Rice molecular markers and genetic mapping:Current status and prospects

Dramatic changes in climatic conditions that supplement the biotic and abiotic stresses pose severe threat to the sustainable rice production and have made it a difficult task for rice molecular breeders to enhance production and productivity under these stress factors. The main focus of rice molecular breeders is to understand the fundamentals of molecular pathways involved in complex agronomic traits to increase the yield. The availability of complete rice genome sequence and recent improvements in rice genomics research has made it possible to detect and map accurately a large number of genes by using linkage to DNA markers. Linkage mapping is an effective approach to identify the genetic markers which are co-segregating with target traits within the family. The ideas of genetic diversity, quantitative trait locus（QTL） mapping, and marker-assisted selection（MAS） are evolving into more efficient concepts of linkage disequilibrium（LD） also called association mapping and genomic selection（GS）, respectively. The use of cost-effective DNA markers derived from the fine mapped position of the genes for important agronomic traits will provide opportunities for breeders to develop high-yielding, stress-resistant, and better quality rice cultivars. Here we focus on the progress of molecular marker technologies, their application in genetic mapping and evolution of association mapping techniques in rice.

Simultaneous Determination of Five Components in Operculina turpethum

[Objectives]This study was conducted to establish a method for the simultaneous determination of caffeic acid, rutin, ononin, luteolin, and apigenin in Operculina turpethum(L.) S. Manso. [Methods]With ononin from O. turpethum as the internal reference, the five components were separated by HPLC, and the contents of various components were calculated according to the relative correction factors of ononin with caffeic acid, rutin, luteolin, and apigenin. Meanwhile, the calculated results of quantitative analysis of multi-components by single marker(QAMS) were compared with the determined values of the external standard method. [Results] The linear relationship of the five components in their respective ranges was good(r=0.999 9). The average recovery was in the range of 97.48%-101.05%, and the RSD values were in the range of 1.04%-2.71%. The results obtained by QAMS were close to those obtained by the external standard method. [Conclusions] The method is accurate, stable and adaptable, and can be used for the determination of five flavonoids in O. turpethum.

Ethyl-iophenoxic acid as a quantitative bait marker for small mammals

Bait markers are indispensable for ecological research but in small mammals,most markers are invasive,expensive and do not enable quantitative analyses of consumption.Ethyl-iophenoxic acid(Et-IPA)is a non-toxic,quantitative bait marker,which has been used for studying bait uptake in several carnivores and ungulates.We developed a bait with Et-IPA,assessed its palatability to common voles(Microtus arvalis),and determined the dose-residue-relation for this important agricultural pest rodent species.Et-IPA concentrations of 40 to 1280μg Et-IPA per g bait were applied to wheat using sunflower oil or polyethylene glycol 300 as potential carriers.In a laboratory study,common voles were offered the bait and blood samples were collected 1,7,and 14 days after consumption.The samples were analyzed with LC-ESI-MS/MS for blood residues of Et-IPA.Sunflower-oil was the most suitable bait carrier.Et-IPA seemed to be palatable to common voles at all test concentrations.Dose-dependent residues could be detected in blood samples in a dose-dependent manner and up to 14 days after uptake enabling generation of a calibration curve of the dose-residue relationship.Et-IPA was present in common vole blood for at least 14 days,but there was dissipation by 33–37%depending on dose.Et-IPA meets many criteria for an“ideal”quantitative bait marker for use in futurefield studies on common voles and possibly other small mammal species.

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

Comprehensive Quality Evaluation of ShuXueNing Injection Employing Quantitative High-Performance Liquid Chromatography Fingerprint and Chemometrics

Objective:In this study,a comprehensive and effective quality method for evaluating the efficacy of ShuXueNing injection(SXNI)was developed.Materials and Methods:Quantitative high-performance liquid chromatography fingerprint,the quantitative analysis of multicomponents by a single marker(QAMS)method,hierarchical cluster analysis(HCA),and orthogonal partial least squares discrimination analysis(OPLS-DA)were used to distinguish 53 batches of SXNI samples from 7 manufacturers.Results:A total of 53 batches of samples were analyzed to establish antithesis fingerprint of SXNI,and 12 peaks of the common model were collected and used for the similarity analysis.Meanwhile,six index flavonoid components were determined by the QAMS method,using rutin as internal reference substance.The accuracy of the QAMS method was confirmed by investigating the relative deviation between the QAMS method and the traditional external standard method.The results demonstrated that there was no significant difference(RE<1%),suggesting that QAMS was a reliable and convenient method for the content determination of multiple components.The HCA and OPLS-DA methods drew a similar conclusion.The 53 batches of SXNI samples from 7 manufacturers were categorized into five groups,indicating that chemometrics could reveal the quality differences of SXNI between the manufacturers.Conclusions:The method established herein was efficient and successful in assessing the quality of SXNI,and that it may be potentially employed in the quality control of related products composed of Ginkgo biloba extract.