bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep Learning-Based Multiresolution Parameterization for Spatially Adaptive Model Updating
Authors: Valiyev Mahammad , Mohd Razak Syamil , Jafarpour Behnam 
Published: March 2023

Abstract
This paper presents a new deep learning-based parameterization approach for model calibration with two important properties: spatial adaptivity and multiresolution representation. The method aims to establish a spatially adaptive multiresolution latent space representation of subsurface property maps that enables local updates to property distributions at different scales. The deep learning model consists of a convolutional neural network architecture that learns successive mapping across multiple scales, from a coarse grid to increasingly finer grid representations. Once trained, the architecture learns latent spaces that encode spatial information across multiple scales. The resulting parameterization can facilitate the integration of data at different resolutions while enabling updates to the desired regions of the domain. Unlike the standard deep learning latent variables that are not localized and do not provide spatial adaptivity, the presented method enables local update capability that can be exploited to incorporate expert knowledge into assisted model updating workflows. Examples with two-dimensional multi-Gaussian random fields are used to introduce the method and its properties, followed by application of the method to a travel-time tomography inverse problem to investigate its model updating performance.
An 18F-FDG-PET/CT-based radiomics signature for estimating malignance probability of Solitary Pulmonary Nodule
Authors: Zheng Jingchi , Hao Yue , Guo Yan , Du Ming , Wang Pengyuan , Xin Jun 
Published: March 2023

Abstract

Background.
Some SPNs as early manifestations of lung cancer, it is difficult to determine its nature, which brings great trouble to clinical diagnosis and treatment. Radiomics can deeply explore the essence of images and provide clinical decision support for clinicians. The purpose of our study was to explore the effect of positron emission tomography (PET) with 2-deoxy-2-[fluorine-18] fluoro-D-glucose integrated with computed tomography (CT; 18F-FDG-PET/CT) combined with radiomics for predicting probability of malignancy of solitary pulmonary nodules (SPNs).
Methods.
We retrospectively enrolled 190 patients with SPNs confirmed by pathology from January 2013 to December 2019 in our hospital. SPNs were benign in 69 patients and malignant in 121 patients. Patients were randomly divided into a training or testing group at a ratio of 7:3. Three-dimensional regions of interest (ROIs) were manually outlined on PET and CT images, and radiomics features were extracted. Synthetic minority oversampling technique (SMOTE) method was used to balance benign and malignant samples to a ratio of 1:1. In the training group, least absolute shrinkage and selection operator (LASSO) regression analyses and Spearman correlation analyses were used to select the strongest radiomics features. Three models including PET model, CT model, and joint model were constructed using multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were plotted to evaluate diagnostic efficiency, calibration degree, and clinical usefulness of all models in training and testing groups.
Results.
The estimative effectiveness of the joint model was superior to the CT or PET model alone in the training and testing groups. For the joint model, CT model, and PET model, area under the ROC curve was 0.929, 0.819, 0.833 in the training group, and 0.844, 0.759, 0.748 in the testing group, respectively. Calibration and decision curves showed good fit and clinical usefulness for the joint model in both training and testing groups.
Conclusion.
Radiomics models constructed by combining PET and CT radiomics features are valuable for distinguishing benign and malignant SPNs. The combined effect is superior to qualitative diagnoses with CT or PET radiomics models alone.
Long-acting recombinant neuritin with increased stability
Authors: Meng Pingping , Liang Mengjie , Wei Yu , Yuan Wumei , Zhu Liyan , Zhu Jingling , Sun Jiawei , Huang Jin 
Published: March 2023

