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Background For the association between time-lapse technology (TLT) and embryo ploidy status, there has not yet been fully understood. TLT has the characteristics of large amount of data and non-invasiveness. If we want to accurately predict embryo ploidy status from TLT, artificial intelligence (AI) technology is a good choice. However, the current work of AI in this field needs to be strengthened. Methods A total of 469 preimplantation genetic testing (PGT) cycles and 1803 blastocysts from April 2018 to November 2019 were included in the study. All embryo images are captured during 5 or 6 days after fertilization before biopsy by time-lapse microscope system. All euploid embryos or aneuploid embryos are used as data sets. The data set is divided into training set, validation set and test set. The training set is mainly used for model training, the validation set is mainly used to adjust the hyperparameters of the model and the preliminary evaluation of the model, and the test set is used to evaluate the generalization ability of the model. For better verification, we used data other than the training data for external verification. A total of 155 PGT cycles from December 2019 to December 2020 and 523 blastocysts were included in the verification process. Results The euploid prediction algorithm (EPA) was able to predict euploid on the testing dataset with an area under curve (AUC) of 0.80. Conclusions The TLT incubator has gradually become the choice of reproductive centers. Our AI model named EPA that can predict embryo ploidy well based on TLT data. We hope that this system can serve all in vitro fertilization and embryo transfer (IVF-ET) patients in the future, allowing embryologists to have more non-invasive aids when selecting the best embryo to transfer.

Large-scale population genomic surveys are essential to explore the phenotypic diversity of natural populations. Here we report the whole-genome sequencing and phenotyping of 1,011 Saccharomyces cerevisiae isolates, which together provide an accurate evolutionary picture of the genomic variants that shape the species-wide phenotypic landscape of this yeast. Genomic analyses support a single ‘out-of-China’ origin for this species, followed by several independent domestication events. Although domesticated isolates exhibit high variation in ploidy, aneuploidy and genome content, genome evolution in wild isolates is mainly driven by the accumulation of single nucleotide polymorphisms. A common feature is the extensive loss of heterozygosity, which represents an essential source of inter-individual variation in this mainly asexual species. Most of the single nucleotide polymorphisms, including experimentally identified functional polymorphisms, are present at very low frequencies. The largest numbers of variants identified by genome-wide association are copy-number changes, which have a greater phenotypic effect than do single nucleotide polymorphisms. This resource will guide future population genomics and genotype–phenotype studies in this classic model system. Whole-genome sequencing of 1,011 natural isolates of the yeast Saccharomyces cerevisiae reveals its evolutionary history, including a single out-of-China origin and multiple domestication events, and provides a framework for genotype–phenotype studies in this model organism.

Cancer cells often exhibit DNA copy number aberrations and can vary widely in their ploidy. Correct estimation of the ploidy of single-cell genomes is paramount for downstream analysis. Based only on single-cell DNA sequencing information, scAbsolute achieves accurate and unbiased measurement of single-cell ploidy and replication status, including whole-genome duplications. We demonstrate scAbsolute’s capabilities using experimental cell multiplets, a FUCCI cell cycle expression system, and a benchmark against state-of-the-art methods. scAbsolute provides a robust foundation for single-cell DNA sequencing analysis across different technologies and has the potential to enable improvements in a number of downstream analyses.

An important assessment prior to genome assembly and related analyses is genome profiling, where the k-mer frequencies within raw sequencing reads are analyzed to estimate major genome characteristics such as size, heterozygosity, and repetitiveness. Here we introduce GenomeScope 2.0 ( https://github.com/tbenavi1/genomescope2.0 ), which applies combinatorial theory to establish a detailed mathematical model of how k-mer frequencies are distributed in heterozygous and polyploid genomes. We describe and evaluate a practical implementation of the polyploid-aware mixture model that quickly and accurately infers genome properties across thousands of simulated and several real datasets spanning a broad range of complexity. We also present a method called Smudgeplot ( https://github.com/KamilSJaron/smudgeplot ) to visualize and estimate the ploidy and genome structure of a genome by analyzing heterozygous k-mer pairs. We successfully apply the approach to systems of known variable ploidy levels in the Meloidogyne genus and the extreme case of octoploid Fragaria   ×   ananassa . Prior to genome assembly, the raw sequencing reads must be analyzed for assessment of major genome characteristics such as genome size, heterozygosity, and repetitiveness. For this purpose, the authors introduce GenomeScope 2.0, an extension of GenomeScope for polyploid genomes, and Smudgeplot, which can estimate a genome’s ploidy.

