Enrofloxacin Induces Intestinal Microbiota-Mediated Immunosuppression in Zebrafish

The immunosuppressive effects of antibiotics and the potential associations with the intestinal microbiota of the host have been increasingly recognized in recent years. However, the detailed underlying mechanisms of immune interference of antibiotics in environmental organisms remain unclear, particularly at the early life stage of high sensitivity. To better understand the gut microbiome and immune function interactions, the vertebrate model, zebrafish, was treated with environmentally relevant concentrations of a frequently detected antibiotic, enrofloxacin (ENR), ranging from 0.01 to 100 μg/L. 16S ribosomal RNA sequencing indicated diminished diversity, richness, and evenness of intestinal flora following ENR treatment. Twenty-two taxa of gut bacteria including Rickettsiales, Pseudomonadales, and Flavobacteriales were significantly correlated with immunosuppressive biomarkers, including a significant decrease in the abundance of macrophages and neutrophils. To validate the immunomodulatory effects due to altered intestinal microbial populations, zebrafish reared under sterile and non-sterile husbandry conditions were compared after ENR treatment. A significant inhibitory effect was induced by ENR treatment under non-sterile conditions, while the number of macrophages and neutrophils, as well as biomarkers of immunosuppressive effects, were significantly salved in zebrafish under sterile conditions, confirming for the first time that immunosuppression by ENR was closely mediated through alterations of the intestinal microbiome in fish.publishedVersio

The associations of residential greenness with fetal growth in utero and birth weight: A birth cohort study in Beijing, China

Background: Although studies have examined the association between residential greenness and birth weight, there is no evidence regarding the association between residential greenness and fetal growth in utero. We aimed to investigate the associations of residential greenness with both fetal growth in utero and birth weight. Methods: A birth cohort (2014–2017) with 18,665 singleton pregnancies was established in Tongzhou Maternal and Child hospital of Beijing, China. Residential greenness was matched with maternal residential address and estimated from remote satellite data using normalized difference vegetation index with 200 m and 500 m buffers (NDVI-200 and NDVI-500). Fetal parameters including estimated fetal weight (EFW), abdominal circumference (AC), head circumference (HC) an

Qwen Technical Report

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.Comment: 59 pages, 5 figure

Nonlinear Effects of Environmental Regulation on Environmental Pollution

This paper classifies environmental regulation into two types and constructs a theoretical framework to explore the influences of fee-based environmental regulation and invest-based environmental regulation on environmental pollution. It then establishes some dynamic spatial autoregressive nonlinear econometric models to test the theoretical hypothesis based on 30-area panel data from 2004 to 2016. The results illustrate that inverted “U” shape curve relationship exists between fee-based environmental regulation and environmental pollution, while a “U” shape curve relationship between invest-based environmental regulation and environmental pollution exists. In addition, the findings suggest that improving the proportion of secondary industry can directly promote the environmental quality while effectively control of foreign direct investment and fiscal decentralization is also indispensable. Thus, the government should make targeted research about the optimal intensity of fee-based environmental regulation and invest-based environmental regulation and make targeted enterprise policy for the environmental pollution reduce, which contains promoting the energy revolution and strengthening the depth and strength of opening-up step by step

Output Layer Structure Optimization for Weighted Regularized Extreme Learning Machine Based on Binary Method

