Stock market trading volumes and economic uncertainty dependence: before and during Sino-U.S. trade friction

This article mainly studies the interaction between the economic uncertainty and stock market trading volumes changes before and during Sino-U.S. trade friction using multifractal detrended fluctuation analysis (M.F.-D.F.A.) and multifractal detrended crosscorrelation analysis (M.F.-D.C.C.A.). Our research aims to reveal whether the economic uncertainty increased by Sino-U.S. trade friction affects stock market trading volume more susceptible, as well as how policymaker strengthen risk management and maintain financial stability. The results show that the dynamic volatility linkages between economic uncertainty and stock market trading volumes changes are multifractal, and the cross-correlation of volatility linkages are anti-persistent. Through the rolling-windows analysis, we also find that the economic uncertainty and trading volumes are anti-persistent dynamic cross-correlated. This means that while economic uncertainty increases, trading volume decreases. Besides, Sino-U.S. trade friction has impact on the cross-correlated behaviour significantly, suggesting that stock markets’ risks are relatively large and trading volumes changes are more susceptible by economic uncertainty during Sino-U.S. trade friction in the U.S. Our study complements existing literature about the stock markets trading volumes and economic uncertainty dependence relationship by multifractal theory’s methods. The overall findings imply that the increased economic uncertainty caused by Sino-U.S. trade friction exacerbates financial risks, which are useful for policymakers and investors

From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning

Video captioning in essential is a complex natural process, which is affected by various uncertainties stemming from video content, subjective judgment, etc. In this paper we build on the recent progress in using encoder-decoder framework for video captioning and address what we find to be a critical deficiency of the existing methods, that most of the decoders propagate deterministic hidden states. Such complex uncertainty cannot be modeled efficiently by the deterministic models. In this paper, we propose a generative approach, referred to as multi-modal stochastic RNNs networks (MS-RNN), which models the uncertainty observed in the data using latent stochastic variables. Therefore, MS-RNN can improve the performance of video captioning, and generate multiple sentences to describe a video considering different random factors. Specifically, a multi-modal LSTM (M-LSTM) is first proposed to interact with both visual and textual features to capture a high-level representation. Then, a backward stochastic LSTM (S-LSTM) is proposed to support uncertainty propagation by introducing latent variables. Experimental results on the challenging datasets MSVD and MSR-VTT show that our proposed MS-RNN approach outperforms the state-of-the-art video captioning benchmarks

Fabrication of CuOx thin-film photocathodes by magnetron reactive sputtering for photoelectrochemical water reduction

The CuOx thin film photocathodes were deposited on F-doped SnO2 (FTO) transparent conducting glasses by alternating current (AC) magnetron reactive sputtering under different Ar:O2 ratios. The advantage of this deposited method is that it can deposit a CuOx thin film uniformly and rapidly with large scale. From the photoelectrochemical (PEC) properties of these CuOx photocathodes, it can be found that the CuOx photocathode with Ar/O2 30:7 provide a photocurrent density of −3.2 mA cm−2 under a bias potential −0.5 V (vs. Ag/AgCl), which was found to be twice higher than that of Ar/O2 with 30:5. A detailed characterization on the structure, morphology and electrochemical properties of these CuOx thin film photocathodes was carried out, and it is found that the improved PEC performance of CuOx semiconductor photocathode with Ar/O2 30:7 attributed to the less defects in it, indicating that this Ar/O2 30:7 is an optimized condition for excellent CuOx semiconductor photocathode fabrication

Inhibition of protein FAK enhances 5-FU chemosensitivity to gastric carcinoma via p53 signaling pathways

