Over-expression of Arabidopsis DnaJ (Hsp40) contributes to NaCl-stress tolerance

DnaJ (Hsp40), a heat shock protein, is a molecular chaperones responsive to various environmental stress. To analyze the protective role of DnaJ, we obtained sense transgenic Arabidopsis plants that constitutively expressed elevated levels of DnaJ. In this study, sense transgenic plants show large thinner, fade color and malformed leaves, as well as less floss of back leaves. Plants with enhanced levels of DnaJ in their transgenic sense lines exhibited tolerance to NaCl stress. Under 120 mM NaCl, root length was higher in transgenic sense plants than wild-type plants. In vitro expression system, DnaJ protein shows tolerance to high NaCl. These results suggest that over-expression of DnaJ can confer NaCl-stress tolerance

A Low-Temperature Specific Heat Study of the Giant Dielectric Constant Materials

Low-temperature specific-heat study has been performed on the insulating giant dielectric constant material CaCu3Ti4O12 and two related compounds, Bi2/3Cu3Ti4O12 and La0.5Na0.5Cu3Ti4O12, from 0.6 to 10 K. From analyzing the specific heat data at very low-temperature range, 0.6 to 1.5 K, and moderately low-temperature range, 1.5 to 5 K, in addition to the expected Debye terms, we noticed significant contributions originated from the linear and Einstein terms, which we attributed as the manifestation of low-lying elementary excitations due to lattice vibrations occurred at the grain boundaries and induced by local defects. Together with the findings on electronic and mechanical properties, a phenomenological model is proposed to explain the high dielectric constant behaviors at both low and high frequency regions

Train post-derailment behaviours and containment methods: a review

Railway accidents, particularly serious derailments, can lead to catastrophic consequences. Therefore, it is essential to prevent derailment escalation to reduce the likelihood of severe derailments. Train post-derailment behaviours and containment methods play a critical role in preventing derailment escalation and providing passive safety protection and accident prevention in the event of a derailment. However, despite the increasing attention on this field from academia and industry in recent years, there is a lack of systematic exploration and summarization of emerging applications and containment methods in train post-derailment research. For this reason, this paper presents a comprehensive review of existing studies on train post-derailment behaviours, encompassing various topics such as post-derailment contact–impact models, dynamic modelling and simulation techniques, and the primary factors influencing post-derailment behaviours. Significantly, this review introduces and elucidates substitute guidance mechanisms (SGMs), which serve as railway-specific passive safety protection and accident prevention measures. The various types of SGMs are depicted, and their ongoing developments and applications are explored in depth. The review additionally points out several unresolved challenges including the adverse effects of SGMs, and proposes future research directions to advance the theoretical understanding and practical application of train post-derailment behaviours and containment methods. This review seeks to be a valuable reference for railway industry professionals in preventing catastrophic derailment consequences through post-derailment containment methods

Temperature dependent surface relaxations of Ag(111)

The temperature dependent surface relaxation of Ag(111) is calculated by density-functional theory. At a given temperature, the equilibrium geometry is determined by minimizing the Helmholtz free energy within the quasiharmonic approximation. To this end, phonon dispersions all over the Brillouin zone are determined from density-functional perturbation theory. We find that the top-layer relaxation of Ag(111) changes from an inward contraction (-0.8 %) to an outward expansion (+6.3%) as the temperature increases from T=0 K to 1150 K, in agreement with experimental findings. Also the calculated surface phonon dispersion curves at room temperature are in good agreement with helium scattering measurements. The mechanism driving this surface expansion is analyzed.Comment: 6 pages, 7 figures, submitted to Phys. Rev. B (May 1998). Other related publications can be found at http://www.rz-berlin.mpg.de/th/paper.htm

SD-Net: joint surgical gesture recognition and skill assessment.

PURPOSE: Surgical gesture recognition has been an essential task for providing intraoperative context-aware assistance and scheduling clinical resources. However, previous methods present limitations in catching long-range temporal information, and many of them require additional sensors. To address these challenges, we propose a symmetric dilated network, namely SD-Net, to jointly recognize surgical gestures and assess surgical skill levels only using RGB surgical video sequences. METHODS: We utilize symmetric 1D temporal dilated convolution layers to hierarchically capture gesture clues under different receptive fields such that features in different time span can be aggregated. In addition, a self-attention network is bridged in the middle to calculate the global frame-to-frame relativity. RESULTS: We evaluate our method on a robotic suturing task from the JIGSAWS dataset. The gesture recognition task largely outperforms the state of the arts on the frame-wise accuracy up to [Formula: see text] 6 points and the F1@50 score [Formula: see text] 8 points. We also keep the 100% predicted accuracy for the skill assessment task using LOSO validation scheme. CONCLUSION: The results indicate that our architecture is able to obtain representative surgical video features by extensively considering the spatial, temporal and relational context from raw video input. Furthermore, the better performance in multi-task learning implies that surgical skill assessment has a complementary effects to gesture recognition task

