Subsequent Extraction Research Articles

Consequences of hyposalivation in relation to cancer treatment and early management of radiation-induced caries: case reports

Given the prevalence of head and neck carcinoma and the salivary changes induced by the oral side effects of radiotherapy, such patients are at higher risk/susceptibility of developing dental caries. Radiation-caries is often under-treated in patients undergoing cervicofacial radiotherapy, yet these lesions can increase the risk of osteoradionecrosis due to necessary subsequent dental extractions. Moreover, xerostomia is accompanied by difficulties with speech, chewing and swallowing. The prevention and/or early management of radiation-induced carious lesions is essential in preventing oral complications and improving patients' quality of life. Based on a French case series, this paper proposes a protocol for the dental management of radiation-induced carious lesions.

Hydrophobic Modification of Halloysite Nanotubes Loaded with a Small Amount of Tungsten Oxide for Efficient Oxidative Desulfurization.

Transition metal oxides can be used as efficient multiphase catalysts in the field of catalysis. In this study, a hydrophobic halloysite nanotube (HNT) catalyst was designed and prepared with a low loading. Tungsten oxide was immobilized on the inner surface of the HNT, through electrostatic adsorption and calcination. Furthermore, a dual-functional W/HMT/M catalyst was prepared by hydrophobic modification of the outer surface of HNT through a harmless and nontoxic method. The catalyst was applied in the oxidative desulfurization (ODS) of dibenzothiophene (DBT), and characterized by inductively coupled plasma (ICP), contact angle tests, and other methods. Systematic characterization further confirmed that W/HNT/M has a low loading (0.48 wt %) and a relatively high contact angle of 92.6°. Oxidative desulfurization experiments demonstrated that the high contact angle corresponds to good hydrophobicity. The low loading and high activity of the catalyst enabled it to achieve a removal efficiency of 100% for DBT under conditions of 60 °C and an O/S = 4. The hydrophobic surface of HNT allowed better dispersion in the oil phase, while its hydrophilic inner cavity could adsorb H2O2 and the converted dibenzothiophene sulfoxide, thereby reducing the subsequent extraction steps after oxidative desulfurization and enhancing the reaction environment for reactants and active oxygen. W/HNT/M maintained high activity for at least 5 cycles. Additionally, the potential mechanism of the catalyst in the aqueous ODS reaction was proposed. This study demonstrates that HNT-supported metal oxides have desulfurization potential and provides ideas for improving ODS catalytic activity of the ODS through low loading, high activity, and unique hydrophobicity design.

Multiple morcelloma of the peritoneal cavity: a case report

Introduction. Uterine fibroids are benign monoclonal hormone-sensitive tumors arising from smooth muscle cells of the cervix or body uterus. Myomectomyhas become a treatment «golden standard» upon transition to the strategy of organ-preserving surgery. The introduction of morcellation into wide clinical practice allowed to expand the indications for laparoscopic myomectomy, which is usually much better tolerated by patients due to lower invasiveness, short period of rehabilitation and low blood loss. However, the use of morcellators along with the obvious advantages has also specific complications particularly emergence of iatrogenic parasitic fibroids of the abdominal cavity upon improper surgery technique.Aim: analysis of a clinical case of multiple morcelloma.Case presentation. Female patient I., 57 years old, underwent laparoscopic myomectomy in 2003. In 2023 during a routine examination at the Central Clinical Hospital «RZD-Medicine» multiple asymptomatic iatrogenic fibroids of the abdominal cavity were revealed. The patient underwent planned surgical intervention to remove all abnormal masses.Results. During the revision upon surgical intervention, a node of 1.5×1.5 cm was found on the parietal peritoneum along the left anterior wall, as well as a node of 1.5×1.5 cm on the parietal peritoneum along the right anterior wall. There was also observed a 2×3 cm node at the edge of the omentum. In the left and right side of peritoneum of the sacro-uterine ligament, there were found nodes of up to 1 cm in size. In the area of the small intestinal mesentery, a dense node of 4.5 cm was observed 70 cm away from the ileocecal valve. Due to small size, all the fibroids were extirpated and removed from the abdominal cavity without morcellation, complete hemostasis was performed. No complications were noted during postoperative period.Conclusion. Iatrogenic fibroids are quite rare but nevertheless a potential complication of laparoscopic myomectomy. Gynecologists should pay special attention to follow proper technique upon surgical intervention. Morcellation and subsequent extraction of fibroids should be performed strictly with use of airtight container to avoid ingress of myomatous tissue into the abdominal cavity followed by emergence of iatrogenic fibroids.

