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Identification of genes associated with resistance trait to VP AHPND in Pacific white shrimp ( Litopenaeus vannamei ) using Bulk Segregation Analysis (BSA)

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Aquaculture Reports 2025年 43卷

作者： Zhenning Bao Yang Yu Pengfei Lin Fuhua Li State Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture Institute of Oceanology Chinese Academy of Sciences Qingdao 266000 China University of Chinese Academy of Sciences Beijing 100049 China Laboratory for Marine Biology and Biotechnology Qingdao Marine Science and Technology Center Qingdao 266071 China

Vibrio parahaemolyticus harboring the PirA/PirB toxin (VP AHPND ) is regarded as one of the main pathogens that caused Acute hepatopancreatic necrosis disease (AHPND) in the Pacific white shrimp Litopenaeus vannamei ( L. vannamei ). Breeding new varieties resistant to VP AHPND is considered to be the most effective way to reduce the economic losses in shrimp aquaculture. Identification of genes and SNP markers associated with resistance trait could accelerate the breeding efficiency by application of marker assisted selection and genomic selection. In the present study, a Bulk Segregation Analysis coupled to whole genome sequencing method was employed to identify SNPs and genes associated with resistance trait to VP AHPND in L. vannamei . The DNA of susceptible and resistant individuals was pooled separately. ED (Euclidean distance) and the chi-square test were applied to calculate the allele frequency and genotype frequency differences between susceptible and resistant individuals. A total of 2238 SNPs and 97 genes were identified to be associated with resistance trait to VP AHPND . KEGG enrichment analysis showed that these genes were significantly enriched in PPAR signaling pathway, PI3K-Akt signaling pathway, NLR pathway and O-Antigen nucleotide sugar biosynthesis pathway. The acyl-CoA delta-9 desaturase gene, 14–3–3 epsilon-like gene, retrovirus-related Pol polyprotein gene, enzymatic polyprotein gene in these pathways were considered as candidate genes associated with resistance trait to VP AHPND of shrimp. This study demonstrated that BSA based on whole genome re-sequencing was a cost-effective method for identifying disease resistant genes in aquaculture animals. These data will not only provide useful information for understanding the genetic basis of shrimp resistance trait to VP AHPND , but also offer a source of SNPs for marker-assisted selection and genomic selection in shrimp breeding.

关键词： Litopenaeus vannamei Bulk Segregation Analysis (BSA) Resistance to VP AHPND Single nucleotide polymorphisms (SNPs) sliding window algorithm

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Knot calculation for spline fitting based on the unimodality property

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COMPUTER AIDED GEOMETRIC DESIGN 2019年 73卷 54-69页

作者： Luo, Jiaqi Kang, Hongmei Yang, Zhouwang Soochow Univ Sch Math Sci Suzhou 215006 Jiangsu Peoples R China Univ Sci & Technol China Sch Math Sci Hefei 30026 Anhui Peoples R China

Determining the knot number and location is the most challenging problem in spline fitting. In this paper, we proposed a two-stage framework for calculating knots in spline fitting based on the unimodality property. Here, the unimodality property means that the jumps of the highest order derivative of the B-spline approximation at the initial knots closest to the true knots from the sampled B-spline are local maximal. Thus, in the first step, we search for the knots associated with local maximal jumps to locate in which intervals true knots should be. Here, the sliding window algorithm is adopted to search for the knots from an initial knot vector. Then, the knots selected in the first step are adjusted locally to reduce the knot number and improve fitting performance. Furthermore, we provide an error estimation for the B-spline approximation when data points are sampled uniformly and densely from a spline, and the initial knots are chosen as the parametrization for the data points. Such an estimation provides evidence of the unimodality of the least squares B-spline approximation. The proposed method is a significant improvement upon the method used in Kang et al. (2015) based on algorithm efficiency and fitting performance. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Knot calculation Spline fitting Least squares fitting Unimodality property sliding window algorithm

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An Efficient sliding window algorithm Using Adaptive-Length Guard window for Turbo Decoders

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JOURNAL OF COMMUNICATIONS AND NETWORKS 2012年第2期14卷 195-198页

作者： Lim, Hyuntack Kim, Yongsang Cheun, Kyungwhoon Pohang Univ Sci & Technol POSTECH Div Elect & Comp Engn Pohang 790784 South Korea POSTECH Div Informat Technol Convergence Engn Pohang South Korea

An efficient sliding window algorithm employing an adaptive-length guard window for turbo decoders is proposed. The proposed algorithm results in significant complexity reductions, especially for small sliding window ... 详细信息

关键词： Iterative maximum a posteriori decoder (MAP) sliding window algorithm turbo code

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Real-time Detection and Recognition of Live Panoramic Traffic Signs Based on Deep Learning 9

Real-time Detection and Recognition of Live Panoramic Traffi...

