Genetic Hybrid Research Articles

Interspecific Hybridization between Gossypium hirsutum (American Cotton) and Gossypium barbadense (Egyptian Cotton) for Quality Improvement

Cotton (Gossypium spp.) is a globally important cash crop, valued for its fiber and economic contributions to the textile industry. This study investigated the variability, heritability, and genetic advance of yield-related traits in interspecific hybrids of American (G. hirsutum) and Egyptian (G. barbadense) cotton. Using a randomized complete block design, 28 hybrids were evaluated for traits such as seed cotton yield, plant height, bolls per plant, and boll weight. Significant genetic variability was observed among hybrids, with high heritability estimates for seed cotton yield (96.84%), plant height (93.51%), and bolls per plant (76.44%), coupled with moderate genetic advance, suggesting additive gene action. Regression analysis revealed that the number of bolls emasculated and pollinated significantly influenced total seed production, with coefficients of determination (R²) of 0.5924 and 0.5102, respectively. The hybrid JA-08/A × JA-12/203 demonstrated superior crossability (100%) and seed production, making it a promising candidate for breeding programs. These findings emphasize the potential for exploiting genetic variability and hybrid vigor to develop high-yielding and high-quality cotton varieties.

PopCluster: A Population Genetics Model-Based Toolset for Simulating, Inferring and Visualising Individual Admixture and Population Structure.

In this computer note I introduce software, PopCluster, that implements a new likelihood method for unsupervised population structure analysis from marker data. To infer a coarse population structure, it assumes the mixture model and adopts a simulated annealing algorithm to make a maximum likelihood clustering analysis, partitioning the sampled individuals into a predefined number of clusters. To deduce a fine population structure, it further assumes the admixture model and employs an expectation maximisation algorithm to estimate individual admixture proportions. PopCluster has many features. First, it is one of just a couple of model-based methods that can handle both biallelic and multiallelic markers in the same framework. Second, it is the first population structure analysis method that uses both Message Passing Interface (MPI) and openMP to exploit multiple CPUs with both shared and distributed memories and has the capacity to handle genomic data with millions of individuals and millions of loci. Third, the algorithms for both mixture and admixture analyses are fast, rendering PopCluster favourably in computational efficiency over previous methods in analysing genomic data. Fourth, PopCluster is built for Windows, Linux and Mac platforms, and its Windows version has an integrated GUI that can conveniently visualise analysis results and facilitate data input. Fifth, its Windows version has a built-in simulation module designed to simulate genotype data under admixture, hybridization or migration models. PopCluster provides a valuable toolset for researchers to simulate, infer and visualise individual admixture and population genetic structure, hybridization and migration using marker data.

Genomic-Guided Conservation Actions to Restore the Most Endangered Conifer in the Mediterranean Basin.

Species with extremely small population sizes are critically endangered because of reduced genetic diversity, increased inbreeding and hybridisation threats. Genomic tools significantly advance conservation by revealing genetic insights into endangered species, notably in monitoring frameworks. Sicilian fir (Abies nebrodensis) is the most endangered conifer in Europe with only 30 adult trees in an 84-ha area. Using 20,824 SNPs from RAD-seq, employing genome assembly and a custom 120 SNP-array, we evaluated genetic diversity, mating patterns, and effective population size in adult trees, 118 natural seedlings, and 2064 nursery seedlings from past conservation actions. We assessed introgression from neighbouring non-native fir plantations (~6%) and established an intra-population assisted gene flow (AGF) program selecting the most genetically dissimilar individuals and investigating the outcome through simulations. Genomic analysis unveiled significant genetic diversity among adult Sicilian firs, comparable to non-endangered Mediterranean firs with larger populations. However, the genetic diversity of the forthcoming generation declined due to high self-fertilisation, leading to marked inbreeding (FIS = 0.38) and an alarmingly low effective population size (Ne = 6). Nursery seedling monitoring revealed similar selfing rates and introgression (~2%) from non-native firs. Although intra-population AGF could help to mitigate genetic loss, it may not alleviate the species vulnerability to imminent environmental challenges, perpetuating the risk of an extinction vortex. Hence, investigating the impact of Sicilian fir population decline and selfing on inbreeding depression, along with exploring the potential of hybrids for genetic load alleviation and future adaptation, is crucial for effective conservation strategies.

