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Longitudinal antibody profiling after dengue reveals distinct dynamics by antibody specificity over 18 months
Abstract The four dengue virus serotypes (DENV1-4) co-circulate worldwide, posing major challenges for vaccine development. One key issue is that certain levels and subsets of cross-reactive antibodies have been associated with enhanced disease during subsequent infection with a different DENV serotype. To understand the heterogeneity of DENV antibody responses and delineate their distinct kinetics, we define the magnitude and kinetics of 84 antiviral antibody subsets (by isotype, subclass, antigen, and cross-reactivity) after primary versus secondary dengue, using longitudinal samples collected <1, 3, 6 and 18 months post-symptom onset from a pediatric hospital study in Nicaragua. Interestingly, we find that after primary infection, cross-reactive IgG antibody responses against the envelope protein rise, not wane, over time. Antibody kinetics vary by specificity as measured by homologous versus cross-reactive subsets, viral antigen, and subdomain of a single antigen. Further, a substantial fraction of subjects still have IgA, IgM, and IgG3 responses above the assay background at 18 months post-infection. Overall, we find that the cross-reactive subset of post-primary anti-DENV antibody responses demonstrates distinct kinetics from overall DENV-binding antibodies as well as from secondary immune responses, which has implications for the outcome of subsequent DENV infections.
A unified platform for the rapid assembly of glutarimides for Cereblon E3 ligase modulatory drugs
Abstract Glutarimide-containing Cereblon (CRBN) ligands are critical motifs for PROTACs, molecular glue degraders and next-generation Cereblon E3 ligase modulatory drugs (CELMoDs), which represent promising therapeutic modalities in targeted protein degradation. However, the multistep synthetic routes required to access glutarimide scaffolds continue to present formidable challenges for medicinal chemists, limiting rapid structure–activity relationship (SAR) exploration and late-stage diversification. To streamline access to these privileged motifs, modular and efficient methodologies are still highly desirable. Here, we report a unified organocatalytic synthesis platform for the rapid assembly of diverse glutarimide derivatives from readily available nitrogen heterocycles. Employing a sequence of phosphine-catalysed C–N bond formation, metal-free Giese addition and acid-mediated cyclisation, this approach provides high selectivity, broad functional group tolerance and operational simplicity under conditions amenable to both multigram synthesis and high-throughput parallel synthesis. Using this platform, we rapidly prepare CRBN binder libraries, access control analogues (for example, N ‑alkylated glutarimides) and perform late‑stage functionalisation of bioactive molecules. This strategy could offer a transformative solution for the efficient and cost-effective synthesis of CRBN-targeted therapeutics and chemical biology probes, overcoming longstanding synthetic bottlenecks in the field.
Sensing centrosome amplification: the interface between centriole duplication and autophagy
Abstract Multipolar mitotic spindles with extra centrosomes, first observed in cancer cells in the late nineteenth century, remain poorly understood. Here, to address how cells overcome proliferation arrest imposed by centrosome amplification, we describe a genome-wide screen revealing that downregulation of the Wnt, Hippo, Tpr53, PIDDosome, ciliary biogenesis, or autophagy pathways enables proliferation of mouse embryonic stem cells having PLK4-mediated centrosome amplification. We select the tumor suppressor, Guanine-nucleotide Activating Protein ARHGAP15, for further study as its depletion activates autophagy, overcomes centrosome amplification, and enables embryonic fibroblast proliferation. Reduction of centrosomes following ARHGAP15 depletion requires autophagy protein, ATG16L1, which associates with ARHGAP15 when the autophagy pathway is inactive. ARHGAP15 is opposed by Guanine-nucleotide Exchange Factor ARHGEF2, which is activated by the centriolar protein CEP170 to generate RAC1-GTP and promote autophagy. Together our findings add extra dimensions to the roles of RAC1 in cytoskeletal regulation and ARHGAP15 as a potential tumor suppressor.
