Performance of solar-induced chlorophyll fluorescence in estimating water-use efficiency in a temperate forest

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing 10 (2018): 796, doi:10.3390/rs10050796.Water-use efficiency (WUE) is a critical variable describing the interrelationship between carbon uptake and water loss in land ecosystems. Different WUE formulations (WUEs) including intrinsic water use efficiency (WUEi), inherent water use efficiency (IWUE), and underlying water use efficiency (uWUE) have been proposed. Based on continuous measurements of carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) at a temperate forest, we analyze the correlations between SIF emission and the different WUEs at the canopy level by using linear regression (LR) and Gaussian processes regression (GPR) models. Overall, we find that SIF emission has a good potential to estimate IWUE and uWUE, especially when a combination of different SIF bands and a GPR model is used. At an hourly time step, canopy-level SIF emission can explain as high as 65% and 61% of the variances in IWUE and uWUE. Specifically, we find that (1) a daily time step by averaging hourly values during daytime can enhance the SIF-IWUE correlations, (2) the SIF-IWUE correlations decrease when photosynthetically active radiation and air temperature exceed their optimal biological thresholds, (3) a low Leaf Area Index (LAI) has a negative effect on the SIF-IWUE correlations due to large evaporation fluxes, (4) a high LAI in summer also reduces the SIF-IWUE correlations most likely due to increasing scattering and (re)absorption of the SIF signal, and (5) the observation time during the day has a strong impact on the SIF-IWUE correlations and SIF measurements in the early morning have the lowest power to estimate IWUE due to the large evaporation of dew. This study provides a new way to evaluate the stomatal regulation of plant-gas exchange without complex parameterizations.This research was supported by U.S. Department of Energy Office of Biological and Environmental Research Grant DE-SC0006951, National Science Foundation Grants DBI 959333 and AGS-1005663, and the University of Chicago and the MBL Lillie Research Innovation Award to Jianwu Tang. This study was also supported by the open project grant (LBKF201701) of Key Laboratory of Land Surface Pattern and Simulation, Chinese Academy of Sciences

Comparison of phenology estimated from reflectance-based indices and solar-induced chlorophyll fluorescence (SIF) observations in a temperate forest using GPP-based phenology as the standard

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing 10 (2018): 932, doi:10.3390/rs10060932.We assessed the performance of reflectance-based vegetation indices and solar-induced chlorophyll fluorescence (SIF) datasets with various spatial and temporal resolutions in monitoring the Gross Primary Production (GPP)-based phenology in a temperate deciduous forest. The reflectance-based indices include the green chromatic coordinate (GCC), field measured and satellite remotely sensed Normalized Difference Vegetation Index (NDVI); and the SIF datasets include ground-based measurement and satellite-based products. We found that, if negative impacts due to coarse spatial and temporal resolutions are effectively reduced, all these data can serve as good indicators of phenological metrics for spring. However, the autumn phenological metrics derived from all reflectance-based datasets are later than the those derived from ground-based GPP estimates (flux sites). This is because the reflectance-based observations estimate phenology by tracking physiological properties including leaf area index (LAI) and leaf chlorophyll content (Chl), which does not reflect instantaneous changes in phenophase transitions, and thus the estimated fall phenological events may be later than GPP-based phenology. In contrast, we found that SIF has a good potential to track seasonal transition of photosynthetic activities in both spring and fall seasons. The advantage of SIF in estimating the GPP-based phenology lies in its inherent link to photosynthesis activities such that SIF can respond quickly to all factors regulating phenological events. Despite uncertainties in phenological metrics estimated from current spaceborne SIF observations due to their coarse spatial and temporal resolutions, dates in middle spring and autumn—the two most important metrics—can still be reasonably estimated from satellite SIF. Our study reveals that SIF provides a better way to monitor GPP-based phenological metrics.This research was supported by U. S. Department of Energy Office of Biological and Environmental Research Grant DE-SC0006951, National Science Foundation Grants DBI 959333 and AGS-1005663, and the University of Chicago and the MBL Lillie Research Innovation Award to Jianwu Tang and China Scholarship Council No. 201506190095 to Z. Liu. Xiaoliang Lu was also supported by the open project grant (LBKF201701) of Key Laboratory of Land Surface Pattern and Simulation, Chinese Academy of Sciences

Preparation of graphene oxide decorated Fe3O4@SiO2 nanocomposites with superior adsorption capacity and SERS detection for organic dyes

