Traditional Extraction Methods Research Articles

Optimization of ultrasound-assisted extraction of mango cotyledon starch: Physicochemical, structural, thermal and functional properties

Mango (Mangifera indica L.) seed, a byproduct of mango pulp and juice processing, is recognized for its abundant starch content. The advancement of the modern food industry has prompted a shift away from traditional starch extraction methods due to their environmental impact and low efficiency. The study aimed to optimize the ultrasound-assisted extraction process of starch from the cotyledons of mango seeds, employing response surface methodology with a customized optimal design and an optimal-I optimality criterion. We investigated the effects of cotyledon/water ratio, time, power, and sonication frequency on maximizing starch extraction yield. We explored the impact of ultrasound on structural, morphological, functional, and pasting properties. The maximum starch extraction yield was 50.74%. This yield was about 82.4% higher than that of conventional wet extraction. Ultrasound-assisted extraction increased starch purity and amylose content; it reduced granule size while enhancing all starch pasting properties without affecting starch's chemical structure and morphological, thermal, and functional properties. The mango cotyledon starch can be classified as medium to normal amylose content starches, exhibiting A-type polymorphs, fast-swelling, and capable of forming strong, firm, low-sticky gels. These results demonstrate the potential applications of mango residue and ultrasound technology in the food and pharmaceutical industry.

Combined ANFIS and numerical methods to reveal the mass transfer mechanism of ultrasound-enhanced extraction of proteins from millet

Millet protein, as a promising plant-based protein substitute source, is an excellent basis for essential amino acids compared to commonly consumed staple grains. Compared with the traditional extraction process, ultrasound has been used to enhance the extraction efficiency of various plant-based proteins. To reveal the mechanism of ultrasound-enhanced extraction of proteins, adaptive neuro-fuzzy inference system (ANFIS) algorithm and numerical simulation based on Fick’s law were applied to illustrate the mass transfer behavior of millet proteins under different ultrasonic conditions including solid–liquid ratios (S/L ratios), pH and acoustic energy density levels (AED). The results showed that AED dominated the changes in effective diffusion coefficient (De), showing a positive correlation relationship (p < 0.05). Specifically, when the AED was 47.07 W/cm2, the De value increased by 95 % compared to that of 23.47 W/cm2. Meanwhile, the ANFIS model successfully predicted protein yields across all investigated parameters, achieving a coefficient of determination (R2) greater than 0.97. This model also elucidated the interactions among four critical factors, among which pH and S/L ratios were the main factors affecting protein yield. Concerning the ultrasonic cavitation bubble dynamics, the bubble collapse efficiency enhanced with an increase in AED, and therefore high AED ultrasound can significantly enhance the cavitation effect. Additionally, the results of the yields and physical properties of millet protein also indicated that in contrast with the traditional extraction methods, the ultrasound impactfully improved extraction yield (by 165 %), and combined with pH condition, it decreased the protein particle size (from 813.55 nm to 299.30 nm) without altering the molecular weight distribution. This study offers a novel perspective on the mechanism underlying ultrasound-enhanced protein extraction, drawing upon principles of ultrasonics and extraction processes. The insights gained can serve as a foundation for the food industry to upscale the extraction process, potentially enhancing efficiency and yield.

Remaining useful life prediction of rolling bearings based on parallel feature extraction

Purpose This study aims to introduce a method for predicting the remaining useful life (RUL) of bearings based on parallel feature extraction. The proposed model provides prior knowledge and removes redundant handcrafted feature information, additionally, which focuses on the important features at different time scales. Design/methodology/approach Distinct from traditional parallel feature extraction methods, which can lead to information redundancy, a one-dimensional convolutional autoencoder is introduced to process selected indicators to remove redundancy and retain useful feature information. To fully capture the important degradation information within different stages in the feature sequences, a novel multi-scale attention feature fusion module is proposed to extract degradation features at different time scales. Considering the impact of degradation modes on RUL prediction, a dual-task prediction module based on no degradation mode labels is designed to obtain accurate RUL. Findings Comparative experiments and ablation studies on the PHM2012 bearing dataset verified the effectiveness of the proposed method. Furthermore, the rationality of the selected parameters is confirmed through model parameter analysis. Originality/value The novelty of the proposed method is that it not only provides prior knowledge but also further removes redundant information from prior knowledge. In addition, the distribution differences between the original features and their multi-scale convolution results are measured through Kullback–Leibler divergence as the attention scores, which allows the proposed method to focus on important information at different time scales.