A structurally unique effector shared between vascular wilt fungi drives cotton and olive defoliation
Abstract Defoliating (D) strains of the vascular wilt fungus Verticillium dahliae cause severe yield losses in cotton and olive, but the genetic basis of this pathotype remained unknown. Using comparative genomics, functional genetics, structural analysis, and phylogenomics, we identify a D-pathotype–specific genomic region encoding two duplicated secreted effector genes. Simultaneous deletion of both copies abolishes pathogenicity and defoliation in cotton and olive, and affects virulence in Nicotiana benthamiana and Arabidopsis thaliana . Expression of the effector in non-defoliating strains induces cotton defoliation, and purified protein causes wilting and leaf drop. Structural analyses reveal a previously uncharacterized protein fold conserved across Verticillium and Fusarium species, with evidence of functional diversification and host specificity. Phylogenomic and genomic context analyses indicate repeated horizontal transfer mediated by giant transposable elements known as Starships . Together, these findings identify the D effector as a central determinant of defoliation and virulence and show how Starship -mediated gene transfer drives emergence of an agriculturally important fungal trait.
Attention modulates value normalization in human reinforcement learning by shaping reward encoding
ARL13B is regulated by the ERK/P90 pathway and mediates TMZ resistance in glioblastoma via microvesicles
GinkgoSense-Net: A method for characterization and real-time detection of ginkgo fruits in complex field environments
Cleansability of zirconia implants after exposure to different contamination media: a methodological assessment
Quantitative estimation and interpretation of non-technical loss severity in smart grids using RFE-optimized XGBoost regression
Abstract Non-technical losses (NTL) remain a major source of economic risk for electricity distribution utilities. Although extensive research has focused on classification-based theft detection, such approaches typically provide binary decisions and offer limited support for quantifying the magnitude of consumption irregularities, which is essential for inspection prioritization and financial planning. This study proposes a regression-driven framework to estimate the severity of deviation from expected demand behavior using the Cumulative Abnormal Deviation (CAD) index. Instead of attempting theft verification, the method provides a continuous risk indicator derived from the difference between measured consumption and a baseline demand model. The framework combines Extreme Gradient Boosting with Recursive Feature Elimination to construct a compact predictor while preserving feature interpretability. Experiments conducted on a publicly available smart-meter dataset demonstrate that the optimized XGBoost-RFE model achieves high predictive consistency (R² = 0.9869, RMSE = 2.4062, MAE = 1.1285) and outperforms ensemble and deep learning benchmarks under identical data conditions. Residual-based prediction intervals show stable uncertainty behavior, with 93.83% of observations captured within the nominal 95% confidence bounds. Feature-attribution analysis indicates that load-related variables dominate deviation formation, supporting the operational plausibility of the model outputs. The results suggest that reliable estimation of deviation severity can be achieved under the evaluated experimental conditions without direct access to inspection outcomes. Nevertheless, the framework should be interpreted as a decision-support and prioritization tool, not as proof of theft. The proposed methodology provides a statistically grounded step toward quantitative, explainable, and practically applicable NTL risk assessment in modern distribution systems.
Temporal dynamics of nutrient elements in biochar and biochar-amended soils over three years: a comparative micro-XRF and SEM–EDX study
Abstract Biochar reshapes soil composition for years, yet most evidence comes from short-term, single-element laboratory incubations, leaving multi-element dynamics in tropical soils poorly resolved. We tracked 11 elements in longan-wood biochar and biochar-amended Ultisols over three years after a single field application, combining micro-XRF with SEM–EDX (one Map Sum Spectrum per group). Soil Fe and Al showed a non-linear response: both peaked at Year 1 (Fe 17.77 ± 3.93; Al 21.33 ± 1.88 wt.%; ~ 3.1- and 1.75-fold above control), fell at Year 2, then rose again at Year 3—a pattern invisible to single time-point studies, consistent with organo-mineral coating formation and reworking. Soil P stayed below detection despite annual fertilisation, indicating persistent Fe–Al phosphate fixation, while soil K peaked at Year 2 (1.82 wt.%). The biochars were Ca-rich (> 60 wt.%), acting as strong liming agents. EM-inoculated biochar showed higher surface carbon (92.7 vs. 77.6 wt.%), consistent with microbial biofilm deposition, though EM did not alter inorganic composition; a high Year-3 N signal (13.52 ± 23.42 wt.%) is a semi-quantitative artefact, not a reliable soil-N value. These findings reveal non-linear multi-element redistribution from a single biochar application and show the value of pairing bulk and surface analysis for long-term biochar–soil studies.
Circulating matrix metalloproteinase profile in early-stage primary biliary cholangitis
Alkali activation of construction and demolition waste for sustainable building materials
Model-driven analysis reveals oxidative stress adaptation enabling efficient energy utilization in a Crabtree-negative Saccharomyces cerevisiae
Abstract Although abolishing the Crabtree effect in Saccharomyces cerevisiae through a pyruvate dehydrogenase bypass eliminates carbon loss through ethanol overflow metabolism, it compromises growth rates. While the Crabtree effect has been a valuable natural adaptation, it is energetically inferior to respiration and is generally undesirable in cell factories engineered to produce assimilatory compounds. Restoring growth efficiency in Crabtree-negative strains remains a central challenge. Through adaptive laboratory evolution of the engineered strain (sZJD23) and subsequent reverse engineering, a variant (sZJD28) with markedly improved growth was identified. This improvement is driven primarily by a mutation in MED2 (encoding a Mediator complex subunit) and, to a lesser extent, a mutation in GPD1 (encoding glycerol-3-phosphate dehydrogenase). By integrating quantitative proteomics with enzyme-constrained genome-scale modelling, we demonstrate that these mutations jointly enable a more efficient mode of oxidative stress adaptation and energy utilization. The GPD1 mutation suppresses a protein-costly, suboptimal NAD⁺-recycling strategy reliant on glycerol synthesis, while the MED2 mutation reshapes the oxidative stress response towards peroxisomal detoxification. Collectively, these adjustments optimize metabolic flux distribution and reduce protein costs in energy metabolism, thereby increasing ATP availability. Our findings reveal how coordinated mutations in regulatory and metabolic genes restore growth fitness in engineered Crabtree-negative yeast.
High-gamma frequency-tagged magnetoencephalography reveals time-resolved task-related functional coupling during spatial cognition
Dietary microplastic exposure and lip–oral cavity cancer: a global ecological analysis with pre-specified spatial econometric sensitivity
GreenAid: a confidence-weighted ensemble deep learning system for real-time plant disease detection and management
Abstract Plant diseases cause 20–40% annual crop losses worldwide, yet conventional detection methods remain slow, subjective, and inaccessible to smallholder farmers. This work presents GreenAid, an end-to-end plant disease detection and management system that bridges the gap between laboratory-level deep learning performance and practical agricultural deployment. The system integrates a confidence-weighted ensemble of three CNN architectures (VGG16, ResNet50, InceptionV3), fused through per-class F1-score reliability weights, with a cross-platform mobile application supporting offline inference via TensorFlow Lite, a web-based analytics dashboard, and an NLP-powered chatbot. On the PlantVillage benchmark (87,000 images, 38 classes, 14 species), the ensemble achieves 98.74% accuracy and 98.48% F1-score. Systematic comparison of six fusion strategies confirms that per-class F1 weighting outperforms alternatives including majority voting, simple averaging, and stacking. The INT8-quantised deployment model (78 MB, 127 ms on a mid-range smartphone) retains 98.43% accuracy with per-class analysis confirming disproportionate impact on the five most challenging categories. All pairwise model comparisons are validated by McNemar’s test ( $$p < 0.05$$ ). The primary contribution is the complete, reproducible integration of competitive classification, edge deployment, and an end-to-end agricultural delivery pipeline (mobile application, web dashboard, and NLP chatbot) rather than the ensemble mechanism itself.
Profiling of immunomodulatory anti-cytokine autoantibodies associated with disease heterogeneity in a multiethnic Asian cohort
Analysis of salt change and desalination effect of saline soil in Xinjiang under the influence of multiple measures
Direct quantification of waterborne viruses via high-temperature and high-pressure treatment: a simplified nucleic acid extraction-free approach
Abstract High-temperature and high-pressure treatment (HTP) is a physical lysis method that enables direct quantification of viral genomes from environmental samples without nucleic acid extraction. This study assessed the applicability of HTP for rapid virus detection using pepper mild mottle virus (PMMoV) as a model. Optimal conditions for PMMoV quantification were identified as 120 °C for 15 s, yielding results comparable to conventional RNA extraction methods. RNA integrity analysis revealed that temperatures above 140 °C may cause genome degradation, supporting the selection of 120 °C as the optimal setting. The method demonstrated high reproducibility across qPCR and dPCR assays and enabled stable quantification with minimal interference from wastewater matrix components. In addition, additional virus species were evaluated to assess the performance of HTP beyond PMMoV under the tested conditions. These findings highlight the potential of HTP as a simplified workflow without nucleic acid extraction for environmental virus monitoring. Future research should focus on expanding its use to diverse virus types and integrating the method into automated platforms for real-time monitoring.