Landraces represent a significant gene pool of African cultivated white Guinea yam diversity. They could, therefore, serve as a potential donor of important traits such as resilience to stresses as well as food quality attributes that may be useful in modern yam breeding. This study assessed the pattern of genetic variability, quantitative trait loci (QTLs), alleles, and genetic merits of landraces, which could be exploited in breeding for more sustainable yam production in Africa.A total of 86 white Guinea yam landraces representing the popular landraces in Nigeria alongside 16 elite clones were used for this study. The yam landraces were genotyped using 4,819 DArTseq SNP markers and profiled using key productivity and food quality traits.Genetic population structure through admixture and hierarchical clustering methods revealed the presence of three major genetic groups. Genome-wide association scan identified thirteen SNP markers associated with five key traits, suggesting that landraces constitute a source of valuable genes for productivity and food quality traits. Further dissection of their genetic merits in yam breeding using the Genomic Prediction of Cross Performance (GPCP) allowed identifying several landraces with high crossing merit for multiple traits. Thirteen landraces were identified as potential genitors to develop segregating progenies to improve multiple traits simultaneously for desired gains in yam breeding. Results of this study provide valuable insights into the patterns and the merits of local genetic diversity which can be utilized for identifying desirable genes and alleles of interest in yam breeding for Africa. Yam plaThe cultivated yam plays an essential role in ensuring food security and improving the livelihoods of millions of people in Africa . AccordiLandraces with different historical origins, distinct identities, values, and adaptations constitute the dominant parts of the cultivated variability exhibited by yam in West Africa . ObidiegDifferent techniques exist for genetic diversity assessment. Of these techniques, morphological markers are routinely used to assess yam genetic diversity . DiversiThe use of molecular markers is, an accurate method of identifying genotypes at the species level and harnessing genetic diversity in yam crop . Differe22.1A panel of 86 white Guinea yam landraces collected from 10 major yam growing regions of Nigeria (2.2The collected landraces (86) alongside the elite clones (16) were evaluated in two cropping seasons in 2018 and 2019 at IITA, Ibadan, Nigeria . The materials were planted using an alpha lattice design with two replications and five plants per plot. A spacing of 1 m × 1 m was used between ridges and plants on ridges. The study materials were phenotyped for key traits such as tuber yield, dry matter, tuber browning (tuber flesh oxidation), yam mosaic virus resistance, and plant vigor as per procedures described in the yam crop ontology . Phenoty2.3D. rotundata reference genome v2  was extracted from the leaf samples using the CTAB procedure with slight modification . DNA quaenome v2 .2.4Hapmap file received from the DArT sequencing platform was converted into a variant call format (VCF). A total of 22,140 SNP markers were identified from the raw data. After filtering for 1% minor allele frequency (MAF), 10% missing values, and low sequence depth (<5), 4,432 SNP markers were retained for further analyses. Data were then imputed using beagle software v4.0  implemented in GAPIT (Genome Association and Prediction Integrated Tool) – R package  and visu2.6th genotype in the jth repetition; b is a 1 × r vector of unknown and unobservable rep fixed effects; u is an m[= 1 × g] vector of unknown and unobservable genotype random effects; X is an n × r design matrix of 0s and 1s relating y to b; Z represents an n × r design matrix generated from marker; e is an n × r vector of random.Each trait was analyzed using a mixed-effect model based on the following equation as given in The total genetic values were used to calculate a multi-trait index based on the factor analysis and ideotype-design (FAI-BLUP) index . FAI-BLU2.7F1 = a(p - q – y) + d[2pq + y(q - p)]We estimated the prediction of cross performance in ASReml-R  using thF1 is the predicted mean genotypic value of the cross (F1), a and d are additive and dominance effect of the SNP marker, p and q represent the allele dosage in one parent and y=pp'=q-q' represents the gene frequency difference between two parents. The total cross merit value was then estimated for all the landraces considering them as parents. The sex of each parent was determined to eliminate cross-combinations between landraces of the same sex. It is noteworthy that D. rotundata is predominantly dioecious, with male and female flowers borne on separate individuals . SNP density plot revealed the presence of a relatively high SNP density in the telomeric region across the 20 chromosomes. Observed heterozygosity (Ho) varied from 0.113 (on chromosome 17) to 0.173 (on chromosome 3), with an average of 0.141. The expected heterozygosity (He) ranged between 0.210 (on chromosome 1) and 0.257 (on chromosome 8), with a mean of 0.234. The minor allele frequency (MAF) ranged between 0.134 (on chromosome 1) and 0.170 (on chromosome 8), with an average of 0.151. Polymorphic information content (PIC) varied from 0.232 (on chromosome 1) to 0.265 (on chromosome 16), with a mean value of 0.247. Gene diversity ranged from 0.259 (on chromosome 1) to 0.297 (on chromosome 16), with an average of 0.275  showed a rapid elbow at clusters Figure 2The genetic distance between the landraces was computed as 1 - IBS (identity by state), with IBS defined as the probability that alleles drawn at random from two individuals at the same locus are the same. The grouping pattern based on the IBS clustered the 86 white yam farmers’ landraces into three clusters Figure 3A pairwise dissimilarity genetic distance matrix revealed that the genetic distance for the entire landraces ranged from 0.0073 to 0.4015. The two landraces that displayed the lowest genetic distance had the same name (Punch) but were collected from different locations and hence considered as the same genetic material. In cluster 1, the lowest genetic distance was 0.0225, while the highest distance was 0.053. Landraces in this cluster had relatively higher fresh tuber yield, lower AUDPC values for YMV disease severity scores, and rough tubers Table 3.Classification based on admixture and hierarchical clustering (HC) methods agreed in assigning most of the landraces Table 4.3.32 and LOD scores above 4. Notably, the region of chromosome 19 was identified as linked with four of the traits analyzed. Also, the SNP marker “chr19_5210667” was associated with yield and plant vigor. Using the naïve analysis, no significant QTL was detected above the suggestive threshold  that explained 74.2% of the total variation among the traits as most discriminative. The communality after the varimax rotation of each trait’s variance explained by the three factors ranged from 0.518 for vigor to 0.843 for tuber oxidative browning. The five traits were grouped into three based on their highest genetic correlations for the first three factors . However, the predicted selection gain was in desired direction for three traits out the five. The strengths and weaknesses of the selected 13 yam landraces were presented in a radar plot which accounted by the proportion of each factor to the FAI-BLUP index of the landraces  generated net crossing merit for 937 cross combinations that ranged from -2.04 to 2.4  were more densely covered with genes and had higher recombination rates than peri-centromeric heterochromatin regions. These results in line with those of D. alata accessions from Côte d’Ivoire using SNP markers. High levels of genetic variation among clusters of yam landraces in Nigeria indicated a lack of gene flow, possibly due to low seed-yam  exchange among farmers in geographically distant areas. In contrast, the low variation within cluster revealed a low degree of genetic differentiation, which may be attributed to regional preferences for some dominant varieties  can be found below: AA conceptualized the study idea. PA, JEO, AA, and AE assembled the genetic materials. AA, AE, and PAA coordinated data generation. PAA analyzed the data and wrote the first draft with inputs from AA. All authors contributed to the article and approved the submitted version.


