Effects of syndication network on specialisation and performance of venture capital firms

The Chinese venture capital (VC) market is a young and rapidly expanding financial subsector. Gaining a deeper understanding of the investment behaviours of VC firms is crucial for the development of a more sustainable and healthier market and economy. Contrasting evidence supports that either specialisation or diversification helps to achieve a better investment performance. However, the impact of the syndication network is overlooked. Syndication network has a great influence on the propagation of information and trust. By exploiting an authoritative VC dataset of thirty-five-year investment information in China, we construct a joint-investment network of VC firms and analyse the effects of syndication and diversification on specialisation and investment performance. There is a clear correlation between the syndication network degree and specialisation level of VC firms, which implies that the well-connected VC firms are diversified. More connections generally bring about more information or other resources, and VC firms are more likely to enter a new stage or industry with some new co-investing VC firms when compared to a randomised null model. Moreover, autocorrelation analysis of both specialisation and success rate on the syndication network indicates that clustering of similar VC firms is roughly limited to the secondary neighbourhood. When analysing local clustering patterns, we discover that, contrary to popular beliefs, there is no apparent successful club of investors. In contrast, investors with low success rates are more likely to cluster. Our discoveries enrich the understanding of VC investment behaviours and can assist policymakers in designing better strategies to promote the development of the VC industry

P14AS upregulates gene expression in the CDKN2A/2B locus through competitive binding to PcG protein CBX7

Background: It is well known that P16INK4A, P14ARF, P15INK4B mRNAs, and ANRIL lncRNA are transcribed from the CDKN2A/2B locus. LncRNA P14AS is a lncRNA transcribed from antisense strand of P14ARF promoter to intron-1. Our previous study showed that P14AS could upregulate the expression level of ANRIL and P16INK4A and promote the proliferation of cancer cells. Because polycomb group protein CBX7 could repress P16INK4A expression and bind ANRIL, we wonder whether the P14AS-upregulated ANRIL and P16INK4A expression is mediated with CBX7.Results: In this study, we found that the upregulation of P16INK4A, P14ARF, P15INK4B and ANRIL expression was induced by P14AS overexpression only in HEK293T and HCT116 cells with active endogenous CBX7 expression, but not in MGC803 and HepG2 cells with weak CBX7 expression. Further studies showed that the stable shRNA-knockdown of CBX7 expression abolished the P14AS-induced upregulation of these P14AS target genes in HEK293T and HCT116 cells whereas enforced CBX7 overexpression enabled P14AS to upregulate expression of these target genes in MGC803 and HepG2 cells. Moreover, a significant association between the expression levels of P14AS and its target genes were observed only in human colon cancer tissue samples with high level of CBX7 expression (n = 38, p < 0.05), but not in samples (n = 37) with low level of CBX7 expression, nor in paired surgical margin tissues. In addition, the results of RNA immunoprecipitation (RIP)- and chromatin immunoprecipitation (ChIP)-PCR analyses revealed that lncRNA P14AS could competitively bind to CBX7 protein which prevented the bindings of CBX7 to both lncRNA ANRIL and the promoters of P16INK4A, P14ARF and P15INK4B genes. The amounts of repressive histone modification H3K9m3 was also significantly decreased at the promoters of these genes by P14AS in CBX7 actively expressing cells.Conclusions: CBX7 expression is essential for P14AS to upregulate the expression of P16INK4A, P14ARF, P15INK4B and ANRIL genes in the CDKN2A/2Blocus. P14AS may upregulate these genes’ expression through competitively blocking CBX7-binding to ANRIL lncRNA and target gene promoters

The cellular composition of the tumor microenvironment is an important marker for predicting therapeutic efficacy in breast cancer

At present, the incidence rate of breast cancer ranks first among new-onset malignant tumors in women. The tumor microenvironment is a hot topic in tumor research. There are abundant cells in the tumor microenvironment that play a protumor or antitumor role in breast cancer. During the treatment of breast cancer, different cells have different influences on the therapeutic response. And after treatment, the cellular composition in the tumor microenvironment will change too. In this review, we summarize the interactions between different cell compositions (such as immune cells, fibroblasts, endothelial cells, and adipocytes) in the tumor microenvironment and the treatment mechanism of breast cancer. We believe that detecting the cellular composition of the tumor microenvironment is able to predict the therapeutic efficacy of treatments for breast cancer and benefit to combination administration of breast cancer

Association between MTR A2756G polymorphism and susceptibility to congenital heart disease: A meta-analysis.

