In this work we demonstrate a facile means to generate fluorescent carbon nanoribbons, nanoparticles, and graphene from graphite electrode using ionic liquid-assisted electrochemical exfoliation. A time-dependence study of products exfoliated from the graphite anode allows the reconstruction of the exfoliation mechanism based on the interplay of anodic oxidation and anion intercalation. We have developed strategies to control the distribution of the exfoliated products. In addition, the fluorescence of these carbon nanomaterials can be tuned from the visible to ultraviolet region by controlling the water content in the ionic liquid electrolyte.

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We report on the development of highly conductive NiCo2S4 single crystalline nanotube arrays grown on a flexible carbon fiber paper (CFP), which can serve not only as a good pseudocapacitive material but also as a three-dimensional (3D) conductive scaffold for loading additional electroactive materials. The resulting pseudocapacitive electrode is found to be superior to that based on the sibling NiCo2O4 nanorod arrays, which are currently used in supercapacitor research due to the much higher electrical conductivity of NiCo2S4. A series of electroactive metal oxide materials, including CoxNi1-x(OH)2, MnO2, and FeOOH, were deposited on the NiCo2S4 nanotube arrays by facile electrodeposition and their pseudocapacitive properties were explored. Remarkably, the as-formed CoxNi1-x(OH)2/NiCo2S4 nanotube array electrodes showed the highest discharge areal capacitance (2.86 F cm(-2) at 4 mA cm(-2)), good rate capability (still 2.41 F cm(-2) at 20 mA cm(-2)), and excellent cycling stability (∼ 4% loss after the repetitive 2000 cycles at a charge-discharge current density of 10 mA cm(-2)).

Highlights d Heterogeneity and plasticity of non-parenchymal cells in healthy and NASH liver d Landscape of intrahepatic ligand-receptor signaling at single-cell resolution d Emergence of Trem2+ NASH-associated macrophages (NAMs) in mouse and human NASH d Stellakine secretion and contractile response to vasoactive hormones by HSCs

Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Key Points • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

To control the spread of Corona Virus Disease , screening large numbers of suspected cases for appropriate quarantine and treatment is a priority.Pathogenic laboratory testing is the diagnostic gold standard but it is time consuming with significant false negative results. Fast and accurate diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we aimed to develop a deep learning method that could extract COVID-19's graphical features in order to provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control.Methods：We collected 1,119 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the Inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results:The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Conclusion:These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis.

The erosion, transport and redeposition of sediments shape the Earth's surface, and a ect the structure and function of ecosystems and society 1,2 . The Yellow River was once the world's largest carrier of fluvial sediment, but its sediment load has decreased by approximately 90% over the past 60 years 3 . The decline in sediment load is due to changes in water discharge and sediment concentration, which are both influenced by regional climate change and human activities. Here we use an attribution approach to analyse 60 years of runo and sediment load observations from the traverse of the Yellow River over China's Loess Plateau -the source of nearly 90% of its sediment load. We find that landscape engineering, terracing and the construction of check dams and reservoirs were the primary factors driving reduction in sediment load from the 1970s to 1990s, but large-scale vegetation restoration projects have also reduced soil erosion from the 1990s onwards. We suggest that, as the ability of existing dams and reservoirs to trap sediments declines in the future, erosion rates on the Loess Plateau will increasingly control the Yellow River's sediment load.Change of soil erosion and the resulting river sediment transport are important components of global change, so understanding the mechanisms behind such change is crucial to developing strategic plans for the sustainable management of catchments 4,5 . In recent decades, significant decreasing trends in river sediment loads have been observed in approximately 50% of the world's rivers 6,7 . The benefits and risks of the change in river sediment load largely depend on the baseline load and the scale of the change 8,9 . Hence, it is important to quantify the change of river sediment loads through time, and to understand the drivers and mechanisms behind them 2,5 .The Huang He, or Yellow River (YR) (Fig. 1), was the most sediment-laden river in the world, but its annual sediment load has continually decreased since the 1950s (refs 10-13). The yearly sediment loads at the main gauging stations along the YR, all show significant decreasing trends (p < 0.01) over the past six decades (Fig. 1b). Sediment load increases most suddenly in the middle reach of the river, when crossing the Loess Plateau (LP), between the Toudaoguai gauging station (TDG) (0.07 Gt yr −1 ) and the Tongguan station (TG) (0.63 Gt yr −1 ), and then gradually declines in the lower reach (Fig. 1b, top right inset). The LP is thus the largest sediment source, nearly 90% (refs 3,11) for the YR, and we therefore focus on this part of the river's catchment. A mass budget over the middle reach of the YR can be obtained from the difference of measured sediment flux and water discharge at TG and TDG (Fig. 1). Both the river discharge and sediment load across the LP show significant decreasing trends (−0.25 km 3 yr −2 , p < 0.001; and −0.02 Gt yr −2 , p < 0.001, respectively) over the past six decades, whereas precipitation decreased slightly (−1.2 mm yr −2 , p = 0.015). As Fig. 2a shows two abrupt falls in sed...

IMPORTANCE Sodium-glucosecotransporter2(SGLT2)inhibitorsfavorablyaffectcardiovascular(CV) and kidney outcomes; however, the consistency of outcomes across the class remains uncertain. OBJECTIVE To perform meta-analyses that assess the CV and kidney outcomes of all 4 available SGLT2 inhibitors in patients with type 2 diabetes.