Browse Articles

MRI mapping of hemodynamics in the human spinal cord

High energy density carbon–cement supercapacitors for architectural energy storage

Electron-conducting carbon concrete (ecˆ3) is a multifunctional cement-based composite material that combines mechanical robustness with electrochemical energy storage. To further expand our understanding of structure–function relationships in this complex multiphase material system and provide a roadmap for transitioning this technology from a simple proof-of-concept to a viable large-scale energy storage alternative, we report insights into the nanoscale connectivity of the electrode’s conductive carbon network, explore different electrolyte compositions and material integration strategies, and highlight opportunities for device scaling. Through the use of FIB-SEM tomography, the electrode’s percolating fractal-like nano-carbon black network has been visualized at the nanoscale, providing insights into the theoretical energy storage capacity of this material. To reduce the required times for the production of functional electrodes, we also present a cast-in electrolyte approach, where centimeter-thick electrodes could be produced without the need for postcuring steps. In these prototypes, device performance scales linearly with electrode thickness and cell count, and a simple analytical model was developed to explain these scaling phenomena. Furthermore, the exploration of alternative ionic and organic electrolytes further contribute to improved electrochemical behavior, with the fabricated designs ultimately achieving a 10-fold increase in supercapacitor energy density compared to previous designs. Finally, we were able to fabricate a 12 V, 50 F supercapacitor module and a 9 V arch prototype that integrate energy storage into load-bearing architectural elements. These functional prototypes highlight the potential for real-time structural health monitoring, while demonstrating the potential of our ecˆ3 technology for the production of a scalable, high-voltage concrete energy-storing infrastructure.

Mitigating greenhouse gas emissions and enhancing composting efficiency using biochar, used oil, and compost inoculum amendments

Abstract This study aimed to evaluate the effectiveness of combining biochar, used cooking oil, and compost inoculum as amendments to improve the composting process of agricultural residues (broccoli and pepper waste) and manure. The objective was to enhance compost maturation, microbial safety, and environmental performance, particularly in terms of greenhouse gas emissions. Six treatments were tested: 50% manure + 50% agricultural residues (T1–1, control), 60% manure + 40% agricultural residues (T1– 2, control), 50% manure + 50% agricultural residues + 20 ml used cooking oil (T2–1), 60% manure + 40% agricultural residues + 20 ml used cooking oil (T2–2), 50% manure + 50% agricultural residues + 20% inoculum compost (T3–1), and 60% manure + 40% agricultural residues + 20% inoculum compost (T3–2), Then the first experiment was conducted to produce compost without adding biochar. After the first experiment was completed, a second experiment was conducted with the addition of 10% biochar at the same proportions as in the first experiment. The resulting mixture was composted in a controlled reactor with aeration and mixing. Results showed that the combination of used cooking oil and biochar significantly increased temperature, prolonged the thermophilic phase, and reduced the composting period to 10 days. This combination also decreased GHG emissions by 27–33% and eliminated methane emissions. Pathogenic bacteria (E. coli and Salmonella) were fully removed within one week. The findings suggest that integrating these amendments can accelerate composting, improve hygienic quality, and reduce environmental impacts.

Rust never sleeps: Climate change, permafrost thaw, and the rapid environmental degradation of wilderness river ecosystems

Anston attentional network for structured data based stroke risk prediction in smart aging

Abstract To reduce the pressure on public health services caused by the aging population, nursing homes need to predict disease risks for the elderly periodically. To improve the disease risks predicting ability of nursing homes, we designed Anston (An Attention Mechanism Network Model for Structured Data Classification) in the application scenario of innovative elderly care. The Anston model can use the physiological indicators and pathogenic factors easily collected by nursing homes to predict disease risks. In the study of disease risk prediction based on physiological indicators and pathogenic factors for thoughtful elderly care, we designed a data enhancement method, a feature weight automatic update method, and a multi-layer perceptron neural network to solve the problems of sample shortage, inconsistent feature weights, and sample imbalance. At the same time, we designed an attention mechanism network model for structured data classification based on the multi-layer perceptron neural network Developed in this paper. To fit the application scenario of competent elderly care, we propose a disease risk prediction model, Anston, based on the data enhancement method, feature automatic update method, and structured data classification attention mechanism network designed in this paper. We use public data sets and subject data as sample data in the experiment. The experimental results show that the Anston model has an accuracy of 95%, a precision of 92%, a recall of 91%, a specificity of 93%, an F1 score of 91%, and an AUC of 93% in predicting disease risks in the experiment, which have achieved the SOTA result.

