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Agricultural And Food Sciences
Biology

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High-Altitude Genetic Selection and Genome-Wide Association Analysis of Yield-Related Traits in Elymus sibiricus L. Using SLAF Sequencing

The genetic adaptations to harsh climatic conditions in high altitudes and genetic basis of important agronomic traits are poorly understood in Elymus sibiricus L. In this study, an association population of 210 genotypes was used for population structure, selective sweep analysis, and genome-wide association study (GWAS) based on 88,506 single nucleotide polymorphisms (SNPs). We found 965 alleles under the natural selection of high altitude, which included 7 hub genes involved in the response to UV, and flavonoid and anthocyanin biosynthetic process based on the protein–protein interaction (PPI) analysis. Using a mixed linear model (MLM), the GWAS test identified a total of 1,825 significant loci associated with 12 agronomic traits. Based on the gene expression data of two wheat cultivars and the PPI analysis, we finally identified 12 hub genes. Especially, in plant height traits, the top hub gene (TOPLESS protein) encoding auxins and jasmonic acid signaling pathway, shoot apical meristem specification, and xylem and phloem pattern formation was highly overexpressed. These genes might play essential roles in controlling the growth and development of E. sibiricus. Therefore, this study provides fundamental insights relevant to hub genes and will benefit molecular breeding and improvement in E. sibiricus and other Elymus species.

INTRODUCTION
Elymus is the largest and most widely distributed genus of Triticeae (Poaceae) with a valuable gene pool that can be used for the genetic improvement of cereal crops such as wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and rye (Secale cereale L.) due to its excellent stress tolerance, disease resistance, and good forage quality (Dewey, 1984;Ma et al., 2012a). Siberian wild rye (Elymus sibiricus L.), the type species of the genus Elymus, is a perennial, sparse-type, selfpollinating, and allotetraploid grass (2n = 4× = 28). Elymus sibiricus is commercially useful in stock raising and serves as a major forage grass in natural and artificial grasslands. In addition, it is widely being used in the restoration of ecosystems, soil stabilization, and erosion control due to its good ecological function (Xie et al., 2015).
Natural selection and artificial domestication in plants have a pivotal role in reshaping the genome, promoting the evolution of populations, high allele frequency in selective sweep regions, local adaptation, and genetically based trait differentiation (Chapman et al., 2013). The geographic distribution of wild E. sibiricus accessions mainly extended from Eastern Europe to Siberia, Mongolia, China, Japan, and even to Alaska and Canada, leaving extensive genetic variations and evolutionary signatures in the genome (Ma et al., 2012a). The Qinghai-Tibet Plateau (QTP) is a hot spot area for studying genetic adaptations, as the average elevation of about 4,000 m above sea level caused the extreme ecological conditions, such as high UV-B radiation and low oxygen concentration (∼30% stronger and ∼40% lower than at sea level, respectively; Yang et al., 2017). The genetic basis of harsh climatic adaptations in high-altitude regions has been reported and many genome selective regions and strongly associated environmentally related genes were found for humans, animals, and plants (Li M. et al., 2013;Yang et al., 2017;Wang et al., 2021a,b). E. sibiricus grows on wet meadows, shrubs, forests, and subalpine meadows, where altitude gradients range from 0 to 4,000 m above the sea level, yet the selective signatures at molecular level between high-altitude and lowaltitude habitats remain unclear.
Recently, increasing attention has been paid to the requirement of high-yield and high-quality forage varieties, such as E. sibiricus (Jin et al., 2021). The annual demand for hay and grass seeds in China exceeds 10 million and 150 thousand tons, respectively. However, traditional breeding and phenotypic selection still rely heavily on experience and manual observation, which is time consuming, low efficiency, and environmentally sensitive. The genome-wide association study (GWAS) is a powerful tool for the rapid identification of loci or key genes associated with agronomic traits by analyzing associations between nucleotide polymorphisms and phenotypic diversity. These associations were measured by using a natural or low-structured population, in which a large number of recombination events were found based on linkage disequilibrium (LD; Flint-Garcia et al., 2003). The abundant genetic variations from GWAS facilitate more targeted and accurate genetic selection for marker-assisted selection (MAS) and molecular design breeding when compared with quantitative traits loci (QTL) mapping. Recently, advances in next-generation high-throughput sequencing technologies provide a strategy for high-density genotyping based on large-scale populations at the genome-wide level. An increasing number of GWASs have been reported in crops and forage grasses for important agronomic traits such as heading date, plant height, and panicle length in rice (Oryza sativa L.; Yano et al., 2016); fiber quality and yield traits in cotton (Gossypium hirsutum L.; Fang et al., 2017a); spike ethylene production and spike dry weight in wheat (Valluru et al., 2017); drought adaptation in maize (Zea mays L.; Zhang et al., 2021); salt germination, frost damage, fall dormancy, and unifoliate internode length in alfalfa (Medicago sativa L.; Shen et al., 2020); and flowering time in switchgrass (Panicum virgatum L.; Grabowski et al., 2017). Limited by high-sequencing costs and difficulties in genome assembly, the genomic information of most species is still unknown, especially for polyploidy plants with large size and complex genomes. Sun et al. (2013) tested an efficient approach (specific-locus amplified fragment sequencing, SLAF-seq) for de novo single nucleotide polymorphism (SNP) discovery, namely reduced-representation genome sequencing (RRGS) technology. Several distinguishing characteristics have propelled much of the practicability of SLAF-seq including the generation of millions of high-density SNPs, improvement of efficiency and performance-cost ratio by missing repetitive sequences, capability of detecting novel SNPs compared with SNP arrays, and suitability for species without a reference genome. The technology has successfully applied to many species with haplotype analysis, linkage mapping, and genetic association studies Zhang et al., 2019a;Zhuo et al., 2021).
Although GWAS test has the potential to detect numerous genetic polymorphisms or QTLs associated with complex agronomic traits, it is difficult to completely avoid possible spurious loci caused by population stratification and unequal relatedness among the studied individuals (Kang et al., 2010;Zhang et al., 2010). The mixed linear model (MLM) has been proven effective as a useful method to control-inflated associations for principal components (or population structure) and kinship modeling as a fixed effect and random effect, respectively (Yu et al., 2006). However, the exact statistics test approach was computationally impractical in dealing with large data sets, for the iterative time of each SNP increased sharply with the number of individuals as a random effect. A P3D (population parameters previously determined) method implemented within the TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) program to reduce the running time from standard MLM proposed an approximate algorithm, which replaced the repeated iteration of population parameters by fixed empirical Bayesian priors (Zhang et al., 2010). The statistical power of P3D has been maintained or improved regardless of the different genetic architectures of the phenotype. Similarly, Efficient Mixed-Model Association eXpedited (EMMAX) simplified the estimation of variance parameters once by using a null hypothesis, based on natural characteristics that the most effective of a single locus is small in complex quantitative traits (Kang et al., 2010). In practice, inaccurate P values may result from the approximation approach when dealing with highly structured populations and strongly associated marker effects. Contrarily, an exact association test was provided from GEMMA (Genome-wide Efficient Mixed Model Association) on which the Newton-Raphson optimization method was presented. GEMMA remained the identical results compared with the exact calculations in EMMA, but was roughly n times faster, where n was the sample size (Zhou and Stephens, 2012). As the GEMMA strictly needs completed or imputed genotype data sets (sample integrity > 0.95), some approximate algorithms remain necessary, especially in the absence of largescale effective SNPs by using an RRGS. A previous research showed that a conjoint analysis of multiple models favored improving the accuracy and reducing the false positive rate.
In this study, we performed a genome-wide SNP identification based on SLAF-seq of 210 E. sibiricus genotypes. Among these, the genetically selected loci and genes were examined by using two extreme groups from low and high altitudes. Further, the phylogenetic relationship, genetic architecture, and a GWAS of twelve grass yield-and seed-related traits were conducted. Our results provided a new genetic resource for molecular breeding in E. sibiricus.

Plant Materials and Field Trials
The 210 E. sibiricus genotypes used in this study were obtained from the Lanzhou University and the U.S. Department of Agriculture Germplasm Resources Information Network (GRIN). The latitude and altitude of the collection site ranged from 32. The seedlings of all genotypes were planted for phenotypic investigation at Yuzhong experimental field of Lanzhou University (35.95 • N, 104.15 • E, altitude 1,720 m). Each individual was planted with 50-cm spacing in a randomized design. Twelve agronomic traits, including flag leaf length (FLL), flag leaf width (FLW), leaf length (LL), leaf width (LW), plant height (PH), culm diameter (CD), tiller number (TN), culm node number (CN), spike length (SL), length of seed (LS), width of seed (WS), and 1,000-seed weight (SW1) were assessed from 2017 to 2018. A total of 10 replications for each trait (except PH and TN) in an individual plant were recorded using the method described by the previous study (Zhang et al., 2018). The R package "corrplot" (v0.92) was used for a pairwise correlation analysis. The Shannon's diversity index (H') of each trait was calculated as previously described by Zhang et al. (2018).

