Effect of Substrate Concentration on the Synthesis of Cefaclor by Penicillin Acylase with in Situ Product Removal

Enzymatic synthesis of 3-chloro-7-D-(2-phenylglycinamide)-3-cephem-4-carboxylic acid (cefaclor) by penicillin acylase (PA) was carried out with in situ product removal (ISPR) under kinetic control. The yield of cefaclor highly depended on substrate concentrations and the ratio of nucleus to acyl donor. Substrate concentrations were optimized as 50 mmol l–1 of 7-aminodesacetoxymethyl-3-chlorocephalosporanic acid (7ACCA) and 100 mmol l–1 of phenylglycine methyl ester (PGME) at the conditions: temperature 20 °C, pH 6.3; and enzyme load was 8 IU ml–1. It is effective to improve the transfer of acyl donor through controlling the substrate concentration with feeding acyl donor. The conversion of nucleus and acyl donor was improved to 93 % and 62 %, respectively

Triton-3^3He relative and differential flows as probes of the nuclear symmetry energy at supra-saturation densities

Using a transport model coupled with a phase-space coalescence after-burner we study the triton-$^3$He ratio, relative and differential transverse flows in semi-central $^{132}Sn+^{124}Sn$ reactions at a beam energy of 400 MeV/A. The neutron-proton ratio, relative and differential flows are also discussed as a reference. We find that similar to the neutron-proton pairs the triton-$^3$He pairs also carry interesting information about the density dependence of the nuclear symmetry energy. Moreover, the nuclear symmetry energy affects more strongly the t-$^3$He relative and differential flows than the $\pi^-/\pi^+$ ratio in the same reaction. The t-$^3$He relative flow can be used as a particularly powerful probe of the high-density behavior of the nuclear symmetry energy.Comment: Discussions added. Version accepted by PR

Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial

On top of machine learning models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Toward this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. Two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage and the remaining useful life prediction of turbofan engines

Formation and sedimentation of Fe-rich intermetallics in Al−Si−Cu−Fe alloy

Formation and sedimentation of Fe-rich intermetallics were studied in a commercial Al−Si−Cu−Fe alloy with extra additions of Mn. It is found that the introduction of extra Mn is an effective approach to lower the Fe level in the equilibrium liquid phase after sedimentation of solid Fe-rich phase at a temperature between its liquidus and solidus. The higher Mn/Fe mass ratio results in the lower Fe content in the retained alloy, during which Mn is also consumed and settled at the bottom of the melt as solid Fe-rich intermetallics. Therefore, the final Fe content in the alloy can be controlled by the Mn content and the holding temperature of the melt. The results confirmed a good agreement of the theoretical calculation and the experimental test with a specially designed 50 mm cylindrical casting. The sedimentation of Fe-rich intermetallics in the Al−Si−Cu−Fe alloy is completed at 600 °C after 10 min. The reduction of Fe content in the retained alloy is 31.4% when m(Mn)/m(Fe)=0.5 and 53.3% when m(Mn)/m(Fe)=1.0 in comparison with that in the original alloy. The settled Fe-rich intermetallics were identified as α-Al15(Fe,Mn)3Si2, which provided the lower balanced Fe concentration in the melt in comparison with other Fe-rich intermetallics.The financial support from TSB (UK) under project No. 101172 is acknowledged. The authors also would like to thank the EPSRC (UK) and Jaguar Cars Ltd. (UK) for financial support under the grant for the EPSRC Centre - LiME

Distinct pre-COVID brain structural signatures in COVID-19-related post-traumatic stress symptoms and post-traumatic growth

Post-traumatic stress symptoms and post-traumatic growth are common co-occurring psychological responses following exposure to traumatic events (such as COVID-19 pandemic), their mutual relationship remains unclear. To explore this relationship, structural magnetic resonance imaging data were acquired from 115 general college students before the COVID-19 pandemic, and follow-up post-traumatic stress symptoms and post-traumatic growth measurements were collected during the pandemic. Voxel-based morphometry was conducted and individual structural covariance networks based on gray matter volume were further analyzed using graph theory and partial least squares correlation. Behavioral correlation found no significant relationship between post-traumatic stress symptoms and post-traumatic growth. Voxel-based morphometry analyses showed that post-traumatic stress symptoms were positively correlated with gray matter volume in medial prefrontal cortex/dorsal anterior cingulate cortex, and post-traumatic growth was negatively correlated with gray matter volume in left dorsolateral prefrontal cortex. Structural covariance network analyses found that post-traumatic stress symptoms were negatively correlated with the local efficiency and clustering coefficient of the network. Moreover, partial least squares correlation showed that post-traumatic stress symptoms were correlated with pronounced nodal properties patterns in default mode, sensory and motor regions, and a marginal correlation of post-traumatic growth with a nodal property pattern in emotion regulation-related regions. This study advances our understanding of the neurobiological substrates of post-traumatic stress symptoms and post-traumatic growth, and suggests that they may have different neuroanatomical features

COVID-19 vicarious traumatization links functional connectome to general distress.

As characterized by repeated exposure of others' trauma, vicarious traumatization is a common negative psychological reaction during the COVID-19 pandemic and plays a crucial role in the development of general mental distress. This study aims to identify functional connectome that encodes individual variations of pandemic-related vicarious traumatization and reveal the underlying brain-vicarious traumatization mechanism in predicting general distress. The eligible subjects were 105 general university students (60 females, aged from 19 to 27 years) undergoing brain MRI scanning and baseline behavioral tests (October 2019 to January 2020), whom were re-contacted for COVID-related vicarious traumatization measurement (February to April 2020) and follow-up general distress evaluation (March to April 2021). We applied a connectome-based predictive modeling (CPM) approach to identify the functional connectome supporting vicarious traumatization based on a 268-region-parcellation assigned to network memberships. The CPM analyses showed that only the negative network model stably predicted individuals' vicarious traumatization scores (q2 = -0.18, MSE = 617, r [predicted, actual] = 0.18, p = 0.024), with the contributing functional connectivity primarily distributed in the fronto-parietal, default mode, medial frontal, salience, and motor network. Furthermore, mediation analysis revealed that vicarious traumatization mediated the influence of brain functional connectome on general distress. Importantly, our results were independent of baseline family socioeconomic status, other stressful life events and general mental health as well as age, sex and head motion. Our study is the first to provide evidence for the functional neural markers of vicarious traumatization and reveal an underlying neuropsychological pathway to predict distress symptoms in which brain functional connectome affects general distress via vicarious traumatization

Experiment and analysis of state preparation for atom interferometry

The state preparation is a crucial procedure in atom interferometry; however, there is a shortage of detailed experimental studies on determining the optimal method for achieving this. This paper investigates and compares two methods for state preparation: the combined use of microwave and Raman light (M-R) and the combined use of optical pumping, microwave, and Raman light (O-M-W). The experimental results demonstrate that the M-R method improves the efficiency of Raman transitions for atom interference, which is helpful in enhancing the contrast of the interference fringes. The O-M-R method increases the quantity of prepared atoms, thereby enhancing the signal-to-noise ratio of the detected signals. This work helps provide a useful experimental basis and reference for researchers to design a suitable state preparation scheme