Meta-analysis finds large variation but no general patterns in the relationship between climate and parasitism in terrestrial animals

Climate can vary spatially and temporally and is becoming increasingly unpredictable due to climate change. It can have a large impact on host–parasite interactions and investigating this effect is vital both for understanding current parasite distribution and epidemiology, and predicting how this will change in the future. Here, we conducted a meta-analysis to determine whether temperature and precipitation have an overall effect on parasite prevalence and infection intensity in terrestrial animals. This is a phylogenetically controlled quantitative synthesis to assess parasite prevalence and infection intensity in terrestrial animals across contrasting temperatures and precipitation. We found large variation in the effect of temperature on parasite prevalence, precipitation on parasite prevalence, and temperature on infection intensity. This provides robust quantitative evidence against the controversial “warmer sicker world” hypothesis. There was no effect of climate on parasitism, irrespective of whether the parasite was an endoparasite or ectoparasite, or across different parasite lifecycles. Although some host and parasite taxa were understudied, we found no consistent taxonomic patterns. Importantly, we revealed large gaps in the literature, including the relationship between humidity, prevalence, and infection intensity. Ectoparasites and reptile hosts were also very underrepresented, and deserve further study. Focusing future research on these gaps will help to confirm whether certain types of host–parasite interactions are more or less sensitive to changes in climate, with implications for conservation.

Genotypic variation in morphological traits, yield, essential oil profiles, and mineral composition of fennel (Foeniculum vulgare L.) across two growing seasons

RETRACTED: Curiouser and curiouser: Meningeal lymphoid structures in the aging brain

Characterization and safety evaluation of highly swellable polymeric nanomatrices for enhanced solubility of acyclovir

Mesoscale avalanche size underpins the rheology of granular yielding

Granular materials under loading exhibit intermittent avalanches of varying sizes prior to full yielding, a hallmark of natural failure phenomena such as landslides and earthquakes. While continuum models for postyield plastic flow are well established, a unified framework connecting preyield avalanche dynamics to bulk rheology remains lacking. Here, we introduce a birefringent double-shear experiment that enables sustained probing of avalanche statistics and quasistatic flow behavior near the yielding transition. We find that the avalanche regime exhibits rate-weakening behavior, while the plastic regime is rate-independent, resulting in dual rheology under identical local shear rates and indicating hysteresis and mechanical instability. Within a stress-activated framework, we identify the mean normalized stress drop, a measure for mesoscale avalanche size, as a key field variable that bridges the two regimes. Incorporating this variable, we formulate a unified constitutive model that captures the entire yielding transition. These findings establish mesoscale avalanche evolution as a central mechanism underlying granular yielding rheology.

Cholinergic modulation enables scalable action selection learning in a computational model of the striatum