The association between methionine synthase (MTR) A2756G (rs1805087) polymorphism and the susceptibility to congenital heart disease (CHD) has not been fully determined. A meta-analysis of case-control studies was performed to systematically evaluate the above association. Studies were identified by searching the PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and WanFang databases from inception to June 20, 2021. Two authors independently performed literature search, data extraction, and quality assessment. Predefined subgroup analyses were carried out to evaluate the impact of the population ethnicity, source of healthy controls (community or hospital-based), and methods used for genotyping on the outcomes. A random-effects model was used to combine the results, and 12 studies were included. Results showed that MTR A2756G polymorphism was not associated with CHD susceptibility under the allele model (odds ratio [OR]: 0.96, 95% confidence interval [CI]: 0.86 to 1.07, P = 0.43, I2 = 4%), heterozygote model (OR: 0.95, 95% CI: 0.84 to 1.07, P = 0.41, I2 = 0%), homozygote model (OR: 1.00, 95% CI: 0.64 to 1.55, P = 0.99, I2 = 17%), dominant genetic model (OR: 0.95, 95% CI: 0.84 to 1.07, P = 0.41, I2 = 0%), or recessive genetic model (OR: 0.94, 95% CI: 0.62 to 1.43, P = 0.32, I2 = 13%). Consistent results were found in subgroup analyses between Asian and Caucasian populations in studies with community and hospital-derived controls as well as in studies with PCR-RFLP and direct sequencing (all P values for subgroup differences > 0.05). In conclusion, current evidence does not support an association between MTR A2756G polymorphism and CHD susceptibility

Syndication network associates with specialisation and performance of venture capital firms

The Chinese venture capital (VC) market is a young and rapidly expanding financial subsector. Gaining a deeper understanding of the investment behaviours of VC firms is crucial for the development of a more sustainable and healthier market and economy. Contrasting evidence supports that either specialisation or diversification helps to achieve a better investment performance. However, the impact of the syndication network is overlooked. Syndication network has a great influence on the propagation of information and trust. By exploiting an authoritative VC dataset of thirty-five-year investment information in China, we construct a joint-investment network of VC firms and analyse the impacts of syndication and diversification on specialisation and investment performance. There is a clear correlation between the syndication network degree and specialisation level of VC firms, which implies that the well-connected VC firms are diversified. More connections generally bring about more information or other resources, and VC firms are more likely to enter a new stage or industry with some new co-investing VC firms when compared to a randomised null model. Moreover, autocorrelation analysis of both specialisation and success rate on the syndication network indicates that feature clustering of similar VC firms is roughly limited to the secondary neighbourhood. When analysing local feature clustering patterns, we discover that, contrary to popular beliefs, there is no apparent successful club of investors. In contrast, investors with low success rates are more likely to cluster. Our discoveries enrich the understanding of VC investment behaviours and can assist policymakers in designing better strategies to promote the development of the VC industry

Automatic Construction Hazard Identification Integrating On-Site Scene Graphs with Information Extraction in Outfield Test

Construction hazards occur at any time in outfield test sites and frequently result from improper interactions between objects. The majority of casualties might be avoided by following on-site regulations. However, workers may be unable to comply with the safety regulations fully because of stress, fatigue, or negligence. The development of deep-learning-based computer vision and on-site video surveillance facilitates safety inspections, but automatic hazard identification is often limited due to the semantic gap. This paper proposes an automatic hazard identification method that integrates on-site scene graph generation and domain-specific knowledge extraction. A BERT-based information extraction model is presented to automatically extract the key regulatory information from outfield work safety requirements. Subsequently, an on-site scene parsing model is introduced for detecting interaction between objects in images. An automatic safety checking approach is also established to perform PPE compliance checks by integrating detected textual and visual relational information. Experimental results show that our proposed method achieves strong performance in various metrics on self-built and widely used public datasets. The proposed method can precisely extract relational information from visual and text modalities to facilitate on-site hazard identification

