The impacts of extreme marine weather and marine scientific and technological innovation on marine economic development: Evidence form China’s coastal regions

The extreme marine weather is a very vital factor and has important implications for of marine economic development. However, there is a lack of systematic and quantitative analyses of its impact on the marine economic development. Here, we study the impacts of extreme marine weather on marine economic development of 11 coastal regions in China, using the dynamic panel model. We found that extreme marine weather exerts a significant negative impact on the marine economic development. The marine scientific and technological innovation promotes marine economic development in a prominent manner. The marine scientific and technological innovation slows down the unfavorable impact of extreme marine weather on the marine economy. After considering different industries for marine economic development and heterogeneity, we found that extreme marine weather and marine scientific and technological innovation have a great impact on marine economic development in the tertiary industry and the areas with high development concerning marine economy level, while deliver a small impact on the marine economic development in the primary industry and the areas low development level. This paper empirically studies the relationship between the two variables of marine extreme weather and marine science and technology innovation and its impact on marine economic development, enriches the research perspective of extreme weather on marine economic development, and provides new method evidence for improving the level of marine scientific and technological innovation and promoting the development of marine economy

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance

Household, community, sub-national and country-level predictors of primary cooking fuel switching in nine countries from the PURE study

Introduction. Switchingfrom polluting (e.g. wood, crop waste, coal)to clean (e.g. gas, electricity) cooking fuels can reduce household air pollution exposures and climate-forcing emissions.While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. Methods.We examined longitudinal survey data from 24 172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology study.We assessed household-level primary cooking fuel switching during a median of 10 years offollow up (∼2005–2015).We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. Results. One-half of study households(12 369)reported changing their primary cookingfuels between baseline andfollow up surveys. Of these, 61% (7582) switchedfrom polluting (wood, dung, agricultural waste, charcoal, coal, kerosene)to clean (gas, electricity)fuels, 26% (3109)switched between different polluting fuels, 10% (1164)switched from clean to polluting fuels and 3% (522)switched between different clean fuels

Remoteness and distance, distance (signless) Laplacian eigenvalues of a graph

Abstract Let G be a connected graph of order n. The remoteness of G, denoted by ρ, is the maximum average distance from a vertex to all other vertices. Let ∂1≥⋯≥∂n $\partial_{1}\geq\cdots\geq\partial_{n}$, ∂1L≥⋯≥∂nL $\partial_{1}^{L}\geq\cdots\geq\partial_{n}^{L}$ and ∂1Q≥⋯≥∂nQ $\partial_{1} ^{Q}\geq\cdots\geq\partial_{n}^{Q}$ be the distance, distance Laplacian and distance signless Laplacian eigenvalues of G, respectively. In this paper, we give lower bounds on ρ+∂1 $\rho+\partial _{1}$, ρ−∂n $\rho-\partial_{n}$, ρ+∂1L $\rho+\partial_{1}^{L}$, ∂1L−ρ $\partial_{1} ^{L}-\rho$, 2ρ+∂1Q $2\rho+\partial_{1}^{Q}$ and ∂1Q−2ρ $\partial_{1}^{Q}-2\rho$ and the corresponding extremal graphs are also characterized

Multiobjective Brain Storm Optimization Community Detection Method Based on Novelty Search

A complex network is characterized by community structure, so it is of great theoretical and practical significance to discover hidden functions by detecting the community structure in complex networks. In this paper, a multiobjective brain storm optimization based on novelty search (MOBSO-NS) community detection method is proposed to solve the current issue of premature convergence caused by the loss of diversity in complex network community detection based on multiobjective optimization algorithm and improve the accuracy of community discovery. The proposed method designs a novel search strategy where novelty individuals are first constructed to improve the global search ability, thus avoiding falling into local optimal solutions; then, the objective space is divided into 3 clusters: elite cluster, ordinary cluster, and novel cluster, which are mapped to the decision space, and finally, the populations are disrupted and merged. In addition, the introduction of a restarting strategy is introduced to avoid stagnation by premature convergence. Experimental results show that the algorithm with good global searchability can find the Pareto optimal network community structure set with uniform distribution and high convergence and excavate the network community with higher quality

Modification of glycosylation mediates the invasive properties of murine hepatocarcinoma cell lines to lymph nodes.

