Search Results (204)

FLASH radiotherapy requires real-time, non-invasive beam monitoring systems capable of operating under ultra-high dose rate (UHDR) conditions without perturbing the therapeutic beam. In this work, we characterized the extraction system of Supersonic Gas Curtain-based Ionization Profile Monitor (SGC-IPM) for its capabilities as a transverse beam profile and position monitor for FLASH protons. The monitor utilizes a tilted gas curtain intersected by the incident beam, leading to the generation of ions that are extracted through a tailored electrostatic field, and detected using a two stage microchannel plate (MCP) coupled to a phosphor screen and CMOS camera. CST Studio Suite was employed to conduct electrostatic and particle tracking simulations evaluating the ability of the extraction system to measure both beam profile and position. The ion interface, at the interaction region of proton beam and gas curtain, was modeled with realistic proton beam parameters and uniform gas curtain density distributions. The ion trajectory was tracked to evaluate the performance across multiple beam sizes. The simulations suggest that the extraction system can reconstruct transverse beam profiles for different proton beam sizes. Simulations also supported the system’s capability as a beam position monitor within the boundary defined by the beam size, the dimensions of the extraction system, and the height of the gas curtain. Some simulation results were benchmarked against experimental data of 28 MeV proton beam with 70 nA average beam current. This study will further help to optimize the design of the extraction system to facilitate the integration of SGC-IPM in medical accelerators. Full article

Background: Menopause affects every woman worldwide, with varying degrees of severity. In addition to traditional treatments such as hormone replacement therapy, there is also a growing interest in alternative treatments. One possible way to address this need is through the use of placenta extracts. This systematic review is the first to evaluate the efficacy of placental extracts in randomized controlled trials (RCTs). Methods: A systematic search of three databases (MEDLINE, Scopus, Embase) identified studies on placental extract treatment of menopausal symptoms in women, yielding 272 records, with 11 eligible studies. Results: Menopausal severity scores (Kupperman Menopausal Index, Simplified Menopausal Index, Menopausal Rating Scale), somatic and vasomotor symptoms, skin conditions, and certain psychological indicators were significantly improved in the 11 enrolled randomized controlled trials, including perimenopausal and postmenopausal women treated with porcine or human dried, purified placental extract. Placental extract was well tolerated in all studies; no significant side effects or clinically significant laboratory abnormalities were recorded. Conclusions: Porcine and human placental extracts appear to offer potential benefits for alleviating menopausal symptoms. Full article

While tropical regions have traditionally been the focus of studies on natural bioactive products, works published within the last decade demonstrate that cyanobacteria from the Baltic Sea also possess significant biotechnological and pharmaceutical potential. The Baltic Pseudanabaena galeata CCNP1313 previously demonstrated activity against breast cancer cell lines (MCF7 and T47D) and several viruses. In the present study, the cytotoxicity of cellular extract and flash chromatography fractions from the strain were evaluated against a wider panel of cancer cells (A549, C-33A, CaSki, DoTC2, HeLa, PC3, SiHa, and T47D). To gain better insight into the compounds potentially responsible for the observed effects, high-resolution mass spectrometry was combined with bioactivity-based molecular networking. Both the extract and hydrophobic fractions showed strong cytotoxicity, particularly against breast cancer cells and selected cervical cancer cells. While HRMS analyses confirmed the production of previously characterised peptides by CCNP1313 (Pseudanabaena galeata peptides and galeapeptins), neither of them was found to be responsible for the activity. Instead, the molecular networking approach linked the cytotoxicity to specific lipid classes, including diacylglycerols (DAGs) and monogalactosyldiacylglycerols (MGDGs). This study highlights the necessity of integrating traditional methods with advanced bioinformatics for the successful discovery of bioactive natural products, especially when complex samples, such as extract or chromatographically separated fractions, are analysed. Full article

