Predicting China's CPI by Scanner Big Data

Scanner big data has potential to construct Consumer Price Index (CPI). This work utilizes the scanner data of supermarket retail sales, which are provided by China Ant Business Alliance (CAA), to construct the Scanner-data Food Consumer Price Index (S-FCPI) in China, and the index reliability is verified by other macro indicators, especially by China's CPI. And not only that, we build multiple machine learning models based on S-FCPI to quantitatively predict the CPI growth rate in months, and qualitatively predict those directions and levels. The prediction models achieve much better performance than the traditional time series models in existing research. This work paves the way to construct and predict price indexes through using scanner big data in China. S-FCPI can not only reflect the changes of goods prices in higher frequency and wider geographic dimension than CPI, but also provide a new perspective for monitoring macroeconomic operation, predicting inflation and understanding other economic issues, which is beneficial supplement to China's CPI

Translithospheric magma plumbing system of intraplate volcanoes as revealed by electrical resistivity imaging

AbstractThe magma plumbing systems of volcanoes in subduction and divergent tectonic settings are relatively well known, whereas those of intraplate volcanoes remain elusive; robust geophysical information on the magma pathways and storage zones is lacking. We inverted magnetotelluric data to image the magma plumbing system of an intraplate monogenetic volcanic field located above the stagnant Pacific slab in northeast China. We identified a complex, vertically aligned, low-resistivity anomaly system extending from the asthenosphere to the surface consisting of reservoirs with finger- to lens-like geometries. We show that magma forms as CO2-rich melts in a 150-km-deep asthenospheric plume crossing the whole lithosphere as hydrated melt, inducing underplating at 50 km depth, evolving in crustal reservoirs, and erupting along dikes. Intraplate volcanoes are characterized by low degrees of melting and low magma supply rates. Their plumbing systems have a geometry not so different from that of volcanoes in subduction settings

The signal pathways and treatment of cytokine storm in COVID-19

The Coronavirus Disease 2019 (COVID-19) pandemic has become a global crisis and is more devastating than any other previous infectious disease. It has affected a significant proportion of the global population both physically and mentally, and destroyed businesses and societies. Current evidence suggested that immunopathology may be responsible for COVID-19 pathogenesis, including lymphopenia, neutrophilia, dysregulation of monocytes and macrophages, reduced or delayed type I interferon (IFN-I) response, antibody-dependent enhancement, and especially, cytokine storm (CS). The CS is characterized by hyperproduction of an array of pro-inflammatory cytokines and is closely associated with poor prognosis. These excessively secreted pro-inflammatory cytokines initiate different inflammatory signaling pathways via their receptors on immune and tissue cells, resulting in complicated medical symptoms including fever, capillary leak syndrome, disseminated intravascular coagulation, acute respiratory distress syndrome, and multiorgan failure, ultimately leading to death in the most severe cases. Therefore, it is clinically important to understand the initiation and signaling pathways of CS to develop more effective treatment strategies for COVID-19. Herein, we discuss the latest developments in the immunopathological characteristics of COVID-19 and focus on CS including the current research status of the different cytokines involved. We also discuss the induction, function, downstream signaling, and existing and potential interventions for targeting these cytokines or related signal pathways. We believe that a comprehensive understanding of CS in COVID-19 will help to develop better strategies to effectively control immunopathology in this disease and other infectious and inflammatory diseases

Self-Supervised Tracking via Target-Aware Data Synthesis

While deep-learning based tracking methods have achieved substantial progress, they entail large-scale and high-quality annotated data for sufficient training. To eliminate expensive and exhaustive annotation, we study self-supervised learning for visual tracking. In this work, we develop the Crop-Transform-Paste operation, which is able to synthesize sufficient training data by simulating various appearance variations during tracking, including appearance variations of objects and background interference. Since the target state is known in all synthesized data, existing deep trackers can be trained in routine ways using the synthesized data without human annotation. The proposed target-aware data-synthesis method adapts existing tracking approaches within a self-supervised learning framework without algorithmic changes. Thus, the proposed self-supervised learning mechanism can be seamlessly integrated into existing tracking frameworks to perform training. Extensive experiments show that our method 1) achieves favorable performance against supervised learning schemes under the cases with limited annotations; 2) helps deal with various tracking challenges such as object deformation, occlusion, or background clutter due to its manipulability; 3) performs favorably against state-of-the-art unsupervised tracking methods; 4) boosts the performance of various state-of-the-art supervised learning frameworks, including SiamRPN++, DiMP, and TransT (based on Transformer).Comment: 11 pages, 7 figure

