Fast Method Research Articles

Leveraging occupancy map to accelerate video-based point cloud compression

Video-based Point Cloud Compression enables point cloud streaming over the internet by converting dynamic 3D point clouds to 2D geometry and attribute videos, which are then compressed using 2D video codecs like H.266/VVC. However, the complex encoding process of H.266/VVC, such as the quadtree with nested multi-type tree (QTMT) partition, greatly hinders the practical application of V-PCC. To address this issue, we propose a fast CU partition method dedicated to V-PCC to accelerate the coding process. Specifically, we classify coding units (CUs) of projected images into three categories based on the occupancy map of a point cloud: unoccupied, partially occupied, and fully occupied. Subsequently, we employ either statistic-based rules or machine-learning models to manage the partition of each category. For unoccupied CUs, we terminate the partition directly; for partially occupied CUs with explicit directions, we selectively skip certain partition candidates; for the remaining CUs (partially occupied CUs with complex directions and fully occupied CUs), we train an edge-driven LightGBM model to predict the partition probability of each partition candidate automatically. Only partitions with high probabilities are retained for further Rate–Distortion (R–D) decisions. Comprehensive experiments demonstrate the superior performance of our proposed method: under the V-PCC common test conditions, our method reduces encoding time by 52% and 44% in geometry and attribute, respectively, while incurring only 0.68% (0.66%) BD-Rate loss in D1 (D2) measurements and 0.79% (luma) BD-Rate loss in attribute, significantly surpassing state-of-the-art works.

Simona: a comprehensive R package for semantic similarity analysis on bio-ontologies

BackgroundBio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in ontologies. It plays an important role in structured and meaningful interpretations and integration of complex data from multiple biological domains.ResultsWe present simona, a novel R package for semantic similarity analysis on general bio-ontologies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona, we conducted a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, thus guiding researchers in the selection of appropriate methods. Additionally, we explored annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies.ConclusionsSimona offers a versatile interface and efficient implementation for processing, visualization, and semantic similarity analysis on bio-ontologies. We believe that simona will serve as a robust tool for uncovering relationships and enhancing the interoperability of biological knowledge systems.

Detection of the stimulant clobenzorex using voltammetry and screen-printed electrodes: A simple and fast screening method for application in seized samples and oral fluid of drivers

Clobenzorex (CBZ) is a stimulant drug, recognized for its anorectic potential, legally used in some countries for obesity treatment. However, CBZ is banned in much of the world and has been often used illegally by drivers needing to stay alert for extended periods. Therefore, the rapid identification of CBZ in forensic samples is of paramount importance for public health and safety. In this context, we introduce an innovative electroanalytical screening method for detecting CBZ in seized tablet samples and human oral fluid using voltammetry with a graphite screen-printed electrode (SPE-Gr). The electrochemical detection was optimized in phosphate buffer solution (0.1 mol L−1, pH 7.0) using square wave voltammetry (SWV). The SPE-Gr combined with SWV exhibited a good stability of the electrochemical responses for CBZ detection, with a relative standard deviation of less than 5% across different days (N = 5) and using different SPEs (N = 3). A linear detection range of 2.5–100.0 μmol/L (R2 = 0.992) was achieved, with a low limit of detection (LOD) of 0.32 μmol L−1. Interference studies with other illicit drugs and adulterants confirmed the selectivity of the proposed method for CBZ detection. In addition, the presence of CBZ in seized tablet samples and authentic oral fluids, collected from drivers, was detected and identified using the proposed method, which results were also confirmed by HPLC-MS. Consequently, integrating SPE-Gr with SWV offers a reliable, rapid, sensitive, selective, reproducible, and direct approach for the preliminary qualitative and quantitative analysis of CBZ in forensic contexts. Furthermore, the proposed method provides an efficient oral fluid test for CBZ screening in drivers under the influence of drugs.

Exopolysaccharides from the Green Microalga Strain Coelastrella sp. BGV-Isolation, Characterization, and Assessment of Anticancer Potential.

