bluuebunny/crossref_metadata_2025 huggingface dataset:
Herding or Hunting? Animal Exploitation at Jiangzhuang (Jiangsu, China) During the Liangzhu Period (3300–2300 BC)
Authors: Liu Yifei , Huang Yucheng , Gan Huiyuan , Dong Ningning
Published: November 2024

The lower Yangtze Valley provides a rich context for studying the interplay between the origins and intensification of agriculture and socio-political complexity. While the adoption of agriculture was closely associated with the developing societal complexity in major core centres during the Liangzhu period (3300–2300 BC), smaller and peripheral sites, which were also integral parts of the regional social fabric, exhibited contrasting subsistence choices and varying societal development. By examining these locations, a better understanding of the core-hinterland relationships within the region’s complex social structure and agricultural background would be clarified. In this paper, we present a zooarchaeological study of the Jiangzhuang site, located on the northern periphery of the Liangzhu culture. The faunal assemblage is primarily composed of cervids with a small proportion of suids, indicating subsistence heavily relying on wild resources. The morphological variation in suids is likely a result of the co-existence of domesticated pigs, wild boar, feral pigs, and probably the hybrid of the three, unveiling a complex human-suid relationship. The intensive utilisation of cervid bones for tool production is also a distinctive feature. While the Jiangzhuang community might have been articulated with the Liangzhu centre through the circulation of ritual jades, the subsistence economy there remained largely self-sufficient. The economic independence in the peripheral region might have contributed to the increasing decentralisation in the late Liangzhu period, providing a plausible explanation for the collapse of Liangzhu society.
Antimicrobial Stewardship Intervention for the Family Caregiver Attending Primary Health Care Setting: A Quasi-Experimental Study
Authors: Binsaleh Ammena Y. , Abdallah Mahmoud S. , Osman Basma Mohamed , Bahaa Mostafa M. , Alsubaie Nawal , Elmasry Thanaa A. , Yasser Mohamed , Eldesoqui Mamdouh , Gadallah Abdel-Naser Abdel-Atty , Hamouda Manal A. , Eltantawy Nashwa , Mokhtar Fatma A. , El Sabaa Ramy M.
Published: November 2024

Background: Antimicrobial resistance (AMR) has been identified as one of the top ten public health threats facing humanity. Aim: The purpose of this study was to assess the effect of an antimicrobial stewardship educational intervention on family caregivers’ knowledge and practices in primary healthcare settings in Egypt. Methods: A quasi-experimental, one-group pretest-posttest design involving a sample of 300 family caregivers attending family health centers. The data were collected using questionnaires that assessed caregiver knowledge and self-reported practices regarding AMR before and after the intervention (primary outcomes). The intervention combined tailored knowledge and practice components that carefully evaluated participants’ knowledge regarding AMR, health risk, antibiotic usage, and prevention of infection. Furthermore, their practice of using antibiotics, including previous antibiotic exposure, their antibiotic use during the past year, reasons for taking antibiotics, ways of obtaining antibiotics, and reasons for discontinuing of antibiotic therapy were also recorded. Results: The pre-intervention assessment revealed poor knowledge and practice regarding antibiotic use. Post-intervention, mean knowledge scores increased significantly from 18.36 to 23.28 (t = 19.5, p < 0.0001), while mean practice scores improved from 9.83 to 12.37 (t = 6.4, p < 0.0001). Conclusions: The intervention successfully improved caregivers’ knowledge and practices regarding AMR. However, there are some limitations that could affect the generalization, and the impact of the results such as the relatively small sample size recruited from a single center, lack of a control group, reliance on self-reported data, and lack of long-term follow-up. Future studies should aim to address these constraints in order to assess the intervention’s effectiveness.
Water Demand (or Specific Surface) of Aggregate as a Dominating Factor for SCC Composition Design
Authors: Urban Maciej
Published: November 2024

In the modern era of superplasticizer-based concrete technology, water demand (or specific surface) of aggregate is a significantly underestimated factor influencing cement paste demand in the self-compacting concrete (SCC) design process. The presented data show that it is the key factor for optimization criterion of SCC cement paste demand. Four models were taken into consideration (Bolomey, Stern, modified Loudon, and Relative Specific Surface), and all of them fit linearly very well (R2 ≥ 0.95) to the relative thickness of coating aggregate with cement paste (trel). This means that all of these models may be used interchangeably in the process of SCC design without any alteration (so there is no need to develop a new model). Including the water demand of aggregate in the design procedure in its proposed version sets the bottom limit of superplasticizer dose for laboratory trials, leaving only small gap for eventual minor adjustments.
Analyzing Important Elements for Improving the Safety of Motorways
Authors: Kim Yejin , Lee Yoseph , Lee Youngtaek , Ko Woori , Yun Ilsoo
Published: November 2024

