bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrative network pharmacology and molecular docking to reveal the mechanism of Jinqiaomai tablet on asthma
Authors: Liu Zhen , Qiu Shi , Liu Wen 
Published: February 2023

Abstract

Background:Jinqiaomai tabletis widely used to treat asthma in China. Despite this, it is less certain how Jinqiaomai tablet worked.Objective:To explore the active components and molecular mechanism of Jinqiaomai tablet treating asthma via network pharmacology and molecular docking.Methods:Through searching the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), we identified the active components and targets of Jinqiaomai tablet (JQM). Pathogenic genes related to asthma were retrieved from DisGeNET and GeneCards database.In turn, the common target genes between JQM and asthma were obtained.Also, GO and KEGG enrichment analysis were conducted on the online platform PANTHER for the common target genes. By utilizing the STRING database, an interaction network for the JQM-asthma common target protein was established. Based on the Cytohubba package, hub genes were calculated. In addition, molecular docking was conducted to confirm whether the targeted compounds bind to potential therapeutic targets.Results:In total, 15 active components and 208 targets of Jinqiaomai tablet were identified. 3701 pathogenic genes related to asthma were obtained, including 3697 from GeneCards database and 110 from DisGeNET database accompanying 106 repetitions. Then, 128 common target genes between JQM and asthma were obtained. GO and KEGG enrichment analysis of the common target genes were performed. An interaction network for the JQM-asthma common target protein was established and the top ten hub genes were AKT1, VEGFA, PTGS2, MMP9, CASP3, TNF, IL6, EGF, IL1B and NFKBIA. The affinity of the molecular docking between the targeted compound and the potential therapeutic target was analyzed, and the ligands and the overall pictures of partial results of molecular docking above were showed.Conclusion:The active components and molecular mechanism of Jinqiaomai tablet treating asthma were revealed via network pharmacology and molecular docking.
Synthesis and Characterization of Some Transition Complexes with 4- [2-(2-chlorobenzylidene)hydrazinyl]-7H-pyrrolo[2, 3-d]pyrimidine: Antimicrobial and Cytotoxic Activity
Authors: Bagul Abhay , Gaikwad Digamber , Patil Yogesh 
Published: February 2023

Abstract

Ten new transition metal complexes (1–10) with 4-[2-(2-chlorobenzy-lidene)hydrazinyl]-7H-pyrrolo[2,3-d]pyrimidine (HPPHoCB) was prepared. 4-[2-(2-Chlorobenzylidene)hydrazinyl]-7H-pyrrolo[2,3-d]pyrimidine of general formula [C13H10ClN5], was prepared through the condensation reaction of 2-chlorobenzaldehyde with pyrrolopyrimidinehydrazide. The characterization of the new formed compounds was done by physico-chemical studies, conductance measurement, magnetic susceptibility data, 1H-NMR, UV-Visible, ESR, FT(IR) spectroscopy and elemental analysis. In addition, the structure of the complexes Cr(III), Fe(II), Co(II), Ni(II) and Cu(II) has been determined by X-ray diffraction method. The prepared HPPHoCB ligand act as bidentate ligand and coordinate with central metal ions through nitrogen of azomethine and pyrrolo groups. The low molar conductance values in nitrobenzene indicate that the metal complexes are non-electrolytes in nature. The magnetic moments and electronic spectral data suggest octahedral geometry for the Cr(III), Mn(II), Co(II), Ni(II), and Fe(II) complexes, tetrahedral geometry for Zn(II), Cd(II), Hg(II) complexes and square planar for Pd(II) and Cu(II) complexes. The HPPHoCB ligand and its metal complexes were screened for antimicrobial activities against two Gram-positive bacteria (Staphylococcus aureus MTCC 96 and Bacillus subtilis MTCC 121), two Gram-negative bacteria (Escherichia coli MTCC 1652 and Pseudomonas aeruginosa MTCC 741), and Fungus (Aspergillus niger, Aspergillus flavous and Fusarium species) as well as for cytotoxic studies against Artemia salina . The synthesised metal complexes were found more active against both bacterial as well as fungi in antimicrobial and cytotoxic screening test than that of HPPHoCB ligand.
Autologous Platelet Gel Healing Effects on Cutaneous Leishmaniasis Wounds
Authors: Shadmand Enayatollah , Solhjoo Kavous , Taghipour Ali , Tayer Akbar Hashemi , Sadeghi Fatemeh , Meshkin Ahmad 
Published: February 2023

Abstract
Background and Objectives

Autologous platelet gel (APG) has healing growth factors and is used in most surgeries to treat a variety of tissue defects. This study was conducted to investigate the healing effects of APG on cutaneous leishmaniasis (CL) wounds.

