In this paper, the spatial construction models of populations M. persicae and its predatory natural enemy E. graminicola during different periods were simulated by geostatistics, and their spatial relationships were analyzed. The spatial structure of M. persicae population was described by spherical model, showing an aggregated spatial arrangement. Its spatial dependence was 2.0252-4.1495 m, heterogeneity degree was 10,281.36-300,216.30, and sample variance was 12,176.81-303,433.70. The spatial structure of E . graminicola population was also simulated by spherical model, showing an aggregated spatial arrangement. Its spatial dependence was 3.7328-4.8983 m, heterogeneity degree was 1.4482-4.4134, and sample variance was 1.6941-5.8167. The results and methods could be applied to monitor the temporal and spatial dynamics of target insect pest population in tobacco field, and provide scientific basis for ecological control.
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Vaccines (Basel)
December 2024
Department of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, São Paulo, Brazil.
Introduction: Vaccines are a significant public health achievement, which are crucial for child survival and disease control globally. In Brazil, the National Immunization Program (PNI) manages vaccination schedules, including essential vaccines like BCG and Hepatitis B, administered at birth. Despite achieving over 95% coverage for years, vaccination rates have declined since 2016, a trend exacerbated by the COVID-19 pandemic.
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November 2024
Laboratory of Proteolytic Enzyme Chemistry, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 117997 Moscow, Russia.
IgA1 protease is one of the virulence factors of , and other pathogens causing bacterial meningitis. The aim of this research is to create recombinant proteins based on fragments of the mature IgA1 protease A-P from serogroup B strain H44/76. These proteins are potential components of an antimeningococcal vaccine for protection against infections caused by pathogenic strains of and other bacteria producing serine-type IgA1 proteases.
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November 2024
WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK.
Many measles endemic countries with suboptimal coverage levels still rely on vaccination campaigns to fill immunity gaps and boost control efforts. Depending on local epidemiological patterns, national or targeted campaigns are implemented, following which post-campaign coverage surveys (PCCSs) are conducted to evaluate their performance, particularly in terms of reaching previously unvaccinated children. Due to limited resources, PCCS surveys are designed to be representative at coarse spatial scales, often masking important heterogeneities in coverage that could enhance the identification of areas of poor performance for follow-up via routine immunization strategies.
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December 2024
Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510630, China.
Real-time online monitoring of track deformation during railway construction is crucial for ensuring the safe operation of trains. However, existing monitoring technologies struggle to effectively monitor both static and dynamic events, often resulting in high false alarm rates. This paper presents a monitoring technology for track deformation during railway construction based on dynamic Brillouin optical time-domain reflectometry (Dy-BOTDR), which effectively meets requirements in the monitoring of both static and dynamic events of track deformation.
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December 2024
Shanghai Research Institute of Microelectronics, Peking University, Shanghai 201203, China.
Despite the accuracy and robustness attained in the field of object tracking, algorithms based on Siamese neural networks often over-rely on information from the initial frame, neglecting necessary updates to the template; furthermore, in prolonged tracking situations, such methodologies encounter challenges in efficiently addressing issues such as complete occlusion or instances where the target exits the frame. To tackle these issues, this study enhances the SiamRPN algorithm by integrating the convolutional block attention module (CBAM), which enhances spatial channel attention. Additionally, it integrates the kernelized correlation filters (KCFs) for enhanced feature template representation.
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