Fast Method Research Articles

Mo-doped Co LDHs as Raman Enhanced Substrate for Detection of Roxarsine

BackgroundRoxarsone (ROX) is widely used as a feed additive, which is indigestible after ingestion by poultry, and most of it can only be excreted into the natural environment and degraded into highly toxic and carcinogenic inorganic arsenic compounds, which pose a hazard to the ecosystem and human health. However, for roxarsone, traditional detection methods require complex and time-consuming procedures, so it is urgent to find a new fast detection method for detection of ROX. ResultsIn this work, we developed a novel Raman enhancement material and utilized the Surface-enhanced Raman scattering (SERS) technique to achieve rapid and sensitive detection of roxarsone. Specifically, Mo-doped cobalt layered double hydroxides (Co-LDHs) semiconductor material (abbreviated as CMM-100) was prepared by a simple method of using ion-assisted MOF etching. Under laser excitation at a wavelength of 532 nm, the CMM-100 showed excellent SERS property to various organic dye molecules such as methylene blue (MB), Toluidine Blue(TB), and Crystal Violet (CV). Especially, an enhancement factor (EF) of 1.4×106 was achieved for MB. Compared with the traditional method, this work utilized the fast and non-destructive SERS technology, which achieved a rapid detection of ROX. The detection limit was as low as 9.73×10-10 M, and the detection range was from 10-9 M to 10-3 M. SignificanceIn this work, SERS technology was adopted for the rapid and sensitive detection of ROX. This study provides a Raman-enhanced substrate named CMMs for detection of food additives, pesticides, biomolecules, etc., which also broadens the application areas of SERS materials.

Reliability, concurrent validity, and clinical feasibility of measurement methods determining volume in patients with lower limb lymphedema and healthy controls

Purpose To investigate reliability, concurrent validity, and clinical feasibility of measurements assessing volume in patients with lower limb lymphedema (LLL) and healthy controls. Materials and methods To investigate intra- and interrater reliability, 47 patients with LLL and 30 healthy controls were assessed three times by two assessors. To investigate between session reliability, 50 participants were reassessed two weeks later. Each assessment included measurements of the midline region (hip circumference; suprapubic volume), leg volume (perimeter every 4 cm; Perometer®), and foot volume (water displacement; figure-of-eight method). Concurrent validity was assessed with correlation coefficients. Measurements were timed and practical limitations were reviewed. Clinical trial registration number: NCT: 05269264. Results Measurements of the total volume of different regions showed weak to very high intraclass correlation coefficients (ICCs) (0.131–998). Absolute and relative volume differences had lower ICC values (0.360–0.976). A strong correlation was found between the total volumes of the same region. The Perometer® and figure-of-eight method were the fastest method for leg and foot volume, respectively. Conclusions The assessed total volumes might be more valuable in assessing the evolution of volume in bilateral LLL than the calculated absolute and relative differences between both limbs. The Perometer® and figure-of-eight method were the most time efficient for leg and foot volume, respectively. Implications for rehabilitation Lymphedema is a chronic condition for which a reliable and clinically feasible assessment of volume is essential for the diagnosis, treatment decisions, and the evaluation of the treatment. This study shows that the total leg/foot volumes were more reliable than the calculated absolute and relative differences between both limbs and could therefore more valuable to evaluate bilateral lower limb lymphedema. For the assessment of leg volume, the Perometer® was the most reliable and fastest method. For the evaluation of the foot volume, the figure-of-eight method was overall the best method.