Single Nucleotide Polymorphism Genotyping
The genomic DNA of each genotype was extracted from young leaf tissues using the Qiagen DNeasy 96-well procedure (QIAGEN, Valencia, Calif). The quantity and quality of samples were measured using a Nano-Drop ND1000 spectrophotometer (NanoDrop, Wilmington, DE, United States). The final concentration of the DNA was adjusted to 50 ng/µl to meet the requirements of SLAF-seq (concentration ≥ 30 ng/µl, DNA quantity ≥ 2 µg).
Elymus sibiricus genome is not available; therefore, the wheat genome was selected as a reference genome 1 (v1.0) for a higher genomic homology (79%) with E. sibiricus genetic linkage map (Zhang et al., 2019a). After the digestion prediction analysis of the reference genome, HaeIII, the appropriate restriction enzyme, was used to digest the genomic DNA and to generate sequencing tags (SLAFs, approximately 464-494 bp in length). This step ensured that the obtained tags were evenly distributed among the chromosomes. The PCR, SLAF library construction, and paired-end sequencing were conducted as previously described (Sun et al., 2013;Zhang et al., 2019a). High-quality raw reads of each sample were mapped onto the reference genome to develop SLAFs by using the Burrows-Wheeler Aligner (BWA) software (v0.7.17; Li and Durbin, 2009). The Genome Analysis Toolkit (GATK, v4.2.0.0) was used for SNP calling (McKenna et al., 2010). The raw DNA sequencing data had been uploaded to NCBI (SRR17714927-SRR17715136). A total of 88,506 highly reliable population SNPs (missing data less than 0.5, minor allele frequency (MAF) greater than 0.05) were selected and used for subsequent analysis. The R package "CMplot" (v4.0.0) was used to show the distribution of SNPs in each chromosome.

Population Structure, Genetic Diversity, and Selective Sweep Analysis
A neighbor-joining phylogenetic tree was constructed using PHYLIP with 500 bootstrap replicates 2 (v3.698) and visualized on the online tool iTOL. 3 The population structure of the tested natural population was assessed by the ADMIXTURE software (v1.3.0; Alexander et al., 2009). The optimum number of subgroups was determined according to cross-validation of the error rate with the presented K-value ranging from 1 to 10. A principal component analysis (PCA) was performed using PLINK 4 (v1.90). In addition, the kinship matrixes among all genotypes were calculated by using GEMMA (v0.98.1) and then visualized by the "pheatmap" package (v1.0.12) in R (Zhou and Stephens, 2012). Nucleotide diversity (π), Tajima's D value, and genetic differentiation (F ST ) for each group were measured with VCFtools (v0.1.16; Danecek et al., 2011). The past evolution of the effective population size (N e ) was estimated by using SMC + + (v1.15.4; Terhorst et al., 2016). A mutation rate of 1.3e-8 per synonymous site per generation was used. Two environmental data sets, surface UV-B radiation and surface pressure, were collected from European Centre for Medium-Range Weather Forecasts (ECMWF 5 , ERA5 monthly averaged data on single levels from 1979 to present) from 1979 to 2010 for the genome-wide selective sweep analysis on high-altitude group (greater than 3,000 m) and low-altitude group (less than Frontiers in Plant Science | www.frontiersin.org 1,000 m). The selected SNPs were annotated to find related genes with response to UV-B and oxidative damage of UV radiation to plants among seven public databases: the NCBI non-redundant protein sequence (Nr, 202009 6 ), Gene Ontology (GO, 20200615 7 ), Protein family (Pfam, v33.1 8 ), Cluster of Orthologous Groups (COG 9 ), euKaryotic Orthologous Groups (KOG 10 ), Annotated protein sequence database (Swiss-Prot, 202005 11 ), and Kyoto Encyclopedia of Genes and Genomes (KEGG, 20191220 12 ; Supplementary Table 4). The Search Tool for Retrieval of Interacting Genes/Proteins (STRING 13 v11.5) was used to predict and construct the PPI network of the candidate genes based on the minimum required interaction score of medium confidence (Szklarczyk et al., 2019). The intersection genes with a high degree were regarded as candidate hub genes and the PPI network was visualized with the Cytoscape software (v3.8.2).

Genome-Wide Association Study of Agronomic Traits
Three publicly GWAS statistical softwares, Genome-wide Efficient Mixed Model Association (GEMMA, v0.98.1), Efficient Mixed-Model Association eXpedited (EMMAX, beta-07Mar2010 version), and Trait Analysis by aSSociation, Evolution, and Linkage (TASSEL, v5.0), were used together to find out the underlying SNPs or genes associated with 12 grass yield-related traits in E. sibiricus (Bradbury et al., 2007;Kang et al., 2010;Zhou and Stephens, 2012). A MLM, with the first three PCA components and the kinship matrix considered a fixed effect and random effect, respectively, was implemented on these programs to carry out GWAS analysis. In addition, the SNP genotyping database was filtered more strictly (marker integrity frequency > 95%, MAF > 0.05) before the GEMMA analysis. Genome-wide significance thresholds were set as P < 1e-4 (EMMAX and TASSEL) and 1e-3 (GEMMA), whereas the Bonferroni-corrected cutoff was 0.1/total applied SNPs (approximately 1.13e-6 for EMMAX and TASSEL and 9.36e-6 for GEMMA; Supplementary Figures 3-5). The genes were located within 100-kb flanking regions around the significant trait-associated SNPs based on the public databases. All the genes were selected based on plant development process terms, including root, seed, leaf, flower, cell cycle, cell division, cell proliferation, as well as some related proteins, enzymes, hormones, transcription factors, and so on (Supplementary Table 6). The gene expression data from leaf-, culm-, and seedrelated traits of two wheat cultivars (Azhurnaya and Chinese Spring) were collected from IWGSC RefSeq Annotations v1.0 14 and used to analyze the gene expression profiles of each identified gene. The candidate genes were identified with an expression value (TPM) of more than 10. In addition, the non-synonymous variations that happened within the gene exon areas were used for the analysis of statistical differences for traits. The PPI network analysis was employed to remove the noise information and focus on the hub genes related to plant growth and development by using the STRING and Cytoscape softwares.

Sequencing and Genome-Wide Polymorphic Single Nucleotide Polymorphism Identification
Using a SLAF high-throughput sequencing approach, a total of 1,176.09 million paired-end reads were generated from the raw sequence data from the panel of 210 E. sibiricus samples (Supplementary Table 2). The average GC (guanine-cytosine) contents were 45.88% and the Q30 percentage was 93.66%. A bioinformatics analysis aligned to the wheat reference genome revealed that the mapping rate of reads was ranged from 72.83 to 87.92%, with an average of 84.67%. Furthermore, 750,108 SLAF tags were identified with an average mapping depth per sample of 18.01x. A total of 13,643,515 SNP markers were generated. Upon filtering the genotype results with MAFs of 0.05 and sample locus integrity of 0.5, a total of 88,506 highly consistent SNPs were retained. The mean integrity and heterozygosity rate for each sample were 72.39 and 0.2271%, respectively. These obtained SNP markers were not evenly distributed on the genomic regions of 22 chromosomes of wheat (Table 1 and Supplementary Figure 1). The largest number and density of SNPs (6,704.6 and 84.2 kb/SNP) were on chromosome D, followed by chromosome B (3,395.6 and 220.1 kb/SNP) and chromosome A (2,332.0 and 314.2 kb/SNP). The mean number of SNPs was 4,023, ranging from 7,923 (chr7D) to 1,481 (chrUn), and the mean density per marker was 211.5 kb, ranging from 80.4 (chr6D) to 361.1 (chr4A).

