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The beating heart possesses the intrinsic ability to adapt cardiac output to changes in mechanical load. The century-old Frank–Starling law and Anrep effect have documented that stretching the heart during diastolic filling increases its contractile force. However, the molecular mechanotransduction mechanism and its impact on cardiac health and disease remain elusive. Here we show that the mechanically activated Piezo1 channel converts mechanical stretch of cardiomyocytes into Ca 2+ and reactive oxygen species (ROS) signaling, which critically determines the mechanical activity of the heart. Either cardiac-specific knockout or overexpression of Piezo1 in mice results in defective Ca 2+ and ROS signaling and the development of cardiomyopathy, demonstrating a homeostatic role of Piezo1. Piezo1 is pathologically upregulated in both mouse and human diseased hearts via an autonomic response of cardiomyocytes. Thus, Piezo1 serves as a key cardiac mechanotransducer for initiating mechano-chemo transduction and consequently maintaining normal heart function, and might represent a novel therapeutic target for treating human heart diseases. The beating heart adapts cardiac output to changes in mechanical load via incompletely understood mechanotransduction mechanisms. Here the authors show that the mechanosensitive Piezo1 channel serves as a mechanotransducer for directly converting mechanical stretch of cardiomyocytes into Ca 2+ and ROS signaling and consequently maintaining normal heart function.

Even though the perovskite solar cell has been so popular for its skyrocketing power conversion efficiency, its further development is still roadblocked by its overall performance, in particular long-term stability, large-area fabrication and stable module efficiency. In essence, the soft component and ionic–electronic nature of metal halide perovskites usually chaperonage large number of anion vacancy defects that act as recombination centers to decrease both the photovoltaic efficiency and operational stability. Herein, we report a one-stone-for-two-birds strategy in which both anion-fixation and associated undercoordinated-Pb passivation are in situ achieved during crystallization by using a single amidino-based ligand, namely 3-amidinopyridine, for metal-halide perovskite to overcome above challenges. The resultant devices attain a power conversion efficiency as high as 25.3% (certified at 24.8%) with substantially improved stability. Moreover, the device without encapsulation retained 92% of its initial efficiency after 5000 h exposure in ambient and the device with encapsulation retained 95% of its initial efficiency after >500 h working at the maximum power point under continuous light irradiation in ambient. It is expected this one-stone-for-two-birds strategy will benefit large-area fabrication that desires for simplicity. Long-term stability and stable efficiency are essential for large-area fabrication of perovskite solar cells. Here, the authors achieve in situ anion-fixation and undercoordinated-Pb passivation using amidino-based ligand, realizing maximum power conversion efficiency of 25.3% with T95 over 500 h.

Research has supported the association between movement behaviors and cognitive ability in preschool children. However, most of the research has independently examined the various movement behaviors (e.g., physical activity, sedentary behavior, and sleep) without considering the dynamic composition of these behaviors in a 24-hour daily cycle. Therefore, this study aimed to explore the relationship between 24-hour movement behaviors and cognitive ability in preschool children. The participants were 191 Chinese preschool children from Zhuzhou aged 3–6 years. We measured light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and sedentary behavior (SB) using the Actigraph Accelerometers and evaluated the children’s sleep time based on reports from parents and teachers. The Chinese version of the Wechsler Young Children Scale of Intelligence (C-WYCSI) was used to assess cognitive ability. Compositional analysis and isotemporal substitution were performed to examine the influence of 24-hour movement behaviors on children’s cognitive ability. After controlling for demographics (e.g., age and sex), the composition of 24-hour movement behaviors was significantly associated with the verbal intelligence quotient (VIQ), performance intelligence quotient (PIQ), and full intelligence quotient (FIQ). Importantly, preschool children demonstrated greater cognitive behavior when time in MVPA replaced that spent in LPA, SB, or sleep than when time spent in LPA, SB, and sleep replaced that spent in MVPA within the 24-hour cycle. Physical activity engaged in a 24-hour daily cycle has a significant effect on cognitive ability in preschool children, with increased MVPA and LPA being associated with higher PIQ and FIQ and increased sleep being associated with lower VIQ, PIQ, and FIQ. Replacing time in SB and LPA with MVPA is promising for children’s cognitive development.

This study investigates the hydrogen‒oxygen crossover behavior induced by membrane perforation in alkaline electrolyzers, assessing explosion risks during operation. Perforation defects were simulated using an experimental platform with transparent end plates to analyze the effects of perforation diameter, position, and operating power on gas mixing. Results demonstrate that membrane perforation significantly increases HTO (Hydrogen to Oxygen), with severity dependent on perforation size and proximity to flow inlets/outlets. Maximum hydrogen-oxygen crossover occurs at high-flow-velocity zones due to flow field heterogeneity and gas accumulation. In addition, Lower operating power intensifies HTO, while higher load dilutes HTO through increased oxygen output. Based on the experimental data, a predictive regression model was developed, capable of forecasting HTO under different perforation conditions with high accuracy (R 2  = 0.9499). The novelty lies in quantifying the correlation between diaphragm perforation and risk in alkaline electrolyzer, and providing reference for early fault detection and preventive maintenance.

