Internet Finance: A Systematic Literature Review and Bibliometric Analysis

Internet finance has gained growing popularity in internet plus environment. While various problems have emerged, and hindered the sustainable growth of internet finance industry. Thus, a summary of existent research and directions for future study are expected. However, few comprehensive literature reviews has been published. This paper presents a thorough bibliometric and network analysis following a systematic literature review methodology. The analysis begins by identifying 331 published studies in Web of Science. Prolific authors, institutions and nations are identified by rigorous bibliometric tools. Based on citation and co-citation analysis, influential papers from different time periods are identified. Established and emergent research clusters are identified for topological analysis by coupling analysis. Future research opportunities are pointed out

A model local interpretation routine for deep learning based radio galaxy classification

Radio galaxy morphological classification is one of the critical steps when producing source catalogues for large-scale radio continuum surveys. While many recent studies attempted to classify source radio morphology from survey image data using deep learning algorithms (i.e., Convolutional Neural Networks), they concentrated on model robustness most time. It is unclear whether a model similarly makes predictions as radio astronomers did. In this work, we used Local Interpretable Model-agnostic Explanation (LIME), an state-of-the-art eXplainable Artificial Intelligence (XAI) technique to explain model prediction behaviour and thus examine the hypothesis in a proof-of-concept manner. In what follows, we describe how \textbf{LIME} generally works and early results about how it helped explain predictions of a radio galaxy classification model using this technique.Comment: 4 pages, 1 figure, accepted summary paper for URSI GASS 2023 J0

Long-Range Temporal Correlations of Patients in Minimally Conscious State Modulated by Spinal Cord Stimulation

Spinal cord stimulation (SCS) has been shown to improve the consciousness levels of patients with disorder of consciousness (DOC). However, the underlying mechanisms of SCS remain poorly understood. This study recorded resting-state electroencephalograms (EEG) from 16 patients with minimally conscious state (MCS), before and after SCS, and investigated the mechanisms of SCS on the neuronal dynamics in MCS patients. Detrended fluctuation analysis (DFA), combined with surrogate data method, was employed to measure the long-range temporal correlations (LRTCs) of the EEG signals. A surrogate data method was utilized to acquire the genuine DFA exponents (GDFAE) reflecting the genuine LRTCs of brain activity. We analyzed the GDFAE in four brain regions (frontal, central, posterior, and occipital) at five EEG frequency bands [delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–45 Hz)]. The GDFAE values ranged from 0.5 to 1, and showed temporal and spatial variation between the pre-SCS and the post-SCS states. We found that the channels with GDFAE spread wider after SCS. This phenomenon may indicate that more cortical areas were engaged in the information integration after SCS. In addition, the GDFAE values increased significantly in the frontal area at delta, theta, and alpha bands after SCS. At the theta band, a significant increase in GDFAE was observed in the occipital area. No significant change was found at beta or gamma bands in any brain region. These findings show that the enhanced LRTCs after SCS occurred primarily at low-frequency bands in the frontal and occipital regions. As the LRTCs reflect the long-range temporal integration of EEG signals, our results indicate that information integration became more “complex” after SCS. We concluded that the brain activities at low-frequency oscillations, particularly in the frontal and occipital regions, were improved by SCS

Case Report: Cancer spectrum and genetic characteristics of a de novo germline POLD1 p.L606M variant-induced polyposis syndrome

Germline variations in the DNA polymerase genes, POLE and POLD1, can lead to a hereditary cancer syndrome that is characterized by frequent gastrointestinal polyposis and multiple primary malignant tumors. However, because of its rare occurrence, this disorder has not been extensively studied. In this report, we present the case of a 22-year-old female patient who had been diagnosed with gastrointestinal polyposis, breast fibroadenoma, multiple primary colorectal cancers, and glioblastoma (grade IV) within a span of 4 years. Next-generation sequencing analysis revealed a germline variant in POLD1 (c.1816C>A; p.L606M). In silico analysis using protein functional predicting software, including SIFT, Polyphen, GERP++, and CADD, further confirmed the pathogenicity of POLD1 p.L606M (classified as ACMG grade Class 4). In line with polymerase deficiency, both rectal cancer and glioblastoma tissues exhibited a high tumor mutation burden, with 16.9 muts/Mb and 347.1 muts/Mb, respectively. Interestingly, the patient has no family history of cancer, and gene examination of both parents confirms that this is a de novo germline variant. Therefore, molecular screening for POLD1 may be necessary for patients with such a cancer spectrum, regardless of their family history

MafA Regulation in β-Cells: From Transcriptional to Post-Translational Mechanisms

β-cells are insulin-producing cells in the pancreas that maintain euglycemic conditions. Pancreatic β-cell maturity and function are regulated by a variety of transcription factors that enable the adequate expression of the cellular machinery involved in nutrient sensing and commensurate insulin secretion. One of the key factors in this regulation is MAF bZIP transcription factor A (MafA). MafA expression is decreased in type 2 diabetes, contributing to β-cell dysfunction and disease progression. The molecular biology underlying MafA is complex, with numerous transcriptional and post-translational regulatory nodes. Understanding these complexities may uncover potential therapeutic targets to ameliorate β-cell dysfunction. This article will summarize the role of MafA in normal β-cell function and disease, with a special focus on known transcriptional and post-translational regulators of MafA expressio

Image-Based Pain Intensity Estimation Using Parallel CNNs with Regional Attention

