Continuous Parameter Research Articles

Vortex loop operators and quantum M2-branes

We study M2-branes in AdS_4× S^7/\mathbb{Z}_kAdS4×S7/ℤk dual to 1/2 and 1/3 BPS vortex loop operators in ABJM theory and compute their one-loop correction beyond the classical M2-brane action. The correction depends only on the parity of kk and is independent of all continuous parameters in the definition of the vortex loops. The result for odd kk agrees with the answers for the 1/2 BPS Wilson loop in the k=1k=1 theory and for even kk with the one in the k = 2k=2 theory. Combining with the classical part, we find that the natural expansion parameter seems to be 1/\sqrt{kN}1/kN rather than 1/\sqrt{N}1/N. This provides a further setting where semiclassical quantisation can be applied to M2-branes and produces new results inaccessible by other methods.

Landau free energy of small clusters beyond mean-field approach.

Landau free energy determines the landscape of order parameter fluctuations that occur in a physical system at thermal equilibrium and, in particular, characterizes the critical phenomena. We propose a semianalytical approach based on the fluctuating local field method, which allows us to estimate Landau free energy for small clusters with discrete (Ising model) and continuous (Heisenberg model) order parameter.

Deep learning CT reconstruction improves liver metastases detection

ObjectivesDetection of liver metastases is crucial for guiding oncological management. Computed tomography through iterative reconstructions is widely used in this indication but has certain limitations. Deep learning image reconstructions (DLIR) use deep neural networks to achieve a significant noise reduction compared to iterative reconstructions. While reports have demonstrated improvements in image quality, their impact on liver metastases detection remains unclear. Our main objective was to determine whether DLIR affects the number of detected liver metastasis. Our secondary objective was to compare metastases conspicuity between the two reconstruction methods.MethodsCT images of 121 patients with liver metastases were reconstructed using a 50% adaptive statistical iterative reconstruction (50%-ASiR-V), and three levels of DLIR (DLIR-low, DLIR-medium, and DLIR-high). For each reconstruction, two double-blinded radiologists counted up to a maximum of ten metastases. Visibility and contour definitions were also assessed. Comparisons between methods for continuous parameters were performed using mixed models.ResultsA higher number of metastases was detected by one reader with DLIR-high: 7 (2–10) (median (Q₁–Q₃); total 733) versus 5 (2–10), respectively for DLIR-medium, DLIR-low, and ASiR-V (p < 0.001). Ten patents were detected with more metastases with DLIR-high simultaneously by both readers and a third reader for confirmation. Metastases visibility and contour definition were better with DLIR than ASiR-V.ConclusionDLIR-high enhanced the detection and visibility of liver metastases compared to ASiR-V, and also increased the number of liver metastases detected.Critical relevance statementDeep learning-based reconstruction at high strength allowed an increase in liver metastases detection compared to hybrid iterative reconstruction and can be used in clinical oncology imaging to help overcome the limitations of CT.Key PointsDetection of liver metastases is crucial but limited with standard CT reconstructions.More liver metastases were detected with deep-learning CT reconstruction compared to iterative reconstruction.Deep learning reconstructions are suitable for hepatic metastases staging and follow-up.Graphical

Wave propagation in a light-temperature neural network under adaptive local energy balance.

External electric and mechanical stimuli can induce shape deformation in excitable media because of its intrinsic flexible property. When the signals propagation in the media is described by a neural network, creation of heterogeneity or defect is considered as the effect of shape deformation due to accumulation or release of energy in the media. In this paper, a temperature-light sensitive neuron model is developed from a nonlinear circuit composed of a phototube and a thermistor, and the physical energy is kept in capacitive and inductive terms. Furthermore, the Hamilton energy for this function neuron is obtained in theoretical way. A regular neural network is built on a square array by activating electric synapse between adjacent neurons, and a few of neurons in local area is excited by noisy disturbance, which induces local energy diversity, and continuous coupling enables energy propagation and diffusion. Initially, the Hamilton energy function for a temperature-light sensitive neuron can be obtained. Then, the finite neurons are applied noise to obtain energy diversity to explore the energy spread between neurons in the network. For keeping local energy balance, one intrinsic parameter is regulated adaptively until energy diversity in this local area is decreased greatly. Regular pattern formation indicates that local energy balance creates heterogeneity or defects and a few of neurons show continuous parameter shift for keeping energy balance in a local area, which supports gradient energy distribution for propagating waves in the network.

