Weighted single-step genome-wide association study to reveal new candidate genes for productive traits of Landrace pig in Korea.

The objective of this study was to identify genomic regions and candidate genes associated with productive traits using a total of 37,099 productive records and 6,683 single nucleotide polymorphism (SNP) data obtained from five Great-Grand-Parents (GGP) farms in Landrace. The estimated of heritabilities for days to 105 kg (AGE), average daily gain (ADG), backfat thickness (BF), and eye muscle area (EMA) were 0.49, 0.49, 0.56, and 0.23, respectively. We identified a genetic window that explained 2.05%-2.34% for each trait of the total genetic variance. We observed a clear partitioning of the four traits into two groups, and the most significant genomic region for AGE and ADG were located on the Sus scrofa chromosome (SSC) 1, while BF and EMA were located on SSC 2. We conducted Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), which revealed results in three biological processes, four cellular component, three molecular function, and six KEGG pathway. Significant SNPs can be used as markers for quantitative trait loci (QTL) investigation and genomic selection (GS) for productive traits in Landrace pig.

Impact of truncating historical data on prediction ability of dairy sheep selection candidates.

Along the last decades, the genetic evaluation methodology has evolved, improving breeding value estimates. Many breeding programmes have historical phenotypic records and large number of generations, but to make use of them could result in more inconveniences than benefits. In this study, the prediction ability of genotyped young animals was assessed by simultaneously evaluating the removal of historical data, two pedigree deepness and two methodologies (traditional BLUP and single-step genomic BLUP or ssGBLUP), using milk yield records of 40 years of three Latxa dairy sheep populations. The linear regression method was used to compare predictions of young rams before and after progeny testing, with six cut-off points, by intervals of 4 years (from 1992 to 2012), and statistics of ratio of accuracies, bias, and dispersion were calculated. The prediction accuracy of selection candidates, when genomic information was included, was the highest in all Latxa populations (between 0.54 and 0.69 with full data set). Nevertheless, the deletion of historical phenotypic data resulted on moderate accuracy gain in the bigger data size populations (mean gain 2.5%), and the smaller population took advantage of a moderate data deletion (2.7% gain by removing data until 2004), reducing accuracy when more records were removed. The bias of validation individuals was lower when the breeding value was predicted based on genomic information (between 2.1 and 13.9), being lower when the biggest amount of data was deleted in the bigger data size populations (5.2% reduction), and the smaller population was benefited from data deletion between 1996 and 2008 (3.8% bias reduction). Meanwhile, the slope of estimated genetic trend was lower when less data were included, and an overestimation of the unknown parent group estimates was observed. The results indicated that ssGBLUP evaluations were outstanding, compared with traditional BLUP evaluations, while the depth of pedigree had a very small influence, and deletion of historical phenotypic data was beneficial. Thus, Latxa routine genetic evaluations would benefit from truncating phenotypic records between 2000 and 2004, the use of two pedigree generations and the implementation of ssGBLUP methodology.

Comparison between a new transepithelial PRK vs. conventional alcohol-assisted PRK: Corneal densitometry and aberrometry study.

PURPOSE: To describe and compare corneal densitometry, subjective refraction, visual acuity, and corneal higher order aberrations (HOA) after corneal refractive surgery using either alcohol-assisted photorefractive keratectomy (aaPRK) or single-step transepithelial PRK (tPRK). METHODS: We conducted a retrospective and observational study. We analyzed 120 right eyes from 120 healthy consecutive myopic patients who underwent aaPRK or tPRK to correct myopia of up to 6 diopters and astigmatism of up to 2 diopters. The WaveLight EX500 excimer laser (Alcon Laboratories, Inc.) was used in all cases. Visual acuity, subjective refraction, and Pentacam AXL® measurements were performed at the preoperative visit and at 6-months follow-up visit. Pentacam AXL® software was used to assess corneal optical density in various annuli for different corneal depths and anterior corneal HOA (6 mm area of analysis). RESULTS: Preoperative spherical equivalent values were similar between groups preoperatively (-3.07 ± 1.52 and -3.38 ± 1.46 in the aaPRK and tPRK groups, respectively). There were no statistically significant differences in visual acuity and postoperative refraction between groups. Postoperative corneal densitometry did not show statistically significant differences in any of the areas studied and both surgical procedures obtained similar results. However, analysis of HOA showed statistically significant differences between the techniques (1.42 ± 0.39 and 1.80 ± 0.62 for the aaPRK and tPRK groups, respectively; p = 0.000). CONCLUSIONS: Both aaPRK and single-step tPRK gave comparable visual, refractive, and corneal density outcomes. Some differences were observed in HOA but were not clinically relevant.

