Microbial immobilization technology for remediation of petroleum hydrocarbon contaminated soil

Petroleum hydrocarbon is a kind of global pollutant that is difficult to degrade. The remediation of petroleum hydrocarbon contaminated soil has always been a challenging subject for environmentalists. Microbial immobilization technology (MIT) has the advantages of high efficiency, stability, low cost and environmental friendliness. It shows great application potential in soil remediation. In recent years, the study of microbial immobilization technology for remediation of petroleum hydrocarbon contaminated soil is in the ascendant. Microbial immobilization technology has become an effective way to improve microbial degradation of petroleum hydrocarbons in soil. This paper discusses the research progress of microbial immobilization technology, summarizes the different characteristics of carrier materials, microorganisms, immobilization methods and influencing factors in the immobilization process and their effects on the immobilization effect, and expounds the research status and development trend of immobilization technology for the remediation of petroleum hydrocarbon contaminated soil

Skill-Based Few-Shot Selection for In-Context Learning

In-context learning is the paradigm that adapts large language models to downstream tasks by providing a few examples. Few-shot selection -- selecting appropriate examples for each test instance separately -- is important for in-context learning. In this paper, we propose Skill-KNN, a skill-based few-shot selection method for in-context learning. The key advantages of Skill-KNN include: (1) it addresses the problem that existing methods based on pre-trained embeddings can be easily biased by surface natural language features that are not important for the target task; (2) it does not require training or fine-tuning of any models, making it suitable for frequently expanding or changing example banks. The key insight is to optimize the inputs fed into the embedding model, rather than tuning the model itself. Technically, Skill-KNN generates the skill-based descriptions for each test case and candidate example by utilizing a pre-processing few-shot prompting, thus eliminating unimportant surface features. Experimental results across five cross-domain semantic parsing datasets and six backbone models show that Skill-KNN significantly outperforms existing methods.Comment: Accepted by EMNLP 2023 main conferenc

Therapeutic effects of neuregulin-1 in diabetic cardiomyopathy rats

BACKGROUND: Diabetic cardiomyopathy (DCM) is a disorder of the heart muscle in people with diabetes, which is characterized by both systolic and diastolic dysfunction. The effective treatment strategy for DCM has not been developed. METHODS: Rats were divided into 3 groups with different treatment. The control group was only injected with citrate buffer (n = 8). The diabetes group and diabetes treated group were injected with streptozotocin to induce diabetes. After success of diabetes induction, the rats with diabetes were treated with (diabetes treated group, n = 8) or without (diabetes group, n = 8) recombinant human Neuregulin-1 (rhNRG-1). All studies were carried out 16 weeks after induction of diabetes. Cardiac catheterization was performed to evaluate the cardiac function. Apoptotic cells were determined by TUNEL staining. Left ventricular (LV) sections were stained with Masson to investigate myocardial collagen contents. Related gene expressions were analyzed by quantitative real-time PCR (qRT-PCR). RESULTS: Diabetes impaired cardiac function manifested by reduced LV systolic pressure (LVSP), maximum rate of LV pressure rise and fall (+dp/dt max and -dp/dt max) and increased LV end-diastolic pressure (LVEDP). The rhNRG-1 treatment could significantly alleviate these symptoms and improve heart function. More TUNEL staining positive cells were observed in the diabetic group than that in the control group, and the rhNRG-1 treatment decreased apoptotic cells number. Furthermore, qRT-PCR assay demonstrated that rhNRG-1 treatment could decrease the expression of bax and caspase-3 and increase that of bcl-2. Collagen volume fraction was higher in the diabetic group than in the control group. Fibrotic and fibrotic related mRNA (type I and type III collagen) levels in the myocardium were significantly reduced by administration of rhNRG-1. CONCLUSION: rhNRG-1 could significantly improve the heart function and reverse the cardiac remodeling of DCM rats with chronic heart failure. These results support the clinical possibility of applying rhNRG-1 as an optional therapeutic strategy for DCM treatment in the future

A bibliometric and visualization analysis on the association between chronic exposure to fine particulate matter and cancer risk

