What drives the latitudinal gradient in open-ocean surface dissolved inorganic carbon concentration?

Previous work has not led to a clear understanding of the causes of spatial pattern in global surface ocean dissolved inorganic carbon (DIC), which generally increases polewards. Here, we revisit this question by investigating the drivers of observed latitudinal gradients in surface salinity-normalized DIC (nDIC) using the Global Ocean Data Analysis Project version 2 (GLODAPv2) database. We used the database to test three different hypotheses for the driver producing the observed increase in surface nDIC from low to high latitudes. These are (1) sea surface temperature, through its effect on the CO2 system equilibrium constants, (2) salinity-related total alkalinity (TA), and (3) highlatitude upwelling of DIC- and TA-rich deep waters. We find that temperature and upwelling are the two major drivers. TA effects generally oppose the observed gradient, except where higher values are introduced in upwelled waters. Temperature-driven effects explain the majority of the surface nDIC latitudinal gradient (182 of the 223 μmol kg1 increase from the tropics to the high-latitude Southern Ocean). Upwelling, which has not previously been considered as a major driver, additionally drives a substantial latitudinal gradient. Its immediate impact, prior to any induced air-sea CO2 exchange, is to raise Southern Ocean nDIC by 220 μmol kg1 above the average low-latitude value. However, this immediate effect is transitory. The long-term impact of upwelling (brought about by increasing TA), which would persist even if gas exchange were to return the surface ocean to the same CO2 as without upwelling, is to increase nDIC by 74 μmol kg1 above the low-latitude average

A critical role for hepatic protein arginine methyltransferase 1 isoform 2 in glycemic control

Appropriate control of hepatic gluconeogenesis is essential for the organismal survival upon prolonged fasting and maintaining systemic homeostasis under metabolic stress. Here, we show protein arginine methyltransferase 1 (PRMT1), a key enzyme that catalyzes the protein arginine methylation process, particularly the isoform encoded by Prmt1 variant 2 (PRMT1V2), is critical in regulating gluconeogenesis in the liver. Liver‐specific deletion of Prmt1 reduced gluconeogenic capacity in cultured hepatocytes and in the liver. Prmt1v2 was expressed at a higher level compared to Prmt1v1 in hepatic tissue and cells. Gain‐of‐function of PRMT1V2 clearly activated the gluconeogenic program in hepatocytes via interactions with PGC1α, a key transcriptional coactivator regulating gluconeogenesis, enhancing its activity via arginine methylation, while no effects of PRMT1V1 were observed. Similar stimulatory effects of PRMT1V2 in controlling gluconeogenesis were observed in human HepG2 cells. PRMT1, specifically PRMT1V2, was stabilized in fasted liver and hepatocytes treated with glucagon, in a PGC1α‐dependent manner. PRMT1, particularly Prmt1v2, was significantly induced in the liver of streptozocin‐induced type 1 diabetes and high fat diet‐induced type 2 diabetes mouse models and liver‐specific Prmt1 deficiency drastically ameliorated diabetic hyperglycemia. These findings reveal that PRMT1 modulates gluconeogenesis and mediates glucose homeostasis under physiological and pathological conditions, suggesting that deeper understanding how PRMT1 contributes to the coordinated efforts in glycemic control may ultimately present novel therapeutic strategies that counteracts hyperglycemia in disease settings.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/10/fsb221018-sup-0005-FigS5.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/9/fsb221018-sup-0001-FigS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/8/fsb221018-sup-0003-FigS3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/7/fsb221018-sup-0008-FigS8.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/6/fsb221018-sup-0002-FigS2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/5/fsb221018_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/4/fsb221018.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/3/fsb221018-sup-0007-FigS7.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/2/fsb221018-sup-0006-FigS6.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/1/fsb221018-sup-0004-FigS4.pd

Integrated analysis of carbon dioxide and oxygen concentrations as a quality control of ocean float data

