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1. Forcespinning technique for the production of poly( d , l ‐lactic acid) submicrometer fibers: Process–morphology–properties relationship

   https://doi.org/10.1002/app.47643  

   Padilla‐Gainza, Victoria ; Morales, Graciela ; Rodríguez‐Tobías, Heriberto ; Lozano, Karen ( February 2019 , Journal of Applied Polymer Science)

   This work addresses a systematic study for the process development and optimization of poly(d,l‐lactic acid) (PDLLA) submicrometer fibers utilizing the centrifugal spinning technique known as Forcespinning. This study analyzes the effect of polymer concentration (8, 10, and 12 wt %) and angular speed on the fiber morphology, diameter distribution, and fiber yield. The increase in polymer concentration and angular speed favored the formation of continuous and homogeneous submicrometer fibers with an absence of bead formation and higher output. The optimal conditions were established considering the morphological characteristics that exhibit a greater surface area (homogeneous and submicrometer fibers); and they were achieved at a polymer concentration of 10 wt % at an angular speed ranging from 8000 to 10 000 rpm. Optimization of PDLLA submicrometer fiber fabrication lays the groundwork for scaling up the process and serves as a platform to further develop promising applications of PDLLA nonwoven mats, particularly in the biomedical area such as in scaffolds for tissue engineering. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci.2019,136, 47643.

    

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2. A process approach to quality management doubles NEON sensor data quality

   https://doi.org/10.1111/2041-210X.13943  

   Sturtevant, Cove ; DeRego, Elizabeth ; Metzger, Stefan ; Ayres, Edward ; Allen, Dan ; Burlingame, Teresa ; Catolico, Nora ; Cawley, Kaelin ; Csavina, Janae ; Durden, David ; et al ( September 2022 , Methods in Ecology and Evolution)
3. Molecular dynamics simulations of enantiomeric separations as an interfacial process in HPLC

   https://doi.org/10.1002/aic.17143  

   Jameson, Cynthia J. ; Wang, Xiaoyu ; Murad, Sohail ( January 2021 , AIChE Journal)

   Since chromatographic separation is a dynamic process, with the interactions between the drug and the chiral stationary phase mediated by the solvent, no single interacting structure, such as could be found by minimizing the energy, could possibly describe and account for the ratio of residence times in the chromatographic column for the enantiomeric pair. We describe the use of explicit‐solvent fully atomistic molecular dynamics simulations, permitting all the interactions between the atoms constituting the chiral stationary phase, solvent molecules and the drug molecule. This allows us to better understand the molecular dynamic chiral recognition that provides the discrimination, which results in the separation of enantiomers by high performance liquid chromatography. It also provides a means of predicting, for a given set of conditions, which enantiomer elutes first and an estimate of the expected separation factor. In this review, we consider the use of molecular dynamics toward this understanding and prediction.

    

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4. tuxnet : a simple interface to process RNA sequencing data and infer gene regulatory networks

   https://doi.org/10.1111/tpj.14558  

   Spurney, Ryan J. ; Van den Broeck, Lisa ; Clark, Natalie M. ; Fisher, Adam P. ; de Luis Balaguer, Maria A. ; Sozzani, Rosangela ( November 2019 , The Plant Journal)

   Predicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA‐sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developedtuxnet, a user‐friendly platform that can process raw RNA‐sequencing data from any organism with an existing reference genome using a modifiedtuxedopipeline (hisat 2 + cufflinkspackage) and infer GRNs from these processed data.tuxnetis implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network (DBN) inference algorithm,genist, or a regression tree‐based pipeline,rtp‐star. We obtained time‐course expression data of aPERIANTHIA(PAN) inducible line and inferred a GRN usinggenistto illustrate the use oftuxnetwhile gaining insight into the regulations downstream of the Arabidopsis root stem cell regulatorPAN. Usingrtp‐star, we inferred the network ofATHB13, a downstream gene of PAN, for which we obtained wild‐type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell‐type data to highlight the use oftuxnetto form new testable hypotheses from previously explored data. Our case studies feature the versatility oftuxnetwhen using different types of gene expression data to infer networks and its accessibility as a pipeline for non‐bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.

    

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5. Latent variable Gaussian process models: A rank‐based analysis and an alternative approach

   https://doi.org/10.1002/nme.6690  

   Tao, Siyu ; Apley, Daniel W. ; Plumlee, Matthew ; Chen, Wei ( April 2021 , International Journal for Numerical Methods in Engineering)

   Gaussian process (GP) models have been extended to emulate expensive computer simulations with both qualitative/categorical and quantitative/continuous variables. Latent variable (LV) GP models, which have been recently developed to map each qualitative variable to some underlying numerical LVs, have strong physics‐based justification and have achieved promising performance. Two versions use LVs in Cartesian (LV‐Car) space and hyperspherical (LV‐sph) space, respectively. Despite their success, the effects of these different LV structures are still poorly understood. This article illuminates this issue with two contributions. First, we develop a theorem on the effect of the ranks of the qualitative factor correlation matrices of mixed‐variable GP models, from which we conclude that the LV‐sph model restricts the interactions between the input variables and thus restricts the types of response surface data with which the model can be consistent. Second, following a rank‐based perspective like in the theorem, we propose a new alternative model named LV‐mix that combines the LV‐based correlation structures from both LV‐Car and LV‐sph models to achieve better model flexibility than them. Through extensive case studies, we show that LV‐mix achieves higher average accuracy compared with the existing two.

    

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