Population Structure, Genetic Diversity, and Selective Sweep Analysis
The kinship analysis showed that no direct genetic relationships between each two E. sibiricus samples in this study, for the kinship coefficients of more than 97.5% paired individuals, were less than.05 (Supplementary Figure 2). Phylogenetic relationships among 210 E. sibiricus genotypes were quantified using 88,506 high-quality SNPs ( Figure 1A). The results showed a monophyletic branch in the neighbor-joining (NJ) tree, indicating a single domestication event happened among all samples. Most of the genotypes of Geo_1, Geo_2, Geo_3, Geo_4, and Geo_5 tended to be grouped according to their geographic origins, and the remains were formed in other admixture clusters. A model-based population structure analysis, reflecting additional inter-and intra-group relationships by the varying K-value (the number of ancestry kinship), had assigned the 210 genotypes into two and five subgroups (cross-validation errors of.498 and.521, respectively; Figure 1B). Different from others, genotypes that originated from the Qinghai-Tibet Plateau (Geo_1) had a close relationship when K was set to two (the best suitable number from the K-value test). When a higher K-value was assumed, more subgroups were further detected, including some groups from Tien Shan Mountains (Geo_2), Altay Mountains (Geo_3), and Mongolian Plateau (Geo_4 and Geo_5). Similar to the NJ tree, five structured populations (K = 5) were consistent with their geographical distribution characteristics ( Figure 1D). The PCA mainly split the samples into two distinct groups (QTP and others) at an overall genomic level, in which the first two PCAs had explained 24.95% of the total variation ( Figure 1E). A continuous distribution showed on the plot indicating no population stratification within the studied genotypes.
The nucleotide diversity (π value) was estimated for six major geographic groups, which was higher for Geo_3, Geo_5, and Geo_6 (average of.302) than Geo_1, Geo_2, and Geo_4 (average of.270; Figure 1F). These results were similar to our previous diversity study based on EST-SSR markers ( Figure 1G; Zhang et al., 2019b). Positive mean Tajima's D values existed within all of E. sibiricus groups which not only implied a lower probability of population expansion under the balancing selection but also less rare allele frequencies resided in specific habitats, especially in Geo_1 (0.984) and Geo_2 (0.856). The farthest genetic distance (average of.058) was inferred between 76 QTP samples and 129 genotypes from the other five groups using a genetic differentiation analysis (F ST ). An evolutionary history analysis among different geo-populations showed similar demographic trajectories: all groups suffered bottleneck events that the effective population size (N e ) decreased first and then increased to a stable size during 2-13 thousand years ago (Kya), and a short common history that the N e size of 4E6 appeared in.4-2 Kya ( Figure 1H). Thereafter, in the next bottleneck, effective population size significantly declined and then rebounded except for Geo_1 group.
Genome regions with low nucleotide diversity were considered to be potential selection signatures from human domestication or different natural environment factors. A survey of meteorological information among these studied genotypes within a span of 32 years (from 1979 to 2010) showed significant correlations among UV-B, surface pressure, and altitude (Figure 2A). Sixty-six wild genotypes originated from two extreme habitats were used for a selective sweep analysis, of which 38 genotypes originated from high-altitude regions (>3,000 m) and 28 genotypes originated from low-altitude regions (< 1,000 m; Figure 2B). Two climate factors were significantly different between the two groups (p < 0.001, Welch's test). The mean monthly cumulative UV-B value was 1535.9 and 2036.0 kJ·m −2 in low and high altitudes, respectively. Correspondingly, the mean surface pressure was 92.9 and 67.0 kPa, respectively. For the selection signals, a total of 965 SNPs were identified from the top 20% θ π ratio (θ π , low altitude/θ π , high altitude) and the top 5% F ST value for high-altitude samples ( Figure 2C and Supplementary Table 3).  Considering the characteristics of SLAF-based molecular markers in this study, the range where the distance between two adjacent markers is less than 100 kb and the distance span is greater than 100 bp was deemed as the selected area. We found 54 selected regions, of which the largest one was on chromosome 2A with eight SNPs and 255.7 kb of physical distance from 459,245,966 to 459,501,653 bp. The mean length was 16.1 kb (Supplementary Table 3). Seventy-three genes in response to harsh climate (such as low oxygen and strong ultraviolet radiation) in high-altitude areas were identified, including UV-B receptor UVR8 (3 genes), response to UV-B (5 genes), regulation of photosynthesis (9 genes), photosystem II (PSII) assembly (9 genes), chlorophyll (9 genes), carotenoid (2 genes), response to superoxide (1 gene), reactive oxygen metabolic process (1 gene), hydrogen peroxide (12 genes), peroxidase (15 genes), proline (7 genes), flavonoid biosynthetic process (7 genes), anthocyanin biosynthetic process (9 genes), L-ascorbic acid biosynthetic process (3 genes), and so on (Supplementary To find out potential hub candidate genes responding to the harsh climate, a PPI network consisting of 57 nodes and 196 lines was obtained by using the online STRING database (Figure 3 and Supplementary Table 4). The analysis showed a tight PPI network, suggesting that the queried proteins have strong relationships and interactions. There were seven hub genes with the node degree greater than seven, including one with 17 degrees (TraesCS1A01G442300), two with 8 degrees (TraesCS2D01G379400 and TraesCSU01G146500), and four with 7 degrees (TraesCS2D01G462700, TraesCS5A01G554500, TraesCS5D01G065700, and TraesCS7B01G136000). These genes are mainly involved in the response to UV light, flavonoid biosynthetic process, anthocyanin accumulation, reactive oxygen species (ROS) metabolic process, and cytochrome P450. Furthermore, the potential hub genes were important homologous genes (such as CYP75B3, FabZ, and GPA1) in Poaceae plants, including Aegilops tauschii, Hordeum vulgare, Lolium rigidum, and Oryza sativa, and may be important components in the genetic basis of the adaptation to high altitudes for E. sibiricus.

Phenotypic Variation and Correlation Analysis
Four leaf-related traits: FLL, FLW, LL, and LW, four culmrelated traits: PH, CD, TN, and CN, and four seed-related traits: SL, LS, WS, and SW1 were investigated within 2 years. A wide range of phenotypic variations were found among 210 E. sibiricus genotypes. The mean coefficient of variation (CV) between 12 phenotypic traits was 28.2%, varying from 6.8% (LS in 2018) to 84.1% (TN in 2018), and the average Shannon's diversity index (H') was 2.008 varying from 1.798 (TN in 2018) to 2.095 (WS in 2018; Table 2).
The phenotypic characters followed a normal or approximate normal distribution without any significant skewness and could be used for the subsequent analysis (Figures 4A,B). The Pearson's correlation coefficient analysis showed significantly positive pairwise correlations between four leaf-related traits (FLL, FLW, LL, and LW;0.60 to.85 in 2017and.36 to.68 in 2018, as well as three seed-related traits (LS, WS, and SW1; 0.35 to.54 in 2017 and.35 to.39 in 2018; Figures 4C,D). These traits may be controlled by similar genetic factors. Two culm-related traits (TN and CD) had a highly significant positive correlation with leaf-related traits, of which the mean phenotypic correlation coefficients of.32,0.60 in 2017 and.44,0.49 in 2018, respectively. Generally, there were weakly positive or negative relations between two seed traits (LS and WS) and two grass yield-related traits (leaf and culm). Eight traits (FLL, FLW, LL, LW, PH, CD, TN, and SW1) had a higher correlation between two consecutive years ( Figure 4E).

