Monte Carlo simulated annealing strategies, carried out on four different potential energy surfaces, are applied to benzene-cyclohexane clusters, BCn, n=3-7, 12, to identify low-energy isomers and to trace the evolution of structures as a function of cluster size. Initial structures are first heated to ensure randomization, and subsequent annealing yields optimized rigid, low-energy clusters. Five major structural isomers are identified for BC3: one assumes the form of a symmetric, modified sandwich; the remaining four lack general symmetry, assuming distorted tetrahedral arrangements. For BC4 and larger clusters, the number of low-temperature isomers is large. It is, nevertheless, feasible to classify isomers into groups based on structural similarities. The evolution of BCn structures as a function of cluster size is observed to follow one of two primary paths: The first maximizes benzene-cyclohexane interactions and places benzene in or near the BCn cluster center; the competing path maximizes cyclohexane-cyclohexane interactions and distances benzene from the cluster's center of mass. Results for BC3 and BC4 are discussed with reference to experimental results and models previously applied to interpret benzene-argon cluster spectra.
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Front Pharmacol
January 2025
Department of Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Purpose: Rituximab has proven efficacy in children with idiopathic nephrotic syndrome (INS). However, vast majority of children inevitably experience relapse with B-cell repletion, necessitating repeat course of rituximab, which may increase the risk of adverse effects. The timing of additional dosing and optional dosing regimen of rituximab in pediatric patients with INS have yet to be determined.
View Article and Find Full Text PDFBr J Math Stat Psychol
January 2025
Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Recent technological advancements have enabled the collection of intensive longitudinal data (ILD), consisting of repeated measurements from the same individual. The threshold autoregressive (TAR) model is often used to capture the dynamic outcome process in ILD, with autoregressive parameters varying based on outcome variable levels. For ILD from multiple individuals, multilevel TAR (ML-TAR) models have been proposed, with Bayesian approaches typically used for parameter estimation.
View Article and Find Full Text PDFBackground: In proton radiotherapy, the steep dose deposition profile near the end of the proton's track, the Bragg peak, ensures a more conformed deposition of dose to the tumor region when compared with conventional radiotherapy while reducing the probability of normal tissue complications. However, uncertainties, as in the proton range, patient geometry, and positioning pose challenges to the precise and secure delivery of the treatment plan (TP). In vivo range determination and dose distribution are pivotal for mitigation of uncertainties, opening the possibility to reduce uncertainty margins and for adaptation of the TP.
View Article and Find Full Text PDFSci Total Environ
January 2025
Department of Science and Engineering of Materials, Environment and Urban Planning - SIMAU, Polytechnic University of Marche, via Brecce Bianche 12, 60131 Ancona, Italy.
The reuse of stormwater represents a potential option for meeting water demands in water stressed regions as well as preventing and mitigating diffuse pollution of receiving water bodies. Particularly, the elaboration of a risk management plan for stormwater reuse may help to understand associated environmental and public health risks and design fit-for-purpose water treatment processes. In this work, it is presented an innovative methodology to perform quantitative microbial risk assessment (QMRA) for stormwater reuse by using data simulated by SWMM software.
View Article and Find Full Text PDFJ Chem Phys
January 2025
Microsoft Research AI for Science, 21 Station Road, Cambridge CB1 2FB, United Kingdom.
Variational ab initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows, in principle, straightforward extraction of any other observable of interest, besides the energy, but, in practice, this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation.
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