Comparison of genomic prediction accuracy using different models for egg production traits in Taiwan country chicken.

In local chickens targeted for niche markets, genotyping costs are relatively high due to the small population size and diverse breeding goals. The single-step genomic best linear unbiased prediction (ssGBLUP) model, which combines pedigree and genomic information, has been introduced to increase the accuracy of genomic estimated breeding value (GEBV). Therefore, this model may be more beneficial than the genomic BLUP (GBLUP) model for genomic selection in local chickens. Additionally, the single-step genome-wide association study (ssGWAS) can be used to extend the ssGBLUP model results to animals with available phenotypic information but without genotypic data. In this study, we compared the accuracy of (G)EBVs using the pedigree-based BLUP (PBLUP), GBLUP, and ssGBLUP models. Moreover, we conducted single-SNP GWAS (SNP-GWAS), GBLUP-GWAS, and ssGWAS methods to identify genes associated with egg production traits in the NCHU-G101 chicken to understand the feasibility of using genomic selection in a small population. The average prediction accuracy of (G)EBV for egg production traits using the PBLUP, GBLUP, and ssGBLUP models is 0.536, 0.531, and 0.555, respectively. In total, 22 suggestive- and 5% Bonferroni genome-wide significant-level SNPs for total egg number (EN), average laying rate (LR), average clutch length, and total clutch number are detected using 3 GWAS methods. These SNPs are mapped onto Gallus gallus chromosomes (GGA) 4, 6, 10, 18, and 25 in NCHU-G101 chicken. Furthermore, through SNP-GWAS and ssGWAS methods, we identify 2 genes on GGA4 associated with EN and LR: ENSGALG00000023172 and PPARGC1A. In conclusion, the ssGBLUP model demonstrates superior prediction accuracy, performing on average 3.41% than the PBLUP model. The implications of our gene results may guide future selection strategies for Taiwan Country chickens. Our results highlight the applicability of the ssGBLUP model for egg production traits selection in a small population, specifically NCHU-G101 chicken in Taiwan.

Efficient AAV9 Purification Using a Single-Step AAV9 Magnetic Affinity Beads Isolation.

Adeno-associated viruses (AAVs) have emerged as promising tools for gene therapy due to their safety and efficacy in delivering therapeutic genes or gene editing sequences to various tissues and organs. AAV serotype 9 (AAV9), among AAV serotypes, stands out for its ability to efficiently target multiple tissues, thus holding significant potential for clinical applications. However, existing methods for purifying AAVs are cumbersome, expensive, and often yield inconsistent results. In this study, we explore a novel purification strategy utilizing Dynabeads™ CaptureSelect™ magnetic beads. The AAV9 magnetic beads capture AAV9 with high specificity and recovery between 70 and 90%, whereas the AAVX magnetic beads did not bind to the AAV9. Through continuous interaction with AAVs in solution, these beads offer enhanced clearance of genomic DNA and plasmids even in the absence of endonuclease. The beads could be regenerated at least eight times, and the used beads could be stored for up to six months and reused without a significant reduction in recovery. The potency of the AAV9-purified vectors in vivo was comparable to that of iodixanol purified vectors.

Ualign: pushing the limit of template-free retrosynthesis prediction with unsupervised SMILES alignment.

MOTIVATION: Retrosynthesis planning poses a formidable challenge in the organic chemical industry, particularly in pharmaceuticals. Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in AI for science. Various deep learning-based methods have been proposed for this task in recent years, incorporating diverse levels of additional chemical knowledge dependency. RESULTS: This paper introduces UAlign, a template-free graph-to-sequence pipeline for retrosynthesis prediction. By combining graph neural networks and Transformers, our method can more effectively leverage the inherent graph structure of molecules. Based on the fact that the majority of molecule structures remain unchanged during a chemical reaction, we propose a simple yet effective SMILES alignment technique to facilitate the reuse of unchanged structures for reactant generation. Extensive experiments show that our method substantially outperforms state-of-the-art template-free and semi-template-based approaches. Importantly, our template-free method achieves effectiveness comparable to, or even surpasses, established powerful template-based methods. SCIENTIFIC CONTRIBUTION: We present a novel graph-to-sequence template-free retrosynthesis prediction pipeline that overcomes the limitations of Transformer-based methods in molecular representation learning and insufficient utilization of chemical information. We propose an unsupervised learning mechanism for establishing product-atom correspondence with reactant SMILES tokens, achieving even better results than supervised SMILES alignment methods. Extensive experiments demonstrate that UAlign significantly outperforms state-of-the-art template-free methods and rivals or surpasses template-based approaches, with up to 5% (top-5) and 5.4% (top-10) increased accuracy over the strongest baseline.

Synthesis, anti-microbial, and docking studies of functionalized chromenyl phosphonates using ionic liquid catalyst.

Synthesis of functionalized chromenyl phosphonates by the reaction among 2-hydorxybenzaldehydes, dicyanoethane, and dialkyl phosphonates that was promoted by choline hydroxide ionic liquid catalyzes the simultaneous, Knoevenagel, Pinner, and phospha-Michael reactions, under neat condition at room temperature. Important phosphorus-containing compounds can be produced at a reasonable cost because of the mild reaction conditions and the inexpensive promoter choline hydroxide. Furthermore, the desired products can be obtained without the need for any extraction or chromatography steps. An alternate technique for the simple and high-yield synthesis of functionalized chromenyl phosphonates is offered by this protocol. The synthesized compounds were studied by anti-microbial activity and docking studies. The title compounds molecular docking investigations demonstrated their efficacy as therapeutic agents against DNA Gyrase B and Aspergillus niger endoglucanase in both antibacterial and antifungal inhibition, and they identified compounds 4a, 4d, 4l, 4p, and 4q as promising candidates for microbial treatment, with binding affinities ranging from - 6.9 to - 7.4 kcal/mol.

Exploring unknown parent groups and metafounders in single-step genomic BLUP: Insights from a simulated cattle population.

This study explores how the metafounder (MF) concept enhances genetic evaluations in dairy cattle populations using single-step genomic best linear unbiased prediction (ssGBLUP). By improving the consideration of relationships among founder populations, MF ensures accurate alignment of pedigree and genomic relationships. The research aims to propose a method for grouping MF based on genotypic information, assess different approaches for estimating the gamma matrix, and compare unknown parent groups (UPG) and MF methodologies across various scenarios, including those with low and high pedigree completeness based on a simulated dairy cattle population. In the scenario where unknown ancestors are rare, the impact of UPG or MF on breeding values is minimal but MF still performs slightly better compared with UPG. The scenario with lower genotyping rates and more unknown parents shows significant differences in evaluations with and without UPG and also compared with MF. The study shows that ssGBLUP evaluations where UPG are considered via Quaas-Pollak-transformation in the pedigree-based and genomic relationship matrix (UPG_fullQP) results in double counting and subsequently in a pronounced bias and overdispersion. Another focus is on the estimation of the gamma matrix, emphasizing the importance of crossbred genotypes for accuracy. Challenges emerge in classifying animals into subpopulations and further into MF or UPG, but the method used in this study, which is based on genotypes, results in predictions which are comparable to those obtained using the true subpopulations for the assignment. Estimated validation results using the linear regression method confirm the superior performance of MF evaluations, although differences compared with true validations are smaller. Notably, UPG_fullQP's extreme bias is less evident in routine validation statistics.

Genomic prediction based on a joint reference population for the Xinjiang Brown cattle.

Introduction: Xinjiang Brown cattle constitute the largest breed of cattle in Xinjiang. Therefore, it is crucial to establish a genomic evaluation system, especially for those with low levels of breed improvement. Methods: This study aimed to establish a cross breed joint reference population by analyzing the genetic structure of 485 Xinjiang Brown cattle and 2,633 Chinese Holstein cattle (Illumina GeneSeek GGP bovine 150 K chip). The Bayes method single-step genome-wide best linear unbiased prediction was used to conduct a genomic evaluation of the joint reference population for the milk traits of Xinjiang Brown cattle. The reference population of Chinese Holstein cattle was randomly divided into groups to construct the joint reference population. By comparing the prediction accuracy, estimation bias, and inflation coefficient of the validation population, the optimal number of joint reference populations was determined. Results and Discussion: The results indicated a distinct genetic structure difference between the two breeds of adult cows, and both breeds should be considered when constructing multi-breed joint reference and validation populations. The reliability range of genome prediction of milk traits in the joint reference population was 0.142-0.465. Initially, it was determined that the inclusion of 600 and 900 Chinese Holstein cattle in the joint reference population positively impacted the genomic prediction of Xinjiang Brown cattle to certain extent. It was feasible to incorporate the Chinese Holstein into Xinjiang Brown cattle population to form a joint reference population for multi-breed genomic evaluation. However, for different Xinjiang Brown cattle populations, a fixed number of Chinese Holstein cattle cannot be directly added during multi-breed genomic selection. Pre-evaluation analysis based on the genetic structure, kinship, and other factors of the current population is required to ensure the authenticity and reliability of genomic predictions and improve estimation accuracy.

Generation of Defective Interfering Particles of Morbilliviruses Using Reverse Genetics.

RNA viruses generate defective genomes naturally during virus replication. Defective genomes that interfere with the infection dynamics either through resource competition or by interferon stimulation are known as defective interfering (DI) genomes. DI genomes can be successfully packaged into virus-like-particles referred to as defective interfering particles (DIPs). Such DIPs can sustainably coexist with the full-length virus particles and have been shown to negatively impact virus replication in vitro and in vivo. Here, we describe a method to generate a clonal DI genome population by reverse genetics. This method is applicable to other RNA viruses and will enable assessment of DIPs for their antiviral properties.

Polysaccharide structure evaluation of Ganoderma lucidum from different regions in China based on an innovative extraction strategy.

The polysaccharides and triterpenes are important functional components of Ganoderma lucidum, but traditional preparation process of G. lucidum functional components can only realize the preparation of single functional component, which has poor targeting and low efficiency. In this study, the existence state of the functional components of G. lucidum was revealed. Then, the single step extraction process for functional components was established, and the precise structure evaluation of polysaccharide and triterpenes was conducted based on the process. The results showed that preparation time required for this strategy is only one-sixth of the traditional one, and 50 % of raw materials can be saved. Structural analysis of the functional components revealed that triterpenes were mainly Ganoderic acid and Lucidenic acid, and the polysaccharide structure was mainly 1,3-glucan and 1,3,6-glucan. The establishment of single step extraction strategy and the evaluation of the fine structure of functional components improved the efficiency of preparation and result determination, and provided an important basis for the development and utilization of green and low-carbon G. lucidum and even edible fungi resources and human nutritional dietary improvement strategies.

Genomic Prediction of Growth Traits in Yorkshire Pigs of Different Reference Group Sizes Using Different Estimated Breeding Value Models.

The need for sufficient reference population data poses a significant challenge in breeding programs aimed at improving pig farming on a small to medium scale. To overcome this hurdle, investigating the advantages of combing reference populations of varying sizes is crucial for enhancing the accuracy of the genomic estimated breeding value (GEBV). Genomic selection (GS) in populations with limited reference data can be optimized by combining populations of the same breed or related breeds. This study focused on understanding the effect of combing different reference group sizes on the accuracy of GS for determining the growth effectiveness and percentage of lean meat in Yorkshire pigs. Specifically, our study investigated two important traits: the age at 100 kg live weight (AGE100) and the backfat thickness at 100 kg live weight (BF100). This research assessed the efficiency of genomic prediction (GP) using different GEBV models across three Yorkshire populations with varying genetic backgrounds. The GeneSeek 50K GGP porcine high-density array was used for genotyping. A total of 2295 Yorkshire pigs were included, representing three Yorkshire pig populations with different genetic backgrounds-295 from Danish (small) lines from Huaibei City, Anhui Province, 500 from Canadian (medium) lines from Lixin County, Anhui Province, and 1500 from American (large) lines from Shanghai. To evaluate the impact of different population combination scenarios on the GS accuracy, three approaches were explored: (1) combining all three populations for prediction, (2) combining two populations to predict the third, and (3) predicting each population independently. Five GEBV models, including three Bayesian models (BayesA, BayesB, and BayesC), the genomic best linear unbiased prediction (GBLUP) model, and single-step GBLUP (ssGBLUP) were implemented through 20 repetitions of five-fold cross-validation (CV). The results indicate that predicting one target population using the other two populations yielded the highest accuracy, providing a novel approach for improving the genomic selection accuracy in Yorkshire pigs. In this study, it was found that using different populations of the same breed to predict small- and medium-sized herds might be effective in improving the GEBV. This investigation highlights the significance of incorporating population combinations in genetic models for predicting the breeding value, particularly for pig farmers confronted with resource limitations.

A comparison of genetic and genomic breeding values in Saanen and Alpine goats.

Nowadays, several countries are developing or adopting genomic selection in the dairy goat sector. The most used method to estimate breeding values is Single-Step Genomic Best Linear Unbiased Prediction (ssGBLUP) which offers several advantages in terms of computational process and accuracy of the estimated breeding values (EBVs). Saanen and Alpine are the predominant dairy goat breeds in Italy, and both have similar breeding programs where EBVs for productive traits are currently calculated using BLUP. This work describes the implementation of genomic selection for these two breeds in Italy, aligning with the selection practices already carried out in the international landscape. The available dataset included 3 611 genotyped animals, 11 470 lactation records, five traits (milk, protein and fat yields, and fat and protein percentages), and three-generation pedigrees. EBVs were estimated using BLUP, GBLUP, and ssGBLUP both with single and multiple trait approaches. The methods were compared in terms of correlation between EBVs and genetic trends. Results were also validated with the linear regression method excluding part of the phenotypic data. In both breeds, EBVs and GEBVs were strongly correlated and the trend of each trait was similar comparing the three methods. The average increase in accuracy across traits and methods amounted to +13 and +10% from BLUP to ssGBLUP for Alpine and Saanen breeds, respectively. Results indicated higher prediction accuracy and correlation for GBLUP and ssGBLUP compared to BLUP, implying that the use of genotypes increases the accuracy of EBVs, particularly in the absence of phenotypic data. Therefore, ssGBLUP is likely to be the most effective method to enhance genetic gain in Italian Saanen and Alpine goats.

Modified single-step apexification and strengthening of thin dentinal walls with Biodentine.

Apexification is a technique used to create a mineralized barrier in a root having an open apex or to continue the apical growth of a root that is still not fully developed in teeth with necrotic pulps. Biodentine is a versatile material which can help in achieving apical closure as well as reinforcement of thin dentinal walls, thus improving the overall prognosis and preserving the natural dentition. In the present case, the radiographic evaluation showed thinner dentinal walls and an open apex with periapical radiolucency in relation to the upper right central incisor. The large open root apex and thin dentin walls of immature permanent teeth render them challenging to treat with root canal therapy. This case report describes a modified single-step apexification procedure and strengthening of the dentinal walls of the root canal using Biodentine.

Live birth outcomes following single-step blastocyst warming technique - optimizing efficiency without impacting live birth rates.

PURPOSE: To evaluate the impact of a single-step (SS) warming versus standard warming (SW) protocol on the survival/expansion of vitrified blastocysts and their clinical outcomes post-frozen embryo transfer (FET). METHODS: Retrospective analysis was performed on 200 vitrified/warmed research blastocysts equally divided amongst two thawing protocols utilizing the Fujifilm Warming NX kits (Fujifilm, CA). SW utilized the standard 14-minute manufacturer's guidelines. SS protocol required only a one-minute immersion in thaw solution (TS) before the embryos were transferred to culture media. A time-interrupted study was performed evaluating 752 FETs (SW: 376 FETs, SS 376 FETs) between April 2021-December 2022 at a single academic fertility clinic in Boston, Massachusetts. Embryologic, clinical pregnancy, and live birth outcomes were assessed using generalized estimated equation (GEE) models, which accounted for potential confounders. RESULTS: There was 100% survival for all blastocysts (n = 952 embryos) with no differences in blastocyst re-expansion regardless of PGT status. Adjusted analysis showed no differences in implantation, clinical pregnancy, spontaneous abortion, or biochemical pregnancy rate. A higher odds of multiple gestation [AdjOR(95%CI) 1.06 (1.01, 1.11), p = 0.019] were noted, even when adjusting for number of embryos transferred [AdjOR(95%CI) 1.05 (1.01, 1.10)]. Live birth outcomes showed no differences in live birth rates or birthweight at delivery. CONCLUSIONS: The study found equivalent outcomes for SS and SW in all parameters except for a slight rise in the rate of multiple gestations. The results suggest that SS warming is an efficient, viable alternative to SW, reducing thaw times without adverse effects on live birth rates or neonatal birth weights.

Genetic parameters and genomic prediction of growth and breast morphological traits in a crossbreed duck population.

Genomic selection (GS) has great potential to increase genetic gain in poultry breeding. However, the performance of genomic prediction in duck growth and breast morphological (BM) traits remains largely unknown. The objective of this study was to evaluate the benefits of genomic prediction for duck growth and BM traits using methods such as GBLUP, single-step GBLUP, Bayesian models, and different marker densities. This study collected phenotypic data for 14 growth and BM traits in a crossbreed population of 1893 Pekin duck × mallard, which included 941 genotyped ducks. The estimation of genetic parameters indicated high heritabilities for body weight (0.54-0.72), whereas moderate-to-high heritabilities for average daily gain (0.21-0.57) traits. The heritabilities of BM traits ranged from low to moderate (0.18-0.39). The prediction ability of GS on growth and BM traits increased by 7.6% on average compared to the pedigree-based BLUP method. The single-step GBLUP outperformed GBLUP in most traits with an average of 0.3% higher reliability in our study. Most of the Bayesian models had better performance on predictive reliability, except for BayesR. BayesN emerged as the top-performing model for genomic prediction of both growth and BM traits, exhibiting an average increase in reliability of 3.0% compared to GBLUP. The permutation studies revealed that 50 K markers had achieved ideal prediction reliability, while 3 K markers still achieved 90.8% predictive capability would further reduce the cost for duck growth and BM traits. This study provides promising evidence for the application of GS in improving duck growth and BM traits. Our findings offer some useful strategies for optimizing the predictive ability of GS in growth and BM traits and provide theoretical foundations for designing a low-density panel in ducks.

Genome-wide association study for stayability at different calvings in Nellore beef cattle.

BACKGROUNDING: Stayability, which may be defined as the probability of a cow remaining in the herd until a reference age or at a specific number of calvings, is usually measured late in the animal's life. Thus, if used as selection criteria, it will increase the generation interval and consequently might decrease the annual genetic gain. Measuring stayability at an earlier age could be a reasonable strategy to avoid this problem. In this sense, a better understanding of the genetic architecture of this trait at different ages and/or at different calvings is important. This study was conducted to identify possible regions with major effects on stayability measured considering different numbers of calvings in Nellore cattle as well as pathways that can be involved in its expression throughout the female's productive life. RESULTS: The top 10 most important SNP windows explained, on average, 17.60% of the genetic additive variance for stayability, varying between 13.70% (at the eighth calving) and 21% (at the fifth calving). These SNP windows were located on 17 chromosomes (1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18, 19, 20, 27, and 28), and they harbored a total of 176 annotated genes. The functional analyses of these genes, in general, indicate that the expression of stayability from the second to the sixth calving is mainly affected by genetic factors related to reproductive performance, and nervous and immune systems. At the seventh and eighth calvings, genes and pathways related to animal health, such as density bone and cancer, might be more relevant. CONCLUSION: Our results indicate that part of the target genomic regions in selecting for stayability at earlier ages (from the 2th to the 6th calving) would be different than selecting for this trait at later ages (7th and 8th calvings). While the expression of stayability at earlier ages appeared to be more influenced by genetic factors linked to reproductive performance together with an overall health/immunity, at later ages genetic factors related to an overall animal health gain relevance. These results support that selecting for stayability at earlier ages (perhaps at the second calving) could be applied, having practical implications in breeding programs since it could drastically reduce the generation interval, accelerating the genetic progress.

Using genomic selection to improve the accuracy of genomic prediction for multi-populations in pigs.

