Automatic Sampling Research Articles

Object Detection and Regression Based Visible Spectrophotometric Analysis: A Demonstration Using Methylene Blue Solution

This study investigates the estimation of the concentration of methylene blue solutions to understand if visible spectrophotometry could be performed using a smartphone and machine learning. The presented procedure consists of taking photos, detecting test tubes and sampling region of interest (ROI) with YOLOv5, finding the hue, saturation, value (HSV) code of the dominant color in the ROI, and regression. 257 photos were taken for the procedure. The YOLOv5 object detection architecture was trained on 928 images and the highest mAP@05 values were detected as 0.915 in 300 epochs. For automatic ROI sampling, the YOLOv5 detect.py file was edited. The trained YOLOv5 detected 254 out of 257 test tubes and extracted ROIs. The HSV code of the dominant color in the exported ROI images was determined and stored in a csv file together with the concentration values. Subsequently, 25 different regression algorithms were applied to the generated data set. The extra trees regressor was the most generalizing model with 99.5% training and 99.4% validation R2 values. A hyperparameter tuning process was performed on the extra trees regressor and a mixed model was created using the best 3 regression algorithms to improve the R2 value. Finally, all three models were tested on unseen data and the lowest MSE value was found in the untuned extra trees regressor and blended model with values of 0.10564 and 0.16586, respectively. These results prove that visible spectrophotometric analysis can be performed using the presented procedure and that a mobile application can be developed for this purpose.

Measuring magnetic susceptibility of particulate matter collected on filters

The magnetic susceptibility (κ) of particulate matter (PM) is a useful tool in estimation concentration of iron-rich particles and provides useful information on the emission sources and pathways of spread of PM in the atmosphere. However, there is currently no established protocol for measuring the magnetic susceptibility of PM collected on filters used in standard monitoring of PM concentration. This paper presents a step-by-step process for collecting PM on filters in automatic samplers and measuring their κ. The procedure outlines requirements for data quality, measurement uncertainty, exposure time and conditions, and the amount of material collected on the filters. The study analyzed a 2-year dataset of magnetic susceptibility measurements by MFK-1 kappabridge (Agico, Czech Republic) for PM10 and PM2.5 collected at two locations, Warsaw and Cracow, in Poland using low-volume PM samplers. By strictly following the procedure for conditioning filters, measuring magnetic susceptibility and mass of PM, the study found that it is possible to obtain repeatable data with good measurement accuracy and acceptable errors. This makes magnetic susceptibility an additional reliable parameter for tracking of emission sources of iron-rich particles. Successful implementation of this magnetic method as a standard procedure for monitoring PM in addition to the PM mass collected on filters could be used to analyze sources of emission of Fe-particles and their contribution to the PM mass, especially in urban and industrial environments.

On the feasibility of Vis–NIR spectroscopy and machine learning for real time SARS-CoV-2 detection

The pandemic caused by Covid-19 is still present around the world. Despite advances in combating the disease, such as vaccine development, identifying infected individuals is still essential to optimize the control of human-to-human transmission of the virus. The main technique for detecting the virus is the RT-PCR method, which, despite its high relative cost, has a high accuracy in detecting the coronavirus. Given this, a method capable of performing the identification quickly, accurately, and inexpensively is necessary. Thus, this work aimed to analyze the feasibility of a new technique for identifying SARS-CoV-2 through the use of optical spectroscopy in the visible and near-infrared range (Vis–NIR) combined with machine learning algorithms. Spectral signals were obtained from nasopharyngeal swab samples previously analyzed using the RT-PCR method. The specimens were provided by the Molecular Diagnosis Laboratory of Covid-19 at Univasf. A total of 314 samples were analyzed, comprising 42 testing positive and 272 testing negative for Covid-19. Digital signal processing techniques, such as Savitzky–Golay filters and statistical methods were used to eliminate spurious elements from the original data and extract relevant features. Supervised machine learning algorithms such as SVM, Random Forest, and Naive Bayes classifiers were used to perform automatic sample identification. To evaluate the performance of the models, a 5-fold cross-validation technique was applied. With the proposed methodology, it was possible to achieve an accuracy of 75%, a sensitivity of 80%, and a specificity of 70%, in addition to an area under the ROC curve of 0.81, in the identification of nasopharyngeal swab samples from previously diagnosed individuals. From these results, it was possible to conclude that Vis–NIR spectroscopy is a promising, fast and relatively low cost technique to identify the SARS-CoV-2.

