Browse Articles

The accelerating loss and shifting dynamics of US tidal wetlands

Abstract Tidal wetlands are critical ecosystems for coastal sustainability, yet despite growing regulatory protection, they continue to decline globally. Their long-term resilience to interacting chronic stressors and extreme events remains uncertain, in part because comprehensive, high-frequency monitoring has been lacking. While direct land-use conversion has been substantially restricted in the United States, the true trajectory of these protected habitats has remained unclear. Here, we use four decades of high-resolution satellite records to analyze the shifting dynamics of US tidal wetlands. We reveal a widespread and previously unquantified acceleration in the rate of tidal wetland loss, amounting to a net loss of −1640 km 2 at the rate of −40.53 km 2 year −1 , accelerating by −0.73 km 2 year −2 , of which tidal marsh contributed the majority of this loss with a cumulative decline of 1567 km 2 . Furthermore, we show that the drivers of this decline are shifting: while chronic stressors like relative sea level rise have caused the largest cumulative loss (~60% of the total area loss), acute shocks from extreme weather now dominate (1.4 times that of the chronic stressors) the acceleration of that loss. By contrast, direct human activities were a minor driver, accounting for only 4% of total observed losses. These findings indicate that the resilience of these protected ecosystems is declining. It provides an urgent warning that existing conservation strategies, initially concerned with direct human impacts and increasingly focused on relative sea level rise as a slow-moving pressure, are ill-equipped for a future of increasing extreme weather events and highlights the need to redesign adaptation policies.

Development of a Polyvinylidene fluoride–based membrane incorporating magnetic iron–nickel alloy for vacuum membrane distillation desalination

Abstract Novel mixed a starfish-like shaped magnetic iron-nickel alloy with Polyvinylidene fluoride (PVDF) matrix membranes were developed for water desalination using vacuum membrane distillation (VMD). This study highlights the alloy’s unique morphology that coated by the hydrophobic polymer, which enhances water vapor transport through innovative pore formation and its permanent magnetic properties, distinguishing it from existing research. The membranes were characterized using scanning electron microscopy (SEM), Energy Dispersive X‑ray (EDX) analysis and mapping, Fourier Transform Infrared Spectroscopy with Attenuated Total Reflection (FTIR-ATR), and Thermal Gravimetric Analysis (TGA), along with measurements of Liquid Entry Pressure (LEP), static water contact angle, tensile strength, thickness, roughness, porosity, pore size and pore size distribution. Performance tests in a VMD system showed that the iron–nickel alloy increased membrane productivity by 47% compared to pristine PVDF membranes. The 0.2 wt% alloy with 14% PVDF achieved the highest porosity (74.32%) and flux (29.1 kg/m²·h), balancing surface roughness and structural integrity. In contrast, higher polymer content (18 wt% PVDF) negatively impacted porosity and led to performance trade-offs. Thus, this study emphasizes the critical interplay between porosity, roughness, thickness, and the magnetic properties of the alloy in optimizing membrane performance for VMD applications.

Regional impacts on decarbonisation under evolving financing conditions for energy technologies

Abstract Energy-sector decarbonisation requires large-scale investment in low-carbon technologies, yet only a limited share flows to low- and middle-income countries, partly due to higher financing costs and perceived risks. Most modelling exercises do not fully account for how the cost of capital may vary across regions and technologies, potentially influencing policy insights. We examine how plausible, expert-informed long-term trends in de-risking clean energy and increasing risks for fossil fuels could shape decarbonisation pathways, using an empirical dataset differentiated by country and technology. We also evaluate a “corrective justice” policy that taxes corporate windfall profits and redistributes revenues to support low-carbon investments in higher-risk regions. Results suggest that incorporating differentiated cost-of-capital trajectories may improve mitigation outcomes and help narrow the gap between current commitments and long-term climate targets, while indicating potential underestimation of risks associated with bioenergy-based negative emissions technologies in mitigation scenarios for high-income nations.

