Optimizing Gantry Breakpoint Angles in Proton Therapy: Enhancing Efficiency and Patient Experience.

Purpose: The breakpoint for a 360° radiotherapy gantry is typically positioned at 180°. This arbitrary setting has not been systematically evaluated for efficiency and may cause redundant gantry rotation and extended setup times. Our study aimed to identify an optimal gantry breakpoint angle for a full-gantry proton therapy system, with the goal of minimizing gantry movement. Materials and Methods: We analyzed 70 months of clinically delivered proton therapy plans (9152 plans, 131 883 fractions), categorizing them by treatment site and mapping the fields from a partial-gantry to full-gantry orientation. For each delivered fraction, we computed the minimum total gantry rotation angle as a function of gantry breakpoint position, which was varied between 0° and 360° in 1° steps. This analysis was performed separately within the entire plan cohort and individual treatment sites, both with and without the capability of over-rotating 10° past the breakpoint from either direction (20° overlap). The optimal gantry breakpoint was identified as one which resulted in a low average gantry rotation per fraction. Results: Considering mechanical constraints, 130° was identified as a reasonable balance between increased gantry-rotation efficiency and practical treatment considerations. With a 20° overlap, this selection reduced the average gantry rotation by 41.4° per fraction when compared to the standard 180° breakpoint. Disease site subgroups showed the following reduction in average gantry rotation: gastrointestinal 192.2°, thoracic 56.3°, pediatric 44.9°, genitourinary 19.9°, central nervous system 10.7°, breast 2.8°, and head and neck 0.1°. Conclusion: For a full-gantry system, a breakpoint of 130° generally outperforms the conventional 180° breakpoint. This reduction is particularly impactful for gastrointestinal, pediatric, and thoracic sites, which constitute a significant proportion of cases at our center. The adjusted breakpoint could potentially streamline patient delivery, alleviate mechanical wear, and enhance treatment precision by reducing the likelihood of patient movement during delivery.

Deep learning based linear energy transfer calculation for proton therapy.

Objective.This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for accuracy, is resource-intensive and slow for dose optimization, while the speedier analytical approximation has compromised accuracy. Our objective was to prototype a deep-learning-based model for calculating dose-averaged LET (LETd) using patient anatomy and dose-to-water (DW) data, facilitating real-time biological dose evaluation and LET optimization within proton treatment planning systems.Approach. 275 4-field prostate proton Stereotactic Body Radiotherapy plans were analyzed, rendering a total of 1100 fields. Those were randomly split into 880, 110, and 110 fields for training, validation, and testing. A 3D Cascaded UNet model, along with data processing and inference pipelines, was developed to generate patient-specific LETddistributions from CT images and DW. The accuracy of the LETdof the test dataset was evaluated against MC-generated ground truth through voxel-based mean absolute error (MAE) and gamma analysis.Main results.The proposed model accurately inferred LETddistributions for each proton field in the test dataset. A single-field LETdcalculation took around 100 ms with trained models running on a NVidia A100 GPU. The selected model yielded an average MAE of 0.94 ± 0.14 MeV cm-1and a gamma passing rate of 97.4% ± 1.3% when applied to the test dataset, with the largest discrepancy at the edge of fields where the dose gradient was the largest and counting statistics was the lowest.Significance.This study demonstrates that deep-learning-based models can efficiently calculate LETdwith high accuracy as a fast-forward approach. The model shows great potential to be utilized for optimizing the RBE of proton treatment plans. Future efforts will focus on enhancing the model's performance and evaluating its adaptability to different clinical scenarios.

A Low-Cost, Tabletop LOD-EPR System for Nondestructive Quantification of Iron Oxide Nanoparticles in Tissues.

