Precise Verification Research Articles

Performance evaluation of four kits for the detection of neutralizing antibody against SARS-CoV-2 in human serum

To evaluate four novel SARS-CoV-2 neutralizing antibody assay kits' application in neutralizing antibodies of population. Questionnaires from the voluntary participating researchers and selected the qualified questionnaires to analyse. For negative and positive coincidence rate, four novel SARS-Cov-2 neutralization antibody assay kits were tested. For within-run and between-run Precision verification study, four serum samples with two high and two low titer neutralizing antibodies were used to analyse. Based on the questionnaires, 175 qualified samples were divided into two groups. (1) negative neutralizing antibodies group: 31 samples had not been infected with the novel SARS-Cov-2 nor received the vaccine within the past one year; (2) positive neutralizing antibodies group: 144 samples were infected by COVID-19. There was 28 negative and 3 positive neutralizing antibodies of the individuals among the 31 negative samples which based on the questionnaires. The negative rates of 28 negative individules tested by GenScript, Vazyme and Hygeianey were 82.14 %, 60.71 % and 17.85 %, while the positive rates of the 147 positive samples were 93.87 %, 95.23 % and 100 %. The within-run coefficient of variations (C·V) of PBNAs, GenScript, Vazyme and Hygeianey were 11.49 %, 9.12 %, 7.97 % and 7.48 %, while the between-run coefficient of variations (C·V) were 21.37 %, 14.21 %, 12.29 % and 11.78 %. Due to the large within-run and between-run coefficient of variations, PBNAs was not suitable for large-scale promotion, while ELISAs could be leveraged for routine monitoring the titer of neutralizing antibodies against SARS-CoV-2.

Small field measurements using electronic portal imaging device

Purpose/Objective. Small-field measurement poses challenges. Although many high-resolution detectors are commercially available, the EPID for small-field dosimetry remains underexplored. This study aimed to evaluate the performance of EPID for small-field measurements and to derive tailored correction factors for precise small-field dosimetry verification. Material/Methods. Six high-resolution radiation detectors, including W2 and W1 plastic scintillators, Edge-detector, microSilicon, microDiamond and EPID were utilized. The output factors, depth doses and profiles, were measured for various beam energies (6 MV-FF, 6 MV-FFF, 10 MV-FF, and 10 MV-FFF) and field sizes (10 × 10 cm2, 5 × 5 cm2, 4 × 4 cm2, 3 × 3 cm2, 2 × 2 cm2, 1 × 1 cm2, 0.5 × 0.5 cm2) using a Varian Truebeam linear accelerator. During measurements, acrylic plates of appropriate depth were placed on the EPID, while a 3D water tank was used with five-point detectors. EPID measured data were compared with W2 plastic scintillator and measurements from other high-resolution detectors. The analysis included percentage deviations in output factors, differences in percentage for PDD and for the profiles, FWHM, maximum difference in the flat region, penumbra, and 1D gamma were analyzed. The output factor and depth dose ratios were fitted using exponential functions and fractional polynomial fitting in STATA 16.2, with W2 scintillator as reference, and corresponding formulae were obtained. The established correction factors were validated using two Truebeam machines. Results. When comparing EPID and W2-PSD across all field-sizes and energies, the deviation for output factors ranged from 1% to 15%. Depth doses, the percentage difference beyond dmax ranged from 1% to 19%. For profiles, maximum of 4% was observed in the 100%–80% region. The correction factor formulae were validated with two independent EPIDs and closely matched within 3%. Conclusion. EPID can effectively serve as small-field dosimetry verification tool with appropriate correction factors.

