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Aging is an important risk factor for tumorigenesis. Metabolic reprogramming is a hallmark of both aging and tumor initiation. However, the manner in which the crosstalk between aging and metabolic reprogramming affects the tumor microenvironment (TME) to promote tumorigenesis was poorly explored. We utilized a computational approach proposed by our previous work, MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to characterize aging-related metabolic plasticity events using pan-cancer bulk RNA-seq data. Our analysis revealed a high degree of metabolically organized heterogeneity across 17 aging-related cancer types. In particular, a higher degree of several energy generation pathways, i.e., glycolysis and impaired oxidative phosphorylation, was observed in older patients. Similar phenomena were also found via single-cell RNA-seq analysis. Furthermore, those energy generation pathways were found to be weakened in activated T cells and macrophages, whereas they increased in exhausted T cells, immunosuppressive macrophages, and Tregs in older patients. It was suggested that aging-induced metabolic switches alter glucose utilization, thereby influencing immune function and resulting in the remodeling of the TME. This work offers new insights into the associations between tumor metabolism and the TME mediated by aging, linking with novel strategies for cancer therapy. [ABSTRACT FROM AUTHOR]

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is a classic multi-criteria decision-making problem. Therefore, this study proposes a multi-criteria decision-making method based on the two-dimensional linguistic cloud model to optimize site selection for offshore wind-to-hydrogen bases. Firstly, the alternative schemes are evaluated using two-dimensional linguistic information, and a new model for transforming two-dimensional linguistic information into a normal cloud is constructed. Then, the cloud area overlap degree is defined to calculate the interaction factor between decision-makers, and a multi-objective programming model based on maximum deviation-minimum correlation is established. Following this, the Pareto solution of criteria weights is solved using the non-dominated sorting genetic algorithm II, and the alternatives are sorted and selected through the cloud-weighted average operator. Finally, an index system was constructed in terms of resource conditions, planning conditions, external conditions, and other dimensions, and a case study was conducted using the location of offshore wind-to-hydrogen production bases in Shanghai. The method proposed in this study demonstrates strong robustness and can provide a basis for these multi-criteria decision-making problems with solid qualitative characteristics. [ABSTRACT FROM AUTHOR]

In this article, an optimized fault-tolerant control (FTC) method without current judgement is proposed for open-switch faults (OSFs) in dual three-phase permanent magnet synchronous motor (DTPMSM) drives. The reason for the torque ripple under OSFs has been investigated. The theoretical analysis reveals a significant increase in torque ripple under OSFs. Then, an optimized FTC method is proposed for a DTPMSM with two isolated neutral points. The proposed method maintains the original control scheme, enabling the smooth transitions of current and torque between faulty operation and FTC without introducing noticeable torque ripples. In addition, the universality and robustness are enhanced by eliminating the need for current judgement, thereby avoiding misjudgments due to sinusoidal current zero crossings, sudden load, or speed changes. The experimental results are presented to validate the effectiveness of the proposed FTC strategy under OSFs on a laboratory DTPMSM. [ABSTRACT FROM AUTHOR]

Within the area of environmental perception, automatic navigation, object detection, and computer vision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which have spawned the creation of a large number of related applications. At the same time, with the rapid increase in autonomous systems in the market, energy consumption has become an increasingly critical issue in computer vision and SLAM (Simultaneous Localization and Mapping) algorithms. This paper presents the results of a detailed review of over 100 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement in computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the state-of-the-art advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis of conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM algorithms in the context of autonomous vehicles was explored. Additionally, in the context of intelligent robots for low-carbon, unmanned factories, we discussed model optimization techniques such as pruning and quantization, highlighting their importance in enhancing energy efficiency. We conducted a comprehensive comparison of the performance and energy consumption of various computer vision algorithms, with a detailed exploration of how to balance these factors and a discussion of potential future development trends. [ABSTRACT FROM AUTHOR]

