Method of predicting Splice Sites based on signal interactions

BACKGROUND: Predicting and proper ranking of canonical splice sites (SSs) is a challenging problem in bioinformatics and machine learning communities. Any progress in SSs recognition will lead to better understanding of splicing mechanism. We introduce several new approaches of combining a priori knowledge for improved SS detection. First, we design our new Bayesian SS sensor based on oligonucleotide counting. To further enhance prediction quality, we applied our new de novo motif detection tool MHMMotif to intronic ends and exons. We combine elements found with sensor information using Naive Bayesian Network, as implemented in our new tool SpliceScan. RESULTS: According to our tests, the Bayesian sensor outperforms the contemporary Maximum Entropy sensor for 5' SS detection. We report a number of putative Exonic (ESE) and Intronic (ISE) Splicing Enhancers found by MHMMotif tool. T-test statistics on mouse/rat intronic alignments indicates, that detected elements are on average more conserved as compared to other oligos, which supports our assumption of their functional importance. The tool has been shown to outperform the SpliceView, GeneSplicer, NNSplice, Genio and NetUTR tools for the test set of human genes. SpliceScan outperforms all contemporary ab initio gene structural prediction tools on the set of 5' UTR gene fragments. CONCLUSION: Designed methods have many attractive properties, compared to existing approaches. Bayesian sensor, MHMMotif program and SpliceScan tools are freely available on our web site. REVIEWERS: This article was reviewed by Manyuan Long, Arcady Mushegian and Mikhail Gelfand

Routing and Wavelength Assignment (RWA) with Power Considerations In All- Optical Wavelength-Routed Networks

Routing and wavelength assignment (RWA) is an important problem that arises in wavelength division multiplexed (WDM) optical networks. Previous studies have solved many variations of this problem under the assumption of perfect conditions regarding the power of a signal. In this paper, we investigate this problem while allowing for degradation of routed signals by components such as taps, multiplexers, and fiber links. We assume that optical amplifiers are preplaced. We investigate the problem of routing the maximum number of connections while maintaining proper power levels. The problem is formulated as a mixed-integer nonlinear program and two-phase hybrid solution approaches employing two different heuristics are develope

Wireless Communication in Data Centers: A Survey

Data centers (DCs) is becoming increasingly an integral part of the computing infrastructures of most enterprises. Therefore, the concept of DC networks (DCNs) is receiving an increased attention in the network research community. Most DCNs deployed today can be classified as wired DCNs as copper and optical fiber cables are used for intra- and inter-rack connections in the network. Despite recent advances, wired DCNs face two inevitable problems; cabling complexity and hotspots. To address these problems, recent research works suggest the incorporation of wireless communication technology into DCNs. Wireless links can be used to either augment conventional wired DCNs, or to realize a pure wireless DCN. As the design spectrum of DCs broadens, so does the need for a clear classification to differentiate various design options. In this paper, we analyze the free space optical (FSO) communication and the 60 GHz radio frequency (RF), the two key candidate technologies for implementing wireless links in DCNs. We present a generic classification scheme that can be used to classify current and future DCNs based on the communication technology used in the network. The proposed classification is then used to review and summarize major research in this area. We also discuss open questions and future research directions in the area of wireless DCs

Design Of An All-Optical WDM Lightpath Concentrator

A design of a nonblocking, all-optical lightpath concentrator using wavelength exchanging optical crossbars and WDM crossbar switches is presented. The proposed concentrator is highly scalable, cost-efficient, and can switch signals in both space and wavelength domains without requiring a separate wavelength conversion stage

