Buckwheat is a highly nutritional pseudocereal with antioxidant potential. The aim of this study was to analyze the genetic variability of 21 varieties of common buckwheat (Fagopyrum esculentum Moench.) and 14 varieties of Tartary buckwheat (Fagopyrum tataricum Gaertn.) using microsatellite markers. By analyzing 21 SSR markers, an average of 11.6 alleles per locus were amplified and an average PIC value of 0.711 was determined. We determined the heterozygous status of the individuals and variability in the set using the SSR analysis on the basis of expected heterozygosity (He, 0.477), observed heterozygosity (Ho, 0.675), Shannon\'s index (I, 0.820), and fixation indices (FST, FIS, FIT). Based on the SSR analyses, the lower level of expected heterozygosity in the analyzed set of Tartary buckwheat genotypes was observed compared to common buckwheat. With the help of a hierarchical cluster analysis using the UPGMA algorithm, Structure analysis, and PCoA analysis for the SSR markers, we divided the buckwheat varieties in the dendrogram into two main clusters according to the species. The AMOVA analysis showed that genetic variability between the individuals prevails in the analyzed set. The SSR technique proved to be a suitable tool for the determination of intra- and inter-varietal genetic variability and for analysis of diversity.

This paper includes a comprehensive taxonomical study based on seed morphology of 31 Euphorbia L species from Türkiye. The studied Euphorbia taxa have been examined for morphological traits such as seed color, dimensions, surface ornamentation, cell wall structures, lipid granule presence, and caruncle shape and dimensions with Scanning electron microscopy (SEM) and stereo microscopy to develop a better understanding of the basis of its species. The outcomes show that the species differ based on seed shape and color. The seed width dimensions are between 0.55 and 3.83 mm and the length dimensions are between 1.03 and 5.87 mm. Euphorbia lathyris, E. prostrata, and E. nutans are marked differently from the rest of the studied species based on their seed dimension. The seed surface ornamentation is classified into 12 different types: tuberculate, reticulate, areolate, colliculate, verrucate, alveolate, rugose, alveolate-reticulate, slightly reticulate, reticulate-areolate, pusticulate, and ruminate. The most common form is reticulate, found in eight species. The tuberculate (in E. helioscopia), areolate (in E. oblongata), slightly reticulate (in E. amygdaloides), and ruminate (in E. herniariifolia) ornamentation types are each characterized by only one species. The presence of lipid granules and anticlinal and periclinal cell walls disclose interspecific relationships within the examined taxa. Also, an identification key is offered for the studied species based on seed characters. RESEARCH HIGHLIGHTS: The seeds of Turkish Euphorbia species have been studied in depth. The morphological characters of seeds of Turkish Euphorbia species have been examined utilizing SEM and light microscopy for the first time and discussed the taxonomic practice of these characteristics. A dichotomous key containing seed morphological data has presented.

Accurate prediction of breeding values is challenging due to the genotype-phenotype relationship is crucial and necessary for producing crops with elite genotypes. This paper is about investigating and predicting the phenotypic trait Height and Yeild in a genotype.
BACKGROUND: Most of the existing studies focus on genetic methods or Machine learning models, in this, we implemented a hybrid combination of genetic methods and machine learning models that accurately predicted phenotypic trait yield, height and subpopulation.
METHODS: Our proposed methodology for genomic prediction of yield in Oryza sativa (rice) involves a two-level classification approach. First, we classify biological sequences and cluster them using the UPGMA algorithm on a phylogenetic tree. Then, we use advanced machine learning techniques like Random Forest, and K-Nearest Neighbours to predict GEBVs with 85- 95% accuracy on rice subpopulations.
RESULTS: we achieved an accuracy of 93% when compared with other stated literature in this paper.
CONCLUSIONS: This approach overcomes limitations and effectively enhances crop breeding by capturing the genotype-phenotype relationship.