Design and analysis of a bionic adhesion coring sampler for space unstructured surface

The better application of robot technology in space unstructured scenes has become an important research topic. How to adapt to limited workspace and maintain high reliability sampling activities on unstructured surfaces with unknown geometric information and physical features poses high requirements for the implementation of automated sampling missions for extraterrestrial celestial bodies (ECBs). This work introduces a biomimetic adhesion sampler PBAS. It expects to achieve multisite core sampling activities on the unstructured surface of ECBs under astronaut assistance or unmanned automatic operation. Different from previous sampler, the system adopts novel modular structure design and has made improvements to the bionic adhesion mechanism, percussive and drilling mechanism, power transmission and sensing mechanism, and control mechanism. PBAS can firmly adhere to unstructured surfaces and efficiently obtain complete coring samples through penetration and drilling activities. It is compact and lightweight and has high portability, good reusability, excellent efficiency, as well as better performance in automatic sampling operations. Finally, to verify the effectiveness and reliability of the PBAS, a testing apparatus was designed. Relevant tests were conducted outdoors and in the laboratory. The experimental results indicate that PBAS can successfully adhere to surfaces with different characteristics and obtain coring samples with a length exceeding 50 mm.

Real-Time and Online Monitoring of Hazardous Volatile Organic Compounds in Environmental Water by an Unmanned Shipborne Mass Spectrometer System.

Hazardous volatile organic compounds (VOCs) are one of the critical concerns in environmental water due to their toxicity to aquatic organisms and drinking water. Therefore, rapid detection of hazardous VOCs in environmental water is highly needed as many analytical methods are limited to on-site monitoring. In this work, we designed a novel unmanned shipborne mass spectrometer (US-MS) system for the real-time and online monitoring of hazardous VOCs in environmental water. The US-MS system consists of a miniaturized mass spectrometer, an automatic sampling device, a robust unmanned ship, and other monitoring and control devices. Along with the navigation route of the US-MS system, environmental water was continuously introduced into the MS system for the online and real-time detection of hazardous VOCs via a liquid/gas exchange membrane. Analytical performances of the US-MS system were investigated by a mixture of 10 VOCs showing low limits of detection (LODs: 0.31-1.26 ng/mL), good reproducibility (RSDs: 2.93-11.03%, n = 7), and excellent quantitative ability (R2 > 0.99). Furthermore, on-site detection and online monitoring of hazardous volatile contaminants such as benzene, chloroprene, and toluene in different aquatic environments such as rivers and lakes were successfully demonstrated, showing excellent field applicability of the US-MS system. Overall, the newly developed US-MS system could perform on-site, online, and real-time monitoring of complex VOCs in environmental water, showing good performances and versatile applications in water analysis.

Fine-grained wetland classification for national wetland reserves using multi-source remote sensing data and Pixel Information Expert Engine (PIE-Engine)

ABSTRACT Timely and accurate wetland information is necessary for wetland resource management. Recent advances in machine learning and remote sensing have facilitated cost-effective monitoring of wetlands. However, reliable methods for fine-grained and rapid wetland mapping are still lacking. To address the issue, a wetland sample set with 20 categories for China was collected based on a sampling strategy that combines automatic sample generation and visual interpretation. Simultaneously, a novel multi-stage method for fine-grained wetland classification was proposed, which integrates pixel-based and object-based strategies using ensemble learning algorithms and multi-source remote sensing data. First, a pixel-based ensemble learning algorithm was implemented to classify five rough wetland categories and six non-wetland categories. Second, an object-based ensemble learning approach was designed to separate the water cover in the pixel-based classification results into eight detailed categories. Third, the merged pixel-based and object-based classification results were refined with knowledge-based post-processing procedures to identify 14 fine-grained wetland categories. Results using the Pixel Information Expert Engine (PIE-Engine) cloud platform proved the effectiveness of the proposed wetland classification method. The overall accuracy, kappa, and weighted F1 reached 87.39%, 82.80%, and 86.02%, respectively. The adopted ensemble learning algorithm yielded better performance than classifiers such as CatBoost, random forest, and XGBoost. The incorporation of spectral, texture, shape, topographic, and geographic features from multi-source data contributed to differentiating wetland categories. According to the relative contribution, spectral indexes (NDVI and NDWI), texture features (sum average and contrast), and topographic features (slope and elevation) were identified as important leading predictors for the first-stage pixel-based classification. Shape features (shape index and compactness) and auxiliary features (geographic location) were crucial predictors for the second-stage object-based classification. Compared with other products, our 10-m wetland mapping results for national wetland reserves were rich in detail and fine in categories. Overall, the constructed sample set and developed classification method show promise in laying a foundation for large-scale wetland mapping. The derived wetland maps can provide support for wetland protection and restoration.

