bluuebunny/crossref_metadata_2025 huggingface dataset:
A radial basis neural network using the optimization of Bayesian regularization of the malaria disease model
Authors: sabir zulqurnain , Ismail Tala , Sleem Hussein
Published: September 2024

The current research investigations are used to provide a stochastic computational radial basis function neural network (RBFNN), which is one of the kinds of the artificial neural network that applies radial basis as an activation function. The optimization is performed through the Bayesian regularization (BR) and the proposed solver is named as RBFNN-BR in order to solve the malaria disease model (MDM). The mathematical form of the MDM is categorized into host and vector populations that are based on pesticides and medication. A dataset is constructed based on the explicit Runge-Kutta scheme, which is used to reduce the mean square error (MSE) by selecting the data for testing 0.15, validation 0.12 and training 0.72 for the numerical solutions of the mathematical MDM. The solution of the MDM is presented by taking twenty numbers of neurons, RBF as an activation function in the hidden layers, RB for the optimization, and data selection based different values. The correctness of the RBFNN solver is observed by using the comparison with the published literature results accurateness and the reference solutions for solving the MDM. Moreover, the negligible absolute error performances also approve the precision of the scheme. The competency of the proposed solver is authenticated by using different performances in the sense of MSE, regression and error histogram.
An optimal solution for classifying Arabic fake news via efficient deep learning algorithms
Authors: alhussayni khaldoon H. , Dawood Amina Atiya , Hanon Wial
Published: September 2024

Misinformation becomes publicly available on social media and websites. Some journalists write fake news in different languages to target different societies. This study aims to develop an optimized algorithm for detecting and classifying fake news in the Arabic language. The proposed work investigates different deep learning techniques to identify effective and efficient algorithms that can handle fake news problems. The utilized techniques involve individual algorithms such as convolutional neural networks (CNNs), gate recurrent neural networks (GRUs), and bidirectional long short-term memory (BiLSTM). This work also considers hybrid algorithms such as CNNs with BiLSTM (CNN-BiLSTM) and CNNs with BiGRU (CNN-BiGRU). This research focused on Arabic news, motivated by the limited works interested in the Arabic language because of its linguistic difficulty. English has been widely investigated due to its simplicity. The proposed model used the Arabic Fake News Dataset (AFND) as an input channel for training and testing purposes. The model was trained to discriminate fake from real news. It achieved premium results with accuracies of 88.63%, 89.7%, 91.73%, and 92% for BiLSTM, BiGRU, CNN-BiLSTM, and CNN-BiGRU, respectively. The significant value achieved by CNN-BiGRU algorithms refers to the power of CNNs in feature extraction, the efficiency of the GRU structure and its simplicity in mitigating the vanishing gradient problem. This research aims to enhance model accuracy for unseen data in Arabic fake news detection.
The Integrated Stress Response Engages a Cell-Autonomous, Ligand-independent, DR5-driven Apoptosis Switch
Authors: Acosta-Alvear Diego , Zappa Francesca , Muniozguren Nerea Lopez , Conrad Julia
Published: September 2024

The integrated stress response (ISR) is a central signaling network that leverages the cell’s biosynthetic capacity against different stresses to restore homeostasis. However, when homeostasis is unattainable, the ISR switches to drive cell death and eliminate irreparably damaged cells. During persistent endoplasmic reticulum (ER) stress, prolonged activity of the ISR kinase PERK induces apoptosis downstream of death receptor 5 (DR5). ER stress provides activating signals that engage the ectodomain (ED) of DR5 to drive its unconventional activation in the Golgi apparatus. Here, using chemical genetics to uncouple stress sensing from ISR activation, we found that DR5 signaling from the Golgi apparatus is integral to the ISR and not specific to ER stress. Furthermore, we show that DR5 activation can be driven solely by increased expression and does not require its ED. These findings indicate that a general ISR kill switch eliminates irreversibly injured cells.
Risks Associated with the Dissemination of Misleading and Deceptive Information on Social Networking Sites and the Requirement for Prevention Strategies
Authors: Kasi BASKAR , Govindaraju Sakthi , Kamalakkannan Kasi , Ramalingam saravanan
Published: September 2024

In today's digital era, the majority of social networking site users prefer communication through oral or video means. Unfortunately, this trend has led to the spread of inaccurate information for two primary reasons. Firstly, individuals knowingly or unknowingly make false claims and influence others to do the same, creating an illusion of accuracy and leading to poor decision-making. Secondly, information related to minors under the age of 13 is being disseminated through websites and social networking platforms. This poses a challenge for young individuals to make informed choices as many people tend to believe and act upon such information. While social networking sites offer various benefits, they also come with inherent risks. To address these issues, we propose a preventive measure using a pre-translated framework system called Deep Support Vector Machine (DSVM). This innovative approach combines a Support Vector Machine (SVM) with a Deep Neural Network (DNN) to effectively reduce these hazards. We emphasize the importance of promoting positive and accurate information about kids, while strictly disallowing the spread of any false or misleading content about minors. In our paper, we thoroughly contrast and evaluate the effectiveness of our suggested DSVM method with the well-established preventive techniques of SVM and LSTM. By doing so, we aim to devise a more robust and reliable system to ensure a safer and more responsible use of social networking sites, particularly when it comes to sharing information about minors.
WITHDRAWN: Microaneurysm Detection in fundus images using Convolutional with Lite Transformer Network
Authors: A Jayachandran
Published: September 2024

