Exploring large chemical reaction networks with automated exploration approaches and accurate quantum chemical methods can require prohibitively large computational resources. Here, we present an automated exploration approach that focuses on the kinetically relevant part of the reaction network by interweaving (i) large-scale exploration of chemical reactions, (ii) identification of kinetically relevant parts of the reaction network through microkinetic modeling, (iii) quantification and propagation of uncertainties, and (iv) reaction network refinement. Such an uncertainty-aware exploration of kinetically relevant parts of a reaction network with automated accuracy improvement has not been demonstrated before in a fully quantum mechanical approach. Uncertainties are identified by local or global sensitivity analysis. The network is refined in a rolling fashion during the exploration. Moreover, the uncertainties are considered during kinetically steering of a rolling reaction network exploration. We demonstrate our approach for Eschenmoser-Claisen rearrangement reactions. The sensitivity analysis identifies that only a small number of reactions and compounds are essential for describing the kinetics reliably, resulting in efficient explorations without sacrificing accuracy and without requiring prior knowledge about the chemistry unfolding.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11163430 | PMC |
http://dx.doi.org/10.1021/acs.jpca.3c08386 | DOI Listing |
Front Psychol
December 2024
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
Introduction: While the fact that visual stimuli synthesized by Artificial Neural Networks (ANN) may evoke emotional reactions is documented, the precise mechanisms that connect the strength and type of such reactions with the ways of how ANNs are used to synthesize visual stimuli are yet to be discovered. Understanding these mechanisms allows for designing methods that synthesize images attenuating or enhancing selected emotional states, which may provide unobtrusive and widely-applicable treatment of mental dysfunctions and disorders.
Methods: The Convolutional Neural Network (CNN), a type of ANN used in computer vision tasks which models the ways humans solve visual tasks, was applied to synthesize ("dream" or "hallucinate") images with no semantic content to maximize activations of neurons in precisely-selected layers in the CNN.
Cureus
December 2024
Pathology, BLDE (Deemed to be University) Shri B.M. Patil Medical College, Hospital, and Research Centre, Vijayapura, IND.
Introduction Occupational health hazards are a significant concern for pathologists due to their unique work environment. These professionals face risks from prolonged microscope use, exposure to chemicals such as formalin, and handling sharp instruments, leading to issues such as musculoskeletal disorders and needlestick injuries. Addressing these hazards is crucial for their well-being and the overall efficiency of medical diagnostics.
View Article and Find Full Text PDFPharmacoepidemiol Drug Saf
January 2025
Hunan Institute for Drug Control, Changsha, Hunan, China.
Background And Objectives: Based on the Adverse Event Reporting System (FAERS) data from the US FDA, this study mined the adverse drug reactions of obeticholic acid (OCA) in the real world and provided reference for clinical safe drug use.
Methods: Adverse event reports for OCA from the second quarter of 2016 to the third quarter of 2023 were extracted. The analysis for adverse reaction signal detection was conducted using reporting odds ratio, proportional reporting ratio, Bayesian confidence propagation neural network, and multi-item gamma Poisson shrinker methods.
Angew Chem Int Ed Engl
January 2025
Institute of Chemistry Chinese Academy of Sciences, Institute of chemistry, Beiyijie number 2, Zhongguancun, 100190, Beijing, CHINA.
Modulating the surface microenvironment of electrodes stands as a pivotal aspect in enhancing the electrocatalytic performance for CO2 electroreduction. Herein, we propose an innovative approach by incorporating a small amount of linear oligomer, polyethylene glycol (PEG), into Cu2O catalysts during the preparation of the CuPEG electrode. The Faradaic efficiency (FE) toward multicarbon products (C2+) increases from 69.
View Article and Find Full Text PDFJ Neurol
January 2025
IRCCS Stella Maris Foundation, Via Dei Giacinti 2, 56128, Pisa, Italy.
The neuronal ceroid lipofuscinoses (NCLs) are incurable pediatric neurodegenerative diseases characterized by accumulation of lysosomal material and dysregulation of autophagy. Given the promising results of treatment with trehalose, an autophagy inducer, in cell and animal models of NCL, we conducted an open-label, non-placebo-controlled, non-randomized 12-month prospective study in NCL patients receiving oral trehalose (4 g/day). All were treated with a commercially available formulation for 6 months, followed by a 6-month washout.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!