EFFECT OF ULTRASOUND ON THE EXTRACTION OF HYDROXYCINNAMIC ACIDS FROM DANDELION ROOTS

Introduction. Ultrasonic extraction is widely used to accelerate the extraction of biologically active substances (BAS). Given the presence of free and bound moisture in medicinal plant raw materials (MPRM), it is rational to assume a positive effect of ultrasound on the MPRM itself in order to increase the yield of BAS. The aim of the work is to study the effect of ultrasonic pre-treatment of dandelion roots on the yield of hydroxycinnamic acids (HCA) from them and to substantiate the feasibility of pre-treatment this MPRM to obtain tinctures. Material and methods. The object of the study was dandelion roots. The mass fraction and composition of HCA were determined by the spectro-photometric method according to the reaction with Arnov's reagent and by high-performance liquid chromatography using standard samples, respec-tively. Results. Preliminary ultrasound exposure of dandelion roots significantly increases the yield of HCA. The maximum content was determined when MPRM with a layer thickness of 2-4 cm and a particle size of 500 μm or less were exposed to ultrasound for 45 min with a frequency of 31 kHz. The highest amount of HCA in tinctures was determined when they were obtained by fourteen-day maceration and subsequent ultrasound extraction for 15 min using 50% ethanol in a volume of 50 ml per 1 g of MPRM with a particle size of 500 μm or less. Defatting, ultrasonic, thermal pre-treatment and their combination enrich dandelion tinctures with HCA; the greatest enrichment of tinctures with this group of BAS was observed with ultrasonic treatment and thermal treatment followed by defatting. Conclusions. The parameters of preliminary ultrasonic pre-treatment and technological parameters for obtaining tinctures of dandelion roots were ex-perimentally determined. It is recommended to use preliminary pre-treatment of raw materials as one of the stages of the technology for obtaining tinctures, ensuring an increase in the yield of HCA.

Geometry-Aware Enhanced Mutual-Supervised Point Elimination with Overlapping Mask Contrastive Learning for Partitial Point Cloud Registration

Point cloud registration is one of the fundamental tasks in computer vision, but faces challenges under low overlap conditions. Recent approaches use transformers and overlapping masks to improve perception, but mask learning only considers Euclidean distances between features, ignores mismatches caused by fuzzy geometric structures, and is often computationally inefficient. To address these issues, we introduce a novel matching framework. Firstly, we fuse adaptive graph convolution with PPF features to obtain rich feature perception. Subsequently, we construct a PGT framework that uses GeoTransformer and combines it with location information encoding to enhance the geometry perception between source and target clouds. In addition, we improve the visibility of overlapping regions through information exchange and the AIS module, aiming at subsequent keypoint extraction, preserving points with distinct geometrical structures while suppressing the influence of non-overlapping regions to improve computational efficiency. Finally, the mask is refined through contrast learning to preserve geometric and distance similarity, which helps to compute the transformation parameters more accurately. We have conducted comprehensive experiments on synthetic and real-world scene datasets, demonstrating superior registration performance compared to recent deep learning methods. Our approach shows remarkable improvements of 68.21% in RRMSE and 76.31% in tRMSE on synthetic data, while also excelling in real-world scenarios with enhancements of 76.46% in RRMSE and 45.16% in tRMSE.

Modelling of enhanced gas extraction in low permeability coal seam by controllable shock wave fracturing

The controlled shock wave (CSW) fracturing is an effective method for enhancing permeability of coal seam to promote gas extraction. Based on Fick’s law, Darcy’s law, the ideal gas law and the Langmuir equation, a damage-seepage-deformation coupling mathematical model of CSW fracturing in coal seam combined with the maximum tensile stress and the Mohr-Coulomb criterion is established. This model is implemented into COMSOL Multiphysics to simulate the coal seam CSW fracturing and subsequent gas extraction. When the shock wave and isotropic in-situ stress are applied on the borehole wall, the coal damage zone is an annular shape, and the permeability in the damage zone increases sharply. The CSW can effectively increase the efficiency of gas extraction and reduce the gas pressure and gas content in coal seam. With the increase of CSW action times, the damage in coal mass reaches a threshold and tends to be stable after several shocks. The damage area and the gas extraction efficiency are positively correlated with the shock intensity. Under the anisotropic ground stress, the larger diversity of the stress in different directions is, the more obvious damage extension in the fractured coal along the maximum stress direction is. Ground stress can inhibit the extension of cracks in the CSW fractured coal seam. This inhibition effect becomes more obvious with the increase of in-situ stress. Parameters are substantiated of controlled shock wave impact on the coal seam, which ensures increased methane extraction from low-permeability reservoirs, are substantiated.