Diabetic Retinopathy (DR) is a complication of diabetes that can cause vision impairment and may lead to permanent blindness in working-age people. The motivation behind this is the significant increase in the number of diabetic patients compared to the limited number of ophthalmologists available. Hence, there is a crucial need for an automated screening tool to promptly detect and diagnose DR in patients. Microaneurysms (MAs) are the initial stage of DR and are crucial for computerized diagnosis. However, automatically detecting microaneurysms in fundus images is challenging due to complex backgrounds and varying illumination conditions. In recent years, convolutional neural networks (CNNs) have experienced significant advancements within the domain of medical imaging. Notably, their efficacy has been pronounced through the application of the u-shaped architecture, which has exhibited exceptional effectiveness in diverse image segmentation tasks. However, these advantages notwithstanding, convolutional operations reveal limitations in capturing extensive global and long-range semantic interactions. Therefore, this manuscript introduces the CRUnet, a converter resembling the architecture of Unet, specifically designed for the task of MAs detection in retinal images. To reduce the computational cost, a lightweight transformer block is designed as the bottleneck layer, this model processes the feature maps derived from the CNN as input sequences, facilitating the extraction of the global context. Simultaneously, the global context information is integrated with the CNN feature map to attain precise localization of features, and a refined block is designed to adjust the deep features. Our assertion is that the Transformer architecture can serve as a robust encoder for medical image segmentation tasks. Its integration with convolutional networks holds the potential to enhance segmentation performance, particularly by facilitating the restoration of local spatial information. The proposed model achieving an average accuracy of 99.13%. In this case, the respective accuracy for the three different data sets, DRIVE, ROC, and e-ophtha, is 99.67%, 98.16%, and 99.76%.
Ex Vivo Machine Perfusion as a Platform for Lentiviral Gene Delivery in Rat Livers
Authors: Uygun Korkut , von Reiterdank Irina Filz , Mojoudi Mohammadreza , Bento Raphaela , Taggart McLean , Dinicu Antonia , Wojtkiewicz Gregory , Coert J. , Molen Aebele Mink van der , Weissleder Ralph , Parekkadan Biju
Published: September 2024

Developing new strategies for local monitoring and delivery of immunosuppression is critical to making allografts safer and more accessible. Ex vivo genetic modification of grafts using machine perfusion presents a promising approach to improve graft function and modulate immune responses while minimizing risks of off-target effects and systemic immunogenicity in vivo. This proof-of-concept study demonstrates the feasibility of using normothermic machine perfusion (NMP) to mimic in vitro conditions for effective gene delivery. In this study, lentiviral vectors carrying biosensor constructs with Gaussia Luciferase (GLuc) were introduced to rodent livers during a 72-hour perfusion period, with a targeted delivery of 3 x 107 infection units (IU). Following the initial 24-hour exposure required for viral transduction, an additional 48 hours was necessary to observe gene expression, analogous to in vitro benchmarks. The perfused livers displayed significantly increased luminescence compared to controls, illustrating successful genetic modification. These findings validate the ex vivo use of lentiviral particles in a rodent liver model and lay the groundwork for a broad range of applications through genetic manipulation of organ systems. Future studies will focus on refining this technology to enhance precision in gene expression and explore its implications for clinical transplantation.
Fine-mapping of the brittle rachis locus Qbr.sau-2DL in Tibetan semi-wild wheat (Triticum aestivum ssp. tibetanum Shao)
Authors: Chen Qing , Zhao Kan , Gao Ting , Li Yang , Li Qingcheng , Zhou Linlin , Zhang Ziyi , Peng Shijing , Tang Jie , Zhang Mi , Zhu Jing , Liu Caihong , Lei Lu , Guo Zhenru , Kong Li , Jiang Yunfeng , Chen Guoyue , Ning Shunzong , Jiang Qiantao , Zhang Yazhou , Xu Qiang , Wei Yuming , Ma Jian , Zheng Youliang , (Qi) 鹏飞 (Peng-Fei) 祁
Published: September 2024

