Traditional Extraction Methods Research Articles

Advances in DNA Extraction Techniques: A Comprehensive Review of Methods and Applications

The extraction of DNA is a fundamental process in molecular biology, underpinning a wide range of applications from genetic research to forensic science and medical diagnostics. This review aims to explore the latest advancements in DNA extraction techniques, highlighting their principles, applications, and suitability for various types of biological samples. Traditional DNA extraction methods, such as phenol-chloroform and ethanol precipitation, have served as the backbone of DNA isolation for decades. However, these techniques often involve hazardous chemicals and can be time-consuming. Recent advancements have focused on developing safer, faster, and more efficient methods, with a focus on automation and scalability. Innovations such as magnetic bead-based extraction, silica column purification, and specialized kits have greatly simplified the process, allowing for high-throughput applications in clinical and research settings. In addition to these technical improvements, new approaches have emerged to address specific challenges, such as extracting DNA from degraded or trace samples, as seen in forensic investigations, or isolating cell-free DNA for non-invasive prenatal testing and cancer diagnostics. Moreover, microfluidic devices and lab-on-a-chip technologies are transforming the landscape by enabling DNA extraction from minute samples with minimal reagent consumption. This review discusses the principles underlying these advanced techniques, their benefits and limitations, and the specific contexts in which they excel. It also considers future trends, including further automation, integration with sequencing platforms, and the potential for point-of-care applications. By examining the current state of DNA extraction technology, this review aims to provide researchers and practitioners with a comprehensive guide to the best methods for their specific needs.

Switchable metal extractant integrated miniaturized 3D-printed device: A semi-online multi-metal separation system for matrix-free ICP-MS analysis

BackgroundThis study tackles the critical challenges in metal analysis by presenting an innovative miniaturized metal extraction device prototype. This device features a functional nanocomposite (FNC) enhanced 3D-printed polylactic acid (PLA) metal extractant (FNC@3D PLA). The research is motivated by the constraints of traditional solid-phase extraction (SPE) methods, specifically their limitations in handling competitive metal ion environments and matrix interference during inductively coupled plasma mass spectrometry (ICP-MS) analysis. The designed prototype aims to overcome these challenges and enhance the extraction efficiency of diverse metals. ResultsThe FNC, designed to incorporate various functional groups critical for metal ion extraction efficiency, was meticulously engineered through the reaction of acid-treated and delaminated graphitic carbon nitride nanosheets (Thiol-gCN NSs) with 3-mercaptopropyl trimethoxysilane (MPTMS). The competitive metal ion extraction efficiency of FNC@3D PLA was demonstrated, showcasing notable limit of detection values of 3.2 ± 0.7 ng mL−1 and 8.57 ± 3.05 ng mL−1 for Cu and Ag, respectively. Furthermore, the miniaturized 3D-printed metal-preconcentration setup incorporating FNC@3D PLA exhibited favorable intraday relative standard deviation (RSD) percentage (%) values ranging from 1.23 to 8.6 for both Cu and Ag. Interday RSD % between 1.41 and 8.14 were observed under spiked real urine sample conditions. The sustainability and robustness of the proposed approach were underscored by substantial recovery % values exhibited by FNC@3D PLA, even after eight consecutive regeneration processes. SignificanceThis study significantly contributes to the advancement of analytical methodologies by providing a reliable and efficient platform for metal extraction and preconcentration in practical metal analysis applications. Developed FNC@3D PLA system demonstrates its potential to address the challenges associated with SPE in metal analysis, especially in complex sample matrices. We believe implications of this research can be extended to various fields, from environmental monitoring to clinical diagnostics, where accurate and reliable metal analysis is paramount.

Direct Extraction and Determination of Free Nicotine in Cigarette Smoke.

