Frontiers Approaches to the Diagnosis of Thrips (Thysanoptera): How Effective Are the Molecular and Electronic Detection Platforms?

Thrips are insect pests of economically important agricultural, horticultural, and forest crops. They cause damage by sucking plant sap and by transmitting several tospoviruses, ilarviruses, carmoviruses, sobemoviruses, and machlomoviruses. Accurate and timely identification is the key to successful management of thrips species. However, their small size, cryptic nature, presence of color and reproductive morphs, and intraspecies genetic variability make the identification of thrips species challenging. The use of molecular and electronic detection platforms has made thrips identification rapid, precise, sensitive, high throughput, and independent of developmental stages. Multi-locus phylogeny based on mitochondrial, nuclear, and other markers has resolved ambiguities in morphologically indistinguishable thrips species. Microsatellite, RFLP, RAPD, AFLP, and CAPS markers have helped to explain population structure, gene flow, and intraspecies heterogeneity. Recent techniques such as LAMP and RPA have been employed for sensitive and on-site identification of thrips. Artificial neural networks and high throughput diagnostics facilitate automated identification. This review also discusses the potential of pyrosequencing, microarrays, high throughput sequencing, and electronic sensors in delimiting thrips species.

Nanoribbon-Based Electronic Detection of a Glioma-Associated Circular miRNA

Nanoribbon chips, based on “silicon-on-insulator” structures (SOI-NR chips), have been fabricated. These SOI-NR chips, whose surface was sensitized with covalently immobilized oligonucleotide molecular probes (oDNA probes), have been employed for the nanoribbon biosensor-based detection of a circular ribonucleic acid (circRNA) molecular marker of glioma in humans. The nucleotide sequence of the oDNA probes was complimentary to the sequence of the target oDNA. The latter represents a synthetic analogue of a glioma marker—NFIX circular RNA. In this way, the detection of target oDNA molecules in a pure buffer has been performed. The lowest concentration of the target biomolecules, detectable in our experiments, was of the order of ~10−17 M. The SOI-NR sensor chips proposed herein have allowed us to reveal an elevated level of the NFIX circular RNA in the blood of a glioma patient.

Adoption of Photoelectric Sensor in Automobile Electronic Detection

The continuous improvement of auto-related electronic detection technology has become a new direction of the development of the automobile industry. First, the ZigBee technology is compared with other wireless communication technologies, and the advantages of ZigBee technology are analyzed. Then it is used in automobile electronic detection jointly with photoelectric sensor. The wireless sensor network (WSN) composed of photoelectric sensor is introduced into automobile electronic detection platform, and a set of information acquisition system for automobile electronic detection based on ZigBee technology is designed. In the hardware field, CC2520 chip is selected as the main data transmission chip according to the actual needs of the system. The ZigBee node with CC2520 and photoelectric sensor as the core architecture is mainly discussed. At the same time, the anti-interference design of the node is added, so that the node has the ability to collect information such as tire pressure, speed, acceleration, temperature, and humidity. In the software field, IAR Embedded Workbench is used to debug the program of ZigBee node based on CC2520+ photoelectric sensor, so as to establish coordination points and optimize network performance. During the experiment, it is necessary to build a WSN based on photoelectric sensor and ZigBee technology. ZigBee nodes have fast response speed, accurate data return, and improved transmission speed. The WSN based on photoelectric sensor +ZigBee technology can maintain good communication quality and low packet loss rate. After applying it to speed information collection, the results further verify the accuracy of data collection of WSN established in this study.