SERS monitoring of methylene blue degradation by Au-Ag@Cu2O-rGO nanocomposite.

A combination of multiple materials effectively improves and enhances the performance of the materials. Thus, a gold-silver@cuprous oxide (Au-Ag@Cu2O)-reduced graphene oxide (rGO) structure was designed and fabricated. We decorated the Au nanoparticles (NPs) on the Ag@Cu2O-rGO composite surface by a redox reaction to form a Au-Ag@Cu2O-rGO structure with two noble metals attached to a Cu2O semiconductor. A comparable Au-Ag@Cu2O structure was also fabricated. After decorating Au NPs into the Ag@Cu2O-rGO composite, the Au-Ag@Cu2O composite structure was loosened, and the surface and interior of the Cu2O shell were decorated with Au and Ag NPs. Moreover, the addition of Au NPs resulted in a proper surface plasmon resonance effect and a significant broadening of the absorption range. The loose structure increased the adsorption of the probe molecules, which increased the surface-enhanced Raman scattering (SERS) intensity. In addition, the fabricated Au-Ag@Cu2O-rGO exhibited excellent catalytic reduction of methylene blue (MB) with sodium borohydride (NaBH4). Therefore, the SERS-based monitoring of the MB degradation was obviously improved.

Toxicity prediction and classification of Gunqile-7 with small sample based on transfer learning method.

Drug-induced diseases are the most important component of iatrogenic disease. It is the duty of doctors to provide a reasonable and safe dose of medication. Gunqile-7 is a Mongolian medicine with analgesic and anti-inflammatory effects. As a foreign substance in the body, even with reasonable medication, it may produce varying degrees of adverse reactions or toxic side effects. Since the cost of collecting Gunqile-7 for pharmacological animal trials is high and the data sample is small, this paper employs transfer learning and data augmentation methods to study the toxicity of Gunqile-7. More specifically, to reduce the necessary number of training samples, the data augmentation approach is employed to extend the data set. Then, the transfer learning method and one-dimensional convolutional neural network are utilized to train the network. In addition, we use the support vector machine-recursive feature elimination method for feature selection to reduce features that have adverse effects on model predictions. Furthermore, due to the important role of the pre-trained model of transfer learning, we select a quantitative toxicity prediction model as the pre-trained model, which is consistent with the purpose of this paper. Lastly, the experimental results demonstrate the efficiency of the proposed method. Our method can improve accuracy by up to 9 percentage points compared to the method without transfer learning on a small sample set.

Potential of Mie-Fluorescence-Raman Lidar to Profile Chlorophyll a Concentration in Inland Waters.

Vertical distribution of phytoplankton is crucial for assessing the trophic status and primary production in inland waters. However, there is sparse information about phytoplankton vertical distribution due to the lack of sufficient measurements. Here, we report, to the best of our knowledge, the first Mie-fluorescence-Raman lidar (MFRL) measurements of continuous chlorophyll a (Chl-a) profiles as well as their parametrization in inland water. The lidar-measured Chl-a during several experiments showed good agreement with the in situ data. A case study verified that MFRL had the potential to profile the Chl-a concentration. The results revealed that the maintenance of subsurface chlorophyll maxima (SCM) was influenced by light and nutrient inputs. Furthermore, inspired by the observations from MFRL, an SCM model built upon surface Chl-a concentration and euphotic layer depth was proposed with root mean square relative difference of 16.5% compared to MFRL observations, providing the possibility to map 3D Chl-a distribution in aquatic ecosystems by integrated active-passive remote sensing technology. Profiling and modeling Chl-a concentration with MFRL are expected to be of paramount importance for monitoring inland water ecosystems and environments.

Comprehensive, Continuous, and Vertical Measurements of Seawater Constituents with Triple-Field-of-View High-Spectral-Resolution Lidar.

Measuring the characteristics of seawater constituent is in great demand for studies of marine ecosystems and biogeochemistry. However, existing techniques based on remote sensing or in situ samplings present various tradeoffs with regard to the diversity, synchronism, temporal-spatial resolution, and depth-resolved capacity of their data products. Here, we demonstrate a novel oceanic triple-field-of-view (FOV) high-spectral-resolution lidar (HSRL) with an iterative retrieval approach. This technique provides, for the first time, comprehensive, continuous, and vertical measurements of seawater absorption coefficient, scattering coefficient, and slope of particle size distribution, which are validated by simulations and field experiments. Furthermore, it depicts valuable application potentials in the accuracy improvement of seawater classification and the continuous estimation of depth-resolved particulate organic carbon export. The triple-FOV HSRL with high performance could greatly increase the knowledge of seawater constituents and promote the understanding of marine ecosystems and biogeochemistry.

Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties.

Lidar techniques present a distinctive ability to resolve vertical structure of optical properties within the upper water column at both day- and night-time. However, accuracy challenges remain for existing lidar instruments due to the ill-posed nature of elastic backscatter lidar retrievals and multiple scattering. Here we demonstrate the high performance of, to the best of our knowledge, the first shipborne oceanic high-spectral-resolution lidar (HSRL) and illustrate a multiple scattering correction algorithm to rigorously address the above challenges in estimating the depth-resolved diffuse attenuation coefficient Kd and the particulate backscattering coefficient bbp at 532 nm. HSRL data were collected during day- and night-time within the coastal areas of East China Sea and South China Sea, which are connected by the Taiwan Strait. Results include vertical profiles from open ocean waters to moderate turbid waters and first lidar continuous observation of diel vertical distribution of thin layers at a fixed station. The root-mean-square relative differences between the HSRL and coincident in situ measurements are 5.6% and 9.1% for Kd and bbp, respectively, corresponding to an improvement of 2.7-13.5 and 4.9-44.1 times, respectively, with respect to elastic backscatter lidar methods. Shipborne oceanic HSRLs with high performance are expected to be of paramount importance for the construction of 3D map of ocean ecosystem.

Retrograde flow in aortic isthmus in normal and fetal heart disease by principal component analysis and computational fluid dynamics.

OBJECTIVES: Reverse flow Retrograde flow (RF) of blood in the aortic isthmus can be observed in different types of fetal heart disease (FHD), including abnormalities in heart structure and function. This study sought to investigate the relationship between RF and blood flow parameters, and develop a computational fluid dynamics (CFD) model to understand the mechanisms underlying this observation. MATERIAL AND METHODS: A total of 281 fetuses (gestational age [GA] 26.6±.3 weeks) with FHD and 2803 normal fetuses (GA: 26.1±.1 weeks) by fetal echocardiography collected from May 2016 to December 2018. Principal component analysis (PCA) was performed to find the relationship and the CFD model reconstructed from 3D/4D spatio-temporal image correlation (STIC) images to simulate hemodynamics. RESULTS: There was a significant difference in the percentages of RF between the study (80/201 (39%)) and control (29/2803 (1%)) groups (p < 0.05). The RF occur when the aorta flow rate (left heart) is reduced to 60% by CFD stimulation. Pearson correlation analysis showed significant correlations between flow rate and wall shear stress(WSS) (r = .883, p = 0.047) variables at the AI. CONCLUSION: Volumetric flow rate of AO or left heart was the main component of the cause of RF. The hemodynamics of the cardiovascular system have highly complex behavior hinge on the turbulent nature of circulating blood flow.

Destabilizing heterochromatin by APOE mediates senescence.

Apolipoprotein E (APOE) is a component of lipoprotein particles that function in the homeostasis of cholesterol and other lipids. Although APOE is genetically associated with human longevity and Alzheimer's disease, its mechanistic role in aging is largely unknown. Here, we used human genetic, stress-induced and physiological cellular aging models to explore APOE-driven processes in stem cell homeostasis and aging. We report that in aged human mesenchymal progenitor cells (MPCs), APOE accumulation is a driver for cellular senescence. By contrast, CRISPR-Cas9-mediated deletion of APOE endows human MPCs with resistance to cellular senescence. Mechanistically, we discovered that APOE functions as a destabilizer for heterochromatin. Specifically, increased APOE leads to the degradation of nuclear lamina proteins and a heterochromatin-associated protein KRAB-associated protein 1 via the autophagy-lysosomal pathway, thereby disrupting heterochromatin and causing senescence. Altogether, our findings uncover a role of APOE as an epigenetic mediator of senescence and provide potential targets to ameliorate aging-related diseases.

Amorphous TiO2 as a multifunctional interlayer for boosting the efficiency and stability of the CdS/cobaloxime hybrid system for photocatalytic hydrogen production.

The construction of both highly efficient and stable hybrid artificial photosynthetic systems comprising semiconductors as photosensitizers and abundant metal-based molecular complexes as cocatalysts for photocatalytic H2 generation remains challenging. Herein, we report an effective and stable CdS/cobaloxime hybrid system prepared by inserting an amorphous TiO2 (a-TiO2) interlayer with adjustable thickness and by covalently-surface-attaching molecular cobaloxime catalysts. This hybrid system displayed outstanding photocatalytic H2 production and reached a maximum rate of ∼25 mmol g-1 h-1, which was ∼20.8 times that of pure CdS and 1.7 times that of the CdS/cobaloxime system without an a-TiO2 interlayer (CdS/Co). More importantly, 6 nm a-TiO2 uniformly coated CdS nanorods (CdS NRs) exhibited exceptional 200 h long-term catalytic behaviour under ≥420 nm visible light irradiation. However, the H2 production performance of the CdS/Co hybrid system decreased significantly over 10 h. Density functional theory (DFT) calculations indicated that the a-TiO2 surface can provide abundant bonding sites for the effective immobilization of molecular catalysts. Moreover, Mott-Schottky electrochemical measurements and femtosecond transient absorption spectroscopy revealed that the a-TiO2 interlayer had favourable band levels that could fasten the photoexcited electron transfer from CdS to molecular cobaloxime and could extract holes with intraband electronic states generated by defects, thus prohibiting CdS photocorrosion and improving the stability of the hybrid system. This study proposes a strategy for designing multifunctional interlayers for the effective immobilization of molecular catalysts, beneficial regulation of photoinduced charge carriers, and improvement of the stability as well as facilitation of the construction of artificial photosynthetic hybrid systems with high efficiency and durability.

Zn2GeO4-x/ZnS heterojunctions fabricated via in situ etching sulfurization for Pt-free photocatalytic hydrogen evolution: interface roughness and defect engineering.

Interface engineering has been regarded as a promising strategy for enhancing the catalytic activities of heterojunction photocatalysts. Herein, we have adopted an in situ etching sulfurization method to construct a Zn2GeO4-x/ZnS intimate heterojunction, which exhibited excellent photocatalytic H2 production in the absence of a Pt co-catalyst. Distinctively, TEM and HRTEM measurements showed that the interface of the Zn2GeO4-x/ZnS heterojunction became rough (topologically) due to in situ etching sulfurization, and etching was found to be strongly dependent on the crystal orientation. Moreover, the surface of the Zn2GeO4 nanorods from flat (100) planes evolved into an irregular coastline-like structure topologized with (110) and (113) high-index planes. ICP and elemental distribution measurements indicated that during the precipitation of ZnS via in situ etching sulfurization, the migration and dissolution of Zn and Ge ions on the Zn2GeO4(100) plane led to the roughening of the interface and the evolution of crystal planes. XPS and EPR analyses showed that Zn2GeO4-x/ZnS contained more oxygen vacancies with structural evolution. The theoretical calculations demonstrated that oxygen defects were prone to be generated on the Zn2GeO4(113) plane and formed the Ge3c3+-VO complexes. Compared to the inactive (100) plane, etching caused the Zn2GeO4(110) planes to have a higher number of threefold coordinated germanium (Ge3c4+) and (113) high-index planes that possessed abundant active sites (Ge3c3+-VO complexes), which dramatically decreased the barrier and reaction energy of H2O dissociation. This work not only provides fundamental insights into the topological interface evolution and facet-dependent defect distribution but also offers a strategy for the design of efficient photocatalysts for H2 production without the use of Pt as a co-catalyst based on a multifunctional interface.

Neural-Response-Based Extreme Learning Machine for Image Classification.