The brittle rachis is the most important trait in wheat domestication and de-domestication. Tibetan semi-wild wheat (Triticum aestivum ssp. tibetanum Shao) is a unique common wheat subspecies that originated through de-domestication. Qbr.sau-2DL on chromosome 2DL is one of the three main brittle rachis loci in the Tibetan semi-wild wheat accession “Q1028.” However, the gene responsible for the brittle rachis trait at the Qbr.sau-2DL locus remains unknown. In this study, “QZ110,” a monogenic line for Qbr.sau-2DL isolated from a recombinant inbred line population derived from hybridization between “Q1028” and the common wheat cultivar “ZM9023,” was crossed with the common wheat cultivar “SM482” to construct F2, F2:3, and F3 populations. Genetic analyses indicated that Qbr.sau-2DL was recessive. Using Bulked Segregant Exome Capture Sequencing data and Kompetitive Allele Specific PCR markers, Qbr.sau-2DL was located within an 836.4-kb interval containing 12 high-confidence predicted genes. One of them, TreasCS2D02G502900, was a homolog of OsLG1, which affects rice shattering. The transcript level of TreasCS2D02G502900 in the disarticulation layer was significantly higher in “QZ110” than in “SM482,” consistent with the transcript profile of OsLG1 in rice. Therefore, TreasCS2D02G502900 was identified as the candidate gene for Qbr.sau-2DL. Microscopic observations indicated that Qbr.sau-2DL reduces cell wall thickness and the lignin content in the disarticulation layer. These results provide a basis for clarifying the function and underlying molecular mechanisms of Qbr.sau-2DL in the brittle rachis trait.
Green Synthesis, Characterization, and Antimicrobial Activity of Silver Nanoparticles from Water-Soluble Fractions of Brazilian Kefir
Authors: Bernardes Lucas Matos Martins , Malta Serena Mares , Santos Ana Carolina Costa , Silva Rafael Alves da , Rodrigues Tamiris Sabrina , Silva Murillo Néia Thomaz da , Mendes-Silva Ana Paula , Ueira-Vieira Carlos
Published: September 2024

Background: Nanotechnology offers innovative approaches to combat drug-resistant diseases. Silver nanoparticles (AgNPs) have emerged as potent antimicrobial agents in vitro and in vivo. Green synthesis methods, which leverage the bioactive components of the water-soluble fractions of Brazilian kefir (whole water-soluble fraction and smaller than 10 kDa fraction), provide sustainable alternatives to conventional nanoparticles production. However, despite the documented therapeutic benefits of kefir, its potential in nanomedicine remains underexplored.
Results: The successful synthesis of silver nanoparticles using water-soluble fractions of kefir was confirmed by UV-Visible spectroscopy and Fourier-transform infrared analyses. The hydrodynamic radius of nanoparticles derived from the entire water-soluble fraction was 1300 nm, while those from the smaller than 10 kDa fraction displayed a radius of 400 nm. All synthesized AgNPs exhibited a zeta potential of -30 mV. The disk diffusion method demonstrated the antimicrobial efficacy of our AgNPs against a range of multidrug-resistant bacteria and Candida fungi (p<0.0001), with no observed toxicity on Drosophila melanogasteron a long-term treatment.
Conclusion:This study highlights the potential of these AgNPs as effective antimicrobial agents, particularly against drug-resistant pathogens. Future research is needed to evaluate the minimum inhibitory concentrations of our AgNPs and enhance specificity through conjugation with other compounds. Additionally, further investigations into electron microscopy analysis and various applications, such as disinfectant solutions, wound healing, and antibiotic production, will advance the utilization of kefir-derived AgNPs in healthcare.
Accelerating Charge Estimation in Molecular Dynamics Simulations Using Physics-Informed Neural Networks: Corrosion Applications
Authors: Zapiain David Montes de Oca , Venkatraman Aditya , Wilson Mark
Published: September 2024

Molecular Dynamics (MD) simulations can be utilized to develop a fundamental understanding of the effect that corrosion has on metallic materials in the presence of a salt brine. The use of reactive force fields in classical MD offers the capability to accurately model the formation and breakage of bonds within the aqueous medium and at the interface between the metal and the electrolyte. Additionally, MD protocols can facilitate dynamic partial charge equilibration. Nevertheless, despite their capabilities, MD simulations are unsuitable for modeling the long time scales characteristic of corrosive phenomena given their computationally intensive nature. Hence, there is a critical need for a protocol to decrease the computational expense associated with variable-charge molecular dynamics simulations. This work addresses this concern by developing reduced-order machine learning models to provide accurate and computationally efficient predictions of the charge density in corrosive environments. Specifically, Long Short Term Memory (LSTM) networks are used to assess and forecast the charge density evolution as a function of the atomic environment, represented using Smooth Overlap of Atomic Positions (SOAP) descriptors. Furthermore, this work utilizes a physics-informed loss function to enforce charge neutrality and the electronegativity equivalence. Lastly, even though the protocols are developed in the context of corrosion, they have been formulated in a phenomena agnostic manner, facilitating their utilization for a wide-class of variable-charge interatomic potentials and related applications.
Global analysis of countries’ exposure to climate driven simultaneous staple crop failures
Authors: Murgatroyd Anna , Verschuur Jasper , Davis Kyle , Tuninetti Marta , Hall Jim
Published: September 2024

The extent to which import-reliant countries may be vulnerable to co-occurring – and geographically distal – climate disruptions in crop production remains poorly understood. By analysing weather variability and covariation in staple food crop production across 181 countries, we identify the countries most exposed to simultaneous crop failures across their import partners and from their domestic production. Results indicate that over half of countries importing maize, wheat or rice are identified as at risk of their top three suppliers experiencing simultaneous crop failures.
Performance-based Seismic Assessments of Steel Fibre Reinforced Concrete Columns
Authors: Han Daguang , Ying Chunli , Chen Liyuan , Wu Xuguang , Lin Huizhong
Published: September 2024

