Traditional Extraction Methods Research Articles

Dihydro-β-ionone production by a one-pot enzymatic cascade of a short-chain dehydrogenase NaSDR and enoate reductase AaDBR1

Dihydro-β-ionone, a high-value compound with distinctive fragrance, is widely utilized in the flavor and fragrance industries. However, its low abundance in plant sources poses a significant challenge to its application through traditional extraction methods. Development of an enzyme cascade reaction with artificial design offers a promising alternative. Herein, a short-chain dehydrogenase NaSDR, was identified from Novosphingobium aromaticivorans DSM 12444, which exhibited a high activity in converting β-ionol to β-ionone. A novel biosynthesis route to produce dihydro-β-ionone from β-ionol was developed, by utilizing alcohol dehydrogenase NaSDR and enoate reductase AaDBR1. Under the optimized conditions (0.29 mg/mL NaSDR, 0.39 mg/mL AaDBR1, 1 mM NADP+ and 2.5 mM β-ionol at 40 °C for 2 h), a maximum yield (173.11 mg/L) of dihydro-β-ionone was achieved with a molar conversion rate of 35.6 %, which was 2.7-fold higher than that before optimization. Additionally, this cascade reaction achieved self-sufficient NADPH regeneration through the actions of NaSDR and AaDBR1. This study offered a fresh perspective for achieving a green and sustainable synthesis of dihydro-β-ionone and could inspire on another natural products biosynthesis.

Antibacterial and antioxidant activities of Chlorella vulgaris and Scenedesmus incrassatulus using natural deep eutectic solvent under microwave assisted by ultrasound

Microalgae are increasingly recognized as promising sources of natural bioactive compounds. However, traditional extraction methods using volatile organic solvents (VOCs) pose environmental risks. This study explores renewable deep eutectic solvents (DES) as sustainable alternatives for extracting bioactive compounds from microalgae biomass, focusing on Chlorella vulgaris and Scenedesmus incrassatulus. Four DES systems, comprising choline chloride (ChCl) and glycerol, citric acid, urea, and glucose, were compared with three conventional solvents (ethanol, methanol, and water). Extraction efficiency was assessed based on total phenolic content (TPC), flavonoid content, and tannin content, followed by antioxidant activity evaluation using DPPH, CAT, and FRAP assays. Additionally, antibacterial activity of the DES extracts was determined against Escherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853), Staphylococcus aureus (ATCC 29213), and Bacillus subtilis (ATCC 3366) using disc diffusion and microplate dilution methods to determine minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). Results reveal that DES, particularly choline chloride: citric acid, outperform conventional solvents in terms of polyphenol extraction efficiency, antioxidant activity, and antibacterial activity against both Gram-negative and Gram-positive bacteria. For instance, the citric acid-based DES (SIDES2) showed a TPC of 4.98 mg/g, while the conventional solvent ethanol exhibited a TPC of 3.27 mg/g. Additionally, SIDES2 exhibiting the highest DPPH scavenging activity of 75 %, compared to 60 % for ethanol. Furthermore, SIDES2 showed an MIC of 0.5 mg/ml against Staphylococcus aureus. This study underscores the potential of DES for sustainable extraction of natural antioxidants from microalgae biomass, contributing to the development of environmentally friendly extraction processes in various industries.

Leveraging Medical Knowledge Graphs and Large Language Models for Enhanced Mental Disorder Information Extraction

The accurate diagnosis and effective treatment of mental health disorders such as depression remain challenging owing to the complex underlying causes and varied symptomatology. Traditional information extraction methods struggle to adapt to evolving diagnostic criteria such as the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) and to contextualize rich patient data effectively. This study proposes a novel approach for enhancing information extraction from mental health data by integrating medical knowledge graphs and large language models (LLMs). Our method leverages the structured organization of knowledge graphs specifically designed for the rich domain of mental health, combined with the powerful predictive capabilities and zero-shot learning abilities of LLMs. This research enhances the quality of knowledge graphs through entity linking and demonstrates superiority over traditional information extraction techniques, making a significant contribution to the field of mental health. It enables a more fine-grained analysis of the data and the development of new applications. Our approach redefines the manner in which mental health data are extracted and utilized. By integrating these insights with existing healthcare applications, the groundwork is laid for the development of real-time patient monitoring systems. The performance evaluation of this knowledge graph highlights its effectiveness and reliability, indicating significant advancements in automating medical data processing and depression management.