Algal metabolites have been extensively studied as potential anticancer therapeutics. Among them, polysaccharides have attracted much attention because of their beneficial biological effects and safety. In the present research, the chemical characteristics, antitumor, and proapoptotic activities of extracellular polysaccharides (EPS) isolated from a new Bulgarian strain of the green microalga Coelastrella sp. BGV were investigated. A fast and convenient method of precipitation with cold ethanol was used to isolate EPS from the culture medium. The chemical characteristics of the isolated EPS were examined by colorimetric and spectrophotometric analyses, HPSEC-RID and HPLC-UV chromatography, and FT-IR spectroscopy. The results showed that the isolated EPS sample consists of three carbohydrate fractions with different molecular weights (11.5 × 104 Da, 30.7 × 104 Da, and 72.4 × 104 Da, respectively) and contains 7.14 (w/w%) protein. HPLC-UV analysis revealed the presence of galactose and fucose. The total uronic acid content in the sample was 4.5 (w/w%). The IR-FT spectrum of EPS revealed the presence of various functional groups typical of a polysaccharide (or proteoglycan) composed primarily of neutral sugars. The anticancer potential of the obtained EPS was assessed using cell lines with cancerous and non-cancerous origins as in vitro experimental models. The results of the performed MTT assay showed that EPS reduced the viability of the cervical and mammary carcinoma cell lines HeLa and MCF-7, while the control non-cancer cell lines BALB/3T3 and HaCaT were less affected. The HeLa cell line showed the highest sensitivity to the effects of EPS and was therefore used for further studies of its anticancer potential. The ability of EPS to inhibit cancer cell migration was demonstrated by wound-healing (scratch) assay. The cell cycle FACS analysis indicated that the EPS treatment induced significant increases in the sub G1 cell population and decreases of the percentages of cells in the G1, S, and G2-M phases, compared to the control. The fluorescent microscopy studies performed using three different staining methods in combination with Annexin V-FITC flow cytometric analysis clearly demonstrate the ability of EPS to induce cancer cell death via the apoptosis pathway. Moreover, an altered pattern and intensity of the immunocytochemical staining for the apoptosis- and proliferation-related proteins p53, bcl2, and Ki67 was detected in EPS-treated HeLa cancer cells as compared to the untreated controls. The obtained results characterize the new local strain of green microalgae Coelastrella sp. BGV as a producer of EPS with selective antitumor activity and provide an opportunity for further studies of its pharmacological and biotechnological potential.

IR Hidden Patterns for Security Labels.

Authentication of a product's originality by anticounterfeiting labels represents a crucial point toward protection against forgery. Fast and scalable fabrication methods of original labels with a high degree of protection are in high demand for the protection of valuable goods. Here, we propose a simple strategy for fabrication of hidden security tags with IR luminescent readout by the direct femtosecond laser patterning of silicon-erbium-silicon sandwiched thin films. The choice of laser processing parameters makes possible the creation of random or quasi-regular self-organized surface nanotextures. The controlled laser-driven oxidation accompanying this process provides simultaneous regulation of the film's optical properties and spontaneous emission yield of the embedded Er atoms. The regimes are detected when optically similar patterned areas demonstrate different Er emission intensities, allowing us to create hidden security tags with facile readout at the C-band telecommunication wavelengths. The obtained results take another step toward the application of IR-luminescent erbium-based anticounterfeiting labels for covert and/or forensic security levels.

Frank–Wolfe and friends: a journey into projection-free first-order optimization methods

AbstractInvented some 65 years ago in a seminal paper by Marguerite Straus-Frank and Philip Wolfe, the Frank–Wolfe method recently enjoys a remarkable revival, fuelled by the need of fast and reliable first-order optimization methods in Data Science and other relevant application areas. This review tries to explain the success of this approach by illustrating versatility and applicability in a wide range of contexts, combined with an account on recent progress in variants, both improving on the speed and efficiency of this surprisingly simple principle of first-order optimization.

Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods

Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of traditional chemometrics and deep learning methods in identifying and quantifying adulterated CAO were compared from a statistical perspective, and no significant difference were founded in the identification of CAO at different levels of adulteration. The recognition rate of pure and adulterated CAO was 100 %, but there were misclassifications among different adulterated CAOs. The deep learning models outperformed chemometrics methods in quantitative prediction of adulteration level, with RP2, RMSEP, and RPD of the optimal ConvLSTM model achieved 0.999, 0.9 % and 31.5, respectively. The classifiers and models developed in this study based on deep learning have wide applicability and reliability, and provide a fast and accurate method for adulteration detection in CAO.