This study presents a comprehensive investigation into the seismic performance of steel fibre reinforced concrete (SFRC) columns, aiming to assess their ductility and energy dissipation capacity. A large-scale database consisting of 116 individual tests of flexure-dominated SFRC and plain concrete (PC) columns subjected to cyclic lateral loading is utilized. The research begins by conducting parametric sensitivity analyses and empirical calibration of hysteretic parameters, crucial for accurately modelling the seismic response of reinforced concrete columns. These analyses are performed in accordance with a nonlinear static procedure. Additionally, fragility analyses of both reinforced PC and SFRC columns are conducted, based on their respective lateral drift ratios, with a specific drift ratio defined as the damage limit state. The findings of this work reveal intriguing insights. It is found that the axial load ratio has a comparable effect on the plastic rotations at the yield and ultimate points of PC and SFRC columns. However, it exhibits an entirely opposite effect on the rotation corresponding to the peak lateral load, especially for those with over 0.75% of fibres by volume. This discrepancy suggests that the current seismic design standards for PC columns are not directly applicable to SFRC columns. Furthermore, it is observed that the benefits of steel fibres in SFRC columns diminish at high lateral drift ratios or when flexural cracks are larger. Under these conditions, the effectiveness of fibres reduces, resulting in similar failure probabilities for both PC and SFRC columns.
Preparation and characterization of CaO-Al2O3-SiO2 glass-ceramic microspheres from coal-based solid waste
Authors: Liu Runze , Guo Yi , Li Haiqing , Chang Zhu , Lu Hao , Wang shuming
Published: September 2024

To alleviate the environmental pollution caused by the stockpile of coal-based solid waste, glass-ceramic microspheres prepared by a simple and low-cost wet process with coal-based solid waste, and the effect of different solid waste content on the network structure and properties were investigated by X-ray diffraction(XRD), Fourier infrared spectroscopy(FTIR), Scanning electron microscopy(SEM) and Universal electronic testing machine. The results show that, with the diversification of coal-based solid waste content, the amount of anorthite crystalline, the glass network structure, and the main structure of glass Q3 changed also. Accordingly, significant changes have occurred in the properties of the crushing strength. Optimizing the raw material ratio, a coal-based solid waste glass-ceramic microsphere with unique network structure is well developed and the crushing strength reaches the maximum value of 221.23N. The findings contribute significantly to improving the properties of matrix materials and the utilization of coal-based solid waste resources.
Beam steering for single antenna element using different optical lens
Authors: Mohamed Aya W. , Mahmoud Korany R. , Montaser Ahmed M.
Published: September 2024

This paper presents a beam steering technique for a single antenna element with optical lenses. The nanoantenna operates at a wavelength of 1550 nm. The plasmonic nanoantenna consists of silicon, silicon dioxide, and silver. It is fed with light from below. The light inside the plasmonic nanoantenna radiates vertically from bottom to top, and we obtain a gain of 8.46 dBi. When we put the lens on the antenna, the efficiency of the antenna increases according to the design of the antenna, the lens, and the material the lens is made of. In this article, we will choose two types of lenses, the hemispherical dielectric lens and the flat lens, which consists of 17 layers of different materials. The lenses are moved above the nanoantenna to obtain the best gain and efficiency. The lenses move right and left in the X direction, and thus we get the beam steering at different angles. In general, using any lens improves the efficiency of a nanoantenna, and different lenses provide different levels of gain and efficiency.
Truncated tau modulates neuron-glia signaling through CX3CL1-CX3CR1 and CD47-SIRP-α pathways.
Authors: Majerova Petra , Zilkova Monika , Bhide Katarina , Kovac Andrej
Published: September 2024

Tauopathies encompass a group of disorders characterized by abnormal accumulation of tau protein into neurofibbrilary pathology and neuroinflammation. Understanding how glial cells interact with neurons is important to unraveling the complex mechanisms driving the progression of tauopathies. In this study, we established a multi-component cellular model for tauopathies comprising neuronal cells inducible expressing pathological truncated tau, primary microglia, and astrocytes. We successfully identified optimal culture conditions, with 1% serum supplemented with B27 proving to be most effective in enhancing neuronal protein expression, tau levels, and cell viability. This condition supported improved neuronal differentiation and synaptic marker expression, reflecting a more robust neuronal phenotype compared to monocultures. The increase in pro-inflammatory cytokines production underscores the model's capability to replicate the inflammatory environment characteristic of neurodegenerative diseases. Importantly, we observed that truncated tau significantly modulates key signalling pathways, notably the CX3CL1-CX3CR1 and CD47-SIRP-α pathways. We found that pathological changes in tauopathies altered neuron-glia interactions, leading them towards a more quiescent glial state.The alterations in signaling were accompanied by changes in tau phosphorylation, with neuroinflammation exacerbating hyperphosphorylation of truncated tau, while minimally affecting endogenous tau. In conclusion, our study highlights that pathological truncated tau significantly affects the expression of membrane-anchored fractalkine. This differential modulation, coupled with changes in inflammatory conditions, suggests that early-stage tau pathology can influence neuroinflammatory responses and may still allow neuronal cells to engage in protective mechanisms.
Outpatient visits for women with postmenopausal bleeding; What do we tell our patients? A Qualitative study
Authors: Vroom Albertine J , Hanegem Nehalennia van , Werner Henrica MJ , Bongers Marlies Y , Geomini Peggy MAJ
Published: September 2024

