No abstract

The paper examines the potential of deep learning to support decisions in financial risk management.We develop a deep learning model for predicting whether individual spread traders secure profits from future trades. This task embodies typical modeling challenges faced in risk and behavior forecasting.Conventional machine learning requires data that is representative of the feature-target relationship and relies on the often costly development, maintenance, and revision of handcrafted features. Consequently, modeling highly variable, heterogeneous patterns such as trader behavior is challenging. Deep learning promises a remedy. Learning hierarchical distributed representations of the data in an automatic manner (e.g. risk taking behavior), it uncovers generative features that determine the target (e.g., trader's profitability), avoids manual feature engineering, and is more robust toward change (e.g. dynamic market conditions). The results of employing a deep network for operational risk forecasting confirm the feature learning capability of deep learning, provide guidance on designing a suitable network architecture and demonstrate the superiority of deep learning over machine learning and rule-based benchmarks.

Newer methods to address errors in intraocular lens power calculations after keratorefractive surgery represent a paradigm shift from the previous gold standard of the clinical history method. Understanding the advantages and limitations of the various methods may be beneficial in obtaining more accurate estimations of the intraocular lens power after corneal refractive surgery, resulting in improved visual outcomes.

Objective: Atherosclerotic lesions are often characterized by accumulation of OxLDL (oxidized low-density lipoprotein), which is associated with vascular inflammation and lesion vulnerability to rupture. Extracellular AIBP (apolipoprotein A-I binding protein; encoded by APOA1BP gene), when secreted, promotes cholesterol efflux and regulates lipid rafts dynamics, but its role as an intracellular protein in mammalian cells remains unknown. The aim of this work was to determine the function of intracellular AIBP in macrophages exposed to OxLDL and in atherosclerotic lesions. Approach and Results: Using a novel monoclonal antibody against human and mouse AIBP, which are highly homologous, we demonstrated robust AIBP expression in human and mouse atherosclerotic lesions. We observed significantly reduced autophagy in bone marrow-derived macrophages, isolated from Apoa1bp −/− compared with wild-type mice, which were exposed to OxLDL. In atherosclerotic lesions from Apoa1bp −/− mice subjected to Ldlr knockdown and fed a Western diet, autophagy was reduced, whereas apoptosis was increased, when compared with that in wild-type mice. AIBP expression was necessary for efficient control of reactive oxygen species and cell death and for mitochondria quality control in macrophages exposed to OxLDL. Mitochondria-localized AIBP, via its N-terminal domain, associated with E3 ubiquitin-protein ligase PARK2 (Parkin), MFN (mitofusin)1, and MFN2, but not BNIP3 (Bcl2/adenovirus E1B 19-kDa-interacting protein-3), and regulated ubiquitination of MFN1 and MFN2, key components of mitophagy. Conclusions: These data suggest that intracellular AIBP is a new regulator of autophagy in macrophages. Mitochondria-localized AIBP augments mitophagy and participates in mitochondria quality control, protecting macrophages against cell death in the context of atherosclerosis.