Diversity on a small scale - phylogeography of the locally endemic dwarf succulent genus Oophytum (Aizoaceae) in the Knersvlakte of South Africa.

Oophytum (Aizoaceae) is a locally endemic genus of the extremely fast evolving subfamily Ruschioideae and consists of only two formally accepted species (O. nanum and O. oviforme). Both species are leaf-succulent dwarf shrubs and habitat specialists on quartz fields in the Knersvlakte, a renowned biodiversity hotspot in the arid winter-rainfall Succulent Karoo Biome of South Africa. Quartz fields present specialised patchy habitats with an island-like distribution in the landscape. Oophytum oviforme grows in the south-western part, whereas O. nanum covers most of the remaining Knersvlakte. These species co-occur in a small area but within different quartz islands. We investigated the effects of the patchy distribution, environmental conditions and potential effects of paleoclimatic changes on the genetics of Oophytum. Phylogenetic and population genetic analyses of 35 populations of the genus, covering its entire distribution area, were conducted using four cpDNA markers and an AFLP dataset. These were combined with environmental data via a principal component analysis and comparative heatmap analyses. The genetic pattern of the Oophytum metapopulation is a tripartite division with a northern, central and western group. This geographical pattern does not correspond to the two-species concept of Oophytum. Only the western O. oviforme populations form a monophyletic lineage, whereas the central populations of O. oviforme are genetic hybrids of O. nanum populations. The highly restricted gene flow often resulted in private gene pools with very low genetic diversity, in contrast to the hybrid gene pools of the central and edge populations. Oophytum is an exceptional example of an extremely fast-evolving genus that illustrates the high speciation rate of the Ruschioideae and their success as one of the leading plant groups of the drought-prone succulent Karoo Biome. The survival strategy of these dwarf quartz-field endemics is an interplay of adaptation to diverse island habitats, highly restricted gene flow, occasional long-distance dispersal, migration, founder effects and hybridisation events within a small and restricted area caused by glacial and interglacial changing climate conditions from Pleistocene up to Present. These findings have important implications for future conservation management strategies.

Eco-evolutionary dynamics of host-microbiome interactions in a natural population of closely related mouse subspecies and their hybrids.

Closely related host species share similar symbionts, but the effects of host genetic admixture and environmental conditions on these communities remain largely unknown. We investigated the influence of host genetic admixture and environmental factors on the intestinal prokaryotic and eukaryotic communities (fungi, parasites) of two house mouse subspecies (Mus musculus domesticus and M. m. musculus) and their hybrids in two settings: (i) wild-caught mice from the European hybrid zone and (ii) wild-derived inbred mice in a controlled laboratory environment before and during a community perturbation (infection). In wild-caught mice, environmental factors strongly predicted the overall microbiome composition. Subspecies' genetic distance significantly influenced the overall microbiome composition, and each component (bacteria, parasites and fungi). While hybridization had a weak effect, it significantly impacted fungal composition. We observed similar patterns in wild-derived mice, where genetic distances and hybridization influenced microbiome composition, with fungi being more stable to infection-induced perturbations than other microbiome components. Subspecies' genetic distance has a stronger and consistent effect across microbiome components than differences in expected heterozygosity among hybrids, suggesting that host divergence and host filtering play a key role in microbiome divergence, influenced by environmental factors. Our findings offer new insights into the eco-evolutionary processes shaping host-microbiome interactions.

