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While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (>70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites in the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underestimation is highly site-specific. We then propose a machine learning based method to correct for this underestimation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (>90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE.

Evaporation (E) is a critical component of the water and energy budget in lake systems yet is challenging to quantify directly and continuously. We examined the magnitude and changes of E and its drivers over Lake Erie-the shallowest and most southern lake of the Laurentian Great Lakes. We deployed two eddy-covariance tower sites in the western Lake Erie Basin-one located nearshore (CB) and one offshore (LI)-from September 2011 through May 2016. Monthly E varied from 5 to 120 mm, with maximum E occurring in August-October. The annual E was 635 ± 42 (±SD) mm at CB and 604 ± 32 mm at LI. Mean winter (October-March) E was 189 ± 61 mm at CB and 178 ± 25 mm at LI, accounting for 29.8% and 29.4% of annual E. Mean daily E was 1.8 mm during the coldest month (January) and 7.4 mm in the warmest month (July). Monthly E exhibited a strong positive linear relationship to the product of wind speed and vapor pressure deficit. Pronounced seasonal patterns in surface energy fluxes were observed with a 2-month lag in E from R n, due to the lake's heat storage. This lag was shorter than reports regarding other Great Lakes. Difference in E between the offshore and nearshore sites reflected within-lake spatial heterogeneity, likely attributable to climatic and bathymetric differences between them. These findings suggest that predictive models need to consider lake-specific heat storage and spatial heterogeneity in order to accurately simulate lake E and its seasonal dynamics.

Lakes are important components for regulating carbon cycling within landscapes. Most lakes are regarded as CO2 sources to the atmosphere, except for a few eutrophic ones. Algal blooms are common phenomena in many eutrophic lakes and can cause many environmental stresses, yet their effects on the net exchange of CO2 (Fco2) at large spatial scales have not been adequately addressed. We integrated remote sensing and Eddy Covariance (EC) technologies to investigate the effects that algal blooms have on FCO2 in the western basin of Lake Erie-a large lake infamous for these blooms. Three years of long-term EC data (2012-2014) at two sites were analyzed. We found that at both sites: (1) daily FCO2 significantly correlated with daily temperature, light, and wind speed during the algal bloom periods; (2) monthly FCO2 was negatively correlated with chlorophyll-a concentration; and (3) the year with larger algal blooms was always associated with lower carbon emissions. We concluded that large algal blooms could reduce carbon emissions in the western basin of Lake Erie. However, considering the complexity of processes within large lakes, the weak relationship we found, and the potential uncertainties that remain in our estimations of Fco2 and chlorophyll-a, we argue that additional data and analyses are needed to validate our conclusion and examine the underlying regulatory mechanisms.

Remote sensing images (RSIs) are widely used in various fields due to their versatility, accuracy, and capacity for earth observation. Direct application of RSIs to harvest optimal results is generally difficult, especially for weak information features in the images. Thus, extracting the weak information in RSIs is reasonable to promote further applications. However, the current techniques for weak information extraction mainly focus on spectral features in hyperspectral images (HSIs), and a universal weak information extraction technology for RSI is lacking. Therefore, this study focused on mining the weak information from RSIs and proposed the deep multi-order spatial–spectral residual feature extractor (DMSRE). The DMSRE considers the global information and three-dimensional cube structures by combining low-rank representation, high-order residual quantization, and multi-granularity spectral segmentation theories. This extractor obtains spatial–spectral features from two derived sequences (deep spatial–spectral residual feature (DMSR) and deep spatial–spectral coding feature (DMSC)), and three RSI datasets (i.e., Chikusei, ZY1-02D, and Pasture datasets) were employed to validate the DMSRE method. Comparative results of the weak information extraction-based classifications (including DMSR and DMSC) and the raw image-based classifications showed the following: (i) the DMSRs can improve the classification accuracy of individual classes in fine classification applications (e.g., Asphalt class in the Chikusei dataset, from 89.12% to 95.99%); (ii) the DMSC improved the overall accuracy in rough classification applications (from 92.07% to 92.78%); and (iii) the DMSC improved the overall accuracy in RGB classification applications (from 63.25% to 63.6%), whereas DMSR improved the classification accuracy of individual classes on the RGB image (e.g., Plantain classes in the Pasture dataset, from 32.49% to 39.86%). This study demonstrates the practicality and capability of the DMSRE method to promote target recognition on RSIs and presents an alternative technique for weak information mining on RSIs, indicating the potential to extend weak information-based applications of RSIs. [ABSTRACT FROM AUTHOR]

Background: CircTADA2A has been demonstrated to play critical roles in the occurrence and development of human cancer. However, the expression pattern and biological mechanisms of circTADA2A in melanoma remains largely unknown. Methods: CircTADA2A were detected by quantitative real-time RT-PCR (qRT-PCR) and validated by Sanger sequencing. Function of circTADA2A and its protein partner in melanoma cells was investigated using RNA interference and overexpression assays. Interaction of circTADA2A, CCHC-type zinc finger nucleic acid binding protein (CNBP) and solute carrier family 38 member 1 (SLC38A1) was confirmed by RNA immunoprecipitation, RNA pull-down, and dual-luciferase reporter assay. The expression of genes and proteins were detected by qRT-PCR and western blot assays. Results: Data from the investigation showed that a novel circRNA (circTADA2A, hsa_circ_0043278) was markedly downregulated in melanoma cells. Functionally, circTADA2A repressed cell proliferation, migration, invasion in melanoma cells. Mechanistically, circTADA2A interacted with CNBP, acting to suppress the binding of CNBP to the SLC38A1 promoter and subsequently restrained SLC38A1 transcription, which resulting in repression of melanoma progression. Conclusions: CircTADA2A suppresses melanoma progression by regulating CNBP/SLC38A1 axis, indicating a potential therapeutic target in melanoma. [ABSTRACT FROM AUTHOR]