Rose essential oils: Current trends, mapping of extraction techniques, chemical analysis, therapeutic applications, and by‐product valorization

AbstractThis paper provides a comprehensive review of recent advancements in the extraction, chemical analysis, therapeutic applications, and valorization of by‐products from rose essential oils, with a focus on emerging trends and technologies. Traditional extraction methods, such as hydrodistillation and steam distillation, are compared with modern techniques, including supercritical CO2 extraction (SFE), ultrasound‐assisted extraction (UAE), and microwave‐assisted extraction (MAE). These modern techniques offer improved efficiency, yield, and sustainability, while preserving the chemical integrity of bioactive compounds. Influential extraction parameters, such as temperature, pressure, and extraction time, are analyzed to highlight their impact on yield and chemical composition. Special attention is given to the valorization of rose by‐products, which show potential for pharmaceutical, cosmetic, and food industry applications. Studies demonstrate the promising antioxidant, antimicrobial, and anti‐cancer activities of rose by‐products. The growing demand for sustainable extraction methods and high‐quality natural products highlights the importance of optimizing these technologies. This review also discusses future research directions in the field of rose essential oils, focusing on the integration of advanced extraction methods and the development of value‐added products.

Ultra turrax-assisted extraction of Amazon oils: Optimization and comparison with Soxhlet and compressed propane extraction

The Amazon region, while rich in biodiversity, experiences low socio-economic development. One promising solution involves using Amazon oilseeds, which offer high nutritional value and industrial potential. However, traditional extraction methods have limitations, such as high energy use, solvent consumption, and low yields. This study optimized the extraction of Brazil nut oil (BNO) and Tucumã-do-Amazonas oil (TAO) using Ultra turrax-assisted extraction (UTAE), comparing it with conventional and non-conventional methods. Using the Plackett-Burman design, only extraction time and solvent/solid ratio were significant factors. Optimizing these variables through response surface methodology achieved high yields: 64.6% for BNO and 61.1% for TAO, outperforming Soxhlet and compressed propane extraction (CPE) by up to 43.4%. UTAE produced comparable oleic acid levels in BNO (37.0 - 37.8%) and TAO (66.1 - 68.2%). Additionally, the total phenolic content was higher in both CPE (7.87 ± 0.77 and 26.71 ± 0.63 mg GAE.100 g−1) and UTAE (4.57 ± 1.00 and 19.63 ± 1.68 mg GAE.100 g−1) compared to Soxhlet (1.25 ± 0.37 and 10.97 ± 1.92 mg GAE.100 g−1) for BNO and TAO, respectively. These results highlight UTAE's efficiency for oil extraction.

Improved Visual SLAM Algorithm Based on Dynamic Scenes

This work presents a novel RGB-D dynamic simultaneous localization and mapping (SLAM) method that improves accuracy, stability, and efficiency of localization while relying on deep learning in a dynamic environment, in contrast to traditional static scene-based visual SLAM methods. Based on the classic framework of traditional visual SLAM, we propose a method that replaces the traditional feature extraction method with a convolutional neural network approach, aiming to enhance the accuracy of feature extraction and localization, as well as to improve the algorithm’s ability to capture and represent the characteristics of the entire scene. Subsequently, the semantic segmentation thread was utilized in a target detection network combined with geometric methods to identify potential dynamic areas in the image and generate masks for dynamic objects. Finally, the standard deviation of the depth information of potential dynamic points was calculated to identify true dynamic feature points, to guarantee that static feature points were used for position estimation. We performed experiments based on the public datasets to validate the feasibility of the proposed algorithm. The experimental results indicate that the improved SLAM algorithm, which boasts a reduction in absolute trajectory error (ATE) by approximately 97% compared to traditional static visual SLAM and about 20% compared to traditional dynamic visual SLAM, also exhibited a 68% decrease in computation time compared to well-known dynamic visual SLAM, thereby possessing absolute advantages in both positioning accuracy and operational efficiency.

MaskDGNets: Masked-attention guided dynamic graph aggregation network for event extraction.

