Explore the vast range of titles available.

Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.

The scale-up of chemical processes involves substantial changes in reactor size, operational modes, and data characteristics, leading to significant challenges in predicting product distribution across scales. This study presents a unified modeling framework that integrates the mechanistic model with deep transfer learning to accelerate chemical process scale-up. The framework is demonstrated through a case study on naphtha fluid catalytic cracking. A molecular-level kinetic model was developed from laboratory-scale experimental data, and a deep neural network was designed and trained to represent complex molecular reaction systems. To address the challenge of discrepancies in data types at various scales, a property-informed transfer learning strategy was developed by incorporating bulk property equations into the neural network. This approach enabled automated prediction of pilot-scale product distribution with minimal data. Moreover, process conditions of the pilot plant were optimized using a multi-objective optimization algorithm. Process scale-up is a persistent challenge in chemical industries. Here, the authors integrated the mechanistic model with transfer learning to accelerate process scale-up, and product distribution was auto-predicted from the laboratory to the pilot.

• Abscisic acid (ABA) plays a crucial role in the adaptation of young seedlings to environmental stresses. However, the role of epigenetic components and core transcriptional machineries in the effect of ABA on seed germination and seedling growth remain unclear. • Here, we show that a histone 3 lysine 4 (H3K4) demethylase, JMJ17, regulates the expression of ABA-responsive genes during seed germination and seedling growth. Using comparative interactomics, WRKY40, a central transcriptional repressor in ABA signaling, was shown to interact with JMJ17. WRKY40 facilitates the recruitment of JMJ17 to the ABI5 chromatin, which removes gene activation marks (H3K4me3) from the ABI5 chromatin, thereby repressing its expression. • Additionally, WRKY40 represses the transcriptional activation activity of HY5, which can activate ABI5 expression by directly binding to its promoter. An increase in ABA concentrations decreases the affinity of WRKY40 for the ABI5 promoter. Thus, WRKY40 and JMJ17 are released from the ABI5 chromatin, activating HY5. The accumulated ABI5 protein further shows heteromeric interaction with HY5, and thus synergistically activates its own expression. • Our findings reveal a novel transcriptional switch, composed of JMJ17–WRKY40 and HY5–ABI5 modules, which regulates the ABA response during seed germination and seedling development in Arabidopsis.

Patients with type 2 diabetes mellitus (T2DM) have a significantly higher risk of cardiovascular disease (CVD) compared to the general population. Accurately predicting this risk is crucial for developing personalized treatment plans and public health interventions. This study aims to develop and validate a model for predicting CVD risk in T2DM patients using the Boruta feature selection algorithm and machine learning methods. We analyzed data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. Six machine learning (ML) models, including Multilayer Perceptron (MLP), Light Gradient Boosting Machine (LightGBM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), and k-Nearest Neighbors (KNN), were employed for model development and validation. Boruta was used for optimal feature selection. The performance of the machine learning models was comprehensively evaluated using ROC curves, accuracy, and other related metrics. Shapley Additive Explanation (SHAP) analysis was conducted for visual interpretation, and the Shinyapps.io platform was utilized to deploy the best-performing models as web-based applications. A total of 4,015 T2DM patients were included, among which 999 (24.9%) had CVD. Model evaluation revealed significant overfitting with the KNN algorithm, which showed perfect discrimination in the training set but performed poorly in the test set (AUC = 0.64). In contrast, XGBoost demonstrated more consistent performance between training and testing datasets (AUC = 0.75 and 0.72, respectively), indicating better generalization ability and making it more suitable for clinical application. Using SHAP analysis, the top 10 important influencing factors identified by the XGBoost model were utilized to construct a CVD risk prediction platform for T2DM patients. The prediction model based on Boruta feature selection and machine learning shows promising results in assessing the CVD risk among T2DM patients. This study provides a viable tool for clinical use, facilitating early intervention and precision treatment.

Climate warming nowadays has caused people to increasingly enhance public awareness about carbon emissions from industries. In the storing industry, inventory management for deteriorating items is crucial in the business competition. To slow down the deterioration and ensure the quality of products, the items are usually stored in certain temperature-controlled environment. However, a lot of carbon emissions of the inventory system are caused by these warehousing activities. In a finite panning period, this paper studies a continuous review inventory system and proposes inventory models to analyze the impacts of carbon emissions on inventory system for deteriorating items with a general time-varying demand, in which shortages are allowed, and the customer demand during shortage period is partially backlogged till the next replenishment. Under carbon emission regulations, the existence and uniqueness of the optimal solution to each model is explored and comparisons of optimality among the proposed models are given. Numerical examples and robust analysis of the models are presented to illustrate the applicability in practice. Some management insights about inventory policies and emission reduction strategies are obtained.

• Allopolyploidization, which entails interspecific hybridization and whole genome duplication (WGD), is associated with emergent genetic and epigenetic instabilities that are thought to contribute to adaptation and evolution. One frequent genomic consequence of nascent allopolyploidization is homoeologous exchange (HE), which arises from compromised meiotic fidelity and generates genetically and phenotypically variable progenies. • Here, we used a genetically tractable synthetic rice segmental allotetraploid system to interrogate genome-wide DNA methylation and gene expression responses and outcomes to the separate and combined effects of hybridization, WGD and HEs. • Progenies of the tetraploid rice were genomically diverse due to genome-wide HEs that affected all chromosomes, yet they exhibited overall methylome stability. Nonetheless, regional variation of cytosine methylation states was widespread in the tetraploids. Transcriptome profiling revealed genome-wide alteration of gene expression, which at least in part associates with changes in DNA methylation. Intriguingly, changes of DNA methylation and gene expression could be decoupled from hybridity and sustained and amplified by HEs. • Our results suggest that HEs, a prominent genetic consequence of nascent allopolyploidy, can exacerbate, diversify and perpetuate the effects of allopolyploidization on epigenetic and gene expression variation, and hence may contribute to allopolyploid evolution.

