Browse Articles

Observed universal continuum morphology of raindrops reveals a concise diagram of heavy precipitation microphysics

Persistent knowledge gaps in precipitation microphysics, particularly the nonlinear coupling between microphysical process hierarchies and raindrop size distribution (DSD) variability, keep introducing systemic uncertainties into precipitation retrievals and model simulations. Here, we address this challenge through a unified framework that integrates observations from China’s national-scale disdrometer network (1,031 sites) and 10-year global dual-frequency precipitation satellite dataset. First, a region-independent DSD continuum characterized by a universal linear relationship between raindrop diameter and concentration across diverse climatic zones is identified, extending and refining the conventional maritime-like and continental-like category. Then, we quantify the vertical stratification of microphysical processes in shaping and shifting this continuum. Implementations of our findings to reduce biases in current microphysics parameterizations are proposed and discussed. This study advances our fundamental understanding of the apparent heterogeneity yet inherent homogeneity in the microphysics of heavy precipitation, providing mechanistic insights to improve the performance of weather and climate models.

HAp@Cell bio-films engineered from local resources involving molecular mechanisms of dye adsorption and antibacterial activity

Convolutional neural networks outperform other presence-only species distribution modeling algorithms

Species distribution models (SDMs) are essential tools for ecologists and conservationists because they can identify environmental determinants of species occurrences and predict species distributions. Unfortunately, most SDM algorithms rely on point-based or localized averages of environmental conditions and do not capture spatial heterogeneity and landscape patterns that shape species distributions. However, species–environment relationships are hierarchical, with different environmental factors shaping realized niche at different spatial extents. Using presence-only data, we evaluated whether convolutional neural networks (CNNs), which capture three crucial aspects of spatial context (heterogeneity, pattern, and multiscale relationships), outperform other SDM algorithms in predicting species distributions. We benchmarked CNNs against other widely used algorithms, including Maxent and ensemble models, and modeled 225 species from diverse geographic regions and taxonomic groups. We also assessed the efficiency of data augmentation in mitigating CNNs’ sensitivity to limited training data. We found that CNNs consistently outperformed other algorithms. CNNs utilizing augmented data achieved median AUC ROC of 0.77 and AUC PRG of 0.78, compared to, for example, 0.74 and 0.61, respectively, for ensemble models. Notably, for rare species with <30 occurrences, CNNs with augmentation maintained high performance (AUC ROC = 0.75), again exceeding ensemble models (AUC ROC = 0.68). While CNNs required longer inference times, their model fitting was as fast as for other algorithms. Our results demonstrate CNNs’ ability to incorporate multiscale spatial complexity and enhance predictive accuracy, particularly for data-limited species. CNNs have the potential to transform biodiversity modeling and inform conservation by enabling more spatially explicit and ecologically meaningful representations of the realized niche.

High-performance graphene oxide desalination membranes enabled by size-sieving, ion exclusion, and cation recognition mechanisms

The <i>DELAYED ABAXIAL TRICHOMES</i> Helitron has dual functions in vegetative and pollen development in <i>Arabidopsis thaliana</i>

Transposons drive genetic diversity and evolution by altering the genomic landscape over time. Here, we describe D ELAYED AB AXIAL TRICHOMES ( DAB ), a Helitron/RC transposable element in Arabidopsis thaliana that has a role in vegetative phase change and gametogenesis. A genome-wide association study (GWAS) for the timing of abaxial trichome development (an adult leaf trait) in A. thaliana revealed a conserved haplotype of polymorphisms within DAB that delays abaxial trichome production. CRISPR-Cas9-induced deletions of DAB are gametophytic pollen-lethal, indicating that this locus is also required for pollen production. DAB produces 24-nucleotide siRNAs with sequence complementarity to genes involved in embryogenesis, gametogenesis, and seed development. DAB also impacts the expression of ARGONAUTE genes, genes involved in RNA-directed DNA methylation (RdDM), as well as genes in several key genetic pathways. This global effect on gene expression suggests that DAB may have functions beyond those identified in this study.

