Abstract Background Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point 18F-FDG PET/CT to predict the malignant risk of GGOs. Methods Retrospectively analyzing 311 patients with 397 GGOs, this study identified 118 low-risk GGOs and 279 high-risk GGOs through pathology and follow-up according to the new WHO classification. The dataset was randomly divided into a training set comprising 239 patients (318 lesions) and a testing set comprising 72 patients (79 lesions), we employed a self-configuring 3D nnU-net convolutional neural network with majority voting method to segment GGOs and predict malignant risk of GGOs. Three independent segmentation prediction models were developed based on thin-section lung CT, early-phase 18F-FDG PET/CT, and dual-time-point 18F-FDG PET/CT, respectively. Simultaneously, the results of the dual-time-point 18F-FDG PET/CT model on the testing set were compared with the diagnostic of nuclear medicine physicians. Results The dual-time-point 18F-FDG PET/CT model achieving a Dice coefficient of 0.84 ± 0.02 for GGOs segmentation and demonstrating high accuracy (84.81%), specificity (84.62%), sensitivity (84.91%), and AUC (0.85) in predicting malignant risk. The accuracy of the thin-section CT model is 73.42%, and the accuracy of the early-phase 18F-FDG PET/CT model is 78.48%, both of which are lower than the accuracy of the dual-time-point 18F-FDG PET/CT model. The diagnostic accuracy for resident, junior and expert physicians were 67.09%, 74.68%, and 78.48%, respectively. The accuracy (84.81%) of the dual-time-point 18F-FDG PET/CT model was significantly higher than that of nuclear medicine physicians. Conclusions Based on dual-time-point 18F-FDG PET/CT images, the 3D nnU-net with a majority voting method, demonstrates excellent performance in predicting the malignant risk of GGOs. This methodology serves as a valuable adjunct for physicians in the risk prediction and assessment of GGOs.

• Book : 25(1)
• Pub. Date : 2025
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• Pub. Date : 2025
• Page : pp.1-1
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Artisanal kefir is produced by fermenting milk with kefir grains, resulting in a lightly carbonated drink with health-promoting bioactive compounds. However, sensory variability and fermentation conditions challenge its standardization, limiting commercialization in Brazil due to regulatory requirements. This study evaluated the physicochemical properties, volatile compounds, and microbiological stability of artisanal kefir produced in southern Brazil during 30 days of storage. Metabarcoding analysis, carried out by sequencing the V3/V4 regions of the 16S rRNA gene (bacteria) and the ITS region (fungi), revealed an increase in bacterial diversity, with a predominance of Enterococcus and Acetobacter, while fungal diversity decreased, with a predominance of Kazachstania. The physicochemical parameters remained stable. The concentration of volatile compounds, analyzed using a gas chromatograph coupled to a mass spectrometer, decreased, except for an increase in 2-heptanol. The aromatic profile was enriched with alcohols and ketones, possibly influenced by Enterococcus and Acetobacter. These findings show that kefir maintained microbiological stability and adequate sensory characteristics throughout the period analyzed. The study provides subsidies for the standardization of artisanal kefir and compliance with Brazilian quality standards, as well as guiding future research into durability, quality, and consumer perception.

