MDPI - Publisher of Open Access Journals

Animals
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
Geosciences
Environmental Effects of a Hellenistic Earthquake, Helike, Greece
Catalysts
Recent Advances in Photocatalytic Degradation of Tetracycline Antibiotics

Recent Articles

13 pages, 789 KiB

Open AccessArticle

The Adducts Lipid Peroxidation Products with 2′-DeoxyNucleosides: A Theoretical Approach of Ionisation Potential

by Boleslaw T. Karwowski

Appl. Sci. 2025, 15(1), 437; https://doi.org/10.3390/app15010437 (registering DOI) - 5 Jan 2025

The human body contains ~10¹⁴ cells—each of which is separated by a lipid bilayer, along with its organeller. Unsaturated fatty acids are located on the external layer and, as a result, are particularly exposed to harmful factors, including xenobiotics and ionising radiation. During this activity, lipid peroxidation products are generated, e.g., 4-hydroxy-2-nonenal (HNA), 4-oxo-2(E)-nonenal (ONE), and malondialdehyde (MDA). The mentioned aldehydes can react with cytosolic 2′-deoxynucleosides via Michael addition. In this paper, the following adducts have been taken into theoretical consideration: ε-dCyt, H-ε-dAde, ε-dCyt, H-ε-dAde, H-ε-dGua, R/S-OH-PdGua, N2,3-ε-dGua, M1-dGua, N1-ε-dGua, and HNE-dGua. The presence of the above molecules can alter a cell’s antioxidant pool. With this in mind, the adiabatic ionisation potential (AIP) and vertical ionisation potential (VIP), as well as the spin and charge distributions, are discussed. For this purpose, DFT studies were performed at the M06-2x/6-31++G** level of theory in the aqueous phase (both non-equilibrated (NE) and equilibrated (EQ) solvent–solute interaction modes), together with a Hirshfeld charge and spin distribution analysis. The obtained results indicate that the AIPs of all the investigated molecules fell within a range of 5.72 and 5.98 eV, which is consistent with the reference value of 7,8-dihydro-8-oxo-2′-deoxyguanosine (^OXOdGua), 5.78 eV. N2,3-ε-dGua and M1-dGua were the only exceptions, whose VIP and AIP were noted as higher. The electronic properties analysis of 2′-deoxynucleoside adducts with lipid peroxidation products reveals their potential influence on the cells’ antioxidant pool, whereby they can affect the communication process between proteins, lipids, and nucleotides. Full article

(This article belongs to the Section Chemical and Molecular Sciences)

23 pages, 10239 KiB

Open AccessArticle

Quantifying Time-Lag and Time-Accumulation Effects of Climate Change and Human Activities on Vegetation Dynamics in the Yarlung Zangbo River Basin of the Tibetan Plateau

by Ning Li and Di Wang

Remote Sens. 2025, 17(1), 160; https://doi.org/10.3390/rs17010160 (registering DOI) - 5 Jan 2025

Abstract

Vegetation, as a fundamental component of terrestrial ecosystems, plays a pivotal role in the flux of water, heat, and nutrients between the lithosphere, biosphere, and atmosphere. Assessing the impacts of climate change and human activities on vegetation dynamics is essential for maintaining the health and stability of fragile ecosystems, such as the Yarlung Zangbo River (YZR) basin of the Tibetan Plateau, the highest-elevation river basin in the world. Vegetation responses to climate change are inherently asymmetric, characterized by distinct temporal effects. However, these temporal effects remain poorly understood, particularly in high-altitude ecosystems. Here, we examine the spatiotemporal changes in leaf area index (LAI) and four climatic factors—air temperature, precipitation, potential evapotranspiration, and solar radiation—in the YZR basin over the period 2000–2019. We further explore the time-lag and time-accumulation impacts of these climatic factors on LAI dynamics and apply an enhanced residual trend analysis to disentangle the relative contributions of climate change and human activities. Results indicated that (1) a modest increase in annual LAI at a rate of 0.02 m² m⁻² dec⁻¹ was detected across the YZR basin. Spatially, LAI increased in 66% of vegetated areas, with significant increases (p < 0.05) in 10% of the basin. (2) Temperature, precipitation, and potential evapotranspiration exhibited minimal time-lag (<0.5 months) but pronounced notable time-accumulation effects on LAI variations, with accumulation periods ranging from 1 to 2 months. In contrast, solar radiation demonstrated significant time-lag impacts, with an average lag period of 2.4 months, while its accumulation effects were relatively weaker. (3) Climate change and human activities contributed 0.023 ± 0.092 and –0.005 ± 0.109 m² m⁻² dec⁻¹ to LAI changes, respectively, accounting for 60% and 40% on the observed variability. Spatially, climate change accounted for 85% of the changes in LAI in the upper YZR basin, while vegetation dynamics in the lower basin was primarily driven by human activities, contributing 63%. In the middle basin, vegetation dynamics were influenced by the combined effects of climate change and human activities. Our findings deepen insights into the drivers of vegetation dynamics and provide critical guidance for formulating adaptive management strategies in alpine ecosystems. Full article