A key component of the global blue economy strategy is the sustainable extraction of marine resources and conservation of marine environments through networks of marine protected areas (MPAs). Connectivity and representativity are essential factors that underlie successful implementation of MPA networks, which can safeguard biological diversity and ecosystem function, and ultimately support the blue economy strategy by balancing ocean use with conservation. New “big data” omics approaches, including genomics and transcriptomics, are becoming essential tools for the development and maintenance of MPA networks. Current molecular omics techniques, including population-scale genome sequencing, have direct applications for assessing population connectivity and for evaluating how genetic variation is represented within and among MPAs. Effective baseline characterization and long-term, scalable, and comprehensive monitoring are essential for successful MPA management, and omics approaches hold great promise to characterize the full range of marine life, spanning the microbiome to megafauna across a range of environmental conditions . Omics tools, such as eDNA metabarcoding can provide a cost-effective basis for biodiversity monitoring in large and remote conservation areas. Here we provide an overview of current omics applications for conservation planning and monitoring, with a focus on metabarcoding, metagenomics, and population genomics. Emerging approaches, including whole-genome sequencing, characterization of genomic architecture, epigenomics, and genomic vulnerability to climate change are also reviewed. We demonstrate that the operationalization of omics tools can enhance the design, monitoring, and management of MPAs and thus will play an important role in a modern and comprehensive blue economy strategy. This emsensuMarine protected areas represent a spatial tool for the conservation of biodiversity, and fit into a broader approach for ecosystem-based management and spatial planning . MPAs caHere, we provide an overview of molecular omics approaches, focusing on genomics, transcriptomics, and environmental DNA (eDNA) metabarcoding, and emphasize how development and application of such methods can address key needs for the successful design and management of MPAs to support the goals of a blue economy. We demonstrate how omics tools can be used to characterize genetic diversity, evaluate functional connectivity, identify adaptive variation and environmental association, and to predict population-level responses to climate change. These tools can reveal fine-scale evolutionary processes required for conservation planning that were previously either not considered or not detectable . We alsoRepresentativity and connectivity are core design principles when establishing MPA networks to ensure that the protection is equitably distributed through space. Genomic approaches, such as reduced representation genome sequencing  revealed that reproductive productivity within an MPA disproportionately contributed to recruitment in surrounding areas . In other cases, genetic methods have revealed evidence of selection for limited dispersal, which correspondingly drove fine-scale genetic structure associated with the MPA protective measures , and have uncovered genome-wide differences between declining and stable populations  leading to design configurations that did not include evolutionarily unique populations and were thus less resilient to environmental change. These results emphasize the value of collecting genetic information a priori. Incorporating population-level stratification into the MPA network design can promote the conservation of genetic diversity and resiliency following the stewardship aspects of the BES.Recent genomic studies have also revealed the magnitudes of population decline using measures of genetic diversity such as effective population size  came an increased pressure on nearshore cod stocks. A phenotypically and genetically distinct population of inshore cod reside within the Gilbert Bay MPA, which was established to protect this population  used genomic data to identify priority areas for MPAs  from a polluted port compared to those in an adjacent MPA, and RNA-sequencing supported corresponding differences in the expression of stress-related genes supporting the conservation efficacy of the MPA. Epigenetic changes, such as DNA methylation, can provide another heritable means by which organisms may alter their phenotype and rapidly respond to environmental or anthropogenic stressors without changes to their underlying DNA sequence (Spatial conservation tools like MPAs help to regulate anthropogenic impacts on populations, and these impacts can be monitored using molecular omics approaches. Short-term human impacts, including overfishing, aquaculture escape events, and pollution, have produced measurable differences in adaptive variation among populations , demonstsequence . As techA growing area of omics application and research is eDNA metabarcoding, which has revolutionized biomonitoring in aquatic ecosystems through detection of invasive and/or rare species  and the New eDNA studies are developing PCR-free methods that employ direct sequencing of genomic DNA through shotgun metagenomic sequencing or “genome skimming” , which cVertebrate Genomes Project. As the number of resources for species grows, so does the ability to integrate datasets into larger-scale single-species studies (Interest in the blue economy and research involving genomics in MPAs has expanded markedly since 2018 . The pro studies , or mult studies . We recoMulti-species genomic approaches can provide information for understanding interactions between various levels of the ecosystem within an MPA network . For exaThe non-invasive nature of eDNA metabarcoding sampling makes it an ideal alternative to direct, in some cases destructive, monitoring techniques, and a strong candidate for real-time autonomous sampling e.g., . SimilarTo contribute to real-time monitoring for the blue economy, it has even been proposed that ships could be designed to filter water along their transport routes, potentially covering large geographic areas and making the data readily available to resource managers and the public . The “reMolecular omics technologies offer significant potential to improve and transform marine conservation planning, and recognition of its importance for monitoring and providing real-time information for the blue economy is quickly taking hold. The ability to use millions of genome-wide markers to delineate populations, assess connectivity, and determine environmental drivers is invaluable when designing networks of MPAs and subsequently managing and monitoring them. While the fields of genomics and eDNA are perpetually evolving, this should not be seen as a reason to refrain from their operationalization now. The data and sequences generated can be incorporated into future or expanded studies, be used as the basis for monitoring programs, or provide historical baselines as we enter an era of unprecedented biodiversity change. The adoption of these techniques toward marine conservation will allow targeted planning of protected areas and effective monitoring of changes in populations from anthropogenic and climate impacts, together enabling targeted and adaptive marine conservation.


Early identification of hereditary cancer risk would save lives, but genetic testing (GT) has been inadequate. We assessed i) trends for hereditary breast and ovarian cancer (HBOC), Lynch syndrome, and other GT and ii) factors associated with receipt of GT.We used data from the Arkansas All-Payer Claims Database from January 2013 through June 2018 , December 2017 (state employee), or December 2016 (Medicare) and identified enrollees with ≥1 month of enrollment. Using Current Procedural Terminology (CPT-4) codes, rates for GT were calculated per 100,000 person-quarters and time series regressions estimated. Second, GT and covariate information for enrollees with 24 months of continuous enrollment were used to estimate separate logistic regression models for each GT category.Among 2,520,575 unique enrollees, HBOC testing rates were 2.2 (Medicaid), 22.0 , 40.4 (state employee), and 13.1(Medicare) per 100,000 person-quarters and increased linearly across all plans. Older age , female sex , higher comorbidity burden , mental disorders , and state employee coverage  were positively associated with HBOC testing. Less than 1 of 10,000 enrollees received Lynch syndrome testing, while < 5 of 10,000 received HBOC testing.GT rates for hereditary cancer syndromes have increased in Arkansas but remain low. Receipt of GT was explained with high discrimination by sex and plan type.Expansion of GT for hereditary cancer risk in Arkansas is needed to identify high-risk individuals who could benefit from risk-reduction strategies.The online version contains supplementary material available at 10.1186/s13053-022-00226-0. BRCA1 in 1994, yet no more than 1%–10% of individuals at risk for these syndromes have been identified [Screening individuals unaffected by cancer for hereditary cancer syndromes (HCS) provides opportunities to reduce cancer risk by offering preventive strategies to those with elevated risk to mitigate or even eliminate cancer development. Significant advances in our understanding of the genes that underlie the most common HCS, hereditary breast and ovarian cancer (HBOC) and Lynch syndrome, have been made since the cloning of entified . The lanentified . Simultaentified , 7. Agaientified .The need to expand utilization of genetic testing for HCS is clear, especially in the group of mutation carriers who are currently unaffected by cancer and therefore have the greatest potential to benefit from risk reduction. Data from the 2015 National Health Interview Survey (NHIS) indicate that around 2.5 million people have ever received cancer genetic testing, representing roughly 1% of the population . SeveralTP53, VHL, and MLH1 based on the complexity of the testing procedure), and a composite measure of receipt of any cancer genetic testing, stratified by those with commercial, Medicaid, state employee, or Medicare coverage in Arkansas. The second objective was to identify demographic, clinical, and insurance coverage factors associated with enrollees receiving those four types of genetic testing.This study sought to describe the trends in genetic testing in a state with historically low levels of cancer genetic testing. The first objective was to assess the quarterly trends of genetic testing in four categories: HBOC testing, Lynch syndrome testing, tier 2 molecular pathology procedures (billing codes which include genes such as The Arkansas All-Payer Claims Database (APCD) is a state-level claims database that is harmonized across multiple payers and provides a nearly universal portrait of healthcare utilization of insured people in the state of Arkansas. It includes claims from commercial, Medicaid, state employee, and Medicare health plans for Arkansans. Data from January 2013 through June 2018 were used for commercial and Medicaid enrollees; due to lags in data availability, data from January 2013 through December 2017 were used for state employees and from January 2013 through December 2016 for Medicare enrollees. Medicare is the federal health insurance program for people who are 65 or older and younger persons with disabilities or end stage renal disease. Medicaid is a public insurance program for low-income families that is administered by states and jointly funded by the federal and state governments.A separate sample set was constructed for each of the two objectives. For the trend analysis, enrollees with at least one month of enrollment in both medical and pharmacy benefits in each quarter were identified for each of the four insurance plan types. Enrollees were excluded from the sample if they had missing age or sex information. Enrollees who had overlapping enrollment in more than one plan type were only included in one plan using the following hierarchy: Medicare > Medicaid > state employee > commercial. For example, if an individual had overlapping enrollment in Medicare and Medicaid, they were only considered enrolled in Medicare. The total number of unique enrollees in each quarter for each plan type were retained as the denominator of