The RING-H2 gene LdXERICO plays a negative role in dormancy release regulated by low temperature in Lilium davidii var. unicolor

Dormancy regulation is the basis of the sustainable development of the lily industry.Therefore,basic research on lily dormancy is crucial for innovation in lily cultivation and breeding.Previous studies revealed that dormancy release largely depends on abscisic acid(ABA)degradation.However,the key genes and potential regulatory network remain unclear.We used exogenous ABA and ABA inhibitors to elucidate the effect of ABA on lily dormancy.Based on the results of weighted gene coexpression network analysis(WGCNA),the hub gene LdXERICO was identified in modules highly related to endogenous ABA,and a large number of coexpressed genes were identified.LdXERICO was induced by exogenous ABA and expressed at higher levels in tissues with vigorous physiological activity.Silencing LdXERICO increased the low-temperature sensitivity of bulblets and accelerated bulblet sprouting.LdXERICO rescued the ABA insensitivity of xerico mutants during seed germination in Arabidopsis,suggesting that it promotes seed dormancy and supporting overexpression studies on lily bulblets.The significant increase in ABA levels in transgenic Arabidopsis expressing LdXERICO indicated that LdXERICO played a role by promoting ABA synthesis.We generated three transgenic lines by overexpressing LdICE1 in Arabidopsis thaliana and showed that,in contrast to LdXERICO,LdICE1 positively regulated dormancy release.Finally,qRT-PCR confirmed that LdXERICO was epistatic to LdICE1 for dormancy release.We propose that LdXERICO,an essential gene in dormancy regulation through the ABA-related pathway,has a complex regulatory network involving temperature signals.This study provides a theoretical basis for further exploring the mechanism of bulb dormancy release.

Design of high-performance and sustainable Co-free Ni-rich cathodes for next-generation lithium-ion batteries

Great attention has been given to high-performance and inexpensive lithiumion batteries(LIBs)in response to the ever-increasing demand for the explosive growth of electric vehicles(EVs).High-performance and low-cost Co-freeNi-rich layered cathodes are considered one of the most favorable candidates for nextgeneration LIBs because the current supply chain of EVs relies heavily on scarce and expensive Co.Herein,we review the recent research progress on Co-free Nirich layered cathodes,emphasizing on analyzing the necessity of replacing Co and the popular improvment methods.The current advancements in the design strategies of Co-free Ni-rich layered cathodes are summarized in detail.Despite considerable improvements achieved so far,the main technical challenges contributing to the deterioration of Co-free Ni-rich cathodes such as detrimental phase transitions,crack formation,and severe interfacial side reactions,are difficult to resolve by a single technique.The cooperation of multiple modification strategies is expected to accelerate the industrialization of Co-free Ni-rich layered cathodes,and the corresponding synergistic mechanisms urgently need to be studied.More effects will be aroused to explore high-performance Co-free Ni-rich layered cathodes to promote the sustainable development of LIBs.

SemiSynBio:A new era for neuromorphic computing

Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence(AI)systems,due to its advantages of adaptive learning and parallel computing.Meanwhile,biocomputing has seen ongoing development with the rise of synthetic biology,becoming the driving force for new generation semiconductor synthetic biology(SemiSynBio)technologies.DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks(ANNs),providing the possibility of executing neuromorphic computing at the molecular level.Herein,we briefly outline the principles of neuromorphic computing,describe the advances in DNA computing with a focus on synthetic neuromorphic computing,and summarize the major challenges and prospects for synthetic neuromorphic computing.We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing,which would be of widespread use in biocomputing,DNA storage,information security,and national defense.

Using amino acid features to identify the pathogenicity of influenza B virus

Background:Influenza B virus can cause epidemics with high pathogenicity, so it poses a serious threat to public health. A feature representation algorithm is proposed in this paper to identify the pathogenicity phenotype of influenza B virus.Methods:The dataset included all 11 influenza virus proteins encoded in eight genome segments of 1724 strains. Two types of features were hierarchically used to build the prediction model. Amino acid features were directly delivered from 67 feature descriptors and input into the random forest classifier to output informative features about the class label and probabilistic prediction. The sequential forward search strategy was used to optimize the informative features. The final features for each strain had low dimensions and included knowledge from different perspectives, which were used to build the machine learning model for pathogenicity identification.Results:The 40 signature positions were achieved by entropy screening. Mutations at position 135 of the hemagglutinin protein had the highest entropy value (1.06). After the informative features were directly generated from the 67 random forest models, the dimensions for class and probabilistic features were optimized as 4 and 3, respectively. The optimal class features had a maximum accuracy of 94.2% and a maximum Matthews correlation coefficient of 88.4%, while the optimal probabilistic features had a maximum accuracy of 94.1% and a maximum Matthews correlation coefficient of 88.2%. The optimized features outperformed the original informative features and amino acid features from individual descriptors. The sequential forward search strategy had better performance than the classical ensemble method.Conclusions:The optimized informative features had the best performance and were used to build a predictive model so as to identify the phenotype of influenza B virus with high pathogenicity and provide early risk warning for disease control.