1. Introduction

Knowledge about the diversity and composition of gut microbiota has accumulated rapidly by cultureindependent methods, especially the 16S rDNA amplicon high-throughput sequencing technique (Su et al., 2012).However, different experimental protocols concerned with material preparation, DNA extraction and PCR primer selection can generate technical biases in drawing the gut microbiota profiles (Larsen et al., 2015; Lozupone et al., 2013; Wagner Mackenzie et al., 2015).

本文利用墨尔本大学Rajkumar Buyya教授开发的Cloudsim-3.0.3云计算仿真平台[8]，测试本文提出的云计算资源调度方法的有效性.实验中考虑了资源的处理速度和待处理任务的长度，以不同规模的资源请求环境下任务的完成时间作为评价指标.具体参数设置为:种群规模为30，计算资源数量为10，学习因子c1、c2均为1.2，权重值wmax和wmin分别设定为0.9和0.4．

Since acquiring high quality genomic DNA is the prerequisite for the downstream analysis of gut microbiota structure, researchers have performed many studies on the effect of material types, sample storage conditions and DNA extraction methods (Choo et al., 2015; Ferrand et al., 2014; Larsen et al., 2015; Song et al., 2016;Wagner Mackenzie et al., 2015). In spite of differences between intestinal and fecal microflora (Eckburg et al.,2005; Nava et al., 2011), feces have been utilized as a noninvasive material to measure the intra- and interindividual variation in composition and diversity of intestinal bacterial communities (Arumugam et al., 2011;Dethlefsen et al., 2008; Yatsunenko et al., 2012). Storage conditions for fecal samples could significantly influence microbiota profiles (Bahl et al., 2012; Choo et al., 2015;Maukonen et al., 2012), though some exceptions exist(Fouhy et al., 2015). DNA extraction protocols can produce technical variations due to different cell lysis and purification methods (Claassen et al., 2013; Maukonen et al., 2012; Wagner Mackenzie et al., 2015; Yuan et al.,2012). However, these technical variations seem not to be large enough to distort the biological variations(Lozupone et al., 2013; Wagner Mackenzie et al., 2015).

Recent research on gut microbiota has shed new light on the associations between gut microbiota and vertebrate physiology, development and evolution (Ley et al.,2008; McFall-Ngai et al., 2013; Nicholson et al., 2012;Yatsunenko et al., 2012), while the number of intestinal microflora studies in amphibians remains far smaller than that in mammals (Bletz et al., 2016; Colombo et al., 2015;Jiménez and Sommer, 2017; Kohl et al., 2013; Kohl et al.,2014; Mashoof et al., 2013; Vences et al., 2016; Weng et al., 2016; Weng et al., 2017). Intensive studies of the gut microbiota in different taxa including amphibians are essential prerequisites to elucidate host-gut microbiota symbioses, e.g., phylosymbiosis (Brooks et al., 2016; Ley et al., 2008; Li et al., 2017a; Li et al., 2017b; Shapira,2016; Vences et al., 2016). In addition, the understanding of gut microbiota in amphibians could help us to take effective measures for amphibian conservation and cultivation (Jiménez and Sommer, 2017). Since many amphibians have been suffering from severe survival conditions (Hof et al., 2011), noninvasive approaches will always be an optimal choice for the study of symbioses between amphibians and gut microbiota. However, it remains unclear whether the feces can be applied as a noninvasive material to study the gut microbiota of amphibians.

In this study, we aimed to test the efficacy of a phenolchloroform method and a commercial fecal reagent kit(TIANGEN Biotech Co., Ltd.) in describing intestinal bacterial communities of Asiatic toad (B. gargarizans)tadpoles by feces. Specifically, DNA extraction quality of different methods and sample types was tested in terms of three parameters, i.e., A260/A280, A260/A230 and DNA yield rate. Furthermore, the structural consistency between bacterial communities was evaluated based on 16S rDNA amplicon high-throughput sequencing.