persons who could have received genetic testing.For the second objective, where exploratory models were estimated, individuals with 24 months of continuous enrollment in both medical and pharmacy benefits were identified for each of the four plan types. For individuals with more than 24 months of continuous enrollment, the most recent 24 months were considered. Individuals with more than one month of overlapping enrollment in two or more plan types were included in only one plan using the following hierarchy: state employee > commercial > Medicare > Medicaid. This hierarchy was adopted based on the empirical results for the rates of healthcare claims in the overlapping segments of the respective plans. The samples in the four plan types were combined into an overall sample. Individuals with missing age or sex were excluded. Enrollees who had a cancer diagnosis prior to genetic testing in the 24-month window and those less than 10 years of age at the start of the window were also excluded . Unfortunately for this study’s purpose, these codes do not distinguish testing for genes related to cancer risk from those with non-cancer health risks; since important HCS genes such as TP53, VHL, and MLH1 are examples of tier 2 procedures, they were included in the analysis to avoid missing any cancer genetic testing. Additionally, a category called “other HCS panel testing” was used for HCS besides HBOC and Lynch syndrome that have specific billing codes, which included testing for hereditary polyposis/colorectal cancer risk, multiple endocrine neoplasia, and Cowden syndrome. Codes for broad test panels that included genes for multiple HCS were also included in the “other HCS” category. A separate analysis for the other HCS testing category was not conducted due to low prevalence; however, genetic tests for these syndromes were included in the composite “any cancer genetic test” category. Genetic tests in the HBOC, Lynch syndrome, tier 2 molecular pathology procedures, and other HCS panel testing categories were all included in the “any cancer genetic test” category. More than one inpatient or outpatient claim for genetic testing on the same day for an enrollee was counted as a single test. The quarterly rates for the genetic test measures were calculated for each quarter across four insurance plan types . Quarterly rates were also calculated for age- and sex-stratified groups: i) male enrollees <18 years old, ii) female enrollees <18 years old, iii) male enrollees 18–64 years old, iv) female enrollees 18–64 years old, v) male enrollees ≥65 years old, and vi) female enrollees ≥65 years old. Because of the different time periods for data across the four insurance plans, the age-sex stratified analyses were conducted using January 2013 through December 2016 data.Genetic tests were identified from the inpatient and outpatient claims using Current Procedural Terminology (CPT-4) codes Table . Tests wFor the second objective, separate binary measures were created for whether a subject received testing in any of the four genetic test categories and were used as the dependent variables in separate exploratory models. The factors explored were: age , age squared, sex (recorded on the member enrollment file), mental health conditions recorded in inpatient or outpatient claims , health plan type, three-digit zip code region of residence in Arkansas , and a measure of comorbidity (modified Elixhauser index). Age and age squared were included to account for a potential non-linear relationship between age and testing. Anxiety disorders, developmental disorders and other miscellaneous mental disorders were identified using ICD-9-CM and ICD-10-CM diagnosis codes. Nicotine dependence was identified using diagnosis codes or prescription claims for nicotine replacement therapy  regressions with a first-order autoregressive error term. Linear and quadratic terms were used for the time (in quarters) variable. If the quadratic terms did not attain statistical significance, the models were re-estimated using only the linear term. For the exploratory models, logistic regression was used. Due to low test rates, Firth’s bias correction was applied. The predictive ability of the models were evaluated using c-statistic and Brier score for discrimination and calibration respectively. All analyses were conducted using SAS 9.4 and R 4.0.1. A two-sided p-value of 0.05 was used to determine statistical significance.A total of 1,516,850 Medicaid, 1,501,828 commercial, 154,529 state employee, and 519,955 Medicare plan enrollees had at least one month of medical and pharmacy benefit enrollment in the aim 1 sample. The overall sample of unduplicated enrollees for the trend analysis was 2,520,575. The average age was 37 years; 53% were female (data not shown). For the second objective (exploratory models), a total of 1,495,960 individuals with 24 months of continuous medical and pharmacy enrollment were identified. The average age was 45 years; 56% were female . Supplementary Table 1. Diagnosis codes used to identify mental health disorders. Supplementary Figure 1. Sample selection for the exploratory models (2nd objective of the study). Supplementary Figure 2. Actual and predicted rates of Hereditary Breast and Ovarian Cancer (HBOC) genetic testing in Medicaid enrollees. Supplementary Figure 3. Actual and predicted rates of Hereditary Breast and Ovarian Cancer (HBOC) genetic testing in Commercial plan enrollees. Supplementary Figure 4. Actual and predicted rates of Hereditary Breast and Ovarian Cancer (HBOC) genetic testing in State Employee plan enrollees. Supplementary Figure 5. Actual and predicted rates of Hereditary Breast and Ovarian Cancer (HBOC) genetic testing in Medicare enrollees. Supplementary Figure 6. Actual and predicted rates of Lynch syndrome genetic testing in Medicaid enrollees. Supplementary Figure 7. Actual and predicted rates of Lynch syndrome genetic testing in Commercial enrollees. Supplementary Figure 8. Actual and predicted rates of Lynch syndrome genetic testing in State employee plan enrollees. Supplementary Figure 9. Actual and predicted rates of Lynch syndrome genetic testing in Medicare enrollees. Supplementary Figure 10. Actual and predicted rates of Tier 2 molecular pathology procedures* in Medicaid enrollees. Supplementary Figure 11. Actual and predicted rates of Tier 2 molecular pathology procedures* in Commercial plan enrollees. Supplementary Figure 12. Actual and predicted rates of Tier 2 molecular pathology procedures* in State employee plan enrollees. Supplementary Figure 13. Actual and predicted rates of Tier 2 molecular pathology procedures* in Medicare enrollees. Supplementary Figure 14. Actual and predicted rates of any cancer genetic testing* in Medicaid enrollees. Supplementary Figure 15. Actual and predicted rates of any cancer genetic testing* in Commercial plan enrollees. Supplementary Figure 16. Actual and predicted rates of any cancer genetic testing* in State employee plan enrollees. Supplementary Figure 17. Actual and predicted rates of any cancer genetic testing* in Medicare enrollees. Supplementary Figure 18. Actual and predicted rates of Hereditary Breast and Ovarian Cancer (HBOC) genetic testing in men 18–64 years old. Supplementary Figure 19. Actual and predicted rates of Hereditary Breast and Ovarian Cancer (HBOC) genetic testing in men ≥65 years old. Supplementary Figure 20. Actual and predicted rates of Hereditary Breast and Ovarian Cancer (HBOC) genetic testing in women 18–64 years old. Supplementary Figure 21. Actual and predicted rates of Hereditary Breast and Ovarian Cancer (HBOC) genetic testing in women ≥65 years old. Supplementary Figure 22. Actual and predicted rates of Lynch syndrome genetic testing in men 18–64 years old. Supplementary Figure 23. Actual and predicted rates of Lynch syndrome genetic testing in men ≥65 years old. Supplementary Figure 24. Actual and predicted rates of Lynch syndrome genetic testing in women 18–64 years old. Supplementary Figure 25. Actual and predicted rates of Lynch syndrome genetic testing in women ≥65 years old. Supplementary Figure 26. Actual and predicted rates of Tier 2 molecular pathology procedures* in male enrollees <18 years old. Supplementary Figure 27. Actual and predicted rates of Tier 2 molecular pathology procedures* in men 18–64 years old. Supplementary Figure 28. Actual and predicted rates of Tier 2 molecular pathology procedures* in men ≥65 years old. Supplementary Figure 29. Actual and predicted rates Tier 2 molecular pathology procedures* in female enrollees <18 years old. Supplementary Figure 30. Actual and predicted rates of Tier 2 molecular pathology procedures* in women 18–64 years old. Supplementary Figure 31. Actual and predicted rates of Tier 2 molecular pathology procedures* in women ≥65 years old. Supplementary Figure 32. Actual and predicted rates of any cancer genetic testing* in male enrollees <18 years old. Supplementary Figure 33. Actual and predicted rates of any cancer genetic testing* in men 18–64 years old. Supplementary Figure 34. Actual and predicted rates of any cancer genetic testing* in men ≥65 years old. Supplementary Figure 35. Actual and predicted rates of any cancer genetic testing* in female enrollees <18 years old. Supplementary Figure 36. Actual and predicted rates of any cancer genetic testing* in women 18–64 years old. Supplementary Figure 37. Actual and predicted rates of any cancer genetic testing* in women.


This study was conducted on eight Indian zebu breeds with an objective to reveal any cryptic genetic bottlenecks in the population. The alarming reduction in the effective population size of these breeds was the major reason for conducting this study. Genotyping of 27 FAO/ISAG-recommended microsatellite markers and further statistical analysis using allele frequency data under three different models of mutation revealed the absence of recent genetic bottlenecks in any of the populations under study. The results from the qualitative test of mode shift distortion were in accordance with the above findings. Even though the results of the present study indicated little or no critical loss of rare alleles in any of the breeds understudy, the chances of this happening cannot be completely ignored. One of the plausible explanations for this scenario could be the potential gene flow from commercial taurine cattle through indiscriminate crossbreeding in the native tract of these breeds. Thus, we here emphasize the need of further initiatives for improving selective breeding practices in order to conserve and effectively utilize the existing South Indian zebu cattle genetic diversity.p > 0.05) in any of the studied breeds. Similarly, the standardized differences test and Wilcoxon sign rank test revealed no concrete evidence for the occurrence of a recent genetic bottleneck in South Indian zebu cattle breeds. The qualitative test for mode-shift distortion revealed a normal L-shaped distribution of allele frequencies, suggesting a lack of evidence for the loss of low-frequency alleles in all the investigated South Indian zebu cattle breeds.During the last few decades, the effective population size of indigenous zebu cattle breeds has declined drastically, resulting in the classification of some of them into the vulnerable, endangered, or critically endangered category. Drastic reductions in the effective size of a population may result in genetic bottlenecks and can affect within-breed genetic variability and its viability. The present study was undertaken with the objective of evaluating South Indian zebu cattle populations for mutation drift equilibrium and to detect the occurrence of recent genetic bottleneck events. A total of 293 cattle from eight indigenous breeds were genotyped at 27 FAO/ISAG-recommended microsatellite marker loci. Three different statistical tests, viz., the sign test, standardized differences test, and Wilcoxon sign rank test were performed using allele frequency data to detect loci with heterozygosity excess under the infinite alleles, stepwise, and two-phase mutation models. Under the infinite alleles model, the observed number of loci with heterozygosity excess (He > Heq) ranged between 10 and 19 among the investigated cattle breeds. However, the observed heterozygosity excess was not statistically significant ( Bos primigenius diverged into Bos taurus and Bos indicus between 280,000 [B. indicus