Packaging and containerization of computational methods.

Methods for analyzing the full complement of a biomolecule type, e.g., proteomics or metabolomics, generate large amounts of complex data. The software tools used to analyze omics data have reshaped the landscape of modern biology and become an essential component of biomedical research. These tools are themselves quite complex and often require the installation of other supporting software, libraries and/or databases. A researcher may also be using multiple different tools that require different versions of the same supporting materials. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging and containerization are different approaches to satisfy this need by delivering omics tools already wrapped in additional software that makes the tools easier to install and use. In this systematic review, we describe and compare the features of prominent packaging and containerization platforms. We outline the challenges, advantages and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers and system administrators. We also propose principles to make the distribution of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research.

Relative Motion Estimation Algorithm for Noncooperative Targets Considering Multiple Solutions of Rotational Parameters.

On-orbit servicing using a space robot is gaining popularity among the space community for both economic and safety aspects. In particular, the estimation of the relative motion of a noncooperative target is a challenging problem. This study presents a relative motion estimation scheme based on stereovision for noncooperative targets considering multiple solutions of rotational parameters. Specifically, the mass distribution of the target is identified based on the least-square method and the principle of conservation of angular momentum. Then, the determination of a unique principal axis coordinate frame of the target is employed to resolve the multiple-solution problem. In addition, an EKF (extended Kalman filter)-based filter with global observability is designed to estimate the full motion states and inertia parameters of the target. The convergence performance of the proposed method is verified by numerical simulation. The results also demonstrate that the method is robust to occlusion.

Research Progress of Single-Crystal Nickel-Rich Cathode Materials for Lithium Ion Batteries.

Single-crystal nickel-rich cathode materials (SC-NRCMs) are the most promising candidates for next-generation power batteries which enable longer driving range and reliable safety. In this review, the outstanding advantages of SC-NRCMs are discussed systematically in aspects of structural and thermal stabilities. Particularly, the intergranular-crack-free morphology exhibits superior cycling performance and negligible parasitic reactions even under severe conditions. Besides, various synthetic methods are summarized and the relation between precursor, sintering process, and final single-crystal products are revealed, providing a full view of synthetic methods. Then, challenges of SC-NRCMs in fields of kinetics of lithium diffusion and the one particularly occurred at high voltage (intragranular cracks and aggravated parasitic reactions) are discussed. The corresponding mechanism and modifications are also referred. Through this review, it is aimed to highlight the magical morphology of SC-NRCMs for application perspective and provide a reference for following researchers.

Addressing bias in big data and AI for health care: A call for open science.

Artificial intelligence (AI) has an astonishing potential in assisting clinical decision making and revolutionizing the field of health care. A major open challenge that AI will need to address before its integration in the clinical routine is that of algorithmic bias. Most AI algorithms need big datasets to learn from, but several groups of the human population have a long history of being absent or misrepresented in existing biomedical datasets. If the training data is misrepresentative of the population variability, AI is prone to reinforcing bias, which can lead to fatal outcomes, misdiagnoses, and lack of generalization. Here, we describe the challenges in rendering AI algorithms fairer, and we propose concrete steps for addressing bias using tools from the field of open science.

A Renewable Sedimentary Slurry Battery: Preliminary Study in Zinc Electrodes.

Low-cost, scalable energy storage is the key to continuing growth of renewable energy technologies. Here a battery with sedimentary slurry electrode (SSE) is proposed. Through the conversion of discrete particles between sedimentary and suspending types, it not only inherits the advantages of semi-solid flow cell but also exhibits high energy density and stable conductive network. Given an example, the zinc SSE (ZSSE) delivers a large discharge capacity of 479.2 mAh g-1 at 10 mA cm-2. More importantly, by renewal of the slurry per 20 cycles, it can run for 112 and 75 cycles before falling below 80% of designed capacity under 10 mA cm-2 (20% DODZn) and 25 mA cm-2 (25% DODZn), respectively. The lost capacity after cycles is able to recover after slurry renewal and the end-of-life SSE can be easily reused by re-formation. The concept of SSE brands a new way for electrochemical energy storage.

Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens.

Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60-70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/).