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.

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.

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.

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.

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.

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.