Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Adsorption & Supply chain. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

Characterizing Neural Entrainment to Hierarchical Linguistic Units using Electroencephalography (EEG).

Abstract: To understand speech, listeners have to combine the words they hear into phrases and sentences. Recent magnetoencephalography (MEG) and electrocorticography (ECoG) studies show that cortical activity is concurrently entrained/synchronized to the rhythms of multiple levels of linguistic units including words, phrases, and sentences. Here we investigate whether this phenomenon can be observed using electroencephalography (EEG), a technique that is more widely available than MEG and ECoG. We show that the EEG responses concurrently track the rhythms of hierarchical linguistic units such as syllables/words, phrases, and sentences. The strength of the sentential-rate response correlates with how well each subject can detect random words embedded in a sequence of sentences. In contrast, only a syllabic-rate response is observed for an unintelligible control stimulus. In sum, EEG provides a useful tool to characterize neural encoding of hierarchical linguistic units, potentially even in individual participants.

NO synthase-generated NO acts downstream of auxin in regulating Fe-deficiency-induced root branching that enhances Fe-deficiency tolerance in tomato plants

Abstract: In response to Fe-deficiency, various dicots increase their root branching which contributes to the enhancement of ferric-chelate reductase activity. Whether this Fe-deficiency-induced response eventually enhances the ability of the plant to tolerate Fe-deficiency or not is still unclear and evidence is also scarce about the signals triggering it. In this study, it was found that the SPAD-chlorophyll meter values of newly developed leaves of four tomato (Solanum lycocarpum) lines, namely line227/1 and Roza and their two reciprocal F1 hybrid lines, were positively correlated with their root branching under Fe-deficient conditions. It indicates that Fe-deficiency-induced root branching is critical for plant tolerance to Fe-deficiency. In another tomato line, Micro-Tom, the increased root branching in Fe-deficient plants was accompanied by the elevation of endogenous auxin and nitric oxide (NO) levels, and was suppressed either by the auxin transport inhibitors NPA and TIBA or the NO scavenger cPTIO. On the other hand, root branching in Fe-sufficient plants was induced either by the auxin analogues NAA and 2,4-D or the NO donors NONOate or SNP. Further, in Fe-deficient plants, NONOate restored the NPA-terminated root branching, but NAA did not affect the cPTIO-terminated root branching. Fe-deficiency-induced root branching was inhibited by the NO-synthase (NOS) inhibitor L-NAME, but was not affected by the nitrate reductase (NR) inhibitor NH4 , tungstate or glycine. Taking all of these findings together, a novel function and signalling pathway of Fe-deficiency-induced root branching is presented where NOS-generated rather than NR-generated NO acts downstream of auxin in regulating this Fe-deficiency-induced response, which enhances the plant tolerance to Fe-deficiency.

Privacy-Enhanced Data Collection Based on Deep Learning for Internet of Vehicles

Abstract: The development of smart cities and deep learning technology is changing our physical world to a cyber world. As one of the main applications, the Internet of Vehicles has been developing rapidly. However, privacy leakage and delay problem for data collection remain as the key concerns behind the fast development of the cyber intelligence technologies. If the original data collected are directly uploaded to the cloud for processing, it will bring huge load pressure and delay to the network communication. Moreover, during this process, it will lead to the leakage of data privacy. To this end, in this article we design a data collection and preprocessing scheme based on deep learning, which adopts the semisupervised learning algorithm of data augmentation and label guessing. Data filtering is performed at the edge layer, and a large amount of similar data and irrelevant data are cleared. If the edge device cannot process some complex data independently, it will send the processed and reliable data to the cloud for further processing, which maximizes the protection of user privacy. Our method significantly reduces the amount of data uploaded to the cloud, and meanwhile protects the user's data privacy effectively.