Design of In-Orbit Sample Container Transfer Mechanism for Chang’E-5 Lunar Sample Return Mission

In this paper, a pawl composite linkage transfer mechanism is designed for the automatic in-orbit sample transfer mission of Chang’E-5 lunar sample return mission, which can realize the sample container in-orbit transfer under various critical constraints such as lightweight, miniaturization, and narrow working space. Resistance during the whole process as well as the sensitive factors that affect the resistance during the sample container transfer process are investigated and designed. The sample container transfer process has been verified by the test system on the ground, indicating that the design can satisfy the requirements of the sample transfer mission. The developed transfer mechanism completed the Chang’E-5 sample return mission successfully with good consistency between space and ground, verifying the correctness and effectiveness of the design.

Mapping 30-m cotton areas based on an automatic sample selection and machine learning method using Landsat and MODIS images

ABSTRACT Cotton is one of the most significant cash crops in the world, and it is also the main source of natural fiber for textiles. It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accurately on a fine scale. However, previous research studies have predominantly concentrated on specific years using remote sensing data. Challenges still exist in the extraction of cotton areas for long time series with high accuracy. To address this issue, a novel cotton sample selection method was proposed and the machine learning method is employed to effectively identify the long time series cotton planting areas at a 30-m resolution scale. Bortala and Shuanghe in Xinjiang, China, were selected as the study cases to demonstrate the approach. Specifically, the cropland in this study was extracted by using an object-oriented classification method with Landsat images and the results were optimized as the vectorized boundary of croplands. Then, the cotton samples were selected using the Normalized Difference Vegetation Index (NDVI) series of Moderate Resolution Imaging Spectroradiometer (MODIS) based on its phenological characteristics. Next, cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model. Finally, the performance was evaluated, and the spatiotemporal distribution characteristics of cotton planting areas were analyzed. The results showed that the proposed approach can achieve high accuracy at a fine spatial resolution. The performance evaluation indicated the applicability and suitability of the method, there is a good correlation between the extracted cotton areas and statistical data, and the cotton area of the study area showed an increasing trend. The cotton spatial distribution pattern developed from dispersion to agglomeration. The proposed approach and the derived 30-m cotton maps can provide a scientific reference for the optimization of agricultural management.

Digital metabolic activity assay enables fast assessment of 2D materials bactericidal efficiency

BackgroundThe identification and quantification of viable Escherichia coli (E. coli) are important in multiple fields including the development of antimicrobial materials, water quality, food safety and infections diagnosis. However, the standard culture-based methods of viable E. coli detection suffer from long detection times (24 h) and complex operation, leaving the unmet requirement for fast assessing the efficiency of antimicrobial materials, early alerting the contamination of water and food, and immediately treatment of infections. ResultsWe present a digital β-d-glucuronidase (GUS) assay in a self-priming polydimethylsiloxane (PDMS) microfluidic chip for rapid E. coli identification and quantification. The GUS expression in viable bacteria was investigated to develop a fast GUS assay at the single-cell level. Single E. coli were stochastically discretized in picoliter chambers and identified by specific GUS activity. The digital GUS assay enabled identifying E. coli within 3 h and quantifying within 4 h for different E. coli subtypes. The specificity of our method was confirmed by using blended bacteria including E. coli, Bacillus, Shigella and Vibrio. We utilized digital GUS assay to enumerate viable E. coli after incubated with antibacterial materials for assessing the antibacterial efficiency. Moreover, the degassed chip can realize automatic sample distribution without external instruments. SignificanceThe results demonstrated the functionality and practicability of digital GUS assay for single E. coli identification and quantification. With air-tight packaging, the developed chip has the potential for on-site E. coli analysis and could be deployed for diagnosis of E. coli infections, antimicrobial susceptibility testing, and warning the fecal pollution of water. Digital GUS assay provides a paradigm, examining the activity of metabolic enzyme, for detecting the viable bacteria other than E. coli.