The accurate determination of the free nicotine content in cigarette smoke is crucial for assessing cigarette quality, studying harm and addiction, and reducing tar levels. Currently, the determination of free nicotine in tobacco products primarily relies on methods such as pH calculation, nuclear magnetic resonance (NMR) spectroscopy, headspace solid-phase microextraction (HS-SPME), and traditional solvent extraction. However, these methods have limitations that restrict their widespread application. In this study, the free nicotine in cigarette smoke was directly extracted by using cyclohexane according to the traditional solvent extraction method and detected via gas chromatography-mass spectrometry. Compared with the traditional two-phase solvent extraction, our experimental method is easy to execute and eliminates the influence of aqueous solutions on the original distribution of nicotine in cigarette smoke particulate matter. Furthermore, the presence of protonated nicotine in tobacco does not affect the determination. Compared with HS-SPME and NMR spectroscopy, our approach, which involves solvent extraction followed by chromatographic separation and instrumental detection, offers simplicity, improved precision, better detection limits, and reduced interference during the instrumental detection stage. The standard addition recoveries in the conducted experiment ranged from 96.2% to 102.5%. The limit of detection was 2.8 μg/cig, and the correlation coefficient (R2) for the quadratic regression of the standard curve exceeded 0.999. The relative standard deviation for parallel samples was between 1.7% and 3.4% (n = 5), fully meeting the requirements for the determination of free nicotine in cigarette smoke. Analysis of cigarette samples from 38 commercially available brands revealed that the content of free nicotine ranged from 0.376 to 0.716 mg/cig, with an average of 0.540 mg/cig, and free nicotine accounted for 39.1%-88.8% of the total nicotine content.

Optimization of Extraction Conditions of Bioactive Compounds From Kurdistan Species Urtica dioica.

Phytotherapeutics derived from medicinal plants treat various illnesses, including viral infections such as SARS, MERS, and SARSCoV-2, as well as bacterial and fungal diseases. It highlights ongoing research into the chemical compositions of plant components for developing new drugs, with a particular emphasis on anti-cytotoxic agents for anticancer drugs. Traditional extraction methods have limitations, leading to the exploration of environmentally friendly technologies such as ultrasound-assisted, supercritical fluid, microwave-assisted, and accelerated solvent extraction. The paragraph concludes by stating the aim of a specific study to optimize extraction conditions of bioactive compounds from Urtica dioica in Kurdistan, comparing conventional and non-conventional extraction methods, solvents, and extraction times. The study was conducted between June 2022 and August 2022, fresh leaves and stems of U. dioica plant were collected and sequentially underwent four extraction methods (maceration, Soxhlet, ultrasound-assisted extraction (UAE), and microwave-assisted extraction (MAE) by using petroleum ether, chloroform, ethanol, and distilled-water as solvents. The results highlighted significant variations in the yields of bioactive compounds based on the extraction method, solvent type, and duration. Among conventional methods, Soxhlet was the most powerful method and had the most extraction yields, while maceration had the lowest yields. The modern techniques surpassed the conventional methods by producing high extraction yields within a shorter time (a few minutes) and using a lesser amount of solvent. Consequently, UAE and MAE emerge as the most efficient techniques. Hence, MAE effectively produced the highest extraction yields and is considered the preferred technique. The choice of solvents significantly influenced the extraction yields, with ethanol consistently emerging as an effective solvent across various extraction methods. In contrast, petroleum ether demonstrated the lowest efficacy as a solvent. Furthermore, the results unveiled the impact of extraction time on yields, indicating a correlation between increased time and extraction yield in certain cases. Extraction is a very critical step in the study of medicinal plants. The amount of extracted compounds is significantly affected by the extraction method, solvent, and time. Ethanol stands out as the most effective solvent, producing the highest yields of bioactive compounds, while petroleum ether yields the least. Additionally, extraction yield shows a direct relation with extraction time. Soxhlet being the most powerful among conventional methods and maceration yields the least. Modern techniques, particularly UAE and MAE, surpass conventional methods by achieving high yields in shorter times with less solvent. MAE, in particular, offers advantages such as shortened extraction time, increased efficiency, reduced labor, and enhanced selectivity, making it the preferred method for extracting bioactive compounds from aerial parts U. dioica.

