Highly Loaded Independent Pt0 Atoms on Graphdiyne for pH-General Methanol Oxidation Reaction.

The emergence of platinum-based catalysts promotes efficient methanol oxidation reactions (MOR). However, the defects of such noble metal catalysts are high cost, easy poisoning, and limited commercial applications. The efficient utilization of a low-cost, anti-poisoning catalyst has been expected. Here, it is skillfully used N-doped graphdiyne (NGDY) to prepare a zero-valent platinum atomic catalyst (Pt/NGDY), which shows excellent activity, high pH adaptability, and high CO tolerance for MOR. The Pt/NGDY electrocatalysts for MOR with specific activity 154.2 mA cm-2 (1449.3 mA mgPt -1 ), 29 mA cm-2 (296 mA mgPt -1 ) and 22 mA cm-2 (110 mA mgPt -1 ) in alkaline, acid, and neutral solutions. The specific activity of Pt/NGDY is 9 times larger than Pt/C in alkaline solution. Density functional theory (DFT) calculations confirm that the incorporation of electronegativity nitrogen atoms can increase the high coverage of Pt to achieve a unique atomic state, in which the shared contributions of different Pt sites reach the balance between the electroactivity and the stability to guarantee the higher performance of MOR and durability with superior anti-poisoning effect.

Highly Dispersed Platinum Chlorine Atoms Anchored on Gold Quantum Dots for a Highly Efficient Electrocatalyst.

The development of efficient and durable electrocatalysts is the only way to achieve commercial fuel cells. A new, efficient method was utilized for epitaxial growth of gold quantum dots using atomically platinum chlorine species with porous graphdiyne as a support (PtCl2Au(111)/GDY), for obtaining successful multicomponent quantum dots with a size of 2.37 nm. The electrocatalyst showed a high mass activity of 175.64 A mgPt-1 for methanol oxidation reactions (MORs) and 165.35 A mgPt-1 for ethanol oxidation reactions (EORs). The data for this experiment are 85.67 and 246.80 times higher than those of commercial Pt/C, respectively. The catalyst also showed highly robust stability for MORs with negligible specific activity decay after 110 h at 10 mA cm-2. Both structure characterizations and theoretical calculations reveal that the excellent catalytic performance can be ascribed to the chlorine introduced to modify the d-band structure on the Pt surface and suppression of the CO poisoning pathway of the MOR. Our results indicate that an atomically dispersed metal species tailoring strategy opens up a new path for the efficient design of highly active and stable catalysts.

Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients.

Heart-transplant recipients are at high risk of developing skin cancer, while Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC) are commonly detected. This paper utilized the database from the United Network for Organ Sharing (UNOS) to study the incidence rate of SCC and BCC among heart transplant recipients. Cox proportional hazards model and two deep neural network-based models were studied, and their performance were compared. In addition, Lasso regression, Chi-square test, and Wilcoxon signed-rank test were applied to identify key risk factors. The neural network-based survival models showed better accuracy compared to the standard Cox regression model, which indicates the advantage of deep learning approaches in survival analysis and risk prediction for post-transplant skin cancer.This study investigates the performance of deep learning (DL) models in clinical applications for predicting the risk of skin cancer in heart transplant recipients. The DL models outperform the standard models in assessing the incidence rate of skin cancer across different time spans.

Control of a Mechanical Blood Pump based on a Trade-off between Aortic Valve Dynamics and Cardiac Outputs.

Left ventricular assist device (LVAD) is a therapeutic option for advanced heart failure (HF) patients. This mechanical device assists a failing heart to circulate blood in the human body by adjusting its pump speed according to cardiac output. However, to use an LVAD for bridge-to-recovery, other criteria (e.g., aortic valve function) should be also considered to reduce complications of the LVAD implantation. In this work, we present an optimization-based control approach to meet the circulatory demand of blood, while maintaining the aortic valve to open and close repeatedly in a cardiac cycle. To validate the performance of the control method, several case studies were investigated, which incorporate different levels of HF severity and physical activity. The results show that the optimization-based control algorithm can quantify the trade-off between the aortic valve function and the blood flow, which will meet clinicians' long quest to improve the myocardial functions for the use of an LVAD as bridge-to-recovery.Clinical Relevance-The efficacy of the control algorithm was validated with computer experiments, showing its potential as a bridge to recovery or as a long-term treatment plan for HF.

