Real-time Verification Research Articles

Face Recognition Using Convolutional Neural Network

This study proposes a Real-time Face Recognition leveraging deep learning techniques to address the computational constraints and processing speed requirements of such applications. This model focuses on efficient feature extraction and comparison mechanisms to enable rapid and accurate face verification in real-time scenarios. By incorporating techniques like transfer learning and model compression, the proposed network achieves a balance between accuracy and computational efficiency. The utilization of deep learning enables the network to automatically learn discriminative features from facial images, enhancing the overall verification performance. Additionally, the lightweight design of the network ensures minimal resource consumption, making it suitable for deployment on low-power devices without compromising on performance. Experimental results demonstrate the effectiveness of the proposed model in achieving real-time face verification while maintaining high accuracy levels. Overall, the model presented in this study offers a promising solution for real-time face verification applications where speed and efficiency are crucial factors, showcasing the potential of deep learning techniques in enhancing biometric authentication systems.

Enhancing neurosurgical navigation operation flow management through personal digital assistant technology: a prospective cohort study.

This prospective cohort study aims to evaluate the impact of digital health technology especially Personal Digital Assistants (PDA) in neurosurgical procedure management, focusing on surgical safety check accuracy, efficiency, and patient satisfaction. The study included 211 neurosurgical cases from January to December 2022. The control group of 106 patients followed traditional verification methods, while the experimental group of 105 patients used PDA. The PDA system facilitated real-time data collection, verification, and transmission. The study compared both groups in terms of check times, accuracy rates, and patient satisfaction, and used multivariate regression to assess the impact of baseline parameters on these outcomes. The study found that the experimental group using the PDA system reduced the average verification time by approximately 8 min, achieving 100.0% accuracy in preoperative and postoperative checks, significantly better than the control group (91.5% pre- and post-operation). Multivariate regression confirmed a 48.1% reduction in postoperative verification time due to the PDA system (p < 0.001), with the model showing high explanatory power (R2 = 0.911). Other examined factors, including patient age and nurse experience, had no significant effects. Similarly, the PDA's introduction markedly improved verification accuracy, with no significant impact from other variables (p = 0.010). The application of the PDA system in neurosurgical operations significantly enhanced the accuracy and efficiency of surgical safety checks, reduced nursing errors, optimized nursing workflows, and improved patient satisfaction. These results provide valuable insights for the application of PDA technology in high-risk medical fields, demonstrating potential of digital health tools in enhancing surgical safety and efficiency.

Monitoring of Laser Powder Bed Fusion process by bridging dissimilar process maps using deep learning-based domain adaptation on acoustic emissions

Advances in sensorization and identification of information embedded inside sensor signatures during manufacturing processes using Machine Learning (ML) algorithms for better decision-making have become critical enablers in building data-driven monitoring systems. In the Laser Powder Bed Fusion (LPBF) process, data-driven-based process monitoring is gaining popularity since it allows for real-time component quality verification. Real-time qualification of the additively manufactured parts has a significant advantage as the cost of conventional post-manufacturing inspection methods can be reduced. Also, corrective actions or build termination could be done to save machine time and resources. However, despite the successful development in addressing monitoring needs in LPBF processes, less research has been paid to the ML model's robustness in decision-making when dealing with variations in data distribution from the laser-material interaction owing to different process spaces. Inspired by the idea of domain adaptation in ML, in this work, we propose a deep learning-based unsupervised domain adaptation technique to tackle shifts in data distribution owing to different process parameter spaces. The temporal waveforms of acoustic emissions from the LPBF process zone corresponding to three regimes, namely Lack of Fusion, conduction, and keyhole, were acquired on two different 316 L stainless steel powder distributions (> 45 µm and < 45 µm) with two different parameter sets. Temporal and spectral analysis of the acoustic waveforms corresponding to the powder distributions treated with different laser parameters showed the presence of offset in the data distribution, which was subsequently treated with the proposed unsupervised domain adaptation technique to have an ML model that could be generalized. Furthermore, the prediction accuracy of the proposed methodology between the two distributions showed the feasibility of adapting to the newer environment unsupervisedly and improving the ML model's generalizability.

