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Purpose: To investigate the clinical efficacy of suspension training and Mulligan technique in combination with celecoxib in the management of chronic lower back ache of unknown causes. Methods: A total of 100 subjects with persistent lower back pain of unknown etiology treated in the Sports Medical Rehabilitation Center, Shijiazhuang, China from June 2019 to December 2020, were assigned at random to control and study cohorts (n = 50/group). Celecoxib (100 to 200 mg/day) was taken orally, once or twice daily by the control cohort, while the other cohort received suspension training, Mulligan technology (once a day, 8 weeks) and celecoxib. The treatment effect, pain and dysfunction scores, inflammatory indicators, quality of life, and complications were compared on Visual Analogue Scale (VAS) and Oswestry Disability Index (ODI). Results: The effect of Macnab standard in study group was significantly better than that in control group (p < 0.05). After treatment, VAS and ODI scores in both cohorts were significantly lower than pretreatment values, but were significantly lesser in the study cohort. Furthermore, TNF-a level, and levels of CRP and IL-6 were significantly reduced when compared with pre-treatment levels, and were significantly lower in study cohort. The GQOL-74 scores in both groups were significantly increased after treatment, but were significantly lower in the control cohort (p < 0.05). No significant variations were seen in cases of complications between both cohorts. Conclusion: The use of suspension training and Mulligan technology in combination with celecoxib in treating persistent lower back pain of unknown cause is beneficial in reducing lower back pain, mitigating dysfunction, and improving patients' quality of life. There is however a need for more studies to validate these findings. [ABSTRACT FROM AUTHOR]

This work presents a probabilistic scheme for solving semilinear nonlocal diffusion equations with volume constraints and integrable kernels. The nonlocal model of interest is defined by a time-dependent semilinear partial integro-differential equation (PIDE), in which the integrodifferential operator consists of both local convection-diffusion and nonlocal diffusion operators. Our numerical scheme is based on the direct approximation of the nonlinear Feynman-Kac formula that establishes a link between nonlinear PIDEs and stochastic differential equations. The exploitation of the Feynman-Kac representation avoids solving dense linear systems arising from nonlocal operators. Compared with existing stochastic approaches, our method can achieve first-order convergence after balancing the temporal and spatial discretization errors, which is a significant improvement of existing probabilistic/stochastic methods for nonlocal diffusion problems. Error analysis of our numerical scheme is established. The effectiveness of our approach is shown in two numerical examples. The first example considers a three-dimensional nonlocal diffusion equation to numerically verify the error analysis results. The second example presents a physics problem motivated by the study of heat transport in magnetically confined fusion plasmas. [ABSTRACT FROM AUTHOR]