Green vehicle routing using mixed fleets for cold chain distribution

Introducing clean energy into urban cold chain distribution to achieve a green transition is crucial for reducing transportation and logistics emissions and promoting sustainable urban development. This study focuses on the Cold Chain Green Multi-Depot Vehicle Routing Problem with Time Windows and Mixed Fleets (CC-GMD-VRPTW-MF) for city logistics distribution, utilizing both electric vehicles (EVs) and gasoline and diesel vehicles (GDVs). To accurately assess energy consumption, a realistic energy consumption model is employed. The CC-GMD-VRPTW-MF aims to minimize total costs (including fixed, energy consumption, damage, and environmental costs). To address the problem, we propose an improved Variable Neighborhood Search (VNS) algorithm that introduces a new balanced mechanism for perturbation and a new memory-based mechanism for local search to enhance computational performance. Numerical studies on newly designed CC-GMD-VRPTW-MF instances are conducted to investigate the effect of incorporating EVs for joint delivery, considering different carbon prices, and adjusting time windows. Furthermore, we evaluate the effectiveness of the proposed improvement strategies in VNS and demonstrate the algorithm’s performance on benchmarks of related problems

Antidepressant fluoxetine alleviates colitis by reshaping intestinal microenvironment

Abstract Background The impact of antidepressants on Inflammatory bowel diseases (IBD) has been extensively studied. However, the biological effects and molecular mechanisms of antidepressants in alleviating colitis remain unclear. Methods We systematically assessed how antidepressants (fluoxetine, fluvoxamine and venlafaxine) affected IBD and chose fluoxetine, the most effective one, for mechanism studies. We treated the C56BL/6 mice of the IBD model with fluoxetine and their controls. We initially assessed the severity of intestinal inflammation in mice by body weight loss, disease Activity Index scores and the length of the colon. The H&E staining and immunohistochemical staining of MUC2 of colon sections were performed to observe the pathological changes. RT-qPCR and western blot were conducted to assess the expression level of the barrier and inflammation-associated genes. Then, single-cell RNA sequencing was performed on mouse intestinal mucosa. Seurat was used to visualize the data. Uniform Manifold Approximation and Projection (UMAP) was used to perform the dimensionality reduction. Cell Chat package was used to perform cell–cell communication analysis. Monocle was used to conduct developmental pseudotime analysis. Last, RT-qPCR, western blot and immunofluorescence staining were conducted to test the phenomenon discovered by single-cell RNA sequencing in vitro. Results We found that fluoxetine treatment significantly alleviated colon inflammation. Notably, single-cell RNA sequencing analysis revealed that fluoxetine affected the distribution of different cell clusters, cell–cell communication and KEGG pathway enrichment. Under the treatment of fluoxetine, enterocytes, Goblet cells and stem cells became the dominating cells. The pseudotime analysis showed that there was a trend for M1 macrophages to differentiate into M2 macrophages. Lastly, we tested this phenomenon in vitro, which exhibited anti-inflammatory effects on enterocytes. Conclusions Fluoxetine exhibited anti-inflammatory effects on intestinal mucosa via remodeling of the intestinal cells and macrophages, which reveals that fluoxetine is a promising therapeutic drug for the treatment of IBD and psychiatric comorbidities

DataSheet1_P14AS upregulates gene expression in the CDKN2A/2B locus through competitive binding to PcG protein CBX7.pdf

