bluuebunny/crossref_metadata_2025 huggingface dataset:
Delving into Hearing Threshold of the Delay Gap of Initial Reflection in a Room by Using the Response of Cortical Brainwaves
Authors: Chen Chiung-Yao
Published: October 2023

In this study, the apparent variation ranges of acoustical parameters were investigated in a concert hall. The initial time delay gap (ITDG) was evaluated in terms of its just noticeable difference (JND) through two instruments, the cello and the trumpet. Even though the IDTG values were prolonged over the measurements and were not significantly varied, an ITDG range of 22–220 ms in increments of 91 steps was produced electro-acoustically in an anechoic chamber. The result of JNDc (Δgap/gap) was rated by 50% “different” judgement ranges for the cello and trumpet tracks, respectively. The effective duration of the autocorrection function (ACF) of the continuous brainwaves (CBWs) within the alpha (8–13 Hz) frequency range in the left hemisphere responding to 91-step ITDG increments revealed that the continuous ratios of τe_min ((τe_min_rear − τe_min_front)/τe_min_front) of CBWs were constantly on the trumpet. Furthermore, a homologous period of resonance between the subjective JNDc and τe values of the ACF of CBWs in the alpha range allowed us to conclude that the subjective JND of the ITDG in a room was related to the W_IACC value of the interaural cross-correlation function, which reflected the characteristics of source signals themselves and aroused the activities of the brain in the right hemisphere (p < 0.01). The dry sources of sound stimuli were first used to link the psychological preference and the neurophysiological activation of the room acoustics.
A Systematic Review and Future Development of Automotive Chassis Control Technology
Authors: Yang Aixi , Zang Yuhui , Xu Liuliu , Li Lanyou , Tan Dapeng
Published: October 2023

Automotive chassis control technology plays a crucial role in ensuring the stability, performance, and safety of vehicles. This paper reviews and discusses automotive steering/braking/driving/suspension systems from perspectives of system composition, the state of the art, and key technologies. Detailed analysis is conducted on critical techniques related to system fault tolerance, road feel feedback, brake force distribution strategy, electric motors, and motor controllers. The development and application of automotive chassis control technology is in line with the goals and objectives for the advancement of the automotive industry in terms of innovation, safety, and environmental sustainability.
The Effects of Toluene Mineralization under Denitrification Conditions on Carbonate Dissolution and Precipitation in Water: Mechanism and Model
Authors: Gan Shuang , Zhang Min , Zhou Yahong , Guo Caijuan , Yang Shuai , Xie Yan , Wang Xinzhe , Sun Lin , Ning Zhuo
Published: October 2023

The mineralization of benzene, toluene, ethylbenzene, and xylene (BTEX) into inorganic substances by microorganisms may affect the water–rock interaction. However, few studies have quantitatively analyzed the processes. To quantitatively reveal this mechanism, in this study, nitrate and toluene were taken as the typical electron acceptor and BTEX, respectively. Based on hydro-geochemical theory, the mechanism and mathematical model were established. In addition, the model was verified with a toluene mineralization experiment. The mechanism model demonstrated that H+ was the main factor in the dissolution or precipitation of CaCO3. The mathematical model derived the equations quantitatively between the amount of toluene mineralization, CaCO3, and some biogeochemical indicators, including temperature, microbial consumption, and other major ions in groundwater. According to the model, the amount of dissolved CaCO3 increased with the increasing proportion of completely reduced nitrate. For a complete reaction, the greater the microorganisms’ consumption of toluene was, the smaller the precipitation of CaCO3. CaCO3 dissolution was a nonmonotonic function that varied with temperature and the milligram equivalent of other ions. Furthermore, the validation experiments agreed well with the mathematical model, indicating its practicality. The established model provides a tool for assessing the biodegradation of toluene by monitoring the concentration of groundwater ions.
Unit Operation Combination and Flow Distribution Scheme of Water Pump Station System Based on Genetic Algorithm
Authors: Zhao Yaping , Zhang Pengli , Pu Yongjian , Lei Hui , Zheng Xiaobo
Published: October 2023

Pump station engineering is a water conservancy project that utilizes water pump units for energy transfer and conversion, and safe water transportation. This study ensures the safe and efficient operation of the pumping station system by taking a pump station project containing seven water pump units as the research object. Under the premise that the total water intake of the pumping station is certain, the flow distribution of the pumping station is optimized with the working flow and head as the constraint function, and the minimum operating cost as the objective function, and the required number of operating units is determined to obtain the optimal combination of flow distribution and the corresponding working flow of each operating unit. Compared with previous studies, the novel feature of this study is that a genetic algorithm is used to optimize the power-up combination and rationally distribute the flow, which can minimize the energy consumption and maximize the operation efficiency of the pumping station. Then, a genetic-algorithm-based optimization method is constructed for unit operation combination and flow distribution. The optimized combination of units is evaluated by using the uniformity of flow velocity at the inlet section of the water pump and the average deviation angle of the section as indicators. The results indicate that, when each operating unit in the pump station has the same working flow distribution, the total input power of the pump station is at maximum. As the differences increase in the working flow rates allocated to each operating unit, the total input power of the pump station decreases. Through the proposed schemes, the optimal operating combination and flow distribution of the pump station system can be achieved when the total water intake is constant, thereby enabling efficient operations and creating maximum economic and social benefits.
Artistic and Laboratory Patinas on Copper and Bronze Surfaces
Authors: Petiti Chiara , Toniolo Lucia , Berti Letizia , Goidanich Sara
Published: October 2023

