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Defining the Role of IgE Against Cross-Reactive Carbohydrates in Dogs
International Journal of Molecular Sciences
The Role of Macrophages in Hepatocellular Carcinoma and Their Therapeutic Potential
Atoms
Gordon Decomposition of the Magnetizability of a Dirac One-Electron Atom in an Arbitrary Discrete Energy State
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Environmental Effects of a Hellenistic Earthquake, Helike, Greece
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Recent Advances in Photocatalytic Degradation of Tetracycline Antibiotics

Recent Articles

16 pages, 1195 KiB

Open AccessArticle

Carbon Emission Reduction Assessment of Ships in the Grand Canal Network Based on Synthetic Weighting and Matter-Element Extension Model

by Zhengchun Sun, Sudong Xu and Jun Jiang

Sustainability 2025, 17(1), 349; https://doi.org/10.3390/su17010349 (registering DOI) - 5 Jan 2025

Abstract

Vessel traffic is an important source of global greenhouse gas emissions. The carbon emissions from ships in the canal network are directly linked to the environmental performance of China’s inland waterway transportation, contributing to the achievement of global carbon reduction goals. Therefore, systematically assessing the carbon emission reduction levels of ships in canal networks is essential to provide a robust foundation for developing more scientific and feasible emission reduction strategies. To address the limitations of current evaluations—which often focus on a single dimension and lack an objective, quantitative representation of the mechanisms driving carbon emission and their synergistic effects—this study took a comprehensive approach. First, considering the factors influencing ship carbon emissions and emission reduction strategies, an evaluation index system was developed. This system included 6 first-level indexes and 22 s-level indexes, covering aspects such as energy utilization, technical equipment, and economic benefits. Second, a novel combination of methods was used to construct an evaluation model. Qualitative weights, determined through the interval binary semantic method, were integrated with quantitative weights calculated using the CRITIC method. These were then combined and assigned using a game-theory-based comprehensive assignment method. The resulting evaluation model, built upon the theory of matter-element topology, represents a significant methodological innovation. Finally, the evaluation method was applied to the empirical analysis of ships operating in Jiangsu section of the Beijing–Hangzhou Grand Canal. This application demonstrated the model’s specificity and feasibility. The study’s findings provide valuable insights for improving carbon emission reduction levels for inland ships and advancing the sustainable development of the shipping industry. Full article

(This article belongs to the Special Issue Sustainable Intelligent Transportation: Cooperative Systems and Vehicle Automation)

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13 pages, 1086 KiB

Open AccessArticle

The Validation of a Novel, Sex-Specific LDL-Cholesterol Equation and the Friedewald, Sampson-NIH, and Extended-Martin–Hopkins Equations Against Direct Measurement in Korean Adults

by Hyun Suk Yang, Soo-Nyung Kim, Seungho Lee and Mina Hur

Metabolites 2025, 15(1), 18; https://doi.org/10.3390/metabo15010018 (registering DOI) - 5 Jan 2025

Abstract

Background/Objectives: The currently established equations for calculating low-density lipoprotein cholesterol (LDLc) do not reflect the sex-specific differences in lipid metabolism. We aimed to develop a sex-specific LDLc equation (SSLE) and validate it with three established equations (Friedewald, Sampson-NIH, and ext-Martin–Hopkins) against direct LDLc measurement in Korean adults. Methods: This study included 23,757 subjects (51% male; median age, 51 years) from the 2009–2022 Korean National Health and Nutrition Examination Survey. We developed the SSLE through multiple linear regression incorporating total cholesterol (TC), high-density lipoprotein cholesterol (HDLc), triglycerides (TG), and sex. The validation metrics included Bland–Altman analysis for mean absolute percentage error (MAPE) and agreement of the categorization based on the NCEP ATP-III guidelines, assessed by sex and lipid subgroups. Results: The derived SSLE equation was as follows: for TG < 200 mg/dL, LDLc = 0.963 × TC − 0.881 × HDLc − 0.111 × TG + 0.982 × Sex − 6.958; for TG ≥ 200 mg/dL, LDLc = 0.884 × TC − 0.646 × HDLc − 0.126 × TG + 3.742 × Sex − 3.214 (male = 1, female = 0). The MAPE was similar between males and females for the SSLE (4.6% for both) and ext-Martin–Hopkins (5.0% vs. 4.9%) but higher in males for the Sampson-NIH (5.4% vs. 4.9%) and Friedewald (7.6% vs. 5.7%). In the TG ≥ 400 mg/dL group, the MAPE increased progressively: SSLE (10.2%), ext-Martin–Hopkins (12.0%), Sampson-NIH (12.7%), and Friedewald (27.4%). In the LDLc < 70 mg/dL group, the MAPE was as follows: SSLE (8.0%), Sampson-NIH (8.6%), ext-Martin–Hopkins (9.7%), and Friedewald (12.8%). At TG 200–400 mg/dL, the SSLE revealed very good agreement (κ = 0.801) versus good agreement for other equations (ext-Martin–Hopkins κ = 0.794, Sampson-NIH κ = 0.782, Friedewald κ = 0.696). Conclusions: The novel SSLE demonstrated superior accuracy and agreement in Korean adults. Further validation studies across different ethnic populations are warranted. Full article

