Center for Nonlinear Dynamics and Department of Physics and ‡School of Biological Sciences, The University of Texas at Austin, Austin, Texas 78712, United States.
Published: April 2014
AI Article Synopsis
Article Abstract
Biofilms are sessile communities of microbes that are spatially structured by an embedding matrix. Biofilm infections are notoriously intractable. This arises, in part, from changes in the bacterial phenotype that result from spatial structure. Understanding these interactions requires methods to control the spatial structure of biofilms. We present a method for growing biofilms from initiating cells whose positions are controlled with single-cell precision using laser trapping. The native growth, motility, and surface adhesion of positioned microbes are preserved, as we show for model organisms Pseudomonas aeruginosa and Staphylococcus aureus. We demonstrate that laser-trapping and placing bacteria on surfaces can reveal the effects of spatial structure on bacterial growth in early biofilm development.
The effective population size ( ) is a key parameter in conservation and evolutionary biology, reflecting the strength of genetic drift and inbreeding. Although demographic estimations of are logistically and time-consuming, genetic methods have become more widely used due to increasing data availability. Nonetheless, accurately estimating remains challenging, with few studies comparing estimates across molecular markers types and estimators such as single-sample methods based on linkage disequilibrium or sibship analyses versus methods based on temporal variance in allele frequencies.
Background: Accurate diagnosis and understanding of brain disorders are crucial for the best treatment. While multimodal neuroimaging is essential, it has its limitations. Conventional computed tomography (CT) and magnetic resonance imaging (MRI) provide detailed anatomical information but lack molecular insights, while 18F-fluorodeoxyglucose-positron emission tomography (FDG PET) offers metabolic data but often has limited spatial resolution.
Introduction: Automatic segmentation of the left atrium (LA) constitutes a crucial pre-processing step in evaluating heart structure and function during clinical interventions, such as image-guided radiofrequency ablation of atrial fibrillation. Despite prior research on LA segmentation, the low contrast in medical images exacerbates the challenge of distinguishing various tissues, rendering accurate boundary delineation of the target area formidable. Moreover, class imbalance due to the small target size further complicates segmentation.
Safety learning during threat and adversity is critical for behavioral adaptation, resiliency, and survival. Using a novel mouse paradigm involving thermal threat, we recently demonstrated that safety learning is highly susceptible to social isolation stress. Yet, our previous study primarily considered male mice and did not thoroughly scrutinize the relative impacts of stress on potentially distinct defensive mechanisms implemented by males and females during the thermal safety task.
Introduction: Emotion recognition using electroencephalography (EEG) is a key aspect of brain-computer interface research. Achieving precision requires effectively extracting and integrating both spatial and temporal features. However, many studies focus on a single dimension, neglecting the interplay and complementarity of multi-feature information, and the importance of fully integrating spatial and temporal dynamics to enhance performance.
