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Abstract Background Recent studies suggest a connection between immunoglobulin light chains (IgLCs) and coronary heart disease (CHD). However, current diagnostic methods using peripheral blood IgLCs levels or subtype ratios show limited accuracy for CHD, lacking comprehensive assessment and posing challenges in early detection and precise disease severity evaluation. We aim to develop and validate a Coronary Health Index (CHI) incorporating total IgLCs levels and their distribution. Additionally, we aim to evaluate its effectiveness by integrating patient data and using machine learning models through diagnostic trial. Methods The CHI was developed and combined with other clinical data. Nine machine learning models were screened to identify optimal diagnostic performance, with the XGBoost model emerging as the top performer. Performance was assessed based on accuracy, sensitivity, and its ability to identify severe CHD cases characterized by complex lesions (SYNTAX score > 33). Results The XGBoost model demonstrated high accuracy and sensitivity in diagnosing CHD, with an area under the curve (AUC) of 0.927. It also accurately identified patients with severe CHD, achieving an AUC of 0.991. An online web tool was introduced for broader external validation, confirming the model’s effectiveness. Conclusions Combining the CHI with the XGBoost model offers significant advantages in diagnosing CHD and assessing disease severity. This approach can guide clinical interventions and improve large-scale CHD screening.

Abstract Current influenza vaccines need to be updated annually owing to constant antigenic drift in the globular head of the viral surface hemagglutinin (HA) glycoprotein. The immunogenic subdominant stem domain of HA is highly conserved and can be recognized by antibodies capable of binding multiple HA subtypes. Therefore, the HA stem antigen is a promising target for the design of universal influenza vaccines. On the basis of an established lipid nanoparticle‐encapsulated mRNA vaccine platform, we designed and developed a novel universal influenza mRNA vaccine (mHAs) encoding the HA stem antigen of the influenza A (H1N1) virus. We tested the efficacy of the mHAs vaccine using a mouse model. The vaccine induced robust humoral and specific cellular immune responses against the stem region of HA. Importantly, two doses of the mHAs vaccine fully protected mice from lethal challenges of the heterologous H1N1 and heterosubtypic H5N8 influenza viruses. Vaccinated mice had less pathological lung damage and lower viral titers than control mice. These results suggest that an mRNA vaccine using the conserved stem region of HA may provide effective protection against seasonal and other possible influenza variants.

ABSTRACTThe crucial function of circulating microbial DNA (cmDNA) in peripheral blood is gaining recognition because of its importance in normal physiology and immunity in healthy individuals. Evidence suggests that cmDNA in peripheral blood is derived from highly abundant, translocating gut microbes. However, the associations with and differences between cmDNA in peripheral blood and the gut microbiome remain unclear. We collected blood, urine, and fecal samples from volunteers to compare their microbial information via 16S rDNA sequencing. The results revealed that, compared with gut microbial DNA, cmDNA in peripheral blood was associated with reduced diversity and a distinct microbiota composition. The cmDNA in the blood reflects the biochemical processes of microorganisms, including synthesis, energy conversion, degradation, and adaptability, surpassing that of fecal samples. Interestingly, cmDNA in blood showed a limited presence of DNA from anaerobes and gram-positive bacteria, which contrast with the trend observed in fecal samples. Furthermore, analysis of cmDNA revealed traits associated with mobile elements and potential pathologies, among others, which were minimal in stool samples. Notably, cmDNA analysis indicated similarities between the microbial functions and phenotypes in blood and urine samples, although greater diversity was observed in urine samples. Source Tracker analysis suggests that gut microbes might not be the main source of blood cmDNA, or a selective mechanism allows only certain microbial DNA into the bloodstream. In conclusion, our study highlights the composition and potential functions associated with cmDNA in peripheral blood, emphasizing its selective presence; however, further research is required to elucidate the mechanisms involved.IMPORTANCEOur research provides novel insights into the unique characteristics and potential functional implications of circulating microbial DNA (cmDNA) in peripheral blood. Unlike other studies that analyzed sequencing data from fecal or blood microbiota in different study cohorts, our comparative analysis of cmDNA from blood, urine, and fecal samples from the same group of volunteers revealed a distinct blood-specific cmDNA composition. We discovered a decreased diversity of microbial DNA in blood samples compared to fecal samples as well as an increased presence of biochemical processes microbial DNA in blood. Notably, we add to the existing knowledge by documenting a reduced abundance of anaerobes and gram-positive bacteria in blood compared to fecal samples according to the analysis of cmDNA and gut microbial DNA, respectively. This observation suggested that a potential selective barrier or screening mechanism might filter microbial DNA molecules, indicating potential selectivity in the translocation process which contrasts with the traditional view that cmDNA primarily originates from random translocation from the gut and other regions. By highlighting these differences, our findings prompt a reconsideration of the origin and role of cmDNA in blood circulation and suggest that selective processes involving more complex biological mechanisms may be involved.