Background
Postmenopausal vaginal bleeding is a common complaint and requires diagnostic work-up to rule out endometrial cancer. Blind endometrial aspiration sampling is commonly used as a diagnostic procedure in the outpatient setting, but can cause severe discomfort and pain. Previous studies show that women highly value clear explanations and appropriate management of expectations during gynecological outpatient procedures. We believe that patients’ satisfaction is significantly influenced by these expectations, and thus, we consider counseling by gynecologists to play a crucial role in a patient’s comfort experience. The aim of this qualitative study is to explore counseling practices with a special focus on managing expectations regarding outpatient endometrial aspiration.
Methods
We conducted a qualitative study to explore communication by gynecologists (in training) with patients during an initial visit for postmenopausal bleeding. We interviewed experienced gynecologists (in training) from multiple medical centers. Grounded theory was used for methodological orientation.
Results
This qualitative study showed that, during the visit, gynecologists do inform patients about the procedure and its anticipated experience, but often reassure patients as a method to alleviate anxiety. Most respondents used alternative terminology to describe the procedure instead of directly mentioning ‘pain’. We also observed that the amount of information women receive prior to their visit (e.g. by the general practitioner or using a leaflet) is limited. Most respondents state that every aspect of the consultation should be detailed in an information leaflet, including information about potentially painful procedures, although some respondents believe this information could provoke anxiety.
Conclusion
Gynecologists do inform patients during their visit about the procedure. It is debatable whether gynecologist should specify the degree and nature of pain patients may potentially experience during a procedure or if we should use alternative terminology to describe the procedure while acknowledging the discomfort without explicitly detailing the extent and nature of pain. At this moment little information is provided prior to the visit. Most respondents supported the idea of a standard information leaflet, sent prior to the procedure, as it may potentially be a helpful tool to reduce anxiety and therefore dissatisfaction.
Research on rigid-flexible coupling dynamics modeling for rack vehicle
Authors: Dong Chenglong , Deng Xingqiao , Liu Jialin , Zhang Zhendong , Gao Chang , Liu Weiping , Wang Shisong
Published: September 2024

In order to address the challenge of quantifying the impact of changes in slope and driving speed on individual components of rack vehicle, which is not feasible through experimental methods in practical engineering. Thus, based on the vehicle-track dynamics theory, this paper establishes a rigid-flexible coupling dynamics model of rack vehicle, which consists of vehicle submodel, track submodel, and gear-rack transmission model. In the model, the bogie frames are linked with the wheelsets through the primary suspensions and linked with the vehicle through the secondary suspensions, respectively, while three-dimensional spring-damper elements are used to represent the stiffness and damping characteristics of the primary and secondary suspension in three directions. Furthermore, regard the connection relationship between rack and sleeper as elastic damping, and the wheel-rail contact relationship is established on the nonlinear Hertz contact theory. Based on this model, the dynamic characteristics of rack vehicle are analyzed under various slopes and driving speeds. Results show that the slope has a substantial effect on the dynamic characteristics of each component of rack vehicle, whereas the driving speed primarily affects the root bending stress of rack and the vibration acceleration of gear. The research results is of great significance to the safety and stability of rack vehicle.
Optimizing NFDRS by Developing Custom Fuel Models for Wildfire Risk Assessment in Golestan Province, NE Iran
Authors: Khalaf Mhd.Wathek Alhaj , Jouibary Shaban Shataee , Jahdi Roghayeh , Jolly William Matthew
Published: September 2024

Wildfire risk assessment is essential for managing and mitigating the effects of wildfires, especially in regions frequently affected by severe fires This study focuses on optimizing the National Fire Danger Rating System (NFDRS) by creating 11 custom fuel models tailored to the climatic and vegetation conditions of Golestan province, NE Iran. These models were developed by sampling 125 homogeneous zones. The number of custom fuel models was determined by K-means analysis with relative squared Euclidean distances and the silhouette method using data collected from the homogeneous zones. The NFDRS outputs include the Spread Component (SC), Energy Release Component (ERC), and Burning Index (BI), with fire danger classes identified using the Static Fire Danger Index (SFDI). The models were evaluated using Overall Accuracy, Kappa, Sorensen, True Positive Rate (TPR), False Positive Rate (FPR), and Area under the Curve (AUC). The results revealed significant variability in fuel properties between vegetation types, affecting SC and ERC values. The custom fuel models outperformed the standard NFDRS models in accuracy, with Overall Accuracy (0.85), Kappa (0.78), and AUC (0.92) compared to the NFDRS's 0.70, 0.65, and 0.80, respectively. This study provides a clear illustration of the improved predictive performance of the custom models in the study area, which can help fire managers make more informed decisions, leading to better preparation and more efficient allocation of resources. these models can help mitigate wildfires' economic and environmental impacts. In addition, this research highlights the need for region-specific, customized fuel models to improve the accuracy of fire risk
Translation stalling induced mitochondrial entrapment of ribosomal quality control related proteins offers cancer cell vulnerability
Authors: ojha Rani , Tantray Ishaq , Banerjee Shouryarudra , Rimal Suman , Thirunavukkarasu Sandiya , Srikris Saripella , Chiu Wah , Mete Uttam , Sharma Aditya , Kakkar Nandita , Lu Bingwei
Published: September 2024

