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In biological micromanipulation, cell penetration is a typical procedure that precedes cell injection or oocyte enucleation. During this procedure, cells usually undergo significant deformation, which leads to cell damage. In this paper, we focus on modeling and evaluating the cell penetration process to reduce cell deformation and stress, thereby reducing cell damage. Initially, a finite element model (FEM) is established to simulate the cell penetration process. The effectiveness of the model is then verified through visual detection and comparison of cell deformation with experimental data. Next, various mechanical responses are analyzed, considering the influence of parameters, such as the radius and shape of the injection micropipettes, material properties, and size of the cells. Finally, the relationship between the intracellular stress and the cell penetration depth of biological cells is obtained. The evaluation results will be applied to develop optimized operation plans, enhancing the efficiency and safety of the cell penetration process. [ABSTRACT FROM AUTHOR]

Oil tea camellia (Camellia spp.) is an important woody oil crop with a high nutritional and economic value. Whole-genome resequencing (WGR) technology can provide an in-depth understanding of the genetic background of this plant as well as a reference for breeding research, germplasm resource conservation, and genetic modification. In this study, we analyzed SNP and InDel variations in 49 individual oil tea camellia germplasm samples collected from five populations located in three provinces of China: Hainan, Guangdong and Guangxi. The samples were analyzed through WGR after the ploidy of the samples was determined through flow cytometry. A total of 239,441,603 high-quality single nucleotide polymorphisms (SNPs) and 23,510,374 high-quality insertion/deletion variation sites (InDels) were obtained. The distribution of SNPs and InDels in different functional regions differed significantly, with a high density of variations in non-coding regions, such as intergenic regions and introns, and a relatively low density of variations in coding regions. Transition was the main type of SNP variation. A population genetic diversity analysis revealed that the sampled oil tea camellia populations exhibited a high genetic diversity and extensive genetic variation. The genetic diversity of the oil tea camellia populations in the Hainan region was higher than inland regions. This study also determined the genetic diversity of and variations between octoploid and decaploid oil tea camellia in the tropics and between Hainan-based and inland oil tea camellia. Such findings provide a reference for the conservation of germplasm resources and the genetic modification of oil tea camellia. [ABSTRACT FROM AUTHOR]

This study analyzed the horizontal and vertical distribution characteristics of temperature and salinity in the central and eastern regions of the Beibu Gulf, based on conductivity measurements in summer 2022, temperature, and depth (CTD) measurement data from the Sino–Vietnamese cooperative project "Demonstration Study on Ecological Protection and Management in Typical Bays: Seasonal Survey of the Beibu Gulf". Furthermore, the study utilized the computational results from the numerical Finite-Volume Coastal Ocean Model (FVCOM) to elucidate the intrinsic patterns that formed the temperature and salinity distribution characteristics in August 2022 from both thermodynamic and dynamic perspectives. The circulation in the Beibu Gulf drives external seawater to move northward from the bay mouth. During this movement, numerous upwelling areas are created by lateral Ekman transport. The formation of different scales of cyclonic and anticyclonic vortices and current convergence zones is influenced by topography, runoff, and the water flux from the Qiongzhou Strait, which are key factors in the formation of upwelling and downwelling. The surface circulation in August 2022 significantly differed from the 20-year average surface circulation, with an influx of 1.15 × 104 m3/s more water entering the Beibu Gulf compared to the multi-year average. The water flux from the Qiongzhou Strait is a critical factor affecting the circulation patterns in the Beibu Gulf. The northeastern waters of the Beibu Gulf are characterized by current convergence zones, where extensive upwelling occurs. The rich nutrient salts in these areas promote the reproduction and growth of phytoplankton and zooplankton, making this the most favorable ecological environment in the Beibu Gulf and serving as a natural reserve for fisheries, coral reefs, dugongs, and Bryde's whales. [ABSTRACT FROM AUTHOR]

Alkali-activated concrete (AAC), produced from industrial by-products like fly ash and slag, offers a promising alternative to traditional Portland cement concrete by significantly reducing carbon emissions. Yet, the inherent variability in AAC formulations presents a challenge for accurately predicting its compressive strength using conventional approaches. To address this, we leverage machine learning (ML) techniques, which enable more precise strength predictions based on a combination of material properties and cement mix design parameters. In this study, we curated an extensive dataset comprising 1756 unique AAC mixtures to support robust ML-based modeling. Four distinct input variable schemes were devised to identify the optimal predictor set, and a comparative analysis was performed to evaluate their effectiveness. After this, we investigated the performance of several popular ML algorithms, including random forest (RF), adaptive boosting (AdaBoost), gradient boosting regression trees (GBRTs), and extreme gradient boosting (XGBoost). Among these, the XGBoost model consistently outperformed its counterparts. To further enhance the predictive accuracy of the XGBoost model, we applied four state-of-the-art optimization techniques: the Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), beetle antennae search (BAS), and Bayesian optimization (BO). The optimized XGBoost model delivered superior performance, achieving a remarkable coefficient of determination (R2) of 0.99 on the training set and 0.94 across the entire dataset. Finally, we employed SHapely Additive exPlanations (SHAP) to imbue the optimized model with interpretability, enabling deeper insights into the complex relationships governing AAC formulations. Through the lens of ML, we highlight the benefits of the multi-faceted synergistic approach for AAC strength prediction, which combines careful input parameter selection, optimal hyperparameter tuning, and enhanced model interpretability. This integrated strategy improves both the robustness and scalability of the model, offering a clear and reliable prediction of AAC performance. [ABSTRACT FROM AUTHOR]

Spacecraft recovery technology is crucial in the field of aerospace, in which the parachute plays a key role in slowing down the descent speed of the spacecraft and realizing a smooth landing. In order to construct a dynamically adjustable parachute deployment strategy, it is necessary to measure the parachute dynamic load accurately in real-time. However, the existing sensor measurement scheme makes it difficult to meet the measurement requirements due to its large structure and complex wiring. In order to meet the current demand for real-time measurement of parachute cords dynamic load, a miniature measuring instrument is designed. According to the function and technical requirements of the miniature measuring instrument, the hardware modules of the acquisition system are selected and designed, and the integration debugging and performance optimization of the microcontroller module, A/D sampling module, signal acquisition circuit, and power supply module are carried out. The software of the parachute cords tension acquisition system based on the miniature measuring instrument is developed. The Load Cell is modeled by using SolidWorks 2022 and statically analyzed by using Ansys 2022 R1 Workbench finite element analysis software. Then the final structure of the Load Cell and the pasting position of the strain gauge are determined through the results analysis as well as experimental verification. The hardware module of the signal acquisition system for the miniature measuring instrument is then encapsulated. The force value of the miniature measuring instrument is calibrated and tested many times by using the microcomputer-controlled electronic universal testing machine. The experimental results show that the designed miniature measuring instrument has accurate data, strong stability, and good real-time performance, which meets the demand for real-time accurate measurement of miniature measuring instruments, and can provide reliable data for parachute cords parameter validation and stepless unfolding design. [ABSTRACT FROM AUTHOR]

