bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emotional Eating – Elusive or Evident? Integrating Laboratory, Psychometric and Daily Life Measures
Authors: Schnepper Rebekka , Blechert Jens , Arend Ann-Kathrin , Yanagida Takuya , Reichenberger Julia 
Published: June 2023

Abstract
Purpose: Emotional eating (EE) refers to eating in response to (negative) emotions. Evidence for the validity of EE is mixed: some meta-analyses find EE only in eating disordered patients, others only in restrained eaters, which suggest that only certain subgroups show EE. Furthermore, EE measures from lab-based assessments, ecological momentary assessment (EMA), and psychometric measures often diverge. This paper tested whether the covariance of these three different EE methods can be modeled through a single latent variable (factorial validity), and if so, how this variable would relate to restrained eating (construct validity), Body-Mass-Index (BMI), and subclinical eating disorder symptomatology (concurrent validity).
Methods: 102 non-eating disordered female participants with a wide BMI range completed EE measures from three methods: psychometric questionnaires, a laboratory experiment (craving ratings of food images in induced neutral vs. negative emotion) and EMA-questionnaires (within-participant correlations of momentary negative emotions and momentary food cravings across nine days). Two measures for each method were extracted and submitted to confirmatory factor analysis.
Results: A one-factor model provided a good fit. The resulting EElat factor correlated positively with subclinical eating disorder symptoms and BMI but not with restrained eating.
Conclusion: The one-factor solution shows that the EE construct can validly be assessed with three different methods. Individual differences in EE are supported by the data and are related to eating and weight problem symptomatology but not to restrained eating. This supports learning accounts of EE and underscores the relevance of the EE construct to physical and mental health.
Counting algorithms with phase sensitive detection for hover and S turn of aircraft flight
Authors: Chen Moli , Wei Xunkai , Wang Hao , Jiang Zhenhe 
Published: June 2023

Abstract
Hover and S turn of an aircraft can be characterized by flight yaw angle, but the yaw angle signal in actual fly parameters is not only non-stationary and non- ergodic, but also often appears violent mutations and weak fluctuations, which makes it difficult to work out service environment spectra related to the numbers of hovering and S-turning training times. This article develops intelligent counting algorithms for the numbers of hovers and S turns based on yaw angle data that from fly parameters. Using the median-crossing de redundant function of Phase Sensitive Detection (PSD) and analyzing the characteristics of 360° hovering fly parameters, a counting algorithm for the number of hovers during a flight profile is presented. Using the split-half function of PSD, a triangle layering algorithm about yaw angle signal is developed to count the number of S turns during a flight profile, where the signal of each sublayer is segmented into the median-crossing intervals so as to eliminate the redundant median-crossing marks from the previous layer. Compared with the artificial means, the counting results of flight example show that the intelligent algorithms presented are effective.
Multicenter preclinical analysis of tenecteplase versus alteplase
Authors: Correa-Paz Clara , Pérez-Mato María , Bellemain-Sagnard Mathys , González-Domínguez Marco , Marie Pauline , Pérez-Gayol Lara , López-Arias Esteban , Pozo-Filíu Lucia , López-Amoedo Sonia , Bugallo-Casal Ana , Alonso-Alonso Mª Luz , Candamo-Lourido María , Santamaría-Cadavid María , Arias-Rivas Susana , Rodríguez-Yañez Manuel , Iglesias-Rey Ramón , Castillo José , Vivien Denis , Rubio Marina , Campos Francisco 
Published: June 2023

