In a multicast scenario, all desired users are divided into K groups. Each group receives its own individual confidential message stream. Eavesdropper group aims to intercept K confidential message ...streams. To achieve a secure transmission, two secure schemes are proposed: Maximum group receive power plus null-space (NS) projection (Max-GRP plus NSP) and leakage. The former obtains its precoding vector per group by maximizing its own group receive power subject to the orthogonal constraint, and its AN projection matrix consist of all bases of NS of all desired steering vectors from all groups. The latter attains its desired precoding vector per group by driving the current confidential message power to its group steering space and reducing its power leakage to eavesdropper group and other K - 1 desired ones by maximizing signal-to-leakage-and-noise ratio. And its AN projection matrix is designed by forcing AN power into the eavesdropper steering space by viewing AN as a useful signal for eavesdropper group and maximizing AN to leakage-and-noise ratio. Simulation results show that the proposed two methods are better than conventional method in terms of both bit-error-rate and secrecy sum-rate per group. Also, the leakage scheme performs better than Max-GRP-NSP, especially in the presence of direction measurement errors. However, the latter requires no channel statistical parameters and, thus, is simpler compared to the former.
•A two-stage algorithm for high-dimensional feature selection is proposed.•A advanced hybrid ant colony optimization algorithm is proposed.•Our method has good selection performance with short ...running time.
Ant colony optimization (ACO) is widely used in feature selection owing to its excellent global/local search capabilities and flexible graph representation. However, the current ACO-based feature selection methods are mainly applied to low-dimensional datasets. For thousands of dimensional datasets, the search for the optimal feature subset (OFS) becomes extremely difficult due to the exponential increase of the search space. In this paper, we propose a two-stage hybrid ACO for high-dimensional feature selection (TSHFS-ACO). As an additional stage, it uses the interval strategy to determine the size of OFS for the following OFS search. Compared to the traditional one-stage methods that determine the size of OFS and search for OFS simultaneously, the stage of checking the performance of partial feature number endpoints in advance helps to reduce the complexity of the algorithm and alleviate the algorithm from getting into a local optimum. Moreover, the advanced ACO algorithm embeds the hybrid model, which uses the features’ inherent relevance attributes and the classification performance to guide OFS search. The test results on eleven high-dimensional public datasets show that TSHFS-ACO is suitable for high-dimensional feature selection. The obtained OFS has state-of-the-art performance on most datasets. And compared with other ACO-based feature selection methods, TSHFS-ACO has a shorter running time.
This paper studies the physical layer security of an unmanned aerial vehicle (UAV) network, where a UAV base station (UAV-B) transmits confidential information to multiple information receivers (IRs) ...with the aid of a UAV jammer (UAV-J) in the presence of multiple eavesdroppers. We formulate an optimization problem to jointly design the trajectories and transmit power of UAV-B and UAV-J in order to maximize the minimum average secrecy rate over all IRs. The optimization problem is non-convex and the optimization variables are coupled, which leads to the optimization problem being mathematically intractable. As such, we decompose the optimization problem into two subproblems and then solve it by employing an alternating iterative algorithm and the successive convex approximation technique. Our results show that the average secrecy rate performance of the proposed scheme provides about 20% and 150% performance gains over the joint trajectory and transmit power optimization without UAV-J scheme and the transmit power optimization with fixed trajectory scheme at flight period <inline-formula><tex-math notation="LaTeX">T=150</tex-math></inline-formula> s, respectively.
