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Screen time and chronic neck pain in Peru: A comparative population-based cross-sectional study in the COVID-19 post-pandemic period
Background Chronic neck pain (CNP) is a prevalent condition worldwide. During the COVID-19 pandemic, its prevalence increased, partly due to the rise in screen time associated with digital device use. However, two years after the pandemic, there is still a lack of epidemiological data on its current prevalence. This study aimed to estimate the prevalence of CNP among adults in Peru in 2025 and assess its association with screen viewing time. Methods We conducted a cross-sectional study involving a nationally representative sample of Peruvian individuals aged 18 years or older, whose data were collected in February 2025. The dependent variable —CNP— was self-reported based on symptoms experienced in the prior six months, and the exposure was the average daily screen time (hours/day). We assessed the association with multinomial logistic regression, adjusting by sex, age, rurality, and region of residence. The prevalences of the outcome and the exposure were described along with the estimates from November 2022, at the end of the pandemic. Results 1208 individuals were surveyed (51.6% women), with a mean age of 40.34 years (95% confidence interval [CI]: 39.3–41.3). CNP daily or near-daily affected 15.6% (95%CI: 13.4–18.0) of the participants; representing a slight but non-significant increase compared to 2022 (14.8%, 95%IC: 12.6–17.3) (p = 0.525). Screen time exceeding 8 hours per day was significantly associated with daily or near-daily CNP (odds ratio: 4.17, 95% CI: 2.15–7.69) compared with individuals who had never experienced neck pain. Conclusions Two years after the end of the COVID-19 social restrictions in Peru, the prevalence of CNP remains high, with 15 out of 100 adults experiencing this condition daily. The association between screen use and CNP was strongest in adults aged 18–29 years, while other factors may be more influential in adults ≥60. As prolonged screen use is a modifiable risk factor, reducing excessive exposure should be prioritized within prevention strategies to lower CNP prevalence.
Prediction of individual optimal drop height in drop jump from anthropometric and strength variables
Oseltamivir aziridines are potent influenza neuraminidase inhibitors and imaging agents
Influenza neuraminidase (NA) is a critical target for seasonal and pandemic antivirals, including the strains of current concern. Current treatments, such as Zanamivir and Oseltamivir, are limited by noncovalent binding and emerging resistance. We hypothesized that Oseltamivir aziridines would unite transition-state mimicry for tight binding, with aziridine-enabled covalent capture of the catalytic tyrosine, thereby supporting both therapy and activity-based quantification. Here, we present oseltamivir-based aziridines, inspired by cyclophellitol chemistry, that act as covalent inhibitors and activity-based probes via an N -acylaziridine warhead. Free-energy calculations, and NMR observations, indicate a 4 H 5 half-chair preference consistent with the NA transition state, and selected analogues inhibit multiple NA subtypes with low nanomolar binding constants. Diverse evidence establishes covalency: time-dependent inactivation, inhibitor washout, intact-mass shifts, MS/MS identification of a tyrosine adduct, and QM/MM reaction profiles, while cryoEM of N1 aligns with the proposed binding mode, revealing an elimination product. The inhibitors demonstrate formidable activity against diverse viral neuraminidases, including H5N1, and further enable imaging and quantification of active NA. With their dual therapeutic and diagnostic potential, these first-in-class inhibitors indeed benefit from transition state mimicry and covalency, and thus offer a powerful platform for antiviral development and neuraminidase imaging, addressing urgent global health needs in influenza treatment and prevention.
Fine-grained identification of tea plantation parcels in UAV remote sensing images based on DVIT-UNet
The complex terrain and diverse management practices in tea-producing regions have resulted in highly fragmented tea plantation plots, posing challenges to precision cultivation, yield estimation, and ecological management. Although remote sensing technology has been increasingly applied to tea plantation mapping, most studies have focused on the overall identification of tea-growing areas, while research on the fine-grained classification and extraction of tea plantation plots within the same spatiotemporal range remains limited. To address this gap, this study proposed a DVIT-UNet model based on ultra-high-resolution unmanned aerial vehicle (UAV) imagery, which integrates a Vision Transformer (ViT) and dilated convolution modules within a UNet framework to effectively capture global semantic dependencies and multi-scale local contextual information. This design specifically targets blurred parcel boundaries, high intra-class heterogeneity, and spectral similarity between tea plantations and surrounding vegetation. Comparative experiments against seven stable deep learning models demonstrated that DVIT-UNet achieved the best performance, with a mean Intersection over Union (mIoU) of 90.48%, F1 score of 94.99%, mean recall (UA) of 94.39%, mean precision (PA) of 95.60%, and a Matthews correlation coefficient (MCC) of 91.13%. Despite its moderate parameter size, the model achieved accurate delineation of small and fragmented tea plots and robustly suppressed false positives in complex backgrounds. The results comprehensively verify the strong capability of DVIT-UNet for fine-grained classification and precise extraction of tea plantation plots from high-resolution UAV imagery, providing a reliable technical foundation for precision tea-plantation management and ecological monitoring.