Serum Tumor Markers for Lung Cancer Diagnosis

Background and objective Progress has been made in research of lung cancer tumor markers in recent years, and these tumor markers have been used in clinical application. This study is to evaluate the regimens of six serum tumor markers in lung cancer diagnosis. Methods The serum levels of the six tumor markers (NSE、pro-GRP、CYFRA21-1、SCC、p53 antibody and CA199) were detected in 80 healthy adults, 170 patients with lung cancer and 80 patients with respiratory infection by ELISA. Results The levels of the six tumor markers in patients with lung cancer were remarkably higher than those in healthy adults and patients with respiratory infection (P<0.01). The levels of the NSE、pro-GRP in patients with small cell lung cancer were significantly higher than those in other subtypes of the lung cancer (P<0.01); The levels of the CYFRA21-1、SCC in patients with squamous carcinoma was remarkably higher than that in other subtypes of the lung cancer (P<0.01). The sensitivity of the NSE、pro-GRP in diagnosing small cell lung cancer was remarkably higher than that in other subtypes of the lung cancer (P<0.01); The sensitivity of the CYFRA21-1、SCC in diagnosing squamous carcinoma was remarkably higher than that in other subtypes of the lung cancer (P<0.01). The sensitivity of the tumor markers combinations in diagnosing lung cancer was remarkably higher than that of the single marker (P<0.01). Conclusion Detection of the six tumor markers is helpful for diagnosis lung cancer. Combination of NSE and pro-GRP is more economic than other combinations in diagnosing small cell lung cancer; Combined CYFRA21-1、SCC is more economic than other combinations in diagnosing squamous carcinoma

Evidence of Bermuda Hot and Wet Upwelling From Novel Three‐Dimensional Global Mantle Electrical Conductivity Image

Abstract A model of electrical conductivity in the mid–mantle transition zone was obtained with improved constraints. An L1‐norm regularization inversion algorithm is proposed here that reduces the influence of noisy data on three‐dimensional geomagnetic depth sounding inversion from C‐responses. Here the regularization is implemented by using an L1‐norm to measure the predicted data error, which is normalized by the C‐response covariance, but an L2‐norm is used to measure the regularization term associated with model parameters. The limited‐memory quasi‐Newton method (L‐BFGS) is used to invert for the three‐dimensional electrical conductivity model. The model is discretized by curved rectangular prisms in spherical coordinates. Sensitivity tests show that for good‐quality data contaminated by Gaussian noise, L1 inversion, which could perform as well as L2 inversion, can adequately recover the main features of the electrical conductivity structure within the region of data coverage. When data errors are drawn from an exponential distribution, L1 inversion obtains relatively reliable reconstruction of the electrical structure, even when the noise level is comparable to that of actual C‐responses. C‐responses from 129 low‐latitude and midlatitude geomagnetic observatories are inverted using L1‐norm minimization of the data error. The resulting model reveals an electrically conductive feature in the lower mantle transition zone and upper lower mantle that is broadly coincident with that found in previous studies. The reduced influence of data with large variances on L1‐norm misfits, along with inclusion of responses estimated from more observatories, makes L1 inversion more clearly identify these deep conductive features while identifying previously obscured anoconductive zones. A feature of particular interest is the high electrical conductivity anomaly beneath the Bermuda‐Sargasso Sea region in the mid–mantle transition zone and the uppermost lower mantle. Rock physics analysis indicates that the anomaly is most possibly caused by the wet upwelling material with excessive ~650 K higher temperature, suggesting a narrow tail with a broad head

Demo abstract: wind measurements for water quality studies in urban reservoirs

Water quality monitoring and prediction are critical for ensuring the sustainability of water resources which are essential for social security, especially for countries with limited land like Singapore. For example, the Singapore government identified water as a new growth sector and committed in 2006 to invest S$ 330 million over the following five years for water research and development [1]. To investigate the water quality evolution numerically, some key water quality parameters at several discrete locations in the reservoir (e.g., dissolved oxygen, chlorophyll, and temperature) and some environmental parameters (e.g., the wind distribution above water surface, air temperature and precipitation) are used as inputs to a three-dimensional hydrodynamics-ecological model, Estuary Lake and Coastal Ocean Model - Computational Aquatic Ecosystem Dynamics Model (ELCOM-CAEDYM) [2]. Based on the calculation in the model, we can obtain the distribution of water quality in the whole reservoir. We can also study the effect of different environmental parameters on the water quality evolution, and finally predict the water quality of the reservoir with a time step of 30 seconds. In this demo, we introduce our data collection system which enables water quality studies with real-time sensor data