The size of the reference group is among the most critical determinants of genomic estimated breeding values (GEBVs) accuracy. However, small- and medium-sized pig farms often need help accumulating adequate reference data, posing significant challenges to breeding programs. To solve this problem, exploring the potential benefits of combining reference groups of different sizes is necessary to improve GEBV accuracy. The primary objective of this investigation was to assess a more effective statistical model for combined multi-populations and its potential to enhance the accuracy of GEBVs for small and medium populations. Three populations were simulated using the QMSim software, each consisting of different sizes (300, 600, and 1 500, respectively). To assess the impact of heritability on the accuracy of GEBVs, four different levels of heritability (0.05, 0.15, 0.35, and 0.5) were simulated. Simultaneously, to investigate the impact of kinship on multi-populations, the study created four distinct scenarios for the three sizes of populations. These scenarios included: (1) the three groups are all independent, (2) the large group and the small group with a familial connection (n = 1 800), a middle group (n = 600) acting independently with no kinship, (3) the large group with a familial connection to the middle group (n = 2 100) but no connection to the small group (n = 300), and (4) the small group with a familial connection to the middle group (n = 900), while the large group (n = 1 500) acted independently with no kinship. This study evaluates and compares the accuracy of predicting breeding values using four different methods, including genomic best linear unbiased prediction (GBLUP), single-stepGBLUP (ssGBLUP), and two Bayesian models (Bayes A and Bayes B), with varying sizes of reference groups. In each scenario, three different prediction strategies were compared: (1) Merging all three different sizes of populations for predicting, (2) predicting each independent population separately, and (3) the other two populations predict the population. Our findings reveal that combining populations enhances the Bayesian models, with Bayes B yielding the highest accuracy. In independent populations, the best linear unbiased prediction (BLUP) models demonstrated the highest accuracy. However, in cases where populations were related and the heritability was high, the Bayes B model exhibited the highest overall accuracy (slightly higher than BLUP models) in the independent population. Our results underscore the importance of considering population combinations when using genetic models to predict breeding values, particularly for pig farmers with limited resources.

Photocurrent response loss of dye sensitized solar cells owing to top surface nanograss growth and bundling of anodic TiO2 nanotubes.

In this research, the effect of anodization time on the length of the titanium oxide nanotube arrays (TNAs) and photovoltaic parameters of back-side illuminated dye-sensitized solar cells (DSSCs) were investigated. The TNAs were characterized using X-ray diffraction (X-ray) or (XRD), and scanning electron microscopy (SEM). Anodic TNAs having tube lengths from 7.9 to 20.17 µm were produced in ethylene glycol containing ammonium fluoride-NH4F by increasing the anodizing time from 20 min to 6 h. Based on I-V curves, the power conversion efficiency (PCE) of back-side illuminated dye sensitized solar cells (DSSCs) increased for TNAs grown for up to 120 min, but decreased afterward. Using electrochemical impedance spectroscopy (EIS), we understand that the resistance of the TNAs decreased from 94.82 Ω cm-2 for TNAs anodized for 20 min down to 50.43 Ω cm-2 for those TNAs anodized for 120 min, however, it increases for TNAs anodized for longer periods of time. Furthermore, the short circuit current density (Jsc) increased from 3.14 to 5.67 mA cm-2 during 2 h anodic oxidation for TNAs, and leading to enhanced efficiency of about 200 % (from 1.19 % to 2.45 %). We interpret this behaviour with the top surface morphology evolution of TNAs as a function of anodization time which is associated with the formation of top surface nanograss and bundling the tubes for specific durations.

Genomic evaluation for breeding and genetic management in Cordia africana, a multipurpose tropical tree species.

BACKGROUND: Planting tested forest reproductive material is crucial to ensure the increased resilience of intensively managed productive stands for timber and wood product markets under climate change scenarios. Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) analysis is a cost-effective option for using genomic tools to enhance the accuracy of predicted breeding values and genetic parameter estimation in forest tree species. Here, we tested the efficiency of ssGBLUP in a tropical multipurpose tree species, Cordia africana, by partial population genotyping. A total of 8070 trees from three breeding seedling orchards (BSOs) were phenotyped for height. We genotyped 6.1% of the phenotyped individuals with 4373 single nucleotide polymorphisms. The results of ssGBLUP were compared with pedigree-based best linear unbiased prediction (ABLUP) and genomic best linear unbiased prediction (GBLUP), based on genetic parameters, theoretical accuracy of breeding values, selection candidate ranking, genetic gain, and predictive accuracy and prediction bias. RESULTS: Genotyping a subset of the study population provided insights into the level of relatedness in BSOs, allowing better genetic management. Due to the inbreeding detected within the genotyped provenances, we estimated genetic parameters both with and without accounting for inbreeding. The ssGBLUP model showed improved performance in terms of additive genetic variance and theoretical breeding value accuracy. Similarly, ssGBLUP showed improved predictive accuracy and lower bias than the pedigree-based relationship matrix (ABLUP). CONCLUSIONS: This study of C. africana, a species in decline due to deforestation and selective logging, revealed inbreeding depression. The provenance exhibiting the highest level of inbreeding had the poorest overall performance. The use of different relationship matrices and accounting for inbreeding did not substantially affect the ranking of candidate individuals. This is the first study of this approach in a tropical multipurpose tree species, and the analysed BSOs represent the primary effort to breed C. africana.