bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using optically pumped magnetometers to replicate task-related responses in next generation magnetoencephalography
Authors: Safar Kristina , Vandewouw Marlee M. , Sato Julie , Devasagayam Jasen , Hill Ryan M. , Rea Molly , Brookes Matthew J. , Taylor Margot J. 
Published: March 2024

AbstractOptically pumped magnetometers (OPMs) offer a new wearable means to measure magnetoencephalography (MEG) signals, with many advantages compared to conventional systems. However, OPMs are an emerging technology, thus characterizing and replicating MEG recordings is essential. Using OPM-MEG and SQUID-MEG, this study investigated evoked responses, oscillatory power, and functional connectivity during emotion processing in 20 adults, to establish replicability across the two technologies. Five participants with dental fixtures were included to assess the validity of OPM-MEG recordings in those with irremovable metal. Replicable task-related evoked responses were observed in both modalities. Similar patterns of oscillatory power to faces were observed in both systems. Increased connectivity was found in SQUID-versus OPM-MEG in an occipital and parietal anchored network. Notably, high quality OPM-MEG data were retained in participants with metallic fixtures, from whom no useable data were collected using conventional MEG.
Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks
Authors: Lee Pei-Hsi , Liao Shih-Lung 
Published: March 2024

AbstractIn recent years, deep learning methods have been widely used in combination with control charts to improve the monitoring efficiency of complete data. However, due to time and cost constraints, data obtained from reliability life tests are often type-I right censored. Traditional control charts become inefficient for monitoring this type of data. Thus, researchers have proposed various control charts with conditional expected values (CEV) or conditional median (CM) to improve efficiency for right-censored data under normal and non-normal conditions. This study combines the exponentially weighted moving average (EWMA) CEV and CM chart with deep learning methods to increase efficiency for gamma type-I right-censored data. A statistical simulation and a real-world case are presented to assess the proposed method, which outperforms the traditional EWMA charts with CEV and CM in various skewness coefficient values and censoring rates for gamma type-I right-censored data.
Generating 3D images of material microstructures from a single 2D image: a denoising diffusion approach
Authors: Phan Johan , Sarmad Muhammad , Ruspini Leonardo , Kiss Gabriel , Lindseth Frank 
Published: March 2024

AbstractThree-dimensional (3D) images provide a comprehensive view of material microstructures, enabling numerical simulations unachievable with two-dimensional (2D) imaging alone. However, obtaining these 3D images can be costly and constrained by resolution limitations. We introduce a novel method capable of generating large-scale 3D images of material microstructures, such as metal or rock, from a single 2D image. Our approach circumvents the need for 3D image data while offering a cost-effective, high-resolution alternative to existing imaging techniques. Our method combines a denoising diffusion probabilistic model with a generative adversarial network framework. To compensate for the lack of 3D training data, we implement chain sampling, a technique that utilizes the 3D intermediate outputs obtained by reversing the diffusion process. During the training phase, these intermediate outputs are guided by a 2D discriminator. This technique facilitates our method’s ability to gradually generate 3D images that accurately capture the geometric properties and statistical characteristics of the original 2D input. This study features a comparative analysis of the 3D images generated by our method, SliceGAN (the current state-of-the-art method), and actual 3D micro-CT images, spanning a diverse set of rock and metal types. The results shown an improvement of up to three times in the Frechet inception distance score, a typical metric for evaluating the performance of image generative models, and enhanced accuracy in derived properties compared to SliceGAN. The potential of our method to produce high-resolution and statistically representative 3D images paves the way for new applications in material characterization and analysis domains.
Global-best brain storm optimization algorithm based on chaotic difference step and opposition-based learning
Authors: Zhao Yanchi , Cheng Jianhua , Cai Jing , Qi Bing 
Published: March 2024

