The low prevalence of some genetic neuro-developmental syndromes associated to psychiatric disorders requests to be integrated in human genome-phenome databases from which pleiotropy can be compiled from by systematic integration of phenotypes associated with genetic loci using phenomic inference tools. Williams-Beuren syndrome (WBS) is a neurodevelopmental disorder related to elastin gene at 7q11.23. Anxiety, depression and attention problems are the main behavioral problems found in WBS with no gender differences. Significant differences between cohorts are reported in particular regarding somatic complaints and aggressive behavior.Here, we report a Tunisian cohort of WBS patients for whom clinical behavioral phenotypes as well as genetic features are detailed.Sixteen patients from Sfax, Tunisia were referred for genetic assessment due to a suspected WBS syndrome.Genetic evaluation using fluorescent in situ hybridization confirmed 7q11.23 microdeletion in only eight patients. Comparison of detailed behavioral phenotypes revealed differences between age groups, gender groups and genetic groups. Anxiety and depression were recorded in the two older male patients and aggressive behavior was recorded in only two boys. The severity of behavioral features were dependent to familial environment and to parental socio-economic and educational levels.A more complete understanding of phenomic space is critical for elucidating genome-phenome relationships mediating neurodevelopmental disorder associated to psychiatric diseases for assessing and managing psychiatric and behavioral risks in young syndromic children.No significant relationships.


This paper demonstrates a manipulation of snowman-shaped soft microrobots under a uniform rotating magnetic field. Each microsnowman robot consists of two biocompatible alginate microspheres with embedded magnetic nanoparticles. The soft microsnowmen were fabricated using a microfluidic device by following a centrifuge-based microfluidic droplet method. Under a uniform rotating magnetic field, the microsnowmen were rolled on the substrate surface, and the velocity response for increasing magnetic field frequencies was analyzed. Then, a microsnowman was rolled to follow different paths, which demonstrated directional controllability of the microrobot. Moreover, swarms of microsnowmen and single alginate microrobots were manipulated under the rotating magnetic field, and their velocity responses were analyzed for comparison. Recent advances in robotics field endeavor to create viable deformable robots in macro and micro scales. Soft robotics is a field of research that has opened new prospects to solve the challenges of authentic tasks that rigid robots cannot overcome ,2,3,4,5.Soft microrobotics has been attracting researchers’ interest due to its advantages, such as biocompatibility, flexibility, adaptability, and motility ,14. A loIn previous work, a microsnowman composed of two rigid microparticles (10 micron diameters) was investigated for swimming propulsion within synthetic mucus . These mThe rest of the paper is organized as follows. In Fabrication process of the alginated-based soft microsnowman is shown in psulated . In thisThe hardware setup for experiments is shown in Exploiting the magnetic properties of iron(III) oxide particles, the soft microsnowman robots are driven wirelessly by manipulating the magnetic field. In this paper, the translation of the robots was achieved by rolling locomotion. The effect of rolling motion under the rotating magnetic field is shown in B, t stand for the amplitude, the heading angle, frequency, and time in seconds for the rotating magnetic field, respectively.The applied rotating magnetic field for rolling motion can be written asThe kinematics for the rolling soft microsnowman is inspired from 2D unicycle robot dynamics. This model was used previously in  for rollVarious experiments were performed to understand the motion of the soft microsnowman under rotating magnetic fields. For each experiment, a different microsnowman robot was utilized. Due to geometrical differences, please note that each robot has its own characteristics in terms of rolling performance. The microsnowman robots were tested with the approximate Helmoltz coil system inside a PDMS chamber filled with DI water.x and y coordinates for each frame. Then, the velocity data is obtained from the location data.The velocity analysis was performed to understand the controllability of the microsnowman robots at various frequencies . The results can be seen in The trajectory of a microsnowman robot following the letters ‘S’, ‘M’, and ‘U’ with the velocity vs. time graphs is shown in By exploiting the global magnetic input, a swarm consisted of single and soft microsnowman alginate microrobots were controlled. In In summary, this paper presents an alginate-based microsnowman soft robots for rolling on the substrate surface under a uniform rotating magnetic field. A centrifuged-based microfluidic droplet method was utilized to create snowman-shape robots in microscale using alginate-based hydrogel. To characterize the motion capacity, the velocity responses of the rolling microsnowman robot were investigated at incremental frequencies. By using swarm of microsnowmen and single alginate particles, the performance of the robots were compared. As a future work, more complex locomotion modes, such as, tumbling and pivot walking, will be implemented, and we will try to fabricate three or more aggregated alginate particles with the pressure-based droplet method.


The genetic diversity of the 4th cycle breeding population was high with nucleotide diversity (iP) of 0.003, and oH and eH of 0.215 and 0.233, respectively, indicating that the breeding population has a broad genetic base. The genetic differentiation level between the different breeding generations and different provenances was low (stF < 0.05), with population structure analysis results dividing the 233 individuals into four subgroups. Each subgroup has a mixed branch with interpenetration and weak population structure, which might be related to breeding rather than provenance, with aggregation from the same source only being in the local branches. Our results provide a reference for further research on the marker-assisted selective breeding of Chinese fir and other coniferous trees.Studying population genetic structure and diversity is crucial for the marker-assisted selection and breeding of coniferous tree species. In this study, using RAD-seq technology, we developed 343,644 high-quality single nucleotide polymorphism (SNP) markers to resolve the genetic diversity and population genetic structure of 233 Chinese fir selected individuals from the 4 The species is widely distributed in 17 provinces and autonomous regions of China and has rich genetic diversity  , amplified fragment length polymorphism (AFLP), and simple sequence repeat (SSR) . SSR and cycles) , and hig cycles) . Using sHigh-throughput sequencing technologies generate substantial high-density SNP information, thereby offering opportunities for the development of new strategies for population genetics research. Among them, simplified genome sequencing technologies are widely used as they are free from the “reference genome” constraints. These include restriction-site related DNA sequencing (RAD-seq) , 2b-RAD th cycle breeding population as the study material to develop high-quality SNPs markers based on RAD-seq simplified genome technology. We expect that this development will not only help elucidating the genetic structure and diversity of the Chinese fir advanced generation breeding population, but also provide theoretical basis and reference for the development and establishment of breeding population and parental selection of seed orchards.Here, we used the Chinese fir 422.1th cycle genetic improvement population initially was selected for fast growth, high wood quality, and disease resistance. Individuals in this population were selected over three cycles of intensive genetic evaluation and were benchmarked against natural stands’ seedstock. The population is comprised of 233 individuals selected for the above-mentioned attributes along with the added knowledge of their flowering propensity (data generated from three years observations post grafting), which according to the genealogical records could be divided into four generations: 1st (n=43), 2nd (n=141), 3rd (n=38), and 4th (n=11) (The Chinese fir 4h (n=11) Figure 12.2th cycle selection population growing in the Yangkou State-owned Forest Farm (Fujian Province) and then preserved on dry ice. DNA was extracted using the Tiangen Biotech kit (DP-320-02), and its quality was checked using Qubit  and Nanodrop  with TE buffer as the blank. DNA purity and integrity were checked using 1% polyacrylamide gel electrophoresis.Current year fresh needles were collected from 233 Chinese fir trees of the 4Sequencing libraries of the 233 Chinese fir germplasm were constructed using the RAD-seq simplified gene sequencing technology. The quality-checked genomic DNA was enzymatically digested with EcoRI, and samples were double-end sequenced on the Illumina HiSeq 2500 platform to an average depth of 10×. The reference genome was assembled and spliced from the 233 genotypes using the simplified genome sequencing data assembled using the Stacks (Version 1.46) software  and its 2.3MAF>0.01, Call rate>0.9, ②MAF>0.05, Call rate>0.8 and ③MAF>0.05, Call rate>0.9. Finally, high-quality SNPs were obtained for genetic diversity analysis.The BWA-MEM method of the Burrows-Wheeler-Aligner (v0.7.16a-r1181) software  was used2.4oH) and expected heterozygosity (eH), and inbreeding coefficient (F) were measured. To equalize the sample size of each population, clusters with larger sample sizes  were randomly sampled each time with repeated sampling to calculate the genetic diversity parameters, and finally the mean was estimated. Using the Vcftools software (https://vcftools.github.io/man_latest.html) to 1) calculate nucleotide diversity parameters (iP); 2) number of conversions and reversals ; and 3) calculate Ts/Tv values. The R package StAMPP’s stamppAmova (https://rdrr.io/cran/StAMPP/man/stamppAmova.html) and PopGenome (https://cran.r-project.org/web/packages/PopGenome/index.html) were used for the analysis of molecular variance (AMOVA) and also for estimating the genetic differentiation indices both between the four generations and between the different geographical origins. The SNP data were used to construct a phylogenetic tree for the 4th cycle breeding population using the MEGA 6 software  module was used for principal component analysis (PCA). The above graphs were visualized using the R software.Using Plink software , geneticsoftware . Using tsoftware  was used33.1Picea abies genome (http://congenie.org/), and it showed 80.48% match, with the RAD-seq sequencing accuracy being reliable for downstream analysis.After RAD-seq sequencing, as shown in MAF > 0.05, Call rate > 0.9) were used as high-quality SNP markers, and 343,644 SNP (1.26%) markers were finally retained for subsequent analyses.After quality control of the raw data, we detected a total of 27,283,139 SNP markers in the whole population as compared to the reference genome, with