County-Level Cultivated Land Quality Evaluation Using Multi-Temporal Remote Sensing and Machine Learning Models: From the Perspective of National Standard

Scientific evaluation of cultivated land quality (CLQ) is necessary for promoting rational utilization of cultivated land and achieving one of the Sustainable Development Goals (SDGs): Zero Hunger. However, the CLQ evaluation system proposed in previous studies was diversified, and the methods were inefficient. In this study, based on China’s first national standard “Cultivated Land Quality Grade” (GB/T 33469-2016), we constructed a unified county-level CLQ evaluation system by selecting 15 indicators from five aspects—site condition, environmental condition, physicochemical property, nutrient status and field management—and used the Delphi method to calculate the membership degree of the indicators. Taking Jimo district of Shandong Province, China, as a case study, we compared the performance of three machine learning models, including random forest, AdaBoost, and support vector regression, to evaluate CLQ using multi-temporal remote sensing data. The comprehensive index method was used to reveal the spatial distribution of CLQ. The results showed that the CLQ evaluation based on multi-temporal remote sensing data and machine learning model was efficient and reliable, and the evaluation results had a significant positive correlation with crop yield (r was 0.44, p < 0.001). The proportions of cultivated land of high-, medium- and poor-quality were 27.43%, 59.37% and 13.20%, respectively. The CLQ in the western part of the study area was better, while it was worse in the eastern and central parts. The main limiting factors include irrigation capacity and texture configuration. Accordingly, a series of targeted measures and policies were suggested, such as strengthening the construction of farmland water conservancy facilities, deep tillage of soil and continuing to construct well-facilitated farmland. This study proposed a fast and reliable method for evaluating CLQ, and the results are helpful to promote the protection of cultivated land and ensure food security.

Mining Association Rules on Time-sensitive Data

Real-world data is continuously produced andcollected quickly; hence, mining association rules are used tofind relationships between item sets. One of these combinedrule mining techniques is Rare Association Rule Mining(RARM), a method that extracts rare, combined rules with lowsupport but high confidence from the database. Additionally,a large amount of time-sensitive data is created in many fields,and combining valid and outdated data leads to low efficiencyin extracting combined rules. This paper explores a miningmethod that increases rare, combined rules on time-sensitivedata to extract common and rare patterns in the database.The primary approach of the research proposes a new treestructure called ILC-tree, inspired by the LC-tree structurepublished in 2022. Specifically, our approach includes a newtree structure and a fast reconstruction method. The proposedapproach reduces execution time and memory consumptionby approximately 2 times compared to the old tree structure.

A fast detection method for metering anomalies of three-phase energy meters based on sliding filter and decision tree

In the actual measurement of three-phase electricity meters, the presence of electromagnetic interference, environmental temperature, and other factors increases the noise in the measurement data, which affects the accuracy of anomaly detection. In this regard, a fast detection method for three-phase meter measurement anomalies based on sliding filters and decision trees is studied. Firstly, sliding filtering and sliding window matrix methods are used to reduce the dimensionality and filter the measurement data of three-phase electric meters. Then, a cascaded random forest is constructed using a CART decision tree, and the filtered three-phase meter measurement data is input into the cascaded random forest. The CART decision tree uses a binary method to partition the nodes. Finally, using the Gini coefficient as a measurement indicator, a cascaded structure is formed through layered stacking to output rapid detection results of outliers in three-phase electricity meter measurements. The experimental results show that this method can quickly detect measurement abnormalities of three-phase energy meters, timely detect meter flying anomalies and unexpected mutations, and improve the fault handling efficiency of three-phase energy meters.

