The Dilemma of Philosophy and the Prospect of Chinese Philosophy

At gatherings with some professionals, many Chinese philosophy scholars often express their sorrow at the decline in first–choice applications of philosophy, even in some famous unive

EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction

Accurate trajectory prediction is crucial for the safe and efficient operation of autonomous vehicles. The growing popularity of deep learning has led to the development of numerous methods for trajectory prediction. While deterministic deep learning models have been widely used, deep generative models have gained popularity as they learn data distributions from training data and account for trajectory uncertainties. In this study, we propose EquiDiff, a deep generative model for predicting future vehicle trajectories. EquiDiff is based on the conditional diffusion model, which generates future trajectories by incorporating historical information and random Gaussian noise. The backbone model of EquiDiff is an SO(2)-equivariant transformer that fully utilizes the geometric properties of location coordinates. In addition, we employ Recurrent Neural Networks and Graph Attention Networks to extract social interactions from historical trajectories. To evaluate the performance of EquiDiff, we conduct extensive experiments on the NGSIM dataset. Our results demonstrate that EquiDiff outperforms other baseline models in short-term prediction, but has slightly higher errors for long-term prediction. Furthermore, we conduct an ablation study to investigate the contribution of each component of EquiDiff to the prediction accuracy. Additionally, we present a visualization of the generation process of our diffusion model, providing insights into the uncertainty of the prediction

miR-26a mediates LC-PUFA biosynthesis by targeting the Lxrα-Srebp1 pathway in the marine teleost Siganus canaliculatus

MicroRNAs (miRNAs) have been recently shown to be important regulators of lipid metabolism. However, the mechanisms of miRNA-mediated regulation of long-chain polyunsaturated fatty acids (LC-PUFA) biosynthesis in vertebrates remain largely unknown. Herein, we for the first time addressed the role of miR-26a in LC-PUFA biosynthesis in the marine rabbitfish Siganus canaliculatus. The results showed that miR-26a was significantly down-regulated in liver of rabbitfish reared in seawater and in S. canaliculatus hepatocyte line (SCHL) incubated with the LC-PUFA precursor α-linolenic acid (ALA), suggesting that miR-26a may be involved in LC-PUFA biosynthesis due to its abundance being regulated by factors affecting LC-PUFA biosynthesis. Opposite patterns were observed in the expression of liver X receptor α (lxrα) and sterol regulatory element-binding protein-1 (srebp1), as well as the LC-PUFA biosynthesis related genes (Δ4 fads2, Δ6Δ5 fads2 and elovl5) in SCHL cells incubated with ALA. Luciferase reporter assays revealed rabbitfish lxrα as a target of miR-26a, and overexpression of miR-26a in SCHL cells markedly reduced protein levels of Lxrα, Srebp1 and Δ6Δ5 Fads2 induced by the agonist T0901317. Moreover, increasing endogenous Lxrα by knockdown of miR-26a facilitated Srebp1 activation and concomitant increased expression of genes involved in LC-PUFA biosynthesis, and consequently promoted LC-PUFA biosynthesis both in vitro and in vivo. These results indicate a critical role of miR-26a in regulating LC-PUFA biosynthesis through targeting the Lxrα-Srebp1 pathway and provide new insights into the regulatory network controlling LC-PUFA biosynthesis and accumulation in vertebrates

miR-27b Suppresses Endothelial Cell Proliferation and Migration by Targeting Smad7 in Kawasaki Disease

Background/Aims: Increasing evidence indicates that microRNAs (miRNAs) play important roles in Kawasaki disease (KD). Our previous study demonstrated that hsa-miR-27b-3p (miR-27b) was up-regulated in KD serum. However, the specific role of miR-27b in KD remains unclear. We aimed to investigate that miR-27b could be a biomarker and therapeutic target for KD treatment. As well, the specific mechanism of miR-27b effecting endothelial cell functions was studied. Methods: The expression of miR-27b and Smad7 was measured by qRT-PCR. Gain-of-function strategy was used to observe the effect of miR-27b on human umbilical vein endothelial cells (HUVECs) proliferation and migration. Bioinformatics analyses were applied to predict miR-27b targets and then we verified Smad7 by a luciferase reporter assay. Western blot was performed to detect the protein expression of Smad7, PCNA, MMP9, MMP12 and TGF-β-related genes. Results: We confirmed that miR-27b was shown to be dramatically up-regulated in KD serum and KD serum-treated HUVECs and that elevated expression of miR-27b suppressed the proliferation and migration of HUVECs. Furthermore, our results verified that miR-27b mediated cell functions by affecting the TGF-β via targeting Smad7 in HUVECs. Conclusion: These results suggested that up-regulated miR-27b had a protective role in HUVECs proliferation and migration via targeting Smad7 and affecting TGF-β pathway. Therefore, miR-27b represented a potential biomarker for KD and may serve as a promising therapeutic target for KD treatment

Causal link between gut microbiota and four types of pancreatitis: a genetic association and bidirectional Mendelian randomization study

