Systemic low-grade inflammation associated with specific depressive symptoms:insights from network analyses of five independent NHANES samples

To the editor:Major depressive disorder(MDD)is a heterogeneous disorder with varying symptom presentations and underlying biological mechanisms.1 The mainstream neurobiological hypotheses of depression involve monoamine neurotransmitters,hypothalamic-pituitary-adrenal axis,immune-inflammation and the glutamate system.

Numerical simulation of gap effect in supersonic flows

The gap effect is a key factor in the design of the heat sealing in super- sonic vehicles subjected to an aerodynamic heat load. Built on S-A turbulence model and Roe discrete format, the aerodynamic environment around a gap on the surface of a supersonic aircraft was simulated by the finite volume method. As the presented results indicate, the gap effect depends not only on the attack angle, but also on the Mach number.

Gas-phase synthesis of Ti_(2)CCl_(2) enables an efficient catalyst for lithiumsulfur batteries

MXenes have aroused intensive enthusiasm because of their exotic properties and promising applications.However,to date,they are usually synthesized by etching technologies.Developing synthetic technologies provides more opportunities for innovation and may extend unexplored applications.Here,we report a bottom-up gas-phase synthesis of Cl-terminated MXene(Ti_(2)CCl_(2)).The gas-phase synthesis endows Ti_(2)CCl_(2) with unique surface chemistry,high phase purity,and excellent metallic conductivity,which can be used to accelerate polysulfide conversion kinetics and dramatically prolong the cyclability of Li-S batteries.In-depth mechanistic analysis deciphers the origin of the formation of Ti_(2)CCl_(2) and offers a paradigm for tuning MXene chemical vapor deposition.In brief,the gas-phase synthesis transforms the synthesis of MXenes and unlocks the hardly achieved potentials of MXenes.

Skill Learning for Human-Robot Interaction Using Wearable Device

With the accelerated aging of the global population and escalating labor costs, more service robots are needed to help people perform complex tasks. As such, human-robot interaction is a particularly important research topic. To effectively transfer human behavior skills to a robot, in this study, we conveyed skill-learning functions via our proposed wearable device. The robotic teleoperation system utilizes interactive demonstration via the wearable device by directly controlling the speed of the motors. We present a rotation-invariant dynamicalmovement-primitive method for learning interaction skills. We also conducted robotic teleoperation demonstrations and designed imitation learning experiments. The experimental human-robot interaction results confirm the effectiveness of the proposed method.

Machine Learning-Based Multi-Modal Information Perception for Soft Robotic Hands

This paper focuses on multi-modal Information Perception(IP)for Soft Robotic Hands(SRHs)using Machine Learning(ML)algorithms.A flexible Optical Fiber-based Curvature Sensor(OFCS)is fabricated,consisting of a Light-Emitting Diode(LED),photosensitive detector,and optical fiber.Bending the roughened optical fiber generates lower light intensity,which reflecting the curvature of the soft finger.Together with the curvature and pressure information,multi-modal IP is performed to improve the recognition accuracy.Recognitions of gesture,object shape,size,and weight are implemented with multiple ML approaches,including the Supervised Learning Algorithms(SLAs)of K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Logistic Regression(LR),and the unSupervised Learning Algorithm(un-SLA)of K-Means Clustering(KMC).Moreover,Optical Sensor Information(OSI),Pressure Sensor Information(PSI),and Double-Sensor Information(DSI)are adopted to compare the recognition accuracies.The experiment results demonstrate that the proposed sensors and recognition approaches are feasible and effective.The recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higer than 85 percent for almost all combinations.Moreover,DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.