The study of characterisation and production of artificial patinas plays a key role in the field of cultural heritage. In particular, artistic patinas should be considered as an integral part of the artworks, as they are deliberately produced by artists and metalworkers as a part of their artistic design. Therefore, it is important to achieve a good knowledge of their composition and corrosion behaviour in order to setup and perform optimal conservation strategies for their preservation. In addition, the possibility of realising laboratory patinas that are as representative as possible of natural corrosion layers is important for the realisation of laboratory specimens which can be used as reliable model systems (mock-ups) for the study of degradation mechanisms and conservative treatments. For this work, both artistic and laboratory patinas have been considered and investigated. In particular, six different artistic patinas produced by Fonderia Artistica Battaglia were characterised. Moreover, a series of laboratory patinas was produced according to chemical procedures adapted from those already reported in the literature. The patina morphology was evaluated by stereomicroscopy observations, their composition was analysed by means of FTIR and XRD analysis and their corrosion behaviour was evaluated by LPR and EIS measurements. Finally, the LPR and EIS analysis have pointed out the low protection provided by the corrosion layers of artistic patinas. In regard to laboratory patinas, the optimized procedures of production were found to be effective for the realization of the main corrosion products of copper-based surfaces. From an electrochemical point of view in particular, quite different electrochemical behaviours were observed on artificial corrosion layers with the same chemical composition.
Automatic Vulgar Word Extraction Method with Application to Vulgar Remark Detection in Chittagonian Dialect of Bangla
Authors: Mahmud Tanjim , Ptaszynski Michal , Masui Fumito
Published: October 2023

The proliferation of the internet, especially on social media platforms, has amplified the prevalence of cyberbullying and harassment. Addressing this issue involves harnessing natural language processing (NLP) and machine learning (ML) techniques for the automatic detection of harmful content. However, these methods encounter challenges when applied to low-resource languages like the Chittagonian dialect of Bangla. This study compares two approaches for identifying offensive language containing vulgar remarks in Chittagonian. The first relies on basic keyword matching, while the second employs machine learning and deep learning techniques. The keyword-matching approach involves scanning the text for vulgar words using a predefined lexicon. Despite its simplicity, this method establishes a strong foundation for more sophisticated ML and deep learning approaches. An issue with this approach is the need for constant updates to the lexicon. To address this, we propose an automatic method for extracting vulgar words from linguistic data, achieving near-human performance and ensuring adaptability to evolving vulgar language. Insights from the keyword-matching method inform the optimization of machine learning and deep learning-based techniques. These methods initially train models to identify vulgar context using patterns and linguistic features from labeled datasets. Our dataset, comprising social media posts, comments, and forum discussions from Facebook, is thoroughly detailed for future reference in similar studies. The results indicate that while keyword matching provides reasonable results, it struggles to capture nuanced variations and phrases in specific vulgar contexts, rendering it less robust for practical use. This contradicts the assumption that vulgarity solely relies on specific vulgar words. In contrast, methods based on deep learning and machine learning excel in identifying deeper linguistic patterns. Comparing SimpleRNN models using Word2Vec and fastText embeddings, which achieved accuracies ranging from 0.84 to 0.90, logistic regression (LR) demonstrated remarkable accuracy at 0.91. This highlights a common issue with neural network-based algorithms, namely, that they typically require larger datasets for adequate generalization and competitive performance compared to conventional approaches like LR.
Study of the Relationships among the Reverse Torque, Vibration, and Input Parameters of Mud Pumps in Riserless Mud Recovery Drilling
Authors: He Guolei , Xu Benchong , Chen Haowen , Qin Rulei , Li Changping , Yin Guoyue
Published: October 2023

Compared with traditional deepwater drilling, riserless mud recovery (RMR) drilling technology has the advantages of improving drilling efficiency, reducing risks, and minimizing environmental effects. Therefore, RMR drilling technology has been widely applied in recent years. This study primarily investigates the relationships among reverse torque, vibration, and input parameters of mud pumps in riserless mud recovery drilling. Firstly, the operating principle and the structure of the mud pump module are analyzed, and an analytical model for the reverse torque and the vibration of the mud pump is established. Secondly, relevant data are derived from theoretical calculations and experiments, and the relationships among the reverse torque, vibration, and input parameters of the mud pump are analyzed using ANSYS (Version 2020 R1) software. Furthermore, the SVR (support vector regression) algorithm is employed to predict and analyze the amplitude of the mud pump’s vibration. Finally, the conclusions are drawn based on the findings of the relationships among the reverse torque, vibration, and input parameters of the mud pump. The findings show that the reverse torque of the mud pump increases approximately linearly with an increase in rotational speed, and the vibration of the mud pump increases and then decreases with an increase in rotational speed. The predicted values obtained through the prediction algorithm closely match the actual values. The findings provide a valuable reference for the application of RMR technology.
Anthropological Comparative Analysis of CCTV Footage in a 3D Virtual Environment
Authors: Maksymowicz Krzysztof , Kuzan Aleksandra , Szleszkowski Łukasz , Tunikowski Wojciech
Published: October 2023

