A Backing-Layer-Shared Miniature Dual-Frequency Ultrasound Probe for Intravascular Ultrasound Imaging: In Vitro and Ex Vivo Validations

Intravascular ultrasound (IVUS) imaging has been extensively utilized to visualize atherosclerotic coronary artery diseases and to guide coronary interventions. To receive ultrasound signals within the vessel wall safely and effectively, miniaturized ultrasound transducers that meet the strict size constraints and have a simple manufacturing procedure are highly demanded. In this work, the first known IVUS probe that employs a backing-layer-shared dual-frequency structure and a single coaxial cable is introduced, featuring a small thickness and easy interconnection procedure. The dual-frequency transducer is designed to have center frequencies of 30 MHz and 80 MHz, and both have an aperture size of 0.5 mm × 0.5 mm. The total thickness of the dual-frequency transducer is less than 700 µm. In vitro phantom imaging and ex vivo porcine coronary artery imaging experiments are conducted. The low-frequency transducer achieves spatial resolutions of 40 µm axially and 321 µm laterally, while the high-frequency transducer exhibits axial and lateral resolutions of 17 µm and 247 µm, respectively. A bandpass filter is utilized to separate the ultrasound images. Combining in vitro phantom imaging analysis with ex vivo imaging validation, a comprehensive demonstration of the promising application of the proposed miniature ultrasound probe is established

Apoptosis Disorder, a Key Pathogenesis of HCMV-Related Diseases

Human cytomegalovirus (HCMV) belongs to the β-herpesvirus family, which is transmitted in almost every part of the world and is carried by more than 90% of the general population. Increasing evidence indicates that HCMV infection triggers numerous diseases by disrupting the normal physiological activity of host cells, particularly apoptosis. Apoptosis disorder plays a key role in the initiation and development of multiple diseases. However, the relationship and molecular mechanism of HCMV-related diseases and apoptosis have not yet been systematically summarized. This review aims to summarize the role of apoptosis in HCMV-related diseases and provide an insight into the molecular mechanism of apoptosis induced by HCMV infection. We summarize the literature on HCMV-related diseases and suggest novel strategies for HCMV treatment by regulating apoptosis

Evaluation of Agricultural Machinery Operational Benefits Based on Semi-Supervised Learning

Judging the efficiency of agricultural machinery operations is the basis for evaluating the utilization rate of agricultural machinery, the driving abilities of operators, and the effectiveness of agricultural machinery management. A range of evaluative factors—including operational efficiency, oil consumption, operation quality, repetitive operation rate, and the proportion of effective operation time—must be considered for a comprehensive evaluation of the quality of a given operation, an analysis of the causes of impact, the improvement of agricultural machinery management and an increase in operational efficiency. In this study, the main factors affecting the evaluation of agricultural machinery operations are extracted, and information about the daily operations of particular items of agricultural machinery is taken as a data source. As regards modeling, a subset of data can be scored manually, and the remaining data is predicted after the training of the relevant model. With a large quantity of data, manual scoring is not only time-consuming and labor-intensive, but also produces sample errors due to subjective factors. However, a small number of samples cannot support an accurate evaluation model, and so in this study a semi-supervised learning method was used to increase the number of training samples and improve the accuracy of the least-squares support vector machine (LSSVM) training model. The experiment used 33,000 deep subsoiling operation data, 500 of which were used as training samples and 500 as test samples. The accuracy rate of the model obtained using 500 training samples was 94.43%, and the accuracy rate achieved with this method with an increased number of training samples was 96.83%. An optimal combination of agricultural machinery and tools is recommended owing to their operational benefits in terms of reduced costs and improved operating capacity

Data Classification and Demand Prediction Methods Based on Semi-Supervised Agricultural Machinery Spare Parts Data

Because of the continuous improvement of technology, mechanization has emerged in various fields. Due to the different suitable seasons for the growth of agricultural plants, agricultural mechanization faces problems different from other industries. That is, agricultural machinery and equipment may be used frequently for a period of time, or may be idle for a long time. This leads to the aging of equipment no longer becoming regular, the maintenance time of spare parts is not fixed, the number of spare parts stored in the spare parts warehouse cannot be too large to occupy funds, and the number cannot be too small to meet the maintenance needs, so the prediction of agricultural machinery spare parts has become particularly important. Due to the lack of information, the difficulty of labeling, and the imbalance of positive and negative sample classification, this paper used a semi-supervised learning algorithm to solve the problem of agricultural machinery spare parts data classification. In order to forecast the demand for spare parts of agricultural machinery, this paper compared the IPSO-BP neural network algorithm and BP neural network algorithm. It was found that the IPSO-BP neural network was used to forecast the demand for spare parts of agricultural machinery, and the error between the predicted value and the actual value was small and met the accuracy requirements

Typing and evaluating heat resistance of Bacillus cereus sensu stricto isolated from the processing environment of powdered infant formula

Bacillus cereus sensu lato is one of the most harmful bacterial groups affecting the quality and safety of powdered infant formula (PIF). In this study, samples were collected from the raw materials and processing environments of PIF. A total of 84 isolates were identified as Bacillus cereus sensu stricto (B. cereus s. s.) by 16S rRNA analysis, molecular typing technology, and physiological and biochemical tests. The 84 B. cereus s. s. strains were assigned to panC group II, group III, and group IV. Then, the 7 housekeeping genes glpF, gmk, ilvD, pta, pur, pycA, and tpi were selected for multilocus sequence typing. Results showed that the 84 isolates were clustered into 24 sequence types (ST), and 14 novel ST were detected. Among the 24 ST, ST999 (19/84, 22.62%) and ST1343 (13/84, 15.48%) predominated. The correlation between processing areas and ST showed that the processing environments of the production and packing areas were the most susceptible to contamination by B. cereus s. s. Spores of these ST showed different heat resistance phenotypes evaluated by the analysis of DT (time in minutes of spore decimal reduction at each temperature) and Z values (temperature increase required to reduce the DT value to one-tenth of the original). Spores from group III according to panC gene analysis were the most heat resistant. These findings will help us to better understand B. cereus s. s. contamination and control in PIF processing environments