Genetic divergence and hybrid potential for yield and fiber quality in cotton (Gossypium hirsutum. L)

ABSTRACT Fiber trait is multigenic and each parent of the population carrying different genes aid in arriving at varied blend of association between yield and fiber quality traits. Eleven parental lines were crossed in half-diallel fashion to arrive at 55 hybrid combinations, which were evaluated under seven environments. The results manifested highly significant differences among the traits studied, indicating wide range of genetic diversity in the parental genotypes. Combining ability studies revealed parental genotypes SCS-PL-01, SCS-PL-04 as best general combiners for seed cotton yield; SCS-PL-04, SCS-PL-12, SCS-PL-51, SCS-PL-59 for fiber length; SCS-PL-51 for fiber strength and SCS-PL-02 for micronaire value. Genetic diversity of parental genotypes estimated using microsatellites was compared with SCA effects to understand the role of genetic distance (GD) in realizing the heterosis. Comparative characterization of parental genotypes indicated efficiency of capillary gel electrophoresis over metaphor agarose gel electrophoresis and could identify 55 unique bands. The study indicated predicting heterosis using GD estimates is competent when markers linked to the QTLs are used. Also, marker-assisted parental selection in combining ability studies would be a better approach in realizing potential crosses with high yield and fiber quality traits.

Genetic analysis and hybrid performance of recommended cocoa (Theobroma cacao L.) clones for bean yield in Ghana

Abstract Attempts to develop high yielding varieties in Ghana have mostly relied on the introduction of new clones to broaden the range of planting materials for yield improvement. The objective of this study was to estimate the genetic variation and heritability for bean yield of six recommended cocoa clones using these as males in crosses with five seed garden parents. Twenty-four families obtained from a 5 × 6 North Carolina II (NC II) incomplete factorial mating design together with 19 high yielding single crosses, of which four were standard mixed hybrids, were planted in a randomized complete block design with four replications and evaluated for bean yield over 6 years. To account for the serial correlation among yield data collected from the same plants over years, six models with different covariance structures were tested. The general covariance model emerged appropriate based on Akaike information criterion values with significant (P < 0.01) family × year interaction. Average bean yield was highest in the NC II families followed by the specific crosses then the standard mixed hybrids. Combining ability analysis among the NC II was significant for female, male and female × male interaction along with a narrow-sense heritability of 0.26. Clone CRG 6035 among the males which had good general combining ability could be added to the seed garden parents, while the promising hybrids (NA 33 × SCA 9, SCA 6 × Pound 10, T60/887 × PA 121 and T79/501 × CRG 6035/103) undergo stability tests before release.

Genetic structure and potential hybridization between populations of two Penstemon species

Abstract Hybridization is a common phenomenon in plants, facilitating genetic exchange and contributing to evolutionary innovation. In Penstemon, which boasts a diverse array of species with varying pollination syndromes, hybridization has been extensively documented. In this study, we employed nine nuclear microsatellites to investigate the genetic differentiation between Penstemon gentianoides and P. roseus populations, along with two populations of potentially hybrid morphotypes. Our results reveal significant genetic differentiation among populations, particularly between sympatric and allopatric populations of P. gentianoides and P. roseus. Bayesian analyses identified three genetic clusters, the first corresponding to individuals of sympatric and allopatric P. roseus populations, and the second and third to intermixed individuals of sympatric and allopatric P. gentianoides populations and intermediate morphotypes. Sympatric populations of both parental species exhibit signs of admixture. Contemporary and historical migration rates were low and asymmetrical among populations, with higher migration rates from the ‘fuchsia’ morphotype to P. gentianoides populations. The significant and positive correlation between migration rates suggests that migration patterns have remained relatively stable over time. These findings underscore the complex dynamics of genetic exchange and hybridization between Penstemon populations, highlighting the need for further research to understand population differentiation in the presence of gene flow.