Optimization of antioxidant activity and phenolic extraction from Ainsliaea acerifolia stem using ultrasound-assisted extraction technology

Ultrasound-assisted extraction for bioactive compound retrieval is a viable alternative to traditional extraction methods. Employing ultrasound-assisted extraction, this study aimed to enhance the scavenging capacity of 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis(3-ethylbenzothia-zoline-6-sulfonic acid) (ABTS), total polyphenol content (TPC), and flavonoid content in Ainsliaea acerifolia (A. acerifolia) through response surface methodology (RSM). Initially, the impact of extraction temperature, time, and ethanol concentration on DPPH scavenging capacity, ABTS, TPC, and flavonoid content was assessed. Optimal conditions for maximizing antioxidant activity and TPC were determined as 78% (v/v) ethanol, 60 °C extraction temperature, and 91 min of extraction time. High-performance liquid chromatography (HPLC) analysis of the optimized extract revealed dicaffeoylquinic acid as the primary polyphenol in A. acerifolia extracts, comprising the majority of phenolic compounds (102.06 mg/g DW). This model enabled the optimization of conditions for phenolic compound extraction with antioxidant properties from A. acerifolia, highlighting its potential as a source of antioxidant compounds for industrial, pharmaceutical, and food applications.

Microbial consortia-driven bioweathering provides new potential for sustainable recovery of rare earth elements (REE) in fly ash: From metagenome exploration to performance verification

This study presents a novel approach to extracting rare earth elements (REE) from fly ash (FA) using environmentally adapted microbial consortia, highlighting a sustainable alternative to traditional extraction methods. Through comprehensive geochemical characterization of FA obtained from a power plant in China, a complex matrix rich in REE was identified. Microbial consortia isolated from both sludge and FA demonstrated distinct and interactive communities with specialized adaptations for REE bioweathering, as revealed by metagenomic analysis and molecular ecological network assessments. These communities exhibited diverse metabolic profiles, emphasizing ion transport, energy production, and carbohydrate and lipid metabolism tailored to their specific environmental contexts. Enzymatic profiling further elucidated the critical roles of siderophore production and redox reactions in the bioweathering process. Ex-situ bioweathering experiments showcased the consortia's capability to efficiently mobilize specific REE. Notably, under optimized conditions (10 % inoculum of Sludge_LB at 30 °C), significant extraction efficiencies were observed, such as 0.119 mg/g for Sc and 0.068 mg/g for Y; similarly, 10 % inoculum of FA_M9 at 30 °C showed an extraction efficiency of 0.036 mg/g for La. Recovery rates for various REE ranged from 29 % to 83 %, were obtained in a short time (48 hours) demonstrates the effectiveness of this method. These results demonstrate the sustainability of microbial consortia for REE extraction, offering reduced environmental impact compared to conventional methods. This research not only provides a viable method for REE recovery from industrial waste but also contributes to broader goals of sustainable resource utilization and environmental protection.

Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks

Background:Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods. Method:Here, we used a unique dataset compilation comprising 7837 T1-weighted (T1w) MR images from 191 different OpenNeuro datasets in combination with advanced deep learning methods to build a fast, high-precision brain extraction tool called deepbet. Results:deepbet sets a novel state-of-the-art performance during cross-dataset validation with a median Dice score (DSC) of 99.0 on unseen datasets, outperforming the current best performing deep learning (DSC=97.9) and classic (DSC=96.5) methods. While current methods are more sensitive to outliers, deepbet achieves a Dice score of >97.4 across all 7837 images from 191 different datasets. This robustness was additionally tested in 5 external datasets, which included challenging clinical MR images. During visual exploration of each method’s output which resulted in the lowest Dice score, major errors could be found for all of the tested tools except deepbet. Finally, deepbet uses a compute efficient variant of the UNet architecture, which accelerates brain extraction by a factor of ≈10 compared to current methods, enabling the processing of one image in ≈2 s on low level hardware. Conclusions:In conclusion, deepbet demonstrates superior performance and reliability in brain extraction across a wide range of T1w MR images of adults, outperforming existing top tools. Its high minimal Dice score and minimal objective errors, even in challenging conditions, validate deepbet as a highly dependable tool for accurate brain extraction. deepbet can be conveniently installed via “pip install deepbet” and is publicly accessible at https://github.com/wwu-mmll/deepbet.