22 pages, 26208 KiB

Open AccessArticle

SwinInsSeg: An Improved SOLOv2 Model Using the Swin Transformer and a Multi-Kernel Attention Module for Ship Instance Segmentation

by Rabi Sharma, Muhammad Saqib, Chin-Teng Lin and Michael Blumenstein

Mathematics 2025, 13(1), 165; https://doi.org/10.3390/math13010165 (registering DOI) - 5 Jan 2025

Abstract

Maritime surveillance is essential for ensuring security in the complex marine environment. The study presents SwinInsSeg, an instance segmentation model that combines the Swin transformer and a lightweight MKA module to segment ships accurately and efficiently in maritime surveillance. Current models have limitations in segmenting multiscale ships and achieving accurate segmentation boundaries. SwinInsSeg addresses these limitations by identifying ships of various sizes and capturing finer details, including both small and large ships, through the MKA module, which emphasizes important information at different processing stages. Performance evaluations on the MariBoats and ShipInsSeg datasets show that SwinInsSeg outperforms YOLACT, SOLO, and SOLOv2, achieving mask average precision scores of 50.6% and 52.0%, respectively. These results demonstrate SwinInsSeg’s superior capability in segmenting ship instances with improved accuracy. Full article

(This article belongs to the Special Issue New Advances and Applications in Image Processing and Computer Vision)

12 pages, 3346 KiB

Open AccessArticle

A Digital Twin of Hot Pumping Waxy Oil Through a Main Pipeline

by Uzak Zhapbasbayev, Timur Bekibayev, Gaukhar Ramazanova and Zhibek Akasheva

Energies 2025, 18(1), 202; https://doi.org/10.3390/en18010202 (registering DOI) - 5 Jan 2025

Abstract

This article presents a digital twin of hot pumping waxy oil through a main pipeline. Digital copies of the original object data were identified through sensor measurements from SCADA and ECMAS, forming the basis of the SmartTranPro 1.7.1 Software. The mathematical model of the software describes the process of hot pumping waxy oil regarding heat exchange with the environment. The intelligent algorithms of the SmartTranPro 1.7.1 Software were used to determine the actual dependencies of the digital twins of the objects, hydraulic parameters, and heat transfer for the Kassymov–Bolshoi Chagan hot main pipeline, which has a length of 450 km. The results of the thermal–hydraulic calculations for the hot pumping of waxy oil are in good agreement with the actual sensor data from SCADA and ECASM. The optimization calculations of the heating temperature for waxy oil show an economic efficiency of 38.9% for the hot pumping method. Full article

(This article belongs to the Section H: Geo-Energy)

► Show Figures

Figure 1

---

Figure 3

---

Figure 4

---

Figure 5

---

Figure 6

---

Figure 7

---

Figure 8

---

Figure 9

---

Figure 10

17 pages, 9958 KiB

Open AccessArticle

Spinorphin Molecules as Opportunities for Incorporation into Spinorphin@AuNPs Conjugate Systems for Potential Sustained Targeted Delivery to the Brain

by Stela Georgieva, Petar Todorov and Jana Tchekalarova

Pharmaceuticals 2025, 18(1), 53; https://doi.org/10.3390/ph18010053 (registering DOI) - 5 Jan 2025

