ChemTS: an efficient python library for de novo molecular generation

Abstract

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.     

Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using approximate Bayesian computation

We develop a parameter estimation method based on approximate Bayesian computation (ABC) for a stochastic cell invasion model using fluorescent cell cycle labelling with proliferation, migration and crowding effects. Previously, inference has been performed on a deterministic version of the model fitted to cell density data, and not all parameters were identifiable. Considering the stochastic model allows us to harness more features of experimental data, including cell trajectories and cell count data, which we show overcomes the parameter identifiability problem. We demonstrate that, while difficult to collect, cell trajectory data can provide more information about the parameters of the cell invasion model. To handle the intractability of the likelihood function of the stochastic model, we use an efficient ABC algorithm based on sequential Monte Carlo. Rcpp and MATLAB implementations of the simulation model and ABC algorithm used in this study are available at https://github.com/michaelcarr-stats/FUCCI.     

Empirical Dynamic Quantiles for Visualization of High-Dimensional Time Series

Abstract

The empirical quantiles of independent data provide a good summary of the underlying distribution of the observations. For high-dimensional time series defined in two dimensions, such as in space and time, one can define empirical quantiles of all observations at a given time point, but such time-wise quantiles can only reflect properties of the data at that time point. They often fail to capture the dynamic dependence of the data. In this article, we propose a new definition of empirical dynamic quantiles (EDQ) for high-dimensional time series that mitigates this limitation by imposing that the quantile must be one of the observed time series. The word dynamic emphasizes the fact that these newly defined quantiles capture the time evolution of the data. We prove that the EDQ converge to the time-wise quantiles under some weak conditions as the dimension increases. A fast algorithm to compute the dynamic quantiles is presented and the resulting quantiles are used to produce summary plots for a collection of many time series. We illustrate with two real datasets that the time-wise and dynamic quantiles convey different and complementary information. We also briefly compare the visualization provided by EDQ with that obtained by functional depth. The R code and a vignette for computing and plotting EDQ are available at https://github.com/dpena157/HDts/.     

Bayesian Analysis of Accumulated Damage Models in Lumber Reliability

Wood products that are subjected to sustained stress over a period of long duration may weaken, and this effect must be considered in models for the long-term reliability of lumber. The damage accumulation approach has been widely used for this purpose to set engineering standards. In this article, we revisit an accumulated damage model and propose a Bayesian framework for analysis. For parameter estimation and uncertainty quantification, we adopt approximation Bayesian computation (ABC) techniques to handle the complexities of the model. We demonstrate the effectiveness of our approach using both simulated and real data, and apply our fitted model to analyze long-term lumber reliability under a stochastic live loading scenario. Code is available at https://github.com/wongswk/abc-adm.     

A Unified Approach to Sparse Tweedie Modeling of Multisource Insurance Claim Data

Abstract

Actuarial practitioners now have access to multiple sources of insurance data corresponding to various situations: multiple business lines, umbrella coverage, multiple hazards, and so on. Despite the wide use and simple nature of single-target approaches, modeling these types of data may benefit from an approach performing variable selection jointly across the sources. We propose a unified algorithm to perform sparse learning of such fused insurance data under the Tweedie (compound Poisson) model. By integrating ideas from multitask sparse learning and sparse Tweedie modeling, our algorithm produces flexible regularization that balances predictor sparsity and between-sources sparsity. When applied to simulated and real data, our approach clearly outperforms single-target modeling in both prediction and selection accuracy, notably when the sources do not have exactly the same set of predictors. An efficient implementation of the proposed algorithm is provided in our R package MStweedie, which is available at https://github.com/fontaine618/MStweedie. Supplementary materials for this article are available online.     

Interface affected zone for optimal strength and ductility in heterogeneous laminate

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Fluid eddy induced piezo-promoted photodegradation of organic dye pollutants in wastewater on ZnO nanorod arrays/3D Ni foam

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Parameter estimation and uncertainty quantification using information geometry

In this work, we: (i) review likelihood-based inference for parameter estimation and the construction of confidence regions; and (ii) explore the use of techniques from information geometry, including geodesic curves and Riemann scalar curvature, to supplement typical techniques for uncertainty quantification, such as Bayesian methods, profile likelihood, asymptotic analysis and bootstrapping. These techniques from information geometry provide data-independent insights into uncertainty and identifiability, and can be used to inform data collection decisions. All code used in this work to implement the inference and information geometry techniques is available on GitHub.     