Comparison of quantity, quality and antibacterial activity of essential oil Mentha longifolia (L.) L. under different traditional and modern extraction methods.

Extraction is the first and most important step in obtaining the effective ingredients of medicinal plants. Mentha longifolia (L.) L. is of considerable economic importance as a natural raw material for the food and pharmaceutical industries. Since the effect of different extraction methods (traditional and modern methods) on the quantity, quality and antimicrobial activity of the essential oil of this plant has not been done simultaneously; the present study was designed for the first time with the aim of identifying the best extraction method in terms of these features. For this purpose, extracting the essential oil of M. longifolia with the methods of hydrodistillation with Clevenger device (HDC), steam distillation with Kaiser device (SDK), simultaneous distillation with a solvent (SDE), hydrodistillation with microwave device (HDM), pretreatment of ultrasonic waves and Clevenger (U+HDC) and supercritical fluid (SF) were performed. Chemical compounds were identified by gas chromatography coupled with mass spectrometer (GC-MS). Antimicrobial activity of essential oils against various clinical microbial strains was evaluated by agar diffusion method and determination of the minimum inhibitory concentration and minimum bactericidal concentration (MIC and MBC). The results showed that the highest and lowest yields of M. longifolia leaf essential oil belonged to HDC (1.6083%) and HDM (0.3416%). The highest number of compounds belonged to SDK essential oil and was equal to 72 compounds (with a relative percentage of 87.13%) and the lowest number of compounds was related to the SF essential oil sample (7 compounds with a relative percentage of 100%). Piperitenone (25.2-41.38%), piperitenone oxide (22.02-0%), pulegone (10.81-0%) and 1,8-cineole (5-35.0%) are the dominant and main components of M. longifolia essential oil were subjected to different extraction methods. Antimicrobial activity results showed that the lowest MIC value belonged to essential oils extracted by HDM, SDK, SDE and U+HDC methods with a value of 1000 μg/mL was observed against Gram-negative bacteria Shigella dysenteriae, which was 5 times weaker than rifampin and 7 times weaker than gentamicin. Therefore, it can be concluded that in terms of efficiency of the HDC method, in terms of the percentage of compounds of the HDM method, and in terms of microbial activity, the SDK, HDM and U+HDC methods performed better.

Dynamic spectral extraction method with approximately the same optical path length of non-invasive quantitative analysis of human blood components

The non-invasive quantitative analysis of blood components using spectroscopic methods based on Beer-Lambert law, and dynamic spectroscopic theory needs to meet the four applicable conditions of this law. However, in practical applications, due to the scattering characteristics of human tissue, it is difficult to meet the two conditions (parallel monochromatic light and non-scattering homogeneous system of absorbing substances), which will reduce the accuracy of quantitative analysis. To reduce the principle error caused by scattering, this paper proposes an extraction method for dynamic spectra with approximately the same optical path length. This method extracts approximately the same absorbance values from photoplethysmographic (PPG) spectral signals. A smaller absorbance value corresponds to a thinner blood layer, in which light can be approximated as parallel light, thus maximizing the satisfaction of the conditions of Beer-Lambert law. In the experimental, compared with the traditional single-edge extraction method, the proposed method has higher prediction accuracy. The correlation coefficients of the total cholesterol and triglycerides increased by 31.22% and 38.05%, and the root mean square errors decreased by 29.63% and 37.14%. The results show that the methods can significantly enhance the robustness of non-invasive quantitative analysis of blood components based on dynamic spectral theory.

