Extraction Approach Research Articles

Layered Bio-Inorganic MXene Membranes: A Green Approach for Uranium Extraction from Seawater Using Genetically Modified E. coli.

With the growing demand for clean energy, efficient uranium extraction technologies are needed, especially from seawater, where uranium reserves are huge. Here, we developed a composite membrane by inserting Escherichia coli engineered with super uranyl-binding protein (SUP) within a two-dimensional (2D) MXene (Ti3C2Tx) layer. SUP endowed the bioinorganic hybrid membrane with ultrahigh selectivity for uranyl ions, while the engineered E. coli improved the mechanical strength and economy of the membranes. Experimental results showed that the membranes achieved precise recognition of uranyl ions and excellent ion screening performance (SFU/V ≈ 43, SFNa/U ≈ 158). Excellent separation performance and cyclic stability tests demonstrated the industrial application potential of the membrane. This method offers a green and sustainable solution, combining biological engineering and nanomaterial innovation, providing an environmentally friendly and efficient approach for uranium extraction from seawater, marking a significant advancement in the field of clean energy resource development.

Signal Enhancement of Selected Norepinephrine Metabolites Extracted from Artificial Urine Samples by Capillary Electrophoretic Separation

The measurement of selected norepinephrine metabolites, such as 3,4-dihydroxyphenylglycol (DHPG), 3-methoxy-4-hydroxyphenylethylenglycol (MHPG), and vanillylmandelic acid (VMA), in biological matrices—including urine—is of great clinical importance for the diagnosis and monitoring of diseases. This fact has forced researchers to evaluate new analytical methodologies for their isolation and preconcentration from biological samples. In this study, the three most popular extraction techniques—liquid-liquid extraction (LLE), solid-phase extraction (SPE), and a new 3D-printed system for dispersive solid-phase extraction (3D-DSPE)—were investigated. Micellar electrokinetic chromatography (MEKC) with a diode array detector (DAD) at 200 nm wavelength was applied to the separation of analytes, allowing for the assessment of the extraction efficiency (R) and enrichment factor (EF) for the tested extraction types. The separation buffer (BGE) consisted of 5 mM sodium tetraborate decahydrate, 50 mM SDS, 15% (v/v) MeOH, 150 mM boric acid, and 1 mM of 1-hexyl-3-methylimidazolium chloride (the apparent pH of the BGE equaled 7.3). The EF for each extraction procedure was calculated with respect to standard mixtures of the analytes at the same concentration levels. The 3D-DSPE procedure, using DVB sorbent and acetone as the desorption solvent, proved to be the most effective approach for the simultaneous extraction and determination of the chosen compounds, achieving over 3-fold signal amplification for DHPG and MHPG and over 2-fold for VMA. Moreover, all extraction protocols used for the selected norepinephrine metabolites were estimated and discussed. It was also confirmed that the 3D-DSPE-MEKC approach could be considered an effective tool for sample pretreatment and separation of chosen endogenous analytes in urine samples.

A Systemic Review on Phytochemicals and Novel Approaches for the Management of Hepatic Cancer

Introduction: Hepatic cancer, an aggressive tumour that often develops along with cirrhosis and chronic liver disease like infections with the hepatitis B and hepatitis C viruses, while nonalcoholic steatohepatitis, is linked to metabolic syndrome or diabetes mellitus. It is the third most prevalent cause of cancer-related mortality globally and ranks fifth in cancer incidence. According to GLOBOCON, the prevalence is expected to rise by 55.0% and the fatalities by 56.4% in the near future. Objective: The present review offered natural plant-based substances and compounds having curative effects on liver cancer, along with novel drug delivery systems and nanocarrier-based therapies. Methods: The literature has been taken from PubMed, Google Scholar, SciFinder, Springer Nature, Bentham Science, PLOS one, or other internet sites. Result: Treatment for heterogeneous malignancy is multidimensional, and care guidelines differ depending on the specialty and location. Several nutritional herbal remedies and their active phytoconstituents may have an abundance of impacts on the management of liver cancer, including preventing the growth and spread of tumor cells, shielding the body from liver carcinogens, boosting the effects of chemotherapy and immunomodulating the body. Conclusion: The treatment of liver cancer involves multidisciplinary and multimodel therapy. The literature is a compilation of extract, compounds, and novel approaches like nanoparticles, microsphere, liposomes, niosomes, phytosomes and microparticles. These approaches not only manage cancer but also boost the immunity of the individuals.

