Insufficient FUNDC1-dependent mitophagy due to early environmental cadmium exposure triggers mitochondrial redox imbalance to aggravate diet-induced lipotoxicity.

Cadmium (Cd) is a toxic contaminant widely spread in natural and industrial environments. Adolescent exposure to Cd increases risk for obesity-related morbidity in young adults including type 2 diabetes and metabolic dysfunction-associated steatotic liver disease (MASLD). Despite this recognition, the direct impact of adolescent Cd exposure on the progression of MASLD later in life, and the mechanisms underlying these effects, remain unclear.

[Electroacupuncture at acupoints of meridians for sarcopenia: a randomized controlled trial].

Objective: To observe the clinical effect of electroacupuncture at acupoints of meridians for sarcopenia.

Methods: A total of 60 patients with sarcopenia were randomized into an observation group and a control group, 30 cases in each group. In the control group, conventional nutrition intervention for sarcopenia was adopted.

Protective effects of fowl-origin cadmium-tolerant lactobacillus against sub-chronic cadmium-induced toxicity in chickens.

Cadmium (Cd) directly endangers poultry health and indirectly causes harm to human health by food chain. Numerous studies have focused on removing Cd using lactic acid bacteria (LAB). However, there is still a lack of in vivo studies to validate whether Cd can be absorbed successfully by LAB to alleviate Cd toxicity.

Hierarchical structure in the world's largest high-speed rail network.

Presently, China has the largest high-speed rail (HSR) system in the world. However, our understanding of the network structure of the world's largest HSR system remains largely incomplete due to the limited data available. In this study, a publicly available data source, namely, information from a ticketing website, was used to collect an exhaustive dataset on the stations and routes within the Chinese HSR system.

Global vs local modularity for network community detection.

Community structures are ubiquitous in various complex networks, implying that the networks commonly be composed of groups of nodes with more internal links and less external links. As an important topic in network theory, community detection is of importance for understanding the structure and function of the networks. Optimizing statistical measures for community structures is one of most popular strategies for community detection in complex networks.

Critical analysis of (Quasi-)Surprise for community detection in complex networks.

Module or community structures widely exist in complex networks, and optimizing statistical measures is one of the most popular approaches for revealing and identifying such structures in real-world applications. In this paper, we focus on critical behaviors of (Quasi-)Surprise, a type of statistical measure of interest for community structure, accompanied by a series of comparisons with other measures. Specially, the effect of various network parameters on the measures is thoroughly investigated.

Dynamic Cluster Formation Game for Attributed Graph Clustering.

Besides the topological structure, there are additional information, i.e., node attributes, on top of the plain graphs.

Social significance of community structure: statistical view.

Community structure analysis is a powerful tool for social networks that can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the significance of a partitioned community structure is an urgent and important question. In this paper, integrating the specific characteristics of real society, we present a framework to analyze the significance of a social community.

Potts model based on a Markov process computation solves the community structure problem effectively.

The Potts model is a powerful tool to uncover community structure in complex networks. Here, we propose a framework to reveal the optimal number of communities and stability of network structure by quantitatively analyzing the dynamics of the Potts model. Specifically we model the community structure detection Potts procedure by a Markov process, which has a clear mathematical explanation.