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9th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Meng, Xiangsong Zhang, Xiangli Yan, Kun Zhang, Hongmei Guilin Univ Key Lab Cognit Radio & Inormat Proc Minist Educ Elect Technol Guilin 541004 Peoples R China

ISBN: (纸本)9781538665657

With the hotness of unmanned driving and the challenge of traffic safety, traffic sign detection and recognition is the core issue of unmanned driving. It is of great significance to study it. At present, due to the application of deep learning, the object recognition method has achieved good results, but there are few researches on real-time recognition of small object in live panoramic high resolution images or videos. This paper proposes a method for small object detection and recognition in high resolution images, and applies it to the detection and recognition of traffic signs in panoramic pictures. Firstly, the data preprocessmg method for training dataset is proposed. Then a YOLOv3 algorithm based Traffic Signs Network(TSNet) is designed. After training and fine-tuning, the testing dataset with reasonable scaling is processed by the new sliding window algorithm proposed in this paper. The predictions for bounding boxes is processed by Delete Subsets- Nonmaximum Suppression(DS-NMS) to obtain the higher accuracy and real-time detection effect.

关键词： traffic signs recognition data preprocessing TSNet network sliding window algorithm DS-NMS algorithm

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Every Drop Counts: Unleashing the prospective locations for Water Harvesting using Geospatial Analytics 17

Every Drop Counts: Unleashing the prospective locations for ...

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International Conference on Internet of Things and Machine Learning (IML)

作者： Gupta, Aparana Garg, Anshul Rawat, Namrata Chigurupati, Sandeep Kumar, Dinesh U. Indian Inst Management Bangalore Karnataka India

ISBN: (纸本)9781450352437

Water is at the heart of 'Sustainable Development Goals (SDGs)'set by United Nations- with an objective to balance the three dimensions of sustainable development: Environment, Social and Economic - and is indirectly associated with the success of all the other Goals. But, with changing climatic patterns, untimely rains, prolonged dry spells, depleting ground water and drought making every drop of water extremely precious, the need of the hour is to gauge and work towards the major aspects of water harvesting- 'Catchment'. Water Harvesting must be a key element of any strategy to bring an end to India's perennial swings between drought and flood and to meet the following SDGs for sustained development. This study presents a structured and meticulous approach, wielding `Geospatial Analytics'to identify the prospective locations for Water Harvesting in arid and semi-arid parts of the country for sustainable *** paper is structured as follows. Section 1 describes the background and motivation for this idea. Section 2 details out the objective. In section 3 we present the 'Literature Survey 'on the work that has already been carried out in this field. While section 4 discerns our area of study, Section 5 provides process flow starting from Data gathering, Data extraction,Data pre-processing, Model selection and Multi Criteria Decision Making (Model Application). In Section 6, we present and validate our experimental results achieved using the proposed methodology. Section 7 concludes our study followed by Section 8 on Recommendations for future enhancements and next steps.

关键词： Geospatial Analytics AHP Analytical Hierarchy Process LAND SAT-8 Digital Elevation Model Image Processing Rain Water Harvesting Water Tanks RWH optimum location selection GIS sliding window algorithm Flood Fill Model Smart Water

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Online traffic prediction in the cloud

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INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT 2016年第4期26卷 29-45页

作者： Dalmazo, Bruno L. Vilela, Joao P. Curado, Marilia Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

Network traffic prediction is a fundamental tool to harness several management tasks, such as monitoring and managing network traffic. Online traffic prediction is usually performed based on large sets of historical data used in training algorithms, for example, to determine the size of static windows to bound the amount of traffic under consideration. However, using large sets of historical data may not be suitable in highly volatile environments, such as cloud computing, where the coupling between time series observations decreases rapidly with time. To fill this gap, this work presents a dynamic window size algorithm for traffic prediction that contains a methodology to optimize a threshold parameter alpha that affects both the prediction and computational cost of our scheme. The alpha parameter defines the minimum data traffic variability needed to justify dynamic window size changes. Thus, with the optimization of this parameter, the number of operations of the dynamic window size algorithm decreases significantly. We evaluate the alpha estimation methodology against several prediction models by assessing the normalized mean square error and mean absolute percent error of predicted values over observed values from two real cloud computing datasets, collected by monitoring the utilization of Dropbox, and a data center dataset including traffic from several common cloud computing services. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： cloud computing network traffic prediction short-range dependence sliding window algorithm

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A New Data-Aided Synchronization Scheme for FBMC-OQAM Systems 8

A New Data-Aided Synchronization Scheme for FBMC-OQAM System...

