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Salvia leucantha is a perennial herb of the genus Salvia in the family Labiatae, which has a wide range of biological activities, mainly including inhibition of acetylcholinesterase, antibacterial, and anti-inflammatory activity. To explore the protective effects and mechanism of action of S. leucantha on Alzheimer's disease (AD), the anti-AD activity of SLE (extracts of S. leucantha) was determined by using a transgenic Caenorhabditis elegans (C. elegans) model (CL4176). Analyses included paralysis assay, phenotypic experiments, transcriptome sequencing, RNA interference (RNAi), heat shock assays, and gas chromatography-mass spectrometry (GC-MS). SLPE (S. leucantha petroleum ether extract) could significantly delay CL4176 paralysis and extend the longevity of C. elegans N2 without harmful effects. A total of 927 genes were significantly changed by SLPE treatment in C. elegans, mainly involving longevity regulatory pathways—nematodes, drug metabolism—cytochrome P450, and glutathione metabolic pathways. RNAi showed that SLPE exerted its anti-AD activity through up-regulation of the gene gst-5; the most abundant compound in SLPE analyzed by GC-MS was 2,4-Di-tert-butylphenol (2,4-DTBP), and the compound delayed nematode paralysis. The present study suggests that active components in S. leucantha may serve as new-type anti-AD candidates and provide some insights into their biological functions. [ABSTRACT FROM AUTHOR]

The width of natural fractures is an important parameter in the leak prevention and plugging operations for oil and gas drilling. To accurately predict the fracture width of the leaking formation when well leakage occurs during oilfield drilling, based on the mud logging data of adjacent oil wells during the loss process, the Spearman correlation analysis and data normalization methods were used to preprocess the data. A new method was then established to predict the natural fracture width based on FDCNN (Fluid Dynamics Constrained Neural Network), which is a neural network algorithm constrained by the prior knowledge of fluid dynamics. In this method, first, based on the fluid dynamics prior knowledge that there is a strict positive partial derivative relationship between the natural fracture width and the leakage volume and pressure difference in the fluid dynamics model, the constraint conditions of the neural network were optimized. Second, the augmented Lagrange multiplier method was used to establish the performance index of the neural network through a multiplier and a penalty factor. Finally, the model was trained using the backpropagation learning rule and gradient descent training methods. The results indicate that this method, utilizing FDCNN, can train the model with a small sample training set, demonstrating superior generalization ability and prediction accuracy compared to traditional fluid dynamics models and Data-Driven Neural Network (which refers to an algorithm training a neural network solely on data samples without using fluid dynamics prior knowledge). This approach can effectively reduce the prediction error of fracture width, providing valuable reference for the development of field plugging programs. [ABSTRACT FROM AUTHOR]

The health of mobile software ecosystems is closely related to the interests of software developers, end‐users, and stakeholders. Therefore, it is crucial to maintain the mobile software ecosystem healthy and functioning. Researchers have done considerable research on mobile software ecosystems like Android and iOS. However, the evolution laws implicit in mobile software ecosystems have not attracted widespread attention. This paper proposes a research framework for investigating the evolution process and influencing factors of mobile software ecosystems based on community mining. Firstly, we mine the evolving ecosystem from many mobile software projects based on a community detection algorithm. Then we analyze the evolution process of the ecosystem by identifying evolution events in different periods. Furthermore, we utilize the multinomial logistics regression model to analyze the relevant indicators and summarize the crucial factors affecting the evolution. Meanwhile, by training the long short term memory (LSTM) model to predict evolution events, our prediction accuracy can reach 75%. This work can be used to maintain and improve the healthy operations of mobile software ecosystems. [ABSTRACT FROM AUTHOR]

