Health-related quality of life after mandibular resection for oral cancer: reconstruction with free fibula flap

Objectives: Mandibular resection for oral cancer is often necessary to achieve an adequate margin of tumor clear - ance. Mandibular resection has been associated with a poor health-related quality of life (HRQOL), particularly before free fibula flap to reconstruct the defect. The aim of this study was to evaluate health-related quality of life in patients who have had mandibular resections of oral cancer and reconstruction with free fibula flap. Study D esigns: There were 115 consecutive patients between 2008 and 2011 who were treated by primary surgery for oral squamous cell carcinoma, 34 patients had a mandibular resection. HRQOL was assessed by means of the 14-item Oral Health Impact Profile (OHIP-14) and University of Washington Quality of Life (UW-QOL) question - naires after 12 months postoperatively. Results: In the UW-QOL the best-scoring domain was mood, whereas the lowest scores were for chewing and saliva. In the OHIP-14 the lowest-scoring domain was social disability, followed by handicap, and psychological disability. Conclusions: Mandible reconstruction with free fibula flap would have significantly influenced on patients' quality of life and oral functions. The socio-cultural data show a fairly low level of education for the majority of patients

μ-3-Thienylmalonato-κ2 O 1:O 3-bis­[triphenyl­tin(IV)]

The title compound, [Sn2(C6H5)6(C7H4O4S)], contains two molecules with similar conformations in the asymmetric unit. In each mol­ecule, the Sn atoms adopt a distorted tetra­hedral geometry arising from three C atoms of three phenyl rings and one O atom from the bridging 3-thienylmalonato ligand. The mol­ecules lie about inversion centers with the ligands facing each other, with C⋯O distances of 3.417 (10) and 3.475 (10) Å

A divergence-free constrained magnetic field interpolation method for scattered data

An interpolation method to evaluate magnetic fields given unstructured, scattered magnetic data is presented. The method is based on the reconstruction of the global magnetic field using a superposition of orthogonal functions. The coefficients of the expansion are obtained by minimizing a cost function defined as the L^2 norm of the difference between the ground truth and the reconstructed magnetic field evaluated on the training data. The divergence-free condition is incorporated as a constrain in the cost function allowing the method to achieve arbitrarily small errors in the magnetic field divergence. An exponential decay of the approximation error is observed and compared with the less favorable algebraic decay of local splines. Compared to local methods involving computationally expensive search algorithms, the proposed method exhibits a significant reduction of the computational complexity of the field evaluation, while maintaining a small error in the divergence even in the presence of magnetic islands and stochasticity. Applications to the computation of Poincar\'e sections using data obtained from numerical solutions of the magnetohydrodynamic equations in toroidal geometry are presented and compared with local methods currently in use

Di-μ-hydroxido-bis­[aqua­trichlorido­tin(IV)] diethyl ether disolvate

The title compound, [Sn2Cl6(OH)2(H2O)2]·2C4H10O, consists of a centrosymmetric molecule and two additional solvent molecules and has an infinite two-dimensional network extending parallel to (101). The Sn atom is six-coordinate with a distorted octa­hedral geometry. Additional O—H⋯O hydrogen bonding leads to stabilization of the crystal structure

CTCF Mediates the Cell-Type Specific Spatial Organization of the Kcnq5 Locus and the Local Gene Regulation

Chromatin loops play important roles in the dynamic spatial organization of genes in the nucleus. Growing evidence has revealed that the multivalent functional zinc finger protein CCCTC-binding factor (CTCF) is a master regulator of genome spatial organization, and mediates the ubiquitous chromatin loops within the genome. Using circular chromosome conformation capture (4C) methodology, we discovered that CTCF may be a master organizer in mediating the spatial organization of the kcnq5 gene locus. We characterized the cell-type specific spatial organization of the kcnq5 gene locus mediated by CTCF in detail using chromosome conformation capture (3C) and 3C-derived techniques. Cohesion also participated in mediating the organization of this locus. RNAi-mediated knockdown of CTCF sharply diminished the interaction frequencies between the chromatin loops of the kcnq5 gene locus and down-regulated local gene expression. Functional analysis showed that the interacting chromatin loops of the kcnq5 gene locus can repress the gene expression in a luciferase reporter assay. These interacting chromatin fragments were a series of repressing elements whose contacts were mediated by CTCF. Therefore, these findings suggested that the dynamical spatial organization of the kcnq5 locus regulates local gene expression

A pseudo-reversible normalizing flow for stochastic dynamical systems with various initial distributions

We present a pseudo-reversible normalizing flow method for efficiently generating samples of the state of a stochastic differential equation (SDE) with different 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 SDE, 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. Existing normalizing flows for SDEs depend on the initial distribution, meaning the model needs to be re-trained 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. We provide a rigorous convergence analysis of the pseudo-reversible 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

Thermodynamic effects of gas adiabatic index on cavitation bubble collapse

In this paper, an improved multicomponent lattice Boltzmann model is employed to investigate the impact of the gas properties, specifically the gas adiabatic index, on the thermodynamic effects of cavitation bubble collapse. The study focuses on analyzing the temperature evolution in the flow field and the resulting thermal effects on the surrounding wall. The accuracy of the developed model is verified through comparisons with analytical solutions of the Rayleigh-Plesset equation and the validation of the adiabatic law. Then, a thermodynamic model of cavitation bubble composed of two-mixed gases collapsing near a wall is established to explore the influence of the gas adiabatic index γ on the temperature behavior. Key findings include the observation that the γ affects the temperature of the first collapse significantly, while its influence on the second collapse is minimal. Additionally, the presence of low-temperature regions near the bubble surface during collapse impacts both bubble and wall temperatures. The study also demonstrates that the γ affects maximum and minimum wall temperatures. The results have implications for selecting specific non-condensable gas properties within cavitation bubbles for targeted cooling or heating purposes, including potential applications in electronic component cooling and environmental refrigeration

Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation

We present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks, normalizing flows, variational auto-encoders, are usually considered as unsupervised learning models, because labeled data are usually unavailable for training. Despite the success of the generative models, there are several issues with the unsupervised training, e.g., requirement of reversible architectures, vanishing gradients, and training instability. To enable supervised learning in generative models, we utilize the score-based diffusion model to generate labeled data. Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method. This approach uses mini-batch-based Monte Carlo estimators to directly approximate the score function at any spatial-temporal location in solving an ordinary differential equation (ODE), corresponding to the reverse-time stochastic differential equation (SDE). This approach can offer both high accuracy and substantial time savings in neural network training. Once the labeled data are generated, we can train a simple fully connected neural network to learn the generative model in the supervised manner. Compared with existing normalizing flow models, our method does not require to use reversible neural networks and avoids the computation of the Jacobian matrix. Compared with existing diffusion models, our method does not need to solve the reverse-time SDE to generate new samples. As a result, the sampling efficiency is significantly improved. We demonstrate the performance of our method by applying it to a set of 2D datasets as well as real data from the UCI repository

Bis(4-amino­benzoato-κO)triphenylanti­mony(V)

The structure of the title compound, [Sb(C6H5)3(C7H6NO2)2], contains two independent mol­ecules of similar configuration. The Sb atoms exhibit a distorted trigonal–bipyramidal geometry with the O atoms of two carboxyl­ate groups in the axial positions and the C atoms of the phenyl groups in the equatorial positions. In the crystal structure, mol­ecules are connected by inter­molecular N—H⋯O and N—H⋯N hydrogen-bonding inter­actions forming a chain structure along [100]