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.

Abstract Background IFN-γ is a pleiotropic cytokine that has been shown to affect multiple cellular functions of bovine mammary epithelial cells (BMECs) including impaired milk fat synthesis and induction of malignant transformation via depletion of arginine, one of host conditionally essential amino acids. But the molecular mechanisms of these IFN-γ induced phenotypes are still unknown. Methods BMECs were treated with IFN-γ for 6 h, 12 h, and 24 h. The metabolomic profiling in BMECs upon IFN-γ induction were assessed using untargeted ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) metabolomic analysis. Key differentially expressed metabolites (DEMs) were quantified by targeted metabolomics. Results IFN-γ induction resulted in significant differences in the contents of metabolites. Untargeted analysis identified 221 significantly DEMs, most of which are lipids and lipid-like molecules, organic acids and derivatives, organ heterocyclic compounds and benzenoids. According to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, DEMs were enriched in fructose and mannose metabolism, phosphotransferase system (PTS), β-alanine metabolism, arginine and proline metabolism, methane metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis. Quantification of selected key DEMs by targeted metabolomics showed significantly decreased levels of D-(-)-mannitol, argininosuccinate, and phenylacetylglycine (PAG), while increased levels in S-hydroxymethylglutathione (S-HMG) and 2,3-bisphospho-D-glyceric acid (2,3-BPG). Conclusions These results provide insights into the metabolic alterations in BMECs upon IFN-γ induction and indicate potential theoretical basis for clarifying IFN-γ-induced diseases in mammary gland.

In this work, phase-apodized silicon grating filters with varying sidewall corrugation width and location were investigated, while the resonance wavelength, extinction ratio, and rejection bandwidth were tuned flexibly. The grating filters with a waveguide width of 500 nm and grating period of 400 nm were fabricated and characterized as a proof of concept. The resonance wavelength of the device can be shifted by 4.54 nm by varying the sidewall corrugation width from 150 to 250 nm. The corresponding rejection bandwidth can be changed from 1.19 to 2.03 nm by applying a sidewall corrugation location offset from 50 to 200 nm. The experimental performances coincide well with the simulation results. The presented sidewall corrugation-modulated apodized grating can be expected to have great application prospects for optical communications and semiconductor lasers.

Abstract Background IFN-γ has been traditionally recognized as an inflammatory cytokine that involves in inflammation and autoimmune diseases. Previously we have shown that sustained IFN-γ induced malignant transformation of bovine mammary epithelial cells (BMECs) via arginine depletion. However, the molecular mechanism underlying this is still unknown. Methods In this study, the amino acids contents in BMECs were quantified by a targeted metabolomics method. The acquisition of differentially expressed genes was mined from RNA-seq dataset and analyzed bioinformatically. Quantitative reverse transcription polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), western blotting, and immunohistochemistry (IHC) assay were performed to detect gene mRNA and protein expression levels. CCK-8 and would healing assays were used to detect cell proliferation and migration abilities, respectively. Cell cycle phase alternations were analyzed by flow cytometry. Results The targeted metabolomics analysis specifically discovered IFN-γ induced arginine depletion through accelerating arginine catabolism and inhibiting arginine anabolism in BMECs. Transcriptome analysis identified leucine aminopeptidase 3 (LAP3), which was regulated by p38 and ERK MAPKs, to downregulate arginine level through interfering with argininosuccinate synthetase (ASS1) as IFN-γ stimulated. Moreover, LAP3 also contributed to IFN-γ-induced malignant transformation of BMECs by upregulation of HDAC2 (histone deacetylase 2) expression and promotion of cell cycle proteins cyclin A1 and D1 expressions. Arginine supplementation did not affect LAP3 and HDAC2 expressions, but slowed down cell cycle process of malignant BMECs. In clinical samples of patients with breast cancer, LAP3 was confirmed to be upregulated, while ASS1 was downregulated compared with healthy control. Conclusions These results demonstrated that LAP3 mediated IFN-γ-induced arginine depletion to malignant transformation of BMECs. Our findings provide a potential therapeutic target for breast cancer both in humans and dairy cows.

