A study on service quality in higher education institutions with special reference to western Tamilnadu

Service quality is regarded as the managerial efforts in facilitating activities of acquiring, creating, storing, sharing, diffusing, developing, and deploying service by individuals and groups. The Learned people and Higher Educational Institutions are considered as the service society, service hub respectively. Academic sector has significant opportunities to apply Service quality practices to support its education, research and also facilitate the nation in achieving the set of objectives. Service quality in higher educational institutions provides a set of practices for linking people such as students, teachers, researchers, business, and external entities also link with technology. It also focuses on how institutions can promote strategies and practices that help different actors and practitioners to share, manage and apply new service in Service quality encompasses much more, going beyond the intrinsic service industry of colleges and universities. At present educational are striving very hard to improve their standard, quality and adding more and more value to the services in order to attract quality of students and faculty members. This situation has raised the need to implement the service quality practices in the educational institutions in order to achieve their mission, be competitive, remain innovative, and ensuring the satisfaction of stakeholder’s expectation

Feature based-Learning with Data Increasing for video Recommendation and Computing

Image content analysis is crucial for determining the reliability of a link between two videos. Video characteristics are increasingly being used in image and video representation as custom pre-trained picture and video convolutional neural networks become generally available. People also have limited access to video editing tools for a variety of reasons, such as ownership and privacy concerns. You don't need to go back to the source video data to use the refined features again. An affine transformation, for instance, can be used to map a well-studied function onto an unfamiliar domain. To do this, we use a unique triplet failure in conjunction with the re-learning strategy. We propose a contemporary data augmentation method that may be applied to functionality on various frames for videos as an alternative to employing specific motion data. Extensive testing on the well-known Hulu content-based Video Relevance challenge demonstrates the process's efficacy and provides solid evidence of state-of-the-art performance

Bench-to-bedside review : targeting antioxidants to mitochondria in sepsis

Peer reviewedPublisher PD

Efficient Code Generation in a Region-based Dynamic Binary Translator

Region-based JIT compilation operates on translation units comprising multiple basic blocks and, possibly cyclic or conditional, control flow between these. It promises to reconcile aggressive code optimisation and low compilation latency in performance-critical dynamic binary translators. Whilst various region selection schemes and isolated code optimisation techniques have been investigated it remains unclear how to best exploit such regions for efficient code generation. Complex interactions with indirect branch tables and translation caches can have adverse effects on performance if not considered carefully. In this paper we present a complete code generation strategy for a region-based dynamic binary translator, which exploits branch type and control flow profiling information to improve code quality for the common case. We demonstrate that using our code generation strategy a competitive region-based dynamic compiler can be built on top of the LLVM JIT compilation framework. For the ARM-V5T target ISA and SPEC CPU 2006 benchmarks we achieve execution rates of, on average, 867 MIPS and up to 1323 MIPS on a standard X86 host machine, outperforming state-of-the-art QEMU-ARM by delivering a speedup of 264%

Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models

<p>Abstract</p> <p>Background</p> <p>Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that genes within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable. The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect.</p> <p>Results</p> <p>In this paper we propose a logistic kernel machine regression model for binary outcomes. This model relates the disease risk to covariates parametrically, and to genes within a genetic pathway parametrically or nonparametrically using kernel machines. The nonparametric genetic pathway effect allows for possible interactions among the genes within the same pathway and a complicated relationship of the genetic pathway and the outcome. We show that kernel machine estimation of the model components can be formulated using a logistic mixed model. Estimation hence can proceed within a mixed model framework using standard statistical software. A score test based on a Gaussian process approximation is developed to test for the genetic pathway effect. The methods are illustrated using a prostate cancer data set and evaluated using simulations. An extension to continuous and discrete outcomes using generalized kernel machine models and its connection with generalized linear mixed models is discussed.</p> <p>Conclusion</p> <p>Logistic kernel machine regression and its extension generalized kernel machine regression provide a novel and flexible statistical tool for modeling pathway effects on discrete and continuous outcomes. Their close connection to mixed models and attractive performance make them have promising wide applications in bioinformatics and other biomedical areas.</p

Disulfiram/copper selectively eradicates AML leukemia stem cells in vitro and in vivo by simultaneous induction of ROS-JNK and inhibition of NF-κB and Nrf2

© 2017 The Authors. Published by Nature Publishing Group. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1038/cddis.2017.176Acute myeloid leukemia (AML) is a heterogeneous malignancy. Despite the advances in past decades, the clinical outcomes of AML patients remain poor. Leukemia stem cells (LSCs) is the major cause of the recurrence of AML even after aggressive treatment making, promoting development of LSC-targeted agents is an urgent clinical need. Although the antitumor activity of disulfiram (DS), an approved anti-alcoholism drug, has been demonstrated in multiple types of tumors including hematological malignancies such as AML, it remains unknown whether this agent would also be able to target cancer stem cells like LSCs. Here, we report the in vitro and in vivo activity of DS in combination with copper (Cu) against CD34(+)/CD38(+) leukemia stem-like cells sorted from KG1α and Kasumi-1 AML cell lines, as well as primary CD34(+) AML samples. DS plus Cu (DS/Cu) displayed marked inhibition of proliferation, induction of apoptosis, and suppression of colony formation in cultured AML cells while sparing the normal counterparts. DS/Cu also significantly inhibited the growth of human CD34(+)/CD38(+) leukemic cell-derived xenografts in NOD/SCID mice. Mechanistically, DS/Cu-induced cytotoxicity was closely associated with activation of the stress-related ROS-JNK pathway as well as simultaneous inactivation of the pro-survival Nrf2 and nuclear factor-κB pathways. In summary, our findings indicate that DS/Cu selectively targets leukemia stem-like cells both in vitro and in vivo, thus suggesting a promising LSC-targeted activity of this repurposed agent for treatment of relapsed and refractory AML

The polycomb group protein EZH2 is involved in progression of prostate cancer

Prostate cancer is a leading cause of cancer-related death in males and is second only to lung cancer. Although effective surgical and radiation treatments exist for clinically localized prostate cancer, metastatic prostate cancer remains essentially incurable. Here we show, through gene expression profiling(1), that the polycomb group protein enhancer of zeste homolog 2 (EZH2)(2,3) is overexpressed in hormone-refractory, metastatic prostate cancer. Small interfering RNA (siRNA) duplexes(4) targeted against EZH2 reduce the amounts of EZH2 protein present in prostate cells and also inhibit cell proliferation in vitro. Ectopic expression of EZH2 in prostate cells induces transcriptional repression of a specific cohort of genes. Gene silencing mediated by EZH2 requires the SET domain and is attenuated by inhibiting histone deacetylase activity. Amounts of both EZH2 messenger RNA and EZH2 protein are increased in metastatic prostate cancer; in addition, clinically localized prostate cancers that express higher concentrations of EZH2 show a poorer prognosis. Thus, dysregulated expression of EZH2 may be involved in the progression of prostate cancer, as well as being a marker that distinguishes indolent prostate cancer from those at risk of lethal progression.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62896/1/nature01075.pd