Minimum output variance control for FSN models: Continuous-time case

In this paper we consider the Finite Signal-to-Noise ratio model for linear stochastic systems. It is assumed that the intensity of noise corrupting a signal is proportional to the variance of the signal. Hence, the signal-to-noise ratio of each sensor and actuator is finite – as opposed to the infinite signal-to-noise ratio assumed in LQG theory. Computational errors in the controller implementation are treated similarly. The objective is to design a state feedback control law such that the closed loop system is mean square asymptotically stable and the output variance is minimized. The main result is a controller which achieves its maximal accuracy with finite control gains – as opposed to the infinite controls required to achieve maximal accuracy in LQG controllers. Necessary and sufficient conditions for optimality are derived. An optimal control law which involves the positive definite solution of a Riccati-like equation is derived. An algorithm for solving the Riccati-like equation is given and its convergence is guaranteed if a solution exists

DNet: distributional network for distributional individualized treatment effects

There is a growing interest in developing methods to estimate individualized treatment effects (ITEs) for various real-world applications, such as e-commerce and public health. This paper presents a novel architecture, called DNet, to infer distributional ITEs. DNet can learn the entire outcome distribution for each treatment, whereas most existing methods primarily focus on the conditional average treatment effect and ignore the conditional variance around its expectation. Additionally, our method excels in settings with heavy-tailed outcomes and outperforms state-of-the-art methods in extensive experiments on benchmark and real-world datasets. DNet has also been successfully deployed in a widely used mobile app with millions of daily active users

Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics

Feynman's path integral approach is to sum over all possible spatio-temporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous potential to reveal quantum processes in classical view. However, the complete characterization of quantum wave function with infinite paths is a formidable challenge, which greatly limits the application potential, especially in the strong-field physics and attosecond science. Instead of brute-force tracking every path one by one, here we propose deep-learning-performed strong-field Feynman's formulation with pre-classification scheme which can predict directly the final results only with data of initial conditions, so as to attack unsurmountable tasks by existing strong-field methods and explore new physics. Our results build up a bridge between deep learning and strong-field physics through the Feynman's path integral, which would boost applications of deep learning to study the ultrafast time-dependent dynamics in strong-field physics and attosecond science, and shed a new light on the quantum-classical correspondence

An Essential Role for RIG-I in Toll-like Receptor-Stimulated Phagocytosis

SummaryRetinoic acid-inducible gene-I (RIG-I) plays an important role in antiviral response by recognizing double-stranded RNA. Here we demonstrate an unanticipated role of RIG-I in Toll-like receptor (TLR)-stimulated phagocytosis. Stimulation with lipopolysaccharide (LPS), a ligand of TLR4, induced the expression of RIG-I in macrophages. Depletion of RIG-I by RNAi or gene targeting inhibited the LPS-induced phagocytosis of bacteria. Cellular processes involved in phagocytosis, such as small GTPase Cdc42/Rac1 activation, actin polymerization, and actin-regulator Arp2/3 recruitment, were also impaired in RIG-I-deficient macrophages activated by LPS. Moreover, RIG-I−/− mice were found to be more susceptible to infection with Escherichia coli as compared to wild-type mice. Thus, the regulatory functions of RIG-I are strikingly broad, including a role not only in antiviral responses but in antibacterial responses as well

Circulating exo-miR-154-5p regulates vascular dementia through endothelial progenitor cell-mediated angiogenesis

BackgroundVascular dementia (VaD) mainly results from cerebral vascular lesions and tissue changes, which contribute to neurodegenerative processes. Effective therapeutic approaches to targeting angiogenesis may reduce mortality of VaD. Endothelial progenitor cells (EPCs) play a key role in postnatal angiogenesis. Many exosomal microRNAs (exo-miRNAs) have been reported to involve in the development of dementia. The present study was designed to investigate whether the expression profile of the exo-miRNAs is significantly altered in patients with VaD and to reveal the function of differentially expressed miRNAs and the relevant mechanisms in EPC-mediated angiogenesis in VaD rat model.ResultsExosomes isolated from serum of patients with VaD (n = 7) and age-matched control subjects (n = 7), and miRNA sequencing and bioinformatics analysis found that circulating exosome miRNA-155-5p, miRNA-154-5p, miR-132-5p, and miR-1294 were upregulated in patients with VaD. The expression of miRNA-154-5p was further verified to be upregulated in clinical samples (n = 23) and 2-vessel occlusion-induced VaD rat model by reverse transcription quantitative PCR (RT-qPCR). Notably, miRNA-154-5p inhibition in bone marrow-EPCs (BM-EPCs) from VaD rats improved EPC functions, including tube formation, migration, and adhesion, and elevated concentrations of vascular endothelial growth factor (VEGF) and stromal cell-derived factor-1α (SDF-1α). The mRNA levels of ICAM-1, VCAM-1, and MCP-1 were reduced in miRNA-154-5p-inhibited EPCs. In addition, miRNA-154-5p inhibition increased the level of superoxide dismutase (SOD), and decreased reactive oxygen species (ROS) in EPCs. PRKAA2 was chosen as a promising target gene of miR-154-5p, and miRNA-154-5p inhibition upregulated the protein expression of AMPKα2. Furthermore, upregulation of miR-154-5p markedly diminished EPC functions and inhibited angiogenesis following EPC transplantation in VaD rats.ConclusionCirculating exo-miR-154-5p was upregulated in patients with VaD, and miR-154-5p upregulation was associated with impaired EPC functions and angiogenesis in VaD rat model. Therefore, miR-154-5p is a promising biomarker and therapeutic strategy for VaD

miR-100 Reverses Cisplatin Resistance in Breast Cancer by Suppressing HAX-1

Background/Aims: Breast cancer (BC) is the most common cancer in women worldwide. Despite great advancements in cancer therapy in recent years, surgery and chemotherapy are still the mainstays of BC treatment. However, cancer cells usually develop mechanisms to evade cell death induced by chemotherapy. Thus, strategies are needed to reverse the chemoresistance of cancer cells. Methods: We established cisplatin-resistant BC models in MDA-MB-231 and MCF-7 BC cell lines through long-term exposure to cisplatin. Quantitative reverse transcription PCR was used to examine the expression of microRNA (miR)-100. MTT cell viability assays were performed to determine cell viability. Regulation of hematopoietic cell-specific protein 1-associated protein X-l (HAX-1) targeted by miR-100 was confirmed by western blotting and luciferase reporter assays. The mitochondrial membrane potential and apoptosis were measured by flow cytometry. Release of cytochrome c from the mitochondria into the cytoplasm, HAX-1 expression, and activation of caspase-9 and caspase-3 were detected by western blotting. Results: A clear decrease in miR-100 expression was observed in cisplatin-resistant MDA-MB-231 and MCF-7 cells (MDA-MB-231/R and MCF-7/R). Overexpression of miR-100 increased the sensitivity of MDA-MB-231/R and MCF-7/R cells to cisplatin treatment and promoted cisplatin-induced mitochondrial apoptosis by targeting HAX-1 gene. Conclusions: MiR-100 targeted HAX-1 to increase the chemosensitivity of BC by mediating the mitochondrial apoptosis pathway