This paper designs a multi-variable hybrid islanding-detection method (HIDM) using signal-processing techniques. The signals of current captured on a test system where the renewable energy (RE) penetration level is between 50% and 100% are processed by the application of the Stockwell transform (ST) to compute the Stockwell islanding-detection factor (SIDF) and the co-variance islanding-detection factor (CIDF). The signals of current are processed by the application of the Hilbert transform (HT), and the Hilbert islanding-detection factor (HIDF) is computed. The signals of current are also processed by the application of the Alienation Coefficient (ALC), and the Alienation Islanding Detection Factor (AIDF) is computed. A hybrid islanding-detection indicator (HIDI) is derived by multiplying the SIDF, CIDF, AIDF, and an islanding weight factor (IWF) element by element. Two thresholds, designated as the hybrid islanding-detection indicator threshold (HIDIT) and the hybrid islanding-detection indicator fault threshold (HIDIFT), are selected to detect events of islanding and also to discriminate such events from fault events and operational events. The HIDM is effectively tested using an IEEE-13 bus power network, where solar generation plants (SGPs) and wind generation plants (WGPs) are integrated. The HIDM effectively identified and discriminated against events such as islanding, faults, and operational. The HIDM is also effective at identifying islanding events on a real-time distribution feeder. The HIDM is also effective at detecting islanding events in the scenario of a 20 dB signal-to-noise ratio (SNR). It is established that the HIDM has a small non-detection zone (NDZ). The effectiveness of the HIDM is better relative to the islanding-detection method (IDM) supported by the discrete wavelet transform (DWT), an IDM using a hybridization of the slantlet transform, and the Ridgelet probabilistic neural network (RPNN). An IDM using wavelet transform multi-resolution (WT-MRA)-based image data and an IDM based on the use of a deep neural network (DNN) were used. The study was performed using the MATLAB software (2017a) and validated in real-time using the data collected from a practical distribution power system network.

In order to make the power transmission more stable and ensure the safe operation of the system, a study on the interaction of controllable series compensation and traditional PSS to suppress low-frequency oscillation is proposed. Firstly, the lead-slip angle is adjusted by the lead-lag module of the PSS limit stabilizer to eliminate the reverse modulation phenomenon, and the series compensation is used to optimize the parallel transmission lines and lines of different voltage levels to improve the stability of the long-distance transmission system. Secondly, the phase compensation method is used to set the parameters of the TCSC and PSS damping controllers, the characteristics of the rotor system are judged by the damping ratio, the sensitivity characteristic root is extracted, and the maximum parameters affecting the mode are determined; finally, the genetic algorithm is introduced to coordinate the damping controller to increase the oscillation. Mode damping, constraining the range of each tuning parameter, enabling coordinated optimization. Experiments show that the method in this paper can effectively suppress low-frequency oscillation and ensure the safety and stability of the power system.

Coronary heart disease (CHD) is caused by coronary atherosclerosis and has a high morbidity and mortality rate worldwide. There are challenges in both early screening and treatment of CHD. The appearance and development of CHD is a complex metabolic disorder process. Therefore, to search for new biomarkers of CHD, we analyzed the peripheral blood metabolome in patients with CHD. In the study, a plasma metabolite, 4′-Phosphopantetheine (4-PPanSH), which was discovered by HPLC-MS/MS, as peripheral blood 4-PPanSH decreases, the degree of coronary blockage gradually aggravates. In addition, the 4-PPanSH supplement limited atherosclerotic plaque formation and endothelial injury in mice. Further, in vascular endothelial cells, 4-PPanSH effectively inhibited ROS generation and ox-LDL accumulation. In summary, 4-PPanSH was associated with the degree of coronary stenosis, and the 4-PPanSH supplement reduced atherosclerotic plaque generation, which could be associated with 4-PPanSH acting as a potent antioxidant that inhibits ROS generation and alleviates vascular endothelial injury.

