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Excitation wavelength controllable lanthanide upconversion allows for real-time manipulation of luminescent color in a composition-fixed material, which has been proven to be conducive to a variety of applications, such as optical anti-counterfeiting and information security. However, current available materials highly rely on the elaborate core–shell structure in order to ensure efficient excitation-dependent energy transfer routes. Herein, multicolor upconversion luminescence in response to both near-infrared I and near-infrared II (NIR-I and NIR-II) excitations is realized in a novel but simple NaYGeO4:Yb3+/Er3+ phosphor. The remarkably enhanced red emission ratio under 1532 nm excitation, compared with that under 980 nm excitation, could be attributed to the Yb3+-mediated cross-relaxation energy transfers. Moreover, multi-wavelength excitable temperature-dependent (295–823 K) upconversion luminescence realizes a ratiometric thermometry relying on the thermally coupled levels (TCLs) of Er3+. Detailed investigations demonstrate that changing excitation wavelength makes little difference for the performances of TCL-based ratiometric thermometry of NaYGeO4:Yb3+/Er3+. These findings gain more insights to manipulate cross-relaxations for excitation controllable upconversion in single activator doped materials and benefit the cognition of the effect of excitation wavelength on ratiometric luminescence thermometry. [ABSTRACT FROM AUTHOR]

The key problem to solving constrained multi-objective optimization problems (CMOPs) is how to achieve a balance between objectives and constraints. Unfortunately, most existing methods for CMOPs still cannot achieve the above balance. To this end, this paper proposes an adaptive constraint relaxation-based evolutionary algorithm (ACREA) for CMOPs. ACREA adaptively relaxes the constraints according to the iteration information of population, whose purpose is to induce infeasible solutions to transform into feasible ones and thus improve the ability to explore the unknown regions. Completely ignoring constraints can cause the population to waste significant resources searching for infeasible solutions, while excessively satisfying constraints can trap the population in local optima. Therefore, balancing constraints and objectives is a crucial approach to improving algorithm performance. By appropriately relaxing the constraints, it induces infeasible solutions to be transformed into feasible ones, thus obtaining more information from infeasible solutions. At the same time, it also establishes an archive for the storage and update of solutions. In the archive update process, a diversity-based ranking is proposed to improve the convergence speed of the algorithm. In the selection process of the mating pool, common density selection metrics are incorporated to enable the algorithm to obtain higher-quality solutions. The experimental results show that the proposed ACREA algorithm not only achieved the best Inverse Generation Distance (IGD) value in 54.6% of the 44 benchmark test problems and the best Hyper Volume (HV) value in 50% of them, but also obtained the best results in seven out of nine real-world problems. Clearly, CP-TSEA outperforms its competitors. [ABSTRACT FROM AUTHOR]

Crepidotus subg. Dochmiopus contributes to more than half of Crepidotus species and exhibits highly hidden diversity. However, C. subg. Dochmiopus is challenging to study because the basidiomata of C. subg. Dochmiopus species are usually small and white, inconspicuous interspecific distinctions, and possess a familiar complex. In this study, we utilized a variety of characteristics for species identification, including habitat, presence or absence of a stipe in mature specimens, pileipellis and cheilocystidia patterns, whether the lamellae edges are fimbriated, and other characteristics. Above all, cheilocystidia and pileipellis patterns will be important in C. subg. Dochmiopus research. Based on the present specimens, we constructed a multigene phylogenetic tree (ITS + LSU) and recognized four new species: C. lamellomaculatus sp. nov., C. capitatocystidiatus sp. nov., C. succineus sp. nov., C. clavocystidiatustustus sp. nov. Detailed morphological descriptions, photographs, line drawings and comparisons with closely related taxa for the new species are provided. The current phylogenetic analysis does not support the previously classifications, indicating that the classification of Crepidotus requires re-evaluation. But the existing molecular datasets and species' descriptions are insufficient to fully resolve the classification. Further integration of new gene segments and a comprehensive review of morphological characteristics will reveal a natural classification for Crepidotus. [ABSTRACT FROM AUTHOR]

Transition metal oxide magnetite (Fe3O4) is recognized as a potential anode material for lithium-ion batteries owing to its high theoretical specific capacity, modest voltage output, and eco-friendly character. It is a challenging task to engineer high-performance composite materials by effectively dispersing Fe3O4 crystals with limited sizes in a well-designed supporting framework following sustainable approaches. In this work, the naturally abundant plant products sodium lignosulfonate (Lig) and sodium cellulose (CMC) were selected to coprecipitate with Fe3+ ions under mild hydrothermal conditions. The Fe-Lig/CMC intermediate sediment with an optimized microstructure can be directly converted to the Lig/CMC-derived carbon matrix-supported Fe3O4 nanocomposite sample (Fe3O4@LigC/CC). Compared with the controlled Fe3O4@LigC material, the Fe3O4@LigC/CC nanocomposite provides superior electrochemical performance in the anode, which has inspiring specific capacities of 820.6 mAh g−1 after 100 cycles under a current rate of 100 mA·g−1 and 750.5 mAh g−1 after 250 cycles, as well as more exciting rate capabilities. The biomimetic sample design and synthesis protocol closely follow the criteria of green chemistry and can be further developed in wider scenarios. [ABSTRACT FROM AUTHOR]

As the share of renewable energy in a grid increases, the grid's frequency support capability weakens, and the spatial distribution of grid frequency becomes more pronounced. As a result, control strategies based on system frequency consistency and traditional frequency regulation dominated by synchronous machines are becoming increasingly inadequate for meeting the frequency regulation requirements of new-type power systems. To enhance the system's frequency support capability, it is imperative to fully utilize the frequency regulation resources within the power system. To address this issue, this paper first introduces a system frequency security posture assessment method that accounts for the spatial distribution characteristics of the grid. Subsequently, a parameter optimization method for diverse frequency regulation resources is proposed in conjunction with the proposed comprehensive evaluation method for frequency regulation. Next, using a renewable energy feeder regional grid as an example, an integrated frequency regulation method based on the system frequency security posture is presented. Finally, the frequency regulation performance and economic costs of different frequency regulation methods are analyzed under various operating scenarios and disturbances using a model based on actual data from the renewable energy feeder regional grid. The simulation and index calculation results demonstrate that the method proposed in this paper effectively enhances the system's frequency support capability, reduces the frequency disparity between different nodes within the grid, and maintains high economic performance. [ABSTRACT FROM AUTHOR]

Doping engineering is crucial for both fundamental science and emerging applications. While transition metal (TM) dopants exhibit considerable advantages in the tuning of magnetism and conductivity in bulk Ga2O3, investigations on TM-doped two-dimensional (2D) Ga2O3 are scarce, both theoretically and experimentally. In this study, the detailed variations in impurity levels within 3d TM-doped 2D Ga2O3 systems have been explored via first-principles calculations using the generalized gradient approximation (GGA) +U method. Our results show that the Co impurity tends to incorporate on the tetrahedral GaII site, while the other dopants favor square pyramidal GaI sites in 2D Ga2O3. Moreover, Sc3+, Ti4+, V4+, Cr3+, Mn3+, Fe3+, Co3+, Ni3+, Cu2+, and Zn2+ are the energetically favorable charge states. Importantly, a transition from n-type to p-type conductivity occurs at the threshold Cu element as determined by the defect formation energies and partial density of states (PDOS), which can be ascribed to the shift from electron doping to hole doping with respect to the increase in the atomic number in the 3d TM group. Moreover, the spin configurations in the presence of the square pyramidal and tetrahedral coordinated crystal field effects are investigated in detail, and a transition from high-spin to low-spin arrangement is observed. As the atomic number of the 3d TM dopant increases, the percentage contribution of O ions to the total magnetic moment significantly increases due to the electronegativity effect. Additionally, the formed 3d bands for most TM dopants are located near the Fermi level, which can be of significant benefit to the transformation of the absorbing region from ultraviolet to visible/infrared light. Our results provide theoretical guidance for designing 2D Ga2O3 towards optoelectronic and spintronic applications. [ABSTRACT FROM AUTHOR]

