Various zircons of Proterozoic to Oligocene ages (1060‐31 Ma) were analysed by laser ablation‐inductively coupled plasma‐mass spectrometry. Calibration was performed using Harvard reference zircon 91500 or Australian National University reference zircon TEMORA 1 as external calibrant. The results agree with those obtained by SIMS within 2s error. Twenty‐four trace and rare earth elements (P, Ti, Cr, Y, Nb, fourteen REE, Hf, Ta, Pb, Th and U) were analysed on four fragments of zircon 91500. NIST SRM 610 was used as the reference material and 29Si was used as internal calibrant. Based on determinations of four fragments, this zircon shows significant intra‐and inter‐fragment variations in the range from 10% to 85% on a scale of 120 μm, with the variation of REE concentrations up to 38.7%, although the chondrite‐normalised REE distributions are very similar. In contrast, the determined age values for zircon 91500 agree with TIMS data and are homogeneous within 8.7 Ma (2s). A two‐stage ablation strategy was developed for optimising U‐Pb age determinations with satisfactory trace element and REE results. The first cycle of ablation was used to collect data for age determination only, which was followed by continuous ablation on the same spot to determine REE and trace element concentrations. Based on this procedure, it was possible to measure zircon ages as low as 30.37 0.39 Ma (MSWD = 1.4; 2s). Other examples for older zircons are also given.

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The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944,

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The effects of adding nitrogen to the central gas flow (Ar + He) of an Ar plasma in laser ablation inductively coupled plasma mass spectrometry are presented. The optimum central gas flow rate was found to be negatively correlated with the N 2 gas flow rate. The addition of 5-10 ml min À1 nitrogen to the central channel gas in LA-ICP-MS increases the sensitivity for most of the 65 investigated elements by a factor of 2-3. The degree of enhancement depends, to some extent, on the 1st ionization energy. Another important advantage of N 2 mixed gas plasma for LA-ICP-MS is that the oxide ratios (ThO + / Th + ) are significantly reduced (one order of magnitude). The hydride ratio (ArH + /Ar + ) is also reduced up to a factor of 3, whereas the doubly charged ion ratio (Ca 2+ /Ca + ) is increased. The background signals at masses 29, 31, 42, 51, 52 and 55 are significantly increased due to the nitrogen based polyatomic interferences. Compared to the spatial profiles of the ion distributions in the normal mode (without nitrogen), the addition of 5 ml min À1 nitrogen leads to significant wider axial profiles and more uniform distribution of ions with different physical and chemical properties. Our results also show that the makeup gas flow (central channel gas) rate has a significant effect on the ion distribution of elements with different physical and chemical properties. A very consistent increase of argon signal by the addition of nitrogen (5 ml min À1 ) corroborates better energy transfer effect of nitrogen in the plasma.

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Recently, the outbreak of Coronavirus Disease 2019 has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting highdimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.