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The accurate acquisition and real-time calculation of the attitude angle of precision firefighting extinguishing projectiles are essential for ensuring stable flight and precise extinguishing agent release. However, measuring the roll attitude angle in such projectiles is challenging due to their highly dynamic nature and environmental disturbances such as fire smoke, high temperature, and electromagnetic interference. Traditional methods for measuring attitude angles rely on multi-sensor fusion schemes, which suffer from complex structure and high cost. This paper proposes a single-gyro attitude calculation method based on micro-electromechanical inertial measurement units (MIMUs). This method integrates Fourier transform time-frequency analysis with a second-order Infinite Impulse Response (IIR) bandpass filtering algorithm optimized by dynamic coefficients. Unlike conventional fixed-coefficient filters, the proposed algorithm adaptively updates filter parameters according to instantaneous roll angular velocity, thereby maintaining tracking capability under time-varying conditions. This theoretical contribution provides a general framework for adaptive frequency-tracking filtering, beyond the specific engineering case of firefighting projectiles. Through joint time-frequency domain processing, it achieves high-precision dynamic decoupling of the roll angle, eliminating the dependency on external sensors (e.g., radar/GPS) inherent in conventional systems. This approach drastically reduces system complexity and provides key technical support for low-cost and high-reliability firefighting projectile attitude control. The research contributes to enhancing the effectiveness of urban firefighting, forest fire suppression, and public safety emergency response.

This study introduces a novel supramolecular thermotropic columnar liquid-crystalline (LC) electrolyte tailored for high-performance ionic electroactive polymer (iEAP) actuators. The electrolyte is designed by integrating lithium salts into a taper-shaped molecule with bisphosphate moieties (BPO), which self-assembles into a columnar hexagonal (Col ) phase, forming 3D continuous ion-conductive pathways. This architecture achieves high ionic conductivity of up to 2 × 10 S cm at room temperature. An actuator was fabricated by embedding this electrolyte into a microporous polyethylene membrane, sandwiched between PEDOT:PSS electrodes. The resulting device exhibits exceptional performance, achieving a bending strain of 0.52% and a force output of 0.5 mN under a ± 2 V, along with outstanding durability, retaining its performance over 9000 cycles. These results underscore the potential of 3D ion-conductive LC electrolytes in advancing iEAP actuator technologies, paving the way for innovative applications in tactile interfaces and soft robotics.

Elucidating the mechanism underlying the poor proliferative capacity of adult pancreatic β-cells is critical to regenerative therapeutic approaches for diabetes. Here, we show that the microRNA (miR)-7/7ab family member miR-7a is enriched in mouse adult pancreatic islets compared with miR-7b. Remarkably, miR-7a targets five components of the mTOR signaling pathway. Further, inhibition of miR-7a activates mTOR signaling and promotes adult β-cell replication in mouse primary islets, which can be reversed by the treatment with a well-known mTOR inhibitor, rapamycin. These data suggest that miR-7 acts as a brake on adult β-cell proliferation. Most importantly, this miR-7–mTOR proliferation axis is conserved in primary human β-cells, implicating miR-7 as a therapeutic target for diabetes.

In transplantation, there is a critical need for noninvasive biomarker platforms for monitoring immunologic rejection. We hypothesized that transplanted tissues release donor-specific exosomes into recipient circulation and that the quantitation and profiling of donor intra-exosomal cargoes may constitute a biomarker platform for monitoring rejection. Here, we have tested this hypothesis in a human-into-mouse xenogeneic islet transplant model and validated the concept in clinical settings of islet and renal transplantation. In the xenogeneic model, we quantified islet transplant exosomes in recipient blood over long-term follow-up using anti-HLA antibody, which was detectable only in xenoislet recipients of human islets. Transplant islet exosomes were purified using anti-HLA antibody-conjugated beads, and their cargoes contained the islet endocrine hormone markers insulin, glucagon, and somatostatin. Rejection led to a marked decrease in transplant islet exosome signal along with distinct changes in exosomal microRNA and proteomic profiles prior to appearance of hyperglycemia. In the clinical settings of islet and renal transplantation, donor exosomes with respective tissue specificity for islet β cells and renal epithelial cells were reliably characterized in recipient plasma over follow-up periods of up to 5 years. Collectively, these findings demonstrate the biomarker potential of transplant exosome characterization for providing a noninvasive window into the conditional state of transplant tissue.