Implementacija dvofaktorske autentifikacije u računarskim sistemima

Introduction/purpose: The paper explores the implementation of two-factor authentication (2FA) in computer systems, addressing the increasing need for enhanced security. It highlights the vulnerabilities of password-based authentication and emphasizes the advantages of 2FA in mitigating digital threats. The development of the VoiceAuth application, integrating 2FA through a combination of password and voice authentication, serves as a practical illustration. Methods: The research adopts a three-tier architecture for the VoiceAuth application, encompassing a database, server-side REST API, and client-side single-page application. Speaker verification is employed for voice authentication, analyzing elements like pitch, rhythm, and vocal tract shapes. The paper also discusses possibilities for future upgrades, suggesting enhancements such as real-time voice verification and additional 2FA methods. Results: The application's implementation involves a detailed breakdown of the REST API architecture, Single Page Applications (SPAs), and the Speaker Verification service. Conclusion: The research underscores the crucial role of two-factor authentication (2FA) in bolstering the security of computer systems. The VoiceAuth application serves as a practical demonstration, showcasing the successful integration of 2FA through a combination of password and voice authentication. The modular architecture of the application allows for potential upgrades.

Experimental Measurement of Secondary Particle Count for Real-Time Proton Range Verification

Experimental Measurement of Secondary Particle Count for Real-Time Proton Range Verification

Interfacial analysis by optical monitoring of layer level multi-material parts fabricated via Powder Bed Fusion–Laser Beam

This experimental work aims to contribute to the development of Powder Bed Fusion–Laser Beam (PBF-LB) processes for the fabrication of multi-material structures by studying the interfacial characteristics using an off-axis optical monitoring system and relating in-situ results with metallurgical and mechanical properties of built parts. The study was developed employing an innovative approach for intralayer multi-material fabrication by PBF-LB, based on the use of a partitioning system in the powder chamber.Image analysis was used to extrapolate information needed to describe possible contamination effects between powders, the size of the interface and the presence of metallurgical defects.Chemical, metallographic and statistical analyses were also carried out to study the interface between the two materials of the samples and to support the results from optical monitoring.The results showed that the optical monitoring system enables real-time verification of the required quality parameters during the joining of different materials with PBF-LB processes.

Resilient Frequency-shifted IMC-PID Scheme for Mitigating Scaling and Random Attacks in Thermal Plants Amid Delay

In recent times, several network-related scenarios like Communication Delays (CDs) and malicious Cyber Attacks (CAs) have posed a major challenge in keeping the operational frequency of automatic generation systems within admissible levels. Hence, the proposed work focuses on investigating the applicability of an Frequency-Shifted Internal Model Control-Proportional Integral Derivative (FIMC-PID) controller for Load Frequency Control (LFC) of a non-identical dual-area Power System (PS) in the presence of inherent CDs and CAs. The FIMC approach is based on the integration of concepts like model order reduction and pole-zero shifting of the reduced order transfer function by the constant (ψ) which indicates the amount by which system robustness can be manipulated. In addition to that, an analytical approach for obtaining a stable numerical range of the tunable parameter ψ is introduced using Routh-Hurwitz criteria. This range serves as the input for the Golden Jackal Optimizer (GJO) for finding the optimal value of ψ by minimizing the Integrated Time-Squared Error (ITSE). False Data Injection (FDI) threats to the cyber-physical systems like scaling and random attacks have been modeled and the efficacy of GJO-tuned FIMC strategy in mitigating the same is vindicated. Finally, hardware-in-loop real-time verification of the suggested strategy in mitigating frequency fluctuations is carried out in the OPAL-RT platform.