Mean-field solution of the neural dynamics in a Greenberg-Hastings model with excitatory and inhibitory units.

We present a mean-field solution of the dynamics of a Greenberg-Hastings neural network with both excitatory and inhibitory units. We analyze the dynamical phase transitions that appear in the stationary state as the model parameters are varied. Analytical solutions are compared with numerical simulations of the microscopic model defined on a fully connected network. We found that the stationary state of this system exhibits a first-order dynamical phase transition (with the associated hysteresis) when the fraction of inhibitory units f is smaller than some critical value f_{t}≲1/2, even for a finite system. Moreover, any solution for f<1/2 can be mapped to a solution for purely excitatory systems (f=0). In finite systems, when the system is dominated by inhibition (f>f_{t}), the first-order transition is replaced by a pseudocritical one, namely a continuous crossover between regions of low and high activity that resembles the finite size behavior of a continuous phase transition order parameter. However, in the thermodynamic limit (i.e., infinite-system-size limit), we found that f_{t}→1/2 and the activity for the inhibition dominated case (f≥f_{t}) becomes negligible for any value of the parameters, while the first-order transition between low- and high-activity phases for f<f_{t} remains.

Predicting the dynamics of heart rate variability in patients with type 1 diabetes against improving glycemic control

The aim of the study: to analyze the effect of improvement of glycemic control on heart rate variability changes in patients with type 1 diabetes against the background of insulin therapy correction and to predict this dynamics based on the parameters of continuous glucose monitoring. We examined 49 patients with the level of glycosylated hemoglobin ≥7% and &lt;10% without late diabetic complications. The average age – 32.0 (21.5; 38.0) years, the average disease duration – 9.5 (5.0; 17.5) years. The study was based on the simultaneous registration of heart rate variability and continuous glucose monitoring before and 3 months after correction of insulin therapy. Logistic regression analysis and ROC-analysis were used to predict the changes. After 3 months, the patients had significant decrease in the level of glycosylated hemoglobin, glycose variability, and a decrease in the frequency of hypoglycemic episodes. Heart rate variability increased in 73.5% of persons. The presence of hypoglycemia, standard deviation of blood glucose levels and glomerular filtration rate after treatment turned out to be prognostic factors for the predicting improvement in heart rate variability (the proportion of correct prediction of the patient's actual belonging to one or another prognostic group was 76.39%). Based on the calculation of the theoretical values of the positive result probability using the logistic equation, a detailed scale for predicting changes in heart rhythm variability for type 1 diabetes patients was proposed: up to 0.07 – a low probability of a positive result; 0.07-0.29 – the probability of a positive result is below average; 0.29-0.51 – a moderate probability of a positive result; 0.51-0.90 – high probability of a positive result; more than 0.90 – a very high probability of a positive result. We found that improvement of glycemic control leads to an increase in both frequency and time characteristics of heart rate variability. The increase in the likelihood of improvement of heart rate variability in patients with type 1 diabetes was more likely to be associated with reduced glycose variability and fewer hypoglycemic episodes. We developed a predictive mathematical model of heart rate variability based on the continuous glucose monitoring parameters for type 1 diabetes with sensitivity of 88.0% and specificity 68.18%, AUC 0.739 (p=0.001).

Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio

This research investigates the comparative predictive efficacy of two leading machine learning methodologies, specifically the XGBoost and Random Forest models, in estimating ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions along the east coast of North America. Using annual temperature datasets and relevant oceanographic parameters, the data is carefully processed, cleaned and sorted into training and test subsets via the RStudio Platform. The performance evaluation model is carried out using predetermined machine learning assessment criteria, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared. The results show the superiority of the XGBoost model compared to Random Forest in terms of prediction accuracy and minimizing prediction errors. The XGBoost model shows lower MSE values and higher R-squared values than the Random Forest model, indicating its better capacity in explaining data variations. XGBoost consistently provides more accurate predictions and shows higher sensitivity in identifying important factors influencing ocean temperature fluctuations than Random Forest. This research significantly improves understanding and prognostic capabilities regarding ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions. Empirical evidence underlines the efficacy of the XGBoost model in predicting ocean temperatures in the studied region. Continuous model evaluation and parameter refinement for both methodologies is critical to establishing standards for optimal prediction performance. The findings of this research have implications for the fields of oceanography and climate science, and offer potential pathways to comprehensively understand and mitigate the impacts of climate change on marine ecosystems.

Influence of donor age and donor-recipient age difference on intimal hyperplasia in pediatric patients with young and adult donors vs. adult patients after heart transplantation.

Optimal selection and allocation of donor hearts is a relevant aspect in transplantation medicine. Donor age and cardiac allograft vasculopathy (CAV) affect post-transplant mortality. To what extent donor age impacts intimal hyperplasia (CAVIH) in pediatric and adult patients after heart transplantation (HTx) is understudied. In a cohort of 98 HTx patients, 58 pediatric (24.1% with adult donors) and 40 adult patients, we assessed the effect of donor age and donor-recipient age difference (D-R) on the continuous parameter of maximal intima thickness (mIT) in optical coherence tomography. We evaluated their predictive value regarding higher mIT and the prevalence of CAVIH, defined as mIT > 0.3 mm, and compared it to established CAV risk factors. In the overall population, donor age correlated with mIT (p < 0.001), while in the pediatric subpopulation, both donor age and D-R correlated with mIT (p < 0.001 and p = 0.002, respectively). In the overall population, donor age was a main predictor of higher mIT and CAVIH (p = 0.001 and p = 0.01, respectively) in addition to post-transplant interval, arterial hypertension, and dyslipidemia. In the pediatric patients, dyslipidemia remained a main predictor of both higher mIT and CAVIH (p = 0.004 and p = 0.040, respectively), while donor age and D-R were not. While there was an effect of the non-modifiable parameter of donor age regarding maximal intimal thickness, a stronger association was seen between the modifiable risk factor dyslipidemia and higher maximal intimal thickness and CAVIH in both the overall population and the pediatric subpopulation.

An Investigative Study on the Oral Health Condition of Individuals Undergoing 3D-Printed Customized Dental Implantation.

The advent of three-dimensional (3D) printing technology has revolutionized the field of dentistry, enabling the precise fabrication of dental implants. By utilizing 3D printing, dentists can devise implant plans prior to surgery and accurately translate them into clinical procedures, thereby eliminating the need for multiple surgical procedures, reducing surgical discomfort, and enhancing surgical efficiency. Furthermore, the utilization of digital 3D-printed implant guides facilitates immediate restoration by precisely translating preoperative implant design plans, enabling the preparation of temporary restorations preoperatively. This comprehensive study aimed to assess the postoperative oral health status of patients receiving personalized 3D-printed implants and investigate the advantages and disadvantages between the 3D-printed implant and conventional protocol. Additionally, variance analysis was employed to delve into the correlation between periodontal status and overall oral health. Comparisons of continuous paired parameters were made by t-test. The results of our study indicate a commendable one-year survival rate of over 94% for 3D-printed implants. This finding was corroborated by periodontal examinations and follow-up surveys using the Oral Health Impact Profile-14 (OHIP-14) questionnaire, revealing excellent postoperative oral health status among patients. Notably, OHIP-14 scores were significantly higher in patients with suboptimal periodontal health, suggesting a strong link between periodontal health and overall oral well-being. Moreover, we found that the operating time (14.41 ± 4.64 min) was less statistically significant than for the control group (31.76 ± 6.83 min). In conclusion, personalized 3D-printed implant surgery has emerged as a reliable clinical option, offering a viable alternative to traditional implant methods. However, it is imperative to gather further evidence-based medical support through extended follow-up studies to validate its long-term efficacy and safety.

