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The use of fertilizers is mainly adopted in arid regions to improve the soil carbon (C) pool and crop productivity. However, the mechanisms underlying improvements in dryland wheat field soils related to microbial metabolic activity and community structure remain poorly understood. Therefore, a field experiment with four fertilization treatments and no fertilizer as the control (CK) was conducted for 10 years in a semi-arid region of China. The results revealed that the combined application of chemical and organic fertilizers (fermented chicken manure) clearly increased the levels of soil organic carbon (SOC), dissolved organic carbon (DOC), and light-fraction organic carbon (LFOC) by 13.54–16.72%, 6.96–9.01%, and 11.00–13.51%, respectively, compared to the sole use of chemical fertilizers (FP treatment). Moreover, the combined treatment not only enhanced the metabolic activity of microorganisms concerning carbon source utilization but also increased the diversity of the bacterial community. This caused noticeable changes in the composition of the bacterial community. A Mantel test analysis revealed that Bacteroidetes and Mortierellomycota significantly enhanced the metabolic activity associated with carbohydrate, amino acid, and carboxylic acid C sources. Actinobacteria, Bacteroidetes, and Mortierellomycota facilitated the accumulation of active C and particulate organic carbon (POC), whereas Mortierellomycota specifically promoted the accumulation of heavy-fraction organic carbon (HFOC), thereby collectively influencing the SOC content. The combined application of chemical and organic fertilizers increased the abundance of Bacteroidetes and Mortierellomycota. This enhancement improved the metabolic utilization of carbohydrates, amino acids, and carboxylic acids, resulting in alterations in the types and quantities of soil metabolites. Consequently, these alterations ultimately affect the composition and quantity of the SOC pool in arid agroecosystems. In conclusion, the combined application of balanced NPK fertilizers and organic fertilizers has a strong positive effect in improving soil microbial activity and the soil C pool.

A novel co-bonded octyl and pyridine silica (OPS) sorbent was prepared and applied for the solid phase extraction (SPE) of cyclopiazonic acid (CPA, a type of mycotoxin) in feed and agricultural products for the first time. A simple mixed-ligand one-pot reaction strategy was employed for OPS sorbent preparation. Nitrogen adsorption–desorption measurements, elemental analysis (EI), thermal gravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FT-IR) analysis demonstrated the successful immobilization of octyl and quaternary ammonium groups onto the surface of silica gel. The large specific surface area, high-density functional groups, and mixed-mode anion-exchange characteristics of these silica particles made them the ideal material for the efficient extraction of CPA. Additionally, the OPS sorbents displayed excellent batch-to-batch reproducibility, satisfactory reusability, and low cost. The SPE parameters were optimized to explore the ionic and hydrophobic interactions between CPA and the functional groups, and the ultra-high performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry (UPLC-MS/MS) parameters were optimized to obtain a desirable extraction efficiency and high sensitivity to CPA. Meanwhile, the OPS sorbent presented a satisfactory extraction selectivity and low matrix effect. Under the optimized conditions, our developed CPA detection method was used to determine CPA level in rice, wheat flour, corn flour, peanut, and feed samples, exhibiting a lower detection limit, better linearity, higher sensitivity, and satisfactory extraction recovery rate than previously reported methods. Therefore, our method can be preferentially used as a method for the detection of CPA in agricultural products and feeds.

The Loess Plateau in China has long grappled with issues such as chronic soil erosion, poor soil structure, and diminished organic matter. Soil organic carbon plays a crucial role in enhancing soil fertility, and fertilization is a key tool that influences it. In a comprehensive field experiment, we examined five fertilization methods: no fertilization (CK); traditional fertilization used by local farmers (FP); nutrient-balanced fertilization with nitrogen, phosphorus, and potassium (OF); chemical fertilizers used in conjunction with organic fertilizers (OFM); and chemical fertilizers paired with bio-organic fertilizers (OFB). Our findings revealed that the OFM and OFB treatments were the most effective, explaining 84.35% and 81.26% of the variation in soil carbon sequestration, respectively. Further, the OF, OFM, and OFB treatments demonstrated superior effectiveness compared with the FP treatment in enhancing the soil carbon fractions. However, these fertilization patterns did not significantly alter the active-to-inert carbon ratio of the soil. OF, OFM, and OFB treatments enhanced the stability of soil carbon pools more than FP treatment. In structural equation modeling, factors such as microbial biomass nitrogen and phosphorus, soil pH, and β-N-acetyl glucosidase indirectly exhibited a limiting effect on the carbon pool stability index (CPSI), while β-glucosidase displayed an indirect positive correlation with the carbon fractions. In contrast, dissolved organic carbon, low-molecular-weight organic carbon, high-molecular-weight organic carbon, and crop yield demonstrated direct positive correlations with the CPSI. Consequently, both a balanced application of chemical fertilizers and the combination of organic and chemical fertilizers were effective in enhancing and sustaining the soil’s organic carbon content, thereby contributing to soil fertility stability. To this end, this study can inform the efficient selection and use of fertilizers, improving crop yield and soil carbon content.

In this paper, the Smoothed Particle Hydrodynamics (SPH) method is used in a C# environment to simulate the interaction between freak waves and bottom-fixed structures by establishing a fluid dynamics model. Paraview software 5.10.1 was used to analyze and visualize the simulation results. In order to simulate wave propagation accurately, the reliability of the model was verified by comparing experimental and simulated data. A two-dimensional numerical wave flume was established based on the SPH method, a conservative Riemann solver was introduced, a repulsive boundary condition was adopted, and a slope was used to eliminate wave reflection. Bottom-fixed structures of different heights and lengths, as well as different wave conditions, were selected to numerically simulate the interaction between freak waves and bottom-fixed structures. The results show that the height of bottom-fixed structures and wave conditions have a significant effect on hindering the propagation of rogue waves, while the length has little effect on the propagation of deformed waves. When the amplitude of the wave remains constant, both the period andthe duration of the deformed wave are longer. This research is of certain significance for the prediction of freak waves in marine engineering and the application and promotion of SPH methods.