(This article belongs to the Special Issue Lipid Biomarkers and Cardiometabolic Diseases—2nd Edition)

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24 pages, 6317 KiB

Open AccessArticle

BIM-Based Machine Learning Application for Parametric Assessment of Building Energy Performance

by Panagiotis Tsikas, Athanasios Chassiakos, Vasileios Papadimitropoulos and Antonios Papamanolis

Energies 2025, 18(1), 201; https://doi.org/10.3390/en18010201 (registering DOI) - 5 Jan 2025

Abstract

The energy performance of buildings has become a main concern globally in response to increased energy demand, the environmental impacts of energy production, and the reality of energy poverty. To improve energy efficiency, proper building design should be secured at the early design phase. Digital tools are currently available for performing energy assessment analyses and can efficiently handle complex and technically demanding buildings. However, alternative designs should be checked individually, and this makes the process time-consuming and prone to errors. Machine learning techniques can provide valuable assistance in developing decision support tools. In this paper, typical residential buildings are considered along with eleven factors that highly affect energy performance. A dataset of 337 instances of such parameters is developed. For each dataset, the building energy performance is estimated based on BIM analysis. Next, statistical and machine learning techniques are implemented to provide artificial models of energy performance. They include statistical regression modeling (SRM), decision trees (DTs), random forests (RFs), and artificial neural networks (ANNs). The analysis reveals the contribution of each factor and highlights the ANN as the best performing model. An easy-to-use interface tool has been developed for the instantaneous calculation of the energy performance based on the independent parameter values. Full article

(This article belongs to the Special Issue Building Energy Performance Modelling and Simulation)

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13 pages, 312 KiB

Open AccessArticle

Market Reaction to Earnings Announcements Under Different Volatility Regimes

by Yusuf Joseph Ugras and Mark A. Ritter

J. Risk Financial Manag. 2025, 18(1), 19; https://doi.org/10.3390/jrfm18010019 (registering DOI) - 5 Jan 2025

Abstract

This study investigates the occurrence and persistence of abnormal stock returns surrounding corporate earnings announcements, particularly emphasizing how varying frequencies of financial reporting influence market behavior. Specifically, this research examines the effects of the timing and frequency of disclosures on market reactions and stock price volatility during critical earnings announcement periods. By analyzing firms within the Dow Jones Industrial Average (DJIA) from 2014 to 2024, this study evaluates the interplay between financial reporting schedules and market responses to stock prices. Furthermore, it considers the impact of peer firms’ reporting practices on the assimilation of firm-specific information into stock prices. Using econometric models, including Vector Auto Regression (VAR), Impulse Response Functions (IRFs), and Self-Exciting Threshold Autoregressive (SETAR) models, causal relationships between reporting frequency, stock price volatility, and abnormal return patterns across different volatility regimes are identified. The findings highlight that quarterly reporting practices intensify market responses and contribute to significant variations in stock price behavior in high-volatility periods. These insights provide a deeper understanding of the role of financial disclosure practices and forward-looking guidance in shaping market efficiency. This study contributes to ongoing discussions about balancing the transparency benefits of frequent reporting with its potential to amplify market volatility and sector-specific risks, offering valuable implications for policymakers, investors, and corporate managers. Full article

(This article belongs to the Special Issue Advances in Accounting & Auditing Research)