• Book : 11(2)
• Pub. Date : 2025
• Page : pp.105-105
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• Pub. Date : 2025
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This study proposes improving the process of the vertical diffusion of temperature in numerical models to enhance the accuracy of sea surface temperature (SST) simulation. SST tends to be underestimated in the coastal and tidal flat regions, such as the Yellow Sea around Korea. In particular, SST in coastal areas is highly sensitive to wet/dry treatment, implying that the sensitivity of SST increases with the slope of coastal bathymetry. Therefore, during the calculation of vertical temperature diffusion terms, the numerical model’s surface boundary condition (SBC) was modified to limit excessive temperature differences below a certain depth in the coastal regions. Under wet or dry conditions defined by the wet/dry treatment, SBC and bottom boundary condition (BBC) adjustments are stabilized within a predefined depth limit. While horizontal diffusion also plays a role in the model, SST is significantly influenced by the balance of heat advection and shortwave radiation. To demonstrate this, Heat Limit Depth (HLD) was added as an input parameter into the vertical diffusion algorithm in the model to enhance sensitivity to the SBC. If the total water depth in the tidal flat is below the HLD and less than 1.0 m, the model is changed to estimate surface sediment temperature instead of SST. The improvement in the vertical diffusion term for SST was effective primarily in tidal flat areas. In contrast, the impact was less pronounced in coastal areas with average depths exceeding 5 m. The rationale for separating SBC and BBC in the improved air–sea interaction process is twofold: SBC adjustments are suitable for reducing heat flux effects, specifically in shallow depths or tidal flats, without significantly affecting the entire model domain, while combined SBC and BBC adjustments are more appropriate for inducing coastal SST changes across the domain.

• Book : 17(4)
• Pub. Date : 2025
• Page : pp.474-474
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Dynamical decoupling multipulse sequences can be applied to solid-state spins for sensing weak oscillating fields from nearby single nuclear spins. By periodically reversing the probing system's evolution, other noises are counteracted and filtered out over the total evolution. However, the technique is subject to intricate interactions resulting in additional resonant responses, which can be misinterpreted with the actual signal intended to be measured. We experimentally characterize three of these effects present in single nitrogen-vacancy centers in diamond, where we also develop a numerical simulation model without rotating-wave approximation, showing robust correlation to the experimental data. Regarding centers with the N15 nitrogen isotope, we observe that a small misalignment in the bias magnetic field causes the precession of the nitrogen nuclear spin to be sensed by the electronic spin of the center. Another studied case of ambiguous resonances comes from the coupling with lattice C13 nuclei, where we use the echo modulation frequencies to obtain the interaction Hamiltonian and then utilize the latter to simulate multipulse sequences. Finally, we also measure and simulate the effects from the free evolution of the quantum system during finite pulse durations. Due to the large data volume and the strong dependence of these ambiguous resonances with specific experimental parameters, we provide a simulations data set with a user-friendly graphical interface, where users can compare simulations with their own experimental data for spectral disambiguation. Although focused on nitrogen-vacancy centers and dynamical decoupling sequences, these results and the developed model can potentially be applied to other solid-state spins and quantum sensing techniques. Published by the American Physical Society 2025

• Book : 111(2)
• Pub. Date : 2025
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Abstract Spurious solar-wind effects are a potential noise source in future Laser Interferometer Space Antenna (LISA) measurements. One noise coupling mechanism is constrained by estimating solar-wind effects on acceleration noise in LISA Pathfinder (LPF). While LISA is designed for drag-free differential measurement, predicting the realistic impact both bounds the operational environment and assesses whether LISA could provide serendipitous space-weather observations. Data from NASA’s Advanced Composition Explorer (ACE), situated at the L1 Lagrange point, serves as a reliable source of solar-wind data. The data sets are compared over the 114-day time period from March 1, 2016 to June 23, 2016. This period gives the longest readily-available open data set, without interference from other commissioning activities. To evaluate space weather effects, the data from both satellites are formatted, gap-filled/interpolated, and Fast-Fourier Transformed for amplitude spectral density and coherence comparisons. Solar wind effects are not seen in a coherence plot between LPF and ACE; modest coherence in the planned LISA observational frequency band can be attributed to chance. This result indicates that measurable correlation due to solar-wind acceleration noise over 3-month timescales will be a negligible noise source. LISA is unlikely to inform solar wind measurements routinely. Another source of noise from the sun, solar radiation pressure, is estimated to impart greater acceleration noise, but has yet to be analyzed.