Understanding the Experience of Workplace Violence in Hospitals as Documented by Nursing Staff: Using the READ Approach.

Background: Workplace violence has a significant impact on patients, families, and staff safety. Workplace violence can produce traumatic results for those involved; the importance of preventive measures needs to be paramount in health service policy and process. Health care staff are required to document their experiences of violent incidents after every occasion, usually via an incident reporting system, which allows for a free text description of the event. There is a lack of understanding of how health care staff document reports of violence and how they explain the events. Methods: This study aims to determine the circumstances surrounding workplace violent events as documented by health care staff. The four-step Review, Extract, Analyze, and Document (READ) approach to document analysis was used to examine workplace violence incident reports over 12 months (September 2021-September 2022) in a tertiary referral hospital. Findings: Six categories of workplace violence were found: (a) "Escalation Dynamics"-patterns and progression of how violence incidents intensify; (b) "Warning Behaviors"-verbal or non-verbal signals that may foreshadow physical violence if not addressed; (c) "Authoritative Institutional Interventions"-how aggression correlates to protocols involving security personnel or law enforcement; (d) "Care Delivery Methods"-how certain treatment procedures and approaches might inadvertently elicit violence; (e) "Situational Stressors"-external circumstances or changes that act as triggers for violence; and (f) "Unprovoked triggers"-sudden and seemingly unprovoked violent outbursts. Conclusions and application to practice: Workplace violence in health care is a multifaceted interplay of events with the nurse involved in all aspects of the process. These findings can be used by occupational health nurses in education and policy development. The findings can be used to focus education on how violent incidents may escalate and provide more opportunities for de-escalation by health care staff.

Optimizing Genomic DNA Extraction from Avian Feathers: A Modified Phenol-Chloroform Approach for Enhanced Efficiency and Cost-Effectiveness.

The procurement of blood and tissue samples for DNA extraction in avian species intended for molecular studies is associated with the induction of discomfort and pain in the subjects, compounded by practical challenges in application and ethical considerations. Consequently, feathers have emerged as a more prevalent source for molecular investigations, particularly in the fields of poultry and ornithology. However, the effective extraction of DNA from feathers necessitates the breakdown of the hard keratinized tissue within the feather structure. This study aimed to devise a highly efficient, cost-effective, and easily adaptable Modified Phenol-Chloroform (MPC) approach for genomic DNA extraction from feathers, addressing shortcomings identified in previous studies on feather-based DNA isolation. The MPC method was employed to extract genomic DNA from feather samples obtained from six distinct avian species (chicken, guinea fowl, canary, pigeon, emu, and goose). Comparative evaluation of DNA isolation efficiency was conducted by employing two different commercial DNA kits alongside the MPC method. The results showed significantly higher DNA concentrations (ng/ml) from chicken feathers using the MPC method compared to those obtained with commercial kits (p < 0.05), along with high DNA purity (1.83 ± 0.11). Subsequent PCR experiments, employing nuclear and mitochondrial DNA-specific primers, illustrated the effective amplification of short and long fragments from MPC-isolated DNA samples. In contrast to commercial kits, the findings underscore the successful application of the MPC method in isolating high-quality genomic DNA from feathers characterized by elevated keratin content.

A novel ionic liquid-based approach for DNA and RNA extraction simplifies sample preparation for bacterial diagnostics.

