State identification of Parkinson's disease based on transfer learning.

Background: The local field potential (LFP) signals are a vital signal for studying the mechanisms of deep brain stimulation (DBS) and constructing adaptive DBS containing information related to the motor symptoms of Parkinson's disease (PD).

Objective: A Parkinson's disease state identification algorithm based on the feature extraction strategy of transfer learning was proposed.

Methods: The algorithm uses continuous wavelet transform (CWT) to convert one-dimensional LFP signals into two-dimensional gray-scalogram images and color images respectively, and designs a Bayesian optimized random forest (RF) classifier to replace the three fully connected layers for the classification task in the VGG16 model, to realize automatic identification of the pathological state of PD patients.

A sensitive enzyme-free electrochemical sensor based on ZnWO@co-MNPC@MIP for specific recognition and determination of chloramphenicol in milk sample.

We have carefully built a new chloramphenicol (CAP) electrochemical sensor, which takes the zinc tungstate @ cobalt magnetic nanoporous carbon @ molecularly imprinted polymer (ZnWO@Co-MNPC@MIP) as the core. First, we successfully prepared Co-MNPC nanomaterials using an efficient one-step hydrothermal method and a direct carbonization method. Next, we recombined ZnWO with Co-MNPC and synthesized the completely new ZnWO@Co-MNPC complex by using the hydrothermal method.

Carrier Tuning in ZnSnN by Forming Amorphous and Microcrystalline Phases.

ZnSnN (ZTN), an earth-abundant element semiconductor, is a potential candidate for photovoltaic applications. However, the excessively high n-type carrier concentration caused by intrinsic defects hinders its progress. In this work, a series of ZnSnN thin films are fabricated by RF-magnetron sputtering deposition.