In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can easily lead to vehicle skidding, loss of control, and even traffic accidents, especially when water has pooled in or submerged the potholes. Therefore, the detection and recognition of road potholes can significantly reduce vehicle damage and the incidence of safety incidents. However, research on road pothole detection lacks high-quality annotated datasets, particularly under low-light conditions at night. To address this issue, this study introduces a novel Nighttime Pothole Dataset (NPD), independently collected and comprising 3831 images that capture diverse scene variations. The construction of this dataset aims to counteract the insufficiency of existing data resources and strives to provide a richer and more realistic benchmark. Additionally, we develop a baseline detector, termed WT-YOLOv8, for the proposed dataset, based on YOLOv8. We also evaluate the performance of the improved WT-YOLOv8 method and eight state-of-the-art object detection methods on the NPD and the COCO dataset. The experimental results on the NPD demonstrate that WT-YOLOv8 achieves a 2.3% improvement in mean Average Precision (mAP) over YOLOv8. In terms of the key metrics—AP@0.5 and AP@0.75—it shows enhancements of 1.5% and 2.8%, respectively, compared to YOLOv8. The experimental results provide valuable insights into each method's strengths and weaknesses under low-light conditions. This analysis highlights the importance of a specialized dataset for nighttime pothole detection and shows variations in accuracy and robustness among methods, emphasizing the need for improved nighttime pothole detection techniques. The introduction of the NPD is expected to stimulate further research, encouraging the development of advanced algorithms for nighttime pothole detection, ultimately leading to more flexible and reliable road maintenance and road safety. [ABSTRACT FROM AUTHOR]

Trichoderma species have been reported as masters in producing cellulolytic enzymes for the biodegradation of lignocellulolytic biomass and biocontrol agents against plant pathogens and pests. In our previous study, a novel Trichoderma strain LZ117, which shows potent capability in cellulase production, was isolated. Herein, we conducted multilocus phylogenetic analyses based on DNA barcodes and performed time-scaled phylogenomic analyses using the whole genome sequences of the strain, annotated by integrating transcriptome data. Our results suggest that this strain represents a new species closely related to T. atrobrunneum (Harzianum clade). Genes encoding carbohydrate-active enzymes (CAZymes), transporters, and secondary metabolites were annotated and predicted secretome in Trichoderma sp. LZ117 was also presented. Furthermore, genetic manipulation of this strain was successfully achieved using PEG-mediated protoplast transformation. A putative transporter gene encoding maltose permease (Mal1) was overexpressed, which proved that this transporter does not affect cellulase production. Moreover, overexpressing the native Cre1 homolog in LZ117 demonstrated a more pronounced impact of glucose-caused carbon catabolite repression (CCR), suggesting the importance of Cre1-mediated CCR in cellulase production of Trichoderma sp. LZ117. The results of this study will benefit further exploration of the strain LZ117 and related species for their applications in bioproduction. [ABSTRACT FROM AUTHOR]

To investigate the applicability and differences in wave loads and the dynamic response calculation principles for SFT on an entire-length scale, two numerical models of entire-length SFT with identical dimensions and parameters were established. These models are employed by a 3D diffraction method based on rigid-body assumptions, the potential flow theory and the Dummy-Connection-Mass (DCM) method based on the lumped mass method and Morison equation while considering hydroelasticity effects. The applicability of the potential flow theory and Morison equation for wave load calculation of SFT is presented along with the differences in their dynamic response calculation, which aim to explore the impact on SFT dynamic responses considering hydroelasticity by comparing the numerical calculation results. Furthermore, a comparison between free-end boundary and fixed-end boundary models, established using the DCM method, is conducted to examine the reasonableness of the commonly adopted free-end boundary condition. [ABSTRACT FROM AUTHOR]

As the development of floating offshore wind turbines (FOWTs) progresses from offshore to deeper sea, the demands on mooring systems to ensure the safety of the structure have become increasingly stringent, leading to a concomitant rise in costs. A parameter optimization method for the mooring system of FOWTs is proposed, with the mooring line length and anchor radial spacing as the optimization variables, and the minimization of surge, yaw, and nacelle acceleration as the objectives. A series of mooring system configuration samples are generated by the fully analytical factorial design method, and the open source program OpenFAST is employed to simulate the global responses in the time domain. To enhance the efficiency of the optimization process, a multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), is utilized to find the Pareto-optimal solutions, alongside a Kriging model, which serves as a surrogate model for the FOWTs. This approach was applied to an IEC 15MW FOWT to demonstrate the optimization procedure. The results indicate that the integration of the genetic algorithm and the surrogate model achieved rapid convergence and high accuracy. Through this optimization process, the longitudinal motion response of FOWTs is reduced by a maximum of 6.46%, the yaw motion by 2.87%, and the nacelle acceleration by 11.55%. [ABSTRACT FROM AUTHOR]

In the clinical diagnosis of pneumonia, particularly during the COVID-19 pandemic, individuals who progress to a critical stage requiring mechanical ventilation are classified as mechanically ventilated critically ill patients. Accurately predicting the discharge outcomes for this specific cohort, especially those with COVID-19, is of paramount clinical importance. Missing data, a common issue in medical research, can significantly impact the validity of analyses. In this work, we address this challenge by employing two missing data imputation techniques: multiple imputation and missForest, to enhance data completeness. Additionally, we utilize the smoothly clipped absolute deviation (SCAD) penalized logistic regression method to select significant features. Our real data analysis compares the predictive performances of extreme learning machines, random forests, support vector machines, and XGBoost using 10-fold cross-validation. The results consistently show that XGBoost outperforms the other methods in predicting discharge outcomes, making it a reliable tool for clinical decision-making in the treatment of severe pneumonia, including COVID-19 cases. Within this context, the random forest imputation method generally enhances performance, underscoring its effectiveness in managing missing data compared to multiple imputation. [ABSTRACT FROM AUTHOR]

Pavement maintenance and rehabilitation decision-making needs to weigh multiple strategic goals to achieve sustainable development through the pavement maintenance management system. Making decisions regarding pavement preventive maintenance is both intricate and costly. This study introduces a multi-criteria decision-making framework aimed at enhancing the scientific basis of such decisions. The framework first establishes an evaluation system for preventive maintenance strategies by considering three primary evaluation criteria—service functionality, pavement performance, and economic benefits, and then identifies nine specific evaluation indicators to influence these criteria, with a comparison matrix constructed to determine the weight of each indicator in relation to the maintenance decision hierarchy. Following this, the technique for order preference by similarity to ideal solution (TOPSIS) is employed to prioritize four commonly utilized preventive maintenance strategies. The results reveal that pavement condition and maintenance costs are the most influential factors in determining the appropriate preventive maintenance strategies for national highways. The priority rankings for the four strategies—slurry seal, micro-surfacing, chip seal, and ultra-thin overlays—are found to be 56.12%, 63.86%, 12.12%, and 83.52%, respectively, with ultra-thin overlays identified as the optimal choice for second-class highways. The decision-making model utilized in this study enables a multi-dimensional analysis, reducing the subjectivity inherent in expert evaluations and facilitating the prompt identification of the most suitable maintenance strategy. [ABSTRACT FROM AUTHOR]

In recent years, smart aggregates have emerged as a promising tool for monitoring the movement of and changes in particles inside asphalt mixtures. However, there remain significant differences between smart aggregates and real rock aggregates, particularly the lack of an asphalt coating on the surface of smart aggregates. Currently, the research on the impact of smart aggregates themselves on the structure and properties of asphalt mixtures is lacking. Therefore, this study focuses on the influence of smart aggregate size on the mesostructure and mechanical properties of asphalt mixtures. Firstly, based on laboratory tests and the discrete element method (DEM), discrete element models of asphalt mixture specimens containing smart aggregates of various sizes were constructed, followed by simulated compaction tests. The effects of smart aggregate size on the mesostructure of asphalt mixture voids were then analyzed. Lastly, in this study, the changes in the dynamic modulus of asphalt mixtures were explored with increasing smart aggregate size and the underlying mechanisms. The results indicate that as the size of smart aggregates increases, the average void ratio of the asphalt mixture specimens decreases, but the heterogeneity of the void distribution increases. Additionally, with the increase in smart aggregate size, the dynamic modulus of the mixture specimens decreases. Further strain analysis of the specimens suggests that the increase in cross-sectional deformation is the primary cause of the reduction in modulus. [ABSTRACT FROM AUTHOR]