DNA- and RNA-based diagnostics play a pivotal role in accurately detecting and characterizing health-relevant bacteria, offering insights into bacterial presence, viability and treatment efficacy. Herein, we present the development of a novel extraction protocol for both DNA and RNA, designed to enable simple and rapid molecular diagnostics. The extraction method is based on the hydrophilic ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate and silica-coated magnetic beads. First, we developed an IL-based cell lysis protocol for bacteria that operates at room temperature. Subsequently, we established a magnetic bead purification procedure to efficiently and reproducibly extract DNA and RNA from the IL-lysates. The IL not only lyses the cells, but also facilitates the adsorption of nucleic acids (NAs) onto the surface of the magnetic beads, eliminating the need for a chaotropic binding buffer and allowing for purification of NAs without significant effort and materials required. Lastly, we combined the cell lysis step and the purification step and evaluated the novel IL-based extraction method on periopathogenic bacterial cultures, comparing it to commercial DNA and RNA extraction kits via (RT)-qPCR. In comparison to the reference methods, the IL-based extraction protocol yielded similar or superior results. Furthermore, costs are lower, required materials and equipment are minimal and the process is fast (30min), simple and automatable. These characteristics favour the developed method for use in routine and high-throughput testing as well as in point-of-care, on-site and low-resource settings, thereby advancing the field of molecular diagnostics.

Comparison of Postorthodontic Occlusal Force Distributions between Nonextraction and Extraction Treatment Approaches of Premolars for Orthodontic Treatment by Using T-Scan III System.

To compare bite force distributions in corresponding mouth regions between postorthodontic patients who underwent nonextraction and extraction approaches using the T-Scan III system (Tekscan, Inc, Massachusetts, United States). Thirty-six postorthodontic patients were divided into two groups: (1) 18 subjects with the nonextraction treatment and (2) 18 subjects with the extraction of four first-premolar treatments. The measurements were performed using the T-Scan to collect the occlusal bite force at the maximal intercuspal position to generate the bite force in the anterior region (incisors and canines) and posterior region (premolars and molars), the bite force in individual teeth, and the anteroposterior bite force ratio (A-P ratio). The mean bite force in each region, individual teeth, and the A-P ratio were compared between the two groups by the Mann-Whitney's U-tests. Within-group comparisons of the mean bite force in each region were performed using Wilcoxon's signed rank tests. The bite force in anterior and posterior regions, and the A-P ratio of the nonextraction group show no significant difference compared with the extraction group (p > 0.05). For individual teeth (central incisors, lateral incisors, canines, second premolars, first molars, and second molars), each tooth exhibited nonsignificantly different bite forces (p > 0.05) except for the second molars. The second molars in the nonextraction group had significantly less bite force than in the extraction group (p = 0.001). The comparison of occlusal bite force between the right and left sides showed that in the nonextraction group, the right side had significantly greater force (p < 0.05). In the extraction group, there was no significant difference between the sides (p > 0.05). Both nonextraction and extraction orthodontic treated patients exhibited the similar bite force distribution patterns in regions and individual teeth except for the higher occlusal force on the second molars in the extractions compared with the nonextractions.

A new strategy based on multi-source remote sensing data for improving the accuracy of land use/cover change classification.

Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping. These methods are applied to UAV LiDAR and UAV HSI data collected from land use types (e.g., residential areas, agricultural lands) and specific species (e.g., tree species) in urban, agricultural, and natural environments to reflect the diversity of the study area and to demonstrate the ability of our methods to be applied in different classification scenarios. We utilize LiDAR and HSI data to extract 157 features, including intensity, height, Normalized Digital Surface Model (nDSM), spectral, texture, and index features, to identify the optimal feature subset. Subsequently, the best feature subset is inputted into a random forest classifier to classify the features. Our findings demonstrate that the SS-PCA method successfully enhances downscaled feature bands, reduces hyperspectral data noise, and improves classification accuracy (Overall Accuracy = 91.17%). Additionally, the CFW method effectively screens appropriate features, thereby increasing classification accuracy for LiDAR (Overall Accuracy = 78.10%), HIS (Overall Accuracy = 89.87%), and LiDAR + HIS (Overall Accuracy = 97.17%) data across various areas. Moreover, the integration of LiDAR and HSI data holds promise for significantly improving ground fine classification accuracy while mitigating issues such as the 'salt and pepper noise'. Furthermore, among individual features, the LiDAR intensity feature emerges as critical for enhancing classification accuracy, while among single-class features, the HSI feature proves most influential in improving classification accuracy.