The image is a particularly valuable data carrier in medical forensic and forensic analyses. One of the analyses, as mentioned above, is to assess whether a graphically captured object is the same object examined in reality. This is a complicated process due to perspective foreshortening, making it difficult to determine the scale and proportion of objects in the frame, as well as the subsequent correct reading of their actual measurements. This paper presented a method for the 3D reconstruction of silhouettes of people recorded in a photo or video, with the aim of identifying these people through subsequent comparative studies. The authors presented an algorithm for dealing with graphic evidence, using the example of the analysis of spatial correlation of the silhouette of the perpetrator of the actual event (recorded via CCTV footage) with the silhouette of the suspect (scanned in 3D in custody). In this paper, the authors posed the thesis that the isometric (devoid of perspective foreshortening) display mode that 3D platforms offer, and the animation of the figure to the desired identical poses, provides the possibility of not only obtaining linear measurements of the person but also of orthophotographic visualization of body proportions, allowing their comparison with another silhouette, which is difficult to achieve in perspective view of the studied image.
Adversarial Example Detection and Restoration Defensive Framework for Signal Intelligent Recognition Networks
Authors: Han Chao , Qin Ruoxi , Wang Linyuan , Cui Weijia , Li Dongyang , Yan Bin
Published: October 2023

Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques. Our design features a detection AE that harnesses reconstruction errors and convolutional neural networks to discern deep features, employing thresholds from reconstruction error and Kullback–Leibler divergence to identify adversarial samples and their origin mechanisms. Additionally, a restoration AE with a multi-layered structure effectively restores adversarial samples generated via optimization methods, ensuring accurate classification. Tested rigorously on the RML2016.10a dataset, our framework proves robust against adversarial threats, presenting a versatile defense solution compatible with various deep learning models.
Research Progress on the Development of the Planter Unit for Furrowing Control and the Depth Measurement Technology
Authors: Li Hang , He Jin , Wang Chao , Yang Wenchao , Lin Han , Wang Quanyu , Yang Hanyu , Tan Lu
Published: October 2023

The article briefly describes the importance of furrowing depth stability for seed germination and growth under precision seeding conditions. By analyzing the application status of furrowing depth control technology of the planter globally, the research method, technical characteristics, and development of furrowing depth stability control technology are reviewed from three key aspects, namely, profiling adjustment device, furrowing depth detection technology, and automatic control system. In this paper, (1) two types of profiling adjustments, active and passive, are described based on the difference in the downforce adjustment method; (2) three furrowing depth detection methods are described based on different sensors; (3) and three ways of regulating the furrowing depth system are summarized based on the different ways of evaluating the stability of furrowing depth. In addition, the characteristics and application requirements of global furrow depth control technology are summarized. It is proposed that the future planter should be developed in the direction of automatic navigation, automatic monitoring and evaluation of seeding quality, variable seeding, high-speed seeding, and other intelligent precision seeding techniques. The summary and outlook of this paper aim to promote the overall development of furrowing depth control technology.
A Real-Time Measurement System for Atmospheric Turbulence Intensity and Distribution Based on the GLAO System
Authors: Ran Xian , Zhang Lanqiang , Bao Hua , Rao Xuejun , Yang Jinsheng , Tong Dingkang , Wang Cheng , Rao Changhui
Published: October 2023

Measuring the intensity and distribution of atmospheric optical turbulence at large-aperture astronomical telescope sites is crucial to optimizing turbulence correction for different layers. A real-time measurement of turbulence distribution in large-aperture telescopes would be valuable for the parameter optimization of adaptive optics (AO) systems, especially for large field-of-view AO systems such as multi-conjugate adaptive optics (MCAO) and ground-layer adaptive optics (GLAO). Based on the GLAO system of NVST at FSO, a real-time measurement system was deployed to assess the site’s atmospheric turbulence intensity and distribution. This system is, to our knowledge, the first real-time turbulence parameter measurement system in the world with an AO system. We adopt pseudo-open loop methods to restore the turbulence information from the close-loop data of GLAO and measure the turbulence strength and distribution. Multiple subaperture pairs are used instead of a pair of subapertures for fitting calculation to increase the measurement accuracy. Two conventional measurement algorithms, SLODAR and S-DIMM+, are compared with the data from the open-source simulator SOAPY, to cross-verify the correctness of our calculation based on the data process of pseudo-open loop data and multiple subaperture pairs. The simulation results show that for two layers’ turbulence input, approximately 93% of the turbulence is correctly detected with the SLODAR method and the given parameters of wavefront sensors and correctors, while the S-DIMM+ is 87%. Real-time measurements of atmospheric turbulence at the NVST site were carried out on 28 May 2023. The observation results indicated that approximately 80% of the turbulence was located below an altitude of 2000 m; only a few appear in the upper height.
Field Test Study on Construction Disturbances of Driven Pile and PGP Pile
Authors: Shao Feiyu , Deng Yuebao , Chen Shuaijiong , Zheng Rongyue , Zhang Rihong
Published: October 2023