Abstract The striatum plays a central role in action selection and reinforcement learning, integrating cortical inputs with dopaminergic signals encoding reward prediction errors. While dopamine modulates synaptic plasticity underlying value learning, the mechanisms that enable selective reinforcement of behaviorally relevant stimulus-action associations–the structural credit assignment problem–remain poorly understood, especially in environments with multiple competing stimuli and actions. Here, we present a computational model in which acetylcholine (ACh), released by striatal cholinergic interneurons, acts as a channel-specific gating signal that restricts plasticity to brief temporal windows following action execution. The model implements a biologically plausible three-factor learning rule requiring presynaptic activity, postsynaptic depolarization, and phasic dopamine, with plasticity gated by cholinergic pauses that temporally align with behaviorally relevant events. This mechanism ensures that only synapses involved in the selected behavior are eligible for modification. Through systematic evaluation across tasks with distractors and contingency reversals, we show that ACh-gated learning promotes synaptic specificity, suppresses cross-channel interference, and yields increasingly competitive performance relative to Q-learning in complex tasks, reflecting the scalability of the proposed learning mechanism. Moreover, the model reveals distinct roles for striatal pathways: direct pathway (D1) neurons maintain stimulus-specific responses, while indirect pathway (D2) neurons are progressively recruited to suppress outdated associations during policy adaptation. These findings provide a mechanistic account of how coordinated cholinergic and dopaminergic signaling can support scalable and efficient reinforcement learning in the striatum, consistent with experimental observations of pathway-specific plasticity.

Computational identification of phytochemicals as glycogen synthase kinase 3 beta (GSK3β) inhibitors for therapeutic applications in chronic diseases

Emerging hemispheric asymmetry of Earth’s radiation

Twenty-four years of satellite observations from the Clouds and the Earth’s Radiant Energy System show a northern hemisphere (NH) minus southern hemisphere (SH) trend difference of 0.34 ± 0.23 Wm −2 dec −1 (5 to 95% CI) in absorbed solar radiation (ASR) and a weaker trend difference of 0.21 ± 0.21 Wm −2 dec −1 in outgoing longwave radiation. The emerging darkening of the NH relative to the SH is associated with changes in hemispheric differences in aerosol–radiation interactions, surface albedo, and water vapor changes. Cloud changes also contribute to a greater ASR hemispheric contrast, but the magnitude is small due to opposing trend differences in the tropics and extratropics. The break in hemispheric symmetry in ASR challenges the notion that clouds naturally compensate for forced hemispheric asymmetries in noncloud properties. Hemispheric (a)symmetry in radiation is linked with the atmosphere–ocean general circulation. How clouds respond to this hemispheric imbalance has important implications for future climate.

Research on nonlinear vibration of transformer winding based on static analysis of axial pressing process

Hidden order in active nematic defects

Hyperuniformity and giant number fluctuations represent opposite ends in a spectrum of statistical correlations found in physical systems outside of thermal equilibrium. Dynamic phase transitions exhibit critical points that are hyperuniform, while anomalously large fluctuations are often quoted as a hallmark of active matter. Here, we show that the apparently disordered state of active nematic defects exhibits suppression of fluctuations on scales as large as the system size, reminiscent of what is seen for ordered lattices. Modeling the nematic defects in terms of continuum densities, we show that their distribution becomes asymptotically hyperuniform in the limit where their proliferation is solely driven by activity, rather than by thermal fluctuations. When both effects are at play, the resultant hyperuniform structure is limited to a finite range of length scales, ℓ < ℓ × , with ℓ × → ∞ as thermal unbinding is suppressed. The system of active nematic defects can therefore be said to possess hidden order across scales provided ℓ is comparable to the system size. This organization is most pronounced when considering the subpopulation of either the positive or the negative defects, illustrating lack of multihyperuniformity. Our experimental findings are supported by hydrodynamic theory and agent-based simulations, which further allow elucidating the role of both creation/annihilation of defect pairs and of their mutual repulsion and attraction in achieving hyperuniformity.