Materials and Methods

In this clinical trial, 15 patients with CL were recruited and followed for two months. The patients had more than one cutaneous wound, one of which was examined as the control and the other one as the intervention wound. APG was applied to the intervention wounds once a week, up to eight times. The response to treatment was evaluated base on the histopathology analyses.

Results

The use of APG on the wounds in the intervention group was associated with complete and faster healing in 66% of the cases and partial healing in 34% of the wounds. During study, none of the control wounds were completely healed. Following eight weeks treatment of CL lesions with APG, the inflammatory process in the epidermis, and dermis were decreased significantly.

Conclusion

This study demonstrated for the first time that APG can be effective in wound healing and removal of scars caused by CL.
Trial registration
IRCT, IRCT20190212042694N1. Registered 20 February 2019, https://en.irct.ir/trial/37522
Some results on maximum likelihood from incomplete data: finite sample properties and an improved M-estimator for resampling method
Authors: Surya Budhi 
Published: February 2023

Abstract
This paper presents some results on the classical maximum likelihood (ML) parameter estimation from incomplete data. Finite sample properties of incomplete-data conditional observed information matrices are established. They possess positive def-initeness and the same Loewner partial ordering as the expected information matrices do. An explicit form of the observed Fisher information (OFI) is derived for the calculation of standard errors of the ML estimates. It simplifies Louis (1982) general formula for the OFI matrix. To prevent from getting an incorrect inverse of the OFI matrix, which may be attributed by the lack of sparsity and large size of the matrix, a monotone convergent recursive equation for the inverse of OFI matrix is developed which in turn generalizes the algorithm of Hero and Fessler (1994) for the Cramér-Rao lower bound. To improve the estimation, in particular when applying repeated sampling to incomplete data, a robust M-estimator is introduced. A closed form sandwich estimator of covariance matrix is proposed to provide the standard errors of the M-estimator. By the resulting loss of information presented in finite-sample incomplete data, the sandwich estimator produces smaller standard errors for the M-estimator than the ML estimates. In the case of complete information or absence of re-sampling, the M-estimator coincides with the ML estimates. Application to parameter estimation of a finite-mixture of Markov jump processes is discussed to verify the results. The simulation study confirms the accuracy and asymptotic properties of both the ML estimates and M-estimator.
2020 MSC: 60J20, 60J27, 62M09, 62H30
Population structure and phylogeographic history of wild soybean (Glycine soja) have implications for its conservation
Authors: Meng Jing , Li Xue-Jiao , Zhao Yan , He Shuilian 
Published: February 2023

Abstract
Glycine soja Sieb. & Zucc., the wild ancestor from which the important crop plant soybean was bred. G. soja provides important germplasm resources for the breeding and improvement of cultivated soybean crops, however the species is threatened by habitat loss and fragmentation, and is experiencing population declines across its natural range. Understanding the patterns of genetic diversity in G. soja populations can help to inform conservation practices. Additionally, wild soybean has a wide distribution across the Sino-Japanese Floristic Region, and therefore provides an opportunity to investigate the effects of fluctuations in the Quaternary climate on the current genetic structure of plant species in this region. In this study, we analyzed the genetic diversity and differentiation of G. soja populations and investigated the phylogeographic history of the species. We obtained 147 wild soybean accessions collected from 16 locations across the natural range of the species from China, Korea and Japan. Samples were analyzed using SLAF-seq (Specific-Locus Amplified Fragment Sequencing), and we obtained a total of 56,489 highly consistent population SNPs. Our results suggested that wild soybean harbored relatively high diversity and that populations of the species were highly differentiated. The 147 wild soybean individuals clustered into two groups: lineage I comprised five populations from Northern and Central China as well as a population from Japan. Lineage II comprised the other 11 populations, which had Northeast Chinese, Korean and Yangtze River origins. Analysis of gene flow suggested that historical migrations of wild soybean may have occurred, from south to north across the East-Asia land-bridge. The phylogeographic history of wild soybean provides us with new insights into the migration patterns of herbaceous plants across the Sino-Japanese Floristic Region.
Deep Learning Neural Network (DLNN) based classification and optimization algorithm for Organ Inflammation Disease diagnosis
Authors: Basha Alavudeen , Ali Azath Mubarak , P Parthasarathy , Tayfour Omer Elsier , Changalasetty Suresh Babu , Shuja Muhammed 
Published: February 2023