Abstract
Objective: To obtain the long-acting protein neuritin we fused the carboxyl-terminal peptide (CTP) to the C-terminal of neuritin and expressed it in Chinese hamster ovarian (CHO) cells.
Methods：The plasmid was constructed by fusion PCR. Affinity chromatography is used for protein purification. Thermal stability and serum stability were used to evaluate protein stability.
Results: The molecular weight of the neuritin-CTP was determined to be approximately 20 kDa. Subsequent functional analysis showed that the purified neuritin-CTP promoted neurite outgrowth in PC12 cells at a rate equivalent to that observed with neuritin. The stability experiments showed that the degradation rate of neuritin was 100% after incubation at 37°C for 72 h, whereas only approximately 20% of the neuritin-CTP was degraded under the same conditions. Similarly, the serum stability analysis results showed that neuritin degraded by approximately 90% and neuritin-CTP degraded by approximately 30% after incubation at 37°C for 72 h.
Conclusions: Fusion with CTP can effectively increase the stability of neuritin without affecting its secretion and activity. These results provide a basis for the construction of long-acting neuritin proteins.
Determining the Characteristics of a Healthy Environment Promoting the Health of Women Working in the Hospital: A Qualitative Study
Authors: Faghihi Mitra , Farshad Ali Asghar , Salehi Nasim , Whitehead Dean , Ghayen Masoud Motalebi , Izadi Bahar , Mansourian Morteza 
Published: March 2023

Abstract
Background and Purpose: The workplace is one of the priorities for health promotion in the 21st century by extending as the adult population extends. Also, the work environment has a significant impact on people's health. Therefore, the aim of this study was to determine the characteristics of a healthy environment promoting the health of women working in the hospital.
Materials & Methods: This study was conducted using a qualitative method and content analysis. The participants were selected using the purposive sampling total of 48 working women, hospital managers, and health promotion specialists participated in the study. In this study, we tried to select participants with maximum diversity in terms of age, work experience, and the sector they are working in. The data were collected using a semi-structured interview. The texts of the interviews were extracted and divided into the meaningful units. The criterion of credibility or acceptability of the data (Credibility) was used to ensure the accuracy of the study results.
Results: In the process of data analysis, 31 basic concepts that had the same characteristics were grouped into 12 subcategories, and based on the common characteristics at a more abstract level: they were transformed into 3 main categories, including organizational management and commitment to the health of working women. The psychological environment supports women's health, and also the social environment promotes women's health in the hospital by providing services.
Discussion and conclusion: Hospital managers play an important role in creating healthy workplaces for women, and to achieve this goal, they should try to improve their health from the aspects of mental and social health and provide special services to working women.
Efficacy and safety of baricitinib for the treatment of hospitalised adults with COVID-19: a systematic review and meta-analysis
Authors: Sun Jing , Wang Shufang , Ma Xin , Wei Qingqing , Peng Yujuan , Bai Ying , Miao Guobin , Meng Chang , Liu Peng 
Published: March 2023

Abstract
Several clinical trials have evaluated the efficacy and safety of baricitinib in COVID-19 patients. Recently, there have been reports on critical patients, which are different from previous research results. Studies were searched in PubMed, Embase, and Cochrane Library databases on January 31, 2023. We performed a meta-analysis to estimate the efficacy and safety of baricitinib for the treatment of hospitalised adults with COVID-19. This study is registered with INPLASY , number 202310086. A total of 3010 patients were included in our analyses. All included studies were randomized controlled trials or prospective study. There was no difference in 14-day mortality between the two groups (OR 0.23 [95% CI 0.03–1.84], I²=72%, P=0.17). In subgroup analyses we found that baricitinib did not seem to improve significantly in 24-day mortality critically ill patients (OR 0.60 [95% CI 0.35–1.02], I²=0%, P=0.06). Fortunately, baricitinib have led to faster recovery and shorter hospital stays for COVID-19 patients. There were no difference in infections and infestations, major adverse cardiovascular events, deep vein thrombosis and pulmonary embolism. Baricitinib is safe. At the same time, we can find that it reduces the mortality of COVID-19 patients, but the prognosis of the critically ill patients is not significantly improved.
A statistical method for quantifying progenitor population reveals incipient cell fate commitments
Authors: He Xionglei , Deng Shanjun , Gong Han , Zhang Di , Zhang Mengdong 
Published: March 2023