Simple Summary: The Anji salamander (Hynobius amjiensis) is an endangered amphibian native primarily to China. Artificial breeding is crucial for its population growth, yet research on its reproduction remains limited. This study identified 25 early embryo stages and observed that these embryos contain less yolk compared to those of other salamanders or frogs. The Anji salamander employs a "mass escape" reproductive strategy, producing fewer but higher-quality offspring due to intense competition within its restricted breeding habitats. This research offers new insights into these salamnders' breeding biology and proposes methods to enhance breeding practices for conservation. Hynobius amjiensis, also known as the Anji salamander, is an amphibian species currently categorized as endangered due to its limited geographical distribution, primarily in China. To address the critical conservation status of this species, artificial breeding is essential for population expansion. However, progress in artificial breeding efforts has been hindered by the scarcity of research on the reproductive biology of the Anji salamander. In this study, we identified 25 distinct early stages of embryo development. Additionally, we observed that Anji salamander embryos contain a lesser amount of yolk compared to other salamanders or frogs. We further discovered that the Anji salamander employs a highly competitive reproductive strategy, producing a smaller number of high-quality offspring. This strategy aims to generate adaptive individuals through intense intraspecific competition, driven by three factors: extremely confined breeding habitats, a substantial number of eggs, and a reduced yolk content. We introduce the term "mass escape" to describe this strategy, which provides a novel perspective on cannibalization, focusing on the consumption of specific body parts rather than a single-individual activity. This study offers valuable insights into artificial breeding techniques designed to mitigate inherent intraspecific competitive pressure, thereby improving metamorphosis and survival rates. Additionally, it provides a comprehensive table detailing the pre-embryonic developmental stages of the Anji salamander. [ABSTRACT FROM AUTHOR]

Simple Summary: The role of fatty acid transport protein 1 (FATP1) in decapod crustaceans is still poorly understood. In this study, we cloned the FATP1 cDNA from the mud crab Scylla paramamosain, identified its sequence features and tissue distribution pattern and analyzed its roles in lipid metabolism in vivo. We found that FATP1 is involved in long-chain polyunsaturated fatty acid metabolism and deposition in crustaceans, which provides new information for understanding the function of FATP1 in invertebrates. In mammals, fatty acid transport protein 1 (FATP1) plays important roles in cellular uptake and activation of long-chain fatty acid (LCFA), especially in processes of transportation, oxidation and triacylglycerol synthesis. However, the role of FATP1 in invertebrates, especially decapod crustaceans, is still poorly understood. In this study, the cDNA of a FATP1 gene from a decapod crustacean, mud crab Scylla paramamosain, was cloned and functionally characterized. The FATP1 gene encoded a polypeptide consisting of 643 amino acids that exhibits all the typical features of the FATP family and shares high homology with the other FATP orthologs of crustaceans. The relative mRNA expression levels of FATP1 were observed to be higher in metabolically active tissues such as hepatopancreas, stomach and gill than in other crab parts. Knockdown of the FATP1 mRNA in vivo significantly reduced triacylglycerols and total lipid levels in the hepatopancreas, accompanied by an increase in the expression of genes related to fatty acid transportation, allocation and hydrolysis, including long-chain acyl-CoA synthetase 3/4 (ACSL3/4) and carnitine palmitoyl transferase 1 (CPT1), and a decrease in the expression of genes related to fatty acid synthesis such as acetyl-CoA carboxylase (ACC) and fatty acid synthase (FAS) in the hepatopancreas. Furthermore, increased dietary n-3 long-chain polyunsaturated fatty acid (LC-PUFA) levels resulted in the up-regulation of the FATP1 expression in the hepatopancreas, accompanied by an increase in LC-PUFA content, especially eicosapentaenoic acid (EPA, 20:5n-3) and docosahexaenoic acid (DHA, 22:6n-3), in both polar (PLs) and neutral lipids (NLs) in the hepatopancreas and muscles of crabs. These findings suggested that the FATP1 gene identified in S. paramamosain might play important roles in regulating long-chain fatty acid metabolism and deposition in crustaceans. [ABSTRACT FROM AUTHOR]