Genetic analysis was applied to different regions of renal-cell cancers. The lesions noted in the tumor were not found in every sample, and regions of the tumor had different gene-expression patterns. This suggests that extrapolation from results of a single biopsy may be problematic. Large-scale sequencing analyses of solid cancers have identified extensive heterogeneity between individual tumors. 1 – 6 Genetic intratumor heterogeneity has also been shown 7 – 15 and can contribute to treatment failure and drug resistance. Intratumor heterogeneity may have important consequences for personalized-medicine approaches that commonly rely on single tumor-biopsy samples to portray tumor mutational landscapes. Studies comparing mutational profiles of primary tumors and associated metastatic lesions 16 , 17 or local recurrences 18 have provided evidence of intratumor heterogeneity at nucleotide resolution. Intratumor heterogeneity within primary tumors and associated metastatic sites has not been systematically characterized by next-generation sequencing. We applied exome sequencing, chromosome aberration analysis, . . .

Abstract Flow cytometry has made a significant contribution to the study of several complex fundamental mechanisms in plant cytogenetics, becoming a useful analytical tool to understand several mechanisms and processes underlying plant growth, development, and function. In this study, the genome size, DNA ploidy level, and A-T/G-C ratio were measured for the first time for two genotypes of chia, Salvia hispanica , an herbaceous plant commonly used in phytotherapy and nutrition. This study also evaluated, for the first time by flow cytometry, the capacity to produce organic acids of tissues stained with LysoTracker Deep Red after elicitation with either yeast extract or cadmium chloride. Rosmarinic acid content differed between the two chia varieties treated with different elicitor concentrations, compared with non-elicited plant material. Elicited tissues of both varieties contained a higher content of rosmarinic acid compared with non-elicited cultures, and cadmium chloride at 500 μM was much better than that at 1000 μM, which led to plant death. For both genotypes, a dose-response was observed with yeast extract, as the higher the concentration of elicitor used, the higher rosmarinic acid content, resulting also in better results and a higher content of rosmarinic acid compared with cadmium chloride. This study demonstrates that flow cytometry may be used as a taxonomy tool, to distinguish among very close genotypses of a given species and, for the first time in plants, that this approach can also be put to profit for a characterization of the cytoplasmic acid phase and the concomitant production of secondary metabolites of interest in vitro, with or without elicitation. Key points • Genome size, ploidy level, A-T/G-C ratio, and cytoplasm acid phase of S. hispanica • Cytometry study of cytoplasm acid phase of LysoTracker Deep Red-stained plant cells • Yeast extract or cadmium chloride elicited rosmarinic acid production of chia tissues Graphical Abstract

PurposeTo determine if creating voting ensembles combining convolutional neural networks (CNN), support vector machine (SVM), and multi-layer neural networks (NN) alongside clinical parameters improves the accuracy of artificial intelligence (AI) as a non-invasive method for predicting aneuploidy. MethodsA cohort of 699 day 5 PGT-A tested blastocysts was used to train, validate, and test a CNN to classify embryos as euploid/aneuploid. All embryos were analyzed using a modified FAST-SeqS next-generation sequencing method. Patient characteristics such as maternal age, AMH level, paternal sperm quality, and total number of normally fertilized (2PN) embryos were processed using SVM and NN. To improve model performance, we created voting ensembles using CNN, SVM, and NN to combine our imaging data with clinical parameter variations. Statistical significance was evaluated with a one-sample t-test with 2 degrees of freedom.ResultsWhen assessing blastocyst images alone, the CNN test accuracy was 61.2% (± 1.32% SEM, n = 3 models) in correctly classifying euploid/aneuploid embryos (n = 140 embryos). When the best CNN model was assessed as a voting ensemble, the test accuracy improved to 65.0% (AMH; p = 0.1), 66.4% (maternal age; p = 0.06), 65.7% (maternal age, AMH; p = 0.08), 66.4% (maternal age, AMH, number of 2PNs; p = 0.06), and 71.4% (maternal age, AMH, number of 2PNs, sperm quality; p = 0.02) (n = 140 embryos).ConclusionsBy combining CNNs with patient characteristics, voting ensembles can be created to improve the accuracy of classifying embryos as euploid/aneuploid from CNN alone, allowing for AI to serve as a potential non-invasive method to aid in karyotype screening and selection of embryos.

