Extraction Method Research Articles

Variation in the metagenomic analysis of fecal microbiome composition calls for a standardized operating approach.

The reproducibility in microbiome studies is limited due to the lack of one gold-standard operating procedure. The aim of this study was to examine the impact of protocol variations on microbiome composition using metagenomic data sets from a single center. We assessed the variation in a data set consisted of 2,722 subjects, including 9 subcohorts harboring healthy subjects and patients with various disorders, such as inflammatory bowel disease, colorectal cancer, and type 2 diabetes. Two different DNA extraction kits, with or without lyticase, and two sample storage methods were compared. Our results indicated that DNA extraction had the largest impact on gut microbiota diversity among all host factors and sample operating procedures. Healthy subjects matched by age, body mass index, and sample operating methods exhibited reduced, yet significant differences (PERMANOVA, P < 0.05) in gut microbiota composition across studies. The variations contributed by DNA extraction were primarily driven by different recovery efficiency of gram-positive bacteria, e.g., phyla Firmicutes and Actinobacteria. This was further confirmed by a parallel comparison of fecal samples from five healthy subjects and a standard mock community. In addition, the DNA extraction method influenced DNA biomass, quality, and the detection of specific lineage-associated diseases. Sample operating approach and batch effects should be considered for cohorts with large sample size or longitudinal cohorts to ensure that source data were appropriately generated and analyzed. Comparison between samples processed with inconsistent methods should be dealt with caution. This study will promote the establishment of a sample operating standard to enhance our understanding of microbiome and translating in clinical practice.IMPORTANCEThe reproducibility of human gut microbiome studies has been suboptimal across cohorts and study design choices. One possible reason for the disagreement is the introduction of systemic biases due to differences in methodologies. In our study, we utilized microbial metagenomic data sets from 2,722 fecal samples generated from a single research center to examine the extent to which sample storage and DNA extraction influence the quantification of microbial composition and compared this variable with other sources of technical and biological variation. Our research highlights the impact of DNA extraction methods when analyzing microbiome data and suggests that the microbiome profile may be influenced by differences in the extraction efficiency of bacterial species. With metagenomics sequencing being increasingly used in clinical biology, our findings provide insight into the challenges using metagenomics sequencing in clinical diagnostics, where the detection of certain species and its abundance relative to a "healthy reference" is key.

The influence of extraction methods on the properties of tropical sisal plant fibers: Agave sisalana

The influence of extraction methods on the properties of tropical sisal plant fibers: Agave sisalana

Extraction and In Vitro and In Vivo Antioxidant Activity Evaluation of Polyphenols from Lychee Pericarp

Background Finding a new promising plant antioxidant to combat or prevent cardiovascular disease remains a major challenge for researchers. Lychee pericarp, which includes a variety of polyphenols, is a prospective source of botanical antioxidants. Purpose In response to this issue, we assessed the effect of lychee bark extract on oxygen radical scavenging capacity and separate vascular protective ability in vivo. Methods In the current study, the lychee pericarp enrichment product (RLP) was obtained by optimizing an extraction method and separated using modern separation methods. The structure of the isolated compounds was assessed using 1H-NMR, 13C-NMR, and electrospray ionization-mass spectrometry, and the content of polyphenols obtained from the extract was quantified using the area normalization method. In addition, the capacity of RLP to scavenge oxygen radicals in vitro was assessed using the DPPH (1,1-diphenyl-2-picrylhydrazyl) and T-AOC (total antioxidant capacity) techniques. Furthermore, an isoprenaline hydrochloride (ISO)-induced rat model of acute myocardial ischemia was established, and four biochemical indicators were used to assess the effect of lychee bark extract on oxygen radical scavenging capacity and cardiovascular protective ability in vivo. Results RLP extraction produced 11 chemicals, 6 of which were structurally assessed. Quantitative analysis revealed that epicatechin, PC-C(epicatechin-(4β↓8,2β↓O↓7)-epicatechin-(4β↓8)-epicatechin), type A proanthocyanidin trimer, proanthocyanidin A2, and type B proanthocyanidin dimer were the most abundant polyphenols. The in vitro DPPH and T-AOC findings revealed that RLP exhibited strong free radical scavenging activity, approximately 1.5-fold that of vitamin C. The four biochemical indicators confirmed the antioxidant activity and cardiovascular protective ability of RLP in vivo. Conclusion RLP has good in vivo and in vitro antioxidant activity, confirming lychee pericarp as a prospective source of botanical antioxidants.