Chelator-Assisted Precipitation-Based Separation of the Rare Earth Elements Neodymium and Dysprosium from Aqueous Solutions.

The rare earth elements (REEs) are critical resources for many clean energy technologies, but are difficult to obtain in their elementally pure forms because of their nearly identical chemical properties. Here, an analogue of macropa, G-macropa, was synthesized and employed for an aqueous precipitation-based separation of Nd3+ and Dy3+. G-macropa maintains the same thermodynamic preference for the large REEs as macropa, but shows smaller thermodynamic stability constants. Molecular dynamics studies demonstrate that the binding affinity differences of these chelators for Nd3+ and Dy3+ is a consequence of the presence or absence of an inner-sphere water molecule, which alters the donor strength of the macrocyclic ethers. Leveraging the small REE affinity of G-macropa, we demonstrate that within aqueous solutions of Nd3+, Dy3+, and G-macropa, the addition of HCO3 - selectively precipitates Dy2(CO3)3, leaving the Nd3+-G-macropa complex in solution. With this method, remarkably high separation factors of 841 and 741 are achieved for 50 : 50 and 75 : 25 mixtures. Further studies involving Nd3+:Dy3+ ratios of 95 : 5 in authentic magnet waste also afford an efficient separation as well. Lastly, G-macropa is recovered via crystallization with HCl and used for subsequent extractions, demonstrating its good recyclability.

Efficient Extraction and Separation of Scandium from Scandium-Bearing Solid Waste and Acid by Synergistically Leaching Followed by Solvent Extraction.

The solid waste and waste acid generated during the production of titanium dioxide contain considerable amount of scandium, which are valuable secondary resources. In this study, the titanium dioxide waste acid was used to leach the scandium-containing solid waste, and the leached solution was pretreated for iron removal by reduction-crystallization process. After that, scandium was recovered from the leached solution by using the P204-TBP co-extraction system. The process parameters were investigated systematically. The results showed that iron powder reduction-crystallization for iron removal at molar ratio of Fe:Fe3+ = 0.25 was most suitable for subsequent extraction, and the scandium extraction efficiency could reach 100% using 15% P204-5% TBP at 25 °C with A/O = 8. This study provided a novel process for treating scandium-bearing solid waste with scandium-bearing waste acid, showing great potential for industrial application.

Non-destructive detection of male and female information in ducklings based on near-infrared spectral wavelength selection and deep learning

The technology for sex identification in ducklings can contribute to increased revenue and cost savings in modern duck farming. However, traditional manual identification techniques require high skill levels and involve significant labor intensity. In this study, a non-destructive, user-friendly, and efficient duckling sex identification technique was proposed using near-infrared spectroscopy and deep learning algorithms. Spectral data from 600 groups of newly hatched ducklings were collected. These data were divided into training, testing, and validation sets in a ratio of 7:2:1. The raw spectral data was preprocessed using the Savitzky-Golay convolution derivative method, which was employed for subsequent spectral feature wavelength extraction and modeling. The characteristic wavelengths were extracted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination combined with SPA (UVE-SPA). Conventional machine learning methods − support vector machine (SVM) and different deep learning models, including multilayer perceptron (MLP), mobile network version 2 (MobileNetV2), and residual neural network (ResNet), were studied and compared. The experiment showed that deep learning algorithms outperform traditional spectral analysis models in terms of classification performance. Furthermore, conducting feature wavelength extraction before constructing the classification model could reduce the model’s testing time and even improve its classification performance. Finally, the four models with better classification performance were validated using a validation set, and the combination of MobileNetV2 model UVE-SPA was selected as the optimized model for ducklings’ gender determination, with a classification accuracy of 98.3 % and an average validation time of 1.1 ms. In summary, the detection model established using near-infrared spectroscopy and MobileNetV2 can achieve non-destructive identification of the gender of ducklings. The findings can provide a preliminary research foundation and technical support for the subsequent design of related online intelligent detection systems.

