カントンゴ ワシャ オヨビ ペキンゴ ワシャ ニ ヨル ヒョウジュン チュウゴクゴ ２ オンセツ ケイセイゴ ノ サンシュツ ニ ツイテ

本稿では、規則軽声と非規則軽声の対立という視点から広東語話者及び北京語話者が標準中国語の2音節軽声語を発音する際に方言の影響を受けるかどうか、また受けるならそれはどのようなものかを考察した。また、先行研究のような検査語の前後における声調と文のフォーカスを厳密に統一した実験手法とそれと異なる実験手法の両方を使い、得られた結果に差があるかどうかを考察した。その結果、次に挙げたことが明らかになった。第一に、広東語話者と北京語話者が軽声を産出する際に方言の影響を受ける。第二に、方言の影響にかかわらず、規則軽声語と比べ、非規則軽声語が広東語話者と北京語話者のどちらにとっても習得しにくい。第三に、検査語の前後における声調と文のフォーカスの違いにもかかわらず、得られた結果が不変性を持つ。第四に、軽声語を誤って非軽声で発音されやすい傾向がある一方、本来非軽声で発音されるべき語は正しく非軽声で発音されることが圧倒的に多い。ただし、漢字の“子”が非軽声の時、軽声に発音されやすい可能性があることが示唆された。音声言語の研究(16

Amorphous shear bands in crystalline materials as drivers of plasticity

Traditionally, the formation of amorphous shear bands (SBs) in crystalline materials has been undesirable, because SBs can nucleate voids and act as precursors to fracture. They also form as a final stage of accumulated damage. Only recently SBs were found to form in undefected crystals, where they serve as the primary driver of plasticity without nucleating voids. Here, we have discovered trends in materials properties that determine when amorphous shear bands will form and whether they will drive plasticity or lead to fracture. We have identified the materials systems that exhibit SB deformation, and by varying the composition, we were able to switch from ductile to brittle behavior. Our findings are based on a combination of experimental characterization and atomistic simulations, and they provide a potential strategy for increasing toughness of nominally brittle materials

Based on disulfidptosis-related glycolytic genes to construct a signature for predicting prognosis and immune infiltration analysis of hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) comprises several distinct molecular subtypes with varying prognostic implications. However, a comprehensive analysis of a prognostic signature for HCC based on molecular subtypes related to disulfidptosis and glycolysis, as well as associated metabolomics and the immune microenvironment, is yet to be fully explored.MethodsBased on the differences in the expression of disulfide-related glycolytic genes (DRGGs), patients with HCC were divided into different subtypes by consensus clustering. Establish and verify a risk prognosis signature. Finally, the expression level of the key gene SLCO1B1 in the signature was evaluated using immunohistochemistry (IHC) and quantitative real-time PCR (qRT-PCR) in HCC. The association between this gene and immune cells was explored using multiplex immunofluorescence. The biological functions of the cell counting kit-8, wound healing, and colony formation assays were studied.ResultsDifferent subtypes of patients have specific clinicopathological features, prognosis and immune microenvironment. We identified seven valuable genes and constructed a risk-prognosis signature. Analysis of the risk score revealed that compared to the high-risk group, the low-risk group had a better prognosis, higher immune scores, and more abundant immune-related pathways, consistent with the tumor subtypes. Furthermore, IHC and qRT-PCR analyses showed decreased expression of SLCO1B1 in HCC tissues. Functional experiments revealed that SLCO1B1 overexpression inhibited the proliferation, migration, and invasion of HCC cells.ConclusionWe developed a prognostic signature that can assist clinicians in predicting the overall survival of patients with HCC and provides a reference value for targeted therapy

Acoustic Denoising Using Artificial Intelligence for Wood-Boring Pests Semanotus bifasciatus Larvae Early Monitoring