SVRS: self-supervised 3D voxel reconstruction network from stereo vision
Amplifying toughness in silica-reinforced natural rubber by preserving long chains
Natural rubber outperforms synthetic rubbers because of its long chains and strain-induced crystallization (SIC). However, these advantages are largely lost when the natural rubber chains are masticated during processing, and silica particles are added for reinforcement. Mastication eases mixing but shortens chains and lowers performance. Silica particles require covalent interlinks with rubber chains, but these interlinks restrict chain stretch and alignment, reducing SIC. Here, we show that the performance of silica-reinforced natural rubber can be markedly enhanced by preserving long natural rubber chains. We use a solvent to dissolve natural rubber latex into individual rubber chains and use the solution to uniformly disperse silica particles. After drying, the uncured compound can be stored and molded prior to curing. The long rubber chains are then sparsely crosslinked with one another and interlinked with the silica particles. The long strands readily align under stretch and increase SIC. Preserving long chains elevates toughness by an order of magnitude, from ~2 to 44 kJ m –2 . High toughness arises from energy dissipation across multiple length scales, over long rubber strands, silica particles, and a zone of SIC. High modulus of ~19 MPa arises from two interpenetrating networks: the network of densely entangled rubber chains and the network of percolated silica particles. The resulting material achieves high toughness while maintaining high modulus, a combination uncommon in silica-reinforced synthetic and natural rubbers.
Predicted antiviral potential of phytochemicals prolific in Cleistanthus bracteosus Jabl. and essential oils of Artemisia scoparia and Thuja orientalis against Nipah virus and Human metapneumovirus: An AI-driven in-silico study
The recent Nipah virus (NiV) epidemic and human metapneumovirus (hMPV) outbreak have had a significant impact on human health and society worldwide. The attachment glycoprotein (G) and fusion glycoprotein (F0) of NiV and hMPV are essential for pathogenesis and are potentially pronounced targets for antiviral treatment. In the present study, we utilised computational methods to analyse the predictive antiviral potential of phytochemicals present in Cleistanthus bracteosus and in the essential oils of Artemisia scoparia and Thuja orientalis against NiV and hMPV. Molecular docking and dynamics simulations were the primary tools for assessing the binding interactions of compounds detected by GC-MS. Three out of four compounds tested (digoxigenin, cedrene and cedrol) exhibited remarkable binding affinities between −7.7 kcal/mol and −6.2 kcal/mol for NiV fusion glycoprotein (F0), and between −8.3 kcal/mol and −7.1 kcal/mol for NiV attachment glycoprotein (G). Similarly for hMPV fusion glycoprotein (F0), the aforesaid compounds showed binding affinities between −8.1 kcal/mol and −6.4 kcal/mol. Moreover, MD simulations illustrated phytochemical interacting amino acid residues associated with each receptor of NiV and hMPV. These phytochemical compounds were further evaluated using ADMET platforms. In conclusion, the present in silico work predicts for the first time the predicted potential of using major compounds present C. bracteosus , A. scoparia and T. orientalis as a novel anti-viral therapeutic strategy to control the entry and pathogenesis of NiV and hMPV. Despite few RMSD fluctuations in protein-ligand complexes stemming from structural alterations in the beta-turn-beta and helix-coil-helix, the simulations remain mostly stable from 50 ns till 100 ns.