Gaussian Process-Based Spatiotemporal Modeling of Electrical Wave Propagation in Human Atrium.

Rhythm regularity of the heart depends on how electrical impulses spread through the cardiac conduction system. Any abnormal activities in the electrical impulses can lead to serious cardiac disorders or sudden death. It is important to understand the electrical activities of the human heart in both healthy and diseased conditions to determine the cause of cardiac disorders and explore the best therapeutic designs. Mathematical models calibrated with clinical and/or in-vitro data are popularly used to study cardiac function and investigate treatment effects. Most of the current human heart models are highly integrated and couple over a hundred equations across different organizational scales of ion channel, cell, and muscle. The model complex poses a significant computational challenge on cardiac simulation. This study developed a metamodel to replace the time-consuming simulation model. Specifically, Gaussian Process (GP) is used to reconstruct the spatiotemporal variations of the cell membrane potential in left atrium. Four different covariance functions were used to infer the potential distributions. The GP model provides an accurate estimation of the spatiotemporal propagation of electrical waves with a small set of data and shows great advantage in computations as compared to traditional models.

An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis.

Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance.

Fundament and Application of Graphdiyne in Electrochemical Energy.

The artificial synthesis of graphdiyne (GDY) in 2010 successfully fills the blank of low temperature preparation of all-carbon allotropes. GDY is an emerging two-dimensional (2D) planar carbon material composed of benzene rings moieties (sp2 carbon atoms), butadiyne (sp carbon atoms) linkers, and well dispersed electron-rich cavities, forming a large π-conjunction structure. GDY has attracted increasing attention in many fields. GDY is the first carbon material with both 2D fast transfer channels for electrons and 3D channels for ions. The 2D electron-rich all-carbon nature endows GDY with considerable conductivity and tunable electronic properties, and the in-plane cavities give it intrinsic selectivity and accessibility for electrochemically active metal ions. In addition, its easy preparation under mild conditions well complements the disadvantages of the traditional sp2-hybridized carbon materials (carbon nanotubes, graphene, and graphite) in the highly efficient synthesis and processing for potential electrochemical applications. As an all-carbon material, the unique advantages of GDY in both structure and preparation match well the urgent demands in key materials for solving many challenging problems in recent electrochemical areas and beyond. During the last decade since the first preparation of GDY, it has already achieved much enlightening and creative progress in both fundamental scientific research and forward-looking applications. This Account is intended not to summarize all this progress in preparation and applications but to outline some newly reported interesting phenomena in both high-quality preparation and electrochemical applications. This Account mainly discusses the recent progress in electrochemical applications: (i) constructing new concepts and new functions in electrochemical interfaces for realizing highly active electrochemical catalysts in the fields of water splitting and oxygen reduction reaction and (ii) building a highly stable conductive network and electrochemical interface for reversible energy storage. In the field of electrochemical catalysis, based on current studies of structural advantages and superior performance, atomic catalysis with metal atoms anchored in GDY is encouraging, owing to the desirable immobilizing capability of electron-rich dialkyne cavities toward metal atoms and corresponding electron transfer. For high-energy batteries, the in situ growth of the all-carbon GDY on the various battery electrodes shows great promise for solving key practical problems (safety, long lifespan, high power), which are ascribed to weak interfacial stability. In addition, the perspective application of GDY to broader interfacial modifications is described, bringing new choices for solving the interfacial challenges in various energy storage devices.

Propagation of Parametric Uncertainty in Aliev-Panfilov Model of Cardiac Excitation.

Models of cardiac electrophysiology are useful for studying heart functions and cardiac disease mechanisms. However, cardiac models often have a great level of complexity, and it is often computationally prohibitive to simulate tissue and organ activities in a real-time fashion. To address the challenge, simplified models such as Aliev-Panfilov model are developed to reduce model complexity, while providing necessary details of cardiac functions. Simplified models may induce uncertainty, which can deteriorate the accuracy and reliability of cardiac models. In addition, model parameters are calibrated with noisy data and cannot be known with certainty. It is important to assess the effect of parametric uncertainty on model predictions. For the probabilistic, time-invariant parametric uncertainty, a generalized polynomial chaos (gPC) expansion-based method is presented in this work to quantify and propagate uncertainty onto model predictions. Using gPC, a measure of confidence in model predictions can be quickly estimated. As compared with sampling-based uncertainty propagation techniques, e.g., Monte Carlo (MC) simulations, the gPC-based method in this work shows its advantages in terms of computational efficiency and accuracy, which has the potentials for dealing with complicated cardiac models, e.g., 2D tissue and 3D organ models.

