Quantum Phases of Self-Bound Droplets of Bose-Bose Mixtures

We systematically investigate the ground-state properties of self-bound droplets of quasi-two-dimensional binary Bose gases by using the Gaussian state theory. We find that quantum droplets consists two macroscopic squeezed phases and a macroscopic coherent phase. We map out the phase diagram and determine all phase boundaries via both numerical and nearly analytical methods. In particular, we find three easily accessible signatures for the quantum phases and the stablization mechanism of the self-bound droplets by precisely measuring their radial size. Our studies indicate that binary droplets represent an ideal platform for in-depth investigations of the quantum nature of the droplet state.Comment: 7+10 pages, 5+5 figure

Opportunistic Relaying in Time Division Broadcast Protocol with Incremental Relaying

In this paper, we investigate the performance of time division broadcast protocol (TDBC) with incremental relaying (IR) when there are multiple available relays. Opportunistic relaying (OR), i.e., the “best” relay is select for transmission to minimize the system’s outage probability, is proposed. Two OR schemes are presented. The first scheme, termed TDBC-OIR-I, selects the “best” relay from the set of relays that can decode both flows of signal from the two sources successfully. The second one, termed TDBC-OIR-II, selects two “best” relays from two respective sets of relays that can decode successfully each flow of signal. The performance, in terms of outage probability, expected rate (ER), and diversity-multiplexing tradeoff (DMT), of the two schemes are analyzed and compared with two TDBC schemes that have no IR but OR (termed TDBC-OR-I and TDBC-OR-II accordingly) and two other benchmark OR schemes that have no direct link transmission between the two sources

Upcycling of PET oligomers from chemical recycling processes to PHA by microbial co-cultivation

Polyethylene terephthalate (PET) is the most widely consumed polyester plastic and can be recycled by many chemical processes, of which glycolysis is most cost-effective and commercially viable. However, PET glycolysis produces oligomers due to incomplete depolymerization, which are undesirable by-products and require proper disposal. In this study, the PET oligomers from chemical recycling processes were completely bio-depolymerized into monomers and then used for the biosynthesis of biodegradable plastics polyhydroxyalkanoates (PHA) by cocultivation of two engineered microorganisms Escherichia coli BL21 (DE3)-LCCICCG and Pseudomonas putida KT2440-ΔRDt-ΔZP46C-M. E. coli BL21 (DE3)-LCCICCG was used to secrete the PET hydrolase LCCICCG into the medium to directly depolymerize PET oligomers. P. putida KT2440-ΔRDt-ΔZP46C-M that mastered the metabolism of aromatic compounds was engineered to accelerate the hydrolysis of intermediate products mono-2- (hydroxyethyl) terephthalate (MHET) by expressing IsMHETase, and biosynthesize PHA using ultimate products terephthalate and ethylene glycol depolymerized from the PET oligomers. The population ratios of the two microorganisms during the co-cultivation were characterized by fluorescent reporter system, and revealed the collaboration of the two microorganisms to bio-depolymerize and bioconversion of PET oligomers in a single process. This study provides a biological strategy for the upcycling of PET oligomers and promotes the plastic circular economy

L dwarfs detection from SDSS images using improved Faster R-CNN

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey(SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs,including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i-z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.Comment: 12 pages, 10 figures, accepted to be published in A

PP-012 Novel blaCTX-M-79 gene from community isolates in association with ISEcp1 in Shenyang, China

Digitalitzat per Artypla

Xuebijing injection alleviates liver injury by inhibiting secretory function of Kupffer cells in heat stroke rats

