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Gibberellic acid (GA3) is a vital plant growth hormone widely used in agriculture. Currently, GA3 production relies on liquid fermentation by the filamentous fungus Fusarium fujikuroi. However, the lack of an effective selection marker recycling system hampers the application of metabolic engineering technology in F. fujikuroi, as multiple-gene editing and positive-strain screening still rely on a limited number of antibiotics. In this study, we developed a strategy using pyr4-blaster and CRISPR/Cas9 tools for recycling orotidine-5′-phosphate decarboxylase (Pyr4) selection markers. We demonstrated the effectiveness of this method for iterative gene integration and large gene-cluster deletion. We also successfully improved GA3 titers by overexpressing geranylgeranyl pyrophosphate synthase and truncated 3-hydroxy-3-methyl glutaryl coenzyme A reductase, which rewired the GA3 biosynthesis pathway. These results highlight the efficiency of our established system in recycling selection markers during iterative gene editing events. Moreover, the selection marker recycling system lays the foundation for further research on metabolic engineering for GA3 industrial production.

Using supplementary cementitious materials in concrete production makes it eco-friendly by decreasing cement usage and the corresponding CO2 emissions. One key measure of concrete’s durability performance is its porosity. An empirical prediction of the porosity of high-performance concrete with added cementitious elements is the goal of this work, which employs machine learning approaches. Binder, water/cement ratio, slag, aggregate content, superplasticizer (SP), fly ash, and curing conditions were considered as inputs in the database. The aim of this study is to create ML models that could evaluate concrete porosity. Gene expression programming (GEP) and multi-expression programming (MEP) were used to develop these models. Statistical tests, Taylor’s diagram, R 2 values, and the difference between experimental and predicted readings were the metrics used to evaluate the models. With R 2 = 0.971, mean absolute error (MAE) = 0.348%, root mean square error (RMSE) = 0.460%, and Nash–Sutcliffe efficiency (NSE) = 0.971, the MEP provided a slightly better-fitted model and improved prediction performance when contrasted with the GEP, which had R 2 = 0.925, MAE = 0.591%, RMSE = 0.745%, and NSE = 0.923. Binder, water/binder ratio, curing conditions, and aggregate content had a direct (positive) relationship with the porosity of concrete, while SP, fly ash, and slag had an indirect (negative) association, according to the SHapley Additive exPlanations study.

Marble cement (MC) is a new binding material for concrete, and the strength assessment of the resulting materials is the subject of this investigation. MC was tested in combination with rice husk ash (RHA) and fly ash (FA) to uncover its full potential. Machine learning (ML) algorithms can help with the formulation of better MC-based concrete. ML models that could predict the compressive strength (CS) of MC-based concrete that contained FA and RHA were built. Gene expression programming (GEP) and multi-expression programming (MEP) were used to build these models. Additionally, models were evaluated by calculating R 2 values, carrying out statistical tests, creating Taylor’s diagram, and comparing theoretical and experimental readings. When comparing the MEP and GEP models, MEP yielded a slightly better-fitted model and better prediction performance (R 2 = 0.96, mean absolute error = 0.646, root mean square error = 0.900, and Nash–Sutcliffe efficiency = 0.960). According to the sensitivity analysis, the prediction of CS was most affected by curing age and MC content, then by FA and RHA contents. Incorporating waste materials such as marble powder, RHA, and FA into building materials can help reduce environmental impacts and encourage sustainable development.

The reliance of the transport sector on fossil fuels has led to increasing concerns related to sustainability and environmental impact. Growing petroleum shortages and global climate change caused by greenhouse gas emissions have given an impetus to the development of renewable and low-carbon energy sources. Fatty acid-derived hydrocarbons are an attractive drop-in biofuel to substitute fossil fuels consumed by the transport sector, especially heavy vehicles. Compared to traditional biofuels such as ethanol and biodiesel, fatty acid-derived hydrocarbons have higher energy density and better compatibility with existing storage and transport systems. As a common microbial platform for biofuel production, yeasts have been engineered to produce fatty acid-derived hydrocarbons. Here, we attempted to comprehensively review the latest advances in metabolic engineering strategies for the production of fatty acid-derived hydrocarbons in the yeasts Saccharomyces cerevisiae and Yarrowia lipolytica. The heterologous hydrocarbon synthesis pathways and combinational metabolic engineering strategies have been summarized, followed by a discussion of future research directions for the development of yeasts as industrial hydrocarbon producers.

