Predicting Groundwater Fluctuations in Hard Rock Watersheds – An Application of Data Visualizations and Machine Learning Algorithms

Groundwater sustainability is critical to the future of agriculture and food security. The challenges are not only technical but have important social, economic, institutional and policy implications. The objective of this research is to predict groundwater levels in rural wells, allowing farmers to use their groundwater more sustainably. Data visualizations and machine learning algorithms are used to examine data collected over a five-year period from rural rock water basins in the northwestern part of India. Preliminary examination shows that the weekly collected time variable proved to be the single most valuable predictor of groundwater level, as it included implied seasonal changes in weather patterns and pumping patterns. However, due to limited rainfall outside of the monsoon season, it proved a less potent variable than previously expected

Still weighting to break the model democracy

This is the final version of the article. Available from AGU via the DOI in this record.Ensembles of climate model simulations are employed to project how climate might change inthe future. How do these ensemble projections relate to what will happen to the real-world climate?M.C. acknowledges support from NERC The Natural Environment Research Council NE/N018486/1

Drone-based imaging to assess the microbial water quality in an irrigation pond: A pilot study

Microbial water quality datasets are essential in irrigated agricultural practices to detect and inform measures to prevent the contamination of produce. Escherichia coli (E. coli) concentrations are commonly used to evaluate microbial water quality. Remote sensing imagery has been successfully used to retrieve several water quality parameters that can be determinants of E. coli habitats in waterbodies. This pilot study was conducted to test the possibility of using imagery from a small unmanned aerial vehicle (sUAV or drone) to improve the estimation of microbial water quality in small irrigation ponds. In situ measurements of pH, turbidity, specific conductance, and concentrations of dissolved oxygen, chlorophyll-a, phycocyanin, and fluorescent dissolved organic matter were taken at depths of 0–15 cm in 23 locations across a pond in Central Maryland, USA. The pond surface was concurrently imaged using a drone with three modified GoPro cameras, and a multispectral MicaSense RedEdge camera with five spectral bands. The GoPro imagery was decomposed into red, blue, and green components. Mean digital numbers for 1-m radius areas in the images were combined with the water quality data to provide input for a regression tree-based analysis. The accuracy of the regression-tree data description with “only imagery” inputs was the same or better than that of trees constructed with “only water-quality parameters” as inputs. From multiple cross-validation runs with “only imagery” inputs for the regression trees, the average (±SD) determination coefficient and root-mean-squared error of the decimal logarithm of E. coli concentrations were 0.793 ± 0.035 and 0.131 ± 0.011, respectively. The results of this study demonstrate the opportunities for using sUAV imagery for obtaining a more accurate delineation of the spatial variation of E. coli concentrations in irrigation ponds

Accounting for the three-dimensional distribution of Escherichia coli concentrations in pond water in simulations of the microbial quality of water withdrawn for irrigation

Evaluating the microbial quality of irrigation water is essential for the prevention of foodborne illnesses. Generic Escherichia coli (E. coli) is used as an indicator organism to estimate the microbial quality of irrigation water. Monitoring E. coli concentrations in irrigation water sources is commonly performed using water samples taken from a single depth. Vertical gradients of E. coli concentrations are typically not measured or are ignored; however, E. coli concentrations in water bodies can be expected to have horizontal and vertical gradients. The objective of this work was to research 3D distributions of E. coli concentrations in an irrigation pond in Maryland and to estimate the dynamics of E. coli concentrations at the water intake during the irrigation event using hydrodynamic modeling in silico. The study pond is about 22 m wide and 200 m long, with an average depth of 1.5 m. Three transects sampled at 50-cm depth intervals, along with intensive nearshore sampling, were used to develop the initial concentration distribution for the application of the environmental fluid dynamic code (EFDC) model. An eight-hour irrigation event was simulated using on-site data on the wind speed and direction. Substantial vertical and horizontal variations in E. coli concentrations translated into temporally varying concentrations at the intake. Additional simulations showed that the E. coli concentrations at the intake reflect the 3D distribution of E. coli in the limited pond section close to the intake. The 3D sampling revealed E. coli concentration hot spots at different depths across the pond. Measured and simulated 3D E. coli concentrations provide improved insights into the expected microbial water quality of irrigation water compared with 1D or 2D representations of the spatial variability of the indicator concentration

Prolonged treatment of genetically obese mice with conjugated linoleic acid improves glucose tolerance and lowers plasma insulin concentration: possible involvement of PPAR activation