With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus. Most existing vulnerability detection methods are based on rules defined by experts, which are inefficient and have poor scalability. Although there have been studies using machine learning methods to extract contract features for vulnerability detection, the features considered are singular, and it is impossible to fully utilize smart contract information. In order to overcome the limitations of existing methods, this paper proposes a smart contract vulnerability detection method based on deep learning and multimodal decision fusion. This method also considers the code semantics and control structure information of smart contracts. It integrates the source code, operation code, and control-flow modes through the multimodal decision fusion method. The deep learning method extracts five features used to represent contracts and achieves high accuracy and recall rates. The experimental results show that the detection accuracy of our method for arithmetic vulnerability, re-entrant vulnerability, transaction order dependence, and Ethernet locking vulnerability can reach 91.6%, 90.9%, 94.8%, and 89.5%, respectively, and the detected AUC values can reach 0.834, 0.852, 0.886, and 0.825, respectively. This shows that our method has a good vulnerability detection effect. Furthermore, ablation experiments show that the multimodal decision fusion method contributes significantly to the fusion of different modalities.

Background: Overweight (OW) and obesity have become increasingly serious public health problems worldwide. The clinical impact of washed microbiota transplantation (WMT) from healthy donors in OW patients is unclear. This study aimed to investigate the effect of WMT in OW patients. Methods: The changes in body mass index (BMI = weight (kg)/height (m)2), blood glucose, blood lipids and other indicators before and after WMT were compared. At the same time, 16S rRNA gene amplicon sequencing was performed on fecal samples of OW patients before and after transplantation. Finally, serum samples were tested for sphingolipids targeted by lipid metabolomics. Results: A total of 166 patients were included, including 52 in the OW group and 114 in the normal weight (NOW) group. For OW patients, WMT significantly improved the comprehensive efficacy of OW. In the short term (about 1 month) and medium term (about 2 months), a significant reduction in BMI was seen. At the same time, in the short term (about 1 month), liver fat attenuation (LFA), triglyceride (TG) and fasting blood glucose (FBG) were significantly reduced. In the long term (about 5 months), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), non-high-density lipoprotein (non-HDL-c), etc. were significantly reduced. WMT improved the gut microbiota of OW patients, and also had an improvement effect on OW patients by regulating sphingolipid metabolism. Conclusion: WMT had a significant improvement effect on OW patients. WMT could restore gut microbiota homeostasis and improve OW patients by regulating sphingolipid metabolism.

Due to its unique biological composition, aquatic products, especially fish, are extremely perishable compared to other muscle products. Herein, we proposed an artificial nanozyme-based colorimetric detection of hypoxanthine (Hx), the indicator of fish freshness, in a minute–time scale without the assistance of a natural enzyme (hypoxanthine oxidase). The principle is based on the interaction between Hx and polyvinylpyrrolidone-modified platinum cubic nanomaterials (PVP-PtNC), in which the catalytic active sites of PVP-PtNC’s surface were blocked by Hx. This causes the downregulation of PVP-PtNC’s catalytic ability and weakened its ability to catalyze the oxidization of 3,3′,5,5′-Tetramethylbenzidine (TMB) by H2O2. Accordingly, the decrease in the UV–vis absorption and the weakening of the colorimetric reaction color is proportional to the Hx concentration. On this basis, a target-triggered colorimetric method for detecting Hx is developed for fish freshness monitoring with a fast detection speed, low cost, high accuracy, and simplified operation. Experiments reveal that the correlation response of Hx is from 0.5 μM to 10 mM with a limit of detection of 0.16 μM. In particular, the Hx detected from real fish indicates that the method possesses a promising potential for practical application. All of these features are expected to promote the development of online detection tools for food safety monitoring.

Fetal growth restriction (FGR) is a common complication of pregnancy and a significant cause of neonatal morbidity and mortality. The adverse effects of FGR can last throughout the entire lifespan and increase the risks of various diseases in adulthood. However, the etiology and pathogenesis of FGR remain unclear. This study comprehensively reviewed metabolomics studies related with FGR in pregnancy to identify potential metabolic biomarkers and pathways. Relevant articles were searched through two online databases (PubMed and Web of Science) from January 2000 to July 2022. The reported metabolites were systematically compared. Pathway analysis was conducted through the online MetaboAnalyst 5.0 software. For humans, a total of 10 neonatal and 14 maternal studies were included in this review. Several amino acids, such as alanine, valine, and isoleucine, were high frequency metabolites in both neonatal and maternal studies. Meanwhile, several pathways were suggested to be involved in the development of FGR, such as arginine biosynthesis, arginine, and proline metabolism, glyoxylate and dicarboxylate metabolism, and alanine, aspartate, and glutamate metabolism. In addition, we also included 8 animal model studies, in which three frequently reported metabolites (glutamine, phenylalanine, and proline) were also present in human studies. In general, this study summarized several metabolites and metabolic pathways which may help us to better understand the underlying metabolic mechanisms of FGR.

Prefabricated buildings that are more environmentally friendly have been vigorously promoted by the Chinese government because of the reduced waste and carbon emissions during the construction process. Most of the construction processes of prefabricated buildings are completed in the prefabricated component factory, but the safety risks during the hoisting process cannot be ignored. In this paper, the initial framework of a Bayesian Network (BN) is obtained from the combination of the improved Human Factors Analysis and Classification System Model (HFACS) and BN. The improved similarity aggregation method (SAM) is used to calculate the prior probability of BN, which can better summarize and deal with the fuzzy judgment of experts on risk accidents. The improved SAM can consider both the weight of experts and the relative consistency of their opinions, which is of great significance for improving the reliability of BN inputted data. This paper uses the construction project in Sanya, Hainan Province, to verify the validity of the model. The results show that the calculation results of the model are basically consistent with the actual situation. The safety risk of this project is relatively low, and the premise of unsafe behaviors and unsafe supervision are the key risk factors of the project. In addition to maintaining good construction conditions and workers’ healthy states, it is also necessary to carefully check the performance of tower cranes and equipment such as spreaders. During the operation process of the tower crane, workers should avoid walking or staying within the hoisting range.

The characteristics of plant and soil bacterial communities in forest ecosystems have been reported, but our understanding of the relationship between plant communities and soil bacteria in different stages of secondary tropical rainforest succession is still poor. In June 2018, three different natural successional stages of tropical lowland rainforests, early (33 years), early-mid (60 years), and mid successional stage (73 years), in Hainan Island, China, were selected for this study. By conducting field investigation and 16S rRNA gene high-throughput sequencing, the composition and diversity of tree communities, the niche overlap of tree species with legumes among tree species, and the diversity and composition of soil bacterial communities and co-occurrence networks within communities across the successional stages were investigated. The results showed that plant richness and species diversity increased significantly during the secondary succession of tropical lowland rainforests. The order of positive correlations between nitrogen-fixing legumes and other species in plant communities was early-mid > mid > early successional stage. Soil nutrient content and soil bacterial richness were highest in the early-mid stages of succession, followed by mid and early stages of succession. Organic matter (OM), total nitrogen (TN), alkali nitrogen (AN), and available phosphorus (AP) had a stronger positive impact on soil bacterial communities. Co-occurrence network analysis showed that with the advancement of rainforests succession, the negative correlation between soil bacterial species decreased, and the community stability increased. Overall, as a result of tropical lowland rainforest secondary natural succession, the richness and diversity of plant communities increased, which altered the living conditions of nitrogen-fixing legumes and the soil properties, and the network complexity of soil bacterial communities increased with the rising of rainforest soil nutrient content.