Recording and Interpretation of Active Calcium Transients in Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

Calcium plays a pivotal role in the excitation-contraction coupling process in cardiomyocytes, a critical multi-parametric event leading to rhythmic contraction. Over the past few decades, calcium signaling in cardiomyocytes has been extensively studied in cardiovascular sciences. However, a standard methodology is needed not only to trace the calcium within cells but also to remove signal processing biases and to accurately interpret the features of calcium transient signals in relation to cardiomyocyte electrophysiology. This article outlines the use of genetically encoded calcium indicator (GCaMP) human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to record calcium transients. These cells express a green fluorescent signal when calcium binds to intracellular calmodulin, a key regulator of calcium signaling. The extraction and processing of calcium transient waveforms are performed using ImageJ and MATLAB software. Key features of these waveforms are then identified and categorized based on their physiological relevance to cardiomyocyte function. Additionally, this work includes a Support Protocol for the successful replating of cardiomyocytes onto non-traditional culture platforms, such as metallic sensors and polymer-based substrates, to facilitate data multiplexing. The three Basic Protocols outlined here provide a comprehensive approach for maintaining, expanding, and differentiating the GCaMP hiPSCs, video recording of calcium transients, and the subsequent signal extraction, preprocessing, analysis, and data visualization. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Maintenance, expansion, and differentiation of genetically encoded calcium indicator hiPSCs Support Protocol: Replating GCaMP hiPSC-CMs for stimulation and multielectrode array studies Basic Protocol 2: Video recording from calcium transients of GCaMP hiPSC-CMs Basic Protocol 3: Signal extraction, preprocessing, analysis, and data visualization.

Glacial cirque identification based on Convolutional Neural Networks

Cirques provide important information about the palaeoclimate conditions that produced past glaciers. However, mapping cirques is challenging, time-consuming, and subjective due to their fuzzy boundaries. A recent study tested the potential of using a deep learning algorithm, Convolutional Neural Networks (CNN), to predict boundary boxes containing cirques. Based on a similar CNN method, RetinaNet, we use a dataset of >8000 cirques worldwide and various combinations of digital elevation models and their derivatives to detect these features. We also incorporate the Convolutional Block Attention Module (CBAM) into RetinaNet for training and prediction. The precision of cirque detection with or without the addition of the CBAM is evaluated for various input data combinations, and training sample sizes, based on comparison with mapped cirques in two test areas on the Kamchatka Peninsula and the Gangdise Mountains. The results show that the addition of CBAM increases the average precision by 4–5 % (p < 0.01), and the trained model can detect the cirque boundary boxes with high precision (84.7 % and 87.0 %), recall (94.7 % and 86.6 %), and F1 score (0.89 and 0.87), for the two test areas, respectively. The inclusion of CBAM also significantly reduces the number of undetected cirques. The model performance is affected by the quantity and quality of the training samples: the performance generally increases with increasing training samples and a training dataset of 6000 cirques produces the best results. The trained model can effectively detect boundary boxes that contain cirques to help facilitate subsequent cirque outline extraction and morphological analysis.

Deciphering the molecular mechanisms of heat stress tolerance in goats: Insights from transcriptome and Gene Co-expression analysis

Climate change poses a significant threat to the sustainability of livestock production systems in developing countries, particularly impacting small ruminants like goats, which are highly susceptible to heat stress. This stressor not only reduces productivity but also undermines economic viability. This study aimed to delve into the molecular mechanisms underlying heat stress tolerance in goats by conducting a comprehensive transcriptome analysis of heat-tolerant (HT, n = 4) and heat-susceptible (HS, n = 6) Jamunapari goats. Physiological metrics, such as rectal temperature, respiratory rate, and heart rate, were meticulously monitored under extreme environmental conditions (Temperature Humidity Index >92) to effectively classify goats based on their distinct heat stress responses. Samples of blood were obtained, and peripheral blood mononuclear cells (PBMCs) were extracted for subsequent RNA extraction. RNA-Seq analysis revealed a sum of 734 differentially expressed genes (DEGs), comprising 251 upregulated and 483 downregulated genes in HT goats compared to their HS counterparts. The WGCNA revealed three key modules, darkorange (tolerance), paleturquoise (respiration rate), and darkmagenta (heart rate). Moreover, functional enrichment analysis revealed that DEGs within these modules played intricate roles in crucial biological processes and pathways, including mitochondrial function, cardiac function, immune response, genomic stability, and metabolic regulation. This research notably enhances our comprehension of the genetic underpinnings of thermo-tolerance in goats and provides invaluable guidance for formulating breeding strategies aimed at bolstering livestock resilience against the challenges of climate change.