AbstractObjectiveTo evaluate the effects of Xuebijing (XBJ) injection in heat stroke (HS) rats and to investigate the mechanisms underlying these effects.MethodsSixty anesthetized rats were randomized into three groups and intravenously injected twice daily for 3 days with 4 mL XBJ (XBJ group) or phosphate buffered saline (HS and Sham groups) per kg body weight. HS was initiated in the HS and XBJ groups by placing rats in a simulated climate chamber (ambient temperature 40°C, humidity 60%). Rectal temperature, aterial pressure, and heart rate were monitored and recorded. Time to HS onset and survival were determined, and serum concentrations of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, alanine-aminotransferase (ALT), and aspartate-aminotransferase (AST) were measured. Hepatic tissue was harvested for pathological examination and electron microscopic examination. Kupffer cells (KCs) were separated from liver at HS initiation, and the concentrations of secreted TNF-α, IL-β and IL-6 were measured.ResultsTime to HS onset and survival were significantly longer in the XBJ than in the HS group. Moreover, the concentrations of TNF-α, IL-1β, IL-6, ALT and AST were lower and liver injury was milder in the XBJ than in the HS group. Heat-stress induced structural changes in KCs and hepatic cells were more severe in the HS than in the XBJ group and the concentrations of TNF-α, IL-β and IL-6 secreted by KCs were lower in the XBJ than in the HS group.ConclusionXBJ can alleviate HS-induced systemic inflammatory response syndrome and liver injury in rats, and improve outcomes. These protective effects may be due to the ability of XBJ to inhibit cytokine secretion by KCs

Large-scale offloading in the Internet of Things

Large-scale deployments of IoT devices are subject to energy and performance issues. Fortunately, offloading is a promising technique to enhance those aspects. However, several problems still remain open regarding cloud deployment and provisioning. In this paper, we address the problem of provi- sioning offloading as a service in large-scale IoT deployments. We design and develop an AutoScaler, an essential component for our offloading architecture to handle offloading workload. In addition, we also develop an offloading simulator to generate dynamic offloading workload of multiple devices. With this toolkit, we study the effect of task acceleration in different cloud servers and analyze the capacity of several cloud servers to handle multiple concurrent requests. We conduct multiple experiments in a real testbed to evaluate the system and present our experiences and lessons learned. From the results, we find that the AutoScaler component introduces a very small overhead of ≈150 milliseconds in the total response time of a request, which is a fair price to pay to empower the offloading architectures with multi-tenancy ability and dynamic horizontal scaling for IoT scenarios

Combined Treatment with MEK and mTOR Inhibitors is Effective in In Vitro and In Vivo Models of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer histotype, characterized by high biological aggressiveness and scarce treatment options. Recently, we have established a clinically relevant murine HCC model by co-expressing activated forms of v-akt murine thymoma viral oncogene homolog (AKT) and oncogene c-mesenchymal-epithelial transition (c-Met) proto-oncogenes in the mouse liver via hydrodynamic tail vein injection (AKT/c-MET mice). Tumor cells from these mice demonstrated high activity of the AKT/ mammalian target of rapamycin (mTOR) and Ras/ Mitogen-activated protein kinase (MAPK) signaling cascades, two pathways frequently co-induced in human HCC. Methods: Here, we investigated the therapeutic efficacy of sorafenib, regorafenib, the MEK inhibitor PD901 as well as the pan-mTOR inhibitor MLN0128 in the AKT/c-Met preclinical HCC model. Results: In these mice, neither sorafenib nor regorafenib demonstrated any efficacy. In contrast, administration of PD901 inhibited cell cycle progression of HCC cells in vitro. Combined PD901 and MLN0128 administration resulted in a pronounced growth constraint of HCC cell lines. In vivo, treatment with PD901 or MLN0128 alone moderately slowed HCC growth in AKT/c-MET mice. Importantly, the simultaneous administration of the two drugs led to a stable disease with limited tumor progression in mice. Mechanistically, combined mitogen-activated extracellular signal-regulated kinase (MEK) and mTOR inhibition resulted in a stronger cell cycle inhibition and growth arrest both in vitro and in vivo. Conclusions: Our study indicates that combination of MEK and mTOR inhibitors might represent an effective therapeutic approach against human HCC