Effects of cross-sectional shape on the low observability of double serpentine nozzles

When unmanned combat aerial vehicles (UCAVs) egress from the battlefield after attacking the target, the defensive radar systems detect signals reflected by the cavity structure of the nozzle and engine of the UCAVs, threatening their survivability. Infrared (IR) guided missiles can also threaten them by detecting the gaseous plume and hot engine parts. We present a comprehensive investigation of the effects of the cross-sectional shape of double serpentine nozzles on thrust, IR signature, and radar cross section (RCS), which are associated with propulsive performance and low observability. The fillet radius and cross-sectional shape of the nozzles were considered as the main design variables. The thermal flow field was analyzed using the compressible Navier-Stokes equations, while the IR signature was analyzed with the radiative heat transfer equation with a narrow band model. The RCS was calculated using a multi-level fast multipole method of full Maxwell's equations instead of the approximate methods often employed. The double serpentine (DS) nozzle with an elliptical cross-section showed high pressure recovery and a 9.66 % lower IR signature than the DS nozzle with a rectangular cross section. The maximum RCS and mean RCS were lowest for the DS nozzle with an elliptical cross section, with a maximum difference in maximum RCS of 383 % and mean RCS of 90 % compared with the DS nozzle with a rectangular cross section. The DS nozzle with an elliptical cross section distributes the current more evenly across the central part of the engine than the DS nozzle with a rectangular cross section. The DS nozzle with an elliptical cross-section showed excellent nozzle performance with a negligible difference in thrust. In addition, the results show that the cross-sectional shape of the nozzles has different levels of impact on thrust and pressure recovery (negligible), IR signature (limited), and RCS (significant).

A fast laser ultrasonic SAFT method with sparse scanning data and F-K based interpolation

ABSTRACT Laser ultrasonic SAFT can be applied for noncontact testing and imaging of defects in structures. However, due to the large demand for signal data by laser scanning, the time consumption of this method would be relatively long. To address this problem, this paper proposes a fast LU-SAFT method with sparse scanning data and F-K based interpolation. It aims to reduce the inspection time but without decreasing the imaging resolution. First, the method determines the minimum number of scanning sparse points using simulation. Then the F-K interpolation method is utilised to recover the full-field unscanned point signal from the sparse scan data. Finally, the reconstructed signal is used for SAFT image reconstruction of the defect. The experimental results show that, for the same scanning area, the proposed method in this paper reduces the number of scanning points by 87.5% and shortens the scanning time by 86.0% compared to conventional LU-SAFT point-by-point scanning. Additionally, the signal-to-noise ratio of the defective SAFT imaging decreases by only about 7.9%.

Calculating traveltimes in 2D general tilted transversely isotropic media using fast sweeping method

Traveltime calculations play an important role in the field of exploration seismology, such as traveltime tomography and seismic imaging and so on. Seismic anisotropy poses a challenge for traveltime calculation, because anisotropic eikonal solvers are more complex than the isotropic counter part. To solve the eikonal equations in 2D tilted transversely isotropic (TTI) media, we have developed a fast algorithm combine with fast sweeping method to compute the first arrival traveltimes of quasi-P (qP)-, quasi-SV (qSV)-, and quasi-SH(qSH)-waves. For the qP- and qSV-waves, we analyzed the quartic coupled slowness surface equation derived from the Christoffel equation. Then, we constructed a local solver to relate traveltime and slowness. We found that in the local solver, one component of the slowness vector is known and the corresponding slowness equation is monotonic. This provides a strong basis for the fast iterative algorithm we proposed, where we use the Newton method to solve the qP- and qSV-wave slowness equation to determine the related traveltimes. For the qSH wave, the slowness equation is quadratic and simple to solve. Numerical experiments demonstrate that the proposed method can obtain accurate traveltimes for simple and complicated 2D TTI models.

Point-of-Care Lung Ultrasound to Evaluate Lung Isolation During One-Lung Ventilation in Children: A Blinded Observational Feasibility Study.