2. Materials and Methods

2.1. Transplantation of B. gargarizans eggs In February-March 2016, we sampled seven broods of B.gargarizans eggs from Xinyang City of Henan Province in China (Table S1). A ～10 cm-length chalaza was taken from each brood of eggs, and then was hatched in labs using plastic cylinders (1 L) filled with about 0.5 L dechlorinated drinking water. Subsequently, about 15 larvae in each brood were reared together with boiled green vegetable leaves rich in cellulose or fish foods rich in protein. We applied semi-natural conditions for rearing all larvae, i.e., a water change and food feeding per three days without controlling the light, humidity and temperature. All procedures used in this study were approved by the Animal Care and Use Committee of Xinyang Normal University.

2.2. Preparation of intestinal and stool samples When tadpoles developed into the lower limb stage (Gosner 35–40), we collected intestinal and stool samples. We first collected 10 samples (S1–S10) of mixed fecal sediments from cultivation water by using aseptic injectors (Table S1). These mixed stool samples spin-dried in a centrifuge were applied for comparing DNA extraction quality of the phenol-chloroform method and the TIANamp Stool DNA Kit. In addition to the DNA extraction quality, 23 tadpoles (S11–S33) were further selected to compare the microbiota structure between the phenol-chloroform method and the commercial stool kit (Table S1). Tadpoles(S11–S16 and S22–S27) were individually cultivated for less than 24 hours after a water change. Then we got their spin-dried stool samples through the same approach for mixed stool samples. We sacrificed tadpoles using 75%ethanol solution before the extraction of the gut samples into sterile microcentrifuge tubes. All intestinal and stool samples were stored in a –20°C freezer before the DNA extraction.

In Gut_ph vs Gut_kit, we identified by the LEfSe analysis that three genera of phylum Proteobacteria showed significant between-group divergence, i.e.,Sphingorhabdus biased to Gut_ph, and Coxiella biased to Gut_kit along with an unnamed genus in order Rhizobiales (Figure 6). As for Stool_ph vs Gut_ph, five genera (i.e., Hydrogenophaga, Rhizobium,Brevundimonas, an unnamed genus in order Rhizobiales,an unnamed genus in family Sphingomonadaceae) in phylum Proteobacteria biased to Stool_ph, and genus Clostridium_XlVa in phylum Firmicutes biased to Gut_ph (Figure 6). In addition, significant betweengroup divergences of four phyla (i.e., Proteobacteria,Bacteroidetes, Verrucomicrobia, Firmicutes) composed of 15 genera were detected in Stool_kit vs Gut_kit, i.e.,12 genera in the phyla of Proteobacteria, Bacteroidetes and Verrucomicrobia biased to Stool_kit, and 3 genera in the phyla of Proteobacteria and Firmicutes biased to Gut_kit (Figure S4). The shared between-material bias of genera in Stool_ph vs Gut_ph and Stool_kit vs Gut_kit included four genera in phylum Proteobacteria biased to Stool (i.e., Hydrogenophaga, Rhizobium, Brevundimonas,an unnamed genus in family Sphingomonadaceae) and one genus in phylum Firmicutes biased to Gut (i.e.,Clostridium_XlVa).

TIANamp stool DNA kit.―TIANamp Stool DNA Kit simplifies the DNA isolation by a fast spincolumn procedure. The protocol recommended by the manufacturer was utilized to extract the metagenomic DNA.

DNA quality evaluation.―The A260/A280, A260/A230 and DNA yield rate were applied for the DNA quality evaluation. The A260/A280, A260/A230 and concentration of DNA products were determined by using NanoVue Plus Spectrophotometer (GE Healthcare Inc.,Germany). The DNA yield rate was given by the ratio of the DNA concentration to the sample weight. The DNA products extracted from feces or intestines of tadpoles(S11–S16 and S22–S27) were subsequently stored in a–20°C freezer until the high-throughput sequencing of 16S rDNA amplicons.