an average of one SNP locus per 46 bp. The content and distribution density of different types of SNP variants varied across the genome. Among them, conversion accounted for 60.41%, A/G and C/T accounted for 30.41 and 30% respectively; reversal accounted for 39.59% , with C/G accounting for 5.51%. After further filtering, we retained a total of 343,644 (1.26%) high quality SNP markers for subsequent analyses. By comparing the genetic diversity parameters of Chinese fir under the three sets of criteria, the results showed that the parameter values under the first set of criteria were significantly smaller than the other two groups, while the values of various genetic parameters calculated under the third set of criteria were higher than the other two groups. Therefore, the SNP markers filtered by the third set of criteria , while eH varied from 0.214 to 0.231 (mean of 0.225). Both oH and eH were the highest in G2, with oH at all SNP loci being smaller than eH, thereby indicating that heterozygous deletions may exist in this Chinese fir germplasm population. G4 had the highest iP (0.003), which may be related to its inclusion of more provenances, followed by G2, which was similar to G3 and G1. Among the origins, oH was smaller than eH in FJ and HN, while eH was larger than oH in the remaining provenances, and iP values are also higher in the other provenances compared to the HN and FJ, probably due to the small sample size (only 3 to 5) in the other provenances, thus suggesting that genetic diversity in each provenance is somewhat related to the population size. This shows that the genetic diversity level of the 4th cycle breeding population was high and had abundant genetic variation.We used the 343,644 high-quality SNP markers to calculate the genetic diversity parameters of the breeding parents of different generations and their origins in the 4pulation Table 1.3.3stF < 0.05), indicating that there was no significant genetic differentiation in Chinese fir between the different provinces and between the breeding populations of the four generations. In contrast, the degree of differentiation between SC, JX and GD was higher. The genetic differentiation among the different breeding generations showed the highest differentiation between G4 and G1, which shared similarity with the nucleotide diversity results, whereas the lowest genetic differentiation was between G2 and G3.We assessed the genetic differentiation for different breeding generations and different germplasm sources 3. GenerThe AMOVA results showed that only 1.29% and 3.02% of the variation originated between breeding population generations and between the different germplasm origins, respectively, and over 96% of the variation was due to among different genotypes Table 4.3.4th cycle breeding population can be divided into four differential classes (I-IV)  Figure 2We used the admixture software to calculate the Q values of each sample  and plotted them in pairs  (Pinus massoniana (37.95%) (Cryptomeria japonica (48.00%) . This uanalysis . This es(37.90%)  and Pinu(37.95%) , and was(48.00%) , probablMAF > 0.01, Call rate > 0.9), thus indicating that setting of MAF filtering criteria had a greater effect on the number of SNP markers obtained. The filtering criteria for Chinese fir SNP selection in this study were more stringent than those implemented for Picea abies  was much higher than the number of SNP markers detected by SLAF-seq simplified sequencing technology . This may be due to either an increase in sample size (233:221/110) or differences in sequencing technology . RAD-seq4.2th cycle breeding population still harbours high genetic diversity (iP = 0.003) and high within-population genetic variation, similar to that reported for Pinus taeda  for long periods is subjected to natural selection, artificial selection, and some anthropogenic activities, leading to the occurrence of pollen and seed exchange and thus gene flow, making it possible for diversity to decrease and the genetic base to narrow  between subgroups, which may either be related to the unbalanced sample size representation across germplasm origins, or that the parental population was widely used due to its excellent phenotype, and the higher level of human activity may have led to enhancing gene flow, thus reducing genetic differentiation among populations , estimates similar to that reported for the same species (0.163/0.250) (Cryptomeria japonica (0.269/0.253) (Keteleeria davidiana var. formosana (0.128/0.096) (Pinus pungens (0.113/0.114) and Pinus rigida (0.098/0.104)  (Pinus strobus (0.477/0.590) (Cedrus (0.460/0.530)   and (0.23/0.250) , Cryptom9/0.253) , and als8/0.096) , Pinus p8/0.104) , but low1/0.423) , Pinus s7/0.590) , Cedrus 0/0.530) . The rea strobus  and Crypjaponica  and otheSNP markers detect significantly more genetic variation than SSRs, probably because SNP markers are obtained from the whole genome, have a low genotyping error rate, and have a high density in genomes ) e.g., , whereas4.3Pinus monticola  high-quality SNP markers. Furthermore, we detected that the genetic diversity of the 4The datasets presented in this study can be found in online repositories. The name of the repository and accession numbers can be found below: NCBI; PRJNA910811 and PRJNA909424.YJ, LB and XFZ contributed to conception and design of the study. YJ, LB, XFZ, BZ, RZ, SS, DY and XYZ organized the database. YJ, LB, XFZ and BZ performed the statistical analysis. YJ, LB and XFZ wrote the first draft of the manuscript. YE-K and JS wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.


In poultry, the population structure of local breeds is usually complex mainly due to unrecorded breeding. Local chicken breeds offer an interesting proxy to understand the complexity of population structure in the context of human-mediated development of diverse morphologies and varieties. We studied 37 traditional Dutch chicken breeds to investigate population structure and the corresponding genomic impact using whole-genome sequence data.Looking at the genetic differences between breeds, the Dutch chicken breeds demonstrated a complex and admixed subdivided structure. The dissection of this complexity highlighted the influence of selection adhering to management purposes, as well as the role of geographic distance within subdivided breed clusters. Identification of signatures of genetic differentiation revealed genomic regions that are associated with diversifying phenotypic selection between breeds, including dwarf size (bantam) and feather color. In addition, with a case study of a recently developed bantam breed developed by crossbreeding, we provide a genomic perspective on the effect of crossbreeding.This study demonstrates the complex population structure of local traditional Dutch chicken, and provides insight into the genomic basis and the factors involved in the formation of this complexity.The online version contains supplementary material available at 10.1186/s12711-022-00775-x. Following the process of domestication, chicken populations have been diversified with distinct morphological features during subsequent worldwide agriculture revolutions. During this process, chickens have been selectively bred for specific types of management, which led to genetic differentiation and population subdivision. Based on human interventions, chicken populations can be generally classified into commercial breeds and local traditional breeds. The commercial breeds have been bred under extensive and specialized selection for either egg-laying (layers) or meat production (broilers) . BreedinVarious factors or forces that influence population structure may coincide with each other and/or outcompete one another, which is subsequently comprised in the complexity of population structure. To understand the complex structure of populations, many studies have been performed to untangle the differentiation between populations by initially looking for genomic regions with divergent genetic signals. Identification of genetic differentiation serves as a good starting point to understand the genetic basis that underlies phenotypic variation. Phenotypes such as body stature, comb type, and coat/plumage pattern may reflect the history of selection –15. Genotraditional or true bantam breeds, Dutch chicken breeds exhibit an important history of bantam crossbreeding, so-called bantamization. The bantamization process used donors from existing bantams and/or neo-bantams of local breeds to create corresponding neo-bantams, the dwarf counterparts of native ‘large’ chickens [In the Netherlands, historic chicken resources have been admired and received attention from the sixteenth century onwards, and continued to be managed in diverse forms , 17. Thechickens , 11, 19.chickens , 11. FroIn this study, we focused on multiple factors that may play a significant role in recent breed formation revealing the complex population structure of Dutch traditional chicken breeds. We used whole-genome sequence data, representing the genetic basis of various breeds to investigate the population structure between breeds, and the effect of management type, geographic distribution, and phenotypic selection. Lastly, we demonstrate the recent hybrid history and provide a genomic perspective on the effect of selective breeding by using the case of a neo-bantam breed (Drenthe Fowl bantam).The chickens sampled for whole-genome sequencing included 136 individuals from 37 breeds, in which the neo-bantam and their normal-sized counterparts were considered as different breeds  was performed on the Illumina HiSeq 3,000 platform, with insert sizes of 350 bp. The processing of whole-genome sequence data was conducted following an in-house analysis pipeline. In brief, raw sequencing reads were trimmed by Sickle  then mapGallus gallus chromosomes GGA16 and GGA30-33), linkage groups without chromosomal location, and the sex chromosomes (Z and W) were excluded. An exception is the genetic map for GGA25 which was derived from an earlier study by Groenen et al. [We used the genotype phasing program Beagle (version 5.0)  to constn et al.  because Detection of identity-by-descent segments was based on the haplotype of individuals using the software package Refined-ibd . The aimhttp://tree.bio.ed.ac.uk/software/figtree/); the phylogenetic relationship was used in the FLK and hapFLK analysis; and (3) we used the genetic analysis software ADMIXTURE to estimate ancestry in individuals [Population structure analyses were performed using three approaches: a principal component analysis (PCA), a population phylogeny constructed based on Reynold’s distance, and an admixture analysis: (1) the PCA was computed using the PLINK (V1.9) software  with autividuals , the sofThe geographical distance is determined by haversine distance given the longitude and latitude of the location of the origin of the breeds using the “geosphere” package  in R V3.2 > 0.3) using PLINK software (-indep-pairwise 10 10 0.3). In total, we ended up with 2.30 million variants covering the genome. The hierarchical structure (Reynolds’ distance matrix) of the populations was accounted for in the genomic scans for signatures of selection. For the single-marker analysis, we performed the FLK test on all variants by