bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of YY1 in the Regulation of LAG-3 Expression in CD8 T Cells and Immune Evasion in Cancer: Therapeutic Implications
Authors: Merenstein Adam , Obeidat Loiy , Zaravinos Apostolos , Bonavida Benjamin
Published: December 2024

The treatment of cancers with immunotherapies has yielded significant milestones in recent years. Amongst these immunotherapeutic strategies, the FDA has approved several checkpoint inhibitors (CPIs), primarily Anti-Programmed Death-1 (PD-1) and Programmed Death Ligand-1/2 (PDL-1/2) monoclonal antibodies, in the treatment of various cancers unresponsive to immune therapeutics. Such treatments resulted in significant clinical responses and the prolongation of survival in a subset of patients. However, not all patients responded to CPIs, due to various mechanisms of immune resistance. One such mechanism is that, in addition to PD-1 expression on CD8 T cells, other inhibitory receptors exist, such as Lymphocyte Activation Gene 3 (LAG-3), T cell Immunoglobulin Mucin 3 (TIM3), and T cell immunoreceptor with Ig and ITIM domains (TIGIT). These inhibitory receptors might be active in the presence of the above approved CPIs. Clearly, it is clinically challenging to block all such inhibitory receptors simultaneously using conventional antibodies. To circumvent this difficulty, we sought to target a potential transcription factor that may be involved in the molecular regulation of more than one inhibitory receptor. The transcription factor Yin Yang1 (YY1) was found to regulate the expression of PD-1, LAG-3, and TIM3. Therefore, we hypothesized that targeting YY1 in CD8 T cells should inhibit the expression of these receptors and, thus, prevent the inactivation of the anti-tumor CD8 T cells by these receptors, by corresponding ligands to tumor cells. This strategy should result in the prevention of immune evasion, leading to the inhibition of tumor growth. In addition, this strategy will be particularly effective in a subset of cancer patients who were unresponsive to approved CPIs. In this review, we discuss the regulation of LAG-3 by YY1 as proof of principle for the potential use of targeting YY1 as an alternative therapeutic approach to preventing the immune evasion of cancer. We present findings on the molecular regulations of both YY1 and LAG-3 expressions, the direct regulation of LAG-3 by YY1, the various approaches to targeting YY1 to evade immune evasion, and their clinical challenges. We also present bioinformatic analyses demonstrating the overexpression of LAG-3, YY1, and PD-L1 in various cancers, their associations with immune infiltrates, and the fact that when LAG-3 is hypermethylated in its promoter region it correlates with a better overall survival. Hence, targeting YY1 in CD8 T cells will result in restoring the anti-tumor immune response and tumor regression. Notably, in addition to the beneficial effects of targeting YY1 in CD8 T cells to inhibit the expression of inhibitory receptors, we also suggest targeting YY1 overexpressed in the tumor cells, which will also inhibit PD-L1 expression and other YY1-associated pro-tumorigenic activities.
The Three-Class Annotation Method Improves the AI Detection of Early-Stage Osteosarcoma on Plain Radiographs: A Novel Approach for Rare Cancer Diagnosis
Authors: Hasei Joe , Nakahara Ryuichi , Otsuka Yujiro , Nakamura Yusuke , Ikuta Kunihiro , Osaki Shuhei , Hironari Tamiya , Miwa Shinji , Ohshika Shusa , Nishimura Shunji , Kahara Naoaki , Yoshida Aki , Fujiwara Tomohiro , Nakata Eiji , Kunisada Toshiyuki , Ozaki Toshifumi
Published: December 2024

Background/Objectives: Developing high-performance artificial intelligence (AI) models for rare diseases is challenging owing to limited data availability. This study aimed to evaluate whether a novel three-class annotation method for preparing training data could enhance AI model performance in detecting osteosarcoma on plain radiographs compared to conventional single-class annotation. Methods: We developed two annotation methods for the same dataset of 468 osteosarcoma X-rays and 378 normal radiographs: a conventional single-class annotation (1C model) and a novel three-class annotation method (3C model) that separately labeled intramedullary, cortical, and extramedullary tumor components. Both models used identical U-Net-based architectures, differing only in their annotation approaches. Performance was evaluated using an independent validation dataset. Results: Although both models achieved high diagnostic accuracy (AUC: 0.99 vs. 0.98), the 3C model demonstrated superior operational characteristics. At a standardized cutoff value of 0.2, the 3C model maintained balanced performance (sensitivity: 93.28%, specificity: 92.21%), whereas the 1C model showed compromised specificity (83.58%) despite high sensitivity (98.88%). Notably, at the 25th percentile threshold, both models showed identical false-negative rates despite significantly different cutoff values (3C: 0.661 vs. 1C: 0.985), indicating the ability of the 3C model to maintain diagnostic accuracy at substantially lower thresholds. Conclusions: This study demonstrated that anatomically informed three-class annotation can enhance AI model performance for rare disease detection without requiring additional training data. The improved stability at lower thresholds suggests that thoughtful annotation strategies can optimize the AI model training, particularly in contexts where training data are limited.
DWI in the Differentiation of Malignant and Benign Breast Lesions Presenting with Non-Mass Enhancement on CE-MRI
Authors: Perić Iva , Brkljačić Boris , Tadić Tade , Jerković Kristian , Dolić Krešimir , Borić Matija , Ćavar Marija
Published: December 2024

