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Purpose: To develop a novel classification of sagittal en bloc resection (SEBR) based on anatomical locations for thoracolumbar spine tumors and assess the clinical outcomes of this surgical procedure. Methods: 31 patients with thoracolumbar tumours treated with SEBR were enrolled in this study. The individualized surgical strategy was adopted based on our surgical classification. Demographics, perioperative outcomes, complications and postoperative outcomes were assessed. Results: Based on our surgical classifications, patients were divided into four types. All bony resection margins were negative, wide resection was achieved in 25 patients, marginal resection in four, and intralesional resection in two. 18 patients underwent anterior reconstruction. Complications were encountered in five patients, and instrumentation failure occurred in one patient. The median follow-up was 24 (range, 6–72) months and recurrence was found in only one patient. Conclusion: SEBR is a safe and effective surgical procedure for patients with thoracolumbar spinal tumours in specific anatomical locations. The proposed surgical classification covers all SEBR types and is easy to apply, it may assist surgical decision-making in patients with spinal tumours. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate whether short course of neoadjuvant denosumab treatment for spinal GCTB could (1) Induce radiological and histological response? (2) Facilitate en bloc resection? (3) Achieve satisfactory oncological and functional outcomes? Methods: The clinical information of ten consecutive patients between 2018 and 2022 with spinal GCTB treated with short course of neoadjuvant denosumab (≤ 5 doses) and en bloc spondylectomy was retrospectively reviewed. The radiological and histological response, operative data, oncological and functional outcomes were analyzed. Results: The mean doses of neoadjuvant denosumab were 4.2 (range 3–5 doses). After neoadjuvant denosumab, there were 9 cases showing new ossification and 5 cases with reappearance of cortical integrity. The values of Hounsfield units (HU) of the soft tissue component were increased by > 50% in 7 cases. The signal intensity (SI) ratios of tumor/muscle in T2WI of plain MRI were decreased by > 10% in 60% of the cases. Shrinkage of soft tissue mass by > 10% was observed in 4 cases. The mean duration of operation was 575 ± 174 min, and the mean estimated blood loss (EBL) was 2790 ± 1934 ml. No obvious adhesion to dura mater or major vessels was encounter intraoperatively. There is no tumor collapse or breakage during surgery. Multinucleated giant cells were decreased in 6 cases (60%) with the remaining 4 cases showing absence of multinucleated giant cells. Mononuclear stromal cells existed in most of the cases (8 cases, 80%). New bone formation was noticed in 8 cases (80%). No patient had a worsening of neurologic function after surgery. No tumor recurrence was noticed within the mean follow-up of 24 ± 20 months. Conclusion: Short-term neoadjuvant denosumab could yield radiological and histological responses and might facilitate en bloc spondylectomy by hardening the tumor and causing less adhesion to segmental vessels, major vessels and nerve roots, which was beneficial to achieve the optimal oncological and functional outcomes. [ABSTRACT FROM AUTHOR]

This paper introduces a new sparse Bayesian learning (SBL) algorithm that jointly recovers a temporal sequence of edge maps from noisy and under-sampled Fourier data. The new method is cast in a Bayesian framework and uses a prior that simultaneously incorporates intra-image information to promote sparsity in each individual edge map with inter-image information to promote similarities in any unchanged regions. By treating both the edges as well as the similarity between adjacent images as random variables, there is no need to separately form regions of change. Thus we avoid both additional computational cost as well as any information loss resulting from pre-processing the image. Our numerical examples demonstrate that our new method compares favorably with more standard SBL approaches. [ABSTRACT FROM AUTHOR]

A new algorithm is developed to jointly recover a temporal sequence of images from noisy and under-sampled Fourier data. Specifically we consider the case where each data set is missing vital information that prevents its (individual) accurate recovery. Our new method is designed to restore the missing information in each individual image by “borrowing” it from the other images in the sequence. As a result, all of the individual reconstructions yield improved accuracy. The use of high resolution Fourier edge detection methods is essential to our algorithm. In particular, edge information is obtained directly from the Fourier data which leads to an accurate coupling term between data sets. Moreover, data loss is largely avoided as coarse reconstructions are not required to process inter- and intra-image information. Numerical examples are provided to demonstrate the accuracy, efficiency and robustness of our new method. [ABSTRACT FROM AUTHOR]

Edge detection from Fourier data has been emerging in many applications. The concentration factor method has been widely used in detecting edges from Fourier data. We present a theoretic analysis of the concentration factor method for non-uniform Fourier data in this paper. Specifically, we propose admissible conditions for the concentration factors such that the edge detector converges to a smoothed approximation of the jump function. Moreover, we also introduce some specific choices of admissible concentration factors and present estimates of convergence rates correspondingly. [ABSTRACT FROM AUTHOR]