The diagnostic value of serum lncRNA CATG00000112921.1 as a marker of multiple myeloma

Multiple myeloma (MM) is a malignant plasma cell disease. At present, numerous studies have shown that lncRNA plays a very important role in the occurrence, development and even drug resistance of multiple myeloma. It may become a potential diagnostic and prognostic marker of multiple myeloma and provide new ideas for targeted therapy. Based on the above research background, this study used gene chip technology to screen out the differentially expressed lncRNA in the serum of MM patients and healthy people, and verified more clinical serum samples to screen out the lncRNA with the largest difference as a biomarker for further research. In this research, the data of hospitalized patients diagnosed with MM and healthy people in the Affiliated Hospital of Guangdong Medical University were retrospectively collected. The lncRNA expression profile of serum samples from patients with multiple myeloma and healthy controls was analyzed by lncRNA chip technology. The serum samples were verified by real-time fluorescence quantitative PCR, and the candidate diagnostic markers were screened out. The ROC working curve was drawn to evaluate the diagnostic efficacy of the candidate markers and to determine their stability at different temperatures and time. A total of 44 MM patients and 37 healthy people were involved in this research. Among them, 4 patients with MM and 4 patients with HD were sent for microarray analysis. According to Fold Change ≥ 2 and P < 0.05, a total of 17 differentially expressed lncRNA molecules were screened, of which 9 were up-regulated RNA molecules and 8 were down-regulated RNA molecules. Through real-time fluorescence quantitative PCR verification, it was found that lncRNA CATG00000112921.1 was highly expressed in the healthy control group and diminished in patients with multiple myeloma, P < 0.001. The ROC curve demonstrated that the area under the curve (AUC) was 0.749, the sensitivity was 0.636, the specificity was 0.789, and the 95 % CI was 0.636-0.862 (P < 0.001). In addition, in order to verify the effects of temperature, time and repeated freezing and thawing on lncRNA, it was placed at 25°C, 4°C, -20°C, -80°C for 0h, 24h, 48h, 72h, and placed at-80°C repeated freezing and thawing 0 times, 2 times, 4 times, 8 times, and the expression level was not significantly changed. Serum lncRNA CATG00000112921.1 may be a potential candidate diagnostic marker for multiple myeloma. The ROC curve shows that it has good diagnostic value, and its high stability at different temperatures and different times is a required condition for becoming a diagnostic marker. As far as we know, this is the first study in the world to find differential expression of lncRNA CATG00000112921.1 in peripheral serum of healthy people and newly diagnosed multiple myeloma patients. This study also highlights the application of gene chip technology in screening differentially expressed genes.

Testing the Verification and Validation Capability of a DCP-Based Interface for Distributed Real-Time Applications

Cyber–physical systems (CPS) integrate diverse elements developed by various vendors, often dispersed geographically, posing significant development challenges. This paper presents an improved version of our previously developed co-simulation interface based on the non-proprietary Distributed Co-Simulation Protocol (DCP) standard, now optimized for broader hardware platform compatibility. The core contributions include a demonstration of the interface’s hardware-agnostic capabilities and its straightforward adaptability across different platforms. Furthermore, we provide a comparative analysis of our interface against the original DCP. It is validated via various X-in-the-Loop simulations, reinforcing the interface’s versatility and applicability in diverse scenarios, such as distributed real-time executions, verification and validation processes, or Intellectual Property protection.

The Role of clinical pharmacists’ interventions and prevention of medication errors in emergency departments

Clinical pharmacists play a vital role in mitigating medication errors within the high-pressure setting of emergency departments (EDs). This study underscores the significance of their interventions in elevating patient safety and reducing adverse drug events. Medication errors in the ED often stem from factors such as time constraints, incomplete patient information, and complex drug regimens. Clinical pharmacists address these challenges by conducting comprehensive medication reconciliation, assessing drug interactions, and adjusting dosages to align with each patient's specific requirements. Their presence in EDs facilitates real-time verification of medication orders, immediate correction of errors, and close collaboration with the healthcare team. Furthermore, clinical pharmacists provide invaluable drug information and education to patients and medical staff, thereby lowering the risk of inappropriate drug usage and promoting safer administration practices. Existing literature affirms that clinical pharmacist interventions in EDs lead to a notable reduction in medication errors, adverse drug events, and subsequent healthcare costs. In summary, the integration of clinical pharmacists in the ED is indispensable for augmenting patient safety, averting medication errors, and ultimately enhancing patient outcomes. Keywords: medication errors, clinical pharmacists, emergency departments, patient safety, healthcare.

In Situ Real-Time Monitoring for Aseptic Drilling: Lessons Learned from the Atacama Rover Astrobiology Drilling Studies Contamination Control Strategy and Implementation and Application to the Icebreaker Mars Life Detection Mission.