Deep Plug-and-Play Non-Iterative Cluster for 3D Global Feature Extraction

Efficient and accurate point cloud feature extraction is crucial for critical tasks such as 3D recognition and semantic segmentation. However, existing global feature extraction methods for 3D data often require designing different models for different input types (point clouds, voxels, and maps). This article proposes an efficient plug-and-play non-iterative clustering method (NICM) to establish a unified point cloud global feature extraction paradigm suitable for any input type to solve the above problems. The core idea of the NICM is to construct the connection between a single point and other global points based only on the cosine similarity between center points to achieve global feature extraction, which has linear complexity characteristics and can be combined with any existing feature extraction model. Additionally, to better integrate the features extracted by NICM and the original model, this article designs an adaptive feature fusion module is designed based on the gate unit, which retains similar features and effectively fuses dissimilar features based on their importance to downstream tasks. We have applied our method to downstream tasks such as point cloud recognition, part segmentation and scene segmentation. Sufficient experiments have proven that our method can provide comprehensive and robust features for the original model, and effectively improve the performance of downstream tasks.

Optimization of extraction for efficient recovery of kenaf seed protein isolates: evaluation of physicochemical and techno-functional characteristics.

Kenaf seeds are a rich source of protein; however, finding the best extraction method is crucial to obtaining high-quality protein from these underutilized seeds. This research devised an optimized extraction process for best recovery of kenaf seeds protein using response surface methodology. The key parameters affecting the yield and protein content were optimized, including extraction pH (2-11), seed:water ratio (5:1-50:1), temperature (30-90 °C), and duration (20-360 min). The physicochemical and techno-functional properties of kenaf seed protein isolates (KSPIs) were examined. A maximum protein yield of 12.05 g/100 g with purity level 91.94 g/100 g was obtained using an optimized extraction with pH 11.0, seed:water ratio 50:1, 360 min duration, and temperature 50 °C. The oil and water retention capacities of KSPI were 1.14 mL g-1 and 1.37 mL g-1 respectively. After 30 min at pH 7, KSPIs demonstrated remarkable emulsion capacity (83.12%) and stability (75.63%), along with high foaming capacity (106%) and stability (18.3%). As per high-performance liquid chromatography analysis, arginine, glutamic acid, leucine, phenylalanine, and lysine were the most abundant amino acids detected in KPSIs. The KSPIs' globular protein structure was successfully verified using analytical approaches, including Fourier transform infrared spectroscopy, protein fraction ratios, and differential scanning calorimetry. Sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis revealed that KPSI has a molecular weight distribution ranging from 10 kDa to 50 kDa. The results of this study support the application of the proposed response-surface-methodology-optimized extraction method for efficient recovery of high-quality kenaf seed proteins that meet the necessary physicochemical and techno-functional requirements. © 2024 Society of Chemical Industry.

Mastering Snow Analysis: Enhancing Sampling Techniques and Introducing ACF Extraction Method with Applications in Svalbard.