Deep intelligence: a four-stage deep network for accurate brain tumor segmentation

Abstract Image segmentation is an essential research field in image processing that has developed from traditional processing techniques to modern deep learning methods. In medical image processing, the primary goal of the segmentation process is to segment organs, lesions or tumors. Segmentation of tumors in the brain is a difficult task due to the vast variations in the intensity and size of gliomas. Clinical segmentation typically requires a high-quality image with relevant features and domain experts for the best results. Due to this, automatic segmentation is a necessity in modern society since gliomas are considered highly malignant. Encoder-decoder-based structures, as popular as they are, have some areas where the research is still in progress, like reducing the number of false positives and false negatives. Sometimes these models also struggled to capture the finest boundaries, producing jagged or inaccurate boundaries after segmentation. This research article introduces a novel and efficient method for segmenting out the tumorous region in brain images to overcome the research gap of the recent state-of-the-art deep learning-based segmentation approaches. The proposed 4-staged 2D-VNET + + is an efficient deep learning tumor segmentation network that introduces a context-boosting framework and a custom loss function to accomplish the task. The results show that the proposed model gives a Dice score of 99.287, Jaccard similarity index of 99.642 and a Tversky index of 99.743, all of which outperform the recent state-of-the-art techniques like 2D-VNet, Attention ResUNet with Guided Decoder (ARU-GD), MultiResUNet, 2D UNet, Link Net, TransUNet and 3D-UNet.

HISTONE DEACETYLASE-1 is required for epigenome stability in <i>Neurospora crassa</i>

Polycomb group (PcG) proteins form chromatin modifying complexes that stably repress lineage- or context-specific genes in animals, plants, and some fungi. Polycomb Repressive Complex 2 (PRC2) catalyzes trimethylation of lysine 27 on histone H3 (H3K27me3) to assemble repressive chromatin. In the model fungus Neurospora crassa, H3K27me3 deposition is regulated by the H3K36 methyltransferase ASH1 and components of constitutive heterochromatin including the H3K9me3-binding protein HETEROCHROMATIN PROTEIN 1 (HP1). Hypoacetylated histones are a defining feature of both constitutive heterochromatin and PcG-repressed chromatin, but how histone deacetylases (HDACs) contribute to normal H3K27me3 and transcriptional repression within PcG-repressed chromatin is poorly understood. We performed a genetic screen to identify HDACs required for repression of PRC2-methylated genes. In the absence of HISTONE DEACETYLASE-1 (HDA-1), PRC2-methylated genes were activated and H3K27me3 was depleted from typical PRC2-targeted regions. At constitutive heterochromatin, HDA-1 deficient cells displayed reduced H3K9me3, hyperacetylation, and aberrant enrichment of H3K27me3 and H3K36me3. CHROMODOMAIN PROTEIN-2 (CDP-2) is required to target HDA-1 to constitutive heterochromatin and is also required for normal H3K27me3 patterns. Patterns of aberrant H3K27me3 were distinct in isogenic ∆ hda-1 strains, suggesting that loss of HDA-1 causes stochastic or progressive epigenome dysfunction. To test this, we constructed a new Δhda-1 strain and performed a laboratory “aging” experiment. Deletion of hda-1 led to progressive epigenome decay over hundreds of nuclear divisions. Together, our data indicate that HDA-1 is a critical regulator of epigenome stability in N. crassa.

Analytical and machine learning approaches identify a sea star steroid with promising activity for COVID-19 therapeutic development

Abstract The pressing demand for safe and efficient COVID-19 treatments has intensified interest in Natural products, especially those derived from marine organisms. In this study, a bioactive steroidal compound, 5α-cholesta-9(11)-en-3β,20β-diol, was successfully isolated from the starfish Acanthaster planci. Structural elucidation was achieved using HREIMS, FTIR, and advanced 1D/2D NMR spectroscopy, confirming a molecular formula of C₂₇H₄₆O₂ and characteristic functionalities including hydroxyl and double bond moieties. The compound demonstrated notable anti-SARS-CoV-2 activity, attaining 85% viral inhibition at 5 ng/µl with an IC₅₀ of 5.86 µM, as demonstrated by plaque reduction assays. Molecular docking studies demonstrated significant binding affinities toward key viral targets Mpro, NSP10, and RNA-dependent RNA polymerase with free binding energies of -26.85, -27.59, and − 35.08 kcal/mol, respectively. These affinities surpassed those of their respective co-crystallized reference ligands. In-silico ADMET profiling indicated favorable pharmacokinetic properties, including high BBB penetration, moderate intestinal absorption, and non-hepatotoxicity. Toxicity assessments predicted low carcinogenic risk, a high rat MTD, and minimal ocular and dermal irritancy. Additionally, we developed a predictive web application based on machine learning to estimate IC₅₀ values of SARS-CoV-2 inhibitors, streamlining the drug discovery process. The forecasted values nearly matched the experimental outcomes, demonstrating the model’s reliability and its potential to reduce time, cost, and risk in early-stage drug development. Moreover, machine learning models, particularly XGBoost, demonstrated excellent performance in predicting pIC₅₀ values (RMSE = 0.1357, MAE = 0.1022), supporting the development of a web-based IC₅₀ prediction application. The bioactivity prediction platform ENHPCG further validated the compound’s antiviral potential, estimating an IC₅₀ of 5.95 µM. Overall, these integrated analytical, biological, and computational approaches highlight 5α-cholesta-9(11)-en-3β,20β-diol as a potential SARS-CoV-2 inhibitor and a candidate for further pharmacological development.