Abstract(#br)The small molecule drug 5-fluorouracil (5-FU) is widely used in the treatment for gastric cancer (GC), however, it exerts poor efficacy and is associated with acquired and intrinsic resistance. Focal adhesion kinase (FAK), a non-receptor tyrosine kinase, plays a key role in adhesion, migration, and proliferation of gastric carcinoma cells, suggesting that this kinase may be a promising therapeutic target. Differentially expressed FAK in GC tissue was detected by RT-qPCR and TCGA database analysis. To investigate the biological functions of FAK, loss-of-function experiments were performed. CCK-8 assay, colony formation assay, flow cytometry, dual-luciferase reporter assays, and western blot assays were conducted to determine the underlying mechanisms of FAK in 5-FU chemosensitivity in GC. FAK is overexpressed in GC patients, and positively correlated with poor prognosis. The use of shRNA interference to target FAK decreased proliferation and increased apoptosis of GC cells in vitro. Importantly, FAK silencing enhanced the therapeutic efficacy of 5-FU, leading to reduced tumor growth in vivo . We further demonstrated that FAK silencing increased 5-FU-induced caspase-3 activity, and promoted p53 transcriptional activities. Clinical data also has shown that patients with higher levels of FAK had significantly shorter overall survival (OS) and time to first progression (FP) than those with lower levels of FAK. These findings indicate that FAK plays a critical role in 5-FU chemosensitivity in GC, and the use of FAK inhibitors as an adjunct to 5-FU might be an effective strategy for patients who undergo chemotherapy

Generation of Trophoblast Stem Cells from Rabbit Embryonic Stem Cells with BMP4

Trophoblast stem (TS) cells are ideal models to investigate trophectoderm differentiation and placental development. Herein, we describe the derivation of rabbit trophoblast stem cells from embryonic stem (ES) cells. Rabbit ES cells generated in our laboratory were induced to differentiate in the presence of BMP4 and TS-like cell colonies were isolated and expanded. These cells expressed the molecular markers of mouse TS cells, were able to invade, give rise to derivatives of TS cells, and chimerize placental tissues when injected into blastocysts. The rabbit TS-like cells maintained self-renewal in culture medium with serum but without growth factors or feeder cells, whilst their proliferation and identity were compromised by inhibitors of FGFs and TGF-β receptors. Taken together, our study demonstrated the derivation of rabbit TS cells and suggested the essential roles of FGF and TGF-β signalings in maintenance of rabbit TS cell self-renewal

Genetic Polymorphisms in CYP2E1: Association with Schizophrenia Susceptibility and Risperidone Response in the Chinese Han Population

CYP2E1 is a member of the cytochrome P450 superfamily, which is involved in the metabolism and activation of both endobiotics and xenobiotics. The genetic polymorphisms of CYP2E1 gene (Chromosome 10q26.3, Accession Number NC_000010.10) are reported to be related to the development of several mental diseases and to be involved in the clinical efficacy of some psychiatric medications. We investigated the possible association of CYP2E1 polymorphisms with susceptibility to schizophrenia in the Chinese Han Population as well as the relationship with response to risperidone in schizophrenia patients.In a case-control study, we identified 11 polymorphisms in the 5' flanking region of CYP2E1 in 228 schizophrenia patients and 384 healthy controls of Chinese Han origin. From among the cases, we chose 130 patients who had undergone 8 weeks of risperidone monotherapy to examine the relationship between their response to risperidone and CYP2E1 polymorphisms. Clinical efficacy was assessed using the Brief Psychiatric Rating Scale (BPRS).Statistically significant differences in allele or genotype frequencies were found between cases and controls at rs8192766 (genotype p = 0.0048, permutation p = 0.0483) and rs2070673 (allele: p = 0.0018, permutation p = 0.0199, OR = 1.4528 95%CI = 1.1487-1.8374; genotype: p = 0.0020, permutation p = 0.0225). In addition, a GTCAC haplotype containing 5 SNPs (rs3813867, rs2031920, rs2031921, rs3813870 and rs2031922) was observed to be significantly associated with schizophrenia (p = 7.47E-12, permutation p<0.0001). However, no association was found between CYP2E1 polymorphisms/haplotypes and risperidone response.Our results suggest that CYP2E1 may be a potential risk gene for schizophrenia in the Chinese Han population. However, polymorphisms of the CYP2E1 gene may not contribute significantly to individual differences in the therapeutic efficacy of risperidone. Further studies in larger groups are warranted to confirm our results