Mass Spectrometry-Based Metabolomics Reveals a Salivary Signature for Low-Severity COVID-19

Omics approaches were extensively applied during the coronavirus disease 2019 (COVID-19) pandemic to understand the disease, identify biomarkers with diagnostic and prognostic value, and discover new molecular targets for medications. COVID-19 continues to challenge the healthcare system as the virus mutates, becoming more transmissible or adept at evading the immune system, causing resurgent epidemic waves over the last few years. In this study, we used saliva from volunteers who were negative and positive for COVID-19 when Omicron and its variants became dominant. We applied a direct solid-phase extraction approach followed by non-target metabolomics analysis to identify potential salivary signatures of hospital-recruited volunteers to establish a model for COVID-19 screening. Our model, which aimed to differentiate COVID-19-positive individuals from controls in a hospital setting, was based on 39 compounds and achieved high sensitivity (85%/100%), specificity (82%/84%), and accuracy (84%/92%) in training and validation sets, respectively. The salivary diagnostic signatures were mainly composed of amino acids and lipids and were related to a heightened innate immune antiviral response and an attenuated inflammatory profile. The higher abundance of thyrotropin-releasing hormone in the COVID-19 positive group highlighted the endocrine imbalance in low-severity disease, as first reported here, underscoring the need for further studies in this area.

Laser speckle interferometry approach for measuring the first wall surface micro-morphology in fusion devices with only single interferogram based on Hilbert transform

During the operation of the EAST device, the surface morphology of the first wall in will change due to the erosion and redeposition from plasma-wall interaction (PWI). In this paper, a novel phase extraction approach using only single interferogram based on twice Hilbert transform (THT) has been proposed and implemented. The current approach has a significant advance for eliminating the phase-shifting errors in principle, but the problems of phase flipping and loss due to ignoring non-physical negative frequency spectrum must to be overcome. To address these problems a judgement method is proposed in current investigation to distinguish the regions of the negative spatial frequency. Then a binary matrix was developed to modify the wrapped phase of the measured interferogram. The validation of the proposed THT approach has been demonstrated by the micro-morphology reconstruction of laser ablation craters on molybdenum (Mo) and tungsten (W) tiles, which are commonly used as the first wall in EAST device. Results indicate that the THT approach has a reasonable measurement accuracy with relative errors around 10%. Also, the current THT approach presents an enhanced robust under the EAST-like vibrational environments.

Design of EEG based thought identification system using EMD & deep neural network

Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction is done by Empirical Mode Decomposition on non- stationary & non-linear kind of EEG signal. EMD helps in the effective time frequency analysis by disintegrating the EEG signal in the form of six Intrinsic Mode Functions with help of the frequency components. In all nine features are extracted from the decomposed IMFs so as to predict the states or messages of the patient. The above computed features are then served to the Deep Neural Network to perform the classification. The performance of suggested method is studied through applying it to the acquired database generated by the designed hardware as well as also in real time message depiction. The maximum classification accuracy 97% for the acquired database & 85% in real time are obtained respectively by comparative analysis. The command messages generated from the proposed system helps the person suffering from neurological disorder to establish the communication link with the outside world in an efficient way. Thus, the proposed novel method shows better performance in real time message depiction purpose as related to other existing methods.