Background Blueberry ( Vaccinium spp.) fruits are rich in flavonoids such as anthocyanins and have a high nutritional value. The zinc finger protein transcription factor B-box (BBX) plays important roles in plant growth and development, hormone response, abiotic stress, and anthocyanin accumulation. However, studies on the BBX family in blueberry are lacking. Results In total, 83 VcBBX and 24 VdBBX genes were identified in tetraploid and diploid blueberry, respectively. A correlation was observed between the number of BBX genes in blueberry and chromosome ploidy. Gene loss and specific replication during blueberry evolution may lead to an imbalance of quantitative relationship between VcBBX and VdBBX genes. The analysis of transcriptome and quantitative reverse transcription–polymerase chain reaction data revealed that the expression pattern of BBX genes depended on the developmental stage of blueberry fruit. Gibberellin inhibited the expression of most VcBBX genes. Abscisic acid promoted the expression of some members of the BBX family. The expression levels of VcBBX15b4 , VcBBX21a1 , and VcBBX30a in blueberry leaves were significantly downregulated under blue light treatment, whereas that of VcBBX15c3 was significantly upregulated under red light treatment. Conclusion In total, 83 VcBBX and 24 VdBBX genes were identified in 2 types of blueberries. Fruit development and transcription profiles under different stresses were analyzed. These findings will support further investigation of how BBX genes are involved in regulating hormone treatment and light stress during the growth and development of blueberry.

PurposeTo determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data.MethodsThis retrospective study utilized a dataset of 1908 blastocyst embryos. The dataset includes ploidy status, morphokinetic features, morphology grades, and 11 clinical variables. Six machine learning (ML) models including Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost (ADA), and Light Gradient-Boosting Machine (LGBM) were trained to predict ploidy status probabilities across three distinct datasets: high-grade embryos (HGE, n = 1107), low-grade embryos (LGE, n = 364), and all-grade embryos (AGE, n = 1471). The model’s performance was interpreted using XAI, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques.ResultsThe mean maternal age was 38.5 ± 3.85 years. The Random Forest (RF) model exhibited superior performance compared to the other five ML models, achieving an accuracy of 0.749 and an AUC of 0.808 for AGE. In the external test set, the RF model achieved an accuracy of 0.714 and an AUC of 0.750 (95% CI, 0.702–0.796). SHAP’s feature impact analysis highlighted that maternal age, paternal age, time to blastocyst (tB), and day 5 morphology grade significantly impacted the predictive model. In addition, LIME offered specific case-ploidy prediction probabilities, revealing the model’s assigned values for each variable within a finite range.ConclusionThe model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process.

Premise of the Study Genome duplication is associated with multiple changes at different levels, including interactions with pollinators and herbivores. Yet little is known whether polyploidy may also shape belowground interactions. Methods To elucidate potential ploidy‐specific interactions with arbuscular mycorrhizal fungi (AMF), we compared mycorrhizal colonization and assembly of AMF communities in roots of diploid and tetraploid Centaurea stoebe s.l. (Asteraceae) co‐occurring in a Central European population. In a follow‐up greenhouse experiment, we tested inter‐cytotype differences in mycorrhizal growth response by combining ploidy, substrate, and inoculation with native AMF in a full‐factorial design. Key Results All sampled plants were highly colonized by AMF, with the Glomeraceae predominating. AMF‐community composition revealed by 454‐pyrosequencing reflected the spatial distribution of the hosts, but not their ploidy level or soil characteristics. In the greenhouse experiment, the tetraploids produced more shoot biomass than the diploids did when grown in a more fertile substrate, while no inter‐cytotype differences were found in a less fertile substrate. AMF inoculation significantly reduced plant growth and improved P uptake, but its effects did not differ between the cytotypes. Conclusions The results do not support our hypotheses that the cytotype structure in a mixed‐ploidy population of C. stoebe is mirrored in AMF‐community composition and that ploidy‐specific fungal communities contribute to cytotype co‐existence. Causes and implications of the observed negative growth response to AMF are discussed.