A Blockchain-Based Model for Traceability and Quality Management of Raw Materials in Clothing Products

In this study, we employ blockchain-based traceability technology to construct a comprehensive model for raw material traceability and quality management in clothing products. Additionally, we investigate engineered water nanostructures (EWNS) for their antibacterial, hydrophilic, and anti-pilling effects on textiles. The EWNS can serve as an effective tool for monitoring quality, enabling precise tracking and verification of nanomaterials during clothing production. The research validated the traceability performance of the blockchain-based model for raw material traceability and quality management in clothing products, achieving a remarkable traceability accuracy of 99.92% and an exceptionally low traceability response time of merely 1.83 seconds. Moreover, after being treated with EWNS, antibacterial rates against E. coli and S. aureus in clothing antibacterial quality management reached 99.9% and 99.0%, respectively. In the hydrophilicity test, EWNS was applied to clothing for a duration of 1 to 7 hours, resulting in a significant reduction in the wetting time of clothing from 10.8 seconds to 1.2 seconds. In the anti-pilling tests, there was a notable enhancement in the effectiveness of anti-pilling properties after EWNS treatment. In summary, our model has achieved remarkable outcomes in material traceability and quality management, offering consumers and the clothing industry a reliable method to ensure product quality and traceability, while simultaneously improving performance aspects such as antibacterial efficacy, hydrophilicity, and resistance against pilling.

Analyzing the impact of deep learning algorithms and fuzzy logic approach for remote English translation

A remote English translation is used for assisting with on-demand support for adaptable sentence conversion and language understanding. The problem with on-demand translations is the precision verification of the words used. This article addresses the precision problem by assimilating deep learning and fuzzy decision algorithm for remote translation support. The method named Fusion-dependent Precision Translation Approach (FPTA) conducts a series of recurrent validations on word usage and sentence completion for the given inputs. First, the completed sentences are verified using the understandability and meaning intended using deep learning in two recurrent layers. The first layer is responsible for identifying word placement and understandability and the second is responsible for meaning verification. The recurrent training is tuned using a fuzzy decision algorithm by selecting the maximum best-afford solution. The constraint’s understandability and meaning are augmented for tuning the outputs by preventing errors consequently. In precise, the error sequences are identified from the first layer for fuzzification across various inputs. This process improves the word adaptability from different languages reducing errors (12.49%) and improves the understandability (11.57%) for various translated sentences.

The shape of the Tz = +1 nucleus 94Pd and the role of proton-neutron interactions on the structure of its excited states

Reduced transition probabilities have been extracted between excited, yrast states in the N=Z+2 nucleus 94Pd. The transitions of interest were observed following decays of the Iπ=14+, Ex=2129-keV isomeric state, which was populated following the projectile fragmentation of a 124Xe primary beam at the GSI Helmholtzzentrum für Schwerionenforschung accelerator facility as part of FAIR Phase-0. Experimental information regarding the reduced E2 transition strengths for the decays of the yrast 8+ and 6+ states was determined following isomer-delayed Eγ1−Eγ2−△T2,1 coincidence method, using the LaBr3(Ce)-based FATIMA fast-timing coincidence gamma-ray array, which allowed direct determination of lifetimes of states in 94Pd using the Generalized Centroid Difference (GCD) method. The experimental value for the half-life of the yrast 8+ state of 755(106) ps results in a reduced transition probability of B(E2:8→+6+) = 205−25+34 e2fm4, which enables a precise verification of shell-model calculations for this unique system, lying directly between the N=Z line and the N=50 neutron shell closure. The determined B(E2) value provides an insight into the purity of (g9/2)n configurations in competition with admixtures from excitations between the (lower) N=3pf and (higher) N=4gds orbitals for the first time. The results indicate weak collectivity expected for near-zero quadrupole deformation and an increasing importance of the T=0 proton-neutron interaction at N=48.

Vulnerability assessment of landslides along the Yunnan section of the Northern Tropic of Cancer based on fuzzy evidence weight model