Abstract

Background: This study explores the potential for the synthesis of peptide nanosystems comprising spinorphin molecules (with rhodamine moiety: Rh-S, Rh-S5, and Rh-S6) conjugated with nanoparticles (AuNPs), specifically peptide Rh-S@AuNPs, peptide Rh-S5@AuNPs, and peptide Rh-S6@AuNPs, alongside a comparative analysis of the biological activities of free and conjugated peptides. The examination of the microstructural characteristics of the obtained peptide systems and their physicochemical properties constitutes a key focus of this study. Methods: Zeta (ζ) potential, Fourier transformation infrared (FTIR) spectroscopy, circular dichroism (CD), scanning electron microscopy (SEM-EDS), transmission electron microscopy (TEM), and UV–Vis spectrophotometry were employed to elucidate the structure–activity correlations of the peptide@nano AuNP systems. Results: The zeta potential values for all the Rh-S@AuNPs demonstrate that the samples are electrically stable and resistant to flocculation and coagulation. The absorption of energy quanta from UV–Vis radiation by the novel nanopeptide systems does not substantially influence the distinctive signal of AuNPs, which is situated at around 531 nm. The FTIR measurements indicate the signals associated with the unique functional groups of the peptides, whereas circular dichroism verifies the synthesis of the conjugated nanocomposites of the spinorphin@AuNP type. An analysis of the SEM and TEM data revealed that most AuNPs have a spherical morphology, with an average diameter of around 21.92 ± 6.89 nm. The results of the in vivo studies showed promising findings regarding the anticonvulsant properties of the nanocompounds, especially the Rh-S@AuNP formulation. Conclusions: All the nanocompounds tested demonstrated the ability to reduce generalized tonic–clonic seizures. This suggests that these formulations may effectively target the underlying neuronal hyperexcitability. In addition, the prepared Rh-S@AuNP formulations also showed anticonvulsant activity in the maximal electroshock test performed in mice, which was evident after systemic (intraperitoneal) administration. The study’s findings indicate that conjugates can be synthesized via a straightforward process, rendering them potential therapeutic agents with biological activity. Full article

(This article belongs to the Special Issue Peptide Drug Conjugates and Their Applications)

► Show Figures

Figure 1

---

Figure 3

---

Figure 4

---

Figure 5

---

Figure 6

---

Figure 7

---

Figure 8

---

Figure 9

---

Scheme 1

11 pages, 268 KiB

Open AccessArticle

A Note on Generalized k-Order F&L Hybrinomials

by Süleyman Aydınyüz and Gül Karadeniz Gözeri

Axioms 2025, 14(1), 41; https://doi.org/10.3390/axioms14010041 (registering DOI) - 5 Jan 2025

Abstract

In this study, we introduce generalized k-order Fibonacci and Lucas (F&L) polynomials that allow the derivation of well-known polynomial and integer sequences such as the sequences of k-order Pell polynomials, k-order Jacobsthal polynomials and k-order Jacobsthal F&L numbers. Within the scope of this research, a generalization of hybrid polynomials is given by moving them to the k-order. Hybrid polynomials defined by this generalization are called k-order F&L hybrinomials. A key aspect of our research is the establishment of the recurrence relations for generalized k-order F&L hybrinomials. After we give the recurrence relations for these hybrinomials, we obtain the generating functions of hybrinomials, shedding light on some of their important properties. Finally, we introduce the matrix representations of the generalized k-order F&L hybrinomials and give some properties of the matrix representations. Full article

(This article belongs to the Special Issue Advances in Generalized Hypergeometric Functions, Integral Transforms and Number Theory)

30 pages, 5754 KiB

Open AccessArticle

Enhancing CO₂ Sequestration Through Corn Stalk Biochar-Enhanced Mortar: A Synergistic Approach with Algal Growth for Carbon Capture Applications

by Suthatip Sinyoung, Ananya Jeeraro, Patchimaporn Udomkun, Kittipong Kunchariyakun, Margaret Graham and Puangrat Kaewlom

Sustainability 2025, 17(1), 342; https://doi.org/10.3390/su17010342 (registering DOI) - 5 Jan 2025

Abstract

This study examines corn stalk biochar (CSB)-enhanced mortar as an innovative material for carbon capture and CO₂ sequestration. CSB, a renewable agricultural byproduct, was incorporated into cement mortar at varying concentrations (2.5% to 75%), and its effects on the mortar’s physicochemical properties, its ability to support algal growth, and the CO₂ absorption capacity of the algae were analyzed. Characterization of CSB showed a high carbon content (62.3%), significant porosity, and a large surface area (680.3 m² g⁻¹), making it ideal for gas capture. At low concentrations (2.5%), CSB slightly improved the mortar’s compressive strength and density. However, higher CSB levels (5% to 75%) led to significant reductions (p < 0.05) in strength and density, while water absorption increased. CO₂ sequestration monitored from algal growth studies revealed that both Chlorella sp. (TISTR 8262) and Scenedesmus sp. (TISTR 9384) thrived on CSB-enhanced mortars. At a 75% CSB concentration, Scenedesmus sp. achieved a 24.2-fold increase in biomass by day 12, outperforming Chlorella sp., which showed a 26.6-fold increase. CO₂ absorption also improved with biochar. Mortars with 75% CSB achieved an 86% CO₂ absorption ratio without algae, while adding algae boosted this to nearly 100%, highlighting the synergistic effect of biochar and algal photosynthesis. Higher CSB levels accelerated CO₂ absorption stabilization, reaching saturation by day 8 at 75% CSB. Scenedesmus sp. showed slightly higher CO₂ absorption efficiency than Chlorella sp., reaching peak absorption earlier and maintaining greater efficiency. Higher CSB concentrations accelerated CO₂ absorption, indicating that biochar–mortar mixtures, particularly when combined with algae, provide a promising solution for enhancing carbon capture and sequestration in green infrastructure. Full article