This study aims to identify the factors that influence the occurrence of traffic accidents to improve motorway traffic safety. Various data, including the frequency of traffic accidents, traffic volume, geometric structure, and congestion level, were collected from individual sections of motorways in South Korea. Using the collected data, a traffic accident frequency prediction model was developed by applying an explainable artificial intelligence (AI)-based approach. The developed deep neural network model was combined with Shapley Additive Explanations to identify the variables that significantly affect the frequency of traffic accidents. The analysis identified five significant factors: segment length, total traffic volume, the proportion of truck traffic, the number of dangerous driving behaviors, and the duration of congestion. The results demonstrate the potential of using explainable AI in predicting traffic accident frequency. By identifying the factors that influence traffic accidents using this model, we can pinpoint areas for improvement, which may ultimately help reduce highway traffic accidents.
An Analysis of WiFi Coverage Modeling for a Hotspot in the Parish of Checa Employing Deterministic and Empirical Propagation Models
Authors: Sánchez Iván , Vallejo Fabricio , Játiva Pablo Palacios , Dehghan Firoozabadi Ali
Published: November 2024

This study presents the analysis and comparison of Wi-Fi coverage modeling for a hotspot using deterministic and empirical propagation models developed by researchers from the Universidad de Las Américas in Quito, Ecuador. Signal intensity measurements were taken from both the hotspot and the repeater at various locations within the Checa parish using a Raspberry Pi and a Global Positioning System (GPS). To assess the accuracy of the models, heat maps were generated using Matlab (R2023A). The results showed that the adjusted model, comparing the received signal levels of the hotspot with the Stanford University Interim Propagation Model (SUI), exhibited a significant error margin, especially at distances below 60 m. However, starting at −70 dBm and beyond 60 m, the sampled data aligned better with the adjusted model. The discrepancy in the heatmaps was explained by the hotspot’s higher transmission power compared to the Wi-Fi repeater. Furthermore, the reception levels of the hotspot were low near the transmitter, which led to new measurements being taken with the Wi-Fi repeater (Raspberry Pi 3). With the new measurements, the adjusted model using logarithmic regression showed a better fit, particularly in the range from −40 dBm to −98 dBm, with a path loss exponent of 8.96. This demonstrated a significant improvement in prediction accuracy, particularly at short distances. The results emphasize the importance of using tools such as Matlab and reference models to optimize network planning, providing the Universidad de Las Américas with a valuable tool to generate heat maps in areas with characteristics similar to those of Checa in the context of their community outreach programs. This approach could be crucial for future research and optimization of community Wi-Fi networks in similar environments.
An Exploratory Study on the Correlation Between Reactive Agility and Downhill Trail Running Performance in Amateur Trail Runners
Authors: García Muñoz Juan Pablo , García-Muro San José Francisco , Rodríguez Fernández Ángel Luis , Neale Ella Jayne , Pérez Gosálvez Arturo
Published: November 2024

Agility appears to be an important skill in the downhill sections of trail running; however, its actual role in the performance of this sport is unknown. The aim of the study was to evaluate the correlation of reactive agility (RA) and certain biomechanical parameters with the performance of runners when performing trail running descents. Nine amateur trail runners (four women and five men) performed a session of change of direction, RA, and 15 m linear sprint tests. In a second session, they performed a trail running race of 400 m with a negative elevation gain of 40 m. The relationship between the variables obtained in both sessions was evaluated using Pearson’s correlation coefficient. The results revealed significant correlations of performance during the trail running descent, with the time of the 15 m linear sprint (r = 0.77, ρ ≤ 0.05.), 5 m linear sprint (r = 0.79, ρ ≤ 0.05.), and with the reaction time in the reactive agility test with a sport-specific stimulus (r = 0.82, ρ ≤ 0.05.). The correlations found in the tests suggest that reactive agility skills and acceleration abilities have a significant relationship with performance on downhill sections in trail running.
Expert-Trajectory-Based Features for Apprenticeship Learning via Inverse Reinforcement Learning for Robotic Manipulation
Authors: Naranjo-Campos Francisco J. , Victores Juan G. , Balaguer Carlos
Published: November 2024

This paper explores the application of Inverse Reinforcement Learning (IRL) in robotics, focusing on inferring reward functions from expert demonstrations of robot arm manipulation tasks. By leveraging IRL, we aim to develop efficient and adaptable techniques for learning robust solutions to complex tasks in continuous state spaces. Our approach combines Apprenticeship Learning via IRL with Proximal Policy Optimization (PPO), expert-trajectory-based features, and the application of a reverse discount. The feature space is constructed by sampling expert trajectories to capture essential task characteristics, enhancing learning efficiency and generalizability by concentrating on critical states. To prevent the vanishing of feature expectations in goal states, we introduce a reverse discounting application to prioritize feature expectations in final states. We validate our methodology through experiments in a simple GridWorld environment, demonstrating that reverse discounting enhances the alignment of the agent’s features with those of the expert. Additionally, we explore how the parameters of the proposed feature definition influence performance. Further experiments on robotic manipulation tasks using the TIAGo robot compare our approach with state-of-the-art methods, confirming its effectiveness and adaptability in complex continuous state spaces across diverse manipulation tasks.
Evaluation of Machine Learning Applications for the Complex Near-Critical Phase Behavior Modelling of CO2–Hydrocarbon Systems
Authors: Magzymov Daulet , Makhatova Meruyert , Dairov Zhasulan , Syzdykov Murat
Published: November 2024