A comparative study of pulsed electric field, ultrasound, milling and soaking as pre-treatments for assistance in the extraction of polyphenols from willow bark (Salix alba)

White willow (Salix alba) has a long history of use as an herbal remedy for treating common pain and inflammation. Pre-treatment is a crucial step that assists the subsequent extraction process and may affect the extraction efficiency of polyphenols from plant materials. The objective of this work was to study the impact of pre-treatment methods on the hot water extraction of polyphenols from willow bark. Pulsed electric field (PEF) and ultrasound (US) pre-treatments were compared to commonly used pre-treatment methods, milling and soaking. All pre-treatments significantly increased the total phenolic content (TPC) and antioxidant activity (p < 0.05) in willow extracts. At a similar energy level, PEF with 400 pulses yielded a higher TPC (44.33mg GAE/g d.w.) and antioxidant activity (DPPH of 74.58mg AAE/g and FRAP of 186.41mmol TE/g) than US pre-treated samples, but a small increase in ultrasonic energy resulted in the highest TPC (46.20mg GAE/g d.w.) and antioxidant activity (DPPH of 76.18mg AAE/g and FRAP of 220.20mmol TE/g) of all the pre-treatments. Compared with PEF and US, milling (42.69mg GAE/g d.w.) and soaking (40.29mg GAE/g d.w.) were less effective in recovering polyphenols but they were comparable with PEF and US when employed at low energy levels. Overall results indicate emerging technologies PEF and US can be alternatives to milling to enhance the extractability of polyphenols and to reduce energy consumption. The obtained willow extracts were a rich source of polyphenols that could be used for food and pharmaceutical applications.

Ameliorated Fick’s law algorithm based multi-threshold medical image segmentation

Medical image segmentation is a critical and demanding step in medical image processing, which provides a solid foundation for subsequent medical image data extraction and analysis. Multi-threshold image segmentation, one of the most commonly used and specialized image segmentation techniques, limits its application to medical images because it requires demanding computational performance and is difficult to produce satisfactory segmentation results. To overcome the above problems, an ameliorated Fick's law algorithm (MsFLA) for multi-threshold image segmentation is developed in this paper. First, an optimized sine–cosine strategy is introduced to extend the molecular diffusion process to alleviate the problem of easily falling into local optima, thus improving the convergence accuracy of the Fick's law algorithm (FLA). Secondly, the introduction of local minimal value avoidance enriches the individual molecular information and enhances the local search ability, thus improving computational accuracy. In addition, the optimal neighborhood learning strategy is added to ensure a more careful and reasonable reliance on the optimal solution, thus reducing the chance of convergence of a local solution. The efficient optimization capability of MsFLA is comprehensively validated by comparing MsFLA with the original FLA and other algorithms in 23 classical benchmark functions. Finally, MsFLA is applied to image segmentation of grayscale images of COVID-19 and brain and color images of Lung and Colon cancer histopathology by using Cross entropy to validate its segmentation capability. The experimental results show that the MsFLA obtains the best segmentation results in three medical image cases compared to other comparison algorithms, which indicates that MsFLA can effectively solve the multi-threshold medical image segmentation problem.Graphical abstract

Evaluation of fouling phenomena and cation-exchange membrane cleaning in Donnan dialysis for separation of ammonium from fermented solutions rich in volatile fatty acids