Abstract
Recombinant tissue plasminogen activator (rtPA/Alteplase) remains the gold standard thrombolytic drug for acute ischemic stroke. However, new rtPA-derived molecules such as Tenecteplase (TNK) with longer plasmatic half-life, practical delivery advantages as a fast, single bolus and increased fibrin binding have been developed. In spite of the recommendations on the bi-directionality of the basic/clinical research relationship, TNK is being tested in clinical trials without a preclinical basis on its toxicity and efficacy. In this study, toxicities of rtPA and TNK were evaluated on endothelial, astrocytes and neuronal culture; and efficacy was independently tested by two research centres in a thromboembolic model of ischemic stroke in mice. Both therapies were tested after early (20 and 30 min) and late administration (4 and 4.5 h) of ischemia onset. rtPA and TNK did not affect the viability of the endothelial cells or astrocytes. In neuronal cultures, rtPA, but not TNK, increased cell death at 24 h by itself. A single bolus dose of TNK showed an infarct volume reduction similar to that obtained after the perfusion of rtPA. TNK has a therapeutic window similar to rtPA and loses its beneficial effect when administered late. Early administration of TNK decreases the risk of haemorrhagic transformations compared to rtPA, but not when it is administered as a late treatment. These two independent preclinical studies support the use of TNK as a promising reperfusion alternative to rtPA treatment, mainly due to lower neurotoxicity and risk of haemorrhagic transformation when administered early after stroke onset.
Answer Me: Pictorial Interrogation on Categorized Queries
Authors: Shahapure Nagamani , V Sahana , M Rekha P 
Published: June 2023

Abstract
The recent years have witnessed massive development in the fields of computer vision, object recognition, and natural language processing (NLP). Artificial Intelligence (AI) applications use NLP to provide the computer with facilities, such as question-answering models. An extension of this method is to integrate NLP with computer vision to perform the Visual Question Answering (VQA) mission, which is to construct systems that can respond to image questions in natural language. In this paper, the application of Convolutional Neural Network (CNN) for the image Question Answering (QA) task is proposed. The proposed CNN provides an end-to-end framework with convolutional architectures for learning not only the image and question representations, but also their inter-modal interactions to produce the answer. More specifically, this model consists of three CNNs: image CNN to encode the image content, sentence CNN to compose the words of the question, and multimodal convolution layer to learn their joint representation for the classification in the space of candidate answer words.
Lagrange radial basis functions collocation method for boundary value problems in 1D
Authors: Arfaj Kawther Al , Levesly Jeremy 
Published: June 2023

Abstract

This paper introduces the Lagrange collocation method with radial basis functions (LRBF) as a novel approach for solving 1D partial differential equations. Our method addresses the trade-off principle, a key challenge in standard RBF collocation methods, by maintaining the accuracy and convergence of the numerical solution while improving stability and efficiency. We prove the existence and uniqueness of the numerical solution for specific differential operators, such as the Laplacian operator, and for positive definite RBFs. Additionally, we introduce a perturbation to the main matrix, developing the perturbed LRBF method (PLRBF) which allows for the application of Cholesky decomposition, significantly reducing the condition number of the matrix to its square root, resulting in the CPLRBF method. In return, this enables us to choose a large value for the shape parameter without compromising stability and accuracy, provided the perturbation is carefully selected. By doing so, highly accurate solutions can be achieved at an early level, significantly reducing CPU time. Furthermore, to overcome stagnation issues in the RBF collocation method, we combine LRBF and CPLRBF with multilevel techniques and get the Multilevel PLRBF (MuC-PLRBF). We illustrate the stability, accuracy, convergence, and efficiency of the presented methods in numerical experiments with a 1D Poisson equation. Although our approach is presented for 1D, we expect to extend it to higher dimensions in future work.
The Effect of Congruent Tibial Inserts in Total Knee Arthroplasty: A Network Meta- analysis of Randomized Controlled Trials
Authors: Tsai Yen-Lin , Tsai Sung Huang Laurent , Lin Chia-Han , Lin Chun-Ru , Hu Chih-Chien 
Published: June 2023