The objective of this study was to evaluate the capability of machine learning models to accurately predict complex near-critical phase behavior in CO2–hydrocarbon systems, which are crucial for enhanced oil recovery and carbon storage applications. We compared the physical Peng–Robinson equation of state model to machine learning algorithms under varying temperatures, pressures, and composition, including challenging near-critical scenarios. We used a direct neural network model and two hybrid model approaches to capture physical behavior in comprehensive compositional space. While all the models showed great performance during training and validation, the Direct Model exhibited unphysical behavior in compositional space, such as fluctuations in equilibrium constants and tie-line crossing. Hybrid Model 1, integrating a single Rachford–Rice iteration for physical constraints, showed an improved consistency in phase predictions. Hybrid Model 2, utilizing logarithmic transformations to better handle nonlinearities in equilibrium constants, further enhanced the accuracy and provided smoother predictions, particularly in the near-critical region. Overall, the hybrid models demonstrated a superior ability to balance computational efficiency and physical accuracy, closely aligning with the reference of the Peng–Robinson equation of state. This study highlights the importance of incorporating physical constraints into machine learning models for reliable phase behavior predictions, especially under near-critical conditions.
Seismic Benefits of a Vibrating Mass Equipped with an Inerter on Frame Structures Due to Soil Interaction
Authors: Di Egidio Angelo , Contento Alessandro
Published: November 2024

The reduction of the seismic effects on new and existing structures is a relevant topic of the structural mechanics applied to the civil engineering. Usually, the conceptual aspects related to a new approach are studied by means of low-dimensional mechanical models able to capture the main dynamic aspects of the method. The present paper can be framed in this context. Specifically, the paper investigates the possibility of reducing the seismic response of a frame structure by using a vibrating mass connected to an inerter device, which interacts through the soil to protect the structure. The problem is studied by using existing soil–structure interaction (SSI) and structure–soil–structure interaction (SSSI) models, which describe the actions between the structure and the soil, and among adjacent structures through linear visco-elastic devices. A seven-degrees-of-freedom mechanical model is used to describe the problem, where a general multi-story frame structure is mathematically described by means of an equivalent 2-degrees-of-freedom system. The external vibrating mass is coupled with the inerter device to increase its inertia without using high real mass. The aim of the paper is to point out the role of the many parameters that characterize the interaction system. Particular attention is devoted to the mechanical characteristics of the soil, in order to know the effectiveness of the SSSI system as a function of the characteristics of the soil. Results show that the vibrating mass equipped with the inerter device is almost always beneficial for the frame structure to be protected. However, sufficient good performances justifying the costs of this method can be reached only in limited ranges of the characterizing parameters.
AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
Authors: Schwarz Klaus , Bollens Kendrick , Arias Aranda Daniel , Hartmann Michael
Published: November 2024

This paper presents the Open-Source Intelligence Disaster Event Tracker (ODET), a modular platform that provides customizable endpoints and agents for each processing step. ODET enables the implementation of AI-enhanced algorithms to respond to various complex disaster scenarios. To evaluate ODET, we conducted two case studies using unmodified AI models to demonstrate its base performance and potential applications. Through our case studies on Hurricane Harvey and the 2023 Turkey earthquake, we show how complex tasks can be quickly broken down with ODET while achieving a score of up to 89% using the AlignScore metric. ODET enables compliance with Berkeley protocol requirements by ensuring data privacy and using privacy-preserving processing methods. Our results demonstrate that ODET is a robust platform for the long-term monitoring and analysis of dynamic environments and can improve the efficiency and accuracy of situational awareness reports in disaster management.
The Roles of the Eastern Atlantic Niño and Central Atlantic Niño in ENSO Prediction
Authors: Gan Yuzhi , Shen Xingchen , Jin Yishuai , Rao Zhengxiang , Pang Yiqun , Huang Shouyou
Published: November 2024

Recent studies have shown that there are two types of Niño events in the Tropical Atlantic, namely the Eastern Atlantic (EA) Niño and Central Atlantic (CA) Niño modes. However, it remains unknown whether these two types of Niño modes still impact El Niño–Southern Oscillation (ENSO) prediction. This paper investigates the impacts of the EA and CA Niño modes on ENSO predictability with an empirical dynamical model: the Linear Inverse Model (LIM). After selectively including in or excluding from the LIM the EA and CA modes of the Tropical Atlantic, respectively, we discover that the EA mode has a greater significance in ENSO prediction compared to the CA mode. The evolution of the EA and CA mode optimum initial structures also confirms the impact of the EA mode on the Tropical Pacific. Further study shows that the EA mode can improve the Eastern Pacific (EP)-ENSO and Central Pacific (CP)-ENSO predictions, while the CA mode plays a less important role. Despite the significant influence of the EA mode, the CA mode has become increasingly important since the 2000s and the EA mode has been weakened in recent years. Therefore, the role of the CA mode in ENSO prediction after 2000 should be considered in the future.
Hepatitis and Hepatitis B Virus Reactivation in Everolimus-Treated Solid Tumor Patients: A Focus on HBV-Endemic Areas
Authors: Su Chien-Hao , Chen Chung-Yu , Liu Chien-Ting , Yang Yi-Hsin , Wu Pao-Chu
Published: November 2024

