Data_Sheet_1_Multi-feature fusion learning for Alzheimer's disease prediction using EEG signals in resting state.PDF

IntroductionDiagnosing Alzheimer's disease (AD) lesions via visual examination of Electroencephalography (EEG) signals poses a considerable challenge. This has prompted the exploration of deep learning techniques, such as Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs), for AD prediction. However, the classification performance of CNN-based methods has often been deemed inadequate. This is primarily attributed to CNNs struggling with extracting meaningful lesion signals from the complex and noisy EEG data.MethodsIn contrast, ViTs have demonstrated proficiency in capturing global signal patterns. In light of these observations, we propose a novel approach to enhance AD risk assessment. Our proposition involves a hybrid architecture, merging the strengths of CNNs and ViTs to compensate for their respective feature extraction limitations. Our proposed Dual-Branch Feature Fusion Network (DBN) leverages both CNN and ViT components to acquire texture features and global semantic information from EEG signals. These elements are pivotal in capturing dynamic electrical signal changes in the cerebral cortex. Additionally, we introduce Spatial Attention (SA) and Channel Attention (CA) blocks within the network architecture. These attention mechanisms bolster the model's capacity to discern abnormal EEG signal patterns from the amalgamated features. To make well-informed predictions, we employ a two-factor decision-making mechanism. Specifically, we conduct correlation analysis on predicted EEG signals from the same subject to establish consistency.ResultsThis is then combined with results from the Clinical Neuropsychological Scale (MMSE) assessment to comprehensively evaluate the subject's susceptibility to AD. Our experimental validation on the publicly available OpenNeuro database underscores the efficacy of our approach. Notably, our proposed method attains an impressive 80.23% classification accuracy in distinguishing between AD, Frontotemporal dementia (FTD), and Normal Control (NC) subjects.DiscussionThis outcome outperforms prevailing state-of-the-art methodologies in EEG-based AD prediction. Furthermore, our methodology enables the visualization of salient regions within pathological images, providing invaluable insights for interpreting and analyzing AD predictions.</p

Role of Capsular Polysaccharides in Biofilm Formation: An AFM Nanomechanics Study

Bacteria form biofilms to facilitate colonization of biotic and abiotic surfaces, and biofilm formation on indwelling medical devices is a common cause of hospital-acquired infection. Although it is well-recognized that the exopolysaccharide capsule is one of the key bacterial components for biofilm formation, the underlying biophysical mechanism is poorly understood. In the present study, nanomechanical measurements of wild type and specific mutants of the pathogen, <i>Klebsiella pneumoniae</i>, were performed <i>in situ</i> using atomic force microscopy (AFM). Theoretical modeling of the mechanical data and static microtiter plate biofilm assays show that the organization of the capsule can influence bacterial adhesion, and thereby biofilm formation. The capsular organization is affected by the presence of type 3 fimbriae. Understanding the biophysical mechanisms for the impact of the structural organization of the bacterial polysaccharide capsule on biofilm formation will aid the development of strategies to prevent biofilm formation

Atomic Force Microscopy Reveals the Mechanobiology of Lytic Peptide Action on Bacteria

Increasing rates of antimicrobial-resistant medically important bacteria require the development of new, effective therapeutics, of which antimicrobial peptides (AMPs) are among the promising candidates. Many AMPs are membrane-active, but their mode of action in killing bacteria or in inhibiting their growth remains elusive. This study used atomic force microscopy (AFM) to probe the mechanobiology of a model AMP (a derivative of melittin) on living <i>Klebsiella pneumoniae</i> bacterial cells. We performed <i>in situ</i> biophysical measurements to understand how the melittin peptide modulates various biophysical behaviors of individual bacteria, including the turgor pressure, cell wall elasticity, and bacterial capsule thickness and organization. Exposure of <i>K. pneumoniae</i> to the peptide had a significant effect on the turgor pressure and Young’s modulus of the cell wall. The turgor pressure increased upon peptide addition followed by a later decrease, suggesting that cell lysis occurred and pressure was lost through destruction of the cell envelope. The Young’s modulus also increased, indicating that interaction with the peptide increased the rigidity of the cell wall. The bacterial capsule did not prevent cell lysis by the peptide, and surprisingly, the capsule appeared unaffected by exposure to the peptide, as capsule thickness and inferred organization were within the control limits, determined by mechanical measurements. These data show that AFM measurements may provide valuable insights into the physical events that precede bacterial lysis by AMPs