AbstractFrequent geological disasters pose significant threats to both human lives and property. High-risk landslides, with features such as rapid occurrence, wide distribution, and strong destructiveness, are of particular concern. To investigate the distribution characteristics and threat range of landslides, the Yunnan section of the Northern Tropic of Cancer was selected as the study area. Based on the fuzzy evidence weight model and using a grid size of 1000 m × 1000 m as the study unit, a vulnerability assessment of landslides along the Northern Tropic of Cancer in Yunnan was conducted. This assessment involved constructing a set of 13 evaluation indicator factors from three aspects: geological environment, geographical environment, and human engineering activities. The assessment results were subsequently subjected to precision verification. The results are as follows: (1) The overall vulnerability of landslides in the Yunnan section of the Northern Tropic of Cancer is moderate, covering an area of 25,968.79 km2. This area constitutes the largest proportion, accounting for 46.30% of the entire study area and is primarily distributed in counties such as Yanshan, Funing, and Shuangjiang. The highly vulnerable area covers 15,942.27 km2, representing 28.43% of the total study area. This region is mainly distributed in counties such as Gejiu, Yuanjiang, and Mojiang. (2) The main triggering factors for landslides are twofold: first, large-scale slope instability caused by excavation in human engineering activities, leading to extensive unstable slopes affected by long-term weathering and gravitational effects. Second, soil erosion resulting from land cultivation leads to the collapse and sliding of weathered and fragmented rocks under the influence of heavy rainfall and water erosion. During routine geological disaster patrols, special attention should be given to these factors, with a focus on implementing engineering measures in areas with more fragile geological environments. (3) Precision verification of the assessment results was conducted using both receiver operating characteristic (ROC) curves and Success rate curves. The area under the curve (AUC) values for the success rate curve and ROC curve were 0.85 and 0.74, respectively. The verification results showed high accuracy and feasibility of the evaluation method.

#2646 Precision verification and method comparison of anti-MPO anti-hsPR3 immunoassay on Alegria2 instrument

Abstract Background and Aims Antibodies to myeloperoxidase (anti-MPO) and proteinase 3 (anti-PR3) are recognized as the only clinically relevant antineutrophil cytoplasmic antibodies (ANCA) specificities so far. ANCA testing can be used in different clinical settings, especially in situations that require rapid diagnosis like renal-pulmonary syndrome that require result within 24 hours. The aim of this study was to perform analytical verification of the new automated single test ELISA based method on Alegria2 instrument to test if the reagents and analyser were acceptable for the routine use. Method For precision assessment we tested two concentration levels of commercial control material in triplicate for five days in a row. We used EASI acceptable criteria for precision for anti-MPO as well as for anti-hsPR3. For method comparison we included 50 patients with history or suspicion of vasculitis. Anti-MPO and anti-PR3 were measured using 5 different reagents: Anti-Myeloperoxidase ELISA (IgG) and anti-PR3-hn-hr ELISA (IgG) on Euroimmun Analyzer I-2P (EUROIMMUN AG, Lubeck, Germany), MPO and PR3 II chemiluminiscent immunoassay (CLIA) on IDS iSYS (IDS ISYS, Pouilly en Auxois, France), QUANTA Flash® MPO and QUANTA Flash® PR3 CLIA on BIO-FLASH® (Inova Diagnostics Inc, San Diego, USA), Anti-MPO and anti-PR3 hs on Alegria2 ELISA based method (Orgentec, Mainz, Germany), and Anti-MPO and anti-PR3 ELISA (Orgentec, Mainz, Germany). Results were categorized as positive or negative and we calculated kappa statistics as well as intraclass correlation coefficient. Complete statistical analysis was done using MedCalc statistical software (MedCalc version 14.8.1, Ostend, Belgium). Results Within-laboratory precision (CV%) for anti-MPO for normal and pathological controls were 5.73% and 15.14% respectively. For anti-PR3 hs within-laboratory precision (CV%) for normal and pathological controls were 5.46% and 18.89% respectively. Intraclass correlation coefficient showed excellent degree of consistency in average measures (0.9778 (95% CI: 0.9665-0.9862)) for anti-MPO and good reliability on single ratings (0.8981 (95% CI: 0.8522-0.9344). For anti PR3 intraclass correlation coefficient showed excellent degree of consistency in average measures (0.9257 (95% CI: 0.8873-0.9539)) but only moderate consistency in single ratings (0.7136 (95% CI: 0.6115-0.8053)). Separate Cohen`s kappa testing revealed almost perfect agreement for most reagent combination for anti- MPO (kappa from 0.825 to 0.958). For anti-PR3 we found moderate agreement between QUANTA Flash® PR3 and PR3 II IDS iSYS (kappa 0.520) but mostly substantial agreement (kappa from 0.629 to 0.865). We also found almost perfect agreement between anti-PR3 hs on Alegria2 and QUANTA Flash® PR3 (kappa = 0.901). Conclusion Our results showed a rather small relative variability and met the precision criteria defined by EASI initiative. Satisfactory agreement between methods is very reassuring but due to the lack of standardisation in autoimmune diagnostics imposes that information about methodological differences must be clearly stated on laboratory report. According to our results the new method for anti-MPO and anti-PR3 proved to be suitable for the routine use.