Natural deep eutectic solvents: promising agents for extracting substances from plant materials

There is a growing trend to replace synthetic products or additives with natural alternatives. Historical experience and developments in human health science demonstrate the adverse effects of xenobiotics on human health and metabolism. The shift toward natural products is essential for enhancing vitality, social activity, and overall quality of life, while preventing adverse events and diseases. Industries such as pharmaceutical, cosmetic, and food are increasingly turning to natural and biologically active chemicals isolated from plants or microorganisms. However, the primary challenge lies in developing effective and eco-friendly methods for their isolation. Deep eutectic solvents are a mixture of two or more components with unique properties like low volatility, low melting points, ease of preparation, cost-effective starting materials, and minimal toxicity to humans. They have gained popularity as environmentally friendly agents for extracting biologically active substances from medicinal plants and as safe alternatives for various industries, including food and pharmaceuticals. Traditional organic solvent extraction methods have several drawbacks, including long extraction times, safety concerns, environmental damage, high cost, and the need for large solvent volumes. This review summarizes research progress on the benefits of using deep eutectic solvents for extracting bioactive compounds, including phenolic acid, flavonoids, isoflavones, catechins, polysaccharides, curcuminoids, proanthocyanidin, phycocyanin, gingerols, ginsenosides, anthocyanins, rutin, and chlorogenic acids. Additionally, it examines the biological activity of these extracts, including antioxidant, antibacterial, and antitumor properties.

Tree parameter extraction method based on new remote sensing technology and terrestrial laser scanning technology

Ground LiDAR is a terrestrial LiDAR system that is often used for terrain and geomorphic mapping. Ground-based LiDAR can be used to collect more local and short-range data, making it ideal for mapping smaller areas with high precision. In order to solve the rapid extraction of tree parameters in the national public welfare forest survey, the ground-based LIDAR was used to obtain the point cloud of trees, and the point cloud data was registered, denoised, normalized, sliced, parameter extracted, etc., and the parameters of individual trees in the forest were obtained. The Bland-Altman consistency test is used to test whether the method of extracting tree parameters from point clouds is consistent with the traditional measurement method. The experimental results show that the point cloud data obtained by the ground-based LIDAR can quickly, conveniently and accurately extract the tree parameters, which is consistent with the traditional tree parameter extraction method, and has the advantages than the traditional tree parameter measurement, such as point cloud, image and traceability. It has a unique advantage in establishing a tree database. It is suggested that LIDAR should be used for forest survey in the future.

Improvement of foaming and emulsifying properties of SPI via weak-base synchronized membrane separation: Insight from tuning the interfacial flexibility

Preparation of soy proteins with high functionalities is significant for enhancing their utilization. Here, we propose a green and mild weak-base synchronous membrane separation (MS) technique to enrich soy protein isolate (SPI), with two traditional extraction methods, alkali extraction-isoelectric point precipitation (AE) and salt extraction (SE), as the control. The interfacial properties of SPI were investigated by Cryo-SEM, interfacial rheology (dilatational and shear), and Langmuir-Blodgett films combined with AFM. Results showed that MS extraction significantly reduced the disulfide bond content of SPI, which induced partial depolymerization of the protein and exposed the internal hydrophobic groups, and thus leading to the reduction of particle size as well as the increase of surface hydrophobicity (H0). These structural properties conferred MS-SPI with a faster interfacial adsorption rate, followed by easier unfolding and rearrangement after anchoring to the interface. Finally, the flexible and denser interfacial layers with high mechanical properties were formed through strong in-plane interactions among SPI, which could effectively prevent the rupture of oil droplets and air bubbles. Therefore, the emulsification stability and foamability of MS-SPI could be maintained for up to ∼252 min and ∼232%, which was at least 2.4 times and 1.2 times than that of AE-SPI and SE-SPI, respectively. These findings demonstrated that MS extraction was an effective approach for broadening the applications of soy proteins in the food ingredient field.