Objectives: This study aimed to investigate whether the apparent diffusion coefficient (ADC) maps values of breast lesions presenting as non-mass enhancement (NME) on MRI could predict benign or malignant pathohistological findings. Materials and Methods: This retrospective single-center study included 136 female patients with NME and corresponding ultrasound correlate and a subsequent ultrasound-guided core needle biopsy. The patients were subdivided into benign or malignant subgroups based on pathology reports, which served as the gold standard. Blinded to the pathological results, two radiologists independently measured the ADC values of the depicted NME using punctate, 10 mm and whole tumor regions of interest (ROIs) wherever applicable. The mean of all measurements was also analyzed and compared with the pathologic subdivision. Results: The sensitivity of whole tumor ROI in detecting benign NME is 91% compared to 74% for 10 mm ROI and 78% for punctate ROI. No significant differences in ADC values were observed when comparing fatty breast tissue and dense breast tissue. Conclusions: There were differences in ADC values between benign and malignant findings using all types of measurements, where the whole tumor ROI was the most sensitive.
Ezrin Polarization as a Diagnostic Marker for Circulating Tumor Cells in Hepatocellular Carcinoma
Authors: Büdeyri Ibrahim , Guckelberger Olaf , Oppermann Elsie , Roy Dhruvajyoti , Sliwinski Svenja , Becker Felix , Struecker Benjamin , Vogl Thomas J. , Pascher Andreas , Bechstein Wolf O. , Lorentzen Anna , Heikenwalder Mathias , Juratli Mazen A.
Published: December 2024

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third leading cause of cancer-related death worldwide, with no precise method for early detection. Circulating tumor cells (CTCs) expressing the dynamic polarity of the cytoskeletal membrane protein, ezrin, have been proposed to play a crucial role in tumor progression and metastasis. This study investigated the diagnostic and prognostic potential of polarized circulating tumor cells (p-CTCs) in HCC patients. CTCs were isolated from the peripheral blood of 20 HCC patients and 18 patients with nonmalignant liver disease (NMLD) via an OncoQuick® kit and immunostained with Ezrin-Alexa Fluor 488®, CD146-PE, and CD45-APC. A fluorescence microscopy was then performed for analysis. The HCC group exhibited significantly higher levels of p-CTCs, with median values of 0.56 p-CTCs/mL, compared to 0.02 p-CTCs/mL (p = 0.03) in the NMLD group. CTCs were detected in 95% of the HCC patients, with a sensitivity of 95% and specificity of 89%. p-CTCs were present in 75% of the HCC patients, with a sensitivity of 75% and a specificity of 94%. Higher p-CTC counts were associated with the significantly longer overall survival in HCC patients (p = 0.05). These findings suggest that p-CTCs could serve as valuable diagnostic and prognostic markers for HCC. The incorporation of p-CTCs into diagnostic strategies could enhance therapeutic decision-making and improve patient outcomes.
Advanced In Vitro Models for Preclinical Drug Safety: Recent Progress and Prospects
Authors: Nair Dileep G. , Weiskirchen Ralf
Published: December 2024

The majority of drugs are typically orally administered. The journey from drug discovery to approval is often long and expensive, involving multiple stages. A major challenge in the drug development process is drug-induced liver injury (DILI), a condition that affects the liver, the organ responsible for metabolizing most drugs. Traditionally, identifying DILI risk has been difficult due to the poor correlation between preclinical animal models and in vitro systems. Differences in physiology between humans and animals or cell lines contribute to the failure of many drug programs during clinical trials. The use of advanced in vitro systems that closely mimic human physiology, such as organ-on-a-chip models like gut–liver-on-a-chip, can be crucial in improving drug efficacy while minimizing toxicity. Additionally, the adaptation of these technologies has the potential to significantly reduce both the time and cost associated with obtaining safe drug approvals, all while adhering to the 3Rs principle (replacement, reduction, refinement). In this review, we discuss the significance, current status, and future prospects of advanced platforms, specifically organ-on-a-chip models, in supporting preclinical drug discovery.
Multi-Step Simulations of Ionized Metal Physical Vapor Deposition to Enhance the Plasma Formation Uniformity
Authors: Cheon Cheongbin , Hur Min Young , Kim Ho Jun , Lee Hae June
Published: December 2024

Ionized metal physical vapor deposition (IMPVD), which is operated at a very low pressure to take advantage of the metal sputtering effect on the target surface, has unique properties compared with conventional DC magnetron sputtering. In this study, we investigated the effect of the rotating magnetic field on the plasma formation of IMPVD to enhance the deposition uniformity. This was accomplished through a multi-step simulation, which enabled plasma analysis, sputtered particle and chemical reaction analysis, and deposition profile analysis. A two-dimensional particle-in-cell Monte Carlo simulation utilizes the exact cross-section data of the Cu ion collisions and calculates the particle trajectories under specific magnetic field profiles. This new methodology gives guidance for the design of the magnetic field profiles of IMPVD and an understanding of the physical mechanism.
Advances in Crystal Growth: Pioneering Materials for Tomorrow’s Technologies
Authors: Liu Yong , Bi David Wenhua , Magrez Arnaud
Published: December 2024