CARMENES input catalog of M dwarfs

Aims. Knowledge of rotation periods (Prot) is important for understanding the magnetic activity and angular momentum evolution of late-type stars, as well as for evaluating radial velocity signals of potential exoplanets and identifying false positives. We measured photometric and spectroscopic Prot for a large sample of nearby bright M dwarfs with spectral types from M0 to M9, as part of our continual effort to fully characterize the Guaranteed Time Observation programme stars of the CARMENES survey. Methods. We analyse light curves chiefly from the SuperWASP survey and TESS data. We supplemented these with our own follow-up photometric monitoring programme from ground-based facilities, as well as spectroscopic indicator time series derived directly from the CARMENES spectra. Results. From our own analysis, we determined Prot for 129 stars. Combined with the literature, we tabulated Prot for 261 stars, or 75% of our sample. We developed a framework to evaluate the plausibility of all periods available for this sample by comparing them with activity signatures and checking for consistency between multiple measurements. We find that 166 of these stars have independent evidence that confirmed their Prot. There are inconsistencies in 27 periods, which we classify as debated. A further 68 periods are identified as provisional detections that could benefit from independent verification. We provide an empirical relation for the Prot uncertainty as a function of the Prot value, based on the dispersion of the measurements. We show that published formal errors seem to be often underestimated for periods longwards of ∼10 d. We examined rotation–activity relations with emission in X-rays, Hα, Ca II H&K, and surface magnetic field strengths for this sample of M dwarfs. We find overall agreement with previous works, as well as tentative differences in the partially versus fully convective subsamples. We show Prot as a function of stellar mass, age, and galactic kinematics. With the notable exception of three transiting planet systems and TZ Ari, all known planet hosts in this sample have Prot ≳ 15 d. Conclusions. Inherent challenges in determining accurate and precise stellar Prot means independent verification is important, especially for inactive M dwarfs. Evidence of potential mass dependence in activity–rotation relations would suggest physical changes in the magnetic dynamo that warrants further investigation using larger samples of M dwarfs on both sides of the fully convective boundary. Important limitations need to be overcome before the radial velocity technique can be routinely used to detect and study planets around young and active stars.

Cross enclosed square split ring resonator based on D.N.G. metamaterial absorber for X-band glucose sensing application

This article presents a novel real-time meta-material (MM) sensor based on a non-invasive method that operates in microwave frequency ranges at 8.524 GHz to measure blood glucose levels with quality factor 184 is designed and fabricated. A cross enclosed between two square shapes produces a strong interaction between glucose samples and electromagnetic waves. In this study, 5 were tested noninvasively using the proposed glucose resonant sensor with a range of glucose-level changes from 50 to 130 mg/dL. For this range of glucose-level changes, the frequency detection resolution is 5.06 MHz/(mg/dL), respectively. Despite simulations, fabrication procedures (F.P.) have been carried out for more precise result verification. For the purpose of qualitative analysis, the proposed MM sensor is considered a viable candidate for determining glucose levels.

Fuzzified Deep Learning based Forgery Detection of Signatures in the Healthcare Mission Records