Iron oxide nanoparticles (IONPs) have wide utility in applications from drug delivery to the rewarming of cryopreserved tissues. Due to the complex behavior of IONPs (e.g., uneven particle distribution and aggregation), further developments and clinical translation can be accelerated by having access to a noninvasive method for tissue IONP quantification. Currently, there is no low-cost method to nondestructively track IONPs in tissues across a wide range of concentrations. This work describes the performance of a low-cost, tabletop, longitudinally detected electron paramagnetic resonance (LOD-EPR) system to address this issue in the field of cryopreservation, which utilizes IONPs for rewarming of rat kidneys. A low-cost LOD-EPR system is realized via simultaneous transmit and receive using MHz continuous-wave transverse excitation with kHz modulation, which is longitudinally detected at the modulation frequency to provide both geometric and frequency isolation. The accuracy of LOD-EPR for IONP quantification is compared with NMR relaxometry. Solution measurements show excellent linearity (R2 > 0.99) versus Fe concentration for both measurements on EMG308 (a commercial nanoparticle), silica-coated EMG308, and PEG-coated EMG308 in water. The LOD-EPR signal intensity and NMR longitudinal relaxation rate constant (R1) of water are affected by particle coating, solution viscosity, and particle aggregation. R1 remains linear but with a reduced slope when in cryoprotective agent (CPA) solution, whereas the LOD-EPR signal is relatively insensitive to this. R1 does not correlate well with Fe concentration in rat kidney sections (R2 = 0.3487), while LOD-EPR does (R2 = 0.8276), with a linear regression closely matching that observed in solution and CPA.

Deep learning-based solution for smart contract vulnerabilities detection.

This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process. In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we propose, Optimized-CodeBERT model surpasses other methods, achieving an f1-score of 93.53%. To precisely extract vulnerability features, we acquire segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately. To demonstrate the feasibility of our proposed solution, we evaluate its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types.

Sensitivity-Tunable Terahertz Liquid/Gas Biosensor Based on Surface Plasmon Resonance with Dirac Semimetal.

In this paper, we study the sensitivity-tunable terahertz (THz) liquid/gas biosensor in a coupling prism-three-dimensional Dirac semimetal (3D DSM) multilayer structure. The high sensitivity of the biosensor originates from the sharp reflected peak caused by surface plasmon resonance (SPR) mode. This structure achieves the tunability of sensitivity due to the fact that the reflectance could be modulated by the Fermi energy of 3D DSM. Besides, it is found that the sensitivity curve depends heavily on the structural parameters of 3D DSM. After parameter optimization, we obtained sensitivity over 100°/RIU for liquid biosensor. We believe this simple structure provides a reference idea for realizing high sensitivity and a tunable biosensor device.

Development and Dosimetric Characterization of a Customizable Shield for Subtotal Skin Electron Beam Therapy.

Purpose: Purpose: Subtotal skin electron beam therapy may be an option for patients with cutaneous lymphoma receiving radiation therapy to treat large areas of their skin but may benefit from sparing specific areas that may have had previous radiation therapy, are of specific cosmetic concern, and/or show no evidence of disease. We report here on the design, implementation, and dosimetric characteristics of a reusable and transparent customizable shield for use with the large fields used to deliver total skin electron beam therapy at extended distance with a conventional linear accelerator. Methods and Materials: A shield was designed and manufactured consisting of acrylic blocks that can be mounted on a steel frame to allow patient-specific shielding. The dosimetry of the device was measured using radiochromic film. Results: The shield is easy to use and well-tolerated for patient treatment, providing minimal electron transmission through the shield with a sharp penumbra at the field edge, with no increase in x-ray dose. We report on the dosimetry of a commercial device that has been used to treat more than 30 patients to date. Conclusions: The customizable shield is well suited to providing patient-specific shielding for subtotal skin electron beam therapy.

A frequency-swept, longitudinal detection EPR system for measuring short electron spin relaxation times at ultra-low fields.

A frequency-swept longitudinal detection (LOD) EPR system is described for ultra-low field spectroscopy and relaxometry. With the capability of performing simultaneous transmit and receive with -80 dB isolation, this LOD-EPR can capture signals with decay constants in the nanosecond range and in theory even sub-nanosecond range, at fields close to the earth's magnetic field. The theoretical principles underlying this LOD-EPR are based on a fictitious field that accounts for the Z-axis magnetization polarized by a radiofrequency field alone. The electron spin relaxation time is obtained directly from a previously derived equation that describes the relationship between the relaxation time and the spectral peak position. Herein, the first frequency-swept LOD-EPR system is described in detail, along with experimental measurements of the short relaxation time (∼30 ns) of the free radical, 2,2-diphenyl-1-picrylhydrazyl, at zero to low field.

EST-SSR Marker Development and Full-Length Transcriptome Sequence Analysis of Tiger Lily (Lilium lancifolium Thunb).

