Acquirer performance and its determinants: Testing cross-border M&A in Asia

Master'sMASTER OF SCIENCE (MANAGEMENT

Environmental Sound Classification Algorithm Based on Region Joint Signal Analysis Feature and Boosting Ensemble Learning

Environmental sound classification is an important branch of acoustic signal processing. In this work, a set of sound classification features based on audio signal perception and statistical analysis are proposed to describe the signal from multiple aspects of the time and frequency domain. Energy features, spectral entropy features, zero crossing rate (ZCR), and mel-frequency cepstral coefficient (MFCC) are combined to form joint signal analysis (JSA) features to improve the signal expression of the features. Then, based on the JSA, a novel region joint signal analysis feature (RJSA) for environment sound classification is also proposed. It can reduce feature extraction computation and improve feature stability, robustness, and classification accuracy. Finally, a sound classification framework based on the boosting ensemble learning method is provided to improve the classification accuracy and model generalization. The experimental results show that compared with the highest classification accuracy of the baseline algorithm, the environmental sound classification algorithm based on our proposed RJSA features and ensemble learning methods improves the classification accuracy, and the accuracy of the LightGBM-based sound classification algorithm improves by 14.6%

Environmental Sound Classification Algorithm Based on Region Joint Signal Analysis Feature and Boosting Ensemble Learning

Environmental sound classification is an important branch of acoustic signal processing. In this work, a set of sound classification features based on audio signal perception and statistical analysis are proposed to describe the signal from multiple aspects of the time and frequency domain. Energy features, spectral entropy features, zero crossing rate (ZCR), and mel-frequency cepstral coefficient (MFCC) are combined to form joint signal analysis (JSA) features to improve the signal expression of the features. Then, based on the JSA, a novel region joint signal analysis feature (RJSA) for environment sound classification is also proposed. It can reduce feature extraction computation and improve feature stability, robustness, and classification accuracy. Finally, a sound classification framework based on the boosting ensemble learning method is provided to improve the classification accuracy and model generalization. The experimental results show that compared with the highest classification accuracy of the baseline algorithm, the environmental sound classification algorithm based on our proposed RJSA features and ensemble learning methods improves the classification accuracy, and the accuracy of the LightGBM-based sound classification algorithm improves by 14.6%

Hazard Assessment of Urban Waterlogging Disaster on Underground Substations in Shanghai

Based on the two methods of the index system and the scenario simulation, hazard assessment of urban waterlogging disaster on underground substations in Shanghai was carried out. The results show that: (1) the return period rainfall of underground substations in Shanghai gradually increases with the extension of the return period; in terms of spatial distribution, the return period precipitation of the stations along the river and within the inner ring is higher than that of the stations outside the inner ring. (2) The waterlogging thresholds of stations between the inner ring and the middle ring are highest. The waterlogging thresholds of stations within the inner ring and between the middle ring and the outer ring are higher than those in the suburban stations. (3) Under the 20-year return period extreme precipitation scenario, the urban waterlogging risks of underground substations in Shanghai are mainly at low and medium levels. Under the 50-year and longer return period extreme precipitation scenario, the risks of inner ring stations increase to medium-high level

New Metabolites and Bioactive Actinomycins from Marine-Derived Streptomyces sp. ZZ338

An extract prepared from the culture of a marine-derived actinomycete Streptomyces sp. ZZ338 was found to have significant antimicrobial and antiproliferative activities. A chemical investigation of this active extract resulted in the isolation of three known bioactive actinomycins (1–3) and two new metabolites (4 and 5). The structures of the isolated compounds were identified as actinomycins D (1), V (2), X0β (3), 2-acetylamino-3-hydroxyl-4-methyl-benzoic acid methyl ester (4), and N-1S-(4-methylaminophenylmethyl)-2-oxo-propyl acetamide (5) based on their nuclear magnetic resonance (NMR) and high resolution electrospray ionization mass spectroscopy (HRESIMS) data as well as their optical rotation. This class of new compound 5 had never before been found from a natural resource. Three known actinomycins showed activities in inhibiting the proliferation of glioma cells and the growth of methicillin-resistant Staphylococcus aureus, Escherichia coli, and Candida albicans and are responsible for the activity of the crude extract. Actinomycin D (1) was also found to downregulate several glioma metabolic enzymes of glycolysis, glutaminolysis, and lipogenesis, suggesting that targeting multiple tumor metabolic regulators might be a new anti-glioma mechanism of actinomycin D. This is the first report of such a possible mechanism for the class of actinomycins

Induction of murine macrophage M2 polarization by cigarette smoke extract via the JAK2/STAT3 pathway.