In an era subjected to digital solutions, handwritten signatures continue playing a crucial role in identity verification and document authentication. These signatures, a form of bio-metric verification, are unique to every individual, serving as a primitive method for confirming identity and ensuring security of an individual. Signatures, apart from being a means of personal authentication, are often considered a cornerstone in the validation of critical documents and processes, especially within the healthcare sector. In healthcare missions, particularly in the regions that are underdeveloped, hand-written records persist as the primary mode of documentation. The credibility of these handwritten documents hinges on the authenticity of the accompanying signatures, making signature verification a paramount safeguard for the integrity and security of medical information. Nonetheless, traditional offline methods of signature identification can be time-consuming and inefficient, particularly while dealing with a massive volume of documents. This arises the evident need for automated signature verification systems. Our research introduces an innovative signature verification system which synthesizes the strengths of fuzzy logic and CNN (Convolutional Neural Networks) to deliver precise and efficient signature verification. Leveraging the capabilities of Fuzzy Logic for feature representation and CNNs for discriminative learning, our proposed hybrid model offers a compelling solution. Through rigorous training, spanning a mere 28 epochs, our hybrid model exhibits remarkable performance by attaining a training accuracy of 91.29% and a test accuracy of 88.47%, underscoring its robust generalization capacity. In an era of evolving security requirements and the persistent relevance of handwritten signatures, our research links the disparity between tradition and modernity.

Ala®sil chemical characterization and toxicity evaluation: an example of the need for the Medical Device Regulation 2017/745.

Introduction: Ala®sil infusion was on the market for clinical use under the Medical Devices Directive (MDD) 93/42/EEC as an irrigating solution based on polydimethylsiloxane (PDMS). The product was withdrawn in 2016, and to the best of our knowledge, it did not cause any health damage. Methods: A bibliographic review and experimental analysis were conducted to evaluate whether this CE-marked product could have been used in patients under the current Medical Device Regulation (MDR) 2017/745. Analytical results from gas chromatography-mass spectrometry (GC-MS) and matrixassisted laser desorption ionization (MALDI) were performed. Citotoxicity studies were also carried out. Results: Only one study related to Ala®sil clinical use was found, describing a pilot series of five patients. The authors rated the product as not helpful in three out of the five cases for internal searching of retinal breaks and in four out of the five cases for drainage of subretinal fluid. No other scientific papers or documentation was found regarding Ala®sil's safety. Nevertheless, the product was introduced in the market after achieving the CE marking. GC-MS and MALDI showed that the polymer has a low molecular weight of 1,000g/mol. Several linear and cyclic low-molecular-weight components (LMWCs) were identified as impurities ranging from L3 to D8, with a molecular weight below 600g/mol. The Ala®sil sample was found to be cytotoxic after 24h of cell culture but non-cytotoxic after 72h, probably due to the cellular regeneration capacity of an immortalized cell line. Tissular cytotoxicity revealed an increased apoptosis rate but without morphological modifications. Discussion: Although Ala®sil cannot be classified as cytotoxic, this substance appears to increase retinal cell death processes. This study supports the notion that the MDDwas not functioning adequately to ensure the safety of medical devices. However, the current MDR 2017/745 imposes stricter standards to prevent the commercialization of medical devices without high-quality preclinical and clinical information, as well as precise clinical verification for their use, information not available for Ala®sil infusion.

Precise Editing and Functional Verification of Pine Disease Resistance Genes

Precise Editing and Functional Verification of Pine Disease Resistance Genes

Smart contract generation for inter‐organizational process collaboration

SummaryCurrently, inter‐organizational process collaboration (IOPC) has been widely used in the design and development of distributed systems that support business process execution. Blockchain‐based IOPC can establish trusted data sharing among participants, attracting more and more attention. The core of such study is to translate the graphical model (e.g., BPMN) into program code called smart contract that can be executed in the blockchain environment. In this context, a proper smart contract plays a vital role in the correct implementation of block‐chain‐based IOPC. In fact, the quality of graphical model affects the smart contract generation. Problematic models (e.g., deadlock) will result in incorrect contracts (causing unexpected behaviors). To avoid this undesired implementation, this article explores to generate smart contracts by using the verified formal model as input instead of graphical model. Specifically, we introduce a prototype framework that supports the automatic generation of smart contracts, providing an end‐to‐end solution from modeling, verification, translation to implementation. One of the cores of this framework is to provide a CSP#‐based formalization for the BPMN collaboration model from the perspective of message interaction. This formalization provides precise execution semantics and model verification for graphical models, and a verified formal model for smart contract generation. Another novelty is that it introduces a syntax tree‐based translation algorithm to directly map the formal model into a smart contract. The required formalism, verification, and translation techniques are transparent to users without imposing additional burdens. Finally, a set of experiments shows the effectiveness of the framework.