Background: Everolimus is approved for treating breast, renal, and pancreatic neuroendocrine cancers but carries the risk of hepatitis B virus (HBV) reactivation (HBVr) and hepatitis. However, data on HBVr in everolimus-treated patients are limited. This study evaluates the risk of hepatitis and HBVr in cancer patients with current or past HBV infection. Methods: This retrospective study analyzed patients prescribed everolimus between 1 January 2011 and 31 May 2022, using a private healthcare system database in Taiwan. Patients with HBsAg positivity or HBsAg negativity and anti-HBs or anti-HBc results were included. The cumulative incidence function and risk of hepatitis from a competing risk model, which estimates Fine-Gray subdistribution hazard (SDH), were analyzed across different HBV serological subgroups. The risk of hepatitis B reactivation was also calculated. Results: Of 377 patients, 45% (36/80) of HBsAg-positive and 0.67% (2/297) of HBsAg-negative patients received nucleos(t)ide analogues (NUCs) prophylaxis. Hepatitis occurred in 28.75% of HBsAg-positive and 17.85% of HBsAg-negative patients. Baseline HBsAg positivity and exemestane use increased hepatitis risk. HBVr occurred in 11.36% (5/44) of HBsAg-positive patients without NUCs and 5.56% (2/36) with prophylaxis. Two HBsAg-negative, anti-HBc-positive patients developed severe HBVr-related hepatitis. Conclusion: Hepatitis occurred in 28.75% of HBsAg-positive and 17.85% of HBsAg-negative patients on everolimus. HBVr was common in HBsAg-positive patients but rare in HBsAg-negative individuals. HBV screening and liver function monitoring are critical for patients with past or current HBV infection receiving everolimus, especially in endemic areas.
Metabolic and Regulatory Pathways Involved in the Anticancer Activity of Perillyl Alcohol: A Scoping Review of In Vitro Studies
Authors: Batista Brochado Ana Carolina , Moraes Júlia Alves de , Rodrigues de Oliveira Bruna , De Souza Lima Victor Hugo , Mariano Eric Domingos , Karande Sachin , Romasco Tea , Leite Paulo Emilio Correa , Mourão Carlos Fernando , Gomes Alves Gutemberg
Published: November 2024

Background/Objectives: Perillyl alcohol (POH), a plant-derived compound, has demonstrated anti-tumor activity across various human cancers. Understanding the regulatory pathways through which POH exerts its effects is crucial for identifying new therapeutic opportunities and exploring potential drug repositioning strategies. Therefore, this scoping review aims to provide a comprehensive overview of the metabolic and regulatory pathways involved in the anticancer effects of POH, based on in vitro evidence. Methods: Following the PRISMA-ScR 2018 guidelines, a systematic search was conducted in the PUBMED, Web of Science, and Scopus databases. Results: A total of 39 studies were included, revealing that POH exerts its biological effects by modulating several pathways, including the regulation of cyclins, CDKs, and p21, thereby affecting cell cycle progression. It inhibits growth and promotes cell death by attenuating AKT phosphorylation, reducing PARP-1 activity, increasing caspase activity and the FAS receptor and its ligand FASL. Additionally, POH reduces ERK phosphorylation, inhibits RAS protein isoprenylation, and decreases Na/K-ATPase activity. Conclusions: In conclusion, this review delineates the key regulatory pathways responsible for mediating the biological effects of POH in cancer.
The Potential of a Novel Cold Atmospheric Plasma Jet as a Feasible Therapeutic Strategy for Gingivitis—A Cell-Based Study
Authors: Negrescu Andreea-Mariana , Zampieri Leonardo , Martines Emilio , Cimpean Anisoara
Published: November 2024

Due to its antimicrobial, anti-inflammatory and pro-healing properties, the application of cold atmospheric plasma (CAP) has emerged as a new and promising therapeutic strategy in various fields of medicine, including general medicine and dentistry. In this light, the aim of the present study was to investigate the effects of a homemade plasma jet on the cellular behaviour of two important cell types involved in gingivitis, namely gingival fibroblasts (HGF-1 cell line) and macrophages (RAW 264.7 cell line), by the direct application of CAP in different experimental conditions. The cellular behaviour of the HGF-1 cells was investigated in terms of viability/proliferation (LIVE/DEAD and CCK-8 assays), morphological features (immunofluorescent staining of the actin cytoskeleton) and fibronectin expression (immunocytochemical staining of the fibronectin network), while the macrophages’ response was evaluated through the assessment of the cellular survival/proliferation rate (LIVE/DEAD and CCK-8 assays), morphological behaviour (immunofluorescent staining of the actin cytoskeleton) and inflammatory activity (pro-inflammatory cytokine secretion profile (ELISA assay) and foreign body giant cells (FBGCs) formation (immunofluorescent staining of the actin cytoskeleton and multinuclearity index determination)). The in vitro biological assessment revealed an upward trend dependent on treatment time and number of CAP applications, in terms of fibroblasts proliferation (p < 0.0001) and fibronectin expression (p < 0.0001). On the other hand, the macrophages exposed to five consecutive CAP applications for longer treatment times (over 120 s) exhibited a strong pro-inflammatory activity, as evinced by their altered morphology, pro-inflammatory cytokine profile (p < 0.0001) and FBGCs formation. Overall, our results demonstrate that CAP exposure, when used with appropriate operating parameters, has a beneficial effect on the cellular response of HGF-1 and RAW 264.7 cells, thus paving the way for further in vitro and in vivo investigations that will allow the translation of CAP treatment from research to clinic as an alternative therapy for gingivitis.
Correction: Xin et al. Dexras1 Induces Dysdifferentiation of Oligodendrocytes and Myelin Injury by Inhibiting the cAMP-CREB Pathway after Subarachnoid Hemorrhage. Cells 2022, 11, 2976
Authors: Xin Yuanjun , Chen Jie , Zhang Hongxia , Ostrowski Robert P. , Liang Yidan , Zhao Jun , Xiang Xiang , Liang Fuming , Fu Wenqiao , Huang Hao , Wu Xintong , Su Jun , Deng Jiewen , He Zhaohui
Published: November 2024

