Adaptive Space Search-based Molecular Evolution Optimization Algorithm.

MOTIVATION: In drug development process, a significant portion of budget and research time are dedicated to the lead compound optimization procedure in order to identify potential drugs. This procedure focuses on enhancing the pharmacological and bioactive properties of compounds by optimizing their local substructures. However, due to the vast and discrete chemical structure space and the unpredictable element combinations within this space, the optimization process is inherently complex. Various structure enumeration-based combinatorial optimization methods have shown certain advantages. However, they still have limitations. Those methods fail to consider the differences between molecules and struggle to explore the unknown outer search space. RESULTS: In this study, we propose an adaptive space search-based molecular evolution optimization algorithm (ASSMOEA). It consists of three key modules: construction of molecule-specific search space, molecular evolutionary optimization, and adaptive expansion of molecule-specific search space. Specifically, we design a fragment similarity tree in molecule-specific search space, and apply a dynamic mutation strategy in this space to guide molecular optimization. Then we utilize an encoder-encoder structure to adaptively expand the space. Those three modules are circled iteratively to optimize molecules. Our experiments demonstrate that ASSMOEA outperforms existing methods in terms of molecular optimization. It not only enhances the efficiency of the molecular optimization process, but also exhibits a robust ability to search for correct solutions. AVAILABILITY AND IMPLEMENTATION: The code is freely available on the web at https://github.com/bbbbb-b/MEOAFST. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Guest-Induced Reversible Single-Crystal-to-Single-Crystal Transformation Involving Displacement of 2D Layers and Spin Crossover Behavior Change in a Hofmann-Type Coordination Polymer.

A novel two-dimensional (2D) Hofmann-type coordination polymer, {FeII(PyHbim)2[Pd(CN)4]}·2CH3OH [1·2CH3OH, PyHbim = 2-(4-pyridyl)benzimidazole], has been synthesized, which can undergo a spontaneous guest exchange, transforming to 1·2H2O in a single-crystal-to-single-crystal (SCSC) manner, shifting from orthorhombic Cmmm to monoclinic C2/m involving the displacement of 2D layers. The solvent-induced SCSC transformation process was reversible and verified through powder X-ray diffraction (PXRD) and single-crystal X-ray crystallography analyses. Both 1·2CH3OH and 1·2H2O exhibit complete and abrupt spin crossover (SCO) behaviors in two steps, while their SCO temperature ranges drastically shift by ca.100 K, spanning room temperature, owing to different intermolecular interactions resulting from diverse interlayer packing manners and host-guest interactions. Besides, a structural phase transition is observed in 1·2CH3OH, contributing to the two-step spin transition.

A Multi-Task Road Feature Extraction Network with Grouped Convolution and Attention Mechanisms.

To cope with the challenges of autonomous driving in complex road environments, the need for collaborative multi-tasking has been proposed. This research direction explores new solutions at the application level and has become a hot topic of great interest. In the field of natural language processing and recommendation algorithms, the use of multi-task learning networks has been proven to reduce time, computing power, and storage usage in various task coupling cases. Due to the characteristics of the multi-task learning network, it has also been applied to visual road feature extraction in recent years. This article proposes a multi-task road feature extraction network that combines group convolution with transformer and squeeze excitation attention mechanisms. The network can simultaneously perform drivable area segmentation, lane line segmentation, and traffic object detection tasks. The experimental results of the BDD-100K dataset show that the proposed method performs well for different tasks and has a higher accuracy than similar algorithms. The proposed method provides new ideas and methods for the autonomous road perception of vehicles and the generation of highly accurate maps in visual-based autonomous driving processes.

Co-cultivation of Chlamydomonas reinhardtii with Azotobacter chroococcum improved H2 production.

OBJECTIVES: To improve H2 production, the green algae Chlamydomonas reinhardtii cc849 was co-cultured with Azotobacter chroococcum. RESULTS: The maximum H2 production of the co-culture was 350% greater than that of the pure algal cultures under optimal H2 production conditions. The maximum growth and the respiratory rate of the co-cultures were about 320 and 300% of the controls, and the dissolved O2 of co-cultures was decreased 74%. Furthermore, the in vitro maximum hydrogenase activity of the co-culture was 250% greater than that of the control, and the in vivo maximum hydrogenase activity of the co-culture was 1.4-fold greater than that of the control. In addition, the maximum starch content of co-culture was 1400% that of the control. CONCLUSIONS: Azotobacter chroococcum improved the H2 production of the co-cultures by decreasing the O2 content and increasing the growth and starch content of the algae and the hydrogenase activity of the co-cultures relative to those of pure algal cultures.

Synthesis of MnO2-CuO-Fe2O3/CNTs catalysts: low-temperature SCR activity and formation mechanism.

MnO2-CuO-Fe2O3/CNTs catalysts, as a low-dimensional material, were fabricated by a mild redox strategy and used in denitration reactions. A formation mechanism of the catalysts was proposed. NO conversions of 4% MnO2-CuO-Fe2O3/CNTs catalyst of 43.1-87.9% at 80-180 °C were achieved, which was ascribed to the generation of amorphous MnO2, CuO and Fe2O3, and a high surface-oxygen (Os) content.

Enhanced Lipid Production in Chlamydomonas reinhardtii by Co-culturing With Azotobacter chroococcum.

The green algae, Chlamydomonas reinhardtii, is one of the model species used to study lipid production, although research has focused on nitrogen-deficient cultures, that inhibit the development of biomass by C. reinhardtii and limit lipid production. In this study, Azotobacter chroococcum was added to the algal culture to improve lipid accumulation and productivity of C. reinhardtii. The maximum lipid content and production of C. reinhardtii in the co-culture were 65.85% and 387.76 mg/L, respectively, which were 2.3 and 5.9 times the control's levels of 29.11% and 65.99 mg/L, respectively. The maximum lipid productivity of C. reinhardtii in the co-culture was 141.86 mg/(L·day), which was 19.4 times the control's levels of 7.33 mg/(L·day). These increases were attributed to the enhanced growth and biomass and the change in the activity of enzymes related to lipid regulation (ACCase, DGAT, and PDAT). Compared to the conventional strategy of nitrogen deprivation, A. chroococcum added to the culture of C. reinhardtii resulted in higher lipid accumulation and activity, greater efficiency in the conversion of proteins to lipids, higher biomass, and increased growth of C. reinhardtii. Therefore, using A. chroococcum to improve the growth and biomass of C. reinhardtii is an efficient, rapid, and economically viable strategy for enhancing lipid production in C. reinhardtii.