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Abstract As the key component of wireless data transmission and powering, stretchable antennas play an indispensable role in flexible/stretchable electronics. However, they often suffer from frequency detuning upon mechanical deformations; thus, their applications are limited to wireless sensing with wireless transmission capabilities remaining elusive. Here, a hierarchically structured stretchable microstrip antenna with meshed patterns arranged in an arched shape showcases tunable resonance frequency upon deformations with improved overall stretchability. The almost unchanged resonance frequency during deformations enables robust on-body wireless communication and RF energy harvesting, whereas the rapid changing resonance frequency with deformations allows for wireless sensing. The proposed stretchable microstrip antenna was demonstrated to communicate wirelessly with a transmitter (input power of − 3 dBm) efficiently (i.e., the receiving power higher than − 100 dBm over a distance of 100 m) on human bodies even upon 25% stretching. The flexibility in structural engineering combined with the coupled mechanical–electromagnetic simulations, provides a versatile engineering toolkit to design stretchable microstrip antennas and other potential wireless devices for stretchable electronics. 

Abstract DNA replication occurs through an intricately regulated series of molecular events and is fundamental for genome stability 1,2 . At present, it is unknown how the locations of replication origins are determined in the human genome. Here we dissect the role of topologically associating domains (TADs) 3–6 , subTADs 7 and loops 8 in the positioning of replication initiation zones (IZs). We stratify TADs and subTADs by the presence of corner-dots indicative of loops and the orientation of CTCF motifs. We find that high-efficiency, early replicating IZs localize to boundaries between adjacent corner-dot TADs anchored by high-density arrays of divergently and convergently oriented CTCF motifs. By contrast, low-efficiency IZs localize to weaker dotless boundaries. Following ablation of cohesin-mediated loop extrusion during G1, high-efficiency IZs become diffuse and delocalized at boundaries with complex CTCF motif orientations. Moreover, G1 knockdown of the cohesin unloading factor WAPL results in gained long-range loops and narrowed localization of IZs at the same boundaries. Finally, targeted deletion or insertion of specific boundaries causes local replication timing shifts consistent with IZ loss or gain, respectively. Our data support a model in which cohesin-mediated loop extrusion and stalling at a subset of genetically encoded TAD and subTAD boundaries is an essential determinant of the locations of replication origins in human S phase. 

General movements (GMs) have been widely used for the early clinical evaluation of infant brain development, allowing immediate evaluation of potential development disorders and timely rehabilitation. The infants’ general movements can be captured digitally, but the lack of quantitative assessment and well‐trained clinical pediatricians presents an obstacle for many years to achieve wider deployment, especially in low‐resource settings. There is a high potential to explore wearable sensors for movement analysis due to outstanding privacy, low cost, and easy‐to‐use features. This work presents a sparse sensor network with soft wireless IMU devices (SWDs) for automatic early evaluation of general movements in infants. The sparse network consisting of only five sensor nodes (SWDs) with robust mechanical properties and excellent biocompatibility continuously and stably captures full‐body motion data. The proof‐of‐the‐concept clinical testing with 23 infants showcases outstanding performance in recognizing neonatal activities, confirming the reliability of the system. Taken together with a tiny machine learning algorithm, the system can automatically identify risky infants based on the GMs, with an accuracy of up to 100% (99.9%). The wearable sparse sensor network with an artificial intelligence‐based algorithm facilitates intelligent evaluation of infant brain development and early diagnosis of development disorders.