Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass

Multiscale modelling of hepatitis B virus at cell level of organization

Multiscale modelling is a promising quantitative approach for studying infectious disease dynamics. This approach garners attention from both individuals who model diseases and those who plan for public health because it has great potential to contribute in expanding the understanding necessary for managing, reducing, and potentially exterminating infectious diseases. In this article, we developed a nested multiscale model of hepatitis B virus (HBV) that integrates the within-cell scale and the between-cell scale at cell level of organization of this disease system. The between-cell scale is linked to the within-cell scale by a once off inflow of initial viral infective inoculum dose from the between-cell scale to the within-cell scale through the process of infection; the within-cell scale is linked to the between-cell scale through the outflow of the virus from the within-cell scale to the between-cell scale through the process of viral shedding or excretion. The resulting multiple scales model is bidirectionally coupled in such a way that the within-cell scale and between-cell scale sub-models mutually affect each other, creating a reciprocal relationship. The computed reproductive number from the multiscale model confirms that the within-host scale and the between-host scale influence each other in a reciprocal manner. Numerical simulations are presented that also confirm the theoretical results and support the initial assumption that the within-cell scale and the between-cell scale influence each other in a reciprocal manner. This multiple scales modeling approach serves as a valuable tool for assessing the impact and success of health strategies aimed at controlling hepatitis B virus disease system.

Mechanisms, methods and applications of machine learning in bio-alcohol production and utilization: A review

Bio-alcohols have been proven promising alternatives to fossil fuels. Machine learning (ML), as an analytical tool for uncovering intrinsic correlations and mining data connotations, is also becoming widely used in the field of bio-alcohols. This article reviews the mechanisms, methods, and applications of ML in the bio-alcohols field. In terms of mechanisms, we describe the workflow of ML applications, emphasizing the importance of a well-defined research problem and complete feature engineering for a robust model. Prediction and optimization are the main application scenarios. In terms of methods, we illustrate the characteristics of different ML models and analyze their applicability in the bio-alcohol field. The role of ML in the production of bio-methanol by pyrolysis and gasification, as well as in the three stages of fermentation for bioethanol production are highlighted. In terms of utilization, ML is used to optimize engine performance and reduce emissions. This review provides guidance on how to use novel ML methods in the bio-alcohol field, showing the potential of ML to streamline work in the whole biofuel field.

AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden

The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their deployment is hindered by the high cost of labelling the crop images to provide data for model training. This study examines the capabilities of semi-supervised and active learning to minimise effort when labelling cotton lint samples while maintaining high classification accuracy. Random forest classification models were developed using supervised learning, semi-supervised learning, and active learning to determine Egyptian cotton grade. Compared to supervised learning (80.20-82.66%) and semi-supervised learning (81.39-85.26%), active learning models were able to achieve higher accuracy (82.85-85.33%) with up to 46.4% reduction in the volume of labelled data required. The primary obstacle when using machine learning for Egyptian cotton grading is the time required for labelling cotton lint samples. However, by applying active learning, this study successfully decreased the time needed from 422.5 to 177.5 min. The findings of this study demonstrate that active learning is a promising approach for developing accurate and efficient machine learning models for grading food and industrial crops.

Optimisation of enzymatic saccharification of wheat straw pre-treated with sodium hydroxide

To enhance the reducing sugar yield in enzymatic hydrolysis, various factors (NaOH concentration, solid content and pre-treatment time) that affect the pre-treatment process were investigated and evaluated based on the reducing sugar yield of the subsequent enzymatic hydrolysis. The enzymatic hydrolysis was based on the cellulase from Trichoderma reesi ATCC 26921, the optimum NaOH pre-treatment conditions were an NaOH concentration of 1.0% (w/w), a solid content of 5.0% (w/v) and a pre-treatment time of 60 min. Various parameters that affect the enzymatic hydrolysis of wheat straw, including the solid content, enzyme loading, pH and hydrolysis time, were investigated and optimized through a Box–Behnken design and response surface methodology. The predicted optimum conditions for enzymatic hydrolysis were a solid content of 8.0% (w/v), an enzyme loading of 35 FPU/g substrate, a temperature of 50 °C, a pH of 5.3 and a hydrolysis time of 96 h. The experimental result showed that the maximum reducing sugar yield was 60.73% (53.35% higher than the wheat straw without NaOH pre-treatment), which is in accordance with the predicted conditions.

Comparisons of protective effects between two sea cucumber hydrolysates against diet induced hyperuricemia and renal inflammation in mice

Differences in the anti-hyperuricemic and anti-inflammation effects between two sea cucumber hydrolysates in diet induced hyperuricemic mice.

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

Multiphase systems are important in minerals processing, and usually include solid–solid and solid–fluid systems, such as in wet grinding, flotation, dewatering, and magnetic separation, among several other unit operations. In this paper, the current trends in the process system engineering tasks of modeling, design, and optimization in multiphase systems, are analyzed. Different scales of size and time are included, and therefore, the analysis includes modeling at the molecular level (molecular dynamic modeling) and unit operation level (e.g., computational fluid dynamic, CFD), and the application of optimization for the design of a plant. New strategies for the modeling, design, and optimization of multiphase systems are also included, with a strong focus on the application of artificial intelligence (AI) and the combination of experimentation and modeling with response surface methodology (RSM). The integration of different modeling techniques such as CFD with discrete element simulation (DEM) and response surface methodology (RSM) with artificial neural networks (ANN) is included. The paper finishes with tools to study the uncertainty, both epistemic and stochastic, based on uncertainty and global sensitivity analyses, which is present in all mineral processing operations. It is shown that all of these areas are very active and can help in the understanding, operation, design, and optimization of mineral processing that involves multiphase systems. Future needs, such as meso-scale modeling, are highlighted.

In silico analysis and in vivo tests of the tuna dark muscle hydrolysate anti-oxidation effect

Hydrolysate is a mixture of various peptides with specific functions. However, functional identification of hydrolysate with high throughput is still a difficult task. Furthermore, using in vivo tests via animal or cell experiments is time and labor-intensive. In this study, the peptides component of hydrolysate derived from the tuna dark muscle was measured via MALDI-TOF/TOF-MS, and the functions of the KEFT (Lys-Glu-Phe-Thr), EEASA (Glu-Glu-Ala-Ser-Ala) and RYDD (Arg-Tyr-Asp-Asp) peptides, which were found with the highest proportion, were predicted via Discovery Studio 2016 software. All three peptides were predicted to bind to the Keap1 protein with the highest fit-value and to affect the activity of Keap1, which is involved in anti-oxidation pathways. Subsequently, mice experiments showed that administration of tuna dark muscle hydrolysate increased the levels of superoxide dismutase and glutathione peroxidase in the serum and liver (P < 0.05) and decreased the malondialdehyde level (P < 0.05) as well as transcription of Keap1 (P > 0.05), which are consistent with the in silico analysis results using Discovery Studio 2016 software. The combination of in silico analysis and in vivo tests provided an alternative strategy for identifying hydrolysate function and provided insight into high-value utilization of protein hydrolysate.

In silico prediction and in vivo confirmation of anti-oxidation effect.