Development of a Resource Optimization Platform for Cross-Regional Operation and Maintenance Service for Combine Harvesters

In view of the centralized operation, high failure rate and large number of harvesters involved in the cross-regional operation of combine harvesters, which has led to a surge in maintenance service demand and a lack of effective maintenance service systems, in order to be able to quickly solve problems arising from failures during the process of cross-regional operation, an operation and maintenance (O&M) service platform for the cross-regional operation of combine harvesters was designed in this research on the basis of data resources, supported by the computing power of a big data platform and centered on an artificial intelligence algorithm. Meeting the demand for maintenance service during cross-regional operation, we built a system platform integrating service order management, maintenance service activity management, and maintenance service resource management, and a technical algorithm for operation and maintenance service resource allocation and service path optimization was developed in order to achieve service function modularization and intelligent monitoring, while early warning and display were realized using multi-dimensional platforms such as a PC, a control screen, and a mobile App. This platform was able to solve problems arising when harvesters break down, maintenance service can be carried out quickly when traditional resource information is blocked and the demand for the service is difficult to meet. The reduction in cost and the increased efficiency for agricultural machinery enterprises was also achieved, while the problem of ensuring continued service was systematically solved during the process of cross-regional operation. Finally, the performance of the software architecture and the effect of path optimization were verified. The results showed that the platform system developed using the three-layer C/S architecture offered more stable characteristics, and the path optimization in the platform system was better able to reduce the maintenance time and distance, thus making it possible to realize the dynamic on-demand configuration and scheduling management of cross-region job service resources

Silver nanoparticles: A novel antibacterial agent for control of Cronobacter sakazakii

Silver nanoparticles (AgNP) have been widely applied because of their broad spectrum of antimicrobial activities against bacteria, fungi, and viruses. However, little research has been done to evaluate their effects on Cronobacter sakazakii, an opportunistic pathogen usually infecting infants and having a high fatality rate. The aims of this work were to investigate the antibacterial property of novel, synthesized, positively charged silver nanoparticles against C. sakazakii and to discuss the potential antibacterial mechanisms involved. In this study, the spherical and face-centered cubic silver nanoparticles had a mean particle size of 31.2 nm and were synthesized by reducing Ag+ using citrate and dispersed by glycerol and polyvinylpyrrolidone (PVP) under alkaline conditions. Minimum inhibitory concentrations (MIC) and inhibition zone tests showed that the AgNP exhibited strong antibacterial activity against 4 tested C. sakazakii strains with mean MIC of 62.5 to 125 mg/L and average inhibition zone diameters of 13.8 to 16.3 mm. Silver nanoparticles caused cell membrane injury accompanied by adsorption of AgNP onto the cell surface, as shown by changes in cell morphology, cell membrane hyperpolarization, and accelerated leakage of intracellular reducing sugars and proteins outward from the cytoplasm. In addition, dysfunction of the respiratory chain was induced after treatment with AgNP, which was supported by a decrease in intracellular ATP and inhibition of related dehydrogenases. This research indicates that AgNP could be a novel and efficient antibacterial agent to control C. sakazakii contamination in environments producing powdered infant formulas from milk

Occlusion and Deformation Handling Visual Tracking for UAV via Attention-Based Mask Generative Network

Although the performance of unmanned aerial vehicle (UAV) tracking has benefited from the successful application of discriminative correlation filters (DCF) and convolutional neural networks (CNNs), UAV tracking under occlusion and deformation remains a challenge. The main dilemma is that challenging scenes, such as occlusion or deformation, are very complex and changeable, making it difficult to obtain training data covering all situations, resulting in trained networks that may be confused by new contexts that differ from historical information. Data-driven strategies are the main direction of current solutions, but gathering large-scale datasets with object instances under various occlusion and deformation conditions is difficult and lacks diversity. This paper proposes an attention-based mask generation network (AMGN) for UAV-specific tracking, which combines the attention mechanism and adversarial learning to improve the tracker’s ability to handle occlusion and deformation. After the base CNN extracts the deep features of the candidate region, a series of masks are determined by the spatial attention module and sent to the generator, and the generator discards some features according to these masks to simulate the occlusion and deformation of the object, producing more hard positive samples. The discriminator seeks to distinguish these hard positive samples while guiding mask generation. Such adversarial learning can effectively complement occluded and deformable positive samples in the feature space, allowing to capture more robust features to distinguish objects from backgrounds. Comparative experiments show that our AMGN-based tracker achieves the highest area under curve (AUC) of 0.490 and 0.349, and the highest precision scores of 0.742 and 0.662, on the UAV123 tracking benchmark with partial and full occlusion attributes, respectively. It also achieves the highest AUC of 0.555 and the highest precision score of 0.797 on the DTB70 tracking benchmark with the deformation attribute. On the UAVDT tracking benchmark with the large occlusion attribute, it achieves the highest AUC of 0.407 and the highest precision score of 0.582