In the original publication [...]
Metabolic Adaptations in Cancer and the Host Using Drosophila Models and Advanced Tools
Authors: Saez-Carrion Ernesto , Aguilar-Aragon Mario , García-López Lucia , Dominguez Maria , Uribe Mary Luz
Published: November 2024

Cancer is a multifactorial process involving genetic, epigenetic, physiological, and metabolic changes. The ability of tumours to regulate new reactive pathways is essential for their survival. A key aspect of this involves the decision-making process of cancer cells as they balance the exploitation of surrounding and distant tissues for their own benefit while avoiding the rapid destruction of the host. Nutrition plays a central role in these processes but is inherently limited. Understanding how tumour cells interact with non-tumoural tissues to acquire nutrients is crucial. In this review, we emphasise the utility of Drosophila melanogaster as a model organism for dissecting the complex oncogenic networks underlying these interactions. By studying various levels—from individual tumour cells to systemic markers—we can gain new insights into how cancer adapts and thrives. Moreover, developing innovative technologies, such as high-throughput methods and metabolic interventions, enhances our ability to explore how tumours adapt to different conditions. These technological advances allow us to explore tumour adaptations and open new opportunities for potential therapeutic strategies.
Serotonin Inhibition of Claustrum Projection Neurons: Ionic Mechanism, Receptor Subtypes and Consequences for Claustrum Computation
Authors: Wong Kelly Li Lin , Graf Martin , Augustine George J.
Published: November 2024

The claustrum is a small but densely interconnected brain structure that is innervated by axons containing serotonin (5-HT), a neuromodulator that has been implicated in control of sleep and in the actions of psychedelic drugs. However, little is known about how 5-HT influences the claustrum. We have combined whole-cell patch-clamp measurements of ionic currents, flash photolysis, and receptor pharmacology to characterize the 5-HT responses of individual claustral projection neurons (PNs) in mouse brain slices. Serotonin application elicited a long-lasting outward current in claustral PNs. This current was due to an increase in membrane permeability to K+ ions and was mediated mainly by the type 1A 5-HT receptor (5-HTR-1A). The 5-HT-induced K+ current hyperpolarized, and thereby inhibited, the PNs by reducing action potential firing. Focal uncaging of 5-HT revealed that inhibitory 5-HTR-1As were located at both the soma and dendrites of PNs. We conclude that 5-HT creates a net inhibition in the claustrum, an action that should decrease claustrum sensitivity to excitatory input from other brain areas and thereby contribute to 5-HT action in the brain.
Evaluation of the Antimicrobial Effect of Ag Nanoparticles on Nickel–Titanium Archwires in the Presence of Streptococcus mutans Bacteria
Authors: Lozoya Sebastián , Rico Raquel Duarte , León Eder Alejandro Carreón , López Meléndez Claudia , Carreño-Gallardo Caleb , Madrigal Rosa Margarita Aguilar , Monreal Romero Humberto Alejandro
Published: November 2024

In this study, Streptococcus mutans bacteria were tested on nickel–titanium archwires in the presence of silver nanoparticles (AgNPs) as coatings. As a growth control, a well containing the BHI broth and bacterial suspension without silver nanoparticles was inoculated. The test was carried out in triplicate. The NiTi archwires in the presence of artificial saliva were incubated at different exposure times between 0–24 h and 15–30 days, respectively. The archwires were then put in contact with S. mutans to evaluate the AgNPs bactericidal effect. The characterization of AgNPs with NiTi archwires was conducted using scanning electron microscopy (SEM), X-ray energy dispersive analysis (EDS), fast fourier transform (FFT), power spectral density (PSD), surface geometry analysis, metal relation analysis, and control process analysis. The results indicate that the bioelectric signal and chemical interaction of NiTi and Ag nanoparticles have an antibacterial effect. In this context, the high wavelength of 17.06 mm and the wave amplitude of 15.66 GL are representative of the light scattering and humidity of the system in which the bacteria and silver nanoparticles interact. The sizes of the Ag nanoparticles in the archwires were less than 150 nm. Under microaerophile conditions, the solution’s pH and temperature were 7.0 at 37 °C, respectively. The NiTi archwires AgNPs functionality and relation to the minimum inhibitory concentration, dominant wavelength in power spectral density, and fast fourier transform analyses were investigated. The analysis was of the interaction between the high and low frequencies of the AgNPs-NiTi archwires and the S. mutans bacteria. This approach opens up a new route for the assessment and management of bacterial growth in various fluids by utilizing alternative biologically acceptable materials.
Microstructure and Oxidation Behaviors of (TiVCr)2AlC MAX-Phase Coatings Prepared by Magnetron Sputtering
Authors: Zhu Yufeng , Zheng Yueqing , Chen Ke , Huang Qing , Meng Fanping
Published: November 2024