Considering that the traditional deep learning event extraction method ignores the correlation between word features and sequence information, it cannot fully explore the hidden associations between events and events and between events and primary attributes. To solve these problems, we developed a new framework for event extraction called the masked attention-guided dynamic graph aggregation network. On the one hand, to obtain effective word representation and sequence representation, an interaction and complementary relationship are established between word vectors and character vectors. At the same time, a squeeze layer is introduced in the bidirectional independent recurrent unit to model the sentence sequence from both positive and negative directions while retaining the local spatial details to the maximum extent and establishing practical long-term dependencies and rich global context representations. On the other hand, the designed masked attention mechanism can effectively balance the word vector features and sequence semantics and refine these features. The designed dynamic graph aggregation module establishes effective connections between events and events, and between events and essential attributes, strengthens the interactivity and association between them, and realizes feature transfer and aggregation on graph nodes in the neighborhood through dynamic strategies to improve the performance of event extraction. We designed a reconstructed weighted loss function to supervise and adjust each module individually to ensure the optimal feature representation. Finally, the proposed MaskDGNets framework is evaluated on two baseline datasets, DuEE and CCKS2020. It demonstrates its robustness and event extraction performance, with F1 of 81.443% and 87.382%, respectively.

Abstract A020: Enhanced cell-free RNA (cfRNA) recovery for liquid biopsy applications: Comparative analysis using controlled RNA samples for early and advanced cancer detection

Abstract Introduction: Extracting cfRNA from biological samples is crucial in liquid biopsy applications, particularly for detecting low-frequency mutations like KRAS G12V, essential for early cancer diagnosis and minimal residual disease (MRD) monitoring. Traditional cfRNA extraction methods involve multiple transfer steps, aliquoting, and pooling, leading to cfRNA loss and affecting mutation detection sensitivity and accuracy. The nRichDX Revolution cfTNA Max 20 kit, with its innovative nRicher cartridge, simplifies the extraction process by eliminating these extra steps, potentially enhancing yield and efficiency. This study evaluates the cfRNA recovery efficiency of the nRichDX kit compared to the Qiagen Circulating Total Nucleic Acid kit. Methods: Plasma samples (5 mL each) were spiked with RNA containing KRAS G12V mutation at concentrations ranging from 100 to 2 million copies, mimicking early-stage cancer detection and MRD monitoring, where low copy numbers represent early disease and high copy numbers reflect advanced cancer stages. cfRNA was extracted using the nRichDX and Qiagen kits, with each condition tested in triplicate. cfRNA recovery efficiency was assessed using quantitative reverse transcription PCR (qRT-PCR) to quantify KRAS G12V RNA copies recovered. High-sensitivity RNA ScreenTape Analysis was also used to evaluate cfRNA profile quality. Results: The nRichDX kit recovered significantly higher cfRNA yields than Qiagen across all concentrations. Recovery rates were consistent and reproducible, with over 70% of input RNA recovered, even at lower inputs (100 to 100,000 copies), where recovery rates were approximately 70-85%. High Sensitivity RNA ScreenTape Analysis showed a linear increase in 18S and 28S ribosomal peaks and high RNA Integrity Number (RIN) scores in nRichDX profiles. The ability to robustly recover low copy numbers is significant for early cancer detection and MRD monitoring. In contrast, the kit’s effectiveness in high copy number samples reflects its ability to monitor advanced cancer stages accurately. Conclusion: The nRichDX cfTNA kit demonstrates exceptional efficiency in cfRNA extraction from plasma samples across a broad dynamic range, from as few as 100 copies to as many as 2 million copies of the KRAS G12V mutation. Its versatility makes it suitable for various cancer detection and monitoring applications. The streamlined process offered by the nRicher cartridge minimizes sample handling and reduces cfRNA loss, enhancing performance. This translates to more accurate and sensitive detection of early-stage and high-burden mutations associated with advanced cancer. The high-quality profiles indicated by RNA ScreenTape Analysis further validate the reliability of the nRichDX kit, making it a valuable tool for liquid biopsy applications, with significant advantages in early cancer detection and monitoring disease progression. Citation Format: Nafiseh Jafari, Mayer Saidian, Jason Saenz, Andrew Dunnigan, Carlos Hernandez, Leila Aghili. Enhanced cell-free RNA (cfRNA) recovery for liquid biopsy applications: Comparative analysis using controlled RNA samples for early and advanced cancer detection [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A020.