The rate of population aging has reached a high level in China that age-friendly design should be concerned when designing gardens in urban place. Although the generation-skipping guardianship phenomenon always exists in present situation of elderly people in China that grandchildren are taken with the elderly and are involved in their daily activities in most family, the needs of children are neglected in urban garden design. The paper generalized guidelines of age-friendly garden design according to LEED, Chinese garden design standard, and the guideline of global age-friendly cities in Europe. Childrenfriendliness have also been included based on the above-mentioned new guidelines. 29 reports have been collected in the case study of WANSHOU Park which can be characterized as an age-friendly garden involving basic information and scoring records on its environment and facilities. Through the increasement of children-friendly features and installments in the park, the design will have great potential to deal with aging problems.

The deep network model relies on sufficient training samples to achieve superior processing performance, which limits its application in hyperspectral image (HSI) classification. In order to perform HSI classification with noisy labels, a robust weakly supervised feature learning (WSFL) architecture combined with multi-model attention is proposed. Specifically, the input noisy labeled data are first subjected to multiple groups of residual spectral attention models and multi-granularity residual spatial attention models, enabling WSFL to refine and optimize the extracted spectral and spatial features, with a focus on extracting clean samples information and reducing the model’s dependence on labels. Finally, the fused and optimized spectral-spatial features are mapped to the multilayer perceptron (MLP) classifier to increase the constraint of the model on the noisy samples. The experimental results on public datasets, including Pavia Center, WHU-Hi LongKou, and HangZhou, show that WSFL is better at classifying noise labels than excellent models such as spectral-spatial residual network (SSRN) and dual channel residual network (DCRN). On Hangzhou dataset, the classification accuracy of WSFL is superior to DCRN by 6.02% and SSRN by 7.85%, respectively.

BACKGROUNDMultiple islet autoantibodies (AAbs) predict the development of type 1 diabetes (T1D) and hyperglycemia within 10 years. By contrast, T1D develops in only approximately 15% of individuals who are positive for single AAbs (generally against glutamic acid decarboxylase [GADA]); hence, the single GADA+ state may represent an early stage of T1D.METHODSHere, we functionally, histologically, and molecularly phenotyped human islets from nondiabetic GADA+ and T1D donors.RESULTSSimilar to the few remaining β cells in the T1D islets, GADA+ donor islets demonstrated a preserved insulin secretory response. By contrast, α cell glucagon secretion was dysregulated in both GADA+ and T1D islets, with impaired glucose suppression of glucagon secretion. Single-cell RNA-Seq of GADA+ α cells revealed distinct abnormalities in glycolysis and oxidative phosphorylation pathways and a marked downregulation of cAMP-dependent protein kinase inhibitor β (PKIB), providing a molecular basis for the loss of glucose suppression and the increased effect of 3-isobutyl-1-methylxanthine (IBMX) observed in GADA+ donor islets.CONCLUSIONWe found that α cell dysfunction was present during the early stages of islet autoimmunity at a time when β cell mass was still normal, raising important questions about the role of early α cell dysfunction in the progression of T1D.FUNDINGThis work was supported by grants from the NIH (3UC4DK112217-01S1, U01DK123594-02, UC4DK112217, UC4DK112232, U01DK123716, and P30 DK019525) and the Vanderbilt Diabetes Research and Training Center (DK20593).

