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Breaking the Birefringence Barrier in B–O Crystals via B–C π-scaffolding
A preliminary bioinformatic screen to identify SRI SMC2 PSIP1 TLE4 and MSX1 as potential diagnostic and prognostic markers of osteoarthritis
Intelligent resource allocation in internet of things using random forest and clustering techniques
Multi-axis compression fusion network for vehicle re-identification
A Material Platform Based on Dissociative CO<sub>2</sub>-Derived <i>N,O-</i>Acetals for Tunable Degradation of 3D Printable Materials
Using transcripts to refine image based cell segmentation with FastReseg
Abstract Spatial transcriptomics (ST) faces persistent challenges in cell segmentation accuracy, which can bias biological interpretations in a spatial-dependent way. FastReseg introduces a novel algorithm that refines inaccuracies in existing image-based segmentations using transcriptomic data, without radically redefining cell boundaries. By combining image-based information with 3D transcriptomic precision, FastReseg enhances segmentation accuracy. Its key innovation, a transcript scoring system based on log-likelihood ratios, facilitates the quick identification and correction of spatial doublets caused by cell proximity or overlap in 2D. FastReseg reduces circularity in boundary derivation, and addresses computational challenges with a modular workflow designed for large datasets. The algorithm’s modularity allows for seamless optimization and integration of advancements in segmentation technology. FastReseg provides a scalable, efficient solution to improve the quality and interpretability of ST data, ensuring compatibility with evolving segmentation methods and enabling more accurate biological insights.
Dimensional Reduction Guides Electronic Structure Evolution in the A<i><sub>n</sub></i>Cu<sub>4–<i>n</i></sub>SnS<sub>4</sub> Semiconductor Series
Personality-based intergenerational effects of prenatal THC exposure in an inherited mouse model of social dominance and submissiveness
Hydrogen Bonding Activates Ferric Porphyrin Hydroperoxo Species and Drives the Regioselective Heme Oxygenase Reaction
A novel rapid rule-out protocol for acute chest pain using H-FABP point-of-care testing
Bidirectional decision analysis of online Ride-hailing enterprises based on fuzzy theory and cloud model
Electrochemical Annulation of Phenothiazines with Alkynes: Access to Anti-Kasha Triple-Emission, Light-Sensitive, and Room-Temperature Phosphorescent Materials
A study on the optimal design of isothermal experiments in predictive microbiology
Abstract This study addresses from the Optimal Experimental Design perspective the use of the isothermal experimentation procedure to precisely estimate the parameters defining models used in predictive microbiology. Starting from a case study set out in the literature, and taking the Baranyi model as the primary model, and the Ratkowsky square-root model as the secondary, D- and c-optimal designs are provided for isothermal experiments, taking the temperature both as a value fixed by the experimenter and as a variable to be designed. The designs calculated show that those commonly used in practice are not efficient enough to estimate the parameters of the secondary model, leading to greater uncertainty in the predictions made via these models. Finally, an analysis is carried out to determine the effect on the efficiency of the possible reduction in the final experimental time.