chromosome. As for the haplotype-based FLK (hapFLK) test, we set the number of haplotype groups in the population to K = 15, as suggested by Boitard et al. [The FLK and hapFLK analyses were conducted to detect the signatures of selection between different populations , 34. Thed et al. , where thttps://omia.org/home/). Second, a gene ontology enrichment analysis was carried out using PANTHER v.11 [We annotated the variants that were within 5-kb up- or downstream regions from a gene or regulatory element (Ensembl gene sets version 95). Selective sweeps that were annotated with more than one significant variant were considered for further investigation. First, we compared the genes with the Online Mendelian Inheritance in Animals (OMIA) dataset, searching for “likely known” causative genes for Mendelian traits in chicken (HER v.11  based onHER v.11 .DB_DrFwB) with the nIBD between DrFw and DrFwB (nIBDDrFw_DrFwB). A positive rIBD shows more haplotype sharing between DB and DrFwB than between DrFw and DrFwB, whereas a negative rIBD shows more haplotype sharing between the counterparts  correspondingly. In order to compare the rIBD pattern in another neo-bantam breed with a similar crossbreeding history, we selected individuals from the GrMwB and GrMw breeds to show the introgression signals. To keep the samples comparable, we chose one individual from each breed, and the average coverage of the samples should be adequate and as good as the coverage of DrFwB. Likewise, we computed the rIBD between DB, GrMwB, and GrMw. The DB introgressed regions were highlighted in the visualization.In the case study of Drenthe Fowl bantam chicken, haplotype sharing was measured by identity-by-descent among the three breeds . The pairwise identity-by-descent segments between two breeds were estimated by bins of 10 kb and then normalized by the possible count between these two breeds (nIBD). The rIBD was computed by comparing the nIBD between DB and DrFwB  separated three breeds and their bantam counterparts from the other breeds: WelSummer (WelSum), Barnevelder (Barnev), and North Hollands Blue (NHBl)  and CL3 , while breeds in CL2 showed a positive but rather weak correlation  , suggesting no clear correlation between genetic and geographical distance across the population Fig. a. AnalysOne of the causes of the complex population structure of Dutch traditional chicken breeds is selective breeding adhering to desired traits and/or management types. The breed features shared between multiple breeds have led to extensive crossbreeding and recycling of the genetic materials of these phenotypes. Over the past decades and among the many desired phenotypic features, selection has focused on downsizing the local breeds and increasing morphological varieties, especially color varieties. To identify genomic regions that underlie the desired features, we tested the dataset of 37 breeds for genetic differentiation of all kinds of breed-specific or breed-overlapping traits. We applied the FLK approach (extended LK test) using both the single variant and haplotype information. The single variant approach FLK was used to detect genomic regions that display genetic differentiation between populations, such as signatures of selection, while accounting for the hierarchical structure of the populations . Likewishigh mobility group AT-hook 2 (HMGA2) and PR/SET domain 16 (PRDM16). These genes have been reported to be associated with body growth and reduced stature (dwarf) in diverse species [ENSGALG00000052273 and ENSGALG00000049778 genes on chromosome 4, encoding two long non-coding RNAs (lncRNAs). In addition, a strong selective sweep was observed around 170.72 Mb on chromosome 1, proximal to the quantitative trait locus (QTL) that is associated with growth and body weight (170.52–172.04 Mb) [potassium channel regulator (KCNRG) and tripartite motif containing 13 (TRIM13) and of the microRNAs, gga-mir-15a and gga-mir-16-1 , and SRY-box 5 (SOX5) genes.We observed 387 significant signals (FDR 5%) in 299 genes across the genome by using the FLK test, suggesting potential genetic differentiation in the population  . Given tWe have addressed the signatures of selection that are related to the introduction of the desired phenotypes and especially the strong selection at bantam associated loci. Therefore, we took a neo-bantam breed as a case study to show the hybrid nature of its genome and the consequence of crossbreeding to introduce desired traits. The bantam-oriented crossbreeding to introduce this dwarf phenotype is an excellent model to understand and predict the introgression of bantam.Drenthe Fowl bantam (DrFwB) as the corresponding neo-bantam counterpart of the traditional breed Drenthe Fowl (DrFw), has a recent history of the bantam crossbreeding process. According to the bantamization record, DrFwB was created by mating DrFw with existing bantam breeds, particularly the Dutch Bantam (DB) . Thus, tTo understand crossbreeding in the context of bantamization, we used the genetic haplotype sharing (identity-by-descent) method to address the contribution of bantam ancestry towards the neo-bantam breed, which is from DB to DrFwB. The number and length of IBD fragments between DB and DrFwB showed abundant segments of shared haplotypes  in terms of length and sequence similarity. The genes in this region showed an overrepresentation of the oxygen transport process . We inferred and compared the localized haplotypes between DrFwB with both the counterpart (DrFW) and the bantam donor (DB). Within this introgressed region, the distribution of genotypes of ~ 2000 variants demonstrated that the sequence of DrFwB is highly homozygous and highly similar to the sequences from DB individuals (three out of six) . This observation suggested that the general management of Dutch breeds was partially confounded by the selective breeding for the bantam phenotype. This consensus highlighted the complexity of the population structure made up by multiple factors and the underlying relatedness between approaches. Although the applied clustering approaches should complement the objectives of our study, as we highlight here, it is important to take population substructure into account.In this study, we performed a set of genomic analyses in 37 Dutch chicken breeds of various management purposes for which whole-genome sequencing data has been generated. We observed that, in general, most Dutch native breeds were genetically closely-related to their bantam counterparts. However, it should be noted that the limited and imbalanced sample size between breeds may influence the interpretation of the results. Nevertheless, a close relationship was found between intermingled breeds, such as between breeds of the ornamental cluster  and Dutch Owl bearded (DOwBd)) and of the country fowl cluster , Assendelft Fowl (AssFw) and Groningen Mew (GrMw)). This pattern of close relationship between intermingled breeds has already been reported based on SNP-array data  or in caTaken together, the population structure of Dutch traditional chickens suggested a clear admixed substructure showing a complex ancestry. The influence of breed management on the substructure is pronounced. The genetic relationship between the three historical clusters may reflect the selection for breed standards and management categories which can be a major force subdividing the population structure and reshaping the characteristics of breeds during breed formation. Unlike other animals with more refined and specialized purebreds and well documented pedigrees , the local traditional chicken breeds are expected to have diverse genetic variations within populations. Furthermore, in the past, gene flow was extensive due to crossing with new breeds and later on gene flow occurred between breeds among local regions particularly with similar characteristics , 43.The results of isolation-by-distance demonstrated that genetic distance is correlated with geographic distance only within the clusters, which is likely due to gene flow being restricted within each cluster and to the management “barrier” between clusters. Given the limited dispersal distance between chickens in the Netherlands (maximum 227 km), strong artificial selective breeding may have created a major human-driven “barrier” that reshaped the genetic landscape over geographic distance. Nevertheless, when we look at dispersal distance within each cluster, breeds with a shared common management purpose can also be distributed across the country at a maximum distance of 200 km . When compared across clusters, management purposes differed and were unique, thus for breeds from different clusters at a close geographic distance, gene flow between them will be limited resulting in greater genetic distance. Consequently, the exclusive exchange of genetic materials within subdivided management clusters leads to a “barrier” between clusters. The limited haplotype sharing between clusters further confirmed the “barrier” between management groups which is likely due to restricted recent gene flow. Moreover, the haplotype sharing within clusters demonstrated some recent common ancestry and genetic exchange within clusters, rather than a deep phylogenetic split with restricted gene flow.Overall, we observed a stronger effect of management than of geographic distance on the subdivision of the Dutch traditional chicken breeds. In other words, the breeds in the same management cluster tended to show a higher level of genetic similarity, outcompeting and masking the geographic separation at the whole population level. During the last century, selective breeding to maintain the characteristics of the traditional breeds has been strengthened in The Netherlands , 17. BasAlthough the productive specializations of breeds are generally homogenous, e.g., for egg production in CL1, it should be noted that breeds could have sub-divergent purposes, e.g., North Holland Blue was kept for its dual purpose i.e. egg-laying and production of meat . The divhttps://omia.org/home/) database for chicken, a deletion upstream of the SOX10 gene is associated with dark/yellow brown feather color in chickens [SOX5 gene is responsible for the shape of the comb through the epistatic interaction with the MNR2 gene [SOX5 and SOX10 genes. We confirmed the copy number variant (CNV) in the first exon of the SOX5 gene in pea comb individuals and also demonstrated the deletion upstream of the SOX10 gene in gold/yellow feather individuals and its absence in other plumage colors  around HMGA2  illustrates the Reynold’s distance between breeds. Figure S2. Manhattan plot of hapFLK results. Figure S3. Distribution of shared identity-by-descent blocks identified between breeds. Figure S4. Distribution of regions of rIBD across the genome, showing the relative fraction of the haplotype sharing between DrFwB and DB and that between DrFwB and DrFw. Figure S5. Correlation of assignments of individuals based on the two clustering approaches. Figure S6. Demonstration of two CNV detected by signatures of selection in the SOX5 and SOX10 genes. Figure S7. Distribution of relative identity-by-descent (rIBD)on chromosome 1, showing the relative fraction of haplotype blocks shared between GrMwB and DB (positive rIBD) and GrMwB and GrMw (negative rIBD).