Robust Control Design of Heart Rate Response during Treadmill Exercise under Parametric Uncertainty.

Rehabilitation (Rehab) exercise can benefit cardiac patients as it can promote the recovery and improve the heart wellness. However, heart failure (HF) patients can only take mild exercise, since excessive exercise may lead to fatal events. It is important to control the exercise intensity at a desired level to maximize exercise benefit. Heart Rate (HR) is an essential factor for measuring exercise intensity. Mathematical models of HR can be used to study exercise physiology. However, HR models involve model uncertainty, resulting from model calibration or variability in patients. It is important to quantify the effect of uncertainty on HR prediction for optimizing exercise intensity, such as treadmill speed. A probabilistic model-based control design is presented in this work to obtain an optimal treadmill speed for Rehab exercise in the presence of uncertainty. To obtain a computationally tractable formulation, the generalized polynomial chaos (gPC) theory is used to propagate uncertainty via a model to HR predictions, and predict slow-acting responses such as peripheral local metabolism that can be used to evaluate exercise outcome for individual patients. The speed control of treadmill is formulated as an optimization problem that can maximize the exercise outcome, while minimizing the slow-acting effects. The effectiveness of the proposed control design was experimentally verified with simulations, showing potentials in the exercise control of individual patients.

Generalized polynomial chaos-based uncertainty quantification and propagation in multi-scale modeling of cardiac electrophysiology.

Uncertainty and physiological variability are ubiquitous in cardiac electrical signaling. It is important to address the uncertainty and variability in cardiac modeling to provide reliable and realistic predictions of heart function, thus ensuring trustworthy computer-aided medical decision-making and treatment planning. Statistical techniques such as Monte Carlo (MC) simulations have been applied to uncertainty quantification and propagation in cardiac modeling. However, MC simulation-based methods are computationally prohibitive for complex cardiac models with a great number of parameters and governing equations. In this paper, we propose to use the Generalized Polynomial Chaos (gPC) expansion in combination with Galerkin projection to analytically quantify parametric uncertainty in ion channel models of mouse ventricular cell, and further propagate the uncertainty across different organizational levels of cell and tissue. To identify the most significant parametric uncertainty in cardiac ion channel and cell models, variance decomposition-based sensitivity analysis was first performed. Following this, gPC was integrated with deterministic cardiac models to propagate uncertainty through ion current, ventricular cell, 1D cable, and 2D tissue to account for the stochasticity and cell-to-cell variability. As compared to MC, the gPC in this work shows the superior performance in terms of computational efficiency. In addition, the gPC models can provide a measure of confidence in model predictions, which can improve the reliability of computer simulations of cardiac electrophysiology for clinical applications.

Segmentation and Quantitative Analysis of Apoptosis of Chinese Hamster Ovary Cells from Fluorescence Microscopy Images.

Accurate and fast quantitative analysis of living cells from fluorescence microscopy images is useful for evaluating experimental outcomes and cell culture protocols. An algorithm is developed in this work to automatically segment and distinguish apoptotic cells from normal cells. The algorithm involves three steps consisting of two segmentation steps and a classification step. The segmentation steps are: (i) a coarse segmentation, combining a range filter with a marching square method, is used as a prefiltering step to provide the approximate positions of cells within a two-dimensional matrix used to store cells' images and the count of the number of cells for a given image; and (ii) a fine segmentation step using the Active Contours Without Edges method is applied to the boundaries of cells identified in the coarse segmentation step. Although this basic two-step approach provides accurate edges when the cells in a given image are sparsely distributed, the occurrence of clusters of cells in high cell density samples requires further processing. Hence, a novel algorithm for clusters is developed to identify the edges of cells within clusters and to approximate their morphological features. Based on the segmentation results, a support vector machine classifier that uses three morphological features: the mean value of pixel intensities in the cellular regions, the variance of pixel intensities in the vicinity of cell boundaries, and the lengths of the boundaries, is developed for distinguishing apoptotic cells from normal cells. The algorithm is shown to be efficient in terms of computational time, quantitative analysis, and differentiation accuracy, as compared with the use of the active contours method without the proposed preliminary coarse segmentation step.

