Genome-wide recombination variation in biparental segregating and reciprocal backcross populations provides information for introgression breeding in Brassica napus

Variation in patterns of recombination in plant genomes provides information about species evolution,genetic diversity and crop improvement. We investigated meiotic crossovers generated in biparental segregating and reciprocal backcross populations of the allopolyploid genome of rapeseed(Brassica napus)(AACC, 2n = 38). A structured set of 1445 intercrossed lines was derived from two homozygous de novo genome-assembled parents that represented the major genetic clusters of semi-winter Chinese and winter European rapeseeds, and was used to increase QTL resolution and achieve genomic reciprocal introgression. A high-density genetic map constructed with 6161 genetic bins and anchored centromere regions was used to establish the pattern of recombination variation in each chromosome. Around 93%of the genome contained crossovers at a mean rate of 3.8 c M Mb^(-1), with the remaining 7% attributed to centromeres or low marker density. Recombination hotspots predominated in the A genome, including two-thirds of those associated with breeding introgression from B. rapa. Genetic background might affect recombination variation. Introgression of genetic diversity from European winter to Chinese semi-winter rapeseed showed an increase in crossover rate under the semi-winter environment. Evidence for an elevated recombination rate having historically contributed to selective trait improvement includes accumulation of favorable alleles for seed oil content on hotspots of chromosome A10. Conversely, strong artificial selection may affect recombination rate variation, as appears to be the case with a coldspot resulting from strong selection for glucosinolate alleles on A09. But the cold region would be promptly reactivated by crossing design indicated by the pedigree analysis. Knowledge of recombination hotspots and coldspots associated with QTL for 22 traits can guide selection strategies for introgression breeding between the two gene pools. These results and rich genomic resources broaden our understanding of recombination behavior in allopolyploids and may advance rapeseed genetic improvement.

An RFI Mitigation Pipeline for CRAFTS Multi-beam Data Based on Signal Cross-Correlation Function and SumThreshold Algorithm

The increasing radio frequency interference(RFI)is a well-recognized problem in radio astronomy research.Pulsars and Fast Radio Bursts(FRBs)are high-priority science targets of the ongoing Commercial Radio Astronomy FAST Survey(CRAFTS).To improve the quality of RFI removal in searches of pulsars and FRBs based on CRAFTS multi-beam data,we here propose an intuitive but powerful RFI mitigation pipeline(CCF-ST).The“CCF-ST”is a spatial filter constructed by signal cross-correlation function(CCF)and Sum-Threshold(ST)algorithm.The RFI marking result is saved in a“mask”file,a binary format for RFI masks in PRESTO.Three known pulsars,PSR B0525-21,PSR B0621-04,and PSR J0943+2252 from CRAFTS L-band 19 beams data are used for evaluation of the performance of CCF-ST in comparison with other methods,such as PRESTO’s“rfifind”,ArPLS-ST and ArPLS-SF.The result shows that CCF-ST can reduce effective data loss rate and improves the detected signal-to-noise ratio of the pulsations by~26%and~18%respectively compared with PRESTO’s“rfifind”and ArPLS-ST.The CCF-ST also has the advantage of low computational cost,e.g.,reducing the time consumption by~40%and memory consumption by~90%compared with ArPLS-SF.We expect that the new RFI mitigation and analysis toolkit(CCF-ST)demonstrated in this paper can be applied to CRAFTS and other multi-beam telescope observations to improve the data quality and efficiency of pulsar and FRB searches.

A GPU based single-pulse search pipeline(GSP)with database and its application to the Commensal Radio Astronomy FAST Survey(CRAFTS)

We developed a GPU based single-pulse search pipeline(GSP)with a candidate-archiving database.Largely based upon the infrastructure of the open source PulsaR Exploration and Search Toolkit(PRESTO),GSP implements GPU acceleration of the de-dispersion and integrates a candidate-archiving database.We applied GSP to the data streams from the Commensal Radio Astronomy FAST Survey(CRAFTS),which resulted in quasi-real-time processing.The integrated candidate database facilitates synergistic usage of multiple machine-learning tools and thus improves efficient identification of radio pulsars such as rotating radio transients(RRATs)and fast radio bursts(FRBs).We first tested GSP on pilot CRAFTS observations with the FAST Ultra-Wide Band(UWB)receiver.GSP detected all pulsars known from the the Parkes multibeam pulsar survey in the corresponding sky area covered by the FAST-UWB.GSP also discovered 13 new pulsars.We measured the computational efficiency of GSP to be~120 times faster than the original PRESTO and~60 times faster than an MPI-parallelized version of PRESTO.