Breaking HER limits with Ni@B40’s single-atom catalytic prowess
Abstract The hydrogen evolution reaction (HER) has emerged as a key process in the pursuit of sustainable alternatives to nonrenewable fuels. Single-atom catalysts (SACs) are particularly promising for HER electrocatalysis due to their exceptional atom utilization, high electrical conductivity, and thermal stability. In this study, we systematically evaluated the catalytic potential of late first-row transition metal-decorated TM@B 40 (TM = Zn, Fe, Co, Cu, and Ni) complexes as SACs for HER using density functional theory (DFT) and ab initio molecular dynamic (AIMD) calculations. The interaction energies (E int of these complexes ranged from − 1.16 to -3.72 eV at B3LYP-D3/6–31 + G (d) method in aqueous phase, confirming their thermodynamic stability. Notably, Ni@B 40 and Cu@B 40 exhibited the lowest Gibb’s free energy of -0.01 eV and 0.01 eV, respectively, identifying them as the most efficient HER catalysts. The H-Ni@B 40 and H-Cu@B 40 complexes further demonstrated a favorable hydrogen adsorption energy (ΔE H* ) of -0.29 eV and − 0.25 eV, reinforcing their stability. Density of states (DOS) analysis revealed the formation of new energy states upon hydrogen adsorption, facilitating charge transfer between Ni@B 40 and H, in agreement with frontier molecular orbital (FMO) analysis. These findings underscore the potential of TM@B 40 complexes as highly efficient SACs for HER, offering a viable strategy for designing cost-effective and high-performance electrocatalysts for hydrogen production.
Status of the sunken nuclear submarine Komsomolets in the Norwegian Sea
The study documents in detail the extent of damage to the exterior of the sunken nuclear submarine Komsomolets and that previous remedial action carried out by Russia was still in place. No evidence was found of any plutonium in the near environment around the damaged forward section of the submarine from the nuclear warheads that were reported to be part of Komsomolets armament in the torpedo compartment. It was confirmed that releases from the reactor were still occurring, but not continuously, with maximum activity concentrations of 90 Sr and 137 Cs that were 400,000 and 800,000 times higher, respectively, than typical levels of these radionuclides in the Norwegian Sea. Elevated levels of 239 Pu, 240 Pu, and 236 U were also detected in the releases from the reactor, with atom ratios of 240 Pu/ 239 Pu and 236 U/ 239 Pu that indicate that the nuclear fuel in the reactor is corroding. Despite that releases from the reactor have occurred for over 30 y, there is little evidence of any accumulation of radionuclides in the near environment around the submarine as the released radionuclides appear to be rapidly diluted in the surrounding seawater. Releases from the reactor in Komsomolets can be expected to continue, so further investigations should be carried out to determine the mechanisms behind the observed releases, the corrosion processes that are occurring within the reactor and the implications of these for further releases and the fate of the remaining nuclear material in the reactor.
Exploring climate smart agriculture in Turkey: Enhancing food security and sustainable practices for the reduction of CO₂ emissions
Climate-smart agriculture entails the reduction of CO₂ emissions, adaptation and modification of technology to enhance resilience to climate change, and sustainable increase of incomes. This study evaluates the effectiveness of smart agricultural practices in Turkey, which significantly impact food security and mitigate CO₂ emissions. The decoupling technique was implemented to estimate the portfolio returns and examine their correlation with climate-smart agriculture and CO₂ emissions over the anticipated period of 1992–2023. The decoupling technique was implemented to accomplish the two primary objectives. First, it is employed to calculate the percentage change in portfolio returns that is linked to both high and low weighted risk allocations. Second, it enables the prediction of CO₂ emissions levels for the next five years, which are influenced by sustainability practices and food security fluctuations. A corresponding difference in the efficacy of climate-smart agriculture has been demonstrated in the agricultural context by a percentage change in continuous, single aeration, and multiple aeration practices. The estimated results suggest that the decoupling trend in portfolio returns is significantly influenced by factors such as rice cultivation, field rise, and soil management, which also contribute to the highest weighted risk. Additionally, this factor consistently shows the highest weighted importance in determining overall portfolio returns, as it exhibits the largest marginal effects. Consequently, this investigation substantiates the detrimental influence of these variables on CO₂ emissions. Turkey’s sustainable smart agriculture process is essential for the efficient expansion of the economy, as it integrates climate change considerations into current policies and initiatives and reinforces the policy indicator of economic consideration with environmental protection in terms of CO₂ emissions.