Semi-volatile organic contaminants (SVOCs) are known for their tendency to evaporate from source regions and undergo atmospheric transport to distant areas. Cold condensation intensifies dry deposition, particle deposition, and scavenging by snow and rain, allowing SVOCs to move from the atmosphere into terrestrial and aquatic ecosystems in alpine and polar regions. However, no standardized methods exist for the sampling, laboratory processing, and instrumental analysis of persistent organic pollutants (POPs) in snow. The lack of reference methods makes these steps highly variable and prone to errors. This study critically reviews the existing literature to highlight the key challenges in the sampling phase, aiming to develop a reliable, consistent, and easily reproducible technique. The goal is to simplify this crucial step of the analysis, allowing data to be shared more effectively through standardized methods, minimizing errors. Additionally, an innovative method for laboratory processing is introduced, which uses activated carbon fibers (ACFs) as adsorbents, streamlining the analysis process. The extraction method is applied to analyze polychlorobiphenyls (PCBs) and chlorinated pesticides (α-HCH, γ-HCH, p,p'-DDE, o,p'-DDT, HCB, and PeCB). The entire procedure, from sampling to instrumental analysis, is subsequently tested on snow samples collected on the Svalbard Islands. To validate the efficiency of the new extraction system, quality control measures based on the EPA methods 1668B and 1699 for aqueous methods are employed. This study presents a new, reliable method that covers both sampling and lab analysis, tailored for detecting POPs in snow.

MAR-YOLOv9: A multi-dataset object detection method for agricultural fields based on YOLOv9

With the development of deep learning technology, object detection has been widely applied in various fields. However, in cross-dataset object detection, conventional deep learning models often face performance degradation issues. This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. Existing technologies still face performance bottlenecks when dealing with diverse scenarios. To address these issues, this study proposes a lightweight, cross-dataset enhanced object detection method for the agricultural domain based on YOLOv9, named Multi-Adapt Recognition-YOLOv9 (MAR-YOLOv9). The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. A more streamlined and lightweight Main Neck structure has been introduced, along with innovative methods for feature extraction, up-sampling, and Concat connection. The hybrid connection strategy allows the model to flexibly utilize features from different levels. This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. At the same time, the model size was reduced by 9.3%, and the number of model layers was decreased, reducing computational costs and storage requirements. Additionally, MAR-YOLOv9 demonstrated significant advantages in detecting complex agricultural images, providing an efficient, lightweight, and adaptable solution for object detection tasks in the agricultural field. The curated data and code can be accessed at the following link: https://github.com/YangxuWangamI/MAR-YOLOv9.

Diclofenac recovery by means of using Emulsion Liquid Membranes: A possible role of surfactant in the mass transfer process

Emulsion Liquid Membranes (ELM) are a useful method for drug extraction in aqueous solution, as they allow their recovery in a highly concentrated acceptor phase after passing through a membrane phase with a high surface area. The success of this process depends on system formulation and operation conditions. One of the most important aspects in the formulation is the addition of a surfactant that stabilizes the system, which can also participate in the mass transfer process. Recently, resistance to mass transfer caused by the use of polymeric surfactants, such as Abil® EM 90, has been reported. To verify this phenomenon observed in previous studies, the effect of various factors on the recovery of diclofenac (DCF) dissolved in an aqueous medium was analyzed using the ELM technique. The study concluded that using a membrane consisting of Abil® EM 90 (2 % w/V), trioctylamine (5 % w/V), and dodecane, along with a stripping phase formed by NaOH (0.10 mol/L), results in successful DCF removal. When emulsified at 15,000 rpm for 10 minutes at room temperature, it is possible to remove over 99 % of DCF after 15 minutes and recover 18.45 % after 30 min. Furthermore, it was determined that the action of the surfactant is the main cause of the resistance to the passage of DCF to the internal phase and its retention in the organic phase, as had already been observed. This is probably due to strong intermolecular interactions between Abil ® EM 90 and DCF and the formation of a complex and stable interfacial structure, similar to a hydrated bilayer, which favors the rigidity of the interface and keeps the drug trapped. A better understanding of the role surfactants play in the system is essential for the future design of more efficient and stable ELMs. This knowledge will enable the adaptation of the technology to a wider range of processes, including drug recovery. As a result, the maximum benefits of this technology can be fully realized.

