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As tunnel engineering advances towards greater depths, larger scales, and longer distances, high rock temperatures and tunnel thermal damage frequently occur, constituting some of the main geological hazards faced by transportation, water conservancy, and other tunnel engineering projects. In this study, we take the alternative option to the No.1 deep-buried water diversion tunnel of the “Diversion of Yellow River to Jining” project as the study area, combined with the geological data of the field investigation, to comprehensively consider the thermodynamic properties of the rocks along the tunnel and the logging data along the tunnel, and we use the least squares method to fit the temperature of the logging wells to obtain the thermal background parameters of the earth’s heat flow as well as other regional heat background parameters to obtain the thermal background parameters of the tunnel. The COMSOL Multiphysics software established a two-dimensional steady-state geothermal field numerical model to predict the rock temperature along the tunnel. The results show that the maximum temperature along the tunnel is 51.1 °C, the length of the tunnel with thermal damage class I is 45.9 km, the length of the tunnel with thermal damage class II is 18.4 km, the length of the tunnel with thermal damage class III is 8.0 km, and the length of the tunnel with thermal damage class IV is predicted to be 0.18 km. Compared with the on-site temperature measurement data, the model prediction error is within ±1.5 °C, which validates the accuracy of the model. This study adopts numerical simulation combined with field geological and logging data to provide a theoretical basis for tunnel heat hazard prevention. Meanwhile, it offers technical support for the design and construction of similar deep-buried high-temperature tunnels.

The South American tomato pinworm, Tuta absoluta (Meyrick), is one of the major invasive pests that causes severe economic damage to several hosts, especially tomato plants, globally. Spinosad, a biopesticide widely used against several insect pests, also shows promising results against T. absoluta. Here, we investigated the evolution of spinosad resistance and its effects on biological traits and related gene effects to fully understand the relationship between resistance degree and associated fitness costs. The spinosad-resistant strain (SpRS) with a moderate level of resistance (14.40-fold) was developed following continuous selection of the susceptible strain (SS) against spinosad for eight generations. Fitness analysis showed that the larval and pupal developmental durations were significantly increased, while the adult emergence was substantially reduced in the SpRS compared to SS. Adult longevity (male and female), fecundity, and hatching rates significantly decreased in the SpRS T. absoluta compared to SS. Additionally, the gene expression analyses indicated the down-regulation of development and reproduction-related genes (Vg, VgR, JHBP, JHAMT, JHE, and JHDK) in the SpRS population. Further, the mRNA expression level of the resistance-related cytochrome P450 gene CYP321C40, followed by CYP4M116, CYP6AW1, CYP339A1, and CYP6AB327, were significantly increased in the SpRS T. absoluta. Taken together, these results indicated that although T. absoluta developed a moderate level of spinosad resistance accompanied by fitness costs, continuous and indiscriminate use might elevate the resistance level. Overall, these findings provide important information about the trade-off between resistance degree and fitness cost that might be crucial to designing resistance management strategies against this key invasive herbivore.

Sodium-ion batteries (SIBs) are advantageous for large-scale energy storage due to the plentiful and ubiquitous nature of sodium resources, coupled with their lower cost relative to alternative technologies. To expedite the market adoption of SIBs, enhancing the energy density of SIBs is essential. Raising the operational voltage of the SIBs cathode is regarded as an effective strategy for achieving this goal, but it requires stable high-voltage cathode materials. Sodium iron sulfate (NFSO) is considered to be a promising cathode material due to its stable framework, adjustable structure, operational safety, and the high electronegativity of SO4−. This paper reviews the research progress of NFSO, discusses its structure and sodium storage mechanism on this basis, and summarizes the advantages and disadvantages of NFSO cathode materials. This study also evaluates the advancements in enhancing the electrochemical characteristics and structural reliability of SIBs, drawing on both domestic and international research. The findings of this paper offer valuable insights into the engineering and innovation of robust and viable SIB cathodes based on NFSO at ambient temperatures, contributing to their commercial viability.

Sodium-ion batteries (SIBs) have garnered significant interest due to their potential as viable alternatives to conventional lithium-ion batteries (LIBs), particularly in environments where low-temperature (LT) performance is crucial. This paper provides a comprehensive review of current research on LT SIBs, focusing on electrode materials, electrolytes, and operational challenges specific to sub-zero conditions. Recent advancements in electrode materials, such as carbon-based materials and titanium-based materials, are discussed for their ability to enhance ion diffusion kinetics and overall battery performance at colder temperatures. The critical role of electrolyte formulation in maintaining battery efficiency and stability under extreme cold is highlighted, alongside strategies to mitigate capacity loss and cycle degradation. Future research directions underscore the need for further improvements in energy density and durability and scalable manufacturing processes to facilitate commercial adoption. Overall, LT SIBs represent a promising frontier in energy storage technology, with ongoing efforts aimed at overcoming technical barriers to enable widespread deployment in cold-climate applications and beyond.

