulators of Notch and Wnt signaling respectively. Methods: Previously, we have shown that the natural product (-)-gossypol as the first known small molecule inhibitor of MSI1 that down-regulates Notch/Wnt signaling and inhibits tumor xenograft growth in vivo. Using a fluorescence polarization (FP) competition assay, we identified gossypolone (Gn) with a > 20-fold increase in Ki value compared to (-)-gossypol. We validated Gn binding to MSI1 using surface plasmon resonance, nuclear magnetic resonance, and cellular thermal shift assay, and tested the effects of Gn on colon cancer cells and colon cancer DLD-1 xenografts in nude mice. Results: In colon cancer cells, Gn reduced Notch/Wnt signaling and induced apoptosis. Compared to (-)-gossypol, the same concentration of Gn is less active in all the cell assays tested. To increase Gn bioavailability, we used PEGylated liposomes in our in vivo studies. Gn-lip via tail vein injection inhibited the growth of human colon cancer DLD-1 xenografts in nude mice, as compared to the untreated control (P < 0.01, n = 10). Conclusion: Our data suggest that PEGylation improved the bioavailability of Gn as well as achieved tumor-targeted delivery and controlled release of Gn, which enhanced its overall biocompatibility and drug efficacy in vivo. This provides proof of concept for the development of Gn-lip as a molecular therapy for colon cancer with MSI1/MSI2 overexpression

Co-Administration of IL-1+IL-6+TNF-α with Mycobacterium tuberculosis Infected Macrophages Vaccine Induces Better Protective T Cell Memory than BCG

BCG has been administered globally for more than 75 years, yet tuberculosis (TB) continues to kill more than 2 million people annually. Further, BCG protects childhood TB but is quite inefficient in adults. This indicates that BCG fails to induce long-term protection. Hence there is a need to explore alternative vaccination strategies that can stimulate enduring T cell memory response. Dendritic cell based vaccination has attained extensive popularity following their success in various malignancies. In our previous study, we have established a novel and unique vaccination strategy against Mycobacterium tuberculosis (M. tb) and Salmonella typhimurium by utilizing infected macrophages (IM). In short-term experiments (30 days), substantial degree of protection was observed. However, remarkable difference was not observed in long-term studies (240 days) due to failure of the vaccine to generate long-lasting memory T cells. Hence, in the present study we employed T cell memory augmenting cytokines IL-1+IL-6+TNF-α and IL-7+IL-15 for the induction of the enhancement of long-term protection by the vaccine. We co-administered the M. tb infected macrophages vaccine with IL-1+IL-6+TNF-α (IM-1.6.α) and IL-7+IL-15 (IM-7.15). The mice were then rested for a reasonably large period (240 days) to study the bona fide T cell memory response before exposing them to aerosolized M. tb. IM-1.6.α but not IM-7.15 significantly improved memory T cell response against M. tb, as evidenced by recall responses of memory T cells, expansion of both central as well as effector memory CD4 and CD8 T cell pools, elicitation of mainly Th1 memory response, reduction in the mycobacterial load and alleviated lung pathology. Importantly, the protection induced by IM-1.6.α was significantly better than BCG. Thus, this study demonstrates that not only antigen-pulsed DCs can be successfully employed as vaccines against cancer and infectious diseases but also macrophages infected with M. tb can be utilized with great efficacy especially in protection against TB