The pre-bored grouted planted (PGP) pile has been more and more used in recent years, but its precise disturbance effect in soft soil areas has not been studied deeply. Therefore, a comparative field test for the disturbances by PGP pile and driven pile construction in deep soft ground was carried out. It revealed that the excess pore pressure caused by the two kinds of piles decreases with the increase of radial distance, and the influence range is less than 7.5 d (d is pile diameter). The maximum excess pore pressure generated by PGP pile construction is about 100 kPa smaller than that generated by driven pile construction. The comparison of the soil pressure and lateral displacement between the two piles is related to the depth and soil type. The typical result is that the soil pressure caused by PGP pile construction is half that of the driven pile, and the maximum lateral displacement of the PGP pile is 50.7~53.8% of that of the driven pile. The noise generated during PGP construction was lower but continuous, and the maximum value at the same distance was reduced by 8 to 15% than the driven pile. The test results reveal the construction disturbance effect of PGP pile and provide a reference for the selection of pile construction method in soft soil areas.
Adaptive Optimal Control of UAV Brake Based on Parameter Estimation
Authors: Lv Hao , Liu Qinrui , Wan Yu , Gao Xuehui , Li Feng , Liu Zhen
Published: October 2023

A new parameter estimation method for a UAV braking system with unknown friction parameters is suggested. The unknown part containing friction is separated from the coupling system. The law of parameter updating is driven by the estimation error extracted by the auxiliary filter. A controller is developed to guarantee the convergence of the parameter estimation and tracking errors simultaneously. Furthermore, a designed performance index function enables the system to track the desired slip rate optimally. The optimal value of the performance function is updated by a single critic neural network (NN) to obtain comprehensive optimization of the control energy consumption, dynamic tracking error, and filtering error during braking. In addition, the unknown bounded interference and neural network approximation errors are compensated by robust integral terms. Simulation results are presented to confirm the effectiveness of the proposed control scheme.
Linear Time Non-Local Cost Aggregation on Complementary Spatial Tree Structures
Authors: Bu Penghui , Wang Hang , Dou Yihua , Zhao Hong
Published: October 2023

Studies on many dense correspondence tasks in the field of computer vision attempt to find spatially smooth results. A typical way to solve these problems is by smoothing the matching costs using edge-preserving filters. However, local filters generate locally optimal results, in that they only take the costs over a small support window into account, and non-local filters based on a minimum spanning tree (MST) tend to overuse the piece-wise constant assumption. In this paper, we propose a linear time non-local cost aggregation method based on two complementary spatial tree structures. The geodesic distances in both the spatial and intensity spaces along the tree structures are used to evaluate the similarity of pixels, and the final aggregated cost is the sum of the outputs from these two trees. The filtering output of a pixel on each tree can be obtained by recursively aggregating the costs along eight sub-trees with linear time complexity. The only difference between the filtering procedures on these two spatial tree structures is the order of the filtering. Experimental results in optical flow estimation and stereo matching on the Middlebury and KITTI datasets demonstrate the effectiveness and efficiency of our method. It turns out that our method outperforms typical non-local filters based on the MST in cost aggregation. Moreover, a comparison of handcrafted features and deep features learned by convolutional neural networks (CNNs) in calculating the matching cost is also provided. The code will be available soon.
Remaining Useful Life Estimation of Turbofan Engines with Deep Learning Using Change-Point Detection Based Labeling and Feature Engineering
Authors: Ensarioğlu Kıymet , İnkaya Tülin , Emel Erdal
Published: October 2023

Accurate remaining useful life (RUL) prediction is one of the most challenging problems in the prognostics of turbofan engines. Recently, RUL prediction methods for turbofan engines mainly involve data-driven models. Preprocessing the sensor data is essential for the performance of the prognostic models. Most studies on turbofan engines use piecewise linear (PwL) labeling, which starts with a constant initial RUL value in normal/healthy operating time. In this study, we designed a prognostic procedure that includes difference-based feature construction, change-point-detection-based PwL labeling, and a 1D-CNN-LSTM (one-dimensional–convolutional neural network–long short-term memory) hybrid neural network model for RUL prediction. The procedure was evaluated on the subset FD001 of the C-MAPSS dataset. The proposed procedure was compared with machine learning and deep learning models with and without the new difference feature. Also, the results were compared with the studies that used similar labeling approaches. Our analysis of the numerical results underscores the clear superiority of the proposed 1D-CNN-LSTM model with the difference feature in RUL prediction, with a score of 437.2 and an RMSE value of 16.1. This result illustrates the superior predictive capability of the 1D-CNN-LSTM model, which outperformed traditional machine learning methods and one of the earliest deep learning methods. These findings emphasize the superior predictive capability of the 1D-CNN-LSTM model and underline the potential of the feature engineering process for more accurate and robust RUL prediction in the context of turbofan engine prognostics.
Modeling an Optimal Environmentally Friendly Energy-Saving Flexible Workshop
Authors: Zhang Tianrui , Wei Mingqi , Gao Xiuxiu
Published: October 2023