The fast detection and removal of organic dyes from contaminated water has become an urgent environmental issue due to their high toxicity, chemical stability, and low biodegradability. In this paper, we have developed graphene oxide decorated Fe3O4@SiO2 (Fe3O4@SiO2-GO) as a novel adsorbent aiming at the rapid adsorption and trace analysis of organic dyes followed by surface enhanced Raman scattering (SERS). The structure and morphology of the nanocomposites were characterized by transmission electron microscopy (TEM), Fourier infrared spectrometry (FT-IR), X-ray diffraction (XRD), and vibrating sample magnetometer (VSM). The obtained nanocomposites were used to adsorb methylene blue (MB) in aqueous solution based on π-π stacking interaction and electrostatic attraction between MB and GO, and the adsorption behaviors of MB were investigated. Moreover, the obtained nanocomposites with adsorbed dyes were separated from the solution and loaded with silver nanoparticles for SERS detection. These nanocomposites showed superior SERS sensitivity and the lowest detectable concentration was 1.0 × 10-7 M

Cellular Phenotype Plasticity in Cancer Dormancy and Metastasis

Cancer dormancy is a period of cancer progression in which residual tumor cells exist, but clinically remain asymptomatic for a long time, as well as resistant to conventional chemo- and radiotherapies. Cellular phenotype plasticity represents that cellular phenotype could convert between epithelial cells and cells with mesenchymal traits. Recently, this process has been shown to closely associate with tumor cell proliferation, cancer dormancy and metastasis. In this review, we have described different scenarios of how the transition from epithelial to mesenchymal morphology (EMT) and backwards (MET) are connected with the initiation of dormancy and reactivation of proliferation. These processes are fundamental for cancer cells to invade tissues and metastasize. Recognizing the mechanisms underlying the cellular phenotype plasticity as well as dormancy and targeting them is likely to increase the efficiency of traditional tumor treatment inhibiting tumor metastasis

Reconstruction of global gridded monthly sectoral water withdrawals for 1971-2010 and analysis of their spatiotemporal patterns

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scale, due to a lack of observations at local and seasonal time scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5 degree) sectoral water withdrawal dataset for the period 1971–2010, which distinguishes six water use sectors, i.e. irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that global total water withdrawal has increased significantly during 1971–2010, mainly driven by the increase of irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of annual total irrigation water withdrawal in mid and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual scales. The reconstructed gridded water withdrawal dataset is open-access, and can be used for examining issues related to water withdrawals at fine spatial, temporal and sectoral scales

Rapid kidney function decline and increased risk of heart failure in patients with type 2 diabetes: findings from the ACCORD cohort : Rapid kidney function decline and heart failure in T2D.

BACKGROUND Impaired kidney function and albuminuria are associated with increased risk of heart failure (HF) in patients with type 2 diabetes (T2D). We investigated whether rapid kidney function decline over time is an additional determinant of increased HF risk in patients with T2D, independent of baseline kidney function, albuminuria, and other HF predictors. METHODS Included in the study were 7,539 participants in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study with baseline urinary albumin-to-creatinine ratio (UACR) data, who had completed 4 years of follow-up and had ≥ 3 eGFR measurements during that period (median eGFR/year = 1.9, IQR 1.7-3.2). The association between rapid kidney function decline (eGFR loss ≥ 5 ml/min/1.73 m2/year) and odds of HF hospitalization or HF death during the first 4 years of follow-up was estimated by logistic regression. The improvement in risk discrimination provided by adding rapid kidney function decline to other HF risk factors was evaluated as the increment in the area under the Receiving Operating Characteristics curve (ROC AUC) and integrated discrimination improvement (IDI). RESULTS Over 4 years of follow-up, 1,573 participants (20.9%) experienced rapid kidney function decline and 255 (3.4%) experienced a HF event. Rapid kidney function decline was associated with a ~ 3.2-fold increase in HF odds (3.23, 95% CI, 2.51-4.16, p < 0.0001), independent of baseline CVD history. This estimate was not attenuated by adjustment for potential confounders, including eGFR and UACR at baseline as well as at censoring (3.74; 95% CI 2.63-5.31). Adding rapid kidney function decline during follow-up to other clinical predictors (WATCH-DM score, eGFR, and UACR at study entry and end of follow-up) improved HF risk classification (ROC AUC = + 0.02, p = 0.027; relative IDI = + 38%, p < 0.0001). CONCLUSIONS In patients with T2D, rapid kidney function decline is associated with a marked increase in HF risk, independent of starting kidney function and/or albuminuria. These findings highlight the importance of serial eGFR measurements over time to improve HF risk estimation in T2D

