A Fair and Efficient Packet Scheduling Scheme for IEEE 802.16 Broadband Wireless Access Systems

This paper proposes a fair and efficient QoS scheduling scheme for IEEE 802.16 BWA systems that satisfies both throughput and delay guarantee to various real and non-real time applications. The proposed QoS scheduling scheme is compared with an existing QoS scheduling scheme proposed in literature in recent past. Simulation results show that the proposed scheduling scheme can provide a tight QoS guarantee in terms of delay, delay violation rate and throughput for all types of traffic as defined in the WiMAX standard, thereby maintaining the fairness and helps to eliminate starvation of lower priority class services. Bandwidth utilization of the system and fairness index of the resources are also encountered to validate the QoS provided by our proposed scheduling scheme

Interaction of Glutathione S-Transferase with Hypericin: A Photophysical Study

The photophysics of hypericin have been studied in its complex with two different isoforms, A1-1 and P1-1, of the protein glutathione S-transferase (GST). One molecule of hypericin binds to each of the two GST subunits. Comparisons are made with our previous results for the hypericin/human serum albumin complex (Photochem. Photobiol. 1999, 69, 633−645). Hypericin binds with high affinity to the GSTs:  0.65 μM for the A1-1 isoform and 0.51 μM for the P1-1 isoform (Biochemistry 2004, 43, 12761−12769). The photophysics and activity of hypericin are strongly modulated by the binding protein. Intramolecular hydrogen-atom transfer is suppressed in both cases. Most importantly, while there is significant singlet oxygen generation from hypericin bound to GST A1-1, binding to GST P1-1 suppresses singlet oxygen generation to almost negligible levels. The data are rationalized in terms of a simple model in which the hypericin photophysics depends entirely upon the decay of the triplet state by two competing processes, quenching by oxygen to yield singlet oxygen and ionization, the latter of these two are proposed to be modulated by A1-1 and P1-1

Functional Analysis of Generalized Linear Models Under Nonlinear Constraints With Artificial Intelligence and Machine Learning Applications to the Sciences

This thesis presents multiple fundamental mathematical contributions to Generalized Linear Models (GLMs) ubiquitous to the sciences. The methodologies considered are shown to overcome biased estimates for parameters of interest in the sciences through new mathematical results and their applications in both nonparametric and parametric settings. The results are shown to be uniformly better in comparison to existing widely used methods in the sciences. In extensive simulation studies the methodologies outperform existing Artificial Intelligence (AI) and Machine Learning (ML) methods in the sciences for all around better Model fits, Inference and Prediction (MIP) results without losing interpretability of the parameter estimates. This is because the mathematical construction and their accompanying mathematical foundations ensure that the estimation procedure strongly converges to the parameters of interest. In the first application, I present a parametric version of the methodology (© Elsevier and Journal of Informetrics) titled “Functional analysis of generalized linear models under non-linear constraints with applications to identifying highly-cited papers.” In the second application, I extend this methodology in an entirely nonparametric setting which gives equivalent results to the parametric formulation under various circumstances, but may outperform it as well in others, especially if the underlying Data Generating Process (DGP) is asymmetric. Furthermore, I show that the categorical data models on which the methodologies are applied can be extended to any GLM, continuous or otherwise, while maintaining model interpretability and convergence results. In addition, I present a new prediction performance diagnostic statistic, called Adjusted ROC Statistic (ARS), which allows us to compare whether the prediction performance of various models fitted are statistically different. The nonparametric methodology is then further extended to give a new formulation of the binary regression framework widely used in the sciences. Through extensive simulation studies I show that this version of the methodology is more robust than the previous versions discussed. This general framework is then extended to various AI and ML applications widely used in the sciences. The entirety of the work also has some important consequences for our continued discussion on “statistical significance” vs. “scientific significance.” This includes the need for us to consider the strength of convergence of our methodology in addition to the subtle connections between Topological Spaces and Measure Spaces. Each of which are crucial to ensure almost sure convergence of the parameter estimates through the estimation algorithm presented termed, Latent Adaptive Hierarchical EM Like algorithm or LAHEML. As such, the results present a significantly expanded and more accurate toolset for Mathematicians, Statisticians, Scientists and Decision Makers at all levels for better model fit, inference and prediction outcomes