From the perspective of energy efficiency and environmental sustainability, the scheduling problem in a flexible workshop with the utilization of automated guided vehicles (AGVs) was investigated for material transportation. Addressing the dual-constrained integrated scheduling challenge involving machining machines and AGVs, a scheduling optimization model was established with makespan, workshop energy consumption, and processing quality as the optimization objectives. To effectively solve this model, an enhanced whale optimization algorithm (IWOA) was proposed. Specifically, nonlinear convergence factors, adaptive inertia weights, and improved helix positions were introduced into the standard whale optimization algorithm to update the model. Furthermore, a loss function was constructed based on fuzzy membership theory to obtain the optimal compromise solution of the multi-objective model. The research results indicate that: (1) The IWOA obtained the optimal solutions on benchmark instances MK01, MK02, MK04, MK07, and MK08; (2) The IWOA outperformed the WOA(1), WOA(2), WOA-LEDE, and NSGA-II algorithms in the two instances provided in this paper, demonstrating strong robustness of the model; (3) Although the multi-objective model constructed in this paper could not surpass the single-objective optimal solution in individual objectives, it achieved compensation in other objectives, effectively balancing the trade-offs among the makespan, workshop energy consumption, and processing quality of the three objectives. This research offers an effective practical approach to address green flexible workshop scheduling with AGV transportation.
Correction: Leyda and Sulimma (2023). Pop/Poetry: Dickinson as Remix. Arts 12: 62
Authors: Leyda Julia , Sulimma Maria
Published: October 2023

In the original publication [...]
The Boundary Integral Equation for Kinetically Limited Dendrite Growth
Authors: Titova Ekaterina A. , Galenko Peter K. , Nikishina Margarita A. , Toropova Liubov V. , Alexandrov Dmitri V.
Published: October 2023

The boundary integral equation defining the interface function for a curved solid/liquid phase transition boundary is analytically solved in steady-state growth conditions. This solution describes dendrite tips evolving in undercooled melts with a constant crystallization velocity, which is the sum of the steady-state and translational velocities. The dendrite tips in the form of a parabola, paraboloid, and elliptic paraboloid are considered. Taking this solution into account, we obtain the modified boundary integral equation describing the evolution of the patterns and dendrites in undercooled binary melts. Our analysis shows that dendritic tips always evolve in a steady-state manner when considering a kinetically controlled crystallization scenario. The steady-state growth velocity as a factor that is dependent on the melt undercooling, solute concentration, atomic kinetics, and other system parameters is derived. This expression can be used for determining the selection constant of the stable dendrite growth mode in the case of kinetically controlled crystallization.
Fixed-Point Convergence of Multi-Valued Non-Expansive Mappings with Applications
Authors: Azam Akbar , Rashid Maliha , Kalsoom Amna , Ali Faryad
Published: October 2023

This paper is dedicated to the advancement of fixed-point results for multi-valued asymptotically non-expansive maps regarding convergence criteria in complete uniformly convex hyperbolic metric spaces that are endowed with a graph. The famous fixed-point theorems of Goebel and Kirk, Khamsi and Khan, along with other recent results in the literature can be obtained as corollaries of these main results. A nice graph and an interesting example are also provided in support of the hypothesis of the main results.
On a Mathematical Model of a General Autoimmune Disease
Authors: Kolev Mikhail , Netov Nikolay , Nikolova Iveta , Naskinova Irina , Kuneva Velika , Milev Marian
Published: October 2023

The proposed paper is devoted to presenting and analyzing a kinetic model describing the development of autoimmune disorders. The proposed model is a nonlinear system of differential equations that considers the biological activity of the interacting populations. The main characteristics of autoimmune diseases are taken into account. Preliminaries to the research area are provided. The modeling problem is discretized and solved approximately. The numerical results illustrate typical outcomes of autoimmune diseases.
Applied Methods to Assess the Antimicrobial Activity of Metallic-Based Nanoparticles
Authors: Chung Etelka , Ren Guogang , Johnston Ian , Matharu Rupy Kaur , Ciric Lena , Walecka Agnieszka , Cheong Yuen-Ki
Published: October 2023