The impressive expressive power of deep neural networks (DNNs) underlies their widespread applicability. However, while the theoretical capacity of deep architectures is high, the practical expressive power achieved through successful training often falls short. Building on the insights gained from Neural ODEs, which explore the depth of DNNs as a continuous variable, in this work, we generalize the traditional fully connected DNN through the concept of continuous width. In the Generalized Deep Neural Network (GDNN), the traditional notion of neurons in each layer is replaced by a continuous state function. Using the finite rank parameterization of the weight integral kernel, we establish that GDNN can be obtained by employing the Integral Activation Transform (IAT) as activation layers within the traditional DNN framework. The IAT maps the input vector to a function space using some basis functions, followed by nonlinear activation in the function space, and then extracts information through the integration with another collection of basis functions. A specific variant, IAT-ReLU, featuring the ReLU nonlinearity, serves as a smooth generalization of the scalar ReLU activation. Notably, IAT-ReLU exhibits a continuous activation pattern when continuous basis functions are employed, making it smooth and enhancing the trainability of the DNN. Our numerical experiments demonstrate that IAT-ReLU outperforms regular ReLU in terms of trainability and better smoothness. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) are environmental pollutants generated from the incomplete combustion of organic material. PAHs have been studied as genotoxicants, but some also act via non-genotoxic mechanisms in estrogen-dependent malignancies, such as breast cancer. PAHs require metabolic activation to electrophilic metabolites to exert their genotoxicity but non-genotoxic properties may also contribute to their carcinogenicity. The role of PAHs in endometrial cancer, a cancer associated with unopposed estrogen action is unknown. We assessed the metabolism of the representative PAH, benzo[a]pyrene (B[a]P), to estrogenic compounds in Ishikawa human endometrial cells in the presence and absence of cytochrome P450 induction. Using stable-isotope dilution high-performance liquid chromatography and APCI tandem mass spectrometry in the selected reaction monitoring mode, we analyzed B[a]P metabolism in Ishikawa cells. Estrogenic activity of B[a]P metabolites was determined by the endogenous estrogen inducible alkaline phosphatase reporter gene and an exogenous estrogen response element (ERE) luciferase reporter gene construct. We also assessed whether PAHs can induce a proliferative phenotype via estrogen receptor (ER)- and non-ER-regulated pathways. We demonstrate that B[a]P can be metabolized in human endometrial cells into 3-OH-B[a]P and B[a]P-7,8-dione in sufficient amounts to activate ERs. We also show that only B[a]P-7,8-dione induces endometrial cell proliferation at concentrations lower than required to activate the ER; instead nongenomic signaling by the EGF receptor (EGFR) and activation of the mitogen-activated protein kinase (MAPK) pathway was responsible. This work indicates that human endometrial cells can metabolize PAHs into estrogenic metabolites, which may induce cell proliferation through non-ER-regulated pathways. [ABSTRACT FROM AUTHOR]

The long noncoding RNA nuclear paraspeckle assembly transcript 1 (NEAT1) is involved in a variety of human cancers. Two overlapping NEAT1 isoforms, NEAT1_1 and NEAT1_2, are produced through mutually exclusive alternative 30 end formation. Previous studies extensively investigated NEAT1 dysregulation in tumors, but often failed to achieve distinct quantification of the two NEAT1 isoforms. Moreover, molecular mechanisms governing the biogenesis of NEAT1 isoforms and the functional impacts of their dysregulation in tumorigenesis remain poorly understood. In this study, we employed an isoform-specific quantification assay and found differential dysregulation of NEAT1 isoforms in patient-derived glioblastoma multiforme cells. We further showed usage of the NEAT1 proximal polyadenylation site (PAS) is a critical mechanism that controls glioma NEAT1 isoform production. CRISPRCas9–mediated PAS deletion reduced NEAT1_1 and reciprocally increased NEAT1_2, which enhanced nuclear paraspeckle formation in human glioma cells. Moreover, the utilization of the NEAT1 PAS is facilitated by the RNA-binding protein quaking (QKI), which binds to the proximal QKI recognition elements. Functionally, we identified transcriptomic changes and altered biological pathways caused by NEAT1 isoform imbalance in glioma cells, including the pathway for the regulation of cell migration. Finally, we demonstrated the forced increase of NEAT1_2 upon NEAT1 PAS deletion is responsible for driving glioma cell migration and promoting the expression of genes implicated in the regulation of cell migration. Together, our studies uncovered a novel mechanism that regulates NEAT1 isoforms and their functional impacts on the glioma transcriptome, which affects pathological pathways of glioma, represented by migration. [ABSTRACT FROM AUTHOR]

We developed a domain decomposition model reduction method for linear steadystate convection-diffusion equations with random coefficients. Of particular interest to this effort are the diffusion equation with random diffusivity and the convection-dominated transport equation with random velocity. We investigated two types of random fields, i.e., colored noises and discrete white noises, both of which can lead to high-dimensional parametric dependence in practice. The motivation is to exploit domain decomposition to reduce the parametric dimension of local problems in subdomains, such that an entire parametric map can be approximated with a small number of expensive partial differential equation (PDE) simulations. The new method combines domain decomposition with model reduction and sparse polynomial approximation, so as to simultaneously handle the high dimensionality and irregular behavior of the PDEs under consideration. The advantages of our method lie in three aspects: (i) online-offline decomposition, i.e., the online cost is independent of the size of the triangle mesh; (ii) sparse approximation of operators involving nonaffine high-dimensional random fields; (iii) an effective strategy to capture irregular behaviors, e.g., sharp transitions of the PDE solutions. Two numerical examples are provided to demonstrate the advantageous performance of our method. [ABSTRACT FROM AUTHOR]

