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Sesquiterpene Biosynthetic Gene vir4 from Trichoderma virens Enhances Direct Herbivore Resistance while Maintaining Indirect Defense
No full textAbstract Trichoderma species are widely used as root-colonizing biocontrol agents that enhance plant resistance to biotic and abiotic stresses while promoting growth. These fungi produce diverse volatile and non-volatile metabolites that mediate interactions with plants. Trichoderma can influence both direct and indirect plant defenses, including the release of herbivore-induced plant volatiles (HIPVs) that attract natural enemies of herbivores. In this study, we examined the effects of T. virens and its vir4 gene ( regulating terpenoid synthesis) knockout-mutant on maize ( Zea mays ), the herbivore Helicoverpa armigera , and its predator Macrolophus pygmaeus . Previous research has shown that T. virens differentially modulates maize root gene expression and specialized metabolite concentrations. Here, we found that caterpillars feeding on maize seedlings colonized by wild-type T. virens gained significantly less weight than those feeding on maize colonized by the vir4 knockout mutant or uncolonized plants, suggesting that the vir4 gene cluster contributes to herbivore resistance. Although fungal colonization led to moderate changes in HIPV composition, total volatile emissions remained unchanged. In Y-tube assays, M. pygmaeus preferred caterpillar-infested maize over healthy plants, but fungal colonization did not significantly affect predator behavior. Our findings demonstrate that T. virens enhances direct plant defense against herbivores while maintaining indirect defense through a mechanism regulated by terpenoid synthesis depending on vir4 gene. Further research is needed to elucidate the metabolic changes in maize induced by T. virens that contribute to reduced herbivore performance
Contrasting responses of particulate and mineral-associated organic carbon stocks to grazing exclusion in an alpine meadow
No full texthttp://dx.doi.org/10.13039/501100009996 Shaanxi Province Postdoctoral Science Foundationhttp://dx.doi.org/10.13039/501100007128 Natural Science Foundation of Shaanxi Provincehttp://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of Chin
Biocatalytic Regioselective C‐Formylation of Resorcinol Derivatives
No full textABSTRACT Although aromatic formylation reactions are highly valuable from a synthetic perspective, a biocatalytic version has not yet been reported. Here, the cofactor‐independent multimeric three‐component acyltransferase from Chromobacterium sphagni ( Cs ATase) was identified to enable the nonnatural promiscuous regioselective C‐formylation of polyphenolic substrates, especially resorcinol derivatives, and thus extending the reaction scope of acyltransferases. Formylation of 4‐ and 5‐substituted resorcinol derivatives gave access to regioselectively mono‐formylated products with up to 99% conversion and up to 74% isolated yield. Formylation of phloroglucinol led to the di‐formylated product with 99% conversion, outperforming chemical methods. Structural analysis of Cs ATase by X‐ray crystallography provided insights into its active site.Erasmus+ https://doi.org/10.13039/501100010790H2020 Marie Skłodowska-Curie Actions https://doi.org/10.13039/10001066
Dynamic mode decomposition for water-energy-food nexus modelling: Data-driven predictions of policy impacts
No full texthttp://dx.doi.org/10.13039/501100007051 Uppsala Universite
Magnetic metal-based conductive MOFs with controllable crystal structures for enhanced electromagnetic interference shielding performance
No full texthttp://dx.doi.org/10.13039/501100012672 Scientific Research Foundation of Zhejiang A and F Universityhttp://dx.doi.org/10.13039/501100004731 Natural Science Foundation of Zhejiang Provincehttp://dx.doi.org/10.13039/501100012166 National Key Research and Development Program of Chin
OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
No full textScaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.3 million neurons from the visual cortex of 78 mice across 323 sessions, totaling more than 150 billion neural tokens recorded during natural movies, images and parametric stimuli, and behavior. We train multi-modal, multi-task transformer models (1M–300M parameters) that support three regimes flexibly at test time: neural prediction (predicting neuronal responses from sensory input and behavior), behavioral decoding (predicting behavior from neural activity), neural forecasting (predicting future activity from current neural dynamics), or any combination of the three. We find that performance scales reliably with more data, but gains from increasing model size saturate -- suggesting that current brain models are limited by data rather than compute. This inverts the standard AI scaling story: in language and computer vision, massive datasets make parameter scaling the primary driver of progress, whereas in brain modeling -- even in the mouse visual cortex, a relatively simple and low-resolution system -- models remain data-limited despite vast recordings. These findings highlight the need for richer stimuli, tasks, and larger-scale recordings to build brain foundation models. The observation of systematic scaling raises the possibility of phase transitions in neural modeling, where larger and richer datasets might unlock qualitatively new capabilities, paralleling the emergent properties seen in large language models