Stochastic representation and conditioning of process-based geological model by deep generative and recognition networks

Accurate and realistic geological modelling is the core of oil and gas development and production. In recent years, process-based methods are developed to produce highly realistic geological models by simulating the physical processes that reproduce the sedimentary events and develop the geometry. However, the complex dynamic processes are extremely expensive to simulate, making process-based models difficult to be conditioned to field data. In this work, we propose a comprehensive generative adversarial network framework as a machine-learning-assisted approach for mimicking the outputs of process-based geological models with fast generation. The main objective of our work is to obtain a continuous parametrization of the highly realistic process-based geological models which enables us to calibrate the models and condition the models to data. Numerical results are presented to illustrate the capability of our proposed methodology.

Conditions for the existence of positive operator valued measures

Sufficient and necessary conditions are presented for the existence of ( N, M)-positive operator valued measures (( N, M)-POVMs) valid for arbitrary-dimensional quantum systems. Firstly, a sufficient condition for the existence of ( N, M)-POVMs is presented. It yields a simple relation determining an upper bound on the continuous parameter of an arbitrary ( N, M)-POVM, below which all its POVM elements are guaranteed to be positive semidefinite. Secondly, for arbitrary optimal ( N, M)-POVMs conditions on their existence are derived, which exhibit a close connection to the existence of sets of ( M − 1) N isospectral, traceless, orthonormal, and hermitian operators with particular common spectra. The specific form of the two possible types of spectra depends on whether M is smaller than the dimension of the quantum system under consideration or not. For the special case of M = 2 and dimensions, which are powers of two, these sets of operators always exist and can be expressed in terms of the Clifford algebra of tensor products of Pauli operators.

Transcutaneous vagal nerve stimulation during lower body negative pressure

BackgroundPara-sympathetic vagal activation has profound influence on heart rate and other cardiovascular parameters. We tested the hypothesis that transcutaneous Vagal Nerve Stimulation (tVNS) through the auricular branch of the vagus nerve would attenuate the normal sympathetic response to central blood volume reduction by lower body negative pressure (LBNP). Method10 healthy volunteers (6 female; age 21 ± 2 years; weight 62 ± 13 kg; height 167 ± 12 cm) were included in this cross-over design trial. After 15 min rest in supine position, subjects underwent three 15-min periods of 30 mmHg LBNP intervention with and without cyclic tVNS stimulation. Continuous cardiovascular parameters (Nexfin) were recorded. ResultsOverall tVNS did not convincingly attenuate sympathetic response to central hypovolemia. Deactivation of the tVNS during LBNP resulted in increased MAP at 2.3 ± 0.5 mmHg (P < 0.001). Comparing the cyclic actual active stimulation periods to periods with pause during tVNS intervention showed a decrease in HR by 72.9 ± 11.2 to 70.2 ± 11.6 bpm (mean ± SD; P < 0.05), and concomitant increases in SV (86.0 ± 12.1 to 87.2 ± 12.6 mL; P < 0.05), MAP (82.9 ± 6.3 to 84.0 ± 6.2 mmHg; P < 0.05) and TPR (1116.0 ± 111.1 to 1153 ± 104.8 dyn*s/cm5; P < 0.05). ConclusiontVNS in 30 s cycles during LBNP can selectively attenuate HR, prompting a compensatory augmented sympathetic response. It would appear the method used in this study at least, has an isolated cardiac inhibitory effect probably mediated by augmented vagal activity on the sinoatrial or atrio-ventricular node, possibly in combination with reduced activity in the sympathetic cardiac nerve.