Detection, Verification and Analysis of Micro Surface Defects in Steel Filament Using Eddy Current Principles, Scanning Electron Microscopy and Energy-Dispersive Spectroscopy.

In the current industrial revolution, advanced technologies and methods can be effectively utilized for the detection and verification of defects in high-speed steel filament production. This paper introduces an innovative methodology for the precise detection and verification of micro surface defects found in steel filaments through the application of the Eddy current principle. Permanent magnets are employed to generate a magnetic field with a high frequency surrounding a coil of sensors positioned at the filament's output end. The sensor's capacity to detect defects is validated through a meticulous rewinding process, followed by a thorough analysis involving scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). Artificial defects were intentionally introduced into a sample, and their amplitudes were monitored to establish a threshold value. The amplitude signal of these created defect was identified at approximately 10% FSH, which corresponds to a crack depth of about 20 µm. In the experimental production of 182 samples covering 38 km, the defect ratio was notably high, standing at 26.37%. These defects appeared randomly along the length of the samples. The verification results underscore the exceptional precision achieved in the detection of micro surface defects within steel filaments. These defects were primarily characterized by longitudinal scratches and inclusions containing physical tungsten carbide.

Spatio-temporal variation and impacting factors of NPP from 2001 to 2020 in Sanjiangyuan region, China: A deep neural network-based quantitative estimation approach

The ecosystem in the Sanjiangyuan region of China is known to all for its simplicity and fragility. Studying the changes in net primary productivity (NPP) of vegetation and its influencing factors will help protect and improve this fragile environment. However, there are missing data in some regions in NPP data collectors, which affect the data availability. Therefore, this article proposes a method for estimating NPP using only a small amount of basic data by constructing a deep neural network (DNN) model suitable for NPP estimation. This method requires only a small amount of basic data to obtain high-precision, complete NPP data. We conducted a precision verification of the NPP estimates in the Sanjiangyuan region and studied the temporal and spatial variation of NPP in the Sanjiangyuan region, the relations between NPP and meteorological factors, and the contributions of climate change and human activities to NPP changes. The results showed that the estimation of NPP was reliable, with coefficient of determination (R2) ranging from 0.893 to 0.933 and root mean square error (RMSE) ranging from 24.904 to 34.550. From 2001 to 2020, the NPP in the study area showed a significant increasing trend (1.32 g C/m2·year−1, P < 0.05). The partial correlation coefficients between climate and NPP were all positive, with the maximum partial correlation coefficient between temperature and NPP (0.308), followed by radiation (0.113) and precipitation (0.067). The contributions of climate change and human activities to NPP changes were 1.298 and − 0.051 g C/m2·year−1, respectively. Climate change was the main driving factor for vegetation regeneration, while human factors were the main cause of vegetation reduction in the study area.

Application of 3-D Ground Laser Scanning Technology to Measure The Scenic Spot of The Best Spring in the World

This paper introduces the main types of 3-D ground laser scanners in the field of surveying and mapping, provides a technical scheme of applying 3-D laser scanning technology to measure ancient buildings in scenic spots, and uses the pulsed 3-D laser scanner which can transmit and receive infinite echoes to solve the problem that vegetation in the surveying area seriously blocks buildings. As an example, the feasibility of the proposed technical scheme is confirmed and the reliability of the proposed technical scheme is demonstrated by precision verification experiments at the scenic spot of the best spring in the world.

A Radiotherapy Positioning Method for Both Coarse Guidance and Precise Verification Based on Integration of AR and Optical Surface Imaging