The fast advancement and deployment of sequencing technologies after the Human Genome Project have greatly increased our knowledge of the eukaryotic genome sequences. However, due to technological concerns, high-quality genomic data has been confined to a few key organisms. Moreover, our understanding of which portions of genomes make up genes and which transcript isoforms synthesize these genes is scarce. Therefore, the current study has been designed to explore the reliability of the tiger lily (Lilium lancifolium Thunb) transcriptome. The PacBio-SMRT was used for attaining the complete transcriptomic profile. We obtained a total of 815,624 CCS (Circular Consensus Sequence) reads with an average length of 1295 bp. The tiger lily transcriptome has been sequenced for the first time using third-generation long-read technology. Furthermore, unigenes (38,707), lncRNAs (6852), and TF members (768) were determined based on the transcriptome data, followed by evaluating SSRs (3319). It has also been revealed that 105 out of 128 primer pairs effectively amplified PCR products. Around 15,608 transcripts were allocated to 25 distinct KOG Clusters, and 10,706 unigenes were grouped into 52 functional categories in the annotated transcripts. Until now, no tiger lily lncRNAs have been discovered. Results of this study may serve as an extensive set of reference transcripts and help us learn more about the transcriptomes of tiger lilies and pave the path for further research.

Circ_0027089 regulates NACC1 by targeting miR-136-5p to aggravate the development of hepatitis B virus-related hepatocellular carcinoma.

Hepatitis B virus (HBV) infection is the main trigger of hepatocellular carcinoma (HCC). Circular RNA plays an indispensable role in cancer development, and this study aimed to disclose the function and mechanism of circ_0027089 in HBV-related HCC. The expression levels of circ_0027089, miR-136-5p and nucleus accumbens associated protein 1 (NACC1) mRNA were measured by quantitative real-time PCR, and the protein level of NACC1 was detected by western blot. For functional analyses, cell proliferation was assessed by cell counting kit-8 assay and colony formation assay. Cell apoptosis and cell cycle were detected by flow cytometry assay, and cell apoptosis was also assessed by caspase 3/7 activity. The capacities of migration and invasion were evaluated by wound healing assay and transwell assay, respectively. The predicted relationship between miR-136-5p and circ_0027089 or NACC1 was validated by dual-luciferase reporter assay and RNA binding protein immunoprecipitation assay. Animal experiments were performed in nude mice to explore the role of circ_0027089 in vivo. Circ_0027089 expression and NACC1 expression were elevated, while miR-136-5p expression was decreased in HBV-related HCC tissues and cells. In function, circ_0027089 knockdown inhibited HepG2.2.15 and HepAD38 (tet-off) cell proliferation, migration and invasion but induced cell cycle arrest and apoptosis, while circ_0027089 overexpression played the reversed effects. For mechanism exploration, miR-136-5p was a target of circ_0027089, and miR-136-5p deficiency could reverse the role of circ_0027089 knockdown. Circ_0027089 functioned as an oncogene to promote the development of HBV-related HCC by regulating NACC1 via competitively targeting miR-136-5p.

Evaluation of SrAl2 O4 :Eu, Dy phosphor for potential applications in thermoluminescent dosimetry.

PURPOSE: To evaluate the use of commercial-grade strontium aluminate phosphorescent powder as a thermoluminescent (TL) dosimeter for clinical radiotherapy beams. MATERIALS AND METHOD: Commercially available Eu2+ , Dy3+ co-doped strontium aluminate powder (SrAl2 O4 :Eu, Dy) was annealed and then irradiated using 20 × 20 cm2 field size, with 6-MV (PDD10  = 70.7) and 18-MV (PDD10  = 79.4) photon beams and and 9-MeV (R50  = 3.6), 15 MeV (R50  = 5.9) and 18-MeV (R50  = 7.2) electron beams. To calibrate the relationship between the TL readings and the irradiated doses, TL glow curves were acquired for doses up to 600 cGy at all beam energies. For the percentage depth dose (PDD) measurement, the SrAl2 O4 :Eu, Dy powder was sandwiched by solid water phantoms, with varying thickness of solid water placed above to determine the depth. PDDs were measured at four representative depths and compared against the commissioning depth dose data for each beam energy. RESULTS: Linear dose response models of doses up to 200 cGy were created for all beam energies. Superlinearity was observed with doses greater than 200 cGy. The PDD measurement acquired experimentally agrees well with the commissioning data of the medical linear accelerator. Trapping parameters such as order of kinetics, activation energy and frequency factor have been obtained via TL glow curve analysis. CONCLUSION: The linear dose response demonstrates that SrAl2 O4 :Eu, Dy is a potential TLD dosimeter for both electron beams and photon beams at different beam energies. The PDD measurements further support its potential use in quality assurance and radiation dosimetry.