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8th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Liu, Xiadong Qu, Daiming Jiang, Tao Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Natl Lab Optoelect Wuhan Peoples R China

ISBN: (纸本)9781509028603

Although the Filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) systems are robust against double selective fading channels, it unfortunately is still sensitive to timing offset and carrier frequency offset (CFO) errors. To solve this synchronization problem, in this paper, we propose a new data-aided timing and CFO synchronization scheme, that exploits the conjugate-symmetry property of a properly designed training sequence. In addition, the sliding window algorithm is applied to the proposed scheme to further improve synchronization performance in multipath fading channels. Simulation results verify that the proposed scheme achieves good performance in AWGN channel and multipath fading channels, in terms of timing capture probability, minimum square-error (MSE) of timing offset and CFO estimation and bit error rate (BER) versus signal-to-noise ratio (SNR). Moreover, it can achieve timing and frequency synchronization independently in steps with relatively lower implementation complexity.

关键词： FBMC-OQAM Data-aided timing and CFO synchronization scheme conjugate-symmetry property sliding window algorithm lower implementation complexity

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Effective Implementation of Edge Detection algorithm on FPGA and Beagle Board

Effective Implementation of Edge Detection Algorithm on FPGA...

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International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： Tushara, D. Bindu Vardhini, P. A. Harsha VITS Dept ECE Hyderabad Telangana India

ISBN: (纸本)9781467399395

Image processing is a major aspect in transmission of data in compact fashion without loss of information. There are several algorithms defined for image compression, edge detection and noise reduction which form image transformation techniques. The proposed paper focuses on edge detection of given image using kernel matrix using sliding window algorithm. The interface system includes FPGA and beagle board which are connected through I2C.

关键词： FPGA beagle board I2C edge detection kernel matrix sliding window algorithm image transformation techniques image processing

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Online Traffic Prediction in the Cloud: A Dynamic window Approach 2

Online Traffic Prediction in the Cloud: A Dynamic Window App...

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2nd International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Dalmazo, Bruno L. Vilela, Joao P. Curado, Marilia Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

ISBN: (纸本)9781479943579

Traffic prediction is a fundamental tool that captures the inherent behavior of a network and can be used for monitoring and managing network traffic. Online traffic prediction is usually performed based on large historical data used in training algorithms. This may not be suitable to highly volatile environments, such as cloud computing, where the coupling between observations decreases quickly with time. We propose a dynamic window size approach for traffic prediction that can be incorporated with different traffic predictions mechanisms, making them suitable to online traffic prediction by adapting the amount of traffic that must be analyzed in accordance to the variability of data traffic. The evaluation of the proposed solution is performed for several prediction mechanisms by assessing the Normalized Mean Square Error and Mean Absolute Percent Error of predicted values over observed values from a real cloud computing data set, collected by monitoring the utilization of Dropbox.

关键词： Cloud computing network traffic prediction short-range dependence sliding window algorithm

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Memory-optimized Hybrid Decoding Method for Multi-rate Turbo Codes

Memory-optimized Hybrid Decoding Method for Multi-rate Turbo...

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77th IEEE Vehicular Technology Conference (VTC)

作者： Yoo, Injae Kim, Bongjin Park, In-Cheol Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea

ISBN: (纸本)9781467363372

To provide near-optimal error-correcting performance for multi-rate turbo codes and minimize the size of additional memory, a new decoding method is proposed in this paper. The proposed decoding is based on two methods: hybrid sliding window and dynamic metric encoding. The new window scheme combines dummy metric calculation and border metric storing methods to halve the border metric memory and improve the error-correcting performance for multi-rate codes. The dynamic encoding of metrics also significantly reduces the border metric memory without degrading the performance. Employing the two proposed methods reduces the conventional border metric memory by 83%. In addition, the superb error-correcting performance of the proposed method is verified for various code rates by conducting intensive simulations for 3GPP LTE codes.

关键词： border metric encoding memory optimization multi-rate turbo codes sliding window algorithm turbo decoding

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