The ringed residual u-net with non-natural regions feature for image splicing forgery detection and localization

Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extraction methods and detection methods based on convolutional neural network (CNN) is poor, and the information provided by the features extracted from the network is limited and single. In this paper, an end-to-end ringed residual U-Net is proposed to detect image splicing forgery by blending features of non-natural regions. Some regions with significant differences from the image background are defined as non-natural regions(such as the irregular border at the splicing of images). In this paper, a feature enhancement module for non-natural regions is constructed, which the image through the pooling of four different scales, and these features are then combined with the original image and input to the backbone network for processing, aiming to highlight regions of the image that differ significantly from the background. Therefore, after adding the feature enhancement module for non-natural regions to the end-to-end ring residual U-Net, more attention will be paid to the tampering regions in the feature extraction stage, image manipulation detection and localization will also become more accurate. Compared with some mainstream methods, this method achieves better performance on the three standard datasets(CASIA2.0, NIST2016, COLUMBIA). In addition, it has excellent robustness under JPEG compression attack and noise corruption attack.

Isolation and chemical characterization of lignocellulosic fiber from Pueraria montana using Box-Behnken design for weed management

The huge demand for natural fibers necessitates the search for non-traditional bioresources including invasive species which are deteriorating the ecosystem and biodiversity. The study aims to utilize Pueraria montana weed for the extraction of lignocellulosic fiber using both traditional (water retting) and chemical extraction methods to determine the better extraction method. Chemically extracted fiber showed 17.09 g/tex bundle strength whereas water-extracted fiber showed 11.7 g/tex bundle strength. Therefore, chemical extraction method was chosen for fiber isolation by optimization of reaction conditions using Box Behnken Design. Based on the design, optimal conditions obtained were 1 % w/v NaOH, 0.75 % v/v H2O2, and 3 days retting time. Solid-state NMR illustrated the breakdown of hemicellulose linkages at 25.89 ppm. FTIR revealed the disappearance of C=O groups of hemicellulose at 1742 cm−1. TGA demonstrated thermal stability of chemically treated fiber up to 220 °C and activation energy of 60.122 KJ/mol. XRD evidenced that chemically extracted fiber has a crystallinity index of 71.1 % and a crystal size of 2 nm. Thus P. montana weed holds potential for the isolation of natural fiber as its chemical composition and properties are comparable to commercial lignocellulosic fibers. The study exemplifies the transformation of weed to a bioresource of natural fibers.

Unveiling the mechanisms for the development of rosehip-based dermatological products: an updated review.

Rosa spp., commonly known as rosehips, are wild plants that have traditionally been employed as herbal remedies for the treatment of a wide range of disorders. Rosehip is a storehouse of vitamins, including A, B complex, C, and E. Among phytonutrients, vitamin C is found in the highest amount. As rosehips contain significant levels of vitamin C, they are perfect candidates for the development of skincare formulations that can be effectively used in the treatment of different skin disorders (i.e., scarring, anti-aging, hyperpigmentation, wrinkles, melasma, and atopic dermatitis). This research focuses on the vitamin C content of several Rosa sp. by their botanical and geographic origins, which according to research studies are in the following order: R. rugosa > R. montana > R. canina > R. dumalis, with lower levels in R. villosa and R. arvensis, respectively. Among rosehip species, R. canina is the most extensively studied species which also displays significant amounts of bioactive compounds, but also antioxidant, and antimicrobial activities (e.g., against Propionibacterium acnes, Staphylococcus aureus, S, epidermis, and S. haemolyticus). The investigation also highlights the use of rosehip extracts and oils to minimise the harmful effects of acne, which primarily affects teenagers in terms of their physical appearance (e.g., scarring, hyperpigmentation, imperfections), as well as their moral character (e.g., low self-confidence, bullying). Additionally, for higher vitamin C content from various rosehip species, the traditional (i.e., infusion, maceration, Soxhlet extraction) and contemporary extraction methods (i.e., supercritical fluid extraction, microwave-assisted, ultrasonic-assisted, and enzyme-assisted extractions) are highlighted, finally choosing the best extraction method for increased bioactive compounds, with emphasis on vitamin C content. Consequently, the current research focuses on assessing the potential of rosehip extracts as medicinal agents against various skin conditions, and the use of rosehip concentrations in skincare formulations (such as toner, serum, lotion, and sunscreen). Up-to-date studies have revealed that rosehip extracts are perfect candidates as topical application products in the form of nanoemulsions. Extensive in vivo studies have revealed that rosehip extracts also exhibit specific activities against multiple skin disorders (i.e., wound healing, collagen synthesis, atopic dermatitis, melasma, and anti-aging effects). Overall, with multiple dermatological actions and efficacies, rosehip extracts and oils are promising agents that require a thorough investigation of their functioning processes to enable their safe use in the skincare industry.