Towards automatic feature extraction and sample generation of grain structure by variational autoencoder

Granularity is one of the most predominant structures for polycrystalline materials, from geological compounds to technical high-performance alloys. In particular, grain structures in metal alloys enable custom-tailored material properties if their formation can be modeled and simulated accurately to analyze, characterize, and generate granular materials. Despite significant advances in the field, established physics-based grain-growth models still need to improve to match real-life experimental data accurately. Instead of relying on physical knowledge, data-driven models provide a complementary way to study grain structures using only the information in the data. However, many machine learning methods require supervised learning on information that is challenging to measure or numerically expensive to compute. In this work, we propose a new strategy that uses unsupervised deep learning on synthetic physics-based data to generate realistic granular structures with customizable features and properties (grain size/number). A variational autoencoder learns to compress grain structures into a low-dimensional latent space, derive data-driven features that characterize grain structures, and use these features to generate new structures with representative grain size distributions in a computationally efficient way. These data-driven features can complement the physics-based features derived from classical materials research and help identify new characterizing properties. Thus, this work represents a prototype application of bridging machine learning methods to material characterization and generation.

Change detection in VHR Remote Sensing Images by automatic sample selection and progressive classification

ABSTRACT This paper proposes an automatic method for land cover change detection in very-high-spatial-resolution optical remote sensing images based on the automatic selection of training samples using expectation-maximization (EM) and an extreme learning machine classifier, which has two key characteristics: (1) combining the advantages of supervised and unsupervised methods with progressive mask classification. (2) training samples of three classes (changed, unchanged, and unknown) were automatically selected by K-Means and EM, and then further refined by the likelihood function. The method was validated by one dataset of SuperView-1 imagery with a spatial resolution of 2.0 m, two datasets of TripleSat-2 imagery with a spatial resolution of 3.2 m, and one open dataset of Zi-Yuan-3 imagery with a spatial resolution of 5.8 m, and the results were compared with that of three unsupervised methods (iterative slow feature analysis, multivariate alteration detection, and adaptive object-oriented spatial-contextual extraction algorithm), two deep learning methods (convolutional-wavelet neural networks and dual-domain networks), and three supervised classifiers (support vector machine, random forest, and Naive Bayes), showing the effectiveness of this method in decrease of false-positive rates and increase of change detection accuracy. The average and maximum of the accuracy metric F1 score of our method are 0.6842 and 0.8708, respectively; the average F1 score of the unsupervised, deep learning, and supervised methods are 0.5049, 0.4943, and 0.633, respectively.

Biosignature preparation for ocean worlds (BioPOW) instrument prototype

In situ sampling missions to detect biosignatures on ocean worlds requires thorough sample preparation to manage the expected chemical complexity of such environments. Proposed instruments must be capable of automatic liquid sample handling to ensure sensitive and accurate detections of biosignatures, regardless of the initial chemical composition. Herein, we outline the design, build, and test of the integrated Biosignature Preparation for Ocean Worlds (BioPOW) system capable of purifying amino acids from icy samples. This four step modular instrument 1) melts ice samples, 2) purifies amino acids via cation exchange chromatography, 3) concentrates via vacuum drying, and 4) derivatizes amino acids to volatilize and enable detection with downstream analytical instruments. Initial experiments validated the thermal performance of the system by melting ice in the sample cup (1 mL sample, 3°C–28°C, <5 min, 1.4 kJ) and heating the derivatization tank past the concentration temperature (20°C–60°C, 12 min, 3.6 kJ) to the derivatization temperature (60°C–90°C, 25 min, 7.5 kJ). Later experiments investigated important factors for automatic cation exchange using a design of experiments approach, and found that initial salt concentration, sample and eluate flow rates, and water wash volumes all play significant roles in reducing conductivity (1.1 x–6.7 x) while maintaining phenylalanine yields between 31% and 94%. The modules were then integrated into a 12 cm × 20 cm × 20 cm fieldable platform for analysis, and the maturation of this design for future spaceflight is discussed.

