Insertable, dual-density dielectric barrier for acoustic pressure level reduction in high-performance human head-only MRI system.

We report use of a dual-density dielectric barrier surrounding a detachable high-pass radiofrequency (RF) birdcage coil to achieve an order-of-magnitude reduction of acoustic noise in a high-performance head gradient system. The barrier consisted of a 4.5 mm-thick mass-loaded vinyl and a 6 mm-thick polyurethane foam. It was inserted into the radial gap between the birdcage coil and the RF shield in a prototype head-only gradient system at 3 T. More than 9 dBA reduction of sound pressure level was achieved on the average with representative, high acoustic-noise imaging sequences. Increased acoustic damping was apparent from acoustic impulse response functions. High dielectric constant of the mass-loaded vinyl effectively added distributed capacitance to the birdcage coil, lowering the resonance frequency, but not seriously degrading the RF transmission performance. The barrier occupied the radial space normally used for air cooling of the RF coil and the RF shield. The resulting omission of air cooling was found to be acceptable with efficient gradient thermal management and use of a high-resistivity RF shield for eddy current reduction. The proposed method can improve patient experience while preserving image quality in a high-power head-only gradient system.

Shortened Radiofrequency Delivery Time to Optimize Efficiency and Safety of Pulmonary Vein Isolation with the Radiofrequency Balloon: Insights from the COLLABORATE registry.

BACKGROUND: Previous clinical studies on pulmonary vein isolation (PVI) with a radiofrequency balloon (RFB) reported safe and effective procedures using Conventional ablation settings with 20/60-second RF delivery via posterior/anterior (PST/ANT) electrodes. The latest evidence suggests that reducing the application time to 15 seconds (s) on the posterior wall when facing the esophageal region is as effective as applying 20 s. OBJECTIVE: To prospectively assess whether reducing RF time on PST/ANT segments to 15/45 s can ensure sufficient quality of lesion metrics and compare the new Shortened ablation settings with the Conventional one in terms of safety, and effectiveness at 1-year. METHODS: A total of 641 patients from 7 European centers were enrolled in a collaborative registry, with 374 in the conventional RF delivery group and 267 in the shortened RF delivery group. Procedural outcomes, lesion metrics, and safety profiles were assessed and compared between the groups. RESULTS: Freedom of any atrial tachycarrythmias at one year were 85.4% and 88.2% in the SHRT and CONV groups, respectively. The shortened RF delivery strategy was associated with significantly shorter procedure times (median 63.5 vs. 96.5 minutes, P < 0.001) and shortened fluoroscopy exposure (median 10.0 vs. 14.0 minutes, P < 0.001) compared to conventional delivery. Efficacy metrics, including first-pass isolation rates and time to isolation, were comparable between groups. Shortened RF delivery was associated with a lower incidence of procedural complications (1.4% vs. 5.3%, P = 0.04) and optimized thermal characteristics. CONCLUSION: Analyses from the COLLABORATE Registry demonstrate that shortening RF energy delivery times to 15/45s (PST/ANT) during PVI with the RFB resulted in comparable freedom from recurrent atrial tachyarrhythmia compared to conventional delivery times with comparable efficiency and safety.

Shirt-color recognition for the color-blindness.

Color-blind is a generic disability whereby the affected individuals are not given the opportunity to benefit from the various functions provided by color that would impact humans physically and psychologically. Although this disability is not fatal, it brought plenty of turbulence in the affected individuals' daily activities. This paper aims to develop a system for recognizing and detecting colors of clothes in images, improve accuracy by using advanced algorithms to handle lighting variations, and provide color matching recommendations to assist color-blind individuals in making informed choices when purchasing shirts. The proposed methodology for color recognition involves:•retrieving the RGB values of a given point from the input image and converting them into HSV values.•creating web application integrated with a machine learning model to classify and predict the corresponding color based on the HSV values.•predicting the color name with suggestions of matching colors will be displayed on the interface.

Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features.

PURPOSE: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS AND METHODS: The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. RESULTS: This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. CONCLUSIONS: Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.

Development and Evaluation of Ferrite Core Inductively Coupled Plasma Radio Frequency Ion Source for High-Current Ion Implanters in Semiconductor Applications.

