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The Transiting Exoplanet Survey Satellite (TESS) is continuing its second extended mission after 55 sectors of observations. TESS publishes full-frame images (FFIs) at a cadence of 1800, 600, or 200 s, allowing light curves to be extracted for stars beyond a limited number of pre-selected stars. Simulations show that thousands of exoplanets, eclipsing binaries, variable stars, and other astrophysical transients can be found in these FFI light curves. To obtain high-precision light curves, we forward model the FFI with the effective point-spread function (PSF) to remove contamination from nearby stars. We adopt star positions and magnitudes from Gaia DR3 as priors. The resulting light curves, called TESS–Gaia light curves (TGLCs), show a photometric precision closely tracking the prelaunch prediction of the noise level. The TGLCs’ photometric precision reaches ≲2% at 16th TESS magnitude even in crowded fields. We publish TGLC aperture and PSF light curves for stars down to 16th TESS magnitude through the Mikulski Archive for Space Telescopes for all available sectors and will continue to deliver future light curves. The open-source package tglc 3 3 Via 10.17909/610m‐9474. is publicly available to enable any user to produce customized light curves.

We introduce the problem of variable-length (VL) source resolvability, in which a given target probability distribution is approximated by encoding a VL uniform random number, and the asymptotically minimum average length rate of the uniform random number, called the VL resolvability, is investigated. We first analyze the VL resolvability with the variational distance as an approximation measure. Next, we investigate the case under the divergence as an approximation measure. When the asymptotically exact approximation is required, it is shown that the resolvability under two kinds of approximation measures coincides. We then extend the analysis to the case of channel resolvability, where the target distribution is the output distribution via a general channel due to a fixed general source as an input. The obtained characterization of channel resolvability is fully general in the sense that, when the channel is just an identity mapping, it reduces to general formulas for source resolvability. We also analyze the second-order VL resolvability.

The radius of a planet is a fundamental parameter that probes its composition and habitability. Precise radius measurements are typically derived from the fraction of starlight blocked when a planet transits its host star. The wide-field Transiting Exoplanet Survey Satellite (TESS) has discovered hundreds of new exoplanets, but its low angular resolution means that the light from a star hosting a transiting exoplanet can be blended with the light from background stars. If not fully corrected, this extra light can dilute the transit signal and result in a smaller measured planet radius. In a study of hundreds of TESS planet discoveries using deblended light curves from our validated methodology, we show that systematically incorrect planet radii are common in the literature: studies using various public TESS photometry pipelines have underestimated the planet radius by a weighted median of 6.1% ± 0.3%, leading to a ∼20% overestimation of planet density. The widespread presence of these biases in the literature has profoundly shaped—and potentially misrepresented—our understanding of the exoplanet population. Addressing these biases will refine the exoplanet mass–radius relation, reshape our understanding of exoplanet atmospheric and bulk composition, and potentially inform prevailing planet formation theories.

Posterior instrumentation is used to treat severe adolescent idiopathic scoliosis (AIS) with a Cobb angle greater than 40 degrees. Clinical studies indicate that AIS patients may develop adjacent segment degeneration (ASD) post-surgery. However, there is limited research on the biomechanical effects on adjacent segments after surgery, and straightforward methods for creating finite element (FE) models that reflect vertebral deformation are lacking. Therefore, this study aims to use biplanar X-ray images to establish a case-specific, parameterized FE model reflecting coronal plane vertebral deformation and employ FE analysis to compare pre- and postoperative changes in the range of motion (ROM), endplate stress, and intervertebral disk stress of adjacent segments. We developed an FE model from biplanar X-ray images of a patient with AIS, using ANSYS software to establish pre- and postoperative models. The shape of the preoperative model was validated using computed tomography (CT) reconstruction. A flexion moment was applied to C7 of the spine model to achieve the same forward bending angle in the pre- and postoperative models. This study successfully developed a case-specific parameterized FE model based on X-ray images. The differences between Cobb angle and thoracolumbar kyphosis angle measurements in X-ray images and CT reconstructions were 6.5 and 5.4 mm. This FE model was used to analyze biomechanical effects on motion segments adjacent to the fixation site, revealing a decrease in maximum endplate and disk stress in the cranial segment and an increase in stress in the caudal segment.

Recent discoveries of transiting giant exoplanets around M-dwarf stars (GEMS), aided by the all-sky coverage of TESS, are starting to stretch theories of planet formation through the core-accretion scenario. Recent upper limits on their occurrence suggest that they decrease with lower stellar masses, with fewer GEMS around lower-mass stars compared to solar-type. In this paper, we discuss existing GEMS both through confirmed planets, as well as protoplanetary disk observations, and a combination of tests to reconcile these with theoretical predictions. We then introduce the Searching for GEMS survey, where we utilize multidimensional nonparameteric statistics to simulate hypothetical survey scenarios to predict the required sample size of transiting GEMS with mass measurements to robustly compare their bulk-density with canonical hot Jupiters orbiting FGK stars. Our Monte Carlo simulations predict that a robust comparison requires about 40 transiting GEMS (compared to the existing sample of ∼15) with 5σ mass measurements. Furthermore, we discuss the limitations of existing occurrence estimates for GEMS and provide a brief description of our planned systematic search to improve the occurrence rate estimates for GEMS.