Visualization and quantitative evaluation of delamination defects in GFRPs via sparse millimeter-wave imaging and image processing

Multilayer glass fiber reinforced polymer (GFRP) has broad applications, playing a pivotal role in various fields including aerospace and wind energy production. These structures may develop internal delamination defects during their manufacture or service, which can significantly impair the integrity and safety of the equipment. Thus, the inspection of such delamination defects during the service life of the equipment is crucial for assessing its operational performance and lifespan. Millimeter-wave (MMW) nondestructive testing (NDT) presents a promising method for detecting these defects in GFRPs, due to its high resolution and powerful penetration characteristics. This paper proposes a visual NDT and quantitative characterization method based on sparse imaging and image processing. This method encompasses the following three key points. (i) To facilitate rapid detection and visualization in monostatic mode, we proposed a detection approach utilizing sparse scanning and imaging. Initially, we developed a sparse sampling control matrix grounded to guide the probe for spatial automatic sparse sampling, thereby enhancing data acquisition efficiency and reducing data volume. Subsequently, we introduced a rapid sparse imaging method, integrating wave spectrum reconstruction and compressed sensing (CS). Beginning from the original data domain, we designed a fast imaging operator employing forward and inverse transformations of spectrum reconstruction imaging. (ii) In order to further refine the quality of the detection images, we proposed an adaptive singular value decomposition (SVD) approach to suppress the coupling signal and enhance the specimen signal. By adaptively adjusting the singular values, we generated a signal with the optimal target-to-background ratio (TBR). (iii) We ascertained the position and area parameters of the delamination defects using the proposed defect quantification method, combining image segmentation and image statistics. Experimental validation affirmed the effectiveness of the proposed method, revealing the following results: 1) The proposed method, despite utilizing a small amount of observational data, generates better visual images while maintaining efficient detection; 2) It can effectively identify all 0.5 mm-thick internal delamination defects in GFRPs, simultaneously retaining high quantitative characterization precision; 3) The positional quantification error remains less than 0.5 mm, and the relative area quantification error is constrained within 8%.

Automatic Soil Sampling Site Selection in Management Zones Using a Multi-Objective Optimization Algorithm

Precision agriculture hinges on accurate soil condition data obtained through soil testing across the field, which is a foundational step for subsequent processes. Soil testing is expensive, and reducing the number of samples is an important task. One viable approach is to divide the farm fields into homogenous management zones that require only one soil sample. As a result, these sample points must be the best representatives of the management zones and satisfy some other geospatial conditions, such as accessibility and being away from field borders and other test points. In this paper, we introduce an algorithmic method as a framework for automatically determining locations for test points using a constrained multi-objective optimization model. Our implementation of the proposed algorithmic framework is significantly faster than the conventional GIS process. While the conventional process typically takes several days with the involvement of GIS technicians, our framework takes only 14 s for a 200-hectare field to find optimal benchmark sites. To demonstrate our framework, we use time-varying Sentinel-2 satellite imagery to delineate management zones and a digital elevation model (DEM) to avoid steep regions. We define the objectives for a representative area of a management zone. Then, our algorithm optimizes the objectives using a scalarization method while avoiding constraints. We assess our method by testing it on five fields and showing that it generates optimal results. This method is fast, repeatable, and extendable.

Fully Cascade Consistency Learning for One-Stage Object Detection

Object detection is usually solved by deploying one single prediction head including classification and localization branches to obtain the final results. Recently proposed works utilize several prediction heads in a cascade learning manner to improve the detection performance. Despite achieving promising performance, existing cascade learning manner methods still meet with two inconsistency issues. Firstly, most of them refine the bounding boxes in different prediction heads only by depending on the localization accuracy ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., IoU), while ignoring the inconsistency between classification confidence and localization accuracy. Moreover, simply increasing the IoU threshold by experience to select positive samples makes the inconsistency issue even worse. Secondly, little consideration has been paid on the feature inconsistency between detection-specific features from different prediction heads and detection-generalized ones from backbone model. The extracted feature from backbone model contains the general representation for the whole images. While prediction heads need to be carefully designed to have specific ability which contains more discriminative expressions for the two sub-tasks classification and regression. The different contexture representations of the output features from these two parts lead to the feature inconsistency between backbone model and prediction head in cascade learning architecture. To solve these two inconsistency issues, this paper proposes a novel cascade consistency learning method for one-stage detector. Specifically, a feature adaptation module is firstly developed to calibrate features from different prediction heads and backbone model for solving the feature inconsistency. Then, we design an automatic positive sample threshold selection strategy for further solve the inconsistency between the classification and localization predictions. Moreover, the quality of bounding boxes in cascade learning manner are evaluated by taking both the classification confidence and localization accuracy into consideration. Experiments on MS COCO show that our proposed cascade consistency learning manner (dubbed C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L) can achieve clear improvement over counterparts based on several different one-stage detectors, while performing favorably against state-of-the-arts.

