1,815 research outputs found

    Ethyl 5-[6-(furan-2-yl)-1,2,4-triazolo[3,4-b][1,3,4]thia­diazol-3-yl]-2,6-di­methylnicotinate

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    In the title compound, C17H15N5O3S, the plane of the triazolo–thia­diazole system forms dihedral angles of 15.68 and 4.46° with the planes of the pyridine and furan rings, respectively. In the mol­ecule, there is an intra­molecular C—H⋯N inter­action. The crystal structure also contains other inter­molecular inter­actions, such as C—H⋯O hydrogen bonds, π–π stacking (centroid–centroid distances = 3.746 and 3.444 Å), non-bonded S⋯N [3.026 (2) Å] and C—H⋯π inter­actions

    Experimental signatures of the quantum-classical transition in a nanomechanical oscillator modeled as a damped driven double-well problem

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    We demonstrate robust and reliable signatures for the transition from quantum to classical behavior in the position probability distribution of a damped double-well system using the Qunatum State Diffusion approach to open quantum systems. We argue that these signatures are within experimental reach, for example in a doubly-clamped nanomechanical beam.Comment: Proceedings of the conference FMQT 1

    Low- and high-thermogenic brown adipocyte subpopulations coexist in murine adipose tissue

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    Brown adipose tissue (BAT), as the main site of adaptive thermogenesis, exerts beneficial metabolic effects on obesity and insulin resistance. BAT has been previously assumed to contain a homogeneous population of brown adipocytes. Utilizing multiple mouse models capable of genetically labeling different cellular populations, as well as single-cell RNA sequencing and 3D tissue profiling, we discovered a new brown adipocyte subpopulation with low thermogenic activity coexisting with the classical high-thermogenic brown adipocytes within the BAT. Compared with the high-thermogenic brown adipocytes, these low-thermogenic brown adipocytes had substantially lower Ucp1 and Adipoq expression, larger lipid droplets, and lower mitochondrial content. Functional analyses showed that, unlike the high-thermogenic brown adipocytes, the low-thermogenic brown adipocytes have markedly lower basal mitochondrial respiration, and they are specialized in fatty acid uptake. Upon changes in environmental temperature, the 2 brown adipocyte subpopulations underwent dynamic interconversions. Cold exposure converted low-thermogenic brown adipocytes into high-thermogenic cells. A thermoneutral environment had the opposite effect. The recruitment of high-thermogenic brown adipocytes by cold stimulation is not affected by high fat diet feeding, but it does substantially decline with age. Our results revealed a high degree of functional heterogeneity of brown adipocytes

    OH reactivity at a rural site (Wangdu) in the North China Plain: contributions from OH reactants and experimental OH budget

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    In 2014, a large, comprehensive field campaign was conducted in the densely populated North China Plain. The measurement site was located in a botanic garden close to the small town Wangdu, without major industry but influenced by regional transportation of air pollution. The loss rate coefficient of atmospheric hydroxyl radicals (OH) was quantified by direct measurements of the OH reactivity. Values ranged between 10 and 20 s−1 for most of the daytime. Highest values were reached in the late night with maximum values of around 40 s−1. OH reactants mainly originated from anthropogenic activities as indicated (1) by a good correlation between measured OH reactivity and carbon monoxide (linear correlation coefficient R2 = 0.33) and (2) by a high contribution of nitrogen oxide species to the OH reactivity (up to 30 % in the morning). Total OH reactivity was measured by a laser flash photolysis–laser-induced fluorescence instrument (LP-LIF). Measured values can be explained well by measured trace gas concentrations including organic compounds, oxygenated organic compounds, CO and nitrogen oxides. Significant, unexplained OH reactivity was only observed during nights, when biomass burning of agricultural waste occurred on surrounding fields. OH reactivity measurements also allow investigating the chemical OH budget. During this campaign, the OH destruction rate calculated from measured OH reactivity and measured OH concentration was balanced by the sum of OH production from ozone and nitrous acid photolysis and OH regeneration from hydroperoxy radicals within the uncertainty of measurements. However, a tendency for higher OH destruction compared to OH production at lower concentrations of nitric oxide is also observed, consistent with previous findings in field campaigns in China

    Prediction model of ocular metastasis from primary liver cancer: Machine learning‐based development and interpretation study

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    Background: Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML). Methods: We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non‐ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10‐fold cross‐validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM. Results: Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA. Conclusion: We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients
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