Lowering the PAPR of the optical OTFS-based 6G radio with a hybrid PTS-PSO genetic approach

Orthogonal Time Frequency Space (OTFS) is regarded as one of the best contenders for sixth-generation (6G) radio systems due to its ability to obtain excellent performance in high-speed scenarios. Peak-to-average power ratio (PAPR) significantly impacts and degrades the efficiency of the power amplifier used in OTFS-based 6G systems. The proposed article presents a genetic hybrid algorithm, Partial transmission sequence-particle swarm optimization (PTS-PSO), obtaining an optimal PAPR performance with low computation complexity. The PSO generates the best phase factor compared to the conventional phase generation method of PTS, making PTS-PSO more effective. The impacts such as PAPR, bit error rate (BER), power spectral density (PSD), and complexity of the proposed PTS-PSO are compared with the conventional selective mapping (SLM) and PTS methods for 64, 256, and 512 OTFS sub-carriers. The projected PTS-PSO attained a PAPR and PSD gain of 1–6 dB and − 540 while preserving the BER performance. The experimental results demonstrate that the projected PTS-PSO outperforms the conventional SLM and PTS algorithms with trivial intricacy.

Establishing the green algae Chlamydomonas incerta as a platform for recombinant protein production.

Chlamydomonas incerta, a genetically close relative of the model green alga Chlamydomonas reinhardtii, shows significant potential as a host for recombinant protein expression. Because of the close genetic relationship between C. incerta and C. reinhardtii, this species offers an additional reference point for advancing our understanding of photosynthetic organisms, and also provides a potential new candidate for biotechnological applications. This study investigates C. incerta's capacity to express three recombinant proteins: the fluorescent protein mCherry, the hemicellulose-degrading enzyme xylanase, and the plastic-degrading enzyme PHL7. We have also examined the capacity to target protein expression to various cellular compartments in this alga, including the cytosol, secretory pathway, cytoplasmic membrane, and cell wall. When compared directly with C. reinhardtii, C. incerta exhibited a distinct but notable capacity for recombinant protein production. Cellular transformation with a vector encoding mCherry revealed that C. incerta produced approximately 3.5 times higher fluorescence levels and a 3.7-fold increase in immunoblot intensity compared to C. reinhardtii. For xylanase expression and secretion, both C. incerta and C. reinhardtii showed similar secretion capacities and enzymatic activities, with comparable xylan degradation rates, highlighting the industrial applicability of xylanase expression in microalgae. Finally, C. incerta showed comparable PHL7 activity levels to C. reinhardtii, as demonstrated by the in vitro degradation of a polyester polyurethane suspension, Impranil® DLN. Finally, we also explored the potential of cellular fusion for the generation of genetic hybrids between C. incerta and C. reinhardtii as a means to enhance phenotypic diversity and augment genetic variation. We were able to generate genetic fusion that could exchange both the recombinant protein genes, as well as associated selectable marker genes into recombinant offspring. These findings emphasize C. incerta's potential as a robust platform for recombinant protein production, and as a powerful tool for gaining a better understanding of microalgal biology.

Model Optimization and Application of Straw Mulch Quantity Using Remote Sensing

Straw mulch quantity is an important indicator in the detection of straw returned to the field in conservation tillage, but there is a lack of large-scale automated measurement methods. In this study, we estimated global straw mulch quantity and completed the detection of straw returned to the field. We used an unmanned aerial vehicle (UAV) carrying a multispectral camera to acquire remote sensing images of straw in the field. First, the spectral index was selected using the Elastic-net (ENET) algorithm. Then, we used the Genetic Algorithm Hybrid Particle Swarm Optimization (GA-HPSO) algorithm, which embeds crossover and mutation operators from the Genetic Algorithm (GA) into the improved Particle Swarm Optimization (PSO) algorithm to solve the problem of machine learning model prediction performance being greatly affected by parameters. Finally, we used the Monte Carlo method to achieve a global estimation of straw mulch quantity and complete the rapid detection of field plots. The results indicate that the inversion model optimized using the GA-HPSO algorithm performed the best, with the coefficient of determination (R2) reaching 0.75 and the root mean square error (RMSE) only being 0.044. At the same time, the Monte Carlo estimation method achieved an average accuracy of 88.69% for the estimation of global straw mulch quantity, which was effective and applicable in the detection of global mulch quantity. This study provides a scientific reference for the detection of straw mulch quantity in conservation tillage and also provides a reliable model inversion estimation method for the estimation of straw mulch quantity in other crops.