Reliability, concurrent validity, and clinical feasibility of measurement methods determining volume in patients with lower limb lymphedema and healthy controls

Purpose To investigate reliability, concurrent validity, and clinical feasibility of measurements assessing volume in patients with lower limb lymphedema (LLL) and healthy controls. Materials and methods To investigate intra- and interrater reliability, 47 patients with LLL and 30 healthy controls were assessed three times by two assessors. To investigate between session reliability, 50 participants were reassessed two weeks later. Each assessment included measurements of the midline region (hip circumference; suprapubic volume), leg volume (perimeter every 4 cm; Perometer®), and foot volume (water displacement; figure-of-eight method). Concurrent validity was assessed with correlation coefficients. Measurements were timed and practical limitations were reviewed. Clinical trial registration number: NCT: 05269264. Results Measurements of the total volume of different regions showed weak to very high intraclass correlation coefficients (ICCs) (0.131–998). Absolute and relative volume differences had lower ICC values (0.360–0.976). A strong correlation was found between the total volumes of the same region. The Perometer® and figure-of-eight method were the fastest method for leg and foot volume, respectively. Conclusions The assessed total volumes might be more valuable in assessing the evolution of volume in bilateral LLL than the calculated absolute and relative differences between both limbs. The Perometer® and figure-of-eight method were the most time efficient for leg and foot volume, respectively. Implications for rehabilitation Lymphedema is a chronic condition for which a reliable and clinically feasible assessment of volume is essential for the diagnosis, treatment decisions, and the evaluation of the treatment. This study shows that the total leg/foot volumes were more reliable than the calculated absolute and relative differences between both limbs and could therefore more valuable to evaluate bilateral lower limb lymphedema. For the assessment of leg volume, the Perometer® was the most reliable and fastest method. For the evaluation of the foot volume, the figure-of-eight method was overall the best method.

Detection of fibrous microplastics and natural microfibers in fish species (Engraulis encrasicolus, Mullus barbatus and Merluccius merluccius) for human consumption from the Tyrrhenian Sea

The occurrence of natural/artificial and synthetic microfibers was assessed in three commercial fish species (Engraulis encrasicolus, Mullus barbatus, Merluccius merluccius) from the Tyrrhenian Sea sold for human consumption. The gastrointestinal tracts of n. 150 samples were analyzed, the isolated microfibers were classified applying a morphological approach, based on the analysis of their morphological features, coupled with the identification of the chemical composition of a subsample of microfibers. All the species contained microfibers at levels ranging from 0-49 items/individual and the number of ingested microfibers significantly differed between pelagic and demersal fishes. The evaluation of fiber morphologies highlighted that natural/artificial microfibers were the most numerous among the isolated microfibers, while the dominant colors were blue, black, and clear in all the species. Chemical characterization confirmed the morphological identification and indicated cellulose and polyester as the most common polymer types. Considering the analytical issues that may affect the evaluation of microfiber pollution, the results pointed out the importance of an accurate morphological approach that allows the distinction between different fiber types, before the spectroscopic analyses. Moreover, the implementation of fast and accessible methods to identify microfibers in fish species intended for human consumption will be beneficial also to make an adequate risk assessment to consumer health.

Separation of fringe patterns in fast deflectometric measurement of transparent optical elements based on neural network-assisted fast iterative filtering method

Separation of fringe patterns in fast deflectometric measurement of transparent optical elements based on neural network-assisted fast iterative filtering method

Development of Electrochemical and Colorimetric Biosensors for Detection of Dopamine

Neurotransmitters are essential chemical messengers required for proper brain function, and any changes in their concentrations can lead to neuronal diseases. Therefore, sensitive and selective detection is crucial. This study presents a fast and simple colorimetric method for dopamine detection using three reagent solutions: AgNP and MPA, Ag/Au nanocomposite, and mercaptophenylacetic acid. TEM images showed a narrow distribution of Ag and Au nanoparticles with average sizes of 20 nm and 13 nm, respectively, with gold nanoparticles bound to the edges of silver nanoparticles. A paper-based biosensor was created using manual wax printing for the colorimetric detection of dopamine. Visual detection onsite showed color changes with both the silver nanoparticles and mercaptophenylacetic acid mixture and the silver–gold nanoparticle composite. Electrochemical detection using a glassy carbon electrode modified with 8 mM mercaptophenylacetic acid demonstrated high selectivity and sensitivity towards dopamine, with a peak in the range of 0.7–0.9 V. Interferences were minimized, ensuring high sensitivity and selective detection of dopamine.