Among the various posttranslational modification reactions, glycosylation is the most common, and nearly 50% of all known proteins are thought to be glycosylated. In fact, changes in glycosylation readily occur in carcinogenesis, invasion and metastasis. This report investigated the modification of glycosylation mediated the invasive properties of Hca-F and Hca-P murine hepatocarcinoma cell lines, which have high, low metastatic potential in the lymph nodes, respectively. Analysis revealed that the N-glycan composition profiling, expression of glycogenes and lectin binding profiling were different in Hca-F cells, as compared to those in Hca-P cells. Further analysis of the N-glycan regulation by tunicamycin (TM) application or PNGase F treatment in Hca-F cells showed partial inhibition of N-glycan glycosylation and decreased invasion both in vitro and in vivo. We targeted glycogene ST6GAL1, which was expressed differently in Hca-F and Hca-P cells, and regulated the expression of ST6GAL1. The altered levels of ST6GAL1 were also responsible for changed invasive properties of Hca-F and Hca-P cells both in vitro and in vivo. These findings indicate a role for glycosylation modification as a mediator of tumor lymphatic metastasis, with its altered expression causing an invasive ability differentially

Reversal effect of ST6GAL 1 on multidrug resistance in human leukemia by regulating the PI3K/Akt pathway and the expression of P-gp and MRP1

β-Galactoside α2, 6-sialyltransferse gene (ST6GAL) family has two members, which encode corresponding enzymes ST6Gal I and ST6Gal II. The present atudy was to investigate whether and how ST6GAL family involved in multidrug resistance (MDR) in human leukemia cell lines and bone marrow mononuclear cells (BMMC) of leukemia patients. Real-time PCR showed a high expression level of ST6GAL1 gene in both MDR cells and BMMCs (*P<0.05). Alternation of ST6GAL1 levels had a significant impact on drug-resistant phenotype changing of K562 and K562/ADR cells both in vitro and in vivo. However, no significant changes were observed of ST6GAL2 gene. Further data revealed that manipulation of ST6GAL1 modulated the activity of phosphoinositide 3 kinase (PI3K)/Akt signaling and consequently regulated the expression of P-glycoprotein (P-gp, *P<0.05) and multidrug resistance related protein 1 (MRP1, *P<0.05), which are both known to be associated with MDR. Therefore we postulate that ST6GAL1 is responsible for the development of MDR in human leukemia cells probably through medicating the activity of PI3K/Akt signaling and the expression of P-gp and MRP1

The Characteristics of Microbiome and Cytokines in Healthy Implants and Peri-Implantitis of the Same Individuals

Aim: To characterize the profile of submucosal microbiome and cytokine levels in peri-implant crevicular fluid (PICF) from clinically healthy implants and peri-implantitis in the same individuals. Material and Methods: A total of 170 patients were screened and, finally, 14 patients with at least one healthy implant and one peri-implantitis implant were included. Submucosal microbiota and cytokines from 28 implants were analyzed using 16S rRNA gene sequencing and multifactor assays, respectively. Correlations of clinical indexes and microbiota or cytokines were analyzed using Spearman’s correlation coefficient. A random forest classification model was constructed. Results: Peri-implantitis sites harbored higher microbial diversity, as well as more Gram-negative bacteria and anaerobic bacteria, compared with healthy implants sites. The genera of Peptostreptococcaceae XIG-1, Treponema, Porphyromonas, and Lachnospiraceae G-8, as well as the cytokines of IL-17A, IL-6, IL-15, G-CSF, RANTES, and IL-1β were significantly higher in peri-implantitis than healthy implants. Furthermore, these genera and cytokines had positive relationships with clinical parameters, including probing depth (PD), bleeding on probing (BOP), and marginal bone loss (MBL). The classification model picked out the top 15 biomarkers, such as IL-17A, IL-6, IL-15, VEGF, IL-1β, Peptostreptococcaceae XIG-1, Haemophilus, and Treponema, and obtained an area under the curve (AUC) of 0.85. Conclusions: There are more pathogenic bacteria and inflammatory cytokines in peri-implantitis sites, and biomarkers could facilitate the diagnosis of peri-implantitis