A solid solution is an effective approach to regulate the microstructure and hence the various properties such as hardness and oxidation behavior of materials. In this study, an M-site solid-solution medium-entropy-alloy MAX-phase coating (TiVCr)2AlC was prepared through combining the magnetron sputter deposition at low- and high-temperature vacuum annealing. The mechanical properties and high-temperature oxidation resistance in the 700–1000 °C temperature range in air of these coatings were then evaluated. The results showed that the 211-MAX-phase can be formed in the 700 °C vacuum for 3 h, and the crystallinity depended on the annealing temperature. Compared to the amorphous coating, the MAX-phase sample demonstrated superior oxidation resistance in terms of the onset temperature of the oxidation and the oxidation products. During high-temperature oxidation, a mixed oxide layer containing V2O5, TiO2, and Cr2O3 was formed at 700 °C on the surface of an amorphous coating, whereas only a thin continuous Al2O3 scale was observed at ≤800 °C for the crystalline (TiVCr)2AlC coating. Additionally, the maximum hardness of the coating reached 18 GPa after annealing. These results demonstrate the application potential of the medium-entropy-alloy MAX-phase coating in extreme environments such as aerospace, nuclear energy, and other fields.
The Microstructure of Aluminide Coatings on 310S and 347H Steels Formed by Pack Aluminizing and Their Corrosion Behavior in Molten Chloride Salts
Authors: Chen Weiqian , La Peiqing , Li Zengpeng , Li Yaming , Wan Lei
Published: November 2024

In order to enhance the resistance of superalloys to high-temperature molten chloride salt corrosion, Fe-Al coatings were prepared on 310S and 347H stainless-steel surfaces via pack aluminizing. Then, the coatings were annealed at different temperatures to explore the influence of temperature on their phase constitution, microstructure, microhardness, and corrosion resistance. The results showed that the annealing temperature had a considerable effect on the corrosion resistance of the Fe-Al coatings, which was related to the change in the phase composition of the coatings that occurred due to the annealing treatment. The growth rate of the coating on 347H steel was higher than that on 310S steel, and their thicknesses from aluminizing at 800 °C for 20 h were 209.6 and 153.5 µm, respectively. When annealing at 900 °C for 30 h, the phase composition of the coatings was completely transformed into (Fe, Cr, Ni) Al. The corrosion loss rate of the annealed coating was clearly reduced, the loss rate of the 310 coating was 6.0 and −0.25 mg/cm2 before and after annealing at 900 °C and that of the 347 coating was 4.89 and −0.7 mg/cm2 before and after annealing at 750 °C, respectively. The two coatings showed good corrosion resistance to molten chloride salts, as demonstrated by the oxide scale (Al2O3) that formed on the surface, which had a thickness of about 30~40 µm.
Comparative Morphometric Study of the Occipital Condyle in Class III and Class I Skeletal Malocclusion Patients
Authors: Gumussoy Ismail , Duman Suayip Burak , Miloglu Ozkan , Demirsoy Mustafa Sami , Dogan Ayhan , Abdelkarim Ahmed Z. , Guller Mustafa Taha
Published: November 2024

Objectives: Since the formation of skeletal malocclusions is closely linked to general craniofacial development, it is crucial to understand the anatomy and growth patterns of the skull base. This study aimed to assess the morphometry of the occipital condyle (OC) on CBCT scans of Class III skeletal malocclusion subjects and compare the findings with those of skeletal Class I malocclusion subjects. Methods: A retrospective analysis was performed on CBCT images based on predefined inclusion and exclusion criteria. The sample consisted of 76 CBCT images of 38 skeletal Class III patients and 38 skeletal Class I patients. CBCT scans were used to measure mesiodistal width, sagittal length, coronal height, effective height of OC, and sagittal OC angle. Statistical analyses were conducted with RStudio software. Results: Significant differences were found in sagittal OC angle and sagittal length of OC between the study groups (p < 0.001). In other metrics, such as coronal height of OC, effective OC height, and mesiodistal width of OC between the groups, no significant differences were found. Class III malocclusions exhibited significantly reduced sagittal OC angle and sagittal length of OC compared to Class I malocclusions. The left side showed a significantly larger sagittal OC angle than the right side (p = 0.002). Conclusions: This preliminary study identified reduced sagittal angle and sagittal length of OC in patients with Class III skeletal malocclusion. Clinicians should recognize potential differences in OC morphometry in patients with skeletal malocclusions. Future studies involving larger populations are recommended to further investigate the relationship between skeletal malocclusions and posterior cranial base structures, including the OC.
Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data
Authors: Amouei Sheshkal Sajad , Gundersen Morten , Alexander Riegler Michael , Aass Utheim Øygunn , Gunnar Gundersen Kjell , Rootwelt Helge , Prestø Elgstøen Katja Benedikte , Lewi Hammer Hugo
Published: November 2024