Background: For precise and reliable gene expression analysis, the acquisition of high-quality RNA is contingent upon excellent tissue preparation and handling. The optimal method for preserving tissues after surgical resection remains challenging due to the delays in delivery or the absence of cold storage equipment. Although RNAlater has been extensively adopted for tissue preservation, few studies have systematically evaluated the effects of various tissue preservation solutions and post-collection intervals on RNA integrity across a range of tissue types. Methods: Ten types of mouse tissues, representing common tissue species in biobanks, were collected after resection. Tissues were either flash-frozen in liquid nitrogen as controls or immersed in one of three RNA preservatives—TRIzol and two commercial RNAlater solutions—and stored at room temperature (RT) for 0, 4, or 8 h before being frozen. Total RNA was extracted using TRIzol method, and its integrity was assessed using the RNA Integrity Number (RIN). Results: The results indicated that both the post-collection interval and the type of RNA preservative significantly impact RNA integrity. Pancreatic tissue showed the poorest RNA integrity (RIN < 5.5), whereas heart and ovary tissue yielded high-quality RNA (RIN > 7) even without any preservatives after 8 h at RT. To maintain baseline RNA integrity (RIN > 5.5), tissues including brain, kidney, muscle, liver, intestine, and uterus should be immersed in preservative and frozen within 8 h. For lung tissue preserved in RNAlater, the maximum recommended time at RT was 4 h. Conclusions: Robust, high-quality RNA can be obtained from most mouse tissues stored in RNA preservatives for up to 8 h at RT, with only minor variations observed across the different preservatives tested. Full article

Atrial fibrillation (AF) is the most common type of heart rhythm disorder worldwide. Early recognition of brief episodes of atrial fibrillation can provide important diagnostic information and lead to prompt treatment. AF is mainly characterized by an irregular heartbeat. Today, many personal devices such as smartphones, smartwatches, smart rings, or small wearable medical devices can detect heart rhythm. Sensors can acquire different types of heart-related signals and extract the sequence of inter-beat intervals, i.e., the instantaneous heart rate. Various algorithms, some of which are very complex and require significant computational resources, are used to recognize AF based on inter-beat intervals (RR). This study aims to verify the possibility of using neural networks algorithms directly on a microcontroller connected to sensors for AF detection. Sequences of 25, 50, and 100 RR were extracted from a public database of electrocardiographic signals with annotated episodes of atrial fibrillation. A custom 1D convolutional neural network (1D-CNN) was designed and then validated via a 5-fold subject-wise split cross-validation scheme. In each fold, the model was tested on a set of 3 randomly selected subjects, which had not previously been used for training, to ensure a subject-independent evaluation of model performance. Across all folds, all models achieved high and stable performance, with test accuracies of 0.963 ± 0.031, 0.976 ± 0.022, and 0.980 ± 0.023, respectively, for models using 25 RR, 50 RR, and 100 RR sequences. Precision, recall, F1-score, and AUC-ROC exhibited similarly high performance, confirming robust generalization across unseen subjects. Performance systematically improved with longer RR windows, indicating that richer temporal context enhances discrimination of AF rhythm irregularities. A complete Edge AI prototype integrating a low-power ECG analog front-end, an ARM Cortex M7 microcontroller and an IoT transmitting module was utilized for realistic tests. Inferencing time, peak RAM usage, flash usage and current absorption were measured. The results obtained show the possibility of using neural network algorithms directly on microcontrollers for real-time AF recognition with very low power consumption. The prototype is also capable of sending the suspicious ECG trace to the cloud for final validation by a physician. The proposed methodology can be used for personal screening not only with ECG signals but with any other signal that reproduces the sequence of heartbeats (e.g., photoplethysmographic, pulse oximetric, pressure, accelerometric, etc.). Full article

Background: The analysis of gunshot residue (GSR) is crucial for gaining information on how a crime occurred. This study presents an innovative proof of concept for measuring shooting distances by performing Flash-Pulse active Thermography (FPT). Compared to conventional chemical methods, FPT offers a significant advantage by digitalizing the residue pattern in a non-destructive manner. Methods: Thermal images of cotton canvases, both white and colored, were analyzed to quantify the distribution of gunshot residues after shooting from several distances, specifically focusing on smoke and semi-burnt powders. The proposed approach uses contrast and radial intensity profiles to extract exponential coefficients, which are dependent on the shooting distance. Results: Employing a sigmoid model to fit the coefficients over distance and to derive a characteristic feature used as a classification metric, firing distances can be classified into short, medium, and long range and can be predicted with an uncertainty of less than 5 cm for distances between 18 and 38 cm under the tested conditions. Considerations regarding the influence of different weapons and ammunition are reported, suggesting the potential for a general approach. Conclusions: The methodology has been validated on several samples, demonstrating its feasibility for specific forensic applications. Its most robust use is as a weapon- and ammunition-specific calibration tool, supporting case-specific distance estimation analysis. Full article