Minimally invasive thoracic surgical techniques require effective lung isolation using one-lung ventilation (OLV). Verification of lung isolation may be confirmed by auscultation, visual confirmation using fiberoptic bronchoscopy (FOB), or more recently, point-of-care ultrasound (POCUS). The aim of this study was to prospectively compare lung ultrasound with clinical auscultation to confirm OLV before thoracic surgery in pediatric patients. This prospectively blinded feasibility study included 40 patients ranging in age from 0 to 20 years. After confirmation of lung separation by the primary anesthesia team using FOB, the sonographer and the auscultator, both blinded to the laterality of surgery and lung separation, entered the operating room. The sonographer evaluated for pleural lung sliding and the auscultator listened for breath sounds. Successful lung separation was definitively confirmed by direct visualization of lung collapse during the operation. In confirming effective single-lung ventilation, lung ultrasound had a diagnostic accuracy of 95% (95% confidence interval [CI], 82.7%-98.5%). In contrast, auscultation could only reliably confirm lung isolation with 68% accuracy (95% CI, 51.5%-80.4%). The McNemar test showed a statistically significant difference between the use of lung ultrasound and auscultation (P < .001). The median time to perform ultrasonography was 67 seconds (interquartile range [IQR], 46-142) and the median time to perform auscultation was 21 seconds (IQR, 10-32). Based on the initial results of our feasibility trial, lung ultrasound proved to be a fast and reliable method to verify single-lung ventilation in pediatric patients presenting for thoracic surgery with a high degree of diagnostic accuracy.

Preparation and Performance of Leather-Finished Plywood.

In order to achieve batch production, we propose a simple and fast method to prepare leather-finished plywood. In this study, ethylene-vinyl acetate was selected as the intermediate layer to prepare EVA/polyurethane (PU) leather composites. ESEM, tensile property test and compressive property test were used to characterize the microstructure and physical-mechanical properties of the composites. The response surface method (RSM) was also used to explore the relationship between hot pressing temperature, hot pressing pressure and hot pressing time. The significance of the factors and the interactions between the two factors were determined by ANOVA, with the most significant effect being that of the temperature. The theoretical optimal hot pressing process conditions were calculated by the regression equation as a temperature of 124.4 °C, a time of 200 s and a pressure of 1.3 MPa. The surface bond strength of the test specimen measured under this condition was 1.89 MPa, it has good finishing properties and the impregnation peel strength and surface bond strength met the requirements of GB/T 15104-2021.

Dual-State Red-Emitting Zinc-Based MOF Accompanied by Dual-Mode and Dual-State Detection: Color Tonality Visual Mode for the Detection of Tetracycline.

Red-emitting metal-organic frameworks (MOFs) are still mostly based on the use of lanthanides or functionalization with red fluorophores. However, production of transition-metal-based MOFs with red-emitting is scarce. This work reports on the synthesis of a novel dual-state red-emitting Zn-based MOF (denoted as UoZ-7) with the capability to detect target molecules in dual state, in solution, and as solid on paper. UoZ-7 gives strong red emission when excited in the solution and in the solid state with 365 nm ultraviolet (UV) lamp irradiation. Coordination-induced emission is the mechanism for the red emission enhancement in the MOF as a restriction of intramolecular rotation occurred to the ligand within the framework structure. UoZ-7 was successfully used for tetracycline (TC) using dual-mode detection, fluorescence-based ratiometry, and color tonality, in the dual state, in solution, and on the paper. TC molecules adsorb on the red-emitting UoZ-7 surface, and a yellow-greenish color emerges due to aggregation-induced emission between TC and UoZ-7. Concurrently, the inner filter effect diminishes the red emission of UoZ-7. The dual-mode or dual-state detection platform provides a simple and reliable fast method for the detection of TC on-site in various environmental and biomedical applications. Moreover, red-emitting UoZ-7 will have further luminescence-based biomedical applications.

A fast method to estimate the Moore-Penrose inverse for well-determined numerical rank matrices based on the Tikhonov regularization

A fast method to estimate the Moore-Penrose inverse for well-determined numerical rank matrices based on the Tikhonov regularization

Improved Cascade-RCNN for automatic detection of coronary artery plaque in multi-angle fusion CPR images