This study aims to screen and characterize the protective effect of polysaccharides from Portulaca oleracea L. (POP) against H2O2-stimulated osteoblast apoptosis in vivo and in vitro. The enzymes viscozyme, celluclast, α-amylase, and β-glucanase were used to extract POPs. Among all enzyme-assisted POPs, the first participating fraction of viscozyme extract POP (VPOP1) exhibited the highest antioxidant activity. Hoechst 33342 and acridine orange/ethidium bromide staining and flow cytometry of MC3T3 cells revealed that VPOP1 inhibited apoptosis in a dose-dependent manner. Moreover, VPOP1 increased the expression levels of heme oxygenase-1 (HO-1) and NADPH quinine oxidoreductase 1 (NQO1) and decreased the expression levels of nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and Kelch-like ECH-associated protein 1 (Keap1) in H2O2-induced cells compared with their controls. The results of an in vivo experiment show that VPOP1 significantly reduced reactive oxygen species generation and lipid peroxidation in zebrafish at 72 h post-fertilization and promoted bone growth at 9 days post-fertilization. Furthermore, VPOP1 was identified via 1-phenyl-3-methyl-5-pyrazolone derivatization as an acidic heteropolysaccharide comprising mannose and possessing a molecular weight of approximately 7.6 kDa. Collectively, VPOP1 was selected as a potential anti-osteoporotic functional food because of its protective activity against H2O2-induced damage in vitro and in vivo.

Isoquercitrin (IQ) widely exists in natural products, with a variety of pharmacological activities. In this study, the anti-apoptotic and antioxidative activities of IQ were evaluated. IQ showed protective activity against 2, 2′-azobis [2-methylpropionamidine] dihydrochloride (AAPH)-induced cell damage, as well as a marked reduction in reactive oxygen species (ROS). The evidence of IQ regulating Keap1-Nrf2-ARE and the mitochondrial-mediated Caspase 3 pathway were found in the MC3T3 osteoblastic cell line. Furthermore, IQ significantly decreased ROS production, apoptosis, and lipid peroxidation in AAPH-treated 72 h post-fertilization (hpf) zebrafish, as observed via DCFH-DA, acridine orange (AO), and a 1,3-bis(diphenylphosphino) propane (DPPP) probe, respectively. In AAPH-treated 9 day post-fertilization (dpf) zebrafish, IQ strongly promoted osteogenic development, with increased concentrations by calcein staining, compared with the untreated group. In a molecular docking assay, among all signal proteins, Keap1 showed the strongest affinity with IQ at −8.6 kcal/mol, which might be the reason why IQ regulated the Keap1-Nrf2-ARE pathway in vitro and in vivo. These results indicated that IQ promotes bone development and repairs bone injury, which is valuable for the prevention and treatment of bone diseases.

Ainaxiang (Blumea balsamifera), also known as Sambong, is an important ancient medicinal herb in Southeast Asia. It is rich in volatile oil, and still widely used nowadays for skin wound healing and treatment of sore throats. We analyzed the volatile oil from Blumea balsamifera (BB oil) by gas chromatography-mass spectrometry (GC-MS). Forty one components, including l-borneol, were identified. Next, the damaging effects of BB oil diluted with olive oil on liver at different concentrations (100%, 50%, 20%), were evaluated, using both normal and wounded skin. Plasma ALT, AST, ALP and TBili were assessed, along with liver histopathology. The results showed that serum levels of liver toxicity markers in the high concentration groups (100% w/v) increased compared with control groups, whereas no significant changes was observed in histopathology of liver samples. In the wound groups, treatment with BB oil resulted in a decrease in serum toxicity index, compared with normal animal groups. This study confirms the safety of short term BB oil consumption, though high BB oil doses may lead to mild liver injury and this response might be weakened in the case of cutaneous wounds. These results are expected to be helpful for guiding appropriate therapeutic use of BB oil.

The main components of sambong (Blumea balsamifera) are listed in this article. The whole plant and its crude extracts, as well as its isolated constituents, display numerous biological activities, such as antitumor, hepatoprotective, superoxide radical scavenging, antioxidant, antimicrobial and anti-inflammation, anti-plasmodial, anti-tyrosinase, platelet aggregation, enhancing percutaneous penetration, wound healing, anti-obesity, along with disease and insect resistant activities. Although many experimental and biological studies have been carried out, some traditional uses such as rheumatism healing still need to be verified by scientific pharmacological studies, and further studies including phytochemical standardization and bioactivity authentication would be beneficial.

How to explore the interaction between an individual researcher and others in scientific research, find out the degree of association among individual researchers, and evaluate the contribution of researchers to the whole according to the mechanism and law of interaction, is of great significance to grasp the overall trend of the field. Scholars mostly use bibliometrics to solve these problems and analyze the citation and cooperation among academic achievements from the dimension of “quantity”. However, there is still no mature method for scholars to explore the evolution of knowledge and the relationship between authors; this paper tries to fill this gap. We narrow down the scope of research and focus the research content on the literature in biology and chemistry, collect all the papers from PubMed system (a very comprehensive authoritative database of biomedical papers) during 2014−2018, and take year as a specific analysis unit so as to improve the accuracy of the analysis. Then, we construct the author cooperation networks. Finally, through the above methods and steps, we identify the core authors of each year, analyze the recent cooperative relationships among authors, and predict some changes in the cooperative relationship among the authors based on the networks’ analytical data, evaluating and estimating the role that authors play in the overall field. Therefore, we expect that the cooperative authorship networks supported by the complex network theory can better explain the author’s cooperative relationship.

