Tourism efficiency of ten cities in Shaanxi (2018).

Tourism efficiency of ten cities in Shaanxi (2018).</p

BCC evaluation of ten cities in Shaanxi (2018).

Exploring the of regional tourism efficiency is of great significance in promoting high-quality development of regional tourism. However, there are not many studies that measure the quality development of tourism destinations from the perspective of inputs and output. Based on this, the data envelopment analysis model is used to measure the overall technical efficiency (TECRS), pure technical efficiency (TEVRS), and scale efficiency (SE) with the help of DEA-SOLVER software, taking the ten prefecture-level cities in Shaanxi Province as examples, to further analyze and evaluate the spatial differences of different tourism destinations and the reasons for the differences. The results of the study found that: the efficiency indicators explain the differences in the development quality of tourism destinations from different sides; the development quality of tourism destinations in Shaanxi as a whole is low, with excessive inputs and insufficient outputs; and the tourism destinations with relatively high development quality are distributed in the Guanzhong. On this basis, corresponding countermeasure suggestions are put forward to promote the improvement of governance efficiency of tourism destinations in Shaanxi Province, and then optimize the quality of development.</div

CCR evaluation of ten cities in Shaanxi (2018).

Exploring the of regional tourism efficiency is of great significance in promoting high-quality development of regional tourism. However, there are not many studies that measure the quality development of tourism destinations from the perspective of inputs and output. Based on this, the data envelopment analysis model is used to measure the overall technical efficiency (TECRS), pure technical efficiency (TEVRS), and scale efficiency (SE) with the help of DEA-SOLVER software, taking the ten prefecture-level cities in Shaanxi Province as examples, to further analyze and evaluate the spatial differences of different tourism destinations and the reasons for the differences. The results of the study found that: the efficiency indicators explain the differences in the development quality of tourism destinations from different sides; the development quality of tourism destinations in Shaanxi as a whole is low, with excessive inputs and insufficient outputs; and the tourism destinations with relatively high development quality are distributed in the Guanzhong. On this basis, corresponding countermeasure suggestions are put forward to promote the improvement of governance efficiency of tourism destinations in Shaanxi Province, and then optimize the quality of development.</div

Supporting information includes statistical yearbooks and models constructed on the basis of statistical yearbook data.

Supporting information includes statistical yearbooks and models constructed on the basis of statistical yearbook data.</p

Comparison of TE_CRS, TE_VRS, and SE of ten cities in Shaanxi (2018).

Comparison of TECRS, TEVRS, and SE of ten cities in Shaanxi (2018).</p

Transfection of HGB cells with the vector pAnti IGF-1.

<p><b>A,</b> Physical map of pAnti IGF-1. MT-1: metallothionein - 1 promotor; IGF-1 3′-5′: human IGF-1 DNA sequence in antisense orientation; SV40 poly A: SV40 poly A termination sequence; Ori: origin of replication; Hyg R: hygromycin resistance gene; Amp R: Ampicillin resistance gene; EBNA-1: Epstein Barr Virus (EBV) encoded nuclear antigen 1; EBV ori-P: EBV origin of replication. <b>B,</b> Expression of IGF-1 cDNA in transfected HGB cells. Primer pairs for RT-PCR used to detect and amplify antisense IGF-1 cDNA are as designated in methods. This set of primers gives rise to a 424 bp cDNA band containing exons 1, 2, 3 and 5 of the IGF-1 molecule in antisense orientation. Lane 1 shows the molecular weight markers of ØX174 DNA cut by HaeIII. Lanes 2, 3 and 4 demonstrate the 424 bp band, and a 327 bp β-actin band (internal control). Lane 5 shows a negative clone, which did not express IGF-I antisense cDNA. Lane 6 depicts a negative control in which all constituents of the reaction were present but not the DNA template. <b>C,</b> Detection of IGF-1 antisense RNA transcripts in HGB cells by Northern blot analysis. 30ug of total RNA from non-Transfected and transfected clones of HGB cells were applied to 1.0% formaldehyde agarose gel. ³²P-labeled IGF-I cDNA was used as probe. Lanes 1, 3, 4, 5, 6, 8, 9 and 10 demonstrate the dominant 1 kb IGF-1 antisense RNA band from each of 8 separately transfected clones. Lanes 2, 7 and 11 represent RNA of non-transfected clones. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058428#pone-0058428-g001" target="_blank"><b>Fig 1</b></a><b>C</b> represents result for one set of the experiments; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058428#pone-0058428-g001" target="_blank"><b>Fig 1</b></a><b>D</b> represents the semi-quantitative densitometry analysis for <b>C</b> as determined by the NIH image J program.</p