DNA methylation is sensitive and responsive to stressful environmental conditions. Nonetheless, the extent to which condition-induced somatic methylation modifications can impose transgenerational effects remains to be fully understood. Even less is known about the biological relevance of the induced epigenetic changes for potentially altered well-being of the organismal progenies regarding adaptation to the specific condition their progenitors experienced. We analyzed DNA methylation pattern by gel-blotting at genomic loci representing transposable elements and protein-coding genes in leaf-tissue of heavy metal-treated rice (Oryza sativa) plants (S0), and its three successive organismal generations. We assessed expression of putative genes involved in establishing and/or maintaining DNA methylation patterns by reverse transcription (RT)-PCR. We measured growth of the stressed plants and their unstressed progenies vs. the control plants. We found (1) relative to control, DNA methylation patterns were modified in leaf-tissue of the immediately treated plants, and the modifications were exclusively confined to CHG hypomethylation; (2) the CHG-demethylated states were heritable via both maternal and paternal germline, albeit often accompanying further hypomethylation; (3) altered expression of genes encoding for DNA methyltransferases, DNA glycosylase and SWI/SNF chromatin remodeling factor (DDM1) were induced by the stress; (4) progenies of the stressed plants exhibited enhanced tolerance to the same stress their progenitor experienced, and this transgenerational inheritance of the effect of condition accompanying heritability of modified methylation patterns. Our findings suggest that stressful environmental condition can produce transgenerational epigenetic modifications. Progenies of stressed plants may develop enhanced adaptability to the condition, and this acquired trait is inheritable and accord with transmission of the epigenetic modifications. We suggest that environmental induction of heritable modifications in DNA methylation provides a plausible molecular underpinning for the still contentious paradigm of inheritance of acquired traits originally put forward by Jean-Baptiste Lamarck more than 200 years ago.

Cytosine methylation at CG sites (ᵐCG) plays critical roles in development, epigenetic inheritance, and genome stability in mammals and plants. In the dicot model plant Arabidopsis thaliana , methyltransferase 1 (MET1), a principal CG methylase, functions to maintain ᵐCG during DNA replication, with its null mutation resulting in global hypomethylation and pleiotropic developmental defects. Null mutation of a critical CG methylase has not been characterized at a whole-genome level in other higher eukaryotes, leaving the generality of the Arabidopsis findings largely speculative. Rice is a model plant of monocots, to which many of our important crops belong. Here we have characterized a null mutant of OsMet1-2 , the major CG methylase in rice. We found that seeds homozygous for OsMet1-2 gene mutation (OsMET1-2 ⁻/⁻), which directly segregated from normal heterozygote plants (OsMET1-2 ⁺/⁻), were seriously maldeveloped, and all germinated seedlings underwent swift necrotic death. Compared with wild type, genome-wide loss of ᵐCG occurred in the mutant methylome, which was accompanied by a plethora of quantitative molecular phenotypes including dysregulated expression of diverse protein-coding genes, activation and repression of transposable elements, and altered small RNA profiles. Our results have revealed conservation but also distinct functional differences in CG methylases between rice and Arabidopsis .

Hyaluronic acid (HA), an important natural polysaccharide and meanwhile, an essential component of extracellular matrix (ECM), has been widely used in tissue repair and regeneration due to its high biocompatibility, biodegradation, and bioactivity, and the versatile chemical groups for modification. Specially, HA-based dynamic hydrogels, compared with the conventional hydrogels, offer an adaptable network and biomimetic microenvironment to optimize tissue repair and the regeneration process with a striking resemblance to ECM. Herein, this review comprehensively summarizes the recent advances of HA-based dynamic hydrogels and focuses on their applications in articular cartilage repair. First, the fabrication methods and advantages of HA dynamic hydrogels are presented. Then, the applications of HA dynamic hydrogels in cartilage repair are illustrated from the perspective of cell-free and cell-encapsulated and/or bioactive molecules (drugs, factors, and ions). Finally, the current challenges and prospective directions are outlined.

Porous Ionic Liquids (PILs) have gained attention but facing challenges in catalysis, especially in liquid‐liquid two‐phase reactions due to limited catalytic sites and hydrophilicity control. This work engineered a Type III PILs (PILS‐M) using zeolitic imidazolate framework‐8 (ZIF‐8) confined phosphomolybdic acid (HPMo) as the microporous framework and N‐butyl pyridine bis(trifluoromethane sulfonyl) imide ionic liquid ([Bpy][NTf2]) as the solvent. The PILS‐M not only combines the advantages of traditional ionic liquids and microporous frameworks, including excellent extraction, high dispersion of catalytically active species, remarkable stability, etc., but also can make the inner surface of ZIF‐8 turned to be hydrophilic that favors the contact between aqueous hydrogen peroxide oxidant and catalytically active sites for the promotion of catalytic performance in reactive extractive desulfurization (REDS) processes of fuel oils. This study demonstrates Type III PILs' potential as catalysts for sustainable chemical processes, offering insights into versatile PILs applications in diverse fields. This work introduces innovative Type III Porous Ionic Liquids (PILS‐M), utilizing ZIF‐8 confined HPMo as the microframework and [Bpy][NTf2] as the steric solvent, to address catalytic challenges, offering a synergistic blend of traditional ionic liquids and microporous frameworks for enhanced catalytic performance in liquid‐liquid two‐phase reactions.