Ribosome-associated quality control (RQC) monitors ribosomes for aberrant translation. While the role of RQC in neurodegenerative disease is beginning to be appreciated, its involvement in cancer is understudied. Here, we show a positive correlation between RQC proteins ABCE1 and ZNF598 and high-grade muscle-invasive bladder cancer. Translational stalling by the inhibitor emetine (EME) leads to increased mitochondrial localization of RQC factors including ABCE1, ZNF598, and NEMF, which are continuously imported into mitochondria facilitated by increased mitochondrial membrane potential caused by EME. This reduces the availability of these factors in the cytosol, compromising the effectiveness of RQC in handling stalled ribosomes in the cytosol and those associated with the mitochondrial outer membrane (MOM). Imported RQC factors form aggregates inside the mitochondria in a process we term stalling-induced mitochondrial stress (SIMS). ABCE1 plays a crucial role in maintaining mitochondrial health during SIMS. Notably, cancer stem cells (CSCs) exhibit increased expression of ABCE1 and consequently are more resistant to EME-induced mitochondrial dysfunction. This points to a potential mechanism of drug resistance by CSCs. Our study highlights the significance of mitochondrial entrapment of RQC factors such as ABCE1 in determining the fate of cancer cells versus CSCs. Targeting ABCE1 or other RQC factors in translational inhibition cancer therapy may help overcome drug resistance.
Chronic Diseases and Catastrophic Health Expenditures in Elderly Chinese Households: A Cohort Study
Authors: Yao Xueying , Wang Dandan , Wang Qian , Zhang Tiantian
Published: September 2024

Background
In light of China's demographic transition towards an increasingly aged population coupled with a high prevalence of chronic conditions, there has been a pronounced augmentation of economic pressures on elderly households as well as on the national healthcare system. This research explores the determinants of catastrophic health expenditure (CHE) among Chinese households that include elderly individuals diagnosed with chronic diseases.
Methods
Utilizing data from the 2011 China Health and Retirement Longitudinal Study (CHARLS), conducted by the National School of Development at Peking University, this research focuses on households with at least one member aged 60 or older diagnosed with a chronic disease. CHE is measured by the proportion of out-of-pocket health payments in relation to total household expenditures, excluding food. The study constructed panel data from the follow-up surveys in 2015, 2018, and 2020, and used logistic regression to analyze the impact of various factors on CHE, including the presence of chronic diseases, age, education level, marital status, household registration (hukou), place of residence, and basic medical insurance coverage.
Result
The analysis indicates that chronic diseases significantly increase the likelihood of incurring CHE in elderly households, even if there is only one chronic illness. Key factors affecting CHE include age, education level, marital status, hukou, and residence. Notably, basic medical insurance did not significantly reduce the risk of CHE. Statistical data show that households with chronic diseases have a higher tendency toward CHE (P < 0.001, 95% CI: 1.36–1.64).
Conclusion
Chronic diseases have emerged as a dominant risk factor for catastrophic health expenditures (CHE) among elderly households in China, yet basic medical insurance has not adequately mitigated these expenditures.The findings highlight the urgent need for policy interventions to strengthen financial protections and enhance social health insurance schemes, thereby reducing the financial vulnerability of elderly households facing health shocks.
Conservation of vulnerable Cerrado birds in an imminently threatened single large reserve
Authors: Ferreira Everton Sousa , Schiavetti Alexandre , Santos Marcos Pérsio Dantas
Published: September 2024

The establishment of protected areas (PAs) is critical for mitigating anthropogenic impacts on biodiversity and maintaining ecosystem functionality. This scenario is more critical in extensively fragmented hotspots such as the Brazilian Cerrado. Therefore, addressing gaps in species composition, vulnerability, and abundance across different zoning interfaces, as well as potential threats to large reserves, are key steps in designing conservation actions. Here we evaluate abundance patterns and use of 17 vulnerable Cerrado birds across ecological zones, particularly Cerrado Stricto sensu and Riparian Forests in Chapada das Mesas National Park (NP). We demonstrate a positive scenario in terms of zoning effectiveness and bird conservation. Notably, the most vulnerable species in the NP are concentrated in areas of higher human occupation and tourist visitation, as well as along restricted-use territories. We also note that Cerrado stricto sensu subtypes constitute a single functional mosaic for endemic birds. Finally, we show that the NP may be imminently threatened by historical fires and potential future destruction of its adjacent ecosystems. Policies and actions focused on reducing deforestation and restoring habitats may represent the only way to prevent this single large reserve from becoming a major extinction site in the future.
Improving smartphone positioning accuracy by adapting measurement covariance with t-test on innovations
Authors: Raghuvanshi Anurag , Bisnath Sunil
Published: September 2024

