Deep feature learning of in-cylinder flow fields to analyze cycle-to-cycle variations in an SI engine

Machine learning (ML) models based on a large data set of in-cylinder flow fields of an IC engine obtained by high-speed particle image velocimetry allow the identification of relevant flow structures underlying cycle-to-cycle variations of engine performance. To this end, deep feature learning is employed to train ML models that predict cycles of high and low in-cylinder maximum pressure. Deep convolutional autoencoders are self-supervised-trained to encode flow field features in low dimensional latent space. Without the limitations ascribable to manual feature engineering, ML models based on these learned features are able to classify high energy cycles already from the flow field during late intake and the compression stroke as early as 290 crank angle degrees before top dead center (-290° CA) with a mean accuracy above chance level. The prediction accuracy from -290° CA to -10° CA is comparable to baseline ML approaches utilizing an extensive set of engineered features. Relevant flow structures in the compression stroke are revealed by feature analysis of ML models and are interpreted using conditional averaged flow quantities. This analysis unveils the importance of the horizontal velocity component of in-cylinder flows in predicting engine performance. Combining deep learning and conventional flow analysis techniques promises to be a powerful tool for ultimately revealing high-level flow features relevant to the prediction of cycle-to-cycle variations and further engine optimization

Adipobiology of the brain: From brain diabetes to adipose Alzheimer‘s disease

Accumulating evidence suggests that brain-adipose tissue bidirectional communications might be promising intervention point for cardiometabolic and neurodegenerative diseases (1). Alzheimer‘s disease (AD) is a progressive and yet incurable disorder characterized by memory loss and cognitive ability deterioration. It is the most common form of dementia, afflicting millions of humans globally. Although the progression of AD currently cannot be stopped or reversed, an increased understanding of its pathogenesis may give patients and their families chance for new therapies, which may at least delay the progress of the disease

Concept of metabotrophins: beginning and prospective growth

Since 2003, we have been focusing on metabotrophic factors (MTF), collectively named metabotrophins (MTs) (from Greek me- tabole – “a change”, and trophe – “nutrition”, means “nutritious for metabolism”). These are signaling proteins which improve glucose, lipid and energy metabolism, also affect positively cardiovascular and cognitive functions. They derived from various tissues, we focused on those secreted by adipose and skeletal muscle tissue. Examples include NGF, BDNF, NT-3, FGF21, GDF11, adiponectin, leptin, irisin, visfatin, meteorin, sirtuin-2, Klotho, etc. The present review highlights the beginning and perspective growth of our concept of a pivotal role of MTs in the pathogenesis and therapy of obesity-related cardiometabolic diseases (e.g., atherosclerosis, hypertension, obesity, type 2 diabetes mellitus and metabolic syndrome) and neurometabolic diseases (e.g., Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis)