2-Oxazolines are well-known organic compounds which are included in a variety of complex biologically active structures and play a role as catalyst ligands and intermediates for functional compounds. In addition, 2-oxazolines serve as monomers for the synthesis of substituted poly(imine)s by cationic ring-opening polymerization. For the latter application, the feasibility of preparing new 2-substituted-2-oxazolines was investigated using an automated synthesizer. The reaction of various nitriles with 2-aminoethanol under Lewis acid catalysis was utilized for this purpose. Twenty-nine different substituted nitriles were selected out of more than 2000 commercial available nitriles to form the corresponding 2-oxazolines. At first, the reaction conditions were optimized for seven nitriles with regard to solvent and catalyst, including reproducibility tests in an automated parallel robot system. In the next step, the synthesis of all 29 2-oxazolines was screened in an automated parallel manner, whereby the reactions were monitored by GC-MS measurements providing novel insights in the scope of this synthesis route. These insights resulting from the high-throughput screening were validated by performing representative larger-scale syntheses of selected 2-oxazolines.
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Lab Chip
January 2025
NASCENT Engineering Research Center, The University of Texas at Austin, Austin, Texas 78758, USA.
Despite being a high-resolution separation technique, deterministic lateral displacement (DLD) technology is facing multiple challenges with regard to design, manufacture, and operation of pertinent devices. This work specifically aims at alleviating difficulties associated with design and manufacture of DLD chips. The process of design and production of computer-aided design (CAD) mask layout files that are typically required for computational modeling analysis, optimization, as well as for manufacturing DLD-based micro/nanofluidic chips is complex, time-consuming, and often necessitates a high level of expertise in the field.
View Article and Find Full Text PDFBioinformatics
January 2025
Department of Biological Sciences, University of Illinois at Chicago, Illinois 60607, United States.
Motivation: Recent advancements in parallel sequencing methods have precipitated a surge in publicly available short-read sequence data. This has encouraged the development of novel computational tools for the de novo assembly of transcriptomes from RNA-seq data. Despite the availability of these tools, performing an end-to-end transcriptome assembly remains a programmatically involved task necessitating familiarity with best practices.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
Beijing National Laboratory for Molecular Sciences, State Key Laboratory of Polymer Physics and Chemistry, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, P. R. China.
The accuracy and efficiency of a coarse-grained (CG) force field are pivotal for high-precision molecular simulations of large systems with complex molecules. We present an automated mapping and optimization framework for molecular simulation (AMOFMS), which is designed to streamline and improve the force field optimization process. It features a neural-network-based mapping function, DSGPM-TP (deep supervised graph partitioning model with type prediction).
View Article and Find Full Text PDFRMD Open
January 2025
Rheumatology Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
Artificial intelligence (AI) is transforming rheumatology research, with a myriad of studies aiming to improve diagnosis, prognosis and treatment prediction, while also showing potential capability to optimise the research workflow, improve drug discovery and clinical trials. Machine learning, a key element of discriminative AI, has demonstrated the ability of accurately classifying rheumatic diseases and predicting therapeutic outcomes by using diverse data types, including structured databases, imaging and text. In parallel, generative AI, driven by large language models, is becoming a powerful tool for optimising the research workflow by supporting with content generation, literature review automation and clinical decision support.
View Article and Find Full Text PDFJ Neurosci Methods
January 2025
College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China.
Background: Recognition of emotion changes is of great significance to a person's physical and mental health. At present, EEG-based emotion recognition methods are mainly focused on time or frequency domains, but rarely on spatial information. Therefore, the goal of this study is to improve the performance of emotion recognition by integrating frequency and spatial domain information under multi-frequency bands.
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