► Show Figures

Figure 1

---

Figure 3

---

Figure 4

---

Figure 4 Cont.

---

Figure 5

---

Figure 5 Cont.

---

Figure 6

---

Figure 6 Cont.

---

Figure 7

---

Figure 7 Cont.

---

Figure 8

---

Figure 8 Cont.

---

Figure 9

---

Figure 9 Cont.

18 pages, 6766 KiB

Open AccessArticle

Study on Deformation Mechanism and Surrounding Rock Strata Control in End-Mining Retracement Roadway in Closely Spaced Coal Seams

by Bin Wang, Hui Liu, Dong Liu, Jie Zhang and Haifei Lin

Appl. Sci. 2025, 15(1), 436; https://doi.org/10.3390/app15010436 (registering DOI) - 5 Jan 2025

Abstract

This paper aims to address the issue of hydraulic support crushing accidents or support failures in the retracement roadway (RR) that frequently occurs when a fully mechanized mining face is retraced during the end-mining stage. The deformation and instability mechanism of surrounding rock in the RR during the end mining of a fully mechanized mining face at the Hanjiawan Coal Mine located in the northern Shaanxi mining area is explored through field measurement, theoretical analysis, similar simulation, and numerical simulation. The results reveal that the stability of the remaining coal pillar (RCP) and the fracture position of the main roof are the main factors contributing to large-scale dynamic load pressure in the RR during the end-mining stage. The plastic zone width limit of the RCP is identified to be 5.5 m. Furthermore, the stress distribution within the RCP during the end-mining stage is determined, and the linear relationship between the load borne by the RCP and the strength of the coal pillar is quantified. A similar simulation experiment is conducted to examine the collapse and instability characteristics of the overlying rock structure during the end-mining stage. UDEC (v.5.0) software is utilized to optimize the roof support parameters of the RR. A surrounding rock control technology that integrates the anchor net cable and hydraulic chock is proposed to ensure RR stability. Meanwhile, a method involving ceasing mining operations and waiting pressure is adopted to ensure a safe and smooth connection between the working face and the RR. This study provides a reference for the surrounding rock control of the RR during end mining in shallow, closely-spaced coal seams under similar conditions. Full article

(This article belongs to the Special Issue Advances in Green Coal Mining Technologies)

19 pages, 4638 KiB

Open AccessArticle

An Improved Method for Human Activity Detection with High-Resolution Images by Fusing Pooling Enhancement and Multi-Task Learning

by Haoji Li, Shilong Ren, Lei Fang, Jinyue Chen, Xinfeng Wang, Guoqiang Wang, Qingzhu Zhang and Qiao Wang

Remote Sens. 2025, 17(1), 159; https://doi.org/10.3390/rs17010159 (registering DOI) - 5 Jan 2025

Abstract

Deep learning has garnered increasing attention in human activity detection due to its advantages, such as not relying on expert knowledge and automatic feature extraction. However, the existing deep learning-based approaches are primarily confined to recognizing specific types of human activities, which hinders scientific decision-making and comprehensive environmental protection. Therefore, there is an urgent need to develop a deep learning model to address multiple-type human activity detection with finer-resolution images. In this study, we proposed a new multi-task learning model (named PE-MLNet) to simultaneously achieve change detection and land use classification in GF-6 bitemporal images. Meanwhile, we also designed a pooling enhancement module (PEM) to accurately capture multi-scale change details from the bitemporal feature maps through combining differencing and concatenating branches. An independent annotated dataset at Yellow River Delta was taken to examine the effectiveness of PE-MLNet. The results showed that PE-MLNet exhibited obvious improvements in both detection accuracy and detail handling compared with other existing methods. Further analysis uncovered that the areas of buildings, roads, and oil depots has obviously increased, while the farmland and wetland areas largely decreased over the five years, indicating an expansion of human activities and their increased impacts on natural environments. Full article

(This article belongs to the Special Issue Intelligent Processing, Mining and Application of Remote Sensing Information)

