SUMMARY Maize (Zea mays ssp. mays) was domesticated in southwestern Mexico ~9,000 years ago from its wild ancestor, teosinte (Zea mays ssp. parviglumis) [1]. From its centre of origin, maize experienced a rapid range expansion and spread over 90°of latitude in the Americas [2–4] which required a novel flowering time adaptation. ZEA CENTRORADIALIS 8 (ZCN8) is the maize florigen gene and has a central role in mediating flowering [5, 6]. Here, we show that ZCN8 underlies a major quantitative trait locus (qDTA8) for flowering time that was consistently detected in multiple maize-teosinte experimental populations. Through association analysis in a large diverse panel of maize inbred lines, we identified a single nucleotide polymorphism (SNP-1245) in the ZCN8 promoter that showed the strongest association with flowering time. SNP-1245 co-segregated with qDTA8 in maize-teosinte mapping populations. We demonstrate that SNP-1245 is associated with differential binding by the flowering activator ZmMADS1. SNP-1245 was a target of selection during early domestication which drove the pre-existing early-flowering allele to near fixation in maize. Interestingly, we detected an independent association block upstream of SNP-1245, wherein the early-flowering allele that most likely originated from Zea mays ssp. mexicana introgressed into the early-flowering haplotype of SNP-1245 and contributed to maize adaptation to northern high latitudes. Our study demonstrates how independent cis-regulatory variants at a gene can be selected at different evolutionary times for local adaptation, highlighting how complex cis-regulatory control mechanisms evolve. Finally, we propose a polygenic map for the pre-Columbian spread of maize throughout the Americas.

No abstract

Electronic tongue (E-Tongue), as a novel taste analysis tool, shows a promising perspective for taste recognition. In this paper, we constructed a voltammetric E-Tongue system and measured 13 different kinds of liquid samples, such as tea, wine, beverage, functional materials, etc. Owing to the noise of system and a variety of environmental conditions, the acquired E-Tongue data shows inseparable patterns. To this end, from the viewpoint of algorithm, we propose a local discriminant preservation projection (LDPP) model, an under-studied subspace learning algorithm, that concerns the local discrimination and neighborhood structure preservation. In contrast with other conventional subspace projection methods, LDPP has two merits. On one hand, with local discrimination it has a higher tolerance to abnormal data or outliers. On the other hand, it can project the data to a more separable space with local structure preservation. Further, support vector machine, extreme learning machine (ELM), and kernelized ELM (KELM) have been used as classifiers for taste recognition in E-Tongue. Experimental results demonstrate that the proposed E-Tongue is effective for multiple tastes recognition in both efficiency and effectiveness. Particularly, the proposed LDPP-based KELM classifier model achieves the best taste recognition performance of 98%. The developed benchmark data sets and codes will be released and downloaded in http://www.leizhang.tk/tempcode.html.

Extracellular vesicles (EVs) play major roles in intracellular communication and participate in several biological functions in both normal and pathological conditions. Surface modification of EVs via various ligands, such as proteins, peptides, or aptamers, offers great potential as a means to achieve targeted delivery of therapeutic cargo, i.e., in drug delivery systems (DDS). This review summarizes recent studies pertaining to the development of EV-based DDS and its advantages compared to conventional nano drug delivery systems (NDDS). First, we compare liposomes and exosomes in terms of their distinct benefits in DDS. Second, we analyze what to consider for achieving better isolation, yield, and characterization of EVs for DDS. Third, we summarize different methods for the modification of surface of EVs, followed by discussion about different origins of EVs and their role in developing DDS. Next, several major methods for encapsulating therapeutic cargos in EVs have been summarized. Finally, we discuss key challenges and pose important open questions which warrant further investigation to develop more effective EV-based DDS.

No abstract