A fully integrated, standalone stretchable device platform with in-sensor adaptive machine learning for rehabilitation.

Post-surgical treatments of the human throat often require continuous monitoring of diverse vital and muscle activities. However, wireless, continuous monitoring and analysis of these activities directly from the throat skin have not been developed. Here, we report the design and validation of a fully integrated standalone stretchable device platform that provides wireless measurements and machine learning-based analysis of diverse vibrations and muscle electrical activities from the throat. We demonstrate that the modified composite hydrogel with low contact impedance and reduced adhesion provides high-quality long-term monitoring of local muscle electrical signals. We show that the integrated triaxial broad-band accelerometer also measures large body movements and subtle physiological activities/vibrations. We find that the combined data processed by a 2D-like sequential feature extractor with fully connected neurons facilitates the classification of various motion/speech features at a high accuracy of over 90%, which adapts to the data with noise from motion artifacts or the data from new human subjects. The resulting standalone stretchable device with wireless monitoring and machine learning-based processing capabilities paves the way to design and apply wearable skin-interfaced systems for the remote monitoring and treatment evaluation of various diseases.

Stretchable and anti-impact iontronic pressure sensor with an ultrabroad linear range for biophysical monitoring and deep learning-aided knee rehabilitation.

Monitoring biophysical signals such as body or organ movements and other physical phenomena is necessary for patient rehabilitation. However, stretchable flexible pressure sensors with high sensitivity and a broad range that can meet these requirements are still lacking. Herein, we successfully monitored various vital biophysical features and implemented in-sensor dynamic deep learning for knee rehabilitation using an ultrabroad linear range and high-sensitivity stretchable iontronic pressure sensor (SIPS). We optimized the topological structure and material composition of the electrode to build a fully stretching on-skin sensor. The high sensitivity (12.43 kPa-1), ultrabroad linear sensing range (1 MPa), high pressure resolution (6.4 Pa), long-term durability (no decay after 12000 cycles), and excellent stretchability (up to 20%) allow the sensor to maintain operating stability, even in emergency cases with a high sudden impact force (near 1 MPa) applied to the sensor. As a practical demonstration, the SIPS can positively track biophysical signals such as pulse waves, muscle movements, and plantar pressure. Importantly, with the help of a neuro-inspired fully convolutional network algorithm, the SIPS can accurately predict knee joint postures for better rehabilitation after orthopedic surgery. Our SIPS has potential as a promising candidate for wearable electronics and artificial intelligent medical engineering owing to its unique high signal-to-noise ratio and ultrabroad linear range. An ultrabroad-linear range (1 MPa) iontronic pressure sensor with superior sensitivity (12.43 kPa-1) and stretchability (up to 20%) was proposed for biophysical monitoring and deep learning-based knee-rehabilitation training.

Molecular mapping of a powdery mildew resistance gene in common wheat landrace Baihulu and its allelism with Pm24.

Powdery mildew is one of the most devastating diseases of wheat in areas with cool and maritime climates. Chinese wheat landrace Baihulu confers a high level of resistance against a wide range of Blumeria graminis DC f. sp. tritici (Bgt) races, especially those currently prevailing in Shaanxi. The objectives of this study were to determine the chromosome bin location of the mlbhl gene from Baihulu and its allelism with Pm24. To investigate the inheritance of powdery mildew resistance and detect adjacent molecular markers, we constructed a segregating population of 301 F(2) plants and corresponding F(2:3) families derived from Baihulu/Shaanyou 225. Genetic analysis revealed that a single dominant gene was responsible for seedling stage powdery mildew resistance in Baihulu. A genetic map comprising Xgwm106, Xgwm337, Xgwm1675, Xgwm603, Xgwm789, Xbarc229, Xgpw4503, Xcfd72, Xcfd83, Xcfd59, Xcfd19, and mlbhl spanned 28.2 cM on chromosome 1D. Xgwm603/Xgwm789 and Xbarc229 were flanking markers tightly linked to mlbhl at genetic distances of 1.5 and 1.0 cM, respectively. The mlbhl locus was located in chromosome bin 1DS 0.59-1.00 delimited by the SSR markers Xgwm337 and Xbarc229. When tested with a differential array of 23 Bgt isolates Baihulu displayed a response pattern that was clearly distinguishable from that of Chiyacao and varieties or lines possessing documented Pm genes. Allelism analysis indicated that mlbhl is a new gene, either allelic or closely linked with Pm24. The new gene was designated Pm24b.