YOLO-FGD: a fast lightweight PCB defect method based on FasterNet and the Gather-and-Distribute mechanism

YOLO-FGD: a fast lightweight PCB defect method based on FasterNet and the Gather-and-Distribute mechanism

A fast modeling method for permanent magnet cylindrical eddy current brakes considering end effects

A fast modeling method for permanent magnet cylindrical eddy current brakes considering end effects

HORIZON PICKING ALGORITHM USING GLOBAL OPTIMIZATION AND COHERENCE MEASUREMENT IN POST-STACKED SEISMIC DATA

Accurate horizon recognition within post-stack seismic sections stands as a pivotal stride in seismic interpretation. Horizon-picking techniques wield importance in diverse seismic structural analyses and inversion methodologies. Despite encountering challenges like computational demands and time constraints, the past years have witnessed the evolution of numerous 2-D and 3-D methods. With the progress of modern computing, more resilient and efficient techniques, harnessing artificial intelligence, have come to the fore. In this context, we propose an innovative, cost-effective algorithm grounded in a global optimization framework. This algorithm amalgamates the Very Fast Simulated Annealing method with a set of stability parameters and coherence measurement of neighboring seismic traces, employed as the objective function. The search is executed on continuous and sequential clusters of seismic traces, with a focus on maximizing coherence amidst the presented events. We evaluate the algorithm's efficacy by applying it to two distinct input data sets—enveloped and non-enveloped seismic traces. The initial application pertains to the time-migrated Marmousi dataset, a synthetic marine dataset originating from a complex geological sedimentary basin situated in Angola, Africa. Subsequently, in the context of land seismic data, we apply the algorithm to depth-migrated field data extracted from a past survey conducted in the Tacutu basin in northern Brazil. Outcomes arising from the application to 2-D synthetic and field data sets underscore the method's viability as a compelling alternative for seismic horizon picking within the time or depth domain.#xD;

A fast Galerkin-spectral method based on discrete Legendre polynomials for solving parabolic differential equation

A fast Galerkin-spectral method based on discrete Legendre polynomials for solving parabolic differential equation

Serodiagnosis of nasal myasis in camels (Camelus dromedaries) in Egypt using third larval instar affinity-purified glycoprotein.

The larvae of Cephalopina titillator cause nasopharyngeal myiasis in camels, which parasitize the living tissues of the nasal and paranasal sinuses, pharynx, and larynx. C. titillator infestation adversely affects camel health, meat, and milk production, and can even cause death. In our study, to improve the immunodiagnosis of camel nasal myiasis, a sensitive and specific enzyme-linked immunosorbent assay (ELISA) was developed and evaluated using the Concanavalin-A (Con-A) affinity purification for the C. titillator-N-acetylglucosamine (Ct-GlucNAc) glycoprotein fraction from third larval instars as an antigen for detecting C. titillator antibodies. Crude antigens were prepared from larval instars of C. titillator and evaluated by indirect ELISA. The third C. titillator larval antigen (L3Ct) had the highest protein content (P < 0.001) and the best diagnostic value; chi-square = 235 (P < 0.001). Four glycoprotein fractions were purified separately from the L3Ct antigen by Con-A purification and evaluated. The Ct-GlucNAc glycoprotein fraction was the fraction of choice with the highest diagnostic accuracy (P < 0.05). Using Ct-GlucNAc as a coating antigen, indirect ELISA showed a 99.3% sensitivity for positive results in camel myiasis samples and 100% specificity for negative results in healthy camel samples. The diagnostic accuracy was 99.7%, and no cross reactivity was detected for other parasitic diseases. The indirect ELISA results were confirmed by the western immunoblotting which was characterized by comparing sera from naturally infested dromedary camels with C. titillator, sera from healthy camels and sera from camels with other parasitic infections (Echinococcus granulosus, Fasciola gigantica, Hard ticks; Hyalomma dromedarii, Trichostronglid sp., Eimeria spp., and Cryptosporidium sp.). Immunoreactive antigenic bands of 63, 50, 30 and 18kDa were predominantly detected in sera from camels with nasopharyngeal myiasis and didn't react with healthy and camel's sera from other parasitic infections. However, seven immunoreactive bands appeared at 120, 70, 63, 48, 35, 29, and 19kDa in the crude L3Ct antigen. In addition, a positive rate of C. titillator immunodiagnosis was detected by indirect ELISA (48.6%, chi-square = 483, P < 0.001), which was significantly greater than that of postmortem diagnosis (31%). In conclusion, the current study introduces a new diagnostic immunoaffinity glycoprotein fraction of C. titillator 3rd larval instar-based ELISA as a highly accurate, simple and fast method to detect specific antibodies of nasal myiasis in camels.