Ultrasonic-assisted DES extraction optimization, characterization, antioxidant and in silico study of polysaccharides from Angelica dahurica Radix

In this study, polysaccharides from Angelica dahurica Radix (ADR) were extracted by using ultrasonic-assisted deep eutectic solvent (DES) extraction method. The DES consist of choline chloride (HBA, hydrogen bonding acceptor) and 1, 4-butanediol (HBD, hydrogen bonding donor) with the molar ratio of 1:3 was proved as the best system for polysaccharides extraction. A Box-Behnken design with three factors and three levels was utilized to optimize ultrasonic power, extraction time and temperature for increased polysaccharides yield. The optimal extraction parameters were determined as follows: DES water content of 20 %, liquid–solid ratio of 10:1 mL/g, ultrasonic time of 21 min, temperature at 60 °C, and ultrasonic power of 435 W. These conditions yielded an extraction rate nearly matching the theoretical predicted value of the response surface model, surpassing the traditional hot water extraction method by 1.33 times and requiring less time. The antioxidant polysaccharides was assessed using DPPH,ABTS and Hydroxyl methods, which indicated that ADRP extracted by ultrasound assisted DES had excellent antioxidant capacity and showed a dose–response relationship with drug concentration. Structural analysis identified ADR polysaccharides as a heteropolysaccharide rich in sugar aldonic acids—namely, fucose, rhamnose, arabinose, galactose and glucose—with molar ratios of 0.47:1.99:17.61:64.76:15.17. The polysaccharides’ number-average molecular weight was determined to be 97.422 kDa, with a weight-average molecular weight of 245.678 kDa. Network pharmacology and molecular docking analysis suggest that the monosaccharides Rha and Ara could potentially interact with the proteins GSR and GSTA1, respectively, indicating that ADRP had good antioxidant potential and could be applied in multiple fields with promising development prospects in the future.

Underwater acoustic target recognition model based on transformer and knowledge distillation

Abstract Traditional methods for feature extraction in underwater acoustics heavily rely on domain expertise and handcrafted features, which are often challenged by the complex and variable ocean environment. In contrast, deep learning models, particularly those based on CNNs and Transformers, offer significant advantages in automatic feature extraction and classification tasks. This study introduces a Spectrogram-Vision Transformer (SViT) model, specifically designed for spectrogram classification, leveraging pre-trained Vision Transformer (ViT) models. The SViT model is further utilized as a teacher model in a knowledge distillation framework, where its robust feature extraction capabilities are transferred to a lightweight ResNet50 student model. Experimental results on the DeepShip dataset demonstrate that the proposed method significantly improves the recognition accuracy and generalization capability of ResNet50, achieving performance close to the more computationally intensive SViT model. This research highlights the effectiveness of Transformers in underwater target recognition and underscores the potential of knowledge distillation in enhancing the performance of lightweight models for real-world applications, particularly in resource-constrained environments.

Enhancing human phenotype ontology term extraction through synthetic case reports and embedding-based retrieval: A novel approach for improved biomedical data annotation

With the increasing utilization of exome and genome sequencing in clinical and research genetics, accurate and automated extraction of human phenotype ontology (HPO) terms from clinical texts has become imperative. Traditional methods for HPO term extraction, such as PhenoTagger, often face limitations in coverage and precision. In this study, we propose a novel approach that leverages large language models (LLMs) to generate synthetic sentences with clinical context, which were semantically encoded into vector embeddings. These embeddings are linked to HPO terms, creating a robust knowledgebase that facilitates precise information retrieval. Our method circumvents the known issue of LLM hallucinations by storing and querying these embeddings within a true database, ensuring accurate context matching without the need for a predictive model. We evaluated the performance of three different embedding models, all of which demonstrated substantial improvements over PhenoTagger. Top recall (sensitivity), precision (positive-predictive value, PPV), and F1 are 0.64, 0.64, and 0.64, respectively, which were 31%, 10%, and 21% better than PhenoTagger. Furthermore, optimal performance was achieved when we combined the best performing embedding model with PhenoTagger (a.k.a. Fused model), resulting in recall (sensitivity), precision (PPV), and F1 values of 0.7, 0.7, and 0.7, respectively, which are 10%, 10%, and 10% better than the best embedding models. Our findings underscore the potential of this integrated approach to enhance the precision and reliability of HPO term extraction, offering a scalable and effective solution for biomedical data annotation.