A key challenge in the utilization of waste polyester fibers (PET fibers) is the development of fiber-reinforced composites with high filler content and the improvement of fiber length retention. Herein, the effects of a two-rotor continuous mixer and a twin-screw extruder on the structure and properties of waste polyester fiber composites were evaluated. The results revealed that the mechanical properties of the composites were improved significantly with increasing fiber content, especially when processed using the twin-rotor continuous mixer. This mixer facilitated the formation of a robust fiber network structure, leading to substantial enhancements in tensile strength, flexural strength, and heat resistance. Specifically, compared to those processed by the twin-screw extruder, with 60 wt% fibers content, the tensile and flexural strengths of specimens processed by the twin-rotor continuous mixer increase by 21% and 13%, respectively. The average fiber length in specimens processed by the twin-rotor continuous mixer was 32% longer than that in specimens processed by the twin-screw extruder, attributable to the lower shear frequency and the higher tensile ratio of the former. This blending technique emerges as an effective strategy, contributing significantly to promoting the development and practical application of waste textile fiber-reinforced polymer composites. [ABSTRACT FROM AUTHOR]

In this study, we developed an optofluidic chip consisting of a guided-mode resonance (GMR) sensor incorporated into a microfluidic chip to achieve simultaneous blood plasma separation and label-free albumin detection. A sedimentation chamber is integrated into the microfluidic chip to achieve plasma separation through differences in density. After a blood sample is loaded into the optofluidic chip in two stages with controlled flow rates, the blood cells are kept in the sedimentation chamber, enabling only the plasma to reach the GMR sensor for albumin detection. This GMR sensor, fabricated using plastic replica molding, achieved a bulk sensitivity of 175.66 nm/RIU. With surface-bound antibodies, the GMR sensor exhibited a limit of detection of 0.16 μg/mL for recombinant albumin in buffer solution. Overall, our findings demonstrate the potential of our integrated chip for use in clinical samples for biomarker detection in point-of-care applications. [ABSTRACT FROM AUTHOR]

Laser range-gated underwater imaging technology, by removing most of the backscattering noise, can effectively increase image contrast and extend the detection range. The optical signal captured by a range-gated imaging system primarily comprises reflected light from the object and backscattered light from the surrounding water. Consequently, surfaces with low reflectivity or highly turbid water environments substantially constrain the applicability of the range-gated imaging system. To enhance the detection capability of underwater laser range-gated imaging, this paper proposes the incorporation of underwater polarized light imaging technology as an enhancement method. Based on polarization differences, backscattered light and reflected light from an object can be distinguished. Experimental results indicate that, compared to images obtained using a conventional range-gated laser imaging system, those captured with a polarization-enhanced system exhibit an increase of up to 47% for the corresponding Enhancement Measure Evaluation (EME) index. The proposed approach, which integrates polarization imaging with range-gated laser imaging, has the potential to broaden the applicability of underwater laser imaging scenarios, such as deep-sea exploration and military applications. [ABSTRACT FROM AUTHOR]

This paper focuses on the direction-of-arrival (DOA) estimation for noncircular coherently distributed (CD) sources with nested arrays. Usually, for point sources, sparse arrays have the potential to improve the estimation performance of algorithms by obtaining more degrees of freedom. However, algorithms have to be reconsidered for CD sources with sparse arrays and many problems arise. One thorny problem is the disappearance of displacement invariance of the virtual array manifold constructed by the virtualization technique. To deal with this issue, a nested array processing method for CD sources transmitting noncircular signals is proposed in this paper. Firstly, we construct the virtual sum-and-difference co-array by leveraging the noncircular quality of signals with a nested array. Then, an approximation is made to degrade CD sources into point sources. In this way, spatial smoothing techniques can be applied to restore the rank. Finally, in order to reduce the complexity, we modify the reduced-dimension MUSIC to estimate DOAs through a one-dimensional peak-searching procedure. The simulation results prove the superiority of our algorithm against other competitors. [ABSTRACT FROM AUTHOR]

Aqueous zinc-ion batteries (AZIBs) have become a promising and cost-effective alternative to lithium-ion batteries due to their low cost, high energy, and high safety. However, dendrite growth, hydrogen evolution reactions (HERs), and corrosion significantly restrict the performance and scalability of AZIBs. We propose the introduction of a BaTiO3 (BTO) piezoelectric polarized coating as an interface modification strategy for ZIBs. The low surface energy of the BTO (110) crystal plane ensures its thermodynamic preference during crystal growth in experimental processes and exhibits very low reactivity toward oxidation and corrosion. Calculations of interlayer coupling mechanisms reveal a stable junction between BTO (110) and Zn (002), ensuring system stability. Furthermore, the BTO (110) coating also effectively inhibits HERs. Diffusion kinetics studies of Zn ions demonstrate that BTO effectively suppresses the dendrite growth of Zn due to its piezoelectric effect, ensuring uniform zinc deposition. Our work proposes the introduction of a piezoelectric material coating into AZIBs for interface modification, which provides an important theoretical perspective for the mechanism of inhibiting dendrite growth and side reactions in AZIBs. [ABSTRACT FROM AUTHOR]