Flexible electronic skin (e-skin) can enable robots to have sensory forms similar to human skin, enhancing their ability to obtain more information from touch. The non-invasive nature of electrical impedance tomography (EIT) technology allows electrodes to be arranged only at the edges of the skin, ensuring the stretchability and elasticity of the skin's interior. However, the image quality reconstructed by EIT technology has deteriorated in multi-touch identification, where it is challenging to clearly reflect the number of touchpoints and accurately size the touch areas. This paper proposed an EIT-based flexible tactile sensor that employs self-made hydrogel material as the primary sensing medium. The sensor's structure, fabrication process, and tactile imaging principle were elaborated. To improve the quality of image reconstruction, the fast iterative shrinkage-thresholding algorithm (FISTA) was embedded into the EIDORS toolkit. The performances of the e-skin in aspects of assessing the touching area, quantitative force sensing and multi-touch identification were examined. Results showed that the mean intersection over union (MIoU) of the reconstructed images was improved up to 0.84, and the tactile position can be accurately imaged in the case of the number of the touchpoints up to seven (larger than two to four touchpoints in existing studies), proving that the combination of the proposed sensor and imaging algorithm has high sensitivity and accuracy in multi-touch tactile sensing. The presented e-skin shows potential promise for the application in complex human–robot interaction (HRI) environments, such as prosthetics and wearable devices. [ABSTRACT FROM AUTHOR]

This study looked at how desalinated seawater, which has low minerals and high boron, could affect bone health. Prior research suggests that low mineral water may harm bone health and boron could be beneficial, but the overall impact on bone health is still unclear. Eighty-nine-week-old male Balb/C mice were allocated into eight groups and administered either tap water or purified water with varying boron concentrations (0, 5, 40, and 200 mg/L). They were kept in an environment mimicking tropical conditions (35–40 °C, 70–80% humidity) and underwent daily treadmill exercise for 13 weeks. At the 14th week, serum, femora, and lumbar vertebrae were collected for mineral metabolism, bone biomarker, microstructure, and biomechanics evaluation. Boron exposure improved bone formation, microstructure, and biomechanics initially but the benefits weakened with higher levels of exposure (p < 0.05). Co-exposure to purified water elevated serum boron but weakened the promotion of boron on bone minerals and the bone benefits of boron compared to tap water (p < 0.05). Thus, when studying the health effects of boron in desalinated seawater, it is crucial to look at various health effects beyond bone health. Furthermore, it is important to consider the mineral composition of drinking water when using boron for bone health benefits. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) semiconductors have attracted much attention regarding their use in flexible electronic and optoelectronic devices, but the inherent poor electron mobility in conventional 2D materials severely restricts their applications. Using first-principles calculations in conjunction with Boltzmann transport theory, we systematically investigated the Si-doped 2D β-Ga2O3 structure mediated by biaxial strain, where the structural stabilities were determined by formation energy, phonon spectrum, and ab initio molecular dynamic simulation. Initially, the band gap values of Si-doped 2D β-Ga2O3 increased slightly, followed by a rapid decrease from 2.46 eV to 1.38 eV accompanied by strain modulations from −8% compressive to +8% tensile, which can be ascribed to the bigger energy elevation of the σ* anti-bonding in the conduction band minimum than that of the π bonding in the valence band maximum. Additionally, band structure calculations resolved a direct-to-indirect transition under the tensile strains. Furthermore, a significantly high electron mobility up to 4911.18 cm2 V−1 s−1 was discovered in Si-doped 2D β-Ga2O3 as the biaxial tensile strain approached 8%, which originated mainly from the decreased quantum confinement effect on the surface. The electrical conductivity was elevated with the increase in tensile strain and the enhancement of temperature from 300 K to 800 K. Our studies demonstrate the tunable electron mobilities and band structures of Si-doped 2D β-Ga2O3 using biaxial strain and shed light on its great potential in nanoscale electronics. [ABSTRACT FROM AUTHOR]

Entoloma is a relatively large genus in Agaricales, with a rich diversity of species and a wide distribution. In this study, seven new species of Entoloma belonging to the subgenus Cubospora have been identified based on morphological and phylogenetic evidence from subtropical regions of China. Morphologically, E. excavatum is characterized by the yellow, depressed, estriate pileus and medium-sized basidiospores; E. lacticolor is recognized by the white and papillate pileus, adnexed lamellae, and presence of clamp connections; E. phlebophyllum is identified by the pink-to-maroon and estriate pileus, and lamellae with lateral veins; E. rufomarginatum differs from other cuboid-spored species by the lamellae edge which is red-brown-underlined; E. subcycneum is characterized by the white pileus and carneogriseum-type cheilocystidia; E. submurrayi is recognized by the pileus margin exceeding the lamellae, 2-layered pileipellis with hyphae of different widths, and the presence of clamp connections; E. tomentosum is identified by the tomentose pileus, heterogeneous lamella edge, and versiform cheilocystidia with brown-yellow contents. Their distinct taxonomic status is confirmed by the positions of the seven new species in both the ITS + LSU and 3-locus (LSU, tef-1α, rpb2) phylogenetic trees. Detailed descriptions, color photos, and a key to related species are presented. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a disabling and highly prevalent condition affecting millions worldwide. Recently discovered, disulfidptosis represents a novel form of cell death induced by the excessive accumulation of cystine. Despite its significance, a systematic exploration of disulfidptosis-related genes (DRGs) in OA is lacking. Methods: This study utilized three OA-related datasets and DRGs. Differentially expressed (DE)-DRGs were derived by intersecting the differentially expressed genes (DEGs) from GSE114007 with DRGs. Feature genes underwent screening through three machine learning algorithms. High diagnostic value genes were identified using the receiver operating characteristic curve. Hub genes were confirmed through expression validation. These hub genes were then employed to construct a nomogram and conduct enrichment, immune, and correlation analyses. An additional validation of hub genes was performed through in vitro cell experiments. Results: SLC3A2 and PDLIM1 were designated as hub genes, displaying excellent diagnostic performance. PDLIM1 exhibited low expression in early chondrocyte differentiation, rising significantly in the late stage, while SLC3A2 showed high overall expression, declining in the late differentiation stage. Cellular experiments corroborated the correlation of SLC3A2 and PDLIM1 with chondrocyte inflammation. Conclusions: Two hub genes, SLC3A2 and PDLIM1, were identified in relation to disulfidptosis, providing potential directions for diagnosing and treating OA. [ABSTRACT FROM AUTHOR]