Aboveground biomass (AGB) is a vital indicator for studying carbon sinks in forest ecosystems. Semiarid forests harbor substantial carbon storage but received little attention due to the high spatial–temporal heterogeneity that complicates the modeling of AGB in this environment. This study assessed the performance of different data sources (annual monthly time-series radar was Sentinel-1 [S1]; annual monthly time series optical was Sentinel-2 [S2]; and single-temporal airborne light detection and ranging [LiDAR]) and seven prediction approaches to map AGB in the semiarid forests on the border between Gansu and Qinghai Provinces in China. Five experiments were conducted using different data configurations from synthetic aperture radar backscatter, multispectral reflectance, LiDAR point cloud, and their derivatives (polarimetric combination indices, texture information, vegetation indices, biophysical features, and tree height- and canopy-related indices). The results showed that S2 acquired better prediction (coefficient of determination [R2]: 0.62–0.75; root mean square error [RMSE]: 30.08–38.83 Mg/ha) than S1 (R2: 0.24–0.45; RMSE: 47.36–56.51 Mg/ha). However, their integration further improved the results (R2: 0.65–0.78; RMSE: 28.68–35.92 Mg/ha). The addition of single-temporal LiDAR highlighted its structural importance in semiarid forests. The best mapping accuracy was achieved by XGBoost, with the metrics from the S2 and S1 time series and the LiDAR-based canopy height information being combined (R2: 0.87; RMSE: 21.63 Mg/ha; relative RMSE: 14.45%). Images obtained during the dry season were effective for AGB prediction. Tree-based models generally outperformed other models in semiarid forests. Sequential variable importance analysis indicated that the most important S1 metric to estimate AGB was the polarimetric combination indices sum, and the S2 metrics were associated with red-edge spectral regions. Meanwhile, the most important LiDAR metrics were related to height percentiles. Our methodology advocates for an economical, extensive, and precise AGB retrieval tailored for semiarid forests.

Tartronic acid is known for its potential to inhibit sugar-to-lipid conversion in the human body, leading to weight loss and fat reduction. This compound is predominantly found in cucumbers and other cucurbit crops. Therefore, cultivating cucumbers with high tartronic acid content holds significant health implications. In this study, we assessed the tartronic acid content in 52 cucumber germplasms with favorable overall traits and identified 8 cucumber germplasms with elevated tartronic acid levels. Our investigation into factors influencing cucumber tartronic acid revealed a decrease in content with fruit development from the day of flowering. Furthermore, tartronic acid content was higher in early-harvested fruits compared to late-harvested ones, with the rear part of the fruit exhibiting significantly higher content than other parts. Foliar spraying of microbial agents increased tartronic acid content by 84.4%. This study provides valuable resources for breeding high tartronic acid cucumbers and offers practical insights for optimizing cucumber production practices.

In this study, the degradation system of Shewanella oneidensis MR-1 and goethite was constructed with chlorpyrifos as the target contaminant. The effects of initial pH, contaminant concentration, and temperature on the removal rate of chlorpyrifos during the degradation process were investigated. The experimental conditions were optimized by response surface methodology with a Box–Behnken design (BBD). The results show that the removal rate of chlorpyrifos is 75.71% at pH = 6.86, an initial concentration of 19.18 mg·L−1, and a temperature of 30.71 °C. LC-MS/MS analyses showed that the degradation products were C4H11O3PS, C7H7Cl3NO4P, C9H11Cl2NO3PS, C7H7Cl3NO3PS, C9H11Cl3NO4P, C4H11O2PS, and C5H2Cl3NO. Presumably, the degradation pathways involved are: enzymatic degradation, hydrolysis, dealkylation, desulfur hydrolysis, and dechlorination. The findings of this study demonstrate the efficacy of the goethite/S. oneidensis MR-1 complex system in the removal of chlorpyrifos from water. Consequently, this research contributes to the establishment of a theoretical framework for the microbial remediation of organophosphorus pesticides in aqueous environments.

Chromium (Cr) contamination, widely present in the environment, poses a significant threat to both ecology and human health. Microbial remediation technology has become a hot topic in the field of heavy metal remediation due to its advantages, such as environmental protection, low cost, and high efficiency. This paper focused on using various characterization and analysis methods to investigate the bioreduction effect and mechanism of microorganisms on Cr(VI) under various influencing factors. The main contents and conclusions were as follows: Shewanella oneidensis MR-1 was selected as the target strain for studying its reduction of Cr(VI) at different inoculation amounts, temperatures, pH values, time intervals, etc. The results indicated that S. oneidensis MR-1 exhibited an optimal reduction effect on Cr(VI) at pH 7 and a temperature of 35 °C. Additionally, electron shuttles (ESs), including humic acid (HA) and 9,10-antraquinone-2,6-disulfonate (AQDS), were introduced into the degradation system to improve the reduction efficiency of S. oneidensis MR-1. Upon adding goethite further, S. oneidensis MR-1 significantly enhanced its reducing ability by converting Fe(III) minerals to Fe(II) and reducing Cr(VI) to Cr(III) during electron transfer.