Abstract Background α-Humulene is an important biologically active sesquiterpene, whose heterologous production in microorganisms is a promising alternative biotechnological process to plant extraction and chemical synthesis. In addition, the reduction of production expenses is also an extremely critical factor in the sustainable and industrial production of α-humulene. In order to meet the requirements of industrialization, finding renewable substitute feedstocks such as low cost or waste substrates for terpenoids production remains an area of active research. Results In this study, we investigated the feasibility of peroxisome-engineering strain to utilize waste cooking oil (WCO) for high production of α-humulene while reducing the cost. Subsequently, transcriptome analysis revealed differences in gene expression levels with different carbon sources. The results showed that single or combination regulations of target genes identified by transcriptome were effective to enhance the α-humulene titer. Finally, the engineered strain could produce 5.9 g/L α‐humulene in a 5‐L bioreactor. Conclusion To the best of our knowledge, this is the first report that converted WCO to α-humulene in peroxisome-engineering strain. These findings provide valuable insights into the high-level production of α-humulene in Y. lipolytica and its utilization in WCO bioconversion. Graphical Abstract

Due to their vital physiological functions, nutritional fatty acids have great potential as nutraceutical food supplements for preventing an array of diseases such as inflammation, depression, arthritis, osteoporosis, diabetes and cancer. Microbial biosynthesis of fatty acids follows the trend of sustainable development, as it enables green, environmentally friendly and efficient production. As a natural oleaginous yeast, Yarrowia lipolytica is especially well-suited for the production of fatty acids. Moreover, it has a variety of genetic engineering tools and novel metabolic engineering strategies that make it a robust workhorse for the production of an array of value-added products. In this review, we summarize recent advances in metabolic engineering strategies for accumulating nutritional fatty acids in Y. lipolytica, including conjugated fatty acids and polyunsaturated fatty acids. In addition, the future prospects of nutritional fatty acid production using the Y. lipolytica platform are discussed in light of the current progress, challenges, and trends in this field. Finally, guidelines for future studies are also emphasized.

Abstract Gibberellic acid (GA3) is a plant growth hormone that plays an important role in the production of crops, fruits, and vegetables with a wide market share. Due to intrinsic advantages, liquid fermentation of Fusarium fujikuroi has become the sole method for industrial GA3 production, but the broader application of GA3 is hindered by low titer. In this study, we combined atmospheric and room-temperature plasma (ARTP) with ketoconazole-based screening to obtain the mutant strain 3-6-1 with high yield of GA3. Subsequently, the medium composition and fermentation parameters were systematically optimized to increase the titer of GA3, resulting in a 2.5-fold increase compared with the titer obtained under the initial conditions. Finally, considering that the strain is prone to substrate inhibition and glucose repression, a new strategy of fed-batch fermentation was adopted to increase the titer of GA3 to 575.13 mg/L, which was 13.86% higher than the control. The strategy of random mutagenesis combined with selection and fermentation optimization developed in this study provides a basis for subsequent research on the industrial production of GA3. Graphical Abstract

Background and Objective: Bone age detection plays an important role in medical care, sports, judicial expertise and other fields. Traditional bone age identification and detection is according to manual interpretation of X-ray images of hand bone by doctors. This method is subjective and requires experience, and has certain errors. Computer-aided detection can effectually enhance the validity of medical diagnosis, especially with the fast development of machine learning and neural network, the method of bone age recognition using machine learning has gradually become the focus of research, which has the advantages of simple data pretreatment, good robustness and high recognition accuracy.Methods: In this paper, the hand bone segmentation network based on Mask R-CNN was proposed to segment the hand bone area, and the segmented hand bone region was directly input into the regression network for bone age evaluation. The regression network is using an enhancd network Xception of InceptionV3. After the output of Xception, the convolutional block attention module is connected to refine the feature mapping from channel and space to obtain more effective features.Results: According to the experimental results, the hand bone segmentation network model based on Mask R-CNN can segment the hand bone region and eliminate the interference of redundant background information. The average Dice coefficient on the verification set is 0.976. The mean absolute error of predicting bone age on our data set was only 4.97 months, which exceeded the accuracy of most other bone age assessment methods.Conclusion: Experiments show that the accuracy of bone age assessment can be enhancd by using the Mask R-CNN-based hand bone segmentation network and the Xception bone age regression network to form a model, which can be well applied to actual clinical bone age assessment.