The rapid progress of urbanization and rural revitalization in developing countries has led to dramatic changes to the rural ecological environment. Assessing the rural ecosystem health (REH) is a crucial foundation for promoting sustainable development in rural areas. This study, focusing on rural areas around the Qinling Mountains in Shaanxi Province, establishes an evaluation system based on appropriate evaluation indicators for assessing the composite ecosystem. This evaluation system comprises four rural ecosystem subsystems: resource, environment, society, and economy. By employing a comprehensive indicator evaluation model and remote sensing image data, this study examines the health status of rural ecosystems in the 40 counties and districts across the study area, as well as their spatial differentiation characteristics, using ArcGIS (10.8) spatial analysis. The REH scores of these areas range from 0.6856 to 0.8818, with a fluctuating downward trend from north to south. This suggests that the rural ecosystems around the Qinling Mountains in Shaanxi Province are relatively healthy, with the northern area being notably healthier than the southern area. Spatial Gini coefficient analysis reveals a much smaller coefficient for the overall ecosystem compared to the subsystems in the study area, indicating that the distribution of health levels is dispersed and not concentrated. After establishing REH grades and quantity metrics, the 40 counties and districts are categorized into 13 types, followed by an analysis of the influencing factors for each type. Recommendations and management strategies are then proposed to enhance the health of rural ecosystems. [ABSTRACT FROM AUTHOR]

Semantic feature space plays a bridging role from 'seen classes' to 'unseen classes' in zero-shot learning (ZSL). However, due to the nature of SAR distance-based imaging, which is drastically different from that of optical imaging, how to construct an appropriate semantic space for SAR ZSL is still a tricky and less well-addressed issue. In this work, three different semantic feature spaces, constructed using natural language, remote sensing optical images, and web optical images, respectively, are explored. Furthermore, three factors, i.e., model capacity, dataset scale, and pre-training, are investigated in semantic feature learning. In addition, three datasets are introduced for the evaluation of SAR ZSL. Experimental results show that the semantic space constructed using remote sensing images is better than the other two and that the quality of semantic space can be affected significantly by factors such as model capacity, dataset scale, and pre-training schemes. [ABSTRACT FROM AUTHOR]

In this work, a language-level Semantics-Conditioned framework for 3D Point cloud segmentation, called SeCondPoint, is proposed, where language-level semantics are introduced to condition the modeling of the point feature distribution, as well as the pseudo-feature generation, and a feature–geometry-based Mixup approach is further proposed to facilitate the distribution learning. Since a large number of point features could be generated from the learned distribution thanks to the semantics-conditioned modeling, any existing segmentation network could be embedded into the proposed framework to boost its performance. In addition, the proposed framework has the inherent advantage of dealing with novel classes, which seems an impossible feat for the current segmentation networks. Extensive experimental results on two public datasets demonstrate that three typical segmentation networks could achieve significant improvements over their original performances after enhancement by the proposed framework in the conventional 3D segmentation task. Two benchmarks are also introduced for a newly introduced zero-shot 3D segmentation task, and the results also validate the proposed framework. [ABSTRACT FROM AUTHOR]

In recent years, remotely controlling an unmanned aerial vehicle (UAV) to perform coverage search missions has become increasingly popular due to the advantages of the UAV, such as small size, high maneuverability, and low cost. However, due to the distance limitations of the remote control and endurance of a UAV, a single UAV cannot effectively perform a search mission in various and complex regions. Thus, using a group of UAVs to deal with coverage search missions has become a research hotspot in the last decade. In this paper, a differential evolution (DE)-based multi-UAV cooperative coverage algorithm is proposed to deal with the coverage tasks in different regions. In the proposed algorithm, named DECSMU, the entire coverage process is divided into many coverage stages. Before each coverage stage, every UAV automatically plans its flight path based on DE. To obtain a promising flight trajectory for a UAV, a dynamic reward function is designed to evaluate the quality of the planned path in terms of the coverage rate and the energy consumption of the UAV. In each coverage stage, an information interaction between different UAVs is carried out through a communication network, and a distributed model predictive control is used to realize the collaborative coverage of multiple UAVs. The experimental results show that the strategy can achieve high coverage and a low energy consumption index under the constraints of collision avoidance. The favorable performance in DECSMU on different regions also demonstrate that it has outstanding stability and generality. [ABSTRACT FROM AUTHOR]

Agaricus bisporus, belonging to the genus Agaricus and the family Agaricaceae, is a popular commercially cultivated mushroom with rich nutritional and medical values. Cultivation of A. bisporus requires superior cultivars. Understanding the differences between wild and cultivated accession at the genetic level is crucial for mining loci and genes associated with cultivation-related traits, informing future breeding directions. Additionally, the identification of loci and genes associated with important agronomic traits (e.g., yield and quality) facilitates mushroom molecular breeding. In this study, we conducted sequencing of 200 strains of A. bisporus and analyzed genomic variations. Population structure and genetic relationships were investigated for 200 strains. Selection signs and genes were also obtained after selection sweep analysis. Thirteen candidate genes in the selective elimination regions had significantly different expression patterns at the fruit body stage. Moreover, six genes were identified for the esterase isozyme type through a combination of GWAS and RNA data. This study provides insight into environmental adaptation at the genetic base, providing valuable genetic resources for button mushroom molecular breeding to improve environmental adaptation, quality, or yield. [ABSTRACT FROM AUTHOR]

To address the problem of accurately evaluating age-friendly retrofit indicators in urban communities, this study constructs an evaluation model that takes into account user preferences and their interaction needs based on the social–ecological–technological systems (SETS) theory. The model aims to establish a set of precise community retrofit indicators, covering aspects such as public participation level, public ancillary facilities, green space layout, living environment building, health service support, and accessible design. By employing prioritization strategies, the model seeks to maximize resident satisfaction and promote harmonious coexistence between the community and the environment. Firstly, the retrofit evaluation indicators are formulated using the SETS theory. The entropy weight method (EWM) is then applied to determine the initial weights of these evaluation indicators, followed by the use of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to analyze the interrelationships among the indicators. The Kano model is integrated to adjust the weights, reflecting their importance. The final weights of the indicators are determined through normalization. Based on this, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is employed to rank and analyze the importance of age-friendly retrofits in the community. Finally, six communities in Dalian, Liaoning Province, China, are selected as samples to evaluate and analyze the age-friendly retrofit indicators. Different methods are compared, and their advantages and disadvantages are ranked to validate the effectiveness and feasibility of the proposed approach. Based on the analysis results, specific design schemes for improving the identified shortcomings in age-friendly aspects of these communities are proposed, considering the needs of aging populations and taking into account community public facilities, accessible design renovations, and the enhancement of green space layouts. This study aims to explore the comprehensive benefits of an age-friendly retrofit in urban communities and provide reference for the deep integration of social, ecological, and technological benefits in urban renewal. The evaluation indicators, methods, and conclusions presented can serve as a decision-making basis for the renovation and renewal of aging urban communities, particularly in terms of age-friendly updates. [ABSTRACT FROM AUTHOR]

Within the genus Mycena, species exhibiting brownish basidiomata present considerable challenges in identification due to similar coloration. This study underscores the significance of pileipellis types and cheilocystidia characteristics as critical in delimiting brownish Mycena species. To clarify the principal taxonomic characters and their utility in distinguishing between brownish Mycena species, a morphological taxonomy and phylogenetic analysis were performed. Five new species from China were introduced and characterized through a comprehensive morphological anatomy and phylogenetic substantiation: M. campanulatihemisphaerica sp. nov., M. digitifurcata sp. nov., M. kunyuensis sp. nov., M. limitis sp. nov., and M. oryzifluens sp. nov. Discussions of these taxa are supplemented with morphological illustrations. The phylogenetic relationships were inferred using Bayesian Inference and Maximum Likelihood methods based on sequences from the internal transcribed spacer and the large subunit regions of nuclear ribosomal RNA. With the addition of these five new species, the worldwide count of brownish Mycena increases to 94, and a key to the 29 known species of brownish Mycena from China is presented. [ABSTRACT FROM AUTHOR]