Performance improvements of recycled concrete and ceramic aggregates using graphene oxide nanocoating

Identification of CD164 as an essential entry receptor for divergent adeno-associated viruses

Recombinant adeno-associated viruses (rAAVs) are widely used for in vivo gene delivery. While KIAA0319L, known as AAV receptor (AAVR), is essential for the transduction of multiserotype AAVs, it is dispensable for AAV4-related (Clade G) AAVs. We conducted a genome-wide CRISPR/Cas9 screen and identified CD164, a type I transmembrane sialomucin, as an essential entry receptor for Clade G AAVs. Ablation of CD164 expression substantially impaired both entry and transduction of Clade G AAVs. CD164-targeting antibodies and soluble CD164 ectodomain effectively blocked transduction. AAV4 capsids colocalized with CD164 at the plasma membrane and in endosomal compartments. In vitro, CD164 interacted with AAV4 or AAVrh32.33 capsids at high affinity. Importantly, systemic administration of rAAV4 or rAAVrh32.33 in CD164 knockout (KO) mice resulted in nearly complete loss of transgene expression. These findings establish CD164 as an essential entry receptor for Clade G AAV vectors and uncover a distinct AAVR-independent mechanism of AAV tropism.

Towards fully automated synthetic ECV quantification: an open-access machine learning-based approach for fast blood draw-free CMR

Abstract Extracellular volume (ECV) quantification involves time-consuming multi-step post-processing and a blood draw for hematocrit analysis. This study aimed to develop a fully automated blood draw-free, machine learning-based approach for synthetic ECV assessment for non-invasive assessment of diffuse myocardial fibrosis. We retrospectively evaluated a large clinical cohort of 1092 patients who underwent CMR and ECV measurement at 1.5T or 3T. Participants were divided into training (n = 767) and validation (n = 325) cohorts. Manual contouring of T1 maps was used to iteratively develop a neural network segmentation model, which was then applied for automated analysis. Fully-automated synthetic ECV was calculated using validated sex- and field strength-specific models. Agreement was assessed using Student’s t-test, Pearson correlation, Bland–Altman analysis, and classification analysis. Fully-automated synthetic ECV showed strong correlation with conventional ECV (r = 0.79, p  &lt; 0.001), with no significant differences (26.9% ± 4.9% vs. 27.3% ± 6.4%, p  = 0.056). Bland–Altman analysis indicated minimal mean difference of 0.4% with moderate limits of agreement (LoA) spanning − 7.24% to + 8.07%, with good agreement for values of up to 35% (mean difference 0.1%, LoA: − 5.38% to + 5.23%). Fully automated synthetic ECV offers a blood-free proof-of-concept for large-scale post-processing, supporting consistent and efficient assessment of myocardial fibrosis in research settings, pending further validation for clinical use at higher ECV ranges.

MondoA mediates transcriptional coordination between the MYC network and the integrated stress response in pancreatic cancer

MYC amplification contributes to poor survival and outcome in pancreatic ductal adenocarcinoma (PDAC). Here we show that in PDAC cell lines with amplified MYC, MondoA is required for viability, facilitating proliferation while suppressing apoptosis in vitro and in vivo. Transcriptional and genomic profiling demonstrates that loss of MondoA leads to altered expression of direct MondoA targets as well as MYC target genes and is accompanied by shifts in genomic occupancy of MYC, MNT, and the MondoA paralog ChREBP. This altered genomic binding by MYC network members is associated with transcriptional perturbation of multiple metabolic and stress pathways, as well as global changes in N6-methyladenosine modification (m 6 A) of messenger RNA (mRNA). MondoA inhibition disrupts coordination between MYC network members and the Integrated Stress Response (ISR), resulting in decreased translation of ATF4 mRNA, discordant gene regulation of shared targets of MYC and ATF4 and, ultimately, apoptosis. Reestablishing ATF4 protein expression rescues the diminished viability due to loss of MondoA expression or activity, providing direct evidence of a link between deregulated MYC and the transcriptional machinery of the ISR. Last, we find that small-molecule inhibition of MondoA is lethal in a subset of PDAC cell lines, including patient-derived organoids, suggesting that the ability to target MYC via chemical inhibition of MondoA transcriptional activity may have broad efficacy.