Research and performance analysis of random forest-based feature selection algorithm in sports effectiveness evaluation

The rapid progress in fields such as data mining and machine learning, as well as the explosive growth of sports big data, have posed new challenges to the research of sports big data. Most of the available sports data mining techniques concentrates on extracting and constructing effective features for basic sports data, which cannot be achieved simply by using data statistics. Especially in the targeted mining of sports data, traditional mining techniques still have shortcomings such as low classification accuracy and insufficient refinement. In order to solve the problem of low accuracy in traditional mining methods, the study combines the random forest algorithm with the artificial raindrop algorithm, and adopts a sports data mining method based on feature selection to achieve effective analysis of sports big data. This study is based on the evaluation method of motion effects using random forests, and uses feature extraction algorithms to study the motion effect impacts. It uses the information gain index to rank the importance of features and accurately gain the degree of influence of exercise on various indicators of the human body. Through simulation verification, the algorithm proposed by the research institute performs the best in accuracy and FI scores on the training and testing sets, with accuracies of 0.849 ± 0.021 and 0.819 ± 0.022, respectively, and F1 scores of 0.837 ± 0.020 and 0.864 ± 0.021, respectively. This indicates that the algorithm proposed by the research institute has high classification accuracy and performance proves that the Random Forest-based feature selection algorithm established in this study is superior to the existing traditional feature extraction and extraction methods in terms of both performance and accuracy. The proposal of this data analysis method has achieved accurate and efficient utilization of sports big data, which is of great significance for the development of the sports education industry.

Optimisation of parameters for the extraction of β-amylase from sweet potato via liquid biphasic floatation

This study explores the application of liquid biphasic flotation (LBF) for the extraction of β-amylase from sweet potato (Ipomoea batatas), aiming to provide a greener and more sustainable alternative to traditional extraction methods. The LBF method combines solvent sublation with liquid biphasic systems, offering potential advantages in terms of energy-efficient extraction, reduced environmental impact, and improved resource efficiency. Experimental investigations were conducted to assess the effects of salt concentration, solvent amount, and flotation time on β-amylase extraction efficiency and separation performance. Results indicate that optimal conditions for β-amylase extraction were achieved at a salt concentration of 0.3 g/mL, a solvent amount of 60 mL, and a flotation time of 15 minutes with a salt/solvent combination of trisodium citrate and tert-butanol. These optimal conditions resulted in an amylase yield of 18.29%, with a separation efficiency of 0.06% and a purification factor of 291.90. Comparative analysis with liquid biphasic systems (LBS) revealed that the LBF method provided higher efficiency and yield, contributing to more sustainable practices in enzyme extraction. The LBF method shows promise due to its improved performance and potential for reducing the environmental footprint of industrial processes, supporting both innovation and environmental sustainability. This research also studied the effect of various parameters on phase formation and separation efficiency, offering a more thorough understanding of the LBF system that can be applied to future studies with broader sustainability goals.

Optimization of ultrasound-assisted extraction of astaxanthin from black tiger shrimp (Penaeus monodon) shells using deep eutectic solvent and ethanol as a co-solvent

The study aimed to enhance the extraction of astaxanthin (ASX) from black tiger shrimp shells by combining Deep eutectic solvent (DES) and ultrasound-assisted extraction (UADE). The DES used in the research consisted of choline chloride (Ch) and lactic acid (LA) in a 1:2 M ratio with 20% water. Response surface methodology (RSM) utilizing a Box-Behnken design (BBD) was employed to optimize the UADE conditions, considering ultrasound power (200–400 W), ethanol concentration in the DES (5–65 %), and extraction time (5–45 min). The optimal UADE parameters identified by BBD were 330 W ultrasound power, 35% ethanol content, and 33 min of extraction, resulting in the highest ASX content of 47.42 μg/g, closely matching the predicted value of 48.65 μg/g. Scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) analysis revealed structural alterations in samples treated with DES-based UAE, setting it apart from soxhlet extraction, maceration, and ethanol-based DES extraction. The outcomes indicated that UADE outperformed traditional extraction methods like soxhlet extraction, maceration, and ultrasound-assisted ethanol extraction (UAET) regarding ASX yield and antioxidant activity, while also needing shorter extraction durations. These results underscore UADE as a productive and eco-friendly approach for extracting ASX from shrimp shells.

