The city cluster of the Yangtze River Delta is a highly dynamic and competitive economic region in China. The integration of the market across the 27 cities is crucial in driving economic growth in ...the area. This paper aims to provide policymakers with recommendations on promoting regional integration, enhancing the structure, and improving overall performance. By utilizing the benefits and resources of each community more effectively, greater economic gains can be achieved. The findings of this study can also be applied to other Chinese towns or business areas. Market integration is a necessary foundation for regional integration, as it enables the seamless movement of goods and factors throughout the region while simultaneously reducing entry barriers and supporting the creation of a unified market. Unfortunately, the "vassal economy" model has impeded the region's economic growth. The integration of regional markets is crucial for economic growth. However, it is equally important to create industrial clusters with central towns as their hubs. The Yangtze River Delta urban agglomeration is a prime example of one of six world-class city clusters demonstrating how market integration can result in high-quality economic progress. The paper's primary discoveries are threefold: firstly, there has been a progressive elevation in the level of market integration among the 27 cities within the Yangtze River Delta city cluster, characterized by increasingly intimate connections concerning trade, investment, and population mobility. Secondly, this heightened market integration exerts a catalytic impact on the real economic growth of the Yangtze River Delta city cluster, particularly concerning regional industrial restructuring, transformation, and upgrading. Finally, market integration is poised to expedite the industrial division of labor and synergistic development between the cities, thus promoting a concentration of advanced manufacturing and new industries in the central cities and furthering the development of profitable industries in the central individual cities.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Long non‐coding RNAs (LncRNAs) and DNA methylation are important epigenetic mark play a key role in liver fibrosis. Currently, how DNA methylation and LncRNAs control the hepatic stellate cell (HSC) ...activation and fibrosis has not yet been fully characterized. Here, we explored the role of antisense non‐coding RNA in the INK4 locus (ANRIL) and DNA methylation in HSC activation and fibrosis. The expression levels of DNA methyltransferases 3A (DNMT3A), ANRIL, α‐Smooth muscle actin (α‐SMA), Type I collagen (Col1A1), adenosine monophosphate‐activated protein kinase (AMPK) and p‐AMPK in rat and human liver fibrosis were detected by immunohistochemistry, qRT‐PCR and Western blotting. Liver tissue histomorphology was examined by haematoxylin and eosin (H&E), Sirius red and Masson staining. HSC was transfected with DNMT3A‐siRNA, over‐expressing ANRIL and down‐regulating ANRIL. Moreover, cell proliferation ability was examined by CCK‐8, MTT and cell cycle assay. Here, our study demonstrated that ANRIL was significantly decreased in activated HSC and liver fibrosis tissues, while Col1A1, α‐SMA and DNMT3A were significantly increased in activated HSC and liver fibrosis tissues. Further, we found that down‐regulating DNMT3A expression leads to inhibition of HSC activation. Reduction in DNMT3A elevated ANRIL expression in activated HSC. Furthermore, we performed the over expression ANRIL suppresses HSC activation and AMPK signalling pathways. In sum, our study found that epigenetic DNMT3A silencing of ANRIL enhances liver fibrosis and HSC activation through activating AMPK pathway. Targeting epigenetic modulators DNMT3A and ANRIL, and offer a novel approach for liver fibrosis therapy.
Overview of the epigenetic silencing of LncRNA ANRIL enhances liver fibrosis and HSC activation through activating AMPK pathway. DNMT3A contributes to down regulation of LncRNA ANRIL, which is removed from its target genes during chronic liver fibrosis. Over expression ANRIL suppresses HSC activation and AMPK signaling pathways. Targeting epigenetic modulators DNMT3A and ANRIL, and offer a novel approach for liver fibrosis therapy.
Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide, and the limited availability of qualified ophthalmologists restricts its early diagnosis. For the past few years, ...artificial intelligence technology has developed rapidly and has been applied in DR screening. The upcoming technology provides support on DR screening and improves the identification of DR lesions with a high sensitivity and specificity. This review aims to summarize the progress on automatic detection and classification models for the diagnosis of DR.