Adenomyosis involves the infiltration of endometrial glands and stroma deep into the uterine tissue, causing disruption to the endometrial–myometrial interface (EMI). The role of interleukin-17 (IL-17) has been extensively studied in endometriosis, but its involvement in adenomyosis remains unclear. This study aimed to investigate the expression of IL-17 in eutopic and ectopic endometrium (adenomyosis) of individuals with adenomyosis at the level of EMI. Paired tissues of eutopic endometrium and adenomyoma were collected from 16 premenopausal women undergoing hysterectomy due to adenomyosis. The IL-17 system was demonstrated in paired tissue samples at the level of EMI by the immunochemistry study. Gene expression levels of IL-17A and IL-17 receptor (IL-17R) were assessed through quantitative real-time reverse transcription polymerase chain reaction (RT-PCR). Comparative gene transcript amounts were calculated using the delta-delta Ct method. By immunohistochemical staining, CD4, IL-17A, and IL-17R proteins were detected in both eutopic endometrium and adenomyosis at the level of EMI. IL-17A and IL-17R were expressed mainly in the glandular cells, and the expression of both IL-17A and IL-17R was found to be stronger in adenomyosis than in endometrium. 3-Diaminobenzidine (DAB) staining revealed greater IL-17A expression in adenomyosis compared to eutopic endometrium. Quantitative RT-PCR showed 7.28-fold change of IL-17A and 1.99-fold change of IL-17R, and the fold change level of both IL-17A and IL-17R is significantly higher in adenomyosis (IL-17A: p = 0.047, IL-17R: p = 0.027) versus eutopic endometrium. We found significantly higher IL-17 levels in adenomyosis compared to eutopic endometrium at the level of EMI. The results showed that the IL-17 system may play a role in adenomyosis. [ABSTRACT FROM AUTHOR]

Soil microorganisms are critical to the occurrence of Cordyceps sinensis (Chinese Cordyceps), a medicinal fungi used in Traditional Chinese Medicine. The over-collection of Chinese Cordyceps has caused vegetation degradation and impacted the sustainable occurrence of Cordyceps. The effects of Chinese Cordyceps collection on soil microorganisms have not been reported. Metagenomic analysis was performed on the soil of collecting and non-collecting areas of production and non-production areas, respectively. C. sinensis collection showed no alteration in alpha-diversity but significantly affected beta-diversity and the community composition of soil microorganisms. In Cordyceps production, Thaumarchaeota and Crenarchaeota were identified as the dominant archaeal phyla. DNA repair, flagellar assembly, propionate metabolism, and sulfur metabolism were affected in archaea, reducing the tolerance of archaea in extreme habitats. Proteobacteria, Actinobacteria, Acidobacteria, Verrucomicrobia, and Nitrospirae were identified as the dominant bacterial phyla. The collection of Chinese Cordyceps enhanced the bacterial biosynthesis of secondary metabolites and suppressed ribosome and carbon metabolism pathways in bacteria. A more complex microbial community relationship network in the Chinese Cordyceps production area was found. The changes in the microbial community structure were closely related to C, N, P and enzyme activities. This study clarified soil microbial community composition and function in the Cordyceps production area and established that collection clearly affects the microbial community function by altering microbial community structure. Therefore, it would be important to balance the relationship between cordyceps production and microbiology. [ABSTRACT FROM AUTHOR]

