Explore the vast range of titles available.

Yangtze River basin (YZB) experienced record‐breaking heat in the summer of 2022. Here, we focused on daytime‐nighttime compound heat waves, and used the magnitude index that considers both duration and intensity to investigate the risk of the 2022 extreme heat. The magnitude of heatwaves in 2022 was much larger than the historical average level, which was estimated as a 1‐in‐64‐year event over 1979–2014 climate. Without mitigation efforts (SSP585), the record‐breaking heat would emerge as normal during 2050s, and would affect ∼70% of land and projected population in the basin before global mean temperature change reaches 3°C. Such an emergence could be progressively delayed and impacts could be reduced under lower warming levels. The affected area would be 60% lesser at 2°C warming, and the emergence could be avoided by limiting warming to 1.5°C. Our results call for urgent mitigation efforts for reducing the risk of compound heat extremes. Plain Language Summary Persistent extreme high temperature occurred in Yangtze River basin (YZB) during summer 2022, leading to widespread droughts, heat‐related disease and socioeconomic losses. The physical drivers and future risk of the heat extremes have attracted extensive attention. Focusing on daytime‐nighttime compound heat waves, which have more adverse effect on human health and ecosystem, we investigated the magnitude of compound heat waves occurred in 2022, and future risk under different warming levels and emission scenarios. Observations show that the magnitude was the strongest since records began in 1979, and the return period was 64 (95% CI: 30–223) years over the 1979–2014 climate. Projections show that such unprecedented heat in observed records would emerge as normal since 2053 (2081) under nonmitigation (moderate mitigation) scenario. Moderate climate mitigation efforts could delay the time by 28 years. Compared with the 2°C global warming level, about 10% of lands and population would be avoided to normally expose to such heat at 1.5°C warming level. However, the exposures would increase by 60% from 2°C to 3°C warming level. This study suggests that mitigation actions for accomplishing 2°C warming goal and further pursuing the ambitious goal of 1.5°C can substantially reduce the exposure risk of such unprecedented heat. Key Points The 2022 record‐breaking compound heat waves in Yangtze River basin would become normal during 2050s under a nonmitigation scenario Moderate climate mitigation efforts would delay the time of emergence by 28 years Compared to 3°C, limiting warming to 2°C and 1.5°C could avoid 60% and 70% of lands that are normally affected by such rare heat, respectively

The nucleus pulposus is in a hypoxic environment in the human body, and when intervertebral disc degeneration (IVDD) occurs, the hypoxic environment is disrupted. Nucleus pulposus cell (NPC) ferroptosis is one of the causes of IVDD. N6‐methyladenosine (m6A) and its reader protein YTHDF1 regulate cellular activities by affecting RNA metabolism. However, the regulation of ferroptosis in NPCs by m6A‐modified RNAs under hypoxic conditions has not been as well studied. In this study, through in vitro and in vivo experiments, we explored the underlying mechanism of HIF‐1α and YTHDF1 in regulating ferroptosis in NPCs. The results indicated that the overexpression of HIF‐1α or YTHDF1 suppressed NPC ferroptosis; conversely, the knockdown of HIF‐1α or YTHDF1 increased ferroptosis levels in NPCs. Luciferase reporter assays and chromatin immunoprecipitation demonstrated that HIF‐1α regulated YTHDF1 transcription by directly binding to its promoter region. Polysome profiling results showed that YTHDF1 promoted the translation of SLC7A11 and consequently the expression of the anti‐ferroptosis protein GPX4 by binding to m6A‐modified SLC7A11 mRNA. In conclusion, HIF‐1α‐induced YTHDF1 expression reduces NPC ferroptosis and delays IVDD by promoting SLC7A11 translation in a m6A‐dependent manner. HIF‐1α regulated YTHDF1 transcription by directly binding to its promoter region. YTHDF1 promoted the translation of SLC7A11 and consequently the expression of the anti‐ferroptosis protein GPX4 by binding to m6A‐modified SLC7A11 mRNA. HIF‐1α‐induced YTHDF1 expression reduces NPC ferroptosis and delays IVDD by promoting SLC7A11 translation in a m6A‐dependent manner.