Genome-Wide Association Analysis Reveals Loci Related to Agronomic Traits
Using a MLM with correction of genetic structure (Q) and kinship (K) bias, the multiple GWAS tests (GEMMA, EMMAX, and TASSEL) were performed to confirm significant SNPs associated with twelve grass yield-and seed-related traits in E. sibiricus (Supplementary Figures 3-5). The quantile-quantile (QQ) plots indicated well-controlled population stratification by using MLM and provided reliability for association analysis. In this study, 1,825 SNP markers were detected above the critical threshold of -lgP ≥ 4 (EMMAX and TASSEL) and 3 (GEMMA), respectively ( Table 3, Supplementary Table 5, and Figure 5A). More than half of the associated loci (917) were distributed on D subgenomes, while 477, 398, and 33 SNPs were on B, A, and Un, respectively. Three softwares identified a total of 848 SNPs for culm-related traits, followed by leaf-related traits (604 SNPs) and seed-related traits (373 SNPs). Especially, 358, 247, and 235 SNPs were related to PH, FLW, and TN, respectively. The TASSEL test showed that the percentage of phenotypic variance explained (R 2 ) ranged from 7.62 to 31.85%, with an average of 15.57%. A gene function annotation analysis was performed to rapidly identify the candidate genes associated with yield-related traits in E. sibiricus. There were 1,064 genes identified from 908 significant SNPs within 100-kb flanking regions, of which 344 loci with multigenic effects (Supplementary Table 6). In the TN traits, a SNP (6,982,833) on chromosome 1D associated with 8 genes related to the process of recognizing pollen, response to growth hormone, tyrosine kinase, ankyrin repeats, and leucine-rich repeat (LRR). Eight closely linked genes identified from a SNP marker on chr1D (37,459,155), annotated with plasmodesma, cytoskeleton, F-box protein, PPR repeat family, and hydrolases family, may result in multigenic effects for plant height traits. A total of 116 highly consistent SNPs that overlapped from more than two softwares were deemed to be highly reliable SNPs. After a comparison of associated genes with gene expression data of two wheat cultivars (Azhurnaya and Chinese Spring), we finally identified 106 candidate genes ( Figure 5A). Sixty-two genes were related to PH, CD, TN, and CN traits. Additionally, 20 and 28 genes were related to leaf-and seed-related traits, respectively. Furthermore, functional classifications of these genes were mainly involved in four enriched biological processes: plant development process (70 genes), protein and enzymes (30 genes), hormones (26 genes), and transcription factors (2 genes; Figure 5B). The functional annotation showed that the biological process of photosynthesis, root, cytoskeleton, microtubule, photorespiration, flower, pollen, and seed were the most highly represented categories in the plant development process. For the biological regulation of hormone signals, many terms were enriched with auxins, abscisic acid (ABA), jasmonic acid, ethylene, and salicylic acid. Tyrosine kinase, E3 ubiquitin ligase, hydrolase, and LRRs were the major proteins and enzymes. For leaf-related traits, the top four gene functions were photosynthesis, photorespiration, jasmonic acid, and salicylic acid, whereas E3 ubiquitin ligase, meristem, flower, and photosynthesis were enriched for seedrelated traits. The candidate genes for culm-related traits covered almost all molecular functional categories, and microtubule, auxins, tyrosine kinase, and E3 ubiquitin ligase were major enrichment terms.
For the PH traits, 40 SNPs with 38 candidate genes were identified (Figure 5C and Supplementary Table 6). The GEMMA test found a SNP marker (chromosome 1A, 483,033,948) on exon region of TraesCS1A01G286300, a plasmodesma-related gene, and the non-synonymous SNP diversity caused amino acid differences between Methionine (Met) and Isoleucine (Ile; Figure 5D). The haplotype-based analysis showed that the Hap. B (SNPs of A) with 100 genotypes was significantly positively correlated with plant height (PH; p < 0.0001, Welch's test) which increased by 32.7% when compared with 53 genotypes with Hap. A (SNPs of G; Figure 5D). In addition, the gene was highly expressed in the internode, stem axis, and stem of two wheat cultivars, respectively. Furthermore, these genes showed four main functional annotations: (1) ethylene, auxins, and jasmonic acid signaling pathway; (2) tyrosine kinase activity; (3) cytoskeleton organization, microtubule, xylem formation, and plasmodesma; (4) cell cycle and cell division (Figure 5E). A total of 12 genes were highly overexpressed across all culm-related traits. Particularly, two SNPs on chromosome 4A (80,134,459) and 6D (378,425,315) were jointly annotated by GEMMA, EMMAX, and TASSEL. A gene TraesCS4B01G205100 had a higher expression level in internode and stem in two wheat cultivars, which was located within the 100-kb flanking regions of significant loci with the highest -lg(P) value (8.929) in the GWAS test.
The PPI network analysis further narrowed down the list of 106 identified yield-related candidate genes in E. sibiricus. Based on the degree algorithm, there were three hub genes, TraesCS4D01G221200 (TPL), TraesCS4D01G221100 (Traes_4DL_A43826645.1), and TraesCS7D01G194800 (LIM), with the degree value more than 4 detected within PH traits ( Figure 6A and Supplementary Table 7). The top gene, TraesCS4D01G221200, was found to be involved in the biological regulation process of a variety of plant hormones and morphological development, including auxins-and jasmonic acid-mediated signaling pathway; meristem and shoot apical meristem (SAM) specification; xylem and phloem pattern formation. Besides, two (TraesCS5A01G457500 and TraesCS1B01G254000), six (TraesCS4D01G221200, TraesCS7D01G194800, TraesCS3D01G190400, TraesCS5D01 G343000, TraesCS1A01G286200, and TraesCS5D01G118000), and four (TraesCS1A01G402400, TraesCS3D01G296900, TraesCS6A01G307700, and TraesCS7D01G056800) hub genes were detected in leaf-, culm-, and seed-related traits, respectively (Figures 6B-D). These genes mainly played the biological function of photosynthesis (leaf-related traits); auxins, jasmonic acid, and ethylene-mediated signaling pathway, meristem, shoot, and xylem apical meristem specification, cell proliferation, tyrosine kinase and E3 ubiquitin-protein ligase activity (culm-related traits); and tyrosine kinase, flower development, photosynthesis, and cytoskeleton organization (seed-related traits).

Selective Signature Detection in Elymus sibiricus at High Altitudes
In this study, we performed SLAF-seq of 210 E. sibiricus genotypes including six geographical populations from the main distribution areas of the species. A total of 88,506 high-consistent SNPs were picked up for genetic research. Phylogenetic relationship and population architecture analysis revealed that the Geo_1 group collected from Qinghai-Tibet Plateau (QTP) regions had different genetic backgrounds from the other groups (Figures 1A,B,D,E). A similar result was supported by some genetic diversity studies in E. sibircus, including chromosome karyotype analysis, gliadin, and SSR marker study (Ma et al., 2012b;De et al., 2014;Yan et al., 2017).
It may indicate that the main climatic factors act as a selective pressure on different geographical populations. The QTP population experienced severe natural pressure to adapt to harsh environmental factors in high-altitude regions, such as low pressure, strong radiation, sharp temperature shifts, and overwintering challenges. To understand the genetic bases of adaptation to high plateaus from a genome perspective, two pools of E. sibiricus genotypes collected from high-altitude regions (>3,000 m) and low-altitude regions (<1,000 m) were used to detect the associated genes for altitude adaptation. More than 70 genes, mainly responding to UV-B and superoxide, UV-B receptor UVR8, regulating photosynthesis, PSII, flavonoid biosynthetic process, and peroxidase activity, were identified among 965 strong selected loci. Among them, seven genes were at the hub position in the PPI network analysis, which play pivotal roles in response to UV light, flavonoid biosynthetic process, anthocyanin accumulation, ROS metabolic process, and cytochrome P450. The selective filter of sunlight in leaf epidermal tissues represented an acclimatizing mechanism by which plants are exposed to strong UV radiation conditions (including UV-B, 280-315 nm) for a long time (Barnes et al., 2016). In response to UV radiations, the common stress-related processes include antioxidant defense and accumulation of UV-absorbing compounds in the vacuoles and cell walls of leaf epidermal cells, such as flavonoids, anthocyanin, and other phenolics, is activated (Siipola et al., 2015;Berland et al., 2019). Besides, the photomorphogenic responses, gene expression, and accumulation of metabolites were triggered by UV RESISTANCE FIGURE 5 | Genome-wide association study for phenotypic traits and identification of the candidate genes for the significant SNPs. (A) Manhattan plot for leaf (green points)-, culm (blue points)-, and seed (purple points)-related traits in E. sibiricus, respectively. Only SNPs with P values smaller than.1 are plotted. Three horizontal dashed lines represent P values at of 1.13e-6, 1e-4, and 1e-3, respectively. The gene expression profiles of Azhurnaya and Chinese Spring in leaf-, culm-, and seed-related traits were shown on the heatmap. The stars (*) represent the candidate genes with an expression (TPM) value of more than 10. (B) Frequency of category annotations and average TPM value for candidate genes. The green, blue, purple, and wathet blue bars represent leaf, culm, seed, and plant height (PH) traits, respectively. (C) Local Manhattan plot for PH. The red dashed lines indicate the significant loci with the highest -lg(P) value. (D) Exon-intron structure and DNA polymorphism of TraesCS1A01G286300. The difference between the two haplotypes was shown on the boxplots and statistically analyzed based on Welch's test for plant height (PH, 2018). The expression profiles of two candidate genes were shown on the bar plots. (E) The expression profiles of twelve candidate genes were identified from PH trait.
LOCUS 8 (UVR8), a specific UV-B photoreceptor in plants (Rizzini et al., 2011). In Arabidopsis, transgenic plants with UVR8 showed elevated levels of anthocyanin and enhanced the acclimation-independent UV-B tolerance (Heijde et al., 2013;Podolec et al., 2021). UV-B induced damage to photosynthetic machinery and limit of PSII activity inhibits growth and biomass accumulation, especially in UV-B sensitive plants. It was reported that ambient UV radiations reduced leaf area by 23% in Lactuca sativa L. in mid-latitude locations (Wargent et al., 2011). E. sibiricus can thrive in high-alpine areas and shows good tolerance. However, the information related to the effects of ultraviolet radiations on E. sibiricus growth among different habitats is limited; thus, future exploration of UV light adaptation mechanisms may provide profound benefits to increase the forage grass production. Furthermore, exposure to UV-B among alpine plants can result in cross-protection against other stress factors at the same time, such as low temperature and hypoxic conditions on Qinghai-Tibet Plateau; thus, a combined stress environment should be considered (Wargent et al., 2011;Schulz et al., 2021). The comprehensive ecological environment on Qinghai-Tibet Plateau drove the adaptation strategies in Leymus secalinus and activated antioxidant defense system with multiple protective strategies, including the reduction of chlorophyll contents and increase in carotenoid content to protect plants against photodamage; the changes in antioxidant enzyme activities enhanced the ROS scavenging capacity; the increases in proline and soluble sugar contents adjust osmotic in plants of alpine regions (Cui et al., 2018). In this study, seven hub genes were all related to the antioxidant defense system and may play a key role in the adaptation strategies of E. sibiricus. As the significant ambient difference existed between the two groups, the genetic signatures and functional genes we identified may be preferentially amplified to respond to specific environment.

