Computational Assessment and Understanding of C6 Product Selectivity for Chromium Phosphinoamidine Catalyzed Ethylene Trimerization

One approach to selectively generate 1-hexene is through ethylene trimerization using highly active Cr N-phosphinoamidine catalysts ((P,N)Cr). Depending on the ligand, (P,N)Cr catalysts can either generate nearly pure 1-hexene or form 1-hexene with significant mixtures of other C6 mass products, for example methylenecyclopentane. Here we report DFT transition state modeling examining 1-hexene catalysis pathways as well as pathways that lead to alternative C6 mass products. This provided qualitative and semi-quantitative modeling of the experimental 1-hexene purity values for several (P,N)Cr catalysts. Consistent with previous computational studies, the key 1-hexene purity-determining transition states were determined to be β-hydrogen transfer structures from the metallacycloheptane intermediate. The origin of selectivity for these (P,N)Cr catalysts can be attributed to steric effects in the transition-state structure with coordinated ethylene that leads to C6 impurities.

Quantitative modeling of fine-scale variations in the Arabidopsis thaliana crossover landscape

In essentially all species where meiotic crossovers have been studied, they occur preferentially in open chromatin, typically near gene promoters and to a lesser extent at the end of genes. Here, in the case of Arabidopsis thaliana, we unveil further trends arising when one considers contextual information, namely summarized epigenetic status, size of underlying genomic regions and degree of divergence between homologs. For instance we find that intergenic recombination rate is reduced if those regions are less than 1.5 kb in size. Furthermore, we propose that the presence of single nucleotide polymorphisms is a factor driving enhanced crossover rate compared to when homologous sequences are identical, in agreement with previous works comparing rates in homozygous and heterozygous blocks. Lastly, by integrating these different factors, we produce a quantitative and predictive model of the recombination landscape that reproduces much of the experimental variation.

A Quantitative Modeling and Prediction Method for Sustained Rainfall-PM2.5 Removal Modes on a Micro-Temporal Scale

PM2.5 is unanimously considered to be an important indicator of air quality. Sustained rainfall is a kind of typical but complex rainfall process in southern China with an uncertain duration and intervals. During sustained rainfall, the variation of PM2.5 concentrations in hour-level time series is diverse and complex. However, existing analytical methods mainly examine overall removals at the annual/monthly time scale, missing a quantitative analysis mode that applies micro-scale time data to describe the removal phenomenon. In order to further achieve air quality prediction and prevention in the short term, it is necessary to analyze its micro-temporal removal effect for atmospheric environment quality forecasting. This paper proposed a quantitative modeling and prediction method for sustained rainfall-PM2.5 removal modes on a micro-temporal scale. Firstly, a set of quantitative modes for sustained rainfall-PM2.5 removal mode in a micro-temporal scale were constructed. Then, a mode-constrained prediction of the sustained rainfall-PM2.5 removal effect using the factorization machines (FM) was proposed to predict the future sustained rainfall removal effect. Moreover, the historical observation data of Nanjing city at an hourly scale from 2016 to January 2020 were used for mode modeling. Meanwhile, the whole 2020 year observation data were used for the sustained rainfall-PM2.5 removal phenomenon prediction. The experiment shows the reasonableness and effectiveness of the proposed method.

Quantitative modeling of the impact of facemasks and associated leakage on the airborne transmission of SARS-CoV-2