Oropharyngeal microbiomes play a significant role in the susceptibility and severity of COVID-19, yet the role of these microbiomes play for the development of COVID-19 Omicron variant have not been reported. A total of 791 pharyngeal swab samples were prospectively included in this study, including 297 confirmed cases of Omicron variant (CCO), 222 confirmed case of Omicron who recovered (CCOR), 73 confirmed cases of original strain (CCOS) and 199 healthy controls (HC). All samples completed MiSeq sequencing. The results showed that compared with HC, conditional pathogens increased in CCO, while acid-producing bacteria decreased. Based on six optimal oropharyngeal operational taxonomy units (OTUs), we constructed a marker microbial classifier to distinguish between patients with Omicron variant and healthy people, and achieved high diagnostic efficiency in both the discovery queue and the verification queue. At same time, we introduced a group of cross-age infection verification cohort and Omicron variant subtype XBB.1.5 branch, which can be accurately distinguished by this diagnostic model. We also analyzed the characteristics of oropharyngeal microbiomes in two subgroups of Omicron disease group—severity of infection and vaccination times, and found that the change of oropharyngeal microbiomes may affect the severity of the disease and the efficacy of the vaccine. In addition, we found that some genera with significant differences gradually increased or decreased with the recovery of Omicron variant infection. The results of Spearman analysis showed that 27 oropharyngeal OTUs were closely related to 6 clinical indexes in CCO and HC. Finally, we found that the Omicron variant had different characterization of oropharyngeal microbiomes from the original strain. Our research characterizes oropharyngeal microbiomes of Omicron variant cases and rehabilitation cases, successfully constructed and verified the non-invasive diagnostic model of Omicron variant, described the correlation between microbial OTUs and clinical indexes. It was found that the infection of Omicron variant and the infection of original strain have different characteristics of oropharyngeal microbiomes. Author summary: COVID-19, caused by the SARS-CoV-2 virus, was first discovered in Wuhan, Hubei Province, China in December 2019, causing a huge impact around the world. In the past three years, novel coronavirus has successively appeared many varieties through gene mutation, among which Omicron strain is the most popular variety. Studies have shown that the imbalance of microflora is closely related to the progress of novel coronavirus infection and the severity of clinical symptoms. In the previous study, we build database of oral-intestinal microbial metagenomics, immunome and metabolome of novel coronavirus Original Strain with mild, medium and severe clinical symptoms and healthy subjects, reveal the role and mechanism of the oral and intestinal microbiome in Original Strain infection and exacerbation, and build the model of susceptibility, severe warning and prognosis of Original Strain pneumonia which based on the microbiome-immunity-metabolism. Our research characterizes oropharyngeal microbiomes of Omicron variant cases and rehabilitation cases, successfully constructed and verified the non-invasive diagnostic model of Omicron variant. We also analyzed the characteristics of oropharyngeal microbiomes in two subgroups of Omicron disease group—severity of infection and vaccination times, and found that the change of oropharyngeal microbiomes may affect the severity of the disease and the efficacy of the vaccine. And we described the correlation between microbial OTUs and clinical indexes. At last, it was found that the infection of Omicron variant and the infection of original strain have different characteristics of oropharyngeal microbiomes. [ABSTRACT FROM AUTHOR]

Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper's improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately. [ABSTRACT FROM AUTHOR]

Mixed traffic is a common phenomenon in urban environment. For the mixed traffic situation, the detection of traffic obstacles, including motor vehicle, non-motor vehicle, and pedestrian, is an essential task for intelligent and connected vehicles (ICVs). In this paper, an improved YOLO model is proposed for traffic obstacle detection and classification. The YOLO network is used to accurately detect the traffic obstacles, while the Wasserstein distance-based loss is used to improve the misclassification in the detection that may cause serious consequences. A new established dataset containing four types of traffic obstacles including vehicles, bikes, riders, and pedestrians is collected under different time periods and different weather conditions in urban environment in Wuhan, China. Experiments are performed on the established dataset on Windows PC and NVIDIA TX2, respectively. From the experimental results, the improved YOLO model has higher mean average precision than the original YOLO model and can effectively reduce intolerable misclassifications. In addition, the improved YOLOv4-tiny model has a detection speed of 22.5928fps on NVIDIA TX2, which can basically realize the real-time detection of traffic obstacles., Author(s): Luyao Du [1]; Xiongjie Chen [2]; Zhonghui Pei [3]; Donghua Zhang [4]; Bo Liu [4]; Wei Chen (corresponding author) [1] 1. Introduction Traffic conditions in urban societies can be [...]

Due to the problem of large latency and energy consumption of fog computing in smart community applications, the fog computing task-scheduling method based on Hybrid Ant Lion Optimizer (HALO) is proposed in this paper. This method is based on the Ant Lion Optimizer (ALO. Firstly, chaotic mapping is adopted to initialize the population, and the quality of the initial population is improved; secondly, the Adaptive Random Wandering (ARW) method is designed to improve the solution efficiency; finally, the improved Dynamic Opposite Learning Crossover (DOLC) strategy is embedded in the generation-hopping stage of the ALO to enrich the diversity of the population and improve the optimization-seeking ability of ALO. HALO is used to optimize the scheduling scheme of fog computing tasks. The simulation experiments are conducted under different data task volumes, compared with several other task scheduling algorithms such as the original algorithm of ALO, Genetic Algorithm (GA), Whale Optimizer Algorithm (WOA) and Salp Swarm Algorithm (SSA). HALO has good initial population quality, fast convergence speed, and high optimization-seeking accuracy. The scheduling scheme obtained by the proposed method in this paper can effectively reduce the latency of the system and reduce the energy consumption of the system. [ABSTRACT FROM AUTHOR]