It is important to observe and study cancer cells' cycle progression in order to better understand drug effects on cancer cells. Time-lapse microscopy imaging serves as an important method to measure ...the cycle progression of individual cells in a large population. Since manual analysis is unreasonably time consuming for the large volumes of time-lapse image data, automated image analysis is proposed. Existing approaches dealing with time-lapse image data are rather limited and often give inaccurate analysis results, especially in segmenting and tracking individual cells in a cell population. In this paper, we present a new approach to segment and track cell nuclei in time-lapse fluorescence image sequence. First, we propose a novel marker-controlled watershed based on mathematical morphology, which can effectively segment clustered cells with less oversegmentation. To further segment undersegmented cells or to merge oversegmented cells, context information among neighboring frames is employed, which is proved to be an effective strategy. Then, we design a tracking method based on modified mean shift algorithm, in which several kernels with adaptive scale, shape, and direction are designed. Finally, we combine mean-shift and Kalman filter to achieve a more robust cell nuclei tracking method than existing ones. Experimental results show that our method can obtain 98.8% segmentation accuracy, 97.4% cell division tracking accuracy, and 97.6% cell tracking accuracy
Abstract
Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many ...sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods suffer from oversmoothing and removal of natural cell-to-cell stochasticity in gene expression. Here, we propose the generative adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cells rather than observed cells to avoid these limitations and balances the performance between major and rare cell populations. Evaluations based on a variety of simulated and real scRNA-seq datasets show that scIGANs is effective for dropout imputation and enhances various downstream analysis. ScIGANs is robust to small datasets that have very few genes with low expression and/or cell-to-cell variance. ScIGANs works equally well on datasets from different scRNA-seq protocols and is scalable to datasets with over 100 000 cells. We demonstrated in many ways with compelling evidence that scIGANs is not only an application of GANs in omics data but also represents a competing imputation method for the scRNA-seq data.
RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in ...post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ∼94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ∼83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred.
Quantitative measurement of cell cycle progression in individual cells over time is important in understanding drug treatment effects on cancer cells. Recent advances in time-lapse fluorescence ...microscopy imaging have provided an important tool to study the cell cycle process under different conditions of perturbation. However, existing computational imaging methods are rather limited in analyzing and tracking such time-lapse datasets, and manual analysis is unreasonably time-consuming and subject to observer variances. This paper presents an automated system that integrates a series of advanced analysis methods to fill this gap. The cellular image analysis methods can be used to segment, classify, and track individual cells in a living cell population over a few days. Experimental results show that the proposed method is efficient and effective in cell tracking and phase identification.
Nicotinamide adenine dinucleotide (NAD
) and its metabolites function as critical regulators to maintain physiologic processes, enabling the plastic cells to adapt to environmental changes including ...nutrient perturbation, genotoxic factors, circadian disorder, infection, inflammation and xenobiotics. These effects are mainly achieved by the driving effect of NAD
on metabolic pathways as enzyme cofactors transferring hydrogen in oxidation-reduction reactions. Besides, multiple NAD
-dependent enzymes are involved in physiology either by post-synthesis chemical modification of DNA, RNA and proteins, or releasing second messenger cyclic ADP-ribose (cADPR) and NAADP
. Prolonged disequilibrium of NAD
metabolism disturbs the physiological functions, resulting in diseases including metabolic diseases, cancer, aging and neurodegeneration disorder. In this review, we summarize recent advances in our understanding of the molecular mechanisms of NAD
-regulated physiological responses to stresses, the contribution of NAD
deficiency to various diseases via manipulating cellular communication networks and the potential new avenues for therapeutic intervention.
Hadoop is a popular implementation of the MapReduce framework for running data-intensive jobs on clusters of commodity servers. Shuffle, the all-to-all input data fetching phase between the map and ...reduce phase can significantly affect job performance. However, the shuffle phase and reduce phase are coupled together in Hadoop and the shuffle can only be performed by running the reduce tasks. This leaves the potential parallelism between multiple waves of map and reduce unexploited and resource wastage in multi-tenant Hadoop clusters, which significantly delays the completion of jobs in a multi-tenant Hadoop cluster. More importantly, Hadoop lacks the ability to schedule task efficiently and mitigate the data distribution skew among reduce tasks, which leads to further degradation of job performance. In this work, we propose to decouple shuffle from reduce tasks and convert it into a platform service provided by Hadoop. We present iShuffle, a user-transparent shuffle service that pro-actively pushes map output data to nodes via a novel shuffle-on-write operation and flexibly schedules reduce tasks considering workload balance. Experimental results with representative workloads and Facebook workload trace show that iShuffle reduces job completion time by as much as 29.6 and 34 percent in single-user and multi-user clusters, respectively.
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