36 pages, 4704 KiB

Open AccessArticle

Computational Fluid Dynamics Prediction of the Sea-Keeping Behavior of High-Speed Unmanned Surface Vehicles Under the Coastal Intersecting Waves

by Xiaobin Hong, Guihong Zheng, Ruimou Cai, Yuanming Chen and Guoquan Xiao

J. Mar. Sci. Eng. 2025, 13(1), 83; https://doi.org/10.3390/jmse13010083 (registering DOI) - 5 Jan 2025

Abstract

To better study the sea-keeping response behavior of unmanned surface vehicles (USVs) in coastal intersecting waves, a prediction is conducted using the CFD method in this paper, in which a USV with the shape of a small-scale catamaran and designed target for high-speed navigating is considered. The CFD method is proved to be good enough at ship response prediction and can be utilized in abundant forms of towing experiment simulations, including planar motion mechanism experiments. The regular and irregular wave generation of numerical CFD can also virtualize the actual wave tank work, making it equally scientific but more efficient than the real test. This research regards the changing trend of encounter characteristics of USVs meeting two trains of waves with different inclination angles and wavelengths by monitoring wave profiles, pitch, heave, acceleration, slamming force, and pressure on specific locations of the USV hull. This paper first introduces the modeling method of intersecting waves in a virtual tank and verifies the wave profiles by comparing them with a theoretical solution. Further, the paper focuses on the sea-keeping motion of USVs and analyzes the complicated influences of encounter parameters. Eventually, this paper analyzes the changing pattern of the motion in encounter frequency and investigates the severity during the sea-keeping period through acceleration analysis. Full article

(This article belongs to the Section Ocean Engineering)

14 pages, 2205 KiB

Open AccessArticle

Germline Single-Nucleotide Polymorphism GFI1-36N Causes Alterations in Mitochondrial Metabolism and Leads to Increased ROS-Mediated DNA Damage in a Murine Model of Human Acute Myeloid Leukemia

by Jan Vorwerk, Longlong Liu, Theresa Helene Stadler, Daria Frank, Helal Mohammed Mohammed Ahmed, Pradeep Kumar Patnana, Maxim Kebenko, Eva Dazert, Bertram Opalka, Nikolas von Bubnoff and Cyrus Khandanpour

Biomedicines 2025, 13(1), 107; https://doi.org/10.3390/biomedicines13010107 (registering DOI) - 5 Jan 2025

Abstract

Background/Objectives: GFI1-36N represents a single-nucleotide polymorphism (SNP) of the zinc finger protein Growth Factor Independence 1 (GFI1), in which the amino acid serine (S) is replaced by asparagine (N). The presence of the GFI1-36N gene variant is associated with a reduced DNA repair capacity favoring myeloid leukemogenesis and leads to an inferior prognosis of acute myeloid leukemia (AML) patients. However, the underlying reasons for the reduced DNA repair capacity in GFI1-36N leukemic cells are largely unknown. Since we have demonstrated that GFI1 plays an active role in metabolism, in this study, we investigated whether increased levels of reactive oxygen species (ROS) could contribute to the accumulation of genetic damage in GFI1-36N leukemic cells. Methods: We pursued this question in a murine model of human AML by knocking in human GFI1-36S or GFI1-36N variant constructs into the murine Gfi1 gene locus and retrovirally expressing MLL-AF9 to induce AML. Results: Following the isolation of leukemic bone marrow cells, we were able to show that the GFI1-36N SNP in our model is associated with enhanced oxidative phosphorylation (OXPHOS), increased ROS levels, and results in elevated γ-H2AX levels as a marker of DNA double-strand breaks (DSBs). The use of free radical scavengers such as N-acetylcysteine (NAC) and α-tocopherol (αT) reduced ROS-induced DNA damage, particularly in GFI1-36N leukemic cells. Conclusions: We demonstrated that the GFI1-36N variant is associated with extensive metabolic changes that contribute to the accumulation of genetic damage. Full article

(This article belongs to the Special Issue Molecular Research on Acute Myeloid Leukemia (AML) Volume II)

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50 pages, 2370 KiB

Open AccessSystematic Review

Movement Disorders and Smart Wrist Devices: A Comprehensive Study

by Andrea Caroppo, Andrea Manni, Gabriele Rescio, Anna Maria Carluccio, Pietro Aleardo Siciliano and Alessandro Leone

Sensors 2025, 25(1), 266; https://doi.org/10.3390/s25010266 (registering DOI) - 5 Jan 2025