probably took place in the Indus valley region in modern-day Pakistan [History depicts, with evidence, that the wild aurochs or  280,000  and 330,Pakistan .Even though India has the second largest cattle population in the world, the indigenous cattle population has shown a decline of six per cent during the last five years . The majIntensified efforts for conservation and the sustainable use of farm animal genetic resources are essential to prevent and reverse genetic diversity erosion. Drastic reductions in the effective size of a population may result in demographic bottlenecks and can affect within-breed genetic variability—particularly allelic diversity . ReducedA total of 293 blood samples from eight different indigenous cattle breeds, viz., Kangayam, Umblachery, Pulikulam, Deoni, Ongole, Hallikar, Vechur, and Punganur were utilized from their breeding tract for the present study. The photographs, distribution, geography, origin, and genetic relationships of the cattle breeds under investigation are available in a related publication from the authors . The catBlood was collected by jugular venipuncture in EDTA-coated vacutainer vials. DNA was extracted following the standard phenol chloroform method . The isoTo identify any loci with heterozygosity excess, three tests, viz., the sign test, standardized differences test and Wilcoxon sign rank test were performed under the assumption of three different mutation models using allele frequency data . The mutThe basic diversity indices like mean observed number of alleles (ko), mean expected Hardy-Weinberg equilibrium (HWE) heterozygosity (He), and mean expected mutation drift equilibrium heterozygosity (Heq) under different mutation models are presented in Among the investigated cattle breeds, the allelic diversity was lowest in Vechur and Punganur cattle (ko = 6.04). Similarly, the mean expected HWE heterozygosity was lowest in Vechur cattle (He = 0.672).The mean expected HWE heterozygosity (He) varied between 0.662 (Kangayam) and 0.741 (Punganur) among the studied cattle breeds. The mean expected mutation drift equilibrium heterozygosity (Heq) was estimated under the assumption of three models of microsatellite evolution, viz., the infinite allele model (IAM), stepwise mutation model (SMM) and two-phase model (TPM). The estimated Heq for the investigated breeds was lowest under the IAM with the mean values ranging from 0.619 (Kangayam) to 0.753 (Pulikulam), while it was highest under the SMM with the mean values varying between 0.725 (Kangayam) and 0.823 (Pulikulam). The estimated Heq under the TPM was intermediate between the IAM and SMM and ranged from 0.676 (Kangayam) to 0.792 (Pulikulam) among the investigated breeds. To evaluate the investigated cattle breeds for deviations from mutation drift equilibrium, three different statistical methods, viz., the sign test, standardized differences test, and Wilcoxon sign rank test were employed. All the three tests compared the mean expected HWE heterozygosity (He) and mean expected mutation drift equilibrium heterozygosity (Heq) under the assumption of the IAM, SMM, and TPM. Under the IAM, the observed number of loci with heterozygosity excess (He > Heq) ranged between 10 (Pulikulam) and 19 (Kangayam and Deoni) among the investigated cattle breeds .P > 0.05). Under SMM, the number of loci with heterozygosity excess (He > Heq) varied from 4 (Deoni) to 10 (Umblachery), and the number of loci with heterozygosity deficiency (Heq > He) exceeded the number with heterozygosity excess in all the breeds investigated. Under the TPM, the observed number of loci with heterozygosity excess was intermediate between the IAM and SMM—thus ranging between 8 (Pulikulam) and 15 (Deoni). Under this mutation model, the observed number of loci with heterozygosity excess exceeded the number of loci with heterozygosity deficit only in the Hallikar breed, but without any statistical significance (P > 0.05). Thus, the sign test did not reveal significant deviations from the mutation drift equilibrium in any of the investigated South Indian draught cattle breeds. With the exception of the Pulikulam, the number of loci with heterozygosity excess (He > Heq) exceeded the number of loci with heterozygosity deficiency (Heq > He). However, the observed heterozygosity excess was not statistically significant in any of the studied breeds (P < 0.05) indicating a deviation from the mutation drift equilibrium. However, under the SMM, the T2 statistics were negative and statistically significant in all the breeds (P < 0.01). Similarly, under the TPM, the T2 statistics were negative in all the studied breeds. but significant in five of them—with the exception of the Kangayam, Hallikar, and Punganur. The results of the one-tailed Wilcoxon sign rank test for heterozygosity under different mutation models are presented in The results of the standardized differences test under different mutation models are presented in P < 0.05). However, such a heterozygosity excess was not observed in these populations when assumed under the SMM and TPM models. These differences in detection of significant heterozygosity excess across the mutation models have been reported earlier in Indian poultry [Under the IAM, the Deoni and Kangayam cattle breeds showed significant heterozygosity excess and deviations from the mutation drift ( poultry , Marathw poultry , and Ind poultry .With genetic bottleneck events expected to result in the loss of rare alleles, a qualitative graphical test plotting the allele frequency distribution will show a characteristic mode-shift distortion from the normal L-shaped distribution. The graphical method consists of grouping alleles into each of 10 allele frequency classes and then plotting a frequency histogram. In the present study, all the eight cattle breeds revealed a normal L-shaped distribution of allele frequencies , suggestThe allelic diversity was lowest in the Vechur and Punganur breeds of cattle and the mean expected HWE heterozygosity was lowest in the Vechur. The population size of the two dwarf cattle breeds has steadily declined in the past few decades. As per the DADF , the totThe purpose of this study was to evaluate indigenous South Indian cattle breeds for the occurrence of recent genetic bottlenecks. Most of these cattle breeds are draught or dual-purpose types that were used for ploughing or carting during earlier days. During the last few decades, due to mechanization and other reasons, the population size of many of these breeds has declined drastically. For example, only few hundred breedable females and <5 breedable males are available for the Vechur breed, with an estimated effective population size of <10. Punganur cattle have a total population of less than 3000 and an effective population size of ~200. Similarly, the effective population size of the Umblachery and Bargur breeds is 1580 and 1581, respectively . SimilarThe estimated Heq is consistent with earlier studies that have reported calculations of the mean expected mutation drift equilibrium heterozygosity under the assumption of different mutation models . The IAMThe deviation of a population from the mutation drift equilibrium was tested here with three parametric tests.viz., the sign test, standardized differences test, and Wilcoxon sign rank test . All theFurthermore, the qualitative test plotting the allele frequency distribution was carried out in all the breeds. This will show a characteristic mode-shift distortion from the normal L-shaped distribution if the population has undergone a genetic bottleneck. The graphical method consists of grouping alleles into each of 10 allele frequency classes and then plotting a frequency histogram. However, none of the breeds showed a distortion of the L-shaped graph, depicting the absence of genetic bottlenecks in all the breeds studied. The detection of bottlenecks is complicated by several factors, including the timing and duration of the bottleneck, the extent of decline in population size, immigration, and the level of pre-bottleneck genetic diversity. All these factors can potentially obscure the genetic signals of population declines ,27,28. GThe present study revealed no concrete evidence for the occurrence of a recent genetic bottleneck in South Indian zebu cattle breeds. As bottlenecks tend to cause reductions in the genetic variability and fitness of populations, resources for the demographic monitoring of South Indian cattle need to be managed carefully and efficiently. Further initiatives need to be made for improving selective breeding practices in order to conserve and effectively utilize the existing South Indian zebu cattle genetic diversity. For example, the breed-specific conservation centres established by state governments may practice open nucleus breeding schemes not only to improve the stock of superior germplasm but also to improve diversity. Such centres may also serve as bull stations for the production and distribution of purebred semen for artificial insemination programs in native breed tracts. Furthermore, the awareness of farmers of data recording and scientific breeding practices needs to be ensured for the conservation and improvement of locally adapted cattle populations in South India.


The combination of multiple imaging modalities in a single microscopy system can enable new insights into biological processes. In this work, we describe the construction and rigorous characterization of a custom microscope with multimodal imaging in a single, cost-effective system. Our design utilizes advances in LED technology, robotics, and open-source software, along with existing optical components and precision optomechanical parts to offer a modular and versatile design. This microscope is operated using software written in Arduino and Python and has the ability to run multi-day automated imaging experiments when placed inside of a cell culture incubator. Additionally, we provide and demonstrate methods to validate images taken in brightfield and darkfield, along with validation and optimization for differential phase contrast (DPC) quantitative phase imaging. Live cell imaging is a powerful approach to study and quantify the behavior of individual cells. The development of new microscopy hardware supports this approach by increasing the accessibility and precision of live cell imaging. In particular, the combination of multiple approaches in a multimodal microscope can be used to obtain complementary data about a sample. Many different microscope modalities can be readily combined on a single microscope such as darkfield (DF) and fluorescence microscopy Open-source systems enable researchers to build their own microscopes for a fraction of the cost of a commercial system while also having the flexibility of being customizable for the unique needs of the researcher, such as the ability to add brightfield, darkfield, fluorescence or phase contrast modalities. One downside to many systems incorporating QPI with one or more of these modalities is that they often require complex optical configurations. This is one of the factors behind the emergence of computational microscopy and LED array microscopes When used alone or in addition to other imaging modalities, label free imaging techniques such as QPI offer opportunities to study live single cell attributes like mass accumulation, heterogeneity, and drug response There are numerous examples of commercially available and open-source microscopes and/or microscope attachments that implement QPI along with other microscopy modalities. This can be achieved, for example, by purchasing a commercial research microscope, such as those available from Olympus, Nikon, or Zeiss, and outfitting it with a commercially available phase imaging system from Phasics or Phi Optics. Then, due to the multiple output ports on the microscope, these commercial research microscopes can be readily adapted to combine QPI with other microscopy modalities such as brightfield, phase contrast, fluorescence, or darkfield microscopy depending on the application. In addition to these, Phase Holographic Imaging produces a microscope for measuring the phase shift of live cells, with a small form factor allowing it to fit inside a small tissue culture incubator while imaging, or with two systems inside a standard size incubator. The Livecyte microscope, sold by PhaseFocus, integrates