Quality of life in children with defecation disorders compared with healthy

Correction for Eames et al., Computer-assisted learning in the real world: How Khan Academy influences student math learning

Optimization of mechanical properties and microstructure characterization of resistance spot welded martensitic stainless steel: in-situ tempering and TLBO approach

N6-methyladenine DNA modification modulates pathogen virulence in nematodes

Understanding the global regulatory mechanisms that control pathogen virulence gene expression is essential for elucidating the molecular basis of pathogenicity. N6-methyladenine (6 mA) plays a crucial role in regulating gene expression in response to various environmental stresses; however, its role in pathogen virulence remains largely unexplored. Here, we report the widespread occurrence of 6 mA across 17 nematode isolates and map its genomic landscape in six notorious agriculturally important pathogen root-knot nematodes (RKNs). We demonstrated that 6 mA is characterized by a conserved GAG motif across nematodes, but exhibits species-specific distribution patterns and distinct effects on gene expression. In particular, its enrichment in transposable elements (TEs) differs between polyploid and diploid nematodes, suggesting lineage-specific epigenetic regulation potentially associated with polyploidy. We further identified two functional 6 mA demethylases, MiNMAD-1 and MiNMAD-2, and confirmed their catalytic activity and active sites. Host-induced gene silencing (HIGS) of minmad-1 significantly increased plant resistance to three polyploid RKN species. A detailed functional analysis revealed that minmad-1 knockdown broadly affected gene expression during the parasitic stage, including genes involved in virulence, thereby reducing nematode infectivity. Together, our findings suggest 6 mA demethylase as a key epigenetic regulator of RKNs’ virulence, providing new insights into nematode biology and offering promising targets for the development of sustainable control strategies.

Strength and cost analysis of geopolymer concrete using rice husk ash and GGBS as sustainable cement alternatives

Abstract The present study aims to develop environmentally friendly concrete by using industrial waste materials, namely ground granulated blast furnace slag (GGBS) and rice husk ash (RHA), to produce green concrete. Geopolymer concrete (GPC) has emerged as an alternative to eliminate the use of cement. The main objective of this study is to design and evaluate geopolymer concrete of M40, M50, and M60 grades. Mixes were prepared by replacing GGBS with RHA at 0%, 10%, 20%, and 30% replacement levels for each grade. The resistance of these mixes against 5% sulphuric acid exposure was also examined. Experiments were conducted to determine compressive strength under different parameters, including NaOH concentration, proportions of RHA and GGBS, and curing duration. Additional tests assessed the effect of acid exposure on strength and weight loss. The cost-effectiveness of GPC production was also compared with that of ordinary Portland cement (OPC). The results revealed that, for all three grades, replacement of GGBS with more than 10% RHA led to a decrease in compressive strength. Furthermore, the production cost of GPC was found to be more economical compared to OPC. Both weight loss and strength loss increased progressively with longer acid exposure. Strength reduction for M40, M50, and M60 grade concretes reached 75.4%, 76.1%, and 79.9%, respectively, when 10% of GGBS was replaced with RHA.

Correction for Yu, Addressing the broader implications of AI–AI bias in decision-making systems

Association of platelet to HDL-C ratio with short-term mortality in critically ill intracerebral hemorrhage patients: a MIMIC-IV analysis

