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Research advances over the past 30 years have confirmed a critical role for genetics in the etiology of dilated cardiomyopathies (DCMs). However, full knowledge of the genetic architecture of DCM remains incomplete. We identified candidate DCM causal gene, C10orf71, in a large family with 8 patients with DCM by whole-exome sequencing. Four loss-of-function variants of C10orf71 were subsequently identified in an additional group of492 patients with sporadic DCM from 2 independent cohorts. C10orf71 was found to be an intrinsically disordered protein specifically expressed in cardiomyocytes. C10orf71-KO mice had abnormal heart morphogenesis during embryonic development and cardiac dysfunction as adults with altered expression and splicing of contractile cardiac genes. C10orf71-null cardiomyocytes exhibited impaired contractile function with unaffected sarcomere structure. Cardiomyocytes and heart organoids derived from human induced pluripotent stem cells with C10orf71 frameshift variants also had contractile defects with normal electrophysiological activity. A rescue study using a cardiac myosin activator, omecamtiv mecarbil, restored contractile function in C10orf71-KO mice. These data support C10orf71 as a causal gene for DCM by contributing to the contractile function of cardiomyocytes. Mutation-specific pathophysiology may suggest therapeutic targets and more individualized therapy., Introduction Cardiomyopathies are a heterogeneous group of cardiac disorders with primary abnormalities in the structure and function of the heart (1). These disorders are commonly divided into primary--including hypertrophic, dilated, [...]

During the process of muscle regeneration post‐injury in adults, muscle stem cells (MuSCs) function is facilitated by neighboring cells within the pro‐regenerative niche. However, the precise mechanism triggering the initiation of signaling in the pro‐regenerative niche remains unknown. Using single‐cell RNA sequencing, 14 different muscle cells are comprehensively mapped during the initial stage following injury. Among these, macrophages and fibro‐adipogenic progenitor cells (FAPs) exhibit the most pronounced intercellular communication with other cells. In the FAP subclusters, the study identifies an activated FAP phenotype that secretes chemokines, such as CXCL1, CXCL5, CCL2, and CCL7, to recruit macrophages after injury. Il1rl1, encoding the protein of the interleukin‐33 (IL‐33) receptor, is identified as a highly expressed signature surface marker of the FAP phenotype. Following muscle injury, autocrine IL‐33, an alarmin, has been observed to activate quiescent FAPs toward this inflammatory phenotype through the IL1RL1‐MAPK/NF‐κB signaling pathway. Il1rl1 deficiency results in decreased chemokine expression and recruitment of macrophages, accompanied by impaired muscle regeneration. These findings elucidate a novel mechanism involving the IL‐33/IL1RL1 signaling pathway in promoting the activation of FAPs and facilitating muscle regeneration, which can aid the development of therapeutic strategies for muscle‐related disorders and injuries. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) have been highly successful in graph representation learning. The goal of GNNs is to enrich node representations by aggregating information from neighboring nodes. Much work has attempted to improve the quality of aggregation by introducing a variety of graph information with representational capabilities. The class of GNNs that improves the quality of aggregation by encoding graph information with representational capabilities into the weights of neighboring nodes through different learnable transformation structures (LTSs) are referred to as implicit GNNs. However, we argue that LTSs only transform graph information into the weights of neighboring nodes in the direction that minimizes the loss function during the learning process and does not actually utilize the effective properties of graph information, a phenomenon that we refer to as graph information vanishing (GIV). To validate this point, we perform thousands of experiments on seven node classification benchmark datasets. We first replace the graph information utilized by five implicit GNNs with random values and surprisingly observe that the variation range of accuracies is less than ± 0.3%. Then, we quantitatively characterize the similarity of the weights generated from graph information and random values by cosine similarity, and the cosine similarities are greater than 0.99. The empirical experiments show that graph information is equivalent to initializing the input of LTSs. We believe that graph information as an additional supervised signal to constrain the training of GNNs can effectively solve GIV. Here, we propose GinfoNN, which utilizes both labels and discrete graph curvature as supervised signals to jointly constrain the training of the model. The experimental results show that the classification accuracies of GinfoNN improve by two percentage points over baselines on large and dense datasets. [ABSTRACT FROM AUTHOR]