HelioPoint – A Fast Airborne Calibration Method for Heliostat Fields

Abstract The initial pointing calibration of a newly built heliostat field can take up to several years. The state-of-the-art method typically used is accurate but slow. A faster method could reduce the commissioning time and therefore increase the viability of a solar tower plant significantly. In this work, we present HelioPoint, a fast airborne heliostat calibration technique, and demonstrate its accuracy to be below 0.3 mrad. The measurement is performed using an LED and a camera fitted to a drone. The only infrastructure required are the roughly pre-aligned heliostats themselves. The method is independent of the sun position and can be performed at any time for arbitrary calibration points.

Ovarian cancer identification technology based on deep learning and second harmonic generation imaging.

Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label-free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues.

Fast Determination of Fat-soluble Vitamins in Pharmaceutical Preparations by High-Performance Liquid Chromatography

Background: Vitamins are needed for the healthy functioning of the body. When these vitamins are not in sufficient amounts in the body, they are usually taken externally with pharmaceutical preparations. Taking vitamins into the body in the right amounts is possible by analyzing the amounts in pharmaceutical preparations with validated methods. Methods: In this study, a fast, simple, easily applicable, and selective normal-phase HPLC method was developed for the simultaneous determination of fat-soluble A, D3, E, and K1 vitamins in pharmaceutical preparations from syrup and tablets. Separation of the vitamins was carried out on a Zorbax CN column (250 x 4.6 mm, 5 µm) using a mixture of hexane-isopropyl alcohol (98:2, v/v) at 30°C column temperature and 1 ml/min flow rate. The detection wavelength is 280 nm. The developed method has been validated according to ICH Harmonised Tripartite Guideline Validation of Analytical Procedures: Text and Methodology Q2(R1) rules. Results: Calibration graphs are linear over the range of 10-1000 µg ml-1 , 1-50 µg ml-1 , 10-2000 µg ml-1 , 0.5-20 µg ml-1, and the limit of detection values were found to be 1.496, 0.280, 1.388 and 0.040 µg ml-1 for A, D3, E and K1 vitamins, respectively. Relative standard deviation values, which express within-day and between-day repeatability, were found below 2.54%. Average recovery values were also found at about 100.28, 101.46, 100.65, and 100.29% for A, D3, E, and K1 vitamins, respectively Conclusion: The developed and validated method was successfully applied to the simultaneous analysis of fat-soluble vitamins A, D3, E, and K1 in pharmaceutical preparations in syrup and tablet form.

Nickel ferrite decorated noble metal containing nitrogen-doped carbon nanotubes as potential magnetic separable catalyst for dinitrotoluene hydrogenation

The 2,4-toluenediamine (TDA) is one of the most important chemicals in the polyurethane industry, produced by the catalytic hydrogenation of 2,4-dinitrotoluene (DNT). The development of novel catalysts that can be easily recovered from the reaction mixture is of paramount importance. In our work, a NiFe2O4/N-BCNT supported magnetic catalyst was prepared by a modified coprecipitation method. The catalyst support alone also showed activity in the synthesis of TDA. Platinum nanoparticles were deposited on the catalyst support surface by a fast, relatively simple, and efficient sonochemical method, resulting in a readily applicable catalytically active system. The prepared catalyst exhibited high activity in hydrogenation tests, which was proved by the exceptionally high DNT conversion (100% for 120 min at 333 K) and TDA yield (99%). Furthermore, the magnetic catalyst can be easily recovered from the reaction medium by the action of an external magnetic field, which can greatly reduce catalyst loss during separation.