Pricing Mechanism Design for Centralized Pollutant Treatment with SME Alliances

In this paper, we assume that a professional pollutant treatment enterprise treats all of the pollutants emitted by multiple small and medium-sized enterprises (SMEs). In order to determine the treatment price, SMEs can bargain with the pollutant treatment enterprise individually, or through forming alliances. We propose a bargaining game model of centralized pollutant treatment to study how the pollutant treatment price is determined through negotiation. Then, we consider that there is a moral hazard from SMEs in centralized pollutant treatment; in other words, they may break their agreement concerning their quantities of production and pollutant emissions with the pollutant treatment enterprise. We study how the pollutant treatment enterprise can prevent this by pricing mechanism design. It is found that the pollutant treatment enterprise can prevent SMEs’ moral hazard through tiered pricing. If the marginal treatment cost of the pollutant treatment enterprise is a constant, SMEs could bargain with the pollutant treatment enterprise individually, otherwise, they should form a grand alliance to bargain with it as a whole

Influence of N:P Ratio of Water on Ecological Stoichiometry of <i>Vallisneria natans</i> and <i>Hydrilla verticillata</i>

Eutrophication is one of the major threats to shallow lake ecosystems, because it causes large-scale degradation of submerged plants. N:P ratio is an important indicator to estimate nutrient supply to water bodies and guide the restoration of submerged plants. The massive input of N and P changes the structure of aquatic communities and ecological processes. However, the mechanism underlying the influence of changes in N and P content and the N:P ratio of a water body on the growth of submerged plants is still unclear. In this study, we simulated gradients of water N:P ratio in lakes in the middle-lower reaches of the Yangtze River using outdoor mesocosm experiments. Using established generalized linear models (GLM), the effects of total nitrogen (TN) content and N:P ratio of water, phytoplankton and periphytic algae biomass, and relative growth rate (RGR) of plants on the stoichiometric characteristics of two widely distributed submerged plants, Hydrilla verticillata and Vallisneria natans, were explored. The results reveal that changes in water nutrient content affected the C:N:P stoichiometry of submerged plants. In a middle-eutrophic state, the stoichiometric characteristics of C, N, and P in the submerged plants were not influenced by phytoplankton and periphytic algae. The P content of H. verticillata and V. natans was positively correlated with their relative growth rate (RGR). As TN and N:P ratio of water increased, their N content increased and C:N decreased. These results indicate that excessive N absorption by submerged plants and the consequent internal physiological injury and growth inhibition may be the important reasons for the degradation of submerged vegetation in the process of lake eutrophication

Fusion Algorithm for Hyperspectral Remote Sensing Image Combined with Harmonic Analysis and Gram-Schmidt Transform

For the defect that harmonic analysis algorithm for hyperspectral image fusion(HAF) in image fusion regardless of spectral reflectance curves, the improved fusion algorithm for hyperspectral remote sensing image combined with harmonic analysis and Gram-Schmidt transform(GSHAF) is proposed in this paper. On the basis of completely retaining waveform of spectrum curve of fused image pixel, GSHAF algorithm can simplify hyperspectral image fusion to between the two-dimensional image by harmonic residual of each pixel spectral curve and high spatial resolution image. It is that the spectral curve of original hyperspectral image can be decomposed into harmonic residual, amplitude and phase, then GS transform with harmonic residual and high spatial resolution image, which can effectively amend spectral reflectance curve of fused image pixel. At last, this fusion image, harmonic amplitude and harmonic phase are inverse harmonic transformed. Finally, with Hyperion hyperspectral remote sensing image and ALI high spatial resolution image to analysis feasibility for GSHAF, then with HJ-1A and other satellite data to verify universality. The result shows that the GSHAF algorithm can not only completely retained the waveform of spectral curve, but also maked spectral reflectance curves of fused image more close to real situation