Objective.To investigate the influence of pristimerin (Pris) on rat hepatocellular carcinoma (HCC) induced by diethylnitrosamine (DEN) by regulating ROS/ASK1/JNK signal pathway. Methods.Six SD rats were randomly selected as the control group, and the remaining rats were injected with DEN to construct the rat model of HCC. The rats successfully modeled were randomly grouped into HCC group, Pris group (0.8 mg/kg Pris), Vaccarin group (100 mg/kg ROS/ASK1/JNK signal pathway inhibitor Vaccarin), and Pris+Vaccarin group (0.8 mg/kg Pris+100 mg/kg Vaccarin), with 6 rats in each group, and were injected continuously for one week. HCC group and control group were injected with the same amount of normal saline. Body weight, liver weight and liver weight ratio were measured, liver function, inflammatory factors, antioxidant index and ROS level were detected by ELISA, HE staining was applied to detect the pathological changes of liver, western blot was applied to detect the expression of apoptotic markers (Bax, Bcl-2 and cleaved-Caspase-3) and ROS/ASK1/JNK signal path- way protein. Results.The rats in the control group showed normal liver tissue structure. In HCC group, necrosis of some hepatocytes and focal nodular hyperplasia were observed, body weight, Bax, cleaved-Caspase-3, ROS, p- ASK1/ASK1, and p-JNK/JNK level in HCC group were obviously lower than those in the control group (P < 0.05), but liver weight, liver weight ratio, ALT, AST, ALP, LDH contents, IL-6, TNF-, CCL-2 contents, SOD, GR, GPx, CAT contents, and Bcl-2 protein level were obviously higher (P < 0.05). Hepatocyte necrosis and focal nodular hyperplasia in Pris group were improved, and body weight, Bax, cleaved-Caspase-3, ROS, p-ASK1/ ASK1, and p-JNK/JNK level in Pris group were obviously higher than those in HCC group (P<0.05), liver weight, liver weight ratio, ALT, AST, ALP, LDH contents, IL-6, TNF-, CCL-2 contents, SOD, GR, GPx, CAT con- tents, and Bcl-2 protein level were obviously lower (P<0.05). The opposite trend was observed in Vaccarin group, Vaccarin reversed the anticancer effect of Pris on HCC rats. Conclusion.Pris may play a certain role in inhibiting HCC rats by activating ROS/ASK1/JNK signal pathway. [ABSTRACT FROM AUTHOR]

In this paper, we provide a characterisation of the ionosphere from April 2018 to September 2022 for 48 investigated months. We used the data of the China Seismo Electromagnetic Satellite (CSES-01), which is a sun-synchronous satellite with five days of revisit time and fixed local time of about 2 a.m. and 2 p.m. The unique orbit of CSES-01 permitted us to produce a monthly background of the ionosphere for night- and daytime with median values acquired during geomagnetic quiet time in equatorial and mid-latitude regions (i.e., between 50° S and 50° N of geographical latitude). We compared the obtained CSES-01 monthly median values with the solar activity in terms of sunspot numbers, and we found a high correlation of 0.89 for nighttime and 0.85 for daytime between the mean sunspot number and the maximum of the characterised CSES-01 Ne map values. In addition, we extracted all the anomalous positive increases in CSES-01 electron density and compared them with the Worldwide M5.5+ shallow earthquakes. We tested two different definitions of anomaly based on median and interquartile range or (mild) outliers. We tried two relationships between anomalies inside Dobrovolsky's area before the earthquake and the magnitude of the same seismic events: one which considers distance in space and time and a second which only uses the anticipation time of the anomaly before the earthquake. Using both anomaly definitions, we searched the best coefficients for these two laws for mid-latitude and equational regions. We found that the best coefficients are independent of the anomaly definition, but better accuracy (greater than 80%) is obtained for the outlier definition. Finally, using receiving operating characteristic (ROC) curves, we show that CSES-01 increases seem statistically correlated to the incoming seismic activity. [ABSTRACT FROM AUTHOR]

On 19 September 2021, La Palma volcano (Canarias Archipelagos) started an eruption that persisted until 13 December 2021. Despite the Volcano Explosive Index (VEI) being estimated equal to 3, corresponding to not so powerful eruption, the long eruption activity posed much scientific interest in this natural hazard event. In this paper, we searched for possible effects of this eruption on the atmosphere and ionosphere, investigating the climatological archive and Swarm magnetic satellite data. In particular, we explored Aerosol, Sulphur Dioxide and Carbon Monoxide concentrations in the atmosphere identifying both the direct emissions from the volcano as well as the plume that drifted toward West-South-West and was reinforced during the eruption period. The vertical profile of temperature from the Saber satellite was analysed to search for the possible presence of acoustic gravity waves induced by volcanic activity. Compared with the year before without eruption in the areas, a lot of Saber profiles present an Energy Potential very much higher than the previous year, proposing the presence of Acoustic Gravity Waves (AGW) induced by volcano eruption activity. We also identified Swarm magnetic disturbances on the day of the eruption and in November. The mechanism of coupling could be different for the latter one, as there is no evidence for AGW. They may be due to a more complex of physical and chemical alterations that propagate from the lower atmosphere to the upper one into the ionosphere. [ABSTRACT FROM AUTHOR]