Polyhydroxyalkanoates (PHA) are promising biobased and biodegradable alternatives to the petroleum-based plastics. For their production, controlling cell growth is crucial and can be achieved by limiting nutrients, such as ammonium. The use of Donnan dialysis (DD) has been considered for this purpose. However, the effects of long–term DD fermentative operations need to be evaluated since fouling and sorption phenomena can affect the process performance. In this study, DD operations were performed over 10 consecutive batches to separate ammonium from a real fermented stream derived from an acidogenic fermentation process using cation-exchange membranes with distinct properties (a homogenous Fumasep FKS–PET-130 and a heterogenous Ralex CMH-PES) and NaCl or HCl aqueous receiver solutions. A cleaning procedure with acidic and alkaline solutions was proposed for the Ralex. It was verified that sorption and fouling phenomena occurred on/inside both membranes using a NaCl solution as receiver. This occurred more intensely with the Ralex, thus reducing the ammonium percent extraction from 55% (first batch) to approximately 40% in the subsequent batches. On the other hand, Fumasep exhibited higher percent extractions, and its values remained constant at around 65% along the batches when only rinsed with distilled water. The proposed cleaning procedure for Ralex effectively regenerated this membrane and increased the subsequent ammonium percent extractions. However, the acidic and/or alkaline solutions formed cavities on/in the membrane. The use of a HCl solution as receiver led to a lower reduction in the percent extraction throughout the batches when compared to NaCl, but the acidic receiver solution also damaged the membrane. It is suggested the use of the Fumasep membrane for the application evaluated herein or that a milder cleaning procedure is developed and applied for the Ralex membrane.

Extraction and characterisation of gelatine from yellowfin tuna skin pretreated with a eutectic solvent

The accumulation and management of waste generated during the processing of seafood products is one of the major current challenges in the fishery industry. Among these residues, fish skin stands out for its high collagen content, from which high-quality gelatine can be obtained. However, conventional methods for gelatine extraction are time-consuming and complex processes that involve the use of strong acidic and alkaline solutions, leading to serious environmental concerns. This research studied the use of a eutectic solvent, formed by two safe and environmentally friendly compounds such as sodium acetate trihydrate and urea, to pretreat the skin of yellowfin tuna before the extraction of gelatine. Two different times of pretreatment were tested, and the subsequent extraction was carried out in warm water, resulting in much higher yields of gelatine (0.3 g/g wet fish skin) than previous studies. Additionally, using FT-IR analysis and amino acid profiling, it was observed that the pretreatment time did not significantly affect the structure or composition of the gelatine. However, some variation was found in its molecular weight distribution and therefore in its rheological properties: with an increase in the pretreatment time, both the molecular weight and the storage modulus of the gel also increased, as well as the gelation and melting temperatures. This contribution offers a simplified process that reduces environmental impact and provides the opportunity to tailor the final product.

In vitro digestion study comparing a predigested glycerolysis product versus long-chain polyunsaturated fatty acid-rich oils (LCPUFA) as a strategy for administering LCPUFA to preterm neonates

Background & aimsMaintaining an adequate supply of arachidonic acid (ARA) and docosahexaenoic acid (DHA) is essential for optimal growth of preterm infants. This study aims to evaluate and compare the digestibility and bioaccessibility of ARA and DHA oils compared to their predigested product through an in vitro digestion model. MethodsAn in vitro gastrointestinal digestion model was used in two stages: gastric digestion and intestinal digestion. Samples of two polyunsaturated rich oils (ARA and DHA oils) and their predigested product (2:1, ARA: DHA) produced by enzymatic glycerolysis have been digested for 120 min. The final digestion product obtained was composed of three phases: an upper oily phase (OP) containing the undigested species, an intermediate micellar phase (MP) containing digested and bioaccessible lipids, and a precipitate phase (PP) with insoluble compounds. The reaction was monitored by taking aliquots and their subsequent lipid extraction and analysis. ResultsPoorer digestibility for ARA and DHA oils was observed based on the percentage of the oily phase (26.7% and 20%, respectively) found compared to the glycerolysis product (GP) oily phase (13.9%). The highest micellar phase was found in the GP (approx. 83%). On the other hand, the monoglyceride (MAG) content was lower in the digestion product (DP) from ARA and DHA oils, 4.3% and 9.2%, respectively, compared to the MAG observed in the DP of GP (15%). ConclusionConsidering the percentage of oily phase, micellar phase, and the MAG content, it can be concluded that the GP is more digestible and ARA and DHA are more bioaccessible than in its precursor oils.