The genus Blumea is one of the most economically important genera of Inuleae (Asteraceae) in China. It is particularly diverse in South China, where 30 species are found, more than half of which are used as herbal medicines or in the chemical industry. However, little is known regarding the phylogenetic relationships and molecular evolution of this genus in China. We used nuclear ribosomal DNA (nrDNA) internal transcribed spacer (ITS) and chloroplast DNA (cpDNA) trnL-F sequences to reconstruct the phylogenetic relationship and estimate the divergence time of Blumea in China. The results indicated that the genus Blumea is monophyletic and it could be divided into two clades that differ with respect to the habitat, morphology, chromosome type, and chemical composition of their members. The divergence time of Blumea was estimated based on the two root times of Asteraceae. The results indicated that the root age of Asteraceae of 76−66 Ma may maintain relatively accurate divergence time estimation for Blumea, and Blumea might had diverged around 49.00−18.43 Ma. This common ancestor had an explosive expansion during the Oligocene and Miocene and two major clades were differentiated during these epochs 29.60 Ma (17.76−45.23 Ma 95% HPD (Highest Posterior Density). Evidence from paleogeography and paleoclimate studies has confirmed that Blumea experienced differentiation and an explosive expansion during the Oligocene and Miocene.

Blumea balsamifera (Ai-na-xiang) is used as an important plant source of natural borneol, which is widely used in the pharmaceutical industry. The aim of this study was to establish the methods based on near infrared (NIR) spectroscopy for determining the geographical origin of B. balsamifera, as well as developing a method for the quantitative rapid analysis of the active pharmaceutical ingredients (APIs). A total of 109 samples were collected from China in 2013 and arbitrarily divided into calibration and prediction sets using the Kennard–Stone algorithm. The l-borneol and total flavone contents of the samples were measured by gas chromatography and ultraviolet-visible spectroscopy, respectively. The NIR spectra were acquired using an integrating sphere and a partial least squares (PLS) model was built using the optimum wavelength regions, which were selected using a synergy interval partial least-squares (SiPLS) algorithm. The root mean square errors of prediction of the l-borneol and total flavone models were 0.0779 and 2.2694 mg/g, with R2 of 0.9069 and 0.8013, respectively. A discriminant model to determine the geographical origin of B. balsamifera (Guizhou and Hainan) was also established using a partial least squares discriminant analysis method with an optimum pretreatment method. The prediction accuracy rate of the model was 100%. NIR spectroscopy can be used as a reliable and environmentally friendly method to determine the API and the origin of different B. balsamifera samples.

Blumea balsamifera, also named Ainaxiang, is widely used as an ancient medicinal herb in tropical and subtropical Asia. It is rich in essential oils. In this work the essential oils of B. balsamifera from different plant organs and in different months were extracted, and then analyzed by gas chromatography-mass spectrometry. The results showed that essential oil yield of young leaves was the highest (0.65 mL/100 g), followed by mature leaves (0.57 mL/100 g), and the oil yield was higher in October (0.47 mL/100 g) than other months. A total of 44 compounds were identified, representing 92.64%–96.71% of the oil. Eighteen common chemical components were found among the six plant organs, representing >80% of the oil constituents. l-borneol was the main ingredient in leaves, and its content was the highest in senescent leaves and in December. In the essential oils of young shoots and young stems, the main component was dimethoxydurene. Antioxidant activity was also determined using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and β-carotene bleaching (BCB) assays. The results indicated that the β-carotene bleaching activity was far stronger than the DPPH radical-scavenging capacity, and the young leaves and young shoots showed stronger antioxidant activity. Dimethoxydurene, β-caryophyllene, and α-caryophyllene play a positive role in good antioxidant activity, while β-eudesmol, phytol, and tetradecanal play a negative role. The antioxidant activity revealed in this study might help in developing this promising bioresource for use in the medicinal and cosmetic industries.

Tripartite motif (TRIM) family proteins, distinguished by their N-terminal region that includes a Really Interesting New Gene (RING) domain with E3 ligase activity, two B-box domains, and a coiled-coil region, have been recognized as significant contributors in carcinogenesis, primarily via the ubiquitin–proteasome system (UPS) for degrading proteins. Mechanistically, these proteins modulate a variety of signaling pathways, including Wnt/β-catenin, PI3K/AKT, and TGF-β/Smad, contributing to cellular regulation, and also impact cellular activities through non-signaling mechanisms, including modulation of gene transcription, protein degradation, and stability via protein–protein interactions. Currently, growing evidence indicates that TRIM proteins emerge as potential regulators in gastric cancer, exhibiting both tumor-suppressive and oncogenic roles. Given their critical involvement in cellular processes and the notable challenges of gastric cancer, exploring the specific contributions of TRIM proteins to this disease is necessary. Consequently, this review elucidates the roles and mechanisms of TRIM proteins in gastric cancer, emphasizing their potential as therapeutic targets and prognostic factors.

Tumor vaccine is a promising immunotherapy for solid tumors. Therapeutic tumor vaccines aim at inducing tumor regression, establishing durable antitumor memory, and avoiding non-specific or adverse reactions. However, tumor-induced immune suppression and immune resistance pose challenges to achieving this goal. In this article, we review multiple challenges currently faced in the development of therapeutic tumor vaccines, with a particular focus on anonymous antigen vaccines in situ as a new direction. We summarize the research progress in this area, aiming to provide a reference for future studies on tumor vaccines.

While encryption enhances data security, it also presents significant challenges for network traffic analysis, especially in detecting malicious activities. To tackle this challenge, this paper introduces combined Attention-aware Feature Fusion and Communication Graph Embedding Learning (AFF_CGE), an advanced representation learning framework designed for detecting encrypted malicious traffic. By leveraging an attention mechanism and graph neural networks, AFF_CGE extracts rich semantic information from encrypted traffic and captures complex relations between communicating nodes. Experimental results reveal that AFF_CGE substantially outperforms traditional methods, improving F1-scores by 5.3% through 22.8%. The framework achieves F1-scores ranging from 0.903 to 0.929 across various classifiers, exceeding the performance of state-of-the-art techniques. These results underscore the effectiveness and robustness of AFF_CGE in detecting encrypted malicious traffic, demonstrating its superior performance.