Many recent trials have investigated the long-term efficacy and safety of endarterectomy versus stenting in treating patients with carotid artery stenosis. We aimed to determine the long-term ...comparative efficacy and safety of both procedures by pooling this evidence in a meta-analysis.
We searched PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials for studies published until May 6, 2016. Randomized controlled trials, which reported outcomes of interest with a median follow-up of at least 4-year, were included.
Eight trials involving 7005 patients and 41824 patient-years of follow-up were included. In terms of the periprocedural outcomes, stenting was associated with a lower risk of myocardial infarction (OR: 0.51; 95% CI: 0.33 to 0.80; P = 0.003) but a higher risk of death or stroke (the composite endpoint, OR: 1.76; 95% CI: 1.38 to 2.25; P < 0.0001), a result that was primarily driven by minor stroke (OR: 2.19; 95% CI: 1.59 to 3.01; P < 0.0001), less so by periprocedural death (OR: 1.68; 95% CI: 0.82 to 3.44; P = 0.16) and major stroke (OR: 1.41; 95% CI: 0.95 to 2.09; P = 0.09). In terms of the long-term outcomes, stenting was associated with a higher risk of stroke (OR 1.45; 95% CI: 1.22 to 1.73; P < 0.0001) and the composite outcome of death or stroke (OR 1.25; 95% CI: 1.05 to 1.48; P = 0.01). No difference was found in long-term all-cause mortality between stenting and endarterectomy (OR: 1.09; 95% CI: 0.95 to 1.26; P = 0.21) and restenosis (OR: 1.48 (95% CI: 0.93 to 2.35; P = 0.10). No evidence of significant heterogeneity was found in any of the analyses.
Carotid endarterectomy was found to be superior to stenting for short- and long-term outcomes, although endarterectomy was associated with a higher risk of periprocedural myocardial infarction. Carotid endarterectomy should be offered as the first choice for carotid stenosis at present, however, more evidence is needed because rapid progress in concurrent devices and medical treatments is being made.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Numerous substrates have been identified for Type I and II arginine methyltransferases (PRMTs). However, the full substrate spectrum of the only type III PRMT, PRMT7, and its connection to type I and ...II PRMT substrates remains unknown. Here, we use mass spectrometry to reveal features of PRMT7-regulated methylation. We find that PRMT7 predominantly methylates a glycine and arginine motif; multiple PRMT7-regulated arginine methylation sites are close to phosphorylations sites; methylation sites and proximal sequences are vulnerable to cancer mutations; and methylation is enriched in proteins associated with spliceosome and RNA-related pathways. We show that PRMT4/5/7-mediated arginine methylation regulates hnRNPA1 binding to RNA and several alternative splicing events. In breast, colorectal and prostate cancer cells, PRMT4/5/7 are upregulated and associated with high levels of hnRNPA1 arginine methylation and aberrant alternative splicing. Pharmacological inhibition of PRMT4/5/7 suppresses cancer cell growth and their co-inhibition shows synergistic effects, suggesting them as targets for cancer therapy.
Music plays an extremely important role in people’s production and life. The amount of music is growing rapidly. At the same time, the demand for music organization, classification, and retrieval is ...also increasing. Paying more attention to the emotional expression of creators and the psychological characteristics of music are also indispensable personalized needs of users. The existing music emotion recognition (MER) methods have the following two challenges. First, the emotional color conveyed by the first music is constantly changing with the playback of the music, and it is difficult to accurately express the ups and downs of music emotion based on the analysis of the entire music. Second, it is difficult to analyze music emotions based on the pitch, length, and intensity of the notes, which can hardly reflect the soul and connotation of music. In this paper, an improved back propagation (BP) algorithm neural network is used to analyze music data. Because the traditional BP network tends to fall into local solutions, the selection of initial weights and thresholds directly affects the training effect. This paper introduces artificial bee colony (ABC) algorithm to improve the structure of BP neural network. The output value of the ABC algorithm is used as the weight and threshold of the BP neural network. The ABC algorithm is responsible for adjusting the weights and thresholds, and feeds back the optimal weights and thresholds to the BP neural network system. BP neural network with ABC algorithm can improve the global search ability of the BP network, while reducing the probability of the BP network falling into the local optimal solution, and the convergence speed is faster. Through experiments on public music data sets, the experimental results show that compared with other comparative models, the MER method used in this paper has better recognition effect and faster recognition speed.