Background: Dry eye disease is a common disorder of the ocular surface, leading patients to seek eye care. Clinical signs and symptoms are currently used to diagnose dry eye disease. Metabolomics, a method for analyzing biological systems, has been found helpful in identifying distinct metabolites in patients and in detecting metabolic profiles that may indicate dry eye disease at early stages. In this study, we explored the use of machine learning and metabolomics data to identify cataract patients who suffer from dry eye disease, a topic that, to our knowledge, has not been previously explored. As there is no one-size-fits-all machine learning model for metabolomics data, choosing the most suitable model can significantly affect the quality of predictions and subsequent metabolomics analyses. Methods: To address this challenge, we conducted a comparative analysis of eight machine learning models on two metabolomics data sets from cataract patients with and without dry eye disease. The models were evaluated and optimized using nested k-fold cross-validation. To assess the performance of these models, we selected a set of suitable evaluation metrics tailored to the data set’s challenges. Results: The logistic regression model overall performed the best, achieving the highest area under the curve score of 0.8378, balanced accuracy of 0.735, Matthew’s correlation coefficient of 0.5147, an F1-score of 0.8513, and a specificity of 0.5667. Additionally, following the logistic regression, the XGBoost and Random Forest models also demonstrated good performance. Conclusions: The results show that the logistic regression model with L2 regularization can outperform more complex models on an imbalanced data set with a small sample size and a high number of features, while also avoiding overfitting and delivering consistent performance across cross-validation folds. Additionally, the results demonstrate that it is possible to identify dry eye in cataract patients from tear film metabolomics data using machine learning models.
A Review of State-of-the-Art Methodologies and Applications in Action Recognition
Authors: Zhao Lanfei , Lin Zixiang , Sun Ruiyang , Wang Aili
Published: November 2024

Action recognition, a vital subfield of computer vision, profoundly enhances security, health, and human–computer interaction through its sophisticated analytical capabilities. The review presents a comprehensive analysis of action recognition methodologies, systematically classified by model architecture and input modalities, encompassing traditional techniques, RGB-based neural networks, skeleton-based networks, and advanced pose estimation methods for extracting skeletal data. A rigorous comparative evaluation of the architectures and outcomes of advanced methods within each category are also conducted. Notably, prior reviews have yet to examine action recognition from the perspective of practical applications. This review, for the first time, presents the practical applications of multiple action recognition techniques and forecasts the future trajectory of the field by integrating recent research trends. It aids in understanding the evolution of behavior recognition systems and exploring advanced strategies, facilitating the integration of intelligent systems into human society.
Superposition of Voltage Disturbances Generated by Direct Current Traction
Authors: Olczykowski Zbigniew
Published: November 2024

Electric traction vehicles require a power supply with appropriate voltage parameters. In Poland, a direct voltage power supply system has been adopted. This requires the use of traction substations with the conversion of alternating voltage to direct voltage, which generates a number of disturbances related to the quality of electric power. Dynamic changes in the substation load resulting from the movement of traction vehicles are another cause of voltage changes. The article presents an analysis of measurement results carried out simultaneously in systems powering various traction substations and non-traction demand lines. Due to the fact that non-traction demand lines supply both recipients related to the movement of traction vehicles (including railway stations) and other industrial and municipal recipients, the quality of energy is therefore very important.
Visualization of Real-Time Forest Firefighting Inference and Fire Resource Allocation Simulation Technology
Authors: Yang Siyu , Huai Yongjian , Nie Xiaoying , Meng Qingkuo , Zhang Rui
Published: November 2024

In recent years, the increasing frequency of forest fires has threatened ecological and social security. Due to the risks of traditional fire drills, three-dimensional visualization technology has been adopted to simulate forest fire management. This paper presents an immersive decision-making framework for forest firefighting, designed to simulate the response of resources during fires. First, a fire resource scheduling optimization model for multiple fire stations is proposed. This model integrates the characteristics of fire spread with a mixed-integer linear programming (MILP) framework, aiming to minimize response time and firefighting costs. It enables flexible resource scheduling optimization under various fire spread scenarios and constraints on firefighting resources. Second, the ant lion optimization algorithm (ALO) is enhanced, incorporating multiple firefighting weighting factors such as the density, distance, and wind direction of burning trees. This improvement allows for the dynamic selection of priority firefighting targets, facilitating the precise allocation of resources to efficiently complete fire suppression tasks. Finally, a three-dimensional virtual forest environment is developed to simulate real-time actions and processes during firefighting operations. The proposed framework provides an immersive and visualized real-time fire simulation method, offering valuable support for decision-making in forest fire management.
Upregulation of ABLIM1 Differentiates Intrahepatic Cholangiocarcinoma from Hepatocellular Carcinoma and Both Colorectal and Pancreatic Adenocarcinoma Liver Metastases
Authors: Draškovič Tina , Ranković Branislava , Zidar Nina , Hauptman Nina
Published: November 2024