CsTs, a C-type lectin receptor-like kinase, regulates the development trichome development and cuticle metabolism in cucumber (Cucumis sativus).

Cucumber (Cucumis sativus) fruit spines are a classic material for researching the development of multicellular trichomes. Some key genes that influence trichome development have been confirmed to be associated with cuticle biosynthesis and secondary metabolism. However, the biological mechanisms underlying trichome development, cuticle biosynthesis, and secondary metabolism in cucumber remain poorly understood. CsTs, a C-type lectin receptor-like kinase gene, reportedly causes a tender trichome phenotype in cucumber when it mutates. In this study, the role of CsTs in cucumber fruit spines morphogenesis was confirmed using gene editing technology. Sectioning and cell wall component detection were used to analyse the main reason of tender fruit spines in the ts mutant. Subsequently, transcriptome data and a series of molecular biology experiments were used to further investigate the relationship between CsTs and cytoskeletal homeostasis in cucumber. CsTs overexpression partially compensated for the abnormal trichome phenotype of an Arabidopsis homolog mutant. Genetic hybridization and metabolic analysis indicated that CsTs and CsMict can affect trichome development and cuticle biosynthesis in the same pathway. Our findings provide important background information for further researching on the molecular mechanism underlying cucumber trichome development and contribute to understanding the biological function of C-type lectin receptor-like kinases.

Genetic fuzzy rules and hybrid QDCNN-F-DSAE for detecting attacker behavior with tuning of firewall

ABSTRACT The application of computer technology is increasing quickly as the field of information technology develops over time. Because of the growth of computer networks, there is a rise in network attacks. Moreover, the majority of transactions are conducted via the Internet, and then the web application security is becoming increasingly important. A network firewall shields the web servers from unauthorised and dangerous traffic. In this work, the hybrid Quantum Dilated Convolutional Neural Network fused Deep-Stacked Auto Encoder (QDCNN-F-DSAE) model with Genetic Fuzzy System (GFS) is developed for network protection. With the utilisation of genetic fuzzy system (GFS) and firewall tuning, the QDCNN-F-DSAE identified the attack, ransomware activity, and prompt decision-making. The data offered by the simulated environment is used to make decisions, and the decisions should be made quickly within a few seconds using the proposed QDCNN-F-DSAE with GFS. Furthermore, it identifies the attacks and alerts the users to store the data from the supplied source. The malicious function and protected private Local Area Network (LAN) are discovered and removed by the firewall after the formation of genetic fuzzy rules. Moreover, this model is estimated using the accuracy, sensitivity, and specificity measures that offered the finest values of 0.915, 0.908, and 0.920.

The Genetic Characterization of the Canarian Endemic Palm (Phoenix canariensis) by Simple Sequence Repeats and Chloroplast Markers: A Tool for the Molecular Traceability of Phoenix Hybridization

The endemic palm from the Canary Islands, Phoenix canariensis, is one of the most distinctive elements of the Canarian vegetation landscape, contributing to cultural, economic and environmental aspects. One of the main conservation problems facing this iconic palm is anthropogenic hybridization with other Phoenix species, particularly Phoenix dactylifera, which has been introduced extensively throughout its geographical range. Therefore, it is important to obtain a genetic tool that addresses different issues that may have an impact on the protection of P. canariensis, including ornamental applications and wild population conservation purposes. Our main goals were to detect a molecular tracer that could reliably distinguish between Phoenix canariensis and P. dactylifera in the Canary archipelago and to characterize the presence and extent of genetic hybridization events between the two species. We used 19 nuclear microsatellites and 1 chloroplast minisatellite set and analysed a large sample size (N = 433) of plants using both Bayesian methods and ordination techniques. Our data showed that a set of 13 nuclear markers revealed diagnostic alleles for P. canariensis, which were defined as the Canarian nuclear genotype (CNG). Moreover, P. canariensis exhibited an exclusive chlorotype of 266 bp that together with the GNC serve as an indicator of genetic purity in the Canarian palm. These markers are sufficient to detect any hybrid, even if it is not related to morphological differences. The occurrence of a considerable number of specimens with different degrees of hybridization is discussed in terms of the existence of different generations of hybrids and different types of crosses. Thus, the genetic tracers represent an invaluable tool to address any proposal for the genetic conservation of Phoenix canariensis.

