Quasiclassical trajectory study of the SiH4+H→SiH3+H2 reaction on a global ab initio potential energy surface

The SiH4+H→SiH3+H2 reaction has been investigated by the quasiclassical trajectory (QCT) method on a recent global ab initio potential energy surface [ M. Wang et al., J. Chem. Phys. 124, 234311 (2006) ]. The integral cross section as a function of collision energy and thermal rate coefficient for the temperature range of 300–1600 K have been obtained. At the collision energy of 9.41 kcal/mol, product energy distributions and rovibrational populations are explored in detail, and H2 rotational state distributions show a clear evidence of two reaction mechanisms. One is the conventional rebound mechanism and the other is the stripping mechanism similar to what has recently been found in the reaction of CD4+H [ J. P. Camden et al., J. Am. Chem. Soc. 127, 11898 (2005) ]. The computed rate coefficients with the zero-point energy correction are in good agreement with the available experimental data

Development and external validation of a mixed-effects deep learning model to diagnose COVID-19 from CT imaging

BackgroundThe automatic analysis of medical images has the potential improve diagnostic accuracy while reducing the strain on clinicians. Current methods analyzing 3D-like imaging data, such as computerized tomography imaging, often treat each image slice as individual slices. This may not be able to appropriately model the relationship between slices.MethodsOur proposed method utilizes a mixed-effects model within the deep learning framework to model the relationship between slices. We externally validated this method on a data set taken from a different country and compared our results against other proposed methods. We evaluated the discrimination, calibration, and clinical usefulness of our model using a range of measures. Finally, we carried out a sensitivity analysis to demonstrate our methods robustness to noise and missing data.ResultsIn the external geographic validation set our model showed excellent performance with an AUROC of 0.930 (95%CI: 0.914, 0.947), with a sensitivity and specificity, PPV, and NPV of 0.778 (0.720, 0.828), 0.882 (0.853, 0.908), 0.744 (0.686, 0.797), and 0.900 (0.872, 0.924) at the 0.5 probability cut-off point. Our model also maintained good calibration in the external validation dataset, while other methods showed poor calibration.ConclusionDeep learning can reduce stress on healthcare systems by automatically screening CT imaging for COVID-19. Our method showed improved generalizability in external validation compared to previous published methods. However, deep learning models must be robustly assessed using various performance measures and externally validated in each setting. In addition, best practice guidelines for developing and reporting predictive models are vital for the safe adoption of such models

Deletion and Down-Regulation of HRH4 Gene in Gastric Carcinomas: A Potential Correlation with Tumor Progression

Background: Histamine is an established growth factor for gastrointestinal malignancies. The effect of histamine is largely determined locally by the histamine receptor expression pattern. Histamine receptor H4 (HRH4), the newest member of the histamine receptor family, is positively expressed on the epithelium of the gastrointestinal tract, and its function remains to be elucidated. Previously, we reported the decreased expression of HRH4 in colorectal cancers and revealed its correlation with tumor proliferation. In the current study, we aimed to investigate the abnormalities of HRH4 gene in gastric carcinomas (GCs). Methodology/Principal Findings: We analyzed H4R expression in collected GC samples by quantitative PCR, Western blot analysis, and immunostaining. Our results showed that the protein and mRNA levels of HRH4 were reduced in some GC samples, especially in advanced GC samples. Copy number decrease of HRH4 gene was observed (17.6%, 23 out of 131), which was closely correlated with the attenuated expression of H4R. In vitro studies, using gastric cancer cell lines, showed that the alteration of HRH4 expression on gastric cancer cells influences tumor growth upon exposure to histamine. Conclusions/Significance: We show for the first time that deletion of HRH4 gene is present in GC cases and is closely correlated with attenuated gene expression. Down-regulation of HRH4 in gastric carcinomas plays a role in histaminemediate

Improved version of parallel programming interface for distributed data with multiple helper servers

We present an improved version of the Parallel Programming Interface for Distributed Data with Multiple Helper Servers (PPIDDv2) library, which provides a common application programming interface that is based on the most frequently used functionality of both MPI-2 and GA. Compared with the previous version, the PPIDDv2 library introduces multiple helper servers to facilitate global data structures, and allows programmers to make heavy use of large global data structures efficiently

Quasiclassical trajectory study of H+SiH4 reactions in full-dimensionality reveals atomic-level mechanisms

This work elucidates new atomic-level mechanisms that may be common in a range of chemical reactions, and our findings are important for the understanding of the nature of polyatomic abstraction and exchange reactions. A global 12-dimensional ab initio potential energy surface (PES), which describes both H+SiH4 abstraction and exchange reactions is constructed, based on the modified Shepard interpolation method and UCCSD(T)/cc-pVQZ energy calculations at 4,015 geometries. This PES has a classical barrier height of 5.35 kcal/mol for abstraction (our best estimate is 5.35 ± 0.15 kcal/mol from extensive ab initio calculations), and an exothermicity of −13.12 kcal/mol, in excellent agreement with experiment. Quasiclassical trajectory calculations on this new PES reveal interesting features of detailed dynamical quantities and underlying new mechanisms. Our calculated product angular distributions for exchange are in the forward hemisphere with a tail sideways, and are attributed to the combination of three mechanisms: inversion, torsion-tilt, and side-inversion. With increase of collision energy our calculated angular distributions for abstraction first peak at backward scattering and then shift toward smaller scattering angles, which is explained by a competition between rebound and stripping mechanisms; here stripping is seen at much lower energies, but is conceptually similar to what was observed in the reaction of H+CD4 by Zare and coworkers [Camden JP, et al. (2005) J Am Chem Soc 127:11898–11899]. Each of these atomic-level mechanisms is confirmed by direct examination of trajectories, and two of them (torsion-tilt and side-inversion) are proposed and designated in this work

Single image super-resolution via Image Quality Assessment-Guided Deep Learning Network.

In recent years, deep learning (DL) networks have been widely used in super-resolution (SR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided single image super-resolution (SISR) method is proposed in DL architecture, in order to achieve a nice tradeoff between perceptual quality and distortion measure of the SR result. Unlike existing DL-based SR algorithms, an IQA net is introduced to extract perception features from SR results, calculate corresponding loss fused with original absolute pixel loss, and guide the adjustment of SR net parameters. To solve the problem of heterogeneous datasets used by IQA and SR networks, an interactive training model is established via cascaded network. We also propose a pairwise ranking hinge loss method to overcome the shortcomings of insufficient samples during training process. The performance comparison between our proposed method with recent SISR methods shows that the former achieves a better tradeoff between perceptual quality and distortion measure than the latter. Extensive benchmark experiments and analyses also prove that our method provides a promising and opening architecture for SISR, which is not confined to a specific network model