Frozen in time for 24,000 years—then suddenly alive again. Scientists have revived ancient microscopic creatures from Siberian permafrost, revealing an extraordinary survival trick that could revolutionize cryobiology. What secrets do these tiny survivors hold?
When researchers injected mRNA encoding this protein into mice, it produced enough to protect DNA from radiation damage.
First commercial lander ever just landed on the moon. Watch to see why this is so important.
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OpenAI and nine national labs bring together leading scientists for first-of-its kind event.
While the robot presents a glimpse into the future of robotic caregiving, it will only be ready by 2030.
Developed by researchers from Waseda University, the AI-driven robot addresses Japan’s caregiver shortage in the wake of an ageing population.
A Texture Enhancement Method for Oceanic Internal Wave Synthetic Aperture Radar Images Based on Non-Local Mean Filtering and Texture Layer Enhancement
Posted in futurism | Leave a Comment on A Texture Enhancement Method for Oceanic Internal Wave Synthetic Aperture Radar Images Based on Non-Local Mean Filtering and Texture Layer Enhancement
Synthetic aperture radar (SAR) is an important tool for observing the oceanic internal wave phenomenon. However, owing to the unstable imaging quality of SAR on oceanic internal waves, the texture details of internal wave images are usually unclear, which is not conducive to the subsequent applications of the images. To cope with this problem, a texture enhancement method for oceanic internal wave SAR images is proposed in this paper, which is based on non-local mean (NLM) filtering and texture layer enhancement (TLE). Since the strong speckle noise commonly present in internal wave images is simultaneously enhanced during texture enhancement, resulting in degraded image quality, NLM filtering is first performed to suppress speckle noise.
Although rock glaciers (RGs) are prevalent in the southwestern Pamirs, systematic studies on them are scarce. This article introduces the first inventory of RGs in the southwestern Pamirs, situated at the western edge of the High Mountain Asia region. The inventory, established through a combination of Google Earth optical imagery and Interferometric Synthetic Aperture Radar (InSAR) techniques, encompasses details on the locations, geomorphological parameters, and kinematic attributes of RGs. A total of 275 RGs were cataloged in an area of 55.52 km2 from 3,620 to 5,210 m in altitude. Our inventory shows that most RGs in this region are talus-connected (213 landforms), with the highest frequency facing northeast (23%). The distribution of RGs thins from west to east and is more abundant in higher altitudes.
Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery
Posted in drones, food, information science, satellites, sustainability | Leave a Comment on Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery
Satellite-based optical remote sensing from missions such as ESA’s Sentinel-2 (S2) have emerged as valuable tools for continuously monitoring the Earth’s surface, thus making them particularly useful for quantifying key cropland traits in the context of sustainable agriculture [1]. Upcoming operational imaging spectroscopy satellite missions will have an improved capability to routinely acquire spectral data over vast cultivated regions, thereby providing an entire suite of products for agricultural system management [2]. The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) [3] will complement the multispectral Copernicus S2 mission, thus providing enhanced services for sustainable agriculture [4, 5]. To use satellite spectral data for quantifying vegetation traits, it is crucial to mitigate the absorption and scattering effects caused by molecules and aerosols in the atmosphere from the measured satellite data. This data processing step, known as atmospheric correction, converts top-of-atmosphere (TOA) radiance data into bottom-of-atmosphere (BOA) reflectance, and it is one of the most challenging satellite data processing steps e.g., [6, 7, 8]. Atmospheric correction relies on the inversion of an atmospheric radiative transfer model (RTM) leading to the obtaining of surface reflectance, typically through the interpolation of large precomputed lookup tables (LUTs) [9, 10]. The LUT interpolation errors, the intrinsic uncertainties from the atmospheric RTMs, and the ill posedness of the inversion of atmospheric characteristics generate uncertainties in atmospheric correction [11]. Also, usually topographic, adjacency, and bidirectional surface reflectance corrections are applied sequentially in processing chains, which can potentially accumulate errors in the BOA reflectance data [6]. Thus, despite its importance, the inversion of surface reflectance data unavoidably introduces uncertainties that can affect downstream analyses and impact the accuracy and reliability of subsequent products and algorithms, such as vegetation trait retrieval [12]. To put it another way, owing to the critical role of atmospheric correction in remote sensing, the accuracy of vegetation trait retrievals is prone to uncertainty when atmospheric correction is not properly performed [13].
Although advanced atmospheric correction schemes became an integral part of the operational processing of satellite missions e.g., [9,14,15], standardised exhaustive atmospheric correction schemes in drone, airborne, or scientific satellite missions remain less prevalent e.g., [16,17]. The complexity of atmospheric correction further increases when moving from multispectral to hyperspectral data, where rigorous atmospheric correction needs to be applied to hundreds of narrow contiguous spectral bands e.g., [6,8,18]. For this reason, and to bypass these challenges, several studies have instead proposed to infer vegetation traits directly from radiance data at the top of the atmosphere [12,19,20,21,22,23,24,25,26].
Nasal anti-CD3 monoclonal antibody ameliorates traumatic brain injury, enhances microglial phagocytosis and reduces neuroinflammation via IL-10-dependent Treg–microglia crosstalk Neuroscience
Posted in neuroscience | Leave a Comment on Nasal anti-CD3 monoclonal antibody ameliorates traumatic brain injury, enhances microglial phagocytosis and reduces neuroinflammation via IL-10-dependent Treg–microglia crosstalk Neuroscience
Nasal anti-CD3 therapy shows promise for treating traumatic brain injury by reducing neuroinflammation and aiding recovery in mice. It induces interleukin-10-producing regulatory T cells that enhance microglial phagocytic activity and reduce chronic inflammation, potentially aiding brain repair.