The Gram-negative marine bacterium GXY010T, which has been isolated from the surface seawater of the western Pacific Ocean, is aerobic, non-motile and non-flagellated. Strain GXY010T exhibits growth across a temperature range of 10–42 °C (optimal at 37 °C), pH tolerance from 7.0 to 11.0 (optimal at 7.5) and a NaCl concentration ranging from 1.0 to 15.0% (w/v, optimal at 5.0%). Ubiquinone-8 (Q-8) was the predominant isoprenoid quinone in strain GXY010T. The dominant fatty acids (>10%) of strain GXY010T were iso-C15:0 (14.65%), summed feature 9 (iso-C17:1 ω9c and/or 10-methyl C16:0) (12.41%), iso-C17:0 (10.85%) and summed feature 3 (C16:1 ω7c and/or C16:1 ω6c) (10.41%). Phosphatidylethanolamine (PE), phosphatidylglycerol (PG), diphosphatidylglycerol (DPG), unidentifiable glycolipid (GL) and four non-identifiable aminolipids (AL1-AL4) were the predominant polar lipids of strain GXY010T. The genomic DNA G+C content was identified as a result of 48.0% for strain GXY010T. The strain GXY010T genome consisted of 2,766,857 bp, with 2664 Open Reading Frames (ORFs), including 2586 Coding sequences (CDSs) and 78 RNAs. Strain GXY010T showed Average Nucleotide Identity (ANI) values of 73.4% and 70.6% and DNA–DNA hybridization (DDH) values of 19.2% and 14.5% with reference species Pseudidiomarina tainanensis MCCC 1A02633T (=PIN1T) and Pseudidiomarina taiwanensis MCCC 1A00163T (=PIT1T). From the results of the polyphasic analysis, a newly named species, Pseudidiomarina fusca sp. nov. within the genus Pseudidiomarina, was proposed. The type strain of Pseudidiomarina fusca is GXY010T (=JCM 35760T = MCCC M28199T = KCTC 92693T).

Vehicle type and brand information constitute a crucial element in intelligent transportation systems (ITSs). While numerous appearance-based classification methods have studied frontal view images of vehicles, the challenge of multi-pose and multi-angle vehicle distribution has largely been overlooked. This paper proposes an appearance-based classification approach for multi-angle vehicle information recognition, addressing the aforementioned issues. By utilizing faster regional convolution neural networks, this method automatically captures crucial features for vehicle type and brand identification, departing from traditional handcrafted feature extraction techniques. To extract rich and discriminative vehicle information, ZFNet and VGG16 are employed. Vehicle feature maps are then imported into the region proposal network and classification location refinement network, with the former generating candidate regions potentially containing vehicle targets on the feature map. Subsequently, the latter network refines vehicle locations and classifies vehicle types. Additionally, a comprehensive vehicle dataset, Car5_48, is constructed to evaluate the performance of the proposed method, encompassing multi-angle images across five vehicle types and 48 vehicle brands. The experimental results on this public dataset demonstrate the effectiveness of the proposed approach in accurately classifying vehicle types and brands.

Anthurium andraeanum Linden is a prominent ornamental plant belonging to the family Araceae and is cultivated worldwide. The morphology characteristics are crucial because they significantly impact ornamental values, commercial properties, and the efficiency of space utilization in production. However, only a few related investigations have been conducted in anthurium to date. In this study, an F1 genetic segregation population containing 160 progenies was generated through hybridization between potted and cut anthurium varieties. Fifteen morphological traits were assessed and revealed substantial levels of genetic variation and widespread positive correlation. Based on specific length amplified fragment (SLAF) sequencing technology, 8171 single nucleotide polymorphism (SNP) markers were developed, and the high-density linkage map of 2202.27 cM in length distributed on 15 linkage groups was constructed successfully, with an average distance of 0.30 cM. Using the inclusive composite interval mapping (ICIM) method, 59 QTLs related to 15 key morphological traits were successfully identified, which explained phenotypic variance (PVE) ranging from 6.21% to 17.74%. Thirty-three of those associated with 13 traits were designated as major QTLs with PVE > 10%. These findings offer valuable insights into the genetic basis of quantitative traits and are beneficial for molecular marker-assisted selection (MAS) in anthurium breeding.