Abstract
Diagnostic image volume and complexity in healthcare system increases in rapid pace where available human proficiency may not sufficient for interpreting this much capacity of image data. Machine learning approaches exposed excessive potential to knob huge amount of two-dimensional annotated images of common illnesses from large databases. Deep learning imitates human for extracting knowledge from dataset and favourable to data scientists for accumulating, analysing, interpreting and predictive modelling. In this paper organ inflammation disease is addressed with Deep Learning Neural Network (DLNN) based classification scheme is incorporated to diagnose or prognoses the patient from severity, based on their historical database. In pandemic environment collecting histopathology tissue score is time consuming process due to a smaller number of physician availability, by implementing proposed DLNN algorithm suits for collecting organ inflammation score and categorizing its brutality by classification of pancreatitis, duodenum and appendix. In order to achieve accuracy and sensitivity of various stages soreness DLNN based algorithm is developed and it supports by classifying the datasets.
Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time
Authors: Schmidt Sebastian , Alanko Jarno N. 
Published: February 2023

Abstract
A fundamental operation in computational genomics is to reduce the input sequences to their constituent k-mers. For maximum performance of downstream applications it is important to store the k-mers in small space, while keeping the representation easy and efficient to use (i.e. without k-mer repetitions and in plain text). Recently, heuristics were presented to compute a near-minimum such representation. We present an algorithm to compute a minimum representation in optimal (linear) time and use it to evaluate the existing heuristics. Our algorithm first constructs the de Bruijn graph in linear time and then uses a Eulerian-cycle-based algorithm to compute the minimum representation, in time linear in the size of the output.
Development and validation of a deacetylation regulators-related prognostic signature in gastric cancer
Authors: Lei Chunyu , Xu Juan , Zhang Chengli , Liang Xiaolong 
Published: February 2023

Abstract
Mounting evidences have indicated the effects of the tumor micro-environment (TME) on gastric cancer (GC) prognosis, tumorigenesis and metastasis. However, there is no reliable biomarker that could effectively predict the prognosis and estimate the immune feature of GC patients. Herein, we obtained the expression data from The Cancer Genome Atlas (TCGA) and the National Center for Biotechnology Information (NCBI) databases to construct a deacetylation regulator associated risk signature that could be used as a predictor of survival outcome and immune feature of GC patients. A total of 13 genes were screened out from the univariate-LASSO-multivariate analyses to generate the risk signature based on the regression coefficients. The risk signature exhibited robust prognostic abilities in both training set and testing set, and this association remained significant in the multivariate analysis after controlling for age, gender, T, N, M or Stage. Functional analyses indicated that the risk signature showed a correlation with immune feature of GC patients. Further analyses indicated the risk signature could be used to estimate the infiltration immune cells and predict the immunotherapy response of GC. Taken together, our risk signature could predict prognosis and immune feature of CC, which might provide crucial clues to formulate individualized treatment and provides promising novel biomarkers for immunotherapy.
Real-Time Text Extraction from Malaysian Road Direction Signs Using Dashboard Camera
Authors: Lim Wan Chin , Shah Noraisyah Mohamed , Mahiyiddin Wan Amirul Wan Mohd 
Published: February 2023