brightfield, QPI, as well as 7 channels of fluorescence imaging with onboard tissue culture conditions for live cell imaging all into a single platform. While several commercial options are available, most are very expensive, often costing upwards of $100,000 for the entire system. Therefore, important alternatives to these commercial systems are open-source platforms that enable researchers to build their own microscopes to substitute for these expensive platforms.However, an important problem facing researchers that use these open-source systems is the rigorous characterization of the system that is required to validate quantitative measurements. For users of commercial systems, such calibration is often provided or performed by the manufacturer. In terms of user-performed calibrations, previous work in QPI has used expensive phase targets or microbeads in commercially available index matching fluid for quantitative phase validation Here we demonstrate the fabrication and validation of a multimodal LED array microscope. We demonstrate the validation and fidelity of quantitative phase measurements using microbeads embedded in an NOA73  medium through accurately and reproducibly assessing the difference in refractive index between the two materials. QPI is then used to demonstrate how to tune the measurement and reconstruction parameters of the system by measuring the optical volume of the polystyrene beads, with validation against the theoretical expectation. Furthermore, we demonstrate how to determine the correct illumination angles for darkfield microscopy and characterize the signal-to-noise ratio (SNR) of the system.The multimodal microscope presented here requires two major components for operation, the LED array, along with the required electronics to drive it, and the optical system a, b. In We used a 16x24 LED array with 4.7 mm pitch for sample illumination a, b. TheWe used an Arduino Metro M4 Express to drive the LED array and the stepper motor used for the Z-stage to focus the microscope. The Arduino Metro M4 Express is a custom Arduino designed by Adafruit built on the open-source Arduino platform and is up to 10x faster than a traditional Arduino Uno with a clock speed of 120 MHz. This faster Arduino microcontroller allows the pattern on the LED array to be changed several times per second. The Arduino Metro M4 is USB compatible, controllable with a computer via a serial connection, and interfaces with devices such as the LED array through the I2C protocol using the open-source libraries provided by Adafruit. The Arduino Metro M4 Express is an inexpensive microcontroller, but can be readily replaced by a Metro M4 Grand Central if more input/output (I/O) pins are needed for niche applications or by an Arduino Uno/Mega if rapidly changing the pattern on the LED array is not required .The Arduino Protoboard is used to interface the Arduino Metro M4 express with the other electronic components it is used to control. Pins are soldered into the protoboard so that it can be plugged into the I/O pins on the Arduino Metro M4 Express, connecting them to leads on the protoboard. Wires are then soldered into the protoboard to create permanent connections between the electrical components, such as the motor and LED array, and the I/O pins on the Arduino .The bipolar stepper motor (3.9 V) is used for focusing the microscope a, b. SteFor detection, we used a simple optical system composed of an objective lens, a mirror, a tube lens, and a camera. Isometric and side views of a 3D model show the fully built detection arm separate from the rest of the constructed microscope a, b. We Increasing the coherence parameter increases the range of spatial frequencies collected by the system. For obtaining QPI images the coherence parameter must be greater than 1, with a value of approximately 1.5 giving improved image quality We used a Grasshopper3 machine vision camera from Teledyne-FLIR for image acquisition (GS3-U3-23S6M-C). This camera readily interfaces with the SM1 threads of the Thorlabs lens tubes used here for optomechanics using a C-mount adapter. The camera can be used with an SDK provided by Teledyne-FLIR, allowing it to be controlled using Python software to modify settings such as exposure time, frame rate, gain, and bit depth, and used for image acquisition.We used a Thorlabs high-speed translation stage for high-speed imaging on this microscope. The stage can be controlled using a joystick for easy use. The Thorlabs XY-stage can also be controlled using Python so that the settings can be changed during long periods of imaging or automated experiments. We chose this stage specifically to match the speed and accuracy requirements of our experiments. However, it can also be swapped for other open hardware designs that significantly lower the overall cost of the system The objective lens on this microscope is mounted on a manual translation stage connected to a bipolar stepper motor to automate its movements. The stage has a range of 1.5 mm, long enough for most focusing applications. The high resolution of this stage is determined by the combination of the low translation per revolution (50 µm/rev) and the 400 steps per revolution operation of the stepper motor giving a translation of 0.125 µm/step.•Begin by screwing the c-mount adapter into the c-mount threads of the camera.•Attach SM1 coupler without its outer retention rings to the SM1 internal threads on the c-mount adapter.•Screw the SM1 adjustable lens tube into the SM1 coupler.•Screw an outer retention ring onto the SM1 adjustable lens tube then screw a 30 mm to 60 mm cage adapter (X-shaped), then another outer retention ring followed by an SM2-SM1 adapter.•Screw the SM2 lens tube onto the SM2-SM1 adapter till the edge of the lens tube is flush against the surface of the adapter.•Prepare the tube lens by screwing it into the tube lens adapter.•Using the SM2 coupler, connect the end of the SM2 lens tube and the image plane side of the tube lens adapter.•Screw the 60 mm cage plate onto the objective plane side of the tube lens adapter.•Using a pair of calipers and following •Slot the 8-inch rods along the 60 mm holes to connect the 60 mm cage plate and the X-shaped adapter to prevent the system from rotating.•Slide another X-shaped adapter onto the other side of the 60 mm cage plate using the 8-inch rods and lock everything along the 8-inch rods in place by tightening the screws•Prepare the mirror by screwing in the half inch rods.•Attach the mirror to the X-shaped adapter by sliding the rails and locking them in place.•Screw the 1-inch rods into the mirror.•Prepare the Z-stage by unscrewing the knob until it is completely out, then screw in the fine hex adjuster.•Screw the objective lens into the objective lens adapter, then screw the group into the Z-stage.•Slide the Z-stage onto the 1-inch rods.•Cover the rails between the mirror and Z-stage with black aluminum foil, being careful to keep it out of the optical path, to help reduce points of entry for dust.The isometric and side views of the 3D model are shown to give an overview of the optical assembly isolated from the rest of the microscope platform a, b. To •Cut a 37 mm hole out of the breadboard or microscope base according to the file titled BreadboardDrawing.pdf. It can either be milled or cut out using a 37 mm hole saw with the pilot bit centered on the breadboard hole at coordinates  such that the origin is the front left corner of the breadboard.•Debur the hole and clean any pieces of debris that may damage the optics.•Attach rubber, vibration damping feet to one side of the posts and the breadboard to the other side.•Place the microscope base into its intended location .•Install the optical rail at the edge furthest from the front of the breadboard as shown in the drawing titled MicroscopePlatformDrawing.pdf.•Attach the rail carrier to the precision optical rail and use the thumb screw to fix it near the center line of the breadboard.•Attach the T-slotted frame mounting foot for 1.5″ rails to the rail carrier using the T-framing fastener.We used a high-precision anodized-aluminum breadboard as the base to install all the microscope components, but it is not required. It can be substituted with an aluminum sheet of similar dimensions or thicker to support the weight of the microscope while providing sufficient strength at a light weight. To prepare the microscope platform:•Screw in the fine hex adjustor all the way, then unscrew it 3 turns in order to avoid damaging the motor during testing and allow the correct installation of the flexible shaft.•Attach the motor to the motor bracket.•Install the motor rail on the bottom side of the microscope breadboard 2 in. away from the fine hex adjustor a, b and •Connect the fine hex adjustor and the motor using the flexible shaft.•Attach the motor bracket to the motor rail using the T-frame fastener by sliding the base inside the motor rail and using the screws to fix them in place.•Adjust the height of the motor to allow the flexible shaft to be straight without putting tension on it and keep the motor from touching the benchtop surface.To attach the motor to the fine hex adjuster on the Z-stage:Using the aluminum bar and a mill, fabricate the adapters for the stage according to the AdapterForStage.pdf drawing provided as a supplemental file. Online services such as SendCutSend or its equivalent can also be used by submitting a scale drawing made in AutoCAD or Adobe Illustrator. This part can be substituted with a 3D printed one if not used with biological samples, as the layer lines of fused deposition modeling (FDM) 3D printed models make it difficult to keep the system sterile for use in a cell culture incubator. Another option is to 3D print the part using a material that can be autoclaved, such as polypropylene or polycarbonate.•Using two right-angle corner brackets, attach the two T-slotted rails together using the T-framing fasteners, as shown in •Fabricate the LED array adapter according to the drawing titled LEDArrayAdapterDrawing.pdf. This can be done using a CNC mill or ordered using the drawing from SendCutSend.•Attach 4 corner brackets to the aluminum LED array adapter.•Attach the LED array adapter using LED screws, LED nuts, and unthreaded spacers. Use the T-framing fasteners and the 1.5″ corner brackets to interface the LED array adapter to the T-slotted rails. Slide the LED array adapter along the T-slotted rail to the place where the 4 panels of the LED array are approximately centered over the breadboard drilled hole then tighten the T-framing fasteners to fix the LED array in place.The first installation of the LED does not need to be precise as it will be adjusted based on both the microscopy modalities required (e.g. brightfield vs. darkfield) and the centering procedure described in the validation and characterization section below.•Fabricate the stage adapters using the plans titled AdapterForStageDrawing.pdf. Alternatively this part can be ordered from SendCutSend.•Attach the stage adapters as shown in the plans titled MicroscopePlatformDrawing.pdf using four ¼-20 screws.•Attach the high-speed XY-stage to the adapters.•Level the stage using a sensitive level (±1°) and shims placed between the stage and its adapter when the microscope is in its final location where it will be used for image acquisition.•Follow the wiring diagram to wire the motor and LED array to the proto shield .Fig. 4Wi•Attach the proto shield to the Metro.•https://learn.adafruit.com/adafruit-metro-m4-express-featuring-atsamd51/setup to set up the Arduino IDE with the appropriate libraries to flash code to SAMD Arduino Boards. Alternatively, you can skip this step if you choose to use an Arduino Uno.Follow the instructions at this link: •https://learn.adafruit.com/16x24-led-matrix/introduction to set up the Arduino IDE with the appropriate libraries to run the LED array.Follow the instructions at this link: •Connect the Metro to a computer using a micro-USB cable.•Flash the provided code  for the Arduino using the Arduino IDE.Make sure to use sufficiently long wires (approximately 30–50 cm) for wiring the Arduino Metro.•Connect the camera to the computer using the camera USB.