22 research outputs found

    Cloud Control of Connected Vehicle under Bi-directional Time-varying delay: An Application of Predictor-observer Structured Controller

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    This article is devoted to addressing the cloud control of connected vehicles, specifically focusing on analyzing the effect of bi-directional communication-induced delays. To mitigate the adverse effects of such delays, a novel predictor-observer structured controller is proposed which compensate for both measurable output delays and unmeasurable, yet bounded, input delays simultaneously. The study begins by novelly constructing an equivalent delay-free inter-connected system model that incorporates the Predictor-Observer controller, considering certain delay boundaries and model uncertainties. Subsequently, a stability analysis is conducted to assess the system's robustness under these conditions. Next, the connected vehicle lateral control scenario is built which contain high-fidelity vehicle dynamic model. The results demonstrate the controller's ability to accurately predict the system states, even under time-varying bi-directional delays. Finally, the proposed method is deployed in a real connected vehicle lateral control system. Comparative tests with a conventional linear feedback controller showcase significantly improved control performance under dominant bi-directional delay conditions, affirming the superiority of the proposed method against the delay

    Phosphoinositide 3-kinase enhancer regulates neuronal dendritogenesis and survival in neocortex

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    Phosphoinositide 3-kinase enhancer (PIKE) binds and enhances phosphatidylinositol 3-kinase (PI3K)/Akt activities. However, its physiological functions in brain have never been explored. Here we show that PIKE is important in regulating the neuronal survival and development of neocortex. During development, enhanced apoptosis is observed in the ventricular zone of PIKE knock-out (PIKE-/-) cortex. Moreover, PIKE-/-neurons show reduced dendritic complexity, dendritic branch length, and soma size. These defects are due to the reduced PI3K/Akt activities in PIKE-/- neurons, as the impaired dendritic arborization can be rescued when PI3K/Akt cascade is augmented in vitro or in PIKE-/-PTEN-/- double-knock-out mice. Interestingly, PIKE-/- mice display behavioral abnormality in locomotion and spatial navigation. Because of the diminished PI3K/Akt activities, PIKE-/- neurons are more vulnerable to glutamateor stroke-induced neuronal cell death. Together, our data established the critical role of PIKE in regulating neuronal survival and development by substantiating the PI3K/Akt pathway. © 2011 the authors.Link_to_subscribed_fulltex

    Studies of 1‑Amino-2,2-difluorocyclopropane-1-carboxylic Acid: Mechanism of Decomposition and Inhibition of 1‑Aminocyclopropane-1-carboxylic Acid Deaminase

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    1-Amino-2,2-difluoro­cyclopropane-1-carboxylic acid (DFACC) is of interest in the study of 1-aminocyclo­propane-1-carboxylic acid (ACC) deaminase due to the increased reactivity of its cyclopropyl functionality. It is shown that DFACC is unstable under near-physiological conditions where it primarily decomposes via specific-base catalysis to 3-fluoro-2-oxobut-3-enoic acid with a rate constant of 0.18 ± 0.01 min<sup>–1</sup>. Upon incubation with ACC deaminase, DFACC is found to be a slow-dissociating inhibitor of ACC deaminase with submicromolar affinity

    Metabolic profiles in community-acquired pneumonia: developing assessment tools for disease severity

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    Abstract Background This study aimed to determine whether community-acquired pneumonia (CAP) had a metabolic profile and whether this profile can be used for disease severity assessment. Methods A total of 175 individuals including 119 CAP patients and 56 controls were enrolled and divided into two cohorts. Serum samples from a discovery cohort (n = 102, including 38 non-severe CAP, 30 severe CAP, and 34 age and sex-matched controls) were determined by untargeted ultra-high-performance liquid chromatography with tandem mass spectrometry (LC-MS/MS)-based metabolomics. Selected differential metabolites between CAP patients versus controls, and between the severe CAP group versus non-severe CAP group, were confirmed by targeted mass spectrometry assays in a validation cohort (n = 73, including 32 non-severe CAP, 19 severe CAP and 22 controls). Pearson’s correlation analysis was performed to assess relationships between the identified metabolites and clinical severity of CAP. The area under the curve (AUC), sensitivity and specificity of the metabolites for predicting the severity of CAP were also investigated. Results The metabolic signature was markedly different between CAP patients and controls. Fifteen metabolites were found to be significantly dysregulated in CAP patients, which were mainly mapped to the metabolic pathways of sphingolipid, arginine, pyruvate and inositol phosphate. The alternation trends of five metabolites among the three groups including sphinganine, p-Cresol sulfate, dehydroepiandrosterone sulfate (DHEA-S), lactate and l-arginine in the validation cohort were consistent with those in the discovery cohort. Significantly lower concentrations of sphinganine, p-Cresol sulfate and DHEA-S were observed in CAP patients than in controls (p  65 years (CURB-65), pneumonia severity index (PSI) and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, while DHEA-S inversely correlated with the three scoring systems. Combining lactate, sphinganine and DHEA-S as a metabolite panel for discriminating severe CAP from non-severe CAP exhibited a better AUC of 0.911 (95% confidence interval 0.825–0.998) than CURB-65, PSI and APACHE II scores. Conclusions This study demonstrates that serum metabolomics approaches based on the LC-MS/MS platform can be applied as a tool to reveal metabolic changes during CAP and establish a metabolite signature related to disease severity. Trial registration ClinicalTrials.gov, NCT03093220. Registered retrospectively on 28 March 2017

    Additional file 2: of Metabolic profiles in community-acquired pneumonia: developing assessment tools for disease severity

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    Supplemental Figures. Figure S1. Metabolite base peak chromatograms of serum samples from a patient in three groups: a non-severe CAP; b severe CAP; c controls. Figure S2. S-plots identifying putative biomarkers on the basis of OPLS-DA models: a CAP patients versus controls; b severe CAP versus non-severe CAP patients. Figure S3. Box–whisker plots of relative intensity of 15 metabolites changed in CAP patients compared to controls. Horizontal line represents median; bottom and top of the box represent 25th and the 75th percentiles; whiskers represent 5% and 95% percentiles. *FDR < 0.05, **FDR < 0.001. NSCAP non-severe CAP, SCAP severe CAP. (DOCX 7213 kb
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