CAR-SPLASH identifies nascent pre-mRNA structures implicated in kinetic coupling and alternative splicing

Pre-mRNA splicing is kinetically coupled to transcription as shown by the widespread effects of transcription speed on alternative splicing (AS) outcomes. The molecular basis for such kinetic coupling is incompletely understood, but one potential mechanism is through elongation rate–dependent alternative folding pathways of the nascent pre-messenger RNA (pre-mRNA). To search for RNA structures in nascent pre-mRNA, we modified Sequencing of Psoralen Crosslinked, Ligated And Selected Hybrids (SPLASH) [J. G. Ashley Aw et al. , Mol. Cell 62 , 603–617 (2016)] for use with C hromatin A ssociated R NA. We applied this method called Chromatin Associated RNA (CAR)-SPLASH to cells expressing wild-type and slow mutant RNA polymerase II and identified &gt;3,000 intramolecular RNA duplexes of which &gt;400 are proximal to splice sites. Antisense oligonucleotide (ASO) disruption of several such duplexes that sequester splice sites has a major impact on AS outcomes, even though the ASOs do not directly disrupt splice sites. ASO disruption of these regulatory elements that we designate “RNA kinetic switches” modified AS of NISCH Exon 18, GAK Exon 7, and MEGF8 Exon 14 in a way that depends on the rate of transcription elongation. We propose that these switches mediate kinetic coupling via the effects of transcription speed on folding of nascent RNA structures that modulate AS and that many nascent RNA structures can thereby serve as targets for splice-modifying ASOs.

Evaluating individual and mixed arbuscular mycorrhizal fungi for controlling Rhizoctonia root rot in lupine

Abstract Lupin is an economically and ecologically important legume crop. However, it is susceptible to infection with Rhizoctonia solani, which causes damping off and root rot diseases. Arbuscular mycorrhizal fungi (AMF) as a biological control agent has emerged as a promising alternative to chemical fungicides. Four species of AMF, namely Entrophospora etunicata, Rhizophagus clarus, Rhizophagus intraradices, Entrophospora lutea and their mixture were evaluated to determine their compatibility with lupine plants, also as a biocontrol agent against damping-off and root-rot diseases in comparison with the chemical fungicide Rizolex-T. All mycorrhizal treatments significantly reduced damping-off disease and increased the surviving plants under greenhouse and field conditions. The most effective isolates were Entrophospora lutea, followed by R. intraradices. Alongside their biocontrol activity, they positively enhance the uptake of macro- and micronutrients, promoting nodulation, and boosting nitrogenase enzyme activity. Additionally, they improved various plant growth parameters, increased yield, and stimulated the activity of peroxidase (PO), polyphenol oxidase (PPO), and elevated phenolic compounds. Moreover, greater accumulations of proline, chlorophyll, and carotenoids were observed. However, Entrophospora lutea treatment was effective as Rizolex-T in disease reduction and superior in enhancing plant growth and yield.