Imidacloprid induces hepatorenal toxicity in male albino rats via oxidative, immune inflammatory, and proliferative effects: a 90-day study

Abstract Imidacloprid (IM), a systemic neonicotinoid pesticide, is widely used globally due to its high effectiveness against a broad range of insects at minimal application rates. The current study aimed to examine how this insecticide can induce oxidative stress, disrupt the inflammatory process, and influence cellular proliferation in hepatorenal tissues, even at low doses. Thirty adult male albino rats were divided into five groups ( n  = 6). The first group served as the control, while the other four groups received IM at 0.3, 3, 30, and 60 ppm, respectively, over 90 days through drinking water. Results revealed significant histopathological changes in the liver and kidneys of rats exposed at all doses. A notable increase in hepatorenal markers, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), urea, and creatinine levels, and a slight decrease in total serum protein (TP) were observed. There was a significant rise in malondialdehyde (MDA) levels, along with increased production of pro-inflammatory cytokines, including interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and NOD-like receptor protein-3 (NLRP3). This was accompanied by strong immunopositivity of proliferating cell nuclear antigen (PCNA) and Ki-67 protein expression, while adipocyte-specific adhesion molecule (ASAM) levels showed insignificant changes. The study concludes that IM can induce hepatorenal damage accompanied by lipid peroxidation, elevated inflammatory mediators, and altered proliferative indicators. The effects were generally more apparent at higher dosages.

Structural basis for dual mechanism of Cas2/3 nuclease inhibition by anti-CRISPR protein AcrIF19

Abstract CRISPR-Cas systems are prokaryotic immune mechanisms often targeted by phage-encoded anti-CRISPR (Acr) proteins. This study characterizes AcrIF19, a potent inhibitor of the type I-F system in Pectobacterium atrosepticum . The cryo-EM structure of the apo Cas2/3 and Cas2/3-AcrIF19 complex reveals a dual inhibitory mechanism. AcrIF19 employs a negatively charged β 2 -β 3 loop to sterically occlude the non-target DNA strand entry channel, acting as a competitive inhibitor to disrupt Cas2/3 recruitment. Concurrently, this steric occlusion impedes ssDNA-mediated allosteric activation, which locks the critical helix-like loop motif in an inhibitory conformation and thereby abrogates DNA cleavage activity. AcrIF19 represents an anti-CRISPR protein inhibiting Cas2/3 via two different mechanisms, integrating a competitive ssDNA inhibitor with an allosteric blockade to suppress both target recruitment and DNA cleavage.

Systemic sclerosis in children and older juvenile-onset patients: real-life map by the Egyptian college of Rheumatology

Abstract The aim of this work was to present the demographic and clinical features of children with juvenile systemic sclerosis (JSSc) compared to their older juvenile-onset (Jo-SSc) counterparts. The study included 12 JSSc children (< 16 years) and 54 Jo-SSc patients > 16 years (including adolescents and adults) recruited from 15 tertiary care centers across Egypt. Patients were classified as diffuse (dcSSc), limited cutaneous SSc (lcSSc) and overlap. The mean age at disease onset was 14 ± 2.5 years. Patients were 62 females and 4 males (F: M 15.5:1); 37 lcSSc, 26 dcSSc and 3 overlap. Disease features were generally comparable across five regions of Egypt. Pitting scars and hand puffiness were significantly more frequent in JSSc (58.3% and 41.7%) compared to Jo-SSc (16.7% and 14.8%) ( p  = 0.006 and p  = 0.049, respectively) while pulmonary hypertension was more common in Jo-SSc (40.7% vs. 8.3%, p  = 0.045). All males, currently with Jo-SSc, had gastrointestinal involvement versus n  = 24 (38.7%) in females, p  < 0.0001. To conclude, SSc in children and in those who grow into adulthood remains a rare disease. Pitting scars and hand puffiness were more frequent in children with JSSc while pulmonary hypertension became prominent as they grew. Children characteristics are special for each nation yet with similarities in some features.

Broadband extreme ultraviolet zeroth order scatterometry for nanostructure metrology

Abstract The continuous shrinkage of critical dimensions in nanofabrication demands nanometrology at the relevant resolution, which can be achieved using short-wavelength light sources. Most industrial metrology uses periodic structures for process control, exploiting diffraction to probe the fabrication quality of actual device structures. Here, we introduce a table-top high-harmonic generation extreme-ultraviolet scatterometry with broadband illumination. Our method exploits the spectrally resolved 0 th diffraction order of the extreme-ultraviolet light, which, while lacking spatial-encoded information, carries valuable spectral information and offers high diffraction efficiency. The use of relative reflectivity removes the need for absolute calibration, and rigorous coupled-wave analysis simulations underpin a library-based reconstruction approach, yielding single-nanometer accuracy for groove height and 10 nm accuracy for critical dimensions. Our work demonstrates broadband extreme-ultraviolet high-harmonic-generation 0 th order scatterometry that delivers fast, reliable, non-destructive metrology for structures with at-wavelength features, providing sensitivity and accuracy for details far below the diffraction limit.