•Install the microscope objective onto the lens tube attached to the top of the Z-stage.github.com/Zangle-Lab, 10.5281/zenodo.6841116). Each piece of hardware has its own Python class, making the software configurable in case alternative parts are used. If hardware from another manufacturer is used, changes to the class corresponding to this hardware will be required. However, if the same functions are used, the other classes will not be affected. For example, use of a camera from another manufacturer would require changes to the functions in the Camera class only. Inheritance is used based on the class hierarchy shown in AcquireSnapshots.py can be used to visit saved positions and automatically acquire brightfield, darkfield, and the four asymmetric illumination images that can be used for phase retrieval with the included software •Install Python 3.6. This is the only version compatible with Spinnaker (Teledyne-FLIR)•Download Spinnaker from the Teledyne-FLIR website and install both Spinnaker and SpinView (camera GUI)•Install Pyspin using the command: $ python -m pip install pyspinMetro.py, and change the default value of the variable serial_port to the appropriate COM port for the Arduino on your computer, which can be identified using Device Manager. Next open the file StageXY.py; the first two functions are:To operate the microscope, a few of the default settings need to be modified. First, navigate to the folder containing the open-source software package written in Python, open the file connectXconnectYChange the variable SerialNum to be equal to the serial number on the back of the stage controller BBD202. There should be a different serial number for both the X and Y stages. This will allow the StageXY class to communicate with the high-speed stage.LaunchMicroscopeGUI.py to make the main window appear , it is often easier to focus the sample using asymmetric illumination due to the lack of contrast when using standard brightfield.Underneath the controls for the LED array are the controls for the •Use the joystick to navigate and find the region of interest to be imaged.•Once located, click the button “save xy-pos” to log the coordinates for this position.•Then use the Z-stage motor controls to focus the sample in the field of view.•Once the sample is in focus, click the button “save z-pos” to log the focus position of the objective lens. This will allow you to return to this position later and automatically refocus it.•Additional positions may be logged by repeating steps 1–4 of this procedure.To create a position list for the coordinates to be imaged:•Saved positions can be reviewed by clicking on the “first pos” button, returning you to the first position that you logged.•The image may be out of focus. It can be focused automatically by clicking the button “focus” as long as a focus position was previously logged for this position.•The “next pos” button will allow you to cycle through positions to review selected positions. You can go back to the previous position by clicking “prev pos.” At each position, click focus to automatically focus the location using the previously saved focus position.•xy locations to be imaged to a.csv file. Then click the “save z-pos list” button to save the focus positions to a file.When finished, click the “save xy-pos list” button to save the •Press the “close button” to close the Find Locations window. This will return you to the Main Window.•Press the button “close stage” to close the connection to the XY-stage.•Press the button “close metro” to close the connection to the Arduino Metro M4 Express.•Press the button “close” to close the GUI.•AcquireSnapshots.py” and set the variable froot to the file path where you would like to save the images.Open the file “•Set the variable framesPerHr to the number of frames you want to image at each saved location each hour. It will then automatically compute the amount of time to wait between each imaging cycle.•Set the variable numFrames to the total number of frames to image at each location.•AcquireSnapshots.py” to cycle through each position and acquire four brightfield images with asymmetric illumination and a darkfield image. The frames for each location will be saved in a different folder named “pos#” such that # is the position number based on the order in which they were saved.Return to the command line and run the script “•To process the brightfield and phase images, open the file “DPC_main.m” in Matlab.•Change the variable froot to the file path containing the folders named “pos#” such that # represents the location number for each imaged location.•Set the variable numPos to the number of positions that were imaged.•Set the variable numFrames to the number of frames imaged at each location.•Set the variable fstart to be equal to a string that describes the experiment, usually “samplename_date_”. The position and frame number will be appended to the end of fstart to give each processed image a unique filename.•Set the variable: rot_an = [90 180];•Set the variable: reg2 = 1e-3;•Click “run” to process the images. The images will be saved in froot, and each file will have a unique name. Each of the saved images will be saved in the following format: fstart_pos#_frame#.mat. Each of the saved.mat files contains two variables. Phase is the raw phase image computed from the four asymmetric images obtained from the microscope. BF is the brightfield image.To acquire images:a priori knowledge of the geometry of microscope elements, such as the locations of individual LEDs on the array relative to the objective lens, is used for the reconstruction of the quantitative data Custom built systems require rigorous characterization to validate proper function. The first important calibration step is to validate that the LED array is centered over the objective lens to ensure that the intensity of light incident on the camera is equal from each half of the LED array illuminated at a given time. This is especially important in the case of quantitative measurements because regardless of whether the measurement is reconstructed either in the spatial domain or Fourier domain, Proper calibration of pixel size is important for everything from adding a relevant scalebar to images as well as for proper computation of optical volume from QPI images. To calibrate the pixel size, we imaged a transparent ruler with 10 µm graduations a. We theσ and found that the larger value of σ, the greater accuracy of the results. At a maximum tested value of σ = 2.2, we found that the measured optical volume was 38.7 µm3, about 5 % greater than the expected value of 36.9 µm3 and at σ = 1.9, the measured optical volume was 2 % lower than the expected value  n = 1.56) is another inexpensive material that is readily available and has a similar refractive index to polystyrene beads. This close match in refractive index makes samples with this construction a reliable phase standard as they do not introduce phase wrapping errors 3, which is 1.4 % greater than the theoretical value of 34.1 µm3  corresponding to using all 8 LEDs up to the edge of the 68 mm wide LED array (r = 32.5 mm) placed at d = 110 mm away from the sample for maximum illumination intensity. The NA of the objective is 0.25, therefore we expected that an inner numerical aperture greater than 0.25  would result in darkfield illumination. This corresponds to an inner radius of 4 LEDs with NA = 0.25. In practice, however, we found that an inner radius of 6 LEDs  is required to achieve a completely dark background  for darkfield, we used the same beads sample used for phase validation. From these images, we estimate SNR as: of 50±7 .Fig. 13S3 cells/cm2 prior to imaging with the multimodal microscope. AM-3 media consists of 80 % MCDB153 , 20 % L-15 medium , 2.5 % FBS , 0.2 % bovine pituitary extract , 10 ng/ml insulin-like growth factor, 5 ng/ml EGF , 5 µg/mL transferrin, 3 ng/ml BFGF, 3 µg/mL heparin, 0.18 µg/mL hydrocortisone, 1.68 mM calcium chloride, and 10 nM endothelin-1. Brightfield imaging with one of the DPC half-illumination patterns highlights gradients in optical path length, enabling clear visualization of cells (The entire microscope assembly is sized to fit within a standard cell culture incubator for live cell imaging. Although many of the microscope parts are not certified for operation in a high humidity (95 %) environment, we have not noted a degradation in performance of the system, including the camera and XY stage, over 3 device-years of operation. As a demonstration, MTG-021 melanoma cells established from a patient derived xenograft (PDX) were cultured in AM-3 media and plated at 3.4x10of cells a. Darkfiof cells b. Finall2 to 2,675 µm2 to 2,063 µm2 . At the same time, the cell grew from 432 pg to 592 pg from 1 to 15 h, then lost mass by the 31 h timepoint to a final mass of 532 pg, indicating the possible onset of cell death.As an example of multimodal imaging, brightfield, darkfield, and QPI data of the same cell over time reveals distinct changes in the cell . BrightfIn summary, we demonstrated how to build, validate, and optimize a multimodal microscope that is research capable and versatile in design. Additionally, our validation protocol covers the needed aspects of quantitative validation for both quantitative phase and darkfield measurements. The resulting microscope is modular and can be extended, e.g. with the addition of illumination and a dichroic filter cube for fluorescence.This system performs well in comparison to commercial systems, either in published work or within our own lab. Optical volume and refractive index measurements of polystyrene beads with this system agree with what we expect from known material properties  as well Open hardware microscope designs tend to be purpose built for specific imaging modalities, including but not limited to fluorescence More than half of the cost for the system presented here is the XY-stage and its controller. This XY-stage has remarkable speed, precision and accuracy, as required for some cases, such as drug screening The work presented in this study did not require the use of any human or animal subjects.Tarek E. Moustafa: Conceptualization, Methodology, Software, Data curation, Formal analysis, Investigation, Validation, Visualization, Writing – original draft. Edward R. Polanco: Conceptualization, Methodology, Software, Data curation, Formal analysis, Investigation, Visualization, Writing – original draft. Rachel L. Belote: Methodology, Writing – review & editing. Robert L. Judson-Torres: Funding acquisition, Writing – review & editing. Thomas A. Zangle: Supervision, Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Maize is one of the leading global cereals, and in South Sudan maize cultivation occurs in nearly all of the country’s agro-ecological zones. Despite its widespread cultivation, farmers in South Sudan depend on undeveloped varieties, which results in very low yields in the field. In the current study, 27 simple sequence repeat (SSR) markers were used to investigate genetic diversity and population structures among 37 landrace maize accessions collected from farmers’ fields in South Sudan. In total, 200 alleles were revealed with an average of 7.4 alleles per locus and a range from 3.0 to 13.0 alleles per locus. The observed heterozygosity values ranged from 0.06 to 0.91 with an average of 0.35. High polymorphic information content (PIC) values were identified with a mean of 0.69, which indicates the informativeness of the chosen SSR loci. Genetic structure analysis revealed a moderate genetic differentiation among the maize populations with a fixation index of 0.16, while there was very high genetic differentiation within the groups of populations of three regions with a mean fixation index (F) of 0.37. An unweighted pair group method with an arithmetic mean (UPGMA) dendrogram clustered the 37 maize accessions into three groups with 43% genetic similarity. The clustering pattern of the maize accessions was moderately consistent with their collection area. The findings of this study will provide maize breeders with a better understanding of maize diversification as well as a reserve of genetic resources for use in the selection of advantageous and useful resources for the development of maize varieties in South Sudan. Zea mays L.) is a well-known staple food and the most cultivated cereal worldwide [Maize  that are being imported from neighboring countries and others that are distributed by humanitarian organizations. The yields of these imported genotypes remain extremely low owing to variations in