Silver nanoprism-loaded eggshell membrane: a facile platform for in situ SERS monitoring of catalytic reactions

We reported the fabrication of an in situ surface-enhanced Raman scattering (SERS) monitoring platform, comprised of a porous eggshell membrane (ESM) bioscaffold loaded with Ag nanoprism via an electrostatic self-assembly approach. The localized surface plasmon resonance (LSPR) property of silver nanoprism leads to the blue color of the treated ESMs. UV-vis diffuse reflectance spectroscopy, scanning electron microscope (SEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) measurements were employed to observe the microstructure and surface property of Ag nanoprisms on the ESMs. The silver nanoprism-loaded eggshell membrane (AgNP@ESM) exhibited strong catalytic activity for the reduction of 4-nitrophenol by sodium borohydride (NaBH4) and it can be easily recovered and reused for more than six cycles. Significantly, the composites also display excellent SERS efficiency, allowing the in situ SERS monitoring of molecular transformation in heterogeneous catalysis. The results indicate that the AgNP@ESM biocomposite can achieve both SERS and catalytic functionalities simultaneously in a single entity with high performance, which promotes the potential applications of ESM modified with functional materials

LncRNA BASP1-AS1 interacts with YBX1 to regulate Notch transcription and drives the malignancy of melanoma

Melanoma is a fatal skin malignant tumor with a poor prognosis. We found that long noncoding RNA BASP1-AS1 is essential for the development and prognosis of melanoma. The methylation, RNA sequencing, copy number variation, mutation data, and sample follow-up information of melanoma from The Cancer Genome Atlas (TCGA) were analyzed using weighted gene co-expression network analysis and 366 samples common to the three omics were selected for multigroup clustering analysis. A four-gene prognostic model (BASP1-AS1, LOC100506098, ARHGAP27P1, and LINC01532) was constructed in the TCGA cohort and validated using the GSE65904 series. The expression of BASP1-AS1 was upregulated in melanoma tissues and various melanoma cell lines. Functionally, the ectopic expression of BASP1-AS1 promoted cell proliferation, migration, and invasion in both A375 and SK-MEL-2 cells. Mechanically, BASP1-AS1 interacted with YBX1 and recruited it to the promoter of NOTCH3, initiating its transcription process. The activation of the Notch signaling then resulted in the transcription of multiple oncogenes, including c-MYC, PCNA, and CDK4, which contributed to melanoma progression. Thus, BASP1-AS1 could act as a potential biomarker for cutaneous malignant melanoma

Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis

Bacteraemia has attracted great attention owing to its serious outcomes, including deterioration of the primary disease, infection, severe sepsis, overwhelming septic shock or even death. Candidemia, secondary to bacteraemia, is frequently seen in hospitalised patients, especially in those with weak immune systems, and may lead to lethal outcomes and a poor prognosis. Moreover, higher morbidity and mortality associated with candidemia. Owing to the complexity of patient conditions, the occurrence of candidemia is increasing. Candidemia-related studies are relatively challenging. Because candidemia is associated with increasing mortality related to invasive infection of organs, its pathogenesis warrants further investigation. We collected the relevant clinical data of 367 patients with concomitant candidemia and bacteraemia in the first hospital of China Medical University from January 2013 to January 2018. We analysed the available information and attempted to obtain the undisclosed information. Subsequently, we used machine learning to screen for regulators such as prognostic factors related to death. Of the 367 patients, 231 (62.9%) were men, and the median age of all patients was 61 years old (range, 52–71 years), with 133 (36.2%) patients aged >65 years. In addition, 249 patients had hypoproteinaemia, and 169 patients were admitted to the intensive care unit (ICU) during hospitalisation. The most common fungi and bacteria associated with tumour development and Candida infection were Candida parapsilosis and Acinetobacter baumannii, respectively. We used machine learning to screen for death-related prognostic factors in patients with candidemia and bacteraemia mainly based on integrated information. The results showed that serum creatinine level, endotoxic shock, length of stay in ICU, age, leukocyte count, total parenteral nutrition, total bilirubin level, length of stay in the hospital, PCT level and lymphocyte count were identified as the main prognostic factors. These findings will greatly help clinicians treat patients with candidemia and bacteraemia