Crystal growth is inherently multidisciplinary, encompassing fields such as thermodynamics and kinetics, fluid dynamics, solution and solid-state chemistry, process engineering, and defects engineering to name a few [...]
Diversity and Conservation of Carnivores in Saudi Arabia
Authors: Al Ahmari Ali , Neyaz Farah , Shuraim Faisal , Al Ghamdi Abdul Rahman , Al Boug Ahmad , Alhlafi Mesfer , Al Jbour Sharif , Angelici Francesco M. , Alaamri Saleh , Al Masabi Khalid , Amr Zuhair Sami
Published: December 2024

In the present study, we updated the taxonomy and distributional data for 14 carnivore species based on previous records and the recent results of camera trapping (4787 camera nights from 58 locations) and depicted species richness. We then identified threats and the conservation measures that are practiced to ensure the protection of the carnivores of Saudi Arabia. A total of 10 species of carnivores (Canis aureus, Canis lupus, Caracal caracal, Felis lybica, Genetta genetta, Hyaena hyaena, Ichneumia albicauda, Mellivora capensis, Vulpes cana, and Vulpes vulpes) were recorded based on camera trapping efforts. The striped hyena was the most recorded species in 13 locations, with the highest number of observations made at Raydah Protected Area, followed by the red fox and the Arabian wolf. Both Genetta genetta and Mellivora capensis were the least recorded species. Vulpes vulpes was the most distributed species and it was recorded from 15 sites. The southwestern corner of Saudi Arabia covering the Asir, Jazan, and Najran Plateaus extending further into the Al Sarawat Mountains hosts the highest number of carnivore species. Major threats affecting carnivores include killing and animal trade. The status of the critically endangered species, Panthera pardus nimr, was discussed. Threats and the conservation status based on regional assessment according to the IUCN Red List of the carnivores of Saudi Arabia were documented and highlighted.
VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction
Authors: Nacar Omer , Abdelkader Mohamed , Ghouti Lahouari , Gabr Khaled , Al-Batati Abdulrahman , Koubaa Anis
Published: December 2024

This paper addresses the challenge of predicting 3D trajectories for Unmanned Aerial Vehicles (UAVs) in real-time, a critical task for applications like aerial surveillance and defense. Current prediction models primarily leverage only position data, which may not provide the most accurate forecasts for UAV movements and usually fail outside the position domain used in the training phase. Our research identifies a gap in utilizing velocity estimates and first-order dynamics to better capture the dynamics and enhance prediction accuracy and generalizability in any position domain. To bridge this gap, we introduce a trajectory prediction scheme using sequence-based neural networks with Gated Recurrent Units (GRUs) to forecast future velocity and positions based on historical velocity estimates instead of position measurements. This approach is designed to improve the predictive capabilities over traditional methods that rely solely on recurrent neural networks (RNNs) or transformers, which can struggle with scalability in this context. Our methodology employs both synthetic and real-world 3D UAV trajectory data, incorporating diverse patterns of agility, curvature, and speed. Synthetic data are generated using the Gazebo robotics simulator and PX4 Autopilot, while real-world data are sourced from the UZH-FPV and Mid-Air drone racing datasets. We train the GRU-based models on drone 3D position and velocity samples to capture the dynamics of UAV movements effectively. Quantitatively, the proposed GRU-based prediction algorithm demonstrates superior performance, achieving a mean square error (MSE) ranging from 2×10−8 to 2×10−7. This performance outstrips existing state-of-the-art RNN models. Overall, our findings confirm the effectiveness of incorporating velocity data in improving the accuracy of UAV trajectory predictions across both synthetic and real-world scenarios, in and out of position data distributions. Finally, we open-source our 5000 trajectories dataset and a ROS2 package to facilitate the integration with existing ROS-based UAV systems.
Parameter Identification of PEMFC Model Using Improved Dung Beetle Optimization Algorithm
Authors: Zhang Jingfeng , Sun Yalu , Dong Haiying , He Xin
Published: December 2024

A proton exchange membrane fuel cell (PEMFC) is a complex system with multiple inputs and outputs, nonlinearity and strong coupling, and the establishment of an accurate model is the basis for evaluating the performance of PEMFC and developing control strategies. As the majority of the current intelligent algorithms tend to become stuck in local optimum when attempting to determine the PEMFC model’s parameters, resulting in low accuracy of parameter identification and poor model generalization ability, we propose an Improved Dung Beetle Optimization (IDBO) algorithm to identify the PEMFC model’s best parameters. To evaluate the IDBO algorithm’s performance, we identify the model optimal parameters of two typical commercial stacks, BCS 500 W and NedStack PS6, and the self-developed 3 kW PEMFC system, with the minimization of the sum of squared errors between the experimental output voltages and the model output voltages as the objective function. The verification results indicate that the IDBO algorithm has better convergence performance and higher parameter identification exactitude than the DBO algorithm. The robustness and applicability of the IDBO algorithm in addressing the issue of parameter identification of the PEMFC models are verified.
Enhanced Aqueous Zinc-Ion Batteries Using 3D MoS2/Conductive Polymer Composite
Authors: Jiang Tongxin , Li Sijie , Luo Zexiang , Li Xue , Zhang Lifeng , San Haisheng , Li Xin , Ma Yifei
Published: December 2024