Background/Objectives: Interindividual variability in polyphenol metabolism may help explain the inconsistent effects of polyphenol intake on health outcomes. This study compared, for the first time, (i) polyphenol-related gut microbiota metabotypes (urolithins: UM0, UMA, UMB; equol: EP, ENP; lunularin: LP, LNP) and their clusters (MCs) in non-medicated premenopausal (Pre-M) and postmenopausal (Post-M) women and (ii) the impact of an 8-week intake of a polyphenol-rich plant extract mixture (PPs) on the quality of life (QoL) of Post-M. Methods: Polyphenol metabotypes were determined in urine via UPLC-QTOF-MS after a 3-day intake of PPs containing resveratrol, pomegranate (ellagitannins and ellagic acid), and red clover (isoflavones) in Pre-M (n = 120) and Post-M (n = 90) women. QoL was assessed with the short-form Cervantes Scale in a randomized, placebo-controlled crossover trial (8-week PPs vs. placebo), completed by 78 Post-M participants. Results: At baseline, Pre-M and Post-M women showed only minor differences in metabotype and MC distributions linked to menopausal status. MC3 (UMA+EP+LP) predominated in Pre-M, while MC7 (UMA+EP+LNP) was most frequent in Post-M. PPs intake in Post-M women led to modest shifts in metabotype and MC distributions toward Pre-M patterns. Quantitative metabolite production was comparable between groups, except for equol, which showed a median 2.8-fold increase after PPs intake in EP Post-M women. Clinically meaningful improvements (score reduction ≥ 6.7 points) in QoL were observed in the Psychic domain in EP women (28%, p = 0.039) and in the Menopause and Health domain, specifically in EP (24.1%, p = 0.004), MC3 (22.5%, p = 0.043), and MC4 (UMB+EP+LP; 41.3%, p = 0.022), were mainly driven by a reduction in hot flashes (p = 0.001). Conclusions: These findings support metabotyping as a tool to guide targeted dietary strategies and enhance QoL through precision health in Post-M women. Full article

Accurate traffic light detection and classification are fundamental for autonomous vehicle (AV) navigation and real-time traffic management in complex urban environments. Existing systems often fall short of reliably identifying and classifying traffic light states in real-time, including their flashing modes. This study introduces FlashLightNet, a novel end-to-end deep learning framework that integrates the nano version of You Only Look Once, version 10m (YOLOv10n) for traffic light detection, Residual Neural Networks 18 (ResNet-18) for feature extraction, and a Long Short-Term Memory (LSTM) network for temporal state classification. The proposed framework is designed to robustly detect and classify traffic light states, including conventional signals (red, green, and yellow) and flashing signals (flash red and flash yellow), under diverse and challenging conditions such as varying lighting, occlusions, and environmental noise. The framework has been trained and evaluated on a comprehensive custom dataset of traffic light scenarios organized into temporal sequences to capture spatiotemporal dynamics. The dataset has been prepared by taking videos of traffic lights at different intersections of Starkville, Mississippi, and Mississippi State University, consisting of red, green, yellow, flash red, and flash yellow. In addition, simulation-based video datasets with different flashing rates—2, 3, and 4 s—for traffic light states at several intersections were created using RoadRunner, further enhancing the diversity and robustness of the dataset. The YOLOv10n model achieved a mean average precision (mAP) of 99.2% in traffic light detection, while the ResNet-18 and LSTM combination classified traffic light states (red, green, yellow, flash red, and flash yellow) with an F1-score of 96%. Full article