Analyzing intelligent tourism development and public services based on a fuzzy genetic hybrid system to promote environmental and cultural values.

Environmental, cultural, and public service-dependent factors encourage the development of a country's tourism. In recent years, automated tourism development using statistical and accumulated data has been exploited to recommend attractive tourist features. This article thus discloses an intelligent development assessment method (IDAM) using cumulative factors (CFs) for deriving development-focused improvement in tourism. This method accounts for public services and environmental and cultural factors that promote tourism for better assessment. The fuzzy process identifies the maximum possible impacting factors by independently evaluating the reviewed values. Based on the reviewed values, the manipulation of factor relationships is derived to identify even trivial factors impacting development. The fuzzy outputs are thus integrated with optimistically impacting development factors to provide attractive recommendations. Such recommendations are analyzed using fuzzy data for previous and current development factors for new decisions. The system's efficiency was evaluated using the recommendation ratio, ensuring a 48.58% success rate, a development rate of 0.105%, a 4-factor detection rate, and a review-based assessment rate of 55.5% for a sample size of 5,000 visitors.

Hard Ticks as Vectors: The Emerging Threat of Tick-Borne Diseases in India.

Hard ticks (Ixodidae) play a critical role in transmitting various tick-borne diseases (TBDs), posing significant global threats to human and animal health. Climatic factors influence the abundance, diversity, and vectorial capacity of tick vectors. It is imperative to have a comprehensive understanding of hard ticks, pathogens, eco-epidemiology, and the impact of climatic changes on the transmission dynamics of TBDs. The distribution and life cycle patterns of hard ticks are influenced by diverse ecological factors that, in turn, can be impacted by changes in climate, leading to the expansion of the tick vector's range and geographical distribution. Vector competence, a pivotal aspect of vectorial capacity, involves the tick's ability to acquire, maintain, and transmit pathogens. Hard ticks, by efficiently feeding on diverse hosts and manipulating their immunity through their saliva, emerge as competent vectors for various pathogens, such as viruses, parasites and bacteria. This ability significantly influences the success of pathogen transmission. Further exploration of genetic diversity, population structure, and hybrid tick vectors is crucial, as they play a substantial role in influencing vector competence and complicating the dynamics of TBDs. This comprehensive review deals with important TBDs in India and delves into a profound understanding of hard ticks as vectors, their biology, and the factors influencing their vector competence. Given that TBDs continue to pose a substantial threat to global health, the review emphasizes the urgency of investigating tick control strategies and advancing vaccine development. Special attention is given to the pivotal role of population genetics in comprehending the genetic diversity of tick populations and providing essential insights into their adaptability to environmental changes.

A New Hybrid Approach for Product Management in E-Commerce

Nowadays, due to the developments in technology and the effects of the pandemic, people have largely switched to e-commerce instead of traditional face-to-face commerce. In this sector, the product variety reaches tens of thousands, which has made it difficult to manage and to make quick decisions on inventory, promotion, pricing, and logistics. Therefore, it is thought that obtaining accurate and fast forecasting for the future will provide significant benefits to such companies in every respect. This study was built on the proposal of creating a cluster-based–genetic algorithm hybrid forecasting model including genetic algorithm (GA), cluster analysis, and some forecasting models as a new approach. In this study, unlike the literature, an attempt was made to create a more successful forecasting model for many products at the same time inside of single product forecasting. The proposed CBGA model success was compared separately to both the single prediction method successes and only genetic algorithm-based hybrid model successes by using real values from a popular B2C company. As a result, it has been observed that the forecasting success of the model proposed in this study is more successful than the forecasting made using single models or only the genetic algorithm.