Ultra-low frequency EPR using longitudinal detection and fictitious-field modulation.

When viewed in a rotating frame of reference, a transverse-plane radiofrequency (RF) field manifests as a longitudinal field component called the fictitious field. By modulating the RF field and thus the fictitious field, detectable longitudinal magnetization patterns have previously been shown to be measurable. By combining fictitious-field modulation and longitudinal detection, here we demonstrate EPR spectroscopy and one-dimensional imaging in a custom-built longitudinal detection system operating at an ultra-low frequency (24 MHz) for detecting electron spins with short (~nanoseconds) relaxation times. Simultaneous transmit and receive with low transmitter leakage level (~80 dB isolation) is also demonstrated.

Predicting the Local Response of Metastatic Brain Tumor to Gamma Knife Radiosurgery by Radiomics With a Machine Learning Method.

PURPOSE: The current study proposed a model to predict the response of brain metastases (BMs) treated by Gamma knife radiosurgery (GKRS) using a machine learning (ML) method with radiomics features. The model can be used as a decision tool by clinicians for the most desirable treatment outcome. METHODS AND MATERIAL: Using MR image data taken by a FLASH (3D fast, low-angle shot) scanning protocol with gadolinium (Gd) contrast-enhanced T1-weighting, the local response (LR) of 157 metastatic brain tumors was categorized into two groups (Group I: responder and Group II: non-responder). We performed a radiomics analysis of those tumors, resulting in more than 700 features. To build a machine learning model, first, we used the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of radiomics features to the minimum number of features useful for the prediction. Then, a prediction model was constructed by using a neural network (NN) classifier with 10 hidden layers and rectified linear unit activation. The training model was evaluated with five-fold cross-validation. For the final evaluation, the NN model was applied to a set of data not used for model creation. The accuracy and sensitivity and the area under the receiver operating characteristic curve (AUC) of the prediction model of LR were analyzed. The performance of the ML model was compared with a visual evaluation method, for which the LR of tumors was predicted by examining the image enhancement pattern of the tumor on MR images. RESULTS: By the LASSO analysis of the training data, we found seven radiomics features useful for the classification. The accuracy and sensitivity of the visual evaluation method were 44 and 54%. On the other hand, the accuracy and sensitivity of the proposed NN model were 78 and 87%, and the AUC was 0.87. CONCLUSIONS: The proposed NN model using the radiomics features can help physicians to gain a more realistic expectation of the treatment outcome than the traditional method.

Asymmetric Mapping Quantization for Nearest Neighbor Search.

Nearest neighbor search is a fundamental problem in computer vision and machine learning. The straightforward solution, linear scan, is both computationally and memory intensive in large scale high-dimensional cases, hence is not preferable in practice. Therefore, there have been a lot of interests in algorithms that perform approximate nearest neighbor (ANN) search. In this paper, we propose a novel addition-based vector quantization algorithm, Asymmetric Mapping Quantization (AMQ), to efficiently conduct ANN search. Unlike existing addition-based quantization methods that suffer from handling the problem caused by the norm of database vector, we map the query vector and database vector using different mapping functions to transform the computation of L-2 distance to inner product similarity, thus do not need to evaluate the norm of database vector. Moreover, we further propose Distributed Asymmetric Mapping Quantization (DAMQ) to enable AMQ to work on very large dataset by distributed learning. Extensive experiments on approximate nearest neighbor search and image retrieval validate the merits of the proposed AMQ and DAMQ.

Hand motion classification using a multi-channel surface electromyography sensor.

The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high.

Peptide fractionation and free radical scavenging activity of zein hydrolysate.