Phyllanthuse emblica polyphenols: optimization of high-voltage pulsed electric field assisted extraction, an antioxidant and anti-inflammatory effects in vitro

BackgroundThe polyphenols extraction of Phyllanthus emblica is primarily carried out using organic solvents, and assisted by physical fields such as ultrasound and microwave for extraction. High voltage pulsed electric field technology (PEF) is a non-thermal processing technology that has high efficiency and minimal damage to thermosensitive substances. PEF has been applied to plant extraction in many studies, however, the extraction of polyphenols from Phyllanthus emblica using the PEF has still not been reported; ObjectiveThis study explores the optimal extraction process of polyphenols from Phyllanthus emblica using the PEF, and investigates its relaxation and anti-wrinkle based on anti-oxidation and anti-inflammatory experiment, in order to develop a Phyllanthus emblica extract with substantial efficacy; Materials and MethodsThe method of Phyllanthus emblica extract using PEF is established, and compared with a traditional extraction method. The experimental conditions, such as electric field intensity (0.5~6.0kV/cm), pulse times (20~120), extraction time (0~60min) and material concentration (0.5%~3%), are investigated and optimized using orthogonal experiments; Resultsthe polyphenols in the Phyllanthus emblica extract were highest at the electric field intensity of 5kV / cm, 120 pulses, extraction time of 30minutes, and 2% material concentration. The PEFcontained more polyphenols than the conventional water extraction and ultrasound-assisted extraction. The Phyllanthus emblica extract had substantial antioxidant and anti-inflammatory effects, with a good clearance effect on DPPH ( IC50 of 1.82%) and ABTS ( IC50 of 1.80%) radicals. At the Phyllanthus emblica extract concentration of 1.25%, inflammatory factors (TNF-α) were reduced by 47.08%; and ConclusionThe PEF is a leading-edge and promising method for preparing Phyllanthus emblica extracts.

An improved Trizol method for extracting total RNA from Eleutherococcus senticosus (Rupr. & Maxim.) Maxim leaves.

High-quality nucleic acids are the basis for molecular biology experiments. Traditional RNA extraction methods are not suitable for Eleutherococcus senticosus Maxim. To find a suitable method to improve the quality of RNA extracted, we modified the RNA extraction methods of Trizol. Based on the conventional Trizol method, the modified Trizol method 1 and modified Trizol method 2 were used as the control for extraction of RNA from E. senticosus Maxim leaves. The modified Trizol method 1 added β-mercaptoethanol on the conventional Trizol method. After RNA was dissolved, a mixed solution of phenol, chloroform, and isoamyl alcohol was added to denature protein and inhibit the degradation of RNA. The modified Trizol method 2 adds PVPP to grind on the basis of modified Trizol method 1, so as to better remove phenols from leaves, and eliminates the step of incubation at -20°C to reduce extraction time and RNA degradation. Chloroform, CTAB, and CH3COONa were used instead of a phenol, chloroform, and isoamyl alcohol mixed solution to ensure complete separation of nucleic acid from plant tissues and to obtain high-purity RNA. The research results showed that the quality of RNA extracted by conventional Trizol method, modified Trizol method 1, was incomplete, accompanied with different degrees of contamination of polysaccharides, polyphenols, and DNA. The modified Trizol method 2 could better extract RNA from E. senticosus Maxim leaves. The ratio of A260/A280 was in the range of 1.8-2.0, and the yield of RNA was the highest, which was 1.68 and 1.15 times compared with that by conventional Trizol method and modified Trizol method 1 extraction, respectively. The reverse transcription cDNA was further tested through PCR with the specific primers. The amplified fragments are displayed in clear and bright bands in accordance with the expected size. The modified Trizol method 2 could better extract RNA from E.senticosus Maxim leaves. High-quality RNA has more advantages in molecular biology study of E.senticosus Maxim.

Improved spectral dynamic features extracted from audio data for classification of marine vessels

Underwater sound classification presents a unique challenge due to the complex propagation characteristics of sound in water, including absorption, scattering, and refraction. These complexities can distort and alter spectral features, hindering the effectiveness of traditional feature extraction methods for vessel classification. To address this challenge, this study proposes a novel feature extraction method that combines Mel-frequency cepstral coefficients (MFCCs) with a spectral dynamic feature (SDF) vector. MFCCs capture the spectral content of the audio signal, whereas SDF provides information on the temporal dynamics of spectral features. This combined approach aims to achieve a more comprehensive representation of underwater vessel sounds, potentially leading to improved classification accuracy. Validation with real-world underwater audio recordings demonstrated the effectiveness of the proposed method. Results indicated an improvement of up to 94.68% in classification accuracy when combining SDF with several classical extractors evaluated. This finding highlights the potential of SDF in overcoming the challenges associated with underwater sound classification.