The rapid development of solar energy technology has led to significant progress in recent years, but the daily maintenance of solar panels faces significant challenges. The diagnosis of solar panel failures by infrared detection devices can improve the efficiency of maintenance personnel. Currently, due to the scarcity of infrared solar panel failure samples and the problem of unclear image effective features, traditional deep neural network models can easily encounter overfitting and poor generalization performance under small sample conditions. To address these problems, this paper proposes a solar panel failure diagnosis method based on an improved Siamese network. Firstly, two types of solar panel samples of the same category are constructed. Secondly, the images of the samples are input into the feature model combining convolution, adaptive coordinate attention (ACA), and the feature fusion module (FFM) to extract features, learning the similarities between different types of solar panel samples. Finally, the trained model is used to determine the similarity of the input solar image, obtaining the failure diagnosis results. In this case, adaptive coordinate attention can effectively obtain interested effective feature information, and the feature fusion module can integrate the different effective information obtained, further enriching the feature information. The ACA-FFM Siamese network method can alleviate the problem of insufficient sample quantity and effectively improve the classification accuracy, achieving a classification accuracy rate of 83.9% on an open-accessed infrared failure dataset with high similarity.

Blue honeysuckle (Lonicera caerulea L.), which belongs to the Caprifoliaceae family, is an emerging fruit crop worldwide. For the development of a transgenic system and multipurpose tissue culture, this study for the first time established an in vitro regeneration system via somatic embryogenesis, as well as improving the previously established indirect organogenesis-based regeneration system. For embryogenesis, Murashige and Skoog (MS) medium supplemented with 1.0 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D) showed the highest induction rate of the embryogenic callus (97.6%), and MS supplemented with 0.1 mg/L 6-benzyladenine (6-BA), 0.1 mg/L α-naphthaleneacetic acid (NAA), and 0.5 g/L activated carbon (AC) achieved the highest somatic embryo rate (28.3%). For indirect organogenesis, MS medium supplemented with 1.0 mg/L 6-BA and 0.1 mg/L NAA resulted in the highest non-embryogenic callus induction rate (98.9%) and adventitious shoot induction rate (51.6%). For adventitious root induction, MS supplemented with 1.0 mg/L indole-3-butyric acid (IBA) achieved the highest root induction rate (96.0%) and average root length (4.6 cm), whereas MS supplemented with 0.5 mg/L indole-3-acetic acid (IAA) resulted in the highest average regenerated root number (8.8). The total time for the regeneration from explants to soil-planted seedlings (10 euphylla) was 105 and 150 days with an efficiency of 44.1% and 23.9% through organogenesis and somatic embryogenesis, respectively. This study provides a powerful tool for rapid propagation, proliferation, and transformation, as well as laying a technological foundation for gene function research and genetic improvement of blue honeysuckle.

Building collapse leads to mechanical injury, which is the main cause of injury and death, with crush syndrome as its most common complication. During the post-disaster search and rescue phase, if rescue personnel hastily remove heavy objects covering the bodies of injured individuals and fail to provide targeted medical care, ischemia-reperfusion injury may be triggered, leading to rhabdomyolysis. This may result in disseminated intravascular coagulation or acute respiratory distress syndrome, further leading to multiple organ failure, which ultimately leads to shock and death. Using bio-radar to detect vital signs and identify compression states can effectively reduce casualties during the search for missing persons behind obstacles. A time-domain ultra-wideband (UWB) bio-radar was applied for the non-contact detection of human vital sign signals behind obstacles. An echo denoising algorithm based on PSO-VMD and permutation entropy was proposed to suppress environmental noise, along with a wounded compression state recognition network based on radar-life signals. Based on training and testing using over 3000 data sets from 10 subjects in different compression states, the proposed multiscale convolutional network achieved a 92.63% identification accuracy. This outperformed SVM and 1D-CNN models by 5.30% and 6.12%, respectively, improving the casualty rescue success and post-disaster precision.

The zero-shot image classification (ZSIC) is designed to solve the classification problem when the sample is very small, or the category is missing. A common method is to use attribute or word vectors as a priori category features (auxiliary information) and complete the domain transfer from training of seen classes to recognition of unseen classes by building a mapping between image features and a priori category features. However, feature extraction of the whole image lacks discrimination, and the amount of information of single attribute features or word vector features of categories is insufficient, which makes the matching degree between image features and prior class features not high and affects the accuracy of the ZSIC model. To this end, a spatial attention mechanism is designed, and an image feature extraction module based on this attention mechanism is constructed to screen critical features with discrimination. A semantic information fusion method based on matrix decomposition is proposed, which first decomposes the attribute features and then fuses them with the extracted word vector features of a dataset to achieve information expansion. Through the above two improvement measures, the classification accuracy of the ZSIC model for unseen images is improved. The experimental results on public datasets verify the effect and superiority of the proposed methods.

The product innovation design is an innovation-intensive process that requires abundant knowledge and design experience, even across industries and disciplines, as a resource for problem solving. Therefore, designers need to have a good command of more design knowledge in this process. However, due to varied expressions of design knowledge (function, principle, structure) in different fields lead to the difficulty in representation of design knowledge, which makes designers unable to innovate by directly using design knowledge. Therefore, to solve the problem above, this paper, analogous to biological gene, proposes a product innovation process model based on functional gene extraction and construction. The proposed process model normalizes design knowledge through functional genes and help designers acquire design knowledge in different fields, which further promotes the product innovation design. The process model consists of four following steps. First, Obtain the overall function of product based on user needs and decompose it. Second, Build the functional gene model based on digital twin ones to expand the breadth of design knowledge and facilitate the retrieval. Third, Screen similar functional gene by the functional similarity algorithm. Finally, Obtain the design scheme through recombination, transcription, and translation of the functional gene. The feasibility of the research method is verified by using a shared bicycle parking device as an example.