Drug–drug interaction extraction based on multimodal feature fusion by Transformer and BiGRU

Understanding drug–drug interactions (DDIs) plays a vital role in the fields of drug disease treatment, drug development, preventing medical error, and controlling health care-costs. Extracting potential from biomedical corpora is a major complement of existing DDIs. Most existing DDI extraction (DDIE) methods do not consider the graph and structure of drug molecules, which can improve the performance of DDIE. Considering the different advantages of bi-directional gated recurrent units (BiGRU), Transformer, and attention mechanisms in DDIE tasks, a multimodal feature fusion model combining BiGRU and Transformer (BiGGT) is here constructed for DDIE. In BiGGT, the vector embeddings of medical corpora, drug molecule topology graphs, and structure are conducted by Word2vec, Mol2vec, and GCN, respectively. BiGRU and multi-head self-attention (MHSA) are integrated into Transformer to extract the local–global contextual DDIE features, which is important for DDIE. The extensive experiment results on the DDIExtraction 2013 shared task dataset show that the BiGGT-based DDIE method outperforms state-of-the-art DDIE approaches with a precision of 78.22%. BiGGT expands the application of multimodal deep learning in the field of multimodal DDIE.

Isolation of Cellulose Nanofibers From Sunflower Seed Husks via Formic Acid Hydrolysis

ABSTRACTNanocellulose has garnered great interest among researchers because of its biodegradability, renewability, biocompatibility, high strength and versatile functionality. The present study focuses on the development of an eco‐friendly, benign method to extract cellulose nanofibrils (CNFs) from waste sunflower husks (SFHs) using formic acid hydrolysis. Both CNFs and microcrystalline cellulose (MCC) were obtained in high yields from SFHs. The enhanced colloidal stability of CNFs was evident from the ζ potential values, which ranged from −11.8 to −19.6 mV. Varying yields of CNFs were obtained by differing reaction times, though all were found to be thermally stable. SEM and TEM were used for the morphological analysis. The removal of non‐cellulose constituents was confirmed by FTIR spectroscopy. The enhancement of crystallinity upon treatment was examined using XRD analysis. The thermal stability of the cellulose extracted was checked via TGA. The present method of extraction of CNFs and MCC from waste SFHs using formic acid could open a new way of bulk production of nanocellulose fibers for the manufacture of green, value‐added products in high‐end fields, including electronics, agriculture and biomedicine, among others.

Research progress of plant α-linolenic acid

α-Linolenic acid is a polyunsaturated fatty acid characterized by three double bonds and is recognized as one of the essential fatty acids for human health. It serves multiple critical functions, including nutritional supplementation, enhancement of brain development, and protection of nerve cells. In recent years, researchers globally have investigated natural plant sources abundant in α-linolenic acid and developed more efficient extraction methods, thereby significantly advancing the study of this fatty acid. This paper reviews the physiological effects and sources of plant-derived α-linolenic acid, summarizes the biosynthetic pathways, and outlines the primary extraction processes to provide the theoretical evidence for ongoing research and innovation in this field.

Pressurized liquid extraction of sulfite for quantitation by capillary zone electrophoresis.

Sulfite is an additive used in shrimp processing to prevent discoloration. However, sulfite can cause health issues for sensitive consumers, making its monitoring necessary. Determining sulfite concentrations is complex because the Monier-Williams reference method is laborious and has low analytical throughput. Additionally, new techniques are needed to stabilize sulfite during the extraction process because this analyte undergoes rapid oxidation. A new method involving extraction and derivatization of sulfite with formaldehyde through automated pressurized liquid extraction (PLE), followed by quantitation by capillary zone electrophoresis with diode-array detector in indirect mode, was developed and optimized using multivariate planning. The PLE procedure was compared to another solid-liquid extraction method. The new method successfully stabilized and extracted sulfite from shrimp in few steps with adequate precision (CV<3.8%), producing extracts that were stable for 10 days. Recovery was satisfactory (97%-99%), and the limits of detection (4.6mgkg-1) and quantitation (15.4mgkg-1) were suitable for the intended purpose.

