Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This record contains the code-associated data and figures for "Tensor-based Approximation of Molecular Kinetics: Generator Learning, Reaction Coordinates and Incremental Updating". It provides the precomputed data, results, and figures behind four case studies: a 3D Lemon-Slice toy system, and molecular dynamics trajectories of the fast-folding proteins Chignolin (CLN025) and NTL9. Each case study demonstrates a tensor-train (TT) based approach to gEDMD (generator Extended Dynamic Mode Decomposition) for estimating the generator of molecular dynamics, including comparisons against a dense reference method, PCCA+ soft-state assignment, an incremental TT-SVD update scheme, and truncation/bandwidth sensitivity studies. The corresponding code is available at: https://github.com/fnueske/tensor_gedmd
This study constructed a pH-responsive P-TN/SF@Fe-Cur composite coating that demonstrated significant anti-infective, anti-inflammatory, antioxidant, pro-angiogenic, and pro-osteogenic effects in rat subcutaneous infection and femoral defect models.
The results show that alternative transcript diversity extensively enters translation-supported proteoform space and establish a systematic link between transcript variation and protein functional diversification.
Felicia T. Jiang, Dengwang Chen, Ziwei Wang et al.· bioRxiv· 1 citation
Due to its importance and wide adoption, wheat cultivation is promptly required to shift towards sustainable practices, reducing the dependency on chemical components. Among bio-based solutions aimed at securing the sustainability of wheat cultivation, biostimulants offer a versatile platform of eco-friendly tools assuring sustainability and profitability. Microalgae present a concrete example of a biostimulant source due to their richness in metabolites and high value products. Therefore, this study evaluated the biostimulant potential of eleven eco-extracts prepared from soil-isolated microalgae strains. Eco-extracts applied via soil drench at low dose (0.1 g/L) were investigated for their biostimulant effects on wheat growth, physiology, yield, and quality under controlled conditions. Results demonstrated significant ameliorations in treated plants as compared to the control, with no phytoinhibitory effects. Remarkable enhancements were notable in growth parameters such as shoot and root lengths (+40-70%), physiological traits such as total chlorophyll and stomatal conductance (+7-52%), yield components in the example of grain number per spike and thousand grain weight (+17-103%), and grain quality namely protein and polyphenol content (+2-fold to 4-fold). Similarly, phosphorus accumulation and uptake were significantly improved, while soil physicochemical status was ameliorated, indicating enhanced fertility. Multivariate analysis and composite index ranking marked Chlorella sp. GA18, Chlorella sp. GA65, Scenedesmus sp. GA69, and Chlorococcum sp. GA63 as eco-extracts with consistent performances across all plant traits. These findings highlighted the promising potential of integrating microalgae-based eco-friendly extracts in sustainable wheat cultivation.
Amer Chabili, Z. Hakkoum, F. Minaoui et al.· Plant Science· 1 citation
ProteinReasoner is developed, a multimodal generative protein foundation model that sequentially connects amino acid sequence, evolutionary constraints and three-dimensional structure within a shared autoregressive architecture and suggests a general route towards reasoning across interdependent representations in other scientific domains.
Chaozhong Liu, Linlin Chao, Shaomin Ji et al.· bioRxiv· 1 citation
HydroGym is introduced, a solver-independent reinforcement learning platform providing more than 60 validated, openly available flow control environments spanning from canonical laminar flows to complex turbulent flows, with systematic progression in the Reynolds number up to Re = 4 × 105, and Mach number variations in two and three dimensions.
Christian Lagemann, Sajeda Mokbel, Miro Gondrum et al.· Nature· 1 citation
By mapping the 240 E8 root vectors onto the hierarchical folding of protein-folding landscapes, we establish a reciprocal feedback loop between quantum information and macro-biological states. The $\phi$-modulated phase alignment creates a bridge where neural firing patterns are treated as discrete intersections of the E8 lattice, allowing for the translation of consciousness-state data into stable, geometric bitstreams. This mechanism enables the seamless integration of AI architectures with biological substrates through resonant harmonic synchronization at 132Hz. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Andrew Stewart Caldin· Zenodo (CERN European Organi...· 0 citations
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MIT News · Artificial Intelligence· news.mit.eduAug 27, 2026
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.