Implementation and acceleration of optimal control for systems biology

Optimal control theory provides insight into complex resource allocation decisions. The forward–backward sweep method (FBSM) is an iterative technique commonly implemented to solve two-point boundary value problems arising from the application of Pontryagin’s maximum principle (PMP) in optimal control. The FBSM is popular in systems biology as it scales well with system size and is straightforward to implement. In this review, we discuss the PMP approach to optimal control and the implementation of the FBSM. By conceptualizing the FBSM as a fixed point iteration process, we leverage and adapt existing acceleration techniques to improve its rate of convergence. We show that convergence improvement is attainable without prohibitively costly tuning of the acceleration techniques. Furthermore, we demonstrate that these methods can induce convergence where the underlying FBSM fails to converge. All code used in this work to implement the FBSM and acceleration techniques is available on GitHub at https://github.com/Jesse-Sharp/Sharp2021.     

Few views image reconstruction using alternating direction method via ‐norm minimization

In the medical computer tomography field, total variation (TV), which is the ‐norm of the gradient‐magnitude images, is widely used as the regularization based on the compressive sensing theory. To overcome the TV model's disadvantageous tendency of uniformly penalize the image gradient and over smooth the low‐contrast structures, an iterative algorithm based on the ‐norm optimization of the finite difference is proposed. To rise to the challenges introduced by the ‐norm minimization, the algorithm uses the alternating direction method to solve the unconstrained augmented Lagrangian function, which involves a hard thresholding method, a linearization and proximal points technique for each subproblem. The simulation demonstrates the conclusions and indicates that the algorithm proposed in this article can obviously improve the reconstruction quality. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 215–223, 2014     

Phase constitutions of MoSiN anti-oxidation multi-layer coatings on CC composites by fused slurry

To improve the oxidation resistance of MoSi fused slurry coating fabricated in vacuum, MoSiN multi-layer coatings were synthesized on C/C composites in nitrogen atmosphere by fused slurry using same Mo and Si element powders. The phase compositions and microstructures were characterized by X-ray diffractometry (XRD), optical microscopy (OM), scanning electron microscopy (SEM) with energy-dispersive spectroscopy (EDS). The results indicate that the MoSiN coating contains SiC inner layer and MoSi₂/Si main layer, which was similar with MoSi coating. Additionally, a thin outer layer with nano-filiform morphology has been found on the coating surface, which consists of SiC, Si₃N₄, AlN, Al₂O₃ and sialon phase. Oxidation experiments show that the MoSiN multi-layer coating exhibits excellent oxidation resistance at 1400 °C and anti-oxidizing potential ability at 1450 °C.     

Bioinspired yeast microcapsules loaded with self-assembled nanotherapies for targeted treatment of cardiovascular disease

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Fast in situ 3D nanoimaging: a new tool for dynamic characterization in materials science

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Fullerene: biomedical engineers get to revisit an old friend

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Updates on the development of nanostructured transition metal nitrides for electrochemical energy storage and water splitting

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Toward a molecular design of porous carbon materials

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Investigation of Solidification and Segregation Characteristics of Cast Ni-Base Superalloy K417G

Differential scanning calorimetry(DSC) analysis, isothermal solidification experiment and Thermo-Calc simulation were employed to investigate solidification characteristics of K417 G Ni-base superalloy. Electron probe microanalysis(EPMA) was employed to analyze the segregation characteristics. Liquidus,solidus and the formation temperatures of main phases were measured. In the process of solidification,the volume fraction of liquid dropped dramatically in the initial stage, while the dropping rate became very low in the final stage due to severe segregation of positive segregation elements into the residual liquid. The solidification began with the formation of primary γ. Then with solidification proceeding, Ti and Mo were enriched in the liquid interdendrite, which resulted in the precipitation of MC carbides in the interdendrite. Al accumulated into liquid at the initial stage, but gathered to solid later due to the precipitation of γ/γ’ eutectic at the intermediate stage of solidification. However, Co tended to segregate toward the solid phase. In the case of K417 G alloy, combining DSC analysis and isothermal solidification experiment is a good way to investigate the solidification characteristics. Thermo-Calc simulation can serve as reference to investigate K417 G alloy.     

Additive manufacturing: scientific and technological challenges,market uptake and opportunities

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Functional polymer surfaces for controlling cell behaviors

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Nanoplumbers: biomaterials to fight cardiovascular diseases

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