A Robust Hybrid Approach for Malware Detection: Leveraging CNN and LSTM for Encrypted Traffic Analysis

The rapid growth in Internet usage and advancements in network technologies have escalated the risk of network attacks. As the adoption of encryption protocols increases, so does the difficulty in identifying malware within encrypted traffic. Malware represents a significant danger in cyberspace, as it compromises personal data and harms computer systems. Network attacks involve unauthorized access to networks, often aiming to disrupt or damage them, with potentially severe consequences. To counter these threats, researchers, developers, and security experts are constantly innovating new malware detection techniques. Recently, deep learning has gained traction in network security and intrusion detection systems (IDSs), with models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) showing promise in detecting malicious traffic. Despite these advancements, extracting relevant features from diverse malware types remains a challenge. Current solutions demand substantial computational resources and are often inefficient for large datasets. Additionally, existing image-based feature extraction methods consume significant resources. This study tackles these issues by employing a 1D CNN alongside LSTM for the detection and classification of encrypted malicious traffic. Using the Malware Analysis benchmark dataset, which consists of 42,797 malware and 1,079 goodware API call sequences, the proposed model achieved an accuracy of 99.2%, surpassing other state-of-the-art models

Treatment of Renal Diseases based on Zang-evil Fu-organ Extraction Method

This paper expounds the theoretical connotation and clinical application of TCM Zang-evil fu-organ extraction method in renal diseases, and briefly discusses the Zang-evil fu-organ extraction method from three aspects: bladder discharge, three-jiao discharge and intestinal discharge. It is clear that Zang-evil fu-organ extraction method has important clinical application value in renal diseases.

Remote Sensing Identification and Information Extraction Method of Glacial Debris Flow Based on Texture Variation Characteristics

The formation mechanism of glacial debris flows in alpine gorge mountain areas is complex, with varying characteristics across different regions. Due to the influence of mountain shadows and the accumulation and ablation of ice and snow, accurately identifying and rapidly extracting glacial debris flows using optical images remains challenging. This study utilizes the Random Forest method to develop a multi-feature spatiotemporal information extraction model based on Landsat-8 images and a glacial debris flow gully identification model. These models were applied to the Songzong–Tongmai section of the Sichuan–Tibet Highway to identify glacial debris flows. The results showed that (1) the multi-feature spatiotemporal extraction model effectively eliminated the interference of mountain shadows and ice–snow phase changes, resulting in a higher accuracy for identifying and extracting glacial debris flows in areas with significant information loss due to deep shadows. The total accuracy was 93.6%, which was 8.9% and 4.2% higher than that of the Neural Network and Support Vector Machine methods, respectively. (2) The accuracy of the glacial debris flow gully identification model achieved 92.6%. The proposed method can accurately and rapidly identify glacial debris flows in alpine gorge mountain areas, facilitating remote sensing dynamic monitoring. This approach reduces the damage caused by debris flows to both transportation and the environment, ensuring the safe passage of highways and promoting the sustainable development of the region.

Improved Early-Stage Maize Row Detection Using Unmanned Aerial Vehicle Imagery

Monitoring row centerlines during early growth stages is essential for effective production management. However, detection becomes more challenging due to weed interference and crop row intersection in images. This study proposed an enhanced Region of Interest (ROI)-based approach for detecting early-stage maize rows. It integrated a modified green vegetation index with a dual-threshold algorithm for background segmentation. The median filtering algorithm was also selected to effectively remove most noise points. Next, an improved ROI-based feature point extraction method was used to eliminate residual noises and extract feature points. Finally, the least square method was employed to fit the row centerlines. The detection accuracy of the proposed method was evaluated using the unmanned aerial vehicle (UAV) image data set containing both regular and intersecting crop rows. The average detection accuracy of the proposed approach was between 0.456° and 0.789° (the angle between the fitted centerline and the expert line), depending on whether crop rows were regular/intersecting. Compared to the Hough Transform (HT) algorithm, the results demonstrated that the proposed method achieved higher accuracy and robustness in detecting regular and intersecting crop rows. The proposed method in this study is helpful for refined agricultural management such as fertilization and irrigation. Additionally, it can detect the missing-seedling regions and replenish seedings in time to increase crop yields.

