The Binding Behavior of Daptomycin on the Bacterial Membranes

Daptomycin (DAP) is a cyclic anionic lipopeptide antibiotic that kills gram-positive bacteria via cell membrane distortion. It is currently approved for treatment of complicated infections caused by gram-positive bacteria, including methicillin-resistant Staphylococcus aureus, vancomycin-resistant S. aureus, coagulase-negative staphylococci, penicillin-resistant streptococci and vancomycin-resistant enterococci. Recent studies showed that the bactericidal activity of DAP on the target membrane is dependent on Ca2+. DAP is also associated with membrane depolarization in the presence of phosphatidylglycerol (PG). Therefore, in this study, we aimed to investigate the binding behavior of DAP on the membrane of S. aureus cells as well as the specific interaction of DAP with target molecules. We first used highly inclined and laminated optical sheet (HILO) microscopy to visualize DAP location on the membrane of S. aureus cells. Then, we quantitatively analyzed DAP distribution on S. aureus cell membranes and its correlation with cell size and aggregate formation in a time- and concentration-dependent manner. We observed septum binding for the concentrations lower than the minimum inhibitory concentration (MIC) of DAP and for the concentration around the MIC until 10 min of incubation. However, overall membrane binding of DAP occurred at longer incubation times and higher DAP concentrations. This result was further supported by the super-resolution imaging of the localization of single DAP molecules on the membrane of S. aureus. We found that DAP accumulation correlated negatively with cell size but positively with aggregate formation. Thus, we further examined the colocalization of 5(6)-TAMRA-X, SE-labeled DAP (DAP-TMR) with the FtsW-GFP fusion protein and lipid II. FtsW is a bacterial cell division protein, which is positioned at the septum. For the short incubation interval, DAP-TMR localized to the septum and was colocalized with FtsW-GFP. For incubation times, DAP bound to the complete cell membrane but the distribution of FtsW-GFP remained unaffected. Furthermore, in cells stained with a BODIPY FL conjugate of vancomycin (Van-BDP FL), considerably less binding of DAP-TMR occurred, indicating that Van-BDP FL prevented the binding of DAP and that lipid II might be the target molecule of DAP. Finally, we used fluid supported lipid bilayers to study the binding behavior of DAP on membranes with different lipid compositions. Bilayers were prepared on coverslips by vesicle fusion. The neutral phosphatidylcholine phospholipids were used as the matrix to which PG or/and bactoprenol lipids (C55-PP, C55-P, lipid II) were added. PG as well as the three bactoprenol lipids enhanced the binding of DAP. Surprisingly, addition of PG in bactoprenol-containing membranes significantly strengthened DAP binding, indicating that the bactoprenol lipids affect the binding of DAP and that the combination of PG and bactoprenol lipids is critical for the bactericidal mechanism of DAP. This explains the preferential binding of DAP to the septum. Our findings describe a new model for the mechanism of action of DAP

EENED: End-to-End Neural Epilepsy Detection based on Convolutional Transformer

Recently Transformer and Convolution neural network (CNN) based models have shown promising results in EEG signal processing. Transformer models can capture the global dependencies in EEG signals through a self-attention mechanism, while CNN models can capture local features such as sawtooth waves. In this work, we propose an end-to-end neural epilepsy detection model, EENED, that combines CNN and Transformer. Specifically, by introducing the convolution module into the Transformer encoder, EENED can learn the time-dependent relationship of the patient's EEG signal features and notice local EEG abnormal mutations closely related to epilepsy, such as the appearance of spikes and the sprinkling of sharp and slow waves. Our proposed framework combines the ability of Transformer and CNN to capture different scale features of EEG signals and holds promise for improving the accuracy and reliability of epilepsy detection. Our source code will be released soon on GitHub.Comment: Accepted by IEEE CAI 202

