A compilation of known QSOs for the Gaia mission

Quasars are essential for astrometric in the sense that they are spatial stationary because of their large distance from the Sun. The European Space Agency (ESA) space astrometric satellite Gaia is scanning the whole sky with unprecedented accuracy up to a few muas level. However, Gaia's two fields of view observations strategy may introduce a parallax bias in the Gaia catalog. Since it presents no significant parallax, quasar is perfect nature object to detect such bias. More importantly, quasars can be used to construct a Celestial Reference Frame in the optical wavelengths in Gaia mission. In this paper, we compile the most reliable quasars existing in literatures. The final compilation (designated as Known Quasars Catalog for Gaia mission, KQCG) contains 1843850 objects, among of them, 797632 objects are found in Gaia DR1 after cross-identifications. This catalog will be very useful in Gaia mission

Alternating Deep Low Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications

Exponential function is a fundamental signal form in general signal processing and biomedical applications, such as magnetic resonance spectroscopy and imaging. How to reduce the sampling time of these signals is an important problem. Sub-Nyquist sampling can accelerate signal acquisition but bring in artifacts. Recently, the low rankness of these exponentials has been applied to implicitly constrain the deep learning network through the unrolling of low rank Hankel factorization algorithm. However, only depending on the implicit low rank constraint cannot provide the robust reconstruction, such as sampling rate mismatches. In this work, by introducing the explicit low rank prior to constrain the deep learning, we propose an Alternating Deep Low Rank approach (ADLR) that utilizes deep learning and optimization solvers alternately. The former solver accelerates the reconstruction while the latter one corrects the reconstruction error from the mismatch. The experiments on both general exponential functions and realistic biomedical magnetic resonance data show that, compared with the state-of-the-art methods, ADLR can achieve much lower reconstruction error and effectively alleviates the decrease of reconstruction quality with sampling rate mismatches.Comment: 14 page

MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priors

High-fidelity 3D scene reconstruction from monocular videos continues to be challenging, especially for complete and fine-grained geometry reconstruction. The previous 3D reconstruction approaches with neural implicit representations have shown a promising ability for complete scene reconstruction, while their results are often over-smooth and lack enough geometric details. This paper introduces a novel neural implicit scene representation with volume rendering for high-fidelity online 3D scene reconstruction from monocular videos. For fine-grained reconstruction, our key insight is to incorporate geometric priors into both the neural implicit scene representation and neural volume rendering, thus leading to an effective geometry learning mechanism based on volume rendering optimization. Benefiting from this, we present MonoNeuralFusion to perform the online neural 3D reconstruction from monocular videos, by which the 3D scene geometry is efficiently generated and optimized during the on-the-fly 3D monocular scanning. The extensive comparisons with state-of-the-art approaches show that our MonoNeuralFusion consistently generates much better complete and fine-grained reconstruction results, both quantitatively and qualitatively.Comment: 12 pages, 12 figure

Engineered M2a macrophages for the treatment of osteoarthritis

BackgroundMacrophage is a central regulator of innate immunity. Its M2 subsets, such as interstitial synovial macrophages, have been found to play critical roles in suppressing chronic inflammation and maintaining homeostasis within the joint. These macrophages have great potential as a disease-modifying cell therapy for osteoarthritis (OA). However, this has not yet been studied.MethodsMacrophages were isolated from the bone marrow of rats. We constructed a stable macrophage that “locked” in anti-inflammatory and pro-regenerative M2a polarity (L-M2a) by simultaneously knocking out tumor necrosis factor receptor 1 (TNFR1) and overexpressing IL-4 using Cas9-ribonuclear proteins (Cas9-RNP) and electroporation. In vitro, these L-M2a macrophages were treated with OA synovial fluid or co-cultured with OA chondrocytes or fibroblast-like synoviocytes (FLS). In vivo, L-M2a macrophages were injected intra-articularly to evaluate their homing and engrafting abilities and therapeutic effects on OA progression using a rat model.ResultsL-M2a macrophages displayed a typical anti-inflammatory phenotype similar to that of M2 macrophages in vitro. In OA microenvironment, L-M2a macrophages maintained a stable anti-inflammatory phenotype, whereas unmodified M2 macrophages lost their phenotype and switched to M1 polarity. L-M2a macrophages demonstrated a potent anti-inflammatory effect in crosstalk with OA-FLSs and an anti-degenerative effect in crosstalk with senescent OA chondrocytes. In vivo, compared with M2 macrophages and exosomes, L-M2a macrophages exhibited significantly superior therapeutic effects in OA by successfully resolving inflammation, restoring tissue homeostasis, and promoting cartilage regeneration.ConclusionThe engineered L-M2a macrophages maintained a superior anti-inflammatory and pro-regenerative capacity in the inflammatory OA microenvironment and represents an ideal new strategy for the disease-modifying therapy of OA