Lactobacillus casei (L. casei LC01) treatment. IECs and L. casei LC01 were co-cultured and then subjected to microRNA microarray assay. qRT-PCR, western blot and ELISA were used to detect the expression of occludin (OCLN) and zonula occludens 1 (ZO1/TJP1). The interaction between miRNAs and L. casei LC01 acting in IECs was investigated through transfection of RNA oligoribonucleotides and pcDNA 3.1 plasmid. The results are as follows: 1) L. casei LC01 decreased the expression of miR-144 and FD4 and promoted OCLN and ZO1 expression in IECs; 2) L. casei LC01 enhanced the barrier function of IECs via downregulation of miR-144 and upregulation of OCLN and ZO1; 3) Under L. casei LC01 treatment, OCLN and ZO1 overexpression could partially eliminate the promoting effect of miR-144 on intestinal permeability in IECs. Our results demonstrate that L. casei LC01 regulates intestinal permeability of IECs through miR-144 targeting of OCLN and ZO1. L. casei LC01 can be a possible therapeutic target for managing dysfunction of the intestinal epithelial barrier.Our previous report determined that miR-144 is a key regulator of intestinal epithelial permeability in irritable bowel syndrome with diarrhea (IBS-D) rats. Recent evidence has shown that lactobacilli play an important role in the relief of IBS-D symptoms. However, few studies have addressed the mechanisms by which microRNAs and lactobacilli exert their beneficial effects on intestinal epithelial permeability. Hence, to elucidate whether miRNAs and lactobacilli play roles in intestinal epithelial barrier regulation, we compared miRNA expression levels in intestinal epithelial cells (IECs) under  Irritable bowel syndrome  is a chrLactobacillus paracasei LC01 (L. casei LC01) has good acid and bile salt resistance, and can tolerate a pH around 4.6 [Streptococcus mutans and can produce bacteriocin or bacteriocin-like polypeptides, usually class II bacteriocins [L. casei LC01 in IBS-D.Probiotics are beneficial bacteria that can improve health and maintain the micro-ecological balance of the host intestine. Screening probiotic strains with excellent probiotic characteristics has become a research hotspot. Studies have shown that the probiotic ound 4.6 , 10. L.ceriocins . This aceriocins . HoweverL. casei LC01 can regulate the expression of miR-144 in intestinal epithelial cells (IEC).MicroRNAs (miRNAs) are a type of non-coding small-molecule RNA endogenously expressed in eukaryotic cell organisms and can act on the 3'-untranslated region (3'-UTR) of target gene mRNA . If the L. casei LC01 affects miR-144 expression in IECs and regulates intestinal epithelial permeability, which may provide potential therapeutic strategies for the prevention and treatment of diseases related to intestinal mucosal barrier function.In this study, we aimed to evaluate whether and how 2 incubator with saturated humidity at 37°C. Fibroblasts were removed using phase difference digestion and adherence. When 80%-90% of the IECs were adherent to culture plates, the IECs were passaged by trypsin digestion.Human IEC lines  were purchased from the Chinese Academy of Sciences Shanghai Cell Bank and cultured in DMEM containing 100 ml/l fetal bovine serum in a COL. casei LC01 (No. 1.570) was purchased from the China General Microbiological Culture Collection Center and stored at a temperature below 25°C until use. L. casei LC01 were cultured and stocked as previously reported [L. casei LC01 were cultured and incubated in a stationary state at 37°C for 48 h with MRS medium and plated to count effective colony forming units (CFU). L. casei LC01 were measured and adjusted to 1 × 109 CFU/ml before use.reported . BrieflyL. casei LC01 for 3 days, then FD4 (2 mg/ml) was added to the cultures, and the fluorescein isothiocyanate intensity of the basolateral fluid was measured with a fluorescence reader . The optical density (OD) value of each well was measured at a wavelength of 450 nm. The FD4 concentration was calculated and was expressed in picomoles (pmol).Intestinal epithelial permeability as a surrogate marker of layer integrity was measured by using fluorescein isothiocyanate-dextran 4 kDa . The IECs were treated with or without L. casei LC01 for 3 days were subjected to a microRNA microarray assay. The method was as described previously [IECs treated with or without eviously . In brieL. casei LC01 for 3 days and then harvested. The contents of OCLN  and ZO1  in the culture medium were then determined using the corresponding ELISA kit according to the manufacturer’s instructions. The measurements were repeated in triplicate and averaged. The OD values (450 nm) indicated the relative amount of OCLN and ZO1.The method was as described previously . BrieflyL. casei LC01 were seeded into 6-well plates, and once cell confluence reached 70%, cells were transfected with 100 nM RNA oligoribonucleotides  in 2 ml serum-free reaction containing lipofectamine 2000 reagent for 6 h. IECs, without L. casei LC01 treatment, transfected with no RNA oligoribonucleotides were treated as the normal control group (NC), while IECs, without L. casei LC01 treatment, transfected with RNA control oligoribonucleotides were treated as negative control group. The IECs were then collected and examined using qRT-PCR and western blot. The sequences for cell transfection were as follows: miR-144 mimic: 5'-UACAGUAUAGAUGAUGUACU-3' (100 nM), miR-144 inhibitor: 5'- AUGUCAUAUCUACUACAUGA-3' (100 nM), miR control: 5'-UCGCAUGCCACAGCCAUGGC-3' (100 nM), siRNA control: 5'-UUCUCCGAACGUGUCACGUTT-3' (100 nM), si-ZO1: 5'-GUUAUACGAGCG AUCUCAU-3' (100 nM), si-OCLN: 5'-GGUUCUGGUGUGAACUAAATT-3' (100 nM).The method was as described previously . Briefly−ΔCt method was used to calculate the relative mRNA expression level. The measurements were repeated in triplicate and averaged. The primers were designed as follows: OCLN: 5’-AAGACGATGAGGTGCAGAAG-3’ (forward), 5’-GTGAAGAGAGCCTGACCAAA-3’ (reverse); ZO1: 5’-GGAGAGGTGTTTCGTGTTGT-3’ (forward), 5’-ACTGCTCAGCCCTGTTCTTA-3’(reverse); β-actin: 5’-CGTGACATTAAGGAGAAGCTG-3’(forward), 5’-CTAGAAGCATTTGCGGTGGAC-3’ (reverse); miR-144: 5'-GCGCGCTACAGTATAGATGATG-3' (forward), 5'-GCTGTCAACGATACGCTACG-3' (reverse).The method was as described previously . BrieflyThe method was as described previously . BrieflyThe method was as described previously . Brieflyp < 0.05.SPSS 17.0  was used for statistical analyses. The measurement data that conformed to a normal distribution were expressed as the mean ± standard deviation. One-way analysis of variance (ANOVA) and least significant difference (LSD) multiple comparison tests were used to compare the differences between groups. Parameters with abnormal distribution were expressed as the median (quartile). Non-parametric (Mann-Whitney U) tests were used to compare the differences between groups. Differences were regarded as statistically significant when L. casei LC01 for 3 days were considered the experimental and control group, respectively. As detected by ELISA, OCLN and ZO1 were significantly more highly expressed in IECs under L. casei LC01 treatment when compared to IECs that did not receive L. casei LC01 treatment  L. casei LC01 suppressed the expression of miR-144 and FD4 and promoted OCLN and ZO1 expression in IECs; 2) L. casei LC01 enhanced the barrier function in IECs via downregulation of miR-144 and upregulation of OCLN and ZO1; 3) Under L. casei LC01 treatment, OCLN and ZO1 overexpression could partially eliminate the promoting effect of miR-144 on intestinal permeability in IECs. These findings suggested that L. casei LC01 regulates intestinal permeability in IECs via miR-144 targeting of OCLN/ZO1, which might be a potential strategy for managing or preventing epithelial barrier dysfunction.The aim of this study was to further identify the molecular mechanisms underlying the mutual effect of L. casei LC01 treatment, similar to previous reports [L. casei LC01 could regulate intestinal permeability of IECs via miR-144.Recent evidence indicates that the communication between the gut microbiome and the host may be partially carried out by miRNAs, which are differentially expressed under specific microflora states . However reports , 22, whi reports , suggestL. casei LC01. Both OCLN and ZO1 are potential direct target genes of miR-144 [L. casei LC01 treatment when compared to untreated IECs. These results suggest that L. casei LC01 could enhance intestinal epithelial barrier function in IECs via downregulation of miR-144 and upregulation of OCLN and ZO1.IECs are key mediators of intestinal homeostasis and are able to establish an immune environment that allows symbiotic bacterial colonization. IECs provide physical and biochemical barriers that sequester host tissues and commensal bacteria to maintain intestinal homeostasis. IECs have a mechanism enabling them to respond to microbial signals, which confers tolerance against continuous exposure to symbiotic bacteria . In this miR-144 , which a miR-144 . ELISA, L. casei LC01 on regulating intestinal permeability in IECs, we transfected IECs with miR-144 and pcDNA3.1-OCLN/ZO1 under L. casei LC01 treatment. As a result, OCLN/ZO1 levels were significantly decreased in the miR-144 and si-OCLN/ZO1 groups compared with the control group and model group, of which expression in the miR-144 group was reduced the most. In other words, miR-144 upregulation showed a stronger effect on attenuating epithelial barrier function and promoting intestinal permeability in IECs than si-OCLN and si-ZO1 did under L. casei LC01 treatment. Furthermore, overexpression of OCLN/ZO1 partially rescued the promoting effect of miR-144 on intestinal permeability in IECs under L. casei LC01 treatment, indicating that L. casei LC01 regulates intestinal permeability in IECs via miR-144 targeting of OCLN/ZO1. The above results suggest that targeted microbial interventions may help restore or control intestinal epithelial permeability.It has also been reported that OCLN and ZO1 are not only capable of controlling paracellular macromolecule permeability but also interacting with intracellular signaling pathways that regulate and maintain intestinal epithelial permeability, subsequently enhancing intestinal epithelial barrier function -29. To fL. casei LC01 could be used together with miR-144 as a therapeutic strategy to promote mucosal barrier function and become a new target for the treatment of intestinal epithelial barrier dysfunction-related diseases.In addition, the results of this study have several important clinical implications. First, we conducted miRNA microarray analysis in this study, which provides examples of identifying differentially expressed miRNAs and their target genes after intervention with beneficial bacteria. Second, we provide the basis for further research and characterization of the beneficial bacteria-miRNA-OCLN/ZO1 relationship, which not only helps to better understand the key molecular switches regarding the permeability of the intestinal mucosa but also to characterize the miRNAs related to intestinal epithelial permeability-related diseases. Third, L. casei LC01 intervention can inhibit miR-144 expression in IECs and target OCLN and ZO1 expression to maintain intestinal homeostasis. Regulation of intestinal epithelial permeability through probiotics may become a new target for the treatment of intestinal epithelial barrier dysfunction-related diseases.In summary, regulation of intestinal epithelial permeability through tight junction proteins is an important mechanism for enhancing mucosal defense functions and maintaining intestinal homeostasis.