Seizure detection via deterministic learning feature extraction

Epileptic seizures have a significant impact on the well-being of a large number of individuals worldwide. Utilizing electroencephalographic (EEG) signals for automatic seizure detection proves to be a valuable solution. However, dealing with raw EEG signals is inherently complex, necessitating a preliminary step of feature extraction prior to detection. Traditional feature extraction methods often amalgamate various types of features for seizure detection, as each type typically captures specific properties. In contrast, this paper focuses on detecting seizures by analyzing the system dynamics. The proposed Deterministic Learning Feature Extraction (DLFE) method extracts a single type of nonlinear dynamical feature rooted in the EEG system dynamics. DLFE employs deterministic learning to discern the inherent system dynamics of the EEG under both seizure and normal conditions. Through the feature extraction process, the infinite-dimensional system dynamics are transformed into feature vectors, exhibiting distinct distributions in seizure and normal states. This disparity can be effectively utilized for classification using standard classifiers. The performance of the proposed seizure detection method was assessed using the CHB-MIT and Bonn datasets. The average classification accuracy was found to be 98.63% with a specificity of 99.19% and a sensitivity of 98.06% on CHB-MIT dataset. Compared with the latest similar methods, the accuracy, specificity and sensitivity are improved by 0.31%, 0.21% and 0.05% respectively. Moreover, the performance was achieved with the short-time interval EEG signals within a few channels. The average classification accuracy was found to be 99.90% with a 0.22% improvement on Bonn dataset, which indicates the good generalization performance.

Evaluation of Yield and Physicochemical Quality of Pentadesma butyracea Butter Obtained by Different Traditional Extraction Methods in Ghana.

Pentadesma butyracea seed butter or fat (PBSB) is a vegetable fat extracted from the seeds of the P. butyracea plant. The butter has potential use in the food, pharmaceutical, and cosmetic industries. The study investigated traditional PBSB processing methods in Ghana and evaluated their effects on yield, physicochemical parameters, and fatty acid composition. Four traditional extraction methods were identified and evaluated, and these methods are direct wet extraction of PBSB from a fresh mixture of the seed paste and water (DEW); wet extraction of PBSB from a 12-hour fermented mixture of the seed paste and water (FWO); direct wet extraction of PBSB from a fresh mixture of the seed paste, salt, and water (DES); and wet extraction of PBSB from a 12-hour fermented mixture of the seed paste, salt, and water (FSO). Results of physicochemical properties of the PBSB samples showed moisture content of 0.06-0.07%, free fatty acid of 1.38-2.43%, iodine value of 56.50-56.85 Wijs, peroxide value of 5.58-8.52 mEq/kg, relative density of 0.91, refractive index of 1.462-1.464, percent impurities of 0.015-0.017%, saponification value of 165.57-178.02 mg KOH/g, and percent unsaponifiable matter of 2.60-3.18%. The PBSB yield varied in the range of 21.68-26.97%, with the highest average butter yield observed for FWO. Seventeen fatty acids were characterized in the PBSB samples, and they included ten saturated fatty acids, five monounsaturated fatty acids, and two polyunsaturated fatty acids. Key fatty acids found in the PBSB samples were oleic acid (51.21-51.31%), stearic acid (43.22-43.33%), palmitic acid (2.91-3.07%), linoleic acid (0.49-0.51%), linolenic acid (0.12-0.20%), and arachidic acid (0.14-0.15%). PBSB samples produced by the various traditional extraction methods in Ghana recorded similar physicochemical characteristics as unrefined shea butter per the Regional Standard for Unrefined Shea Butter (CXS 325R-2017) as well as Cook Brand Margarine, a common commercial baking fat, and thus, their potential food application such as an alternative shortening/ingredient could be explored in a future study.