Interior design requires designer creativity and significant workforce investments. Meanwhile, Artificial Intelligence (AI) is crucial for enhancing the creativity and efficiency of interior design. Therefore, this study proposes an innovative method to generate multistyle interior design and videos with AI. First, this study created a new indoor dataset to train an AI that can generate a specified design style. Subsequently, video generation and super-resolution modules are integrated to establish an end-to-end workflow that generates interior design videos from texture-free 3D models. The proposed method utilizes AI to produce diverse interior design videos directly, thus replacing the tedious tasks of texture selection, lighting arrangement, and video rendering in traditional design processes. The research results indicate that the proposed method can effectively provide diverse interior design videos, thereby enriching design presentation and improving design efficiency. Additionally, the proposed workflow is versatile and scalable, thus holding significant reference value for transforming traditional design toward intelligence. [ABSTRACT FROM AUTHOR]

Co-fermentation with bacteria and enzymes can reduce sugar content in palm kernel cake (PKC); however, the chemical changes and their effects on cell functionality are unclear. This study investigated the active components in pre-treated PKC extracts and their effects on pig small intestine IPEC-J2 cell proliferation and LPS-induced inflammation. The extracts contained 60.75% sugar, 36.80% mannose, 1.75% polyphenols and 0.59% flavone, as determined by chemical analyses, suggesting that the extracts were palm kernel cake oligosaccharides (PKCOS). Then, we found that 1000 µg/mL PKCOS counteracted the decrease in cell viability (CCK8 kit) caused by LPS induction by 5 µg/mL LPS (p < 0.05). Mechanistic studies conducted by RNA-seq and qPCR analyses suggested PKCOS promoted cell proliferation through the upregulation of TNF-α, PI3KAP1, MAP3K5 and Fos in the PI3K/MAPK signalling pathway; alleviated inflammation caused by LPS via the downregulation of the target genes Casp3 and TNF-α in association with apoptosis; and regulated the expression of the antioxidant genes SOD1, SOD2 and GPX4 to exert positive antioxidant effects (p < 0.05). Furthermore, PKCOS upregulated SLC5A1 (encoding SLGT1), HK and MPI in the glycolytic pathway (p < 0.05), suggesting cell survival. In summary, PKCOS has positive effects on promoting swine intestine cell proliferation against inflammation. [ABSTRACT FROM AUTHOR]

This study aims to understand the main nutrient composition and comprehensively evaluate the differences in the mineral element contents of fruits of different macadamia cultivars, as well as screen good cultivars that are suitable for use in rocky desert mountains. Nine macadamia nut cultivars were selected as test materials in rocky desert mountain orchards. The contents of crude fat, crude protein, and total soluble sugar in kernels and N, P, K, Ca, Mg, Fe, Mn, Cu, Zn, and B in peels and kernels were determined, respectively. Then, the kernels' mineral element contents were comprehensively evaluated based on principal component analysis. The results showed that the kernels were rich in crude fat, protein, and soluble sugar, with the crude fat content reaching 75% or greater, and the variation among cultivars was small. However, the variation in soluble sugar content was extensive. The content of mineral elements varied in different cultivars and parts of the fruit, with the average macronutrient content being K > N > Ca > P > Mg in the pericarp and N > K > P > Mg > Ca in the kernel, and the content of micronutrients in the pericarp and the kernel being Mn > Fe > Zn > Cu > B. By principal component analysis, the 10 mineral nutrient indexes were calculated as four principal components, with a cumulative contribution rate of 88.051%. Using the affiliation function value method and the calculation of the comprehensive evaluation value, the nine cultivars could be classified into three categories. The cultivar with the highest comprehensive evaluation value of the mineral element content was O.C. The one with the lowest value was H2, which indicated that O.C is a suitable variety for popularization in rocky desert mountainous areas. Stepwise regression analysis concluded that P, K, Fe, Mn, and Cu were the indicators significantly influencing the mineral element content of macadamia nuts and fruits in rocky desert mountains. [ABSTRACT FROM AUTHOR]

The consumption of low-mineral water has been increasing worldwide. Drinking low-mineral water is associated with cardiovascular disease, osteopenia, and certain neurodegenerative diseases. However, the specific mechanism remains unclear. The liver metabolic alterations in rats induced by drinking purified water for 3 months were investigated with a metabolomics-based strategy. Compared with the tap water group, 74 metabolites were significantly changed in the purified water group (6 increased and 68 decreased), including 29 amino acids, 11 carbohydrates, 10 fatty acids, 7 short chain fatty acids (SCFAs), and 17 other biomolecules. Eight metabolic pathways were significantly changed, namely aminoacyl–tRNA biosynthesis; nitrogen metabolism; alanine, aspartate and glutamate metabolism; arginine and proline metabolism; histidine metabolism; biosynthesis of unsaturated fatty acids; butanoate metabolism; and glycine, serine and threonine metabolism. These changes suggested that consumption of purified water induced negative nitrogen balance, reduced expression of some polyunsaturated fatty acids and SCFAs, and disturbed energy metabolism in rats. These metabolic disturbances may contribute to low-mineral-water-associated health risks. The health risk of consuming low-mineral water requires attention. [ABSTRACT FROM AUTHOR]

In the task of upper-limb pattern recognition, effective feature extraction, channel selection, and classification methods are crucial for the construction of an efficient surface electromyography (sEMG) signal classification framework. However, existing deep learning models often face limitations due to improper channel selection methods and overly specific designs, leading to high computational complexity and limited scalability. To address this challenge, this study introduces a deep learning network based on channel feature compression—partial channel selection sEMG net (PCS-EMGNet). This network combines channel feature compression (channel selection) and feature extraction (partial block), aiming to reduce the model's parameter count while maintaining recognition accuracy. PCS-EMGNet extracts high-dimensional feature vectors from sEMG signals through the partial block, decoding spatial and temporal feature information. Subsequently, channel selection compresses and filters these high-dimensional feature vectors, accurately selecting channel features to reduce the model's parameter count, thereby decreasing computational complexity and enhancing the model's processing speed. Moreover, the proposed method ensures the stability of classification, further improving the model's capability of recognizing features in sEMG signal data. Experimental validation was conducted on five benchmark databases, namely the NinaPro DB4, NinaPro DB5, BioPatRec DB1, BioPatRec DB2, and BioPatRec DB3 datasets. Compared to traditional gesture recognition methods, PCS-EMGNet significantly enhanced recognition accuracy and computational efficiency, broadening its application prospects in real-world settings. The experimental results showed that our model achieved the highest average accuracy of 88.34% across these databases, marking a 9.96% increase in average accuracy compared to models with similar parameter counts. Simultaneously, our model's parameter size was reduced by an average of 80% compared to previous gesture recognition models, demonstrating the effectiveness of channel feature compression in maintaining recognition accuracy while significantly reducing the parameter count. [ABSTRACT FROM AUTHOR]