Chorus subpackets are the wave packets with modulated amplitudes in chorus waves, commonly observed in the magnetospheres of Earth and other planets. Nonlinear wave‐particle interactions have been suggested to play an important role in subpacket formation, yet the corresponding electron dynamics remain not fully understood. In this study, we have investigated the electron trapping through cyclotron resonance with subpackets, using a self‐consistent general curvilinear plasma simulation code simulation model in dipole fields. The electron trapping period has been quantified separately through electron dynamic analysis and theoretical derivation. Both methods indicate that the electron trapping period is shorter than the subpacket period/duration. We have further established the relation between electron trapping period and subpacket period through statistical analysis using simulation and observational data. Our study demonstrates that the nonlinear electron trapping through cyclotron resonance is the dominant mechanism responsible for subpacket formation. Plain Language Summary The spectrum of chorus waves comprises a series of subpackets, characterized by modulated amplitudes within a timescale of ∼10–100 milliseconds. In this study, we have investigated the self‐consistent wave‐particle interactions with subpackets, using two‐dimensional particle‐in‐cell simulations in dipole fields. Cyclotron resonant electrons are trapped in wave phases, and we have measured their trapping period. Since these electrons move in the opposite direction of subpacket propagation, the corresponding trapping period is smaller than the period of subpackets. We have further established the relation between the two periods and validated it through both simulation and observational data. This relation facilitates evaluating electron trapping period from direct measurement of subpackets in observations. Our study sheds important lights on the key role of nonlinear electron trapping through cyclotron resonance in the formation of subpackets. Key Points Electron trapping dynamics in the formation of quasi‐parallel chorus subpackets have been investigated The linkage between electron trapping period and subpacket period is quantified via a geometric relation, where the trapping period is shorter The proposed relation between electron trapping period and subpacket period is an extension of the classical results of O’Neil (1965)

The precipitation of tens to hundreds of keV electrons from Earth's magnetosphere plays a crucial role in magnetosphere‐ionosphere coupling, primarily driven by chorus wave scattering. Most existing simulations of electron precipitation rely on test particle models that neglect particle feedback on waves. However, both theoretical and observational studies indicate that the feedback from energetic electrons significantly influences chorus wave excitation and evolution. In this study, we quantify electron precipitation driven by chorus waves using self‐consistent simulations at L = 6 with typical magnetospheric plasma parameters. Electrons in the ∼10–200 keV range are precipitated, exhibiting energy‐dispersive characteristics. The precipitation intensity reaches ∼108–109 ${10}^{8}\\!\\mathit{\\mbox{--}}\\!{10}^{9}$ keV/s/sr/cm2/MeV $\\mathrm{k}\\mathrm{e}\\mathrm{V}/\\mathrm{s}/\\mathrm{s}\\mathrm{r}/{\\mathrm{c}\\mathrm{m}}^{2}/\\mathrm{M}\\mathrm{e}\\mathrm{V}$, consistent with the typical values in observations. As a comparison, test particle simulations underestimate the precipitation intensity by nearly an order of magnitude. These results highlight the importance of self‐consistent simulations in quantifying electron precipitation and investigating wave‐particle interactions that modulate magnetospheric dynamics.

We introduce a versatile generative learning framework that integrates probabilistic diffusion models, observational data, and domain knowledge for stochastic modeling of flow in porous media. The framework begins by pretraining an unconditional diffusion model to approximate the joint distribution of subsurface parameters and state variables, effectively capturing prior information of dynamical systems. By leveraging Bayesian conditional sampling, the model flexibly incorporates specific constraints and adapts to multiple modeling tasks without retraining or fine‐tuning. Furthermore, we devise a training‐free knowledge alignment strategy that embeds domain‐specific knowledge into the sampling process to generate spatiotemporal fields more compatible with physical principles. Extensive evaluations on diverse subsurface flow problems demonstrate that a single pretrained diffusion model, equipped with optimized generative paths, delivers superior performance in unconditional generation, forward prediction, uncertainty quantification, and inverse modeling with sparse and noisy data. These findings underscore the potential of knowledge‐aligned generative learning to advance subsurface flow modeling research.