Abstract
The road direction sign, which serves as a navigation tool, aids the driver in travelling on the road. A real-time text extraction from road direction signs is investigated, using input video images acquired from a dashboard camera while driving on Malaysian roads. This paper proposed a three-stage approach for real-time text extraction: (i) traffic direction signboard detection; (ii) text detection; and (iii) text recognition. In the direction signboard detection, the YOLOv5s model was applied and achieved a precision of 97%and a recall of 96%. In the text detection stage, three text detection models, EAST, DB and PAN, were investigated to select the best text detection model. The DB model is chosen with the highest IoU of 0.8238 and the shortest inference time of 0.1938s. In the text recognition stage, four models (Pytesseract, CRNN, RobustScanner and ABINet) are employed to perform evaluation and pick the best model. The CRNN is selected with the lowest character error rate of 5.85%, the highest accuracy of 97.84%, and a fast inference time of 0.324s. The proposed models, YOLOv5s, DB, and CRNN are combined to run sequentially in the real-time system during daytime conditions. It is performed by using a laptop that is equipped with a GPU and programmed in Python. The real-time text extraction system can run at an adaptive frame rate of 6.5 frames per second with an accuracy of 97%.
Multi-label emotion classification of Tweets with transformer models
Authors: Hettikankanamge T. L. , Pinidiyaarachchi A. J. 
Published: February 2023

Abstract
Analysis and classification of emotions expressed in social media content such as tweets have been useful for numerous commercial and social purposes for tasks like hate speech detection. Emotion classification of social media content has been performed using traditional techniques such as Recurrent Neural Networks (RNN) and Multivariate Long Short Term Memory (LSTM) in the past, which can be outperformed by the new transformer models. The ‘SemEval-2018 Task 1: Affect in Tweets’ (Mohommad et al. 2018) presents a challenge on multi-label classification of emotions expressed in tweets into 11 sentiment classes. The datasets given for this challenge are used in this work to explore the accuracy of the transformer models against other techniques used by the competitors of the particular challenge. Additionally the transformer models (BERT, RoBERTa and XLM RoBERTa) were compared with each other on their performance based on accuracy and speed. The best performing BERT-large model which is trained using bert-large-uncased tokenizer has shown a multi-label accuracy (Jaccard Index) which is higher than the sixth recorded score in the SemEval-2018 Task 1 competition, F1-micro which is higher than the best F1 score recorded.
Informality: Family Ties and Retirement Income
Authors: Göktuna Bilge Öztürk , Dayangaç Renginar 
Published: February 2023

Abstract
This paper suggests a demand side analysis of informal employmentcharacterised by incompliances with labour tax regulation, using a general equilibrium model with overlapping generations. A public social insurance provide benefits to formal employees in retirement, while we allow for an informal insurance mechanism for informal employees through a social norm of mutual support. The objective of the paper is to evaluate impact of auditing policy and social norms on growth and social welfare. We define the private transfer rate to the informal population and provide an analysis on the impact of social norms to wage levels, growth and welfare.
Real-Time Detection of Large Scale Stealthy DDoS Attacks Targeting Financial Institutions
Authors: Duan Qi Duan Qi , Al-Shaer Ehab Al-Shaer Ehab 
Published: February 2023

Abstract
DDoS attackers targeting the financial services firms can cause chaos by disrupting key public services including payment processing and intra-bank links, manipulating markets to influence stock prices, and exposing firms to extremely large losses in earnings, capital, productivity, consumer confidence, etc. State-of-the-art DDoS detectors are mostly based on black listing and rate limiting which are not effective against large scale stealthy (low and slow) attacks. The objectives of this paper is to detect large scale stealthy DDoS attacks targeting financial institutions using data-driven security analytics which can (1) protect application-level services by detecting large scale stealthy DDoS attacks as early as possible, (2) automate the traffic/log analysis process, and (3) provide the guidance for real-time mitigation. The major results of the paper include (1) the data-driven approach to identify features for DDoS detection based on real-life dataset and the methodology to identify the cross-correlation between features, (2) the information theoretical and statistic analysis to develop a model of the normal behavior of users, and (3) the summary of experimental results based on the data from real bank web log and the comparison with a real DDoS attack dataset. We believe that these technical approaches can provide the defense-in-depth against large scale stealthy DDoS attacks targeting financial institutions.
ChatGPT Performs on the Chinese National Medical Licensing Examination
Authors: Wang Xinyi , Gong Zhenye , Wang Guoxin , Jia Jingdan , Xu Ying , Zhao Jialu , Fan Qingye , Wu Shaun , Hu Weiguo , Li Xiaoyang 
Published: February 2023