16 pages, 293 KiB

Open AccessArticle

New Perspectives on Generalised Lacunary Statistical Convergence of Multiset Sequences

by María C. Listán-García, Ömer Kişi and Mehmet Gürdal

Mathematics 2025, 13(1), 164; https://doi.org/10.3390/math13010164 (registering DOI) - 5 Jan 2025

Abstract

This paper explores the concepts of

J

-lacunary statistical limit points,

J

-lacunary statistical cluster points, and

J

-lacunary statistical Cauchy multiset sequences. Building upon previous work in the field, we investigate the relationships between

J

-lacunary statistical convergence and

J^{*}

-lacunary [...] Read more.

This paper explores the concepts of

J

-lacunary statistical limit points,

J

-lacunary statistical cluster points, and

J

-lacunary statistical Cauchy multiset sequences. Building upon previous work in the field, we investigate the relationships between

J

-lacunary statistical convergence and

J^{*}

-lacunary statistical convergence in multiset sequences. The findings contribute to a deeper understanding of the convergence behaviour of multiset sequences and provide new insights into the application of ideal convergence in this context. Full article

(This article belongs to the Section Difference and Differential Equations)

11 pages, 401 KiB

Open AccessArticle

The Impact of Karate and Yoga on Children’s Physical Fitness: A 10-Week Intervention Study

by Tomasz Rutkowski and Agnieszka Chwałczyńska

Appl. Sci. 2025, 15(1), 435; https://doi.org/10.3390/app15010435 (registering DOI) - 5 Jan 2025

Abstract

This study investigated the effects of a 10-week intervention incorporating karate and yoga on the physical fitness of children aged 10–13. Conducted on a sample of 67 boys, the participants were divided into three groups: karate, yoga, and a control group. Each group underwent physical assessments before and after the intervention, focusing on various fitness components measured by the International Physical Fitness Test (MTSF). The intervention sessions were held twice a week and included warm-up exercises, karate or yoga elements, and cool-down. The results demonstrated significant improvements (p > 0.05) in most physical fitness parameters among the karate and yoga groups in trials: run 50 m, jump, run, endurance, hanging, run, agility, sit-ups, and forward bend. However, there was no significant improvement in hand strength, indicating that the intervention might not have adequately targeted this aspect of fitness. The overall MTSF values changed as follows. For the karate group, the results improved significantly, with the mean increasing by 6.95 ± 0.71. In the yoga group, the results also improved significantly, with the mean increasing by 8.74, while the standard deviation changed by −0.19. In contrast, the control group, which did not participate in additional activities, showed a decline in performance in some areas, notably hand strength and jumping ability. Total MTSF value decreased by −1.05, and the standard deviation changed by 0.71. The findings suggest that both karate and yoga can effectively enhance children’s physical fitness, bringing their performance closer to age-adjusted norms. The study highlights the value of structured physical activity programs in promoting the physical development of children. Although no significant differences were observed between the karate and yoga groups, both interventions contributed similarly to overall fitness improvements. Future research should explore additional factors, such as diet and other physical activities, to provide a more comprehensive understanding of the intervention’s effects. Full article

(This article belongs to the Special Issue The Impact of Sport and Exercise on Physical Health)

22 pages, 1173 KiB

Open AccessArticle

Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea

by Daniele Padovano, Arturo Martinez-Rodrigo, José M. Pastor, José J. Rieta and Raul Alcaraz

Appl. Sci. 2025, 15(1), 433; https://doi.org/10.3390/app15010433 (registering DOI) - 5 Jan 2025

Abstract

Obstructive sleep apnea (OSA) represents a significant health concern. While polysomnography (PSG) remains the gold standard, its resource-intensive nature has encouraged the exploration of further alternative approaches. Most of these were based on the heart rate variability (HRV) analysis, but only a few of them have presented a recurrence-based approach. The present paper addresses this gap by integrating convolutional neural networks (CNNs) with HRV recurrence analysis. Employing three different and publicly available databases from PhysioNet’s official repository (Apnea-ECG, MIT-BIH, and UCD-DB), the presented method was able to expose concealed patterns within the distance matrix of HRV’s phase space, which is discernible at an appropriate level of abstraction through CNNs. Under the challenging context of external validation (MIT-BIH and UCD for training, and Apnea-ECG for testing), the results obtained were comparable to those presented in the state of the art, achieving a peak accuracy of 75%, while maintaining balanced sensitivity and specificity at 74% and 75%, respectively. Moreover, these results obtained by the proposed CNN-based recurrence analysis of HRV also outperformed traditional time–frequency models, which have yielded values of accuracy lower than 65%. Hence, this paper highlights the importance of the proposed method in gaining new insights into OSA’s HRV dynamics, offering a contribution that adds to the existing analytical approaches in the state of the art. Full article