Traditional methods of radiotherapy positioning have shortcomings such as fragile skin-markers, additional doses and lack of information integration. Emerging technologies may provide alternatives for the relevant clinical practice. We proposed a noninvasive radiotherapy positioning method integrating augmented reality (AR) and optical surface, and evaluated its feasibility in clinical workflow. AR and structured light-based surface were integrated to implement the coarse-to-precise positioning through two coherent steps, i) the AR-based coarse guidance. To implement quality assurance, recognition of face and pattern was used for patient authentication, case association and accessory validation in AR scenes. The holographic images reconstructed from simulation computed tomography (CT) images, guided the initial posture correction by virtual-real alignment. ii) optical surface-based precise verification. The point clouds were fused, with the calibration and pose estimation of structured light cameras, and segmented according to the preset regions of interest (ROIs). The global-to-local registration for cross-source point clouds was achieved to calculate couch shifts in 6 degrees-of-freedom (DoF), which were ultimately transmitted to AR scenes. The evaluation based on phantom and human-body (4 volunteers) included, i) quality assurance workflow, ii) errors of both steps and correlation analysis, and iii) receiver operating characteristic (ROC). The maximum errors in phantom evaluation were 3.4±2.5 mm in Vrt and 1.4±1.0° in Pitch for the coarse guidance step, while 1.6±0.9 mm in Vrt and 0.6±0.4° in Pitch for the precise verification step. The Pearson correlation coefficients between precise verification and cone beam CT (CBCT) results were distributed in the interval [0.81, 0.85]. In ROC analysis, the areas under the curve (AUC) were 0.87 and 0.89 for translation and rotation respectively. In human body-based evaluation, the errors of thorax and abdomen (T&A) were significantly greater than those of head and neck (H&N) in Vrt (2.6±1.3 vs. 1.7±1.1, p<0.01), Lng (2.4±1.3 vs. 1.4±0.1, p<0.01) and Rtn (0.8±0.5 vs. 0.6±0.4, p = 0.03) while relatively similar in Lat (1.7±1.0 vs. 1.9±1.1, p = 0.13). The combination of AR and optical surface has utility and feasibility for patient positioning, in terms of both safety and accuracy.

Deep Learning-Based Single-View Fluorescence Dose Reconstruction for 3D Dosimetry

3D dose distribution measurement is crucial for precise radiotherapy. Radiation-excited fluorescence imaging has potential for the 3D dosimetry with high spatial resolution, but multiple fluorescence images from different view-angles are required for analytical reconstruction techniques. Furthermore, the imaging data are contaminated by anisotropic Cherenkov light emission and statistical noise. This project aims to establish a novel deep learning-based model to predict 3D dose distributions from a single-view 2D fluorescence image while simultaneously removing the adverse effects of Cherenkov signals and other noises. A total of 124 single-aperture static photon beams were delivered to an acrylic tank containing 1 g/L quinine hemisulfate water solution with varying aperture shapes and collimator angle. The emitted optical signals were detected by a low-cost CMOS camera for 20 seconds, and image pre-processing was performed to obtain input 2D fluorescence images with 0.3 × 0.3 mm spatial resolution. 3D back-projected dose distribution images were also calculated from the input fluorescence images. Ground-truth of 3D dose distributions and 2D field map images were obtained from a clinical treatment planning system with 1.4 × 1.4 × 1.4 mm spatial resolution. The proposed deep learning-based dose reconstruction method involved 3 steps. First, 2D fluence map images at the bottom plane of the tank were predicted from the fluorescence images by using a customized convolutional neural network (CNN). Second, the predicted fluence map images were transformed into the 2D field map images on the isocenter plane by applying perspective transformation. Finally, 2D dose distributions at a given radiological depth were calculated by using the predicted field map images, the back-projected dose distribution images, and the radiological depth value as inputs of a shallow CNN. Both CNN models were trained separately, and the 3D dose distributions were predicted by concatenating the output 2D dose distributions at various radiological depths. The proposed CNN model yielded accurate 2D field map images. Averaged Dice similarity coefficient and mean absolute error of the field maps in the test data was 92.0% ± 4.6% and 0.0132 ± 0.0113, respectively. Moreover, our deep learning-based approach was able to predict accurate 3D dose distributions from the 2D fluorescence images. Mean squared error and averaged 3D gamma passing ratio (3%/3mm) were 9.55 mGy ± 6.8 mGy and 86.3% ± 9.86%, respectively. Theproposed deep learning-based method calculated accurate 3D dose distributions from a single-view 2D fluorescence image. Since this technique require only a single CMOS camera image and fluorescent material, it can be readily used for any external radiotherapy modalities, including SRS/SBRT with small fields. This method is useful for acquiring 3D dose distribution data for precise dose verification within a few seconds.

