Thrombin-induced events in non-platelet cells are mediated by the unique proteolytic mechanism established for the cloned platelet thrombin receptor.

We recently isolated a cDNA clone encoding a functional platelet thrombin receptor that defined a unique mechanism of receptor activation. Thrombin cleaves its receptor's extracellular amino terminal extension, unmasking a new amino terminus that functions as a tethered peptide ligand and activates the receptor. A novel peptide mimicking this new amino terminus was a full agonist for platelet secretion and aggregation, suggesting that this unusual mechanism accounts for platelet activation by thrombin. Does this mechanism also mediate thrombin's assorted actions on non-platelet cells? We now report that the novel thrombin receptor agonist peptide reproduces thrombin-induced events (specifically, phosphoinositide hydrolysis and mitogenesis) in CCL-39 hamster lung fibroblasts, a naturally thrombin-responsive cell line. Moreover, these thrombin-induced events could be recapitulated in CV-1 cells, normally poorly responsive to thrombin, after transfection with human platelet thrombin receptor cDNA. Our data show that important thrombin-induced cellular events are mediated by the same unusual mechanism of receptor activation in both platelets and fibroblasts, very likely via the same or very similar receptors

Offloading Energy Efficiency with Delay Constraint for Cooperative Mobile Edge Computing Networks

© 2018 IEEE. We propose a novel edge computing network architecture that enables edge nodes to cooperate in sharing computing and radio resources to minimize the total energy consumption of mobile users while meeting their delay requirements. To find the optimal task offloading decisions for mobile users, we first formulate the joint task offloading and resource allocation optimization problem as a mixed integer non-linear programming (MINLP). The optimization involves both binary (offloading decisions) and real variables (resource allocations), making it an NP-hard and computational intractable problem. To circumvent, we relax the binary decision variables to transform the MINLP to a relaxed optimization problem with real variables. After proving that the relaxed problem is a convex one, we propose two solutions namely ROP and IBBA. ROP is adopted from the interior point method and IBBA is developed from the branch and bound algorithm. Through the numerical results, we show that our proposed approaches allow minimizing the total energy consumption and meet all delay requirements for mobile users

Rabies in Nigeria: A review of literature

Rabies, also known as hydrophobia is an acute, viral disease of all warm blooded animals including man. It is caused by the rabies virus (RABV), a bullet–shaped, enveloped RNA virus, 45-100 nm in diameter & 100-430 nm in length with projections and helical nucleocapsid, one of the better known encephalitis viruses of the family Rhabdoviridae and genus Lyssavirus type 1It is a major public-health problem in most parts of the developing world. The domestic dog (Canis familiaris) plays a principal role (accounting for over 99%) as a reservoir and transmitter of the disease to humans. Developing countries account for almost all the reported human deaths (99.9%) and most cases of human post-exposure treatments. Rabies is an important public health problem especially in the developing countries and this articles aims to draw attention to this neglected disease.Keywords: rhabdoviridae, rabie

Learning Latent Distribution for Distinguishing Network Traffic in Intrusion Detection System

© 2019 IEEE. We develop a novel deep learning model, Multi-distributed Variational AutoEncoder (MVAE), for the network intrusion detection. To make the traffic more distinguishable, MVAE introduces the label information of data samples into the Kullback-Leibler (KL) term of the loss function of Variational AutoEncoder (VAE). This label information allows MVAEs to force/partition network data samples into different classes with different regions in the latent feature space. As a result, the network traffic samples are more distinguishable in the new representation space (i.e., the latent feature space of MVAE), thereby improving the accuracy in detecting intrusions. To evaluate the efficiency of the proposed solution, we carry out intensive experiments on two popular network intrusion datasets, i.e., NSL-KDD and UNSW-NB15 under four conventional classifiers including Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The experimental results demonstrate that our proposed approach can significantly improve the accuracy of intrusion detection algorithms up to 24.6% compared to the original one (using area under the curve metric)