Alcalase-treated zein hydrolysate (ZH) was separated by gel filtration, ultrafiltration, and reversed-phase HPLC, and the scavenging activities for 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS*+), 1,1-diphenyl-2-picrylhydrazyl (DPPH*), and superoxide anion (O2*-) radicals of different peptide fractions were measured to establish the antioxidant efficacy. Results showed that the ability to stabilize water-soluble free radicals (ABTS*+) by ZH components was insensitive to the peptide size, whereas that against ethanol-soluble free radicals (DPPH*) and O2*- was molecular weight dependent. Antioxidative peptides of <1 kDa were further separated by HPLC into 30 components, of which those with great hydrophobicity exhibited strong DPPH* and O2*- scavenging ability and those with intermediate hydrophobicity displayed the maximum ABTS*+ scavenging activity. Two dominant components (fractions 8 and 17) were further purified and identified by LC-PDA-ESI-MS to be Tyr-Ala and Leu-Met-Cys-His, respectively. The results demonstrated that the free radical scavenging activity of ZH depended on the radical species and was strongly related to the molecular weight and hydrophobicity of the constituting peptides.

Rheological and microstructural properties of porcine myofibrillar protein-lipid emulsion composite gels.

The objective of the study was to investigate the role of emulsified fat (lard) and oil (peanut oil) in the rheology and microstructure of porcine myofibrillar protein (MP) gels. Heat-induced composite gels were prepared from 2% MP with 0% to 15% pre-emulsified lipids at 0.6 M NaCl, pH 6.2. Dynamic rheological testing upon temperature sweeping (20 to 80 degrees C at 2 degrees C/min) showed substantial increases in G' (an elastic modulus) of MP sols/gels with the addition of emulsions. Gel hardness was markedly enhanced (P < 0.05) by incorporating >or=10% emulsions, and the composite gel with 15% lard was 33% more rigid (P < 0.05) than that with 15% peanut oil. Incorporation of both emulsions at 10% or higher levels improved the water holding capacity of the gels by 28% to 44% (P < 0.05). Light microscopy revealed a compact gel structure filled with protein-coated fat/oil globules that interacted with the protein matrix via disulfide bonds. The results indicated that both physical and chemical forces contributed to the enhancements in the rheology, moisture retention, and lipid stabilization in the MP-emulsion composite gels.

Reducing, radical scavenging, and chelation properties of in vitro digests of alcalase-treated zein hydrolysate.

The objective of the study was to assess the antioxidant potential of alcalase-treated zein hydrolysate (ZH) during a two-stage (1 h of pepsin --> 0.5-2 h of pancreatin, 37 degrees C) in vitro digestion. Sephadex gel filtration and high-performance size exclusion chromatography were used to separate ZH into fractions. The amino acid composition, 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS(+*)) and 1,1-diphenyl-2-picrylhydrazyl (DPPH*) free radical scavenging activity, reducing power, and Cu (2+) chelation ability were tested to determine the antioxidant efficacy of ZH. Results showed that in vitro digests of ZH contained up to 16.5% free amino acids, with short peptides (<500 Da) making up the rest of the mass. The ABTS(+*) scavenging activity of ZH was decreased by 27% (P<0.05) after pepsin treatment but was fully recovered upon subsequent pancreatin digestion, while the DPPH* scavenging activity of ZH was substantially less than ABTS(+*) scavenging activity and showed a 7-fold reduction following pancreatin treatment. The reducing power of ZH increased 2-fold (P<0.05) following pancreatin digestion when compared with nondigested ZH. The ability of ZH to sequester Cu (2+) was reduced by pepsin digestion but was reestablished following pancreatin treatment. The antioxidant activity demonstrated by in vitro digests of ZH (1-8 mg/mL) was comparable to or exceeded (P<0.05) that of 0.1 mg/mL of ascorbic acid or BHA. The results suggested that dietary zein alcalase hydrolysate may have the benefit to promote the health of the human digestive tract.

Radio frequency heating of beef rolls from biceps femoris muscle.

Chemical, physical and sensory aspects of quality were compared on encased rolls (1kg) prepared from single muscle beef (biceps femoris) cooked in a steam oven (80°C) or by radio frequency (RF) heating (500W, 27.12MHz) under recirculating water at 80°C. The RF protocol reduced cooking times to 23 and 31% of steam cooking times, respectively, in non-injected meat (PG1) and in rolls prepared with curing brines possessing similar dielectric properties (PG2-4). Compared to steam heating, cooking yields were significantly higher (P<0.05) and instrumental texture measurements related to toughness significantly lower (P<0.05) for RF cooked PG1 rolls and for meat injected with brines containing water binding dielectrically inactive additives (PG4) but not for brined rolls lacking the latter ingredients (PG2 and PG3). Participants in a 50 member untrained sensory panel were unable to detect texture differences which had been indicated by instrumental analysis for PG1 and PG4.