This study presents the development of a ferrite core inductively coupled plasma (ICP) radio frequency (RF) ion source designed to improve the lifetime of ion sources in commercial ion implanters. Unlike existing DC methods, this novel approach aims to enhance the performance and lifetime of the ion source. We constructed a high-vacuum evaluation chamber to thoroughly examine RF ion source characteristics using a Langmuir probe. Comparative experiments assessed the extraction current of two upgraded ferrite core RF ion sources in a commercial ion implanter setting. Additionally, we tested the plasma lifetime of the ICP source and took temperature measurements of various components to verify the operational stability and efficiency of the innovative design. This study confirmed that the ICP RF ion source operated effectively under a high vacuum of 10-5 torr and in a high-voltage environment of 30 kV. We observed that the extraction current increased linearly with RF power. We also confirmed that BF3 gas, which presents challenging conditions, was stably ionized in the ICP RF ion sources.

A Study of MgZnO Thin Film for Hydrogen Sensing Application.

This research introduces a hydrogen sensor made from a thin film of magnesium zinc oxide (MgZnO) deposited using a technique called radiofrequency co-sputtering (RF co-sputtering). Separate magnesium oxide (MgO) and zinc oxide (ZnO) targets were used to deposit the MgZnO film, experimenting with different deposition times and power levels. The sensor performed best (reaching a sensing response of 2.46) when exposed to hydrogen at a concentration of 1000 parts per million (ppm). This peak performance occurred with a MgZnO film thickness of 432 nanometers (nm) at a temperature of 300 °C. Initially, the sensor's responsiveness increased as the film thickness grew. This is because thicker films tend to have more oxygen vacancies, which are imperfections that play a role in the sensor's function. However, further increases in film thickness beyond the optimal point harmed performance. This is attributed to the growth of grains within the film, which hindered its effectiveness. X-ray diffraction (XRD) and field-emission scanning electron microscopy (FE-SEM) were employed to thoroughly characterize the quality of the MgZnO thin film. These techniques provided valuable insights into the film's crystal structure and morphology, crucial factors influencing its performance as a hydrogen sensor.

Slice-specific B1 + shimming improves the repeatability of multishot DWI at 7 T.

PURPOSE: Compared with lower field strengths, DWI at 7 T faces the combined challenges of increased distortion and blurring due to B0 inhomogeneity, and increased signal dropouts due to B1 + inhomogeneity. This study addresses the B1 + limitations using slice-specific static parallel transmission (pTx) in a multi-shot, readout-segmented EPI diffusion imaging sequence. METHODS: DWI was performed in 7 healthy subjects using MRI at 7 T and readout-segmented EPI. Data were acquired with non-pTx circular-polarized (CP) pulses (CP-DWI) and static pTx pulses (pTx-DWI) using slice-specific B1 + shim coefficients. Each volunteer underwent two scan sessions on the same day, with two runs of each sequence in the first session and one run in the second. The sequences were evaluated by assessing image quality, flip-angle homogeneity, and intrasession and intersession repeatability in ADC estimates. RESULTS: pTx-DWI significantly reduced signal voids compared with CP-DWI, particularly in inferior brain regions. The use of pTx also improved RF uniformity and symmetry across the brain. These effects translated into improved intrasession and intersession repeatability for pTx-DWI. Additionally, re-optimizing the pTx pulse between repeat scans did not have a negative effect on ADC repeatability. CONCLUSION: The study demonstrates that pTx provides a reproducible image-quality increase in multishot DWI at 7 T. The benefits of pTx also extend to quantitative ADC estimation with regard to the improvement in intrasession and intersession repeatability. Overall, the combination of multishot imaging and pTx can support the development of reliable, high-resolution DWI for clinical studies at 7 T.

Quantitative analysis of soil potassium by near-infrared (NIR) spectroscopy combined with a three-step progressive hybrid variable selection strategy.