Acoustic detection technology is a new method for early monitoring of wood-boring pests, and the effective denoising methods are the premise of acoustic detection in forests. This paper used sensors to record Semanotus bifasciatus larval feeding sounds and various environmental noises, and two kinds of sounds were mixed to obtain the noisy feeding sounds with controllable noise intensity. Then, the time domain denoising models and frequency domain denoising models were designed, and the denoising effects were compared using the metrics of a signal-to-noise ratio (SNR), a segment signal-noise ratio (SegSNR), and log spectral distance (LSD). In the experiments, the average SNR increment could achieve 17.53 dB and 11.10 dB using the in the test data using the time domain features and frequency domain features, respectively. The average SegSNR increment achieved 18.59 dB and 12.04 dB, respectively, and the average LSD between pure feeding sounds and denoised feeding sounds were 0.85 dB and 0.84 dB, respectively. The experimental results demonstrated that the denoising models based on artificial intelligence were effective methods for S. bifasciatus larval feeding sounds, and the overall denoising effect was more significant, especially at low SNRs. In view of that, the denoising models using time domain features were more suitable for the forest area and quarantine environment with complex noise types and large noise interference

Effect of antiamyloid-β drugs on Alzheimer’s disease: study protocol for a systematic review and meta-analysis

Introduction Alzheimer’s disease (AD) is a neurodegenerative disease with a complex aetiology involving multiple targets and pathways. With the continuous growth of the ageing population, the burden of AD is increasing year by year. However, there has not been new drug approved for over a decade. In addition, the efficacy of memantine and cholinesterase inhibitors is not satisfactory. As amyloid-β (Aβ) is regarded as the core pathological change and the trigger mechanism of AD, anti-Aβ therapy may be an effective therapy. In recent years, a lot of clinical trials have been carried out in this field, but the results have not been well summarised and analysed.Methods and analysis In this study, we will study the effect of anti-Aβ antibodies versus placebo on the clinical efficacy, biomarkers, neuroimaging and safety in different stages of AD, as well as the factors that may affect the efficacy. Drugs that only target the existing Aβ are regarded as anti-Aβ antibodies. Following electronic databases will be searched from inception to April 2021: Medline-Ovid, EMBase-Ovid, Cochrane Central and clinical trial registration platform ClinicalTrials.gov. After identifying eligible studies through screening title, abstract and read full text of each retrieved literature, we will contact the correspondence authors for additional information and grey literatures. To get more reliable results, random effect model will be conducted for meta-analysis and analysis of subgroups or subsets. Funnel plot, Egger’s test and sensitivity analysis will be conducted to explore potential heterogeneity. Meta-regression will be conducted to identify the factors that may affect clinical efficacy. Evidence quality assessment and trial sequential analysis will be conducted to assess the quality of evidence and confirm the reliability of the results in this study.Ethics and discussion This study does not require formal ethical approval. The findings will be submitted to a peer-review journal.PROSPERO registration number CRD42020202370

Learning and Compressing: Low-Rank Matrix Factorization for Deep Neural Network Compression

Recently, the deep neural network (DNN) has become one of the most advanced and powerful methods used in classification tasks. However, the cost of DNN models is sometimes considerable due to the huge sets of parameters. Therefore, it is necessary to compress these models in order to reduce the parameters in weight matrices and decrease computational consumption, while maintaining the same level of accuracy. In this paper, in order to deal with the compression problem, we first combine the loss function and the compression cost function into a joint function, and optimize it as an optimization framework. Then we combine the CUR decomposition method with this joint optimization framework to obtain the low-rank approximation matrices. Finally, we narrow the gap between the weight matrices and the low-rank approximations to compress the DNN models on the image classification task. In this algorithm, we not only solve the optimal ranks by enumeration, but also obtain the compression result with low-rank characteristics iteratively. Experiments were carried out on three public datasets under classification tasks. Comparisons with baselines and current state-of-the-art results can conclude that our proposed low-rank joint optimization compression algorithm can achieve higher accuracy and compression ratios

Convolutional Recurrent Neural Networks for Observation-Centered Plant Identification

Traditional image-centered methods of plant identification could be confused due to various views, uneven illuminations, and growth cycles. To tolerate the significant intraclass variances, the convolutional recurrent neural networks (C-RNNs) are proposed for observation-centered plant identification to mimic human behaviors. The C-RNN model is composed of two components: the convolutional neural network (CNN) backbone is used as a feature extractor for images, and the recurrent neural network (RNN) units are built to synthesize multiview features from each image for final prediction. Extensive experiments are conducted to explore the best combination of CNN and RNN. All models are trained end-to-end with 1 to 3 plant images of the same observation by truncated back propagation through time. The experiments demonstrate that the combination of MobileNet and Gated Recurrent Unit (GRU) is the best trade-off of classification accuracy and computational overhead on the Flavia dataset. On the holdout test set, the mean 10-fold accuracy with 1, 2, and 3 input leaves reached 99.53%, 100.00%, and 100.00%, respectively. On the BJFU100 dataset, the C-RNN model achieves the classification rate of 99.65% by two-stage end-to-end training. The observation-centered method based on the C-RNNs shows potential to further improve plant identification accuracy

microRNA‐637 promotes apoptosis and suppresses proliferation and autophagy in multiple myeloma cell lines via NUPR1