Liver function tests, CD4+ counts, and viral load among people living with HIV on dolutegravir compared to efavirenz-based cART; a comparative cross-sectional study

BackgroundRecently, dolutegravir-based therapy has become the first-line treatment when compared to others. However, dolutegravir-associated side effects in the liver and levels of efficacy haven't been addressed yet in underdeveloped countries such as Ethiopia. ObjectiveThe purpose of this study was to compare liver function tests, CD4+ counts, and viral load among people living with HIV on dolutegravir and efavirenz-based antiretroviral regimens at Debre Markos Comprehensive Specialized Hospital in Northwest Ethiopia. MethodsAn institutional-based comparative cross-sectional study was carried out from May 20 to July 10, 2020. An equal number of dolutegravir and efavirenz-prescribed patients (n = 53 each) for 6 months and above were included, and a judgmental sampling technique was used. A comparison of categorical and continuous parameters was analyzed with chi-square and an independent t-test, respectively, using SPSS version 26. A multivariable logistic regression was conducted and considered statistically significant at a p-value of <0.05. ResultsThe magnitude of liver enzyme (AST/ALT) abnormalities was 22.4 % (12/53) and 30.2 % (16/53) among dolutegravir- and efavirenz-prescribed patients, respectively. The dolutegravir group had significantly higher mean CD4+ counts than the efavirenz group (589.40 ± 244.38 vs. 450.64 ± 203.54 cell/mm3; p = 0.002). The efavirenz group had a significantly higher mean viral load than the dolutegravir group (783.83 ± 476.82 vs. 997.98 ± 439.11 cp/ml; p = 0.032). There was a statistically insignificant difference in AST (p = 0.709) or ALT (p = 0.687) between dolutegravir and efavirenz-based regimens. The multivariable logistic regression analysis revealed that BMI ≥25 kg/m2 was associated with liver enzyme abnormalities (AOR = 6.60, 95 % CI: 1.17, 42.82). ConclusionA dolutegravir-based regimen was more likely to result in patients achieving higher efficacy for viral suppression and a CD4+ count increase. Although the differences were statistically insignificant, the mean AST and ALT levels were marginally higher in efavirenz-treated groups than in dolutegravir-treated groups.

Experiments for control of boundary layer transition by plasma actuators

Plasma flow control technology is a new type of active flow control technology, and this paper carries out certain research on the basic principle of plasma flow control technology and its application on the wing model. The paper introduces the theory of plasma discharge and analyzes how plasma can control the flow field. A parallel dielectric barrier discharge plasma exciter is then designed and tested by using the Particle Image Velocimetry (PIV) technique to investigate the spatial flow field distributions of single-stage and three-stage actuators, and the mechanism of jet generation by the actuator is analyzed. Finally, the plasma actuator was utilized to control flow separation on a super-zero-boundary airfoil, and the oil droplet interferometric method was used to study the actuator’s role in controlling boundary layer transition. The experiment revealed that increasing the actuator discharge voltage can further reduce wall drag and more effectively delay the transition. Under the given conditions of a head-on angle of 6° and the incoming wind speed of 20 m/s, a two-stage actuator’s continuous discharge parameter was set to an output voltage of 12 kV and an output frequency of 4.7 kHz. The results showed that the transition position can be delayed by 13%, indicating the effectiveness of the actuator’s jet effect in enhancing the stability of the boundary layer flow.

State of charge estimation for lithium batteries based on multi-timescale FFRLS-UKF

Abstract State of charge (SOC) can represent the remaining battery capacity, and accurately estimating SOC is very useful for the drivers of electric vehicles. This study proposes a lithium battery SOC estimation technique based on multi-timescale forgetting factor recursive least squares as well as an unscented Kalman filter (MTS-FFRLS-UKF). The MTS-FFRLS-UKF uses a circuit with a 2-RC network as the battery model, whose parameters are continuously updated at a timescale that is different from SOC estimation. The experimental results under a dynamic test prove the availability and accuracy of MTS-FFRLS-UKF. Continuous parameter update enhances the accuracy of battery modelling and furthermore the SOC estimation accuracy. The mean absolute error (MAE) and root-mean square error (RMSE) of SOC estimation decrease to 0.58% and 0.62%, respectively.