Genome-Wide Association Study in Elymus sibiricus
Recently, advances in next-generation sequencing have rapidly increased the density of high-quality molecular markers in many non-model plants (Fang et al., 2017a;Pang et al., 2020;Shen et al., 2020;Wei et al., 2021). As the gigantic genome existed in E. sibiricus, with an estimate of 6.86 Gbp, we used a mature reduced whole-genome sequencing technology (SLAF-seq) to investigate the SNP information among 210 genotypes (Sun et al., 2013;Xiong et al., 2021). At present, there is no report on the assembled genome; thus, generated SNPs were mapped on the closest wheat reference genome (v1.0), with 79% of homogeneity and 84.67% of mapping rate of reads (Zhang et al., 2019a). Our study generated a total of 88,506 highly consistent SNPs, with an average sequencing depth of 18.01-fold and a marker density of 211.5 kb per SNP. Generally, the LD decay in cross-pollinated plants is much faster than in self-pollinated. A precise LD decay distance in E. sibiricus was unavailable in the study, but some self-pollinated species provided certain reference information. The physical distance of LD decay in rice, cottons, wheat, and soybean occurred at 445 kb (r 2 = 0.2; Yano et al., 2016), 1,000 kb (r 2 = 0.3; Fang et al., 2017b), 4.4 Mb (r 2 = 0.5; Pang et al., 2020), and 223.2 kb (r 2 = 0.4; Sui et al., 2020), respectively. Similarly, the mean distance between the markers was very less than the LD decay distances in our study, and SNP set provided sufficient resolution for capturing the genetic variation in general GWAS.
The identification of genes associated with important agronomic traits such as biomass and quality in forage grass is indispensable for breeding improvement (Yano et al., 2016). Previously, we identified 29 QTLs of seed-related traits among 14 linkage groups in E. sibiricus by using a biparental allelic separation population (F 2 ; Zhang et al., 2019a). However, the restrictions with limited allelic segregation and recombination in linkage mapping, low genome coverage of molecular markers, low resolution of QTL regions, and small effects of polygenic interaction had hindered the effective identification of critical genes. The GWAS can detect the relationship between natural allelic variations and phenotypic variation and can provide an effective strategy for the rapid identification of key genes responsible for complex quantitative traits. The method overcomes the limitation of biparental populations and offers more variation sites from historical recombination events. The diversity panel of germplasm resources provides abundant variation sites for GWAS. In this study, the PCA and NJ-tree analyses showed a continuous distribution without a strong population structure, and the genotypes we selected were helpful to control false positives in GWAS. The MLM further controlled the population stratification and familial kinship of the accessions and is extensively used for crops and forage grass research as a popular method Valluru et al., 2017;Ramstein et al., 2018;Kang et al., 2019;Chen et al., 2021;Li et al., 2021). In addition, highthroughput sequencing generated more than tens of thousands of markers, and many loci have strongly LD against the estimation of false-positive rate by using independent tests among the actual number of markers in the Bonferroni assumption (Li et al., 2012;Li H. et al., 2013). A secondary identification of GWAS signals using an enlarged candidate region, considering the deviation of observed P-values from expected distribution in quantile-quantile (Q-Q) plots, increased some of the falsenegative SNPs filtered by too strict and conservative Bonferroni correction (Valluru et al., 2017;Li et al., 2019;Mandozai et al., 2021).

Candidate Genes for Yield-Related Traits
Based on the phenotypic observation and joint analysis with three programs, the GWAS efficiently detected a total of 1,825 significant loci associated with 12 grass yield-and seed-related traits in E. sibiricus. To evaluate the effects of identified genes, each of these was used to analyze the gene expression profiles, and we finally generated 106 candidate genes with higher expression levels (TPM values > 10) and indicated different functions (Figure 5). For example, 7 photosynthesis genes and 4 photorespiration genes were highly expressed in plant leaves. Four genes involved in plant hormone salicylic acid and 4 genes involved in jasmonic acid play an important role in plant growth and development, as well as plant photosystems in response to light stress (Nitschke et al., 2016;Chen et al., 2020). Furthermore, the PPI network analysis was employed to focus on the hub genes related to yield-related traits in E. sibiricus (Figure 6).
For PH traits, the top hub gene, TraesCS4D01G221200, is involved in a variety of biological regulation processes, such as auxins-and jasmonic acid-mediated signaling pathway, SAM, xylem, and phloem pattern formation. Meanwhile, the corresponding protein of this gene, TOPLESS (TPL), a conserved co-repressor, regulates a wide array of plant physiology process as central to multiple signaling pathways in higher plants, such as development, meristem maintenance, and hormone signaling (auxins, jasmonic acid, and ethylene; Causier et al., 2012;Wang et al., 2013). During post-embryonic development, the shoot and the root apical meristems are responsible for the formation of the main axis of plant growth. In Arabidopsis embryogenesis, the topless-1 (tpl-1) mutation failed to form a SAM, for the absence of repression system (TPL), which acts to the expression of root-promoting genes in the top half of the embryo (Long et al., 2006). Cell-to-cell communication is a prerequisite for the process of growth and development for multicellular organisms. Plasmodesmata (PD) connects the adjacent individual cells to the entire organism, allowing transduction and exchange of diverse macromolecules, metabolites, proteins, nutrients, and phytohormones during plant growth and development (Vu et al., 2020). Mutants of multiple genes have been shown to be responsible for PD function. At the stage of embryogenesis in Arabidopsis, ise1 and ise2 mutants increased the PD-mediated transport and the PD permeability (Kobayashi et al., 2007;Stonebloom et al., 2009;Carlotto et al., 2016). Whereas, dse1 mutant reduced the transport via PD and exhibited developmental defects at all development stages including smaller plants and delayed flower initiation than the wild type (Xu et al., 2012). Induction of a synthetic allele icals3m can effectively block the movement of proteins and RNAs via PD by increasing callose and decreasing the size of PD aperture. In root meristem of Arabidopsis, this interference in cell-to-cell communication significantly caused the loss of symplastic signaling of cells and tissues around and is critical for the regulation of cell divisions and cell expansion (Wu S. et al., 2016). In our study, we associated a non-synonymous SNP within the exon coding region of TraesCS1A01G286300, which was functionally related to plasmodesma. Our data showed a phenotype difference of 32.7% (PH) in the two haplotypes. The gene that was highly expressed in the internode, stem axis, and stem of two wheat cultivars may be involved in the development process in E. sibiricus and caused the trait differences.
In addition, the candidate genes which encode auxin signaling (9 genes), tyrosine kinase (6 genes), E3 ubiquitin ligase (5 genes), microtubule (6 genes), and cytoskeleton (5 genes) were largely involved in culm-related traits. Moreover, the genes related to the hormone signaling pathway, tyrosine kinase activity, cytoskeleton, and microtubule structure tend to be in the center position in the PPI analysis. As a multi-functional phytohormone, auxins modulate numerous plant processes including SAM development, vascular elongation, and axillary root formation (De Smet and Jürgens, 2007). The polar auxin transport was impaired in the maize mutant with qph1, a major plant height QTL, and decreased plant height by 20% (Xing et al., 2015). In addition, microtubule growth and cell cytoskeleton rearrangements have been shown to be accompanied by auxin regulation (Winnicki, 2020). In the meristematic cells of the root epidermis of Arabidopsis thaliana, the auxin signaling influences actin cytoskeleton, contributing to the vacuolar morphogenesis and leading to the regulation of cell size in plants (Scheuring et al., 2016). Furthermore, the plant receptors play a crucial regulatory role in many cell signaling pathways leading to cell growth, development, and disease resistance. Eukaryotic protein kinases (ePKs) facilitate the signal transduction and activation (or inactivation) of downstream reaction by phosphorylation of substrate protein of serine (Ser)/threonine (Thr)/tyrosine (Tyr) residues (Schwessinger et al., 2011;Jose et al., 2020). In model plants, rice and Arabidopsis, the proportions of conserved phosphorylation sites of serine and threonine were about 80-85% and 10-15%, respectively, while tyrosine was less than 5% (Nakagami et al., 2010). Despite lower abundance, Tyr phosphorylation has been demonstrated to be involved in different stages and processes of plant development, including the ABA pathway in seeds (Ghelis et al., 2008), leaf size, flowering time (Oh et al., 2009), plant growth (Jaillais et al., 2011;Nemoto et al., 2017), and root hair growth and development (Yemets et al., 2008).

DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

AUTHOR CONTRIBUTIONS
YW, WL, SB, and WX conceived the research. WL and SB provided the germplasm resources and reviewed the manuscript. ZZ, YZ, and NW implemented the experiment. ZZ, YZ, and JZ performed the data curation and analysis. ZZ and WX wrote the manuscript. All authors approved the final manuscript.


provenance: AgriculturalAndFoodSciences,2022-2022-Agricultural And Food Sciences-2022.jsonl.gz.gz

text:
An extensive dataset for successful recognition of fresh and rotten fruits

Detection of rotten fruits is very crucial for agricultural productions and fruit processing as well as packaging industries. Usually, the detection of fresh and rotten fruits is done manually which is an ineffective and lengthy process for farmers. For this reason, the development of a new classification model is required which will reduce human effort, cost, and production time in the agriculture industry by recognizing defects in the fruits. This article offers a major dataset to the researchers to develop effective algorithms for recognizing more variety of fruits and overcome the limitations by increasing accuracy as well as decreasing computation time. This dataset contains sixteen types of fruit classes, namely fresh grape, rotten grape, fresh guava, rotten guava, fresh jujube, rotten jujube, fresh pomegranate, rotten pomegranate, fresh strawberry, rotten strawberry, fresh apple, rotten apple, fresh banana, rotten banana, fresh orange, rotten orange. We collected various fresh and rotten fruit images from 16th to 31st March 2022 from different fruit shops and real fields with the help of a domain specialist from an agricultural organization. The dataset is hosted by the Department of Computer Science and Engineering, Jahangirnagar University, and is freely available at https://data.mendeley.com/datasets/bdd69gyhv8/1

a b s t r a c t
Detection of rotten fruits is very crucial for agricultural productions and fruit processing as well as packaging industries. Usually, the detection of fresh and rotten fruits is done manually which is an ineffective and lengthy process for farmers. For this reason, the development of a new classification model is required which will reduce human effort, cost, and production time in the agriculture industry by recognizing defects in the fruits. This article offers a major dataset to the researchers to develop effective algorithms for recognizing more variety of fruits and overcome the limitations by increasing accuracy as well as decreasing computation time. This dataset contains sixteen types of fruit classes, namely fresh grape, rotten grape, fresh guava, rotten guava, fresh jujube, rotten jujube, fresh pomegranate, rotten pomegranate, fresh strawberry, rotten strawberry, fresh apple, rotten apple, fresh banana, rotten banana, fresh orange, rotten orange. We collected various fresh and rotten fruit images from 16th to 31st March 2022 from different fruit shops and real fields with the help of a domain specialist from an agricultural organization. The dataset is hosted by the Department of Computer Science and Engineering, Jahangirnagar University, and is freely available at https://data.mendeley.com/datasets/ bdd69gyhv8/1

Value of the Data
• Classification of fresh and rotten fruits is usually carried out by people, which is ineffective for fruit farmers as well as fruit sellers. For this reason, the development of a new classification model is required which will reduce human effort, cost, and production time in the agriculture industry by recognizing defects in the fruits. To build a better classification model, an efficient dataset is needed. • This dataset gives a visual representation of fresh and rotten fruits in a hyperspectral order of images. Therefore, it empowers researchers to recognize and classify fresh and rotten fruits at an early stage. • Gathered Images can be utilized to construct, train, test, analyze, and compare various deep learning algorithms for recognizing fresh and rotten fruits based on different features. • Original fresh and rotten images are captured from a wider perspective. This dataset contains a larger number of fruit classes compared to other existing datasets [1][2][3] . This will help researchers to detect large number of fruit classes and get better performance. • This data was captured from different fruit shops and real fields in natural weather with inhomogeneous lighting conditions. As a result, researchers may find it difficult to detect exceptions with the naked eye. • Early recognition of fresh and rotten fruits based on research findings may enable farmers to produce large quantities of fruit and put on value to the national economy.

Dataset Description
Fruits play a vital role in any country's economic development. Everyone wants to buy fresh, high-quality fruits. Since fruits decay with time, it may have a bad effect on the economy. Approximately one-third of the fruits are projected to be rotten, resulting in significant financial loss. Furthermore, customers believe that damaged fruits are dangerous to their health, and the sale of fruits will be affected. This dataset can be a state-of-the-art mentor in creating algo- rithms for the early detection and classification of fresh and rotten fruits in the agricultural field in these situations [1] . To develop computer vision-based algorithms, an extensive fruit dataset is presented containing sixteen types of fruit classes, namely fresh apple, rotten apple, fresh banana, rotten banana, fresh orange, rotten orange, fresh grape, rotten grape, fresh guava, rotten guava, fresh jujube, rotten jujube, fresh pomegranate, rotten pomegranate, fresh strawberry, and rotten strawberry. This dataset has a total number of three thousand two hundred (3200) original images and 12,335 augmented images. Our initial training dataset contains 2560 images. We took the pictures using a digital camera with the assistance of a domain expert from an agricultural organization. All the images hold a constant width and height of 512 × 512 pixels. Table 1 contains details of the dataset and Table 2 contains brief details of the dataset for each fruit class.

Camera Specification
All the images were taken with a Single-lens reflex digital camera (Nikon D5600), which has an effective pixel of 24.2 million and 23.5 × 15.6 mm CMOS sensor. The total pixel of this camera is 24.78 million.

Preprocessing
For training our dataset using any deep learning model, we have to perform five steps named (a) Augmentation, (b) Resizing images, (c) Splitting dataset, (d) Generating Model, (e) Performance Analysis. We visualized these steps in Fig 1 . Since we require a vast amount of data for training the deep learning model, we increased our data using augmentation. Augmentation was done through Random rotation, scaling, shearing, cropping, shifting, adjusting brightness, contrast, saturation, and hue. Some Sample augmented images for this fruit dataset are given in Fig. 2 . We performed rotation with 45 °, 60 °, and 90 °angles. Some techniques that we applied for re-scaling are Nearest-Neighbor Interpolation, Bicubic Interpolation, and Bilinear Interpolation. Histogram Equalization is another approach that we used to improve image contrast. The value of the width shift range, height shift range, and sheer range is 0.2 used for augmentation [4] . The value of the width shift range, height shift range, and sheer range is 0.2 used for augmentation [4] . We used the fill mode() function from the Keras package in Python for estimating parameters. Before being fed into the neural network model, we have resized all the images in a fixed size and shape. We used 512 × 512 pixels to resize the images. Then we divided resized images into training and testing sets. In the future, we will investigate the deep learning models in detail using this dataset to find an optimum method for real-life applications.    Table 2 Brief Details of fruit dataset.

Class Name Description Visualization
Fresh Grape Fresh grape can be identified through its colors, textures and condition of stems. The grapes should be firm, plump, and well-attached to the stems. Fresh grapes have flexible green stems.

Rotten Grape
Rotten grapes are those that have become sticky, fungal, or wrinkled at the stem connection. A mushy texture, brown dis-coloration, and a slight vinegar smell are some of the characteristics of rotten grapes. Mold will eventually emerge in rotten grape.
Fresh Guava Guavas are oval in form when they are fresh. Fresh guava has a distinct aroma, identical to that of lemon rind. The outer skin might be harsh and bitter, or it can be delicate and pleasant. The skin can be any thickness and is normally green before maturity, but can be maroon, green, or yellow when ripe, depending on the species.
Rotten Guava Inside, rotten guava turns brown or black. The guava has probably gone rotten if the peel splits, falls easily, or rots away. In the imperfect stage, Physalopara psidii produces stem canker and Diplodia metalenses produce dry grave fruit rot. The infection targets the plant's primary branches and stem, fracturing the lesion. The afflicted branches wilt when the stem tissues are killed. Fresh Jujube When Jujubes are immature, they are green. As they ripen, they turn yellow-green with red-brown patches, and the completely mature fruit is completely red. However, we may consume them at any stage of development, from yellow-green to fully red; the redder they are, the sweeter they will taste. They have an apple-like texture and are crisp and delicious.
Rotten Jujube Small, round reddish to black marks appeared first on rotten jujube, and as they grew larger, they became deep brown to red-brown. Internal tissues became deep brown, while the damaged parts' surface became sunken with thicker fruit peel.