Selection Of Switching Sites In All-Optical Network Topology Design

In this paper, we consider the problem of topology design for both unprotected and one-link protected all-optical networks. We investigate the problem of selecting switching sites to minimize total cost of the network. The cost of an optical network is expressed as a sum of three main factors: the site cost, the link cost, and the switch cost. For unprotected networks with linear cost model, we present a mixed integer linear programming (MILP) formulation of the problem. We also present an efficient heuristic to approximate the solution. The experimental results show good performance of the linear cost model heuristic. In 16% of the experiments with 10 nodes network topologies, the linear cost model heuristic had no error. Moreover, for 54% and 86% of the experiments with 10 nodes network topologies, the linear cost model heuristic’s solution is within 2% and 5% of its optimal value respectively. Finally, we extend our approach to one-link protected networks, and present an efficient survivable heuristic, and representative experimental results

DESIGN FOR TESTABILITY AND TEST GENERATION WITH TWO CLOCKS

We propose a novel design for testability method that enhances the controllability of storage elements by use of additional clock lines Our scheme is applicable to synchronous circuits but is otherwise transparent to the designer. The associated area and speed penalties are minimal compared to scan based methods, however, a sequential ATPG system is necessary for test generation. The basic idea Is to use independent clock lines to control disjoint groups of flip-flops. No cyclic path are permitted among the flip-flops of the same group. During testing, a selected group can be made to hold its state by disabling its clock lines In the normal mode, all clock lines carry the same system clock signal. With the appropriate partitioning of flip-flops, the length of the vector sequence produced by the test generator for a fault is drastically reduced. An n-stage binary counter is used for experimental verification of reduction in test length by the proposed technique

Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks

Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization metrics to evaluate our method. First, the original RGBcolored images are utilized for the training phase, and then compression methods such as channel reduction and sparsity are applied. Our results show an accuracy increase of 6% from RGB on 32-bit (baseline) to the optimized representation of sparsity on RGB with a lower bit-width, i.e., \u3c8:8\u3e. For energy estimation on the used CNN model, we found that the energy used in RGB color mode with 32-bit is considerably higher than the other lower bit-width and compressed color modes. Moreover, we show that lower bit-width implementations yield higher resource utilization and a lower memory bottleneck ratio. This work is suitable for inference on energy-limited devices, which are increasingly being used in the Internet of Things (IoT) systems that facilitate healthcare systems

Network Coding for WDM All-Optical Multicast

Network coding has become a useful means for achieving efficient multicast, and the optical community has started to examine its application to optical networks. However, a number of challenges, including limited processing capability and coarse bandwidth granularity, need to be overcome before network coding can be effectively used in optical networks. In this paper, we address some of these problems. We consider the problem of finding efficient routes to use with coding, and we study the effectiveness of using network coding for optical-layer dedicated protection of multicast traffic. We also propose architectures for all-optical circuits capable of performing the processing required for network coding. Our experiments show that network coding provides a moderate improvement in bandwidth efficiency for unprotected multicast while significantly outperforming existing approaches for dedicated multicast protection

Genome mining for anti-CRISPR operons using machine learning

Motivation: Encoded by (pro-)viruses, anti-CRISPR (Acr) proteins inhibit the CRISPR-Cas immune system of their prokaryotic hosts. As a result, Acr proteins can be employed to develop more controllable CRISPR-Cas genome editing tools. Recent studies revealed that known acr genes often coexist with other acr genes and with phage structural genes within the same operon. For example, we found that 47 of 98 known acr genes (or their homologs) co-exist in the same operons. None of the current Acr prediction tools have considered this important genomic context feature. We have developed a new software tool AOminer to facilitate the improved discovery of new Acrs by fully exploiting the genomic context of known acr genes and their homologs. Results: AOminer is the first machine learning based tool focused on the discovery of Acr operons (AOs). A two-state HMM (hidden Markov model) was trained to learn the conserved genomic context of operons that contain known acr genes or their homologs, and the learnt features could distinguish AOs and non-AOs. AOminer allows automated mining for potential AOs from query genomes or operons. AOminer outperformed all existing Acr prediction tools with an accuracy¼0.85. AOminer will facilitate the discovery of novel anti-CRISPR operons