A PRESTO-based parallel pulsar search pipeline used for FAST drift scan data

We developed a pulsar search pipeline based on PulsaR Exploration and Search TOolkit(PRESTO).This pipeline simply runs dedispersion,Fast Fourier Transform(FFT)and acceleration search in process-level parallel to shorten the processing time.With two parallel strategies,the pipeline can highly shorten the processing time in both normal searches and acceleration searches.This pipeline was first tested with Parkes Multibeam Pulsar Survery(PMPS)data and discovered two new faint pulsars.Then,it was successfully applied in processing the Five-hundred-meter Aperture Spherical radio Telescope(FAST)drift scan data with tens of new pulsar discoveries up to now.The pipeline is only CPU-based and can be easily and quickly deployed in computing nodes for testing purposes or data processing.

基于变权模糊综合评判的信息安全等级保护测评模型(英文)

Nowadays,clear evaluation models and methods are lacking in classified protection of information system,which our country is making efforts to promote.The quantitative evaluation of classified protection of information system security is studied.An indicators system of testing and evaluation is established.Furthermore,a model of unit testing and evaluation and a model of entirety testing and evaluation are presented respectively.With analytic hierarchy process and two-grade fuzzy comprehensive evaluation,the subjective and uncertain data of evaluation will be quantitatively analyzed by comprehensive evaluation.Particularly,the variable weight method is used to model entirety testing and evaluation.It can solve the problem that the weights need to be adjusted because of the relationship role which enhances or reduces security of information system.Finally,the paper demonstrates that the model testing and evaluation can be validly used to evaluate the information system by an example.The model proposed in this paper provides a new valuable way for classified protection of information system security.

Application of Improved PSO-LSSVM on Network Threat Detection

To solve the problem of the design of classifier in network threat detection, we conduct a simulation experiment for the parameters’ optimal on least squares support vector machine （LSSVM） using the classic PSO algorithm, and the experiment shows that uneven distribution of the initial particle swarm exerts a great impact on the results of LSSVM algorithm’s classification. This article proposes an improved PSO-LSSVM algorithm based on Divide-and-Conquer （DCPSO- LSSVM） to split the optimal domain where the parameters of LSSVM are in. It can achieve the purpose of distributing the initial particles uniformly. And using the idea of Divide-and-Conquer, it can split a big problem into multiple sub-problems, thus, completing problems’ modularization Meanwhile, this paper introduces variation factors to make the particles escape from the local optimum. The results of experiment prove that DCPSO-LSSVM has better effect on classification of network threat detection compared with SVM and classic PSOLSSVM.

Pulsar candidate selection using ensemble networks for FAST drift-scan survey

The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope(FAST) Survey(CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts are not likely to thoroughly examine these signals, and various machine sorting methods are used to aid the classification of the FAST candidates. In this study, we propose a new ensemble classification system for pulsar candidates. This system denotes the further development of the pulsar image-based classification system(PICS), which was used in the Arecibo Telescope pulsar survey, and has been retrained and customized for the FAST drift-scan survey. In this study, we designed a residual network model comprising 15 layers to replace the convolutional neural networks(CNNs) in PICS. The results of this study demonstrate that the new model can sort >96% of real pulsars to belong the top 1% of all candidates and classify >1.6 million candidates per day using a dual-GPU and 24-core computer. This increased speed and efficiency can help to facilitate real-time or quasi-real-time processing of the pulsar-search data stream obtained from CRAFTS. In addition, we have published the labeled FAST data used in this study online, which can aid in the development of new deep learning techniques for performing pulsar searches.

The first pulsar discovered by FAST

To assist with the commissioning [1] of the Five-hundredmeter Aperture Spherical radio Telescope (FAST), we performed a pulsar search, with the primary goal of developing and testing the pulsar data acquisition and processing pipelines. We tested and used three pipelines, two (Pl and P2 hereafter) searched for the periodic signature of pulsars whereas the other one was used to search for bright single pulses (P3 hereafter).