Deep-learning-based multi-sensor hyperspectral image classification algorithms can automatically acquire the advanced features of multiple sensor images, enabling the classification model to better characterize the data and improve the classification accuracy. However, the currently available classification methods for feature representation in multi-sensor remote sensing data in their respective domains do not focus on the existence of bottlenecks in heterogeneous feature fusion due to different sensors. This problem directly limits the final collaborative classification performance. In this paper, to address the bottleneck problem of joint classification due to the difference in heterogeneous features, we innovatively combine self-supervised comparative learning while designing a robust and discriminative feature extraction network for multi-sensor data, using spectral–spatial information from hyperspectral images (HSIs) and elevation information from LiDAR. The advantages of multi-sensor data are realized. The dual encoders of the hyperspectral encoder by the ConvNeXt network (ConvNeXt-HSI) and the LiDAR encoder by Octave Convolution (OctaveConv-LiDAR) are also used. The adequate feature representation of spectral–spatial features and depth information obtained from different sensors is performed for the joint classification of hyperspectral images and LiDAR data. The multi-sensor joint classification performance of both HSI and LiDAR sensors is greatly improved. Finally, on the Houston2013 dataset and the Trento dataset, we demonstrate through a series of experiments that the dual-encoder model for hyperspectral and LiDAR joint classification via contrastive learning achieves state-of-the-art classification performance.

Background: Lentinan (LNT), an isolated traditional Chinese herbal component, has antitumor potential. In the current study, the intrinsic mechanism of LNT-induced immunity against bladder cancer was explored in a mouse model. Methods: In the mouse model of bladder cancer, we used flow cytometry to detect the LNT caused population changes of T cells, macrophages, MDSC cells, and Treg cells. ELISA was used to evaluate cytokines expression in the supernatant of splenocytes. Results: We found that the administration of LNT increased the proportions of CD3+CD4+ and CD3+CD8+ T cell subsets as well as CD11b+F480+ macrophages, whereas it diminished the subpopulations of CD4+CD25+Foxp3+ regulatory T cells (Tregs) and Gr-1+CD11b+ myeloid-derived suppressor cells (MDSCs). LNT also upregulated the expression of interferon (IFN)-γ and interleukin (IL)-12, accompanied by a significant reduction in IL-10 and tumor growth factor (TGF)-β (P < .05). Our research further confirmed the synergy between LNT and gemcitabine (GEM) to activate immunity and inhibit the growth of bladder tumors in mouse model. Conclusions: LNT induced macrophage activation, followed by the enhanced proliferation of CD4+ and CD8+ T cells, and the upregulated expression of IFN-γ and IL-2. Meanwhile, the proportions of MDSCs and Tregs were downregulated, leading to a reduced expression of the anti-inflammatory cytokines IL-10 and TGF-β. The synergy between LNT and GEM provides additional evidence supporting the application of this traditional Chinese herbal component for bladder cancer therapy.

Diabetes is linked to loss of pancreatic beta-cells. Pluripotent stem cells offer a valuable source of human beta-cells for basic studies of their biology and translational applications. However, the signalling pathways that regulate beta-cell development and functional maturation are not fully understood. Here we report a high content chemical screen, revealing that H1152, a ROCK inhibitor, promotes the robust generation of insulin-expressing cells from multiple hPSC lines. The insulin expressing cells obtained after H1152 treatment show increased expression of mature beta cell markers and improved glucose stimulated insulin secretion. Moreover, the H1152-treated beta-like cells show enhanced glucose stimulated insulin secretion and increased capacity to maintain glucose homeostasis after transplantation. Conditional gene knockdown reveals that inhibition of ROCKII promotes the generation and maturation of glucose-responding cells. This study provides a strategy to promote human beta-cell maturation and identifies an unexpected role for the ROCKII pathway in the development and maturation of beta-like cells. Our incomplete understanding of how pancreatic beta cells form limits the generation of beta-like cells from human pluripotent stem cells (hPSC). Here, the authors identify a ROCKII inhibitor H1152 as increasing insulin secreting cells from hPSCs and improving beta-cell maturation on transplantation in vivo.