DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks.

Protein solubility is an important thermodynamic parameter that is critical for the characterization of a protein's function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g., pharmaceutical) applications. Experimental approaches to predict protein solubility are costly, time-consuming, and frequently offer only low success rates. To reduce cost and expedite the development of therapeutic and industrially relevant proteins, a highly accurate computational tool for predicting protein solubility from protein sequence is sought. While a number of in silico prediction tools exist, they suffer from relatively low prediction accuracy, bias toward the soluble proteins, and limited applicability for various classes of proteins. In this study, we developed a novel deep learning sequence-based solubility predictor, DSResSol, that takes advantage of the integration of squeeze excitation residual networks with dilated convolutional neural networks and outperforms all existing protein solubility prediction models. This model captures the frequently occurring amino acid k-mers and their local and global interactions and highlights the importance of identifying long-range interaction information between amino acid k-mers to achieve improved accuracy, using only protein sequence as input. DSResSol outperforms all available sequence-based solubility predictors by at least 5% in terms of accuracy when evaluated by two different independent test sets. Compared to existing predictors, DSResSol not only reduces prediction bias for insoluble proteins but also predicts soluble proteins within the test sets with an accuracy that is at least 13% higher than existing models. We derive the key amino acids, dipeptides, and tripeptides contributing to protein solubility, identifying glutamic acid and serine as critical amino acids for protein solubility prediction. Overall, DSResSol can be used for the fast, reliable, and inexpensive prediction of a protein's solubility to guide experimental design.

Dynamic polymer systems with self-regulated secretion for the control of surface properties and material healing.

Approaches for regulated fluid secretion, which typically rely on fluid encapsulation and release from a shelled compartment, do not usually allow a fine continuous modulation of secretion, and can be difficult to adapt for monitoring or function-integration purposes. Here, we report self-regulated, self-reporting secretion systems consisting of liquid-storage compartments in a supramolecular polymer-gel matrix with a thin liquid layer on top, and demonstrate that dynamic liquid exchange between the compartments, matrix and surface layer allows repeated, responsive self-lubrication of the surface and cooperative healing of the matrix. Depletion of the surface liquid or local material damage induces secretion of the stored liquid via a dynamic feedback between polymer crosslinking, droplet shrinkage and liquid transport that can be read out through changes in the system's optical transparency. We foresee diverse applications in fluid delivery, wetting and adhesion control, and material self-repair.

Transfer Learning in Deep Reinforcement Learning: A Survey.

Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the recent progress of transfer learning approaches in the context of deep reinforcement learning. Specifically, we provide a framework for categorizing the state-of-the-art transfer learning approaches, under which we analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications. We also draw connections between transfer learning and other relevant topics from the reinforcement learning perspective and explore their potential challenges that await future research progress.

RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection.

Unsupervised anomaly detection (AD) plays a crucial role in many critical applications. Driven by the success of deep learning, recent years have witnessed growing interest in applying deep neural networks (DNNs) to AD problems. A common approach is using autoencoders to learn a feature representation for the normal observations in the data. The reconstruction error of the autoencoder is then used as outlier score to detect the anomalies. However, due to the high complexity brought upon by over-parameterization of DNNs, the reconstruction error of the anomalies could also be small, which hampers the effectiveness of these methods. To alleviate this problem, we propose a robust framework using collaborative autoencoders to jointly identify normal observations from the data while learning its feature representation. We investigate the theoretical properties of the framework and empirically show its outstanding performance as compared to other DNN-based methods. Empirical results also show resiliency of the framework to missing values compared to other baseline methods.

Stochastic convex sparse principal component analysis.

Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data. However, there is an obvious disadvantage of traditional PCA when it is applied to analyze data where interpretability is important. In applications, where the features have some physical meanings, we lose the ability to interpret the principal components extracted by conventional PCA because each principal component is a linear combination of all the original features. For this reason, sparse PCA has been proposed to improve the interpretability of traditional PCA by introducing sparsity to the loading vectors of principal components. The sparse PCA can be formulated as an ℓ1 regularized optimization problem, which can be solved by proximal gradient methods. However, these methods do not scale well because computation of the exact gradient is generally required at each iteration. Stochastic gradient framework addresses this challenge by computing an expected gradient at each iteration. Nevertheless, stochastic approaches typically have low convergence rates due to the high variance. In this paper, we propose a convex sparse principal component analysis (Cvx-SPCA), which leverages a proximal variance reduced stochastic scheme to achieve a geometric convergence rate. We further show that the convergence analysis can be significantly simplified by using a weak condition which allows a broader class of objectives to be applied. The efficiency and effectiveness of the proposed method are demonstrated on a large-scale electronic medical record cohort.

Alkaline quinone flow battery.

Storage of photovoltaic and wind electricity in batteries could solve the mismatch problem between the intermittent supply of these renewable resources and variable demand. Flow batteries permit more economical long-duration discharge than solid-electrode batteries by using liquid electrolytes stored outside of the battery. We report an alkaline flow battery based on redox-active organic molecules that are composed entirely of Earth-abundant elements and are nontoxic, nonflammable, and safe for use in residential and commercial environments. The battery operates efficiently with high power density near room temperature. These results demonstrate the stability and performance of redox-active organic molecules in alkaline flow batteries, potentially enabling cost-effective stationary storage of renewable energy.

Extended local resection for treatment of periampullary carcinoma of vater.

BACKGROUND: The trauma caused by pancreatoduodenectomy for periampullary carcinoma of vater is often severe and extensive. The purpose of this study was to evaluate the effect of extended local resection in the treatment of periampullary carcinoma of vater. METHODS: The extra-hepaticobiliary tract, the confluence of the pancreatic and biliary duct, vater ampulla and duodenal papilla were resected en bloc in 8 patients with periampullary carcinoma from 1995 to 1998. RESULTS: One patient died perioperatively. Duodenal obstruction developed postoperatively in one of 7 survived patients and was relieved after reoperation. All the 7 patients were followed up for more than 6 months without recurrence. CONCLUSION: Extended local resection fulfils the task of radical treatment of periampullary malignancy.