Practice, Pathways and Lessons Learned from Building a Digital Data Flow with Tools: Focusing on alien invasive species, from occurrence via measures to documentation

The SLU Swedish Species Information Centre (SSIC, SLU Artdatabanken) accumulates, analyses and disseminates information concerning species and habitats occurring in Sweden. The SSIC provides an open access biodiversity reporting and analysis infrastructure including the Swedish Species Observation System, the Swedish taxonomic backbone Dyntaxa, and tools for species information including traits, terminology, quality assurance and species identification.*1 The content is available to scientists, conservationists and the public. All systems, databases, APIs and web applications, rely on recognized standards to ensure interoperability. The SSIC is a leading partner within the Swedish Biodiversity Data Infrastructure (SBDI). Here we present a data flow (Fig. 1) that exemplifies the strengthening of the cooperation and transfer of experiences between research, community, non-governmental organizations (NGOs), citizen science and governmental agencies, and also presents solutions to current data challenges (e.g., data fragmentation, taxonomic issues or platform relations). This data flow aimed to facilitate the process for evaluating and understanding the distribution and spread of species (e.g., invasive alien species). It provides Findable, Accessible, Interoperable and Reusable (FAIR) data and links related information between different parties such as universities, NGOs, county administrative boards (CABs) and environmental protection agencies (EPAs). The digital structure is built on the national Swedish taxonomic backbone Dyntaxa, which prevents data fragmentation due to taxonomic issues and acts as a common standard for all users. The chain of information contains systems, tools and a linked data flow for reporting observations, verification procedures, and it can work as an early warning system for surveillance regarding certain species. After an observation is reported, an alert can be activated, field checks can be carried out, and if necessary, eradication measures can be activated. The verification tool that traditionally has been focused on the quality of species identification has been improved, providing verification of geographic precision. This is equally important for eradication actions as is species accuracy. A digital catalogue of eradication methods is in use by the CABs but there are also recommendations on methods for ‘public’ use, and collaboration between Invasive Alien Species (IAS) coordinators in regional CABs is currently being developed. The CABs have a separate tool for documentation of eradication measures and, if/when measures are carried out (by CABs), this information can be fed back from the CAB-tool into the database in SSIC where it is possible to search for, and visualize, this information. Taxonomic integrity over time should be intact and related to the taxon identifier (ID) provided by Dyntaxa. However, metadata, such as geographic position, date, verification status, mitigation results, etc., will be fully used when reporting under the IAS Regulation 1143/2014 (EU). The development of the digital structure is a collaboration with the Swedish Environmental Protection Agency (Naturvårdsverket) and the Swedish Agency for Marine and Water Management (Havs-och Vattenmyndigheten).

Patient-specific QA for the HyperArc technique using gafchromic film with multiple calibration methods

Purpose This study aims to evaluate different methods for calibrating EBT-XD films to develop a precise pre-treatment verification method for stereotactic radiotherapy (SRT) patients using the HyperArc (HA, Varian Medical System) technique. Methods Gafchromic EBT-XD films were calibrated using three different approaches: manual calibration, EDW calibration, and PDD calibration. Films were digitalized with an Epson V850 Pro scanner applying the local scanning protocol. Three clinical treatment plans were selected for evaluation. Patient-specific QA films were irradiated in the Mobius MVP phantom and the STEEV phantom. Scanned film images were converted into dose images using the calibration curves. Gamma analysis was performed to compare film dose and TPS calculated dose with various criteria. Results The scan-to-scan variation was evaluated to be ≤ 0.2%. The accuracy of the calibration curves was verified and the deviation from the converted dose deviates ≤ 3% from the known delivered dose. The gamma passing rate for all calibration methods was found to be over 94% with clinically relevant criteria. EDW calibration demonstrated higher average gamma passing rates compared to the manual method for single target plans, which is 99% ± 1.2% and 98.8% ± 1.5%, respectively. PDD method demonstrated improved agreement for multiple targets with the result of 99.3% ± 0.8%. Conclusions The three calibration methods were validated, and they produced accurate calibration curves for EBT-XD films to enable pre-treatment patient-specific QA for stereotactic radiotherapy.