Smartphone-based location-based services (LBS) require enhanced horizontal position accuracy with integrity. Due to the mass-market nature and compact design of smartphones, they utilize low-cost antennas and receivers, making them susceptible to multipath effects and other errors, which complicates the differentiation between reliable and unreliable measurements. To address these challenges, this paper explores the application of an adaptive Kalman filter technique to improve smartphone positioning accuracy. Adaptive Kalman filters adjust parameters such as process noise covariance or measurement noise covariance to modify the filter gain. When augmented with outlier detection mechanisms, the filter becomes more robust. This paper introduces a robust adaptive Kalman filter to enhance smartphone position accuracy. Outliers are detected using standardized innovations as a learning statistic, and a t-test is applied to these statistics to identify and mitigate outliers and adapt the measurement noise covariance accordingly. While previous research used empirical values for thresholds to adapt measurement noise covariance matrix, this study derives thresholds from t-tests, contingent on the normal distribution of learning statistics. By eliminating clock reset effects, innovations are transformed from bimodal to a normal distribution. Testing across multiple datasets demonstrates reductions of up to 42% in horizontal positioning root mean square error, with 50th, 68th, and 95th percentile statistics showing improvements of up to 53%, 41%, and 61%, respectively.
Tooth movement with clear aligners with different elastic modulus: a three-dimensional finite element analysis
Authors: Niu Qin , Lu Wenhsuan , Bai Rushui , Tong Xin , Yao Yang , Tian Lei , Tian Zhanfeng , Yu Tingting , Chen Si , Han Bing
Published: September 2024

Background
Compared with traditional fixed appliances, clear aligners (CAs) still lack the ability to accurately control the three-dimensional tooth movement. The present study was devoted to the comparison of treatment outcomes of CAs diaphragms with different elastic modulus and to explore the improvement of the tooth movement efficiency for CAs.
Methods
In present study, the three-dimensional finite element analysis was used to explore the tooth movement effect resulted from CAs with different elastic modulus in five orthodontic treatment conditions (translation, rotation, torque control, root control and extrusion) on a single tooth model.
Results
For translation, the diaphragm M04 with an elastic modulus of 2086.47 MPa performed relatively well. For rotation, torque control and extrusion, the tooth movement efficiency increased with the elevation of the elastic modulus. However, none of the diaphragms showed ideal results for root control. Additionally, under the same working condition, the greater the elastic modulus of the CA diaphragm, the greater the initial force on the tooth and the periodontal supporting tissue stress.
Conclusions
Diaphragms with different elastic modulus should be used to fabricate CAs to achieve better tooth movement in various tooth movement conditions. In the initial stages of tooth movement, CAs with smaller elastic modulus can be used to reduce the initial force on the teeth, and then CAs with larger elastic modulus can be applied to avoid adverse tooth movement, such as the “roller coaster” effect.
Cross-Layer Attentive Feature Upsampling for Low-latency Semantic Segmentation
Authors: Cheng Tianheng , Wang Xinggang , Liao Junchao , Liu Wenyu
Published: September 2024

Semantic segmentation is a fundamental problem in computer vision and it requires high-resolution feature maps for dense prediction. Current coordinate-guided low-resolution feature interpolation methods, e.g., bilinear interpolation, produce coarse high-resolution features which suffer from feature misalignment and insufficient context information. Moreover, enriching semantics to high-resolution features requires a high computation burden, so that it is challenging to meet the requirement of lowlatency inference. We propose a novel Guided Attentive Interpolation (GAI) method to adaptively interpolate fine-grained high-resolution features with semantic features to tackle these issues. Guided Attentive Interpolation determines both spatial and semantic relations of pixels from features of different resolutions and then leverages these relations to interpolate high-resolution features with rich semantics. GAI can be integrated with any deep convolutional network for efficient semantic segmentation. In experiments, the GAI-based semantic segmentation networks, i.e., GAIN, can achieve 78.8 mIoU with 22.3 FPS on Cityscapes and 80.6 mIoU with 64.5 on CamVid using an NVIDIA 1080Ti GPU, which are the new state-of-the-art results of low-latency semantic segmentation. Code and models will be available.
A Decentralized Authentication Scheme for Smart Factory based on Blockchain
Authors: Cao Zhong , Wen Xudong , Ai Shan , Shang Wenli , Huan Sha
Published: September 2024

The Industrial Internet of Things (IIoT) is recognized as one of the revolutionary technologies driving smart manufacturing and improving productivity. As manufacturing processes grow increasingly intricate, the entire manufacturing ecosystem encompasses multiple managed IoT domains. Within this highly interconnected environment, devices from diverse domains must collaborate, leading to considerable apprehensions about the security and privacy of device-to-device communications. Current authentication methods encounter several challenges. Traditional authentication schemes overly rely on trusted third parties, rendering them susceptible to external attacks or internal spoofing. This susceptibility gives rise to a range of security and privacy concerns. In response to these challenges, this paper aims to contribute to a more secure and efficient service scheme for smart factories by devising a blockchain-based distributed IoT architecture. The proposed scheme introduces a federated blockchain to establish trust among different domains, thereby enabling secure connections between devices in distinct domains. Through security analysis, it is proved that the proposed authentication scheme has integrity, mutual authentication, scalability, and resistance to four attacks. Furthermore, efficiency analysis experiments show that our scheme is feasible for smart factories, and as the number of peer nodes increases, the performance and efficiency of the blockchain network become better.
Health inequalities tackled through intersectoral collaboration: process issues and insights
Authors: Woodall James , Davies Paige , Parker Fallon
Published: September 2024