Multiple myeloma (MM) is a heterogeneous disease with poor prognosis. Increasing evidence has revealed that microRNAs (miRNAs) are strongly associated with the pathogenesis and progression of MM. Here, we investigated the role of microRNA‐637 (miR‐637) in MM to identify potential therapeutic targets. We measured the expression of miR‐637 in bone marrow samples of MM patients and MM cell lines by quantitative real‐time PCR and western blot. The effect of miR‐637 on proliferation and apoptosis of MM primary cells was also investigated. Analyses of four bioinformatics databases showed that miR‐637 is associated with nuclear protein 1 (NUPR1) in MM cells, which was confirmed by luciferase reporter assay. We found that the overexpression of miR‐637 suppressed the development of MM. miR‐637 mimics increased the levels of Bax, cleaved caspase 3, and P62, and decreased the levels of Bcl2 and LC3. Additionally, luciferase reporter assays were performed to demonstrate that NUPR1 is the main target of miR‐637 in MM cells. Overexpression of NUPR1 reversed the effects of miR‐637 mimics in MM cells. Our results suggest that miR‐637 inhibits cell proliferation and autophagy, and promotes apoptosis in MM cells by targeting NUPR1. Our findings also suggest that miR‐637 may have potential as a novel molecular therapeutic target for MM treatment

Acoustic Denoising Using Artificial Intelligence for Wood-Boring Pests <i>Semanotus bifasciatus</i> Larvae Early Monitoring

Acoustic detection technology is a new method for early monitoring of wood-boring pests, and the effective denoising methods are the premise of acoustic detection in forests. This paper used sensors to record Semanotus bifasciatus larval feeding sounds and various environmental noises, and two kinds of sounds were mixed to obtain the noisy feeding sounds with controllable noise intensity. Then, the time domain denoising models and frequency domain denoising models were designed, and the denoising effects were compared using the metrics of a signal-to-noise ratio (SNR), a segment signal-noise ratio (SegSNR), and log spectral distance (LSD). In the experiments, the average SNR increment could achieve 17.53 dB and 11.10 dB using the in the test data using the time domain features and frequency domain features, respectively. The average SegSNR increment achieved 18.59 dB and 12.04 dB, respectively, and the average LSD between pure feeding sounds and denoised feeding sounds were 0.85 dB and 0.84 dB, respectively. The experimental results demonstrated that the denoising models based on artificial intelligence were effective methods for S. bifasciatus larval feeding sounds, and the overall denoising effect was more significant, especially at low SNRs. In view of that, the denoising models using time domain features were more suitable for the forest area and quarantine environment with complex noise types and large noise interference

The Economically Sustainable Hydrothermal Synthesis of Dextrosil-Viologen as a Robust Anolyte in Aqueous Redox Flow Batteries

Aqueous organic redox flow batteries (RFBs) are promising for grid-scale energy storage, but identifying stable and inexpensive organic redox couples suitable for practical applications has been challenging. Here we report a new, inexpensive, and robust anolyte, Dextrosil-Viologen (Dex-Vi), that demonstrates a record overall RFB performance for anolyte redox species in neutral aqueous media, including ultralow anion-exchange membrane permeability, high volumetric capacity capability, and outstanding chemical stability. Remarkably, at a high concentration of 1.5 M (40.2 Ah·L-1 theoretical anolyte volumetric capacity), Dex-Vi shows extremely stable cycling performance without observable capacity decay over one-month cycling. Furthermore, by rationalizing a high-yield hydrothermal synthetic approach that has never been applied to viologen RFB molecules along with a low-cost precursor, the predicted mass production cost of Dex-Vi is below $10/kAh. These results not only establish a new benchmark organic anolyte promising for practical RFB applications but also shows that the properties of organic redox species can be enhanced with minute performance tradeoffs through rationalized structural and synthetic design