Molecular Analysis of Volatile Metabolites Synthesized by Candida albicans and Staphylococcus aureus in In Vitro Cultures and Bronchoalveolar Lavage Specimens Reflecting Single- or Duo-Factor Pneumonia

Current microbiological methods for pneumonia diagnosis require invasive specimen collection and time-consuming analytical procedures. There is a need for less invasive and faster methods to detect lower respiratory tract infections. The analysis of volatile metabolites excreted by pathogenic microorganisms provides the basis for developing such a method. Given the synergistic role of Candida albicans in increasing the virulence of pathogenic bacteria causing pneumonia and the cross-kingdom metabolic interactions between microorganisms, we compare the emission of volatiles from Candida albicans yeasts and the bacteria Staphylococcus aureus using single and mixed co-cultures and apply that knowledge to human in vivo investigations. Gas chromatography–mass spectrometry (GC-MS) analysis resulted in the identification of sixty-eight volatiles that were found to have significantly different levels in cultures compared to reference medium samples. Certain volatiles were found in co-cultures that mainly originated from C. albicans metabolism (e.g., isobutyl acetate), whereas other volatiles primarily came from S. aureus (e.g., ethyl 2-methylbutyrate). Isopentyl valerate reflects synergic interactions of both microbes, as its level in co-cultures was found to be approximately three times higher than the sum of its amounts in monocultures. Hydrophilic–lipophilic-balanced (HLB) coated meshes for thin-film microextraction (TFME) were used to preconcentrate volatiles directly from bronchoalveolar lavage (BAL) specimens collected from patients suffering from ventilation-associated pneumonia (VAP), which was caused explicitly by C. albicans and S. aureus. GC-MS analyses confirmed the existence of in vitro-elucidated microbial VOCs in human specimens. Significant differences in BAL-extracted amounts respective to the pathogen-causing pneumonia were found. The model in vitro experiments provided evidence that cross-kingdom interactions between pathogenic microorganisms affect the synthesis of volatile compounds. The TFME meshes coated with HLB particles proved to be suitable for extracting VOCs from human material, enabling the translation of in vitro experiments on the microbial volatilome to the in vivo situation involving infected patients. This indicates the direction that should be taken for further clinical studies on VAP diagnosis based on volatile analysis.

Fast approximation of fiber reinforced injection molding processes using eikonal equations and machine learning

Injection molding is a popular production process for short fiber reinforced components. The mechanical properties of such components depend on process-induced fiber orientations which are commonly predicted via numerical simulations. However, high computational costs prevent process simulations from being used in iterative procedures, such as topology optimization or finding optimal injection locations. We propose a fast approximation method that extracts nodal features and train a regression model to predict fill states, cooling times, volumetric shrinkage, and fiber orientations. The features are determined by solving eikonal equations with a fast iterative method and computing spatial moments to characterize node-adjacent material distributions. Subsequently, we use these features to train feed forward neural networks and gradient boosted regression trees with simulation data of a large dataset of geometries. This approach is significantly faster than conventional methods, providing 20x speed-up for single simulations and more than 200x speed-up in gate location optimization. It generalizes to arbitrary geometries and injection locations.