Abstract
Objective
This study aims to determine whether modern ultracongruent tibial inserts are associated with different outcomes in Total Knee Arthroplasty (TKA).
Background
Ultracongruent fixed-bearing (UCFB) and medial congruent fixed-bearing (MCFB) inserts have been known to be effective in total knee arthroplasty with patient satisfaction. Nonetheless, no supporting evidence to date exists to rank the clinical outcomes of these various congruent inserts in TKA.
Methods
We searched for PubMed, Embase, The Cochrane Central Register of Controlled Trials, Web of Science, and Scopus up to May 15, 2022. We selected studies involving an active comparison of UCFB or MCFB in TKAs. We performed a network meta-analysis (NMA) of randomized controlled trials (RCTs) and compared different congruent inserts. We ranked the clinical outcomes by SUCRA score with the estimate of the best treatment probability. Our primary outcomes were revision rates and radiolucent lines. Secondary outcomes were functional scores, including the range of motion (ROM), the Knee Society Score (KSS), Oxford Knee Score (OKS), and WOMAC.
Results
18 RCTs with 1793 participants were obtained. The MCFB performed similar revision rates as CRFB and PSRP. CRFB and UCFB had the lowest radiolucent lines. UCFB and MCFB had the best OKS score overall.
Conclusions
The ranking probability for better clinical outcomes in congruent inserts demonstrated the superiority of congruent tibial inserts, including UCFB and MCFB. UCFB may be associated with improved postoperative functional outcomes. However, integrating future RCTs for high-level evidence is necessary to confirm these findings.
Inversion of Rayleigh Wave Dispersion Curves Via BP Neural Network and PSO
Authors: Luo Yijian 
Published: June 2023

Abstract
Rayleigh wave is widely applied in engineering exploration and geotectonic research. While how to reconstruct the corresponding geological information via Rayleigh wave is the critical process and difficulty. This paper presents an inversion method of Rayleigh wave dispersion curves based on BP neural network and PSO. In this work, a sample set that referring to the actual stratum distribution is firstly generated. Then, BP neural network is adopted to train the nonlinear mapping relationship between the dispersion curves and the shear wave velocity of each stratum. The trained BP neural network can quickly output a predicted value with rationality but poor precision, which can be utilized as the initial model of PSO inversion. PSO will then be adopted to further optimize the inversion result on the basis of BP neural network prediction. The combination of BP neural network and PSO aims at overcoming the defects of BP neural network that unable to carry out continual optimization and the slow optimization of PSO in the absence of reasonable initial solution. Finally, the effectiveness of the proposed algorithm is verified by a series of synthetic models and an active-source Rayleigh wave experiment carried out in a new railway project from Baotou, Inner Mongolia to Yinchuan, Ningxia.
Impact of vaccination strategies on the spread of COVID-19 pandemics: A mathematical modelling approach
Authors: Khoshnaw Sarbaz H. A. , Abdulrahman Kawther Yusuf , Mustafa Arkan N. 
Published: June 2023

Abstract
Mathematical models with computational simulations are useful tools for analyzing the spread and control of COVID-19 disease. In this study, we develop the previous models of COVID-19 pandemics. The vaccination compartment and its reaction rates are considered. Some key computational simulations and sensitivity analysis are applied on the model. Accordingly, three techniques of sensitivity analysis are discussed to compute the local sensitivities between model states and parameters. In addition, the basic reproduction number 𝑅0 is calculated for the model equilibrium points. The elasticity between the basic reproduction number, and the model parameters is also calculated. Furthermore, the daily real data of vaccinated cases in the United Kingdom from 15th March, 2021 to 31st December, 2021 are used. Results based on sensitivity analysis show that vaccination rate, contact tracing, rapid testing are the most important parameters to reduce the basic reproduction number. This helps international efforts to reduce the number of infected individuals from the disease. Another novelty in this work is that there is a good fit between the real data and model results. The model results will provide further control strategies and help local efforts to control this disease more effectively widely.
Survival benefit for HPV+ OPSCC patients with dominant serum IgA over IgG antibodies against HPV16 L1
Authors: Weiland Thomas , Zgubic Jakob , Brcic Luka , Thurnher Dietmar 
Published: June 2023

