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Aluminum chloride (AlCl3) is a potent neurotoxic substance known to cause memory impairment and oxidative stress‐dependent neurodegeneration. Naringenin (NAR) is a dietary flavonoid with potent antioxidant and anti‐inflammatory properties which was implemented against AlCl3‐induced neurotoxicity to ascertain its neuroprotective efficacy. Experimental neurotoxicity in mice was induced by exposure of AlCl3 (10 mg/kg, p.o.) followed by treatment with NAR (10 mg/kg, p.o.) for a total of 63 days. Assessed the morphometric, learning memory dysfunction (novel object recognition, T‐ and Y‐maze tests), neuronal oxidative stress, and histopathological alteration in different regions of the brain, mainly cortex, hippocampus, thalamus, and cerebellum. AlCl3 significantly suppressed the spatial learning and memory power which were notably improved by administration of NAR. The levels of oxidative stress parameters nitric oxide, advanced oxidation of protein products, protein carbonylation, lipid peroxidation, superoxide dismutase, catalase, glutathione reductase, reduced glutathione, and the activity of acetylcholine esterase were altered 1.5–3 folds by AlCl3 significantly. Treatment of NAR remarkably restored the level of oxidative stress parameters and maintained the antioxidant defense system. AlCl3 suppressed the expression of neuronal proliferation marker NeuN that was restored by NAR treatment which may be a plausible mechanism. NAR showed therapeutic efficacy as a natural supplement against aluminum‐intoxicated memory impairments and histopathological alteration through a mechanism involving an antioxidant defense system and neuronal proliferation. [ABSTRACT FROM AUTHOR]

The difference between observed and modelled precession/nutation reveals unmodelled signals commonly referred to as Celestial Pole Offsets (CPO), denoted by dX and dY. CPO are currently observed only by Very Long Baseline Interferometry (VLBI), but there is nearly 4 weeks of latency by which the data centers provide the most accurate, final CPO series. This latency problem necessitates predicting CPO for high-accuracy, real-time applications that require information regarding Earth rotation, such as spacecraft navigation. Even though the International Earth Rotation and Reference Systems Service (IERS) provides so-called rapid CPO, they are usually less accurate and therefore, may not satisfy the requirements of the mentioned applications. To enhance the quality of CPO predictions, we present a new methodology based on Neural Additive Models (NAMs), a class of interpretable machine learning algorithms. We formulate the problem based on long short-term memory neural networks and derive simple analytical relations for the quantification of prediction uncertainty and feature importance, thereby enhancing the intelligibility of predictions made by machine learning. We then focus on the short-term prediction of CPO with a forecasting horizon of 30 days. We develop an operational framework that consistently provides CPO predictions. Using the CPO series of Jet Propulsion Laboratory as the input to the algorithm, we show that NAMs predictions improve the IERS rapid products on average by 57% for dX and 25% for dY under fully operational conditions. Our predictions are both accurate and overcome the latency issue of final CPO series and thus, can be used in real-time applications. [ABSTRACT FROM AUTHOR]

We prove rigorous bounds on the errors resulting from the approximation of the incompressible Navier–Stokes equations with (extended) physics-informed neural networks. We show that the underlying partial differential equation residual can be made arbitrarily small for tanh neural networks with two hidden layers. Moreover, the total error can be estimated in terms of the training error, network size and number of quadrature points. The theory is illustrated with numerical experiments. [ABSTRACT FROM AUTHOR]