Prefabricated construction (PC) is considered to be a low-carbon construction method. Implementing prefabricated construction projects (PCPs) requires multiple industry organizations to participate and collaborate. As different PCPs are initiated and implemented, industry organizations will gradually gather into a complex and evolving collaborative network at the industry level based on specific collaboration relationships with different project backgrounds. The evolution of the collaborative network is related to how industry organizations interact with each other, and how PC-related knowledge and innovation has spread among organizations in the long term. However, the laws of network evolution and the micro effects that drive network evolution are still unknown. This study analyzes 236 prefabricated construction projects (PCPs) in Shanghai during 2015–2023, using the stochastic actor-oriented models (SAOM) method to explore how the macro structure of project-based inter-organizational collaborative networks for prefabricated construction (PC) technology implementation evolves over time, and how micro effects jointly support the evolution of the networks. The macro-level descriptive analysis of the network indicates that the collaborative network has become increasingly dense over time and continues to show a core–peripheral structure, with a small number of super-connected organizations. The micro-level SAOM analysis further reveals that the evolution of the collaborative network structure is driven by structure-based preferential attachment and geographic proximity effects, as well as attribute-based ownership similarity effect. This exploratory effort applies a network dynamics model to investigate the micro mechanism of the evolution of inter-organizational collaboration. The research results provide theoretical guidance and decision-making references for PC industry organizations to develop efficient network action strategies. In addition, it can help industry managers to formulate appropriate network management strategies. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing technology is an advanced and powerful tool that enables fine identification of the numerous soil reflectance spectrum characteristics. Heavy metal(loid)s (HMs) are the primary pollutants affecting the soil biodiversity and ecosystem services. Estimating HMs' concentrations in soils using hyperspectral data is an effective method but is challenging due to the effects of varied soil properties and measurement-related errors inflicted by atmospheric effects. This study focused on typical mining areas in the Fenwei Plain (FWP), China. Soil-related data were collected by leveraging airborne- and ground-based integrated remote sensing observations. The concentrations of eight HMs, namely copper (Cu), lead (Pb), zinc (Zn), nickel (Ni), chromium (Cr), cadmium (Cd), arsenic (As), and mercury (Hg), were measured by laboratory analysis from 100 in situ soil samples. Soil reflectance spectra were processed based on resampling and envelope methods. The combination datasets of the concentrations and optimal soil reflectance spectra were used to build the soil-related parameter retrieval models using three machine learning (ML) methods, and the feasibility of applying the high-performance retrieval model to estimate the HM concentrations in mining areas was evaluated and explored. Spectral analysis results show that four hundred and twenty-eight bands of five wavelength ranges are of high quality and obviously demonstrate the spectral characteristics selected to build the soil-related parameter models. The evaluation results of eight combination data subsets and three methods show that the preprocessing of spectral data (ground- and airborne-based reflectance) and soil samples with the random forest (RF) method can obtain higher accuracy than support vector machine (SVM) and partial least squares (PLS) methods, denoted as the AER-ACS-RF and GER-GCS-RF models (the average RMSE values of eight HMs were 2.61 and 2.53 mg/kg, respectively). The highest R2 values were observed in Cd and As, with an equal value of 0.98, followed by that of Pb (R2 = 0.97). The relative prediction deviation (RPD) values of Cu and AS were greater than 1.9. Moreover, the airborne-based AER-ACS-RF model presents a good mapping effect about the concentrations (mg/kg) of eight HMs in mining areas, ranging from 21.65 to 31.25 (Cu), 16.38 to 30.45 (Pb), 62.02 to 109.48 (Zn), 23.33 to 32.47 (Ni), 49.81 to 66.56 (Cr), 0.09 to 0.23 (Cd), 7.31 to 12.24 (As), and 0.03 to 0.17 (Hg), respectively. [ABSTRACT FROM AUTHOR]

Osteoporotic fractures are induced by osteoporosis, which may lead to the degradation of bone tissues and microstructures and impair their healing ability. Conventional internal fixation therapies are ineffective in the treatment of osteoporotic fractures. Hence, developing tissue engineering materials is crucial for repairing osteoporotic fractures. It has been demonstrated that nanomaterials, particularly graphene oxide (GO), possess unique advantages in tissue engineering due to their excellent biocompatibility, mechanical properties, and osteoinductive abilities. Based on that, GO-nanocomposites have garnered significant attention and hold promising prospects for bone repair applications. This paper provides a comprehensive insight into the properties of GO, preparation methods for nanocomposites, advantages of these materials, and relevant mechanisms for osteoporotic fracture applications. [ABSTRACT FROM AUTHOR]

The Danjiangkou Reservoir supplies drinking water to most residents in northern China. However, signs of eutrophication have been observed in the inlet tributaries of the reservoir, including the Shending River backwater. This research used data from the Sentinel-2 Multispectral instrument and findings from a 2021 aquatic ecological survey to analyze the spatial and temporal characteristics of phytoplankton distribution in the Shending River backwater region. The average chlorophyll a (Chl-a) concentrations by season, ranked from largest to smallest, are as follows: summer (63.96 μg/L) > autumn (41.26 μg/L) > spring (27.47 μg/L) > winter (16.21 μg/L); the upstream of the backwater area and the near-shore tributary bay had relatively higher Chl-a concentration. Bacillariophyceae (Cyclotella meneghiniana and Synedra sp.) and Cryptophyceae species (Chroomonas acuta) were dominant in spring, whereas Chlorophyceae (Scenedesmus sp. and Chlorella vulgaris) and Cyanophyceae (Dactylococcopsis acicularis, Microcystis aeruginosa and Oscillatoria tenuis) species were dominant in summer. The seasonal succession characteristics of the phytoplankton community were consistent with those of the Plankton Ecology Group model. The average phytoplankton cell density was 4.80 × 107 cells/L, and the Shannon–Wiener average diversity index was 1.95, indicating that the Shending River backwater area was moderately eutrophic. According to Pearson correlation analysis and Mantel test, the main factors causing temporal and spatial differences in phytoplankton production in the Shending River's backwater were water level (WL), water temperature (WT), ammonia nitrogen (NH3-N) and total nitrogen (TN). In particular, WL was significantly positively correlated with Bacillariophyceae, Chlorophyceae and Cyanophyceae, whereas WT was significantly correlated with Cryptophyceae and Chlorophyceae. NH3-N and TN were significantly correlated with Cyanophyceae. Therefore, intensive nitrogen removal from the tailwater of sewage treatment plants may be considered a feasible measure to prevent cyanobacterial bloom in the Shending River backwater of the Danjiangkou Reservoir. [ABSTRACT FROM AUTHOR]

Adsorption heat storage holds great promise for solar energy applications. The development of new adsorbent materials is currently the research focus in this area. The present work designs several activated carbon models with different functional groups, including -OH, -NH2, -COOH, and -SO3H, and explores the influence of functional groups' categories and numbers on the water adsorption capacity of the activated carbon using the GCMC method. The adsorption mechanism between functional groups and water molecules is analyzed using density functional theory. The results show that the functional groups could significantly improve the water adsorption capacity of activated carbon due to the hydrogen bond between functional groups and water molecules. In the scope of this paper, under low pressure, the activated carbon with -SO3H exhibits the best adsorption capacity, followed by the activated carbon with -COOH. Under low and medium pressure, increasing the number of -SO3H functional groups could increase the water adsorption capacity; however, when the pressure is high, increasing the functional group numbers might decrease the water adsorption capacity. As the temperature increases, the water adsorption capacity of activated carbons decreases, and the activated carbon with -SO3H is proven to have excellent application prospects in heat energy storage. [ABSTRACT FROM AUTHOR]