The aim of this study was to evaluate the impact of salt stress on morphological, yield, biochemical, and molecular attributes of different barley genotypes. Ten genotypes were cultivated at Fayoum Research Station, El-Fayoum Governorate, Egypt, during two seasons (2020-2021 and 2021-2022), and they were exposed to two different salt concentrations (tap water as a control and 8000 ppm). The results showed that genotypes and salt stress had a significant impact on all morphological and physiological parameters. The morphological parameters (plant height) and yield attributes (spike length, number of grains per spike, and grain yield per plant) of all barley genotypes were significantly decreased under salt stress as compared to control plants. Under salt stress, the total soluble sugars, proline, total phenol, total flavonoid, ascorbic acid, malondialdehyde, hydrogen peroxide, and sodium contents of the shoots of all barley genotypes significantly increased while the potassium content decreased. L1, which is considered a sensitive genotype was more affected by salinity stress than the tolerance genotypes L4, L6, L9, and Giza 138. SDS-PAGE of seed proteins demonstrated high levels of genetic variety with a polymorphism rate of 42.11%. All genotypes evaluated revealed significant variations in the seed protein biochemical markers, with new protein bands appearing and other protein bands disappearing in the protein patterns of genotypes cultivated under various conditions. Two molecular marker techniques (SCoT and ISSR primers) were used in this study. Ten Start Codon Targeted (SCoT) primers exhibited a total of 94 fragments with sizes ranging from 1800 base pairs to 100 base pairs; 29 of them were monomorphic, and 65 bands, with a polymorphism of 62.18%, were polymorphic. These bands contained 21 unique bands (9 positive specific markers and 12 negative specific markers). A total of 54 amplified bands with molecular sizes ranging from 2200 to 200 bp were produced using seven Inter Simple Sequence Repeat (ISSR) primers; 31 of them were monomorphic bands and 23 polymorphic bands had a 40.9% polymorphism. The techniques identified molecular genetic markers associated with salt tolerance in barley crop and successfully marked each genotype with distinct bands. The ten genotypes were sorted into two main groups by the unweighted pair group method of arithmetic averages (UPGMA) cluster analysis based on molecular markers and data at a genetic similarity coefficient level of 0.71.

This work proposes a machine learning-based phylogenetic tree generation model based on agglomerative clustering (PTGAC) that compares protein sequences considering all known chemical properties of amino acids. The proposed model can serve as a suitable alternative to the Unweighted Pair Group Method with Arithmetic Mean (UPGMA), which is inherently time-consuming in nature. Initially, principal component analysis (PCA) is used in the proposed scheme to reduce the dimensions of 20 amino acids using seven known chemical characteristics, yielding 20 TP (Total Points) values for each amino acid. The approach of cumulative summing is then used to give a non-degenerate numeric representation of the sequences based on these 20 TP values. A special kind of three-component vector is proposed as a descriptor, which consists of a new type of non-central moment of orders one, two, and three. Subsequently, the proposed model uses Euclidean Distance measures among the descriptors to create a distance matrix. Finally, a phylogenetic tree is constructed using hierarchical agglomerative clustering based on the distance matrix. The results are compared with the UPGMA and other existing methods in terms of the quality and time of constructing the phylogenetic tree. Both qualitative and quantitative analysis are performed as key assessment criteria for analyzing the performance of the proposed model. The qualitative analysis of the phylogenetic tree is performed by considering rationalized perception, while the quantitative analysis is performed based on symmetric distance (SD). On both criteria, the results obtained by the proposed model are more satisfactory than those produced earlier on the same species by other methods. Notably, this method is found to be efficient in terms of both time and space requirements and is capable of dealing with protein sequences of varying lengths.

BACKGROUND: Phoenix dactylifera L. belongs to the subfamily Coryphoideae. Saudi Arabia is the third producing country of dates in the world with over a million tons of dates every year. P. dactylifera is one of the most important species that grows in Al-Madinah and has cultivars that are distinguished by their appearance and taste.
RESULTS: This study aimed to investigate the importance of morphology among P. dactylifera cultivars by using statistical analysis and the ability to identify the cultivars just by looking at them in the obvious characters of palms. Plant specimens were collected from different areas in the Al-Madinah region. All the data obtained from morphology were transferred to numerical characters and used in the multivariate statistical package (MVSP) to study the similarity between the cultivars and give phenetic clusters. One-way ANOVA test and the least significant difference test (LSD) were used to find the significant differences among cultivars in p = 0.05. The numerical data that was recorded indicated significant differences among cultivars. Principal coordinates analysis and cluster analysis (UPGMA) were utilized to study the distance of similarities and differences between cultivars.
CONCLUSIONS: The most distinguishing characteristics were fruit and seed, and the least characteristic was the trunk. However, the features of spine, frond and leaflet were also important in distinguishing between cultivars.

The western part of South America is a centre of diversity for tomatoes, but genetic diversity studies are lacking for parts of that region, including Bolivia. We used 11 simple sequence repeat (SSR) markers (including seven novel markers) to evaluate genetic diversity and population structure of 28 accessions (four modern cultivars, four advanced lines, nine landraces, 11 wild populations), and to compare their genetic variation against phenotypic traits, geographical origin and altitude. In total, 33 alleles were detected across all loci, with 2-5 alleles per locus. The top three informative SSRs were SLM6-11, LE20592 and TomSatX11-1, with polymorphism information content (PIC) of 0.65, 0.55 and 0.49, respectively. The genetic diversity of Bolivian tomatoes was low, as shown by mean expected heterozygosity (He) of 0.07. Analysis of molecular variance (AMOVA) revealed that 77.3% of the total variation was due to variation between accessions. Significant genetic differentiation was found for geographical origin, cultivation status, fruit shape, fruit size and growth type, each explaining 16-23% of the total variation. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) tree and principal coordinate analysis (PCoA) scatter plot both revealed differentiation between accessions with determinate flowers and accessions with indeterminate flowers, regardless of cultivation status. The genetic profiles of the accessions suggest that the Bolivian tomato gene pool comprises both strictly self-pollinating and open-pollinating genotypes.

