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Probing Voltage- and Electrolyte-Dependent Monolayer Dynamics with 2D-IR Spectroscopy
Reduced aerosol pollution diminished cloud reflectivity over the North Atlantic and Northeast Pacific
Auditory stimulation at individual gamma frequency enhances cognitive performance
Mapping the Role of Monomer Conformation in the Amyloid Formation of α-Synuclein Splice Variants
There is urgent need for a Global Data Resource for Antimicrobial PK/PD: CAMO-Net GDR Initiative
The tumor microenvironment enhances the expression of cssDNA by modulating cell cycle signaling pathways via SKP2
Tuning the Quantum-Well Structure of Single-Crystal Layered Perovskite Heterostructures
Magnetoactive bistable soft actuators for programmable large shape transformations at low magnetic fields
Abstract As the demand for advanced actuation strategies in soft robotics and intelligent material systems grows, magnetoactive soft actuators have attracted increasing attention for their ability to achieve flexible shape transformations through remote and untethered control. However, existing designs typically rely on continuous high magnetic fields to generate large deformations, limiting both efficiency and applicability, especially under constrained boundary conditions. Here we report a hemispherical bistable soft actuator embedded with magnetic microparticles, which enables substantial shape changes under low-intensity pulsed magnetic torques and remains stable in two configurations without external fields. We analyze the relationship between design parameters and actuator performance to clarify the bistable mechanism, and show that the actuator can achieve a large shape change ratio exceeding 0.8 under magnetic fields below 20 mT. We further demonstrate its versatility through three applications: a high-efficiency soft pump with closed-loop fluid control, a reprogrammable metamaterial, and a variable-stiffness soft gripper.
Using item response theory as a methodology to impute categorical missing values
Abstract Most datasets suffer from partial or complete missing values, which has downstream limitations on the available models on which to test the data and on any statistical inferences that can be made from the data. Several imputation techniques have been designed to replace missing data with stand in values. The various approaches have implications for calculating clinical scores, model building and model testing. The work showcased here supports using an Item Response Theory (IRT) based approach for categorical imputation, comparing it against several methodologies currently used in the machine learning field including k-nearest neighbors (kNN), multiple imputed chained equations (MICE) and Amazon Web Services (AWS) deep learning method, DataWig. Analyses comparing these techniques were performed on three different datasets that represented ordinal, nominal and binary categories. The data were modified so that they also varied on both the proportion of data missing and the systematization of the missing data. Two different assessments of performance were conducted: accuracy in reproducing the missing values, and predictive performance using the imputed data. Results demonstrated that the proposed method, Item Response Theory for categorical imputation, fared quite well compared to currently used multiple imputation methods, outperforming several of them in many conditions. Given the theoretical basis for the approach, and the unique generation of probabilistic terms for determining category belonging for missing cells, IRT for categorical imputation offers a viable alternative to current approaches.
Stereoselective Photoenzymatic Hydroarylation for the Construction of Quaternary Stereocenters
Structural and functional characterization of TgGSK3, a druggable kinase in Toxoplasma gondii
Abstract Toxoplasma gondii and Cryptosporidium species are apicomplexan parasites of significant medical and veterinary importance. Although current therapeutic options for toxoplasmosis and cryptosporidiosis demonstrate notable efficacy, their clinical efficacy is often limited by suboptimal efficacy and frequent adverse effects. Moreover, therapeutic alternatives remain limited or nonexistent, particularly for cryptosporidiosis, for which nitazoxanide is currently the only approved medication to treat diarrhea in adults and children older than 1 year of age. To identify alternative therapeutic options for addressing these health challenges, we performed a phenotypic screening of an FDA-approved drug repurposing library against Toxoplasma . This screening identifies LY2090314 as a potent inhibitor of T. gondii and Cryptosporidium growth in mammalian cells. Through a target deconvolution strategy combining forward genetics, transcriptome sequencing, and computational mutation analysis, we elucidate the parasiticidal mechanism of LY2090314 and demonstrate that Tg GSK3 kinase is its primary molecular target. We also report the first X-ray crystal structure of LY2090314 bound to Tg GSK3, resolved at 2.1 Å, which reveals an interaction mode characteristic of type I ATP-competitive inhibitors. Furthermore, interactome analysis uncovers functional connections between Tg GSK3 and key cytoskeletal and signaling regulators, providing insights into compound’s effects. Collectively, these findings validate Tg GSK3 as a promising therapeutic target for toxoplasmosis and offer mechanistic insights into apicomplexan GSK3 biology.
Connected, digitalized wire arc additive manufacturing: utilizing data in the internet of production to enable industrie 4.0
Abstract This work explores the potential of connected, digitalized Wire Arc Additive Manufacturing (WAAM) within the framework of Industrie 4.0, analyzing it through distinct process layers: workpiece, assembly, and product. Each layer presents unique timeframes and stakeholder interactions, necessitating varied data infrastructure demands, including a consideration of data security and privacy challenges. The workpiece layer mostly covers the local production setup and is thus directly coupled with the product and process quality as well as maintaining a safe operation. In the assembly layer, ensuring interoperability among diverse stakeholders is crucial, requiring clear definitions of responsibilities and access rights to enhance data exchange. The product layer prioritizes the reliability and trustworthiness of information for informed decision-making, advocating for solutions that guarantee authenticity and verifiability while addressing privacy concerns through techniques like privacy-preserving computing. The paper identifies a critical gap in real-world applications of these concepts in additive manufacturing. It proposes a data-driven quality control approach to enhance process and product quality in arc welding, leveraging digital shadows to create effective interfaces within production networks. This approach has demonstrated potential reductions in welding fume emissions by 12–40%, alongside connected applications that minimize exposure and energy consumption.