2.4. 16S rDNA amplicon high-throughput sequencing The library construction of 16S rDNA amplicons and high-throughput sequencing on MiSeq (Illumina Inc.,USA) were achieved in a commercial company (Genergy Inc., China). Specifically, the hypervariable regions of V3–V4 in the bacterial 16S rDNA were amplified from the microbiota DNA products using the universal primer pair 341F–CCTACGGGNGGCWGCAG and 785R–GACTACHVGGGTATCTAATCC (Klindworth et al.,2013). The amplicons were generated by a two-step,tailed PCR on the DNA products in terms of the 16S Metagenomic Sequencing Library Preparation protocol with some modifications (http://support.illumina.com/downloads/16s_metagenomic_sequencing_library_preparation.html). The volume of each PCR solution was 25 μL, which consisted of 10 ng DNA, 2.5 μL 10 ×Takara Ex Taq Buffer, dNTP (2.5 mmol/L each), 5 pmol/L forward primer, 5 pmol/L reverse primer, 0.1 μL Takara Ex Taq, and ddH2O. The 1st PCR condition was 94°C for 3 min, 20 cycles (94°C for 10 s, 55°C for 15 s, 72°C for 30 s) and 72°C for 7 min. The 2nd PCR condition was 94°C for 3 min, 5 cycles (94°C for 10 s, 55°C for 15 s,72°C for 30 s) and 72°C for 7 min. Finally, the 16S rDNA amplicons quantified by Quant-iT™ PicoGreen® dsDNA Assay Kit (Life Technologies, USA) were paired-end sequenced on the Illumina MiSeq platform.

无人船通过遥控器或NB-IoT 基站进行自动水质采样，测试通信距离在100 米以内，水深度2 米左右，采样量为8 升，采样结束后生成水质信息报告。无人船按照预先设定好的路线自动巡航，驶向指定水质采样位置，无人船15 分钟能采集8 瓶水样采样后返航，通过水质监测系统生成采样及上报水质数据[4]。将装配有8 个水质传感器的水质监测无人船，放置在城市河涌进行试验，步骤如下：

2.5. Bioinformatic analyses of 16S rDNA amplicons The raw paired-end reads, after the removal of barcodes,were filtered by Trimmomatic software with three minimum thresholds (Bolger et al., 2014), i.e., terminal base quality score equal to 25, average quality score equal to 25 in sliding windows of 50 bp with a step of 1 bp, fragment length equal to 100 bp. The paired-end sequences were merged with the flash software for a minimum overlap of 10 bp and a maximum mismatch proportion of 0.2. The merged sequences including ambiguous bases were excluded. Subsequently, the 16S rDNA reference database downloaded from NCBI was utilized for the validation of V3–V4 regions. In this study,16S rDNA amplicons were successfully sequenced in 32 samples excluding S14_S, S17_G and S20_G (Tables 1,S1).

To test the effect of experimental material and DNA extraction method on the microbiota structures, we executed two-way ANOVA to compare α diversity indexes of intestinal and fecal microbiota in S11–S33 tadpoles. We hypothesized that the factors, i.e., host genetic background and diet, could bias the comparative analysis of microbiota structure. Therefore, we used paired t-Test or Wilcoxon Signed Rank Test to compare α diversity indexes of gut microbiota between 10 pairs of littermate tadpoles (S11–S16, S18–S21 and S24–S33),i.e., S11_G–S16_G, S18_G–S21_G versus S24_G–S33_G(Gut_ph vs Gut_kit). Subsequently, the comparisons of α diversity indexes between intestinal and fecal microbiota were performed on 12 tadpoles (S11–S16 and S22–S27),i.e., S11_S–S16_S versus S11_G–S16_G (Stool_ph vs Gut_ph) and S22_S–S27_S versus S22_G–S27_G (Stool_kit vs Gut_kit). The intestinal/fecal metagenomic DNA of S11–S16 and S22–S27 was extracted using the phenolchloroform method and TIANamp Stool DNA Kit,respectively.