Classification of Normal and Apoptotic Cells from Fluorescence Microscopy Images Using Generalized Polynomial Chaos and Level Set Function.

Accurate automated quantitative analysis of living cells based on fluorescence microscopy images can be very useful for fast evaluation of experimental outcomes and cell culture protocols. In this work, an algorithm is developed for fast differentiation of normal and apoptotic viable Chinese hamster ovary (CHO) cells. For effective segmentation of cell images, a stochastic segmentation algorithm is developed by combining a generalized polynomial chaos expansion with a level set function-based segmentation algorithm. This approach provides a probabilistic description of the segmented cellular regions along the boundary, from which it is possible to calculate morphological changes related to apoptosis, i.e., the curvature and length of a cell's boundary. These features are then used as inputs to a support vector machine (SVM) classifier that is trained to distinguish between normal and apoptotic viable states of CHO cell images. The use of morphological features obtained from the stochastic level set segmentation of cell images in combination with the trained SVM classifier is more efficient in terms of differentiation accuracy as compared with the original deterministic level set method.

[Expression and characterization of a novel ω-transaminase from Burkholderia phytofirmans PsJN].

Production of chiral amines and unnatural amino-acid using ω-transaminase can be achieved by kinetic resolution and asymmetric synthesis, thus ω-transaminase is of great importance in the synthesis of pharmaceutical intermediates. By genomic data mining, a putative ω-transaminase gene hbp was found in Burkholderia phytofirmans PsJN. The gene was cloned and over-expressed in Escherichia coli BL21 (DE3). The recombinant enzyme (HBP) was purified by Ni-NTA column and its catalytic properties and substrate profile were studied. HBP showed high relative activity (33.80 U/mg) and enantioselectivity toward ß-phenylalanine (ß-Phe). The optimal reaction temperature and pH were 40 ℃ and 8.0-8.5, respectively. We also established a simpler and more effective method to detect the deamination reaction of ß-Phe by UV absorption method using microplate reader, and demonstrated the thermodynamic property of this reaction. The substrate profiling showed that HBP was specific to ß-Phe and its derivatives as the amino donor. HBP catalyzed the resolution of rac-ß-Phe and its derivatives, the products (R)-amino acids were obtained with about 50% conversions and 99% ee.

Synthesis of α,ß-unsaturated esters via a chemo-enzymatic chain elongation approach by combining carboxylic acid reduction and Wittig reaction.

α,ß-Unsaturated esters are versatile building blocks for organic synthesis and of significant importance for industrial applications. A great variety of synthetic methods have been developed, and quite a number of them use aldehydes as precursors. Herein we report a chemo-enzymatic chain elongation approach to access α,ß-unsaturated esters by combining an enzymatic carboxylic acid reduction and Wittig reaction. Recently, we have found that Mycobacterium sp. was able to reduce phenylacetic acid (1a) to 2-phenyl-1-ethanol (1c) and two sequences in the Mycobacterium sp. genome had high identity with the carboxylic acid reductase (CAR) gene from Nocardia iowensis. These two putative CAR genes were cloned, overexpressed in E. coli and one of two proteins could reduce 1a. The recombinant CAR was purified and characterized. The enzyme exhibited high activity toward a variety of aromatic and aliphatic carboxylic acids, including ibuprofen. The Mycobacterium CAR catalyzed carboxylic acid reduction to give aldehydes, followed by a Wittig reaction to afford the products α,ß-unsaturated esters with extension of two carbon atoms, demonstrating a new chemo-enzymatic method for the synthesis of these important compounds.

Ordered mesoporous titanosilicates with catalytically stable and active four-coordinated titanium sites.

The stable ordered mesoporous titanosilicate (Ti-JLU-20) has been successfully synthesized from an assembly of mixed surfactants (fluorocarbon and triblock copolymer surfactants) with preformed titanosilicate zeolite precursors at high temperature (180-220 degrees C), and catalytic tests show that Ti-JLU-20 has highly stable and active four-coordinated titanium sites in oxidations.