No abstract

Printed flexible electronics have emerged as versatile functional components of wearable intelligent devices that bridge the digital information networks with biointerfaces. Recent endeavors in plant wearable sensors provide real‐time and in situ insights to study phenotyping traits of crops, whereas monitoring of ethylene, the fundamental phytohormone, remains challenging due to the lack of flexible and scalable manufacturing of plant wearable ethylene sensors. Here the all‐MXene‐printed flexible radio frequency (RF) resonators are presented as plant wearable sensors for wireless ethylene detection. The facile formation of additive‐free MXene ink enables rapid, scalable manufacturing of printed electronics, demonstrating decent printing resolution (2.5% variation), ≈30000 S m−1 conductivity and mechanical robustness. Incorporation of MXene‐reduced palladium nanoparticles (MXene@PdNPs) facilitates 1.16% ethylene response at 1 ppm with 0.084 ppm limit of detection. The wireless sensor tags are attached on plant organ surfaces for in situ and continuously profiling of plant ethylene emission to inform the key transition of plant biochemistry, potentially extending the application of printed MXene electronics to enable real‐time plant hormone monitoring for precision agriculture and food industrial management.

IMPORTANCEDeep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis. OBJECTIVE To develop and clinically validate a deep learning system for automated identification and classification of 18 types of brain tumors from patient MRI data. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study was conducted using MRI data collected between 2000 and 2019 from 37 871 patients. A deep learning system for segmentation and classification of 18 types of intracranial tumors based on T1-and T2-weighted images and T2 contrast MRI sequences was developed and tested. The diagnostic accuracy of the system was tested using 1 internal and 3 external independent data sets. The clinical value of the system was assessed by comparing the tumor diagnostic accuracy of neuroradiologists with vs without assistance of the proposed system using a separate internal test data set. Data were analyzed from March 2019 through February 2020. MAIN OUTCOMES AND MEASURES Changes in neuroradiologist clinical diagnostic accuracy inbrain MRI scans with vs without the deep learning system were evaluated. RESULTS A deep learning system was trained among 37 871 patients (mean [SD] age, 41.6 [11.4] years; 18 519 women [48.9%]). It achieved a mean area under the receiver operating characteristic curve of 0.92 (95% CI, 0.84-0.99) on 1339 patients from 4 centers' data sets in diagnosis and classification of 18 types of tumors. Higher outcomes were found compared with neuroradiologists for accuracy and sensitivity and similar outcomes for specificity (for 300 patients in the Tiantan