Background: Pomegranate peel (Punica granatum L.) is a rich source of phenols, particularly ellagitannins, highlighting punicalagin, a bioactive compound with recognized antioxidant potential. However, efficient recovery and purification methods are required to enable its application in food and health-related products. This study aimed to obtain a partially purified fraction of punicalagin from pomegranate peel using optimized extraction and purification strategies. Methods: A Taguchi L9 (3)³ experimental design was employed to optimize ultrasound-assisted extraction, evaluating extraction time (10, 20, 30 min), ethanol concentration (20, 40, 80%), and solid-to-solvent ratio (1:12, 1:14, 1:16). Total polyphenol content was quantified using the Folin–Ciocalteu method. Extracts obtained under optimized conditions were concentrated by rotary evaporation and subjected to semipurification using flash chromatography with Amberlite XAD-16 resin. Subsequently, the fractions were lyophilized and analyzed by HPLC/ESI/MS. Results: The Statistica software determined the optimal conditions for polyphenol extraction (20 min, 40% ethanol, 1:12), with the signal-to-noise (S/N) ratio reaching 88.43 ± 0.66, surpassing the predicted value of 77.42. Flash chromatography yielded four fractions, and HPLC/ESI/MS analysis revealed the presence of ellagitannins in all of them, with fraction number 2 showing the highest relative abundance of punicalagin (89.25%). Conclusions: The combination of ultrasound-assisted extraction and flash chromatography proved effective for obtaining punicalagin-rich fractions from pomegranate peel, supporting its potential for nutraceutical applications. Full article

Three previously undescribed clerodane diterpenoids, including two cis-clerodanes, solidagolactone IX (1) and solidagoic acid K (2), and one trans-clerodane, solidagodiol (3), along with two known cis-clerodane diterpenoids, (−)-(5R,8R,9R,10S)-15,16-epoxy-ent-neo-cleroda-3,13,14-trien-18-ol (4) and solidagoic acid J (5), were isolated and comprehensively characterized from the ethanolic and ethyl acetate root extract of Solidago gigantea Ait. (giant goldenrod). Compound 4 has previously been reported from the roots of this species, whereas compound 5 was identified from the leaves of S. gigantea but not from the roots. The bioassay-guided isolation involved thin-layer chromatography–direct bioautography (TLC–DB) with a Bacillus subtilis antibacterial assay, preparative flash column chromatography, and TLC–mass spectrometry (MS). The chemical structures of the isolated compounds (1–5) were elucidated through extensive in-depth spectroscopic and spectrometric analyses, including one- and two-dimensional nuclear magnetic resonance (NMR) spectroscopy, high-resolution tandem mass spectrometry (HRMS/MS), and attenuated total reflectance Fourier-transform infrared (ATR–FTIR) spectroscopy. Their antimicrobial activities were evaluated using in vitro microdilution assays against B. subtilis and different plant pathogens. Compound 3 was the most active against the tested Gram-positive strains, exerting particularly potent effects against Clavibacter michiganensis with a minimal inhibitory concentration (MIC) value of 5.1 µM as well as B. subtilis and Curtobacterium flaccumfaciens pv. flaccumfaciens (MIC 21 µM for both). Compound 4 also strongly inhibited the growth of C. michiganensis (MIC 6.3 µM). Compounds 2, 4, and 5 displayed moderate to weak activity against B. subtilis and C. flaccumfaciens pv. flaccumfaciens with MIC values ranging from 100 to 402 µM. Rhodococcus fascians bacteria were moderately inhibited by compounds 3 (MIC 41 µM) and 4 (MIC 201 µM). Bactericidal activity was observed for compound 3 against C. michiganensis with a minimal bactericidal concentration (MBC) value of 83 µM. Compounds 2 and 3 demonstrated weak antifungal activity against Fusarium graminearum. Our findings underscore the value of bioassay-guided approaches in discovering previously undescribed bioactive compounds. Full article