Advanced Extraction Techniques and Physicochemical Properties of Carrageenan from a Novel Kappaphycus alvarezii Cultivar.

Carrageenans are valuable marine polysaccharides derived from specific species of red seaweed (Rhodophyta) widely used as thickening and stabilizing agents across various industries. Kappaphycus alvarezii, predominantly cultivated in tropical countries, is the primary source of kappa-carrageenan. Traditional industrial extraction methods involve alkaline treatment for up to three hours followed by heating, which is inefficient and generates substantial waste. Thus, developing improved extraction techniques would be helpful for enhancing efficiency and reducing environmental impacts, solvent costs, energy consumption, and the required processing time. In this study, we explored innovative extraction methods, such as ultrasound-assisted extraction (UAE) and supercritical water extraction (SFE), together with other extraction methods to produce kappa-carrageenan from a new strain of K. alvarezii from the Philippines. FTIR-ATR spectroscopy was employed to characterize the structure of the different carrageenan fractions. We also examined the physicochemical properties of isolated phycocolloids, including viscosity, and the content of fatty acids, proteins, and carbohydrates. For refined carrageenan (RC), both the traditional extraction method and the UAE method used 1 M NaOH. Additionally, UAE (8% KOH) was employed to produce semi-refined carrageenan (SRC). UAE (8% KOH) produced a high yield of carrageenan, in half the extraction time (extraction yield: 76.70 ± 1.44), and improved carrageenan viscosity (658.7 cP), making this technique highly promising for industrial scaling up. On the other hand, SFE also yielded a significant amount of carrageenan, but the resulting product had the lowest viscosity and an acidic pH, posing safety concerns as classified by the EFSA's re-evaluation of carrageenan as a food additive.

A Study on the Influence of Mobile Fans on the Smoke Spreading Characteristics of Tunnel Fires

Mobile fans, as flexible and convenient new longitudinal ventilation and smoke extraction equipment for tunnels, demonstrate more significant effectiveness in an emergency response to tunnel fires compared to traditional smoke extraction methods. This study employs computational fluid dynamics simulation methods, selecting two fire scenarios to investigate the effects of fan inclined angles and fan airflow volumes on the longitudinal temperature distribution and smoke back-layering length in tunnels. The results indicate that when using mobile fans for longitudinal ventilation in tunnels, at a lower fan airflow volume, the temperature distribution along the longitudinal axis is nearly symmetrical. The fire source and the fan installed in the upstream are within a certain range, and it is more effective to use the horizontal angle for longitudinal ventilation. As the fan airflow volume increases, the back-layering length significantly decreases (210,000 m3/h &lt; V &lt; 270,000 m3/h). However, as the fan flow volume continues to increase (270,000 m3/h &lt; V &lt; 300,000 m3/h), the reduction in the back-layering length becomes less pronounced, the smoke spread distance of the latter is only 11% of that of the former. Therefore, selecting appropriate fan airflow volumes and fan inclined angles them can effectively enhance the performance of tunnel smoke extraction systems. Moreover, by comparing with traditional fans, we find that mobile fans provide an alternative effective strategy during firefighting by allowing adjustments in distance from the fire source and fan inclination angles, enhancing fire suppression effectiveness while reducing energy losses. The research findings can serve as a reference for tunnel fire prevention design.

Strategies for Hydrocarbon Removal and Bioleaching-Driven Metal Recovery from Oil Sand Tailings

Oil sand tailings from bitumen extraction contain various contaminants, including polycyclic aromatic hydrocarbons, BTEX, and naphthenic acids, which can leak into surrounding environments, threatening aquatic ecosystems and human health. These tailings also contribute to environmental issues such as habitat disruption and greenhouse gas emissions. Despite these challenges, oil sand tailings hold significant potential for waste-to-resource recovery as they contain valuable minerals like rare earth elements (REEs), titanium, nickel, and vanadium. Traditional metal extraction methods are environmentally damaging, requiring high energy inputs and generating dust and harmful emissions. Furthermore, the coating of hydrocarbons on mineral surfaces presents an additional challenge, as it can inhibit the efficiency of metal extraction processes by blocking access to the minerals. This highlights the need for alternative, eco-friendly approaches. Bioleaching, which uses microorganisms to extract metals, emerges as a sustainable solution to unlock the valuable metals within oil sand tailings. This review discusses the minerals found in oil sand tailings, the challenges associated with their extraction, methods from hydrocarbon removal from minerals, and bioleaching as a potential metal recovery method.

