Object Perceptibility Prediction for Transmission Load Reduction in Vehicle-Infrastructure Cooperative Perception.

Vehicle-infrastructure cooperative perception is an ingenious way to eliminate environmental perception blind areas of connected and autonomous vehicles (CAVs). However, if the infrastructure transmits all environmental information to the nearby CAVs, the transmission load is so heavy that it causes a waste of network resources, such as time and bandwidth, because parts of the information are redundant for the CAVs. It is an efficient manner for the infrastructure to merely transmit the information about objects which cannot be perceived by the CAVs. Therefore, the infrastructure needs to predict whether an object is perceptible for a CAV. In this paper, a machine-leaning-based model is established to settle this problem, and a data filter is also designed to enhance the prediction accuracy in various scenarios. Based on the proposed model, the infrastructure transmits the environmental information selectively, which significantly reduces the transmission load. The experiments prove that the prediction accuracy of the model achieves up to 95%, and the transmission load is reduced by 55%.

29 immune-related genes pairs signature predict the prognosis of cervical cancer patients.

To screen the key immune genes in the development of cervical cancer, construct immune related gene pairs (IRGPs), and evaluate their influence on the prognosis of cervical cancer. Tumor Genome Atlas (TCGA) database and geo database were downloaded as training set and validation set respectively, and immune related gene data were downloaded from immport. IRGPs model is established by machine learning, and the model is analyzed and evaluated. Using the Uclcan to analyze the immune genes expression in cervical cancer, and to further explore the association with the expression level and the clinical stage and prognosis of cervical cancer. According to the analysis of training set, we identified 29 IRGPs as key gene pairs and constructed the model. The AUC value of the model was greater than 0.9, and the model group survival rate was conspicuous different (P < 0.001). The reliability of the model was confirmed in the validation group. Our IRGPs play an important role in the occurrence and development of cervical cancer, and can be used as a prognostic marker and potential new target of cervical cancer.

Frame Transmission Efficiency-Based Cross-Layer Congestion Notification Scheme in Wireless Ad Hoc Networks.

Different from the traditional wired network, the fundamental cause of transmission congestion in wireless ad hoc networks is medium contention. How to utilize the congestion state from the MAC (Media Access Control) layer to adjust the transmission rate is core work for transport protocol design. However, recent works have shown that the existing cross-layer congestion detection solutions are too complex to be deployed or not able to characterize the congestion accurately. We first propose a new congestion metric called frame transmission efficiency (i.e., the ratio of successful transmission delay to the frame service delay), which describes the medium contention in a fast and accurate manner. We further present the design and implementation of RECN (ECN and the ratio of successful transmission delay to the frame service delay in the MAC layer, namely, the frame transmission efficiency), a general supporting scheme that adjusts the transport sending rate through a standard ECN (Explicit Congestion Notification) signaling method. Our method can be deployed on commodity switches with small firmware updates, while making no modification on end hosts. We integrate RECN transparently (i.e., without modification) with TCP on NS2 simulation. The experimental results show that RECN remarkably improves network goodput across multiple concurrent TCP flows.

Low-Latency and Energy-Efficient Data Preservation Mechanism in Low-Duty-Cycle Sensor Networks.

Similar to traditional wireless sensor networks (WSN), the nodes only have limited memory and energy in low-duty-cycle sensor networks (LDC-WSN). However, different from WSN, the nodes in LDC-WSN often sleep most of their time to preserve their energies. The sleeping feature causes serious data transmission delay. However, each source node that has sensed data needs to quickly disseminate its data to other nodes in the network for redundant storage. Otherwise, data would be lost due to its source node possibly being destroyed by outer forces in a harsh environment. The quick dissemination requirement produces a contradiction with the sleeping delay in the network. How to quickly disseminate all the source data to all the nodes with limited memory in the network for effective preservation is a challenging issue. In this paper, a low-latency and energy-efficient data preservation mechanism in LDC-WSN is proposed. The mechanism is totally distributed. The data can be disseminated to the network with low latency by using a revised probabilistic broadcasting mechanism, and then stored by the nodes with LT (Luby Transform) codes, which are a famous rateless erasure code. After the process of data dissemination and storage completes, some nodes may die due to being destroyed by outer forces. If a mobile sink enters the network at any time and from any place to collect the data, it can recover all of the source data by visiting a small portion of survived nodes in the network. Theoretical analyses and simulation results show that our mechanism outperforms existing mechanisms in the performances of data dissemination delay and energy efficiency.

Identification of important positions within miRNAs by integrating sequential and structural features.

MicroRNA(miRNA) is a small, single stranded non-coding RNA which plays an important regulatory role in gene expression. Additionally, miRNAs perform crucial functions in a wide range of biological processes. These functions may be exploited for miRNA-mediated regulation of protein-protein interaction and thus protein function. Many computational methods have been developed to predict the miRNA targets and to explore the regulatory mechanism between miRNA and protein. However, the efforts to investigate important positions within miRNAs are not comprehensive. This paper presents a framework to identify important positions using collision entropy. The information of contained in the sequence and secondary structure of miRNAs is considered. Further, the single base collision entropy and the adjacent base related collision entropy are integrated to measure the importance of miRNA position. Two thresholds are employed to select those positions with more biological meaning. A dataset of Drosophila melanogaster is used in the experiments. The results demonstrate that our approach can find interesting and important positions within miRNAs and may lead to a better understanding of miRNA biogenesis and function.