Abstract
Quantifying the number of progenitor cells that founder an organ, tissue or cell population is of fundamental value for understanding the development and homeostasis of a multicellular organism. Previous efforts rely on marker genes that are specifically expressed in progenitors. The strategy is, however, often hindered by the lack of ideal markers. By exploring the cell phylogenetic tree that records the cell division history during development, we here describe a general statistical method able to quantify the progenitors of any tissues or cell populations defined in an organism without progenitor-specific markers. The method, termed targeting coalescent analysis (TCA), computes for all cells of tissue the average coalescent rate at the monophyletic clades of the target tissue, the inverse of which then measures the progenitor number of the tissue. Both theoretical deduction and computer simulations supported the underlying logics of TCA, which was then validated by empirical data of nematode, fruit fly and mouse, all with required cell phylogenetic trees. We further showed that TCA can be used to identify lineage-specific gene expression upregulations during embryogenesis, revealing incipient cell fate commitments in mouse embryos.
Modeling Electrochemical Oxide Film Growth – Passive and Transpassive Behavior of Iron Electrodes
Authors: Bösing Ingmar 
Published: March 2023

Abstract
The phenomenon of passivity is crucial for many areas of our technological and metal-based civilization. Nevertheless, the mechanisms leading to passivity are still under investigation and not fully understood. Modeling passive film growth can bring insights into these processes which are experimentally hard or impossible to access. Therefore, this paper presents a comprehensive model which describes oxide film growth and dissolution under an external potential by interfacial reactions and transport of defects and electrons and holes. The simulation gives insights into the defect con-centrations and electric field during passive film growth and film dissolution and delivers an anodic transpassive dissolution mechanism.
An adaptive method to recover high dynamic range images from multi-camera systems in back-lighting scenario
Authors: Abedi Firas , Yang You , Liu Qiong 
Published: March 2023

Abstract

The reconstruction of multi-view high dynamic range images faces several challenges, including holes and artifact in the disparity map in non-linear and large baseline between the involved cameras. Furthermore, these challenges increased when a back-lighting scenario is involved, with the currently industrial growth. To dispense the disparity map estimation and it’s related challenges, and generate backlighting multi-view high dynamic range images, we propose a method that relays on accurately detected and matched features between the adjustment viewpoints instead of the estimation of the disparity map, thus providing a flexibility on the camera baseline. Next, we estimate exposure gain between the matched features. After that, we restore the multi-view low dynamic range images based on the estimated gain, and generate a final high dynamic range image per view. Experimental results demonstrate superior performance for the proposed method over state-of-the-art methods in both objective and subject comparisons. These results suggest that our method is suitable to improve the visual quality of multi-view low dynamic range images captured in low back-lighting conditions via commercial cameras sparsely located among each other in any general public infrastructure.
Highly Accurate Trimesh and PointNet based algorithm for Gesture and Hindi air writing recognition
Authors: Sharma Jayash Kumar , Gupta Rajeev , Sharma Shakti , Pathak Vinay , Sharma Manoj 
Published: March 2023

Abstract
Air writing and gesture recognition has been one of the most comprehensive and intensively researched areas due to their wide variety of applications in several domains of Human-Computer Interaction (HCI),such as gaming, medicines, and automobiles. This article presents ahighly accurate point-based algorithm for identifying dynamic air writing and free hand gesture recognition for Devanagari Hindi characters.Usually, the written gestures are limited in nature. In order to create a training data set, we are using Leap Motion Controller (LMC) that generates twenty-four sample points from the gesture drawn. In the pre-processing stage, a trimesh is generated using these sample points; the developed trimesh is then fed as input to PointNet. The algorithm developed measures the similarity of the drawn gesture with the training samples to identify and recognize the gesture drawn using Mahalanobis distance. The validation test indicates that the approach is quite accurate, giving a recognition rate of more than 97%. Comparative studies also show that the methodology performs better when trimesh is used as input for the PointNet and more training samples are used.
Cerebral Venous Sinus Thrombosis After a Third Dose of mRNA COVID-19 Vaccine in an Adolescent: A case report
Authors: Mizuno Shinsuke , Koyama Junji , Horikawa Shogo , Kishimoto Kenji , Hasegawa Daiichiro , Kosaka Yoshiyuki , Kasai Masashi 
Published: March 2023