Background: It is well known that P16INK4A, P14ARF, P15INK4B mRNAs, and ANRIL lncRNA are transcribed from the CDKN2A/2B locus. LncRNA P14AS is a lncRNA transcribed from antisense strand of P14ARF promoter to intron-1. Our previous study showed that P14AS could upregulate the expression level of ANRIL and P16INK4A and promote the proliferation of cancer cells. Because polycomb group protein CBX7 could repress P16INK4A expression and bind ANRIL, we wonder whether the P14AS-upregulated ANRIL and P16INK4A expression is mediated with CBX7.Results: In this study, we found that the upregulation of P16INK4A, P14ARF, P15INK4B and ANRIL expression was induced by P14AS overexpression only in HEK293T and HCT116 cells with active endogenous CBX7 expression, but not in MGC803 and HepG2 cells with weak CBX7 expression. Further studies showed that the stable shRNA-knockdown of CBX7 expression abolished the P14AS-induced upregulation of these P14AS target genes in HEK293T and HCT116 cells whereas enforced CBX7 overexpression enabled P14AS to upregulate expression of these target genes in MGC803 and HepG2 cells. Moreover, a significant association between the expression levels of P14AS and its target genes were observed only in human colon cancer tissue samples with high level of CBX7 expression (n = 38, p INK4A, P14ARF and P15INK4B genes. The amounts of repressive histone modification H3K9m3 was also significantly decreased at the promoters of these genes by P14AS in CBX7 actively expressing cells.Conclusions: CBX7 expression is essential for P14AS to upregulate the expression of P16INK4A, P14ARF, P15INK4B and ANRIL genes in the CDKN2A/2Blocus. P14AS may upregulate these genes’ expression through competitively blocking CBX7-binding to ANRIL lncRNA and target gene promoters.</p

Omega-3 polyunsaturated fatty acid biomarkers and risk of type 2 diabetes, cardiovascular disease, cancer, and mortality.

Background & aims Considerable attention has focused on the role of omega-3 polyunsaturated fatty acids (PUFA) in the prevention of cardiometabolic diseases, which has led to dietary recommendations to increase omega-3 fatty acid intake. A meta-analysis was conducted to summarize evidence from prospective studies regarding associations between omega-3 PUFA biomarkers and risk of developing major chronic diseases. Methods Four electronic databases were searched for articles from inception to March 1, 2022. Random-effects model was used to estimate the pooled relative risk (RR) and 95% confidence intervals (CIs) for the association of omega-3 PUFAs, including α-linolenic acid (ALA), eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and docosahexaenoic acid (DHA), with risk of developing type 2 diabetes (T2D), cardiovascular disease (CVD), including coronary heart disease (CHD) and stroke, cancer, and mortality. The Grades of Recommendation, Assessment, Development and Evaluation assessment tool was used to rates the confidence in estimates. Results A total of 67 prospective studies comprised of 310,955 participants were identified. Individual omega-3 PUFAs showed divergent associations with the study outcomes of interest. A significant inverse association with T2D risk was observed across categories of ALA (relative risk [RR]: 0.89, 95% confidence interval [CI]: 0.82–0.96), EPA (RR: 0.85, 95% CI: 0.72–0.99) and DPA (RR: 0.84, 95% CI: 0.73–0.96) biomarkers. The marine-origin omega-3 fatty acids biomarkers but not ALA was significantly associated with lower risks of total CVD, CHD, and overall mortality, with RRs ranging from 0.70 for DHA-CHD association to 0.85 for EPA-CHD association. A lower risk of colorectal cancer was observed at higher levels of DPA (RR: 0.76, 95% CI: 0.59–0.98) and DHA (RR: 0.80; 95% CI: 0.65–0.99), whereas no association was noted for other outcomes. In addition, a dose–response relationship was observed between an increasing level of EPA, DPA, or DHA biomarker and lower risk of CVD. Conclusions Higher concentrations of marine-derived omega-3 PUFA biomarkers were associated with a significantly reduced risk of total CVD, CHD, and total mortality. Levels of ALA were inversely associated with a lower risk of T2D but not CVD-related outcomes. These data support the dietary recommendations advocating the role of omega-3 PUFAs in maintaining an overall lower risk of developing cardiovascular disease and premature deaths