China collection 2.1: Aerosol Optical Depth dataset for mainland China at 1km resolution

A wide range of data products have been published since the operation of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's TERRA and AQUA satellites. Based on DarkTarget and DeepBlue method, NASA has published Aerosol Optical Depth (AOD) products Collection 6.0 with spatial resolution of 3km. Although validated globally, regional and systematic errors are still found in the MODIS-retrieved AOD products. This is especially remarkable for bright heterogeneous land surface, such as mainland China. In order to solve the aerosol retrieval problem over heterogeneous bright land surface, the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) has been developed based on the synergetic use of the MODIS data of TERRA and AQUA satellites. Using the SRAP algorithm, we produced AOD dataset-China Collection 2.1 at 1km spatial resolution, dated from August 2002 to 2012. We compared the China Collection 2.1 AOD datasets for 2010 with AERONET data. From those 2460 collocations, representing mutually cloud-free conditions, we find that 62% of China Collection 2.1 AOD values comparing with AERONET-observed values within an expected error envelop of 20% and 55% within an expected error envelop of 15%. Compared with MODIS Level 2 aerosol products, China Collection 2.1 AOD datasets have a more complete coverage with fewer data gaps over the study region

Comparison of two methods for aerosol optical depth retrieval over North Africa from MSG/SEVIRI data

A comparison between the algorithm for Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS) and a daily estimation of aerosol optical depth (AOD) algorithm (AERUS-GEO) over land surface using MSG/SEVIRI data over North Africa is presented. To obtain indications about the quantitative performance of two AOD retrieval methods mentioned above, daily SEVIRI AOD values is considered with respect to those measured from the global aerosol-monitoring Aerosol Robotic Network (AERONET) data. The correlation coefficient (R2) between retrieved SEVIRI AOD at 650 nm from the AERUS-GEO algorithm and the AERONET Level 2.0 daily average AOD at 675 nm is 0.80 and root mean square error (RMSE) is 0.044, and R2 between retrieved AOD from the LABITS algorithm and AERONET AOD is 0.80 and RMSE is 0.037

GFI1 downregulation promotes inflammation-linked metastasis of colorectal cancer.

Inflammation is frequently associated with initiation, progression, and metastasis of colorectal cancer (CRC). Here, we unveil a CRC-specific metastatic programme that is triggered via the transcriptional repressor, GFI1. Using data from a large cohort of clinical samples including inflammatory bowel disease and CRC, and a cellular model of CRC progression mediated by cross-talk between the cancer cell and the inflammatory microenvironment, we identified GFI1 as a gating regulator responsible for a constitutively activated signalling circuit that renders CRC cells competent for metastatic spread. Further analysis of mouse models with metastatic CRC and human clinical specimens reinforced the influence of GFI1 downregulation in promoting CRC metastatic spread. The novel role of GFI1 is uncovered for the first time in a human solid tumour such as CRC. Our results imply that GFI1 is a potential therapeutic target for interfering with inflammation-induced CRC progression and spread

Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China

One of four main focus areas of the PEEX initiative is to establish and sustain long-term, continuous, and comprehensive ground-based, airborne, and seaborne observation infrastructure together with satellite data. The Advanced Along-Track Scanning Radiometer (AATSR) aboard ENVISAT is used to observe the Earth in dual view. The AATSR data can be used to retrieve aerosol optical depth (AOD) over both land and ocean, which is an important parameter in the characterization of aerosol properties. In recent years, aerosol retrieval algorithms have been developed both over land and ocean, taking advantage of the features of dual view, which can help eliminate the contribution of Earth's surface to top-of-atmosphere (TOA) reflectance. The Aerosol_cci project, as a part of the Climate Change Initiative (CCI), provides users with three AOD retrieval algorithms for AATSR data, including the Swansea algorithm (SU), the ATSR-2ATSR dual-view aerosol retrieval algorithm (ADV), and the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC). The validation team of the Aerosol-CCI project has validated AOD (both Level 2 and Level 3 products) and AE (Ångström Exponent) (Level 2 product only) against the AERONET data in a round-robin evaluation using the validation tool of the AeroCOM (Aerosol Comparison between Observations and Models) project. For the purpose of evaluating different performances of these three algorithms in calculating AODs over mainland China, we introduce ground-based data from CARSNET (China Aerosol Remote Sensing Network), which was designed for aerosol observations in China. Because China is vast in territory and has great differences in terms of land surfaces, the combination of the AERONET and CARSNET data can validate the L2 AOD products more comprehensively. The validation results show different performances of these products in 2007, 2008, and 2010. The SU algorithm performs very well over sites with different surface conditions in mainland China from March to October, but it slightly underestimates AOD over barren or sparsely vegetated surfaces in western China, with mean bias error (MBE) ranging from 0.05 to 0.10. The ADV product has the same precision with a low root mean square error (RMSE) smaller than 0.2 over most sites and the same error distribution as the SU product. The main limits of the ADV algorithm are underestimation and applicability; underestimation is particularly obvious over the sites of Datong, Lanzhou, and Urumchi, where the dominant land cover is grassland, with an MBE larger than 0.2, and the main aerosol sources are coal combustion and dust. The ORAC algorithm has the ability to retrieve AOD at different ranges, including high AOD (larger than 1.0); however, the stability deceases significantly with increasing AOD, especially when AOD > 1.0. In addition, the ORAC product is consistent with the CARSNET product in winter (December, January, and February), whereas other validation results lack matches during winter

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.Comment: 59 pages, 5 figures, review articl

Innovative use of industrially produced steel slag powders in asphalt mixture to replace mineral fillers

Using steel slag to partially replace the natural aggregate in asphalt mixture to produce high-performance asphalt mixture has gained significant interest in recent years as a value-added option to recycle steel slag. However, the poor homogeneity of the material properties of steel slag aggregates remains a concern for this recycling approach. In this study, an innovative method of using industrially produced steel slag powder (SSP) to replace the mineral filler in asphalt mixture was proposed to address this concern. Five fillers, including four SSP fillers, obtained by grinding different steel slag aggregates with an industrialized production line, and one conventional limestone powder (LP) filler, were evaluated. The chemical compositions and micro-morphologies of the SSPs were first characterized to evaluate the material homogeneity and gain insights into the advantages of using SSPs as fillers. Then, asphalt mixtures with different fillers were designed and produced, and their moisture stability, rutting resistance, and low-temperature crack resistance, were characterized. It was found that the industrially produced SSPs possessed homogeneous properties, and improved the compatibility between filler particles and asphalt binder. Besides, the asphalt mixtures with SSP fillers showed better resistance to the moisture damage, permanent deformation, low-temperature crack in terms of fracture energy, than the asphalt mixture with LP filler. Therefore, it was concluded that using SSPs as a replacement of mineral fillers in asphalt mixture provided a reliable and value-added solution to recycle steel slag

Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion

Microseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency noise in the non-microseismic section remaining. Based on this, we have developed a novel MS denoising method combining variational mode decomposition (VMD) and Akaike information criterion (AIC). The method first applied VMD to decompose a signal into several limited-bandwidth intrinsic mode functions and adaptively determined the effective components by the difference of correlation coefficient. After reconstructing, the improved AIC method was used to determine the location of the valuable waveform, and the residual fluctuations in other positions were further removed. A synthetic wavelet signal and some synthetic MS signals with different signal-to-noise ratios (SNRs) were used to test its denoising effect with ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), and the VMD method. The experimental results depicted that the SNRs of the proposed method were obviously larger than that of other methods, and the waveform and spectrum became cleaner based on VMD. The processing results of the MS signal of Shuangjiangkou Hydropower Station also illustrated its good denoising ability and robust performance to signals with different characteristics

Spatial and Temporal Variability and Driving Factors of Carbon Dioxide and Nitrous Oxide Fluxes in Alpine Wetland Ecosystems