Abstract
Background: Several effective vaccines against Coronavirus disease 2019 (COVID-19) have been developed to control the spread of the disease. A few cases of thrombosis have been reported post-vaccination, especially among young adult women immunized with viral vector-based vaccines; although pediatric cases of cerebral venous sinus thrombosis (CVST) have been rarely reported after messenger ribonucleic acid (mRNA) vaccine administration.
Case presentation: Here, we report a case of CVST in a 14-year-old girl immunized with the BNT16B2b2 vaccine. Other than this recent COVID-19 vaccination, there were no precipitant risk factors in her medical history. Laboratory work-up showed low levels of protein S activity. Further research revealed no pathological gene mutation. She was treated with anticoagulant therapy and discharged with mildly impaired coordination/movement of the fingers.
Conclusion: CVST may occur following a mRNA COVID-19 vaccination, even among children. Further investigations are needed to establish whether thrombotic events are merely incidental or are a complication associated with mRNA-based vaccines.
Toward Building an Open domain Hindi Dialogue System: A Study on Sequence-to-Sequence Architectures
Authors: Rana Sandeep Kumar , Singh Thoudam Doren 
Published: March 2023

Abstract
Open domain conversation systems have gained popularity in recent years due to their versatility and scalability, making them great virtual companions in dialog exchange. Artificial intelligence-based approaches are preferred over rule-based methods as they enable continuous growth and learning, making them increasingly becoming the preferred choice for many applications. However, creating chatbots for Hindi language presents a unique challenge due to the lack of publicly accessible benchmark datasets. To overcome this, a new dataset is derived by translating the Daily Dialog dataset into Hindi using AI4Bharat's IndicTrans model. Various sequence-to-sequence architectures are then trained and evaluated using this dataset to build a dialogue system for Hindi language. The analysis demonstrated that the sequence-to-sequence model with RNN-Gated Recurrent Unit (GRU)+Temporal Convolutional Network(TCN)+ Attention performed the best with an F1 score of 26.31. This study offers a new dataset for Hindi open-domain dialogue systems and sheds light on the performance of various sequence-to-sequence architectures on the dataset. This approach can be beneficial for creating open-domain dialogue systems for other under-resourced languages as well.
Fractal geometry of coronary plaque images within optical coherence tomography: A novel approach of plaque vulnerability in patients with acute coronary syndrome versus stable angina pectoris
Authors: Morikawa Tomoyuki , Hiro Takafumi , Mineki Takashi , Kojima Keisuke , Kogo Takaaki , Iida Korehito , Akutsu Naotaka , Murata Nobuhiro , Sudo Mitsumasa , Kitano Daisuke , Fukamachi Daisuke , Okumura Yasuo 
Published: March 2023

Abstract
Background and Purposes:
The main cause of acute coronary syndrome (ACS) is plaque rupture and thrombus formation. However, it has not been fairly successful to identify vulnerable plaque to rupture using conventional parameters of intravascular imaging modalities. Fractal analysis is one of the mathematical models to examine geometrical features of picture image using a specific parameter called as fractal dimension (FD) which suggests geometric complexity of the image. This study examined FD of the optical coherence tomography (OCT)-derived images of the culprit plaque in patients with ACS versus stable angina pectoris (SAP) in order to evaluate the feasibility of FD for identifying vulnerable coronary plaques, because coronary plaques in ACS patients are known to be more vulnerable than those in patients with SAP.
Methods and Results
We examined 65 cases (34 ACS patients, 31 SAP patients) in which the culprit lesion was imaged by OCT before percutaneous coronary intervention in patients with ACS and SAP. The culprit plaque lesion in the ACS group had a significantly larger mean lipid arc (203.8 ± 39.4° vs 152.3 ± 34.5°, p < 0.001) and a larger lipid plaque length (12.6 ± 5.1mm vs 7.7 ± 2.7mm, p < 0.001) and a thinner fibrous cap thickness (75.3 ± 22.3 µm vs 134.8 ± 53.2 µm, p < 0.001) than those in the SAP group. The prevalence of OCT -derived macrophage infiltration (MφI) in the entire culprit coronary vessel as well as that of the OCT-derived thin-cap fibroatheroma (TCFA) at the culprit lesion were significantly greater in the ACS group than those in the SAP group, respectively (MφI: 61.8% vs 35.5%, p = 0.048; TCFA: 44.1% vs 6.4%, p < 0.001). The FD of culprit plaque in the ACS group was significantly greater than in the SAP group (2.401 ± 0.073 vs 2.341 ± 0.051, p < 0.001). In multivariate regression analysis, the presence of MφI was a significant determinant of FD (regression coefficient estimate 0.049, CI: 0.018–0.079, p = 0.002).
Conclusions
The FD of OCT-derived image of culprit coronary plaque in the ACS group was significantly greater than that in the SAP group, indicating that the culprit plaque in ACS were structurally more complex. Therefore, fractal analysis of coronary OCT images might be clinically useful for identifying vulnerable coronary plaques.
Authorship Attribution using Tf-Idf weight with Machine Learning Approaches
Authors: Mahor Urmila , Kumar Aarti 
Published: March 2023