In 2019, the Atacama Rover Astrobiology Drilling Studies (ARADS) project field-tested an autonomous rover-mounted robotic drill prototype for a 6-Sol life detection mission to Mars (Icebreaker). ARADS drilled Mars-like materials in the Atacama Desert (Chile), one of the most life-diminished regions on Earth, where mitigating contamination transfer into life-detection instruments becomes critical. Our Contamination Control Strategy and Implementation (CCSI) for the Sample Handling and Transfer System (SHTS) hardware (drill, scoop and funnels) included out-of-simulation protocol testing (out-of-sim) for hardware decontamination and verification during the 6-Sol simulation (in-sim). The most effective five-step decontamination combined safer-to-use sterilants (3%_hydrogen-peroxide-activated 5%_sodium-hypochlorite), and in situ real-time verification by adenosine triphosphate (ATP) and Signs of Life Detector (SOLID) Fluorescence Immunoassay for characterization hardware bioburden and airborne contaminants. The 20- to 40-min protocol enabled a 4-log bioburden reduction down to <0.1 fmoles ATP detection limit (funnels and drill) to 0.2-0.7 fmoles (scoop) of total ATP. The (post-cleaning) hardware background was 0.3 to 1-2 attomoles ATP/cm2 (cleanliness benchmark background values) equivalent to ca. 1-10 colony forming unit (CFU)/cm2. Further, 60-100% of the in-sim hardware background was ≤3-4 bacterial cells/cm2, the threshold limit for Class <7 aseptic operations. Across the six Sols, the flux of airborne contaminants to the drill sites was ∼5 and ∼22 amoles ATP/(cm2·day), accounting for an unexpectedly high Fluorescence Intensity (FI) signal (FI: ∼6000) against aquatic cyanobacteria, but negligible anthropogenic contribution. The SOLID immunoassay also detected microorganisms from multiple habitats across the Atacama Desert (anoxic, alkaline/acidic microenvironments in halite fields, playas, and alluvial fans) in both airborne and post-cleaning hardware background. Finally, the hardware ATP background was 40-250 times lower than the ATP in cores. Similarly, the FI peaks (FImax) against the microbial taxa and molecular biomarkers detected in the post-cleaned hardware (FI: ∼1500-1600) were 5-10 times lower than biomarkers in drilled sediments, excluding significant interference with putative biomarker found in cores. Similar protocols enable the acquisition of contamination-free materials for ultra-sensitive instruments analysis and the integrity of scientific results. Their application can augment our scientific knowledge of the distribution of cryptic life on Mars-like grounds and support life-detection robotic and human-operated missions to Mars.

Augmented Reality-Based BIM Data Compatibility Verification Method for FAB Digital Twin implementation

With the advancement of state-of-the-art technologies, the semiconductor industry plays a key role as an essential component in the manufacture of various electronic products. Since the manufacturing of a semiconductor goes through very sophisticated and complex processes, efficient and accurate work and management are essential in the design, construction, and operation stages of the semiconductor fabrication (FAB) plant. Recently, the combined application of building information modeling (BIM) and augmented reality (AR) technology has gained increased attention in this semiconductor FAB industry as an advanced way to improve work efficiency and accuracy while eliminating other related problems, such as human errors. Despite the perceived benefits of combined use of BIM and AR, many technical problems still exist when integrating the target test model and the 3D virtual object model using BIM data and existing AR visualization technology, due to the unique characteristics of the FAB sites. To solve these problems, this study proposed an AR-based real-time BIM data compatibility verification method for future FAB digital twin implementation and demonstrated that it could be converted into a system and applied to actual FAB sites. As a result of the development and verification of this system, the proposed AR-based real-time BIM data compatibility verification system enables the accurate fitting of the AR model and actual object through AR tracking and anchoring technology considering the characteristics of FAB sites. After the fitting, the system was able to maintain compatibility, even when the camera moved and the marker moved away from the screen. By expanding the effective distance of compatibility between the AR model and the actual object, it was possible to increase the AR application range between the 3D virtual object model and the test target model and to improve the compatibility.