This paper proposes a novel and simple multilayer feature learning method for image classification by employing the extreme learning machine (ELM). The proposed algorithm is composed of two stages: the multilayer ELM (ML-ELM) feature mapping stage and the ELM learning stage. The ML-ELM feature mapping stage is recursively built by alternating between feature map construction and maximum pooling operation. In particular, the input weights for constructing feature maps are randomly generated and hence need not be trained or tuned, which makes the algorithm highly efficient. Moreover, the maximum pooling operation enables the algorithm to be invariant to certain transformations. During the ELM learning stage, elastic-net regularization is proposed to learn the output weight. Elastic-net regularization helps to learn more compact and meaningful output weight. In addition, we preprocess the input data with the dense scale-invariant feature transform operation to improve both the robustness and invariance of the algorithm. To evaluate the effectiveness of the proposed method, several experiments are conducted on three challenging databases. Compared with the conventional deep learning methods and other related ones, the proposed method achieves the best classification results with high computational efficiency.

Variational Hamiltonian Monte Carlo via Score Matching.

Traditionally, the field of computational Bayesian statistics has been divided into two main subfields: variational methods and Markov chain Monte Carlo (MCMC). In recent years, however, several methods have been proposed based on combining variational Bayesian inference and MCMC simulation in order to improve their overall accuracy and computational efficiency. This marriage of fast evaluation and flexible approximation provides a promising means of designing scalable Bayesian inference methods. In this paper, we explore the possibility of incorporating variational approximation into a state-of-the-art MCMC method, Hamiltonian Monte Carlo (HMC), to reduce the required expensive computation involved in the sampling procedure, which is the bottleneck for many applications of HMC in big data problems. To this end, we exploit the regularity in parameter space to construct a free-form approximation of the target distribution by a fast and flexible surrogate function using an optimized additive model of proper random basis, which can also be viewed as a single-hidden layer feedforward neural network. The surrogate function provides sufficiently accurate approximation while allowing for fast computation in the sampling procedure, resulting in an efficient approximate Bayesian inference algorithm. We demonstrate the advantages of our proposed method using both synthetic and real data problems.

Hamiltonian Monte Carlo acceleration using surrogate functions with random bases.

For big data analysis, high computational cost for Bayesian methods often limits their applications in practice. In recent years, there have been many attempts to improve computational efficiency of Bayesian inference. Here we propose an efficient and scalable computational technique for a state-of-the-art Markov chain Monte Carlo methods, namely, Hamiltonian Monte Carlo. The key idea is to explore and exploit the structure and regularity in parameter space for the underlying probabilistic model to construct an effective approximation of its geometric properties. To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process. The resulting method provides a flexible, scalable, and efficient sampling algorithm, which converges to the correct target distribution. We show that by choosing the basis functions and optimization process differently, our method can be related to other approaches for the construction of surrogate functions such as generalized additive models or Gaussian process models. Experiments based on simulated and real data show that our approach leads to substantially more efficient sampling algorithms compared to existing state-of-the-art methods.

A semi-implicit augmented IIM for Navier-Stokes equations with open, traction, or free boundary conditions.

In this paper, a new Navier-Stokes solver based on a finite difference approximation is proposed to solve incompressible flows on irregular domains with open, traction, and free boundary conditions, which can be applied to simulations of fluid structure interaction, implicit solvent model for biomolecular applications and other free boundary or interface problems. For some problems of this type, the projection method and the augmented immersed interface method (IIM) do not work well or does not work at all. The proposed new Navier-Stokes solver is based on the local pressure boundary method, and a semi-implicit augmented IIM. A fast Poisson solver can be used in our algorithm which gives us the potential for developing fast overall solvers in the future. The time discretization is based on a second order multi-step method. Numerical tests with exact solutions are presented to validate the accuracy of the method. Application to fluid structure interaction between an incompressible fluid and a compressible gas bubble is also presented.

A Multi-Scale Method for Dynamics Simulation in Continuum Solvent Models I: Finite-Difference Algorithm for Navier-Stokes Equation.

A multi-scale framework is proposed for more realistic molecular dynamics simulations in continuum solvent models by coupling a molecular mechanics treatment of solute with a fluid mechanics treatment of solvent. This article reports our initial efforts to formulate the physical concepts necessary for coupling the two mechanics and develop a 3D numerical algorithm to simulate the solvent fluid via the Navier-Stokes equation. The numerical algorithm was validated with multiple test cases. The validation shows that the algorithm is effective and stable, with observed accuracy consistent with our design.

Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study.

Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT represents averaged patient images over one breathing cycle and the fourth dimension is actually breathing phase instead of time. In many clinical applications, it is desirable to obtain true 4DCBCT with the fourth dimension being time, i.e., each constituent CBCT image corresponds to an instantaneous projection. Theoretically it is impossible to reconstruct a CBCT image from a single projection. However, if all the constituent CBCT images of a 4DCBCT scan share a lot of redundant information, it might be possible to make a good reconstruction of these images by exploring their sparsity and coherence/redundancy. Though these CBCT images are not completely time resolved, they can exploit both local and global temporal coherence of the patient anatomy automatically and contain much more temporal variation information of the patient geometry than the conventional 4DCBCT. We propose in this work a computational model and algorithms for the reconstruction of this type of semi-time-resolved CBCT, called cine-CBCT, based on low rank approximation that can utilize the underlying temporal coherence both locally and globally, such as slow variation, periodicity or repetition, in those cine-CBCT images.

Numerical Poisson-Boltzmann Model for Continuum Membrane Systems.

Membrane protein systems are important computational research topics due to their roles in rational drug design. In this study, we developed a continuum membrane model utilizing a level set formulation under the numerical Poisson-Boltzmann framework within the AMBER molecular mechanics suite for applications such as protein-ligand binding affinity and docking pose predictions. Two numerical solvers were adapted for periodic systems to alleviate possible edge effects. Validation on systems ranging from organic molecules to membrane proteins up to 200 residues, demonstrated good numerical properties. This lays foundations for sophisticated models with variable dielectric treatments and second-order accurate modeling of solvation interactions.

Exploring a charge-central strategy in the solution of Poisson's equation for biomolecular applications.

Continuum solvent treatments based on the Poisson-Boltzmann equation have been widely accepted for energetic analysis of biomolecular systems. In these approaches, the molecular solute is treated as a low dielectric region and the solvent is treated as a high dielectric continuum. The existence of a sharp dielectric jump at the solute-solvent interface poses a challenge to model the solvation energetics accurately with such a simple mathematical model. In this study, we explored and evaluated a strategy based on the "induced surface charge" to eliminate the dielectric jump within the finite-difference discretization scheme. In addition to the use of the induced surface charges in solving the equation, the second-order accurate immersed interface method is also incorporated to discretize the equation. The resultant linear system is solved with the GMRES algorithm to explicitly impose the flux conservation condition across the solvent-solute interface. The new strategy was evaluated on both analytical and realistic biomolecular systems. The numerical tests demonstrate the feasibility of utilizing induced surface charge in the finite-difference solution of the Poisson-Boltzmann equation. The analysis data further show that the strategy is consistent with theory and the classical finite-difference method on the tested systems. Limitations of the current implementations and further improvements are also analyzed and discussed to fully bring out its potential of achieving higher numerical accuracy.

ANALYSIS OF A NUMERICAL SOLVER FOR RADIATIVE TRANSPORT EQUATION.

We analyze a numerical algorithm for solving radiative transport equation with vacuum or reflection boundary condition that was proposed in [4] with angular discretization by finite element method and spatial discretization by discontinuous Galerkin or finite difference method.

Exploring accurate Poisson-Boltzmann methods for biomolecular simulations.

Accurate and efficient treatment of electrostatics is a crucial step in computational analyses of biomolecular structures and dynamics. In this study, we have explored a second-order finite-difference numerical method to solve the widely used Poisson-Boltzmann equation for electrostatic analyses of realistic bio-molecules. The so-called immersed interface method was first validated and found to be consistent with the classical weighted harmonic averaging method for a diversified set of test biomolecules. The numerical accuracy and convergence behaviors of the new method were next analyzed in its computation of numerical reaction field grid potentials, energies, and atomic solvation forces. Overall similar convergence behaviors were observed as those by the classical method. Interestingly, the new method was found to deliver more accurate and better-converged grid potentials than the classical method on or nearby the molecular surface, though the numerical advantage of the new method is reduced when grid potentials are extrapolated to the molecular surface. Our exploratory study indicates the need for further improving interpolation/extrapolation schemes in addition to the developments of higher-order numerical methods that have attracted most attention in the field.