With the rise of antibiotic resistance, the drive to discover novel antimicrobial substances and standard testing methods with the aim of controlling transmissive diseases are substantially high. In healthcare sectors and industries, although methods for testing antibiotics and other aqueous-based reagents are well established, methods for testing nanomaterials, non-polar and other particle-based suspensions are still debatable. Hence, utilities of ISO standard validations of such substances have been recalled where corrective actions had to be taken. This paper reports a serial analysis obtained from testing the antimicrobial activities of 10 metallic-based nanomaterials against 10 different pathogens using five different in vitro assays, where the technique, limitation and robustness of each method were evaluated. To confirm antimicrobial activities of metallic-based nanomaterial suspensions, it was found that at least two methods must be used, one being the agar well diffusion method, which was found to be the most reliable method. The agar well diffusion method provided not only information on antimicrobial efficacy through the size of the inhibitory zones, but it also identified antimicrobial ions and synergistic effects released by the test materials. To ascertain the effective inhibitory concentration of nanoparticles, the resazurin broth dilution method is recommended, as MIC can be determined visually without utilising any equipment. This method also overcomes the limit of detection (LoD) and absorbance interference issues, which are often found in the overexpression of cell debris and nanoparticles or quantum dots with optical profiles. In this study, bimetallic AgCu was found to be the most effective antimicrobial nanoparticle tested against across the bacterial (MIC 7 µg/mL) and fungal (MIC 62.5 µg/mL) species.
Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs
Authors: Hwang Elizabeth E. , Chen Dake , Han Ying , Jia Lin , Shan Jing
Published: October 2023

Glaucomatous optic neuropathy (GON) can be diagnosed and monitored using fundus photography, a widely available and low-cost approach already adopted for automated screening of ophthalmic diseases such as diabetic retinopathy. Despite this, the lack of validated early screening approaches remains a major obstacle in the prevention of glaucoma-related blindness. Deep learning models have gained significant interest as potential solutions, as these models offer objective and high-throughput methods for processing image-based medical data. While convolutional neural networks (CNN) have been widely utilized for these purposes, more recent advances in the application of Transformer architectures have led to new models, including Vision Transformer (ViT,) that have shown promise in many domains of image analysis. However, previous comparisons of these two architectures have not sufficiently compared models side-by-side with more than a single dataset, making it unclear which model is more generalizable or performs better in different clinical contexts. Our purpose is to investigate comparable ViT and CNN models tasked with GON detection from fundus photos and highlight their respective strengths and weaknesses. We train CNN and ViT models on six unrelated, publicly available databases and compare their performance using well-established statistics including AUC, sensitivity, and specificity. Our results indicate that ViT models often show superior performance when compared with a similarly trained CNN model, particularly when non-glaucomatous images are over-represented in a given dataset. We discuss the clinical implications of these findings and suggest that ViT can further the development of accurate and scalable GON detection for this leading cause of irreversible blindness worldwide.
Identifying Fibroblast Growth Factor Receptor 3 as a Mediator of Periosteal Osteochondral Differentiation through the Construction of microRNA-Based Interaction Networks
Authors: Wells Leah M. , Roberts Helen C. , Luyten Frank P. , Roberts Scott J.
Published: October 2023

Human periosteum-derived progenitor cells (hPDCs) have the ability to differentiate towards both the chondrogenic and osteogenic lineages. This coordinated and complex osteochondrogenic differentiation process permits endochondral ossification and is essential in bone development and repair. We have previously shown that humanised cultures of hPDCs enhance their osteochondrogenic potentials in vitro and in vivo; however, the underlying mechanisms are largely unknown. This study aimed to identify novel regulators of hPDC osteochondrogenic differentiation through the construction of miRNA-mRNA regulatory networks derived from hPDCs cultured in human serum or foetal bovine serum as an alternative in silico strategy to serum characterisation. Sixteen differentially expressed miRNAs (DEMis) were identified in the humanised culture. In silico analysis of the DEMis with TargetScan allowed for the identification of 1503 potential miRNA target genes. Upon comparison with a paired RNAseq dataset, a 4.5% overlap was observed (122 genes). A protein–protein interaction network created with STRING interestingly identified FGFR3 as a key network node, which was further predicted using multiple pathway analyses. Functional analysis revealed that hPDCs with the activating mutation FGFR3N540K displayed increased expressions of chondrogenic gene markers when cultured under chondrogenic conditions in vitro and displayed enhanced endochondral bone formation in vivo. A further histological analysis uncovered known downstream mediators involved in FGFR3 signalling and endochondral ossification to be upregulated in hPDC FGFR3N540K-seeded implants. This combinational approach of miRNA-mRNA-protein network analysis with in vitro and in vivo characterisation has permitted the identification of FGFR3 as a novel mediator of hPDC biology. Furthermore, this miRNA-based workflow may also allow for the identification of drug targets, which may be of relevance in instances of delayed fracture repair.
Editorial: Special Issue—Understanding and Targeting Heart Failure: From Biology to Therapeutics
Authors: Pu Jun , Zhu Wuqiang , Ye Lei
Published: October 2023

An estimated 64 [...]
Taxonomic and Feeding Trait-Based Analysis of Macroinvertebrates in the Antisana River Basin (Ecuadorian Andean Region)
Authors: Cabrera-García Santiago , Goethals Peter L. M. , Lock Koen , Domínguez-Granda Luis , Villacís Marcos , Galárraga-Sánchez Remigio , Van der heyden Christine , Eurie Forio Marie Anne
Published: October 2023

