Motico: An attentional mechanism network model for smart aging disease risk prediction based on image data classification.

The current disease risk prediction model with many parameters is complex to run smoothly on mobile terminals such as tablets and mobile phones in imaginative elderly care application scenarios. In order to further reduce the number of parameters in the model and enable the disease risk prediction model to run smoothly on mobile terminals, we designed a model called Motico (An Attention Mechanism Network Model for Image Data Classification). During the implementation of the Motico model, in order to protect image features, we designed an image data preprocessing method and an attention mechanism network model for image data classification. The Motico model parameter size is only 5.26 MB, and the memory only takes up 135.69 MB. In the experiment, the accuracy of disease risk prediction was 96 %, the precision rate was 97 %, the recall rate was 93 %, the specificity was 98 %, the F1 score was 95 %, and the AUC was 95 %. This experimental result shows that our Motico model can implement classification prediction based on the image data classification attention mechanism network on mobile terminals.

Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State-of-the-Art and Future Directions.

Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each have unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, the combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with the hope of arousing the research community's interest in this emerging field.

Inflammatory responses and histopathological changes in a mouse model of Staphylococcus aureus-induced bloodstream infections.

INTRODUCTION: Staphylococcus aureus-induced bloodstream infections (BSIs) remain a prevalent clinical challenge and the underlying pathogenesis is still poorly understood. The aim of this study was to investigate the inflammatory responses and histopathological changes in BSIs in mice. METHODOLOGY: Male C57BL/6 mice were inoculated with S. aureus intravenously to induce BSIs. The survival rate, weight loss, and murine sepsis scores (MSS) were monitored in BSI and phosphate-buffered saline (PBS) control mice. Blood samples and tissue homogenates were plated on agar plates to determine the bacterial burden. Inflammatory proteins and cytokines were determined by enzyme-linked immunosorbent assay (ELISA) kits. Histopathologic changes were assessed by pathological inflammation score (PIS) and macroscopic and microscopic examinations. RESULTS: BSI mice induced by 4.5 × 108 CFU/mL S. aureus showed ~70% survival rate, higher sepsis scores, significantly decreased body weight, elevated levels of white blood cell (WBC) counts, C-reactive protein (CRP), procalcitonin (PCT), interleukin (IL)-1ß, IL-6, and tumor necrosis factor (TNF)-α. Prominent correlations were found between elevated CRP and PCT levels as well as among IL-1ß, IL-6, and TNF-α. Pathological changes and higher PIS were also observed in BSI mice. CONCLUSIONS: Our results demonstrate that inflammatory proteins (PCT and CRP) and cytokines (IL-6, IL-1ß and TNF-α) play an important role in the inflammatory responses and histopathological changes in S. aureus-induced BSIs.