Time Series Analysis for Encrypted Traffic Classification: A Deep Learning Approach

© 2018 IEEE. We develop a novel time series feature extraction technique to address the encrypted traffic/application classification problem. The proposed method consists of two main steps. First, we propose a feature engineering technique to extract significant attributes of the encrypted network traffic behavior by analyzing the time series of receiving packets. In the second step, we develop a deep learning-based technique to exploit the correlation of time series data samples of the encrypted network applications. To evaluate the efficiency of the proposed solution on the encrypted traffic classification problem, we carry out intensive experiments on a raw network traffic dataset, namely VPN-nonVPN, with three conventional classifier metrics including Precision, Recall, and F1 score. The experimental results demonstrate that our proposed approach can significantly improve the performance in identifying encrypted application traffic in terms of accuracy and computation efficiency

Optimal Energy Efficiency with Delay Constraints for Multi-layer Cooperative Fog Computing Networks

We develop a joint offloading and resource allocation framework for a multi-layer cooperative fog computing network, aiming to minimize the total energy consumption of multiple mobile devices subject to their service delay requirements. The resulting optimization involves both binary (offloading decisions) and real variables (resource allocations), making it an NP-hard and computationally intractable problem. To tackle it, we first propose an improved branch-and-bound algorithm (IBBA) that is implemented in a centralized manner. However, due to the large size of the cooperative fog computing network, the computational complexity of the proposed IBBA is relatively high. To speed up the optimal solution searching as well as to enable its distributed implementation, we then leverage the unique structure of the underlying problem and the parallel processing at fog nodes. To that end, we propose a distributed framework, namely feasibility finding Benders decomposition (FFBD), that decomposes the original problem into a master problem for the offloading decision and subproblems for resource allocation. The master problem (MP) is then equipped with powerful cutting-planes to exploit the fact of resource limitation at fog nodes. The subproblems (SP) for resource allocation can find their closed-form solutions using our fast solution detection method. These (simpler) subproblems can then be solved in parallel at fog nodes. The numerical results show that the FFBD always returns the optimal solution of the problem with significantly less computation time (e.g., compared with the centralized IBBA approach). The FFBD with the fast solution detection method, namely FFBD-F, can reduce up to $60\%$ and $90\%$ of computation time, respectively, compared with those of the conventional FFBD, namely FFBD-S, and IBBA

A formal proof of the Kepler conjecture

This article describes a formal proof of the Kepler conjecture on dense sphere packings in a combination of the HOL Light and Isabelle proof assistants. This paper constitutes the official published account of the now completed Flyspeck project

Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M <; N. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance

Validity constraints for data analysis workflows

\ua9 2024Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software stack, or even only a new dataset with some notably different properties is often challenging. Despite the structured definition of the steps (tasks) and their interdependencies during a complex data analysis in the DAW specification, relevant assumptions may remain unspecified and implicit. Such hidden assumptions often lead to crashing tasks without a reasonable error message, poor performance in general, non-terminating executions, or silent wrong results of the DAW, to name only a few possible consequences. Searching for the causes of such errors and drawbacks in a distributed compute cluster managed by a complex infrastructure stack, where DAWs for large datasets typically are executed, can be tedious and time-consuming. We propose validity constraints (VCs) as a new concept for DAW languages to alleviate this situation. A VC is a constraint specifying logical conditions that must be fulfilled at certain times for DAW executions to be valid. When defined together with a DAW, VCs help to improve the portability, adaptability, and reusability of DAWs by making implicit assumptions explicit. Once specified, VCs can be controlled automatically by the DAW infrastructure, and violations can lead to meaningful error messages and graceful behavior (e.g., termination or invocation of repair mechanisms). We provide a broad list of possible VCs, classify them along multiple dimensions, and compare them to similar concepts one can find in related fields. We also provide a proof-of-concept implementation for the workflow system Nextflow