High-elevation tropical streams are under increasing threat from human activities and climate change. Specifically, Ecuadorian Andean streams require priority actions such as bioassessment (e.g., biodiversity and functional ecology of macroinvertebrates) in order to generate adequate environmental management policies. Therefore, we investigated the distribution and composition of the macroinvertebrate taxa and their functional feeding groups in relation to the environmental variables in the Antisana river basin (Andean–Ecuadorian Region). We sampled macroinvertebrates from 15 locations to assess ecological conditions (ECs), expressed as the Biological Monitoring Working Party Colombia (BMWP-Col) classes, the Andean Biotic Index (ABI) and the Andean–Amazon Biotic Index (AAMBI). Results indicate that dissolved oxygen saturation, elevation, nutrient concentration and conductivity contributed significantly to the composition of the taxa and functional feeding groups (FFGs). Taxa diversity and FFGs were more abundant in the best EC sites. Shredders (SH) were, overall, dominant and abundant at sites with medium-high ECs. Scrapers constituted the second most prevalent assemblage, exerting dominance at moderate ecological conditions (high altitude and high oxygen saturation). Collector–gathers (CGs) are less sensitive to contamination than the previous two groups but were equally abundant at medium-high EC sites. Collector–filterers (CFs) and parasites (PAs) were less abundant, although the presence of the former was slightly related to better environmental conditions. Predators (PRs) were almost absent throughout the study, but they were collected from poor EC sites. CGs, PAs and PRs showed more tolerance to the presence of human disturbances (e.g., hydraulic constructions or slope erosion). The BMWP-Col index seems to be the best fit for this ecosystem, showing a significant difference in FFG between the index classes, compared to the other indices evaluated. The results of this investigation may be regarded as a fundamental starting point and used in future bioassessment work in other similar ecosystems, particularly high-altitude tropical Ecuadorian streams.
Biological Effects of HDAC Inhibitors Vary with Zinc Binding Group: Differential Effects on Zinc Bioavailability, ROS Production, and R175H p53 Mutant Protein Reactivation
Authors: Flores Brianna M. , Uppalapati Chandana K. , Pascual Agnes S. , Vong Alan , Baatz Margaux A. , Harrison Alisha M. , Leyva Kathryn J. , Hull Elizabeth E.
Published: October 2023

The coordination of zinc by histone deacetylase inhibitors (HDACi), altering the bioavailability of zinc to histone deacetylases (HDACs), is key to HDAC enzyme inhibition. However, the ability of zinc binding groups (ZBGs) to alter intracellular free Zn+2 levels, which may have far-reaching effects, has not been explored. Using two HDACis with different ZBGs, we documented shifts in intracellular free Zn+2 concentrations that correlate with subsequent ROS production. Next, we assayed refolding and reactivation of the R175H mutant p53 protein in vitro to provide greater biological context as the activity of this mutant depends on cellular zinc concentration. The data presented demonstrates the differential activity of HDACi in promoting R175H response element (RE) binding. After cells are treated with HDACi, there are differences in R175H mutant p53 refolding and reactivation, which may be related to treatments. Collectively, we show that HDACis with distinct ZBGs differentially impact the intracellular free Zn+2 concentration, ROS levels, and activity of R175H; therefore, HDACis may have significant activity independent of their ability to alter acetylation levels. Our results suggest a framework for reevaluating the role of zinc in the variable or off-target effects of HDACi, suggesting that the ZBGs of HDAC inhibitors may provide bioavailable zinc without the toxicity associated with zinc metallochaperones such as ZMC1.
Potential Causal Association between Elevated Gamma-Glutamyl Transferase Level and Stroke: A Two-Sample Mendelian Randomization Study
Authors: Lee Young , Seo Je Hyun
Published: October 2023

Researchers have suggested a potential relationship between gamma-glutamyl transferase (GGT) level and stroke. We investigated a potential causal relationship between GGT level as exposures and stroke and stroke subtypes (cardioembolic, small vessel, and large artery) in a European population. We performed a two-sample Mendelian randomization (MR) study using the genome-wide association study (GWAS) data from the UK Biobank as the exposure set. For the outcome set, we used stroke in the GWAS data from the GIGASTROKE Consortium. We considered alcohol consumption, atrial fibrillation, and body mass index as confounders. We used PhenoScanner searches for removal of SNPs and multivariable MR analysis for assessing confounders. We observed significant causal associations between GGT level and stroke (odds ratio [OR] = 1.23, 95% CI = [1.05–1.44], and p = 0.012 with IVW; OR = 1.19, 95% CI= [1.02–1.39], and p = 0.031 with MR-PRESSO). These results were consistent after removing SNPs related to confounding factors. Similarly, in multivariable MR, GGT was associated with stroke after adjusting for confounding factors (OR = 1.30, 95% CI 1.07–1.60), p = 0.010). Because GGT level has a causal relationship with stroke, researchers should test its significance as a potential risk factor for stroke. Additional research is required to validate these results.
Neuroproteomic Analysis after SARS-CoV-2 Infection Reveals Overrepresented Neurodegeneration Pathways and Disrupted Metabolic Pathways
Authors: Basak Indranil , Harfoot Rhodri , Palmer Jennifer E. , Kumar Abhishek , Quiñones-Mateu Miguel E. , Schweitzer Lucia , Hughes Stephanie M.
Published: October 2023