The distributions of dissolved O2 and CO2 have not previously been systematically compared across the global surface ocean, despite their significance for life and climate. Here we analyze carbon dioxide and oxygen concentrations relative to saturation (equilibrium with the atmosphere) in surface waters, using two large datasets (ship-collected and float-collected data). When applied to a high-quality global ship-collected dataset, CO2 and O2 concentrations relative to saturation exhibit large seasonal and geographic variations. However, linear fits of CO2 and O2 deviations from saturation (ΔCO2 against ΔO2) yield y-intercepts close to zero, which suggests a requirement for data validity. We utilize this finding to investigate the accuracy of carbonate system data from biogeochemical-Argo floats. We find significant discrepancies in ΔCO2-ΔO2 y-intercepts compared to the global reference, implying overestimations of float-based CO2 release in the Southern Ocean. We conclude that this technique can be applied to data from autonomous platforms for quality assessment

Investigation of surface ocean carbon distribution using large global dataset

Despite considerable progress in our understanding of marine biogeochemistry there are many unknowns. We have probably identified all the major processes (physical and geological as well as biological and chemical) influencing the carbon cycle, but the exact nature and magnitude of the different impacts remain to be fully determined. This study aims at taking advantage of a new wealth of carbonate system observational data coming out of the expansion of research into ocean carbon uptake and ocean acidification. The release of new large datasets (e.g., GLODAPv2: Global Ocean Data Analysis Project version 2) provides an opportunity to make advances in our fundamental understanding (Chapter 2). I compare the distributions of carbon, and some other related parameters (e.g., sea surface temperature, total alkalinity, and nutrients in Chapter 3; dissolved oxygen in Chapter 4) in the surface open ocean to expectations based on current understanding, and derive new understanding (including improved quantification and geographical localization of key processes) from investigation of discrepancies. To contribute to these goals, I have firstly improved the understanding of the drivers of the global open ocean surface DIC latitudinal gradient (Chapter 3), demonstrating that sea surface temperature effects on CO2 solubility and high-latitude upwelling (particularly in the Southern Ocean) are the two major factors. I have also clarified the different effects of upwelling depending on the timescale: the short-term effect of upwelling acts immediately, accounting for 98% of the observed nDIC latitudinal gradient; the long-term effect of upwelling acts on timescales of months to a year, accounting for 33% of the observed nDIC latitudinal gradient. Secondly, I have combined and compared the coupled changes in the surface ocean dissolved O2 and CO2 (Chapter 4) by developing a new technique, namely Carbon and Oxygen Relative to Saturation (CORS). By using this technique, I have identified regions and periods where processes are driving O2 and CO2 away from their equilibrium with the atmosphere. Thirdly, I have used a surface carbon balance calculation (by taking the Drake Passage as an example) to test the claim, based on SOCCOM float data, of significant rates of CO2 outgassing from the high-latitude Southern Ocean (Chapter 5). I have shown the implausibility of this finding in the Drake Passage, but with limitation in extrapolating my result to the broader Southern Ocean. I have also applied the CORS technique to float-measured/estimated O2 and CO2 data, showing that CORS is capable of distinguishing suspect data from credible data

Contrasting plant–microbe interrelations on soil Di-(2-ethylhexyl) phthalate and pyrene degradation by three dicotyledonous plant species

Plants and associated microbial communities can actively participate in the biodegradation of organic pollution. Potexperiments were conducted to explore the plant–microbe interrelations on Di-(2-ethylhexyl) phthalate (DEHP) and pyrene degradation in a soil culture system. Three dicotyledonous plant species, Ceylon spinach (Gynuracusimbua (D. Don) S. Moore), sunflower (Helianthus annuus L.), and Shuidong mustard (Brassica juncea (L.) Coss.var. foliosa Bailey), were cultivated for 45 days in DEHP and pyrene co-contaminated soils using three initial content levels: 0 (T0), 20 (T20) and 50 mg kg−1 (T50) with no plants (NP) as control. The results demonstrated that Shuidong mustard biomass and sunflower biomass had significantly positive correlations with the removal rate of DEHP (P < .05), respectively, while Ceylon spinach biomass has no significant correlation with the removal rate of DEHP. Shuidong mustard–actinomycetes and Ceylon spinach–actinomycetes accelerated the removal rate of pyrene, and sunflower–gram-positive bacteria could also enhance the removal rate of pyrene. Our results suggest that a better understanding of plant–microbe interrelations could be exploited to enhance the phytoremediation of organic co-contaminated soils

Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data

Eutrophication is a serious environmental problem in Qiantang River, the largest river in the Zhejiang Province of southeast China. Increased phosphorus concentration is thought to be the major cause of water eutrophication. The objective of this study was to develop an empirical remote sensing model using Landsat Thematic Mapper (TM) data to estimate phosphorus concentration and characterize the spatial variability of the phosphorus concentration in the mainstream of Qiantang River. Field water quality data were collected across a spatial gradient along the river and geospatially overlaid with Landsat satellite images. Various statistical regression models were tested to correlate phosphorus concentration with a combination of other water quality indicators and remotely sensed spectral reflectance, including Secchi depth (SD) and chlorophyll-a (Chl-a) concentration. The optimal regression model was subsequently used to map and characterize the spatial variability of the total phosphorus (TP) concentration in the mainstream of Qiantang River. The results suggest that spectral reflectance from the Landsat satellite is spatially and implicitly correlated with phosphorus concentration (R-2 = 0.77). The approach proved to be effective and has the potential to be applied over large areas for water quality monitoring.Eutrophication is a serious environmental problem in Qiantang River, the largest river in the Zhejiang Province of southeast China. Increased phosphorus concentration is thought to be the major cause of water eutrophication. The objective of this study was to develop an empirical remote sensing model using Landsat Thematic Mapper (TM) data to estimate phosphorus concentration and characterize the spatial variability of the phosphorus concentration in the mainstream of Qiantang River. Field water quality data were collected across a spatial gradient along the river and geospatially overlaid with Landsat satellite images. Various statistical regression models were tested to correlate phosphorus concentration with a combination of other water quality indicators and remotely sensed spectral reflectance, including Secchi depth (SD) and chlorophyll-a (Chl-a) concentration. The optimal regression model was subsequently used to map and characterize the spatial variability of the total phosphorus (TP) concentration in the mainstream of Qiantang River. The results suggest that spectral reflectance from the Landsat satellite is spatially and implicitly correlated with phosphorus concentration (R(2) = 0.77). The approach proved to be effective and has the potential to be applied over large areas for water quality monitoring

A TCF-Based Carbon Monoxide NIR-Probe without the Interference of BSA and Its Application in Living Cells

As toxic gaseous pollution, carbon monoxide (CO) plays an essential role in many pathological and physiological processes, well-known as the third gasotransmitter. Owning to the reducibility of CO, the Pd0-mediated Tsuji-Trost reaction has drawn much attention in CO detection in vitro and in vivo, using allyl ester and allyl ether caged fluorophores as probes and PdCl2 as co-probes. Because of its higher decaging reactivity than allyl ether in the Pd0-mediated Tsuji-Trost reaction, the allyl ester group is more popular in CO probe design. However, during the application of allyl ester caged probes, it was found that bovine serum albumin (BSA) in the fetal bovine serum (FBS), an irreplaceable nutrient in cell culture media, could hydrolyze the allyl ester bond, and thus give erroneous imaging results. In this work, dicyanomethylenedihydrofuran (TCF) and dicyanoisophorone (DCI) were selected as electron acceptors for constructing near-infrared-emission fluorophores with electron donor phenolic OH. An allyl ester and allyl ether group were installed onto TCF-OH and DCI-OH, constructing four potential CO fluorescent probes, TCF-ester, TCF-ether, DCI-ester, and DCI-ether. Our data revealed that ester bonds of TCF-ester and DCI-ester could completely hydrolyze in 20 min, but ether bonds in TCF-ether and DCI-ether tolerate the hydrolysis of BSA and no released fluorescence was observed even up to 2 h. Moreover, passing through the screen, it was concluded that TCF-ether is superior to DCI-ether due to its higher reactivity in a Pd0-mediated Tsuji-Trost reaction. Also, the large stokes shift of TCF-OH, absorption and emission at 408 nm and 618 nm respectively, make TCF-ether desirable for fluorescent imaging because of differentiating signals from the excitation light source. Lastly, TCF-ether has been successfully applied to the detection of CO in H9C2 cells

Quantitative Prediction Method for Distribution Power Grid Risk

The electric power distribution grid is directly oriented to the majority of the ordinary users. Traditional operation and maintenance are performed mainly based on experience, which disable to rationally evaluate the status of the line and predict faults. Based on big data, the risk of the line is evaluated through principal component analysis in this paper, so that a machine learning algorithm is carried out to calculate the risk value of the distribution grid line unit. Finally, GA-BP neural network is used to build a line risk value prediction model for improvement