Urban floods induced by rainstorms can lead to severe losses of lives and property, making rapid flood prediction essential for effective disaster prevention and mitigation. However, traditional deep learning (DL) models often overlook the spatial heterogeneity of rainstorms and lack interpretability. Here, we propose an end‐to‐end rapid prediction method for urban flood inundation incorporating spatiotemporal varying rainstorms using a Convolutional Long Short‐Term Memory Network (ConvLSTM) DL model. We compare the performance of the proposed method with that of a 3D Convolutional Neural Network (3D CNN) model and introduce the spatial visualization technique Grad‐CAM to interpret the rainstorms contributions to flood predictions. Results demonstrate that: (a) Compared to the physics‐based model, the proposed ConvLSTM model achieves satisfactory accuracy in predicting flood inundation evolution under spatio‐temporal varying rainstorms, with an average Pearson correlation coefficient (PCC) of 0.958 and a mean absolute error (MAE) of 0.021 m, successfully capturing the locations of observed inundation points under actual rainstorm conditions. (b) The ConvLSTM model can rapidly predict urban rainstorm inundation process in just 2 s for a study area of 74 km2, which is 170 times more efficient than a physics‐based model. (c) The interpretability of the ConvLSTM model for urban flood prediction can be enhanced through Grad‐CAM, revealing the model naturally focuses on local or upstream rainfall concentration areas most responsible for inundation, aligning well with hydrological understanding. Overall, the ConvLSTM model serves as a powerful surrogate for rapid urban flood simulation, providing an important reference for real‐time flood early warning and mitigation.

•Holistic thinking and analytic thinking are advanced modes of thinking, which neural mechanisms have not been fully explored.•We used the frame-line task and the triad task, with multivariate pattern analysis (MVPA).•We mapped the fundamental neural substrates of holistic thinking and analytic thinking.•We provided a new method to explore the neural representation of cultural constructs. Holistic and analytic thinking are two distinct modes of thinking used to interpret the world with relative preferences varying across cultures. While most research on these thinking styles has focused on behavioral and cognitive aspects, a few studies have utilized functional magnetic resonance imaging (fMRI) to explore the correlations between brain metrics and self-reported scale scores. Other fMRI studies used single holistic and analytic thinking tasks. As a single task may involve processing in spurious low-level regions, we used two different holistic and analytic thinking tasks, namely the frame-line task and the triad task, to seek convergent brain regions to distinguish holistic and analytic thinking using multivariate pattern analysis (MVPA). Results showed that brain regions fundamental to distinguish holistic and analytic thinking include the bilateral frontal lobes, bilateral parietal lobes, bilateral precentral and postcentral gyrus, bilateral supplementary motor areas, bilateral fusiform, bilateral insula, bilateral angular gyrus, left cuneus, and precuneus, left olfactory cortex, cingulate gyrus, right caudate and putamen. Our study maps brain regions that distinguish between holistic and analytic thinking and provides a new approach to explore the neural representation of cultural constructs. We provide initial evidence connecting culture-related brain regions with language function to explain the origins of cultural differences in cognitive styles.

Purpose of Review We reviewed the literature that explored the use of central and peripheral neuromodulation techniques for chronic daily headache (CDH) treatment. Recent Findings Although the more invasive deep brain stimulation (DBS) is effective in chronic cluster headache (CCH), it should be reserved for extremely difficult-to-treat patients. Percutaneous occipital nerve stimulation has shown similar efficacy to DBS and is less risky in both CCH and chronic migraine (CM). Non-invasive transcutaneous vagus nerve stimulation is a promising add-on treatment for CCH but not for CM. Transcutaneous external trigeminal nerve stimulation may be effective in treating CM; however, it has not yet been tested for cluster headache. Transcranial magnetic and electric stimulations have promising preventive effects against CM and CCH. Summary Although the precise mode of action of non-invasive neuromodulation techniques remains largely unknown and there is a paucity of controlled trials, they should be preferred to more invasive techniques for treating CDH.