Fresh Pomegranate
The pomegranate is a deciduous shrub belonging to the Lythraceae family and the Punicoideae subfamily. The pomegranate has a thick crimson skin that varies in color from yellow to purple and can contain up to 600 seeds. Fresh pomegranates are fruits that are hard on the outside and feel weighty for their size, have no cracks or bruises, and have firm, blemish-free skin.

Rotten Pomegranate
Alternaria fruit rot causes the pomegranate to decay. It's also known as black rot because it causes wounds and decay on the inside of the fruit. Mold is arguably the most obvious indicator of a rotten pomegranate. The pomegranate has turned bad if the seeds are dark in color and have soft and mushy regions.
( continued on next page ) Fresh Apple Fresh apple skin is often green, yellow, pink, red, or russeted however, there are numerous bi-or tri-colored varieties available. Fresh apples have a firm texture, brilliant skin, and a nice, delicious aroma. They will be free of bruises, soft patches, and discoloration. They are crunchy and juicy when we bite into them.

Rotten Apple
Apples that have gone bad will be mushy and brown in appearance. On the outer surface of the decaying apple, mold or fungus will start to grow. It will be light gray and may resemble a powdery patch.

Fresh Banana
A banana has a curved shape and a thick, sweet-tasting skin. Depending on ripeness, the hue should range from green to dark yellow with brownish flecks. A banana is a tropical fruit that is widely consumed around the world. Fresh bananas are those that are slightly but not excessively green, bright in color, full and plump, and have no depressed, wet, or black spots on the peel.

Rotten Banana
In a process known as enzymatic browning, high levels of ethylene cause the yellow pigments in bananas to degrade into those distinctive brown marks. If the banana has a lot of brown or black areas inside the peel mildew appears, or if it has an awful odor, it's probably past its prime. Fresh Orange A firm orange with a thin, smooth peel and no soft patches is fresh. The fruit is tiny, not brightly colored, and delicious, with a pale yellow-hued juice, especially in lemon rootstock fruits. It's possible that the fresh orange is seedless or it may contain multiple tiny seeds.

Rotten Orange
The peel of an orange is the most evident sign of its rottenness. Rotten orange is mushy and unpleasant with a sour odor. The flesh of a rotting orange is squishy and discolored. It might have a green and white soft spot on it.

Fresh Strawberry
Strawberries have a juicy texture and are soft, sweet, vivid red fruit. Fresh strawberries have a vivid red hue, a natural gloss, and green crowns that appear to be new. Strawberry seeds are tiny edible seeds that develop all over the top of fresh strawberries. It should have a firm consistency but not be crunchy. Overly ripe strawberries might be excessively soft.

Rotten Strawberry
A moldy odor or feel defines a rotten strawberry. It has a rotten smell and a damaged appearance. If the color, flavor, or texture of a strawberry changes from its original tone, it can be categorized as rotten strawberry. If there are any stains, flaws, or other symptoms of degradation, the strawberry has already become rotten.


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Variability total phenolic content and antioxidant activity of Curcuma zanthorrhiza and C. aeruginosa cultivated in three different locations in West Java, Indonesia

March 2022. Abstract. Suryani, Al Anshory AC, Marlin , Artika IM, Ambarsari L, Nurcholis W. 2022. Variability total phenolic content and antioxidant activity of Curcuma zanthorrhiza and C. aeruginosa cultivated in three different locations in West Java, Indonesia. Biodiversitas 23: 1998-2003. Curcuma zanthorrhiza Roxb. and C. aeruginosa Roxb. are medicinal plants belonging to the Zingiberaceae family, which contained phenolic compounds. Phenolic is one of the antioxidant compounds. This study aimed to investigate variation on the total phenolic and antioxidant activity of three C. zanthorrhiza varieties and twenty C. aeruginosa genotypes cultivated in three different locations in West Java, Indonesia. Total phenolic content was determined using the Folin-Ciocalteu reagent, whereas antioxidant activity was used spectrophotometry using DPPH method. The total phenolic content ranged from 2.403 to 7.539 mg GAE/ g DW) differed significantly among the C. zanthorrhiza and C. aeruginosa varieties/genotypes, with the highest concentration found in the Cursina 3 variety of C. zanthorrhiza in Cianjur. The antioxidant activity ranged from 2.443 to 14.960 µmol TE/g DW, with the maximum activity identified in the Cursina 2 variety of C. zanthorrhiza in the Bogor location. G1, G6, G13, and G16 were identified as stable genotypes for phenolic antioxidant production. Three C. zanthorrhiza cultivars demonstrated higher total phenolic content and antioxidant activity than all C. aeruginosa genotypes in all planting areas. C. zanthorrhiza cultivars are the most promising source of phenolic content and antioxidant activity and are thus recommended for mass plantation at suitable locations in West Java, Indonesia to

INTRODUCTION
Indonesia has a wide variety of medicinal plants, having between 2500 and 2700 plant species (Cahyaningsih et al. 2021). Rhizomes of some species within the Zingiberaceae family are frequently utilized as medicinal plants, two species of which are Curcuma zanthorrhiza Roxb. and C. aeruginosa Roxb. (Yuandani et al. 2021). Several studies reported pharmacological activities from these plants, including anticancer, antidiabetic, antimicrobial, and antioxidant properties (Akarchariya et al. 2017;Nurcholis et al. 2017;Nurcholis et al. 2018;Fitria et al. 2019). This demonstrates the significance of these plants as potential Indonesian medicinal herbs.
Secondary metabolites, such as polyphenols influence the pharmacological activities of C. zanthorrhiza and C. aeruginosa rhizomes. Curcuminoid compounds consisting of curcumin, dimethoxy curcumin, and bisdemethoxy curcumin are polyphenolic compounds that have been reported to be present in the rhizomes of C. zanthorrhiza and C. aeruginosa (Nurcholis et al. 2016a;Nurcholis et al. 2016b;Nurcholis et al. 2019;Atun et al. 2020;). Many studies have found polyphenols to be effective as antioxidants (Bahukhandi et al. 2018;Feduraev et al. 2019;Xiang et al. 2019), and as a result, evaluating polyphenol in samples will be excellent compared to antioxidant activity. These parameters have been used in several studies of C. zanthorrhiza and C. aeruginosa. Sandrasari et al. (2019) evaluated phenolic and antioxidant activity in rhizomes of five species of Zingiberaceae native to Indonesia with maximum identified results in Zingiber officinale followed by C. longa, C. zedoaria, C. zanthorrhiza and C. aeruginosa. Phenolic and antioxidant characteristics were also employed in other studies, such as the use of solar drier (Vitanti et al. 2016), identifying elite accessions Rosidi 2020), and evaluating Indonesian Zingiberaceae herbs (Muflihah et al. 2021).
Plant genotype and environmental conditions and interactions between genotype x environment are essential factors affecting the productivity of medicinal plant secondary metabolites. Productivity secondary metabolites have been shown in several plants, including C. longa (Pal et al. 2020), Solanum viarum (Patel et al. 2022), Andrographis paniculata (Kalariya et al. 2021), and Amelanchier alnifolia (Lachowicz et al. 2017), which are affected by genotype x environment. According to Seno et al. (2020), C. zanthorrhiza varieties and C. aeruginosa genotypes grown at different locations were affected by photosynthetic parameters and rhizome productivity. Photosynthesis is an essential parameter for producing primary plant metabolites which are precursors in the biosynthesis of secondary metabolites in plants (Nocchi et al. 2020).
We suspect that C. zanthorrhiza and C. aeruginosa cultivated at different locations will also affect the content of secondary metabolites, one of which is phenolic compounds. Until present, research on the total phenolic content and antioxidant activity of C. zanthorrhiza varieties and C. aeruginosa cultivated in different locations have not been reported. Therefore, this study aimed to investigate total phenolic content and antioxidant activity in C. zanthorrhiza and C. aeruginosa extract as well as identified stable genotype that cultivated in three different locations in West Java, Indonesia.

Study area
Plant materials used in the study were collected from three locations in West Java, Indonesia from October 2019 to July 2020 (Table 1).

Plant materials
Curcuma zanthorrhiza varieties namely Cursina 1, Cursina 2, and Cursina 3 (C1-C3) used in the study were received from Indonesian Spices and Medicinal Crops Research Institute, Bogor, West Java, Indonesia. The C. aeruginosa genotypes (G1-G20) were obtained from Agricultural Biochemistry Laboratory, Department Biochemistry, IPB University, Bogor, Indonesia. All C. zanthorrhiza and C. aeruginosa were grown under identical conditions, except for the cultivated sites, and harvested at the same time in the same regions. Three samples of each C. zanthorrhiza variety and C. aeruginosa genotype from each site were evaluated in this work. All of the samples' rhizomes were broken into small pieces and air-dried before being processed into a powder.

