It is a great challenge to fabricate piezoresistive sensors that possess high elasticity, large-area compliance, and excellent detectability to satisfy both extremely tiny and large human activity monitoring. Herein, a novel and facile strategy is reported to manufacture highly elastic channel crack-based gold@PU sponge piezoresistive material. The elastic 3D conductive network is successfully prepared by gold ion sputtering, and channel cracks are skillfully designed on the 3D sponge skeletons. Such novel structure makes these fabricated sensors are capable of monitoring both tiny and large human motions, which originate from the nanocrack joint sensing mechanism and physical contact of conductive interconnected network. Meanwhile, our sensors possess excellent elasticity, fast response time (9 ms), and ultralow detection limit (0.568 Pa), as well as good reproducibility over 1000 cycles. The desirable elasticity of channel crack-based gold@PU sensor is comparable to recently reported pressure sensors, together with advantages of reliable fabrication and large-area compliance, makes them attractive in various electronic devices, for example, biological health monitoring, sport performance monitoring, and man-machine interfaces.

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.

Ionic liquids (ILs) are regarded as ideal components in the next generation of strain sensors because their ultralow modulus can commendably circumvent or manage the mechanical mismatch in traditional strain sensors. In addition to strain sensors, stretchable conductors with a strain-insensitive conductance are also indispensable in artificial systems for connecting and transporting electrons, similar to the function of blood vessels in the human body. In this work, two types of ILs-based conductive fibers were fabricated by developing hollow fibers with specific microscale channels, which were then filled with ILs. Typically, the ILs-based fiber with straight microchannels exhibited a high strain sensitivity and simultaneously rapid responses to strain, pressure, and temperature. The other ILs-based fiber with helical microchannels exhibited a good strain-isolate conductance under strain. Due to the high transparency of ILs along with the sealing process, the as-prepared ILs-based fibers are both highly transparent and waterproof. More importantly, owing to the low modulus of ILs and the core-shell structure, both conductive fiber prototypes demonstrated a high durability (>10 000 times) and a long-term stability (>4 months). Ultimately, the ILs-based fibrous sensors were successfully woven into gloves, flaunting the ability to detect human breathing patterns, sign language, hand gestures, and arm motions. The ILs-based strain-insensitive fibers were successfully applied in stretchable wires as well.

A liquid metal fiber with low modulus, high conductivity, and that is hysteresis-free is fabricated and serves as a high-performance fiber strain sensor.

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.