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• Pub. Date : 2025
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The discovery of effective cysteine protease inhibitors with crude protein kiwi extracts (CPKEs) has created novel challenges and prospects for pharmaceutical development. Despite extensive research on CPKEs, limited research has been conducted on treating atopic dermatitis (AD). Therefore, the objective of this work was to investigate the anti-inflammatory effects of CPKEs on TNF-α activation in a HaCaT cell model and in a DNCB (1-chloro-2, 4-dinitrochlorobenzene)-induced atopic dermatitis animal model. The molecular weight of the CPKE was determined using SDS-PAGE under non-reducing (17 kDa and 22 kDa) and reducing conditions (25 kDa, 22 kDa, and 15 kDa), whereas gelatin zymography was performed to examine the CPKE’s inhibitory impact on cysteine protease (actinidin and papain) activity. Moreover, the CPKE remains stable at 60 °C, with pH levels varying from 4 to 11, as determined by the azocasein assay. CPKE treatment decreased the phosphorylation of mitogen-activated protein kinase (MAPK) and Akt, along with the activation of nuclear factor-kappa B (NF-κB)-p65 in tumor necrosis factor-α (TNF-α)-stimulated HaCaT cells. Five-week-old BALB/c mice were treated with DNCB to act as an AD-like animal model. The topical application of CPKE to DNCB-treated mice for three weeks substantially decreased clinical dermatitis severity and epidermal thickness and reduced eosinophil infiltration and mast cells into ear and skin tissues. These findings imply that CPKE derived from kiwifruit might be a promising therapy option for inflammatory skin diseases such as AD.

• Book : 26(4)
• Pub. Date : 2025
• Page : pp.1534-1534
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AbstractThe sub‐Terahertz and Terahertz bands play a critical role in next‐generation wireless communication and sensing technologies, thanks to the large amount of available bandwidth in this spectral regime. While long‐wavelength (microwave to mm‐Wave) and short‐wavelength (near‐infrared to ultraviolet) devices are well‐established and studied, the sub‐THz to THz regime remains relatively underexplored and underutilized. Traditional approaches used in the aforementioned spectral regions are more difficult to replicate in the THz band, leading to the need for the development of novel devices and structures that can manipulate THz radiation effectively. Herein a novel organic, solid‐state electrochemical device is presented, capable of achieving modulation depths of over 90% from ≈500 nm of a conducting polymer that switches conductivity over a large dynamic range upon application of an electronically controllable external bias. The stability of such devices under long‐term, repeated voltage switching, as well as continuous biasing at a single voltage, is also explored. Switching stabilities and long‐term bias stabilities are achieved over two days for both use cases. Additionally, both depletion mode (always “ON”) and accumulation mode (always “OFF”) operation are demonstrated. These results suggest applications of organic electrochemical THz modulators in large area and flexible implementations.

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• Pub. Date : 2025
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An effective neural network system for monitoring sensors in helicopter turboshaft engines has been developed based on a hybrid architecture combining LSTM and GRU. This system enables sequential data processing while ensuring high accuracy in anomaly detection. Using recurrent layers (LSTM/GRU) is critical for dependencies among data time series analysis and identification, facilitating key information retention from previous states. Modules such as SensorFailClean and SensorFailNorm implement adaptive discretization and quantisation techniques, enhancing the data input quality and contributing to more accurate predictions. The developed system demonstrated anomaly detection accuracy at 99.327% after 200 training epochs, with a reduction in loss from 2.5 to 0.5%, indicating stability in anomaly processing. A training algorithm incorporating temporal regularization and a combined optimization method (SGD with RMSProp) accelerated neural network convergence, reducing the training time to 4 min and 13 s while achieving an accuracy of 0.993. Comparisons with alternative methods indicate superior performance for the proposed approach across key metrics, including accuracy at 0.993 compared to 0.981 and 0.982. Computational experiments confirmed the presence of the highly correlated sensor and demonstrated the method’s effectiveness in fault detection, highlighting the system’s capability to minimize omissions.

• Book : 25(4)
• Pub. Date : 2025
• Page : pp.990-990
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