The evolution of pulmonary disease in cystic fibrosis (CF) usually begins when bacteria get trapped in mucus in the lungs and become established as a chronic infection. While most CF patients experience periods of stability, pulmonary exacerbations (PEs) can occur multiple times per year and result in permanent damage to the lungs. Little is known of the shift from a period of stability to a PE, but this shift is likely to be attributed to changes in the bacterial community. Here, we identified changes in the lung microbiota to determine if they reflect patient health, indicate the onset of exacerbations, or are related to antibiotic treatment. In contrast to most bacterial studies on CF, we collected weekly samples from an adult CF patient over a period of 3 years and performed quantitative PCR (qPCR) and Illumina sequencing on those samples. While many DNA-based studies have shown the CF microbiota to be relatively stable, we observed an increase in the total bacterial abundance over time (P < 0.001), while the number of different taxa (bacterial richness) and the number of different taxa and their abundances (diversity) significantly decreased over time (P < 0.03), which was likely due to repeated antibiotic exposure. Using genus-specific primers with qPCR, we observed an increase in the abundance of Burkholderia multivorans, a CF-associated pathogen, prior to the occurrence of a PE (P ؍ 0.006). Combining these DNA-based techniques with frequent sampling identified a potential initiator for exacerbations and described a response of the CF microbiota to time and antibiotic treatment not observed in previous CF microbiota studies. Bacterial infections with consequent progressive lung disease are the leading cause of death in persons with cystic fibrosis (CF), a disease that affects an estimated 30,000 people in the United States and 70,000 people worldwide (1). Prior to the past 2 decades, it was assumed that the CF lungs were colonized with only a few different bacteria, including Pseudomonas aeruginosa, Haemophilus influenzae, Staphylococcus aureus, and members of the Burkholderia cepacia complex (BCC) (2). It has been shown in CF patients that chronic infection with these CF-related bacteria (CFRB) is linked to an increase in mortality (3, 4).Pulmonary exacerbations (PEs), which may develop multiple times per year in CF patients, are often caused by a disturbance to a stable chronic bacterial infection (5, 6). The exact cause of a PE, often identified by an increase in pulmonary disease symptoms, remains uncertain but is commonly attributed to factors associated with established bacteria, and possibly to viruses or newly acquired bacterial strains (7,8). In 2004, Rogers et al. used terminal restriction fragment length polymorphism (TRFLP) analysis to target the bacterial 16S rRNA gene in order to analyze DNA extracted from sputum samples from CF patients. This method of analysis revealed a complexity that included 15 species not previously identified in the lungs. The study by Rogers et al. laid the founda...
This article describes a multimedia system consisting of two sensors: (1) a laser range scanner (LIDAR) and (2) a conventional digital camera. Our work specifies a mathematical calibration model that allows for this data to be explicitly integrated. Data integration is accomplished by calibrating the system, i.e., estimating for each variable of the model for a specific LIDARand-camera pair. Our approach requires detection of feature points in both the LIDAR scan and the digital images. Using correspondences between feature points, we can then estimate the model variables that specify an explicit mathematical relationship between sensed (x,y,z) LIDAR points and (x,y) digital image positions. Our system is designed for 3D line scanners, i.e., scanners that detect positions that lie in a 3D plane which requires some special theoretical and experimental treatment. Results are provided for simulations of the system in a virtual environment and for a real LIDAR-and-camera system consisting of a SICK LMS200 and an inexpensive USB web-camera. Calibrated systems can integrate the data in real-time which is of particular use for autonomous vehicular and robotic navigation.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.