Evaluation of in-house-built pipette-tip micro-solid-phase extraction devices for sample preparation in the analysis of amino compounds by liquid chromatography-tandem mass spectrometry

Amino compounds are of significant interest in dietary, clinical, and quality control fields. Efficient extraction is crucial for comprehensive metabolomics, especially for amino acids and biogenic amines, but traditional solid-phase extraction (SPE) methods are costly and require large solvent volumes. Miniaturized SPE techniques, like pipette-tip micro-solid-phase extraction (PT-µ-SPE), offer promising alternatives by improving throughput and reducing solvent and sorbent usage. This study presents PT-µ-SPE for the screening and quantification of amino compounds in bee products, particularly honey. The method involves derivatization with diethyl ethoxymethylenemalonate (DEEMM) and analysis using liquid chromatography-triple quadrupole mass spectrometry. Silica-based SCX sorbents were effective for a broad range of amino compounds, while WCX sorbents were better for aliphatic amines. The method's extraction efficiency was assessed across sample loading, washing, and elution solution, with recovery rates of 70 - 120% in oat bran, sea buckthorn leaves, and honey extracts. Matrix effects were observed for four amino compounds in honey. Limits of detection (LoD) and quantification (LoQ) ranged from 0.37 to 57 µg L⁻¹ and 1.13 to 174 µg L⁻¹, respectively. Covering 48 amino compounds under different PT-µ-SPE conditions, this method has been applied to several samples, demonstrating accuracy, environmental sustainability, cost-effectiveness, portability, and versatility in amino compound analysis.

Autism Identification Based on the Intelligent Analysis of Facial Behaviors: An Approach Combining Coarse- and Fine-Grained Analysis.

Facial behavior has emerged as a crucial biomarker for autism identification. However, heterogeneity among individuals with autism poses a significant obstacle to traditional feature extraction methods, which often lack the necessary discriminative power. While deep-learning methods hold promise, they are often criticized for their lack of interpretability. To address these challenges, we developed an innovative facial behavior characterization model that integrates coarse- and fine-grained analyses for intelligent autism identification. The coarse-grained analysis provides a holistic view by computing statistical measures related to facial behavior characteristics. In contrast, the fine-grained component uncovers subtle temporal fluctuations by employing a long short-term memory (LSTM) model to capture the temporal dynamics of head pose, facial expression intensity, and expression types. To fully harness the strengths of both analyses, we implemented a feature-level attention mechanism. This not only enhances the model's interpretability but also provides valuable insights by highlighting the most influential features through attention weights. Upon evaluation using three-fold cross-validation on a self-constructed autism dataset, our integrated approach achieved an average recognition accuracy of 88.74%, surpassing the standalone coarse-grained analysis by 8.49%. This experimental result underscores the improved generalizability of facial behavior features and effectively mitigates the complexities stemming from the pronounced intragroup variability of those with autism, thereby contributing to more accurate and interpretable autism identification.

Assessment of greenness for solid phase microextraction techniques of sample preparation for flavors by AGREE Prep & SPMS tools

Assessing the greenness of sample preparation techniques is essential due to the use of various solvents, chemicals, reagents, sorbents, pH adjustments, and energetic inputs. Solid-Phase Microextraction (SPME) is preferred over traditional flavor extraction methods such as Simultaneous Distillation Extraction (SDE) and Solvent-Assisted Flavor Evaporation (SAFE) because of its sensitivity, efficiency, speed, versatility, and economy. SPME fibers coated with stationary phases can extract volatile and semi-volatile flavor compounds from food samples, which are then desorbed and analyzed by Gas Chromatography-Mass Spectrometry (GC–MS). This article provides an assessment of the greenness of 50 SPME techniques for flavor analysis using the AGREE Prep and Sample Preparation Metric of Sustainability (SPMS) tools. This evaluation offers valuable insights into the environmental impact and sustainability of SPME as sample preparation techniques for flavour analysis. As per AGREE Prep, method 34 was found to be environmental friendly with score of 0.66, attributed to safe solvents, minimized waste, high sample throughput, and low energy consumption where as method 7 with SPMS scores of 7.05 is more sustainable due to miniaturization, fewer steps, and low energy use.