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High-performance flexible strain sensing has shown enormous application value in the fields of motion monitoring and biomedical engineering. However, developing strain sensors that combine high sensitivity, wide strain sensing range, and environmental stability still remains a lot of challenge. In this work, we proposed a strain sensor with high sensitivity, wide strain sensing range, low-temperature resistance, and adhesiveness by embedding the silver nanowires (AgNWs) film into the eutectic gel matrix. The double-layer conductive embedded structure gives the sensor unparalleled sensitivity and sensing range, while the eutectic gel gives the sensor low-temperature resistance and excellent skin conformal ability. The results indicate that the sensor has high sensitivity (GF > 1345), wide strain sensing range (0%–300%), long-term stability (over 3000 cycles), and can still maintain stability in low-temperature environments. In addition, a series of human motion monitoring and real-time electrocardiogram (ECG) applications have demonstrated the enormous potential of the prepared sensors in human healthcare monitoring.

Accurate scatter estimation is important in quantitative SPECT for improving image contrast and accuracy. With a large number of photon histories, Monte-Carlo (MC) simulation can yield accurate scatter estimation, but is computationally expensive. Recent deep learning-based approaches can yield accurate scatter estimates quickly, yet full MC simulation is still required to generate scatter estimates as ground truth labels for all training data. Here we propose a physics-guided weakly supervised training framework for fast and accurate scatter estimation in quantitative SPECT by using a $100\times $ shorter MC simulation as weak labels and enhancing them with deep neural networks. Our weakly supervised approach also allows quick fine-tuning of the trained network to any new test data for further improved performance with an additional short MC simulation (weak label) for patient-specific scatter modelling. Our method was trained with 18 XCAT phantoms with diverse anatomies / activities and then was evaluated on 6 XCAT phantoms, 4 realistic virtual patient phantoms, 1 torso phantom and 3 clinical scans from 2 patients for 177Lu SPECT with single / dual photopeaks (113, 208 keV). Our proposed weakly supervised method yielded comparable performance to the supervised counterpart in phantom experiments, but with significantly reduced computation in labeling. Our proposed method with patient-specific fine-tuning achieved more accurate scatter estimates than the supervised method in clinical scans. Our method with physics-guided weak supervision enables accurate deep scatter estimation in quantitative SPECT, while requiring much lower computation in labeling, enabling patient-specific fine-tuning capability in testing.

Training end-to-end unrolled iterative neural networks for single-photon emission computerized tomography (SPECT) image reconstruction requires a memory-efficient forward–backward projector for efficient backpropagation. This article describes an open-source, high-performance Julia implementation of a SPECT forward–backward projector that supports memory-efficient backpropagation with an exact adjoint. Our Julia projector uses only ~5% of the memory of an existing MATLAB-based projector. We compare unrolling a CNN-regularized expectation–maximization (EM) algorithm with end-to-end training using our Julia projector with other training methods, such as gradient truncation (ignoring gradients involving the projector) and sequential training, using XCAT phantoms and virtual patient (VP) phantoms generated from SIMIND Monte Carlo (MC) simulations. Simulation results with two different radionuclides (90Y and 177Lu) show that: 1) for 177Lu XCAT phantoms and 2) 90Y VP phantoms, training unrolled EM algorithm in an end-to-end fashion with our Julia projector yields the best reconstruction quality compared to other training methods and ordered-subset EM (OSEM), both qualitatively and quantitatively. For VP phantoms with 177Lu radionuclide, the reconstructed images using end-to-end training are in higher quality than using sequential training and OSEM, but are comparable with using gradient truncation. We also find there exists a tradeoff between computational cost and reconstruction accuracy for different training methods. End-to-end training has the highest accuracy because the correct gradient is used in backpropagation; sequential training yields worse reconstruction accuracy, but is significantly faster and uses much less memory.

