An Asymmetrical Model for High Energy Radiation of Cassiopeia A

Cassiopeia A (Cas A) supernova remnant shows strong radiation from radio to gamma-ray bands. The mechanism of gamma-ray radiation in Cas A and its possible contribution to PeV cosmic rays are still under debate. The X-ray imaging reveals an asymmetric profile of Cas A, suggesting the existence of a jet-like structure. In this work, we propose an asymmetrical model for Cas A, consisting of a fast moving jet-like structure and a slowly expanding isotropic shell. This model can account for the multi-wavelength spectra of Cas A, especially for the power-law hard X-ray spectrum from $\sim$ 60 to 220 keV. The GeV to TeV emission from Cas A should be contributed by both hadronic and leptonic processes. Moreover, the jet-like structure may produce a gamma-ray flux of $\sim 10^{-13}\rm erg\ cm^{-2}\ s^{-1}$ at $\sim 100$ TeV, to be examined by LHAASO and CTA.Comment: 7 pages, 7 figures. MNRAS in pres

An Experimental Study Of Seismoelectric Signals In Logging While Drilling

Acoustic logging while drilling (LWD) may be complicated because of contamination by waves propagating along the drill collar (the tool waves). In this paper we propose a new method for separating tool waves from the true formation acoustic arrivals in borehole acoustic LWD. The method utilizes the seismoelectric signal induced by the acoustic wave at the fluid-formation boundary. The basis for seismoelectric conversion is the electric double layer (EDL) that exists in most rock-water systems. EDL does not exist at the tool (conductor) water interface. Therefore, there should be no seismoelectric signals due to tool modes. In this paper, borehole monopole and dipole LWD acoustic and seismoelectric phenomena are investigated with laboratory measurements. The main thrust of the paper is the utilization of the difference between acoustic and seismoelectric signals, to eliminate the tool waves and enhance the formation acoustic signals in acoustic LWD.Massachusetts Institute of Technology. Earth Resources LaboratoryMassachusetts Institute of Technology. Borehole Acoustics and Logging Consortiu

Elimination of LWD (Logging-While-Drilling) Tool Modes Using Seismoelectric Data

Borehole acoustic logging-while-drilling (LWD) for formation evaluation has become an indispensable part of hydrocarbon reservoir assessment (Tang et al., 2002; Cittá et al., 2004; Esmersoy et al., 2005). However, the detection of acoustic formation arrivals1over tool mode contamination has been a challenging problem in acoustic LWD technology. This is because the tool mode contamination in LWD is more severe than in wireline tools in most geological environments (Tang et al., 2002; Huang, 2003). In this paper we propose a new method for separating tool waves from formation acoustic waves in acoustic LWD. This method is to measure the seismoelectric 2signal excited by the LWD acoustic waves. The acoustic waves propagating along the borehole or in the formation can induce electric fields. The generated electric field is localized around the wave pulses and carried along the borehole at the formation acoustic wave velocity. The LWD tool waves which propagate along the rigid tool rim can not excite any electric signal. This is due to the effectively grounding of the drill string during the LWD process makes it impossible to accumulate any excess charge at the conductive tool – borehole fluid interface. Therefore, there should be no contribution by the tool modes to the recorded seismoelectric signals. In this study, we designed the laboratory experiments to collect simulated LWD monopole and dipole acoustic and seismoelectric signals in a borehole in sandstone. By analyzing the acoustic and electric signals, we can observe the difference between them, which are the mainly tool modes and noise. Then we calculate the similarity of the two signals to pick out the common components of the acoustic and seismoelectric signals, which are the pure formation modes. Using the seismoelectric signals as reference, we could filter out the tool modes. The method works well. To theoretically understand the seismoelectric conversion in the LWD geometry, we also calculate the synthetic waveforms for the multipole LWD seismoelectric signals based on Pride’s theory (Pride, 1994). The synthetic waveforms for the electric field induced by the LWD-acoustic-wave along the borehole wall demonstrate the absence of the tool mode, which is consistent with the conclusions we get in the experimental study

Reliability Evaluation of a Distribution Network with Microgrid Based on a Combined Power Generation System

Distributed generation (DG), battery storage (BS) and electric vehicles (EVs) in a microgrid constitute the combined power generation system (CPGS). A CPGS can be applied to achieve a reliable evaluation of a distribution network with microgrids. To model charging load and discharging capacity, respectively, the EVs in a CPGS can be divided into regular EVs and ruleless EVs, according to their driving behavior. Based on statistical data of gasoline-fueled vehicles and the probability distribution of charging start instant and charging time, a statistical model can be built to describe the charging load and discharging capacity of ruleless EVs. The charge and discharge curves of regular EVs can also be drawn on the basis of a daily dispatch table. The CPGS takes the charge and discharge curves of EVs, daily load and DG power generation into consideration to calculate its power supply time during islanding. Combined with fault duration, the power supply time during islanding will be used to analyze and determine the interruption times and interruption duration of loads in islands. Then the Sequential Monte Carlo method is applied to complete the reliability evaluation of the distribution system. The RBTS Bus 4 test system is utilized to illustrate the proposed technique. The effects on the system reliability of BS capacity and V2G technology, driving behavior, recharging mode and penetration of EVs are all investigated