Automatic pain estimation plays an important role in the field of medicine and health. In the previous studies, most of the entire image frame was directly imported into the model. This operation can allow background differences to negatively affect the experimental results. To tackle this issue, we propose the parallel CNNs framework with regional attention for automatic pain intensity estimation at the frame level. This modified convolution neural network structure combines BlurPool methods to enhance translation invariance in network learning. The improved networks can focus on learning core regions while supplementing global information, thereby obtaining parallel feature information. The core regions are mainly based on the tradeoff between the weights of the channel attention modules and the spatial attention modules. Meanwhile, the background information of the non-core regions is shielded by the DropBlock algorithm. These steps enable the model to learn facial pain features adaptively, not limited to a single image pattern. The experimental result of our proposed model outperforms many state-of-the-art methods on the RMSE and PCC metrics when evaluated on the diverse pain levels of over 12,000 images provided by the publicly available UNBC dataset. The model accuracy rate has reached 95.11%. The experimental results show that the proposed method is highly efficient at extracting the facial features of pain and predicts pain levels with high accuracy

The Ecosystem Protection and Promotion of Mogao Grottoes

The cultural heritage of the Dunhuang Mogao Caves is a valuable as set for China, but the site is located in Gansu Province in northern China, where the ecological environment is fragile, the environmental space is relatively small and the environmental carrying capacity is limited. In the process of accelerating the tourism development and construction of Mogao Caves cultural heritage, the ecological balance of the environment has been upset and problems have emerged. This paper investigates the ecological and environmental management and enhancement of the site, as well as the conservation and protection of the non-renewable resources of the Mogao Caves cultural heritage

Natural history of glaucomatous optic neuropathy in highly myopic Chinese: study protocol for a registry cohort study

Introduction Myopic maculopathy and glaucoma belong to the most common causes of irreversible blindness worldwide and, having an ocular axial elongation as one of their main risk factors, can occur together. The detection of glaucomatous optic neuropathy (GON) in highly myopic eyes is clinically and technically difficult, and there is no information available, neither about the natural course of GON or about the course of GON under intraocular pressure-lowering therapy. We therefore designed this study to explore the natural course of GON in highly myopic eyes.Methods and analysis In this single-centred longitudinal registry cohort study, 813 highly myopic individuals will be recruited and undergo detailed ophthalmic examinations. High myopia is defined by a myopic refractive error of ≥−6 D or an axial length of ≥26.5 mm. GON is defined by a glaucomatous appearance of the optic nerve head or glaucomatous visual field (VF) defects. GON progression is defined by either change of the optic disc or VF.Ethics and dissemination Ethical approval has been obtained from the ethical committee of the Zhongshan Ophthalmic Center (ZOC), Sun Yat-sen University, China (ID: 2019KYPJ079). All the participants are required to provide informed consents. Results will be disseminated through scientific meetings and published in peer-reviewed journals. The data will be deposited at the clinical research centre in ZOC using electronic data capture system, and a copy of paper files will also be kept. Only members of the project team will have access to these data.Trial registration number NCT04302220

MafA regulation in β‐Cells: from transcriptional to post‐translational mechanisms

β-cells are insulin-producing cells in the pancreas that maintain euglycemic conditions. Pancreatic β-cell maturity and function are regulated by a variety of transcription factors that enable the adequate expression of the cellular machinery involved in nutrient sensing and commensurate insulin secretion. One of the key factors in this regulation is MAF bZIP transcription factor A (MafA). MafA expression is decreased in type 2 diabetes, contributing to β-cell dysfunction and disease progression. The molecular biology underlying MafA is complex, with numerous transcriptional and post-translational regulatory nodes. Understanding these complexities may uncover potential therapeutic targets to ameliorate β-cell dysfunction. This article will summarize the role of MafA in normal β-cell function and disease, with a special focus on known transcriptional and post-translational regulators of MafA expressionThis work was supported by NIH T32DK00732 (to M.C.), R01DK132661 (to U.P.) and the “Programa de Atracción de Talento” (2020-T1/BMD-20162, Comunidad Autónoma de Madrid, Spain) to A.B

Total process of fault diagnosis for wind turbine gearbox, from the perspective of combination with feature extraction and machine learning: A review

With the increasing of the installed capacity of wind power, the condition monitoring and maintains technique is becoming more important. Wind Turbines (WT) gearbox is one of the key wind power components as it plays the role of power transmission and speed regulation. Towards this, a number of scholars have pay attention to the fault diagnosis of WT gearbox. The efficiency of Machine Learning (ML) algorithms is highly correlated with signal type, data quality, and extracted features employed. The implementation of ML techniques has proven to be advantageous in simplifying the comprehension prerequisites for fault diagnosis technology concerning fault mechanisms. More and more current studies predominantly concentrate on the utilization and fine-tuning of ML algorithms, while providing limited insights into the features of the acquired data. Therefore, it is necessary to review the research in recent years from the perspective of the combination of feature extraction and ML algorithms, and provide a detailed direction for future WT gearbox fault diagnosis technology research. In this paper, data processing algorithms and typical fault diagnosis methods based on ML methods for WT gearbox are reviewed. For the using of ML method in WT gearbox fault diagnosis, the data prepared for training is very important. The paper firstly reviewed the data analysing method which will support the ML method. The data analysing methods include data acquisition, data preprocessing and feature extraction method. Feature extraction plays a pivotal role in the realm of gearbox fault diagnosis, as it serves as the essence of effective detection. This review will primarily focus on exploring methods that enable the utilization of efficient features in combination with ML techniques to achieve accurate gearbox fault diagnosis. Then typical ML method for WT gearbox fault diagnosis are carefully reviewed. Moreover, some prospects for future research directions are discussed in the end