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We previously proposed range-guided adaptive proton therapy (RGAPT) that uses mid-range treatment beams as probing beams and intra-fractionated range measurements for online adaptation. In this work, we demonstrated experimental verification and reported the dosimetric accuracy for RGAPT. A STEEV phantom was used for the experiments, and a 3 × 3 × 3 cm3 cube inside the phantom was assigned to be the treatment target. We simulated three online range shift scenarios: reference, overshoot, and undershoot, by placing upstream Lucite sheets, 4, 0, and 8 that corresponded to changes of 0, 6.8, and −6.8 mm, respectively, in water-equivalent path length. The reference treatment plan was to deliver single-field uniform target doses in pencil beam scanning mode and generated on the Eclipse treatment planning system. Different numbers of mid-range layers, including single, three, and five layers, were selected as probing beams to evaluate beam range (BR) measurement accuracy in positron emission tomography (PET). Online plans were modified to adapt to BR shifts and compensate for probing beam doses. In contrast, non-adaptive plans were also delivered and compared to adaptive plans by film measurements. The mid-range probing beams of three (5.55MU) and five layers (8.71MU) yielded accurate range shift measurements in 60 s of PET acquisition with uncertainty of 0.5 mm while the single-layer probing (1.65MU) was not sufficient for measurements. The adaptive plans achieved an average gamma (2%/2 mm) passing rate of 95%. In contrast, the non-adaptive plans only had an average passing rate of 69%. RGAPT planning and delivery are feasible and verified by the experiments. The probing beam delivery, range measurements, and adaptive planning and delivery added a small increase in treatment delivery workflow time but resulted in substantial dose improvement. The three-layer mid-range probing was most suitable considering the balance of high range measurement accuracy and the low number of probing beam layers. [ABSTRACT FROM AUTHOR]

An integration of multiplexed luminescence and photocatalysis assay was proposed for the detection of acetylcholinesterase (AChE) by manipulating the coordination immobilization of photocatalytic Au nanoclusters on Eu-HOF nanorods. Eu-HOF-AuNCs served as a luminescence indicator and mimicked the enzyme toolbox for universally generating reactive oxygen species (ROS). Under light irradiation, Eu-HOF-AuNCs may accelerate the catalytic oxidation of oPD to produce more oligomers with yellow fluorescence and in situ-regulate photoluminescence variations of the indicator, in which oligomers can weaken the phosphorescence of the indicator based on overlapped spectra. By utilizing thiocholine (enzymolysis product of acetylthiocholine) to preferentially compete for ROS produced by photocatalysis of Eu-HOF-AuNCs, AChE activity was detected by the reversal of the indicator signal due to the attenuation of oPD oligomers. The emerged ratiometric fluorescence F573/F340 and F617/F340 exhibit improved sensitivity with detection limits of AChE as low as 0.09 and 0.14 U/L, respectively. Also, the cooperation of phosphorescence of Eu-HOF-AuNCs boosts detection accuracy. Serum experiments show that the Eu-HOF-AuNCs can be used to determine AChE, promoting the possibility of application in disease diagnosis. This sensor driven by photocatalysis has a great potential to be developed into a universal oxidase platform for quantifying analytes as long as the reasonable redox reaction. [ABSTRACT FROM AUTHOR]

Objective. Develop a prototype on-line positron emission tomography (PET) scanner and evaluate its capability of on-line imaging and intra-fractionated proton-induced radioactivity range measurement. Approach. Each detector consists of 32 × 32 array of 2 × 2 × 30 mm3 Lutetium–Yttrium Oxyorthosilicate scintillators with single-scintillator-end readout through a 20 × 20 array of 3 × 3 mm2 Silicon Photomultipliers. The PET can be configurated with a full-ring of 20 detectors for conventional PET imaging or a partial-ring of 18 detectors for on-line imaging and range measurement. All detector-level readout and processing electronics are attached to the backside of the system gantry and their output signals are transferred to a field-programable-gate-array based system electronics and data acquisition that can be placed 2 m away from the gantry. The PET imaging performance and radioactivity range measurement capability were evaluated by both the offline study that placed a radioactive source with known intensity and distribution within a phantom and the online study that irradiated a phantom with proton beams under different radiation and imaging conditions. Main results. The PET has 32 cm diameter and 6.5 cm axial length field-of-view (FOV), ∼2.3–5.0 mm spatial resolution within FOV, 3% sensitivity at the FOV center, 18%–30% energy resolution, and ∼9 ns coincidence time resolution. The offline study shows the PET can determine the shift of distal falloff edge position of a known radioactivity distribution with the accuracy of 0.3 ± 0.3 mm even without attenuation and scatter corrections, and online study shows the PET can measure the shift of proton-induced positron radioactive range with the accuracy of 0.6 ± 0.3 mm from the data acquired with a short-acquisition (60 s) and low-dose (5 MU) proton radiation to a human head phantom. Significance. This study demonstrated the capability of intra-fractionated PET imaging and radioactivity range measurement and will enable the investigation on the feasibility of intra-fractionated, range-shift compensated adaptive proton therapy. [ABSTRACT FROM AUTHOR]