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R2) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations. [ABSTRACT FROM AUTHOR]

Degeneration and death of dopaminergic neurons in the substantia nigra of the midbrain are the main pathological changes in Parkinson's disease (PD); however, the mechanism underlying the selective vulnerability of specific neuronal populations in PD remains unclear. Here, we used single-cell RNA sequencing to identify seven cell clusters, including oligodendrocytes, neurons, astrocytes, oligodendrocyte progenitor cells, microglia, synapse-rich cells (SRCs), and endothelial cells, in the substantia nigra of a rotenone-induced rat model of PD based on marker genes and functional definitions. We found that SRCs were a previously unidentified cell subtype, and the tight interactions between SRCs and other cell populations can be improved by icaritin, which is a flavonoid extracted from Epimedium sagittatum Maxim. and exerts anti-neuroinflammatory, antioxidant, and immune-improving effects in PD. We also demonstrated that icaritin bound with transcription factors of SRCs, and icaritin application modulated synaptic characterization of SRCs, neuroinflammation, oxidative stress, and survival of dopaminergic neurons, and improved abnormal energy metabolism, amino acid metabolism, and phospholipase D metabolism of astrocytes in the substantia nigra of rats with PD. Moreover, icaritin supplementation also promotes the recovery of the physiological homeostasis of the other cell clusters to delay the pathogenesis of PD. These data uncovered previously unknown cellular diversity in a rat model of Parkinson's disease and provide insights into the promising therapeutic potential of icaritin in PD. [ABSTRACT FROM AUTHOR]

It is well known that different muscles of yak exhibit distinctive characteristics, such as muscle fibers and metabolomic profiles. We hypothesized that different muscles could alter the flavor profile of yak jerky. Therefore, the objective of this study was to investigate the differences in flavor profiles of yak jerky produced by longissimus thoracis (LT), triceps brachii (TB) and biceps femoris (BF) through electronic nose (E-nose), electronic tongue (E-tongue), gas chromatography–mass spectrometry (GC-MS) and gas chromatography–ion mobility spectrometry (GC-IMS). The results indicated that different muscles played an important role on the flavor profile of yak jerky. And E-nose and E-tongue could effectively discriminate between yak jerky produced by LT, TB and BF from aroma and taste points of view, respectively. In particular, the LT group exhibited significantly higher response values for ANS (sweetness) and NMS (umami) compared to the BF and TB groups. A total of 65 and 47 volatile compounds were characterized in yak jerky by GC-MS and GC-IMS, respectively. A principal component analysis (PCA) model and robust principal component analysis (rPCA) model could effectively discriminate between the aroma profiles of the LT, TB and BF groups. Ten molecules could be considered potential markers for yak jerky produced by different muscles, filtered based on the criteria of relative odor activity values (ROAV) > 1, p < 0.05, and VIP > 1, namely 1-octen-3-ol, eucalyptol, isovaleraldehyde, 3-carene, D-limonene, γ-terpinene, hexanal-D, hexanal-M, 3-hydroxy-2-butanone-M and ethyl formate. Sensory evaluation demonstrated that the yak jerky produced by LT exhibited superior quality in comparison to that produced by BF and TB, mainly pertaining to lower levels of tenderness and higher color, taste and aroma levels. This study could help to understand the specific contribution of different muscles to the aroma profile of yak jerky and provide a scientific basis for improving the quality of yak jerky. [ABSTRACT FROM AUTHOR]

Simple Summary: MiR-206 inhibits lipid production in hepatocytes by promoting insulin signaling pathways. Fatty acid elongase 6 (ELOVL6) is involved in fatty acid metabolism in the liver and adipocytes and is critical for lipid synthesis and triacylglycerol accumulation. Data obtained in the present study revealed that miR-206 impacted fatty acid metabolism and the triacylglycerol concentration directly via ELOVL6 in bovine mammary epithelial cells. Interference with ELOVL6 inhibited the genes related to fatty acid de novo synthesis and desaturation, fatty acid transport, triacylglycerol synthesis, triacylglycerol hydrolysis, and concentration of long-chain fatty acids. Thus, manipulating the abundance of miR-206 in vivo might be a way to improve the quality of milk. Cow milk possesses high nutritional value due to its rich array of beneficial fatty acids. It is important to understand the mechanisms involved in lipid metabolism in dairy cows. These mechanisms are driven by a complex molecular regulatory network. In addition, there are many regulatory factors involved in the process of fatty acid metabolism, including transcription factors and non-coding RNAs, amongst others. MicroRNAs (miRNAs) can regulate the expression of target genes and modulate various biological processes, including lipid metabolism. Specifically, miR-206 has been reported to impair lipid accumulation in nonruminant hepatocytes. However, the effects and regulatory mechanisms of miR-206 on lipid metabolism in bovine mammary cells remain unclear. In the present study, we investigated the effects of miR-206 on lipid-related genes and TAG accumulation. The direct downstream gene of miR-206 was subsequently determined via a dual-luciferase assay. Finally, the fatty acid content of bovine mammary epithelial cells (BMECs) upon ELOVL6 inhibition was examined. The results revealed that miR-206 overexpression significantly decreased triacylglycerol (TAG) concentration and abundances of the following: acetyl-coenzyme A carboxylase alpha (ACACA); fatty acid synthase (FASN); sterol regulatory element binding transcription factor 1 (SREBF1); diacylglycerol acyltransferase 1 (DGAT1); 1-acylglycerol-3-phosphate O-acyltransferase 6 (AGPAT6); lipin 1 (LPIN1); and fatty acid elongase 6 (ELOVL6). Overexpression of miR-206 was also associated with an increase in patatin-like phospholipase domain-containing 2 (PNPLA2), while inhibition of miR-206 promoted milk fat metabolism in vitro. In addition, we found that ELOVL6 is a direct target gene of miR-206 through mutation of the binding site. Furthermore, ELOVL6 intervention significantly decreased the TAG levels and elongation indexes of C16:0 and C16:1n-7 in BMECs. Finally, ELOVL6 siRNA partially alleviated the increased TAG accumulation caused by miR-206 inhibition. In summary, we found that miR-206 inhibits milk fatty acid synthesis and lipid accumulation by targeting ELOVL6 in BMECs. The results presented in this paper may contribute to the development of strategies for enhancing the quality of cow milk and its beneficial fatty acids, from the perspective of miRNA–mRNA networks. [ABSTRACT FROM AUTHOR]

Over the past decades, Na0.5Bi0.5TiO3 (NBT)-based ceramics have received increasing attention in energy storage applications due to their high power density and relatively large maximum polarization. However, their high remnant polarization (Pr) and low breakdown field strength are detrimental for their practical applications. In this paper, a new solid solution (1−x)Na0.5Bi0.5TiO3–xCaHfO3 (x = 0.04, 0.08, 0.12, 0.16) was constructed by introducing CaHfO3 into NBT, and thus was prepared using a conventional solid-state reaction. With the addition of CaHfO3, the disorder of the structure increased, A-site vacancies formed, and thus oxygen vacancies were suppressed due to the replacement of the Na+ by Ca2+, resulting in the enhanced relaxation behavior and the reduced Pr, the refined grain, and improved breakdown strength. Furthermore, an optimal recoverable energy storage density (Wrec) of 1.2 J/cm3 was achieved in 0.92Na0.5Bi0.5TiO3–0.08CaHfO3 ceramics under the breakdown strength of 140 kV/cm, which is mainly attributed to the resultant defect of Na+ vacancy. [ABSTRACT FROM AUTHOR]