The purpose of the current study was to assess the differences between children with developmental language disorder (DLD) and typically developing (TD) children in their performance of executive functions from working memory, inhibitory control, and cognitive flexibility. We performed a systematical search of PubMed, Embase, Cochrane, and Web of Science for case control studies (published in English between January 1, 1950, and October 11, 2023) comparing the differences in the performance of executive functions between DLD and TD children. Forty eligible studies were included in the present study (  = 3,168 participants). In comparison with TD children, DLD children exhibited significantly poorer performances in all six verbal working memory tasks (backward digit recall task, SMD -1.4321, 95% CI -2.2692 to -0.5950; listening recall task, SMD -1.4469, 95% CI -1.7737 to -1.1202; counting recall task, SMD -0.9192, 95% CI -1.4089 to -0.4295; digit recall task, SMD -1.2321, 95% CI -1.4397 to -1.0244; word list recall task, SMD -1.1375, 95% CI -1.5579 to -0.7171; non-word recall task, SMD -1.5355, 95% CI -1.8122 to -1.2589). However, regarding inhibitory control and cognitive flexibility, the differences between DLD and TD children depended on specific circumstances. In subgroup analyses of all verbal working memory tasks, DLD children presented notably lower performance than TD children in both the monolingual English and monolingual non-English groups, and in both the preschooler and school-aged groups. This study proves that verbal working memory deficits can be seen as a marker for children with DLD and are not affected by age or language type. https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=391070, CRD42023391070.

Under dynamic conditions, motion blur is introduced to star images obtained by a star sensor. Motion blur affects the accuracy of the star centroid extraction and the identification of stars, further reducing the performance of the star sensor. In this paper, a star image restoration algorithm is investigated to reduce the effect of motion blur on the star image. The algorithm includes a blur kernel calculation aided by a MEMS gyroscope, blur kernel correction based on the structure of the star strip, and a star image reconstruction method based on scaled gradient projection (SGP). Firstly, the motion trajectory of the star spot is deduced, aided by a MEMS gyroscope. Moreover, the initial blur kernel is calculated by using the motion trajectory. Then, the structure information star strip is extracted by Delaunay triangulation. Based on the structure information, a blur kernel correction method is presented by utilizing the preconditioned conjugate gradient interior point algorithm to reduce the influence of bias and installation deviation of the gyroscope on the blur kernel. Furthermore, a speed-up image reconstruction method based on SGP is presented for time-saving. Simulated experiment results demonstrate that both the blur kernel determination and star image reconstruction methods are effective. A real star image experiment shows that the accuracy of the star centroid extraction and the number of identified stars increase after restoration by the proposed algorithm.

Ship detection in large-scale synthetic aperture radar (SAR) images has achieved breakthroughs as a result of the improvement of SAR imaging technology. However, there still exist some issues due to the scattering interference, sparsity of ships, and dim and small ships. To address these issues, an anchor-free method is proposed for dim and small ship detection in large-scale SAR images. First, fully convolutional one-stage object detection (FCOS) as the baseline is applied to detecting ships pixel by pixel, which can eliminate the effect of anchors and avoid the missing detection of small ships. Then, considering the particularity of SAR ships, the sample definition is redesigned based on the statistical characteristics of ships. Next, the feature extraction is redesigned to improve the feature representation for dim and small ships. Finally, the classification and regression are redesigned by introducing an improved focal loss and regression refinement with complete intersection over union (CIoU) loss. Experimental simulation results show that the proposed R-FCOS method can detect dim and small ships in large-scale SAR images with higher accuracy compared with other methods.

For meter-wave multiple-input multiple-output (MIMO) radar, the multipath of target echoes may cause severe errors in height measurement, especially in the case of complex terrain where terrain fluctuation, ground inclination, and multiple reflection points exist. Inspired by a sparse array with greater degrees of freedom and low mutual coupling, a height measurement method based on a sparse array is proposed. First, a practical signal model of MIMO radar based on a sparse array is established. Then, the modified multiple signal classification (MUSIC) and maximum likelihood (ML) estimation algorithms based on two classical sparse arrays (coprime array and nested array) are proposed. To reduce the complexity of the algorithm, a real-valued processing algorithm for generalized MUSIC (GMUSIC) and maximum likelihood is proposed, and a reduced dimension matrix is introduced into the real-valued processing algorithm to further reduce computation complexity. Finally, sufficient simulation results are provided to illustrate the effectiveness and superiority of the proposed technique. The simulation results show that the height measurement accuracy can be efficiently improved by using our proposed technique for both simple and complex terrain.

Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet) to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial “distance” of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral “distance” between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pretrained deep CNNs to other remote sensing tasks.

Polygonatum sibiricum polysaccharides (PSPs) are used to improve immunity, alleviate dryness, promote the secretion of fluids, and quench thirst. However, the PSP biosynthetic pathway is largely unknown. Understanding the genetic background will help delineate that pathway at the molecular level so that researchers can develop better conservation strategies. After comparing the PSP contents among several different P. sibiricum germplasms, we selected two groups with the largest contrasts in contents and subjected them to HiSeq2500 transcriptome sequencing to identify the candidate genes involved in PSP biosynthesis. In all, 20 kinds of enzyme-encoding genes were related to PSP biosynthesis. The polysaccharide content was positively correlated with the expression patterns of β-fructofuranosidase (sacA), fructokinase (scrK), UDP-glucose 4-epimerase (GALE), Mannose-1-phosphate guanylyltransferase (GMPP), and UDP-glucose 6-dehydrogenase (UGDH), but negatively correlated with the expression of Hexokinase (HK). Through qRT-PCR validation and comprehensive analysis, we determined that sacA, HK, and GMPP are key genes for enzymes within the PSP metabolic pathway in P. sibiricum. Our results provide a public transcriptome dataset for this species and an outline of pathways for the production of polysaccharides in medicinal plants. They also present more information about the PSP biosynthesis pathway at the molecular level in P. sibiricum and lay the foundation for subsequent research of gene functions.