climate. Landraces are diverse, heterogeneous populations that are commonly sought by farmers because of their adaptation to the local environment of a particular agro-ecological zone and local demand [In South Sudan, maize is the second most dominant cereal after sorghum and accounts for approximately 20% of the total area of cereals. In 2021, the total yield of maize in South Sudan was 131,000 tons , averagil demand ,14.Morphological characterization is a prerequisite for breeding, but it is greatly affected by the environment. Reciprocally, molecular characterization is free from environmental effects and enables highly valuable complementary genetic information to be obtained ,16. MoleTo our knowledge, no diversity studies based on molecular markers on South Sudanese maize landraces have been carried out to date, and there has only been very limited research regarding morphological characteristics and maize adaptation. This study is a pioneer in revealing genetic diversity and the population structure of maize landrace germplasm collected in South Sudan. A total of 15 different agricultural locations were explored in our germplasm collection survey to gather maize landrace accessions from traditional maize farms. These accessions represent different environments of maize production areas in South Sudan and were collected with the aim of understanding the genetic diversity of the indigenous accessions. For this purpose, 27 SSR primers have been constructed to study the 37 maize landrace accessions collected from South Sudan. The object of this study is to estimate the genetic diversity and population structure.To analyze the genetic diversity of maize genetic resources in South Sudan with regard to geographical conditions, we used 37 maize landrace accessions that were collected from three regions: northern (six populations), central (four populations), and southern (five populations). These maize accessions were also divided into 15 populations from the three geographic locations of South Sudan , Table 1The genetic diversity indices per population are presented in . This stAdditionally, in this study, in order to understand the genetic diversity in accordance with the climatic characteristics and geographical areas of South Sudan, we conducted a genetic diversity analysis at the regional level within the groups of populations, namely the southern region population, central region population, and northern region population . The Na ΔK was K = 2  for the 37 accessions of maize landraces  algorithm  showed tA comprehensive understanding of the genetic diversity of native genetic resources is crucial for initiating and managing strategies for the conservation of crop genetic resources. Despite the long history of maize cultivation in South Sudan, a lack of developed varieties still forces farmers to turn to indigenous germplasm for traditional farming. To change this prevalent pattern, the formation of knowledge about the level of genetic diversity in landraces is essential because it will allow for the more efficient and effective use of resources in plant improvement . LandracIn the present study, a total of 200 alleles was revealed using 27 SSR markers with an average of 7.4 alleles per locus, which is similar to or higher than the average number of alleles observed in previous studies using SSR markers ,28,29. TThe fixation index (F) calculates population differences by virtue of the genetic structure, and a value over 0.15 can be considered significant in differentiating populations ,41. In o∆K value . SSR amplification was performed using EX Taq PCR kit . A total volume of 20 µL of product was composed of 20 ng genomic DNA, 0.2 mM dNTPs, 0.5 µM forward and reverse primers, 1×EX Taq buffer, and 1 unit of EX Taq polymerase for polymerase chain reaction (PCR) of the SSR loci. The PCR protocol was conducted as follows: first step, initial denaturation at 94 °C for 5 min; second step, 1 min of denaturation at 94 °C, 1 min of annealing at 65 °C, and 2 min of extension at 72 °C. The second step was repeated 36 times with the temperature of the annealing stage lowered by decreases of 1 °C at every annealing stage until an eventual annealing temperature of 55 °C was reached. After completing the first two steps, a final third step was carried out of 5 min of extension at 72 °C. DNA electrophoresis analysis was conducted with a mini vertical electrophoresis system  for the PCR products. Three µL of the final product was mixed with 3 µL of formamide loading dye . Two µL from each sample was loaded onto a 6% acrylamide-bis acrylamide gel (19:1) in 0.5X Tris-borate-EDTA (TBE) buffer and electrophoresed at 250 V for 30–40 min. The separated fragments were then visualized using ethidium bromide (EtBr).In this study, a total of 100 SSR primer sets (distributed over 10 maize chromosomes) were used in a preliminary experiment using six landrace accessions collected from three populations  of South Sudan. The 27 SSR primer sets, as shown in K statistic, based on the degree of change in the log probability of data between K values [http://taylor0.biology.ucla.edu/structHarvester/, accessed on 25 February 2022) based on the STRUCTURE results. AMOVA was performed using GenAlEx 6.5 software [DNA fragments amplified using the SSR primers were scored as present (1) or absent (0). The number of alleles, PIC, MAF, and GD were identified using Powermarker version 3.25 . Popgen3K values , was calsoftware . Fixatiosoftware (1)F=1−HThis study is a pioneering approach to the evaluation of genetic diversity and population structure of South Sudan maize. A wide understanding of genetic relatedness is crucial for the improvement of any crop. In order to carry out this study, we used 27 SSR markers to investigate the genetic diversity and population structure of native maize accessions collected from across South Sudan. The amplification of polymorphic loci disclosed the potential of the selected SSR markers for investigating genetic diversity among the collected genotypes. Generally, the indices revealed distinct molecular diversity among the maize landrace populations. In addition, the overall genetic index of inbreeding coefficient (Fst) showed a moderate genetic differentiation among the maize populations and a huge genetic differentiation within the groups of populations from the three regions . AMOVA revealed greater genetic variation within the populations rather than among the regions. Moreover, although the results of population structure and UPGMA analysis based on the molecular data of the SSR markers appeared to be mainly in accordance with the geographic location, a number of accessions from different collection areas were positioned in the same group. This result suggests that the mixing of landrace genotypes of South Sudan maize across the regions by local farmers might have occurred in different ways. Even though this paper remains a preliminary study for understanding the diversity of maize in South Sudan, these findings will be very useful in forming standards for genetic materials and for the selection of allelic resources with substantial genetic diversity for breeding programs.


Bletilla spp. Rchb. F. is a traditional Chinese medicinal material. In this study, Bletilla striata (Thunb. ex A. Murray) Rchb F, Bletilla formosana (Hayata) Schltr, and Bletilla ochracea Schltr were collected to analyze the genetic diversity of 16 materials using specific site-amplified fragment sequencing (SLAF-seq) and fluorescence in situ hybridization (FISH). The results showed that the phylogenetic tree of the single-nucleotide polymorphism (SNP) data rendering system was correlated with the shape and geographical distribution of the material. The results of the population structural analysis showed that all the materials containing yellow labellum came from the same ancestor. The results of the principal component analysis were able to preliminarily judge the genetic distance and provided a reference for the selection of hybrid parents. The FISH analysis showed that the chromosomes of B. striata were 2n = 32 and the chromosomes of the B. striata (safflower) mutant were 2n = 34 and the chromosomes of B. ochracea and B. formosana were 2n = 34–36. The (AG3T3)3 non-terminal signal was different from the 5S rDNA signal. These results revealed that the 16 materials had rich genetic diversity, which can provide molecular and cytogenetic data for the study of the genus and its relatives and serve as a reference for the breeding of new genus varieties and improve breeding efficiency and cost. Bletilla spp. Rchb. F. is a perennial orchid herb that is usually purple or yellow [Bletilla in the world, and they are widely distributed in northern Myanmar, Japan, and China. There are four species in China [B. striata (Thunb. ex A. Murray) Rchb F. (hyacinth orchid), B. formosana (Hayata) Schltr. (Taiwan ground orchid), B. ochracea Schltr. (Chinese butterfly orchid), and Bletilla sinensis (Rolf) Schltr. Bletilla has medicinal [Bletilla polysaccharides are used in medical materials [Bletilla resources have been over-collected and are on the verge of extinction, and its medicinal sources are becoming exhausted [Bletilla [r yellow . There ain China : B. striedicinal  and ornaedicinal ,5, and Baterials ,7,8,9,10aterials , and whiaterials ,13. Bletxhausted . TherefoBletilla .Melocalamus arrectus Yi. [Humulus lupulus Linn. [Allium cepa L. [Glycine max (L.) Merr. [Molecular markers based on genetic material are a direct reflection of DNA information and are not affected by the developmental stage and state of plants, growing environment, organ sampling location, etc. . They hactus Yi. , Humulusus Linn. , Allium  cepa L. , and Gly.) Merr. .Bletilla, information about the origin, phylogeny, genetic breeding, and variety improvements in different species can be learned [Bletilla in the genomics era. A combination of 5S rDNA, and (AG3T3)3 probes has been used in Hibiscus mutabilis L. [Hippophae rhamnoides L. [Chimonanthus campanulatus R. H. Chang & C. S. Ding [Juglans regia L. and J. sigillata Dode. [Considering the number of chromosomes is a useful way to distinguish between species. FISH has widely been used to measure the genetic relationships among plant species ,30, phyl learned ,36. The bilis L. , Hippophoides L. , Chimona S. Ding , Juglansta Dode. . Bletilla using 5S rDNA and (AG3T3)3 probes, which facilitated the counting of chromosomes of Bletilla and the comparison of genetic relationships among genera. Specific fragments of the whole genome of Bletilla were obtained via SLAF, and SNP markers were developed. Through the asymmetry and ploidy of the karyotype and signal, we can judge the evolutionary degree and genetic relationship of plant materials, as well as the genetic distance [Bletilla.In the present study, FISH was used for the karyotype analysis of distance ,42. Our B. striata, B. formosana, and B. ochracea were successively collected in Sichuan and Yunnan, China, as shown in B. formosana had leaves that were thin and short, as well as small flowers. The leaves of B. ochracea were slender, and the flower type was medium. The flower type of B. striata was large, and the leaves were wide and thick. The phenological periods of the different varieties were obviously different. The germination time of B. striata was generally from February to March; for B. formosana, it was from February; and for B. ochracea, it was from March to April. In terms of the flowering period, B. striata blooms the earliest and begins to bloom successively around March, and has a shorter duration. B. formosana generally blooms in early May and has a longer duration. B. ochracea blooms the latest and has a longer flowering period, as shown in Sixteen materials were collected from 2017 to 2018. Germplasm resources of Bletilla that was released, the dual-digestion scheme was selected according to the genome size and GC content [Bletilla (reads) material for subsequent quality analysis [Bletilla samples. The reads were clustered according to similarity, and the reads that were clustered together were from the same SLAF label. Reference sequence development was based on genome-wide SNP markers that were of the sequence type, with the highest depth in each SLAF tag. Restriction endonuclease predictions, SLAF library construction, and library quality assessment were performed by Biomarker