Construction of a Multipurpose M13KE Phage Display System

In this study, a multipurpose M13KE phage display vector was constructed from wild-type M13KE phage for long peptide or protein display libraries without helper phage to expand the scope of targeted high-throughput screening. Based on the relationship between the structure and function of minor coat protein of wild-type MI3KE （wt-plII）, a truncated gene III （tglll） encoding minor coat protein from M13KE phage was cloned. A fusion gene fragment harboring a hw/tac promoter, signal peptide and C-terminal region sequence of gill was assembled with SOEing-PCR （splice-overlapping-extension polymerase chain reaction） method and inserted into M13KE vector. SDS-PAGE and Western blot analysis with anti-M13 pIII moneclonal antibody were employed to detect the expression of re- combinant protein, c-Myc and HA tag sequences were fused into the recombinant protein. The results showed that tglll was inserted into an unessential region of M13KE. According to the results of SDS-PAGE and Western blot with anti-M13 pIII antibody, pIII was expressed by wt-gIII and tgIII, glII harboring two tags ex- pressed both c-Myc and HA peptides using SDS-PAGE and Western blot with the corresponding monoclonal antibodies. In this study, a multipurpose M13KE phage display system was successfully constructed, which could express both short and long peptide libraries without helper phage. In future, the obtained M13KE phage display system may be used for targeted high-throughput screening of long peptide libraries without helper phage.

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

Crop type data are an important piece of information for many applications in agriculture.Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions.In this research,we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device(CCD)data.We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a longterm time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution.To increase accuracy,four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images.These phenological metrics were used to further identify each of the crop types with less,but easier to access,ancillary field survey data.We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment.The results show that our classification accuracy was 92%when compared with the highly accurate but limited ZY-3 images and matched up to 80%to the statistical crop areas.