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Abstract Type 1 diabetes (T1D) is a chronic disease characterized by self‐destruction of insulin‐producing pancreatic β cells by cytotoxic T cell activity. However, the pathogenic mechanism of T cell infiltration remains obscure. Recently, tissue‐resident memory T (TRM) cells have been shown to contribute to cytotoxic T cell recruitment. TRM cells are found present in human pancreas and are suggested to modulate immune homeostasis. Here, the role of TRM cells in the development of T1D is investigated. The presence of TRM cells in pancreatic islets is observed in non‐obese diabetic (NOD) mice before T1D onset. Mechanistically, elevated fatty acid‐binding protein 4 (FABP4) potentiates the survival and alarming function of TRM cells by promoting fatty acid utilization and C‐X‐C motif chemokine 10 (CXCL10) secretion, respectively. In NOD mice, genetic deletion of FABP4 or depletion of TRM cells using CD69 neutralizing antibodies resulted in a similar reduction of pancreatic cytotoxic T cell recruitment, a delay in diabetic incidence, and a suppression of CXCL10 production. Thus, targeting FABP4 may represent a promising therapeutic strategy for T1D.

Abstract Lower‐limb motion monitoring is highly desired in various application scenarios ranging from rehabilitation to sports training. However, there still lacks a cost‐effective, energy‐saving, and computational complexity‐reducing solution for this specific demand. Here, a motion capturing and energy harvesting hybridized lower‐limb (MC‐EH‐HL) system with 3D printing is demonstrated. It enables low‐frequency biomechanical energy harvesting with a sliding block‐rail piezoelectric generator (S‐PEG) and lower‐limb motion sensing with a ratchet‐based triboelectric nanogenerator (R‐TENG). A unique S‐PEG is proposed with particularly designed mechanical structures to convert lower‐limb 3D motion into 1D linear sliding on the rail. On the one hand, high output power is achieved with the S‐PEG working at a very low frequency, which realizes self‐sustainable systems for wireless sensing under the Internet of Things framework. On the other hand, the R‐TENG gives rise to digitalized triboelectric output, matching the rotation angles to the pulse numbers. Additional physical parameters can be estimated to enrich the sensory dimension. Accordingly, demonstrative rehabilitation, human‐machine interfacing in virtual reality, and sports monitoring are presented. This developed hybridized system exhibits an economic and energy‐efficient solution to support the need for lower‐limb motion tracking in various scenarios, paving the way for self‐sustainable multidimensional motion tracking systems in near future.

Abstract Rapid advancements of artificial intelligence of things (AIoT) technology pave the way for developing a digital‐twin‐based remote interactive system for advanced robotic‐enabled industrial automation and virtual shopping. The embedded multifunctional perception system is urged for better interaction and user experience. To realize such a system, a smart soft robotic manipulator is presented that consists of a triboelectric nanogenerator tactile (T‐TENG) and length (L‐TENG) sensor, as well as a poly(vinylidene fluoride) (PVDF) pyroelectric temperature sensor. With the aid of machine learning (ML) for data processing, the fusion of the T‐TENG and L‐TENG sensors can realize the automatic recognition of the grasped objects with the accuracy of 97.143% for 28 different shapes of objects, while the temperature distribution can also be obtained through the pyroelectric sensor. By leveraging the IoT and artificial intelligence (AI) analytics, a digital‐twin‐based virtual shop is successfully implemented to provide the users with real‐time feedback about the details of the product. In general, by offering a more immersive experience in human–machine interactions, the proposed remote interactive system shows the great potential of being the advanced human–machine interface for the applications of the unmanned working space.