Besides respiratory illness, SARS-CoV-2, the causative agent of COVID-19, leads to neurological symptoms. The molecular mechanisms leading to neuropathology after SARS-CoV-2 infection are sparsely explored. SARS-CoV-2 enters human cells via different receptors, including ACE-2, TMPRSS2, and TMEM106B. In this study, we used a human-induced pluripotent stem cell-derived neuronal model, which expresses ACE-2, TMPRSS2, TMEM106B, and other possible SARS-CoV-2 receptors, to evaluate its susceptibility to SARS-CoV-2 infection. The neurons were exposed to SARS-CoV-2, followed by RT-qPCR, immunocytochemistry, and proteomic analyses of the infected neurons. Our findings showed that SARS-CoV-2 infects neurons at a lower rate than other human cells; however, the virus could not replicate or produce infectious virions in this neuronal model. Despite the aborted SARS-CoV-2 replication, the infected neuronal nuclei showed irregular morphology compared to other human cells. Since cytokine storm is a significant effect of SARS-CoV-2 infection in COVID-19 patients, in addition to the direct neuronal infection, the neurons were treated with pre-conditioned media from SARS-CoV-2-infected lung cells, and the neuroproteomic changes were investigated. The limited SARS-CoV-2 infection in the neurons and the neurons treated with the pre-conditioned media showed changes in the neuroproteomic profile, particularly affecting mitochondrial proteins and apoptotic and metabolic pathways, which may lead to the development of neurological complications. The findings from our study uncover a possible mechanism behind SARS-CoV-2-mediated neuropathology that might contribute to the lingering effects of the virus on the human brain.
Relationships between Oral Health and the Sustainable Development Goals: A Scoping Review
Authors: Abodunrin Olunike Rebecca , Olagunju Mobolaji Timothy , Alade Omolola Titilayo , Foláyan Morẹ́nikẹ́ Oluwátóyìn
Published: October 2023

The aim of this scoping review was to map the research that links oral health and sustainable development goals (SDGs). The question that guided this review was what is the published evidence specifically linking oral health and SDGs? The search of articles published between 2015 and June 2023 in English was conducted in June 2023 in PubMed, Web of Science, Cumulative Index to Nursing and Allied Health Literature, and Scopus. The extracted data from the 12 of the 520 publications that met the inclusion criteria were synthesized. These were publications from the African (n = 2) and European (n = 1) regions. Ten of the studies were not country specific. There were three observational studies. The referenced SDGs were SDG 1 (n = 1), SDG 3 (n = 11), SDG 4 (n = 3), SDG 5 (n = 2), and SDG 13 (n = 1). The studies linking oral health to SDG3 discussed a range of issues related to integrated oral health care, dental education on the SDGs, improved health equalities, access to universal health care, and sustainable health approaches. The small number of studies identified suggests the need to take proactive steps to generate research-based evidence that explores the interplay between oral diseases, oral health, and SDGs to address the dearth in the literature. By so doing, resources allocated to the SDGs can also be effectively uses to control oral diseases.
Self-Cleavage of Human Chloride Channel Accessory 2 Causes a Conformational Shift That Depends on Membrane Anchorage and Is Required for Its Regulation of Store-Operated Calcium Entry
Authors: Ramena Grace T. , Sharma Aarushi , Chang Yan , Pan Zui , Elble Randolph C.
Published: October 2023

Human CLCA2 regulates store-operated calcium entry (SOCE) by interacting with Orai1 and STIM1. It is expressed as a 943aa type I transmembrane protein that is cleaved at amino acid 708 to produce a diffusible 100 kDa product. The N-terminal ectodomain contains a hydrolase-like subdomain with a conserved HEXXH zinc-binding motif that is proposed to cleave the precursor autoproteolytically. Here, we tested this hypothesis and its link to SOCE. We first studied the conditions for autocleavage in isolated membranes and then in a purified protein system. Cleavage was zinc-dependent and abolished by mutation of the E in the HEXXH motif to Q, E165Q. Cleavage efficiency increased with CLCA2 concentration, implying that it occurs in trans. Accordingly, the E165Q mutant was cleaved by co-transfected wildtype CLCA2. Moreover, CLCA2 precursors with different epitope tags co-immunoprecipitated. In a membrane-free system utilizing immunopurified protease and target, no cleavage occurred unless the target was first denatured, implying that membranes provide essential structural or conformational cues. Unexpectedly, cleavage caused a conformational shift: an N-terminal antibody that immunoprecipitated the precursor failed to precipitate the N-terminal product unless the product was first denatured with an ionic detergent. The E165Q mutation abolished the stimulation of SOCE caused by wildtype CLCA2, establishing that the metalloprotease activity is required for this regulatory function.