MoS2, a typical transition metal dichalcogenide, features a layered structure, multi-phase transition, and tunable band gap, which is a promising candidate for aqueous zinc-ion batteries (AZIBs). Recent studies have focused on the metastable 1T-MoS2 phase, which exhibits superior electrical conductivity and electrochemical activity compared to the more stable 2H phase. Herein, a straightforward one-step hydrothermal method was used to synthesize three-dimensional MoS2/polymer composites (H-MoS2-PEDOT). Under acidic conditions, the polymerization and intercalation of EDOT molecules in the MoS2 layers promote the phase transition from 2H to 1T, thereby enhancing its conductivity and electrochemical performance. Additionally, it was found that the intercalated PEDOT and small amounts of water molecules have contributed to enhancing Zn2+ ion diffusion and cycle stability. As a result, AZIBs based on the H-MoS2-PEDOT composite deliver a high specific capacity of 173.6 mAh g−1 at 1 A g−1, maintaining a specific capacity of 116 mAh g−1 and a capacity retention of 82.8% after 1000 cycles at 5 A g−1.
Model for Joint Operation of Multi-Energy Systems in Energy and Frequency Regulation Ancillary Service Markets Considering Uncertainty
Authors: Hao Wenqi , Liu Yuxing , Wang Tao , Zhang Mingmin
Published: December 2024

A new type of power system with a high proportion of renewable energy sources (RES) penetration has become a global development trend. Meanwhile, the marketization reforms of the electricity market pose challenges to traditional energy. A multi-energy model including a wind turbine (WT), photovoltaic (PV) energy, energy storage (ES), and a thermal power system is proposed in this paper, participating in a joint market mechanism for energy and frequency regulation ancillary services. Unlike existing joint markets, this paper considers the market coupling clearing of various energy sources and the uncertainty of RES generation. Specially, a mechanism for the participation of storage and thermal power units in the frequency regulation ancillary service market is designed. Finally, a practical 118-node case study is provided to validate the impact of renewable generation uncertainty on the participation of multi-energy coupled systems in joint and single energy markets. Compared to the single electricity energy market, the simulation results show that the model can reduce the impact of RES uncertainty on ES generation and increase the cleared electricity quantity of thermal power units by 16%. Moreover, the model also increases the market revenue of thermal power units and storage by 30% and 44%, respectively.
Energy Transitions in Cities: A Comparative Analysis of Policies and Strategies in Hong Kong, London, and Melbourne
Authors: Wong Philip , Lai Joseph
Published: December 2024

This paper reports a comparative analysis of energy transition policies in Hong Kong, London, and Melbourne, highlighting their approaches to achieving carbon neutrality. Utilizing a qualitative research approach, the study combines desktop research and policy analysis to examine secondary data from academic literature and policy reports. A structured policy analysis was developed to compare the strategies of each city, focusing on legislative tools, regulatory mechanisms, and decarbonization goals. The findings reveal that, while all three cities aim to reduce greenhouse gas emissions through energy transition policies, they adopt different strategies shaped by their socio-economic contexts. Hong Kong emphasizes regulatory measures like the Buildings Energy Efficiency Ordinance, London uses market-based instruments such as carbon pricing, and Melbourne prioritizes community engagement and renewable energy integration. Despite progress, challenges remain, including compliance with standards, funding, and public awareness. Recommendations include developing benchmarking strategies, fostering public–private partnerships, and investing in education. This analysis provides actionable insights for future policy development, emphasizing adaptability and innovation in combating climate change and fostering sustainable urban environments.
Studying Forest Species Classification Methods by Combining PolSAR and Vegetation Spectral Indices
Authors: Zhu Hongbo , Song Weidong , Zhang Bing , Lu Ergaojie , Dai Jiguang , Zhao Wei , Hu Zhongchao
Published: December 2024

Tree species are important factors affecting the carbon sequestration capacity of forests and maintaining the stability of ecosystems, but trees are widely distributed spatially and located in complex environments, and there is a lack of large-scale regional tree species classification models for remote sensing imagery. Therefore, many studies aim to solve this problem by combining multivariate remote sensing data and proposing a machine learning model for forest tree species classification. However, satellite-based laser systems find it difficult to meet the needs of regional forest species classification characters, due to their unique footprint sampling method, and SAR data limit the accuracy of species classification, due to the problem of information blending in backscatter coefficients. In this work, we combined Sentinel-1 and Sentinel-2 data to construct a machine learning tree classification model based on optical features, vegetation spectral features, and PolSAR polarization observation features, and propose a forest tree classification feature selection method featuring the Hilbert–Huang transform for the problem of mixed information on the surface of SAR data. The PSO-RF method was used to classify forest species, including four temperate broadleaf forests, namely, aspen (Populus L.), maple (Acer), peach tree (Prunus persica), and apricot tree (Prunus armeniaca L.), and two coniferous forests, namely, Chinese pine (Pinus tabuliformis Carrière) and Mongolian pine (Pinus sylvestris var. mongolica Litv.). In this study, some experiments were conducted using two Sentinel-1 images, four Sentinel-2 images, and 550 measured forest survey sample data points pertaining to the forested area of Fuxin District, Liaoning Province, China. The results show that the fusion model constructed in this study has high accuracy, with a Kappa coefficient of 0.94 and an overall classification accuracy of 95.1%. In addition, this study shows that PolSAR data can play an important role in forest tree species classification. In addition, by applying the Hilbert–Huang transform to PolSAR data, other feature information that interferes with the perceived vertical structure of forests can be suppressed to a certain extent, and its role in the classification of forest species, combined with PolSAR, should not be ignored.
Impact of Insect Foliar Herbivory on Soil N₂O Emission and Nitrogen Dynamics in Subtropical Tree Species
Authors: Yan Bin , Xu Qinqin , Yang Yunyun , Hu Yalin
Published: December 2024