Soil potassium is a crucial nutrient element necessary for crop growth, and its efficient measurement has become essential for developing rational fertilization plans and optimizing crop growth benefits. At present, data mining technology based on near-infrared (NIR) spectroscopy analysis has proven to be a powerful tool for real-time monitoring of soil potassium content. However, as technology and instruments improve, the curse of the dimensionality problem also increases accordingly. Therefore, it is urgent to develop efficient variable selection methods suitable for NIR spectroscopy analysis techniques. In this study, we proposed a three-step progressive hybrid variable selection strategy, which fully leveraged the respective strengths of several high-performance variable selection methods. By sequentially equipping synergy interval partial least squares (SiPLS), the random forest variable importance measurement (RF(VIM)), and the improved mean impact value algorithm (IMIV) into a fusion framework, a soil important potassium variable selection method was proposed, termed as SiPLS-RF(VIM)-IMIV. Finally, the optimized variables were fitted into a partial least squares (PLS) model. Experimental results demonstrated that the PLS model embedded with the hybrid strategy effectively improved the prediction performance while reducing the model complexity. The RMSET and RT on the test set were 0.01181% and 0.88246, respectively, better than the RMSET and RT of the full spectrum PLS, SiPLS, and SiPLS-RF(VIM) methods. This study demonstrated that the hybrid strategy established based on the combination of NIR spectroscopy data and the SiPLS-RF(VIM)-IMIV method could quantitatively analyze soil potassium content levels and potentially solve other issues of data-driven soil dynamic monitoring.

The selective chemical modification of the 6-amino group of adenosine of the premature termination codon induces readthrough to produce full-length peptide in the reconstituted E. Coli translation system.

Nonsense mutations in the coding region turn amino acid codons into termination codons, resulting in premature termination codons (PTCs). In the case of the in-frame PTC, if translation does not stop at the PTC but continues to the natural termination codon (NTC) with the insertion of an amino acid, known as readthrough, the full-length peptide is formed, albeit with a single amino acid mutation. We have previously developed the functionality-transfer oligonucleotide (FT-Probe), which forms a hybrid complex with RNA of a complementary sequence to transfer the functional group, resulting in modification of the 4-amino group of cytosine or the 6-amino group of adenine. In this study, the FT-Probe was used to chemically modify the adenosines of the PTC (UAA, UAG, and UGA) of mRNA, which were assayed for the readthrough in a reconstituted Escherichia coli translation system. The third adenosine-modified UAA produced three readthrough peptides incorporating tyrosine, glutamine and lysine at the UAA site. It should be noted that the additional modification with a cyclodextrin only induced glutamine incorporation. The adenosine modified UGA induced readthrough very efficiently with selective tryptophan incorporation. Readthrough of the modified UGA is caused by inhibition of the RF2 function. This study has demonstrated that the chemical modification of the adenosine 6-amino group of the PTC is a strategy for effective readthrough in a prokaryotic translation system.

PETcoil: first results from a second-generation RF-penetrable TOF-PET brain insert for simultaneous PET/MRI.

Simultaneous PET/MRI provides concurrent information about anatomic, functional, and molecular changes in disease. We are developing a second generation MR-compatible RF-penetrable TOF-PET insert. The insert has a smaller scintillation crystal size and ring diameter compared to clinical whole-body PET scanners, resulting in higher spatial resolution and sensitivity. This paper reports the initial system performance of this full-ring PET insert. The global photopeak energy resolution and global coincidence time resolution, 11.74 ± 0.03 % FWHM and 238.1 ± 0.5 ps FWHM, respectively, are preserved as we scaled up the system to a full ring comprising 12,288 LYSO-SiPM channels. Throughout a ten-hour experiment, the system performance remained stable, exhibiting a less than 1% change in all measured parameters. In a resolution phantom study, the system successfully resolved all 2.8 mm diameter rods, achieving an average VPR of 0.28 ± 0.08 without TOF and 0.24 ± 0.07 with TOF applied. Moreover, the implementation of TOF in the Hoffman phantom study also enhanced image quality. Initial MR compatibility studies of the full PET ring were performed with it unpowered as a milestone to focus on looking for material and geometry-related artifacts. During all MR studies, the MR body coil functioned as both the transmit and receive coil, and no observable artifacts were detected. As expected, using the body coil also as the RF receiver, MR image signal-to-noise ratio exhibited degradation (∼30%), so we are developing a high quality receive-only coil that resides inside the PET ring.

Rapid identification model of mine water inrush source using random forest optimized by multi-strategy improved sparrow search algorithm.