The ongoing worldwide outbreak of COVID-19 has set personal protective equipment in the spotlight. A significant number of countries impose the use of facemasks in public spaces and encourage it in the private sphere. Even in countries where relatively high vaccination rates are achieved at present, breakthrough infections have been frequently reported and usage of facemasks in certain settings has been recommended again. Alternative solutions, including community masks fabricated using various materials, such as cotton or jersey, have emerged alongside facemasks following long-established standards (e.g., EN 149, EN 14683). In the present work, we present a computational model to calculate the ability of different types of facemasks to reduce the exposure to virus-laden respiratory particles, with a focus on the relative importance of the filtration properties and the fitting on the wearer’s face. The model considers the facemask and the associated leakage, the transport of respiratory particles and their accumulation around the emitter, as well as the fraction of the inhaled particles deposited in the respiratory system. Different levels of leakages are considered to represent the diversity of fittings likely to be found among a population of non-trained users. The leakage prevails over the filtration performance of a facemask in determining the exposure level, and the ability of a face protection to limit leakages needs to be taken into account to accurately estimate the provided protection. Filtering facepieces (FFP) provide a better protection efficiency than surgical and community masks due to their higher filtration efficiency and their ability to provide a better fit and thus reduce the leakages. However, an improperly-fitted FFP mask loses a critical fraction of its protection efficiency, which may drop below the protection level provided by properly-worn surgical and community masks.

A Mechanistic Site-Of-Action Model: A Tool for Informing Right Target, Right Compound, And Right Dose for Therapeutic Antagonistic Antibody Programs

Quantitative modeling is increasingly utilized in the drug discovery and development process, from the initial stages of target selection, through clinical studies. The modeling can provide guidance on three major questions–is this the right target, what are the right compound properties, and what is the right dose for moving the best possible candidate forward. In this manuscript, we present a site-of-action modeling framework which we apply to monoclonal antibodies against soluble targets. We give a comprehensive overview of how we construct the model and how we parametrize it and include several examples of how to apply this framework for answering the questions postulated above. The utilities and limitations of this approach are discussed.

NucHMM: a method for quantitative modeling of nucleosome organization identifying functional nucleosome states distinctly associated with splicing potentiality

We develop a novel computational method, NucHMM, to identify functional nucleosome states associated with cell type-specific combinatorial histone marks and nucleosome organization features such as phasing, spacing and positioning. We test it on publicly available MNase-seq and ChIP-seq data in MCF7, H1, and IMR90 cells and identify 11 distinct functional nucleosome states. We demonstrate these nucleosome states are distinctly associated with the splicing potentiality of skipping exons. This advances our understanding of the chromatin function at the nucleosome level and offers insights into the interplay between nucleosome organization and splicing processes.

Determination of the embedded electronic states at nanoscale interface via surface-sensitive photoemission spectroscopy

The fabrication of small-scale electronics usually involves the integration of different functional materials. The electronic states at the nanoscale interface plays an important role in the device performance and the exotic interface physics. Photoemission spectroscopy is a powerful technique to probe electronic structures of valence band. However, this is a surface-sensitive technique that is usually considered not suitable for the probing of buried interface states, due to the limitation of electron-mean-free path. This article reviews several approaches that have been used to extend the surface-sensitive techniques to investigate the buried interface states, which include hard X-ray photoemission spectroscopy, resonant soft X-ray angle-resolved photoemission spectroscopy and thickness-dependent photoemission spectroscopy. Especially, a quantitative modeling method is introduced to extract the buried interface states based on the film thickness-dependent photoemission spectra obtained from an integrated experimental system equipped with in-situ growth and photoemission techniques. This quantitative modeling method shall be helpful to further understand the interfacial electronic states between functional materials and determine the interface layers.

Biophysical and Quantitative Principles of Centrosome Biogenesis and Structure

The centrosome is a main orchestrator of the animal cellular microtubule cytoskeleton. Dissecting its structure and assembly mechanisms has been a goal of cell biologists for over a century. In the last two decades, a good understanding of the molecular constituents of centrosomes has been achieved. Moreover, recent breakthroughs in electron and light microscopy techniques have enabled the inspection of the centrosome and the mapping of its components with unprecedented detail. However, we now need a profound and dynamic understanding of how these constituents interact in space and time. Here, we review the latest findings on the structural and molecular architecture of the centrosome and how its biogenesis is regulated, highlighting how biophysical techniques and principles as well as quantitative modeling are changing our understanding of this enigmatic cellular organelle. Expected final online publication date for the Annual Review of Cell and Developmental Biology, Volume 37 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.