Well-oriented immobilized immunoaffinity magnetic beads for detection of fumonisins in grains and feeds via pre-column automatic derivatization of high-performance liquid chromatography.

In this study, based on the high-throughput automatic sample pretreatment with immunoaffinity magnetic beads with oriented immobilized antibodies, grain and feed fumonisin (FB) content was detected using pre-column automatic derivatization of high-performance liquid chromatography (HPLC). The FB capacity of well-oriented antibody immunoaffinity magnetic beads was 1.5-1.8 times that of magnetic beads with randomly fixed antibody. This pre-column automatic derivatization method using an autosampler can reduce error from manual injection and improve detection efficiency. The spiked recoveries for three different concentrations in maize, husked rice, and pig feed under optimized conditions were 84.6-104.0% (RSD<9.3%). Our novel method was also applied to the analysis of FBs in 63 maize samples collected from the main maize-production regions in China. The results showed that as latitude increased, the contamination level of FBs tended to decrease. High temperature and high humidity are also more favorable for FB growth.

Investigation on atmospheric radioactivity sample association using consistency with isotopic ratio decay over time at IMS radionuclide stations

For the enhancement of the International Data Centre's products, specifically the Standard Screened Radionuclide Event Bulletin, an important step is to establish methods to associate the detections of the Comprehensive Nuclear-Test-Ban Treaty-relevant nuclides in different atmospheric radioactivity samples with the same radionuclide release to characterize its source for the purpose of nuclear explosion monitoring. Episodes of anomalously high activity concentrations in samples at the International Monitoring System radionuclide stations are used as the primary assumption for being related to the same release. For multiple isotope observations, the consistency of their isotopic ratios in subsequent samples with radioactive decay is another plausible hint for one unique release. The radioxenon observations that are associated with the nuclear test announced by the Democratic People's Republic of Korea in 2013 serve as case study to demonstrate the effectiveness of this basic approach and how the additionally associated samples improve the source location. We use two distinct puff releases, both of short duration, for the atmospheric transport modelling simulations to gain further evidence and confidence in our sample association study by identifying the air masses that link the releases to multiple samples. This basic approach will support the definition of analysis procedures and criteria for automatic sample association to be implemented in the Standard Screened Radionuclide Event Bulletin, which is of relevance for an expert technical analysis.

Advancing cardiovascular disease prediction: portable or wearable devices for automatic and rapid blood sample collection for biomarker detection with simultaneous physiological marker measurement

Advancing cardiovascular disease prediction: portable or wearable devices for automatic and rapid blood sample collection for biomarker detection with simultaneous physiological marker measurement

A-070 A fully automated HbA1c Assay on the DxC 500 AU Clinical Chemistry Analyser