AbstractRecently, the following global-best strategy and discussion mechanism have been prevailing to solve the slow convergence and the low optimization accuracy in the brain storm optimization (BSO) algorithm. However, the traditional BSO algorithm also suffers from the problem that it is easy to fall into local optimum. Therefore, this work innovatively designed the chaotic difference step strategy. This strategy introduced four commonly used chaotic maps and difference step to expand the population search space to improve the situation. Moreover, opposition-based learning thought was innovatively adopted into the BSO algorithm. The thought aims to generate the opposition-based population, increase the search density, and make the algorithm out of the local optimum as soon as possible. In summary, this work proposed a global-best brain storm optimization algorithm based on the chaotic difference step and opposition-based learning (COGBSO). According to the CEC2013 benchmark test suit, 15 typical benchmark functions were selected, and multiple sets of simulation experiments were conducted on MATLAB. The COGBSO algorithm was also compared to recent competitive algorithms based on the complete CEC2018 benchmark test suit. The results demonstrate that the COGBSO outperforms BSO and other improved algorithms in solving complex optimization problems.
DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system
Authors: Saboor Abdus , Li Jian Ping , Ul Haq Amin , Shehzad Umer , Khan Shakir , Aotaibi Reemiah Muneer , Alajlan Saad Abdullah 
Published: March 2024

AbstractThis research explores the use of gated recurrent units (GRUs) for automated brain tumor detection using MRI data. The GRU model captures sequential patterns and considers spatial information within individual MRI images and the temporal evolution of lesion characteristics. The proposed approach improves the accuracy of tumor detection using MRI images. The model’s performance is benchmarked against conventional CNNs and other recurrent architectures. The research addresses interpretability concerns by employing attention mechanisms that highlight salient features contributing to the model’s decisions. The proposed model attention-gated recurrent units (A-GRU) results show promising results, indicating that the proposed model surpasses the state-of-the-art models in terms of accuracy and obtained 99.32% accuracy. Due to the high predictive capability of the proposed model, we recommend it for the effective diagnosis of Brain tumors in the E-healthcare system.
Effective combination of cold physical plasma and chemotherapy against Ewing sarcoma cells in vitro
Authors: Nitsch Andreas , Qarqash Sara , Römer Sarah , Schoon Janosch , Singer Debora , Bekeschus Sander , Ekkernkamp Axel , Wassilew Georgi I. , Tzvetkov Mladen V. , Haralambiev Lyubomir 
Published: March 2024

AbstractEwing's sarcoma (ES) is the second most common bone tumor in children and adolescents and is highly malignant. Although the new chemotherapy has significantly improved the survival rate for ES from about 10 to 75%, the survival rate for metastatic tumors remains around 30%. This treatment is often associated with various side effects that contribute to the suffering of the patients. Cold physical plasma (CPP), whether used alone or in combination with current chemotherapy, is considered a promising adjunctive tool in cancer treatment. This study aims to investigate the synergistic effects of CPP in combination with cytostatic chemotherapeutic agents that are not part of current ES therapy. Two different ES cell lines, RD-ES and A673, were treated with the determined IC20 concentrations of the chemotherapeutic agents cisplatin and methotrexate (MTX) in combination with CPP. The effects on population doubling, cell viability, and apoptotic processes within these cell lines were assessed. This combination therapy has led to a reduction of population doubling and cell viability, as well as an increase in apoptotic activity in cells compared to CPP monotherapy. The results of this study provide evidence that combining CPP with non-common chemotherapy drugs such as MTX and CIS in the treatment of ES enhances the anticancer effects of these drugs. These findings open up new possibilities for the effective use of these drugs against ES.
Targeting glucosylceramide synthase induces antiproliferative and proapoptotic effects in osimertinib-resistant NSCLC cell models
Authors: La Monica Silvia , Vacondio Federica , Eltayeb Kamal , Lodola Alessio , Volta Francesco , Viglioli Martina , Ferlenghi Francesca , Galvani Francesca , Galetti Maricla , Bonelli Mara , Fumarola Claudia , Cavazzoni Andrea , Flammini Lisa , Verzè Michela , Minari Roberta , Petronini Pier Giorgio , Tiseo Marcello , Mor Marco , Alfieri Roberta 
Published: March 2024