Abstract
INTRODUCTION:
ChatGPT, a language model developed by OpenAI, uses a 175 billion parameter Transformer architecture for natural language processing tasks. This study aimed to compare the knowledge and interpretation ability of ChatGPT with those of medical students in China by administering the Chinese National Medical Licensing Examination (NMLE) to both ChatGPT and medical students.
METHODS
We evaluated the performance of ChatGPT in two years' worth of the NMLE, which consists of four units. At the same time, the exam results were compared to those of medical students who had studied for five years at medical colleges.
RESULTS
ChatGPT’s performance was lower than that of the medical students, and ChatGPT’s correct answer rate was related to the year in which the exam questions were released.
CONCLUSION
ChatGPT’s knowledge and interpretation ability for the NMLE were not yet comparable to those of medical students in China. It is probable that these abilities will improve through deep learning.
The impact of patient factors and tumor characteristics on language neuroplasticity in left hemispheric diffuse gliomas prior to surgical resection
Authors: Chang Wei-Han , Wei Kuo-Chen , Chen Pin-Yuan , Chen Yi-Chun , Wu Yah-Yuan , Tsai Hong-Chieh , Chen Mei-Hui , Chao Yi-Ping , Chen Ko-Ting 
Published: February 2023

Abstract
Purpose
Language networks are reorganized during glioma growth, leading to varying language performance in patients with gliomas located in language-eloquent areas. Therefore, pre-treated language performance reflects the neuroplasticity potential. Different domains of language processing, such as speech expression, repetition, and comprehension, involving different neural networks. We analyzed the effects of patient factors and tumor characteristics on the pre-treated performance to investigate neuroplastic potential of different language domains.
Methods
Patient age, sex, education level, tumor grade, language pathway involvement, T1 contrast enhanced (C+), and FLAIR (T2) volume were selected as variables. The correlation with abnormal language performance was verified using univariate and multivariate logistic regression.
Results
In total, 104 patients were enrolled in this study. 44% of patients had repetitive abnormalities, 34.9% had comprehension abnormalities, and 32.1% had expression abnormalities. The proportion of language pathway involvement and normal language performance was 60% in grade 2 and 3 gliomas and 16% in grade 4 gliomas. Tumor grade (p = 0.006) and T2 volume (p = 0.008) were associated with abnormal performance in the expressive domain, education level (p = 0.004) and T1 C + volume (p = 0.049) in the repetitive domain, and education level (p = 0.013), T2 volume (p = 0.011), and tumor grade (p = 0.089) in the comprehensive domain.
Conclusion
Different clinical and radiological factors affected the abnormal performance of the three language domains, indicating their functional connectivity and neuroplastic potential are inherently varied. The dynamic interactions between patient factors, tumor characteristics, and language processing should be considered when resecting left hemispheric gliomas.
Effectiveness of Indocyanine Green Flourescence Imaging in Resection of Hepatoblastoma
Authors: Shen Qiyang , Liu Xingyu , Pan Sirui , Li Tao , Zhou Jianfeng 
Published: February 2023

Abstract
Objective
The present research aimed to analyze the application of indocyanine green (ICG) fluorescence contrast technique in the resection of hepatoblastoma (HB) in children, and to discuss the use of ICG in the surgery of HB and the value of guidance.
Methods
We retrospectively analyzed the data of 23 children with HB resected by using ICG fluorescence contrast technique at the Children’s Hospital of Nanjing Medical University from June 2020 to September 2022, including 16 boys and 7 girls, aged 5 days to 80 months. The patients were administered with an ICG injection of 0.1mg/kg around 24 hours before surgery. The surgical margin was detected by real-time fluorescence imaging and confirmed by postoperative pathology.
Results
All primary lesions showed bright fluorescence in 23 HB cases. 22 had clear borders with normal liver tissue, while one neonatal case showed no difference between tumor and background. 13 anatomic resection and 10 non-anatomic resection were performed with ICG fluorescence navigation. The surface of the residual liver was scattered with multiple tumor fluorescence, which was then locally enucleated according to the fluorescence. 22 isolated specimens were dissected and fluorescently visualized. Pathology identified deformed, vacuolated and densely arranged hepatocytes resembling pseudo-envelope changes without tumor residual, due to the compression of the tissue at the site of circumferential imaging.
Conclusion
The ring ICG fluorescence imaging of HB indicates the tumor resection boundary effectively, especially in multiple lesions cases.