Technologies .DNA was extracted from the different samples using CTAB , and the content , followeanalysis . In totaanalysis  was evalBletilla. The root tips were cut and placed in a centrifugal tube, placed in 0.001 mol/L 8-hydroxyquinoline, and stored at 4 °C for 24 h, transferred into Carnoy for 4 °C for 24 h, and then had 70% alcohol added before being stored at −20 °C. The root tips were cleaned in ddH2O and then digested in cellulase and pectinase (4:2) enzymolysis solution, followed by water and anhydrous ethanol cleaning, and the addition of 20 μL glacial acetic acid drops on the slide. Then, the metaphase chromosomes were observed. The probes of the 5S rDNA sequences (41bp) [3T3)3 sequences (21 bp) [From 8:00 a.m. to 10:00 a.m. in the spring and autumn, when soil temperature was appropriate, 1~2 cm root tips were taken from the new growth meristematic zone of s (41bp)  and (AG3 (21 bp)  were synBletilla and population heritability analysis. A phylogenetic tree was generated to describe the tree diagram of the phylogenetic relationship of the 16 materials; the distance matrix was constructed based on the genetic data, and the phylogenetic tree was based on the distance matrix. MEGA X [Bletilla materials. The 16 samples could be divided into different subpopulations according to their gene frequencies, and there were different genetic distances among the different subpopulations. Based on the SNP location, admixture software [Bletilla populations was analyzed via principal component analysis. The FISH images were processed with Photoshop 2021 . They were named according to their length, statistical signal pattern, and karyotype data. (AG3T3)3 and 5S rDNA were used for labeling, wherein (AG3T3)3 was the red signal, and 5S rDNA was the green signal. The metaphase and prometaphase chromosomes of the three Bletilla species were analyzed with FISH. According to the relative length of the chromosomes, the ratio of ≥1.26 to the average length is L type; 1.25–1.01 is M2 type; 1.00–0.76 is M1 type; and ≤0.75 is S type.Through bioinformatics analysis, SNP markers were developed using the sequence type with the highest depth in each SLAF tag as the reference sequence, and sequencing reads were compared with the reference genome using a Burrows–Wheeler Aligner (BWA) . SNPs we. MEGA X  was usedsoftware  was usedsoftware , the genBletilla materials by means of sequence analysis, with an average sequencing depth of 14.26X. There were 283,972 polymorphic SLAF tags, with a total of 4,612,941 SNP markers. The SNP integrity was 5.87–14.82%, and the heterozygosity was 38.24–64.66%. Details are shown in The total number of reads obtained from each sample was 2,034,833–7,385,716. The GC content was 38.93–41.41%, with an average of 39.98%, and the Q30 quality value of the sequenced bases was 90.38–94.86%, with an average value of 93.70%. The sequencing depth was 1,072,634–4,262,652, with an average value of 14.26X, indicating that the sequencing error rate was low and that the data obtained were reliable. A total of 484,764 SLAF tags were obtained from 16 The SNP data based on mutation detection were filtered with the secondary allele frequency (MAF:0.05) and locus integrity (INT:0.8) to obtain high-conformance SNPs  for downstream analysis. The genetic relationships of 16 plant materials were evaluated using three methods.Bletilla. The evolutionary tree was divided into four groups: The first group was the labellum color with the white-only phenotype; the second group included four copies of the materials from the Sichuan province varieties that were purple in color, flowered, and with a more prominent design and color; the third group included the varieties from Yunnan Province, with a larger flower type and a relatively large leaf size. All four groups included B. formosana, B. ochracea, and B. striata (safflower) or, more specifically, their common characteristic labellum with a yellow block. In terms of geographical location, the geographical distribution of Bletilla is correlated with the evolutionary tree to a certain extent. For example, the species of Bletilla growing in similar geographical locations are evolutionarily similar, but there is little correlation between B. ochracea, indicating that B. ochracea are highly mobile and are distributed and cross through Sichuan and Yunnan. As shown in B. formosana was collected in this experiment, and the samples were not representative. The 16 samples were divided into two subgroups. BO-CD2, BO-CD3, BF-LS, BO-NJ, BO-QJ, and BO-CD1 are probably derived from one ancestor, and 61.32% of BS-CD1 and 47.65% of BS-CD3 were derived from this ancestor, respectively. This branch was similar to the fourth group of phylogenetic trees, and the results could accurately distinguish the plants with middle yellow patches in the labellum. The first principal component (PC1), second principal component (PC2), and third principal component (PC3) explained 25.64%, 9.23%, and 5.58% of the genetic variation, respectively. The 16 materials could be divided into three nonoverlapping subgroups—namely, B. formosana, B. ochracea, and B. striata. The spatial distance reflected the genetic relationship. As shown in MEGA X software was used to construct a phylogenetic evolutionary tree with a unique root node and a possible unique ancestor for 16 members of the genus B. striata and B. formosana was 2n = 32. The varieties of B. striata (safflower) had 34 chromosomes. The chromosome number of B. ochracea was 2n = 34~36. In terms of the number and width of chromosomes in different provenances of Bletilla, there were great differences in their length. For example, the total length difference between BS-NJ and BS-AB1 was 57.27 μm, and the difference between the length of B. ochracea was small. The staining length is related to the division stage and chromosome fixation method. Repeated experiments with three root tips yielded consistent results. The longest chromosome length was 8.54 μm, the shortest was 2.12 μm, the longest total length was 182.54 μm, and the shortest was 125.37 μm. When the chromosome length was larger, the difference between the B. striata chromosome length was smaller than that of B. ochracea and B. formosana, and the difference between the B. formosana chromosome length was the largest. This means that the degree of chromosome asymmetry was higher. The results indicate that the three species of Bletilla have higher degrees of asymmetry than other orchids, especially B. ochracea and B. formosana.Fluorescence in situ hybridization involved 9 out of the 16 materials and involved all the phenotypes and 3 varieties, as shown in 3T3)3 signals were divided into five groups according to the signal pattern, as shown in 3T3)3 had a strong signal location. Only the pure white labellum groups (BS-PE) were consistent with the first group of the SNP evolutionary tree. The second group, which had no (AG3T3)3 strong signal locus, represents BS-AB1, BS-BS, and BS-NJ, and was also consistent with the purple and yellow block group of the labellum group and groups 2 and 3 in the SNP evolutionary tree. Groups 3, 4, and 5 had strong (AG3T3)3 frequency zoning, which may be related to chromosome rearrangement and evolution. This region corresponds to the yellow labellum block group and group 4 in the SNP evolutionary tree. For example, in group 3, BS-CD1 had four non-5S rDNA signal chromosome-strong (AG3T3)3 signal loci; in group 4, it had relatively strong (AG3T3)3 signal loci; this group only had BS-LS; in group 5, it had multiple strong (AG3T3)3 signal loci, similar to BO-NJ, BO-CD1, BO-QJ, and another B. ochracea. The FISH karyotype analysis showed that there was abundant genetic diversity among nine materials from three species of Bletilla.The combination patterns of the 5S rDNA signals and  is between that of B. striata and B. ochracea, and the labellum pattern is more similar to B. ochracea and may be formed by natural hybridization between the two species. In terms of regional distribution, multiple locations were considered in Sichuan and Yunnan provinces. In addition to the one sample of B. formosana that was collected, three samples were collected for both B. striata and B. ochracea. In all the groups, there are differences between habitats, and distance also causes differentiation between groups. The greater the ecological environment differences are, the more unique traits, such as color, leaf type, disease and insect resistance, and the content of tuber polysaccharides determined by natural selection formed in the long term. The direct reflection of these traits on the chromosome may be missing. Translocation and inversion structure variation, such as major rearrangements, and divergence in the chromosome structure may make adaptation more obvious [B. striata, B. formosana, and B. ochracea.Karyotype analysis was used to reveal the genome of the chromosome-level information. Karyotypes provide the cytological features  to deternly Wang  has carr complex . The nume D. Don ,61. In toriginal . The deg obvious . The dif3T3)3 signals are usually detected at terminal positions, with occasional signals detected around and inside the centromeres, and have been verified in plants belonging to the genera Hydrocerus, Syringa, and Ligustrum [3T3)3 probe signals can be found in all chromosomes and show that the chromosome shape is complete. In Bletilla, the size of the (AG3T3)3 signal in the genus is different. In some samples, the end signal of (AG3T3)3, may be related to the chromosome rearrangement structure. B. ochracea has a high degree of chromosome rearrangement and a stronger signal site. The 5S rDNA signal binds around the ribosome and is conducive to chromosome differentiation [Piptanthus concolor Harrow ex Craib [Bletilla had two rDNA signals, consistent with previous results for other Bletilla species, indicating that Bletilla are diploids and that different species have different signal intensities. The combination of (AG3T3)3 and 5S rDNA has been verified in plant species such as C.campanulatus [H. rhamnoides [3T3)3 signals of B. formosana and B. ochracea were unstable, which may be related to the degree of evolution. In addition, the non-terminal signal of (AG3T3)3 with the labellum with a characteristic yellow block was strong, similar to the differentiation of the SNP evolutionary tree, indicating that this type of material has the same ancestor and evolutionary trend and can be divided into separate groups. SNP, oligo-FISH signal pattern, and the phenotypic labellum can verify each other. Therefore, the experiment has certain accuracy.(AGigustrum . (AG3T3)ntiation . This isex Craib . In addiex Craib , which iex Craib  and chroex Craib . All theanulatus  and H. ramnoides . HoweverBletilla, providing evidence for ploidy breeding and variety identification [Bletilla varieties are. SNP sequencing cannot distinguish between B. striata and B. formosan and is able to distinguish them at the chromosome level. Chromosome data can be used to study species evolution, classification, and breeding [B. striata and B. formosan at the chromosome level. The lack of differentiation at the molecular level may be related to the evolution of B. formosan, which is reflected in the labellum valve phenotype. At the same time, due to the great depth of sequencing, it is difficult to cluster the results. Of course, these results may also be related to sampling errors and their test data. Combined with population genetic analysis to determine the SNP molecular markers, the fluorescence in situ hybridization results for Bletilla have a certain degree of correlation with SNP population genetic analysis, showing the polymorphism differences on the basis of cytogenetics, which also provide a basis for the differentiation of Bletilla. Improved varieties are often selected for higher phenotypic variation [Bletilla varieties and to understand the genetic backgrounds of the bred varieties, laying a foundation for parental selection in future Bletilla breeding projects [FISH data reflect the ploidy and karyotype relationship of fication . The molfication . SNP genbreeding . It is sariation  or becauariation  to improprojects .Bletilla shows rich polymorphism in different populations and that (AG3T3)3 and 5S rDNA can effectively distinguish three species of Bletilla via their signals.Through population genetic analysis, it was found that