BackgroundA number of recent observational studies have indicated a correlation between the constitution of gut microbiota and the incidence of pancreatitis. Notwithstanding, observational studies are unreliable for inferring causality because of their susceptibility to confounding, bias, and reverse causality, the causal relationship between specific gut microbiota and pancreatitis is still unclear. Therefore, our study aimed to investigate the causal relationship between gut microbiota and four types of pancreatitis.MethodsAn investigative undertaking encompassing a genome-wide association study (GWAS) comprising 18,340 participants was undertaken with the aim of discerning genetic instrumental variables that exhibit associations with gut microbiota, The aggregated statistical data pertaining to acute pancreatitis (AP), alcohol-induced AP (AAP), chronic pancreatitis (CP), and alcohol-induced CP (ACP) were acquired from the FinnGen Consortium. The two-sample bidirectional Mendelian randomization (MR) approach was utilized. Utilizing the Inverse-Variance Weighted (IVW) technique as the cornerstone of our primary analysis. The Bonferroni analysis was used to correct for multiple testing, In addition, a number of sensitivity analysis methodologies, comprising the MR-Egger intercept test, the Cochran’s Q test, MR polymorphism residual and outlier (MR-PRESSO) test, and the leave-one-out test, were performed to evaluate the robustness of our findings.ResultsA total of 28 intestinal microflora were ascertained to exhibit significant associations with diverse outcomes of pancreatitis. Among them, Class Melainabacteria (OR = 1.801, 95% CI: 1.288–2.519, p = 0.008) has a strong causality with ACP after the Bonferroni-corrected test, in order to assess potential reverse causation effects, we used four types of pancreatitis as the exposure variable and scrutinized its impact on gut microbiota as the outcome variable, this analysis revealed associations between pancreatitis and 30 distinct types of gut microflora. The implementation of Cochran’s Q test revealed a lack of substantial heterogeneity among the various single nucleotide polymorphisms (SNP).ConclusionOur first systematic Mendelian randomization analysis provides evidence that multiple gut microbiota taxa may be causally associated with four types of pancreatitis disease. This discovery may contribute significant biomarkers conducive to the preliminary, non-invasive identification of Pancreatitis. Additionally, it could present viable targets for potential therapeutic interventions in the disease’s treatment

Marine organic matter in the remote environment of the Cape Verde islands-an introduction and overview to the MarParCloud campaign

The project MarParCloud (Marine biological production, organic aerosol Particles and marine Clouds: a process chain) aims to improve our understanding of the genesis, modification and impact of marine organic matter (OM) from its biological production, to its export to marine aerosol particles and, finally, to its ability to act as ice-nucleating particles (INPs) and cloud condensation nuclei (CCN). A field campaign at the Cape Verde Atmospheric Observatory (CVAO) in the tropics in September-October 2017 formed the core of this project that was jointly performed with the project MARSU (MARine atmospheric Science Unravelled). A suite of chemical, physical, biological and meteorological techniques was applied, and comprehensive measurements of bulk water, the sea surface microlayer (SML), cloud water and ambient aerosol particles collected at a ground-based and a mountain station took place. Key variables comprised the chemical characterization of the atmospherically relevant OM components in the ocean and the atmosphere as well as measurements of INPs and CCN. Moreover, bacterial cell counts, mercury species and trace gases were analyzed. To interpret the results, the measurements were accompanied by various auxiliary parameters such as air mass back-trajectory analysis, vertical atmospheric profile analysis, cloud observations and pigment measurements in seawater. Additional modeling studies supported the experimental analysis. During the campaign, the CVAO exhibited marine air masses with low and partly moderate dust influences. The marine boundary layer was well mixed as indicated by an almost uniform particle number size distribution within the boundary layer. Lipid biomarkers were present in the aerosol particles in typical concentrations of marine background conditions. Accumulation-and coarse-mode particles served as CCN and were efficiently transferred to the cloud water. The ascent of ocean-derived compounds, such as sea salt and sugar-like compounds, to the cloud level, as derived from chemical analysis and atmospheric transfer modeling results, denotes an influence of marine emissions on cloud formation. Organic nitrogen compounds (free amino acids) were enriched by several orders of magnitude in submicron aerosol particles and in cloud water compared to seawater. However, INP measurements also indicated a significant contribution of other non-marine sources to the local INP concentration, as (biologically active) INPs were mainly present in supermicron aerosol particles that are not suggested to undergo strong enrichment during ocean-atmosphere transfer. In addition, the number of CCN at the supersaturation of 0.30 % was about 2.5 times higher during dust periods compared to marine periods. Lipids, sugar-like compounds, UV-absorbing (UV: ultraviolet) humic-like substances and low-molecularweight neutral components were important organic compounds in the seawater, and highly surface-active lipids were enriched within the SML. The selective enrichment of specific organic compounds in the SML needs to be studied in further detail and implemented in an OM source function for emission modeling to better understand transfer patterns, the mechanisms of marine OM transformation in the atmosphere and the role of additional sources. In summary, when looking at particulate mass, we see oceanic compounds transferred to the atmospheric aerosol and to the cloud level, while from a perspective of particle number concentrations, sea spray aerosol (i.e., primary marine aerosol) contributions to both CCN and INPs are rather limited. © Author(s) 2020