Purpose: We propose a formal framework for the modeling and segmentation of minimally invasive surgical tasks using a unified set of motion primitives (MPs) to enable more objective labeling and the aggregation of different datasets. Methods: We model dry-lab surgical tasks as finite state machines, representing how the execution of MPs as the basic surgical actions results in the change of surgical context, which characterizes the physical interactions among tools and objects in the surgical environment. We develop methods for labeling surgical context based on video data and for automatic translation of context to MP labels. We then use our framework to create the COntext and Motion Primitive Aggregate Surgical Set (COMPASS), including six dry-lab surgical tasks from three publicly available datasets (JIGSAWS, DESK, and ROSMA), with kinematic and video data and context and MP labels. Results: Our context labeling method achieves near-perfect agreement between consensus labels from crowd-sourcing and expert surgeons. Segmentation of tasks to MPs results in the creation of the COMPASS dataset that nearly triples the amount of data for modeling and analysis and enables the generation of separate transcripts for the left and right tools. Conclusion: The proposed framework results in high quality labeling of surgical data based on context and fine-grained MPs. Modeling surgical tasks with MPs enables the aggregation of different datasets and the separate analysis of left and right hands for bimanual coordination assessment. Our formal framework and aggregate dataset can support the development of explainable and multi-granularity models for improved surgical process analysis, skill assessment, error detection, and autonomy.

316 stainless steel was fabricated with novel low-power pulsed laser-induced gas tungsten arc additive manufacturing (LGTA-AM). Compared with gas tungsten arc additive manufacturing (GTA-AM) process, the microstructure, phase composition and room temperature mechanical property were investigated. Compared with the micro-molding of walls fabricated by GTA-AM, the room temperature micro-structure of the two processes are composed of A and δ ferrite. The austenite grains show cell-like crystals → columnar crystals → dendrites → equiaxed crystals from bottom to top of 8-layer wall. The ferrite morphology of the GTA-AM specimens captured by SEM was strip-shaped, skeleton-shaped, sieve-shaped, and worm-shaped in order from bottom to top. The ferrite morphology of LGTA-AM specimens captured by SEM is strip-shaped, fine-mesh-shaped, reticulate-shaped, and fish-scaled in order from bottom to top. LGTA-AM can uniformly refine the micro-structure and accelerate the transformation rate from ferrite to austenite. The hardness value of the AM wall from the bottom to the top shows a trend of decreasing first and then increasing. The LGTA-AM process can improve the uniformity of the interlayer transition and reduce the anisotropy of the grains.

FcγRIIB, the only inhibitory IgG Fc receptor, functions to suppress the hyper-activation of immune cells. Numerous studies have illustrated its inhibitory function through the ITIM motif in the cytoplasmic tail of FcγRIIB. However, later studies revealed that in addition to the ITIM, the transmembrane (TM) domain of FcγRIIB is also indispensable for its inhibitory function. Indeed, recent epidemiological studies revealed that a non-synonymous single nucleotide polymorphism (rs1050501) within the TM domain of FcγRIIB, responsible for the I232T substitution, is associated with the susceptibility to systemic lupus erythematosus (SLE). In this review, we will summarize these epidemiological and functional studies of FcγRIIB-I232T in the past few years, and will further discuss the mechanisms accounting for the functional loss of FcγRIIB-I232T. Our review will help the reader gain a deeper understanding of the importance of the TM domain in mediating the inhibitory function of FcγRIIB and may provide insights to a new therapeutic target for the associated diseases.

The creation of interconnected ionic nanoaggregates within solid electrolytes is a crucial yet challenging task for fabricating high-performance alkaline fuel cells. Herein, we present a facile and generic approach to embedding ionic nanoaggregates via predesigned hybrid core–shell nanoarchitecture within nonionic polymer membranes as follows: (i) synthesizing core–shell nanoparticles composed of SiO2/densely quaternary ammonium-functionalized polystyrene. Because of the spatial confinement effect of the SiO2“core”, the abundant hydroxide-conducting groups are locally aggregated in the functionalized polystyrene “shell”, forming ionic nanoaggregates bearing intrinsic continuous ion channels; (ii) embedding these ionic nanoaggregates (20–70 wt %) into the polysulfone matrix to construct interconnected hydroxide-conducting channels. The chemical composition, physical morphology, amount, and distribution of the ionic nanoaggregates are facilely regulated, leading to highly connected ion channels with high effective ion mobility comparable to that of the state-of-the-art Nafion. The resulting membranes display strikingly high hydroxide conductivity (188.1 mS cm–1at 80 °C), which is one of the highest results to date. The membranes also exhibit good mechanical properties. The independent manipulation of the conduction function and nonconduction function by the ionic nanoaggregates and nonionic polymer matrix, respectively, opens a new avenue, free of microphase separation, for designing high-performance solid electrolytes for diverse application realms.