Improving the pleasantness of the policy environment in Central China stimulates economic growth, but it also contains higher risks of pollution. Based on the data of 80 cities in Central China from 2006 to 2018, the entropy method was used to estimate the pleasantness of policy environment in the region. How the policy environment has changed and whether the pleasant policy environment in the region is conducive to green growth were empirically studied. The results show the following: (1) The current attempts to improve the pleasantness of the policy environment in Central China is not conducive to green growth of the region. (2) Improving the pleasantness of the policy environment has indirect negative impacts on green growth through widening the development gap between prefecture-level cities and provincial capitals, as well as encouraging foreign trade; meanwhile, it also has an indirect positive impact through stimulating industrial diversification. The policy environment does not indirectly affect green growth through affecting technological innovation. (3) The policy environment in Central China will have a heterogeneous effect on the green growth of cities from the perspective of spatial heterogeneity and heterogeneity of city characteristics. In this paper, policy implications are proposed. Policy environment, which refers to whether the government’s intervention in a region is friendly to the operation of enterprises, is an important factor affecting industrial development in Central China. After benefiting from “The Third-Line Construction” movement in the 1960s and 1970s, Central China has built a relatively complete industrial system . However, after 1978, the Chinese government’s economic focus has been shifted to the eastern coastal areas and western ethnic minority areas. The lack of industrial stimulating policies has made Central China a typical sub-developed region. From the perspective of industrial structure, there are relatively fewer types of industries in Central China; there are mainly a raw material industry, fuel power industry, and agricultural processing industry . In ordeThe pleasant policy environment fostered by these interventions may contain high risks of damaging the ecological environment in Central China, making it ineffective to promote regional green growth. The following can be illustrated from the composition of government’s intervention in Central China: Both encouraging policy and restrictive policy are frequently used by governments in emerging markets for their efficiency of creating advantages for industrial development . From thhttp://www.gov.cn/xinwen/2021-03/13/content_5592681.htm, accessed on 13 March 2021). It is urgent for Central China to improve its green economic efficiency. Since governments’ intervention is an un-substitutable driving force for the growth of Central China in the short term . The 14th “Five-Year Plan for the National Economic and Social Development of the People’s Republic of China and the Outline of Long-Term Goals for 2035” states that “governments should resolutely curb the blind development of high-energy-consuming and high-emission projects, and promote green transformation to achieve positive development”  and OECD (2010) have defined the concept of green growth, stating that green growth is a sustainable economy, which essentially seeks the relative or absolute decoupling between economic growth and environmental degradation ,17. In r12 and OEBesides of measuring and commenting on the status of China’s green growth, more studies have explored its possible influencing factors. From the perspective of industrial development, on the one hand, domestic industries, including capital and talents, tend to agglomerate to big cities, and thus will have a direct impact on China’s green total factor productivity ; on the There is no consensus on whether a pleasant policy environment can promote green growth. “Promotion theory” holds that government’s behavior can improve green growth from two aspects: On the one hand, the government can improve cities’ attraction to talents and high-quality enterprises through reducing the taxation burden, improving the quality of public services and infrastructure construction, and then promoting industrial upgrading ,31. On tRecognizing the importance of policy environment in economic activities, existing research studies have conducted in-depth explorations of how to measure the policy environment and whether industrial policies will be conducive to green growth. However, these research studies have failed to clarify the changing trend of policy environment in Central China and also are unable to answer whether there is serious “regulation capture” behind the improvement of policy environment in Central China. From the perspective of policy environment measurement, we believe both restrictive and encouraging policies can affect the pleasantness of industrial development, whereas most studies fail to integrate these two kinds of policies into one policy environment index, resulting in the inaccurate evaluation of the pleasantness of policy environment in the existing literature. From the perspective of data type, most studies use national-level data, whereas governments in Central China are more easily “captured” since Central China is a sub-developed region with inflexible industrial structure and faces greater competitive pressure in the process of development, so conclusions drawn from researches at the national level are not necessarily applicable to Central China. Based on the existing research, this paper has made the following marginal innovations: Firstly, we used the entropy method to accurately estimation of policy environment pleasantness of central cities by considering both influence of restrictive and encouraging policies. Secondly, the relationship between the pleasant policy environment and green growth in Central China was theoretically and empirically analyzed. Lastly, implications to reduce the adverse impact of administration on green growth of the region were proposed.This paper uses the data of 80 prefecture-level cities in Central China. Governments’ expenditure, fixed assets, public service data, pollution emission data, foreign investments, GDP of the city, employment, population, and financial data can be found in China City Statistical Yearbooks from 2007 to 2019. Energy consumption and capital stock can be found in China Statistical Yearbooks, Statistical Yearbooks of 6 provinces in Central China from 2007 to 2019. Foreign trade scale can be found in Statistical Bulletins of each city from 2006 to 2018. Data of patents are from CNRDS database and the official website of National Bureau of Statistics. Data of some cities are excluded: data of Chaohu, which has been incorporated into Hefei, are excluded; data of Tianmen, Xiantao, Qianjiang, and Jiyuan are excluded, for these cities are county-level cities governed by provincial governments; and data of Enshi and Xiangxi are excluded. Indicators such as income, expenditure, and cost are all deflated by the price index of corresponding year, removing the impact of inflation.From 2006 to 2018, there were three landmark events that changed the policy environment for industrial development in Central China. We list these three important events on the timeline of For enterprises, a pleasant policy environment is composed of encouraging policies and loose restrictive policies. We used the method of Li et al. (2021) and Zhou, Ang, and Poh (2008) to integrate these two types of policies into one indicator based on the entropy method to measure policy environment in prefecture-level cities of Central China ,38. The n represents the number of observations, and m represents the number of indicators.The first step is to perform nonnegative processing to the data. Equations (1) and (2) adapt for the nonnegative processing of positive indicators and negative indicators separately; The second step is to calculate information entropy. Equation (4) is the information entropy. Then we can calculate weight value of each indicator:At last, we can calculate the composite index of policy environment:iS in Equation (6) is the composite index of policy environment. m is 8 and n is 1040 in our paper. A higher direct investment and public-service level for a government can make the city more attractive to enterprises and talents, which are positive indicators; on the other hand, loose environmental regulation implies lower pollution-control cost, making cities more attractive to enterprises, so that environmental regulation is a negative indicator. Limited by data availability, the government’s direct investment is measured by general budget expenditure of government and fixed asset investment per unit of GDP. The level of public service is measured by the number of schools, hospitals, and libraries. Environmental regulation is measured by the reciprocal of pollution emission per unit of GDP; pollution emission refers to sulfur dioxide emission, industrial smoke emission, and industrial wastewater emission. Policy environment in this paper is expressed as POLEN; in our paper, POLEN is iS in Equation (6). A larger POLEN value implies more of a pleasant policy environment for industrial development.POLEN) in Central China from 2006 to 2018. Besides a brief decline during the financial crisis in 2008, the pleasantness of the policy environment in Central China maintained a rising trend from 2006 to 2016. According to To further clarify the differences of in the policy environment among cities in Central China, we counted the number of cities whose policy environments are more pleasant than the average level of that year . The numFrom the above analysis, improving the pleasantness of the policy environment in Central China seems to not be conducive to regional green growth, so we propose Hypothesis 1. Hypothesis 1 (H1).Improving the policy environment in Central China will cause a direct negative effect on regional green growth.ENE represents green total factor productivity; POLEN represents policy environment of cities; X represents the vector composed of control variables, such as real income level of residents, foreign capital utilization, proportion of manufacturing, natural resources, level of urbanization, and financial support; i and t are the labels of city units and years, respectively; and iεt, is the random disturbance.Based on Hypothesis 1, the following econometric model was constructed:In addition to the direct impact, a change of policy environment may have indirect impacts on cities’ green growth. Firstly, in order to cope with the competition of cities and promote the coordinated development of cities at different hierarchies within the region, governments in Central China have issued industrial policies and coordination policies, which changed the spatial location of industries and affected the relative industrial scale of cities at different hierarchies. In Central China, provincial capitals have a huge “siphon effect” on the industries of other cities, thus indicating there are only two directions of industrial flow: ordinary cities to provincial capitals and provincial capitals to ordinary cities . SecondlHere we put forward Hypothesis 2:Hypothesis 2 (H2).Improving the pleasantness of the policy environment in Central China will have indirect impacts on green growth through affect relative economic scale between cities, industrial structure of cities, technological innovation, and foreign trade.ENE, POLEN, and X are consistent with Equation (7). MED represents mediation variables. In order to test Hypothesis 2, MED was replaced by the development gap with provincial capital , industrial specialization, foreign trade, and technological innovation. If β1 and β2 in Equation (9) are significant, then β1 in Equation (8) is significant at the same time, and we can then prove that change of the policy environment in sub-developed regions will have indirect effects on the green total factor productivity. Moreover, i and t are the labels of city units and years, respectively; and iεt, is the random disturbance.To test Hypothesis 2, we further constructed a set of empirical test models:ρ is green economic efficiency, which is used to indicate the level of green growth; kx and ky represent the input and output vectors of DMU, respectively; X and Y are input-output matrices; λ is a column vector with all elements equal to 1; m is the number of input variables; q is the number of output variables; and s− and s+ are slack variables. The input variables are capital, labor, and energy utilization. Since prefecture-level cities do not directly report capital stock, capital investment is measured by the value of fixed assets estimated