Identification of Escherichia coli multidrug resistance transporters involved in anthocyanin biosynthesis.

The anthocyanin compound cyanidin 3-O-glucoside (C3G) is a natural pigment widely used in food and nutraceutical industries. Its microbial synthesis by E. coli is a promising alternative to the traditional extraction methods. However, part of the synthesized C3G accumulates in the cytoplasm, thus potentially causing growth inhibition and product degradation. Therefore, it is necessary to enhance C3G secretion via exploration of native transporters facilitating C3G export. In this study, we report the screening and verification of native multidrug resistance transporters from 40 candidates in E. coli that can improve the extracellular C3G production when using catechin as the substrate. Overexpression of single transporter genes including fsr, yebQ, ynfM, mdlAB, and emrKY were found to increase C3G production by 0.5- to 4.8-fold. Genetic studies indicated that mdlAB and emrKY are vital transporters in the secretion of C3G. Our study reveals a set of new multidrug resistance transporters for the improvement of microbial biosynthesis of C3G and other anthocyanins.

Machine learning based detection of T–wave alternans in real ambulatory conditions

Background and objectiveT-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis. However, TWA detection in ambulatory settings remains a challenge due to the absence of standardized evaluation metrics and detection thresholds. MethodsIn this work we use traditional TWA analysis signal processing-based methods for feature extraction, and two machine learning (ML) methods, namely, K–nearest–neighbor (KNN) and random forest (RF), for TWA detection, addressing hyper–parameter tuning and feature selection. The final goal is the detection in ambulatory recordings of short, non-sustained and sparse TWA events. ResultsWe train ML methods to detect a wide variety of alternant voltage from 20 to 100 μV, i.e., ranging from non–visible micro–alternans to TWA of higher amplitudes, to recognize a wide range in concordance to risk stratification. In classification, RF outperforms significantly the recall in comparison with the signal processing methods, at the expense of a small lost in precision. Despite ambulatory detection stands for an imbalanced category context, the trained ML systems always outperform signal processing methods. ConclusionsWe propose a comprehensive integration of multiple variables inspired by TWA signal processing methods to fed learning-based methods. ML models consistently outperform the best signal processing methods, yielding superior recall scores.

REACT: Relation Extraction Method Based on Entity Attention Network and Cascade Binary Tagging Framework

With the development of the Internet, vast amounts of text information are being generated constantly. Methods for extracting the valuable parts from this information have become an important research field. Relation extraction aims to identify entities and the relations between them from text, helping computers better understand textual information. Currently, the field of relation extraction faces various challenges, particularly in addressing the relation overlapping problem. The main difficulties are as follows: (1) Traditional methods of relation extraction have limitations and lack the ability to handle the relation overlapping problem, requiring a redesign. (2) Relation extraction models are easily disturbed by noise from words with weak relevance to the relation extraction task, leading to difficulties in correctly identifying entities and their relations. In this paper, we propose the Relation extraction method based on the Entity Attention network and Cascade binary Tagging framework (REACT). We decompose the relation extraction task into two subtasks: head entity identification and tail entity and relation identification. REACT first identifies the head entity and then identifies all possible tail entities that can be paired with the head entity, as well as all possible relations. With this architecture, the model can handle the relation overlapping problem. In order to reduce the interference of words in the text that are not related to the head entity or relation extraction task and improve the accuracy of identifying the tail entities and relations, we designed an entity attention network. To demonstrate the effectiveness of REACT, we construct a high-quality Chinese dataset and conduct a large number of experiments on this dataset. The experimental results fully confirm the effectiveness of REACT, showing its significant advantages in handling the relation overlapping problem compared to current other methods.

