СЕЛЕВОЕ СОБЫТИЕ 2014 ГОДА И ЕГО ВЛИЯНИЕ НА АККУМУЛЯЦИЮ ТВЕРДОЙ ФРАКЦИИ ВОДОКАМЕННОГО СЕЛЯ В РУСЛЕ РЕКИ КЫНГАРГА, ТУНКИНСКАЯ ДОЛИНА, ЮГО-ЗАПАДНОЕ ПРИБАЙКАЛЬЕ

On 28 June 2014, debris flows brought large volumes of loose material into the Kyngarga river valley. The material was sourced from rock collapse and rock sliding on the valley slopes and delivered mainly to the river by debris flows from the side valleys of the river basin. Our field studies and analysis of the satellite images revealed that the potential debris volume received by the river amounted to about 1×106 m3. The morphometric parameters of the Kyngarga river basin are favorable for the river-channel processes associated with floods, debris flows and waterrock flows.В результате селевых потоков в долину реки Кынгарга 28 июня 2014 года поступило большое количество рыхлого материала. Источниками материала явились обвалы, осыпи со склонов бортов долины, большую часть материала поставили в русло реки селевые потоки, сошедшие с боковых долин бассейна. В результате полевых работ и дешифрирования космоснимков установлено, что в русло реки поступил потенциальный селевой материал в объеме около 1×106 м3. Морфометрические параметры бассейна реки Кынгарга способствуют формированию русловых процессов, связанных с паводками и водокаменными селями

Compensation effect in carbon nanotube quantum dots coupled to polarized electrodes in the presence of spin-orbit coupling

We study theoretically the Kondo effect in carbon nanotube quantum dot attached to polarized electrodes. Since both spin and orbit degrees of freedom are involved in such a system, the electrode polarization contains the spin- and orbit-polarizations as well as the Kramers polarization in the presence of the spin-orbit coupling. In this paper we focus on the compensation effect of the effective fields induced by different polarizations by applying magnetic field. The main results are i) while the effective fields induced by the spin- and orbit-polarizations remove the degeneracy in the Kondo effect, the effective field induced by the Kramers polarization enhances the degeneracy through suppressing the spin-orbit coupling; ii) while the effective field induced by the spin-polarization can not be compensated by applying magnetic field, the effective field induced by the orbit-polarization can be compensated; and iii) the presence of the spin-orbit coupling does not change the compensation behavior observed in the case without the spin-orbit coupling. These results are observable in an ultraclean carbon-nanotube quantum dot attached to ferromagnetic contacts under a parallel applied magnetic field along the tube axis and it would deepen our understanding on the Kondo physics of the carbon nanotube quantum dot.Comment: 8 pages, 6 figure

Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations

Stochastic partial differential equations (SPDEs) are crucial for modelling dynamics with randomness in many areas including economics, physics, and atmospheric sciences. Recently, using deep learning approaches to learn the PDE solution for accelerating PDE simulation becomes increasingly popular. However, SPDEs have two unique properties that require new design on the models. First, the model to approximate the solution of SPDE should be generalizable over both initial conditions and the random sampled forcing term. Second, the random forcing terms usually have poor regularity whose statistics may diverge (e.g., the space-time white noise). To deal with the problems, in this work, we design a deep neural network called Deep Latent Regularity Net (DLR-Net). DLR-Net includes a regularity feature block as the main component, which maps the initial condition and the random forcing term to a set of regularity features. The processing of regularity features is inspired by regularity structure theory and the features provably compose a set of basis to represent the SPDE solution. The regularity features are then fed into a small backbone neural operator to get the output. We conduct experiments on various SPDEs including the dynamic Φ^{4}_{1} model and the stochastic 2D Navier-Stokes equation to predict their solutions, and the results demonstrate that the proposed DLR-Net can achieve SOTA accuracy compared with the baselines. Moreover, the inference time is over 20 times faster than the traditional numerical solver and is comparable with the baseline deep learning models

VENNTURE–A Novel Venn Diagram Investigational Tool for Multiple Pharmacological Dataset Analysis

As pharmacological data sets become increasingly large and complex, new visual analysis and filtering programs are needed to aid their appreciation. One of the most commonly used methods for visualizing biological data is the Venn diagram. Currently used Venn analysis software often presents multiple problems to biological scientists, in that only a limited number of simultaneous data sets can be analyzed. An improved appreciation of the connectivity between multiple, highly-complex datasets is crucial for the next generation of data analysis of genomic and proteomic data streams. We describe the development of VENNTURE, a program that facilitates visualization of up to six datasets in a user-friendly manner. This program includes versatile output features, where grouped data points can be easily exported into a spreadsheet. To demonstrate its unique experimental utility we applied VENNTURE to a highly complex parallel paradigm, i.e. comparison of multiple G protein-coupled receptor drug dose phosphoproteomic data, in multiple cellular physiological contexts. VENNTURE was able to reliably and simply dissect six complex data sets into easily identifiable groups for straightforward analysis and data output. Applied to complex pharmacological datasets, VENNTURE’s improved features and ease of analysis are much improved over currently available Venn diagram programs. VENNTURE enabled the delineation of highly complex patterns of dose-dependent G protein-coupled receptor activity and its dependence on physiological cellular contexts. This study highlights the potential for such a program in fields such as pharmacology, genomics, and bioinformatics