Abstract

In the medical field, there are several very different movement disorders, such as tremors, Parkinson’s disease, or Huntington’s disease. A wide range of motor and non-motor symptoms characterizes them. It is evident that in the modern era, the use of smart wrist devices, such as smartwatches, wristbands, and smart bracelets is spreading among all categories of people. This diffusion is justified by the limited costs, ease of use, and less invasiveness (and consequently greater acceptability) than other types of sensors used for health status monitoring. This systematic review aims to synthesize research studies using smart wrist devices for a specific class of movement disorders. Following PRISMA-S guidelines, 130 studies were selected and analyzed. For each selected study, information is provided relating to the smartwatch/wristband/bracelet model used (whether it is commercial or not), the number of end-users involved in the experimentation stage, and finally the characteristics of the benchmark dataset possibly used for testing. Moreover, some articles also reported the type of raw data extracted from the smart wrist device, the implemented designed algorithmic pipeline, and the data classification methodology. It turned out that most of the studies have been published in the last ten years, showing a growing interest in the scientific community. The selected articles mainly investigate the relationship between smart wrist devices and Parkinson’s disease. Epilepsy and seizure detection are also research topics of interest, while there are few papers analyzing gait disorders, Huntington’s Disease, ataxia, or Tourette Syndrome. However, the results of this review highlight the difficulties still present in the use of the smartwatch/wristband/bracelet for the identified categories of movement disorders, despite the advantages these technologies could bring in the dissemination of low-cost solutions usable directly within living environments and without the need for caregivers or medical personnel. Full article

(This article belongs to the Special Issue Intelligent Sensors and Signal Processing in Industry)

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Abstract

Background: Periprosthetic joint infections (PJIs) are a significant issue in joint replacement surgery patients, affecting results and mortality. Recent research focuses on developing hydrogels (HG) and antimicrobial coatings to reduce pressure injuries, with DAC^TM HG showing lower infection risk in hip revision surgery. However, the effectiveness of DAC^TM hydrogel in PIJs is still unknown. Here, we attempt to update the literature in this field, pointing out methodological flaws and providing guidance for further research. Methods: We conducted a systematic literature review using the PRISMA guidelines. Quality assessment was performed with the Newcastle–Ottawa Scale (NOS) and the Coleman Methodology Score (CMS). Results: Among 27 records from the initial search, 3 studies resulted eligible for final evaluation. It was observed that following the three surgical procedures performed in combination with DAC™ loaded with specific antibiotics, the quality of life of the treated patients had improved. No side effects associated with DAC™ treatment were in fact observed. Conclusion: The amount and quality of scientific evidence are yet insufficient to either encourage or dissuade the use of such hydrogels in hip prosthesis, despite some intriguing first results. These challenges will be better addressed by randomized controlled trials or longitudinal prospective investigations. Full article

20 pages, 3925 KiB

Open AccessArticle

Non-Rigid Cycle Consistent Bidirectional Network with Transformer for Unsupervised Deformable Functional Magnetic Resonance Imaging Registration

by Yingying Wang, Yu Feng and Weiming Zeng

Brain Sci. 2025, 15(1), 46; https://doi.org/10.3390/brainsci15010046 (registering DOI) - 5 Jan 2025

Abstract

Background: In neuroscience research about functional magnetic resonance imaging (fMRI), accurate inter-subject image registration is the basis for effective statistical analysis. Traditional fMRI registration methods are usually based on high-resolution structural MRI with clear anatomical structure features. However, this registration method based on structural information cannot achieve accurate functional consistency between subjects since the functional regions do not necessarily correspond to anatomical structures. In recent years, fMRI registration methods based on functional information have emerged, which usually ignore the importance of structural MRI information. Methods: In this study, we proposed a non-rigid cycle consistent bidirectional network with Transformer for unsupervised deformable functional MRI registration. The work achieves fMRI registration through structural MRI registration, and functional information is introduced to improve registration performance. Specifically, we employ a bidirectional registration network that implements forward and reverse registration between image pairs and apply Transformer in the registration network to establish remote spatial mapping between image voxels. Functional and structural information are integrated by introducing the local functional connectivity pattern, the local functional connectivity features of the whole brain are extracted as functional information. The proposed registration method was experimented on real fMRI datasets, and qualitative and quantitative evaluations of the quality of the registration method were implemented on the test dataset using relevant evaluation metrics. We implemented group ICA analysis in brain functional networks after registration. Functional consistency was evaluated on the resulting t-maps. Results: Compared with non-learning-based methods (Affine, Syn) and learning-based methods (Transmorph-tiny, Cyclemorph, VoxelMorph x2), our method improves the peak t-value of t-maps on DMN, VN, CEN, and SMN to 18.7, 16.5, 16.6, and 17.3 and the mean number of suprathreshold voxels (p < 0.05, t > 5.01) on the four networks to 2596.25, and there is an average improvement in peak t-value of 23.79%, 12.74%, 12.27%, 7.32%, and 5.43%. Conclusions: The experimental results show that the registration method of this study improves the structural and functional consistency between fMRI with superior registration performance. Full article

(This article belongs to the Section Neurotechnology and Neuroimaging)

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