Background: The glandular trichomes of tobacco (Nicotiana tabacum) can efficiently produce secondary metabolites. They act as natural bioreactors, and their natural products function to protect plants against insect-pests and pathogens and are also components of industrial chemicals. To clarify the molecular mechanisms of tobacco glandular trichome development and secondary metabolic regulation, glandular trichomes and glandless trichomes, as well as other different developmental tissues, were used for RNA sequencing and analysis. Results: By comparing glandless and glandular trichomes with other tissues, we obtained differentially expressed genes. They were obviously enriched in KEGG pathways, such as cutin, suberine, and wax biosynthesis, flavonoid and isoflavonoid biosynthesis, terpenoid biosynthesis, and plant–pathogen interaction. In particular, the expression levels of genes related to the terpenoid, flavonoid, and wax biosynthesis pathway mainly showed down-regulation in glandless trichomes, implying that they lack the capability to synthesize certain exudate compounds. Among the differentially expressed genes, 234 transcription factors were found, including AP2-ERFs, MYBs, bHLHs, WRKYs, Homeoboxes (HD-ZIP), and C2H2-ZFs. These transcription factor and genes that highly expressed in trichomes or specially expressed in GT or GLT. Following the overexpression of R2R3-MYB transcription factor Nitab4.5_0011760g0030.1 in tobacco, an increase in the number of branched glandular trichomes was observed. Conclusions: Our data provide comprehensive gene expression information at the transcriptional level and an understanding of the regulatory pathways involved in glandular trichome development and secondary metabolism. [ABSTRACT FROM AUTHOR]

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences. [ABSTRACT FROM AUTHOR]

Measurement of lightning current plays a critical role in the field of atmospheric electricity. Traditionally, Rogowski coils or low-resistance shunts were employed for measuring lightning currents in the range from several amperes up to several hundreds of kilo-amperes, and high-value resistors were utilized for measuring corona discharge currents at sub-ampere levels. However, these approaches were not suitable for continuously recording the vast range of lightning currents. For this sake, we have developed a lightning current measurement system equipped with a shock-tolerant low-noise amplifier module. With the system installed on a tall tower, sub-ampere level currents just before the lightning initiation were observed for the first time. To confirm the authenticity of the recorded currents, the background noise of the measurement system and surrounding environment were identified, and a digital multi-frequency notch filter was proposed for de-noising. Results show that the system can achieve a current identification level of 50 mA even in complex electromagnetic environments, while having a measurement capability of 220 kA. [ABSTRACT FROM AUTHOR]

Recognition of partial discharge (PD) patterns is essential for insulation diagnosis of covered conductors in overhead lines. Current research has not sufficiently addressed the complex background noise in real environments, and most detection methods depend primarily on feature engineering or deep learning, suggesting potential for improvement in accuracy and efficiency. This has led the authors to propose a PD pattern recognition algorithm that integrates feature selection and deep learning. This algorithm incorporates the design of a discrete wavelet denoising function specifically tailored to the characteristics of PD for data preprocessing. It employs Bayesian optimization algorithms and light gradient boosting machines for characterizing corona discharge features. Furthermore, it develops multi‐scale clustering features and phase‐resolved features for feature fusion, and constructs insightful features based on the light gradient boosting machine. Finally, a novel deep learning model is formulated, demonstrating exceptional detection performance for early faults in covered conductors. Experimental results show that this algorithm attains an Matthews correlation coefficient score of 0.814, a 13.2% improvement over the baseline algorithm's 0.719, and a speed increase of 39.18%. The final accuracy amounts to 97.85%. This algorithm demonstrates exceptional performance in detecting early insulation faults in conductors. [ABSTRACT FROM AUTHOR]