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For millions of years, plants evolve plenty of structurally diverse secondary metabolites (SM) to support their sessile lifestyles through continuous biochemical pathway innovation. While new genes commonly drive the evolution of plant SM pathway, how a full biosynthetic pathway evolves remains poorly understood. The evolution of pathway involves recruiting new genes along the reaction cascade forwardly, backwardly, or in a patchwork manner. With three chromosome-scale Papaver genome assemblies, we here reveal whole-genome duplications (WGDs) apparently accelerate chromosomal rearrangements with a nonrandom distribution towards SM optimization. A burst of structural variants involving fusions, translocations and duplications within 7.7 million years have assembled nine genes into the benzylisoquinoline alkaloids gene cluster, following a punctuated patchwork model. Biosynthetic gene copies and their total expression matter to morphinan production. Our results demonstrate how new genes have been recruited from a WGD-induced repertoire of unregulated enzymes with promiscuous reactivities to innovate efficient metabolic pathways with spatiotemporal constraint.

Microsatellite instability (MSI) is a key biomarker for cancer therapy and prognosis. Traditional experimental assays are laborious and time-consuming, and next-generation sequencing-based computational methods do not work on leukemia samples, paraffin-embedded samples, or patient-derived xenografts/organoids, due to the requirement of matched normal samples. Herein, we developed MSIsensor-pro, an open-source single sample MSI scoring method for research and clinical applications. MSIsensor-pro introduces a multinomial distribution model to quantify polymerase slippages for each tumor sample and a discriminative site selection method to enable MSI detection without matched normal samples. We demonstrate that MSIsensor-pro is an ultrafast, accurate, and robust MSI calling method. Using samples with various sequencing depths and tumor purities, MSIsensor-pro significantly outperformed the current leading methods in both accuracy and computational cost. MSIsensor-pro is available at https://github.com/xjtu-omics/msisensor-pro and free for non-commercial use, while a commercial license is provided upon request.

AgSbSe2 possesses extremely low thermal conductivity and high Seebeck coefficient, but the low electronic conductivity leads to a low ZT value. In this paper, Na is used to substitute Sb to improve the electronic conductivity. The results show that Na doping not only improves the power factor caused by the enhanced carrier concentration, but also decreases the thermal conductivity due to point defects, nanoscale stacking faults and Na-rich precipitate. Consequently, a high ZT value of 0.92 is achieved in the AgSb0.99Na0.01Se2 sample.

SnS2 nanosheets with unique properties are excellent candidate materials for fabricating high-performance NO2 gas sensors. However, serious restacking and aggregation during sensor fabrication have greatly impacted the sensing response. In this study, flower-like hierarchical SnS2 was prepared by a simple microwave method and partially thermally oxidized to form hierarchical SnS2/SnO2 nanocomposites to further improve the sensing performance at low operating temperature. The fabricated SnS2/SnO2 sensor exhibited ultrahigh response (resistance ratio = 51.1) toward 1 ppm NO2 at 100 °C, roughly 10.2 times higher than that of pure SnS2 nanoflowers. The excellent and enhanced NO2 sensing performances of hierarchical SnS2/SnO2 nanocomposites were attributed to the novel hierarchical structure of SnS2 and the nanoheterojunction between SnS2 and the ultrafine SnO2 nanoparticles. The SnS2/SnO2 sensors also exhibited excellent selectivity and reliable repeatability. The simple fabrication of high performance sensing materials may facilitate the large-scale production of NO2 gas sensors.

This paper gives a comprehensive review of the application of meta-heuristics to optimization problems in systems biology, mainly focussing on the parameter estimation problem (also called the inverse problem or model calibration). It is intended for either the system biologist who wishes to learn more about the various optimization techniques available and/or the meta-heuristic optimizer who is interested in applying such techniques to problems in systems biology. First, the parameter estimation problems emerging from different areas of systems biology are described from the point of view of machine learning. Brief descriptions of various meta-heuristics developed for these problems follow, along with outlines of their advantages and disadvantages. Several important issues in applying meta-heuristics to the systems biology modelling problem are addressed, including the reliability and identifiability of model parameters, optimal design of experiments, and so on. Finally, we highlight some possible future research directions in this field.

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