The mothur program was used to calculate α diversity indexes (Schloss et al., 2009), i.e., richness (OTU observed, ACE and Chao1), diversity and evenness(Shannon, Simpson and Shannoneven), and Good’s coverage. The rarefaction and Shannon curves were drawn to measure whether the sequencing depth was appropriate for the richness and diversity calculation. To statistically analyze the differences in α and β diversity indexes between samples, 16 881 (i.e., minimum number of sequences in all samples) sequences in each sample were subsampled. In addition, we drew stackbars and Venn charts to show the taxonomic composition and abundance of samples. Furthermore, we used Past software (v3.14) to execute principal coordinates analysis(PCoA) based on Bray-Curtis dissimilarity calculated from taxonomic abundances to analyze the β diversity of microbiota structure (Hammer et al., 2001). The transformation exponent was set to the default value (i.e.,2).

礼之可以为国也久矣，与天地并。君令臣共，父慈子孝，兄爱弟敬，夫和妻柔，姑慈妇听，礼也。君令而不违，臣共而不贰；父慈而敬，子孝而箴；兄爱而友，弟敬而顺；夫和而义，妻柔而正；姑慈而从，妇听而婉，礼之善物也。

In addition to permutational multivariate ANOVA(PERMANOVA), we also performed a Mantel test on the Bray-Curtis dissimilarities calculated from taxonomic(i.e., OTU, genus and phylum) abundances in the above three cases, i.e., Gut_ph vs Gut_kit, Stool_ph vs Gut_ph and Stool_kit vs Gut_kit. The number of permutations was set to 9999 for PERMANOVA and Mantel tests. To test which taxonomies significantly affected the structural divergence of microbiota in these three cases, Lda Effective Size (LEfSe) were executed with recommended options (http://huttenhower.sph.harvard.edu/galaxy/).

传统的对问题认知的方式可能阻碍一般创造力，因而，培养对问题的创造性认知非常重要。在认识事物和问题时，需要有意识地打破常规，深入挖掘问题背后的实质，从而重新审视问题表征，实现对问题的“重定中心”。另外，培养多种创造性思维方式，如逆向思维、横向思维、负向思维、发散思维等，培养认知的灵活性、复杂性和多方位性。最后，赋予个体更多的解决问题的机会，在具体的问题情境中训练创造性认知习惯，也能够起到提升一般创造力的作用。

Operational taxonomic units (OTUs) were assigned to the 16S rDNA amplicons using UPARSE software with the identity threshold of 97% (Edgar, 2013). The annotation of representative OTU sequences was executed through sequence alignments on the RDP database using the RDP classifier (confidence threshold = 0.8) (Wang et al., 2007).

2.6. Statistical analyses of the DNA extraction quality and the microbiota structure To compare the DNA extraction quality of phenol-chloroform method and the TIANamp Stool DNA Kit, we utilized SPSS software(v20.0, IBM Corporation) to perform Mann-Whitney Rank Sum Test and two-way ANOVA (two factors:experimental material and DNA extraction method) in terms of A260/A280, A260/A230 and DNA yield rate.Pairwise multiple comparisons were performed in twoway ANOVA with Bonferroni correction when overall significance level was less than 0.05. In addition, fitting curves, which sketched the relationship of the DNA yield rate and sample weight, were compared between the phenol-chloroform method and the commercial stool kit.

3. Results

3.2 Efficacy comparisons in terms of microbiota structures In Gut_ph vs Gut_kit, total number of OTUs was 121, among which 4 belonged to Gut_ph (3.3%), 4 belonged to Gut_kit (3.3%) and 113 belonged to between groups (93.4%) (Figure 3). In Stool_ph vs Gut_ph, total number of OTUs was 100, among which 6 belonged to Stool_ph (6%), 8 belonged to Gut_ph (8%) and 86 belonged to between groups (86%). In Stool_kit vs Gut_kit, total number of OTUs was 114, among which 9 belonged to Stool_ kit (7.9%), 7 belonged to Gut_ kit(6.1%) and 98 belonged to between groups (86%). The Venn chart indicates that the majority of OTUs could be extracted by using the phenol-chloroform method or the commercial stool kit from intestines or stools.