According to the survey statistics, most traffic accidents are caused by the driver’s behavior and status irregularities. Because there is no multi-level dangerous state grading system at home and abroad, this paper proposes a complex state grading system for real-time detection and dynamic tracking of the driver’s state. The system uses OpenMV as the acquisition camera combined with the cradle head tracking system to collect the driver’s current driving image in real-time dynamically, combines the YOLOX algorithm with the OpenPose algorithm to judge the driver’s dangerous driving behavior by detecting unsafe objects in the cab and the driver’s posture, and combines the improved Retinaface face detection algorithm with the Dlib feature-point algorithm to discriminate the fatigue driving state of the driver. The experimental results show that the accuracy of the three driver danger levels (R1, R2, and R3) obtained by the proposed system reaches 95.8%, 94.5%, and 96.3%, respectively. The experimental results of this system have a specific practical significance in driver-distracted driving warnings.

Essential oils (EOs) from citrus fruits are excellent aromatic resources that are used in food, cosmetics, perfume, and cleaning products. EOs extracted from four citrus varieties, sweet orange, grapefruit, mandarin, and lemon, were separated into two fractions by molecular distillation. The composition, physicochemical properties, cleaning ability, and antimicrobial activity of each EO were then systematically evaluated. The relationships between each of the aforementioned characteristics are also discussed. In keeping with the principle of “like dissolves like,” most citrus EOs show better cleaning ability than acetone and all tend to dissolve the fat-soluble pigment. The key components of citrus EOs are 1-Decanol, α-terpineol, geraniol, and linalool for the inhibition of Staphylococcus aureus, Escherichia coli, Candida albicans, and Vibrio parahaemolyticus, respectively. The findings of this study will be of significant importance for the effective utilization of citrus peel resources and in the development of future applications for citrus EOs.Chemical Compounds Studied in This Article: (+)-α-Pinene (PubChem CID: 6654); β-Phellandrene (PubChem CID: 11142); 3-Carene (PubChem CID: 26049); β-Myrcene (PubChem CID: 31253); D-Limonene (PubChem CID: 440917); γ-Terpinene (PubChem CID: 7461); Octanal (PubChem CID: 454); Decanal (PubChem CID: 8175); Linalool (PubChem CID: 6549); 1-Octanol (PubChem CID: 957); β-Citral (PubChem CID: 643779); α-Terpineol (PubChem CID: 17100); Hedycaryol (PubChem CID: 5365392); α-Citral (PubChem CID: 638011); 1-Decanol (PubChem CID: 8174); Geraniol (PubChem CID: 637566).

The TDM of voriconazole which exhibits wide inter-individual variability is indispensable for treatment in clinic. In this study, a method that high-performance liquid chromatography tandem mass spectrometry cubed (HPLC-MS3) is first built and validated to quantify voriconazole in human plasma. The system is composed of Shimadzu Exion LCTM UPLC coupled with a Qtrap 5500 mass spectrometer. The separation of voriconazole is performed on a Poroshell 120 SB-C18 column at a flow rate of 0.8 mL/min remaining 7 min for each sample. The calibration curves are linear in the concentration range of 0.25–20 μg/mL. Intra-day and inter-day accuracies and precisions are within 8.0% at three concentrations, and the recoveries and matrix effect are all within accepted limits. In terms of stability, there is no significant degradation of voriconazole under various conditions. The HPLC-MS3 and HPLC-MRM (multiple reaction monitoring) methods are compared in 42 patients with Passing–Bablok regression and Bland–Altman plots, and the results show no significant difference between the two methods. However, HPLC-MS3 has a higher S/N (signal-to-noise ratio) and response than the MRM. Finally, the HPLC-MS3 assay is successfully applied to monitor the TDM (therapeutic drug monitoring) of voriconazole in human plasma, and this verifies that the dosing guidelines for voriconazole have been well implemented in the clinic and patients have received excellent treatment.