A survey of parametric modelling methods for designing the head of a high-speed train

With the continuous increase of the running speed, the head shape of the high-speed train (HST) turns out to be a critical factor for further speed boost. In order to cut down the time used in the HST head design and improve the modelling efficiency, various parametric modelling methods have been widely applied in the optimization design of the HST head to obtain an optimal head shape so that the aerodynamic effect acting on the head of HSTs can be reduced and more energy can be saved. This paper reviews these parametric modelling methods and classifies them into four categories: 2D, 3D, CATIA-based, and mesh deformation-based parametric modelling methods. Each of the methods is introduced, and the advantages and disadvantages of these methods are identified. The simulation results are presented to demonstrate that the aerodynamic performance of the optimal models constructed by these parametric modelling methods has been improved when compared with numerical calculation results of the original models or the prototype models of running trains. Since different parametric modelling methods used different original models and optimization methods, few publications could be found which compare the simulation results of the aerodynamic performance among different parametric modelling methods. In spite of this, these parametric modelling methods indicate more local shape details will lead to more accurate simulation results, and fewer design variables will result in higher computational efficiency. Therefore, the ability of describing more local shape details with fewer design variables could serve as a main specification to assess the performance of various parametric modelling methods. The future research directions may concentrate on how to improve such ability

Structure and dynamics of Rh surfaces

Lattice relaxations, surface phonon spectra, surface energies, and work functions are calculated for Rh(100) and Rh(110) surfaces using density-functional theory and the full-potential linearized augmented plane wave method. Both, the local-density approximation and the generalized gradient approximation to the exchange-correlation functional are considered. The force constants are obtained from the directly calculated atomic forces, and the temperature dependence of the surface relaxation is evaluated by minimizing the free energy of the system. The anharmonicity of the atomic vibrations is taken into account within the quasiharmonic approximation. The importance of contributions from different phonons to the surface relaxation is analyzed.Comment: 9 pages, 7 figures, scheduled to appear in Phys. Rev. B, Feb. 15 (1998). Other related publications can be found at http://www.rz-berlin.mpg.de/th/paper.htm

Consistent as-similar-as-possible non-isometric surface registration

© 2017 The Author(s)Non-isometric surface registration, aiming to align two surfaces with different sizes and details, has been widely used in computer animation industry. Various existing surface registration approaches have been proposed for accurate template fitting; nevertheless, two challenges remain. One is how to avoid the mesh distortion and fold over of surfaces during transformation. The other is how to reduce the amount of landmarks that have to be specified manually. To tackle these challenges simultaneously, we propose a consistent as-similar-as-possible (CASAP) surface registration approach. With a novel defined energy, it not only achieves the consistent discretization for the surfaces to produce accurate result, but also requires a small number of landmarks with little user effort only. Besides, CASAP is constrained as-similar-as-possible so that angles of triangle meshes are preserved and local scales are allowed to change. Extensive experimental results have demonstrated the effectiveness of CASAP in comparison with the state-of-the-art approaches

SwarmDeepSurv: Swarm Intelligence Advances Deep Survival Network for Prognostic Radiomics Signatures in Four Solid Cancers

Survival models exist to study relationships between biomarkers and treatment effects. Deep learning-powered survival models supersede the classical Cox proportional hazards (CoxPH) model, but substantial performance drops were observed on high-dimensional features because of irrelevant/redundant information. To fill this gap, we proposed SwarmDeepSurv by integrating swarm intelligence algorithms with the deep survival model. Furthermore, four objective functions were designed to optimize prognostic prediction while regularizing selected feature numbers. When testing on multicenter sets (n = 1,058) of four different cancer types, SwarmDeepSurv was less prone to overfitting and achieved optimal patient risk stratification compared with popular survival modeling algorithms. Strikingly, SwarmDeepSurv selected different features compared with classical feature selection algorithms, including the least absolute shrinkage and selection operator (LASSO), with nearly no feature overlapping across these models. Taken together, SwarmDeepSurv offers an alternative approach to model relationships between radiomics features and survival endpoints, which can further extend to study other input data types including genomics