Abstract
Authorship attribution means automatically determining the authorship of an unrecognize document based on a given sample of text. Authorship attribution has a long history and a wide range of applications. As digitalization is being increased Authorship attribution is playing a vital role in forensics, plagiarism detection, authorship dispute, and research fields. It is very helpful when two persons claim ownership of the same document written by him/her. Authorship attribution is a classification problem, although it uses the techniques of text classification for the text pre-processing despite that it is far different from the goal of text classification because it purely depends on the non-deterministic writing style of the author. The success of the author attribution task highly depends on the writing style characteristics of the authors. Several researchers proposed many different kinds of features such as linguistic, syntactic, semantic, and content-based features to identify the writing style of the authors and many of them have been used by different classifiers to classify the documents. In this paper, we applied tf-idf frequency with FW and sylometric features with support vector machines and parametric and nonparametric methods with supervised and unsupervised classification techniques in authorship attribution. We conducted various experiments with the English corpus gathered from PANCLEF'12. We performed experiments on different feature sets extracted from Corpus with different classifiers and combined these results to improve our success rates. We identified which classifiers give good results on which feature sets. According to experiments, the success rates dramatically change with different combinations of feature sets, however, the test among them are support vector classifier with a bag of words, and Gaussian with function words.
A case of lead helix winding tricuspid chordae tendineae
Authors: Liu Tengfei , Ding Chunhua 
Published: March 2023

Abstract
As left bundle branch pacing (LBBP) is more likely physiological pacing，which has emerged as a novel pacing strategy that uses the native conduction system to improve ventricular synchrony with stable pacing parameters. Operation process of the lead implanation was performed relatively complex, lack ofa standard operation may lead to serious consequences. Recently, we encountered a case of the lead helix was winding the tricuspid chordae. Radio frequency ablation effectively solved this problem without causing serious complications. It is an effective and reliable method to solve the problem of electrode winding chordae. This case provided more clinical experience in handing the operative complications.
Dynamical analysis and optimal harvesting policy of a prey-predator model with Holling type-III functional response
Authors: Kumar Ankit , Poonia Ashish 
Published: March 2023

Abstract
In this study, we propose and analyze a two-dimensional prey-predator model with the harvesting of both prey and predator. The species are harvested according to the catch-per-unit effort (CPUE) hypothesis. It is assumed that the predator is partially dependent upon the prey population and followed by Holling type-III functional response. Steady states of the system are obtained, and local and global stability analysis is performed to examine the long-term behavior of the system. We also examine the effect of harvesting on the dynamics of the system. It is noticed that for higher values of harvesting efforts, the prey population becomes extinct via a transcritical bifurcation. We derive an optimal harvesting policy and obtain the optimal steady state solution and optimal harvesting efforts using Pontryagin’s Maximum Principle. Lastly, some numerical illustrations are presented in support of our theoretical findings. For a chosen set of parameters, we obtain the threshold value of some parameters beyond which the harvesting of prey biomass must be stopped for the sustainable growth of the species.