Promiscuous potato: elucidating genetic identity and the complex genetic relationships of a cultivated potato germplasm collection.

A total of 3,860 accessions from the global in trust clonal potato germplasm collection w3ere genotyped with the Illumina Infinium SolCAP V2 12K potato SNP array to evaluate genetic diversity and population structure within the potato germplasm collection. Diploid, triploid, tetraploid, and pentaploid accessions were included representing the cultivated potato taxa. Heterozygosity ranged from 9.7% to 66.6% increasing with ploidy level with an average heterozygosity of 33.5%. Identity, relatedness, and ancestry were evaluated using hierarchal clustering and model-based Bayesian admixture analyses. Errors in genetic identity were revealed in a side-by-side comparison of in vitro clonal material with the original mother plants revealing mistakes putatively occurring during decades of processing and handling. A phylogeny was constructed to evaluate inter- and intraspecific relationships which together with a STRUCTURE analysis supported both commonly used treatments of potato taxonomy. Accessions generally clustered based on taxonomic and ploidy classifications with some exceptions but did not consistently cluster by geographic origin. STRUCTURE analysis identified putative hybrids and suggested six genetic clusters in the cultivated potato collection with extensive gene flow occurring among the potato populations, implying most populations readily shared alleles and that introgression is common in potato. Solanum tuberosum subsp. andigena (ADG) and S. curtilobum (CUR) displayed significant admixture. ADG likely has extensive admixture due to its broad geographic distribution. Solanum phureja (PHU), Solanum chaucha (CHA)/Solanum stenotomum subsp. stenotomum (STN), and Solanum tuberosum subsp. tuberosum (TBR) populations had less admixture from an accession/population perspective relative to the species evaluated. A core and mini core subset from the genebank material was also constructed. SNP genotyping was also carried out on 745 accessions from the Seed Savers potato collection which confirmed no genetic duplication between the two potato collections, suggesting that the collections hold very different genetic resources of potato. The Infinium SNP Potato Array is a powerful tool that can provide diversity assessments, fingerprint genebank accessions for quality management programs, use in research and breeding, and provide insights into the complex genetic structure and hybrid origin of the diversity present in potato genetic resource collections.

Use of simulation to optimize a sweet corn breeding program: implementing genomic selection and doubled haploid technology.

Genomic selection and doubled haploids hold significant potential to enhance genetic gains and shorten breeding cycles across various crops. Here, we utilized stochastic simulations to investigate the best strategies for optimize a sweet corn breeding program. We assessed the effects of incorporating varying proportions of old and new parents into the crossing block (3:1, 1:1, 1:3, and 0:1 ratio, representing different degrees of parental substitution), as well as the implementation of genomic selection in two distinct pipelines: one calibrated using the phenotypes of testcross parents (GSTC scenario) and another using F1 individuals (GSF1). Additionally, we examined scenarios with doubled haploids, both with (DH) and without (DHGS) genomic selection. Across 20 years of simulated breeding, we evaluated scenarios considering traits with varying heritabilities, the presence or absence of genotype-by-environment effects, and two program sizes (50 vs 200 crosses per generation). We also assessed parameters such as parental genetic mean, average genetic variance, hybrid mean, and implementation costs for each scenario. Results indicated that within a conventional selection program, a 1:3 parental substitution ratio (replacing 75% of parents each generation with new lines) yielded the highest performance. Furthermore, the GSTC model outperformed the GSF1 model in enhancing genetic gain. The DHGS model emerged as the most effective, reducing cycle time from 5 to 4 years and enhancing hybrid gains despite increased costs. In conclusion, our findings strongly advocate for the integration of genomic selection and doubled haploids into sweet corn breeding programs, offering accelerated genetic gains and efficiency improvements.

A Hybrid Genetic Algorithm for Ground Station Scheduling Problems

In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks.