Optically stimulated luminescence dating of sandy deposits from Gulang county at the southern margin of the Tengger Desert, China

Aeolian deposits from the deserts in northern China have been used for palaeoenvironmental research to understand aeolian sedimentology and its dynamic connection to past climate conditions. The Tengger Desert in China is sensitive to the waxing and waning of the monsoonal system. In response to past climate change, the southern margin of the Tengger Desert has evolved significantly since the last glacial period. However, previous attempts to date aeolian deposits in this region were mainly based on radiocarbon dating, which has problems when applied to aeolian deposits. Moreover, sedimentary records are limited. Accordingly, past aeolian activity in this desert remains poorly understood. In the present study, we dated sand samples from Gulang county at the southern margin of the Tengger Desert using optically stimulated luminescence (OSL) to understand the history of aeolian activity in this region. Our samples represented well-sorted aeolian sands and sandy loess. Aeolian sands are evidence of dune field buildup and sparse vegetation cover whereas sandy loess is evidence of improved stabilization of sand dunes resulting from ameliorated vegetation cover. Certain samples showed a decline in the equivalent dose (D e ) values when successive integration intervals were applied, which resulted from unstable OSL signals from non-fast components in the initial part of the decay curve. In order to obtain reliable D e estimates, we investigated component-resolved and different background subtraction approaches, and compared the resultant D e estimates. We adopted the early background subtraction method to derive D e values. Luminescence chronologies and sedimentary records indicated that sand dunes accumulation occurred before 10 ka, and sandy loess developed between 9.5 and 7.6 ka when sand dunes were stabilized as a result of increased effective moisture levels. The transition between sand dune mobilization and stabilization emphasizes the significance of an effective moisture threshold in controlling aeolian activity. Mobilization of sand dunes at ~2.3 ka might be related to an increased aridity during the Late Holocene.

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation,followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and,more recently,by incorporation of molecular marker genotypes.However,plant performance or phenotype(P)is determined by the combined effects of genotype(G),envirotype(E),and genotype by environment interaction(GEI).Phenotypes can be predicted more precisely by training a model using data collected from multiple sources,including spatiotemporal omics(genomics,phenomics,and enviromics across time and space).Integration of 3D information profiles(G-P-E),each with multidimensionality,provides predictive breeding with both tremendous opportunities and great challenges.Here,we first review innovative technologies for predictive breeding.We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy,particularly envirotypic data,which have largely been neglected in data collection and are nearly untouched in model construction.We propose a smart breeding scheme,integrated genomic-enviromic prediction(iGEP),as an extension of genomic prediction,using integrated multiomics information,big data technology,and artificial intelligence(mainly focused on machine and deep learning).We discuss how to implement iGEP,including spatiotemporal models,environmental indices,factorial and spatiotemporal structure of plant breeding data,and cross-species prediction.A strategy is then proposed for prediction-based crop redesign at both the macro(individual,population,and species)and micro(gene,metabolism,and network)scales.Finally,we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives.We call for coordinated efforts in smart breeding through iGEP,institutional partnerships,and innovative technological support.

Different Survival Benefits of Chinese Medicine for Pancreatic Cancer:How to Choose?

Objective：To assess the efficacy of Chinese medicine（CM） on patients with pancreatic cancer（PC） in a retrospective population-based study.Methods：Between January 1,2013,and August 30,2016,according to whether received Western medicine treatment,the patients were included into either integrative medicine（IM） group or CM group.All enrolled patients were orally administrated with Gexia Zhuyu Decoction（膈下逐瘀汤） or Liujun Ermu Decoction（六君二母汤） by syndrome differentiation,twice a day,last for at least 2 months.The primary end point was overall survival（OS）.Results：A total of 174 patients with PC were enrolled in this study.In stage Ⅰ/Ⅱ,the median OS was 20.5 months in the IM group [95% confidence interval（CI）,12.499 to 28.501] and 11.17 months in the CM group（95% CI,5.160 to 17.180,P=0.015）.The 1-and 2-year survival rates for the two groups were 47.0%,40.0% and 21.0%,21.0%,respectively.In stage Ⅲ/Ⅳ,median OS was 13.53 months（95% CI,8.665 to 18.395） in the IM group versus 6.4 months（95% CI,0.00 to 15.682） in the CM group,respectively（P=0.32）.The 1-and 2-year survival rate for the IM and CM groups were 27.0%,7.0% and 20.0%,2.0%,respectively.Conclusions：Intervention of CM contributes to the different survival benefits for PC in different stages.Multimodality treatment might be a promising strategy for PC patients in early stage.While,in advanced stage,CM might be an alternative candidate for PC patients.