Prognostics and Health Management (PHM), including fault diagnosis and Remaining Useful Life (RUL) prediction, is critical for ensuring the reliability and efficiency of industrial equipment. However, traditional AI-based methods require extensive expert intervention in data preprocessing, model selection, and hyperparameter tuning, making them less scalable and accessible in real-world applications. To address these limitations, this study proposes an autonomous agent powered by Large Language Models (LLMs) to automate predictive modeling for fault diagnosis and RUL prediction. The proposed agent processes natural language queries, extracts key parameters, and autonomously configures AI models while integrating an iterative optimization mechanism for dynamic hyperparameter tuning. Under identical settings, we compared GPT-3.5 Turbo, GPT-4, GPT-4o, GPT-4o-mini, Gemini-2.0-Flash, and LLaMA-3.2 on accuracy, latency, and cost, using GPT-4 as the baseline. The most accurate model is GPT-4o with an accuracy of 0.96, a gain of six percentage points over GPT-4. It also reduces end-to-end time to 1.900 s and cost to $0.00455 per 1 k tokens, which correspond to reductions of 32% and 59%. For speed and cost efficiency, Gemini-2.0-Flash reaches 0.964 s and $0.00021 per 1 k tokens with accuracy 0.94, an improvement of four percentage points over GPT-4. The agent operates through interconnected modules, seamlessly transitioning from query analysis to AI model deployment while optimizing model selection and performance. Experimental results confirmed that the developed agent achieved stable performance under ideal configurations, attaining accuracy 0.97 on FordA for binary fault classification, accuracy 0.95 on CWRU for multi-fault classification, and an asymmetric score of 380.74 on C-MAPSS FD001 for RUL prediction, while significantly reducing manual intervention. By bridging the gap between domain expertise and AI-driven predictive maintenance, this study advances industrial automation, improving efficiency, scalability, and accessibility. The proposed approach paves the way for the broader adoption of autonomous AI systems in industrial maintenance. Full article

Nowadays, Internet of Things (IoT) application systems are broadly applied to various sectors of society for efficient management by monitoring environments using sensors, analyzing sampled data, and giving proper feedback. For their fast deployment, we have developed Smart Environmental Monitoring and Analysis in Real Time (SEMAR) as an integrated IoT application server platform and implemented the input setup assistance service using prompt engineering and a generative AI model to assist connecting sensors to SEMAR with step-by-step guidance. However, the current service cannot assist in connections of the sensors not learned by the AI model, such as newly released ones. To address this issue, in this paper, we propose an extension to the service for handling unlearned sensors by utilizing datasheets with four steps: (1) users input a PDF datasheet containing information about the sensor, (2) key specifications are extracted from the datasheet and structured into markdown format using a generative AI, (3) this data is saved to a vector database using chunking and embedding methods, and (4) the data is used in Retrieval-Augmented Generation (RAG) to provide additional context when guiding users through sensor setup. Our evaluation with five generative AI models shows that OpenAI’s GPT-4o achieves the highest accuracy in extracting specifications from PDF datasheets and the best answer relevancy (0.987), while Gemini 2.0 Flash delivers the most balanced results, with the highest overall RAGAs score (0.76). Other models produced competitive but mixed outcomes, averaging 0.74 across metrics. The step-by-step guidance function achieved a task success rate above 80%. In a course evaluation by 48 students, the system improved the student test scores, further confirming the effectiveness of our proposed extension. Full article

Streak cameras, essential for ultrahigh temporal resolution diagnostics in laser-driven inertial confinement fusion, underpin the streak tube imaging LiDAR (STIL) system—a flash LiDAR technology offering high spatiotemporal resolution, precise ranging, enhanced sensitivity, and wide field of view. This study establishes a theoretical model of the STIL system, with numerical simulations predicting limits of temporal and spatial resolutions of ~6 ps and 22.8 lp/mm, respectively. Dynamic simulations of laser backscatter signals from targets at varying depths demonstrate an optimal distance reconstruction accuracy of 98%. An experimental STIL platform was developed, with the key parameters calibrated as follows: scanning speed (16.78 ps/pixel), temporal resolution (14.47 ps), and central cathode spatial resolution (20 lp/mm). The system achieved target imaging through streak camera detection of azimuth-resolved intensity profiles, generating raw streak images. Feature extraction and neural network-based three-dimensional (3D) reconstruction algorithms enabled target reconstruction from the time-of-flight data of short laser pulses, achieving a minimum distance reconstruction error of 3.57%. Experimental results validate the capability of the system to detect fast, low-intensity optical signals while acquiring target range information, ultimately achieving high-frame-rate, high-resolution 3D imaging. These advancements position STIL technology as a promising solution for applications that require micron-scale depth discrimination under dynamic conditions. Full article

