Research Progress of Improving MDD by Fecal Microbiota Transplantation Affecting Intestinal Microbiota-Gut- Brain Axis

It has been improved that fecal microbiota transplantation (FMT) can alleviate gastrointestinal disorders such as Clostridium difficile infection (CDI). Moreover, some studies have also concluded that FMT is available in alleviating Major Depressive Disorder (MDD), also known as depression widely, by regulating microbiota-gut-brain axis (MGBA), hence, this paper summarized the relationship between MGBA and MDD and mechanisms of MDD which is related with MGBA. And this review retrospected the animal experiments and clinical studies on the treatment of depression with FMT in recent years and discussed the future development of FMT, in order to assess whether FMT is potential and credible in the treatment of depression

A magnetic field sensor based on a dual S-tapered multimode fiber interferometer

A multimode interferometer (MMI) for the measurement of a magnetic field based on concatenated S-tapered fibers is proposed and experimentally demonstrated. Spectrally interrogated magnetic field sensing is achieved by integrating the proposed MMI with magnetic fluids. The magnetic sensitivity of the MMI reaches 0.011 dB Oe?1. Owing to its desirable advantages such as compactness, low cost, fast response and flexible structure, the proposed MMI is anticipated to find potential applications in in situ measurements of the magnetic field

The long noncoding RNA lncNB1 promotes tumorigenesis by interacting with ribosomal protein RPL35

The majority of patients with neuroblastoma due to MYCN oncogene amplification and consequent N-Myc oncoprotein over-expression die of the disease. Here our analyses of RNA sequencing data identify the long noncoding RNA lncNB1 as one of the transcripts most over-expressed in MYCN-amplified, compared with MYCN-non-amplified, human neuroblastoma cells and also the most over-expressed in neuroblastoma compared with all other cancers. lncNB1 binds to the ribosomal protein RPL35 to enhance E2F1 protein synthesis, leading to DEPDC1B gene transcription. The GTPase-activating protein DEPDC1B induces ERK protein phosphorylation and N-Myc protein stabilization. Importantly, lncNB1 knockdown abolishes neuroblastoma cell clonogenic capacity in vitro and leads to neuroblastoma tumor regression in mice, while high levels of lncNB1 and RPL35 in human neuroblastoma tissues predict poor patient prognosis. This study therefore identifies lncNB1 and its binding protein RPL35 as key factors for promoting E2F1 protein synthesis, N-Myc protein stability and N-Myc-driven oncogenesis, and as therapeutic targets

AI hardware for neuromorphic computing applications – memory device fabrication and characteristics

In this project, an overview of the field of both conventional and emerging memory technologies is provided. Then, a novel Resistive Random Access Memory (RRAM) is proposed to design and fabricate. Besides, RRAM devices under different parameters are tested and the performance test results are analysed. The presented RRAM is one kind of three-terminal electronic synapse device. The main innovative part of the device is the use of chalcogenide material, Ge2Sb2Te5 (GST), instead of traditional transition metal oxide (TMO). Under the applied electric field, GST acts as an adjustable conductive path by allowing small metal atoms like Ag to diffuse in. By changing the structure and thickness of the electrodes and GST layer, devices with analog resistance switching characteristics and different electrical properties are investigated. Eventually, a high on/off ratio (~20) of the device with good linearity for conductance updates is achieved. Due to the superiority of good linearity of conductance update and low power consumption, this new type of RRAM is supposed to have a promising future with a wide range of application prospects, such as In-Memory Computation, Neuromorphic Computing, Security Applications, and Non-volatile SRAM. Keywords: Resistive Random Access Memory (RRAM), three-terminal electronic synapse devices, Ge2Sb2Te5 (GST), Neuromorphic ComputingMaster of Science (Green Electronics

Iterative Design Algorithm for Robust Disturbance-Rejection Control

An iterative design algorithm is developed for robust disturbance–rejection control of uncertain systems with time-varying parameter perturbations in this paper. For more design degrees of freedom, a generalized equivalent-input-disturbance estimator is adopted to approximate the effect of both disturbances and uncertainties. By the bound real lemma, the H∞ norm is used to evaluate the robust disturbance–rejection performance of the closed-loop uncertain system. To avoid the constraints introduced by the widely used commutative condition, the control gains are divided into two groups and calculated by steps. Further, two robust quadratic stability conditions are derived, and an iterative design algorithm is developed to optimize the robust H∞ disturbance–rejection performance. Finally, the effectiveness and advantages of the developed method are demonstrated by a case study of a suspension system of modern vehicles

Jakarta

Jakarta, Indonesi

Deep learning for highly efficient curvature recognition using fiber scattering speckles

A flexible fiber-optic sensor enabled by deep learning is proposed and experimentally demonstrated for highly efficient curvature sensing application. This sensing modulation system combines a deep optical neural network based on a small training dataset, aiming to simplify speckle data capture and sensor model evaluation. The multimode fiber concatenated with a section of single stress-applying fiber serves as a sensing unit as well as an image transport medium. A type of hybrid scattering speckle images is collected and employed to provide more freedom to identify the bending curvature with and without external disturbances. In a perturbed environment, the trained optical classification model is suitable for the speckle dataset recognition with high accuracy rate of 98.3%. Moreover, the deep-learning-enabled fiber curvature sensor shows great potential for practical applications in real-time structural safety test, including studies on health monitoring of infrastructure equipment and aerospace wings

Study on the Coupling Effect of a Solar-Coal Unit Thermodynamic System with Carbon Capture

Based on the structural theory of thermo-economics, a 600 MW unit was taken as an example. An integration system which uses fuel gas heat and solar energy as a heat source for post-combustion carbon capture was proposed. The physical structure sketch and productive structure sketch were drawn and a thermo-economics model and cost model based on the definition of fuel-product were established. The production relation between units was analyzed, and the composition and distribution of the exergy cost and thermo-economic cost of each unit were studied. Additionally, the influence of the fuel price and equipment investment cost of the thermo-economic cost for each product was studied. The results showed that the main factors affecting the unit cost are the fuel exergy cost, component exergy efficiency, and irreversible exergy cost of each unit, and the main factors affecting the thermo-economics cost are the specific irreversible exergy cost and investment exergy cost. The main factors affecting the thermal economics of solar energy collectors and low-pressure economizers are the invested exergy cost, negentropy exergy cost, and irreversible exergy cost of each unit