Iron impurities are believed to act as deep acceptors that can compensate for the n-type conductivity in as-grown Ga2O3, but several scientific issues, such as the site occupation of the Fe heteroatom and the complexes of Fe-doped β-Ga2O3 with native defects, are still lacking. In this paper, based on first-principle density functional theory calculations with the generalized gradient approximation approach, the controversy regarding the preferential Fe incorporation on the Ga site in the β-Ga2O3 crystal has been addressed, and our result demonstrates that Fe dopant is energetically favored on the octahedrally coordinated Ga site. The structural stabilities are confirmed by the formation energy calculations, the phonon dispersion relationships, and the strain-dependent analyses. The thermodynamic transition level Fe3+/Fe2+ is located at 0.52 eV below the conduction band minimum, which is consistent with Ingebrigtsen's theoretical conclusion, but slightly smaller than some experimental values between 0.78 eV and 1.2 eV. In order to provide direct guidance for material synthesis and property design in Fe-doped β-Ga2O3, the defect formation energies, charge transitional levels, and optical properties of the defective complexes with different kinds of native defects are investigated. Our results show that VGa and Oi can be easily formed for the Fe-doped β-Ga2O3 crystals under O-rich conditions, where the +3 charge state FeGaGai and −2 charge state FeGaOi are energetically favorable when the Fermi level approaches the valence and conduction band edges, respectively. Optical absorption shows that the complexes of FeGaGai and FeGaVGa can significantly enhance the optical absorption in the visible-infrared region, while the energy-loss function in the β-Ga2O3 material is almost negligible after the extra introduction of various intrinsic defects. [ABSTRACT FROM AUTHOR]

In recent years, human pose estimation, as a subfield of computer vision and artificial intelligence, has achieved significant performance improvements due to its wide applications in human-computer interaction, virtual reality, and smart security. However, most existing methods are designed for single-person scenes and suffer from low accuracy and long inference time in multi-person scenes. To address this issue, increasing attention has been paid to developing methods for multi-person pose estimation, such as utilizing Partial Affinity Field (PAF)-based bottom-up methods to estimate 2D poses of multiple people. In this study, we propose a method that addresses the problems of low network accuracy and poor estimation of flexible joints. This method introduces the attention mechanism into the network and utilizes the joint point extraction method based on hard example mining. Integrating the attention mechanism into the network improves its overall performance. In contrast, the joint point extraction method improves the localization accuracy of the flexible joints of the network without increasing the complexity. Experimental results demonstrate that our proposed method significantly improves the accuracy of 2D human pose estimation. Our network achieved a notably elevated Average Precision (AP) score of 60.0 and outperformed competing methods on the standard benchmark COCO test dataset, signifying its exceptional performance. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) represents the foremost degenerative joint disease observed in a clinical context. The escalating issue of population aging significantly exacerbates the prevalence of OA, thereby imposing an immense annual economic burden on societies worldwide. The current therapeutic landscape falls short in offering reliable pharmaceutical interventions and efficient treatment methodologies to tackle this growing problem. However, the scientific community continues to dedicate significant efforts towards advancing OA treatment research. Contemporary studies have discovered that the progression of OA may be slowed through the strategic influence on peroxisome proliferator-activated receptors (PPARs). PPARs are ligand-activated receptors within the nuclear hormone receptor family. The three distinctive subtypes—PPARα, PPARβ/δ, and PPARγ—find expression across a broad range of cellular terminals, thus managing a multitude of intracellular metabolic operations. The activation of PPARγ and PPARα has been shown to efficaciously modulate the NF-κB signaling pathway, AP-1, and other oxidative stress-responsive signaling conduits, leading to the inhibition of inflammatory responses. Furthermore, the activation of PPARγ and PPARα may confer protection to chondrocytes by exerting control over its autophagic behavior. In summation, both PPARγ and PPARα have emerged as promising potential targets for the development of effective OA treatments. [ABSTRACT FROM AUTHOR]

The detection of skin lesions involves a resource-intensive and time-consuming process, necessitating specialized equipment and the expertise of dermatologists within medical facilities. Lesion segmentation, as a critical aspect of skin disorder assessment, has garnered substantial attention in recent research pursuits. In response, we developed a portable automatic dermatology detector and proposed a dual-CAM weakly supervised bootstrapping model for skin lesion detection. The hardware system in our device utilizes a modular and miniaturized design, including an embedded board, dermatoscope, and display, making it highly portable and easy to use in various settings. Our software solution uses a convolutional neural network (CNN) with a dual-class activation map (CAM) weakly supervised bootstrapping model for skin lesion detection. The model boasts two key characteristics: the integration of segmentation and classification networks, and the utilization of a dual CAM structure for precise lesion localization. We conducted an evaluation of our method using the ISIC2016 and ISIC2017 datasets, which yielded findings that demonstrate an AUC of 86.3% for skin lesion classification for ISIC2016 and an average AUC of 92.9% for ISIC2017. Furthermore, our system achieved diagnostic results of significant reference value, with an average AUC of 92% when tested on real-life skin. The experimental results underscore the portable device's capacity to provide reliable diagnostic information for potential skin lesions, thereby demonstrating its practical applicability. [ABSTRACT FROM AUTHOR]

The intrinsic n-type conduction in Gallium oxides (Ga2O3) seriously hinders its potential optoelectronic applications. Pursuing p-type conductivity is of longstanding research interest for Ga2O3, where the Cu- and Zn-dopants serve as promising candidates in monoclinic β-Ga2O3. However, the theoretical band structure calculations of Cu- and Zn-doped in the allotrope α-Ga2O3 phase are rare, which is of focus in the present study based on first-principles density functional theory calculations with the Perdew–Burke–Ernzerhof functional under the generalized gradient approximation. Our results unfold the predominant Cu1+ and Zn2+ oxidation states as well as the type and locations of impurity bands that promote the p-type conductivity therein. Furthermore, the optical calculations of absorption coefficients demonstrate that foreign Cu and Zn dopants induce the migration of ultraviolet light to the visible–infrared region, which can be associated with the induced impurity 3d orbitals of Cu- and Zn-doped α-Ga2O3 near the Fermi level observed from electronic structure. Our work may provide theoretical guidance for designing p-type conductivity and innovative α-Ga2O3-based optoelectronic devices. [ABSTRACT FROM AUTHOR]

It is crucial to detect high-severity defects, such as memory leaks that can result in system crashes or severe resource depletion, in order to reduce software development costs and ensure software quality and reliability. The primary cause of high-severity defects is usually resource scheduling errors, and in the program source code, these defects have contextual features that require defect context to confirm their existence. In the context of utilizing machine learning methods for defect automatic confirmation, the single-feature label method cannot achieve high-precision defect confirmation results for high-severity defects. Therefore, a multi-feature fusion defect automatic confirmation method is proposed. The label generation method solves the dimensionality disaster problem caused by multi-feature fusion by fusing features with strong correlations, improving the classifier's performance. This method extracts node features and basic path features from the program dependency graph and designs high-severity contextual defect confirmation labels combined with contextual features. Finally, an optimized Support Vector Machine is used to train the automatic detection model for high-severity defects. This study uses open-source programs to manually implant defects for high-severity defect confirmation verification. The experimental results show that compared with existing methods, this model significantly improves the efficiency of confirming high-severity defects. [ABSTRACT FROM AUTHOR]

Osteosarcoma is an aggressive malignant neoplasm, and it is of great significance to the fabrication and investigation of the anti-tumor mechanism of nanomedicine in the treatment of osteosarcoma. Herein, a cinnamaldehyde polymeric prodrug micelle with pH-sensitive charge-conversion ability (mPEG-b-P(C7-co-CA)) was fabricated, and the anti-osteosarcoma mechanism of mPEG-b-P(C7-co-CA) micelle was investigated. mPEG-b-P(C7-co-CA) micelles were prepared by self-assembly method, and their diameter was 227 nm. mPEG-b-P(C7-co-CA) micelles could regulate the cell cycle and inhibit the proliferation of 143B cells, which was demonstrated by flow cytometry analysis, CCK-8 assay and 5-Ethynyl-2′-deoxyuridine (EdU) staining. The wound-healing assay and transwell assay showed that mPEG-b-P(C7-co-CA) micelles effectively inhibited the migration and invasion of 143B cells. It was proven that mPEG-b-P(C7-co-CA) micelles downregulated the levels of proliferation and apoptosis-related proteins and affected osteosarcoma migration and invasion by inhibiting the epithelial-mesenchymal transition (EMT). In addition, mPEG-b-P(C7-co-CA) micelles can also inhibit the transcriptional activity of the PI3K/Akt signaling pathway. Therefore, these findings provide new evidence for the pharmacological effects of mPEG-b-P(C7-co-CA) micelles. [ABSTRACT FROM AUTHOR]

Calcitonin gene-related peptide (CGRP) has 37 amino acids. Initially, CGRP had vasodilatory and nociceptive effects. As research progressed, evidence revealed that the peripheral nervous system is closely associated with bone metabolism, osteogenesis, and bone remodeling. Thus, CGRP is the bridge between the nervous system and the skeletal muscle system. CGRP can promote osteogenesis, inhibit bone resorption, promote vascular growth, and regulate the immune microenvironment. The G protein-coupled pathway is vital for its effects, while MAPK, Hippo, NF-κB, and other pathways have signal crosstalk, affecting cell proliferation and differentiation. The current review provides a detailed description of the bone repair effects of CGRP, subjected to several therapeutic studies, such as drug injection, gene editing, and novel bone repair materials. [ABSTRACT FROM AUTHOR]

A physiological disease of the pineapple fruit called pineapple translucency causes the pulp to become water-soaked, which affects the fruit's taste, flavor, shelf life, and integrity. In the present study, we analyzed seven pineapple varieties, of which three were watery and four were non-watery. There were no apparent macronutritional (K, P, or N) differences in their pulp, but the non-watery pineapple varieties had higher dry matter and soluble sugar content. The metabolomic analysis found 641 metabolites and revealed differential expression of alkaloids, phenolic acids, nucleotide derivatives, lipids, and other metabolites among the seven species. Transcriptome analysis and further KEGG enrichment showed downregulation of 'flavonoid biosynthesis' pathways, differential expression of metabolic pathways, secondary metabolites biosynthesis, plant–pathogen interaction, and plant hormone signal transduction. We believe this study will provide critical molecular data supporting a deeper understanding of pineapple translucency formation and greatly benefit future research on this commercially important crop. [ABSTRACT FROM AUTHOR]

The instability of the soil at the shield end is an important safety hazard in shield tunnel construction. In loose and weak strata, the risk of instability and damage is higher, and the loss is greater. In this paper, the instability and failure of the end soil in loose and weak strata are studied. To ensure the smooth start and arrival of the shield and avoid engineering accidents such as end soil instability, surface subsidence, landslides, etc., during the construction process, this paper summarizes the commonly used reinforcement technology and characteristics of the shield in and out of the tunnel. Through numerical simulation, the influence of the thickness of the added solid on the formation and diaphragm wall is analyzed. It is found that the reinforcement effect increases with the increase in thickness of the added solid, but the change rate becomes smaller and smaller. The indoor triaxial test is used to find out the physical and mechanical properties of loose sand and soft clay. The biaxial compression numerical model is built with PFC2D (Particle Flow Code 2D PFC 6.0), and the wall constraint type is improved. With the help of the PFC biaxial test, the influence of model parameters on the macroscopic properties of the simulated material was analyzed. The research has certain reference values for the actual construction of the project. [ABSTRACT FROM AUTHOR]

Osteoarthritis refers to a degenerative disease with joint pain as the main symptom, and it is caused by various factors, including fibrosis, chapping, ulcers, and loss of articular cartilage. Traditional treatments can only delay the progression of osteoarthritis, and patients may need joint replacement eventually. As a class of organic compound molecules weighing less than 1000 daltons, small molecule inhibitors can target proteins as the main components of most drugs clinically. Small molecule inhibitors for osteoarthritis are under constant research. In this regard, by reviewing relevant manuscripts, small molecule inhibitors targeting MMPs, ADAMTS, IL-1, TNF, WNT, NF-κB, and other proteins were reviewed. We summarized these small molecule inhibitors with different targets and discussed disease-modifying osteoarthritis drugs based on them. These small molecule inhibitors have good inhibitory effects on osteoarthritis, and this review will provide a reference for the treatment of osteoarthritis. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the effectiveness of donor in vitro fertilization (IVF-D) and donor artificial insemination (AI-D) in clinical outcomes, risks, and costs. Methods: This study analyzed the cycle changes and clinical outcomes in 20,910 IVF-D and 16,850 AI-D cycles between 2013 and 2021 in the Reproductive and Genetic Hospital of CITIC-Xiangya. A cost-effectiveness analysis was performed to evaluate the costs per couple and per live birth cycle in the two treatment groups. Results: IVF-D had higher pregnancy and live birth rates than AI-D (p < 0.001). The cumulative pregnancy and live birth rates for three AI-D cycles were 41.01% and 32.42%, respectively, higher than the rates for one or two AI-D cycles. The multiple birth and birth defect rate of AI-D was lower than that of IVF-D significantly. IVF-D mean cost per couple was higher than that of AI-D (CNY32,575 vs. CNY11,062, p < 0.001), with a mean cost difference of CNY21,513 (95% confidence interval, CNY20,517–22,508). The mean costs per live birth cycle for IVF-D and AI-D were CNY49,411 and CNY31,246, respectively. Conclusion: AI-D is more cost-effective and poses a lower risk for infertility couples than IVF-D, and patients should undergo three AI-D cycles to obtain the highest success rate. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs), as the main components of petroleum hydrocarbons (PHCs), are carcinogenic organic pollutants that occur widely in the atmospheric environment with increasing concentration. Moreover, PAHs are widespread all over the world due to their high volatility and long-range transport potential. The monitoring of atmospheric PAHs is often limited by working conditions, especially around oil field operation areas and other industrial areas. Mosses (Bryophyta), the most sensitive atmosphere pollution indicators, can be easily collected and have been used to monitor atmospheric pollutants including PAHs. Thus, characteristics and influencing factors of mosses' absorption and accumulation of PAHs in the atmosphere were discussed, and the application of mosses in the biomonitoring of atmospheric PAHs were summarized. Biomonitoring mosses include Dicranum scoparium, Hypnum cupressiforme, Thamnobryum alopecurum, Thuidium tamariscinum, Hylocomium splendens, Pleurozium scheberi, etc. Currently, the main methods for monitoring PAHs by mosses are biomonitoring with the chemical analysis method, the index of atmospheric purity (IAP) method, the ecological survey method, and the Moss-bag technique. Biomonitoring of atmospheric PAHs using mosses has a relatively wide range of prospects. [ABSTRACT FROM AUTHOR]

In plants, guanosine deaminase (GSDA) catalyzes the deamination of guanosine for nitrogen recycling and re-utilization. We previously solved crystal structures of GSDA from Arabidopsis thaliana (AtGSDA) and identified several novel substrates for this enzyme, but the structural basis of the enzyme activation/inhibition is poorly understood. Here, we continued to solve 8 medium-to-high resolution (1.85–2.60 Å) cocrystal structures, which involved AtGSDA and its variants bound by a few ligands, and investigated their binding modes through structural studies and thermal shift analysis. Besides the lack of a 2-amino group of these guanosine derivatives, we discovered that AtGSDA's inactivity was due to the its inability to seclude its active site. Furthermore, the C-termini of the enzyme displayed conformational diversities under certain circumstances. The lack of functional amino groups or poor interactions/geometries of the ligands at the active sites to meet the precise binding and activation requirements for deamination both contributed to AtGSDA's inactivity toward the ligands. Altogether, our combined structural and biochemical studies provide insight into GSDA. [ABSTRACT FROM AUTHOR]