Light intensity flicker is the most basic and important effect of turbulence. Fiber coupling efficiency is the primary parameter that ensures the system's communication quality. The light intensity flicker effect caused by atmospheric turbulence significantly affects the coupling efficiency of space light in a single-mode optical fiber. Based on the principle of fiber coupling efficiency, this paper first establishes the relationship between light intensity flicker and spatial coherence radius, then analyzes the influence of light intensity flicker on fiber coupling efficiency through the spatial coherence radius. A laser communication system was built, and real-time measurement experiments on atmospheric light intensity flicker and optical fiber coupling efficiency at different transmission distances and altitudes above the ground were completed. The experimental results show that the constructed experimental system can simultaneously measure the scintillation index and fiber coupling efficiency. When the communication distance is 12,000 m and the height is 600 m above the ground, the scintillation index is measured to be 0.63, and the coupling efficiency is 0.05. The results of the study provide an experimental and theoretical basis and data support to promote the development of atmospheric laser communications. [ABSTRACT FROM AUTHOR]

Olivine is the most abundant mineral in mantle peridotite and a typical inclusion in diamonds, providing essential evidence for the characterization of the diamondiferous lithospheric mantle. Three olivine inclusions in diamonds (OlDia) from Liaoning in the North China Craton (NCC) were exposed for in situ measurements, and the compositional data of 62 other OlDia from Liaoning were collected based on previous reports. The enrichment of TiO2 (>0.1 wt.%) with high Cr# (>50; Cr# = 100Cr/(Cr + Al) by atom) was revealed, despite the predominance of depleted TiO2 contents and high Mg# (92.8–93.2; Mg# =100Mg/(Mg + Fe) by atom) for OlDia. Silica fluid accompanying olivine still trapped in the host diamond was recognized using Raman spectroscopy. Three thermometers were applied to the OlDia, based on the data from Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) and Electron Probe Micro-Analysis (EPMA), and a temperature range (TLA and TEPMA) of 1080–1380 °C was yielded. With respect to the TLA, Al-in-olivine thermometers were preferred, although there was a smaller deviation between TEPMA and TLA when using the Cr-in-olivine thermometer. The results of these thermometers show a high correlation with TEPMA, enabling their application based on EPMA data. Projections onto 39–42 mW/m2 model geotherms underline a diamondiferous base of the lithospheric mantle beneath the NCC. The lithospheric mantle is characterized by refractory and depleted sections, where enrichment metasomatism may have occurred at the lower roots (161–178 km). [ABSTRACT FROM AUTHOR]

Early pregnancy diagnostic techniques are of significant importance in livestock farming, particularly in dairy farming. This study aimed to screen artificially inseminated cows for potential biomarkers at day 21 of pregnancy using microbiota–metabolomics analysis. The microbiome analysis revealed significant changes (p < 0.05) in the composition and abundance of the vaginal microbiota in cows after pregnancy. Notably, there was an increase in the abundance of [Eubacterium]_hallii_group (p < 0.05) associated with the production of short-chain fatty acids in the pregnant group compared with the non-pregnant group. Furthermore, significant alterations were observed in the serum metabolome, with notable changes in the concentrations of prolyl-hydroxyproline (Pro-Hyp) (p < 0.01) and bonactin (p < 0.01). The majority of differential metabolites clustered within the pathways of amino acid metabolism and lipid metabolism, with lipid metabolism exhibiting a higher proportion and playing a pivotal role in early pregnancy. An enzyme-linked immunosorbent assay was employed to quantify three key metabolites of the arachidonic acid pathway. The results demonstrated significant decreases in serum concentrations of leukotriene B4 (LTB4) (p < 0.05) and prostaglandin F2α (PGF2α) (p < 0.01) and no significant changes in arachidonic acid (AA) (NS) concentrations after 21 days of gestation in cows. Spearman's correlation analysis was utilized to investigate the interrelationship between the vaginal microbiota and serum metabolites. In conclusion, the present study demonstrated that biomaterials such as bonactin, Pro-hyp, LTB4, PGF2α in serum metabolites and [Eubacterium]_hallii_group in the vaginal flora of cows could be utilized as potential biomarkers for 21 days of gestation in cows. [ABSTRACT FROM AUTHOR]

Most real-time semantic segmentation networks use shallow architectures to achieve fast inference speeds. This approach, however, limits a network's receptive field. Concurrently, feature information extraction is restricted to a single scale, which reduces the network's ability to generalize and maintain robustness. Furthermore, loss of image spatial details negatively impacts segmentation accuracy. To address these limitations, this paper proposes a Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network (BMSeNet). First, to address the limitation of singular semantic feature scales, a Multiscale Context Pyramid Pooling Module (MSCPPM) is introduced. By leveraging various pooling operations, this module efficiently enlarges the receptive field and better aggregates multiscale contextual information. Moreover, a Spatial Detail Enhancement Module (SDEM) is designed, to effectively compensate for lost spatial detail information and significantly enhance the perception of spatial details. Finally, a Bilateral Attention Fusion Module (BAFM) is proposed. This module leverages pixel positional correlations to guide the network in assigning appropriate weights to the features extracted from the two branches, effectively merging the feature information of both branches. Extensive experiments were conducted on the Cityscapes and CamVid datasets. Experimental results show that the proposed BMSeNet achieves a good balance between inference speed and segmentation accuracy, outperforming some state-of-the-art real-time semantic segmentation methods. [ABSTRACT FROM AUTHOR]

Ceramsites are extensively employed as substrates for adsorbents in studies focused on phosphorus adsorption, leaving ceramsites crafted from diatomite less explored. Diatomite-based ceramsite, with its distinct porous architecture and surface silanol functionalities, is adept at supporting a variety of metal oxides, presenting a distinct advantage over other ceramsite variants. In light of this, the present study embarked on producing diatomite-based ceramsite using diatomite as the foundational material, subsequently enhancing it through the incorporation of Fe, thus yielding an Fe-modified diatomite-based ceramsite. Through adsorption testing, the modified ceramsite demonstrated a significantly improved adsorption capacity of 4.06 mg P/g, marking a substantial enhancement from the initial capacity of 0.9 mg P/g. The process of phosphorus adsorption exhibited a strong alignment with the Langmuir isotherm model and the pseudo-second-order kinetic model. In-depth analyses employing XRD, FTIR, zeta potential, and XPS techniques have revealed that the principal mechanisms driving the adsorption process are centered on interactions involving electrostatic forces, the formation of chemical precipitates, and the exchange of ligands. This investigation not only opens new avenues for the application of diatomite-based ceramsite but also lays down a theoretical foundation for its modification, thereby enriching the spectrum of its utility. [ABSTRACT FROM AUTHOR]

Depth image has been widely involved in various tasks of 3D systems with the advancement of depth acquisition sensors in recent years. Depth images suffer from serious distortions near object boundaries due to the limitations of depth sensors or estimation methods. In this paper, a simple method is proposed to rectify the erroneous object boundaries of depth images with the guidance of reference RGB images. First, an RGB–Depth boundary inconsistency model is developed to measure whether collocated pixels in depth and RGB images belong to the same object. The model extracts the structures of RGB and depth images, respectively, by Gaussian functions. The inconsistency of two collocated pixels is then statistically determined inside large-sized local windows. In this way, pixels near object boundaries of depth images are identified to be erroneous when they are inconsistent with collocated ones in RGB images. Second, a depth image rectification method is proposed by embedding the model into a simple weighted mean filter (WMF). Experiment results on two datasets verify that the proposed method well improves the RMSE and SSIM of depth images by 2.556 and 0.028, respectively, compared with recent optimization-based and learning-based methods. [ABSTRACT FROM AUTHOR]

Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior. [ABSTRACT FROM AUTHOR]

The Regional Comprehensive Land Price System (RCLPS) in China serves as the compensation standard for land expropriation, as established by the new Land Management Law in 2019. Its primary objectives include safeguarding the legitimate rights and interests of expropriated landowners and promoting social fairness and justice—critical components for achieving Sustainable Development Goal 10. Despite its significance, the existing literature lacks a comprehensive exploration of the RCLPS's legal connotation and implementation challenges. This study first analyzes the research background and legal connotation of China's RCLPS. It provides a comprehensive compensation standard for land expropriation, aiming to ensure consistent compensation levels within the same "expropriated districts". Then, an empirical study examines the practical implementation of the RCLPS in Wuhan City, China. Through a comparative analysis of the 2020 Regional Comprehensive Land Price (RCLP) and state-owned land transfer fees in Wuhan, several deficiencies in the city's RCLPS are identified. First, the distribution of land value-added benefits lacks reasonableness. Second, the district-level delineation of "expropriated districts" inadequately captures variations in land value. From a sustainable development perspective, specific countermeasures can be proposed, including re-evaluating the allocation of land value-added benefits and incorporating them into the RCLP assessment. Additionally, enhancing the precision in delineating "expropriated districts" within the administrative region is necessary. This study provides valuable guidance for achieving equitable compensation in land expropriation and improving the RCLPS. [ABSTRACT FROM AUTHOR]

Dendrocalamus farinosus bamboo shoots, a species with rich nutritional value, are important in Southwest China. Lignin is an important factor affecting the postharvest flavor quality of bamboo shoots; however, the underlying mechanism of lignin deposition in D. farinosus bamboo shoots during cold storage is still not fully understood. In this study, the mutant D. farinosus XK4 with low lignin content at 3.11% and the cultivated variety ZPX at 4.47% were used as experimental materials. The lignin content of D. farinosus XK4 and ZPX, as well as the gene expression differences between them, were compared and analyzed during cold storage using transcriptomic and physiological methods. Our analysis revealed several key genes and found that D. farinosus CCoAOMT1 plays a key role in the regulatory network of bamboo shoots during cold storage. Tobacco heterologous transformation experiments demonstrated that overexpression of DfCCoAOMT1 significantly increases lignin content. This study provides a novel foundation for future research aimed at improving the postharvest quality and flavor of D. farinosus bamboo shoots through targeted genetic manipulation during cold storage. [ABSTRACT FROM AUTHOR]

Sperm, a crucial gamete for reproduction in sexual reproduction, is generated through the proliferation, differentiation, and morphological transformations of spermatogonial stem cells within the specialized microenvironment of the testes. Replicating this environment artificially presents challenges. However, interdisciplinary advancements in physics, materials science, and cell engineering have facilitated the utilization of innovative materials, technologies, and structures for inducing in vitro sperm production. This article offers a comprehensive overview of research progress on inducing in vitro sperm production by categorizing techniques into two major systems based on matrix-based and non-matrix-based approaches, respectively. Detailed discussions are provided for both types of technology systems through comparisons of their similarities and differences, as well as research advancements. The aim is to provide researchers in this field with a comprehensive panoramic view while presenting our own perspectives and prospects. [ABSTRACT FROM AUTHOR]

In order to obtain stable and reliable configuration transformation ability, reliability optimization design is regarded as an effective way to reduce the probability of kinematic function failure for the constrained metamorphic mechanism. Based on the structural composition principle of multi-configuration source metamorphic mechanism that can operate in an under-actuated state, the modularized calculation methods are established for the force analysis of augmented Assur groups including metamorphic kinematic joints. According to the equivalent resistance gradient model of metamorphic mechanisms, with considering the uncertainties in the link dimensions, masses, and compliance parameters et al., a probabilistic evaluation method for describing the configuration transformation ability of the constrained metamorphic mechanism is established. Based on reliability evaluation and reliability sensitivity analysis, a reliability optimization design method for improving the configuration transformation ability is proposed, and then the optimization design is carried out for tolerances of random variables focusing on those structural parameters with higher reliability sensitivity, so that the optimized results can satisfy the requirements of both reliability and economic simultaneously. Finally, the feasibility and effectiveness of the proposed method is verified by the illustration of a paper folding metamorphic mechanism. The research provides the foundation of reliability design of metamorphic mechanisms to obtain the high-probability repeated execution ability of configuration transformation, it also has theoretical and practical significance to promote the engineering application of metamorphic mechanisms. [ABSTRACT FROM AUTHOR]

Non-contact temperature sensors utilising the fluorescence intensity ratio and the unique up-conversion (UC) luminescence of rare-earth ions have numerous benefits; however, their operational temperature range has remained limited. In this study, NaLuF4:Yb3+/Ho3+ samples were prepared by the hydrothermal method. The samples exhibited exceptional UC luminescence properties at low temperatures. The intensity of the green emission (with peak wavelengths of 540 and 546 nm) gradually decreased with increasing temperature, and the green emissions showed a unique change at low temperatures. In addition, we studied the dependence of the UC luminescence intensity on the excitation power and the variation in the decay lifetime with temperature. The experiments revealed excellent luminous performance and significantly enhanced sensitivity at low temperatures; the maximum absolute sensitivity Sa and relative sensitivity Sr of the 540 and 546 nm thermally coupled energy levels were 1.02% and 0.55% K−1, respectively. The potential temperature sensing properties of Yb3+/Ho3+-co-doped NaLuF4 makes it suitable for temperature sensing applications at temperatures as low as 30 K. This study offers a novel approach for the advancement of temperature sensing technology at low temperatures. [ABSTRACT FROM AUTHOR]

As a severe inflammatory response syndrome, sepsis presents complex challenges in predicting patient outcomes due to its unclear pathogenesis and the unstable discharge status of affected individuals. In this study, we develop a machine learning-based method for predicting the discharge status of sepsis patients, aiming to improve treatment decisions. To enhance the robustness of our analysis against outliers, we incorporate robust statistical methods, specifically the minimum covariance determinant technique. We utilize the random forest imputation method to effectively manage and impute missing data. For feature selection, we employ Lasso penalized logistic regression, which efficiently identifies significant predictors and reduces model complexity, setting the stage for the application of more complex predictive methods. Our predictive analysis incorporates multiple machine learning methods, including random forest, support vector machine, and XGBoost. We compare the prediction performance of these methods with Lasso penalized logistic regression to identify the most effective approach. Each method's performance is rigorously evaluated through ten iterations of 10-fold cross-validation to ensure robust and reliable results. Our comparative analysis reveals that XGBoost surpasses the other models, demonstrating its exceptional capability to navigate the complexities of sepsis data effectively. [ABSTRACT FROM AUTHOR]