The working environment of contemporary mechanical products is becoming more complex, and the working conditions are becoming more extreme. This has led to a significant increase in the frequency of problems in mechanical products. In order to reduce the frequency of human repair after problems, the application of the self-recovery concept has become a hot research topic in the area of smart design. However, the current application of the self-recovery concept is mostly limited to the structural and parametric levels, with less research at the functional level, which may lead to a waste of resources within products. To solve this problem, this research combines the functional-level product research method with the self-recovery concept and establishes a design process model of complex products under functional self-recovery. This model extends the application scope of the self-recovery concept and improves the efficiency of resource utilization in the product. The design process model has six steps. First, according to the user requirements and the existing product, the initial function solving is carried out, and the initial function model of the product is established. Next, the main functions of the product are determined based on the initial function model of the product. Then, according to the determined main functions of the product, combined with the parameters marked in the function structure, the self-diagnosis function is designed. After that, the LT matrix and effect library are used to design the self-regulation function corresponding to the main functions, and the parameters are used to screen the self-regulation function design scheme. Finally, according to the design scheme of the self-diagnosis function and self-regulation function, the functional period oriented to self-recovery is constructed to ensure the realization of the main functions of the product. The effectiveness of the design process model is proved through the design process of an intelligent photovoltaic power generation system at the end of the paper.

Research on the security of lattice-based public-key encryption schemes against misuse attacks is an important part of the cryptographic assessment of the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standardization process. In particular, many NIST-PQC cryptosystems follow the same meta-cryptosystem. At EUROCRYPT 2019, Ba˘etu et al. mounted a classical key recovery under plaintext checking attacks (KR-PCA) and a quantum key recovery under chosen ciphertext attacks (KR-CCA). They analyzed the security of the weak version of nine submissions to NIST. In this paper, we focus on learning with error (LWE)-based FrodoPKE, whose IND-CPA security is tightly related to the hardness of plain LWE problems. We first review the meta-cryptosystem and quantum algorithm for solving quantum LWE problems. Then, we consider the case where the noise follows a discrete Gaussian distribution and recompute the success probability for quantum LWE by using Hoeffding bound. Finally, we give a quantum key recovery algorithm based on LWE under CCA attack and analyze the security of Frodo. Compared with the existing work of Ba˘etu et al., our method reduces the number of queries from 22 to 1 with the same success probability.

BRs (Brassinosteroids) regulate many essential pathways related to growth, cell elongation, cell expansion, plant architecture, and fruit development. The potential exogenous application of BR-derivatives has been proven to stimulate plant growth and development, including quality attributes of fruits, whereas its biosynthesis inhibition has shown the opposite effect. In this study, BR-insensitive tomato mutants were used to reveal the potential function of BR signaling in the regulation of fruit development to elaborate the regulatory mechanism of BR signaling in tomato fruits. The BR-signaling mutant exhibited a typical dwarf phenotype and reduced vegetative growth, fruit size, and weight. Microscopic and transcriptional evaluation of the abs1 mutant fruits implies that reduced cell size and number are responsible for the phenotypic variations. Additionally, we also found that the altered content of phytohormones, such as auxin, gibberellin, cytokinin, and ethylene levels, contributed to altered fruit development. Moreover, fruit growth and cell development-specific gene expression levels were downregulated in BR-insensitive plants; culminating in reduced cell size, cell number, and cell layers. These findings provide insight into physio-chemical changes during fruit development in response to BR-insensitivity.

Molecular vegetable breeding has been progressed intensively in recent years. Huge advances have been made in germplasm evaluation, gene isolation, plant transformation, gene editing and molecular-marker-assisted breeding. The goal of this Special Issue is to highlight, through selected works, frontier research from basic to applied molecular vegetable breeding. The selected papers published in Special Issue of Horticulturae exhibit a diversity in molecular vegetable breeding. The papers listed in this editorial are especially noteworthy.

MCPH1 is the first gene identified to be responsible for the human autosomal recessive disorder primary microcephaly (MCPH). Mutations in the N-terminal and central domains of MCPH1 are strongly associated with microcephaly in human patients. A recent study showed that the central domain of MCPH1, which is mainly encoded by exon 8, interacts with E3 ligase βTrCP2 and regulates the G2/M transition of the cell cycle. In order to investigate the biological functions of MCPH1’s central domain, we constructed a mouse model that lacked the central domain of MCPH1 by deleting its exon 8 (designated as Mcph1-Δe8). Mcph1-Δe8 mice exhibited a reduced brain size and thinner cortex, likely caused by a compromised self-renewal capacity and premature differentiation of Mcph1-Δe8 neuroprogenitors during corticogenesis. Furthermore, Mcph1-Δe8 mice were sterile because of a loss of germ cells in the testis and ovary. The embryonic fibroblasts of Mcph1-Δe8 mice exhibited premature chromosome condensation (PCC). All of these findings indicate that Mcph1-Δe8 mice are reminiscent of MCPH1 complete knockout mice and Mcph1-ΔBR1 mice. Our study demonstrates that the central domain of MCPH1 represses microcephaly, and is essential for gonad development in mammals.

