Short- and long-term impact of remarkable economic events on the growth causes of China-Germany trade in agri-food products

This paper focuses on a systematic quantitative discussion of the short- and long-term impact of remarkable economic events on international trade in a two-stage framework. Firstly, procedures based on dummy variables are proposed to detect structural breaks, types and sizes of jumps caused by such events. Then we propose to apply a hierarchical CMS (Constant Market Share) model to all sub-periods defined by the detected change points to study the short- and long-term impact of those events on growth causes. Application to China-Germany trade in agri-food products shows that China’s accession to WTO had a negative short-term impact on corresponding series. But its long-term impact on China’s export competitiveness was definitely positive. The short-term impact of the EU’s CAP (Common Agricultural Policy) reform on Germany’s exports to China was also negative. Its long-term impact on export competitiveness was sometimes positive and sometimes negative. The financial crisis of 2008 caused a significant reduction of China’s agri-food exports to Germany. But Germany’s exports to China in 2009 were not affected by the financial crisis as much.Growth causes of agri-food trade; the CMS model; the EU’s CAP reform; China’s accession to WTO; financial crisis

Integration of ecological innovation, institutional governance, and human capital development for a sustainable environment in Asian Countries

The study evaluates the dynamic influence of institutional quality, green innovation, and human capital on the ecological footprint in South Asian countries from 1990 to 2018. For empirical estimation of panel data, the study applied the cross-section autoregressive distributed lag (CS-ARDL) estimator to address the issues of crosssection dependency and slope heterogeneity. The long-run findings reveal that institutional governance and ecological innovation reduce the ecological footprint. Likewise, human development decreases the ecological footprint. The short-run outcomes are identical to the long-run; however, the short-run estimates’ magnitude is smaller than the long-run. The results also support the Environmental Kuznets Curve Hypothesis in the long run. The error correction term (ECT) with a significant negative value endorsed the conversion towards the long-run equilibrium position with a 26.5% annual adjustment rate in case of short-run deviation. The augmented mean group estimator ensures the robustness of estimates. The findings recommend that South Asian economies should promote green technology and human capital through R&D allocations in industrial and academic sectors

The GATAD2B-NuRD complex drives DNA:RNA hybrid-dependent chromatin boundary formation upon DNA damage

Double-strand breaks (DSBs) are the most lethal form of DNA damage. Transcriptional activity at DSBs, as well as transcriptional repression around DSBs, are both required for efficient DNA repair. The chromatin landscape defines and coordinates these two opposing events. However, how the open and condensed chromatin architecture is regulated remains unclear. Here, we show that the GATAD2B–NuRD complex associates with DSBs in a transcription- and DNA:RNA hybrid-dependent manner, to promote histone deacetylation and chromatin condensation. This activity establishes a spatio-temporal boundary between open and closed chromatin, which is necessary for the correct termination of DNA end resection. The lack of the GATAD2B–NuRD complex leads to chromatin hyperrelaxation and extended DNA end resection, resulting in homologous recombination (HR) repair failure. Our results suggest that the GATAD2B–NuRD complex is a key coordinator of the dynamic interplay between transcription and the chromatin landscape, underscoring its biological significance in the RNA-dependent DNA damage response

Unlocking the Power of Large Language Models for Entity Alignment

Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their effectiveness is constrained by the limited input KG data and the capabilities of the representation learning techniques. Against this backdrop, we introduce ChatEA, an innovative framework that incorporates large language models (LLMs) to improve EA. To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy. To overcome the over-reliance on entity embedding comparisons, ChatEA implements a two-stage EA strategy that capitalizes on LLMs' capability for multi-step reasoning in a dialogue format, thereby enhancing accuracy while preserving efficiency. Our experimental results affirm ChatEA's superior performance, highlighting LLMs' potential in facilitating EA tasks

Mechanism of Virus Inactivation by Cold Atmospheric-Pressure Plasma and Plasma-Activated Water

ABSTRACT Viruses cause serious pathogenic contamination that severely affects the environment and human health. Cold atmospheric-pressure plasma efficiently inactivates pathogenic bacteria; however, the mechanism of virus inactivation by plasma is not fully understood. In this study, surface plasma in argon mixed with 1% air and plasma-activated water was used to treat water containing bacteriophages. Both agents efficiently inactivated bacteriophages T4, ϕ174, and MS2 in a time-dependent manner. Prolonged storage had marginal effects on the antiviral activity of plasma-activated water. DNA and protein analysis revealed that the reactive species generated by plasma damaged both nucleic acids and proteins, consistent with the morphological examination showing that plasma treatment caused the aggregation of bacteriophages. The inactivation of bacteriophages was alleviated by the singlet oxygen scavengers, demonstrating that singlet oxygen played a primary role in this process. Our findings provide a potentially effective disinfecting strategy to combat the environmental viruses using cold atmospheric-pressure plasma and plasma-activated water. IMPORTANCE Contamination with pathogenic and infectious viruses severely threatens human health and animal husbandry. Current methods for disinfection have different disadvantages, such as inconvenience and contamination of disinfection by-products (e.g., chlorine disinfection). In this study, atmospheric surface plasma in argon mixed with air and plasma-activated water was found to efficiently inactivate bacteriophages, and plasma-activated water still had strong antiviral activity after prolonged storage. Furthermore, it was shown that bacteriophage inactivation was associated with damage to nucleic acids and proteins by singlet oxygen. An understanding of the biological effects of plasma-based treatment is useful to inform the development of plasma into a novel disinfecting strategy with convenience and no by-product

Constructing a robust protein-protein interaction network by integrating multiple public databases

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are a critical component for many underlying biological processes. A PPI network can provide insight into the mechanisms of these processes, as well as the relationships among different proteins and toxicants that are potentially involved in the processes. There are many PPI databases publicly available, each with a specific focus. The challenge is how to effectively combine their contents to generate a robust and biologically relevant PPI network.</p> <p>Methods</p> <p>In this study, seven public PPI databases, BioGRID, DIP, HPRD, IntAct, MINT, REACTOME, and SPIKE, were used to explore a powerful approach to combine multiple PPI databases for an integrated PPI network. We developed a novel method called <it>k</it>-votes to create seven different integrated networks by using values of <it>k</it> ranging from 1-7. Functional modules were mined by using SCAN, a Structural Clustering Algorithm for Networks. Overall module qualities were evaluated for each integrated network using the following statistical and biological measures: (1) modularity, (2) similarity-based modularity, (3) clustering score, and (4) enrichment.</p> <p>Results</p> <p>Each integrated human PPI network was constructed based on the number of votes (<it>k</it>) for a particular interaction from the committee of the original seven PPI databases. The performance of functional modules obtained by SCAN from each integrated network was evaluated. The optimal value for <it>k</it> was determined by the functional module analysis. Our results demonstrate that the <it>k</it>-votes method outperforms the traditional union approach in terms of both statistical significance and biological meaning. The best network is achieved at <it>k</it>=2, which is composed of interactions that are confirmed in at least two PPI databases. In contrast, the traditional union approach yields an integrated network that consists of all interactions of seven PPI databases, which might be subject to high false positives.</p> <p>Conclusions</p> <p>We determined that the k-votes method for constructing a robust PPI network by integrating multiple public databases outperforms previously reported approaches and that a value of k=2 provides the best results. The developed strategies for combining databases show promise in the advancement of network construction and modeling.</p