The black skimmer (Rynchops niger) is a state-threatened, colonially nesting seabird in Florida, USA. Conservation threats include habitat alteration, human disturbances, severe weather, and predation. During nest monitoring (May–September, 2020–2022), black skimmer juveniles at colonies on Fort Myers Beach and Marco Island, Florida, had polyarthritis and died or were euthanized due to severe illness. Similarly-aged skimmers from geographically distant (considered unaffected) colonies were evaluated for comparison (2021–2023). We documented field, clinical, radiographical, and pathological findings to characterize disease and purported pathogenesis. The majority were lame and lethargic, in poor nutritional condition, and dehydrated. Additionally, 8/23 of the skimmers with dermatitis and arthritis from affected colonies also had penetrating sandspurs associated with skin ulceration, scabbing, and/or hemorrhage. The affected joints were often in limbs (interphalangeal and hock; less commonly stifle, elbow, carpus). A postmortem evaluation and bacteriology revealed Staphylococcal aureus-associated dermatitis, arthritis, tenosynovitis, and/or osteomyelitis in 21/22 of the juvenile skimmers from southwestern nest colonies. Staphylococcus aureus dissemination to internal organs occurred in 10/13 of the skimmers tested. Among skimmers evaluated from distant colonies, 5/10 that were examined histologically had skin crusting and inflammation but lacked arthritis. Occasional coinfections were documented (e.g., West Nile virus, Gram-negative bacilli). The results suggest that staphylococcal joint disease originated from sandspur-induced skin damage, followed by hematogenous dissemination to the joints and, occasionally, the internal organs. Additional nest sites should be tested to evaluate disease risk and potentially contributing environmental factors. We recommend that site managers employ techniques that reduce the risk of skimmer interactions with sandspurs.

In the context of rapid urbanization and digitalization, scientifically assessing the spatio-temporal interaction between digital inclusive finance (DIF) and urban ecological resilience (UER) is crucial for promoting the coordinated development of the regional ecology and economy. This study investigates the spatiotemporal evolution of the coupled coordination degree (CCD), the decoupling phenomenon, and its hindering factors in the Yangtze River Economic Belt (YREB) by utilizing the kernel density analysis, standard deviation ellipse, decoupling model, and obstacle degree analysis. Through systematic analyses, this paper aims to elucidate the development disparities among regions within the YREB, identify problematic areas, and propose targeted improvement measures. The results show that (1) The CCD between DIF and UER in the YREB has increased annually from 2011 to 2020. However, there are persistent imbalances, with an overall low level of coordination and uneven spatial development, and a trend of “higher coordination in the east and lower coordination in the west”. (2) The overall CCD of the YREB has reached at least the primary coordination level, with the coupling enhancement speed ranked as “downstream > midstream > upstream”, and regional differences decreasing. (3) The decoupling analysis reveals a predominant decoupling trend between DIF and UER, indicating that the digitization of financial services has not concurrently increased ecological pressures. (4) The obstacle degree analysis identifies resilience and digitalization as major barriers hindering CCD. This study provides a scientific basis and analytical framework for understanding the current spatiotemporal interaction between DIF and UER in the YREB, offering an important reference for formulating more effective policies.

Oxidative stress is a major factor that limits the development of the poultry industry. Ferulic acid (FA) has an antioxidant effect in birds, but the mechanism is not fully understood. In this study, we stimulated oxidative stress in 28-day-old female Linwu ducks by lipopolysaccharide (LPS) and fed them a diet supplemented with FA for 28 days. Results showed that FA alleviated LPS-induced growth performance regression, oxidative stress, and microbiota imbalance in ducks. An integrated metagenomics and metabolomics analysis revealed that s_Blautia_obeum, s_Faecalibacterium_prausnitzii, s_gemmiger_formicilis, and s_Ruminococcaceae_bacterium could be the biomarkers in the antioxidant effect of FA, which interacted with dihydro-3-coumaric acid, L-phenylalanine, and 13(S)-HODE, and regulated the phenylalanine metabolism and PPAR signaling pathway. This study revealed the mechanism of the antioxidant effect of FA, which provided evidence of applying FA as a new antioxidant in commercial duck production.

In isolated operation, DC microgrids require multiple distributed energy storage units (DESUs) to accommodate the variability of distributed generation (DG). The traditional control strategy has the problem of uneven allocation of load current when the line impedance is not matched. As the state-of-charge (SOC) balancing proceeds, the SOC difference gradually decreases, leading to a gradual decrease in the balancing rate. Thus, an improved SOC droop control strategy is introduced in this paper, which uses a combination of power and exponential functions to improve the virtual impedance responsiveness to SOC changes and introduces an adaptive acceleration factor to improve the slow SOC balancing problem. We construct a sparse communication network to achieve information exchange between DESU neighboring units. A global optimization controller employing the consistency algorithm is designed to mitigate the impact of line impedance mismatch on SOC balancing and current allocation. This approach uses a single controller to restore DC bus voltage, effectively reducing control connections and alleviating the communication burden on the system. Lastly, a simulation model of the DC microgrid is developed using MATLAB/Simulink R2021b. The results confirm that the proposed control strategy achieves rapid SOC balancing and the precise allocation of load currents in various complex operational scenarios.

Knowledge of wind field characteristics and variation principles in complex topographical regions is of great importance for the development of numerical prediction models, aviation safety support, and wind energy utilization. However, there has been limited research focused on the lower-tropospheric wind fields in the Qinghai-Tibet Plateau. This paper aims to study the wind characteristics, vertical distributions, and temporal variations in the northeast of the plateau by analyzing a four-year continuous dataset collected from a Doppler wind LiDAR deployed in Xining, Qinghai Province of China. The results indicate that the prevailing horizontal wind direction in the low levels is primarily influenced by the mountain-valley wind circulation. However, as the altitude increases, the prevailing winds are predominantly affected by the westerlies. From a diurnal perspective, noticeable transition processes between up-valley and down-valley winds can be observed. The west-northwest wind (down-valley wind) dominates from late night to morning, while the east-southeast wind (up-valley wind) prevails from afternoon to early evening. The vertical winds in the low levels exhibit a downward motion during the daytime and an upward motion during the nighttime. In this plateau valley, the wind shear exponent is found to be highest in spring and lowest in winter, and it is generally lower during the daytime compared to the nighttime.

The space–air–ground integrated network can provide services to ground users in remote areas by utilizing high-altitude platform (HAP) drones to support stable user access and using low earth orbit (LEO) satellites to provide large-scale traffic backhaul. However, the rapid movement of LEO satellites requires dynamic maintenance of the matching relationship between LEO satellites and HAP drones. Additionally, different traffic types generated at HAP drones hold varying levels of values. Therefore, a tripartite matching problem among LEO satellites, HAP drones, and traffic types jointly considering multi-dimensional characteristics such as remaining visible time, channel condition, handover latency, and traffic storage capacity is formulated as mixed integer nonlinear programming to maximize the average transmitted traffic value. The traffic generation state for HAP drones is modeled as a mixture of stochasticity and determinism, which aligns with real-world scenarios, posing challenges for traditional optimization solvers. Thus, the original problem is decoupled into two independent sub-problems: traffic–drone matching and LEO–drone matching, which are addressed by mathematical simplification and multi-agent deep reinforcement learning with centralized training and decentralized execution, respectively. Simulation results verify the effectiveness and superiority of the proposed tripartite matching approach.