Cytotoxic Terpenoids from the Roots of Dracocephalum taliense

A chemical investigation of methanol extract from the roots of Dracocephalum taliense led to the isolation of a new aromatic abietane diterpenoid, 12-methoxy-18-hydroxy-sugiol (1), and one highly-oxygenated ursane triterpenoid, 2α,3α-dihydroxy-11α,12α-epoxy-urs-28,13β-olide (2), together with 15 known natural products (3–17). Among these, compounds 1–13 and 15–17 were detected for the first time in the genus of Dracocephalum. The structures of all of these isolates were determined by extensively spectroscopic analyses. In the anti-inflammatory assay, compounds 1 and 2 had no obvious inhibitory activity on the release of cytokine IL-2 in lipopolysaccharide-induced RAW 264.7 macrophages. However, compound 2 exhibited significant cytotoxic activity against cell lines HepG2 (IC50 = 6.58 ± 0.14 μM) and NCI-H1975 (IC50 = 7.17 ± 0.26 μM)

DataSheet1_Improvement of gut-vascular barrier by terlipressin reduces bacterial translocation and remote organ injuries in gut-derived sepsis.docx

Gut-vascular barrier (GVB) serves as the last barrier to limit the migration of intestinal toxins into the blood circulation. The efficacy of terlipressin (a vasopressin V1 receptor agonist) in reducing GVB and multiple organ damage in gut-derived sepsis is unknown. In this study, we hypothesized that, besides other intestinal barriers, GVB play a key role in gut-derived sepsis and terlipressin improve GVB damage and then reduce bacterial translocation and organ injuries. In vivo, a cecal ligation and puncture mouse model was established. The mice were subjected to examine the damage of GVB determined by intestinal plasmalemma vesicle-associated protein-1(PV-1) and vascular endothelial-cadherin. And the intestinal permeability was assessed by translocation of intestinal bacteria and macromolecules. In vitro, transendothelial electrical resistance (TER) during interleukin (IL)-1β stimulation was measured on endothelial cells with or without small interfering RNA targeting β-catenin (si β-catenin). Terlipressin significantly improved GVB damage and reduced translocation of intestinal macromolecules and bacteria by activating PI3K signaling. Of note, intestinal PV-1 expression was significantly correlated with translocation of macromolecules, and dramatic increase of macromolecules was observed in intestinal tissues whereas fewer macromolecules and bacteria were observed in blood, liver and lung following terlipressin treatment. In vitro, terlipressin restored TER during IL-1β stimulation and si β-catenin transfection blocked the changes delivered by terlipressin. Collectively, terlipressin alleviated GVB damage and subsequent bacterial translocation via blood vessels after sepsis challenge, resulting in reduced distant organ injuries and the responsible mechanisms may involve the activation of PI3K/β-catenin pathway.</p

Predicting recurrence in osteosarcoma via a quantitative histological image classifier derived from tumour nuclear morphological features

Abstract Recurrence is the key factor affecting the prognosis of osteosarcoma. Currently, there is a lack of clinically useful tools to predict osteosarcoma recurrence. The application of pathological images for artificial intelligence‐assisted accurate prediction of tumour outcomes is increasing. Thus, the present study constructed a quantitative histological image classifier with tumour nuclear features to predict osteosarcoma outcomes using haematoxylin and eosin (H&E)‐stained whole‐slide images (WSIs) from 150 osteosarcoma patients. We first segmented eight distinct tissues in osteosarcoma H&E‐stained WSIs, with an average accuracy of 90.63% on the testing set. The tumour areas were automatically and accurately acquired, facilitating the tumour cell nuclear feature extraction process. Based on six selected tumour nuclear features, we developed an osteosarcoma histological image classifier (OSHIC) to predict the recurrence and survival of osteosarcoma following standard treatment. The quantitative OSHIC derived from tumour nuclear features independently predicted the recurrence and survival of osteosarcoma patients, thereby contributing to precision oncology. Moreover, we developed a fully automated workflow to extract quantitative image features, evaluate the diagnostic values of feature sets and build classifiers to predict osteosarcoma outcomes. Thus, the present study provides a novel tool for predicting osteosarcoma outcomes, which has a broad application prospect in clinical practice