The objective of this study is to develop a novel approach to in situfunctionalizing multiwalled carbon nanotubes (MWCNTs) and exploring their application in Nafion-based composite membranes for efficient proton conduction. Covalent grafting of acrylate-modified MWCNTs with poly(methacrylic acid-co-ethylene glycol dimethacrylate), poly(vinylphosphonic acid-co-ethylene glycol dimethacrylate), and sulfonated poly(styrene-co-divinylbenzene) was achieved via surface-initiated distillation precipitation polymerization. The formation of core–shell structure was verified by TEM images, and polymer layers with thickness around 30 nm were uniformly covered on the MWCNTs. The graft yield reached up to 93.3 wt % after 80 min of polymerization. The functionalized CNTs (FCNTs) were incorporated into the Nafion matrix to prepare composite membranes. The influence of various functional groups (−COOH, −PO3H2, and −SO3H) in FCNTs on proton transport of the composite membranes was studied. The incorporation of FCNTs afforded the composite membranes significantly enhanced proton conductivities under reduced relative humidity. The composite membrane containing 5 wt % phosphorylated MWCNTs (PCNTs) showed the highest proton conductivity, which was attributed to the construction of lower-energy-barrier proton transport pathways by PCNTs, and excellent water-retention and proton-conduction properties of the cross-linked polymer in PCNTs. Moreover, the composite membranes exhibited an enhanced mechanical stability.

Zwitterionic microcapsules (ZMCs) based on sulfobetaine with tunable hierarchical structures, superior water retention properties, and high proton conduction capacities are synthesized via precipitation polymerization. The incorporation of ZMCs into a Nafion matrix renders the composite membranes with significantly enhanced proton conductivity especially under low humidity. The composite membrane with 15 wt % ZMC-I displayed the highest proton conductivity of 5.8 × 10–2S cm–1at 40 °C and 20% relative humidity after 90 min of testing, about 21 times higher than that of the Nafion control membrane. The increased proton conductivity is primarily attributed to the versatile roles of ZMCs as water reservoirs and proton conductors for rendering a stable water environment and an additional proton conduction pathway within the membranes. This study may contribute to the rational design of water-retaining and proton-conducting materials.

Because China is becoming an aging society, the incidence of diabetes and diabetic foot have been increasing. Diabetic foot has become one of the main health-related killers due to its high disability and mortality rates. Negative pressure wound therapy (NPWT) is one of the most effective techniques for the treatment of diabetic foot wounds and great progress, both in terms of research and its clinical application, has been made in the last 20 years of its development. However, due to the complex pathogenesis and management of diabetic foot, irregular application of NPWT often leads to complications, such as infection, bleeding and necrosis, that seriously affect its treatment outcomes. In 2020, under the leadership of Burns, Trauma and Tissue Repair Committee of the Cross-Straits Medicine Exchange Association, the writing group for ‘Consensus on the application of negative pressure wound therapy of diabetic foot wounds’ was established with the participation of scholars from the specialized areas of burns, endocrinology, vascular surgery, orthopedics and wound repair. Drawing on evidence-based practice suggested by the latest clinical research, this consensus proposes the best clinical practice guidelines for the application and prognostic evaluation of NPWT for diabetic foot. The consensus aims to support the formation of standardized treatment schemes that clinicians can refer to when treating cases of diabetic foot.

Landslide is one of the serious geological disasters that bring serious impact on transportation construction and highway traffics. It is critical to improve the level of warning and emergency response to the highway landslide. Based on the application of unmanned aerial vehicles (UAV), the highway landslide warning and emergency response system is developed in this paper. The system consists of two main components, including landslide warning sub-system and the UAV emergency response sub-system. The establishment of this system will contribute to: 1) improving the warning and monitoring of highway landslide; 2) quickly understanding the situation of landslide disaster, and therefore helping the policy-makers with emergency rescue, disaster assessment, and reconstruction; and 3) using it as a research platform for large-scale landslides, debris flow monitoring and warning, and other emergency relief work.