A Method for Cropland Layer Extraction in Complex Scenes Integrating Edge Features and Semantic Segmentation

Cultivated land is crucial for food production and security. In complex environments like mountainous regions, the fragmented nature of the cultivated land complicates rapid and accurate information acquisition. Deep learning has become essential for extracting cultivated land but faces challenges such as edge detail loss and limited adaptability. This study introduces a novel approach that combines geographical zonal stratification with the temporal characteristics of medium-resolution remote sensing images for identifying cultivated land. The methodology involves geographically zoning and stratifying the study area, and then integrating semantic segmentation and edge detection to analyze remote sensing images and generate initial extraction results. These results are refined through post-processing with medium-resolution imagery classification to produce a detailed map of the cultivated land distribution. The method achieved an overall extraction accuracy of 95.07% in Tongnan District, with specific accuracies of 92.49% for flat cultivated land, 96.18% for terraced cultivated land, 93.80% for sloping cultivated land, and 78.83% for forest intercrop land. The results indicate that, compared to traditional methods, this approach is faster and more accurate, reducing both false positives and omissions. This paper presents a new methodological framework for large-scale cropland mapping in complex scenarios, offering valuable insights for subsequent cropland extraction in challenging environments.

Chelator‐Assisted Precipitation‐Based Separation of the Rare Earth Elements Neodymium and Dysprosium from Aqueous Solutions

AbstractThe rare earth elements (REEs) are critical resources for many clean energy technologies, but are difficult to obtain in their elementally pure forms because of their nearly identical chemical properties. Here, an analogue of macropa, G‐macropa, was synthesized and employed for an aqueous precipitation‐based separation of Nd3+ and Dy3+. G‐macropa maintains the same thermodynamic preference for the large REEs as macropa, but shows smaller thermodynamic stability constants. Molecular dynamics studies demonstrate that the binding affinity differences of these chelators for Nd3+ and Dy3+ is a consequence of the presence or absence of an inner‐sphere water molecule, which alters the donor strength of the macrocyclic ethers. Leveraging the small REE affinity of G‐macropa, we demonstrate that within aqueous solutions of Nd3+, Dy3+, and G‐macropa, the addition of HCO3− selectively precipitates Dy2(CO3)3, leaving the Nd3+−G‐macropa complex in solution. With this method, remarkably high separation factors of 841 and 741 are achieved for 50 : 50 and 75 : 25 mixtures. Further studies involving Nd3+:Dy3+ ratios of 95 : 5 in authentic magnet waste also afford an efficient separation as well. Lastly, G‐macropa is recovered via crystallization with HCl and used for subsequent extractions, demonstrating its good recyclability.

Harnessing AI for precision tonsillitis diagnosis: a revolutionary approach in endoscopic analysis.

Diagnosing and treating tonsillitis pose no significant challenge for otolaryngologists; however, it can increase the infection risk for healthcare professionals amidst the coronavirus pandemic. In recent years, with the advancement of artificial intelligence (AI), its application in medical imaging has also thrived. This research is to identify the optimal convolutional neural network (CNN) algorithm for accurate diagnosis of tonsillitis and early precision treatment. Semi-supervised learning with pseudo-labels used for self-training was adopted to train our CNN, with the algorithm including UNet, PSPNet, and FPN. A total of 485 pharyngoscopic images from 485 participants were included, comprising healthy individuals (133 cases), patients with the common cold (295 cases), and patients with tonsillitis (57 cases). Both color and texture features from 485 images are extracted for analysis. UNet outperformed PSPNet and FPN in accurately segmenting oropharyngeal anatomy automatically, with average Dice coefficient of 97.74% and a pixel accuracy of 98.12%, making it suitable for enhancing the diagnosis of tonsillitis. The normal tonsils generally have more uniform and smooth textures and have pinkish color, similar to the surrounding mucosal tissues, while tonsillitis, particularly the antibiotic-required type, shows white or yellowish pus-filled spots or patches, and shows more granular or lumpy texture in contrast, indicating inflammation and changes in tissue structure. After training with 485 cases, our algorithm with UNet achieved accuracy rates of 93.75%, 97.1%, and 91.67% in differentiating the three tonsil groups, demonstrating excellent results. Our research highlights the potential of using UNet for fully automated semantic segmentation of oropharyngeal structures, which aids in subsequent feature extraction, machine learning, and enables accurate AI diagnosis of tonsillitis. This innovation shows promise for enhancing both the accuracy and speed of tonsillitis assessments.