Various error factors influence the inversion of forest canopy height using GEDI full-waveform LiDAR data, and the interaction of these factors impacts the accuracy of forest canopy height estimation. From an error perspective, there is still a lack of methods to fully correct the impact of various error factors on the retrieval of forest canopy height from GEDI. From the modeling perspective, establishing clear coupling models between various environments, collection parameters, and GEDI forest canopy height errors is challenging. Understanding the comprehensive impact of various environments and collection parameters on the accuracy of GEDI data is crucial for extracting high-quality and precise forest canopy heights. First, we quantitatively assessed the accuracy of GEDI L2A data in forest canopy height inversion and conducted an error analysis. A GEDI forest canopy height error correction model has been developed, taking into account both forest density and terrain effects. This study elucidated the influence of forest density and terrain on the error in forest canopy height estimation, ultimately leading to an improvement in the accuracy of forest canopy height inversion. In light of the identified error patterns, quality control criteria for GEDI footprints are formulated, and a correction model for GEDI forest canopy height is established to achieve high-precision inversion. We selected 19 forest areas located in the United States with high-accuracy Digital Terrain Models (DTMs) and Canopy Height Models (CHMs) to analyze the error factors of GEDI forest canopy heights and assess the proposed accuracy improvement for GEDI forest canopy heights. The findings reveal a decrease in the corrected RMSE value of forest canopy height from 5.60 m to 4.19 m, indicating a 25.18% improvement in accuracy. [ABSTRACT FROM AUTHOR]

Continuous and extensive monitoring of forest height is essential for estimating forest above-ground biomass and predicting the ability of forests to absorb CO2. In particular, forest height at the national scale is an important indicator reflecting the national forestry economic construction, environmental governance, and ecological balance. However, the lack of inventory data restricts large-scale monitoring of forest height to some extent. Conducting manual surveys of forest height for large-scale areas would be labor-intensive and time-consuming. The successful launch of the new generation of spaceborne light detection and ranging (LiDAR) (The Ice, Cloud, and Land Elevation Satellite-2/the Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS) has brought new opportunities for national-scale forestry resource surveys. This paper explores a method to survey national forest canopy height from the new generation of ICESat-2/ATLAS data. In view of the sparse sampling and little overlap between repeated spaceborne LiDAR data, a strategy for assessing the overall change of canopy height for large scales is provided. Some spatially continuous ancillary data were used to assist ICESat-2/ATLAS data to generate a wall-to-wall (spatially continuous) forest canopy height map in China by using the machine learning approach and then quantifying the analysis of forest canopy height in various provinces. The results show that there is a good correlation between the model forest height and the verification data, with a root mean squared error (RMSE) of 3.30 m and a coefficient of determination (R2) of 0.87. This indicates that the method for retrieving national forest canopy height is reliable. There are some limitations in areas with lower vegetation coverage or complex topography which need additional filtering or terrain correction to achieve higher accuracy in measuring forest canopy height. Our analysis suggests that ICESat-2/ATLAS data can achieve the retrieval of national forest height at an overall level, and it would be feasible to use ICESAT-2/ATLAS products to estimate forest canopy height change for large-scale areas. [ABSTRACT FROM AUTHOR]

Influenza A virus is an important pathogenic virus known to induce host cell cycle arrest in G(0)/G(1) phase and create beneficial conditions for viral replication. However, how the virus achieves arrest remains unclear. We investigated the mechanisms underlying this process and found that the nonstructural protein 1 (NS1) is required. Based on this finding, we generated a viable influenza A virus (H1N1) lacking the entire NS1 gene to study the function of this protein in cell cycle regulation. In addition to some cell cycle regulators that were changed, the concentration and activity of RhoA protein, which is thought to be pivotal for G(1)/S phase transition, were also decreased with overexpressing NS1. And in the meantime, the phosphorylation level of cell cycle regulator pRb, downstream of RhoA kinase, was decreased in an NS1-dependent manner. These findings indicate that the NS1 protein induces G(0)/G(1) cell cycle arrest mainly through interfering with the RhoA/pRb signaling cascade, thus providing favorable conditions for viral protein accumulation and replication. We further investigated the NS1 protein of avian influenza virus (H5N1) and found that it can also decrease the expression and activity of RhoA, suggesting that the H5N1 virus may affect the cell cycle through the same mechanism. The NS1/RhoA/pRb cascade, which can induce the G(0)/G(1) cell cycle arrest identified here, provides a unified explanation for the seemingly different NS1 functions involved in viral replication events. Our findings shed light on the mechanism of influenza virus replication and open new avenues for understanding the interaction between pathogens and hosts.