Considering that the human brain uses ≈1015 synapses to operate, the development of effective artificial synapses is essential to build brain‐inspired computing systems. In biological synapses, the ...voltage‐gated ion channels are very important for regulating the action‐potential firing. Here, an electrolyte‐gated transistor using WO3 with a unique tunnel structure, which can emulate the ionic modulation process of biological synapses, is proposed. The transistor successfully realizes synaptic functions of both short‐term and long‐term plasticity. Short‐term plasticity is mimicked with the help of electrolyte ion dynamics under low electrical bias, whereas the long‐term plasticity is realized using proton insertion in WO3 under high electrical bias. This is a new working approach to control the transition from short‐term memory to long‐term memory using different gate voltage amplitude for artificial synapses. Other essential synaptic behaviors, such as paired pulse facilitation, the depression and potentiation of synaptic weight, as well as spike‐timing‐dependent plasticity are also implemented in this artificial synapse. These results provide a new recipe for designing synaptic electrolyte‐gated transistors through the electrostatic and electrochemical effects.
An electrolyte‐gated transistor using WO3 with a unique tunnel structure to successfully emulate the synaptic functions of both short‐term and long‐term plasticity is proposed. Short‐term plasticity is mimicked with the help of electrolyte ion dynamics under low gate bias, and the long‐term plasticity is realized via proton insertion in WO3 under high gate bias.
We consider the optimal packet scheduling problem in a single-user energy harvesting wireless communication system. In this system, both the data packets and the harvested energy are modeled to ...arrive at the source node randomly. Our goal is to adaptively change the transmission rate according to the traffic load and available energy, such that the time by which all packets are delivered is minimized. Under a deterministic system setting, we assume that the energy harvesting times and harvested energy amounts are known before the transmission starts. For the data traffic arrivals, we consider two different scenarios. In the first scenario, we assume that all bits have arrived and are ready at the transmitter before the transmission starts. In the second scenario, we consider the case where packets arrive during the transmissions, with known arrival times and sizes. We develop optimal off-line scheduling policies which minimize the time by which all packets are delivered to the destination, under causality constraints on both data and energy arrivals.
This paper develops an unsupervised discriminant projection (UDP) technique for dimensionality reduction of high-dimensional data in small sample size cases. UDP can be seen as a linear approximation ...of a multimanifolds-based learning framework which takes into account both the local and nonlocal quantities. UDP characterizes the local scatter as well as the nonlocal scatter, seeking to find a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter. This characteristic makes UDP more intuitive and more powerful than the most up-to-date method, locality preserving projection (LPP), which considers only the local scatter for clustering or classification tasks. The proposed method is applied to face and palm biometrics and is examined using the Yale, FERET, and AR face image databases and the PolyU palmprint database. The experimental results show that UDP consistently outperforms LPP and PCA and outperforms LDA when the training sample size per class is small. This demonstrates that UDP is a good choice for real-world biometrics applications
Speech emotion recognition (SER) is a difficult task due to the complexity of emotions. The SER performances are heavily dependent on the effectiveness of emotional features extracted from the ...speech. However, most emotional features are sensitive to emotionally irrelevant factors, such as the speaker, speaking styles, and environment. In this letter, we assume that calculating the deltas and delta-deltas for personalized features not only preserves the effective emotional information but also reduces the influence of emotionally irrelevant factors, leading to reduce misclassification. In addition, SER often suffers from the silent frames and emotionally irrelevant frames. Meanwhile, attention mechanism has exhibited outstanding performances in learning relevant feature representations for specific tasks. Inspired by this, we propose a three-dimensional attention-based convolutional recurrent neural networks to learn discriminative features for SER, where the Mel-spectrogram with deltas and delta-deltas are used as input. Experiments on IEMOCAP and Emo-DB corpus demonstrate the effectiveness of the proposed method and achieve the state-of-the-art performance in terms of unweighted average recall.
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