In order to safely and efficiently use their power as well as to extend the life of Li-ion batteries, it is important to accurately analyze original battery data and quickly predict SOC. However, today, most of them are analyzed directly for SOC, and the analysis of the original battery data and how to obtain the factors affecting SOC are still lacking. Based on this, this paper uses the visualization method to preprocess, clean, and parse collected original battery data (hexadecimal), followed by visualization and analysis of the parsed data, and finally the K-Nearest Neighbor (KNN) algorithm is used to predict the SOC. Through experiments, the method can completely analyze the hexadecimal battery data based on the GB/T32960 standard, including three different types of messages: vehicle login, real-time information reporting, and vehicle logout. At the same time, the visualization method is used to intuitively and concisely analyze the factors affecting SOC. Additionally, the KNN algorithm is utilized to identify the K value and P value using dynamic parameters, and the resulting mean square error (MSE) and test score are 0.625 and 0.998, respectively. Through the overall experimental process, this method can well analyze the battery data from the source, visually analyze various factors and predict SOC.

Situational detection in the traffic system is of great significance to traffic management and even urban management. Traditional detection methods are generally based on roadside equipment monitoring roads, and it is difficult to support large-scale and fine-grained traffic incident detection. In this study, we propose a detection method applied to the mobile edge, which detects traffic incidents based on the video captured by vehicle cameras, so as to overcome the limitations of roadside terminal perception. For swarm intelligence detection, we propose an improved YOLOv5s object detection network, adding an atrous pyramid pooling layer to the network and introducing a fusion attention mechanism to improve the model accuracy. Compared with the raw YOLOv5s, the mAP metrics of our improved model are increased by 3.3% to 84.2%, enabling it to detect vehicles, pedestrians, traffic accidents, and fire traffic incidents on the road with high precision in real time. This provides information for city managers to help them grasp the abnormal operation status of roads and cities in a timely and effective manner.

Based on the research of domestic and foreign scholars, this paper has improved and established a double oligopoly market model of renewable energy, and analyzed the complex dynamic characteristics of a system based on entropy theory and chaos theory, such as equilibrium point, stability, Hopf bifurcation conditions, etc. This paper also studied and simulated the effects of the natural growth rate of energy and the single delay decision on the renewable energy system by minimizing the entropy of the system and reducing the system instability to a minimum, so that the degree of disorder within the system was reduced. The results show that with the increase of the natural growth rate of energy, the stability of the system is not affected, but the market demand of the oligopoly 1 is gradually reducing and the market demand of the oligopoly 2 is gradually increasing. At the same time, a single oligopoly making the time delay decision will affect the stability of the two oligopolies. With the increase of delay, the time required to reach the stable state will grow, and the system will eventually enter the Hopf bifurcation, thus the system will have its entropy increased and fall into an unstable state. Therefore, in the actual market of renewable energy, oligopolies should pay attention to the natural growth rate of energy and time delay, ensuring the stability of the game process and the orderliness of the system.

Based on the traditional IS–LM economic theory, which shows the relationship between interest rates and output in the goods and services market and the money market in macroeconomic. We established a four-dimensional IS–LM model involving four variables. With the Caputo fractional calculus theory, we improved it into a fractional order nonlinear model, analyzed the complexity and stability of the fractional order system. The existences conditions of attractors under different order conditions are compared, and obtain the orders when the system reaches a stable state. Have the detail analysis on the dynamic phenomena, such as the strange attractor, sensitivity to initial values through phase diagram and the power spectral. The order changes in two ways: orders changes synchronously or single order changes. The results show regardless of which the order situation is, the economic system will enter into multiple states, such as strong divergence, strange attractor and the convergence, finally, system will enter into the stable state under a certain order; parameter changes have similar effects on the economic system. Therefore, selecting an appropriate order is significant for an economic system, which guarantees a steady development. Furthermore, this paper construct the chaos control to IS–LM fractional-order macroeconomic model by means of linear feedback control method, by calculating and adjusting the feedback coefficient, we make the system return to the convergence state.

In this paper, a duopoly game model with double delays in hydropower market is established, and the research focus on the influence of time delay parameter on the complexity of the system. Firstly, we established a game model for the enterprises considering both the current and the historical output when making decisions. Secondly, the existence and stability of Hopf bifurcation are analyzed, and the conditions and main conclusions of Hopf bifurcation are given. Thirdly, numerical simulation and analysis are carried out to verify the conclusions of the theoretical analysis. The effect of delay parameter on the stability of the system is simulated by a bifurcation diagram, the Lyapunov exponent, and an entropic diagram; in addition, the stability region of the system is given by a 2D parameter bifurcation diagram and a 3D parameter bifurcation diagram. Finally, the method of delayed feedback control is used to control the chaotic system. The research results can provide a guideline for enterprise decision-making.