Tsallis Entropy-Based Complexity-IPE Casualty Plane: A Novel Method for Complex Time Series Analysis.

Due to its capacity to unveil the dynamic characteristics of time series data, entropy has attracted growing interest. However, traditional entropy feature extraction methods, such as permutation entropy, fall short in concurrently considering both the absolute amplitude information of signals and the temporal correlation between sample points. Consequently, this limitation leads to inadequate differentiation among different time series and susceptibility to noise interference. In order to augment the discriminative power and noise robustness of entropy features in time series analysis, this paper introduces a novel method called Tsallis entropy-based complexity-improved permutation entropy casualty plane (TC-IPE-CP). TC-IPE-CP adopts a novel symbolization approach that preserves both absolute amplitude information and inter-point correlations within sequences, thereby enhancing feature separability and noise resilience. Additionally, by incorporating Tsallis entropy and weighting the probability distribution with parameter q, it integrates with statistical complexity to establish a feature plane of complexity and entropy, further enriching signal features. Through the integration of multiscale algorithms, a multiscale Tsallis-improved permutation entropy algorithm is also developed. The simulation results indicate that TC-IPE-CP requires a small amount of data, exhibits strong noise resistance, and possesses high separability for signals. When applied to the analysis of heart rate signals, fault diagnosis, and underwater acoustic signal recognition, experimental findings demonstrate that TC-IPE-CP can accurately differentiate between electrocardiographic signals of elderly and young subjects, achieve precise bearing fault diagnosis, and identify four types of underwater targets. Particularly in underwater acoustic signal recognition experiments, TC-IPE-CP achieves a recognition rate of 96.67%, surpassing the well-known multi-scale dispersion entropy and multi-scale permutation entropy by 7.34% and 19.17%, respectively. This suggests that TC-IPE-CP is highly suitable for the analysis of complex time series.

Extraction of chitin from flammulina velutipes waste: A low-concentration acid pretreatment and aspergillus Niger fermentation approach

In recent years, with the booming of the edible mushroom industry, chitin production has become increasingly dependent on fungi and other non-traditional sources. Fungal chitin has advantages including superior performance, simpler separation processes, abundant raw materials, and the absence of shellfish allergens. As a kind of edible mushroom, flammulina velutipes (F. velutipes) also has the advantages of wide source and large annual yield. This provided the possibility for the extraction of chitin. Here, a procedure to extract chitin from F. velutipes waste be presented. This method comprises low-concentration acid pretreatment coupled with consolidated bioprocessing with Aspergillus niger. Characterization by SEM, FTIR, XRD, NMR, and TGA confirmed that the extracted chitin was β-chitin. To achieve optimal fermentation of F. velutipes waste (80 g/L), ammonium sulfate and glucose were selected as nitrogen and carbon sources (5 g/L), with a fermentation time of 5 days. The extracted chitin could be further deacetylated and purified to obtain high-purity chitosan (99.2 % ± 1.07 %). This chitosan exhibited a wide degree of deacetylation (50.0 % ± 1.33 % – 92.1 % ± 0.97 %) and a molecular weight distribution of 92–192 kDa. Notably, the yield of chitosan extracted in this study was increased by 56.3 % ± 0.47 % compared to the traditional chemical extraction method.