3.1. Efficacy comparison in terms of DNA purity and yield rate Among the tests of the between-method difference in A260/A280, A260/A230 and DNA yield rate, a significant difference was detected in A260/A230(Mann-Whitney Rank Sum Test: U = 205, n1 = 23, n2 =27, P = 0.04) and DNA yield rate (U = 66, n1 = 15, n2 =17, P = 0.02) but not A260/A280 (U = 300.5, n1 = 23, n2= 27, P = 0.85) (Table S1; Figures 1, 2). Whereas when the two-way ANOVA was applied (Table 2), a significant between-method difference was detected in A260/A230 and DNA yield rate but not in A260/A280. A significant between-material difference was detected in A260/A280 and A260/A230 but not in DNA yield rate. No significant interaction was detected between these two factors (i.e.,experimental material and DNA extraction method). In the pairwise multiple comparisons, A260/A230 (avg.± std. err.) of stool samples (1.05 ± 0.11, n = 12) was significantly different from that of gut samples (1.51 ±0.08, n = 23) and mixed stool samples (1.51 ± 0.10, n =15). Furthermore, the fitting curves for DNA yield rate and sample weight show that the DNA yield rate in the phenol-chloroform method and the commercial stool kit decreases along with saturated DNA extraction materials in solutions (Figure S1). The phenol-chloroform method has a greater DNA yield rate than the commercial stool kit does at a given sample weight.

No significant differences in α diversity indexes were detected in between- material groups and between-method groups by using two-way ANOVA (Table S2). When host genetic background and diet factors were taken into account, we detected no significant differences in almost all α diversity indices in Gut_ph vs Gut_kit, Stool_ph vs Gut_ph and Stool_kit and Gut_kit (Table 3; Figure 4). One exception was that the richness values (i.e., number of observed OTUs) showed a significant difference between Stool_kit and Gut_kit (P = 0.02, n = 6) (Table 3; Figure 4).

None of the Bray-Curtis dissimilarities calculated from OTU, genus and phylum abundances showed a significant between-group difference in Gut_ph vs Gut_kit (Table 4; Figures 5, S2, S3). However, only the Bray-Curtis dissimilarity based on genus abundances gave rise to a weak between-group correlation (R = 0.32, P= 0.04). In the PERMANOVA for Stool_ph vs Gut_ph(Table 4; Figures 5, S2, S3), the OTU, genus and phylum abundances also showed an insignificant between-group variation in terms of Bray-Curtis dissimilarity. In addition,both Bray-Curtis dissimilarities calculated from OTU and genus abundances showed a significant between-group correlation, i.e., R = 0.64 (P = 0.04) and R = 0.58 (P =0.02). As for the Stool_kit vs Gut_kit (Table 4; Figures 5,S2, S3), both Bray-Curtis dissimilarities calculated from OTU and genus abundances also showed a significant between-group correlation, i.e., R = 0.65 (P = 0.01) and R = 0.64 (P = 0.01). Nevertheless, significant betweengroup variations was detected in both Bray-Curtis dissimilarities based on OTU and genus abundances(PERMANOVA: F1,10 = 4.33, P = 0.02) and F1,10 = 4.37, P= 0.03).