Insect foliar herbivory is ubiquitous in terrestrial ecosystems, yet its impacts on soil nitrogen cycling processes remain not yet well known. To examine the impacts of insect foliar herbivory on soil N2O emission flux and available nitrogen (N), we conducted a pot experiment to measure soil available N content and soil N2O emission flux among three treatments (i.e., leaf herbivory, artificial defoliation, and control,) in two broad-leaved trees (Cinnamomum camphora and Liquidambar formosana) and two conifer trees (Pinus massonianna and Cryptomeria fortunei). Our results showed that insect foliar herbivory significantly increased soil inorganic N (i.e., NH4+–N and NO3−–N), dissolved organic nitrogen (DON) and microbial biomass nitrogen (MBN) contents, and urease activity compared to control treatment. However, there were no differences in soil available N contents and urease activity between artificial defoliation and control treatments, implying that insect foliar herbivory had greater impacts on soil available N contents compared to physical damage of leaves. Moreover, soil N2O emission fluxes were increased by insect foliar herbivory in Cinnamomum camphora and Pinus massonianna, but not for the other two tree species, indicating various effect of insect foliar herbivory on soil N2O emission among tree species. Furthermore, our results showed the positive correlations between soil N2O emission flux and soil NO3−–N, DON, MBN, and acid protease activity, and soil inorganic N, pH, and MBN mainly explained soil N2O emission. Our results implied that insect foliar herbivory can speed up soil nitrogen availability in subtropical forests, but the impacts on soil N2O emission are related to tree species.
Degradation of Oil Paint Coating Based on Wood Under the Combined Effect of UV Light and Heat
Authors: Zuo Shaojun , Li Tongtong , Chen Tong , Li Jianing , Liu Xinyou
Published: December 2024

This study examined the degradation of oil paint coatings on wood under UV light and heat, focusing on three drying oils: tung oil (TO), linseed oil (LO), and walnut oil (WO). Model coatings were prepared with malachite pigment on rubber wood, then exposed to 240 h of UV light at temperatures of 40 °C, 50 °C, and 60 °C. The results showed that tung oil (TO) was the most prone to degradation. After exposure to 60 °C, the lightness (L value) of TO decreased from 51.44 to 50.98, while LO and WO maintained higher lightness. The color differences (ΔE) for TO, LO, and WO were 3.08, 3.26, and 2.87, respectively. Gloss measurements revealed that TO had the lowest initial gloss (3.87 GU), while WO had the highest gloss value. After UV exposure, all three coatings showed a decrease in gloss to varying degrees. Fourier transform infrared spectroscopy (FTIR) analysis confirmed oxidative degradation in TO, characterized by increased hydroxyl and carbonyl bands, while LO and WO exhibited better chemical stability. Scanning electron microscopy (SEM) images revealed that the surface of TO was the roughest, while the WO surface was the smoothest. After UV exposure, the surface of TO became significantly rougher, while the WO coating showed almost no changes, maintaining better structural integrity. The results suggest that LO and WO are more resilient to UV light and thermal stress, making them more suitable for protecting wooden products.
Microbial Characteristics and Functions in Coffee Fermentation: A Review
Authors: Shen Xiaojing , Wang Qi , Wang Hongsheng , Fang Guoqing , Li Ying , Zhang Jilai , Liu Kunyi
Published: December 2024

Based on coffee’s unique and fascinating flavor, coffee has become the most popular nonalcoholic drink in the world and is a significant agricultural economic crop in tropical- and subtropical-planted coffee countries and regions. It is also beneficial for human health because of its rich active compounds, such as caffeine, chlorogenic acids, trigonelline, tryptophan alkaloids, diterpenes, melanoidins, etc. These compounds often relate to the prevention of cardiovascular disease, Alzheimer’s disease, and antibacterial, anti-diabetic, neuroprotection, and anti-cancer activities. The formation of coffee’s flavor results from various influence factors, including genetics, shade, elevation, post-harvest processing, fermentation, roasted methods, etc. The first stage of coffee production is obtaining green coffee beans through the primary process. Fermentation is critical in the primary process of coffee, which is often related to yeasts, bacteria, and filamentous fungi. Therefore, microorganisms play a key role in coffee fermentation and coffee flavor. To provide an understanding of the role of microorganisms in coffee fermentation, the coffee fermentation overview and microbial characteristics in different coffee primary processing methods and different coffee fermentation regions were reviewed in this paper. Brazil and China are the main study countries in coffee fermentation, which contribute a large number of technologies and methods to improve coffee flavor by fermentation. Different primary processing methods (wet, dry, or semi-dry processing) and coffee producer countries had obvious microbial community characteristics. Moreover, the application of yeast and bacteria for improving coffee flavor by microbial fermentation was also introduced.
The Largest Mesosaurs Ever Known: Evidence from Scanty Records
Authors: Piñeiro Graciela , Núñez Demarco Pablo , Laurin Michel
Published: December 2024