Celestial Pole Offsets (CPO), denoted by dX and dY, describe the differences in the observed position of the pole in the celestial frame with respect to a certain precession-nutation model. Precession and nutation components are part of the transformation matrix between terrestrial and celestial systems. Therefore, various applications in geodetic science such as high-precision spacecraft navigation require information regrading precession and nutation. For this purpose, CPO can be added to the precession-nutation model to precisely describe the motion of the celestial pole. However, as Very Long Baseline Interferometry (VLBI)—currently the only technique providing CPO—requires long data processing times resulting in several weeks of latency, predictions of CPO become necessary. Here we present a new methodology named Deep Ensemble Geophysics-Informed Neural Networks (DEGINNs) to provide accurate CPO predictions. The methodology has three main elements: (1) deep ensemble learning to provide the prediction uncertainty; (2) broad-band Liouville equation as a geophysical constraint connecting the rotational dynamics of CPO to the atmospheric and oceanic Effective Angular Momentum (EAM) functions and (3) coupled oscillatory recurrent neural networks to model the sequential characteristics of CPO time-series, also capable of handling irregularly sampled time-series. To test the methodology, we use the newest version of the final CPO time-series of International Earth Rotation and Reference Systems Service (IERS), namely IERS 20 C04. We focus on a forecasting horizon of 90 days, the practical forecasting horizon needed in space-geodetic applications. Furthermore, for validation purposes we generate an independent global VLBI solution for CPO since 1984 up to the end of 2022 and analyse the series. We draw the following conclusions. First, the prediction performance of DEGINNs demonstrates up to 25 and 33 percent improvement, respectively, for dX and dY, with respect to the rapid data provided by IERS. Secondly, predictions made with the help of EAM are more accurate compared to those without EAM, thus providing a clue to the role of atmosphere and ocean on the excitation of CPO. Finally, free core nutation period shows temporal variations with a dominant periodicity of around one year, partially excited by EAM. [ABSTRACT FROM AUTHOR]

The melting of ice sheets and global glaciers results in sea-level rise, a pole-to-equator mass transport increasing Earth's oblateness and resulting in an increase in the length of day (LOD). Here, we use observations and reconstructions of mass variations at the Earth's surface since 1900 to show that the climate-induced LOD trend hovered between 0.3 and 1.0 ms/cy in the 20th century, but has accelerated to 1.33 [Formula: see text] 0.03 ms/cy since 2000. We further show that surface mass transport fully explains the accelerating trend in the Earth oblateness observed in the past three decades. We derive an independent measure of the decreasing LOD trend induced by Glacial Isostatic Adjustment (GIA) of [Formula: see text]0.80 [Formula: see text] 0.10 ms/cy, which provides a constraint for the mantle viscosity. The sum of this GIA rate and lunar tidal friction fully explains the secular LOD trend that is inferred from the eclipse record in the past three millennia prior to the onset of contemporary climate change. Projections of future climate warming under high emission scenarios suggest that the climate-induced LOD rate may reach 2.62 [Formula: see text] 0.79 ms/cy by 2100, overtaking lunar tidal friction as the single most important contributor to the long-term LOD variations., Competing Interests: Competing interests statement:The authors declare no competing interest.

Alumunium usage and toxicity has been a global concern especially an increased use of nanoparticulated aluminum (Al-NPs) products from the environment and the workplace. Al degrades in to nanoparticulate form in the environment due to the routine process of bioremediation in human body. Al-NPs toxicity plays key role in the pathophysiology of neurodegeneration which is characterised by the development of neurofibrillary tangles and neuritic plaques which correlates to the Alzheimer's disease. This study evaluated the Al-NPs induced neurodegeneration and causative behavioral alterations due to oxidative stress, inflammation, DNA damage, β-amyloid aggregation, and histopathological changes in mice. Furthermore, the preventive effect of naringenin (NAR) as a potent neuroprotective flavonoid against Al-NPs induced neurodegeneration was assessed. Al-NPs were synthesized and examined using FTIR, XRD, TEM, and particle size analyzer. Mice were orally administered with Al-NPs (6 mg/kg b.w.) followed by NAR treatment (10 mg/kg b.w. per day) for 66 days. The spatial working memory was determined by novel object recognition, T-maze, Y-maze, and Morris Water Maze tests. We measured nitric oxide, advanced oxidation of protein products, protein carbonylation, lipid peroxidation, superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase, reduced glutathione, oxidised glutathione, and acetylcholine esterase, as well as cytokines analysis, immunohistochemistry, and DNA damage. Al-NPs significantly reduced the learning memory power, increased oxidative stress, reduced antioxidant enzymatic activity, increased DNA damage, altered the levels of cytokines, and increased β-amyloid aggregation in the cortex and hippocampus regions of the mice brain. These neurobehavioral impairments, neuronal oxidative stress, and histopathological alterations were significantly attenuated by NAR supplementation. In conclusion, Al-NPs may be potent neurotoxic upon exposure and that NAR could serve as a potential preventive measure in the treatment and management of neuronal degeneration., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)