Simple Summary: Anthracyclines have a central role in anti-cancer therapy, but their side effects of chemotherapy-induced cardiotoxicity (AIC) during systemic administration have always been a problem for both patients and clinicians. The application of anthracycline agents leads to arrhythmias, causing nonspecific ECG changes. And the incidence rates of abnormal QT changes could be early markers of AIC. Therefore, we conducted a systematic review, meta-analysis, and network meta-analysis to evaluate the arrhythmic risk of anthracyclines and the comparative risk for each agent. We included 4 cohort studies, 8 RCTs, and 18 single-arm studies, and the result shows anthracyclines were associated with a statistically significant 90% increase in the risk of arrhythmia and a 114% increase in the risk of supraventricular arrhythmia. Among them, epirubicin ranked the most likely to have the highest risk of arrhythmia compared with non-anthracycline antineoplastic drugs in the analysis. The purpose of this research is to improve the monitoring of ECGs by clinicians while applying anthracyclines and to try to use molecular imaging technology to study the mechanism of drug action on cardiac electrophysiology in the future. The effects of anthracycline agents on the electrophysiological properties of cardiac cells can be better understood with the help of imaging molecular targets in cardiac cells, which may result in safer and more effective clinical use of the drugs. Background: Chemotherapy is the main first-line treatment, but there is a problem of adverse reactions to systemic drugs. Chemotherapeutic agents may cause adverse effects on the body, influencing the prognosis. Whether the clinical application of anthracyclines is associated with an increased arrhythmic risk remains controversial. To evaluate the arrhythmic risk of anthracyclines as a class, and the comparative risk for each drug, we conducted a systematic review, meta-analysis, and network meta-analysis. Methods: PubMed, Web of Science, EMBASE, and the Cochrane Library were searched, up to March 2022, for randomized controlled trials, cohort studies, and case–control studies that investigated the association between anthracyclines treatment and the risk of arrhythmia. We followed the PRISMA 2020 guidelines for data selection and extraction. Outcomes were pooled using fixed effects models in cohort studies and randomized controlled studies, and random models in single-arm studies. Direct and indirect comparisons in network meta-analysis were performed using frequentist methods. Results: In total, 4 cohort studies, 8 RCTs, and 18 single-arm studies were included in our analysis. Anthracyclines' use was associated with a statistically significant 90% increase in the risk of arrhythmia (odds ratio [OR] 1.90; 95% confidence interval [CI] 1.62–2.24) and a 114% increase in the risk of supraventricular arrhythmia (OR 2.14; 95% CI 1.18–3.89). And the single-arm studies also indicated that the incidence of arrhythmia rate is 20% and the 95% CI is 15/100–25/100. Epirubicin ranked most likely to have the highest risk of arrhythmia compared with non-anthracycline antineoplastic drugs in the analysis (OR 43.07 [95% CI 2.80–2105.83]) by network meta-analysis. Conclusions: Our findings show a significant association between anthracyclines' use and an increased risk of arrhythmia, especially supraventricular arrhythmia. Epirubicin ranked with the highest probability of arrhythmia. These results indicated that cardiac rhythm should be strictly monitored during the application of anthracyclines in clinical practice, and a possible therapy for anthracycline-associated arrhythmia should be explored. Molecular imaging technology is an important means to study the mechanism of drug action on cardiac electrophysiology in the future. By imaging molecular targets in cardiac cells, the effects of drugs on the electrophysiological properties of cardiac cells can be understood, which provides information for the development of safer and more effective drugs. [ABSTRACT FROM AUTHOR]

In the context of the current information age, leveraging Earth observation (EO) technology and spatial analysis methods enables a more accurate understanding of the characteristics of small towns. This study conducted an in-depth analysis of the urban morphology of small towns in the Qinba Mountain Area of Southern Shaanxi by employing large-scale data analysis and innovative urban form measurement methods. The U-Net3+ model, based on deep learning technology, combined with the concave hull algorithm, was used to extract and precisely define the boundaries of 31,799 buildings and small towns. The morphological characteristics of the town core were measured, and the core areas of the small towns were defined using calculated tessellation cells. Hierarchical clustering methods were applied to analyze 12 characteristic indicators of 89 towns, and various metrics were calculated to determine the optimal number of clusters. The analysis identified eight distinct clusters based on the towns' morphological differences. Significant morphological differences between the small towns in the Qinba Mountain Area were observed. The clustering results revealed that the towns exhibited diverse shapes and distributions, ranging from irregular and sparse to compact and dense forms, reflecting distinct layout patterns influenced by the unique context of each town. The use of the morphometric method, based on cellular and biological morphometry, provided a new perspective on the urban form and deepened the understanding of the spatial structure of the small towns from a micro perspective. These findings not only contribute to the development of quantitative morphological indicators for town development and planning but also demonstrate a novel, data-driven approach to conventional urban morphology studies. [ABSTRACT FROM AUTHOR]

This article mainly studies the selection of the matching area in gravity matching navigation systems of underwater vehicles. Firstly, we comprehensively consider 14 types of gravity field feature parameters, and a new gravity field feature parameters selection method is proposed based on feature selection principles and support vector machine algorithms. Secondly, according to the new gravity field feature parameters selection method, the five feature parameters, including range, pooling difference, standard deviation of gravity anomaly, roughness, and correlation coefficient, were selected from the 14 gravity field features parameters. The selected five feature parameters are integrated using SVM, and a classification model is constructed with carefully chosen training and testing sets and parameters for validation. Based on the experimental results, compared to the pre-calibrated results, the classification accuracy of the testing set reaches 91%, demonstrating the effectiveness of the gravity field feature parameter selection method in distinguishing between the suitable and the unsuitable areas. Finally, this method is applied to another area, and we carried out navigation experiments in the areas that were suitable areas in all four directions, as not all areas were suitable in four directions. The results showed that the areas that were suitable in all four directions provided better matching effects, the mean positioning accuracy was less than 100 m, and the accuracy was more than 90%. In path planning, priority can be given to areas that are suitable in all four directions. [ABSTRACT FROM AUTHOR]

Considering the situation of limited resources in practical applications, it is significant to design control algorithms with high resource utilization rates for a class of nonlinear systems subject to mismatched disturbances. In contrast to common recursive methods, this paper proposes a novel event-triggered tracking control approach by co-designing the triggering event and the controller within a non-recursive design framework that combines disturbance estimation techniques and feedforward compensation strategies. Through rigorous Lyapunov stability analysis, the global boundedness of each state in the closed-loop system is demonstrated, and the absence of the Zeno phenomenon is further verified. A representative numerical simulation and a practical implementation for speed regulation of permanent magnet synchronous motor (PMSM) system confirm the effectiveness and simplicity of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Highly efficient and cost-effective electrocatalysts are of critical significance in the domain of water electrolysis. In this study, a Ni3N-CeO2/NF heterostructure is synthesized through a facile hydrothermal technique followed by a subsequent nitridation process. This catalyst is endowed with an abundance of oxygen vacancies, thereby conferring a richer array of active sites. Therefore, the catalyst demonstrates a markedly low overpotential of 350 mV for the Oxygen Evolution Reaction (OER) at 50 mA cm−2 and a low overpotential of 42 mV for the Hydrogen Evolution Reaction (HER) at 10 mA cm−2. Serving as a dual-function electrode, this electrocatalyst is employed in overall water splitting in alkaline environments, demonstrating impressive efficiency at a cell voltage of 1.52 V of 10 mA cm−2. The in situ Raman spectroscopic analysis demonstrates that cerium dioxide (CeO2) facilitates the rapid reconfiguration of oxygen vacancy-enriched nickel oxyhydroxide (NiOOH), thereby enhancing the OER performance. This investigation elucidates the catalytic role of CeO2 in augmenting the OER efficiency of nickel nitride (Ni3N) for water electrolysis, offering valuable insights for the design of high-performance bifunctional catalysts tailored for water splitting applications. [ABSTRACT FROM AUTHOR]