This research, which involved a comprehensive methodology, including depositing electroplated copper on a copper seed layer and Al-doped ZnO (AZO) thin films on textured silicon substrates using DC magnetron sputtering with varying substrate heating, has yielded significant findings. The study thoroughly investigated the effects of substrate temperature (Ts) on copper adhesion strength and morphology using the peel force test and electron microscopy. The peel force test was conducted at angles of 90°, 135°, and 180°. The average adhesion strength was about 0.2 N/mm for the samples without substrate heating. For the samples with substrate heating at 100 °C, the average peeling force of the electroplated copper film was about 1 N/mm. The average peeling force increased to 1.5 N/mm as the substrate heating temperature increased to 200 °C. The surface roughness increases as the annealing temperature of the Cu/AZO/Si sample increases. These findings not only provide a reliable and robust method for applying AZO transparent conductive films onto silicon solar cells but also underscore its potential to significantly enhance the efficiency and durability of solar cells significantly, thereby instilling confidence in the field of solar cell technology. [ABSTRACT FROM AUTHOR]

The application of remote sensing technology in water body detection has become increasingly widespread, offering significant value for environmental monitoring, hydrological research, and disaster early warning. However, the existing methods face challenges in multi-scene and multi-temporal water body detection, including the diverse variations in water body shapes and sizes that complicate detection; the complexity of land cover types, which easily leads to false positives and missed detections; the high cost of acquiring high-resolution images, limiting long-term applications; and the lack of effective handling of multi-temporal data, making it difficult to capture the dynamic changes in water bodies. To address these challenges, this study proposes a novel network for multi-scene and multi-temporal water body detection based on spatiotemporal feature extraction, named TSAE-UNet. TSAE-UNet integrates convolutional neural networks (CNN), depthwise separable convolutions, ConvLSTM, and attention mechanisms, significantly improving the accuracy and robustness of water body detection by capturing multi-scale features and establishing long-term dependencies. The Otsu method was employed to quickly process Sentinel-1A and Sentinel-2 images, generating a high-quality training dataset. In the first experiment, five rectangular areas of approximately 37.5 km2 each were selected to validate the water body detection performance of the TSAE-UNet model across different scenes. The second experiment focused on Jining City, Shandong Province, China, analyzing the monthly water body changes from 2020 to 2022 and the quarterly changes in 2022. The experimental results demonstrate that TSAE-UNet excels in multi-scene and long-term water body detection, achieving a precision of 0.989, a recall of 0.983, an F1 score of 0.986, and an IoU of 0.974, significantly outperforming FCN, PSPNet, DeepLabV3+, ADCNN, and MECNet. [ABSTRACT FROM AUTHOR]

Achieving precise and swift monitoring of aquaculture ponds in coastal regions is essential for the scientific planning of spatial layouts in aquaculture zones and the advancement of ecological sustainability in coastal areas. However, because the distribution of many land types in coastal areas and the complex spectral features of remote sensing images are prone to the phenomenon of 'same spectrum heterogeneous objects', the current deep learning model is susceptible to background noise interference in the face of complex backgrounds, resulting in poor model generalization ability. Moreover, with the image features of aquaculture ponds of different scales, the model has limited multi-scale feature extraction ability, making it difficult to extract effective edge features. To address these issues, this work suggests a novel semantic segmentation model for aquaculture ponds called MPG-Net, which is based on an enhanced version of the U-Net model and primarily comprises two structures: MS and PGC. The MS structure integrates the Inception module and the Dilated residual module in order to enhance the model's ability to extract the features of aquaculture ponds and effectively solve the problem of gradient disappearance in the training of the model; the PGC structure integrates the Global Context module and the Polarized Self-Attention in order to enhance the model's ability to understand the contextual semantic information and reduce the interference of redundant information. Using Sentinel-2 and Planet images as data sources, the effectiveness of the refined method is confirmed through ablation experiments conducted on the two structures. The experimental comparison using the FCN8S, SegNet, U-Net, and DeepLabV3 classical semantic segmentation models shows that the MPG-Net model outperforms the other four models in all four precision evaluation indicators; the average values of precision, recall, IoU, and F1-Score of the two image datasets with different resolutions are 94.95%, 92.95%, 88.57%, and 93.94%, respectively. These values prove that the MPG-Net model has better robustness and generalization ability, which can reduce the interference of irrelevant information, effectively improve the extraction precision of individual aquaculture ponds, and significantly reduce the edge adhesion of aquaculture ponds in the extraction results, thereby offering new technical support for the automatic extraction of aquaculture ponds in coastal areas. [ABSTRACT FROM AUTHOR]