This work proposes a hyperspherical sparse approximation framework for detecting jump discontinuities in functions in high-dimensional spaces. The need for a novel approach results from the theoretical and computational inefficiencies of well-known approaches, such as adaptive sparse grids, for discontinuity detection. Our approach constructs the hyperspherical coordinate representation of the discontinuity surface of a function. Then sparse approximations of the transformed function are built in the hyperspherical coordinate system, with values at each point estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of the hypersurface, the new technique can identify jump discontinuities with significantly reduced computational cost, compared to existing methods. Several approaches are used to approximate the transformed discontinuity surface in the hyperspherical system, including adaptive sparse grid and radial basis function interpolation, discrete least squares projection, and compressed sensing approximation. Moreover, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. Rigorous complexity analyses of the new methods are provided, as are several numerical examples that illustrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

A significant challenge in realizing the promise of the dissolution dynamic nuclear polarization technique for signal enhancement in high-resolution NMR lies in the nonrenewability of the hyperpolarized spin state. This property prevents the application of traditional two-dimensional correlation spectroscopy, which relies on regeneration of spin polarization before each successive increment of the indirect dimension. Since correlation spectroscopy is one of the most important approaches for the identification and structural characterization of molecules by NMR, it is important to find easily applicable methods that circumvent this problem. Here, we introduce the application of scaling of heteronuclear couplings by optimal tracking (SHOT) to achieve this goal. SHOT decoupling pulses have been numerically optimized on the basis of optimal control algorithms to obtain chemical shift correlations in C–H groups, either by acquiring a single one-dimensional 13C spectrum with ¹H off-resonance decoupling or vice versa. Vanillin, which contains a number of functional groups, was used as a test molecule, allowing the demonstration of SHOT decoupling tailored toward simplified and accurate data analysis. This strategy was demonstrated for two cases: First, a linear response to chemical shift offset in the correlated dimension was optimized. Second, a pulse with alternating linear responses in the correlated dimension was chosen as a goal to increase the sensitivity of the decoupling response to the chemical shift offset. In these measurements, error ranges of ±0.03 ppm for the indirectly determined ¹H chemical shifts and of ±0.4 ppm for the indirectly determined 13C chemical shifts were found. In all cases, we show that chemical shift correlations can be obtained from information contained in a single scan, which maximizes the ratio of signal to stochastic noise. Furthermore, a comprehensive discussion of the robustness of the method toward nonideal conditions is included based on experimental and simulated data. Unique features of this technique include the abilities to control the accuracy of chemical shift determination in spectral regions of interest and to acquire such chemical shift correlations rapidly—the latter being of interest for potential application in real-time spectroscopy. [ABSTRACT FROM AUTHOR]

We present a pseudoreversible normalizing flow method for efficiently generating samples of the state of a stochastic differential equation (SDE) with various initial distributions. The primary objective is to construct an accurate and efficient sampler that can be used as a surrogate model for computationally expensive numerical integration of SDEs, such as those employed in particle simulation. After training, the normalizing flow model can directly generate samples of the SDE's final state without simulating trajectories. The existing normalizing flow model for SDEs depends on the initial distribution, meaning the model needs to be retrained when the initial distribution changes. The main novelty of our normalizing flow model is that it can learn the conditional distribution of the state, i.e., the distribution of the final state conditional on any initial state, such that the model only needs to be trained once and the trained model can be used to handle various initial distributions. This feature can provide a significant computational saving in studies of how the final state varies with the initial distribution. Additionally, we propose to use a pseudoreversible network architecture to define the normalizing flow model, which has sufficient expressive power and training efficiency for a variety of SDEs in science and engineering, e.g., in particle physics. We provide a rigorous convergence analysis of the pseudoreversible normalizing flow model to the target probability density function in the Kullback--Leibler divergence metric. Numerical experiments are provided to demonstrate the effectiveness of the proposed normalizing flow model. [ABSTRACT FROM AUTHOR]

