恶性黑色素瘤合并嗜肺军团菌感染性肺炎病例报告

Legionella pneumonia is mainly in community-acquired and nosocomial pneumonias caused by the L. pneumophila. The paper reported a case of Legionella pneumonia caused by Legionella pneumophila Sg1 in a man with malignant melanoma. The method for diagnosing Legionella pneumonia by standard culture method，serotyping，PCR-enzymatic digestion analysis and gene sequencing was elaborate. To confirm the diagnosis result of this rapid diagnostic method, sequencing of the bacteria in patient’s sputum partial gene was also carried out. The diagnosis result of this rapid diagnostic method was consistent with the culture method which indicated that it was effective in diagnosing L. pneumophila infection.军团菌肺炎主要是由嗜肺军团菌感染引起的一种社区获得性或医院内感染性肺炎。本文报告了1例临床上极为罕见的恶性黑色素瘤合并嗜肺军团菌血清1型感染引起的军团菌肺炎，并对其实验室诊断作了系统描述，包括病人痰液标本的细菌分离培养、血清学分型、PCR-酶切分型和基因测序鉴定等分子生物学诊断技术，结果表明PCR-酶切分型对于诊断军团菌病是一种快速、准确可靠的试验方法

Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement

This paper investigates different trade-offs between the number of model parameters and enhanced speech qualities by employing several deep tensor-to-vector regression models for speech enhancement. We find that a hybrid architecture, namely CNN-TT, is capable of maintaining a good quality performance with a reduced model parameter size. CNN-TT is composed of several convolutional layers at the bottom for feature extraction to improve speech quality and a tensor-train (TT) output layer on the top to reduce model parameters. We first derive a new upper bound on the generalization power of the convolutional neural network (CNN) based vector-to-vector regression models. Then, we provide experimental evidence on the Edinburgh noisy speech corpus to demonstrate that, in single-channel speech enhancement, CNN outperforms DNN at the expense of a small increment of model sizes. Besides, CNN-TT slightly outperforms the CNN counterpart by utilizing only 32\% of the CNN model parameters. Besides, further performance improvement can be attained if the number of CNN-TT parameters is increased to 44\% of the CNN model size. Finally, our experiments of multi-channel speech enhancement on a simulated noisy WSJ0 corpus demonstrate that our proposed hybrid CNN-TT architecture achieves better results than both DNN and CNN models in terms of better-enhanced speech qualities and smaller parameter sizes.Comment: Accepted to InterSpeech 202

Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network

We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key idea is to cast the conventional deep neural network (DNN) based vector-to-vector regression formulation under a tensor-train network (TTN) framework. TTN is a recently emerged solution for compact representation of deep models with fully connected hidden layers. Thus TTN maintains DNN's expressive power yet involves a much smaller amount of trainable parameters. Furthermore, TTN can handle a multi-dimensional tensor input by design, which exactly matches the desired setting in multi-channel speech enhancement. We first provide a theoretical extension from DNN to TTN based regression. Next, we show that TTN can attain speech enhancement quality comparable with that for DNN but with much fewer parameters, e.g., a reduction from 27 million to only 5 million parameters is observed in a single-channel scenario. TTN also improves PESQ over DNN from 2.86 to 2.96 by slightly increasing the number of trainable parameters. Finally, in 8-channel conditions, a PESQ of 3.12 is achieved using 20 million parameters for TTN, whereas a DNN with 68 million parameters can only attain a PESQ of 3.06. Our implementation is available online https://github.com/uwjunqi/Tensor-Train-Neural-Network.Comment: Accepted to ICASSP 2020. Update reproducible cod

An Improved Chloroplast DNA Extraction Procedure for Whole Plastid Genome Sequencing

Background: Chloroplast genomes supply valuable genetic information for evolutionary and functional studies in plants. The past five years have witnessed a dramatic increase in the number of completely sequenced chloroplast genomes with the application of second-generation sequencing technology in plastid genome sequencing projects. However, costeffective high-throughput chloroplast DNA (cpDNA) extraction becomes a major bottleneck restricting the application, as conventional methods are difficult to make a balance between the quality and yield of cpDNAs. Methodology/Principal Findings: We first tested two traditional methods to isolate cpDNA from the three species, Oryza brachyantha, Leersia japonica and Prinsepia utihis. Both of them failed to obtain properly defined cpDNA bands. However, we developed a simple but efficient method based on sucrose gradients and found that the modified protocol worked efficiently to isolate the cpDNA from the same three plant species. We sequenced the isolated DNA samples with Illumina (Solexa) sequencing technology to test cpDNA purity according to aligning sequence reads to the reference chloroplast genomes, showing that the reference genome was properly covered. We show that 40–50 % cpDNA purity is achieved with our method. Conclusion: Here we provide an improved method used to isolate cpDNA from angiosperms. The Illumina sequencing results suggest that the isolated cpDNA has reached enough yield and sufficient purity to perform subsequent genom