MgNO: Efficient Parameterization of Linear Operators via Multigrid

In this work, we propose a concise neural operator architecture for operator learning. Drawing an analogy with a conventional fully connected neural network, we define the neural operator as follows: the output of the $i$-th neuron in a nonlinear operator layer is defined by $\mathcal O_i(u) = \sigma\left( \sum_j \mathcal W_{ij} u + \mathcal B_{ij}\right)$. Here, $\mathcal W_{ij}$ denotes the bounded linear operator connecting $j$-th input neuron to $i$-th output neuron, and the bias $\mathcal B_{ij}$ takes the form of a function rather than a scalar. Given its new universal approximation property, the efficient parameterization of the bounded linear operators between two neurons (Banach spaces) plays a critical role. As a result, we introduce MgNO, utilizing multigrid structures to parameterize these linear operators between neurons. This approach offers both mathematical rigor and practical expressivity. Additionally, MgNO obviates the need for conventional lifting and projecting operators typically required in previous neural operators. Moreover, it seamlessly accommodates diverse boundary conditions. Our empirical observations reveal that MgNO exhibits superior ease of training compared to other CNN-based models, while also displaying a reduced susceptibility to overfitting when contrasted with spectral-type neural operators. We demonstrate the efficiency and accuracy of our method with consistently state-of-the-art performance on different types of partial differential equations (PDEs)

Mitigating spectral bias for the multiscale operator learning with hierarchical attention

Neural operators have emerged as a powerful tool for learning the mapping between infinite-dimensional parameter and solution spaces of partial differential equations (PDEs). In this work, we focus on multiscale PDEs that have important applications such as reservoir modeling and turbulence prediction. We demonstrate that for such PDEs, the spectral bias towards low-frequency components presents a significant challenge for existing neural operators. To address this challenge, we propose a hierarchical attention neural operator (HANO) inspired by the hierarchical matrix approach. HANO features a scale-adaptive interaction range and self-attentions over a hierarchy of levels, enabling nested feature computation with controllable linear cost and encoding/decoding of multiscale solution space. We also incorporate an empirical $H^1$ loss function to enhance the learning of high-frequency components. Our numerical experiments demonstrate that HANO outperforms state-of-the-art (SOTA) methods for representative multiscale problems

The Effect Of Physician Ownership On Quality Of Care For Outpatient Procedures

Ambulatory surgery centers (ASCs) play an important role in providing surgical and diagnostic services in an outpatient setting. They can be owned by physicians who staff them. Previous studies focused on patient “cherry picking” and over-utilization of services due to physician ownership. Few studies examined the relationship between physician ownership and quality of care. Using a retrospective cohort of patients who underwent colonoscopy, this study examined the effect of physician ownership of ASCs on the occurrence of adverse events after outpatient colonoscopy. Agency theory is used to as a conceptual framework. Depending on the extent to which consumers are able to assess quality of care differences across health care settings, physician ownership can function as a mechanism to improve quality or as a deterrent to quality. Four adverse event measures are used in this study: same day ED visit or hospitalization, 30-day serious gastrointestinal events resulting in ED visit or hospitalization, 30-day other gastrointestinal events resulting in ED visit or hospitalization, and 30-day non-gastrointestinal events resulting in ED visit or hospitalization. Physician ownership status is determined based on a court decision in California in 2007. Data sources include the State Ambulatory Surgery Databases (SASD), State Inpatient Databases (SID), Emergency Department Databases (SEDD), State Utilization Data Files, the Area Resource File (ARF), and HMO/PPO data from Health Leaders. After controlling for confounding factors, the study found that colonoscopy patients treated at a physician-owned ASC had similar odds of experiencing same day ED visit or hospitalization and 30-day non-gastrointestinal events resulting in ED visit or hospitalization as those treated in a hospital-based outpatient facility. But the former had significantly higher odds of experiencing 30-day serious gastrointestinal events and 30-day other gastrointestinal events resulting in ED visit or hospitalization. The results are robust to changes in propensity score adjustment approach and to the inclusion of a lagged quality indicator. They suggest that physician ownership of ASCs was not associated with better quality of care for colonoscopy patients. As more complex procedures are shifted from hospital-based outpatient facilities to ASCs, expanded efforts to monitor and report quality of care will be worthwhile