Cigarette smoking is a major pathogenic factor in lung cancer. Macrophages play an important role in host defense and adaptive immunity. These cells display diverse phenotypes for performing different functions. M2 type macrophages usually exhibit immunosuppressive and tumor-promoting characteristics. Although macrophage polarization toward the M2 phenotype has been observed in the lungs of cigarette smokers, the molecular basis of the process remains unclear. In this study, we evaluated the possible mechanisms for the polarization of mouse macrophages that are induced by cigarette smoking (CS) or cigarette smoke extract (CSE). The results showed that exposure to CSE suppressed the production of reactive oxygen species (ROS) and nitric oxide (NO) and down-regulated the phagocytic ability of Ana-1 cells. The CD163 expressions on the surface of macrophages from different sources were significantly increased in in vivo and in vitro studies. The M1 macrophage cytokines TNF-α, IL-12p40 and enzyme iNOS decreased in the culture supernatant, and their mRNA levels decreased depending on the time and concentration of CSE. In contrast, the M2 phenotype macrophage cytokines IL-10, IL-6, TGF-β1 and TGF-β2 were up-regulated. Moreover, phosphorylation of JAK2 and STAT3 was observed after the Ana-1 cells were treated with CSE. In addition, pretreating the Ana-1 cells with the STAT3 phosphorylation inhibitor WP1066 inhibited the CSE-induced CD163 expression, increased the mRNA level of IL-10 and significantly decreased the mRNA level of IL-12. In conclusion, we demonstrated that the M2 polarization of macrophages induced by CS could be mediated through JAK2/STAT3 pathway activation

Roles of Fibroblast Growth Factors in the Axon Guidance

Fibroblast growth factors (FGFs) have been widely studied by virtue of their ability to regulate many essential cellular activities, including proliferation, survival, migration, differentiation and metabolism. Recently, these molecules have emerged as the key components in forming the intricate connections within the nervous system. FGF and FGF receptor (FGFR) signaling pathways play important roles in axon guidance as axons navigate toward their synaptic targets. This review offers a current account of axonal navigation functions performed by FGFs, which operate as chemoattractants and/or chemorepellents in different circumstances. Meanwhile, detailed mechanisms behind the axon guidance process are elaborated, which are related to intracellular signaling integration and cytoskeleton dynamics

A Comparison of In-House Real-Time LAMP Assays with a Commercial Assay for the Detection of Pathogenic Bacteria

Molecular detection of bacterial pathogens based on LAMP methods is a faster and simpler approach than conventional culture methods. Although different LAMP-based methods for pathogenic bacterial detection are available, a systematic comparison of these different LAMP assays has not been performed. In this paper, we compared 12 in-house real-time LAMP assays with a commercialized kit (Isothermal Master Mix) for the detection of Listeria monocytogenes, Salmonella spp, Staphylococcus aureus, Escherichia coli O157, E. coli O26, E. coli O45, E. coli O103, E. coli O111, E. coli O121, E. coli O145 and Streptococcus agalactiae. False-positive results were observed in all 12 in-house real-time LAMP assays, while all the negative controls of Isothermal Master Mix remained negative after amplification. The detection limit of Isothermal Master Mix for Listeria monocytogenes, Salmonella spp, Staphylococcus aureus, Escherichia coli O157, E. coli O26, E. coli O45, E. coli O103, E. coli O111, E. coli O121 and Streptococcus agalactiae was 1 pg, whereas the sensitivity of the commercialized kit for E. coli O145 was 100 pg. In conclusion, the 12 in-house real-time LAMP assays were impractical to use, while the commercialized kit Isothermal Master Mix was useful for the detection of most bacterial pathogens