Sparse learning models are popular in many application areas. Objective functions in sparse learning models are usually non-smooth, which makes it difficult to solve them numerically. We develop a fast and convergent two-step iteration scheme for solving a class of non-differentiable optimization models motivated from sparse learning. To overcome the difficulty of the non-differentiability of the models, we first present characterizations of their solutions as fixed-points of mappings involving the proximity operators of the functions appearing in the objective functions. We then introduce a two-step fixed-point algorithm to compute the solutions. We establish convergence results of the proposed two-step iteration scheme and compare it with the alternating direction method of multipliers (ADMM). In particular, we derive specific two-step iteration algorithms for three models in machine learning: ℓ 1 -SVM classification, ℓ 1 -SVM regression, and the SVM classification with the group LASSO regularizer. Numerical experiments with some synthetic datasets and some benchmark datasets show that the proposed algorithm outperforms ADMM and the linear programming method in computational time and memory storage costs. [ABSTRACT FROM AUTHOR]

Edge detection plays an important role in identifying regions of interest in an underlying signal or image. In some applications, such as magnetic resonance imaging (MRI) or synthetic aperture radar (SAR), data are sampled in the Fourier domain. Many algorithms have been developed to efficiently extract edges of images when uniform Fourier data are acquired. However, in cases where the data are sampled non-uniformly, such as in non-Cartesian MRI or SAR, standard inverse Fourier transformation techniques are no longer suitable. Methods exist for handling these types of sampling patterns, but are often ill-equipped for cases where data are highly non-uniform or when the data are corrupted or otherwise not usable in certain parts of the frequency domain. This investigation further develops an existing approach to discontinuity detection, and involves the use of concentration factors. Previous research shows that the concentration factor technique can successfully determine jump discontinuities in non-uniform data. However, as the distribution diverges further away from uniformity so does the efficacy of the identification. Thus we propose a method that employs the finite Fourier approximation to specifically tailor the design of concentration factors. We also adapt the algorithm to incorporate appropriate smoothness assumptions in the piecewise smooth regions of the function. Numerical results indicate that our new design method produces concentration factors which can more precisely identify jump locations than those previously developed in both one and two dimensions. [ABSTRACT FROM AUTHOR]

This paper presents thermodynamic evaluations of the agriculture residual-to-SNG process by thermochemical conversion, which mainly consists of the interconnected fluidized beds, hot gas cleaning, fluidized bed methanation reactor and Selexol absorption unit. The process was modeled using Aspen Plus software. The process performances, i.e., CH content in SNG, higher heating value and yield of SNG, exergy efficiencies with and without heat recovery, unit power consumption, were evaluated firstly. The results indicate that when the other parameters remain unchanged, the steam-to-biomass ratio at carbon boundary point is the optimal value for the process. Improving the preheating temperatures of air and gasifying agent is beneficial for the SNG yield and exergy efficiencies. Due to the effects of CO removal efficiency, there are two optimization objectives for the SNG production process: (I) to maximize CH content in SNG, or (II) to maximize SNG yield. Further, the comparison among different feedstocks indicates that the decreasing order of SNG yield is: corn stalk > wheat straw > rice straw. The evaluation on the potential of agriculture-based SNG shows that the potential annual production of agriculture residual-based SNG could be between 555×10 ∼ 611×10 m with utilization of 100% of the available unexplored resources. The agriculture residual-based SNG could play a significant role on solving the big shortfall of China's natural gas supply in future. [ABSTRACT FROM AUTHOR]

In this paper, we consider multivariate interpolation with radial basis functions of finite smoothness. In particular, we show that interpolants by radial basis functions in ℝ with finite smoothness of even order converge to a polyharmonic spline interpolant as the scale parameter of the radial basis functions goes to zero, i.e., the radial basis functions become increasingly flat. [ABSTRACT FROM AUTHOR]

This paper focuses on developing fast numerical algorithms for selection of a kernel optimal for a given training data set. The optimal kernel is obtained by minimizing a cost functional over a prescribed set of kernels. The cost functional is defined in terms of a positive semi-definite matrix determined completely by a given kernel and the given sampled input data. Fast computational algorithms are developed by approximating the positive semi-definite matrix by a related circulant matrix so that the fast Fourier transform can apply to achieve a linear or quasi-linear computational complexity for finding the optimal kernel. We establish convergence of the approximation method. Numerical examples are presented to demonstrate the approximation accuracy and computational efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]