Introduction:As one of the major pollutants in ambient air pollution, fine particulate matter (PM2.5) has attracted public attention. A large body of laboratory and epidemiological research has shown that PM2.5 exposure is harmful to human health.MethodsTo investigate its association with the commonly observed PM-related cancer, a bibliometric study was performed on related publications from 2012 to 2021 from a macroscopic perspective with the help of the Web of Science database and scientometric software VOSviewer, CiteSpace V, HistCite, and Biblioshiny.ResultsThe results indicated that of the 1,948 enrolled documents, scientific productions increased steadily and peaked in 2020 with 348 publications. The most prolific authors, journals, organizations, and countries were Raaschou-Nielsen O, Science of the Total Environment, the Chinese Academy of Sciences, and China, respectively. The top five keywords in frequency order were “air pollution,” “particulate matter,” “lung cancer,” “exposure,” and “mortality.”DiscussionThe toxic mechanism of carcinogenicity was explained and is worthy of further investigation. China and the US collaborated most closely, and it is hoped the two countries can strengthen their collaboration to combat air pollution. There is also a need to identify the components of PM2.5 and refine the models to assess the global burden of disease attributed to PM2.5 exposure

A scientometric analysis of research trends on targeting mTOR in breast cancer from 2012 to 2022

Over the past decade, thousands of articles have been published on the mechanistic target of rapamycin (mTOR) and its role in breast cancer. However, the variability and heterogeneity of academic data may impact the acquisition of published research information. Due to the large number, heterogeneity, and varying quality of publications related to mTOR and breast cancer, sorting out the present state of the research in this area is critical for both researchers and clinicians. Therefore, scientometric techniques and visualization tools were employed to analyze the large number of bibliographic metadata related to the research area of mTOR and breast cancer. The features of relevant publications were searched from 2012 to 2022 to evaluate the present status of research and the evolution of research hotspots in this particular field. Web of Science was utilized to extract all relevant publications from 2012 to 2022. Subsequently, Biblioshiny and VOSviewer were utilized to obtain data on the most productive countries, authors, and institutions, annual publications and citations, the most influential journals and articles, and the most frequently occurring keywords. In total, 1,471 publications were retrieved, comprising 1,167 original articles and 304 reviews. There was a significant rise in publications between 2015 and 2018, followed by a sharp decline in 2019 and a rebound since then. The publication with the highest number of citations was a 2012 review authored by Baselga et al. The United States had the highest number of publications, citations and connections among all countries. Oncotarget had the highest number of published articles among all the journals, and José Baselga had the strongest links with other authors. Excluding the search topics, the most frequently used words were “expression” (n = 297), “growth” (n = 228), “activation” (n = 223), “pathway” (n = 205), and “apoptosis” (n = 195). mTOR is crucially involved in breast cancer pathogenesis, but its exact mechanism of action remains controversial and warrants further investigation. The scientometric analysis provides a distinct overview of the existing state of research and highlights the topical issues that deserve further exploration

Structural properties and digestion of green banana flour as a functional ingredient in pasta

Gluten free pasta was made from raw banana flour in combination with vegetable gums and protein for comparison to pasta similarly made from wheat flour. After cooking, it was found that the banana flour pasta was less susceptible to alpha-amylase digestion compared to conventional wheat flour pasta. Release of glucose by alpha-amylase digestion followed first order kinetics with an initial rapid rate of digestion and a subsequent second slower phase. The structure of green banana pasta starch at the inner and outer pasta surfaces was observed under confocal laser scanning microscopy (CLSM) and the viscosities of the flour mixtures were measured by a Rapid Visco Analyser (RVA). The digestibility of banana flour pasta was found to be related, not only to the properties of the starch granules, but also to the protein network of the surrounding food matrix. The effects of gums and proteins on pasta formation and digestibility are discussed in the context of its potential use as a gluten free lower glycaemic alternative to conventional wheat based pastas

Hyperledger for IoT: A Review of Reconstruction Diagrams Perspective

The convergence of blockchain with the internet of things (IoT) attracted widespread attention. Blockchain mainly solved the problem of secure storage and trusted transactions. The convergence of these two emerging technologies enhanced the security of smart services. However, there were some technical barriers to the deployment of practical IoT systems. In order to further promote the popularity and application of blockchain in the IoT, Hyperledger became the ideal technology to overcome these obstacles. In recent years, the mainstream application fields of IoT tried to carry out integration with Hyperledger to achieve high security, fine-grained privacy protection, real-time data flow, robustness, and other business requirements. However, there was a lack of literature review on this topic. This study obtained the latest related literature of Hyperledger in IoT from Web of Science, Wordlib, and EBSCO databases. To demonstrate more intuitive differences and provide a technology convergence process, this study proposes a reconstruction diagram analysis method. Reconstruction is the process of fusing the core design and the original architecture diagram in the literature and reconstructing the diagram so that it can show the core ideas of the literature. This approach aims to visualize the core ideas of the literature. Finally, this paper prospected and concluded by proposing four directions for future work, including a low-energy consensus algorithm, intelligent transaction validation, mixed on-chain and off-chain storage, and customized incentives