Abstract
Background
Although the therapy response of HPV-positive oropharyngeal carcinomas (OPSCC) is known to be significantly better compared to HPV-negative tumors, about 25% of them will have a recurrence within the first 5 years after therapy. Therefore, it is of great scientific interest to find relevant biomarkers to identify subgroups with a poorer prognosis. In this prospective observational study, we examine for the first time the behavior of HPV-L1 antibody subclasses IgA, IgG, and IgM in HPV-positive OPSCC patients under therapy.
Material and Methods
The study cohort included patients with HPV-positive OPSCCs from the Department of Otorhinolaryngology, Head and Neck Surgery, Medical University of Graz. Serum samples were collected before and during tumor-specific therapy and follow-up. They were analyzed for the presence of HPV-L1 antibody (AB) subclasses IgA, IgM, and IgG using an HPV-L1-specific immuno-assay. In addition, a PCR-based HPV-DNA detection and subtyping from the tumor tissue was performed. Statistical analysis included Chi2- and Kruskal-Wallis-H-Test for significance testing. Correlation between two ordinal sizes has been tested using Spearmen-Rho.
Results
Altogether, 34 patients with a mean age of 63.5 years and a mean follow-up of 55 months were included. The most common L1-AB-subclass pattern was characterized by IgG > > IgA > IgM (13 cases) without significant fluctuation. The second most common pattern was IgG > IgA > IgM (6 cases). In 5 cases an antibody pattern of IgA > IgG > IgM while in 4 cases IgG = IgA > IgM was seen. Three out of 4 recurrences had previously shown an increase in IgG antibodies. Patients with excessive IgG levels tended to have higher tumor stages. IgA dominance at diagnosis was associated with better disease-free survival.
Conclusions
In this prospective longitudinal observational study, we show for the first time the behavior of HPV-L1 antibody subclasses IgA, IgG, and IGM in HPV-positive OPSCC patients under therapy. IgA dominance at diagnosis was associated with better disease-free survival, whereas a higher IgG/IgA ratio correlated with higher tumor stages. In three recurrent tumors, an increase in the IgG AB level could be shown beforehand. HPV-L1 AB-subclass detection might be of use for future risk stratification in HPV-pos. OPSCC patients.
Smart Anonymity: A Mechanism for Recommending Data Anonymization Algorithms Based on Data Profiles for IoT Environments.
Authors: Neves Flavio , Souza Rafael , Lima Wesley , Raul Wellison , Bonfim Michel , Garcia Vinicius 
Published: June 2023

Abstract
The Internet of Things (IoT) has grown rapidly in recent years. However, coupled with a large number of devices and the huge volume of data generated by these devices, an impasse arises: the privacy of the data of IoT device users. The goal of this research is to propose a solution that recommends which data anonymization algorithm is the most suitable for the dataset according to its characteristics. Among the contributions of this research, we can highlight the creation of metrics for classifying IoT data. Using the metrics to create two Ontologies that use Description Logic for classification of IoT data. The use of Description Logic brings as positive points the verification of inconsistency and discovery of new facts to validate data. The evaluation results demonstrate that the use of the proposed approach brings benefits for user privacy.
Detect Melanoma Skin Cancer Using An Improved Deep Learning CNN Model With Improved Computational Costs
Authors: Ganesh Gourav Ganesh , Somasundaram K Somasundaram 
Published: June 2023

Abstract
Melanoma cancer has been considered as one of the most deadliest cancer. Melanoma is a highly malignant form of skin cancer that originates from melanocytes, the cells responsible for producing skin pigment. It is characterized by the uncontrolled proliferation of abnormal cells, which have the potential to invade surrounding tissues and spread to distant parts of the body. In this work, we aim to classify the skin disease into 7 classes. Our objective is to propose a deep learning CNN model to improve the accuracy of melanoma detection by customizing the number of layers in the network architecture and activation functions followed by reducing the computational cost. A comparitive study is made between the improved model and the use of pre-trained Models like Resnet, DenseNet, Inception, VGG and DenseNet-II which has been giving impeccable accuracy. The HAM10000 dataset is used for research and we have got better results for the proposed model. Also, graphical results have been obtained for the same.
Identifying Cuproptosis-Related Hub Genes and Their Clinical Significance in Tumors
Authors: Gao Hui , Lv Liqiong , Lan YanDan 
Published: June 2023