AbstractThe EGFR tyrosine kinase inhibitor osimertinib has been approved for the first-line treatment of EGFR-mutated Non-Small Cell Lung Cancer (NSCLC) patients. Despite its efficacy, patients develop resistance. Mechanisms of resistance are heterogeneous and not fully understood, and their characterization is essential to find new strategies to overcome resistance. Ceramides are well-known regulators of apoptosis and are converted into glucosylceramides (GlcCer) by glucosylceramide synthase (GCS). A higher content of GlcCers was observed in lung pleural effusions from NSCLC patients and their role in osimertinib-resistance has not been documented. The aim of this study was to determine the therapeutic potential of inhibiting GCS in NSCLC EGFR-mutant models resistant to osimertinib in vitro and in vivo. Lipidomic analysis showed a significant increase in the intracellular levels of glycosylceramides, including GlcCers in osimertinib resistant clones compared to sensitive cells. In resistant cells, the GCS inhibitor PDMP caused cell cycle arrest, inhibition of 2D and 3D cell proliferation, colony formation and migration capability, and apoptosis induction. The intratumoral injection of PDMP completely suppressed the growth of OR xenograft models. This study demonstrated that dysregulation of ceramide metabolism is involved in osimertinib-resistance and targeting GCS may be a promising therapeutic strategy for patients progressed to osimertinib.
Water-richness evaluation method and application of clastic rock aquifer in mining seam roof
Authors: Qiu Mei , Shao Zhendong , Zhang Weiqiang , Zheng Yan , Yin Xinyu , Gai Guichao , Han Zhaodi , Zhao Jianfei 
Published: March 2024

AbstractClastic rock aquifer of the coal seam roof often constitutes the direct water-filling aquifer of the coal seam and its water-richness is closely related to the risk of roof water inrush. Therefore, the evaluation of the water-richness of clastic rock aquifer is the basic work of coal seam roof water disaster prevention. This article took the 4th coal seam in Huafeng mine field as an example. It combined the empirical formula method and generalized regression neural network (GRNN) to calculate the development height of water-conducting fracture zone, determined the vertical spatial range of water-richness evaluation. Depth of the sandstone floor, brittle rock ratio, lithological structure index, fault strength index, and fault intersections and endpoints density were selected as the main controlling factors. A combination weighting method based on the analytic hierarchy process (AHP), rough set theory (RS), and minimum deviation method (MD) was proposed to determine the weight of the main controlling factors. Introduced the theory of unascertained measures and confidence recognition criteria to construct an evaluation model for the water-richness of clastic rock aquifers, the study area was divided into three zones: relatively weak water-richness zones, medium water-richness zones, and relatively strong water-richness zones. By comparing with the water inrush points and the water inflow of workfaces, the evaluation model's water yield zoning was consistent with the actual situation, and the prediction effect was good. This provided a new idea for the evaluation of the water-richness of the clastic rock aquifer on the roof of the mining coal seam.
Generation of OAM beams with quasi omnidirectional pattern using simple slotted waveguide array
Authors: Dhanade Yuvraj B. , Patnaik Amalendu 
Published: March 2024

AbstractTheory and design aspects of the Slotted Waveguide Antenna (SWA) array for the simultaneous generation of Orbital Angular Momentum (OAM) modes are proposed in this paper. The proposed method is a novel approach to OAM generation that uses a simple feeding scheme and has better performance, especially in terms of mode purity. Due to its all-metallic structure, the high-power OAM beams with first-order modes ($$ +1 \, \& \,-1$$

+
1

&

-
1

) can be generated simultaneously along the propagation axis but in opposite directions, which improves the coverage and makes the proposed SWA be used in applications like radar target detection by mounting it on a mechanically rotating platform. The proposed theory is verified by the electromagnetic (EM) simulations, followed by the experimental verification done on the laboratory prototype. The near-field characteristics of the proposed antenna confirm the generation of first-order OAM beams with high mode purity. The far-field characteristics show the high gain and quasi-omnidirectional radiation pattern. Transmission of high-power OAM beams with good far-field characteristics makes the proposed antenna mitigate the issues of long-range OAM communication to a certain extent. However, the proposed antenna can be served well in strategic applications where the mostly high-power microwave is involved.
Enhancing scanning electron microscopy imaging quality of weakly conductive samples through unsupervised learning
Authors: Gao Xin , Huang Tao , Tang Ping , Di Jianglei , Zhong Liyun , Zhang Weina 
Published: March 2024