In this study, we investigated the effects of modified aging treatments on the microstructures and hardness in a commercial 6016 Al alloy through hardness tests and transmission electron microscopy (TEM) observations. The results demonstrate that many fine needle-like β″ phases contribute to the high hardness of peak-aged (T6) alloys. Over-aging treatments lead to the precipitation of lath-like β′, β″/disordered, or B′/disordered composite phases. Moderate over-aging treatment results in the coarsening of grain boundary precipitates (GBPs) and widening of the precipitate-free zone (PFZ), while heavy over-aging treatment triggers the re-precipitation of Cu-containing GBPs and increases the number density of GBPs. A retrogression and re-aging (RRA) treatment precipitates β″, lath-like β′, and disordered phases, while a two-step aging (T78) treatment precipitates β″, B′, and disordered phases. Both the T78 and the RRA treatments lead to the coarsening of GBPs and the widening of PFZs. The decreased hardness during over-aging treatments is attributed to a combination of coarsening intragranular precipitates and/or wider PFZs. The T78 and RRA tempers achieve 95.5% and 94% of the hardness values of the T6 treatment, respectively. The hardness values of the RRA and T78 treated alloys are related to the finer nano-sized precipitates formed during the high temperature process. These precipitates can compensate for the loss of hardness caused by the increase in the widths of the PFZs and the coarsening of the matrix precipitates. The relationship between the hardness and microstructures such as PFZs and precipitates in the matrix during various heat treatments is elucidated.

Faced with the dual challenges of ecological degradation and economic deceleration, promoting urban green high-quality development (UGHQD) is pivotal for achieving economic transformation, ecological restoration, and regional sustainable development. While the existing literature has delved into the theoretical dimensions of UGHQD, there remains a notable dearth of empirical studies that quantitatively assess its developmental levels, spatio-temporal evolution, and driving factors. This study examines 107 cities of China’s five major urban agglomerations from 2003 to 2020, constructing a comprehensive evaluation indicator system for UGHQD. By employing methodologies, including the Dagum Gini coefficient, Kernel density estimation, Markov chain, and geographical detector, this study extensively assesses the spatial difference, dynamic evolution, and underlying driving forces of UGHQD in these urban agglomerations. The findings indicate: (1) The UGHQD level of the five major urban agglomerations has witnessed a consistent year-over-year growth trend, with coastal agglomerations like the Pearl River Delta (PRD) and Yangtze River Delta (YRD) outperforming others. (2) Pronounced regional differences exist in UGHQD levels across the urban agglomerations, with inter-regional differences primarily contributing to these differences. (3) The dynamic evolution of UGHQD distribution generally transitions from a centralized to a decentralized pattern, with a marked “club convergence” characteristic hindering cross-type leaps. (4) While a range of factors drive UGHQD in these agglomerations, technological innovation stands out as the principal factor inducing spatial differentiation. The comprehensive analysis and findings presented in this research not only contribute to academic knowledge but also hold practical implications for policymakers and practitioners striving for environmentally conscious land use planning and urban management.

This research investigates the relationship between capital structure and firm value for companies listed on the Vietnamese stock market. The study utilizes data from audited financial statements of 769 companies spanning from 2012 to 2022, amounting to 8459 observations. Employing various estimation methods, such as ordinary least squares (OLS), fixed effects model (FEM), random effects model (REM), and generalized least squares (GLS), the impact of capital structure on key financial indicators, namely, return on assets (ROA), return on equity (ROE), and Tobin’s Q, is assessed. The findings indicate that the debt ratio exhibits a positive influence on ROA, ROE, and Tobin’s Q, with Tobin’s Q displaying the most pronounced impact (0.450) and ROA showing the weakest impact (0.011). However, the long-term debt ratio does not significantly affect firm value. Interestingly, both short-term and long-term debt ratios have negative effects on ROA, ROE, and Tobin’s Q, with the most substantial impact on Tobin’s Q reduction (0.562). Based on these research outcomes, the authors offer valuable recommendations to companies, investors, business leaders, and policymakers to make informed decisions in selecting an optimal and sensible capital structure.

Systematic codes are of important practical interest for communications. Network coding, however, seems to conflict with systematic codes: although the source node can transmit message packets, network coding at the intermediate network nodes may significantly reduce the number of message packets received by the destination node. Is it possible to obtain the benefit of network coding while preserving some properties of the systematic codes? In this paper, we study the systematic design of batched network coding, which is a general network coding framework that includes random linear network coding as a special case. A batched network code has an outer code and an inner code, where the latter is formed by linear network coding. A systematic batched network code must take both the outer code and the inner code into consideration. Based on the outer code of a BATS code, which is a matrix-generalized fountain code, we propose a general systematic outer code construction that achieves a low encoding/decoding computation cost. To further reduce the number of random trials required to search a code with a close-to-optimal coding overhead, a triangular embedding approach is proposed for the construction of the systematic batches. We introduce new inner codes that provide protection for the systematic batches during transmission and show that it is possible to significantly increase the expected number of message packets in a received batch at the destination node, without harm to the expected rank of the batch transfer matrix generated by network coding.

Since plant–plant interaction has been the fundamental issue of the ecology community and is essential for the multispecies forest community, it is necessary to analyze the interaction mechanisms and provide suggestions for collaborative management of multispecies forests with the background of double carbon goals in China. To explore the interaction mechanisms in different interaction modes and assist China’s green development, we choose the most promising area, the Daxing’an Mountains, and its dominant species, Larch and Betula platyphylla, as research objects and establish a delayed two-species interaction model with an age structure. First, we calculate the equilibria of our model and analyze the stability of equilibria. Then, we study the existence of the Hopf bifurcation near the equilibria. Furthermore, we determine reasonable parameter values based on official data through mathematical methods, such as cluster analysis and model fitting. Finally, we carry out numerical simulations from three aspects, the evolution of a stand structure without interactions, the population dynamics in different interaction modes, and the influences of the parameters on the equilibria. Combined with simulation results, we provide biological interpretations for simulations of the stand structure evolution process and the interactions between Larch and Betula platyphylla; we also give reasonable values of the growth rates and mortalities for developing forest strategies.