A Deep Learning Multifractal Texture Analysis Using Principal Line Extraction Approach for Palmprint Recognition System

A Deep Learning Multifractal Texture Analysis Using Principal Line Extraction Approach for Palmprint Recognition System

Segmentation-based truncated-SVD for effective feature extraction in hyperspectral image classification

ABSTRACT Remote sensing hyperspectral images (HSIs) are rich sources of information about land cover captured across hundreds of narrow, contiguous spectral wavelength bands. However, using the entire original HSI for practical applications can lead to suboptimal classification accuracy. To address this, band reduction techniques, categorized as feature extraction and feature selection methods, are employed to enhance classification results. One commonly used feature extraction approach for HSIs is Principal Component Analysis (PCA). However, PCA may fall short of capturing the local and specific characteristics present in the HSI data. In this paper, we introduce two novel feature extraction methods: Segmented Truncated Singular Value Decomposition (STSVD) and Spectrally Segmented Truncated Singular Value Decomposition (SSTSVD) to improve classification performance. Segmentation is carried out based on highly correlated bands’ segments and spectral bands’ segments within the HSI data. Our study evaluates and compares these newly proposed methods against classical feature extraction methods, including PCA, Incremental PCA, Sparse-PCA, Kernel PCA, Segmented-PCA (SPCA), and Truncated Singular Value Decomposition (TSVD). We perform this analysis on three distinct HSI datasets, namely the Indian Pines HSI, the Pavia University HSI, and the Kennedy Space Center HSI, using per-pixel Support Vector Machine (SVM) and Random Forest (RF) classification. The experimental results demonstrate the superiority of our proposed methods for all three datasets. The best-performing feature extraction methods when classification is performed using an SVM classifier are STSVD3 (89.03%), SSTSVD2 (95.55%), and STSVD3 (97.74%) for the Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively. Similarly, for the RF classifier, the best-performing feature extraction methods are SSTSVD4 (88.98%), SSTSVD3 (96.04%), and SSTSVD4 (96.09%) for Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively.

Characterization of Natural Bioactive Compounds from Greek Oregano Accessions Subjected to Advanced Extraction Techniques.

Nowadays, eco-friendly extraction techniques are often used to develop natural plant extracts for commercial use. In the current investigation, Greek oregano (Origanum vulgare) phenolic extracts from different cultivated accessions were recovered employing ultrasonic-assisted extraction (UAE), microwave-assisted extraction (MAE), and accelerated solvent extraction (ASE). The phytochemical profile of the oregano extracts, as determined by spectrophotometric and chromatographic techniques, as well as antioxidant activity (ABTS, DPPH, and FRAP assays), was used to compare the three extraction approaches. The results showed that oregano phenolic extracts obtained by MAE held the highest total phenolic content, total flavonoid content, and also a higher content of the main phenolic compounds identified, rosmarinic acid, salvianolic acid B and carvacrol, as determined by LC-MS analysis, followed by those extracted by UAE and ASE. All of the tested extracts exhibited relatively high antioxidant activities, especially the MAE extracts. Oregano extracts produced by various extraction techniques were subjected to a multivariate data analysis to highlight differences in phytochemical profiles, and their correlation to antioxidant activity. According to our findings, it was evident that MAE offers more efficient and effective extraction of bioactive compounds in terms of obtaining phytochemical-rich oregano extracts, with applications in the food and pharmaceutical industries.

Schema Extraction in NoSQL Databases: A Systematic Literature Review

Introduction: Nowadays, NoSQL databases have taken on an increasingly important role in the storage of massive data within companies. Due to a common property called schema-less, NoSQL databases offer great flexibility, particularly for the storage of data in different formats. However, despite their success in data storage, schema-less databases are a major obstacle in areas requiring precise knowledge of this schema, especially in the field of data integration. Method: This study presents a Systematic Literature Review (SLR) to explore, evaluate, and discuss relevant existing research and endeavors using novel schema extraction approaches. Furthermore, we conducted this study using a well-defined methodology to examine and study the problem of schema extraction from NoSQL databases. Results: Our research results highlight and emphasize the scheme extraction approaches and provide knowledge to researchers and practitioners by proposing schema extraction approaches and their limitations, which contributes to inventing new, more efficient approaches. Conclusion: In our future work, inspired by the recent advances in quantum computing and the emergence of post-quantum cryptography (PQC), we aim to propose a schema extraction approach that blends cutting-edge technologies with a strong focus on database security.