(This article belongs to the Special Issue AI-Based Biomedical Signal Processing)

14 pages, 442 KiB

Open AccessArticle

Associations Between Dynamic Strength Index and Jumping, Sprinting and Change of Direction Performance in Highly Trained Basketball Players

by Jernej Pleša, Filip Ujaković, Chris Bishop, Nejc Šarabon and Žiga Kozinc

Appl. Sci. 2025, 15(1), 434; https://doi.org/10.3390/app15010434 (registering DOI) - 5 Jan 2025

Abstract

The aim of this study was to investigate associations and differences between dynamic strength index (DSI) and multi-directional jumping, linear and curvilinear sprinting, and change of direction (CoD). Highly trained basketball players (n = 44) performed a 20 m linear sprint, 20 m 3-point line (curvilinear) sprint, countermovement jump (CMJ), drop jump (DJ), bilateral horizontal jump, unilateral horizontal jump, lateral jump, basketball-specific lateral jump and isometric mid-thigh pull (IMTP). The results showed weak to moderate associations between IMTP performance and horizontal jump, lateral jump and curvilinear sprint (r = −0.33–0.41; p < 0.05). No correlations were found between CMJ peak force and performance variables, while weak correlations were observed between DSI and unilateral horizontal jump (r = −0.36; p < 0.05), lateral jumps, linear sprint and CoD deficit (r = −0.37, −0.38; p < 0.05), showing that lower magnitude of DSI is associated with better performance in those tests. Additional analysis revealed that the low DSI subgroup had the highest IMTP peak force, while the high DSI subgroup had the highest CMJ peak force. The low DSI group showed better performance in vertical, horizontal and lateral jumps, while no significant differences were observed in DJ and curvilinear sprint performance compared to other groups. The findings indicate that athletes with lower DSI values exhibit superior physical performance, suggesting that a strength-oriented training approach may be beneficial for basketball players. Due to the ballistic nature of basketball, more maximal strength training is required to optimize the DSI ratio in basketball players. Additional studies are needed to determine the precise benchmarks for navigating training based on DSI values. Full article

(This article belongs to the Special Issue Applied Sports Performance Analysis)

24 pages, 2099 KiB

Open AccessArticle

Unveiling the Mechanisms of a Remission in Major Depressive Disorder (MDD)-like Syndrome: The Role of Hippocampal Palmitoyltransferase Expression and Stress Susceptibility

by Careen A. Schroeter, Anna Gorlova, Michael Sicker, Aleksei Umriukhin, Alisa Burova, Boris Shulgin, Sergey Morozov, Joao P. Costa-Nunes and Tatyana Strekalova

Biomolecules 2025, 15(1), 67; https://doi.org/10.3390/biom15010067 (registering DOI) - 5 Jan 2025

Abstract

Post-translational modifications of proteins via palmitoylation, a thioester linkage of a 16-carbon fatty acid to a cysteine residue, reversibly increases their affinity for cholesterol-rich lipid rafts in membranes, changing their function. Little is known about how altered palmitoylation affects function at the systemic level and contributes to CNS pathology. However, recent studies suggested a role for the downregulation of palmitoyl acetyltransferase (DHHC) 21 gene expression in the development of Major Depressive Disorder (MDD)-like syndrome. Here, we sought to investigate how susceptibility (sucrose preference below 65%) or resilience (sucrose preference > 65%) to stress-induced anhedonia affects DHHC gene expression in the hippocampus of C57BL/6J mice during the phase of spontaneous recovery from anhedonia. Because MDD is a recurrent disorder, it is important to understand the molecular mechanisms underlying not only the symptomatic phase of the disease but also a state of temporary remission. Indeed, molecular changes associated with the application of pharmacotherapy at the remission stage are currently not well understood. Therefore, we used a mouse model of chronic stress to address these questions. The stress protocol consisted of rat exposure, social defeat, restraint stress, and tail suspension. Mice from the stress group were not treated, received imipramine via drinking water (7 mg/kg/day), or received intraperitoneal injections of dicholine succinate (DS; 25 mg/kg/day) starting 7 days prior to stress and continuing during a 14-day stress procedure. Controls were either untreated or treated with either of the two drugs. At the 1st after-stress week, sucrose preference, forced swim, novel cage, and fear-conditioning tests were carried out; the sucrose test and 5-day Morris water maze test followed by a sacrifice of mice on post-stress day 31 for all mice were performed. Transcriptome Illumina analysis of hippocampi was carried out. Using the RT-PCR, the hippocampal gene expression of Dhhc3, Dhhc7, Dhhc8, Dhhc13, Dhhc14, and Dhhc21 was studied. We found that chronic stress lowered sucrose preference in a subgroup of mice that also exhibited prolonged floating behavior, behavioral invigoration, and impaired contextual fear conditioning, while auditory conditioning was unaltered. At the remission phase, no changes in the sucrose test were found, and the acquisition of the Morris water maze was unchanged in all groups. In anhedonic, but not resilient animals, Dhhc8 expression was lowered, and the expression of Dhhc14 was increased. Antidepressant treatment with either drug partially preserved gene expression changes and behavioral abnormalities. Our data suggest that Dhhc8 and Dhhc14 are likely to be implicated in the mechanisms of depression at the remission stage, serving as targets for preventive therapy. Full article