To solve the problems of susceptibility to image noise, subjectivity of training sample selection, and inefficiency of state-of-the-art change detection methods with heterogeneous images, this study proposes a post-classification change detection method for heterogeneous images with improved training of hierarchical extreme learning machine (HELM). After smoothing the images to suppress noise, a sample selection method is defined to train the HELM for each image, in which the feature extraction is respectively implemented for heterogeneous images and the parameters need not be fine-tuned. Then, the multi-temporal feature maps extracted from the trained HELM are segmented to obtain classification maps and then compared to generate a change map with changed types. The proposed method is validated experimentally by using one set of synthetic aperture radar (SAR) images obtained from Sentinel-1, one set of optical images acquired from Google Earth, and two sets of heterogeneous SAR and optical images. The results show that compared to state-of-the-art change detection methods, the proposed method can improve the accuracy of change detection by more than 8% in terms of the kappa coefficient and greatly reduce run time regardless of the type of images used. Such enhancement reflects the robustness and superiority of the proposed method.

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K-nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction.

Heterogeneous change detection (CD) is widely applied in various fields such as urban planning, environmental monitoring, and disaster management. It enhances the accuracy and comprehensiveness of surface change monitoring by integrating multi-sensor remote sensing data. Scholars have proposed many graph-based methods to address the issue of incomparable heterogeneous images caused by imaging differences. However, these methods often overlook the influence of changes in vertex status on the graph structure, which limits their ability to represent image structural features. To tackle this problem, this paper presents an unsupervised heterogeneous CD method based on enhanced graph structure representation (EGSR). This method enhances the representation capacity of the graph structure for image structural features by measuring the unchanged probabilities of vertices, thereby making it easier to detect changes in heterogeneous images. Firstly, we construct the graph structure using image superpixels and measure the structural graph differences of heterogeneous images in the same image domain. Then, we calculate the unchanged probability of each vertex in the structural graph and reconstruct the graph structure using this probability. To accurately represent the graph structure, we adopt an iterative framework for enhancing the representation of the graph structure. Finally, at the end of the iteration, the final change map (CM) is obtained by binary segmentation of the graph vertices based on their unchanged probabilities. The effectiveness of this method is validated through experiments on four sets of heterogeneous image datasets and two sets of homogeneous image datasets.

Guangsangon E (GSE) is a novel Diels–Alder adduct isolated from leaves of Morus alba L, a traditional Chinese medicine widely applied in respiratory diseases. It is reported that GSE has cytotoxic effect on cancer cells. In our research, we investigated its anticancer effect on respiratory cancer and revealed that GSE induces autophagy and apoptosis in lung and nasopharyngeal cancer cells. We first observed that GSE inhibits cell proliferation and induces apoptosis in A549 and CNE1 cells. Meanwhile, the upregulation of autophagosome marker LC3 and increased formation of GFP–LC3 puncta demonstrates the induction of autophagy in GSE-treated cells. Moreover, GSE increases the autophagy flux by enhancing lysosomal activity and the fusion of autophagosomes and lysosomes. Next, we investigated that endoplasmic reticulum (ER) stress is involved in autophagy induction by GSE. GSE activates the ER stress through reactive oxygen species (ROS) accumulation, which can be blocked by ROS scavenger NAC. Finally, inhibition of autophagy attenuates GSE-caused cell death, termed as “autophagy-mediated cell death.” Taken together, we revealed the molecular mechanism of GSE against respiratory cancer, which demonstrates great potential of GSE in the treatment of representative cancer.

Single-object tracking (SOT) in satellite videos (SVs) is a promising and challenging task in the remote sensing community. In terms of the object itself and the tracking algorithm, the rotation of small-sized objects and tracking drift are common problems due to the nadir view coupled with a complex background. This article proposes a novel rotation adaptive tracker with motion constraint (RAMC) to explore how the hybridization of angle and motion information can be utilized to boost SV object tracking from two branches: rotation and translation. We decouple the rotation and translation motion patterns. The rotation phenomenon is decomposed into the translation solution to achieve adaptive rotation estimation in the rotation branch. In the translation branch, the appearance and motion information are synergized to enhance the object representations and address the tracking drift issue. Moreover, an internal shrinkage (IS) strategy is proposed to optimize the evaluation process of trackers. Extensive experiments on space-born SV datasets captured from the Jilin-1 satellite constellation and International Space Station (ISS) are conducted. The results demonstrate the superiority of the proposed method over other algorithms. With an area under the curve (AUC) of 0.785 and 0.946 in the success and precision plots, respectively, the proposed RAMC achieves optimal performance while running at real-time speed.