A high-permeability method for extracting purple yam saponins based on ultrasonic-assisted natural deep eutectic solvent

The extraction of active ingredients from traditional Chinese medicine has received considerable attentions. In this study, 16 kinds of natural deep eutectic solvent (NADES) with ultrasonic were selected to extract saponins from purple yam root and the extraction mechanism was investigated. The results showed that chloride/acrylic acid (1:2; n/n) had the highest extraction yield for saponins. The optimal extraction process parameters were 24% water content, 20 mL/g liquid-solid ratio, and ultrasonic extraction for 85 min (81 °C, 600 W). The extraction rate (ER) of purple yam saponins was 0.935%, close to the fitted result of 96.5 mg/g. Molecular dynamics simulations and FT-IR results showed that the NADES may extract the saponin constituents from purple yam through hydrogen bonding. Compared with traditional extraction methods and molecularly imprinted polymer methods, NADES has a higher ER and lower cost (1.53 $/g), which provides a reference for subsequent industrial quantitative production.

Adaption and application of cell-based bioassays to whole-water samples

The increasing presence of contaminants of emerging concern in wastewater and their potential environmental risks require improved monitoring and analysis methods. Direct toxicity assessment (DTA) using bioassays can complement chemical analysis of wastewater discharge, but traditional in vivo tests have ethical considerations and are expensive, low-throughput, and limited to apical endpoints (mortality, reproduction, development, and growth). In vitro bioassays offer an alternative approach that is cheaper, faster, and more ethical, and can provide higher sensitivity for some environmentally relevant endpoints. This study explores the potential benefits of using whole water samples of wastewater and environmental surface water instead of traditional solid phase extraction (SPE) methods for in vitro bioassays testing. Whole water samples produced a stronger response in most bioassays, likely due to the loss or alteration of contaminants during SPE sample extraction. In addition, there was no notable difference in results for most bioassays after freezing whole water samples, which allows for increased flexibility in testing timelines and cost savings. These findings highlight the potential advantages of using whole water samples in DTA and provide a framework for future research in this area.

An Improved Current Signal Extraction-Based High-Frequency Pulsating Square-Wave Voltage Injection Method for Interior Permanent-Magnet Synchronous Motor Position-Sensorless Control

The high-frequency (HF) voltage injection method is widely applied in achieving position-sensorless control for interior permanent-magnet synchronous motors (IPMSMs). This method necessitates precise and rapid extraction of the current signal for accurate position estimation and field-oriented control (FOC). In the traditional methods, the position error signal and fundamental current are extracted from the current signal using band-pass filters (BPFs) and low-pass filters (LPFs), or a method based on time-delay filters. However, the traditional extraction method falls short in ensuring simultaneous dynamic performance and accuracy, particularly when the switching frequency is limited or when encountering harmonic and noise interference. In this article, a novel HF pulsating square-wave voltage injection method based on an improved current signal-extraction strategy is proposed to improve the extraction accuracy while maintaining good dynamic performance. The newly devised current signal-extraction method is crafted upon a notch filter (NF). Through harnessing NF’s effective separation characteristics of specific frequency signals, the current signal is meticulously processed. This process yields the extraction of the position error signal and fundamental-current component, crucial for accurate position estimation and motor FOC. Simulation and hardware-in-the-loop (HIL) testing are conducted to validate the effectiveness of the proposed approach.

Research progress and latest application of ultrasonic treatment on protein extraction and modification

SummaryThe advancement of the modern food industry has prompted a shift away from traditional protein extraction methods due to their inherent limitations. As a result, there is a growing interest in exploring alternative methods for protein extraction and modification such as ultrasonic (US) treatment. This shift towards more sustainable and efficient protein extraction methods is a key research focus in obtaining safe proteins in an environmentally friendly manner. Furthermore, enhancing the functional properties of proteins through modification can expand their application. This paper reviews recent research progress and applications of US treatment on protein extraction and modification as well as the impact on protein structure and functional properties. In addition, the latest applications of ultrasound‐modified‐proteins, including nano‐proteins, 3D printing and interactions between proteins and other food components, are reviewed. This review may provide some inspiration for the potential application of US technologies in food fields.