by the perpetual inventory method. Labor input is measured by total employment of prefecture-level cities at the end of the year. The data for energy consumption in prefecture-level cities are not complete, so we used a method to estimate energy input. First, provincial-level data were used to calculate the energy consumption per unit GDP  of each province, and then the total energy consumption of prefecture-level cities was estimated by multiplying the GDP of prefecture-level cities by energy consumption per unit GDP of each province. The output variables are divided into good output variables (desirable output variables) and bad output variables (undesirable output variables). The good output variable is measured by the real regional GDP of prefecture-level cities. The bad output variable is pollution emission of Prefecture-level cities, which is measured by industrial wastewater emissions, sulfur dioxide emissions, and industrial smoke emissions. In this paper, ENE is used to represent green economic efficiency. On the other hand, the total industrial electricity consumption of prefecture-level cities is used to measure energy consumption, and then the green growth index is re-estimated as the dependent variable in the robust test, and it is represented by ENEA.This paper uses the green total factor productivity to measure the level of green growth. We draw on the Super-SBM (Slack-Based Model) Model of Tone (2001), using non-oriented and non-radical distance function, including undesirable outputs to measure the green economic efficiency of prefecture-level cities in Central China . The modENE) in the six central provinces from 2006 to 2018. First, the green total factor productivity in Central China decreased first and then increased. It shows that Central China used to pursue economic growth at the cost of destroying the environment, but environmental-protection pressure and sustainable-growth pressure forced Central China to change its development mode, as well as promote industrial transformation, and then improve the level of green growth. Second, the level of green growth in each province was very low in 2012; however, the green growth level of different provinces were quite different. The green total factor productivity of Anhui was significantly higher than that of other provinces in 2006 and 2018, while the green total factor productivity of Shanxi was always the lowest. This indicates that provinces with a high initial green growth level can quickly transform and return to the green growth path based on early industrial foundation and development experience. It also reflects the development dilemma after the shrinkage of coal industry in Shanxi Province. Third, the green total factor productivity of provincial capitals has been becoming higher than that of other cities in the province.AGG). A smaller AGG represents a larger development gap between prefecture-level cities and provincial capital. Due to high industrial isomorphism in Central China, the change of industrial structure can be described by regional specialization. The relative-diversification index can accurately reflect the industrial specialization of a region. At the same time, the relative-diversification index is more suitable for the horizontal comparison between cities than the absolute-diversification index is [RDI. We used the ratio of total foreign trade and GDP to measure the status of foreign trade of prefecture-level cities in Central China, excluding the impact of city size on the absolute amount of foreign trade. FX is used to represent the status of foreign trade in the cities in Central China. The level of technological innovation is measured by the number of invention patents granted in prefecture-level cities, and it is expressed as CRE.The change of the relative economic scale of ordinary prefecture-level cities and provincial capital is very conspicuous in Central China. Therefore, the development gap between prefecture-level cities and provincial capital is used to express change of industrial relocation, which is measured by the ratio of GDP of prefecture level city and GDP of provincial capital  of residents is measured by the per capita real wage of prefecture-level cities. The real utilization level of foreign capital (FDI) is measured by real foreign direct investment in prefecture-level cities. The proportion of manufacturing (MAN) is measured by the proportion of manufacturing employment in total employment of the city. The level of urbanization (CD) is measured by the proportion of non-agricultural population in total population of the city. The natural resource (NR) value is measured by the proportion of employment in agriculture, forestry, animal husbandry, sideline fisheries, and extractive industries in the total employment of the city; a higher NR means that the city has more natural resources [FANJR). The per capita real income level . Firstly, we excluded the data of six provincial capitals and re-estimated the coefficients of Equation (7). The economic scale of provincial capitals in Central China is so large that the regression results, including the data of provincial capitals, may not reflect the real impacts of changes in the policy environment on green growth within the region. The results of Model (2) show that, after excluding the data of provincial capitals, the coefficient of POLEN is still significantly negative and lower than the coefficient in Model (1), indicating that the pleasant policy environment of ordinary prefecture-level cities is more detrimental to green growth. Secondly, we excluded the data before 2010. The regression results of Model (3) show that the coefficient of POLEN is significantly negative, indicating that a more pleasant policy environment after 2011 caused higher ecological risks. Thirdly, we replaced the dependent variable ENE with ENEA. The corresponding regression results are reported in Model (4), showing that the coefficient of POLEN is significantly negative. Fourthly, because fixed-effects regression may have endogenous problems, the total number of industrial enterprises above designated size in the prefecture-level cities was selected as the instrumental variable of policy environment, and the instrumental variable method was adopted to estimate the coefficients of Equation (7). A pleasant policy environment intensifies the attraction of a city to enterprises, under which more large-scale enterprises can be cultivated. Therefore, the number of enterprises above the designated size in prefecture-level cities reflects the pleasantness of the policy environment. Theoretically, the number of enterprises does not directly affect environmental efficiency, so this instrumental variable is reasonable. Model (5) reports the corresponding regression results, showing that the coefficient of POLEN is still significantly negative. Lastly, the differential GMM regression method is adopted to deal with the endogeneity problems of other variables. The first-order lag term of ENE was added to the control variables; At most second-order lag terms of POLEN, CD, and MAN were selected as their own instrumental variables. Model (6) reports the corresponding results, showing that the coefficient of POLEN is significantly negative and there are no problems in regard to weak instrumental variables and overidentification. According to the results of the robustness test, it can be concluded that a pleasant policy environment in Central China is not conducive to green growth. The “promotion theory” of policy on green growth is not tenable in Central China.During this period, economic scale and growth rate of Central China have increased conspicuously, meaning that the decline of green total factor productivity is caused by the increase of pollution emissions. From the results, Central China excessively pursued economic goals, whereas it ignored environmental protection to some extent. Improving the pleasantness of the policy environment should have been “efficient administration” to attract talents and high-quality enterprises to promote industrial transformation and upgrading, but the governments were “captured” under the pressure of short-term economic growth, and then they increased the risks of pollution. As can be seen from POLEN in Model (1) is acceptable. Prefecture-level cities with more pleasant policy environment may bear greater industrial outflow pressure, even improving the pleasantness of policy environment cannot change the “siphon effect” of provincial capitals. Positive coefficient of AGG in Model (2) indicates current industrial agglomeration to provincial capitals is conducive to the improvement of green total factor productivity. From the perspective of productivity, industries that move into provincial capitals may improve productivity through positive externality of agglomeration, while industries stay in other cities can also improve productivity by improving the level of specialization. From the perspective of reducing pollution, the relocation of some industries to provincial capitals has reduced the pressure of pollution control in ordinary prefecture level cities and improved the overall efficiency of pollution control in Central China. The results of Model (3) and Model (4) show that improving the pleasantness of the policy environment can indirectly improve green total factor productivity through industrial diversification, indicating. However, this indirect positive effect can be found to be quite low by comparing coefficients of POLEN and AGG in Model (4). Positive coefficient of RDI in Model (3) indicate improving the pleasantness of the policy environment will encourage the development of diversified industries. Positive coefficient of RDI in Model (4) implies diversified agglomeration is more beneficial to green innovation through making the innovation chain more complete. Some scholars believe that developing countries have become “pollution refuge paradise” in the process of international trade: With the expansion of foreign trade, the environmental quality will decrease significantly [lnCRE in Model (8) is negative, which indicates that enterprises in Central China may pay more attention to technological innovation of improving productivity and has ignored environmental protection objectives to some extent.ficantly . As the Cities in Central China have great differences in geographical location, urban characteristics, and development status. By comparing FX variable as the standard to divide cities into two categories: cities with a large foreign-trade scale and cities with a small foreign-trade scale. According to the results of Model (3) and Model (4), improving the pleasantness of the policy environment in cities with a larger foreign-trade scale will have a greater adverse impact on green growth. This also confirms the conclusion of the mediating effect test: foreign trade in Central China has the problem of “pollution refuge”; the quality of foreign trade needs to be further improved. Finally, we used the proportion of manufacturing employment to measure the development level of manufacturing and used its average value in Central China as the standard to divide cities into two categories: cities with a high proportion of manufacturing and cities with a low proportion of manufacturing. The results of Model (5) and Model (6) show that a pleasant policy environment in cities with a high proportion of manufacturing in Central China has a greater adverse impact on green growth. Manufacturing is one of the pillar industries in Central China. Different from cities with a large proportion of light manufacturing, in Eastern China, cities in Central China have a large number of enterprises that belong to heavy industries which need huge investment in the early stage, such as equipment manufacturing, steel, automobile, and aviation. Thus, a high transformation cost makes it difficult for such cities to achieve leapfrog development through industrial transformation, and it induces more serious damage to the environment. Moreover, Central China needs to improve the overall operating profits and ecological efficiency of manufacturing in the future.Whether growth and environmental problems can be solved simultaneously through the intervention of policies has generated hot debates. The regression results in this paper suggest that, when improving the attraction of city to industries, governments in Central China will cause a net negative effect on regional green growth, meaning that governments in sub-developed regions take economic growth more seriously than environmental protection. According to the empirical test results, should cities in Central China eliminate governments’ intervention in industrial development? In this part, we demonstrate that it is very arbitrary to do so from two aspects.Market mechanism is not necessarily effective in promoting green growth. Price fundamentalism proposes that a price on emissions will reduce emissions more efficiently without the intervention of governments. Proponents of this viewpoint regard pollution as a market failure which will induce negative externalities. Pigouvian tax or purchase on emission right can reduce emissions effectively ,46. The There exists one important reason why sub-developed region such as Central China will not give up the option of the government’s intervention to promote economic growth, even though this will induce pollution: growth might not be easy to achieve without the support from governments. On the one hand, enterprises in Central China often have no comparative advantages for reasons such as relatively shorter survival time, lack of investments, and selling products with low profit margin, so subsidies’ removal will directly reduce their international competitiveness. On the other hand, fierce competition faced by sub-developed region forces local governments to actively participate in economic activities to break through the bottleneck. Green growth requires no substantial reduction in economic growth when improving the ecological environment. Thus, neither a sole market mechanism nor the current governments’ behavior in Central China can achieve this. It is more realistic to avoid regulation capture by improving the quality of administration than eliminating governments’ intervention to achieve green growth in such regions.