(This article belongs to the Section Molecular Medicine)

18 pages, 23143 KiB

Open AccessArticle

Effect of Al/Cu Ratio on Microstructure and High-Temperature Oxidation Resistance of Al_xCoCrCu_yFeNi High-Entropy Alloy Coatings

by Ling Zhou, Hongxi Liu, Qinghua Zhang, Jiazhu Liang, Yuanrun Peng, Xuanhong Hao, Chen Yang, Yaxia Liu and Yueyi Wang

J. Manuf. Mater. Process. 2025, 9(1), 13; https://doi.org/10.3390/jmmp9010013 (registering DOI) - 5 Jan 2025

Abstract

To improve high-temperature oxidation resistance for Ti6Al4V alloy, Al_xCoCrCu_yFeNi (x = 0, 0.3, 0.5, 0.7, 1.0; y = 1.0, 0.7, 0.5, 0.3, 0, x + y = 1.0) high-entropy alloy (HEA) coatings were prepared on the Ti6Al4V alloy substrate by a laser cladding technique. The results show that the coatings were mainly composed of FCC, BCC, and Ti-rich phases. Severe segregation of the Cu element occurred in the CoCrCuFeNi HEA coatings as a Cu-rich phase (FCC2). The Cu-rich phases decreased with a decreasing Cu content and completely disappeared until the Al content reached 1.0. The microhardnesses of the Cu_1.0, Cu_0.7Al_0.3, Cu_0.5Al_0.5, Cu_0.3Al_0.7, and Al_1.0 HEA coatings were 2.01, 2.06, 2.08, 2.09, and 2.11 times that of the substrate, and compared with those of a Ti6Al4V alloy substrate, the oxidation rates of the HEA coatings decreased by 55%, 51%, 47%, 42%, and 35%, respectively. The surface oxides of the five coatings were mainly composed of CuO, TiO₂, Fe₃O₄, Cr₂O₃, and Al₂O₃. The increase in the Al content promoted the generation of Al₂O₃ film and Cr₂O₃ on the surfaces of the coatings, which significantly improved the high-temperature antioxidant performance of the high-entropy alloy coatings for 50 h at 800 °C. When x = 1.0, the coating showed the best high-temperature antioxidant performance. Full article

(This article belongs to the Topic Advanced Manufacturing and Surface Technology)

► Show Figures

Figure 1

---

Figure 3

---

Figure 4

---

Figure 5

---

Figure 6

---

Figure 7

---

Figure 8

---

Figure 9

---

Figure 10

---

Figure 11

---

Figure 12

22 pages, 3522 KiB

Open AccessArticle

Life Cycle Carbon Emission Analysis of Buildings with Different Exterior Wall Types Based on BIM Technology

by Yuelong Lyu, Nikita Igorevich Fomin, Shuailong Li, Wentao Hu, Shuoting Xiao, Yue Huang and Chong Liu

Buildings 2025, 15(1), 138; https://doi.org/10.3390/buildings15010138 (registering DOI) - 5 Jan 2025