Mine water inrush accident is one of the most threatening disasters in coal mine production process. In order to improve the identification accuracy of mine water inrush source, a fast identification method of mine water inrush source based on improved sparrow search (SSA) algorithm coupled with Random Forest algorithm was proposed. Firstly, taking Zhaogezhuang Mine as the research object, six factors were selected as the discriminant index and three principal components were extracted by kernel principal component analysis. Secondly, four strategies are employed to enhance the SSA for achieving the ISSA, while multiple benchmark functions are utilized to validate its performance. The extracted principal components serve as input, and the categories of water inrush sources act as output. Subsequently, the prediction results of Random Forest (RF) algorithm after optimizing hyperparameters through Improve SSA are compared with those obtained from other models. The research findings demonstrate that optimizing the RF model using Improve SSA yields superior predictive performance compared to alternative models. Finally, this model is applied to identify water inrush sources in a mine located in Shandong province. The discrimination results exhibit higher accuracy, precision, recall, and F1 index than other models, thereby confirming the reliability and stability of this approach. The results demonstrate that the kernel principal component analysis-based rapid identification model for mine water outburst source, combined with an improved sparrow search algorithm to optimize Random Forest, exhibits excellent robustness and accuracy. This model effectively fulfills the requirements of identifying mine water outbursts and provides a reliable guarantee for ensuring mining safety production.

Prostate-Specific Membrane Antigen Expression in Patients with Primary Prostate Cancer: Diagnostic and Prognostic Value in Positron Emission Tomography-Prostate-Specific Membrane Antigen.

Prostate cancer represents a significant public health challenge, with its management requiring precise diagnostic and prognostic tools. Prostate-specific membrane antigen (PSMA), a cell surface enzyme overexpressed in prostate cancer cells, has emerged as a pivotal biomarker. PSMA's ability to increase the sensitivity of PET imaging has revolutionized its application in the clinical management of prostate cancer. The advancements in PET-PSMA imaging technologies and methodologies, including the development of PSMA-targeted radiotracers and optimized imaging protocols, led to diagnostic accuracy and clinical utility across different stages of prostate cancer. This highlights its superiority in staging and its comparative effectiveness against conventional imaging modalities. This paper analyzes the impact of PET-PSMA on prostate cancer management, discussing the existing challenges and suggesting future research directions. The integration of recent studies and reviews underscores the evolving understanding of PET-PSMA imaging, marking its significant but still expanding role in clinical practice. This comprehensive review serves as a crucial resource for clinicians and researchers involved in the multifaceted domains of prostate cancer diagnosis, treatment, and management.

Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran.

Floods are among the natural hazards that have seen a rapid increase in frequency in recent decades. The damage caused by floods, including human and financial losses, poses a serious threat to human life. This study evaluates two machine learning (ML) techniques for flood susceptibility mapping (FSM) in the Gamasyab watershed in Iran. We utilized random forest (RF), support vector machine (SVM), ensemble models, and a geographic information system (GIS) to predict FSM. The application of these models involved 10 effective factors in flooding, as well as 82 flood locations integrated into the GIS. The SVM and RF models were trained and tested, followed by the implementation of resampling techniques (RT) using bootstrap and subsampling methods in three repetitions. The results highlighted the importance of elevation, slope, and precipitation as primary factors influencing flood occurrence. Additionally, the ensemble model outperformed both the RF and SVM models, achieving an area under the curve (AUC) of 0.9, a correlation coefficient (COR) of 0.79, a true skill statistic (TSS) of 0.83, and a standard deviation (SD) of 0.71 in the test phase. The tested models were adapted to available input data to map the FSM across the study watershed. These findings underscore the potential of integrating an ensemble model with GIS as an effective tool for flood susceptibility mapping.

Shaping the RF Transmit Field in 7T MRI Using a Nonuniform Metasurface Constructed of Short Conducting Strips.

The ability of metamaterial structures to offer unique properties and new solutions has opened new avenues in a wide range of applications, including super-resolution in optics and efficient antennas in radiofrequency (RF) engineering. In magnetic resonance imaging (MRI), metamaterials hold the promise of increasing the RF magnetic field intensity while minimizing power deposition. Here, we propose a metasurface based on a two-dimensional (2D) array of short conducting strips combined with a high dielectric substrate, which was tuned to operate at ultrahigh field 7T human MRI. While studied in optics and electromagnetics in the GHz-to-THz range, this study is the first to design such a metasurface for proton imaging at 7T MRI. We performed electromagnetic (EM) simulation of the brain MRI setup with the new metasurface placed in the proximity to the temporal lobe, which showed 2.2-fold local increase in the RF transmit efficiency, with superior performance than an array of electric dipoles. In this study, we also investigate the effect of the spatial distribution of the subunits to control the target RF field's distribution. While the common design is based on a uniform distribution of the subunits, nonuniform distribution, such as a denser center (convex) or more condensed edges (concave), provides an extra dimension to tailor both the magnetic and electric fields. The concave distribution achieved 1.5-1.8-fold reduction in the power deposition compared to the uniform distribution in the brain MRI setups examined.