Physics-based modeling and friction parameter identification of a proportional spool valve
Abstract This paper presents the development of a physics-based model of a proportional directional valve with the aim of describing its dynamic behavior while accounting for the influence of friction forces acting on the spool. The model is derived from the spool’s equation of motion, and in order to achieve accurate parameter tuning, an experimental analysis of the individual forces acting on the spool was carried out. Particular attention is given to the parameters of the friction force, as this component represents a key factor affecting the dynamic response of the system. Due to the difficulty of direct measurement, the identification of friction parameters was performed through a combined numerical–experimental comparison of the system’s response to step control inputs. A significant advantage of the proposed model lies in its capability to parameterize the individual force components independently, which facilitates straightforward adaptation to different operating conditions or valve types. For experimental validation, a series of dynamic measurements was conducted on a specialized hydraulic test rig. The acquired data were used both to identify the friction model parameters and to validate the simulation model. A comparison of the simulation results with experimental data demonstrates good agreement across a wide range of operating conditions. The proposed model therefore provides a flexible and accurate tool for predicting the behavior of proportional directional valves, applicable both in the design of control algorithms and in the optimization of valve construction.
CD47 stabilizes ROBO2 to regulate glioblastoma progression by preventing ITCH-mediated ubiquitination
CD47 is an innate immune checkpoint that inhibits phagocytosis by myeloid cells, contributing to immune evasion by cancer cells. CD47-blocking antibodies have limited efficacy in glioblastoma (GBM), and the cell-intrinsic role of CD47 is poorly understood. In this study, we show that CD47 is highly expressed at the invasive edge of GBM tumors, and its elevated expression correlates with poor patient survival. We demonstrate that CD47 loss impairs GBM cell proliferation, migration, and invasion, independent of immune activity, and leads to reduced tumor burden and prolonged survival in vivo. Our study identifies ROBO2 signaling as a key downstream effector of CD47 and demonstrates that loss of ROBO2 similarly reduces GBM cell proliferation and migration. Importantly, we have uncovered that CD47 stabilizes ROBO2 by sequestering the E3 ubiquitin ligase ITCH, thereby blocking ubiquitination and proteasomal degradation of ROBO2. These findings establish CD47 as a key regulator of GBM cell plasticity and highlight the therapeutic potential of targeting CD47 – ROBO2 signaling in GBM.
Correction: Back to the future: The advantage of studying key events in human evolution using a new high resolution radiocarbon method
Risk attitudes and value of hope: survey results from Japanese hematologists and oncologists treating patients with diffuse large B-cell lymphoma
Using wavelet decomposition to determine the dimension of structures from projected images
Mesoscale structures in turbulent media can often be described as fractional dimensional across a wide range of scales. The goal of this paper is to determine the structure’s dimension from a projected image. Our method exploits the laws of scaling of wavelet power spectra under projection and does not carry any restrictions on the embedding and projected dimensions. We show that the wavelet power spectrum of a projected γ dimensional measure is P j = 2 − j γ , where j is the wavelet scale. We contrast the wavelet method with the popular box-counting approach. For projected images, the use of box-counting at fixed thresholds often leads to erroneous results. We apply the method to James Webb Space Telescope (JWST) infrared and Chandra X-ray observations of the supernova remnant Cassiopeia A. We find that the emissions can be represented by projections of mesoscale substructures with fractal dimensions varying from γ = 1.69 ± 0.02 for the warm CO layer observed by JWST, up to γ = 2.49 ± 0.03 for the hot X-ray emitting gas layer in the supernova remnant.
Characterizing inflammatory biomarkers in post-stroke seizure risk and outcome prognostication
Post-stroke seizures (PSS) are sequelae of intracerebral hemorrhage (ICH) that may negatively impact patient outcomes. Available literature suggests that inflammatory biomarkers contribute to epileptogenesis as well as mortality. This retrospective cohort study aimed to elucidate the prognostic ability of CCL2, IL6, and IL8 in PSS development. ICH data were collected at Yale New Haven Hospital from 2014 to 2021. Plasma biomarker levels were measured via cytometric bead array. Patients with EEG-recorded epileptiform discharges, EEG-recorded seizures, or clinical seizures after ICH were defined as seizures and epileptiform discharges (SED), while those without were defined as non-SED. SED was further divided into early and late, occurring before and 7 days post-ICH, respectively. Additionally, we examined if biomarkers were associated with poor outcome (modified Rankin Scale score of 3–6) and mortality at 90, 180, and 365 days post-ICH. We conducted univariable and multivariable logistic regression analyses and reported the findings as Odds Ratio (OR) and 95% CI. We examined 172 patients with ICH, of whom 33 had early SED, 29 had late SED, and 110 had no SED. In univariable analyses, CCL2, ICH volume, diabetes, and lobar ICH were significantly associated with late SED, whereas NIHSS score at admission, ICH volume, and lobar ICH were significantly associated with early SED (p < 0.05). Lower CCL2 levels were independent predictors of late SED in multivariable analyses (OR 0.58; 95% CI 0.41–0.80, p < 0.001), but not of early SED. Additionally, we identified an independent association between higher CCL2 levels and 90-day mortality in multivariable analysis for the combined early and late SED cohorts (OR 1.87; 95% CI 1.03–3.37, p = 0.038). Additional studies investigating additional aspects of biomarkers, such as their temporal profile post-stroke within 24 hours or beyond 72 hours, are needed.