Applications of encoding-decoding network on numerical simulation mapping for propeller wake

Numerical Simulation methods of RANS and LES have been widely used to simulate the velocity field induced by propellers. In order to combine advantages of quick computation time from RANS and detailed information from LES, this paper innovatively proposed a customized data-driven encoding-decoding network to achieve mapping from RANS results to LES results. Firstly, an encoding-decoding neural network model was constructed to extract features from RANS/LES for propeller wake. The extracted features with additional information concatenation during the encoding steps were then used to reconstruct the original samples and got a reconstruction error of 1%, which proved the effectiveness of feature extraction ability of the proposed model. The proposed model was then used to map directly from RANS to LES data with a better performance compared to traditional PCA feature extraction and mapping method. Afterwards, the proposed network was applied to two more RANS to LES mapping scenarios to validate the generalization ability. The model could map a coarse-grid RANS to LES of ship propeller with less than 9% MSE error. Similarly, mapped LES of nominal wake was 98% more alike the original LES samples than the RANS samples. As a result, the proposed model could be used either to extract features for potential feature-based performance prediction, or as a quicker method for detailed simulation for propeller wake and nominal wake, thus expedites the design process of ship design.

Eco-friendly extraction of six different rosemary (Rosmarinus officinalis L.) genotypes-using natural deep eutectic solvents: Optimization and modeling via response surface methodology (RSM)

New environmentally friendly extraction methods and solvents such as ultrasound assisted extraction (UAE) and natural deep eutectic solvents (NADES) are replacing the traditional extraction methods with organic solvents. In the present study, NADES prepared with choline chloride 1,2-propylene glycol (ChCl-PG) were characterized and running parameters for the UAE process of phenolics were optimized and modeled with response surface methodology (RSM) using ChCl-PG. The optimum conditions obtained from running parameters were used to compare efficiency of NADES with four traditional solvents and NADES was found to be for phenolics extraction from rosemary. Experimental data was used to maximize extraction yields of phenolics and antioxidant activities, rosmaranic acid, chlorophyll and carotene content using six rosemary genotypes under optimal conditions with NADES and a traditional solvent (ethanol:water, 80:20 v/v) with UAE. Rosemary genotypes showed significant differences for bioactive compound yields except chlorophyll and carotene content. NADES and traditional solvents did not affect rosmarinic acid and carotenoid yield, but extraction yield of other compounds and their antioxidant capacities were significantly affected by the solvent. While rosmarinic acid content was not affected, chlorophyll and carotenoid contents were affected by the solvent type. Out of 21 Pearson correlation coefficients, 5 and 7 had significant positive correlations between traditional and NADES solvents, respectively. Results showed that NADES with UAE is an effective method could be used for extraction of secondary compounds.

A novel bioaccessibility prediction method for complex petroleum hydrocarbon mixtures in soil.

More evidence shows that bioaccessibility instead of total concentrations based on exhaustive extraction methods can better reflect the actual risk level of petroleum hydrocarbon contaminated sites, so it is essential to establish an effective assessment method for bioaccessibility. This study utilized Tenax extraction, butanol extraction, hydroxypropyl-β-cyclodextrin (HPCD) extraction, and a composite extraction method involving HPCD with LMWOAs (citric acid, CA) and surfactants (rhamnolipid, RL; Tween80, TW80; sodium dodecyl sulfate, SDS) at varying concentrations. These methods were employed to predict the bioaccessibility of earthworms to soil at different aging time of petroleum hydrocarbons. The results showed that traditional extraction methods such as Tenax 6h extraction and n-butanol extraction were ineffective in evaluating petroleum hydrocarbons' bioaccessibility. In contrast, the composite extraction of HPCD and solubilizer enhanced the extraction efficiency of HPCD greatly, and the extraction results showed a significant positive correlation with earthworm accumulation. By the comparison of the extraction results of different fractions of petroleum hydrocarbons, heavy fractions of petroleum hydrocarbons (C29-C40) are essential factors affecting chemical extraction effects. The correlation coefficients of four composite extraction methods and total petroleum hydrocarbons (TPH) of earthworm accumulation by linear regression analysis ranged from 1.1797 to 1.7990, and the slopes ranged from 0.8727 to 0.9792. Among them, the combined extraction method of 50 mmol/L HPCD and 0.5 mmol/L rhamnolipid had the best effect (r2 = 0.9792, slope = 1.1797), which could be used as an evaluation method suitable for the bioaccessibility of petroleum hydrocarbons in soil. This study could provide a new method for evaluating the bioaccessibility of organic pollutants and technically supporting risk assessment and bioremediation of complex petroleum hydrocarbons in soil.