Transition metal catalysts coordinated with electrochemistry for preparation of graphene

It is still crucial to produce mass high-quality graphene through simple and low-cost methods for its wide applications. Herein, a simple and fast method by combining inexpensive transition metal catalysts with electrochemistry to exfoliate natural flake graphite is reported. The electrode is prepared in a sandwich structure, where the titanium mesh will enwrap the nickel net coated with graphite powder. This method can quickly achieve the large-scale preparation of graphene within 15 min. The quality of graphene with large lateral size and few layers has been characterized by XRD, SEM, TEM, Raman, and AFM. The possible electrochemical exfoliation mechanism has been proposed. Oxygen evolution reaction occurs at the anode due to the transition metal catalysts, and hydrogen ions (H+) are generated along with oxygen, H+ and SO42− ions will form a local strong acid. The formation of strong acidic H2SO4 in-situ around electrode will oxidize the edge of the graphite, allowing the intercalation of SO42−, H2O, SO2 and O2 into the graphite sheets, which will inflate the graphite and exfoliate of graphite into graphene. This strategy is reliable, environmentally friendly, and suitable for industrial production.

Label-Free Poisoning Attack Against Deep Unsupervised Domain Adaptation

Deep unsupervised domain adaptation (UDA) has significantly boosted the performance of deep models on different domains by transferring knowledge from a source domain to a target domain. However, its robustness against adversarial attacks has not been explored due to the challenges of highly non-convex deep models and different data distribution. In this paper, we give the first attempt to analyze the vulnerability of deep UDA and propose a label-free poisoning attack (LFPA), which injects poisoning data into the training data to mislead adaptation between the two domains without ground truth in target domain. Specifically, we design an unsupervised adversarial loss as the attack goal, in which the pseudo-labels are used to approximate the ground-truth. Since retraining the model will gradually degrade the attack performance, we also add a regularization term to the unsupervised loss, which eliminates negative interactions between the training goal and the attack goal. To accelerate the craft of poisons, we select influential samples as the initial poisons and propose a fast reverse-mode optimization method which updates poisons according to the approximate truncated gradients. Experimental results on multiple state-of-the-art deep UDA methods demonstrate the effectiveness of the proposed LFPA and the high sensitivity of UDA to poisoning attacks.

Detection of Anthocyanins in Potatoes Using Micro-Hyperspectral Images Based on Convolutional Neural Networks.

The color potato has the function of both a food and vegetable. The color potato not only contains various amino acids and trace elements needed by the human body but also contains anthocyanins. Anthocyanins have many functions, such as antioxidation, inflammation inhibition, vision improvement, and cancer prevention, so colored potatoes are deeply loved by consumers and have good market prospects. However, at present, the detection of anthocyanin content in color potatoes mainly depends on chemical methods, which are time-consuming and laborious, so it is necessary to study a fast and accurate detection method. In this study, microscopic hyperspectral equipment was used to collect the spectral information of the outer skin and inner skin of potatoes. The original spectrum, pretreatment spectrum, and characteristic spectrum variables of the outer skin and inner skin were predicted by the convolution neural network (CNN) algorithm and partial least squares regression (PLS) algorithm, respectively, and the performance of the model was evaluated by the prediction set correlation coefficient (Rp), prediction set root mean square error (RMSEP), correction set correlation coefficient (Rc), correction set root mean square error (RMSEC), and residual prediction deviation (RPD). The results revealed that the inner skin Raw + CNN model constructed under raw spectral data is optimal with Rc = 0.9508, RMSEC = 0.0374%, Rp = 0.9461, RMSEP = 0.2361% and RPD = 4.4933. The inner skin Savitzky-Golay (SG) + Detrend (DET) + CNN model constructed from pre-processed spectral data is optimal with Rc = 0.9499, RMSEC = 0.0359%, Rp = 0.9439, RMSEP = 0.2384%, RPD = 4.6516. The inner skin DET + competitive adaptive reweighted sampling (CARS) +CNN model constructed from the feature-based spectral data was optimal with Rc = 0.9527, RMSEC = 0.0708%, Rp = 0.9457, RMSEP = 0.2711%, and RPD = 4.1623. It can be seen that the Rp, RMSEP, Rc, RMSEC, and RPD values for modeling the spectral information of the inner skin were higher than those of the outer skin under the three different spectral data. The prediction accuracy of the model built by the CNN algorithm was better than the conventional algorithm PLS, the application of the CNN algorithm in inner skin can achieve accurate prediction of anthocyanin content in potato.