| 1 | Validation of the DNATyper^(TM)15 PCR Genotyping System for Forensic Application显示文摘We describe the optimization and validation of the DNATyper^(TM)15 multiplex polymerase chain reaction(PCR)genotyping system for autosomal short tandem repeat(STR)amplification at 14 autosomal loci(D6S1043,D21S11,D7S820,CSF1PO,D2S1338,D3S1358,D13S317,D8S1179,D16S539,Penta E,D5S818,vWA,D18S51,and FGA)and amelogenin,a sex‑determining locus.Several DNATyper^(TM)15 assay variables were optimized,including hot start Taq polymerase concentration,Taq polymerase activation time,magnesium concentration,primer concentration,annealing temperature,reaction volume,and cycle number.The performance of the assay was validated with respect to species specificity,sensitivity to template concentration,stability,accuracy,influence of the DNA extraction methods,and the ability to genotype the mixture samples.The performance of the DNATyper^(TM)15 system on casework samples was compared with that of two widely used STR amplification kits,Identifiler^(TM)(Applied Biosystems,Carlsbad,CA,USA)and PowerPlex 16®(Promega,Madison,WI,USA).The conditions for PCR‑based DNATyper^(TM)15 genotyping were optimized.Contamination from forensically relevant nonhuman DNA was not found to impact genotyping results,and full profiles were generated for all the reactions containing≥0.125 ng of DNA template.No significant difference in performance was observed even after the DNATyper^(TM)15 assay components were subjected to 20 freeze‑thaw cycles.The performances of DNATyper^(TM)15,Identifiler^(TM),and PowerPlex 16®were comparable in terms of sensitivity and the ability to genotype the mixed samples and case‑type samples,with the assays giving the same genotyping results for all the shared loci.The DNA extraction methods did not affect the performance of any of the systems.Our results demonstrate that the DNATyper^(TM)15 system is suitable for genotyping in both forensic DNA database work and case‑type samples. | Jian Ye Chengtao Jiang Xingchun Zhao Le Wang Caixia Li Anquan Ji Li Yuan Jing Sun Shuaifeng Chen | 2015 | Journal of Forensic Science and Medicine2015，1，1： | 3 |
| 3 | Highly-efficient Stereo-cultivation Model in Kiwifruit Orchards Interplanting Konjak显示文摘The kiwifruit orchards with plants growing for over two years were chosen for interplanting of konjak, which takes advantages of complementation of the two plants in terms of habits, reducing water and soil erosions effectively, and decreasing water evaporation, and konjak diseases. Furthermore, the interplanting doubles planting benefits and constitutes a highly-efficient planting model. | Jinping WU Zili DING Anquan LIU Chaozhu YANG Jiang Zhengjun Zhengming QIU | 2015 | Agricultural Science & Technology2015，16，6： | 1 |
| 4 | Analog ferroelectric domain-wall memories and synaptic devices integrated with Si substrates显示文摘Brain-inspired neuromorphic computing can overcome the energy and throughput limitations of traditional von Neumann-type computing systems,which requires analog updates of their artificial synaptic strengths for the best recognition performance and low energy consumption.Here,we report synaptic devices made from highly insulating ferroelectric LiNbO_(3)(LNO)thin films bonded to SiO_(2)/Si wafers.Through the creation/annihilation of periodically arrayed antiparallel domains within LNO nanocells,which are stimulated using positive/negative voltage pulses(synaptic plasticity),we can modulate the synaptic conductance linearly by controlling the number of the conducting domain walls.The multilevel conductance is nonvolatile and reproducible with negligible dispersion over 100 switching cycles,representing much better performance than that of random defect-based nonlinear memristors,which generally exhibit large-scale resistance dispersion.The simulation of a neuromorphic network using these LNO artificial synapses achieves 95.6%recognition accuracy for faces,thus approaching the theoretical yield of ideal neuromorphic computing devices. | Chao Wang Tianyu Wang Wendi Zhang Jun Jiang Lin Chen Anquan Jiang | 2022 | Nano Research2022，15，4： | 0 |
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