(1)Strengthen environmental regulation. Implement stricter pollution control standards for enterprises. Establish a random inspection mechanism to ensure that enterprises deal with pollution emissions according to the required standards. Recruit professionals to strengthen law enforcement in environmental protection. Through the promotion of environmental supervision APPs on mobile devices, mobilize the public to participate in environmental supervision.(2)Restrict governments’ behaviors to prevent “regulation capture”. Strengthen supervision over officials to prevent bribery of polluting enterprises to corrupt officials. Strengthen the ability of the people’s Congress to identify economic development plans formulated by government, avoiding short-term growth at the expense of environmental damage. Establish a lifelong accountability and punishment mechanism for officials, restrict the investment attraction behavior of officials, and prevent environmental risks caused by preferential policies issued to achieve the goal of investment attraction.(3)Establish a reasonable transfer system on ecological and environmental payments between large cities and small cities to improve overall pollution-control capability in the region. Establish a trading market for pollution-emission rights; cities with serious pollution need to buy pollution-emission rights from cities with less pollution. Economically developed cities shall make financial transfer payments to economically underdeveloped cities. Ordinary cities need to subsidize cities in important ecological regions, because cities in ecological regions bear greater pressure in regard to pollution prevention and control. Encourage enterprises to introduce advanced production equipment and technology.(4)Improve the quality of foreign trade industries and stimulate innovations in environmental protection and pollution control. Cultivate a high-tech foreign trade industry and reduce the number of polluting foreign trade enterprises in the region. Shut down polluting foreign trade enterprises located in forest areas or along major rivers. Subsidize R&D cost of patents for pollution prevention and control and give tax relief to the income of such patents.The estimation results based on the data of cities in Central China from 2006 to 2018 show that the pleasantness of the policy environment for industrial development in Central China is increasing, but it contains the risks of damaging the ecological environment. Through empirically testing the relationship between policy environment and green growth, we came to the following conclusions: (1) The current attempts at improving the pleasantness of the policy environment in Central China is not conducive to the green growth of this region. After excluding the data of provincial capitals, changing the time span of the panel data, replacing dependent variable, and performing instrumental variable regression and differential GMM regression, the result is still robust. (2) Improving the pleasantness of the policy environment will have indirect negative impacts on green growth through widening the development gap between ordinary prefecture-level cities and provincial capitals, as well as encouraging foreign trade. A pleasant policy environment will stimulate industrial diversification in Central China, and this will then have an indirect positive effect on green growth. However, the policy environment does not indirectly affect green growth through changing the level of technological innovation. (3) The policy environment in Central China will have a heterogeneous effect on the green growth of cities. From the perspective of spatial heterogeneity, improving the pleasantness of the policy environment in southern cities, cities along major rivers, and cities belonging to provincial capital city groups will have a greater adverse impact on green growth. From the perspective of the heterogeneity of urban characteristics, the adverse impact of improving the pleasantness of the policy environment on green growth is more significant in small- and medium-sized cities, cities with a larger foreign-trade scale, and cities with a high proportion of manufacturing. This paper has two limitations: First, it only identified the possibility of regulation capture in governments’ behavior of Central China, whereas it did not clarify how regulation capture occurred and in which way regulation capture dominant governments’ behavior. Second, the comprehensive tax burden was not considered in estimating the policy environment for lack of data. Next, we, on the one hand, focus on the analysis of regulation capture and, on the other hand, expand data collection to improve the accuracy of policy environment estimated. In order to promote efficient administration, we propose the following:


The optimal positions of different types of nebulizer for aerosol delivery remain unclear.2O and positive end expiratory pressure (PEEP) set to 5 cmH2O, and with a target Vt of 500 ml. The bias flow of all the ventilators was set to 2 L/min. The three nebulizers were the continuous jet nebulizer (c-JN), the inspiratory synchronized jet nebulizer (i-JN), and the vibrating mesh nebulizer (VMN). The five nebulizer positions were as follows: at the Y-piece (position 1) and 15 cm from the Y-piece (position 2) between the endotracheal tube and the Y-piece, at the Y-piece (position 3) and 15 cm from the Y-piece (position 4) in the inspiratory limb; and at the humidifier inlet (position 5). Aerosols were collected with a disposable filter placed at the simulated lung outlet (n = 3) and were measured by UV spectrophotometry (276 nm). The measurements were compared under different experimental conditions.Three ICU ventilators employing three types of nebulizer were separately connected to a simulated lung to simulate nebulization during invasive ventilation. Assist/control-pressure control (A/C-PC) mode was utilized, with inspiratory pressure (Pi) set to 12 cmHThe aerosol delivery of c-JN, i-JN, and VMN was 5.33 ± 0.49~11.12 ± 0.36%, 7.73 ± 0.76~13.75 ± 0.46% and 11.13 ± 56–30.2 ± 1.63%, respectively. The higher aerosol delivery: for c-JN~Positions 2 (10.95 ± 0.15%), fori-JN~Positions 1 or 2 (12.91 ± 0.88% or 13.45 ± 0.42%), for VMN~Positions 4(29.03 ± 1.08%); the lower aerosol delivery: for c-JN~Positions 1, 3 or 5, fori-JN~Positions 4 or 5, for VMN~Positions 5.P < .05). However, no differences were observed between the highest aerosol delivery with c-JN and the lowest aerosol delivery with i-JN. Similar results were found between the lowest aerosol delivery with VMN and the highest aerosol delivery with c-JN /i-JN in the Avea ventilator. There were no differences in the highest aerosol delivery of each nebulizer among the different ventilators .The highest aerosol delivery:For c-JN at Position 2 (10.95 ± .15%), for i-JN at Position 1 or 2 (12.91 ± .88% or 13.45 ± .42%), for VMN at Positions 4 (29. 03 ± 1.08%); the lower aerosol delivery: for c-JN at Positions 1, 3 or 5, for i-JN at Positions 4 or 5, for VMN at Positions 5. The highest aerosol deliveryof c-JN was lower than that of i-JN while the VMN was the highest (all During adult mechanical ventilation, the type and position of nebulizer influences aerosol delivery efficiency, with no differences between ventilators. Delivery of therapeutic aerosol is an important component of treatment in many respi ratory disorders , 2, and Early studies found that aerosol delivery with a nebulizer to the distal airways/alveoli of a mechanically ventilated patient is influenced by ventilator settings for gas flow, residual volume, and bias flow, with the type of nebulizer employed, mode of nebulization, and position of the nebulizer in the circuit having particular importance . Howeverin vitro to investigate the position of c-JN, i-JN, and VMN, and the effect of different nebulizer types/modes and different ventilators on aerosol delivery.However, in all of these bench studies, only two or three positions were compared for each nebulizer type/mode. No consensus has emerged on the most favorable position for drug deposition in a laboratory setting. More importantly, to our knowledge, there are no studies comparing the effects of different ventilators on aerosol delivery. Here, we conducted a comprehensive comparison of aerosol delivery with three nebulizer types and five positions in three ICU ventilators during invasive ventilation 2O, inspiratory resistance 10 cmH2O/L/s, expiratory resistance 15 cmH2O/L/s, and maximum inspiratory pressure drop −8 cmH2O. To simulate the profile of the negative pressure created by respiratory muscles, 5% of the respiratory cycle time was spent in active inspiration, 3% in end inspiratory hold, and 15% in return of pressure to baseline. The breathing rate was set at 15 breaths/min.The Active Servo Lung 5 000 respiratory simulation system  is a precision breathing simulator that contains a piston that moves in a cylinder and can simulate different breathing patterns in candidates for invasive ventilation. The following parameters were applied for the simulated lung –20: comp2O and positive end expiratory pressure (PEEP) set to 5 cmH2O, and with a target Vt of 500 ml. The bias flow of all the ventilators was set to 2 L/min.Three ICU ventilators were used: V300 , Avea , and Servo-s . Of these, both the V300 and Avea ventilators have i-JN, whereas the Servo-s ventilator does not. The ventilators were connected to a humidifier  and an adult-sized, dual-limb, heated wire circuit , and then a Y-piece was connected to the lung model with a 30-cm long, 2.2-cm diameter tube. The humidifier was used with distilled water, set to invasive mode (37°C), and was turned on for a minimum of 20 min before the start of the experiment. The assist/control-pressure control (A/C-PC) mode was utilized, with inspiratory pressure (Pi) set to 12 cmHvia compressed oxygen and the flow meter  maintained the oxygen flow at 8 L/min; (2) i-JN where the nebulizer  was driven directly with the V300 and Avea ventilators during the inspiratory phase; and (3) VMN .Three nebulization modes were used: (1) c-JN where the nebulizer  was driven The bench model is illustrated in For nebulization, a 1-ml solution of 0.5% salbutamol  was diluted with 3 ml normal saline, and the solution was placed into the nebulizer. Aerosol particles were intercepted with a collection filter  placed at the simulated lung outlet. A stopwatch was used to record the nebulization time; the nebulization of JN was considered successfully completed when no visible evidence of nebulization was seen for 30 s; the nebulization of VMN was considered successfully completed when there was no visible evidence of nebulization. After nebulization, the ventilators and the simulated lung were turned off for at least 1 min. The experiments were run thrice under each experimental condition.2 = 0.99). The standard curve was used to calculate the corresponding salbutamol concentration and amount. All the experiments were performed by the same investigator (XD).Following each nebulization, the filter was washed with 10 ml normal saline to collect the aerosol. The filter was shaken using