Comparison of HLA-1 cell surface antigens in pAnti IGF-1 transfected, mock transfected, pAnti IGF-2 transfected and non-transfected T98G cells.

<p>A, Histogram of fluorescence intensity from isotype control (non-transfected cells+mouse IgG FITC); NT (non-transfected cells+mouse anti-human HLA-1 mAb+goat anti-mouse IgG FITC); Mock TX (cells transfected with vector devoid of anti IGF-1 cDNA+mouse anti-human HLA-1 mAb+goat anti-mouse IgG FITC); pAnti IGF-2 TX (cell transfected with vector expressing IGF-2 RNA in antisense orientation+mouse anti-human HLA-1 mAb+goat anti-mouse IgG FITC); and, pAnti IGF-1 TX (cells transfected with vector pAnti IGF-1+mouse anti-human HLA-1 mAb+goat anti-mouse IgG FITC). B, Specific fluorescence showing differential expression of HLA-1 from pAnti IGF-1 transfected cells when compared to parental non-transfected cells and other controls (P <0.05). C, Group ANOVA comparison was done by boxplot with Newman-Keuls graphical representation for comparison in expression of HLA-1 in the experimental groups. The data in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058428#pone-0058428-g003" target="_blank">Fig 3</a> B was repeated×3.</p

Primer pairs used for RT-PCR.

<p>Primer pairs used for RT-PCR.</p

Down-regulation in expression of IGF-1 in pAnti IGF-1 transfected HGB cell lines. A,

<p>Demonstration of intracellular IGF-1 levels in the HG-2 cell line by Flowcytometry. Isotype control (non-transfected Cells+mouse IgG FITC); Non-transfected (non-transfected cells+mouse anti-human IGF-1 mAb+goat antimouse IgG FITC); Transfected (transfected cells+mouse antihuman IGF-1 mAb+goat antimouse IgG FITC); Mock transfected (cells transfected with vector minus antisense IGF-1 cDNA+mouse antihuman IGF-1 mAb+goat antimouse IgG FITC). <b>B,</b> Bar graph comparison of IGF-1 expression in transfected and corresponding parental, non-tranfected HGB Cell Lines. Cell lines were established from discarded tumor tissue of Glioblastoma patients. The experiment design was as depicted in legend <b>A</b> of this Fig. % IGF-1 content = %Fs (specific fluorescence) = [Target (Fluorescence mean value) – Control (Fluorescence mean value)]/Target (Fluorescence mean value)×100%. NT = non-transfected, TX = pAnti-IGF-1 transfected. The experiments of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058428#pone-0058428-g002" target="_blank"><b>Fig 2B</b></a> were done×3. The paired t-test was used to determine P values. The statistical procedures were performed on the average difference for each cell line before (NT) and after (TX) transfection. The calculated t and associated p-values are given. Grouped comparisons between TX and NT cell lines for IGF-1 from summarized data by two-way ANOVA were statistically significant at p<0.001 (5 cases) or p <0.05 (3 cases).</p

Comparison in expression of HLA-1 and B-7.1 for pAnti IGF-1 transfected and non-transfected HGB cell lines by flow cytometry.

<p>Fs: Specific fluorescence. Fs defined as total mean fluorescence of sample (Ft) minus that of the background fluorescence (Fb) divided by Ft, i.e. %Fs = (Ft-Fb)/Ft×100.</p><p>Control: cells not transfected with vector pAnti IGF-1.</p><p>TX: cells transfected with vector pAnti IGF-1.</p><p>SD: standard deviation.</p><p>P value: Grouped comparison of TX vs. NT (control) by two-way ANOVA at p<0.05 is statistically significant.</p