In this study, the rotating welding process of Chinese fir (Keteleeriafortunei) in Guizhou, China, was systematically analyzed. The effects of rotating welding conditions, including the dowel-to-guide hole diameter ratio, welding time, depth, base surface, angle, and dowel type, on the performance of welded Chinese fir were explored. Moreover, the physical and chemical changes oftheChinese fir interface during welding were revealed by Fourier-Transform Infrared Spectroscopy (FT-IR), X-ray Photoelectron Spectroscopy (XPS), X-ray Diffraction (XRD), and Scanning Electron Microscopy (SEM). The results indicated the following: (1) The rotating welding technology can quickly achieve a strong connection between wood through friction heat without chemical adhesives and compared with traditional wood connection technology such as gluing or mechanical fixing;it has the advantages of simple operation, high production efficiency; and environmental friendliness. (2) Aftertherotating welding, the wood underwent significant pyrolysis, especially the degradation of hemicellulose. The heat generated in the welding process caused good melting and mechanical interlocking between the dowel and the wall of the guide hole, but it was also accompanied by afriction loss of the dowel and the substrate. (3) The welding parameters affected the wood's connection strength and stability by altering heat production, distribution, transfer, and frictional losses. The impact of the dowel-to-guide hole diameter ratio had a great influence on the connection strength. When the diameter ratio was 1:0.7, the tensile strength was the highest, reaching 2.27 MPa. (4) The analyses of XPS, FTIR, XRD, and SEM proved thatthechemical composition changes at the interface, leading to a more structured crystalline bond and enhanced connection strength due to fiber entanglement and interlocking. This research providesatheoretical and experimental basis forthefurther innovation and development of wood processing technology and provides a new technical path forthegreen manufacturing of wood structure buildings. [ABSTRACT FROM AUTHOR]

Simple Summary: Spermatogenesis is a highly efficient process that depends on the self-renewal and regulated differentiation of spermatogonial stem cells (SSCs) within the seminiferous tubules of the male mammalian testis. Male fertility requires the production of millions of gametes through the meiotic division of spermatocytes. We discovered that knocking out Etv5 in mice alters the expression of Wdr17, a gene crucial for spermatocyte development, by integrating bulk transcriptome sequencing data from the testes of Etv5 knockout mice with previously published single-cell transcriptome sequencing results. The levels of Wdr17 are strongly linked to cell survival and proliferation in mouse spermatocyte cell line GC-2spd(ts). Wdr17 could be a valuable gene target for investigating and controlling the processes of meiosis and spermatogenesis. We identified Wdr17 as a highly expressed gene in pachytene spermatocytes by transcriptomic analysis of mouse testis. Germ cell-deficient infertile mouse models had significantly reduced Wdr17 expression. We performed gene interference and overexpression in the mouse spermatocyte cell line GC-2spd(ts) and investigated how Wdr17 affects spermatocyte growth and development. Our results showed that Wdr17 suppression significantly decreased cell growth rate and increased cell apoptosis in GC-2spd(ts) cells. Wdr17 suppression also arrested the cell cycle at the G1 phase. On the contrary, Wdr17 overexpression significantly promoted cell proliferation and inhibited cell apoptosis in GC-2spd(ts) cells. More cells were enriched at the S stage with a concomitant reduction of cells at the G1 stage. Wdr17 promotes mouse spermatocyte proliferation by advancing cell cycle progression and inhibiting cell apoptosis, indicating its potential role in regulating spermatogenesis in the mouse. [ABSTRACT FROM AUTHOR]

In this study, we investigate micrometer-sized NaYF4 crystals double-doped with Yb3+/Er3+ lanthanide ions, designed for temperature-sensing applications. In contrast to previous studies, which focused predominantly on the high-temperature regime, our investigation spans a comprehensive range of both high and ultralow temperatures. We explore the relationship between temperature and the upconversion luminescence (UCL) spectra in both frequency and time domains. Our findings highlight the strong dependence of these spectral characteristics of lanthanide-doped NaYF4 crystals on temperature. Furthermore, we introduce a dual-mode luminescence temperature measurement technique, leveraging the upconversion emission intensity ratio for both green and red emissions. This study also examines the correlation between temperature sensing, energy level disparities, and thermal coupling in Er3+ ions across various temperature scales. Our research contributes to advancing the understanding and application of lanthanide-doped materials, setting a foundation for future innovations in temperature sensing across diverse fields. [ABSTRACT FROM AUTHOR]

There is plenty of evidence to suggest that global carbon emission transfer has evolved into a mutually related system, where a realistic and complex network is formed. To profile the structures and features in the global carbon emission transfer network, a carbon-connectedness network model is adapted and combined with the multiregional input–output analysis framework, on the basis of massive and multi-layer global carbon flow data. This study formulates the topological features, spatio-temporal features, dynamic features and core–periphery features from a brand-new perspective on China. Meanwhile, this study identifies the network effects in the global carbon transfer network, including spillover, spillin and spillback effects. In general, an increase in China's carbon emission transfer would lead to significant spillover effects on most economies worldwide, especially on developing economies and those with weaker tertiary industry or situated at the upstream of the global value chain. Simultaneously, China itself would also face substantial spillback effects. Spillovers and spillbacks underscore a broader negative impact that exceeds its initial magnitude. Focused on the connectedness network centered on China, this study is complementary to traditional insights, helping to comprehend the connections and relationships of carbon emissions among economies. This understanding is of substantive significance for the formulation of multi-national mitigation strategies and fostering global climate governance cooperation. [ABSTRACT FROM AUTHOR]

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results. [ABSTRACT FROM AUTHOR]

This study is based on the research results of frazil ice evolution in recent years and proposes an improved frazil ice evolution mathematical model. Based on the NSGA-II genetic algorithm, seven key parameters were used as optimization design variables, the minimum average difference between the number of frazil ice, the mean and the standard deviation of particle diameter of the simulation results, and the observed data were used as the optimization objective, the Pareto optimal solution set was optimized, and the importance of each objective function was analyzed and discussed. The results show that compared to previous models, the improved model has better agreement between simulation results and experimental results. The optimal parameters obtained by the optimization model reduces the difference rate of water temperature process by 5.75%, the difference rate of quantity process by 39.13%, the difference rate of mean particle size process by 47.64%, and the difference rate of standard deviation process by 56.84% during the period of intense evolution corresponding to the initial parameter group. The results prove the validity of the optimization model of frazil ice evolution parameters. [ABSTRACT FROM AUTHOR]

In the realm of medical image analysis, the cost associated with acquiring accurately labeled data is prohibitively high. To address the issue of label scarcity, semi-supervised learning methods are employed, utilizing unlabeled data alongside a limited set of labeled data. This paper presents a novel semi-supervised medical segmentation framework, DCCLNet (deep consistency collaborative learning UNet), grounded in deep consistent co-learning. The framework synergistically integrates consistency learning from feature and input perturbations, coupled with collaborative training between CNN (convolutional neural networks) and ViT (vision transformer), to capitalize on the learning advantages offered by these two distinct paradigms. Feature perturbation involves the application of auxiliary decoders with varied feature disturbances to the main CNN backbone, enhancing the robustness of the CNN backbone through consistency constraints generated by the auxiliary and main decoders. Input perturbation employs an MT (mean teacher) architecture wherein the main network serves as the student model guided by a teacher model subjected to input perturbations. Collaborative training aims to improve the accuracy of the main networks by encouraging mutual learning between the CNN and ViT. Experiments conducted on publicly available datasets for ACDC (automated cardiac diagnosis challenge) and Prostate datasets yielded Dice coefficients of 0.890 and 0.812, respectively. Additionally, comprehensive ablation studies were performed to demonstrate the effectiveness of each methodological contribution in this study. [ABSTRACT FROM AUTHOR]