Abstract Intracerebral hemorrhage (ICH) is associated with substantial early mortality, yet prognostic biomarkers integrating coagulation, inflammation, and lipid metabolism are limited. The platelet to high-density lipoprotein cholesterol ratio (PHR) may reflect this balance. In a retrospective cohort of 878 critically ill ICH patients from the MIMIC-IV database (mean age 69.6 ± 13.8 years, 55.0% female), overall in-hospital and 30-day mortality were 15.0% and 20.5%, respectively. Short-term mortality was defined as death occurring either during hospitalization (in-hospital mortality) or within 30 days of admission (30-day mortality). Higher PHR at ICU admission was independently associated with lower short-term mortality. Each 1-SD increase corresponded to a 20–28% reduction in risk for in-hospital (adjusted HR 0.72, 95% CI 0.59–0.87) and 30-day mortality (HR 0.80, 95% CI 0.68–0.94). When analyzed by quartiles, patients in the highest PHR group had the lowest mortality (in-hospital HR 0.49, 95% CI 0.29–0.83; 30-day HR 0.55, 95% CI 0.35–0.88), consistent with Kaplan-Meier survival analyses. Restricted cubic spline analysis indicated a linear inverse relationship. Results were robust in subgroup analyses and largely consistent in sensitivity analyses, with modest attenuation for in-hospital mortality. These findings suggest PHR has an independent inverse relationship with short-term mortality. Important prognostic factors, including hematoma volume and location, were unavailable; PHR may reflect underlying disease severity rather than causal protection.

Five decades of seasonal phytoplankton succession examined with principal traits—An approach linking composition to function

Understanding how changes in community composition and function are driven by environmental change remains a fundamental challenge—to which trait-based approaches offer a mechanistic perspective. To examine links between functional and compositional changes we introduce a metric—the “principal trait”—relating the mean traits of a community to principal components (PC) of community composition. We demonstrate the benefits of this approach by analyzing nearly five decades of phytoplankton monitoring data from Lake Constance. Based on principal traits, PC-scores, and PC-rotations we identify the transition of a predation susceptible winter-spring to a defended summer community and distinguish patterns of compositional and functional change during eutrophic and oligotrophic conditions. Remarkably, this approach uncovered the independent emergence of a strong tradeoff between the resource acquisition traits phosphate and light affinity at two temporal scales: once in the response of the predation susceptible winter-spring community to seasonal changes in light and phosphorus availability, and once in the response of the defended summer community to long-term changes in the lake’s nutrient status. The analysis identified community members and mechanisms involved in functional changes, demonstrating how the interplay between multiple traits determined the responses of phytoplankton community composition and function to environmental change.

Adult spinal process impingement syndrome: progression and staging

Gas-sensing neurons prime mitochondrial fitness to offset metabolic stress

The mitochondrial unfolded protein response (UPR mt ) is triggered by cells to alleviate proteotoxicity in response to metabolic stress. The ability to anticipate and prime cells against mitochondrial stress, by sensing potentially toxic changes in the external or internal environment, would provide a survival advantage. Yet, whether and how animals anticipate mitochondrial stress remains unclear. Here, we show that the Caenorhabditis elegans receptor guanylyl cyclase GCY-9 regulates neuropeptide signaling from carbon dioxide–sensing neurons to govern a noncanonical mitochondrial stress response in the intestine. This noncell autonomous stress response induces atypical mitochondrial chaperone transcription, confers mitochondrial stress resistance, and increases mitochondrial membrane potential and respiration. We show that starvation decreases GCY-9 expression and propose that the resultant cytoprotective program is launched to offset metabolic and proteotoxic risks. Thus, environmental sensing by peripheral neurons can preemptively enhance systemic mitochondrial function in response to metabolic uncertainty.

AI-driven fault detection and classification in photovoltaic systems using deep learning techniques

Abstract The growing needs of the world regarding electricity and the exhaustion of fossil fuel resources have aggravated the need to use renewable energy, especially the photovoltaic (PV) systems. Nevertheless, internal defects and external environmental conditions are often known to affect the operational efficiency and reliability of PV modules. This paper presents PVDefectNet, a deep learning-based fault detector and classifier of the PV systems. The proposed solution applies a resnet architecture with data augmentation techniques to enhance its resistance to operating in different operating environments. PVDefectNet is a process based on five stages that include data preparation and preprocessing, model architecture design, training, evaluation and visualization, and performance analysis. The experimental findings indicate that the proposed framework has a high classification performance with an average accuracy of 98, precision of 97.1, recall of 96.5 and F1-score of 96.8 that is better than some of the current methods. Moreover, the visualizations provided by Grad-CAM prove that the model is concentrated on physically significant defect areas, which increases interpretability and reliability. These results suggest that PVDefectNet is a good and clear solution in intelligent monitoring and maintenance of PV systems.