The study was designed to explore whether 24-hour urinary sodium excretion could predict the therapeutic effectiveness of oral rehydration saline in pediatric cases of vasovagal syncope. Eighty children suffering from vasovagal syncope with oral rehydration saline treatment in Department of Pediatrics, Peking University First Hospital, China, were recruited into the study. They were followed up for 3 (2, 3) months after treatment. Pre-treatment demographic, clinical, head-up tilt test-based hemodynamic and laboratory variables were compared between responders and non-responders. After univariate analysis, variables with p value < 0.05 in the comparison between responders and non-responders were further analyzed by binary logistic regression analysis. Receiver operating characteristic (ROC) curve was conducted to assess the value in predicting effectiveness of oral rehydration saline treatment. The results showed that 33 cases were responders, and 47 were non-responders. Blood sodium (138 ± 2 mmol/L vs. 139 ± 2 mmol/L, p < 0.05) and pre-treatment 24-hour urinary sodium excretion (74 ± 29 mmol/24 h vs. 109 (93, 141) mmol/24 h, p < 0.001) were lower in responders than in non-responders. The baseline 24-hour urinary sodium excretion was positively correlated to the duration from tilting to the positive response appearance in head-up tilt test (r = 0.289, p < 0.01). The cut-off value of baseline 24-hour urinary sodium excretion of the therapeutic effectiveness of oral rehydration saline on vasovagal syncope cases was 83 mmol/24 h, yielding a sensitivity of 87% and a specificity of 73% with AUC of 0.842 (p < 0.001). In conclusion, 24-hour urinary sodium excretion could be a useful biomarker to predict the therapeutic response to oral rehydration saline in pediatric cases of vasovagal syncope.

Eye drops are the most common and convenient route of topical administration and the first choice of treatment for many ocular diseases. However, the ocular bioavailability of traditional eye drops (i.e., solutions, suspensions, and ointments) is very low because of ophthalmic physiology and barriers, which greatly limits their therapeutic effect. Over the past few decades, many novel eye drop delivery systems, such as prodrugs, cyclodextrins, in situ gels, and nanoparticles, have been developed to improve ophthalmic bioavailability. These novel eye drop delivery systems have good biocompatibility, adhesion, and propermeation properties and have shown superior performance and efficacy over traditional eye drops. Therefore, the purpose of this review was to systematically present the research progress on novel eye drop delivery systems and provide a reference for the development of dosage form, clinical application, and commercial transformation of eye drops.

In this study, a particle swarm optimization with a sigmoid increasing inertia weight (SIPSO) algorithm is proposed for structural damage identification based on the optimization of structural vibration response constraints. In view of the existing problems for particle swarm optimization algorithms used for structural damage identification, such as low accuracy of damage identification and easy misjudgment of damage location, the sigmoid increasing inertia weight is introduced to improve the global and local search ability of the algorithm. Simulation results show that the parameters of the sigmoid increasing inertia weight have a significant effect on the performance of the SIPSO algorithm for structural damage identification. Compared with similar improved particle swarm optimization algorithms, the SIPSO algorithm has some advantages of fast convergence speed, high identification accuracy, and strong robustness ability in structural damage identification.

Multicopper oxidases (MCOs) are a diverse group of enzymes that could catalyze the oxidation of different xenobiotic compounds, with simultaneous reduction in oxygen to water. Aside from laccase, one member of the MCO superfamily has shown great potential in the biodegradation of mycotoxins; however, the mycotoxin degradation ability of other MCOs is uncertain. In this study, a novel MCO-encoding gene, StMCO, from Streptomyces thermocarboxydus, was identified, cloned, and heterologously expressed in Escherichia coli. The purified recombinant StMCO exhibited the characteristic blue color and bivalent copper ion-dependent enzyme activity. It was capable of oxidizing the model substrate ABTS, phenolic compound DMP, and azo dye RB5. Notably, StMCO could directly degrade aflatoxin B1 (AFB1) and zearalenone (ZEN) in the absence of mediators. Meanwhile, the presence of various lignin unit-derived natural mediators or ABTS could significantly accelerate the degradation of AFB1 and ZEN by StMCO. Furthermore, the biological toxicities of their corresponding degradation products, AFQ1 and 13-OH-ZEN-quinone, were remarkably decreased. Our findings suggested that efficient degradation of mycotoxins with mediators might be a common feature of the MCOs superfamily. In summary, the unique properties of MCOs make them good candidates for degrading multiple major mycotoxins in contaminated feed and food.