Ultra-fast multi-parametric 4D-MRI image reconstruction for real-time applications using a downsampling-invariant deformable registration (D2R) model

Background and purposeMotion estimation from severely downsampled 4D-MRI is essential for real-time imaging and tumor tracking. This simulation study developed a novel deep learning model for simultaneous MR image reconstruction and motion estimation, named the Downsampling-Invariant Deformable Registration (D2R) model. Materials and methodsForty-three patients undergoing radiotherapy for liver tumors were recruited for model training and internal validation. Five prospective patients from another center were recruited for external validation. Patients received 4D-MRI scans and 3D MRI scans. The 4D-MRI was retrospectively down-sampled to simulate real-time acquisition. Motion estimation was performed using the proposed D2R model. The accuracy and robustness of the proposed D2R model and baseline methods, including Demons, Elastix, the parametric total variation (pTV) algorithm, and VoxelMorph, were compared. High-quality (HQ) 4D-MR images were also constructed using the D2R model for real-time imaging feasibility verification. The image quality and motion accuracy of the constructed HQ 4D-MRI were evaluated. ResultsThe D2R model showed significantly superior and robust registration performance than all the baseline methods at downsampling factors up to 500. HQ T1-weighted and T2-weighted 4D-MR images were also successfully constructed with significantly improved image quality, sub-voxel level motion error, and real-time efficiency. External validation demonstrated the robustness and generalizability of the technique. ConclusionIn this study, we developed a novel D2R model for deformation estimation of downsampled 4D-MR images. HQ 4D-MR images were successfully constructed using the D2R model. This model may expand the clinical implementation of 4D-MRI for real-time motion management during liver cancer treatment.

IMPLEMENTASI APLIKASI PENGENALAN SOP PENGOLAHAN LIMBAH INDUSTRI PT X BERBASIS ANDROID

The management and proper handling of industrial waste are crucial for environmental sustainability and regulatory compliance. This project presents the development and implementation of an Android-based application designed to recognize and facilitate the adherence to Standard Operating Procedures (SOPs) in the industrial waste processing operations of PT X. The application employs advanced image recognition and processing techniques to identify and validate SOPs associated with different waste processing tasks, ensuring that employees follow the prescribed guidelines accurately. By providing real-time guidance and verification, the application aims to enhance operational efficiency, reduce errors, and promote responsible waste management practices. This paper outlines the key features, technical aspects, and benefits of the application, underscoring its potential to contribute to a more sustainable and compliant industrial waste processing environment at PT X.

Low-cost system for real-time verification of personal protective equipment in industrial facilities using edge computing devices

Ensure worker safety in the industry is crucial. Despite efforts to improve safety, statistics show a plateau in the reduction of these accidents in recent years. To decrease the number of accidents, compliance with established industrial safety standards and regulations by competent authorities must be ensured, including the use of Personal Protective Equipment (PPE). PPE usage is of paramount importance, as it is essential to prevent accidents from occurring. This work aims to improve worker safety by verifying PPE usage. Technology plays a key role here. A cost-effective solution is proposed to monitor PPE usage in real time. Most existing safety control systems are costly and require considerable maintenance. A low-cost computer vision system is proposed to supervise safety in industrial facilities. This system uses object detection and tracking technology in low-cost embedded devices and can generate alarms in real time if PPE is not used. Unlike other works, temporal information is used to generate the alarms. Safety managers receive this information to take necessary actions. Emphasis has been placed on cost, scalability, and ease of use to facilitate system implementation in industrial plants. The result is an effective system that improves worker safety by verifying established safety measures at a reduced cost. The methodology used improves the Average Precision of PPE detection by 6%. In addition, unlike other studies, the problem of application deployment is addressed, which has an impact on its cost.