Gabapentsal ameliorate cisplatin-induced vomiting in pigeons: neurochemical evidences and the involvement of serotonin and dopamine modulation

Symmetry-controlled multi-gap superconductivity and higher-order topological phases of MoTe2

Abstract The transition-metal dichalcogenide MoTe 2 has been proposed as an ideal platform to intertwine superconductivity with band topology, yet a key experiment—tracking how its properties evolve across a pressure-tuned structural and topological phase transition—has remained elusive. Here, we map the superconducting landscape across these high-pressure regimes from the noncentrosymmetric type-II Weyl semimetal T d phase to the centrosymmetric $$1{{{{\rm{T}}}}}^{{\prime} }$$ 1 T ′ phase using surface-sensitive soft point-contact Andreev spectroscopy combined with quantitative theoretical analysis. In the T d phase, our spectra consistently reveal two distinct superconducting gaps that remain resolvable under an external magnetic field, implying robust and pressure-independent multi-gap superconductivity consistent with muon-spin-rotation evidence for two s -wave gaps at ambient pressure. In the $$1{{{{\rm{T}}}}}^{{\prime} }$$ 1 T ′ phase, reached by pressure along a topological pathway that connects the Weyl to the higher-order topological phase, we observe an s  +  p -wave surface response whose p -wave component follows the s -wave gap in temperature and is rapidly suppressed by a magnetic field—fingerprints of proximity-induced p -wave pairing between a bulk s -wave superconducting band and second-order topological surface states. This phenomenology aligns with theoretical analysis showing that the T d phase hosts type-II Weyl points, whereas the $$1{{{{\rm{T}}}}}^{{\prime} }$$ 1 T ′ phase realizes a higher-order topological insulator arising from double-band inversion. Finally, we further propose that the resulting higher-order hinge boundary channels provide a natural route toward potential zero-energy Majorana corner modes under the observed s  +  p -wave proximity pairing, suggesting MoTe 2 as an intrinsic, pressure-tunable platform for multi-gap and s  +  p -wave topological superconductivity.

Patient-derived prostate organoids identify MAOA as a disease severity-associated molecular marker in chronic pelvic pain syndrome

Abstract Chronic pelvic pain syndrome (CPPS) is a multifactorial condition with unclear pathophysiology and a lack of objective biomarkers for assessing disease activity. To investigate its molecular basis, we generated patient-derived prostate organoids from biopsy tissues of nine patients with CPPS, categorized as mild, moderate, or severe according to the NIH CP Symptom Index scores. The organoids were treated with Eviprostat or tadalafil, followed by transcriptomic profiling, quantitative RT-PCR validation, and immunohistochemical analysis of candidate genes. Among the differentially expressed genes, monoamine oxidase A (MAOA) and calbindin-D28K (CALB1) were consistently associateds with symptom severity. Expression of both genes decreased in organoids and prostate tissues as symptom severity increased, whereas serum MAOA levels were significantly elevated in patients with severe CPPS. These findings suggest that MAOA and CALB1 reflect molecular alterations linked to symptom intensity and may serve as potential biomarkers for CPPS. Furthermore, patient-derived prostate organoids offer a valuable experimental platform for elucidating disease mechanisms and evaluating therapeutic interventions in prostatitis-related disorders.