Plants regulate greenhouse gas (GHG) fluxes in wetland ecosystems, but the mechanisms of plant removal and plant species that contribute to GHG emissions remain unclear. In this study, the fluxes of carbon dioxide (CO2) and nitrous oxide (N2O) were measured using the static chamber method from an island forest dominated by two different species, namely Betula platyphylla (BP) and Larix gmelinii (LG), in a marsh wetland in the Great Xing’an Mountains. Four sub-plots were established in this study: (1) bare soil after removing vegetation under BP (SBP); (2) bare soil after removing vegetation under LG (SLG); (3) soil with vegetation under BP (VSBP); and (4) soil with vegetation under LG (VSLG). Additionally, the contributions of the dark respiration from plant aerial parts under BP (VBP) and LG (VLG) to GHG fluxes were calculated. We found that the substantial spatial variability of CO2 fluxes ranged from −25.32 ± 15.45 to 187.20 ± 74.76 mg m−2 h−1 during the study period. The CO2 fluxes decreased in the order of SBP > VSLG > VSBP > SLG > VLG > VBP, indicating that vegetation species had a great impact on CO2 emissions. Particularly, the absence of vegetation promoted CO2 emission in both BP and LG. Additionally, CO2 fluxes showed dramatically seasonal variations, with high CO2 fluxes in late spring (May) and summer (June, July, and August), but low fluxes in late summer (August) and early autumn (September). Soil temperatures at 0–20 cm depth were better predictors of CO2 fluxes than deeper soil temperatures. N2O fluxes were varied in different treatments with the highest N2O fluxes in SLG and the lowest N2O fluxes in VBP. Meanwhile, no significant correlation was found between N2O fluxes and air or soil temperatures. Temporally, negative N2O fluxes were observed from June to October, indicating that soil N2O fluxes were reduced and emitted as N2, which was the terminal step of the microbial denitrification process. Most of the study sites were CO2 sources during the warm season and CO2 sinks in the cold season. Thus, soil temperature plays an important role in CO2 fluxes. We also found that the CO2 flux was positively related to pH in a 10 cm soil layer and positively related to moisture content (MC) in a 50 cm soil layer in VSBP and VSLG. However, the CO2 flux was negatively related to pH in a 30 cm soil layer in SBP and SLG. Our findings highlight the effects of vegetation removal on GHG fluxes, and aid in the scientific management of wetland plants

Instability of an intersecting fault-dyke system during deep rock excavation

Intersecting discontinuities are often encountered in rock engineering and sometimes associated with damaging geohazards. Our understanding of intersecting discontinuities instability remains vastly insufficient due to difficulties in comprehensively monitoring the failure process. Here we use microseismic (MS) monitoring to virtualize the MS events in the rock masses surrounding a powerhouse crown and investigate the effect of geological features on the occurrence of MS events. We subsequently build a three-dimensional numerical model and validate this model using the in-situ measurements by multipoint displacement meters. The numerical results demonstrate how the displacements of surrounding rock masses near the fault and the dyke increase and reveal possible causes, such as stress condition as well as geometry and orientation of rock discontinuities. We also discuss the correlation between the tempo-spatial distribution of MS events and the failure pattern of rock masses and confirm the weakened dyke as the main cause of the rock collapse. This study highlights that the stability of intersecting discontinuities can be controlled by both the geometrical and mechanical properties of individual discontinuities, and attentions should be paid to key properties favorable for rock instability.This work was supported by the Science Foundation for Distinguished Young Scholars of Sichuan Province (Grant No. 2020JDJQ0011) and the National Natural Science Foundation of China (Grant No. 51809221)

Drivers and Changes of the Poyang Lake Wetland Ecosystem

This paper serves as both a review of the latest science on the Poyang Lake wetland ecosystem and as an introduction to this Special Issue guest-edited by Jun Xu. Poyang Lake is the largest freshwater lake in China and one of the largest freshwater lake/wetland complexes in Asia. Poyang Lake's associated floodplain wetlands play a critical role for regional and global biodiversity conservation, particularly wintering migratory birds. Since the Three Gorges Dam became operative in 2003, the magnitude and frequency of extreme seasonal water-level fluctuations in Poyang Lake have increased significantly. We review the existing literature and contributions to this Special Issue on the Poyang Lake wetland ecosystem and their biota in relation to impacts from the Three Gorges Dam. Resulting impacts on, and adaptations of, Poyang Lake biota to the hydrological changes caused by operation of the dam are poorly understood. Adaptive management of this lake and its associated wetlands needs to be further assessed through comprehensive, long-term monitoring covering a wide range of parameters related to the system's hydrological characteristics, water quality, geomorphology, aquatic biota, wetland vegetation, and associated wetland fauna