Efficient power management and control of DC microgrid with supercapacitor-battery storage systems

This paper introduces a novel power management strategy (PMS) that aims to facilitate power-sharing between battery and supercapacitor (SC) energy storage systems. The proposed technique is employed to resolve the discrepancy between power demand and generation, as well as to regulate the voltage of the dc bus. The implementation of a hybrid adaptive fuzzy integrated fractional order PD-PI controller (HAFI FPD-PI controller), in the hybrid energy storage system (HESS) results in enhanced regulation of the dc bus, while simultaneously minimising the levels of stress imposed on the battery. Moreover, the proposed PMS improves the lifespan of batteries by redirecting unwaged battery currents to SCs for rapid compensations. In addition, the stability study of dc microgrid is performed. The efficacy of the proposed PMS is evaluated by the utilisation of varying solar irradiation, wind speed, and load. The effectiveness of the proposed PMS was examined by simulations, which involved a comparison of peak deviations in the dc bus voltage with conventional PMS techniques used in literature. The real-time verification of the results has been conducted using OPAL-RT real-time simulator.

Realization of a new PMOS transistors based first-order delay equalizer suitable for communication networks

In this paper, a novel first-order delay equalizer based on a PMOS transistor is proposed which is highly suitable for the communication systems and networks. The core circuit consists of three unity-gain PMOS inverting amplifiers, one resistor and one capacitor. The newly proposed delay equalizer has ability to provide both inverting all pass and non-inverting all pass responses simultaneously from the same structure without restriction of any matching of passive elements to achieve desired performance. High input and low output impedances enable cascadability in the proposed circuit. The circuit's behaviour under loading effects and the effect of parasitic components are also examined in detail. To assess the applicability of the proposed structure, nth-order delay equalizer is also presented. The performance of the proposed work is validated through PSPICE simulations utilizing TSMC 0.18 μm level-7 CMOS technology process parameters, with voltage levels set at ±0.9 V. Furthermore, real-time post-layout verification is being carried out to ensure the confirmed performance.

Cherenkov and Plan Integration for Real-Time Delivery Verification: The Opportunity for Automated Visualization and Guidance of All Treatments in EBRT

Initiatives in the national radiation oncology incident learning system (RO-ILS) have been exceptionally useful in discovery of factors that lead to incidents and learning of best practices that can help make the national practice of RO safer as a whole. Incident learning systems come from the flight industry, where both visual cues and instrument control are essential parts of implementation, and similarly both human and instrument tools are used in RO. However, numerous RO studies have reported that human factors are a leading cause of incidents discovered and that timelines, such as QA, setup, delivery, and verification are areas where most incidents are found. Tools such as Cherenkov imaging and SGRT can be used to automate many of the riskiest human decisions and/or providing both human vision and instrument-guided oversight in these areas of treatment delivery process. The value of continuous online imaging is reviewed and the two parts have been tested towards complete automation. The conceptual framework of comparing images to the patient treatment plan is outlined with software examples. Development towards a combined SGRT & Cherenkov imaging system that could achieve fully automated incident detection is outlined. Cherenkov imaging has shown direct visualization of many instances of beam delivery to patients that were sub-optimal. These are seen mostly in isolated cases of normal tissue in the beam where it was not expected, such as limbs, chin, contralateral breast or axilla. Incorrect placement of bolus is also readily visualized. Comparisons can be made on a day-to-day basis, but also on a delivery to plan basis if the plan was incorporated into the treatment delivery process. Analysis of the incidents seen indicates that there are automatable metrics of image quality that could have detected them. Overall, if the system detection of variations were fully automated, these could be detected without human intervention. The capabilities for reducing nearly all human error in setup and delivery are available or emerging, and Cherenkov imaging is perhaps one of the most direct ways to capture and computationally analyze the treatment in real time. These tools require further integration with automated analysis and plan integration, but the initial steps are well underway and individual parts are now possible with advances in R&D of the system integration.

Hybrid-Supervised Deep Learning for Proton-Acoustic Reconstruction for 3D In Vivo Proton Dose Verification