a vortex shaker  for 1 min to fully mix the normal saline and salbutamol aerosol particles. The washing solution was placed in a 1-ml quartz glass cup, and a UV spectrophotometer was used to measure the absorbance of the solution at a wavelength of 276 nm. The absorbance at this wavelength had a linear relationship with the concentration of the salbutamol solution over the range of 0 to 0.1 mg/ml, and the slope of the standard curve was 0.1426 . Aerosol delivery and ventilator performance are presented as means and standard deviations. A one-way analysis of variance (ANOVA) was conducted to compare the effects of the different interfaces on aerosol delivery and ventilator performance. The LSD-t method was used for pairwise comparison. Two-tailed formance –20.The aerosol delivery of c-JN, i-JN, and VMN was 5.33 ± 0.49~11.12 ± 0.36%, 7.73 ± 0.76~13.75 ± 0.46% and 11.13 ± 56~30.2 ± 1.63%, respectively. The mean aerosol delivery times were 10.68 ± 0.64, 46.43 ± 10.33, and 13.47 ± 0.67 min, respectively.p < 0.001), but there were no differences in aerosol delivery among the different ventilators   (p < 0.05).Aerosol delivery was higher when the nebulizer was placed in position 2 (10.95 ± 0.15%) than in the other positions  . Aerosolp < 0.001), but there were no differences in aerosol delivery among the different ventilators   (p < 0.001).Aerosol delivery was higher when the nebulizer was placed in position 1 (12.91 ± 0.88%) or 2 (13.45 ± 0.42%) than in the other positions  . Aerosolp < 0.001), but there were no differences in aerosol delivery among the different ventilators   (p < 0.001).Aerosol delivery was higher when the nebulizer was placed in position 4 (29.03 ± 1.08%) than in the other positions  . AerosolThe mean aerosol delivery of c-JN, i-JN, and VMN were 8.48 ± 1.76, 11.07 ± 2.27, and 20.03 ± 5.85%, respectively, with no significant difference between c-JN and i-JN, but both of them had significantly lower values than VMN.p < 0.05). However, no differences in aerosol delivery were observed between the highest aerosol delivery with c-JN  and the lowest aerosol delivery with i-JN . Similar results were found between the lowest aerosol delivery with VMN  and the highest aerosol delivery with c-JN /i-JN  in the Avea ventilator.The highest aerosol delivery of c-JN  was lower than that of i-JN , while VMN had the highest  , 1 (close to the Y-piece) or 2 (15 cm before the Y-piece), and 4 (15 cm from the Y-piece in the inspiratory limb), respectively. Besides, no differences were seen in the highest aerosol delivery for the nebulizer types among the different ventilators.Similar to a previous study , we founTwo studies , 26 have2 rebreathing remains to be further studied.In previous studies, i-JN was placed in completely different positions, including between the Y-piece and ETT, before the Y-piece, 15 cm from the Y-piece, and 30 cm from the Y-piece in the inspiratory limb , 17, 27.In five previous studies on the subject with VMN, four reported that the higher nebulizer position was at the humidifier inlet/15 cm from the ventilator with bias flow during invasive ventilation , 30, 31,To our knowledge, there is no study to compare the effects of different ventilators on aerosol delivery. We found no difference in the highest aerosol delivery among the different ventilators. However, the experiment was only conducted under one ventilator setting (PC mode) with the same bias flow, and the results need to be confirmed under more ventilator settings in further research, such as different respiratory rates, inspiratory pressures tidal volumes, and volume control modes.in vitro. Many factors influence the emitted dose and make it difficult to provide objective comparisons of clinical outcomes. Additional studies with healthy volunteers or patients are warranted to establish whether the in vitro results are clinically relevant. We only focused on the effects of the position and nebulizer type on aerosol delivery in vitro. The risks and benefits of placement of nebulizers, interruption of ventilation and increase risk of VAP in patients, impact of additional mechanical dead space, impact of the position in treating patients with heat moisture exchangers (HMEs), etc., should also be considered in the clinic. We did not use an ETT to connect the ventilator circuit and the lung simulator; instead we used a 30-mm tube. Second, the behavior of the nebulizer and the distribution of particle size may be affected by the properties of the drug solution and the brand of the nebulizer tested; only one brand of jet nebulizer was used in our study. For i-JN, we did not use the nebulizer developed for the ventilator because it was not available. However, all ventilator manufacturers recommend using their own i-JN, and it is difficult to use the recommended i-JN as disposable consumables in clinical practice, but i-JN is widely used in the clinical setting. Besides, the driving flow of the c-JN we used in this study was 8 L/min, which is close to that of the two ventilators . Third, the nebulization of JN was considered finished when there was no visible evidence of nebulization for a period of 30 s in our study, but not 1 min, which is consistent with some reports in the literature but not with international standards. Only three ventilators were used in our study with the same ventilator settings. Additional commercially available ventilators and ventilator settings need to be tested to confirm the results.Some limitations should be considered when interpreting the results of this study. First, the aerosol delivery dose was estimated During adult mechanical ventilation, positions matter in optimizing delivery efficiency. For the highest aerosol delivery in the proper nebulizer position, i-JN is marginally more efficient than c-JN, but VMN was much more efficient; If not, this phenomenon has changed. Besides, there were no differences between the ventilators with the same settings.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.HH, DX, BD, and WT conducted the study. HH, DX, and WT wrote the manuscript. DX, BD, and WT contributed to data collection. BD, HZ, WW, JK, and WT revised the manuscript and designed the study. All authors contributed to the article and approved the final version of the manuscript.This research was funded by the Science and Technology Planning Project, Shenyang (21-172-9-12).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Cyclocybe chaxingu is an edible and medicinal fungal species commonly cultivated in China. The major problems currently facing by growers of C. chaxingu is the random labeling of strains and strains aging and degeneration. Therefore, an evaluation of genetic diversity is essential for the conservation and reproducing programs of this species.C. chaxingu strains was analyzed using inter-simple sequence repeat (ISSR) and sequence-related amplified polymorphism (SRAP) markers.In the present study, 24 widely cultivated strains were collected from the main producing areas of China, and the genetic diversity analysis was performed. DNA polymorphism among these Chinese C. chaxnigu strains fall into five groups with a genetic distance values ranging from 0.06 to 0.60 by ISSR analysis, while the SRAP analysis divided the test strains into four groups within the range of genetic distance from 0.03 to 0.57. The results of the present study reveal a high level of genetic diversity among the widely cultivated C. chaxingu strains.Eight ISSR primers amplified a total of 75 DNA fragments of which 61 (81.33%) were polymorphic. Fifteen SRAP primer combinations amplified 166 fragments of which 132 (79.52%) were polymorphic. Cluster analysis showed that the  Cyclocybe chaxingu is an edible and medicinal species, which commonly cultivated in Jiangxi, Yunnan and Fujian provinces of China   method . The quaA total of 23 primers/primer pairs  that produced clearly distinguishable and reproducible fragments were selected and used in this study for ISSR and SRAP analysis . All of 2O. Amplification program was: 4 min of denaturing at 94 °C, 35 cycles of 35 s at 94 °C, 45 s at 46–55 °C , 1 µL of primer (10 µM/L), 1 µL of template DNA, and 10.5 µL of ddH2O. The amplification included an initial denaturation at 94 °C for 5 min, 5 cycles of 94 °C for 1 min, 35 °C for 1 min and 72 °C for 1 min, followed by 35 cycles of 1 min at 94 °C, 1 min at 50 °C, and 1 min at 72 °C, and a final extension of 10 min at 72 °C.For SRAP analysis, the reaction mixtures contained 12.5 µL of 2 × Taq PCR Master Mix , 1 µL of each primer (10 µM/L), 1 µL of template DNA, and 9.5 µL of ddHAmplified products were fractionated by electrophoresis in 2% (w/v) agarose/TAE gels, visualized under UV after staining with TS-Gelred , and documented using a gel documentation and image analysis system .Qualitative scoring of bands was done from gel photographs obtained from ISSR and SRAP analysis with “1″for presence and “0″for absence to generate a binary matrix. Only those bands amplified consistently were considered. Smeared and weak bands were excluded from the analysis. For data structures with only 0 and 1, Jaccard similarity coefficient is generally adopted . The genIn total, eight ISSR and 15 SRAP primers or primer pairs gave reproducible results that were further considered for data analysis. A total of 24 primers were initially screened to produce polymorphic patterns and only 8 of them were selected which gave reproducible and distinct polymorphic amplified products. For representational purposes, the extent of polymorphism revealed by primer P10 is shown in C. chaxingu strains, the genetic distances estimated based on Jaccard coefficient using ISSR data , and the two most distantly related strains were JAUCC 1847 and JAUCC 2924 with lowest similarity index (genetic distance was 0.57). Genetic distance estimated based on Jaccard coefficient obtained by SRAP profile with an average value of 0.39. Cluster analysis of SRAP data see  based onA total of 23 primers/primer pairs were used for the analysis of combined ISSR and SRAP data, genetic distance among all the test strains ranged from 0.06 (JAUCC 2192 and JAUCC 2196) up to 0.54 (JAUCC 1851 and JAUCC 1920). A total of 241 loci in 24 strains, out of which 193 (80.08%) were polymorphic, with an average of 10.48 polymorphic fragments. The co-phenetic correlation for the combined ISSR and SRAP dendrogram was estimated 0.97, corresponding to a very good fit. Dendrogram by using UPGMA and Jaccard coefficient grouped the 24 test strains into five main clusters with a maximum average contour width value . Within The PCoA based on ISSR and SRAP data revealed that the strains belonging to a particular cluster were grouped together in the PCoA plot . GroupinC. chaxingu is relatively intensive, but there is a lack of research on genetic diversity. Therefore, efforts have been made in the present study to characterize twenty-four strains of C. chaxingu collected from different parts of China, using inter-simple sequence repeat (ISSR) and sequence-related amplified polymorphism (SRAP) markers.As one of the most important edible fungi in China, the research on cultivation of C. chaxingu. Wild strains of C. chaxingu mostly occurs on decayed and dead wood of broadleaf trees such as Camellia oleifera, Populus spp. and Salix spp. It is mainly distributed in temperate and subtropical regions of China, such as Jiangxi, Fujian, Hunan, Sichuan and Yunnan Provinces   technique, the percentage of polymorphic loci amplified by AFLP primers (C. chaxingu strains (C. chaxingu for economic benefit, especially in Jiangxi and Fujian provinces. The lack of new germplasm and the haphazard-introduction of species from one region to another may impede breeding efforts. Therefore, explore more wild resources and breeding good character, high yield and stable C. chaxingu strains is conducive to the development of C. chaxingu industry. The success of strain selection depends on the investigation of genetic diversity (The host and geographical diversity were the root causes of the genetic diversity of ovinces  . In the  primers  was 94.1 primers . ISSR, S strains , suggestiversity .C. chaxingu should be introduced into breeding programs to increase the genetic heterogeneity and to improve the commercial characters of the cultivated strains. In summary, this study laid a foundation for genetic improvement and breeding of C. chaxingu.DNA maker system is an efficient tool to reveal genetic relationship among different genotypes through the numbers of polymorphisms detected  . Surveyi10.7717/peerj.14037/supp-1Table S1Click here for additional data file.10.7717/peerj.14037/supp-2Table S2Click here for additional data file.10.7717/peerj.14037/supp-3Supplemental Information 1Click here for additional data file.