Mesosaurs have long been considered to be small to mid-sized aquatic to semiaquatic amniotes that lived in Gondwana during the Early Permian or Late Carboniferous, according to recent research that showed their ghost range extending back to the Pennsylvanian. Previous morphometric analyses based on several hundred mesosaur specimens, including materials from Uruguay, Brazil, South Africa, Namibia, and the Paris National History Museum, provided a comprehensive understanding of mesosaur ontogeny, documented from fetus to adults. As a result, it was possible to determine the approximate size of any individual, measuring just one isolated limb bone, vertebrae, or even cranial elements. Herein, we describe large, poorly preserved and incomplete skulls, as well as axial and appendicular bones, from the Mangrullo Formation Konservat-Lagerstätte of Uruguay that suggest the existence of gigantism in mature mesosaurs reaching more than twice the size of previously described adults and type specimens. The sporadic occurrence of these giant individuals contrasts sharply with the abundant remains of young mesosaurs and, in general, with what is commonly found in the fossil record of vertebrates. The poor preservation of the mature individuals and their presence in coastal areas of the basin is consistent with the hypothesis that older mesosaurs have spent more time near the coast. An alternative hypothesis suggesting pelagic lifestyles is less supported by the available data. Given the preservation of unborn and hatchlings, as well as early juvenile, mature and very mature individuals, the mesosaur record is considered exceptional among early amniotes.
Morphology of Graphene Aerogel as the Key Factor: Mechanical Properties Under Tension and Compression
Authors: Rozhnova Elizaveta , Baimova Julia
Published: December 2024

Graphene aerogels with high surface areas, ultra-low densities, and thermal conductivities have been attracted a lot of attention in recent years. However, considerable difference in their deformation behavior and mechanical properties lead to their poor performance. The problem can be solved by preparing graphene aerogel of given morphology and by control the properties through the special structure of graphene cells. In the present work, molecular dynamics simulation is used to overview the mechanical properties of four different morphologies of graphene aerogel: honeycomb, cellular, lamellar and randomly distributed graphene flakes. All the structures are considered under uniaxial compression and tension with the detailed analysis of the deformation behavior. It is found that cellular structures have much better compressibility and elasticity. During both compression and tension, cellular structures can be transformed from one to another by controlling the compression/tensile direction. The highest strength and fracture strain are found for the lamellar GA under tension along the direction perpendicular to the alignment of the graphene walls. This reveals that the mechanical properties of graphene aerogels can be controlled by enhancing the structural morphology. The obtained results is the contribution which provide the insights into recent developments concerning the design of carbon-based structures and their application.
UAV-Based Survey of the Earth Pyramids at the Kuklica Geosite (North Macedonia)
Authors: Milevski Ivica , Aleksova Bojana , Dragićević Slavoljub
Published: December 2024

This paper presents methods for a UAV-based survey of the site “Kuklica” near Kratovo, North Macedonia. Kuklica is a rare natural complex with earth pyramids, and because of its exceptional scientific, educational, touristic, and cultural significance, it was proclaimed to be a Natural Monument in 2008. However, after the proclamation, the interest in visiting this site and the threats in terms of its potential degradation rapidly grew, increasing the need for a detailed survey of the site and monitoring. Given the site’s small size (0.5 km2), the freely available satellite images and digital elevation models are not suitable for comprehensive analysis and monitoring of the site, especially in terms of the individual forms within the site. Instead, new tools are increasingly being used for such tasks, including UAVs (unmanned aerial vehicles) and LiDAR (Light Detection and Ranging). Since professional LiDAR is very expensive and still not readily available, we used a low-cost UAV (DJI Mini 4 Pro) to carry out a detailed survey. First, the flight path, the altitude of the UAV, the camera angle, and the photo recording intervals were precisely planned and defined. Also, the ground markers (checkpoints) were carefully selected. Then, the photos taken by the drone were aligned and processed using Agisoft Metashape software (v. 2.1.4), producing a digital elevation model and orthophoto imagery with a very high (sub-decimeter) resolution. Following this procedure, more than 140 earth pyramids were delineated, ranging in height from 1 to 2 m and to 30 m at their highest. At this stage, a very accurate UAV-based 3D model of the most remarkable earth pyramids was developed (the accuracy was checked using the iPhone 14 Pro LiDAR module), and their morphometrical properties were calculated. Also, the site’s erosion rate and flash flood potential were calculated, showing high susceptibility to both. The final goal was to monitor the changes and to minimize the degradation of the unique landscape, thus better protecting the geosite and its value.
The Facial Approximation of the Skull Attributed to Jan Žižka (ca. AD 1360–1424)
Authors: Moraes Cicero , Abdullah Johari Yap , Šindelář Jiri , Šindelář Matěj , Thomová Zuzana , Smrčka Jakub , Vaccarezza Mauro , Beaini Thiago , Galassi Francesco Maria
Published: December 2024

