Genome Assembly for a Yunnan-Guizhou Plateau “3E” Fish, Anabarilius grahami (Regan), and Its Evolutionary and Genetic Applications

A Yunnan-Guizhou Plateau fish, the Kanglang white minnow (Anabarilius grahami), is a typical “3E” (Endangered, Endemic, and Economic) species in China. Its distribution is limited to Fuxian Lake, the nation’s second deepest lake, with a significant local economic value but a drastically declining wild population. This species has been evaluated as VU (Vulnerable) in the China Species Red List. As one of the “Four Famous Fish” in Yunnan province, the artificial breeding has been achieved since 2003. It has not only re-established its wild natural populations by reintroduction of the artificial breeding stocks, but also brought a wide and popular utilization of this species to the local fish farms. A. grahami has become one of the main native aquaculture species in Yunnan province, and the artificial production has been emerging in steady growth each year. To promote the conservation and sustainable utilization of this fish, we initiated its whole genome sequencing project using an Illumina Hiseq2500 platform. The assembled genome size of A. grahami is 1.006 Gb, accounting for 98.63% of the estimated genome size (1.020 Gb), with contig N50 and scaffold N50 values of 26.4 kb and 4.41 Mb, respectively. Approximately about 50.38% of the genome was repetitive. A total of 25,520 protein-coding genes were subsequently predicted. A phylogenetic tree based on 4,580 single-copy genes from A. grahami and 18 other cyprinids revealed three well-supported subclades within the Cyprinidae. This is the first inter-subfamily relationship of cyprinids at genome level, providing a simple yet useful framework for understanding the traditional but popular subfamily classification systems. Interestingly, a further population demography of A. grahami uncovered a historical relationship between this fish and Fuxian Lake, suggesting that range expansion or shrinkage of the habitat has had a remarkable impact on the population size of endemic plateau fishes. Additionally, a total of 33,836 simple sequence repeats (SSR) markers were identified, and 11 loci were evaluated for a preliminary genetic diversity analysis in this study, thus providing another useful genetic resource for studying this “3E” species

Exporting, Converting and Importing Between Learning Management Systems

Learning Management Systems (LMS) are ubiquitous in higher education. In addition to the traditional LMSs used inside schools, Massive Open Online Course (MOOC) platforms, such as Coursera and edX, are forming a new generation of LMSs. The ongoing changes in platforms force instructors to frequently migrate their course content from one LMS to another. In 2015-2016, Rice University migrated content of open-enrollment online courses on Coursera to on-campus courses hosted on Canvas. Such migrations are not easy tasks. There is neither a standard structure for LMSs, nor a standard format for content stored in an LMS. Different LMSs have different features and data formats. This research presents a migration tool that automatically downloads and migrates the whole course, including wiki pages, videos, quizzes and assignments, from Coursera to Canvas

Robust exponential stability for discrete-time interval BAM neural networks with delays and Markovian jump parameters

This paper investigates the problem of global robust exponential stability for discrete-time interval BAM neural networks with mode-dependent time delays and Markovian jump parameters, by utilizing the Lyapunov-Krasovskii functional combined with the linear matrix inequality (LMI) approach. A new Markov process as discrete-time, discrete-state Markov process is considered. An exponential stability performance analysis result is first established for error systems without ignoring any terms in the derivative of Lyapunov functional by considering the relationship between the time-varying delay and its upper bound. The delay factor depends on the mode of operation. Three numerical examples are given to demonstrate the merits of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.Jiqing Qiu, Kunfeng Lu, Peng Shi and Magdi S. Mahmou

Radio–Image Transformer: Bridging Radio Modulation Classification and ImageNet Classification

Radio modulation classification is widely used in the field of wireless communication. In this paper, in order to realize radio modulation classification with the help of the existing ImageNet classification models, we propose a radio–image transformer which extracts the instantaneous amplitude, instantaneous phase and instantaneous frequency from the received radio complex baseband signals, then converts the signals into images by the proposed signal rearrangement method or convolution mapping method. We finally use the existing ImageNet classification network models to classify the modulation type of the signal. The experimental results show that the proposed signal rearrangement method and convolution mapping method are superior to the methods using constellation diagrams and time–frequency images, which shows their performance advantages. In addition, by comparing the results of the seven ImageNet classification network models, it can be seen that, except for the relatively poor performance of the architecture MNASNet1_0, the modulation classification performance obtained by the other six network architectures is similar, indicating that the proposed methods do not have high requirements for the architecture of the selected ImageNet classification network models. Moreover, the experimental results show that our method has good classification performance for signal datasets with different sampling rates, Orthogonal Frequency Division Multiplexing (OFDM) signals and real measured signals

FM-Based Positioning via Deep Learning

Frequency modulation (FM) broadcast signals, as opportunity signals, hold significant potential for indoor and outdoor positioning applications. The existing FM-based positioning methods primarily rely on received signal strength (RSS) for positioning, the accuracy of which needs improvement. In this paper, we introduce an end-to-end FM-based positioning method that leverages deep learning, known as FM-Pnet. This method utilizes the time-frequency representation of FM signals as the network input, allowing the network to automatically learn deep features for positioning. We further propose two strategies, noise injection and enriching training samples, to enhance the model's generalization performance over long time spans. We construct datasets for both indoor and outdoor scenarios and conduct extensive experiments to validate the performance of our proposed method. Experimental results demonstrate that FM-Pnet significantly outperforms traditional RSS-based positioning methods in terms of both positioning accuracy and stability.</p