АНАЛІЗ МЕТОДІВ ОПТИМІЗАЦІЇ ГЛИБИНИ КВАНТОВОЇ СХЕМИ

This work reviews methods for optimizing quantum circuit depth, encompassing theoretical techniques, algorithmic innovations, and practical applications. Recent advancements demonstrate substantial improvements in circuit depth optimization, yet the issue of scalability remains unresolved. Key research contributions, such as the advanced exact synthesis of Clifford+T circuits, have shown promising results in reducing the number of T-gates, thus enhancing computational efficiency. However, the practical implementation of these methods in real-world noisy environments has yet to be fully evaluated. Further studies have developed automated optimization techniques for large quantum circuits with continuous parameters, significantly minimizing gate counts while maintaining structural integrity. Despite these advancements, challenges persist in assessing the performance of these optimized circuits on actual quantum hardware and integrating them into various quantum algorithms. The qubit routing problem also presents significant hurdles, particularly in systems with limited communication, necessitating effective methodologies to optimize qubit placement and movement. A comprehensive full-stack approach, incorporating hardware, software, and compiler optimizations, has provided valuable insights into architectural comparisons and design strategies. This approach highlights the importance of noise-aware compilation and the need for microarchitecture-specific optimizations to improve program success rates on current quantum devices. This paper underscores the need for scalable, hardware-agnostic optimization methods and extensive experimental validation. Integrating graph neural networks for quantum circuit optimization presents a promising future research direction. The analysis concludes that while significant progress has been made, unresolved issues such as computational complexity, limited experimental validation, and the diversity of quantum algorithms necessitate ongoing research to fully realize the potential of quantum computing. In conclusion, optimizing the depth of quantum circuits is a multifaceted problem requiring continuous research and development. The integration of theoretical advancements with practical implementations is crucial for overcoming current limitations and unlocking the full potential of quantum computing. Continued efforts in this area will be vital for achieving scalable and efficient quantum computations

Investigating the sensitivity of dual-phase steel microstructure to continuous annealing line parameters

The mechanical properties of dual-phase (DP) steels are influenced by ferrite grain size, martensite distribution/morphology, and strength differences between ferrite/martensite phases. A sensitivity study was conducted on a continuous annealing line cycle of DP steel manufacturing process to understand the effect of individual segments on the final microstructural features. From the study results, an optimised cycle was developed to obtain a DP steel microstructure with reduced martensite banding, fine ferrite grain size and less untempered martensite. The main modifications are austenite soaking temperature above Ac3 temperature, higher cooling rate of the slow cooling stage, increased soak time at ferrite soaking temperature, and higher over-aging temperature. The optimised cycle produced DP steel microstructure with reduced martensite banding at a similar phase fraction (35%) and decreased ferrite grain size from 8.4 to 5.1 µm. This improved the mechanical properties, increasing tensile strength from 789 ± 21 to 831 ± 14 MPa and total elongation from 15 ± 3 to 20 ± 2%.

Oxygen reserve index versus conventional peripheral oxygen saturation for prevention of hypoxaemia: A randomised controlled trial.