2.3. Protocols of DNA extraction and DNA quality evaluation Phenol-chloroform method. ―The 500 μL SDS (1%) and each weighed sample were mixed in a sterile 1.5 mL microtube and bathed in the water for 10 min at 60°C. During the water bath, microtubes were overturned and blended for three times. Subsequently,30 μL EDTA (0.5 M) and 20 μL protease K (20 mg/mL)were added into each microtube, and the mixture was bathed in the water for 1 hour at the same temperature of the previous step. And then the samples were centrifuged at 12 000 rpm for 5 min at 4°C. The supernatant fraction was transferred to a new 1.5 mL microtube, and blended with an equal volume of phenol-chloroformisoamyl alcohol (25:24:1 in volume). The samples were centrifuged at 12000 rpm for 5 min at 4°C once again.The supernatant fraction was transferred to a new 1.5 mL microtube, and blended with an equal volume of chloroform-isoamyl alcohol (24:1 in volume). The samples were centrifuged at 12000 rpm for 5 min at 4°C once again. The supernatant fraction was transferred to a new 1.5 mL microtube, and blended with twofold absolute ethanol. After cooled at –20°C for 20 min, the samples were centrifuged at 12 000 rpm for 10 min at 4°C. After the removal of the supernatant fractions, the DNA precipitates were washed in 200 μL ethanol (70%)for 5 min, and then the mixtures were centrifuged at 12 000 rpm for 5 min at 4°C. This step was repeated three times. The dry DNA was dissolved in 40 μL TE buffer solution.

日前，温州瓯江特大桥主体工程正式完工。作为乐清湾港区铁路支线项目的控制性工程，瓯江特大桥的贯通，为乐清湾港区铁路支线今年年底的建成夯实了基础。

4. Discussion

The phenol-chloroform method is a classic and costeffective approach to extract eukaryotic or prokaryotic genomic DNA (Ausubel, 2002). The DNA yield rate of the phenol-chloroform method is significantly higher than those of several commercial methods (Wagner Mackenzie et al., 2015). Similarly, this study demonstrated the DNA extraction efficiency of the phenol-chloroform method outweighed that of TIANamp Stool DNA Kit in terms of DNA yield rate. In addition, the phenol-chloroform method produced a better A260/A230 than TIANamp Stool DNA Kit did, which probably resulted from more effective DNA washing in the last step of phenolchloroform method. Due to the poor operationality and technical overlook of mucosal bacteria in separating gut contents from intestines, we extracted the mixture of gut microbiota DNA and host genomic DNA from intestinal samples. Nevertheless, we applied the universal primer pairs to specifically amplify V3–V4 regions of bacterial 16S rDNA (Klindworth et al., 2013). It has been reported that the mechanical treatments (e.g., bead beating) of samples can produce more efficient cell lysis of Grampositive bacteria and yield high amounts of DNA (Ferrand et al., 2014; Guo and Zhang, 2013). However, no method has become a gold standard for 16S rDNA highthroughput sequencing (Yamagishi et al., 2016). Methods with a mechanical treatment face a tradeoff between cell lysis efficiency and DNA disruption level. Due to the easy digestion of tadpole intestines and feces we did not take the mechanical treatments into account.

The composition and diversity of gut microbiota are undoubtedly affected by multiple factors, e.g., host genetic background, dietary profile and environmental situation(Dabrowska and Witkiewicz, 2016; Davenport, 2016; Jin et al., 2017; Voreades et al., 2014). The annotation on the gut microbiota structure can be susceptibly biased by the experimental protocols (Choo et al., 2015; Claassen et al., 2013; Yuan et al., 2012). However, many studies have demonstrated that biological variations outweigh technical variations generated by DNA extraction methods (Salonen et al., 2010; Wagner Mackenzie et al., 2015; Wesolowska-Andersen et al., 2014) and sample storage conditions (Blekhman et al., 2016; Fouhy et al., 2015). Here we homogenized and minimized biological variations in between groups as far as possible.Nevertheless, it is impossible to eliminate each interfering factor. For instance, the littermate tadpole pairs used in Gut_ph vs Gut_kit possess a similar but not an identical genetic background, thereby the between-method heterogeneity is possibly enhanced. From the analyses of Gut_ph vs Gut_kit, the technical variation generated by DNA extraction methods was outweighed by the inter-subject variation. However, the phenol-chloroform method and the commercial stool kit probably resulted in a significant inconsistency in the structural composition of microbiota, e.g., OTU and phylum abundances. To ensure biological differences outweigh systematic biases,we had better use the identical standardized protocols for the comparative analysis of gut microbial consortia.