Characterization of Fresh, Deactivated, and Regenerated TS‐1 for Propylene Epoxidation: Investigating Surface Deactivation Species

AbstractTitanium silicalite‐1 (TS‐1) serves as an effective catalyst for propylene epoxidation in the hydrogen peroxide propylene oxide (HPPO) process, attracting wide attention from researchers and industry. Nonetheless, the TS‐1 catalyst experienced inevitable deactivation during industrial applications, which adversely impacts its lifespan. To enhance reaction stability of catalyst, the present work conducted a systematic investigation into the deactivation reason of the TS‐1 catalyst. The fresh, deactivated, and regenerated catalysts were characterized by using various analytical techniques to compare their physicochemical properties. The surface coke species of the deactivated catalysts were extracted by the Soxhlet extraction method and subsequently analyzed using GC–MS. Additionally, the pathways for by‐product formation were simulated through Gauss software. The results indicated that the so‐called coking deposits formed during the reaction predominantly consist of propylene glycol, ethers, and oligomers. These coke species covered the partial active sites on the catalyst surface, blocked the pores of the catalyst, and accordingly led to the deactivation of the catalyst, although the crystalline structure of the catalyst hardly changed after deactivation. These results provide a theoretical basis and novel insights for the rational design and development of efficient catalysts for the HPPO process.

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.

Sericin Protein: Structure, Properties, and Applications

Silk sericin, the glue protein binding fibroin fibers together, is present in the Bombyx mori silkworms’ cocoons. In recent years, sericin has gained attention for its wide range of properties and possible opportunities for various applications, as evidenced by the meta-analysis conducted in this review. Sericin extraction methods have evolved over the years to become more efficient and environmentally friendly, preserving its structure. Due to its biocompatibility, biodegradability, anti-inflammatory, antibacterial, antioxidant, UV-protective, anti-tyrosinase, anti-aging, and anti-cancer properties, sericin is increasingly used in biomedical fields like drug delivery, tissue engineering, and serum-free cell culture media. Beyond healthcare, sericin shows promise in industries such as textiles, cosmetics, and food packaging. This review aims to highlight recent advancements in sericin extraction, research, and applications, while also summarizing key findings from earlier studies.

Cinnamon – A Competent Drug: A Review on Extraction, Analysis and Anticancer Action

AbstractCinnamon, an Indigenous species, is extensively used as a folk medicine in India, China, and other parts of the world due to its therapeutic potential inherited via the latent chemical composition. The vital component presented is cinnamaldehyde, along with cinnamic acid and cinnamate, which contributes to being an anti‐inflammatory, antimicrobial, antidiabetic, and anticancer agent together with the capability to control neurological syndromes like Alzheimer's and even Parkinson's diseases. Given the importance of the anticarcinogenic properties of cinnamon on various cell strains concerning the curable effect, this review focuses on evaluating different extraction methods like steam distillation, Soxhlet extraction, microwave‐assisted extraction, and more, in addition to a summary of new technologies like gas chromatography, HPLC, DART‐MS, and NMR, etc. which paved the way in characterizing the chemical composition of cinnamon. Cinnamaldehyde showed its apoptosis through various mechanistic pathways on an adequate number of cell lines and antineoplastic potential on specific multifaceted cancerous cells, which advocates for continued research and investment in this vital area of drug discovery and suggestions for future scope.