When AlN thin films are deposited directly on the high-resistance silicon (HR-Si) substrate, a conductive layer will be formed on the HR-Si surface. This phenomenon is called the parasitic surface conduction (PSC) effect. The presence of the PSC effect will increase the power consumption of electronic components. Therefore, it is necessary to reduce the PSC effect. In prior technology, the polysilicon layer is usually used as the trap-rich layer to reduce the PSC effect. Experiments show that compared to AlN films deposited directly on HR-Si, the AlN substrates with polysilicon introduced on HR-Si have less radio frequency (RF) loss. To verify the effect of polysilicon on RF loss, polysilicon films of three different thicknesses and several different roughnesses were introduced. The results show that the thickness of the polysilicon will affect the RF loss, while the roughness has almost no effect on it. The polysilicon trap-rich layer can reduce the RF loss, which gradually becomes smaller as the polysilicon thickness increases.

Due to the influence of the optical proximity effect (OPE), it is easy for a pattern of photoresistance to be inconsistent with a design pattern, thus damaging the performance of a SAW resonator. To solve this problem, this paper proposes an optimization method for SAW filters based on optical proximity correction (OPC). This method can avoid the tip discharge problem of SAW filters by suppressing the problem of rounding and shrinking of dummy electrode and electrode tail caused by OPE. This method increases the quality factor (Q) of the SAW resonator and thus decreases the insertion loss of the SAW filter. The filter increases the bandwidth by 1.8 MHz at −1.5 dB after applying the OPC method. Additionally, it improves the stability of the filter under high power conditions.

In this study, a novel end-to-end path planning algorithm based on deep reinforcement learning is proposed for aerial robots deployed in dense environments. The learning agent finds an obstacle-free way around the provided rough, global path by only depending on the observations from a forward-facing depth camera. A novel deep reinforcement learning framework is proposed to train the end-to-end policy with the capability of safely avoiding obstacles. The Webots open-source robot simulator is utilized for training the policy, introducing highly randomized environmental configurations for better generalization. The training is performed without dynamics calculations through randomized position updates to minimize the amount of data processed. The trained policy is first comprehensively evaluated in simulations involving physical dynamics and software-in-the-loop flight control. The proposed method is proven to have a 38% and 50% higher success rate compared to both deep reinforcement learning-based and artificial potential field-based baselines, respectively. The generalization capability of the method is verified in simulation-to-real transfer without further training. Real-time experiments are conducted with several trials in two different scenarios, showing a 50% higher success rate of the proposed method compared to the deep reinforcement learning-based baseline.

The civilian airplane is a common transportation mode for the local people in the Qinghai-Tibet Plateau (QTP). Due to the profound dynamic and thermal effects, the QTP can trigger strong windstorms during the warm season, during which downbursts can cause severe low-level wind shear and threaten aviation safety. However, the study of downbursts over QTP has not been given much attention. This study analyzes and interprets a typical traveling dry microburst line that happened at the Xining Caojiapu International Airport (ZLXN) on 14 May 2020, intending to show a better understanding of the dry downbursts over QTP and explore the synergetic usage of different remote sensing technologies for downburst detection and warning in plateau airports. Specifically, the characteristics of synoptic conditions, the convective system formation process, and the structure and evolution of downbursts and relevant low-level winds are comprehensively investigated. The results show that, under the control of an upstream shallow trough, features of the local atmosphere state, including a dry-adiabatic stratification, a shallow temperature inversion, increases in solar radiation heating, and strong vertical shears of horizontal winds, can be favorable atmospheric prerequisites for the formation and development of dry storms and downbursts. Low-reflectivity storm cells of the Mesoscale Convective System (MCS) organize to form narrow bow echoes, and downbursts show features of radial wind convergences and rapid descending reflectivity cores with hanging virga as observed by a Doppler weather radar. Moreover, details of gales, gust fronts, convergences, turbulences, wind collisions, and outflow interactions triggered by the downburst line are also detected and interpreted by a scanning Doppler wind lidar from different perspectives. In addition, the findings in this work have been compared with the results observed in Denver, U.S., and some simulation studies. Finally, a few conceptual models of low-level wind evolutions influenced by the dry downburst line are given.

Egg production is a pivotal indicator for evaluating the fertility of poultry, and the ovary is an essential organ for egg production and plays an indispensable role in poultry production and reproduction. In order to investigate different aspects of egg production mechanisms in different poultry, in this study we performed a metabolomic analysis of the transcriptomic combination of the ovaries of two chicken breeds, the high-production Ninghai indigenous chickens and the low-production Wuliangshan black-boned chickens, to analyze the biosynthesis and potential key genes and metabolic pathways in the ovaries during egg production. We predicted four genes in the transcriptomic that are associated with egg production, namely P2RX1, INHBB, VIPR2, and FABP3, and identified three important pathways during egg production, “Calcium signaling pathway”, “Neuroactive ligand–receptor interaction” and “Cytokine–cytokine receptor interaction”, respectively. In the metabolomic 149 significantly differential metabolites were identified, 99 in the negative model and 50 in the positive model, of which 17α-hydroxyprogesterone, iloprost, spermidine, and adenosine are important metabolites involved in reproduction. By integrating transcriptomics and metabolomics, the correlation between specific differential genes and differential metabolites identified important gene-metabolite pairs “VIPR2-Spermidine” and “P2RX1-Spermidine” in egg production. In conclusion, these data provide a better understanding of the molecular differences between the ovaries of low- and high-production hens and provide a theoretical basis for further studies on the mechanics of poultry egg production.

The improvements in muscle growth rate and meat quality are the major breeding aims in pigeon industry. Liver and muscle are recognized as important sites for fatty acid metabolism; understanding the role of specific transcripts in the breast muscle and liver might lead to the elucidation of interrelated biological processes. In this study, RNA-Sequencing (RNA-Seq) was applied to compare the transcriptomes of breast muscle and liver tissues among pigeons at five developmental periods (0, 1, 2, 3, 4 weeks post-hatching) to identify candidate genes related to muscle growth and lipid metabolism. There were 3142 differentially expressed genes (DEGs) identified in the breast muscle libraries; 1794 genes were up-regulated while 1531 genes were down-regulated. A total of 1323 DEGs were acquired from the liver libraries, with 791 up-regulated genes and 591 down-regulated genes. By pathway enrichment analysis, a set of significantly enriched pathways were identified for the DEGs, which are potentially involved in cell proliferation and differentiation, lipid metabolism and energy metabolism in pigeon breast muscle and liver. Our results are consistent with previous partial reports from domestic animals and poultry and provide some unidentified genes involved in muscle growth and lipid metabolism. The reliability of the sequencing data was verified through qPCR analysis of 16 genes from eight comparison groups (two genes per group). The findings from this study could contribute to future investigations of muscle growth and lipid metabolism mechanisms and establish molecular approaches to improve muscle growth rate and meat quality in domestic pigeon breeding.