A previously undescribed cis-clerodane diterpenoid, diangelate solidagoic acid J (1), along with two known cis-clerodane diterpenoids, solidagoic acid C (2) and solidagoic acid D (3), as well as two known unsaturated monoacylglycerols, 1-linoleoyl glycerol (4) and 1-α-linolenoyl glycerol (5), were isolated and characterized from the n-hexane leaf extract of Solidago gigantea (giant goldenrod). Compounds 2–5 were identified first in this species, and compounds 4 and 5 are reported here for the first time in the Solidago genus. The bioassay-guided isolation procedure included thin-layer chromatography (TLC) coupled with a Bacillus subtilis antibacterial assay, preparative flash column chromatography, and TLC–mass spectrometry (MS). Their structures were elucidated via extensive spectroscopic and spectrometric techniques such as one- and two-dimensional nuclear magnetic resonance (NMR) spectroscopy and high-resolution tandem mass spectrometry (HRMS/MS). The antimicrobial activities of the isolated compounds were evaluated by a microdilution assay. All compounds exhibited weak to moderate antibacterial activity against the Gram-positive plant pathogen Clavibacter michiganensis, with MIC values ranging from 17 to 133 µg/mL, with compound 5 being the most potent. Only compound 1 was active against Curtobacterium flaccumfaciens pv. flaccumfaciens, while compound 3 demonstrated a weak antibacterial effect against B. subtilis and Rhodococcus fascians. Additionally, the growth of B. subtilis and R. fascians was moderately inhibited by compounds 1 and 5, respectively. None of the tested compounds showed antibacterial activity against Gram-negative Pseudomonas syringae pv. tomato and Xanthomonas arboricola pv. pruni. No bactericidal activity was observed against the tested microorganisms. Compounds 2 and 3 displayed weak antifungal activity against the crop pathogens Bipolaris sorokiniana and Fusarium graminearum. Our results demonstrate the efficacy of bioassay-guided strategies in facilitating the discovery of novel bioactive compounds. Full article

Flash floods are natural disasters that have significant impacts on human life and economic damage. The detection of flash floods using remote-sensing techniques provides essential data for subsequent flood-risk assessment through the preparation of flood inventory samples. In this research, a new deep-learning approach for bi-temporal flash-flood detection in Synthetic Aperture Radar (SAR) is proposed. It combines a U-Net convolutional network with a Transformer model using a compact Convolutional Tokenizer (CCT) to improve the efficiency of long-range dependency learning. The hybrid model, namely CCT-U-ViT, naturally combines the spatial feature extraction of U-Net and the global context capability of Transformer. The model significantly reduces the number of basic blocks as it uses the CCT tokenizer instead of conventional Vision Transformer tokenization, which makes it the right fit for small flood detection datasets. This model improves flood boundary delineation by involving local spatial patterns and global contextual relations. However, the method is based on Sentinel-1 SAR images and focuses on Erbil, Iraq, which experienced an extreme flash flood in December 2021. The experimental comparison results show that the proposed CCT-U-ViT outperforms multiple baseline models, such as conventional CNNs, U-Net, and Vision Transformer, obtaining an impressive overall accuracy of 91.24%. Furthermore, the model obtains better precision and recall with an F1-score of 91.21% and mIoU of 83.83%. Qualitative results demonstrate that CCT-U-ViT can effectively preserve the flood boundaries with higher precision and less salt-and-pepper noise compared with the state-of-the-art approaches. This study underscores the significance of hybrid deep-learning models in enhancing the precision of flood detection with SAR data, providing valuable insights for the advancement of real-time flood monitoring and risk management systems. Full article