Abstract Development of liver fibrosis results in drastic changes in the liver microenvironment, which in turn accelerates disease progression. Although the pathological function of various hepatic cells in fibrogenesis is identified, the crosstalk between them remains obscure. The present study demonstrates that hepatic expression of adipocyte fatty acid binding protein (A‐FABP) is induced especially in the liver sinusoidal endothelial cells (LSECs) in mice after bile duct ligation (BDL). Genetic ablation and pharmacological inhibition of A‐FABP attenuate BDL‐ or carbon tetrachloride‐induced liver fibrosis in mice associating with reduced collagen accumulation, LSEC capillarization, and hepatic stellate cell (HSC) activation. Mechanistically, elevated A‐FABP promotes LSEC capillarization by activating Hedgehog signaling, thus impairs the gatekeeper function of LSEC on HSC activation. LSEC‐derived A‐FABP also acts on HSCs in paracrine manner to potentiate the transactivation of transforming growth factor β1 (TGFβ1) by activating c‐Jun N‐terminal kinase (JNK)/c‐Jun signaling. Elevated TGFβ1 subsequently exaggerates liver fibrosis. These findings uncover a novel pathological mechanism of liver fibrosis in which LSEC‐derived A‐FABP is a key regulator modulating the onset and progression of the disease. Targeting A‐FABP may represent a potential approach against liver fibrosis.

Abstract Sensory and nerve systems play important role in mediating the interactions with the world. The pursuit of neuromorphic computing has inspired innovations in artificial sensory and nervous systems. Here, an all‐in‐one, tailorable artificial perception, and transmission nerve (APTN) was developed for mimicking the biological sensory and nervous ability to detect and transmit the location information of mechanical stimulation. The APTN shows excellent reliability with a single triboelectric electrode for the detection of multiple pixels, by employing a gradient thickness dielectric layer and a grid surface structure. The sliding mode is used on the APTN to eliminate the amplitude influence of output signal, such as force, interlayer distance. By tailoring the geometry, an L‐shaped APTN is demonstrated for the application of single‐electrode bionic artificial nerve for 2D detection. In addition, an APTN based prosthetic arm is also fabricated to biomimetically identify and transmit the stimuli location signal to pattern the feedback. With features of low‐cost, easy installation, and good flexibility, the APTN renders as a promising artificial sensory and nervous system for artificial intelligence, human–machine interface, and robotics applications.

Abstract Wearable photonics offer a promising platform to complement the thriving complex wearable electronics system by providing high‐speed data transmission channels and robust optical sensing paths. Regarding the realization of photonic computation and tunable (de)multiplexing functions based on system‐level integration of abundant photonic modulators, it is challenging to reduce the overwhelming power consumption in traditional current‐based silicon photonic modulators. This issue is addressed by integrating voltage‐based aluminum nitride (AlN) modulator and textile triboelectric nanogenerator (T‐TENG) on a wearable platform to form a nano‐energy‐nano‐system (NENS). The T‐TENG transduces the mechanical stimulations into electrical signals based on the coupling of triboelectrification and electrostatic induction. The self‐generated high‐voltage from the T‐TENG is applied to the AlN modulator and boosts its modulation efficiency regardless of AlN's moderate Pockels effect. Complementarily, the AlN modulator's capacitive nature enables the open‐circuit operation mode of T‐TENG, providing the integrated NENS with continuous force sensing capability which is notably uninfluenced by operation speeds. Furthermore, a physical model is proposed to describe the coupled AlN modulator/T‐TENG system. With the enhanced photonic modulation and the open‐circuit operation mode enabled by synergies between the AlN modulator and the T‐TENG, optical Morse code transmission and continuous human motion monitoring are demonstrated for practical wearable applications.

Abstract The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical linkage between human and machines. Using a glove as an intuitive and low‐cost HMI can expediently track the motions of human fingers, resulting in a straightforward communication media of human–machine interactions. When combining several triboelectric textile sensors and proper machine learning technique, it has great potential to realize complex gesture recognition with the minimalist‐designed glove for the comprehensive control in both real and virtual space. However, humidity or sweat may negatively affect the triboelectric output as well as the textile itself. Hence, in this work, a facile carbon nanotubes/thermoplastic elastomer (CNTs/TPE) coating approach is investigated in detail to achieve superhydrophobicity of the triboelectric textile for performance improvement. With great energy harvesting and human motion sensing capabilities, the glove using the superhydrophobic textile realizes a low‐cost and self‐powered interface for gesture recognition. By leveraging machine learning technology, various gesture recognition tasks are done in real time by using gestures to achieve highly accurate virtual reality/augmented reality (VR/AR) controls including gun shooting, baseball pitching, and flower arrangement, with minimized effect from sweat during operation.