AbstractScanning electron microscopy (SEM) is a crucial tool for analyzing submicron-scale structures. However, the attainment of high-quality SEM images is contingent upon the high conductivity of the material due to constraints imposed by its imaging principles. For weakly conductive materials or structures induced by intrinsic properties or organic doping, the SEM imaging quality is significantly compromised, thereby impeding the accuracy of subsequent structure-related analyses. Moreover, the unavailability of paired high–low quality images in this context renders the supervised-based image processing methods ineffective in addressing this challenge. Here, an unsupervised method based on Cycle-consistent Generative Adversarial Network (CycleGAN) was proposed to enhance the quality of SEM images for weakly conductive samples. The unsupervised model can perform end-to-end learning using unpaired blurred and clear SEM images from weakly and well-conductive samples, respectively. To address the requirements of material structure analysis, an edge loss function was further introduced to recover finer details in the network-generated images. Various quantitative evaluations substantiate the efficacy of the proposed method in SEM image quality improvement with better performance than the traditional methods. Our framework broadens the application of artificial intelligence in materials analysis, holding significant implications in fields such as materials science and image restoration.
Psychological resilience mediates the relationship between diabetes distress and depression among persons with diabetes in a multi-group analysis
Authors: Wojujutari Ajele Kenni , Idemudia Erhabor Sunday , Ugwu Lawrence Ejike 
Published: March 2024

AbstractThe aim to examine the link between diabetes distress and depression in individuals with diabetes, assess the mediating role of psychological resilience in this relationship, and analyses if these relationships differ between Type 1 and Type 2 diabetes. The study utilized a cross-sectional design. A total of 181 (age 33–72 years, mean = 54.76 years, and SD = 9.05 years) individuals diagnosed with diabetes who were receiving treatment from State Specialist Hospitals in Okitipupa were selected for the study using the convenient sampling technique. The data were analysed using Pearson Multiple correlation and multi-group mediation analysis. The analyses were carried out with Smartpls and IBM/SPSS Version 28.0. The results revealed a significant positive correlation between diabetes distress and depression (r = .80, p < .05), suggesting that higher levels of diabetes distress were associated with increased depression scores. Additionally, psychological resilience partially mediated the relationship between diabetes distress and depression (b = − 0.10, p < .05), signifying that resilience played a crucial role in mitigating the impact of diabetes distress on depression. Furthermore, a multi-group analysis was conducted to explore potential differences between Type 1 and Type 2 diabetes subgroups. The relationship between diabetes distress and depression was found to be more pronounced in the Type 1 subgroup (difference = 0.345, p < .05), while the relationship between psychological resilience and depression was negatively stronger in the Type 2 subgroup (difference = − 0.404, p < .05) compared to the Type 1 subgroup. There is an intricate linkage between diabetes distress, resilience, and depression, emphasizing the differential roles of resilience in Type 1 and Type 2 diabetes. The insights gleaned from this study underscore the importance of considering the type of diabetes when designing interventions and support mechanisms for individuals with diabetes who are also suffering from depression. By advancing our understanding of these dynamics, we can strive for more effective and personalized approaches to improve the overall well-being of those living with diabetes.
Geophysical survey based on hybrid gravimetry using relative measurements and an atomic gravimeter as an absolute reference
Authors: Shettell Nathan , Lee Kai Sheng , Oon Fong En , Maksimova Elizaveta , Hufnagel Christoph , Wei Shengji , Dumke Rainer 
Published: March 2024