Due to the curse of dimensionality and the limitation on training data, approximating high-dimensional functions is a very challenging task even for powerful deep neural networks. Inspired by the Nonlinear Level-set Learning (NLL) method, which uses the reversible residual network (RevNet), in this paper, we propose a new method of Dimension Reduction via Learning Level Sets (DRiLLS) for function approximation. Our method contains two major components: one is the pseudoreversible neural network (PRNN) module, which effectively transforms high-dimensional input variables to low-dimensional active variables, and the other is the synthesized regression module for approximating function values based on the transformed data in the low-dimensional space. The PRNN not only relaxes the invertibility constraint of the nonlinear transformation present in the NLL method due to the use of RevNet but also adaptively weights the influence of each sample and controls the sensitivity of the function to the learned active variables. The synthesized regression uses Euclidean distance in the input space to select neighboring samples whose projections on the space of active variables are used to perform local least-squares polynomial fitting. This helps to resolve numerical oscillation issues present in traditional local and global regressions. Extensive experimental results demonstrate that our DRiLLS method outperforms both the NLL and active subspace methods, especially when the target function possesses critical points in the interior of its input domain. [ABSTRACT FROM AUTHOR]

Context: Retrorectal tumours are rare with developmental cysts being the most common type. Conventionally, large retrorectal developmental cysts (RRDCs) require the combined transabdomino‑sacrococcygeal approach. Aims: This study aims to investigate the surgical outcomes of the laparoscopic approach for large RRDCs. Settings and Design: A retrospective case series analysis. Subjects and Methods: Data of patients with RRDCs of 10 cm or larger in diameter who underwent the laparoscopic surgery between 2012 and 2017 at our tertiary centre were retrospectively analyzed. Statistical Analysis Used: Results are presented as median values or mean ± standard deviation for continuous variables and numbers (percentages) for categorical variables. Results: Twenty consecutive cases were identified (19 females; median age, 36 years). Average tumour size was 10.9 ± 1.1 cm. Cephalic ends of lesions ranged from S1/2 junction to S4 level. Caudally, 18 cysts extended to the sacrococcygeal hypodermis. Seventeen patients underwent the pure laparoscopy; three patients received a combined laparoscopic‑posterior approach. The operating time was 167.1 ± 57.3 min for the pure laparoscopic group and 212.0 ± 24.5 min for the combined group. The intraoperative haemorrhage was 68.2 ± 49.7 and 66.7 ± 28.9 (mL), respectively. Post‑operative complications included one trocar site hernia, one wound infection and one delayed rectal wall perforation. The median post‑operative hospital stay was 7 days. With a median follow‑up period of 36 months, 1 lesions recurred. Conclusions: The laparoscopic approach can provide a feasible and effective alternative for large RRDCs, with advantages of the minimally invasive surgery. For lesions with ultra‑low caudal ends, especially those closely clinging to the rectum, a combined posterior approach is still necessary. [ABSTRACT FROM AUTHOR]

Multilayer colloidal crystal has been prepared by the layer-by-layer deposition of silica microspheres on a glass slide. Each layer is a slab consisting of a fcc close-packed colloidal arrays. By properly choosing the sizes of spheres, the whole spectral feature of multilayer colloidal crystal can be tuned. Here, we engineered a multilayer superlattice structure with an effective passband (380 nm) between two stop bands (366 nm and 400 nm). This gives a strong narrowing effect on emission spectrum (378 nm). With the stop bands at the shortwave and longwave edges of emission spectrum, the passband in the central wavelength region can be regarded as a strong decrease of suppression effect and enhancement of a narrow wavelength region of emission. The FWHM values of stop band modified emission spectrum were narrowed from 59 nm to 22 nm. The spectral narrowing modification effect of suitably engineered colloidal crystals shows up their importance in potential application as optical filters and lasing devices. [ABSTRACT FROM AUTHOR]