Six degree-of-freedom (6-DOF) robotic manipulators have been increasingly adopted in various applications in industries due to various advantages, such as large operation space, more degrees of freedom, low cost, easy placement, and convenient programming. However, the robotic manipulator has the problem of insufficient stiffness due to the series structures, which will cause motion errors of the manipulator end. In this paper, taking a 6-DOF robotic manipulator as an example, forward and inverse kinematics models are established, and a new modeling method for the joint angle and space stiffness of the end of the manipulator is proposed, which can establish the composite stiffness model of joint link stiffness and joint stiffness. An error compensation model is subsequently established. The experimental results indicate that the proposed error compensation method can effectively reduce the end motion error of the robotic manipulator, and hence, the working performance and accuracy of the manipulator can be improved. The proposed research is helpful for extending the application of robotic manipulators in precision machining and measurement.

Antifibrinolytic site-specific pharmaco-laser therapy (SSPLT) is an experimental treatment modality for refractory port wine stains (PWS). Conceptually, antifibrinolytic drugs encapsulated in thermosensitive liposomes are delivered to thrombi that form in semi-photocoagulated PWS blood vessels after conventional laser treatment. Local release of antifibrinolytics is induced by mild hyperthermia, resulting in hyperthrombosis and complete occlusion of the target blood vessel (clinical endpoint). In this study, 20 thermosensitive liposomal formulations containing tranexamic acid (TA) were assayed for physicochemical properties, TA:lipid ratio, encapsulation efficiency, and endovesicular TA concentration. Two candidate formulations (DPPC:DSPE-PEG, DPPC:MPPC:DSPE-PEG) were selected based on optimal properties and analyzed for heat-induced TA release at body temperature (T), phase transition temperature (Tm), and at T > Tm. The effect of plasma on liposomal stability at 37 °C was determined, and the association of liposomes with platelets was examined by flow cytometry. The accumulation of PEGylated phosphocholine liposomes in laser-induced thrombi was investigated in a hamster dorsal skinfold model and intravital fluorescence microscopy. Both formulations did not release TA at 37 °C. Near-complete TA release was achieved at Tm within 2.0–2.5 min of heating, which was accelerated at T > Tm. Plasma exerted a stabilizing effect on both formulations. Liposomes showed mild association with platelets. Despite positive in vitro results, fluorescently labeled liposomes did not sufficiently accumulate in laser-induced thrombi in hamsters to warrant their use in antifibrinolytic SSPLT, which can be solved by coupling thrombus-targeting ligands to the liposomes.

A fast algorithm is established to transform points of the unit sphere into fundamental region symmetrically. With the resulting algorithm, a flexible form of invariant mappings is achieved to generate aesthetic patterns with symmetries of the regular polyhedra.

Lateral flow immunoassay (LFIA) technology serves a significant role as a simple and rapid biosensor in the detection of influenza viruses. The focus of this study is the development of a rapid and convenient screening method for influenza B virus (IBV) proteins using a fluorescence lateral flow biosensor based on Ag-doped ZnIn2S4 quantum dots (Ag: ZIS QDs) as signal reporters. These Ag: ZIS QDs-emitting orange fluorescence are loaded onto dendritic mesoporous silica nanoparticles (DMSNs) and are further coated with a layer of silica shell to form a core–shell structured composite nanomaterial (SiO2 @ Ag: ZIS QDs @ DMSNs). The orange fluorescence effectively eliminates the interference of blue background fluorescence, significantly enhancing the detection sensitivity. This technology demonstrates outstanding performance in the immediate detection of IBV, with a minimum detection limit of 1 ng/mL, compared to the traditional colloidal gold strip with a detection limit of 6 ng/mL. Furthermore, both intra-assay and inter-assay coefficients of variation (CV) are less than 9%. This method holds promise for wide application in early diagnosis, epidemiological investigation, and epidemic surveillance of IBV.

The sensitivity of fluorescent lateral flow immunoassay (LFIA) test strips is compromised by the low fluorescence intensity of the signaling molecules. In this study, we synthesized novel phosphorus-doped carbon-dot-based dendritic mesoporous silica nanoparticles (DMSNs-BCDs) with a quantum yield as high as 93.7% to break this bottleneck. Meanwhile, the in situ growth method increased the loading capacity of carbon dots on dendritic mesoporous silica, effectively enhancing the fluorescence intensity of the composite nanospheres. Applied DMSNs-BCDs in LFIA can not only semi-quantitatively detect a single component in a short time frame (procalcitonin (PCT), within 15 min) but also detect the dual components with a low limit of detection (LOD) (carbohydrate antigen 199 (CA199) LOD: 1 U/mL; alpha-fetoprotein (AFP) LOD: 0.01 ng/mL). And the LOD of PCT detection (0.01 ng/mL) is lower by 1.7 orders of magnitude compared to conventional colloidal gold strips. For CA199, the LOD is reduced by a factor of four compared to LFIA using gold nanoparticles as substrates, and for AFP, the LOD is lowered by two orders of magnitude compared to colloidal gold LFIA. Furthermore, the coefficients of variation (CV) for intra-assay and inter-assay measurements are both less than 11%.

Using a deep learning algorithm in the development of a computer-aided system for colon polyp detection is effective in reducing the miss rate. This study aimed to develop a system for colon polyp detection and classification. We used a data augmentation technique and conditional GAN to generate polyp images for YOLO training to improve the polyp detection ability. After testing the model five times, a model with 300 GANs (GAN 300) achieved the highest average precision (AP) of 54.60% for SSA and 75.41% for TA. These results were better than those of the data augmentation method, which showed AP of 53.56% for SSA and 72.55% for TA. The AP, mAP, and IoU for the 300 GAN model for the HP were 80.97%, 70.07%, and 57.24%, and the data increased in comparison with the data augmentation technique by 76.98%, 67.70%, and 55.26%, respectively. We also used Gaussian blurring to simulate the blurred images during colonoscopy and then applied DeblurGAN-v2 to deblur the images. Further, we trained the dataset using YOLO to classify polyps. After using DeblurGAN-v2, the mAP increased from 25.64% to 30.74%. This method effectively improved the accuracy of polyp detection and classification.