AbstractGravimetry is a versatile metrological approach in geophysics to accurately map subterranean mass and density anomalies. There is a broad diversification regarding the working principle of gravimeters, wherein atomic gravimeters are one of the most technologically progressive class of gravimeters which can monitor gravity at an absolute scale with a high-repetition without exhibiting drift. Despite the apparent utility for geophysical surveys, atomic gravimeters are (currently) laboratory-bound devices due to the vexatious task of transportation. Here, we demonstrated the utility of an atomic gravimeter on-site during a gravity survey, where the issue of immobility was circumvented with a relative spring gravimeter. The atomic gravimeter served as a means to map the relative data from the spring gravimeter to an absolute measurement with an effective precision of 7.7$${\upmu }$$
μ
Gal. Absolute measurements provide a robust and feasible method to define and control gravity data taken at different sites, or a later date, which is critical to analyze underground geological units, in particular when it is combined with other geophysical approaches.
A computational strategy for estimation of mean using optimal imputation in presence of missing observation
Authors: Yadav Subhash Kumar , Vishwakarma Gajendra K. , Sharma Dinesh K. 
Published: March 2024

AbstractIn this study, we suggest an optimal imputation strategy for the elevated estimation of the population mean of the primary variable utilizing the known auxiliary parameters for the missing observations. Under this strategy, we suggest a new modified Searls type estimator, and we study its sampling properties, mainly bias and mean squared error (MSE), for an approximation of order one. The introduced estimator is compared theoretically with the estimators of population mean in competition under the imputation method. The efficiency conditions for the introduced estimator to be more efficient than the estimators in the competition are derived. To be sure about the efficiencies, these efficiency conditions are verified through the three natural populations. We have also conducted a simulation study and generated an artificial population with the same parameters as a natural population. The estimator with minimum MSE and the highest Percentage Relative Efficiency (PRE) is recommended for practical use in different areas of applications.
Multi-omics pan-cancer analyses identify MCM4 as a promising prognostic and diagnostic biomarker
Authors: Li Yanxing , Gao Wentao , Yang Zhen , Hu Zhenwei , Li Jianjun 
Published: March 2024

AbstractMinichromosome Maintenance Complex Component 4 (MCM4) is a vital component of the mini-chromosome maintenance complex family, crucial for initiating the replication of eukaryotic genomes. Recently, there has been a growing interest in investigating the significance of MCM4 in different types of cancer. Despite the existing research on this topic, a comprehensive analysis of MCM4 across various cancer types has been lacking. This study aims to bridge this knowledge gap by presenting a thorough pan-cancer analysis of MCM4, shedding light on its functional implications and potential clinical applications. The study utilized multi-omics samples from various databases. Bioinformatic tools were employed to explore the expression profiles, genetic alterations, phosphorylation states, immune cell infiltration patterns, immune subtypes, functional enrichment, disease prognosis, as well as the diagnostic potential of MCM4 and its responsiveness to drugs in a range of cancers. Our research demonstrates that MCM4 is closely associated with the oncogenesis, prognosis and diagnosis of various tumors and proposes that MCM4 may function as a potential biomarker in pan-cancer, providing a deeper understanding of its potential role in cancer development and treatment.
TOR regulates variability of protein synthesis rates
Authors: Basier Clovis , Nurse Paul 
Published: March 2024

AbstractCellular processes are subject to inherent variability, but the extent to which cells can regulate this variability has received little investigation. Here, we explore the characteristics of the rate of cellular protein synthesis in single cells of the eukaryote fission yeast. Strikingly, this rate is highly variable despite protein synthesis being dependent on hundreds of reactions which might be expected to average out at the overall cellular level. The rate is variable over short time scales, and exhibits homoeostatic behaviour at the population level. Cells can regulate the level of variability through processes involving the TOR pathway, suggesting there is an optimal level of variability conferring a selective advantage. While this could be an example of bet-hedging, but we propose an alternative explanation: regulated ‘loose’ control of complex processes of overall cellular metabolism such as protein synthesis, may lead to this variability. This could ensure cells are fluid in control and agile in response to changing conditions, and may constitute a novel organisational principle of complex metabolic cellular systems.