Unmanned Aerial Systems (UAVs, Drones), initially known only for their military applications, are getting increasingly popular in the civil sector as well. Over the military canvas, drones have already proven themselves as a potent force multiplier through unmanned, round-the-clock, long-range and high-endurance missions for surveillance, reconnaissance, search and rescue, and even armed combat applications. With the emergence of the Internet of Things (IoT), commercial deployments of drones are also growing exponentially, ranging from cargo and taxi services to agriculture, disaster relief, risk assessment and monitoring of critical infrastructures. Irrespective of the deployment sector, drones are often entrusted to conduct safety, time and liability critical tasks, thus requiring secure, robust and trustworthy operations. In contrast, the rise in UAVs’ demand, coupled with market pressure to reduce size, weight, power and cost (SwaP-C) parameters, has caused vendors to often ignore security aspects, thus inducing serious safety and security threats. As UAVs rely on Global Positioning System (GPS) for positioning and navigation, they can fall prey to GPS jamming and spoofing attacks. The vulnerability of GPS to spoofing has serious implications for UAVs, as victim drones using civil GPS can be misdirected or even completely hijacked for malicious intents, as already demonstrated in several academic research efforts using commercially available GPS spoofing hardware. Beside UAVs, GPS spoofing attacks are equally applicable to other GPS-dependent platforms, including manned aircraft, ground vehicles, and cellular systems. This paper conducts a comprehensive review of GPS spoofing threats, with a special focus on their applicability over UAVs and other GPS-dependent mobile platforms. It presents a novel taxonomy of GPS spoofing attacks and critically analyzes different spoofing techniques based upon placement of spoofing device, attack stealthiness, attack methodologies, and objectives of the attacker. We also discuss some of the recent experiments from open literature which utilized commercially available hardware for successfully conducting spoofing attacks.

Background: Fluoride has been heralded as an agent which can prevent caries in people but in cases of excess long-term exposure, there can be certain consequences on the system as a whole. There are in vitro and animal data that fluoride may decrease muscle protein synthesis and neuromuscular function, but no human data exist. Assessing the effects of two dose levels of fluoride supplementation (2.0 mg/day vs. 0.7 mg/day) and placebo on muscle hypertrophy, increases in strength, bone-muscle endocrine signaling, and neuromuscular activation in eight weeks of supervised resistance training in a high-fluoride area of China. Methods: Sixty control participants aged 1835 years were allocated randomly to high-fluoride, low-fluoride, or placebo contingents (n = 20 each) and underwent three times week training at 70 80 percent 1-RM. The main results were the MRI measured quadriceps cross-sectional area and 1-RM strength. Secondaries included serum osteocalcin, myostatin, surface EMG amplitude and urinary fluoride. Group x Time effects were measured using Mixed-model ANOVA; Fluoride burden relationships analyzed using dose-response and correlation analysis. Results: The increases in quadriceps CSA(+4.2 %, vs. +5.4 % placebo, p = 0.02) and squat 1-RM(+12.5 %, vs. +15.2 %, p = 0.03) were less in high-fluoride participants. They also showed diminished osteocalcin responses, upregulated myostatin rises and low gains of EMG. Trends of dose response were significant in all outcomes ( p < 0.01), and an association in changes in urinary fluoride with changes in muscle adaptation showed a negative relationship (r = 0.42, p = 0.001). Conclusions: Fluoride exposure lowers moments of morphological and neuronal elements of musculoskeletal adaptation in people, and the impacts are clearly dose related. The recommendation of the intake levels of fluoride should be regionally-specific with consideration of the positive effect on dental issues and the risks of musculoskeletal defects.