Effect of alkyl chain length on the corrosion inhibition performance of 2-thioxo-2,3-dihydroquinazolin-4(1H)-one derivatives for carbon steel in HCl solution
Abstract This article investigates two quinazoline derivatives for reducing carbon steel corrosion in acidic media, commonly encountered in various chemical processes, which contribute significantly to corrosion. The structurally simple and easily synthesized compounds, 3-methyl-2-thioxo-2,3-dihydroquinazolin-4(1H)-one (Q-C1) and 3-butyl-2-thioxo-2,3-dihydroquinazolin-4 (1H)-one (Q-C4), were evaluated as corrosion inhibitors for carbon steel in 1.0 M HCl solution using chemical, electrochemical, surface analysis, and quantum computational methods, with particular attention to the effect of alkyl chain length on the inhibition efficiency. The inhibitory efficiency reached 88.91% for Q-C4 at 4.0 × 10 -5 , achieved during weight-loss testing on the carbon steel surface in 1.0 M HCl at 298 K. The temperature impact (ranging from 298 to 318 K) on the corrosion inhibition was investigated, revealing that with an increase in temperature, the inhibition efficiency diminishes. The kinetic and thermodynamic parameters were determined and analyzed to express the adsorption behavior of the studied inhibitors. The adsorption of the compounds on the carbon steel surface conforms to the Langmuir adsorption isotherm. The electrochemical measurements were conducted through the electrochemical impedance (EIS) and potentiodynamic polarization (PP) techniques, which confirm that the studied compounds were categorized as mixed-type inhibitors. EIS data reveal that charge-transfer resistance increases from 31.3 Ω cm 2 in the uninhibited solution to 261.61 Ω cm 2 in the inhibited solution of Q-C4. The surface of carbon steel was examined using scanning electron microscopy (SEM), energy dispersive X-ray (EDX), and Fourier-transform infrared spectroscopy (FT-IR), demonstrating the formation of a protective layer on the CS surface, mitigating the corrosion. Finally, the adsorption affinity of these compounds on the carbon steel surface was theoretically examined utilizing quantum calculations. The outcomes of the theoretical study in quantum chemistry were corroborated by data from chemical and electrochemical methods.
Correction for Te et al., Broad beta-CoV immunity and transmission blockade by a single-dose live-attenuated vaccine with atypical codon usage
Multi-layer resource configuration and safety optimization for integrated modular avionics with resource sharing and isolation
The aviation industry extensively employs integrated modular avionics (IMA) to enhance system efficiency by sharing resources across various functions. Despite the benefits, the design of IMA systems is not without its challenges, particularly in achieving cost-effectiveness, ensuring availability, and addressing safety concerns. Optimizing resource utilization in IMA systems is increasingly complex due to the growing functionality and quantity of resources in aviation modules and technological flexibility. This paper presents a multi-layer resource configuration and optimal design method for IMA systems, with a dual focus on resource sharing and isolation mechanisms (which prevent fault propagation and enhance system safety). Our approach takes into account not only the strategic planning of resource sharing but also the intricacies of isolation scheme design, especially in the context of risk propagation. The architecture of the multi-layer resource configuration is meticulously structured and formalized to account for the unique dynamics of risk propagation across different configurations. The optimization process for the configuration scheme is formalized as a constrained multi-objective optimization problem. An optimal performance configuration scheme for the IMA systems can be identified that requires the minimum amount of resources. Finally, the effectiveness of the proposed method is demonstrated through the presentation of an illustrative example. The results show that the proposed approach effectively balances safety, efficiency, and cost in IMA resource management.