Abstract Background Diabetes mellitus is a condition characterized by hyperglycemia resulting from the body’s inability to use blood glucose for energy. In Type-1 diabetes, the pancreas no longer makes insulin and blood glucose cannot enter the cells to be used for energy. In Type-2 diabetes, the pancreas does not make enough insulin or the body is unable to use insulin correctly. According to the World Health Organization (WHO), 422 million adults were living with diabetes globally in 2014 with an estimated 1.6 million deaths directly associated with diabetes annually. HbA1c is the major species of glycohemoglobin in human blood. HbA1c formation occurs through a non-enzymatic reaction, called glycation, which occurs between glucose and the n-terminal valine of the hemoglobin â-chain of HbA. A Schiff base intermediate product is formed, which rearranges to form a stable ketoamine in an irreversible reaction. The rate of glycation is proportional to the glucose concentration in the bloodstream and is an accurate reflection of average blood glucose over a period of eight to twelve weeks. HbA1c testing is recommended for the diagnosis of diabetes by the International Expert Committee (IEC), the American Diabetes Association (ADA), and the WHO, who recommended a diagnostic threshold of = 6.5% ( = 48 mmoL/moL) HbA1c and a range for pre-diabetes of 5.7%–6.4% (39–46 mmoL/moL) HbA1c. Methods The HbA1c Advanced assay on the DxC 500 AU* utilizes automatic sample pre-treatment, has batch and random access capability. No manual pre-treatment of the whole blood sample or additional washing steps are required. Firstly the red blood cells are hemolyzed automatically, total hemoglobin and glycated hemoglobin are then measured colorimetrically and immunoturbidimetrically respectively. Results Precision studies were conducted according to CLSI EP15-A3. Commercial controls and four native K2 EDTA whole blood samples ranging from 5.1% to 12.0% HbA1c, were run twice daily, over twenty days using three lots of reagent on three DxC500AU Clinical Chemistry analyzers at a single site. Repeatability ranged from 0.86% to 1.44% CV and Total Precision ranged from 1.55% to 2.43% CV. Linearity studies were conducted according to CLSI EP06-A, and verified an analytical measuring range of 4.0%–15.0% (NGSP) and 20–140 mmol/mol (IFCC). Method comparison and bias estimation was evaluated using CLSI EP09-A3. K2 EDTA patient samples (n = 150) across the analytical range were run vs a Secondary Reference method and yielded a slope of 1.031, intercept −0.147% HbA1c, correlation coefficient R = 0.997 for Weighted Deming regression, and slope 1.000 and intercept 0.06% HbA1c for Passing Bablok regression. Interference studies carried out demonstrated no significant interference from common endogenous interferences, a large panel of drugs, common Hb variants (HbC, HbD, HbE, HbA2 and HbS) and cross reactants (HbA0, HbA1a+b, acetylated hemoglobin, carbamylated hemoglobin, glycated hemoglobin, glycated albumin and labile HbA1c). Conclusion The HbA1c Advanced assay on the DxC 500 AU is a precise and accurate assay, requiring no manual pretreatment and can be used for monitoring and diagnosing diabetes. *Pending clearance by the United States Food and Drug Administration and achievement of CE compliance. Not currently available for in vitro diagnostic use.

FARM: A fully automated rice mapping framework combining Sentinel-1 SAR and Sentinel-2 multi-temporal imagery

Rice farming exemplifies intensive agriculture, demanding significant inputs to achieve optimal yields. Thus, accurate and precise mapping of rice cultivation is vital for effective agricultural management and food security. However, such studies have been limited by the challenges of obtaining optical cloud-free data and dealing with radar data's speckle noise. Identifying crops using a single data source poses many difficulties. Additionally, acquiring sufficient representative training samples that accurately reflect diverse phenological patterns is challenging for large-scale monitoring and rice cultivation classification. To address these challenges, this study proposed a fully automated rice-mapping framework (FARM) that combines the strengths of time-series synthetic aperture radar (SAR) and optical satellite imagery for large-scale rice mapping without manual sample collection. First, an object-based, fully automatic training sample generation strategy is introduced. The phenology constraint rule, based on time-series SAR satellite images and specific rice-flooding features, is used to extract rice sample objects. Second, the extracted rice sample objects, adhering to phenological rules, serve as training samples for paddy rice extraction by integrating multiple random forest (RF) classifiers, referred to as the multi-RF method, where each RF is individually built using images acquired during each phenological phase of the growing season. Furthermore, the study explored the availability of the method in early-season rice identification by transferring the training samples acquired by the FARM to a new year. The proposed FARM approach was then validated under different cropping conditions at three study sites in China and two sites in other countries. The results showed that the FARM framework proved to be more effective than other methods at all five study sites, achieving average overall accuracies (OA) ranging from 89.67%−97.00%. In addition, when transferring the training samples from 2021 to other years (2020/2022), the OAs of site A, site B and site C were high during the heading period, with accuracies of 97.57%, 84.28% and 89.27%, respectively. These results demonstrated that, first, the FARM framework exhibits high efficiency and accuracy in different study areas without the need for extensive fieldwork to collect training samples. Second, the method has good performance in the early-season rice mapping of the new year and can be used to perform timely and accurate rice identification and monitoring tasks. The method shows great potential in obtaining large-area automatic rice mapping results in a timely and accurate manner. The latest release of FARM can be viewed at https://feature-selected.users.earthengine.app/view/farm, and the code is available on GitHub at the link https://github.com/gactyxc/FARM.git.