Optimizing cropland use to reduce groundwater arsenic hazards in a naturally arsenic-enriched grain-producing region.

In the Hetao Basin, a grain-producing region plagued by naturally occurring arsenic (As) pollution, understanding the role of agricultural cultivation activities in mobilizing As in groundwater is worthwhile. Here we investigated the impact of cropland use characteristics on groundwater As hazards using a model that combines Random Forest (RF) classification with SHapley Additive exPlanation (SHAP). The analysis incorporated eight cropland use characteristics and three natural factors across 1258 groundwater samples as independent variables. Additionally, an optimized cropland use strategy to mitigate groundwater As hazards was proposed. The results revealed that crop cultivation area, especially within a 2500m-radius buffer around sampling points, most significantly influenced the probability of groundwater As concentrations exceeding an irrigation safety threshold of 50 µg/L, achieving an AUC of 0.86 for this prediction. The relative importance of crop areas on As hazards were as follows: sunflower > melon > wheat > maize. Specifically, a high proportion of sunflower area (>30%), particularly in regions with longer cropland irrigation history, tended to elevate groundwater As hazards. Conversely, its negative driving force on groundwater As hazards was more pronounced with the increase in the proportion of wheat area (>5%), in contrast to other crops. Transitioning from sunflower to wheat or melon cultivation in the northeast of the Hetao Basin may contribute to lower groundwater As hazards. This study provides a scientific foundation for balancing food production with environmental safety and public health considerations.

RF coil that minimizes electronic components while enhancing performance for rodent MRI at 7 Tesla.

This study introduces a novel volume coil design that features two slotted end-plates connected by six rungs, resembling the traditional birdcage coil. The end rings are equipped with six evenly distributed circular slots, inspired by Mansfield's cavity resonator theory, which suggests that circular slots can generate a baseline resonant frequency. One notable advantage of this proposed coil design is its reduced reliance on electronic components compared to other volume coils, making it more efficient. Additionally, the dimensions of the coil can be theoretically computed in advance, enhancing its practicality. To evaluate the performance and safety of the coil, electromagnetic field and specific absorption rate simulations were simulated using a cylindrical saline phantom and the finite element method. Furthermore, a transceiver coil prototype optimized for 7 Tesla and driven in quadrature was constructed, enabling whole-body imaging of rats. The resonant frequency of the coil prototype obtained through experimental measurements closely matched the theoretical frequency derived from Mansfield's theory. To validate the coil design, phantom images were acquired to demonstrate its viability and assess its performance. These images also served to validate the magnetic field simulations. The experimental results aligned well with the simulation findings, confirming the reliability of the proposed coil design. Importantly, the prototype coil showcased significant improvements over a similarly-sized birdcage coil, indicating its potential for enhanced performance. The noise figure was lower in the prototype versus the birdcage coil (NFbirdcage-NFslotcage= 0.7). Phantom image data were also used to compute the image SNR, giving SNRslotcage/SNRbirdcage= 34.36/24.34. By proving the feasibility of the coil design through successful rat whole-body imaging, the study provides evidence supporting its potential as a viable option for high-field MRI applications on rodents.

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC-α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

U-TAG: Electromagnetic Wireless Sensing System for Robotic Hand Pre-Grasping.

In order to perform complex manipulation and grasp tasks, robotic hands require sensors that can handle increasingly demanding functionality and degrees of freedom. This research paper proposes a radiofrequency sensor that uses a wireless connection between a probe and a tag. A compact and low-profile antenna is mounted on the hand and functions as a probe to read a printed passive resonator on the plastic object being targeted, operating within a pre-touch sensing range. The grasping strategy consists of four stages that involve planar alignment in up-to-down and left-to-right directions between the probe and tag, the search for an appropriate distance from the object, and rotational (angular) alignment. The real and imaginary components of the probe-input impedance are analyzed for different orientation strategies and positioning between the resonator on the object and the probe. These data are used to deduce the orientation of the hand relative to the target object and to determine the optimal position for grasping.