Coronary heart disease is a disease that seriously endangers human health and life which is caused by plaque formation due to coronary artery atherosclerosis or spasm. The detection of coronary plaques through medical imaging is a non-destructive and fast diagnostic method, which holds significant medical and clinical value for the diagnosis of coronary heart disease. In this paper, We proposed an automatic detecting method for coronary artery vulnerable plaque based on CTA image sequence. We collected CTA image sequences from 132 patients and obtained the coronary artery tree by performing three-dimensional (3D) segmentation. The coronary artery centerline was extracted using the skeleton thinning method. To address the issue of insufficient features in a single CPR image, we constructed Curved Planar Reconstructed (CPR) images from multiple directions perpendicular to the coronary artery, following the centerline. In total, we established 8797 images in the training dataset and 1553 images in the test dataset. We proposed an attentive detection method that fused a new Cascade RCNN ResNet50-FPN neural network model with a convolutional attention module (CBAM). This method achieved automatic recognition and detection of plaques on the two-dimensional multi-angle fused CPR images. The proposed ResNet50-FPN network performed feature extraction, and CBAM was added after the first and last convolutional layers to enhance the network’s attention to plaque features. Our experimental results demonstrate that deep learning neural networks can provide a feasible approach to automatically detecting coronary artery plaques. Our method achieved a plaque detection accuracy of 94.6%, outperforming the RCNN method proposed by Zriek.

Cost-effectiveness and Accuracy Analysis of Different Screening Strategies for COVID-19

Background: Coronavirus disease 2019 (COVID-19) is mainly confirmed through reverse transcription polymerase chain reaction (RT-PCR), which is both expensive and time-consuming and requires specific equipment. Thus, demand for rapid diagnostic methods, such as rapid antigen detection (RAD) tests, has emerged. Here, we assessed the efficiency and reliability of RAD for early diagnosis of COVID-19. Methods: We performed a retrospective study based on systemic COVID-19 screening and diagnosis using RAD and RT-PCR at Taipei City Hospital during the first COVID-19 outbreak week in Taiwan (28 May-6 June 2021). We postulated five strategies for COVID-19 screening in the general population: one RAD test, one RT-PCR test, one RAD test (negative) followed by one RT-PCR, one RAD test (positive) followed by one RT-PCR test, and two RAD tests and calculated their cost and accuracy. We also conducted a cost-effectiveness analysis to identify the most economically efficient strategy for COVID-19 diagnosis. This study included 6368 cases in total. Results: Although RAD exhibited low sensitivity (mean 66%), other values were acceptable: mean specificity, positive predictive value, negative predictive value, and accuracy were 99%, 82.8%, 97.5%, and 94.2%, respectively. RAD was the most cost-effective method. Additionally, it may be an effective diagnostic strategy in the later stages of the epidemic, warranting further research. Conclusion: Although RT-PCR remains the standard method for diagnosing COVID-19, RAD is faster and a more cost-effective, convenient, and globally available method, allowing for early detection of SARS-CoV-2. conclusion: We propose that RAD could be a fast and feasible method for diagnosing SARS-CoV-2 infection, allowing for early detection. Although RT-PCR remains the standard method for diagnosing SARS-CoV-2 infection, RAD is a more cost-effective, convenient, fast, and globally available method.

Fast, Easy, and Reproducible Fingerprint Methods for Endotoxin Characterization in Nanocellulose and Alginate-Based Hydrogel Scaffolds.

Nanocellulose- and alginate-based hydrogels have been suggested as potential wound-healing materials, but their utilization is limited by the Food and Drug Administration (FDA) requirements regarding endotoxin levels. Cytotoxicity and the presence of endotoxin were assessed after gel sterilization using an autoclave and UV treatment. A new fingerprinting method was developed to characterize the compounds detected in cellulose nanocrystal (CNC)- and cellulose-nanofiber (CNF)-based hydrogels using both positive- and negative-ion mode electrospray ionization Fourier transform ion cyclotron resonance mass spectroscopy (ESI FT-ICR MS). These biobased hydrogels were used as scaffolds for the cultivation and growth of human dermal fibroblasts to test their biocompatibility. A resazurin-based assay was preferred over all other biocompatibility methodologies since it allowed for the evaluation of viability over time in the same sample without causing cell lysis. The CNF dispersion of 6 EU mL-1 was slightly above the limits, and it did not affect the cell viability, whereas CNC hydrogels induced a reduction of metabolic activity by the fibroblasts. The chemical structure of the detected endotoxins did not contain phosphates, but it encompassed hydrophobic sulfonate groups, requiring the development of new high-pressure sterilization methods for the use of cellulose hydrogels in medicine.