Plant extraction
All dried rhizomes were extracted utilizing the sonication with maceration procedure, as described in Calvindi et al. (2020), with minor modifications. Rhizome sample of 4 g was extracted with 80% ethanol (20 mL) and then sonicated using Branson 1510 for 30 min. The solution was macerated for 3 h at room temperature in dark condition. Then, the solution was filtered using filter paper. All of the extraction steps were done again three times. Finally, the supernatant was concentrated or adjusted to a final concentration extract of 0.2 g/mL and this extract was used for the evaluation of total phenolic content and antioxidant activity.

Total phenolic content
The Folin-Ciocalteu spectrophotometric technique described by Batubara et al. (2020) with minor modifications was used to estimate the total phenolic content (TPC) by using a nano-spectrophotometer (SPECTROstarNano BMG LABTECH). TPC was reported as mg of gallic acid equivalents per g of dry weight (mg GAE/ g DW).

Antioxidant activity
The determination was performed using the previously described 2,2-diphenyl picrylhydrazyl (DPPH) technique (Nurcholis et al. 2021). The amount of antioxidant activity was expressed in micromol Trolox equivalents per g of dry weight (µmol TE/ g DW).

Data analysis
ANOVA was determined using the ExpDes package in the R program, followed by a Scott-Knot test (Ferreira et al. 2014). When p <0.05 is evaluated, considerable significant differences. The stability of samples was determined using additive main effect and multiplicative interaction (AMMI) with using PBSTAT-GE (www.pbstat.com). Visualization of genotype stability was used AMMI-2 biplot based on data of total phenolic content and antioxidant activity obtained from the pbstat.com on the GGE (genotype plus genotype-byenvironment) menu.

Total phenolic contents
Total phenolic contents (TPC) in 20 genotypes of C. aeruginosa rhizome ranged from 2.403 to 5.118 mg/g GAE DW and 3 varieties of C. zanthorrhiza rhizome ranged from 4.871 to 7.539 mg/g GAE DW. The average TPC of C. aeruginosa and C. zanthorrhiza was the highest produced by C. aeruginosa and C. zanthorrhiza cultivated in the Bogor (3.872 mg/g GAE DW), followed by C. aeruginosa and C. zanthorrhiza that cultivated in Cianjur (3.728 mg/g GAE DW) and Sukabumi (3.651 mg/g GAE DW). The highest mean total phenolic content of C. aeruginosa based on its genotype was G14 (3.909 mg/g GAE DW) after comparison Cursina 1 (C1), Cursina 2 (C2), and Cursina 3 (C3). The maximum TPC of C. zanthorrhiza varieties was found in C3 cultivated in Cianjur, followed C2 and C3 in Bogor. C. zanthorrhiza varieties rhizome contained high TPC compared to the C. aeruginosa genotypes (Table 2).

Antioxidant activity
The antioxidant activity values of C. aeruginosa ranged from 2.443 to 6.794 µmol TE/g DW and C. zanthorrhiza ranged from 5.265 to 14.960 mol TE/g DW. Genotype G1 (5.547 µmol TE /g DW) has the highest mean value of antioxidant after comparison and Bogor (5.034 µmol TE/g DW) is the location with the highest antioxidant activity value compared to Sukabumi (4.951 µmol TE /g DW) and Cianjur (4.333 µmol TE /g DW). The highest antioxidant activity of C. zanthorrhiza varieties was found in C2 followed C1 and C3 cultivated in Bogor (Table 3).

Genotype stability
The results of the combined variance analysis of 20 genotypes of C. aeruginosa and 3 varieties of C. zanthorrhiza showed that the genotypes had a very significant effect with p-value less than 0.001 for TPC and antioxidant activity (Table 4). The p-value less than 0.001 for the total phenolic content and antioxidant activity indicated that there was variation in yield based on the genotype. Experimental replicates on antioxidant activity had a p-value of 0.011 and the interaction between genotype and environment had a significant effect with a pvalue of 0.009 for antioxidant activity of DPPH. The interaction between genotype and environment, PC1 has pvalue of 0.088 for total phenolic content and p-value of less than 0.001 for antioxidant activity.
The AMMI-2 biplot can be used to describe the genotype's stability across all experimental sites by utilizing the confidence region of the ellipse with a center point of (0.0) and the first and second most significant interaction principal component (PCA) values. The contribution of the diversity of interaction effects on the total phenolic parameters and antioxidant activity that can be explained by the main component of the interaction, which is 100% with each component PC1 and PC2 on the total phenolic parameters were 67.1% and 32.9% (Figure 1) and the antioxidant activity parameters of components PC1 and PC2 were 89.1% and 10.9%, respectively (Figure 2).  Stable genotypes are presented by the AMMI-2 biplot, which is the genotype inside the circle (ellipse) (Figure 1). There are 8 genotypes namely G1, G2, G3, G4, G6, G12, G13, and G16 based on TPC, while the specific genotype for total phenolic parameters, C. aeruginosa genotype G7 is specific for the Sukabumi area, G18 is specific for the Bogor area and G5 and G15 were specific for the Cianjur area. There are 6 stable genotypes with antioxidant activity parameters that is G1, G5, G6, G13, G16, and G17 ( Figure  2). Specific genotypes based on antioxidant activity parameters in the Cianjur area were G7, G8, G15, G18, and G19. The C. zanthorrhiza variety C1 is a specific variety for the Bogor area and C. aeruginosa genotypes G9 and G13 are the specific genotypes for the Sukabumi area.

Discussion
Phenolics are key secondary metabolites that are effective antioxidants (Zeb 2020). Antioxidant qualities in plants can lower the risk of a variety of diseases, such as cancer (Roleira et al. 2015), diabetes (Lin et al. 2016), hypercholesterolemia (Wang et al. 2012), and other cardiovascular disorders (Olas 2017). As a result, there is a strong demand for plant materials with high phenolic content and antioxidant capacities (Machu et al. 2015). C. zanthorrhiza showed a greater total phenolic content and antioxidant activity than C. aeruginosa (Tables 2 and 3). These results are consistent with the findings of previous studied (Nurcholis et al. 2012;Sandrasari et al. 2019). The C2 variety of C. zanthorrhiza cultivated in Bogor produced the highest TPC (7.304 mg GAE/g DW) and antioxidant activity (14.960 µmol TE /g DW) compared to other plant samples. C. zanthorrhiza variety C2 in Bogor, West Java, Indonesia, has great promise as a source of antioxidant phenolic commercially.
Environmental factors influenced the total phenolic content and antioxidant activity in plants studied in this work. Bogor with the highest rainfall and temperature conditions showed maximum phenolic antioxidant contents than Cianjur and Sukabumi. This finding is consistent with prior studies on Salvia officinalis, which have shown that rainfall and growth environment temperature affected phenolic and antioxidant production (Generalić Mekinić et al. 2019). Additionally, various studies have demonstrated that altitude affects the antioxidant phenolic content of plants (Cirak et al. 2022). The low altitude was found in Bogor, which identified the plant's highest phenolic and antioxidant production. Previous studies on Achillea collina found that the production of antioxidant phenolics increased when the plants were cultivated in environments with a high altitude (Giorgi et al. 2010). This research indicated that the phenolic antioxidant content in C. zanthorrhiza and C. aeruginosa plants is influenced by altitude, rainfall, and temperature conditions of cultivated area locations.
Evaluating genotypes/cultivars in different environments is crucial in a plant breeding program. The results presented the genotypes/varieties (C. zanthorrhiza and C. aeruginosa) main effects were significant (p <0.001) but not significant in environmental as well as the genotype-by-environment interaction for total phenolic content and antioxidant activity (Table 4). This finding differs from our prior findings on rhizome productivity and photosynthetic rate, which showed that genotype and environment interacted to influence C. zanthorrhiza and C. aeruginosa samples (Seno et al. 2020). Both the total phenolic and antioxidant characteristics of the C2 variety indicated a specific location in Bogor. Numerous stable C. aeruginosa genotypes, namely G1, G6, G13, and G16, suggest a high potential for new varieties to be generated through plant breeding programs (Elias et al. 2016;Pour-Aboughadareh et al. 2019).
In conclusion, the C. zanthorrhiza varieties cultivated in Bogor have the highest total phenolic content and antioxidant activity. Cursina 2 variety of C. zanthorrhiza has emerged as one of the most promising cultivars, and hence mass planting in their ideal site is recommended. The stable genotypes for phenolic antioxidant production were G1, G6, G13, and G16. These genotypes were potentially to develop a new variety through plant breeding programs.