Abstract

Building energy conservation and emission reduction are crucial in addressing global climate change. High-performance insulated building envelopes can significantly reduce energy consumption over a building’s lifecycle. However, few studies have systematically analyzed carbon reduction potential through a life cycle assessment (LCA), incorporating case studies and regional differences. To address this, this study establishes an LCA carbon emission calculation model using Building Information Modeling (BIM) technology and the carbon emission coefficient method. We examined four residential buildings in China’s cold regions and hot summer–cold winter regions, utilizing prefabricated concrete sandwich insulation exterior walls (PCSB) and autoclaved aerated concrete block self-insulating exterior walls (AACB). Results indicate that emissions during the operational phase account for 75% of total lifecycle emissions, with heating, ventilation, and air conditioning systems contributing over 50%. Compared to AACB, PCSB reduces lifecycle carbon emissions by 18.54% and by 20.02% in hot summer–cold winter regions. The findings demonstrate that PCSB offers significant energy-saving and emission-reduction benefits during the construction and operation phases. However, it exhibits higher energy consumption during the materialization and demolition phases. This study provides a practical LCA carbon calculation framework that offers insights into reducing lifecycle carbon emissions, thereby guiding sustainable building design. Full article

(This article belongs to the Section Building Energy, Physics, Environment, and Systems)

22 pages, 2619 KiB

Open AccessArticle

A Novel Power Prediction Model Based on the Clustering Modification Method for a Heavy-Duty Gas Turbine

by Jing Kong, Wei Yu, Jinwei Chen and Huisheng Zhang

Appl. Sci. 2025, 15(1), 432; https://doi.org/10.3390/app15010432 (registering DOI) - 5 Jan 2025

Abstract

Data-driven models utilizing machine learning algorithms provide an effective approach for predicting power in heavy-duty gas turbines, extracting valuable insights from large-scale operational datasets. However, global unified models often struggle to meet the accuracy requirements of all data when dealing with complex and variable operating conditions, leading to limited prediction accuracy for local conditions. To address this problem, a clustering modification method is introduced to develop a novel power prediction model for heavy-duty gas turbines. In this study, the Support Vector Regression (SVR) prediction model is combined with a k-means clustering modification model, enabling the model to adapt to different operational conditions. Operational data from an E-class gas turbine are carefully preprocessed, including filtering, noise reduction, and steady-state selection, to enhance data quality. Then, the k-means algorithm is employed to classify operational conditions, with tailored modification models trained for each category. These modification models refine predictions to accommodate variations in specific operating states. Experimental results demonstrate that the composite model achieves a 32.66% reduction in MAPE and an increase in R² to 0.9982 compared to single-model approaches. The analysis further highlights that training the model with 70% of the annual data achieves optimal prediction accuracy and stability. Additionally, the model significantly reduces high-error occurrences, with 75% of predictions having errors below 0.2946%. This method improves the precision and adaptability of power prediction for gas turbines, providing a practical framework that enhances the reliability of real-world applications and supports the advancement of data-driven energy systems. Full article

18 pages, 5467 KiB

Open AccessArticle

Stem and Leaf Segmentation and Phenotypic Parameter Extraction of Tomato Seedlings Based on 3D Point

by Xuemei Liang, Wenbo Yu, Li Qin, Jianfeng Wang, Peng Jia, Qi Liu, Xiaoyu Lei and Minglai Yang

Agronomy 2025, 15(1), 120; https://doi.org/10.3390/agronomy15010120 (registering DOI) - 5 Jan 2025

Abstract

High-throughput measurements of phenotypic parameters in plants generate substantial data, significantly improving agricultural production optimization and breeding efficiency. However, these measurements face several challenges, including environmental variability, sample heterogeneity, and complex data processing. This study presents a method applicable to stem and leaf segmentation and parameter extraction during the tomato seedling stage, utilizing three-dimensional point clouds. Focusing on tomato seedlings, data was captured using a depth camera to create point cloud models. The RANSAC, region-growing, and greedy projection triangulation algorithms were employed to extract phenotypic parameters such as plant height, stem thickness, leaf area, and leaf inclination angle. The results showed strong correlations, with coefficients of determination for manually measured parameters versus extracted 3D point cloud parameters being 0.920, 0.725, 0.905, and 0.917, respectively. The root-mean-square errors were 0.643, 0.168, 1.921, and 4.513, with absolute percentage errors of 3.804%, 5.052%, 5.509%, and 7.332%. These findings highlight a robust relationship between manual measurements and the extracted parameters, establishing a technical foundation for high-throughput automated phenotypic parameter extraction in tomato seedlings. Full article

(This article belongs to the Section Precision and Digital Agriculture)

► Show Figures

Figure 1

---

Figure 3

---

Figure 4

---

Figure 5

---

Figure 6

---

Figure 7

---

Figure 8

---

Figure 9

---

Figure 10

---

Figure 11

---

Figure 12

---

Figure 13

---

Figure 14

---

Figure 15

---

Figure 16

---

Figure 17

---

Figure 18

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

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)

► Show Figures