Attention-augmented hybrid framework with evolutionary optimization for robust deepfake detection

Impact of stem cell therapy on brain metabolic profile in cerebral palsy assessed by magnetic resonance spectroscopy in a randomized clinical trial

Correction: Somatotopy-independent reduction of audio-tactile intersensory facilitation for looming sounds within the peripersonal space during arm movements execution

An experimental study on the impact behavior of geopolymer concrete incorporating recycled asphalt pavement aggregate

Development of a mortality prediction nomogram for dementia patients using the MIMIC-IV database

An advanced YOLO-based image processing framework for automated sperm cell detection

Abstract Detection of sperm cell is an extremely important procedure in medical diagnostics and fertility research. Given the need to identify sperm in an efficient manner, this paper offers a powerful image processing pipeline. This method starts with image grayscale conversion, which is used to simplify the image and reduce the complexity of color, which is followed by Gaussian blur and Wiener filter to remove noise and improve image quality. An optimal-threshold is obtained by thresholding to obtain the sperm cells out of the background with the binarization method of Otsu which ends by concluding. Alternatively, adaptive Havrda-Charvat entropy thresholding is also discussed to allow better accuracy under difficult circumstances, correcting to differences in the intensity of the image. The sperm separation is then carried out in the segmentation phase, in which the sperm cells are segregated out of non-relevant objects. The last one is an automatic system that can identify sperm cells with high accuracy, which allows quicker and more accurate analysis by deep convolutional neural network of YOLOv5s to be used in clinical and research applications. The offered technique is tested with the help of a collection of microscopic images, which proves its efficiency under various light conditions and morphological changes of sperm.

Using a mixed opinion dynamics and innovation diffusion model to explore the ‘best game no one played’ phenomenon

Innovative products that receive favorable reviews but never catch on with consumers belong to a category known as the “best game no one played.” We combined an adoption threshold model with an opinion dynamics model to examine reasons why certain high-quality products and ideas never achieve expected levels of commercial success. Computational social scientists use opinion dynamics models to analyze consensus formation, and adoption threshold models to study acceptance scenarios. However, most studies based on the first type focus on opinion exchanges without discussing follow-up actions, and most based on the second type only examine ways that individual decisions are dependent on numbers or proportions of friends and neighbors already engaged in specific behaviors, regardless of opinion differences. For this study, four kinds of theoretical networks (regular lattice, random, small-world, scale-free) served as underlying social network structures, and an agent-based simulation approach was used to analyze opinion exchange dynamics and product acceptance. Results indicate that computational agents were capable of changing pro/con opinions regarding issues, products, policies, etc. based on communication with neighboring agents via underlying social networks, and of making acceptance/rejection choices based on a combination of individual adoption threshold plus observations of their neighbors’ behaviors. A series of sensitivity analysis simulation experiments was conducted to identify model-related factors, determine non-linear correlations among them, and quantify degrees of influence. Factors exerting the strongest influence or requiring greater care when applied to cases of innovation diffusion were examined. Sensitivity analysis results indicate that agent adoption threshold mean exerted the greatest influence, followed by agent attitude mean and bounded confidence. Mechanism decomposition experiments revealed that the testimony effect neutralizes opinion clustering, making coordination failure the dominant driver of the opinion–adoption gap. These findings yield predictions distinguishing the model from information cascades, network externalities, and global games.

Seasonal shifts in Leopard (Panthera pardus) occupancy driven by prey availability and proximity to human settlements in Margalla Hills National Park, Pakistan

Deep Learning outperforms physicians in myopathy and neuropathy classification based on Needle Electromyography Signal

Needle electromyography (nEMG) is a valuable tool for diagnosing patients with neuromuscular diseases. However, it is labor-intensive and is prone to diagnostic inaccuracies stemming from human biases. To address these challenges, we validated an nEMG diagnosis-aiding system with minimal preprocessing using deep learning model to classify patients into three categories: normal, myopathy, and neuropathy. Using 376 nEMG signals from 57 patients from a tertiary university hospital database through nested k-fold cross validation, deep learning model surpassed the classification performance of six electromyographers. The median patient classification accuracy, precision, sensitivity, and specificity of the deep learning model was 0.70, 0.70, 0.70, and 0.85, respectively, whereas those of the physicians were 0.55, 0.60, 0.54, and 0.78, respectively. Model interpretability and failure analysis showed that the deep learning model classifies based on relevant signal features. Despite higher accuracy of DL model, the number of unanimously misclassified cases were higher in the DL model than physicians. Our study validates deep learning is a fast, accurate, and practical application to aid physicians in diagnosing patients using nEMG signals.

Parallel fusion model for complex multi-source vibration time-series prediction