Determination of metal ions such as zinc in solution remains an important task in analytical and biological chemistry. We describe a novel zinc ion biosensing approach using a carbonic anhydrase–Oplophorus luciferase fusion protein that employs bioluminescence resonance energy transfer (BRET) to transduce the level of free zinc as a ratio of emission intensities in the blue and orange portions of the spectrum. In addition to high sensitivity (below nanomolar levels) and selectivity, this approach allows both quantitative determination of "free" zinc ion (also termed "mobile" or "labile") using bioluminescence ratios and determination of the presence of the ion above a threshold simply by the change in color of bioluminescence, without an instrument. The carbonic anhydrase metal ion sensing platform offers well-established flexibility in sensitivity, selectivity, and response kinetics. Finally, bioluminescence labeling has proven an effective approach for molecular imaging in vivo since no exciting light is required; the expressible nature of this sensor offers the prospect of imaging zinc fluxes in vivo. [ABSTRACT FROM AUTHOR]

We extracted, purified, and characterized three neutral and three acidic polysaccharides from the roots, stems, and leaves of Aralia continentalis Kitigawa. The results of the analysis of monosaccharide composition indicated that the polysaccharides from the roots and stems were more similar to each other than they were to the polysaccharides from the leaves. The in vitro antioxidant results demonstrated that the acidic polysaccharides had stronger antioxidant activity than the neutral fractions. Therefore, we investigated the primary purified acidic polysaccharide fractions (WACP(R)-A-c, WACP(S)-A-c, and WACP(L)-A-d) by NMR and enzymatic analysis. The structural analytical results indicated that WACP(R)-A-c contained homogalacturonan (HG); WACP(S)-A-c contained HG and rhamnogalacturonan II (RG-II), and WACP(L)-A-d contained HG, RG-II, and rhamnogalacturonan I (RG-I) domains. Our findings offer insights into the screening of natural polysaccharide-based antioxidants and provide a theoretical basis for the application of A. continentalis. [ABSTRACT FROM AUTHOR]

Objective: To establish a method for the simultaneous determination of seven alkaloids in Semen Nelumbinis and its products, the extraction technology and HPLC method were optimized by the single factor experiment. Methods: Firstly, the samples were extracted ultrasonically with 1% formic acid ethanol and purified by PXC SPE column. Then, the extracts and the purified liquid were taken after concentration with nitrogen for quantitative analysis of seven alkaloids by HPLC method. Next, the contents of alkaloids in five samples were determined. Results: The method was fully validated and the result showed that seven kinds of alkaloids had good linear relation in the corresponding range of mass concentration, r2 > 0.999, where the detection limit was 0.5–1.5 mg/kg, the quantification limit was 1.25–4.5 mg/kg, the recovery was 83.33–116.04%, and the RSD of detection method was 1.06–5.25% (n = 7). In five samples, the contents of Lotusine and Neferine were the highest, Berberine Hydrochloride was not detected. Conclusion: This method is simple, sensitive, accurate and reproducible, and it can realize the quantitative analysis and chemical separation of seven kinds of common alkaloids in Semen Nelumbinis and its products and provide a theoretical method for the simultaneous determination of alkaloids. The extraction yields of alkaloids in Semen Nelumbinis can be increased through the extraction process, which is optimized by a single factor experiment. [ABSTRACT FROM AUTHOR]

Brucellosis is a severe zoonotic infectious disease caused by the infection of the Brucella, which is widespread and causes considerable economic losses in underdeveloped areas. Brucella is a facultative intracellular bacteria whose main target cells for infection are macrophages, placental trophoblast cells and dendritic cells. The main clinical signs of Brucella infection in livestock are reproductive disorders and abortion. At present, the pathogenesis of placentitis or abortion caused by Brucella in livestock is not fully understood, and further research on the effect of Brucella on placental development is still necessary. This review will mainly introduce the research progress of Brucella infection of placental trophoblast cells as well as the inflammatory response caused by it, explaining the molecular regulation mechanism of Brucella leading to reproductive system disorders and abortion, and also to provide the scientific basis for revealing the pathogenesis and infection mechanism of Brucella. [ABSTRACT FROM AUTHOR]

The large-scale production and frequent use of endocrine-disrupting chemicals (EDCs) have led to the continuous release and wide distribution of these pollutions in the natural environment. At low levels, EDC exposure may cause metabolic disorders, sexual development, and reproductive disorders in aquatic animals and humans. Adsorption treatment, particularly using nanocomposites, may represent a promising and sustainable method for EDC removal from wastewater. EDCs could be effectively removed from wastewater using various carbon-based nanomaterials, such as carbon nanofiber, carbon nanotubes, graphene, magnetic carbon nanomaterials, carbon membranes, carbon dots, carbon sponges, etc. Important applications of carbon nanocomposites for the removal of different kinds of EDCs and the theory of adsorption are discussed, as well as recent advances in carbon nanocomposite synthesis technology and characterization technology. Furthermore, the factors affecting the use of carbon nanocomposites and comparisons with other adsorbents for EDC removal are reviewed. This review is significant because it helps to promote the development of nanocomposites for the decontamination of wastewater. [ABSTRACT FROM AUTHOR]

Megaprojects have been redefined as dynamic collaboration networks, which are characterized by the strength of the ties between teams. In China, megaprojects have long been criticized for low productivity, poor project implementation processes, and inter-team confrontation. Few studies in the construction domain have explored the relationship between the strength of ties, inter-team conflict, and project success. This research aims to investigate the relationship between the strength of ties, inter-team conflict, and project success in megaprojects. Strong ties and weak ties reflect the strength of ties. Inter-team conflict is measured by task-related conflict and relationship-related conflict. A questionnaire survey was conducted on Chinese megaprojects, and 306 questionnaires were collected. The data were analyzed by a structural equation model. The results indicate that strong ties positively impact task-related conflict while negatively impacting relationship-related conflict. Weak ties positively impact inter-team conflict. Task-related conflict and relationship-related conflict have positive and negative effects on project success, respectively. The effect of the strength of the ties on a project's success is mediated by conflict. This study adds to existing research on the strength of ties and emphasizes the significance of megaproject network governance. The results reveal the constructive effects of the strength of ties and task-related conflict, as well as the non-constructive effects of relationship-related conflict. This provides a reference for megaproject managers to implement project governance. [ABSTRACT FROM AUTHOR]

Foliar dust retention is a crucial source of uncertainty when monitoring the vegetation index using satellite remote sensing. As ground sampling conditions are limited by vegetation dust retention, separating the extinction effect of foliar dust retention from the normalized difference vegetation index (NDVI) poses a significant challenge. In this study, we conducted a correlation test between the relative change in NDVI (δNDVI, an indicator of extinction effect) retrieved by the Gaofen-4 satellite and the atmospheric PM10 concentration in different meteorological periods (before, during, and after rainfall) across 14 stations in Shenzhen City, China. The results showed a significant correlation between δNDVI and atmospheric PM10 concentration during the before-rainfall period and weaker correlations for the other periods (R = 0.680, p < 0.001, n = 63 when excluding the during- and after-rainfall data). The correlation was more significant for the stations with low NDVI values, and a coastal station had a distinct regression slope of δNDVI versus PM10 from the other stations, indicating that the extinction effect of foliar dust retention in high-NDVI and coastal areas may not be well predicted by the general δNDVI–PM10 relationship. This provides a new quantitative basis for estimating the extinction effect of foliar dust retention using PM10 data for future improvement of the accuracy of vegetation monitoring by remote sensing. [ABSTRACT FROM AUTHOR]