Background: Altered gene expression in cancers holds great potential to improve the diagnostics and differentiation of primary and metastatic liver cancers. In this study, the expression of the protein-coding genes ring finger protein 135 (RNF135), ephrin-B2 (EFNB2), ring finger protein 125 (RNF125), homeobox-C 4 (HOXC4), actin-binding LIM protein 1 (ABLIM1) and oncostatin M receptor (OSMR) and the long non-coding RNAs (lncRNA) prospero homeobox 1 antisense RNA 1 (PROX1-AS1) and leukemia inhibitory factor receptor antisense RNA 1 (LIFR-AS1) was investigated in hepatocellular carcinoma, cholangiocarcinoma, colorectal liver metastases and pancreatic ductal adenocarcinoma liver metastases. Methods: This study included 149 formalin-fixed, paraffin-embedded samples from 80 patients. After RNA isolation, quantification, reverse transcription and preamplification, real-time qPCR was performed. The gene expression between different groups was calculated relative to the expression of the reference genes using the ∆∆Cq method and statistically analyzed. The expression of the genes was additionally analyzed using the AmiCA and UCSC Xena platforms. Results: In primary cancers, our results showed differential expression between primary tumors and healthy tissues for all the genes and lncRNA examined. Moreover, we found downregulation of RNF135 in hepatocellular carcinoma, downregulation of OSMR in colorectal liver metastases and upregulation of HOXC4 in cholangiocarcinoma compared to primary liver cancers and metastatic cancers. The major finding is the upregulation of ABLIM1 in cholangiocarcinoma compared to hepatocellular carcinoma, colorectal liver metastases, pancreatic ductal adenocarcinoma liver metastases and healthy liver tissue. We propose ABLIM1 as a potential biomarker that differentiates cholangiocarcinoma from other cancers and healthy liver tissue. Conclusions: This study emphasizes the importance of understanding the differences in gene expression between healthy tissues and primary and metastatic cancers and highlights the potential use of altered gene expression as a diagnostic biomarker in these malignancies.
Clinical and Cytogenetic Impact of Maternal Balanced Double Translocation: A Familial Case of 15q11.2 Microduplication and Microdeletion Syndromes with Genetic Counselling Implications
Authors: Vieira Daniela Koeller R. , Lima Ingrid Bendas Feres , Rosenberg Carla , Fonseca Carlos Roberto da , Gomes Leonardo Henrique Ferreira , Guida Letícia da Cunha , Mazzonetto Patrícia Camacho , Llerena Juan , Bastos Elenice Ferreira
Published: November 2024

Background: Balanced chromosomal translocations occur in approximately 0.16 to 0.20% of live births. While most carriers are phenotypically normal, they are at risk of generating unbalanced gametes during meiosis, leading to genetic anomalies such as aneuploidies, deletions, duplications, and gene disruptions. These anomalies can result in spontaneous abortions or congenital anomalies, including neurodevelopmental disorders. Complex chromosomal rearrangements (CCRs) involving more than two chromosomes are rare but further increase the probability of producing unbalanced gametes. Neurodevelopmental disorders such as Angelman syndrome (AS) and duplication 15q11q13 syndrome (Dup15q) are associated with such chromosomal abnormalities. Methods: This study describes a family with a de novo maternal balanced double translocation involving chromosomes 13, 19, and 15, resulting in two offspring with unbalanced chromosomal abnormalities. Cytogenetic evaluations were performed using GTG banding, fluorescence in situ hybridization (FISH), and low-pass whole-genome sequencing (LP-WGS). Methylation analysis was conducted using methylation-sensitive high-resolution melting (MS-HRM) to diagnose Angelman syndrome. Results: The cytogenetic and molecular analyses identified an 8.9 Mb duplication in 15q11.2q13.3 in one child, and an 8.9 Mb deletion in the same region in the second child. Both abnormalities affected critical neurodevelopmental genes, such as SNRPN. FISH and MS-HRM confirmed the chromosomal imbalances and the diagnosis of Angelman syndrome in the second child. The maternal balanced translocation was found to be cryptic, contributing to the complex inheritance pattern. Conclusion: This case highlights the importance of using multiple genetic platforms to uncover complex chromosomal rearrangements and their impact on neurodevelopmental disorders. The findings underscore the need for thorough genetic counseling, especially in families with such rare chromosomal alterations, to manage reproductive outcomes and neurodevelopmental risks.
TrAnnoScope: A Modular Snakemake Pipeline for Full-Length Transcriptome Analysis and Functional Annotation
Authors: Pektas Aysevil , Panitz Frank , Thomsen Bo
Published: November 2024

Background/Objectives: Transcriptome assembly and functional annotation are essential in understanding gene expression and biological function. Nevertheless, many existing pipelines lack the flexibility to integrate both short- and long-read sequencing data or fail to provide a complete, customizable workflow for transcriptome analysis, particularly for non-model organisms. Methods: We present TrAnnoScope, a transcriptome analysis pipeline designed to process Illumina short-read and PacBio long-read data. The pipeline provides a complete, customizable workflow to generate high-quality, full-length (FL) transcripts with broad functional annotation. Its modular design allows users to adapt specific analysis steps for other sequencing platforms or data types. The pipeline encompasses steps from quality control to functional annotation, employing tools and established databases such as SwissProt, Pfam, Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and Eukaryotic Orthologous Groups (KOG). As a case study, TrAnnoScope was applied to RNA-Seq and Iso-Seq data from zebra finch brain, ovary, and testis tissue. Results: The zebra finch transcriptome generated by TrAnnoScope from the brain, ovary, and testis tissue demonstrated strong alignment with the reference genome (99.63%), and it was found that 93.95% of the matched protein sequences in the zebra finch proteome were captured as nearly complete. Functional annotation provided matches to known protein databases and assigned relevant functional terms to the majority of the transcripts. Conclusions: TrAnnoScope successfully integrates short and long sequencing technologies to generate transcriptomes with minimal user input. Its modularity and ease of use make it a valuable tool for researchers analyzing complex datasets, particularly for non-model organisms.