Hypoxaemia occurs frequently during paediatric laryngeal microsurgery. The oxygen reserve index is a noninvasive and continuous parameter to assess PaO2 levels in the range of 100 to 200 mmHg. It ranges from 0 to 1.0. We investigated whether monitoring the oxygen reserve index can reduce the incidence of SpO2 90% or less. Randomised controlled trial. A tertiary care paediatric hospital. Paediatric patients aged 18 years or less scheduled to undergo laryngeal microsurgery. The patients were randomly allocated to the oxygen reserve index or control groups, and stratified based on the presence of a tracheostomy tube. Rescue intervention was performed when the oxygen reserve index was 0.2 or less and the SpO2 was 94% or less in the oxygen reserve index and control groups, respectively. The primary outcome was the incidence of SpO2 90% or less during the surgery. Data from 88 patients were analysed. The incidence of SpO2 ≤ 90% did not differ between the oxygen reserve index and control groups [P = 0.114; 11/44, 25% vs. 18/44, 40.9%; relative risk: 1.27; and 95% confidence interval (CI): 0.94 to 1.72]. Among the 128 rescue interventions, SpO2 ≤ 90% event developed in 18 out of 75 events (24%) and 42 out of 53 events (79.2%) in the oxygen reserve index and control groups, respectively (P < 0.001; difference: 55.2%; and 95% CI 38.5 to 67.2%). The number of SpO2 ≤ 90% events per patient in the oxygen reserve index group (median 0, maximum 3) was less than that in the control group (median 0, maximum 8, P = 0.031). Additional monitoring of the oxygen reserve index, with a target value of greater than 0.2 during paediatric airway surgery, alongside peripheral oxygen saturation, did not reduce the incidence of SpO2 ≤ 90%.

Joint track initiation of resolvable group target

This paper studies the problem of resolvable group target track initiation (RGTI) and proposes a novel method to obtain the discrete association vectors and continuous trajectory parameters of individual targets within the group, which benefits from the fitting representations of target tracks and the utilization of group target motion constraint. Specifically, every target track within the group is modeled as a weighted sum of a same set of time basis functions with different weights, and the aim is to solve multiple pairs of association vectors and trajectory parameters, i.e., unknown weights, according to the measurement equation and group target motion constraint. Next, a joint RGTI framework, which includes track extraction, track purification, trajectory parameter and association vector correction, is proposed to transform the original complex constraint set solving problem into multiple solvable sub-problems. And the convergence of RGTI is ensured through iterative optimization of track purification, trajectory parameter correction and association vector correction. Finally, simulation example demonstrates the superiority of the proposed method compared with existing multi-target track initiation methods.

Gross Tumor and Intracranial Control Benefits with Fractionated Radiotherapy Compared with Stereotactic Radiosurgery for Patients with WHO Grade 2 Meningioma

ObjectiveSurgical resection is the mainstay of treatment for WHO grade 2 meningioma. Fractionated radiation therapy (RT) is frequently employed following surgery, though many centers utilize stereotactic radiosurgery (SRS) for recurrence or progression. Herein, we report disease control outcomes from an institutional cohort with adjuvant fractionated RT versus salvage SRS. MethodsWe identified 32 patients from an institutional database with WHO grade 2 meningioma and residual/recurrent tumor treated with either SRS or fractionated RT. Patients were treated between 2007 and 2021 and had at least 1 year of follow-up. Kaplan-Meier estimators were used to determine gross tumor control (GTC) and intracranial control (IC). Univariate Cox proportional hazards models using biologically effective dose (BED) as a continuous parameter were used to assess for dose responses. ResultsWith a median follow-up of 5.5 years, 13 patients (41%) received SRS to a recurrent or progressive nodule, 2 (6%) fractionated radiation to a recurrent or progressive nodule, and 17 (53%) adjuvant fractionated radiation following subtotal resection. 5-year GTC was higher with fractionated RT versus SRS (82% vs. 38%, p=0.03). 5-year IC was also better with fractionated RT versus SRS (82% vs. 11%, p<0.001). On univariate analysis, increasing BED10 was significantly associated with better GTC (p=0.039); increasing BED3 was not (p=0.82). ConclusionsIn this patient cohort, GTC and IC were significantly higher in patients treated with adjuvant fractionated RT compared to salvage SRS. Increasing BED10 was associated with better GTC. Fractionated RT may provide a better therapeutic ratio than SRS for grade 2 meningiomas.