In dynamic space networks, achieving high precision and fast initial pointing of the optical line of sight (LOS) is the key goal in developing this technology. It is the premise and basis of realizing optical LOS capture. Based on the composition and working principle of space optical networking systems, and the effect of real-time position and attitude changes on LOS initial pointing between networks, the matrix transformation and transfer principle is used to establish a multi-link LOS initial pointing model and analyze the factors affecting the size of the field of uncertainty (FOU). In a dynamic space optical networking experiment, the "one-to-two" simultaneous LOS pointing test is carried out, which shows that the model can realize the function of multi-link LOS initial pointing. The sizes of the FOU of the test terminal are 8.67 mrad and 8.34 mrad, respectively, with an average capture time of 18.3 s. [ABSTRACT FROM AUTHOR]

Flower color is an important ornamental feature that is often modulated by the contents of flavonoids. Chalcone synthase is the first key enzyme in the biosynthesis of flavonoids, but little is known about the role of R. delavayi CHS in flavonoid biosynthesis. In this paper, three CHS genes (RdCHS1-3) were successfully cloned from R. delavayi flowers. According to multiple sequence alignment and a phylogenetic analysis, only RdCHS1 contained all the highly conserved and important residues, which was classified into the cluster of bona fide CHSs. RdCHS1 was then subjected to further functional analysis. Real-time PCR analysis revealed that the transcripts of RdCHS1 were the highest in the leaves and lowest in the roots; this did not match the anthocyanin accumulation patterns during flower development. Biochemical characterization displayed that RdCHS1 could catalyze p-coumaroyl-CoA and malonyl-CoA molecules to produce naringenin chalcone. The physiological function of RdCHS1 was checked in Arabidopsis mutants and tobacco, and the results showed that RdCHS1 transgenes could recover the color phenotypes of the tt4 mutant and caused the tobacco flower color to change from pink to dark pink through modulating the expressions of endogenous structural and regulatory genes in the tobacco. All these results demonstrate that RdCHS1 fulfills the function of a bona fide CHS and contributes to flavonoid biosynthesis in R. delavayi. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithms (MOEA) have attracted much attention because of their good global exploration ability; however, their local search ability near the optimal value is weak, and for large-scale decision-variable optimization problems the number of populations and iterations required by MOEA are very large, so the optimization efficiency is low. Gradient optimization algorithms can overcome these difficulties well, but gradient search methods are difficult to apply to multi-objective optimization problems (MOPs). To this end, this paper introduces a stochastic weighting function based on the weighted average gradient and proposes two multi-objective stochastic gradient operators. Further, two efficient evolutionary algorithms, MOGBA and HMOEA, are developed. Their local search capability has been greatly enhanced while retaining the good global exploration capability by using different offspring update strategies for different subpopulations. Numerical experiments show that HMOEA has excellent capture ability for various Pareto formations, and it can easily solve multi-objective optimization problems with many objectives, which improves the efficiency by a factor of 5–10 compared with typical multi-objective evolutionary algorithms. HMOEA is further applied to the multi-objective aerodynamic optimization design of the RAE2822 airfoil and the ideal Pareto front is obtained, which indicates that HMOEA is an efficient optimization algorithm with potential applications in aerodynamic optimization design. [ABSTRACT FROM AUTHOR]

The measurement of partial pressures of oxygen (O2) and carbon dioxide (CO2) is fundamental for evaluating a patient's conditions in clinical practice. There are many ways to retrieve O2/CO2 partial pressures and concentrations. Arterial blood gas (ABG) analysis is the gold standard technique for such a purpose, but it is invasive, intermittent, and potentially painful. Among all the alternative methods for gas monitoring, non-invasive transcutaneous O2 and CO2 monitoring has been emerging since the 1970s, being able to overcome the main drawbacks of ABG analysis. Clark and Severinghaus electrodes enabled the breakthrough for transcutaneous O2 and CO2 monitoring, respectively, and in the last twenty years, many innovations have been introduced as alternatives to overcome their limitations. This review reports the most recent solutions for transcutaneous O2 and CO2 monitoring, with a particular consideration for wearable measurement systems. Luminescence-based electronic paramagnetic resonance and photoacoustic sensors are investigated. Optical sensors appear to be the most promising, giving fast and accurate measurements without the need for frequent calibrations and being suitable for integration into wearable measurement systems. [ABSTRACT FROM AUTHOR]

In order to realize the goal of ice-free water conveyance in the winter for water conveyance projects in cold regions, the operation principle of ice-free water conveyance through channels is described based on the two ice-melting measures of a solar heating gallery and heated storage tank. Based on the multi-year meteorological data and the theory of a product probability event, the concept of a "comprehensive satisfaction rate" was proposed, and then the joint optimal regulating model under two ice-melting measures was established, and the genetic algorithm was used to solve the problem, which solved the important limitations of the economic and efficiency optimization of different ice-melting measures. This paper applies this model to the Zhanghe control gate–Mangniuhe control gate section of the middle route of the South-to-North Water Transfer Project. According to the optimization analysis of a large number of operating conditions, the operating costs of the ice-melting measures have also increased with the increase in the comprehensive satisfy rate. In the operation process, the water temperature along the lines presents a "ladder-like" shape. The average hourly flow and average hourly water temperature of the heated water storage tank have the characteristics of overall unity and local complementarity. With the increase in the water flow and downstream depth before the gate, its operating cost also increases. The increase in the flow velocity at the same time can increase the heat transfer efficiency, reducing the operating costs. In addition, the water temperature of the channel with a solar heating gallery decreased more slowly than that without a solar heating gallery due to its good thermal insulation effect. [ABSTRACT FROM AUTHOR]

A new species Diorygma tiantaiense Z.F. Jia, sp. nov. was found, which is characterized by a corticolous thallus with norstictic acid, oval or oblong apothecia, immersed to ± raised discs with white pruina, and large muriform ascospores (120–210 × 35–60 µm). Detailed morphological descriptions, photographs, and a comparison and discussion of similar species are provided. A checklist and key to the species of Diorygma known from China is presented. [ABSTRACT FROM AUTHOR]

Microinjection is usually applied to the treatment of some retinal disorders, such as retinal vein cannulation and displaced submacular hemorrhage. Currently, the microinjection procedure is usually performed by using the viscous fluid control of a standard vitrectomy system, which applies a fixed air pressure through foot pedal activation. The injection process with the fixed pressure is uncontrollable and lacks feedback, the high flow rate of the injected drug may cause damage to the fundus tissue. In this paper, a liquid-driven microinjection system with a flow sensor is designed and developed specifically for fundus injection. In addition, a PID sliding mode control (SMC) method is proposed to achieve precise injection in the injection system. The experimental results of fundus simulation injection demonstrate that the microinjection system meets the requirements of fundus injection and reduces the impact of the injection process on the fundus tissue. [ABSTRACT FROM AUTHOR]

In piezoelectric drive, resonant drive is an important driving mode in which the external elastic force and electric drive signal are the key factors. In this paper, the effects of the coupling of external elastic force and liquid parameters with the structure on the vibrator resonance frequency and liquid drive are analyzed by numerical simulation. The fluid-structure coupling model for numerical analysis of the elastic force was established, the principle of microdroplet generation and the coupling method of the elastic force were studied, and the changes in the resonant frequency and mode induced by the changes in the liquid parameters in different cavities were analyzed. Through the coupled simulation and calculation of the pressure and deformation of the cavity, the laser vibration measurement test was carried out to test the effect of the vibration mode analysis. The driving model of the fluid jet driven by the elastic force on the piezoelectric drive was further established. The changing shape of the fluid jet under different elastic forces was analyzed, and the influence law of the external elastic force on the change in the droplet separation was determined. It provides reference support for further external microcontrol of droplet motion. [ABSTRACT FROM AUTHOR]