First-principles calculations of the high-temperature phase transformation in yttrium tantalate

The high-temperature phase transition between the tetragonal (scheelite) and monoclinic (fergusonite) forms of yttrium tantalite (YTaO4 ) has been studied using a combination of first-principles calculations and a Landau free-energy expansion. Calculations of the Gibbs free energies show that the monoclinic phase is stable at room temperature and transforms to the tetragonal phase at 1430 °C, close to the experimental value of 1426±7 °C. Analysis of the phonon modes as a function of temperature indicate that the transformation is driven by softening of transverse acoustic modes with symmetry Eu in the Brillouin zone center rather than the Raman-active Bg mode. Landau free-energy expansions demonstrate that the transition is second order and, based on the fitting to experimental and calculated lattice parameters, it is found that the transition is a proper rather than a pseudoproper type. Together these findings are consistent with the transition being ferroelastic.Engineering and Applied Science

A Comprehensive Analysis of Fermi Gamma-Ray Burst Data. IV. Spectral Lag and its Relation to E p Evolution

The spectral evolution and spectral lag behavior of 92 bright pulses from 84 gamma-ray bursts observed by the Fermi Gamma-ray Burst Monitor (GBM) telescope are studied. These pulses can be classified into hard-to-soft pulses (H2S; 64/92), H2S-dominated-tracking pulses (21/92), and other tracking pulses (7/92). We focus on the relationship between spectral evolution and spectral lags of H2S and H2S-dominated-tracking pulses. The main trend of spectral evolution (lag behavior) is estimated with ( ), where E p is the peak photon energy in the radiation spectrum, t + t 0 is the observer time relative to the beginning of pulse −t 0, and is the spectral lag of photons with energy E with respect to the energy band 8–25 keV. For H2S and H2S-dominated-tracking pulses, a weak correlation between and k E is found, where W is the pulse width. We also study the spectral lag behavior with peak time of pulses for 30 well-shaped pulses and estimate the main trend of the spectral lag behavior with . It is found that is correlated with k E . We perform simulations under a phenomenological model of spectral evolution, and find that these correlations are reproduced. We then conclude that spectral lags are closely related to spectral evolution within the pulse. The most natural explanation of these observations is that the emission is from the electrons in the same fluid unit at an emission site moving away from the central engine, as expected in the models invoking magnetic dissipation in a moderately high-σ outflow

A comprehensive analysis of Fermi Gamma-Ray Burst Data: IV. Spectral lag and Its Relation to Ep Evolution

The spectral evolution and spectral lag behavior of 92 bright pulses from 84 gamma-ray bursts (GRBs) observed by the Fermi GBM telescope are studied. These pulses can be classified into hard-to-soft pulses (H2S, 64/92), H2S-dominated-tracking pulses (21/92), and other tracking pulses (7/92). We focus on the relationship between spectral evolution and spectral lags of H2S and H2S-dominated-tracking pulses. %in hard-to-soft pulses (H2S, 64/92) and H2S-dominating-tracking (21/92) pulses. The main trend of spectral evolution (lag behavior) is estimated with $\log E_p\propto k_E\log(t+t_0)$ (${\hat{\tau}} \propto k_{\hat{\tau}}\log E$), where $E_p$ is the peak photon energy in the radiation spectrum, $t+t_0$ is the observer time relative to the beginning of pulse $-t_0$, and ${\hat{\tau}}$ is the spectral lag of photons with energy $E$ with respect to the energy band $8$-$25$ keV. For H2S and H2S-dominated-tracking pulses, a weak correlation between $k_{{\hat{\tau}}}/W$ and $k_E$ is found, where $W$ is the pulse width. We also study the spectral lag behavior with peak time $t_{\rm p_E}$ of pulses for 30 well-shaped pulses and estimate the main trend of the spectral lag behavior with $\log t_{\rm p_E}\propto k_{t_p}\log E$. It is found that $k_{t_p}$ is correlated with $k_E$. We perform simulations under a phenomenological model of spectral evolution, and find that these correlations are reproduced. We then conclude that spectral lags are closely related to spectral evolution within the pulse. The most natural explanation of these observations is that the emission is from the electrons in the same fluid unit at an emission site moving away from the central engine, as expected in the models invoking magnetic dissipation in a moderately-high-$\sigma$ outflow.Comment: 58 pages, 11 figures, 3 tables. ApJ in pres

Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth

Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions. Existing dynamic-object-focused methods only partially solved the mismatch problem at the training loss level. In this paper, we accordingly propose a novel multi-frame monocular depth prediction method to solve these problems at both the prediction and supervision loss levels. Our method, called DynamicDepth, is a new framework trained via a self-supervised cycle consistent learning scheme. A Dynamic Object Motion Disentanglement (DOMD) module is proposed to disentangle object motions to solve the mismatch problem. Moreover, novel occlusion-aware Cost Volume and Re-projection Loss are designed to alleviate the occlusion effects of object motions. Extensive analyses and experiments on the Cityscapes and KITTI datasets show that our method significantly outperforms the state-of-the-art monocular depth prediction methods, especially in the areas of dynamic objects. Our code will be made publicly available

Bayesian analysis of complex mutations in HBV, HCV, and HIV studies

This article provides a review of the Bayesian-inference-based methods applied to Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and Human Immunodeficiency Virus (HIV) studies with a focus on the detection of the viral mutations and various problems which are correlated to these mutations. The authors also provide a summary of the Bayesian methods' applications toward these viruses' studies, where several important and useful results have been discovered

Screening Key Indicators for Acute Kidney Injury Prediction Using Machine Learning

Acute kidney injury is a common critical disease with a high mortality. The large number of indicators in AKI patients makes it difficult for clinicians to quickly and accurately determine the patient’s condition. This study used machine learning methods to filter key indicators and use key indicator data to achieve advance prediction of AKI so that a small number of indicators could be measured to reliably predict AKI and provide auxiliary decision support for clinical staff. Sequential forward selection based on feature importance calculated by XGBoost was used to screen out 17 key indicators. Three machine learning algorithms were used to make predictions, namely, logistic regression (LR), decision tree, and XGBoost. To verify the validity of the method, data were extracted from the MIMIC III database and the eICU-CRD database for 1,009 and 1,327 AKI patients, respectively. The MIMIC III database was used for internal validation, and the eICU-CRD database was used for external validation. For all three machine learning algorithms, the prediction performance from using only the key indicator dataset was very close to that from using the full dataset. The XGBoost algorithm performed the best, and LR was the next best. The decision tree performed the worst. The key indicator screening method proposed in this study can achieve a good predictive performance while streamlining the number of indicators

Mitigation of chronic unpredictable stress–induced cognitive deficits in mice by Lycium barbarum L (Solanaceae) polysaccharides

Purpose: To investigate the neuroprotective effects of Lycium barbarum polysaccharide (LBP) against concomitant cognitive dysfunction and changes in hippocampal CREB-BDNF signaling pathway in chronically unpredictable stressed mice.Methods: The mice were subjected to different unpredictable stressors for a period of 4 weeks. Behavioral tests, including open field (OFT) and Morris water maze (MWMT) tests were used to evaluate pharmacological effects. Serum corticosterone levels, protein expression level of BDNF and pCREB/CREB in hippocampus were assessed by ELISA, Western blot and immunohistochemistry methods, respectively. Morphological changes in pyramidal neurons in the hippocampus were studied by Nissl staining.Results: LBP improved mice performance in MWMT, indicating that it reversed chronic unpredictable stress (CUS)-induced cognitive deficits. LBP treatment reduced serum corticosterone levels and prevented neuron loss in the hippocampus. It maintained expression levels of BDNF and phosphorylation of CREB in hippocampus during CUS procedure.Conclusion: Lycium barbarum polysaccharide protects CREB-BDNF signaling pathway in hippocampus and relieves CUS-induced cognitive deficits. These results suggest that Lycium barbarum polysaccharides is potentially an alternative neuro-protective agent against stress-induced psychopathological dysfunction.Keywords: Lycium barbarum, Polysaccharide, Chronic unpredictable stress, Cognitive deficits, Brainderived neurotrophic factor, Calcium/cyclic-AMP responsive binding protei