Kondo effect of an adatom in graphene and its scanning tunneling spectroscopy

We study the Kondo effect of a single magnetic adatom on the surface of graphene. It was shown that the unique linear dispersion relation near the Dirac points in graphene makes it more easy to form the local magnetic moment, which simply means that the Kondo resonance can be observed in a more wider parameter region than in the metallic host. The result indicates that the Kondo resonance indeed can form ranged from the Kondo regime, to the mixed valence, even to the empty orbital regime. While the Kondo resonance displays as a sharp peak in the first regime, it has a peak-dip structure and/or an anti-resonance in the remaining two regimes, which result from the Fano resonance due to the significant background leaded by dramatically broadening of the impurity level in graphene. We also study the scanning tunneling microscopy (STM) spectra of the adatom and they show obvious particle-hole asymmetry when the chemical potential is tuned by the gate voltages applied to the graphene. Finally, we explore the influence of the direct tunneling channel between the STM tip and the graphene on the Kondo resonance and find that the lineshape of the Kondo resonance is unaffected, which can be attributed to unusual large asymmetry factor in graphene. Our study indicates that the graphene is an ideal platform to study systematically the Kondo physics and these results are useful to further stimulate the relevant experimental studies on the system.Comment: 8 pages, 5 figure

Integration of transcriptomic, proteomic, and metabolomic data to identify lncRNA rPvt1 associations in lipopolysaccharide-treated H9C2 cardiomyocytes

Background: Recent evidence has shown that the long non-coding RNA (lncRNA) rPvt1 is elevated in septic myocardial tissues and that its knockdown attenuates sepsis-induced myocardial injury. However, the mechanism underlying the role of rPvt1 in septic myocardial dysfunction has not been elucidated.Methods: In this study, we performed transcriptomic, proteomic, and metabolomic assays and conducted an integrated multi-omics analysis to explore the association between rPvt1 and lipopolysaccharide (Lipopolysaccharide)-induced H9C2 cardiomyocyte injury. LncRNA rPvt1 silencing was achieved using a lentiviral transduction system.Results: Compared to those with the negative control, rPvt1 knockdown led to large changes in the transcriptome, proteome, and metabolome. Specifically, 2,385 differentially expressed genes (DEGs), 272 differentially abundant proteins and 75 differentially expressed metabolites (DEMs) were identified through each omics analysis, respectively. Gene Ontology functional annotation, Kyoto Encyclopedia of Genes and Genomes, Nr, eukaryotic orthologous groups, and Clusters of Orthologous Groups of Proteins pathway analyses were performed on these differentially expressed/abundant factors. The results suggested that mitochondrial energy metabolism might be closely related to the mechanism through which Pvt1 functions.Conclusion: These genes, proteins, metabolites, and their related dysregulated pathways could thus be promising targets for studies investigating the rPvt1-regluatory mechanisms involved in septic myocardial dysfunction, which is important for formulating novel strategies for the prevention, diagnosis and treatment of septic myocardial injury

Molecular mechanisms and functions of pyroptosis in sepsis and sepsis-associated organ dysfunction

Sepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, is a leading cause of death in intensive care units. The development of sepsis-associated organ dysfunction (SAOD) poses a threat to the survival of patients with sepsis. Unfortunately, the pathogenesis of sepsis and SAOD is complicated, multifactorial, and has not been completely clarified. Recently, numerous studies have demonstrated that pyroptosis, which is characterized by inflammasome and caspase activation and cell membrane pore formation, is involved in sepsis. Unlike apoptosis, pyroptosis is a pro-inflammatory form of programmed cell death that participates in the regulation of immunity and inflammation. Related studies have shown that in sepsis, moderate pyroptosis promotes the clearance of pathogens, whereas the excessive activation of pyroptosis leads to host immune response disorders and SAOD. Additionally, transcription factors, non-coding RNAs, epigenetic modifications and post-translational modifications can directly or indirectly regulate pyroptosis-related molecules. Pyroptosis also interacts with autophagy, apoptosis, NETosis, and necroptosis. This review summarizes the roles and regulatory mechanisms of pyroptosis in sepsis and SAOD. As our understanding of the functions of pyroptosis improves, the development of new diagnostic biomarkers and targeted therapies associated with pyroptosis to improve clinical outcomes appears promising in the future

Brown Carbon Aerosol in Urban Xi’an, Northwest China: TheComposition and Light Absorption Properties

Light-absorbing organic carbon (i.e., brown carbon or BrC) in the atmospheric aerosol has significant contribution to light absorption and radiative forcing. However, the link between BrC optical properties and chemical composition remains poorly constrained. In this study, we combine spectrophotometric measurements and chemical analyses of BrC samples collected from July 2008 to June 2009 in urban Xi'an, Northwest China. Elevated BrC was observed in winter (5 times higher than in summer), largely due to increased emissions from wintertime domestic biomass burning. The light absorption coefficient of methanol-soluble BrC at 365 nm (on average approximately twice that of water-soluble BrC) was found to correlate strongly with both parent polycyclic aromatic hydrocarbons (parent-PAHs, 27 species) and their carbonyl oxygenated derivatives (carbonyl-OPAHs, 15 species) in all seasons (r(2) > 0.61). These measured parent-PAHs and carbonyl-OPAHs account for on average similar to 1.7% of the overall absorption of methanol-soluble BrC, about 5 times higher than their mass fraction in total organic carbon (OC, similar to 0.35%). The fractional solar absorption by BrC relative to element carbon (EC) in the ultraviolet range (300-400 nm) is significant during winter (42 +/- 18% for water-soluble BrC and 76 +/- 29% for methanol-soluble BrC), which may greatly affect the radiative balance and tropospheric photochemistry and therefore the climate and air quality