Marine organic matter in the remote environment of the Cape Verde islands – an introduction and overview to the MarParCloud campaign

The project MarParCloud (Marine biological production, organic aerosol Particles and marine Clouds: a process chain) aims to improve our understanding of the genesis, modification and impact of marine organic matter (OM) from its biological production, to its export to marine aerosol particles and, finally, to its ability to act as ice-nucleating particles (INPs) and cloud condensation nuclei (CCN). A field campaign at the Cape Verde Atmospheric Observatory (CVAO) in the tropics in September–October 2017 formed the core of this project that was jointly performed with the project MARSU (MARine atmospheric Science Unravelled). A suite of chemical, physical, biological and meteorological techniques was applied, and comprehensive measurements of bulk water, the sea surface microlayer (SML), cloud water and ambient aerosol particles collected at a ground-based and a mountain station took place. Key variables comprised the chemical characterization of the atmospherically relevant OM components in the ocean and the atmosphere as well as measurements of INPs and CCN. Moreover, bacterial cell counts, mercury species and trace gases were analyzed. To interpret the results, the measurements were accompanied by various auxiliary parameters such as air mass back-trajectory analysis, vertical atmospheric profile analysis, cloud observations and pigment measurements in seawater. Additional modeling studies supported the experimental analysis. During the campaign, the CVAO exhibited marine air masses with low and partly moderate dust influences. The marine boundary layer was well mixed as indicated by an almost uniform particle number size distribution within the boundary layer. Lipid biomarkers were present in the aerosol particles in typical concentrations of marine background conditions. Accumulation- and coarse-mode particles served as CCN and were efficiently transferred to the cloud water. The ascent of ocean-derived compounds, such as sea salt and sugar-like compounds, to the cloud level, as derived from chemical analysis and atmospheric transfer modeling results, denotes an influence of marine emissions on cloud formation. Organic nitrogen compounds (free amino acids) were enriched by several orders of magnitude in submicron aerosol particles and in cloud water compared to seawater. However, INP measurements also indicated a significant contribution of other non-marine sources to the local INP concentration, as (biologically active) INPs were mainly present in supermicron aerosol particles that are not suggested to undergo strong enrichment during ocean–atmosphere transfer. In addition, the number of CCN at the supersaturation of 0.30 % was about 2.5 times higher during dust periods compared to marine periods. Lipids, sugar-like compounds, UV-absorbing (UV: ultraviolet) humic-like substances and low-molecular-weight neutral components were important organic compounds in the seawater, and highly surface-active lipids were enriched within the SML. The selective enrichment of specific organic compounds in the SML needs to be studied in further detail and implemented in an OM source function for emission modeling to better understand transfer patterns, the mechanisms of marine OM transformation in the atmosphere and the role of additional sources. In summary, when looking at particulate mass, we see oceanic compounds transferred to the atmospheric aerosol and to the cloud level, while from a perspective of particle number concentrations, sea spray aerosol (i.e., primary marine aerosol) contributions to both CCN and INPs are rather limited

How to Improve Customer Satisfaction Leading to Pay for Premium Service-Shanbay

With the increasing number of people studying online, self-aid learning platforms which help customers（users）study by themselves are more and more prevalent in China. Self-aid online learning is a relatively innovative field which has not been widely and thoroughly researched. This paper used Shanbay which is one of the largest self-aid English learning platforms in China as an example to investigate what and how factors influence customer satisfaction leading to their (re)purchase intention. Based on the previous models and empirical studies of some related fields, this paper outlined a new framework and generated eight propositions to explore these two research questions. Both free users and premium users of Shanbay were interviewed to gather the research material, and the data got from the interview were analyzed to develop the propositions. This paper found that positive service experience can facilitate customer satisfaction from their perceived utilitarian value and hedonic value. Among the proposed five factors influencing the two values, perceived usefulness was considered as the most important factor while perceived playfulness was the least one. What’ more, the relationship between customer satisfaction and (re)purchase intention was suggested to be positive in this paper

How to Improve Customer Satisfaction Leading to Pay for Premium Service-Shanbay

With the increasing number of people studying online, self-aid learning platforms which help customers（users）study by themselves are more and more prevalent in China. Self-aid online learning is a relatively innovative field which has not been widely and thoroughly researched. This paper used Shanbay which is one of the largest self-aid English learning platforms in China as an example to investigate what and how factors influence customer satisfaction leading to their (re)purchase intention. Based on the previous models and empirical studies of some related fields, this paper outlined a new framework and generated eight propositions to explore these two research questions. Both free users and premium users of Shanbay were interviewed to gather the research material, and the data got from the interview were analyzed to develop the propositions. This paper found that positive service experience can facilitate customer satisfaction from their perceived utilitarian value and hedonic value. Among the proposed five factors influencing the two values, perceived usefulness was considered as the most important factor while perceived playfulness was the least one. What’ more, the relationship between customer satisfaction and (re)purchase intention was suggested to be positive in this paper