The present study aims to approximate the face from the alleged skull of Jan Žižka (ca. AD 1360–1424), a military commander and national hero in the Czech Republic. Found in 1910, the skull has only a fraction of its original structure, which required an initial effort to reconstruct the missing regions from data collected in CT scans of living people’s heads. The forensic facial approximation consisted of projecting the skin boundaries with soft tissue markers and cross-referencing data from statistical projections from CT scans of living people and the use of the anatomical deformation technique, where the digital head of a virtual donor was adjusted until it matched the alleged skull of the Czech general. The final face was the result of the cross-referencing of all data and the completion of the structure respected the iconography attributed to Jan Žižka.
Evaluation of the Timed Up and Go Test in Patients with Knee Osteoarthritis Using Inertial Sensors
Authors: Gianzina Elina , Yiannakopoulos Christos K. , Kalinterakis Georgios , Delis Spilios , Chronopoulos Efstathios
Published: December 2024

Background: There has been a growing interest in using inertial sensors to explore the temporal aspects of the Timed Up and Go (TUG) test. The current study aimed to analyze the spatiotemporal parameters and phases of the TUG test in patients with knee osteoarthritis (KOA) and compare the results with those of non-arthritic individuals. Methods: This study included 20 patients with KOA and 60 non-arthritic individuals aged 65 to 84 years. All participants performed the TUG test, and 17 spatiotemporal parameters and phase data were collected wirelessly using the BTS G-Walk inertial sensor. Results: Significant mobility impairments were observed in KOA patients, including slower gait speed, impaired sit-to-stand transitions, and reduced turning efficiency. These findings highlight functional deficits in individuals with KOA compared to their non-arthritic counterparts. Conclusions: The results emphasize the need for targeted physiotherapy interventions, such as quadriceps strengthening, balance training, and gait retraining, to address these deficits. However, the study is limited by its small sample size, gender imbalance, and limited validation of the BTS G-Walk device. Future research should include larger, more balanced cohorts, validate sensor reliability, and conduct longitudinal studies. Despite these limitations, the findings align with previous research and underscore the potential of inertial sensors in tailoring rehabilitation strategies and monitoring progress in KOA patients.
The Power of Words from the 2024 United States Presidential Debates: A Natural Language Processing Approach
Authors: Jiménez-Preciado Ana Lorena , Álvarez-García José , Cruz-Aké Salvador , Venegas-Martínez Francisco
Published: December 2024

This study analyzes the linguistic patterns and rhetorical strategies employed in the 2024 U.S. presidential debates from the exchanges between Donald Trump, Joe Biden, and Kamala Harris. This paper examines debate transcripts to find underlying themes and communication styles using Natural Language Processing (NLP) advanced techniques, including an n-gram analysis, sentiment analysis, and lexical diversity measurements. The methodology combines a quantitative text analysis with qualitative interpretation through the Jaccard similarity coefficient, the Type–Token Ratio, and the Measure of Textual Lexical Diversity. The empirical results reveal distinct linguistic profiles for each candidate: Trump consistently employed emotionally charged language with high sentiment volatility, while Biden and Harris demonstrated more measured approaches with higher lexical diversity. Finally, this research contributes to the understanding of political discourse in high-stakes debates through NLP and can offer information on the evolution of the communication strategies of the presidential candidates of any country with this regime.
K-Nearest Neighbors with Third-Order Distance for Flooding Attack Classification in Optical Burst Switching Networks
Authors: Nuha Hilal H. , Mugitama Satria Akbar , Absa Ahmed Abo , Sutiyo
Published: December 2024

Optical burst switching (OBS) is a network architecture that combines the advantages of packet and circuit switching techniques. However, OBS networks are susceptible to cyber-attacks, such as flooding attacks, which can degrade their performance and security. This paper introduces a novel machine learning method for flooding attack detection in OBS networks, based on a third-order distance function for k-nearest neighbors (KNN3O). The proposed distance is expected to improve detection accuracy due to higher sensitivity with respect to the distance difference between two points. The developed method is compared with seven other machine learning methods, namely standard KNN, KNN with cosine distance (KNNC), multi-layer perceptron (MLP), naive Bayes classifier (NBC), support vector machine (SVM), decision tree (DT), and discriminant analysis classifier (DAC). The methods are further assessed using five metrics: accuracy, precision, recall, F1-score, and specificity. The proposed method achieved an accuracy of 99.3%, outperforming the original KNN, MLP, and SVM, which achieved accuracies of 99%, 76.4%, and 94.7%, respectively. The results show that KNN3O is the best method for flooding attack detection in OBS networks, as it achieves the highest scores in all five metrics.
The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection
Authors: Berghout Tarek
Published: December 2024

Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-prone. The rise of deep learning has led to advanced models for automated brain tumor feature extraction, segmentation, and classification. Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 research papers over past half-decade (2019–2024), this review fills that gap, exploring the latest methods and paradigms, summarizing key concepts, challenges, datasets, and offering insights into future directions for brain tumor detection using deep learning. This review also incorporates an analysis of previous reviews and targets three main aspects: feature extraction, segmentation, and classification. The results revealed that research primarily focuses on Convolutional Neural Networks (CNNs) and their variants, with a strong emphasis on transfer learning using pre-trained models. Other methods, such as Generative Adversarial Networks (GANs) and Autoencoders, are used for feature extraction, while Recurrent Neural Networks (RNNs) are employed for time-sequence modeling. Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. However, the adoption of eXplainable AI (XAI) remains limited, despite its importance in building trust in medical diagnostics. Finally, this review outlines future opportunities, focusing on image quality, underexplored deep learning techniques, expanding datasets, and exploring deeper learning representations and model behavior such as recurrent expansion to advance medical imaging diagnostics.
Forecasting Influenza Trends Using Decomposition Technique and LightGBM Optimized by Grey Wolf Optimizer Algorithm
Authors: Duan Yonghui , Li Chen , Wang Xiang , Guo Yibin , Wang Hao
Published: December 2024

Influenza is an acute respiratory infectious disease marked by its high contagiousness and rapid spread, caused by influenza viruses. Accurate influenza prediction is a critical issue in public health and serves as an essential tool for epidemiological studies. This paper seeks to improve the prediction accuracy of influenza-like illness (ILI) proportions by proposing a novel predictive model that integrates a data decomposition technique with the Grey Wolf Optimizer (GWO) algorithm, aiming to overcome the limitations of current prediction methods. Firstly, the most suitable indicators were selected using Spearman correlation coefficient. Secondly, a GWO-LightGBM model was established to obtain the residuals between the predicted and actual values. The residual sequence from the GWO-LightGBM model was then decomposed and corrected using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which led to the development of the GWO-LightGBM-CEEMDAN model. The incorporation of the Baidu Index was shown to enhance the precision of the proposed model’s predictions. The proposed model outperforms comparison models in terms of evaluation metrics such as RMSE and MAPE. Additionally, our study found that the revised Baidu Index indicators show a notable association with ILI trends.
Coupled Optimization of UAV Cluster Path Optimization and Task Assignment on a Mobile Platform
Authors: Fu Gaohua , Song Yexin , Wu Yanjie
Published: December 2024

This paper focuses on the coupled optimization problem of path optimization and task assignment for UAVs mounted on mobile platforms. Combining the UAV turning angle, minimum direct flight trajectory and other flight characteristics, the path optimization model on the 3D raster map is established with the objectives of shortest flight time and minimum UAV destruction, and the optimal path between the vertices of each mission is derived by using an improved Gray Wolf Optimization algorithm. Combining the takeoff and landing time-window constraints with the range and mission resource constraints, this mission planning model is established with the objective of maximizing the efficiency ratio of mission revenue–UAV damage consumption. Combining the optimal paths between vertices, a complete UAV flight path is formed, which provides a path optimization and goal assignment method for UAV clusters mounted on mobile platforms to perform multiple tasks cooperatively, and its feasibility and effectiveness are verified through simulation experiments.
Central Serous Chorioretinopathy Associated with Corticosteroid Use in a Patient with Leber Hereditary Optic Neuropathy: A Case Report
Authors: Zoric Lepsa , Petrovic Aleksandra , Milutinovic Vladimir
Published: December 2024

Introduction. Leber hereditary optic neuropathy (LHON) is a condition characterized by bilateral acute or subacute vision loss in seemingly healthy individuals. Depending on the disease stage and initial presentation, it is often diagnosed as optic neuritis. Elevated levels of endogenous and exogenous glucocorticoids have been associated with the onset of central serous chorioretinopathy (CSCR). In our patient, CSCR developed after only three days of pulse corticosteroid therapy, prescribed due to initial presentation as bilateral optic neuritis (papillitis). Objective. Through our case report, we aimed to highlight that CSCR can develop after the initiation of pulse corticosteroid therapy in a patient with LHON and to propose choroidal thickness as a potential contributing factor for this complication. Case Presentation. A 27-year-old male patient presented with painless subacute vision loss in both eyes. The decline in vision developed gradually over 20 days, prior to the patient’s referral to the UKCS Eye Disease Clinic for further examination and treatment, and was not accompanied by pain during eye movements. Initial investigations upon admission to the clinic established the diagnosis of optic neuritis. Consequently, pulse corticosteroid therapy was administered. Three days after the initiation of intravenous methylprednisolone, the patient developed bilateral central serous chorioretinopathy. After cessation of therapy, there was a rapid resolution of choroidopathy, but no improvement in visual acuity, prompting genetic testing. Subsequent laboratory results revealed a positive test for the LHON mutation m.3460 G>A (MT-ND1). Conclusions. LHON is often misdiagnosed as optic neuritis, as upon initial presentation the optic nerve disk often does not exhibit the apparent characteristics of LHON. Numerous studies have documented the development of central serous chorioretinopathy following corticosteroid treatment, though none have reported the onset of CSCR after only three days of pulse corticosteroid therapy. Increased choroidal thickness is a characteristic of the acute phase of LHON and may be associated with the development of CSCR in our patient.