The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 107 A/W and a specific detectivity of 2 × 1016 Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm2.

The mammalian gut, the site of digestion and nutrients absorption, harbors diverse microbes that play an essential role in maintaining physiological homeostasis of the gastrointestinal system. These commensal microbes are important for the normal development of the host immune system and alteration of the microbiota of gastrointestinal system has been found to play an important role in the development of obesity, metabolic syndromes such as type 2 diabetes, and cardiovascular diseases. Several recent studies with mouse models and in humans have demonstrated that intestinal microbiota has important role in host metabolism by regulating energy absorption and modulating the endocrine functions. A variety of nutrients and metabolites derived from commensal bacteria have been proved to be important regulators in improving gut barrier functions and immune homeostasis. Here we review current literature on the interactions between microbes and host in the Gastrointestinal (GI) tract and based on these interactions we proposed a hypothesis in which the microbiota interacts with the host gastrointestine through a gut-brainendocrine- immune system. By understanding this system, we should be in better position to develop treatment for metabolic diseases and inflammation in human and animals.

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Marrow adipogenesis is synchronized with bone loss in the development of GIOP, which was characterized by a significant increase in the number of small-sized marrow adipocytes in the relatively early stage and concomitant volume increase later on. MR spectroscopy appears to be the most powerful tool for detecting the sequential changes in marrow lipid content.

De novo drug design is a stationary way to build novel ligands in the confined pocket of receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation is a dynamical way to study the interaction mechanism between the ligands and receptors based on the molecular force field. De novo drug design and MD simulation are effective tools for novel drug discovery. With the development of technology, deep learning methods, and interpretable machine learning (IML) have emerged in the research area of drug design. Deep learning methods and IML can be used further to improve the efficiency and accuracy of de novo drug design and MD simulations. The application summary of deep learning methods for de novo drug design, MD simulations, and IML can further promote the technical development of drug discovery. In this article, two major workflow methods and the related components of classical algorithm and deep learning are described for de novo drug design from a new perspective. The application progress of deep learning is also summarized for MD simulations. Furthermore, IML is introduced for the deep learning model interpretability of de novo drug design and MD simulations. Our paper deals with an interesting topic about deep learning applications of de novo drug design and MD simulations for the scientific community. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

Artificial intelligence can train the related known drug data into deep learning models for drug design, while classical algorithms can design drugs through established and predefined procedures. Both deep learning and classical algorithms have their merits for drug design. Here, the webserver WADDAICA is built to employ the advantage of deep learning model and classical algorithms for drug design. The WADDAICA mainly contains two modules. In the first module, WADDAICA provides deep learning models for scaffold hopping of compounds to modify or design new novel drugs. The deep learning model which is used in WADDAICA shows a good scoring power based on the PDBbind database. In the second module, WADDAICA supplies functions for modifying or designing new novel drugs by classical algorithms. WADDAICA shows better Pearson and Spearman correlations of binding affinity than Autodock Vina that is considered to have the best scoring power. Besides, WADDAICA supplies a friendly and convenient web interface for users to submit drug design jobs. We believe that WADDAICA is a useful and effective tool to help researchers to modify or design novel drugs by deep learning models and classical algorithms. WADDAICA is free and accessible at https://bqflab.github.io or https://heisenberg.ucam.edu:5000 .

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Objective This study aimed to investigate the predictive value of inflammatory cells in peripheral blood on the prognosis of patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI). Methods Patients (n=1558) were consecutively enrolled and the median follow-up was 1142 days. Patients were divided into the major adverse cardiac events (MACE) 1 group (n=63) (all-cause mortality [n=58] and rehospitalization for severe heart failure [n=5], no MACE1 group (n=1495), MACE2 group (n=38) (cardiac mortality [n=33] and rehospitalization for severe heart failure [n=5]), and no MACE2 group (n=1520). The neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) were analyzed. Results The NLR, MLR, and PLR were higher in the MACE groups than in the no MACE groups. Different subsets of inflammatory cells had similar diagnostic values for MACE. Kaplan–Meier curves showed that the survival time gradually decreased with an increase in the degree of risk as determined by the NLR, MLR, and PLR. The risk of MACE was highest in the extremely high-risk group. Conclusion Peripheral blood inflammatory cell subsets can predict MACE in patients with ACS undergoing PCI. These cell subsets could be important laboratory markers for the prognosis and clinical treatment of these patients.