Optimizing the extraction parameters of phenolics and flavonoids from Bouea macrophylla Griff leaves using ultrasound-assisted extraction and evaluating their antioxidant abilities

Bouea macrophylla Griff is a plant species that primarily thrives in the wet tropical regions of Southeast Asia, such as Indonesia, Myanmar, Malaysia, and Thailand. B. macrophylla leaves contain a potential source of phenolics and flavonoids that exhibit biological activities. This research aimed to maximize the total phenolic and flavonoid contents of B. macrophylla Griff leaves extract using an ultrasound-assisted extraction (UAE) and response surface methodology (RSM) with a four-factor and three-level Box Behnken design (BBD) for optimizing the extraction parameters. The values of four independent variables, namely ethanol concentration (X1), extraction temperature (X2), sample weight-to-solvent volume ratio (X3), and extraction time (X4), were investigated and determined through single-factor analysis experiments, and their optimal values were found to be 47% ethanol concentration, 46°C extraction temperature, 1/69 (g/ml) sample to solvent ratio, and 69 minutes of extraction time. Under the optimized conditions, the total phenolic and flavonoid contents of B. macrophylla Griff leaves were determined to be 15.83 ± 0.05 mg GAE/g and 88.37 ± 0.23 mg QE/g, respectively. These experimental values were found to be close with the expected values. Moreover, this study revealed that UAE resulted in significantly higher antioxidant activity in B. macrophylla Griff compared to traditional extraction methods. The findings suggest that the extract obtained from its leaves through the UAE could be a potential source for various cosmetic or medicinal applications.

STIRUnet: SwinTransformer and inverted residual convolution embedding in unet for Sea–Land segmentation

Extraction of coastline from optical remote sensing images is of paramount importance for coastal zone management, erosion monitoring, and intelligent ocean construction. However, nearshore marine environment complexity presents a challenge when capturing small-scale and detailed information regarding coastlines. Furthermore, the presence of numerous tidal flats, suspended sediments, and coastal biological communities exacerbates the reduction in segmentation accuracy, which is particularly noticeable in medium–high-resolution remote sensing image segmentation tasks. Most previous related studies, based primarily on convolutional neural networks (CNNs) or traditional feature extraction methods, faced challenges in detailed pixel-level refinement and lacked comprehensive understanding of the studied images. Therefore, we proposed a new U-shaped deep learning model (STIRUnet) that combines the excellent global modeling ability of SwinTransformer with an improved CNN using an inverted residual module. The proposed method has the capability of global supervised feature learning and layer-by-layer feature extraction, and we conducted sea–land segmentation experiments using GF-HNCD and BSD remote sensing image datasets to validate the performance of the proposed model. The results indicate the following: 1) suspended sediments and coastal biological communities are major contributors to coastline blurring, and 2) the recovery of minute features (e.g., narrow watercourses and microscale artificial structures) effectively enhances edge details and leads to more realistic segmentation outcomes. The findings of this study are highly important in relation of accurate extraction of sea–land information in complex marine environments, and they offer novel insights regarding mixed-pixel identification.

A new method of ship detection in complex background of SAR images based on YOLO

Synthetic Aperture Radar (SAR) is a remote sensing technology capable of high-resolution imaging of the ground surface under any weather and light conditions. SAR image ship detection is a technique that uses the ship target features in SAR images to identify and locate ships, which is of great value for applications in the fields of maritime traffic management, marine environment monitoring, and maritime security and defence. However, SAR image ship detection also faces some challenges, such as complex sea surface background, low signal-to-noise ratio, and the diversity and density of ship targets, which lead to the shortcomings of traditional feature extraction and classifier-based detection methods in terms of accuracy and efficiency. To address these challenges, this paper proposes a new method for ship detection in SAR images based on YOLOv7. When dealing with SAR images containing clutter interference and complex backgrounds, many models face the dual challenge of insufficient real-time and accuracy. To address this problem, this study proposes a new PCAN aggregation network, which firstly introduces the SimAM attention mechanism, which significantly enhances the model's ability to identify the differences between ships and background clutter, further improves the ability of focusing on target features, and effectively It further improves the ability to focus on target features, effectively reduces the interference of background noise, and improves the detection accuracy in complex scenes. At the same time, the PConv strategy is added to further lighten the model structure, thus accelerating the computation process and improving the inference efficiency. We trained and tested the model on the SAR-Ship-Dataset dataset, and achieved an accuracy of 91.1%, an F1 score of 92.9, and an FPS of 46, demonstrating its excellent detection capability. By comparing and analysing the model with several classical and cutting-edge methods in the current field, the proposed model in this study demonstrates obvious performance advantages, and in addition, through ablation experiments, we further confirm the effectiveness and contribution of PCAN.