Collaborations, comprising organisations from a diverse range of sectors, are often perceived as being well-placed to tackle persistent health inequalities, but often fail due to a myriad of reasons including political, organizational and cultural barriers. This paper explores issues in relation to the formation of a multi-sector collaboration in one geographic area in the UK, working under the banner of the Health Determinants Research Collaboration (HDRC) – a programme which seeks to further understand health determinants and to improve health outcomes in communities. Through qualitative interviews with eleven of the constituents of the collaboration, the data demonstrated a clear and shared vision for the collaboration and a neat ‘dovetailing’ of skill-sets related to community brokerage; academic rigour; and statutory legitimacy. While the collaboration under focus here was in its infancy, cultural and practical tensions in ways of working; pace of working; and philosophy were predicted to emerge and required careful monitoring to ensure intended outcomes were not derailed.
Energy Landscape of Conformational Changes for a Single Unmodified Protein
Authors: Peters Matthew , Zhao Tianyu , George Sherin , Truong Viet Giang , Chormaic Síle Nic , Ying Cuifeng , Nome René A. , Gordon Reuven
Published: September 2024

Free-energy landscapes govern protein biophysics and natural conformational changes linked to function can be described by trajectories on these landscapes. Resolving the free energy landscape is obscured by ensemble averaging. A direct measurement of conformational energy changes in a single unmodified protein has been a challenge, although folding dynamics have been observed with techniques that modify the proteins with fluorescent labels and/or tethers. Here we use nanoaperture optical tweezers to resolve the energy landscape of a single unmodified protein. We quantify changes in the three-state conformation dynamics and the underlying free energy landscape of Bovine Serum Albumin (BSA). The impact of temperature on the energy barrier height and energy minima is quantified, allowing for the extraction of the entropy change between states of 0.4 kcal/(mol K). A Markov model with Kramers' theory transition rates is used to model the dynamics, showing good agreement with the observed state transitions. In addition to single BSA molecules, we also observe the formation of a BSA dimer in real time and uncover the energy landscape associated with hinge motion of this oligomer. This first look at the intrinsic energy landscape of proteins provides a transformative tool for protein biophysics and may be applied broadly, including to map out the energy landscape of particularly challenging intrinsically disordered proteins.
Statistical optimization of process variables for improved Polyethylene terephthalate plastic degradation by a rhizospheric bacterial consortium
Authors: Dhaka Vaishali , Singh Simranjeet , Rao Raman , Garg Shashank , Samuel Jastin , Khan Nadeem A , RAMAMURTHY PRAVEEN C , Singh Joginder
Published: September 2024

The current study focuses on the polyethylene terephthalate (PET) powder degradation potential of a rhizobacterial consortium screened from the rhizosphere of plants growing at plastic-polluted sites. The rhizobacterial isolates were screened, and the ability of PET powder degradation was studied for up to 18 days. For observing the efficiency of degradation, all three rhizobacterial strains with the highest percentage of degradation were combined to formulate the consortium. The Response Surface Methodology was used to optimize the process variables. The combinations demonstrating the highest weight reduction percentage for PET were selected for further degradation studies. The changes in the structure and surfaces that occurred after biodegradation on the plastic were observed through SEM and FTIR analysis. The obtained results showed the disappearance and elongation of the peak, signifying that the rhizobacterial consortium could modify the PET plastic. The weight reduction percentage of PET powder (300 µm) was 71.12% at optimized conditions (29.8ºC, 7.02 pH and 1g/L carbon source). The mathematical model developed through RSM is found to be significant (P < 0.05), and optimization and validation experiments were also well correlated for the process.
Rolling Bearing Fault Diagnosis Method Based on Multi-scale Convolutional Neural Network with Selective Kernel Attention Mechanism
Authors: Zhao Shuping , Liu Xilong , Liang Changyong , Lu Wenxing , Cui Yong
Published: September 2024

Rolling bearings, as core components of rotating machinery, require timely and accurate identification of fault types to ensure the safe and stable operation of mechanical equipment. However, existing fault diagnosis techniques for rolling bearings face challenges in terms of comprehensive feature extraction and noise resistance. To address these issues, this paper proposes a fault diagnosis method based on a multi-scale convolutional neural network (MSCNN) integrated with a selective kernel attention mechanism. First, a MSCNN is constructed as the feature learning framework. It employs dilated convolutional kernels to capture fault features across multiple frequencies and incorporates batch normalization layers to mitigate overfitting. Second, the selective kernel attention mechanism is enhanced with adaptive max pooling, enabling the dynamic adjustment of pooling region sizes to effectively reduce information redundancy. Experimental validation using two bearing datasets demonstrates that the proposed method outperforms existing techniques in terms of fault diagnosis accuracy, robustness, and generalization capability.