Bi-parental graph strategy to represent and analyze hybrid plant genomes.

Hybrid plants are found extensively in the wild, and they often demonstrate superior performance of complex traits over their parents and other selfing plants. This phenomenon, known as heterosis, has been extensively applied in plant breeding for decades. However, the process of decoding hybrid plant genomes has seriously lagged due to the challenges associated with genome assembly and the lack of appropriate methodologies for their subsequent representation and analysis. Here, we present the assembly and analysis of two hybrids, an intraspecific hybrid between two maize (Zea may ssp. mays) inbred lines and an interspecific hybrid between maize and its wild relative teosinte (Zea may ssp. parviglumis), utilizing a combination of PacBio High Fidelity (HiFi) sequencing and chromatin conformation capture sequencing data. The haplotypic assemblies are well-phased at chromosomal scale, successfully resolving the complex loci with extensive parental structural variations (SVs). By integrating into a bi-parental genome graph, the haplotypic assemblies can facilitate downstream short-reads-based SV calling and allele-specific gene expression analysis, demonstrating outstanding advantages over a single linear genome. Our work offers a comprehensive workflow that aims to facilitate the decoding of numerous hybrid plant genomes, particularly those with unknown or inaccessible parentage, thereby enhancing our understanding of genome evolution and heterosis.

MSH7 confers quantitative variation in pollen fertility and boosts grain yield in maize.

Fertile pollen is critical for the survival, fitness, and dispersal of flowering plants, and directly contributes to crop productivity. Extensive mutational screening studies have been carried out to dissect the genetic regulatory network determining pollen fertility, but we still lack fundamental knowledge about whether and how pollen fertility is controlled in natural populations. We used a genome-wide association study (GWAS) to show that ZmGEN1A and ZmMSH7, two DNA repair-related genes, confer natural variation in maize pollen fertility. Mutants defective in these genes exhibited abnormalities in meiotic or post-meiotic DNA repair, leading to reduced pollen fertility. More importantly, ZmMSH7 showed evidence of selection during maize domestication, and its disruption resulted in a substantial increase in grain yield for both inbred and hybrid. Overall, our study describes the first systematic examination of natural genetic effects on pollen fertility in plants, providing valuable genetic resources for optimizing male fertility. In addition, we find that ZmMSH7 represents a candidate for improvement of grain yield.

Soybean steroids improve crop abiotic stress tolerance and increase yield.

Sterols have long been associated with diverse fields, such as cancer treatment, drug development, and plant growth; however, their underlying mechanisms and functions remain enigmatic. Here, we unveil a critical role played by a GmNF-YC9-mediated CCAAT-box transcription complex in modulating the steroid metabolism pathway within soybeans. Specifically, this complex directly activates squalene monooxygenase (GmSQE1), which is a rate-limiting enzyme in steroid synthesis. Our findings demonstrate that overexpression of either GmNF-YC9 or GmSQE1 significantly enhances soybean stress tolerance, while the inhibition of SQE weakens this tolerance. Field experiments conducted over two seasons further reveal increased yields per plant in both GmNF-YC9 and GmSQE1 overexpressing plants under drought stress conditions. This enhanced stress tolerance is attributed to the reduction of abiotic stress-induced cell oxidative damage. Transcriptome and metabolome analyses shed light on the upregulation of multiple sterol compounds, including fucosterol and soyasaponin II, in GmNF-YC9 and GmSQE1 overexpressing soybean plants under stress conditions. Intriguingly, the application of soybean steroids, including fucosterol and soyasaponin II, significantly improves drought tolerance in soybean, wheat, foxtail millet, and maize. These findings underscore the pivotal role of soybean steroids in countering oxidative stress in plants and offer a new research strategy for enhancing crop stress tolerance and quality from gene regulation to chemical intervention.

Factor-augmented transformation models for interval-censored failure time data.

Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This work proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates. We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event. Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation. We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study is provided. An R package ICTransCFA is also available for practitioners. Data used in preparation of this article were obtained from the ADNI database.

Order selection for heterogeneous semiparametric hidden Markov models.

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion-based methods become computationally expensive with the model space growing. A few studies have conducted order selection and parameter estimation simultaneously, but they only considered homogeneous parametric instances. This study proposes a Bayesian double penalization (BDP) procedure for simultaneous order selection and parameter estimation of heterogeneous semiparametric HMMs. To overcome the difficulties in updating the order, we create a brand-new Markov chain Monte Carlo algorithm coupled with an effective adjust-bound reversible jump strategy. Simulation results reveal that the proposed BDP procedure performs well in estimation and works noticeably better than the conventional criterion-based approaches. Application of the suggested method to the Alzheimer's Disease Neuroimaging Initiative research further supports its usefulness.

Construction and validation of a risk model of proteinuria in patients with omicron COVID-19: retrospective cohort study.

BACKGROUND: To explore the risk factors of proteinuria in Omicron variant patients and to construct and verify the risk predictive model. METHODS: 1091 Omicron patients who were hospitalized from August 2022 to November 2022 at Tianjin First Central Hospital were defined as the derivation cohort. 306 Omicron patients who were hospitalized from January 2022 to March 2022 at the same hospital were defined as the validation cohort. The risk factors of proteinuria in derivation cohort were screened by univariate and multivariate logistic regression analysis, and proteinuria predicting scoring system was constructed and the receiver operating characteristic(ROC)curve was drawn to test the prediction ability. The proteinuria risk model was externally validated in validation cohort. RESULTS: 7 factors including comorbidities, blood urea nitrogen (BUN), serum sodium (Na), uric acid (UA), C reactive protein (CRP) and vaccine dosages were included to construct a risk predictive model. The score ranged from -5 to 16. The area under the ROC curve(AUC) of the model was 0.8326(95% CI 0.7816 to 0.8835, p < 0.0001). Similarly to that observed in derivation cohort, the AUC is 0.833(95% CI 0.7808 to 0.9002, p < 0.0001), which verified good prediction ability and diagnostic accuracy in validation cohort. CONCLUSIONS: The risk model of proteinuria after Omicron infection had better assessing efficiency which could provide reference for clinical prediction of the risk of proteinuria in Omicron patients.