Agglomerative hierarchical clustering has become a common tool for the analysis and visualization of data, thus being present in a large amount of scientific research and predating all areas of bioinformatics and computational biology. In this work, we focus on a critical problem, the nonuniqueness of the clustering when there are tied distances, for which several solutions exist but are not implemented in most hierarchical clustering packages. We analyze the magnitude of this problem in one particular setting: the clustering of microsatellite markers using the Unweighted Pair-Group Method with Arithmetic Mean. To do so, we have calculated the fraction of publications at the Scopus database in which more than one hierarchical clustering is possible, showing that about 46% of the articles are affected. Additionally, to show the problem from a practical point of view, we selected two opposite examples of articles that have multiple solutions: one with two possible dendrograms, and the other with more than 2.5 million different possible hierarchical clusterings.

Plant genetic diversity has a significant role in providing traits that can help meet future challenges, such as the need to adapt crops to changing climatic conditions or outbreaks of disease. Our aim in this study was to evaluate the diversity of 61 forage pea specimens (P. sativum ssp. arvense L.) collected from the northeastern Anatolia region of Turkey using 28 simple sequence repeat (SSR) markers. These primers generated a total of 82 polymorphic bands. The number of observed alleles (Na) per primer varied from 2 to 4 with a mean of 2.89 alleles/locus. The mean value of expected heterozygosity (Exp-Het = 0.50) was higher than the mean value of observed heterozygosity (Obs-Het = 0.22). The mean of polymorphic information content (PIC) was 0.41 with a range of 0.03-0.70. The mean number of effective alleles (Ne) was found to be 2.15, Nei\'s expected heterozygosity (H) 0.49, and Shannon\'s information index (I) 0.81. Cluster analysis through the unweighted pair-group mean average (UPGMA) method revealed that 61 forage pea landraces were divided into three main clusters. Genetic dissimilarity between the genotypes, calculated with the use of NTSYS-pc software, varied between 0.10 (G30 and G34) and 0.66 (G1 and G32). Principal coordinate analysis (PCoA) revealed that three principal coordinates explained 51.54% of the total variation. Moreover, population structure analysis showed that all genotypes formed three sub-populations. Expected heterozygosity values varied between 0.2669 (the first sub-population) and 0.3223 (third sub-population), with an average value of 0.2924. Average population differentiation measurement (Fst) was identified as 0.2351 for the first sub-population, 0.3838 for the second sub-population, and 0.2506 for the third sub-population. In general, current results suggest that SSR markers could be constantly used to illuminate the genetic diversity of forage pea landraces and can potentially be incorporated into future studies that examine the diversity within a larger collection of forage pea genotypes from diverse regions.

BACKGROUND: Onobrychis viciifolia Scop. is a short-lived perennial cool-season legume used for forage production. It is a common native species in Asia Minor, especially in Turkey, the districts of the Caucasus, and the Caspian fringes. It can grow well in a broad range of climatic and soil types found in Asia, Europe, and North America. It is a non-bloating crop, making it suitable for use in both hay and pasture.
RESULTS: The aim was to assess the diversity of the 83 sainfoin genotypes selected based on their high agronomic performance from a germplasm collection evaluated in the experimental field of Tekirdag Namik Kemal University, Turkey. Ten nuclear simple sequence repeat (nSSR) primers (OVK036, OVK046, OVK094, OVK101, OVK125, OVK161, OVK174, OVM033, OVM061, and OVM125) were used in the study. All nSSR loci were found to be polymorphic and totally 92 alleles were detected. The mean observed number of alleles per locus was calculated as 9.2. Among the genetic diversity parameters, Shannon Index (I = 0.375), unbiased genetic diversity value (uh = 0.243), and mean polymorphic information content (PIC = 0.240) were calculated. The genetic distance value varied between 0.43 and 0.95. Based on the dendrogram built by the UPGMA clustering method using genetic distance values, it was observed that the studied sainfoin genotypes were divided into two main clusters, whereas the STRUCTURE analysis results had high support for three clusters.
CONCLUSIONS: The results obtained from this study provide important information on the genetic structures of the studied sainfoin genotypes and their genetic relationship. Therefore acquired genetic data will be useful in designing more efficient polycross nurseries, allowing open pollination of best performing and genetically diverse genotypes in the isolated conditions, which will increase genetic gain in sainfoin breeding programs.