Proton-acoustic (PA) image has shown great potential to provide real-time 3D dose verification of proton therapy. However, the PA image quality suffers from severe limited view artifacts, which significantly impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for PA reconstruction to address the limited-view issues. Our method consists of two stages. In the first stage, a transformer-based network was proposed to reconstruct initial pressure maps from protoacoustic signals. The network was first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the second stage, the PA image was further enhanced by a 3D U-net. The final PA images were converted to dose maps using conversion coefficients derived from CT images. Data from 126 prostate cancer patients treated by proton therapy were collected under an IRB protocol and were split into 86 and 40 patients for model training and testing, respectively. Data of each patient contains the planning CT scan, the corresponding clinical treatment plan, and the dose map calculated by commercial software. The radiofrequency signals were generated by performing proton acoustic simulation based on CT images and the ground truth pressure map derived from the treatment plan. An ultrasound detector matrix with 64 × 64 size and 500kHz central frequency was simulated under the perineum to acquire the signals in the prostate area. In the testing results, the method's accuracy was evaluated using Root-mean-squared-error (RMSE) and structural-similarity-index-measure (SSIM) between the reconstructed and ground truth pressure map and dose distribution. Testing results showed that the reconstructed pressure map achieved an average RMSE/SSIM of 0.0292/0.96, demonstrating excellent 3D information with details. Dose maps derived from the pressure map achieved an average RMSE/SSIM of 0.018/0.99 with a gamma index of 94.7% and 95.7% for 1%/3 mm and 1%/5 mm criteria compared to the ground truth dose maps. The reconstruction time was 6s, which can be further reduced using GPU. Our study achieves start-of-the-art performance in the challenging task of direct reconstruction from limited-view radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool for in-vivo 3D proton dose verification. Such high-precision 3D online dose verification can substantially reduce the range uncertainties of proton therapy to significantly improve its precision and outcomes.

Genome-wide identification of the AOMT gene family in wax apple and functional characterization of SsAOMTs to anthocyanin methylation.

Anthocyanins are major pigments in the peels of red-series wax apple fruits, and two principal components of them, namely, the cyanin and the peonidin, are non-methoxylated and methoxylated anthocyanins, respectively. Anthocyanin O-methyltransferases (AOMTs) are an important group of enzymes that have the ability to catalyze anthocyanins methylation to promote the solubility, stability, and bioactivity of anthocyanins. Although AOMT genes have been studied in a variety of plants, the function of them in wax apple is generally not well understood. The anthocyanin composition in peels of two wax apple cultivars was determined by High Performance Liquid Chromatography Tandem Mass Spectrometry (HPLS-MS). The genome-wide analysis of the AOMT genes was performed with bioinformatics technology, and the expression patterns of different plant tissues, cultivars, fruit ripening stages, and exogenous abscisic acid (ABA) treatments were analyzed by transcriptome sequencing analysis and real-time quantitative PCR verification. An initial functional evaluation was carried out in vitro using recombinant the Anthocyanin O-methyltransferase Gene 5 of S. samarangense (SsAOMT5) protein. Only two main compositions of anthocyanin were found in peels of two wax apple cultivars, and it was worth noting that Tub Ting Jiang cultivar contained non-methoxylated anthocyanin (Cy3G) only, whereas Daye cultivar contained both non-methoxylated and methoxylated (Pn3G) anthocyanins. A total of six SsAOMT genes were identified in the whole genome of wax apple, randomly distributing on three chromosomes. A phylogenic analysis of the protein sequences divided the SsAOMT gene family into three subgroups, and all SsAOMTs had highly conserved domains of AOMT family. In total, four types of stress- related and five types of hormone- related cis-elements were discovered in the promoter region of the SsAOMTs. Expression pattern analysis showed that SsAOMT5 and SsAOMT6 were expressed in all tissues to varying degrees; notably, the expression of SsAOMT5 was high in the flower and fruit and significantly higher in Daye peels than those of other cultivars in the fruit ripening period. Exogenous ABA treatment significantly increased anthocyanin accumulation, but the increase of methoxylated anthocyanin content did not reach significant level compared with those without ABA treatment, whereas the expression of SsAOMT5 upregulated under ABA treatment. We identified two homologous SsAOMT5 genes from Daye cultivar (DSsAOMT5) and Tub Ting Jiang cultivar (TSsAOMT5); the results of functional analyses to two SsAOMT5 recombinant proteins in vitro demonstrated that DSsAOMT5 showed methylation modification activity, but TSsAOMT5 did not. In conclusion, SsAOMT5 was responsible for methylated anthocyanin accumulation in the peels of wax apple and played an important role in red coloration in wax apple peels.