An Object-Based Approach to Extract Aquaculture Ponds with 10-Meter Resolution Sentinel-2 Images: A Case Study of Wenchang City in Hainan Province

Coastal aquaculture has made an important contribution to global food security and the economic development of coastal zones in recent decades. However, it has also damaged these coastal zones’ ecosystems. Moreover, coastal aquaculture is poised to play a key role in the achievement of Sustainable Development Goals (SDGs). Consequently, extracting aquaculture has become crucial and valuable. However, due to the limitations of remote sensing image spatial resolution and traditional extraction methods, most research studies focus on aquaculture areas containing dikes rather than individually separable aquaculture ponds (ISAPs). This is not an accurate estimation of these aquaculture areas’ true size. In our study, we propose a rapid and effective object-based method of extracting ISAPs. We chose multi-scale segmentation to generate semantically meaningful image objects for various types of land cover, and then built a decision tree classifier according to the unique features of ISAPs. The results show that our method can remove small rivers and other easily confused features, which has thus far been difficult to accomplish with conventional methods. We obtained an overall precision value of 85.61% with a recall of 84.04%; compared to the support vector machine’s (SVM) overall precision value of 78.85% and recall rate of 61.21%, our method demonstrates greater accuracy and efficiency. We used this method to test the transferability of the algorithm to nearby areas, and the obtained accuracy exceeded 80%. The method proposed in this study could provide a readily available solution for the simple and efficient extracting of ISAPs and shows high spatiotemporal transferability.

Characterizing low-grade refractory gold ores using automated mineralogy coupled with LA ICP-MS

Researchers have made notable progress in quantifying refractory components causing gold losses in traditional extraction methods. The potential for improving gold recoveries lies in targeted re-treatment of the 'invisible' gold fraction hosted within various ore minerals, even at low concentrations. This study presents a methodology for analysing the refractory components that cause gold losses in traditional extraction methods. The approach utilizes automated mineralogical techniques in conjunction with in-situ laser ablation inductively coupled plasma mass spectrometry (LA ICP-MS) to characterize low-grade refractory ores and assess gold deportment. The methodology combines pre-concentration gravity separation, traditional chemical and mineral techniques, and LA ICP-MS to generate comprehensive datasets. To demonstrate the practical application of the methodology, a case study was conducted on historical tailings material from Witwatersrand's Evander Goldfield in South Africa. The case study aimed to determine the mineralogical distribution of gold that cannot be extracted through cyanidation. The results indicated that invisible gold is primarily hosted in pyrite (with an average concentration of 10.21 ppm representing 32 % of total gold), other sulphides (with an average concentration of 1.29 ppm representing 0.42 % of total gold), and oxides (with an average concentration of 0.50 ppm representing 1 % of total gold). The remaining gold is present in discrete fine cyanide amenable phases (with 28 % deportment) and silicate-hosted phases (with 39 % deportment). Environmental monitoring of the Witwatersrand tailings material revealed a net “acid-producing” nature, with high concentrations of heavy metals (such as As, Cu, Pb, Mn, Hg, and Ni) associated with sulphides (with over 78 % deportment). The deportment information obtained through this approach can optimize ore processing strategies and provide valuable insights for future remediation plans. By characterizing the refractory components causing gold losses and understanding the distribution of gold within the ore, this methodology offers the potential to enhance gold recovery and improve the efficiency of ore processing operations.