Although the microbiota structure shows a significant variation between feces and intestinal contents in the case of the sophisticated intestines (Gu et al., 2013), feces has been applied as an effective noninvasive material for the study of gut microbiota in mammals. Larsen et al.(2015) used ribosomal intergenic spacer analysis to reveal that the microbiota structure of fishes was similar but significant different in feces and intestines. However, their preparation procedure of fecal and intestinal samples,i.e., the fecal samples were squeezed from the intestinal samples, probably enhance the between-material variations. In this study, the fecal microbiota based on the phenol-chloroform method can more efficiently reflect the gut microbiota in terms of composition and diversity. On the contrary, the feces and intestines possess more inconsistent microbiota structures deduced from the commercial stool kit. When we applied the PCoA to compare the samples fed with different food types, the experimental material factor seemed to be dominant rather than genetic background and food type factors in the case of commercial stool kit. We will explore and discuss the effects of multi-factors (e.g., genetic background and food type) on the microbiota structure of Asiatic toad tadpoles in a further study. Even though no significant difference between fecal and intestinal microbiota was detected in the phenol-chloroform method, betweenmaterial variations do exist, e.g., four genera in phylum Proteobacteria were more abundant in feces and one genus in phylum Firmicutes in intestines. We argue that the inconsistency possibly resulted from the moderate interference of microorganisms in water and tadpole skin to feces.

5. Conclusion

According to the DNA extraction quality and structural comparisons between fecal and intestinal microbiota,the phenol-chloroform method is probably more robust than a commercial fecal reagent kit (TIANGEN Biotech Co., Ltd.) in evaluating the gut microbiota structure of amphibian tadpoles with feces. To the best of our knowledge, this study provides the first evidence that feces of amphibian tadpoles can be applied as an effective noninvasive material for the study of gut microbiota.

在教育信息化时代，大学英语教学改革大力推进信息技术与教育教学的融合发展，旨在促进教育公平，提高教育质量，培养具有国际化视野的现代化建设高素质人才。国际化视野即是对高素质英语人才的需求。因此，许多教育工作者对如何提高学生的英语综合应用能力进行着不懈的探索，但是，目前这些研究依然未能脱离教师如何“教”的范畴，学生不能有效发挥学习的主动性。混合式学习结合传统学习方法与数字化、网络化在线学习模式的优势，充分以学生为中心开展教学活动，大大提高了学生的学习主动性。基于此，笔者提出基于混合式学习的大学英语学习策略研究，旨在分析英语学习策略的使用现状，进而提出英语学习策略培养对策，提高大学生的英语学习效率。

Acknowledgements We thank Weizhao YANG(Lund University), Yunfei WANG (MD Anderson Cancer Center), Bruce WALDMAN (Seoul National University) and anonymous referees for their comments and suggestions. This study was supported by the National Natural Science Foundation of China (NSFC 31600104), Key Scientific Research Project of Higher Education in Henan Province (No. 17B180004), National Undergraduate Training Program for Innovation and Entrepreneurship (No. 201610477013), Ph.D. Research Startup Foundation of Xinyang Normal University (No.0201424), and Nanhu Scholars Program for Young Scholars of Xinyang Normal University.

经济犯罪案件现场访问的重点在于被害人对被害过程的陈述、知情人所了解的与案件有关的情况、对会计凭证、账簿资料的调取等。经过多年的经济犯罪侦查实践，对经济犯罪案件进行现场访问已经成为经济犯罪侦查人员的工作习惯。囿于探讨的必要性和篇幅，本文侧重于探讨经济犯罪案件侦查中需要进行的实地勘验情况。

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