Predicting stock price of construction companies using weighted ensemble learning.

Modeling the behavior of stock price data has always been one of the challenging applications of Artificial Intelligence (AI) and Machine Learning (ML) due to its high complexity and dependence on various conditions. Recent studies show that this will be difficult to do with just one learning model. The problem can be more complex for companies in the construction sector, due to the dependency of their behavior on more conditions. This study aims to provide a hybrid model for improving the accuracy of prediction for the stock price index of companies in the construction section. The contribution of this paper can be considered as follows: First, a combination of several prediction models is used to predict stock prices so that learning models can cover each other's errors. In this research, an ensemble model based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Classification and Regression Tree (CART) is presented for predicting the stock price index. Second, the optimization technique is used to determine the effect of each learning model on the prediction result. For this purpose, first, all three mentioned algorithms process the data simultaneously and perform the prediction operation. Then, using the Cuckoo Search (CS) algorithm, the output weight of each algorithm is determined as a coefficient. Finally, using the ensemble technique, these results are combined and the final output is generated through weighted averaging on optimal coefficients. The proposed system was implemented, and its efficiency was evaluated by real stock data of construction companies. The results showed that using CS optimization in the proposed ensemble system is highly effective in reducing prediction error. According to the results, the proposed system can predict the price index with an average accuracy of 96.6 %, which shows a reduction of at least 2.4 % in prediction error compared to the previous methods. Comparing the evaluation results of the proposed system with similar algorithms indicates that our model is more accurate and can be useful for predicting the stock price index in real-world scenarios.

Controlling the Architecture of Freeze-Dried Collagen Scaffolds with Ultrasound-Induced Nucleation.

Collagen is a naturally occurring polymer that can be freeze-dried to create 3D porous scaffold architectures for potential application in tissue engineering. The process comprises the freezing of water in an aqueous slurry followed by sublimation of the ice via a pre-determined temperature-pressure regime and these parameters determine the arrangement, shape and size of the ice crystals. However, ice nucleation is a stochastic process, and this has significant and inherent limitations on the ability to control scaffold structures both within and between the fabrication batches. In this paper, we demonstrate that it is possible to overcome the disadvantages of the stochastic process via the use of low-frequency ultrasound (40 kHz) to trigger nucleation, on-demand, in type I insoluble bovine collagen slurries. The application of ultrasound was found to define the nucleation temperature of collagen slurries, precisely tailoring the pore architecture and providing important new structural and mechanistic insights. The parameter space includes reduction in average pore size and narrowing of pore size distributions while maintaining the percolation diameter. A set of core principles are identified that highlight the huge potential of ultrasound to finely tune the scaffold architecture and revolutionise the reproducibility of the scaffold fabrication protocol.

Wnt/ß-catenin signalling, epithelial-mesenchymal transition and crosslink signalling in colorectal cancer cells.

Colorectal cancer (CRC), with its significant incidence and metastatic rates, profoundly affects human health. A common oncogenic event in CRC is the aberrant activation of the Wnt/ß-catenin signalling pathway, which drives both the initiation and progression of the disease. Persistent Wnt/ß-catenin signalling facilitates the epithelial-mesenchymal transition (EMT), which accelerates CRC invasion and metastasis. This review provides a summary of recent molecular studies on the role of the Wnt/ß-catenin signalling axis in regulating EMT in CRC cells, which triggers metastatic pathogenesis. We present a comprehensive examination of the EMT process and its transcriptional controllers, with an emphasis on the crucial functions of ß-catenin, EMT transcription factors (EMT-TFs). We also review recent evidences showing that hyperactive Wnt/ß-catenin signalling triggers EMT and metastatic phenotypes in CRC via "Destruction complex" of ß-catenin mechanisms. Potential therapeutic and challenges approache to suppress EMT and prevent CRC cells metastasis by targeting Wnt/ß-catenin signalling are also discussed. These include direct ß-catenin inhibitors and novel targets of the Wnt pathway, and finally highlight novel potential combinational treatment options based on the inhibition of the Wnt pathway.

Molecular characteristics and systemic treatment options of liposarcoma: A systematic review.

Liposarcoma (LPS) is a rare soft tissue sarcoma that develops from the differentiation of fat cells, typically occurring in the lower extremities and retroperitoneal space. Depending on its histological morphology and molecular changes, LPS can be divided into various subtypes, each exhibiting distinct biological behaviors. During treatment, especially for LPS arising in the retroperitoneum, the extent and quality of the initial surgery are critically important. Treatment strategies must be tailored to the specific type of LPS. Over the past few decades, the treatment of LPS has undergone numerous advancements, with new therapeutic approaches such as targeted drugs and immunotherapies continually emerging. This paper reviews the biological characteristics, molecular alterations, as well as surgical and pharmacological treatments of various LPS subtypes, with the aim of enhancing clinicians' understanding and emphasizing the importance of individualized precision therapy. With a deeper understanding of the biological characteristics and molecular alterations of LPS, future treatment trends are likely to focus more on developing personalized treatment plans to better address the various types of LPS.