Ligninolytic enzymes, including laccase, manganese peroxidase, and dye-decolorizing peroxidase (DyP), have attracted much attention in the degradation of mycotoxins. Among these enzymes, the possible degradation pathway of mycotoxins catalyzed by DyP is not yet clear. Herein, a DyP-encoding gene, StDyP, from Streptomyces thermocarboxydus 41291 was identified, cloned, and expressed in Escherichia coli BL21/pG-Tf2. The recombinant StDyP was capable of catalyzing the oxidation of the peroxidase substrate 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), phenolic lignin compounds 2,6-dimethylphenol, and guaiacol, non-phenolic lignin compound veratryl alcohol, Mn2+, as well as anthraquinone dye reactive blue 19. Moreover, StDyP was able to slightly degrade zearalenone (ZEN). Most importantly, we found that StDyP combined the catalytic properties of manganese peroxidase and laccase, and could significantly accelerate the enzymatic degradation of ZEN in the presence of their corresponding substrates Mn2+ and 1-hydroxybenzotriazole. Furthermore, the biological toxicities of the main degradation products 15-OH-ZEN and 13-OH-ZEN-quinone might be remarkably removed. These findings suggested that DyP might be a promising candidate for the efficient degradation of mycotoxins in food and feed.

The co-occurrence of multiple mycotoxins, including aflatoxin B1 (AFB1), zearalenone (ZEN) and deoxynivalenol (DON), widely exists in cereal-based animal feed and food. At present, most reported mycotoxins degrading enzymes target only a certain type of mycotoxins. Therefore, it is of great significance for mining enzymes involved in the simultaneous degradation of different types of mycotoxins. In this study, a dye-decolorizing peroxidase-encoding gene BsDyP from Bacillus subtilis SCK6 was cloned and expressed in Escherichia coli BL21/pG-Tf2. The purified recombinant BsDyP was capable of oxidizing various substrates, including lignin phenolic model compounds 2,6-dimethylphenol and guaiacol, the substrate 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid), anthraquinone dye reactive blue 19 and azo dye reactive black 5, as well as Mn2+. In addition, BsDyP could efficiently degrade different types of mycotoxins, including AFB1, ZEN and DON, in presence of Mn2+. More important, the toxicities of their corresponding enzymatic degradation products AFB1-diol, 15-OH-ZEN and C15H18O8 were significantly lower than AFB1, ZEN and DON. In summary, these results proved that BsDyP was a promising candidate for the simultaneous degradation of multiple mycotoxins in animal feed and food.

The interaction of soy protein isolate (SPI) and its hydrolysates (SPIHs) with cyanidin-3-O-glucoside (C3G) at pH 7.0 were investigated to clarify the changes in the antioxidant capacity of their complexes. The results of intrinsic fluorescence revealed that C3G binds to SPI/SPIHs mainly through hydrophobic interaction, and the binding affinity of SPI was stronger than that of SPIHs. Circular dichroism and Fourier-transform infrared spectroscopy analyses revealed that the interaction with C3G did not significantly change the secondary structures of SPI/SPIHs, while the surface hydrophobicity and average particle size of proteins decreased. Furthermore, the SPI/SPIHs-C3G interaction induced an antagonistic effect on the antioxidant capacity (ABTS and DPPH) of the complex system, with the masking effect on the ABTS scavenging capacity of the SPIHs-C3G complexes being lower than that of the SPI-C3G complexes. This study contributes to the design and development of functional beverages that are rich in hydrolysates and anthocyanins.

The structure of C60 thin films grown on Cd (0001) surface has been investigated from submonolayer to second monolayer regimes with a low-temperature scanning tunneling microscopy (STM). There are different C60 domains with various misorientation angles relative to the lattice directions of Cd (0001). In the (2√3 × 2√3) R30° domain, orientational disorder of the individual C60 molecules with either pentagon, hexagon, or 6:6 bond facing up has been observed. However, orientation ordering appeared in the R26° domain such that all the C60 molecules adopt the same orientation with the 6:6 bond facing up. In particular, complex chiral motifs composed of seven C60 molecules with clockwise or anticlockwise handedness have been observed in the R4° and R8° domains, respectively. Scanning tunneling spectroscopy (STS) measurements reveal a reduced HOMO–LOMO gap of 2.1 eV for the C60 molecules adsorbed on Cd (0001) due to the substrate screening and charge transfer from Cd to C60 molecules.

The evaluation of clustering results plays an important role in clustering analysis. However, the existing validity indices are limited to a specific clustering algorithm, clustering parameter, and assumption in practice. In this paper, we propose a novel validity index to solve the above problems based on two complementary measures: boundary points matching and interior points connectivity. Firstly, when any clustering algorithm is performed on a dataset, we extract all boundary points for the dataset and its partitioned clusters using a nonparametric metric. The measure of boundary points matching is computed. Secondly, the interior points connectivity of both the dataset and all the partitioned clusters are measured. The proposed validity index can evaluate different clustering results on the dataset obtained from different clustering algorithms, which cannot be evaluated by the existing validity indices at all. Experimental results demonstrate that the proposed validity index can evaluate clustering results obtained by using an arbitrary clustering algorithm and find the optimal clustering parameters.

Attackers can eavesdrop and exploit user privacy by classifying traffic into different types of in-app service usage to identify user actions. WeChat is the largest social messaging platform, which is a popular application in China. When WeChat is shut down, it is unable to generate traffic; that is, traditional traffic. However, the traffic still can be generated by system. How to identify the message types within WeChat with traffic generated by a system instead of traditional traffic becomes a new challenge. To deal with this challenge, we designed a system to identify and analyze the traffic of the Apple Push Notification service (APNs) to identify the message types of WeChat. In detail, we designed a system to identify and analyze the traffic of the APNs. First, the system clusters the traffic based on the session and divides it into multiple bursts. Then, it extracts the features of each burst and sends these features to the learning-based classifier to extract APNs’s traffic from the background traffic. Finally, it uses a hash-based lookup table method to analyze message types from APNs traffic. Extensive evaluation results show that we can accurately identify the six message types of APN and WeChat. In addition, we propose two coping strategies for the method proposed in this article.

Aflatoxin B1 (AFB1) and zearalenone (ZEN) exert deleterious effects to human and animal health. In this study, the ability of a CotA laccase from Bacillus subtilis (BsCotA) to degrade these two mycotoxins was first investigated. Among the nine structurally defined chemical compounds, methyl syringate was the most efficient mediator assisting BsCotA to degrade AFB1 (98.0%) and ZEN (100.0%). BsCotA could also use plant extracts, including the Epimedium brevicornu, Cucumis sativus L., Lavandula angustifolia, and Schizonepeta tenuifolia extracts to degrade AFB1 and ZEN. Using hydra and BLYES as indicators, it was demonstrated that the degraded products of AFB1 and ZEN using the laccase/mediator systems were detoxified. Finally, a laccase of fungal origin was also able to degrade AFB1 and ZEN in the presence of the discovered mediators. The findings shed light on the possibility of using laccases and a mediator, particularly a natural plant-derived complex mediator, to simultaneously degrade AFB1 and ZEN contaminants in food and feed.

Mobile payment apps have been widely-adopted, which brings great convenience to people’s lives. However, at the same time, user’s privacy is possibly eavesdropped and maliciously exploited by attackers. In this paper, we consider a possible way for an attacker to monitor people’s privacy on a mobile payment app, where the attacker aims to identify the user’s financial transactions at the trading stage via analyzing the encrypted network traffic. To achieve this goal, a hierarchical identification system is established, which can acquire users’ privacy information in three different manners. First, it identifies the mobile payment app from traffic data, then classifies specific actions on the mobile payment app, and finally, detects the detailed steps within the action. In our proposed system, we extract reliable features from the collected traffic data generated on the mobile payment app, then use a series of well-performing ensemble learning strategies to deal with three identification tasks. Compared with prior works, the experimental results demonstrate that our proposed hierarchical identification system performs better.

Hedyotis diffuse Willd. (HD) and Hedyotis corymbosa (L.) Lam. (HC), two closely related species of the same genus, are both used for health benefits and disease prevention in China. HC is also indiscriminately sold as HD in the wholesale chain and food markets. This confusion has led to a growing concern about their identification and quality evaluation. In order to further understand the molecular diversification between them, we focus on the screening of chemical components and the analysis of non-targeted metabolites. In this study, UPLC-QTOF-MSE, UNIFI platform and multivariate statistical analyses were used to profile them. Firstly, a total of 113 compounds, including 80 shared chemical constituents of the two plants, were identified from HC and HD by using the UNIFI platform. Secondly, the differences between two herbs were highlighted with the comparative analysis. As a result, a total of 33 robust biomarkers enabling the differentiation were discovered by using multivariate statistical analyses. For HC, there were 18 potential biomarkers (either the contents were much greater than in HD or being detected only in HC) including three iridoids, eight flavonoids, two tannins, two ketones, one alcohol and two monoterpenes. For HD, there were15 potential biomarkers (either the contents were much greater than in HC or being detected only in HD) including two iridoids, eight flavonoids, one tannin, one ketone, and three anthraquinones. With a comprehensive consideration of the contents or the MS responses of the chemical composition, Hedycoryside A and B, detected only in HC, could be used for rapid identification of HC. The compounds 1,3-dihydroxy-2-methylanthraquinone and 2-hydroxy-3-methylanthraquinone, detected only in HD, could be used for rapid identification of that plant. The systematic comparison of similarities and differences between two confusing Chinese herbs will provide reliable characterization profiles to clarify the pharmacological fundamental substances. HC should not be used as the substitute of HD.