Interaction of Glutathione S-Transferase with Hypericin: A Photophysical Study

The photophysics of hypericin have been studied in its complex with two different isoforms, A1-1 and P1-1, of the protein glutathione S-transferase (GST). One molecule of hypericin binds to each of the two GST subunits. Comparisons are made with our previous results for the hypericin/human serum albumin complex (Photochem. Photobiol. 1999, 69, 633−645). Hypericin binds with high affinity to the GSTs:  0.65 μM for the A1-1 isoform and 0.51 μM for the P1-1 isoform (Biochemistry 2004, 43, 12761−12769). The photophysics and activity of hypericin are strongly modulated by the binding protein. Intramolecular hydrogen-atom transfer is suppressed in both cases. Most importantly, while there is significant singlet oxygen generation from hypericin bound to GST A1-1, binding to GST P1-1 suppresses singlet oxygen generation to almost negligible levels. The data are rationalized in terms of a simple model in which the hypericin photophysics depends entirely upon the decay of the triplet state by two competing processes, quenching by oxygen to yield singlet oxygen and ionization, the latter of these two are proposed to be modulated by A1-1 and P1-1.Reprinted (adatped) with permission from Journal of Physical Chemistry B 109 (2005): 19484, doi: 10.1021/jp051645u. Copyright 2005 American Chemical Society.</p

IPMK Mediates Activation of ULK Signaling and Transcriptional Regulation of Autophagy Linked to Liver Inflammation and Regeneration

Summary: Autophagy plays a broad role in health and disease. Here, we show that inositol polyphosphate multikinase (IPMK) is a prominent physiological determinant of autophagy and is critical for liver inflammation and regeneration. Deletion of IPMK diminishes autophagy in cell lines and mouse liver. Regulation of autophagy by IPMK does not require catalytic activity. Two signaling axes, IPMK-AMPK-Sirt-1 and IPMK-AMPK-ULK1, appear to mediate the influence of IPMK on autophagy. IPMK enhances autophagy-related transcription by stimulating AMPK-dependent Sirt-1 activation, which mediates the deacetylation of histone 4 lysine 16. Furthermore, direct binding of IPMK to ULK and AMPK forms a ternary complex that facilitates AMPK-dependent ULK phosphorylation. Deletion of IPMK in cell lines and intact mice virtually abolishes lipophagy, promotes liver damage as well as inflammation, and impairs hepatocyte regeneration. Thus, targeting IPMK may afford therapeutic benefits in disabilities that depend on autophagy and lipophagy—specifically, in liver inflammation and regeneration. : Guha et al. show that IPMK is a physiological determinant of autophagy and is critical in liver inflammation. Two signaling axes, IPMK-AMPK-Sirt-1 and IPMK-AMPK- ULK1, appear to mediate the influence of IPMK on autophagy. Deletion of IPMK impairs lipophagy and hepatocyte regeneration. Keywords: IPMK, ULK, AMPK, autophagy, liver, regeneration, liver damage, lipophagy, inositol, Sirt-

High expression of mesothelin in plasma and tissue is associated with poor prognosis and promotes invasion and metastasis in gastric cancer

Mesothelin (MSLN), a tumor-associated antigen, is upregulated in various malignancies, including gastric cancer (GC). In addition, MSLN is found in the blood-stream of affected individuals, where it is referred to as soluble MSLN-related protein (SMRP). This study aims to investigate the role of MSLN in GC and evaluate its potential as a plasma biomarker for diagnosis and prognosis. Toward that end, GC tissues were obtained, upon signed consent, from affected individuals undergoing surgery or endoscopy (n = 82). Quantitative RT-PCR and immunohistochemistry were performed to determine MSLN expression. Simultaneously, The Cancer Genome Atlas (TCGA) database was mined to evaluate global status of MSLN gene expression in gastric cancer. Next, in vitro cell-culture studies were conducted to evaluate MSLN-driven proliferation properties. Using ELISA, sera from 55 GC-affected individuals were tested for MSLN level. Additionally, plasma mesothelin levels were compared in 6 cases before and after surgery. Upregulated MSLN expression was found in GC tissues, compared to adjacent normal tissues (p < 0.001). Cell culture studies with a MSLN-overexpressing stable GC line showed increased cell proliferation and invasion with ectopic MSLN. Additionally, gene-set-enrichment-analysis (GSEA) revealed an association of MSLN with the genes involved in the epithelial-mesenchymal transition and G2/M checkpoint. GC-affected cases showed higher serum MSLN levels, compared to healthy controls, with rapid decrease post-surgery. We found that MSLN upregulation correlates with poor clinical outcome and promotes growth advantage to GC cells in vitro. With further experimental evidences, we propose that MSLN could potentially be used as a plasma biomarker for diagnosis of GC