Abstract
The freshly detected Cuproptosis is a novel form of tumor cell death whose role in pan-cancer is still not completely unintelligible. Methods: We analyzed and assessed the Cuproptosis-related genes (FDX1, LIPT1, DLD, LIAS, DLAT, PDHA1, PDHB, MTF1, GLS and CDKN2A) conversion through the STING database and Sytoscpy software, performed functional enrichment analysis and identified the Hub genes. We extracted the expression data of Hub gene in tumor through TGCA and UCSC database, and analyzed its clinical prognosis. The cancer single-cell state atlas (CSEA) database was used to evaluate the effect of Hub genes on tumor phenotype in single cell lines. Finally, we analyzed the Hub genes mutation and evaluated its correlation with tumor immune infiltration. Results: DLD and LIAS were identified as the Hub genes of Cuproptosis, which have the same CCM score. Mutation analysis revealed that the expression of Hub genes is steady in pan-cancer, DLD and LIAS have been identified as tumor suppressor genes or oncogenes in different tumors. ROC curve analysis showed that DLD and LIAS had a very high predictive effect on survival and prognosis in LAML, and DLD also had a very high predictive effect in the other 4 tumor types. Through the expression of Hub gene in single cell lines, we found that DLD and LIAS displayed significant inhibitory and facilitative effects in different phenotypes of different tumors. Lastly, we explored the relationship between Hub genes and immune infiltration, and found that DLD was substantially associated with immune infiltration of 23 different tumors, and LIAS was significantly associated with immune infiltration of 31 different tumors. Conclusion: We preliminarily evaluated the effect of Cuproptosis on pan-cancer through the hub gene of Cuproptosis, suggesting that copper death has different effects in different tumors.
Investigating impostorism among undergraduate medical students at Sultan Qaboos University: a questionnaire-based study
Authors: Lawati Abdullah Al , Wahaibi Anas Al , Kharusi Fatma Al , Chan Moon Fai , Sinawi Hamed Al 
Published: June 2023

Abstract
Background:
Imposter syndrome (IS) is a psychological pattern where successful individuals fail to recognize their achievements and often attribute them to good luck and timing. The presence of Imposter syndrome among medical students is gaining more attention, with studies reporting a strong association with burnout phenomenon, anxiety, and depression.
Objectives:
This study sought to determine the prevalence of IS among Omani medical students and classify the levels of severity among the sample.
Methodology:
This was a cross-sectional, observational study conducted at a public university in Muscat, Oman. The Clance Impostor Phenomenon Scale (CIPS), a validated publicly available questionnaire, was used to determine whether students exhibited impostorism and, if so, its severity. Students who scored 63 or higher in the CIPS were classified as impostors.
Results:
A total of 276 students participated (M 34%, F 66%), of which 144 (52.2%) were found to have Impostor Syndrome with 12.7% exhibiting severe impostorism.
Conclusion:
The results show that IS present in significant frequencies among medical students; further studies are needed to address this problem.
The use of stem cells and 3D bioprinting of pancreatic islets in the treatment of type 1 diabetes mellitus: history and perspectives
Authors: Bulgakova S. V. , Dolgikh Y. A. , Treneva E. V. , Sharonova L. A. , Kosareva O. V. , Kurmaev D. P. 
Published: June 2023

Type 1 diabetes mellitus (DM) is a disease characterized by hyperglycemia due to an absolute insulin deficiency caused by autoimmune destruction of insulin-producing β-cells. The disease occurs at a young age, is characterized by a labile course, a tendency to ketoacidosis and leads to the development of vascular complications that contribute to disability and early death of patients. To date, insulin therapy is the only treatment for type 1 diabetes. However, this method of treatment is not perfect. Patients need to adjust their diet, conduct frequent glycemic control and multiple injections of insulin. In this regard, the search for more effective methods of treating type 1 diabetes continues. The transformation of stem cells as a potential source of αand β-cells and their transplantation to the patient seems promising. However, it is not enough to simply obtain α- and β-cells from any stem cell. An important role is played by the interaction between the cells of the pancreatic islets. Currently, attempts are being made to develop functional in vitro models of pancreatic islets in which the cellular microenvironment would be completely preserved. The possibility of culturing and monitoring cells in a permeable three-dimensional microenvironment has been demonstrated. Combining different types of cells with each other in biologically suitable protein hydrogels allows the formation of spatial tissue systems. Cell microvascularization is also important, which is critical for adequate glucose homeostasis. 3D bioprinting can help ensure proper cell distribution in the scaffold and help reduce hypoxia through vascularization. 3D bioprinting technology will solve the problems of creating a natural environment for pancreatic islets with extracellular matrix and vasculature, since this technology will help to create organs in fully controlled conditions in vitro. However, this technology is still developing and further research is required in this direction.