In this paper, we focus on the redesign of the output layer for the weighted regularized extreme learning machine (WRELM). For multi-classification problems, the conventional method of the output layer setting, named “one-hot method”, is as follows: Let the class of samples be r; then, the output layer node number is r and the ideal output of s-th class is denoted by the s-th unit vector in Rr (1≤s≤r). Here, in this article, we propose a “binarymethod” to optimize the output layer structure: Let 2p−1r≤2p, where p≥2, and p output nodes are utilized and, simultaneously, the ideal outputs are encoded in binary numbers. In this paper, the binary method is employed in WRELM. The weights are updated through iterative calculation, which is the most important process in general neural networks. While in the extreme learning machine, the weight matrix is calculated in least square method. That is, the coefficient matrix of the linear equations we solved is symmetric. For WRELM, we continue this idea. And the main part of the weight-solving process is a symmetry matrix. Compared with the one-hot method, the binary method requires fewer output layer nodes, especially when the number of sample categories is high. Thus, some memory space can be saved when storing data. In addition, the number of weights connecting the hidden and the output layer will also be greatly reduced, which will directly reduce the calculation time in the process of training the network. Numerical experiments are conducted to prove that compared with the one-hot method, the binary method can reduce the output nodes and hidden-output weights without damaging the learning precision

Design of material regulatory mechanism for electrocatalytic converting NO/NO3− to NH3 progress

Abstract Nitric oxide (NO)/nitrate (NO3−) exists as the most hazardous pollutions in the air/water that severely impacts human health. Conventional disposing methods are energy‐consuming and uneconomic. Moreover, ammonia (NH3) fertilizer resources acquire urgent, eco‐friendly, and economical strategies that can remove NO/NO3− pollution and simultaneously convert nitrate species, maintaining nitrogen balance. Electrochemical nitrogen (N) reduction is attracting more attention, particularly electrocatalytic NO/NO3− reduction (ENR) to ammonia supply an approach to fixed nitrogen and generate ammonia. ENR is capable of achieving high NH3 yield and Faradaic efficiency (FE), avoiding competitive hydrogen evolution reactions and easily overcoming strong N≡N triple bond (941 kJ mol−1). There are abundant research studies related to ENR for decreasing hazardous NO/NO3− and supplying profitable NH3. In this review, we discuss different electrocatalytic regulations for crystalline facet engineering, heteroatom doping, heterostructure, surface vacancy engineering, and single‐atom structure, which bring various metal/nonmetal and their combined catalysts to the preferable performance, such as reactivity, selectivity, FE, and stability. Finally, we summarize the challenges and provide the perspectives to promote the industrial application of ENR. Key Points This review focusing on systematically introduce the different modification strategies and regulatory mechanism to enhance the electrochemical performance for NORR/NO3RR, including crystalline facet engineering, heteroatom doping, heterostructure, surface vacancy engineering, and single atom structure

Asymmetric Coordination Environment Engineering of Atomic Catalysts for CO₂ Reduction

Single-atom catalysts (SACs) have emerged as well-known catalysts in renewable energy storage and conversion systems. Several supports have been developed for stabilizing single-atom catalytic sites, e.g., organic-, metal-, and carbonaceous matrices. Noticeably, the metal species and their local atomic coordination environments have a strong influence on the electrocatalytic capabilities of metal atom active centers. In particular, asymmetric atom electrocatalysts exhibit unique properties and an unexpected carbon dioxide reduction reaction (CO2RR) performance different from those of traditional metal-N4 sites. This review summarizes the recent development of asymmetric atom sites for the CO2RR with emphasis on the coordination structure regulation strategies and their effects on CO2RR performance. Ultimately, several scientific possibilities are proffered with the aim of further expanding and deepening the advancement of asymmetric atom electrocatalysts for the CO2RR

Multiple Osteomas in Middle Ear

Since the first description of middle ear osteomas by Thomas in 1964, only few reports were published within the English literatures (Greinwalid et al., 1998; Shimizu et al., 2003; Cho et al., 2005; and Jang et al., 2009), and only one case of the multiple osteomas in middle ear was described by Kim et al., 2006, which arose from the promontory, lateral semicircular canal, and epitympanum. Here we describe a patient with multiple middle ear osteomas arising from the promontory, incus, Eustachian tube, and bony semicanal of tensor tympani muscle. This patient also contracted the chronic otitis media in the ipsilateral ear. The osteomas were successfully removed by performing type III tympanoplasty in one stage