CRMSP: A semi-supervised approach for key information extraction with Class-Rebalancing and Merged Semantic Pseudo-Labeling

CRMSP: A semi-supervised approach for key information extraction with Class-Rebalancing and Merged Semantic Pseudo-Labeling

An approach for built-up area extraction using different indices and deep neural network (DNN) model

Mapping urbanization and built-up areas from remotely sensed data poses a formidable challenge, particularly when the spectral reflectance of built-up regions intersects with other land types. To address this, numerous spectral indices have been developed. This paper utilizes multiple indices: Normalized Difference Built-up Index (NDBI), Built-up Area Extraction Index (BAEI), Normalized Built-up Area Index (NBAI), New Built-up Index (NBI), Modified Built-up Index (MBI), Band Ratio for Built-up Area (BRBA), and Normalized Difference Vegetation Index (NDVI) to delineate built-up regions. An intersection approach between BAEI, NBAI, and NDVI refines the methodology, resulting in a final built-up map with 92.5 % accuracy and a 0.848 Kappa coefficient. Subsequently, a Deep Neural Network (DNN) model trained on this map achieves over 95 % accuracy in predicting built-up areas from Landsat 5 imagery, and the resultant built-up map achieved an overall accuracy of 92 % and a Kappa coefficient of 0.85. The proposed methodology demonstrates efficiency for time-series analysis and addresses misclassification in built-up areas. Moreover, the optimization of the DNN model proves effective when meticulous training and validation processes incorporate more precise sample datasets.

AI-Based Feature Extraction Approaches for Dual Modalities of Autism Spectrum Disorder Neuroimages

AI-Based Feature Extraction Approaches for Dual Modalities of Autism Spectrum Disorder Neuroimages

Review and analysis on numerical simulation and compact modeling of InGaZno thin-film transistor for display SENSOR applications

Recent years have seen an increase in the use of Organic Thin Film Transistors (OTFTs), with applications ranging from flexible, low-cost displays to organic memory, RFID tag components, low-cost electronic appliances, and polymer circuits and sensors. Thin-film transistors (TFTs) have developed into a critical business on the grounds levels to their wide scope of utilizations in display; Radio-Frequency ID labels (RFID SENSOR), intelligent computation, and different areas. Reduced models are basic in the turn of events and execution of TFTs on the grounds that they overcome any barrier between the manufacture cycle and circuit plan. The motivation behind this exploration is to assemble a hypothetical structure for nanoscale TFT models made of polysilicon, indistinct silicon, natural, and In-Ga-Zn-O (IGZO) semiconductors. Extraordinary consideration is paid to surface-expected based smaller models of silicon-based TFTs. Surface-potential-based compact models and parameter extraction approaches were presented based on our knowledge of charge transport characteristics and TFT needs in organic and IGZO TFTs.

Performance Evaluation of Thermography-Based Computer-Aided Diagnostic Systems for Detecting Breast Cancer: An Empirical Study

Among women, breast cancer is one of the most commonly occurring cancers besides skin and cervix cancer. Developing countries are at a higher risk of mortality due to late-stage presentation, inaccessible diagnosis, and treatment. Thermography-based technology, aided with machine learning, for screening/diagnosing breast cancer is non-invasive, cost-wise appropriate, and requires very little equipment in rural areas with limited facilities. In this article, we systematically compare the state-of-the-art feature extraction approaches on a uniform platform, using two common datasets, three feature selection methods, four well-known classifiers, and three cross-validation strategies and analyze the results, for a fair comparison. Also, we evaluated the performance when all the features were combined (Unified Model) on the same platform. Experimental results show that the classification accuracy improves considerably with the use of feature selection methods. Among all the combinations considered, the classification model with Union \(\_\) FeatureSet and mRMR gave the best performance for both datasets. We obtained a feature subset of 26 and 34 features (from Union \(\_\) FeatureSet) with a combination of mRMR and SVM, which are relevant, non-redundant, and distinguish normal and abnormal thermal patterns with the accuracy of 95.73% on the DMR-IR dataset and 92.533% on the RGC-IR dataset.