BACKGROUND: Studies in rodents and some studies in humans have shown that conjugated linoleic acid (CLA), especially its trans-10, cis-12 isomer, reduces body fat content. However, some but not all studies in mice and humans (though none in rats) have found that CLA promotes insulin resistance. The molecular mechanisms responsible for these effects are unclear, and there are conflicting reports on the effects of CLA on peroxisomal proliferator-activated receptor-γ (PPARγ) activation and expression. We have conducted three experiments with CLA in obese mice over three weeks, and one over eleven weeks. We have also investigated the effects of CLA isomers in PPARγ and PPARα reporter gene assays. RESULTS: Inclusion of CLA or CLA enriched with its trans-10, cis-12 isomer in the diet of female genetically obese (lep(ob)/lep(ob)) mice for up to eleven weeks reduced body weight gain and white fat pad weight. After two weeks, in contrast to beneficial effects obtained with the PPARγ agonist rosiglitazone, CLA or CLA enriched with its trans-10, cis-12 isomer raised fasting blood glucose and plasma insulin concentrations, and exacerbated glucose tolerance. After 10 weeks, however, CLA had beneficial effects on glucose and insulin concentrations. At this time, CLA had no effect on the plasma TNFα concentration, but it markedly reduced the plasma adiponectin concentration. CLA and CLA enriched with either isomer raised the plasma triglyceride concentration during the first three weeks, but not subsequently. CLA enriched with its trans-10, cis-12 isomer, but not with its cis-9, trans-11 isomer, stimulated PPARγ-mediated reporter gene activity; both isomers stimulated PPARα-mediated reporter gene activity. CONCLUSIONS: CLA initially decreased but subsequently increased insulin sensitivity in lep(ob)/lep(ob )mice. Activation of both PPARγ and PPARα may contribute to the improvement in insulin sensitivity. In the short term, however, another mechanism, activated primarily by trans-10, cis-12-CLA, which probably leads to reduced adipocyte number and consequently reduced plasma adiponectin concentration, may decrease insulin sensitivity

The terrestrial carbon budget of South and Southeast Asia

Accomplishing the objective of the current climate policies will require establishing carbon budget and flux estimates in each region and county of the globe by comparing and reconciling multiple estimates including the observations and the results of top-down atmospheric carbon dioxide (CO2) inversions and bottom-up dynamic global vegetation models. With this in view, this study synthesizes the carbon source/sink due to net ecosystem productivity (NEP), land cover land use change (ELUC), fires and fossil burning (EFIRE) for the South Asia (SA), Southeast Asia (SEA) and South and Southeast Asia (SSEA=SA+SEA) and each country in these regions using the multiple top-down and bottom-up modeling results. The terrestrial net biome productivity (NBP=NEP-ELUC-EFIRE) calculated based on bottom-up models in combination with EFIRE based on GFED4s data show net carbon sinks of 217±147, 10±55, and 227±279 TgC yr?1 for SA, SEA, and SSEA. The top-down models estimated NBP net carbon sinks were 20±170, 4±90 and 24±180 TgC yr?1. In comparison, regional emissions from the combustion of fossil fuels were 495, 275, and 770 TgC yr?1, which are many times higher than the NBP sink estimates, suggesting that the contribution of the fossil fuel emissions to the carbon budget of SSEA results in a significant net carbon source during the 2000s. When considering both NBP and fossil fuel emissions for the individual countries within the regions, Bhutan and Laos were net carbon sinks and rest of the countries were net carbon source during the 2000s. The relative contributions of each of the fluxes (NBP, NEP, ELUC, and EFIRE, fossil fuel emissions) to a nation’s net carbon flux varied greatly from country to country, suggesting a heterogeneous dominant carbon fluxes on the country-level throughout SSEA

Reflections on a crisis: political disenchantment, moral desolation, and political integrity

Declining levels of political trust and voter turnout, the shift towards populist politics marked by appeals to ‘the people’ and a rejection of ‘politics-as-usual’, are just some of the commonly cited manifestations of our culture of political disaffection. Democratic politics, it is argued, is in crisis. Whilst considerable energy has been expended on the task of lamenting the status of our politics and pondering over recommendations to tackle this perceived crisis, amid this raft of complaints and solutions lurks confusion. This paper seeks to explore the neglected question of what the precise nature of the crisis with which we are confronted involves, and, in so doing, to go some way towards untangling our confusion. Taking my cue from Machiavelli and his value-pluralist heirs, I argue that there is a rift between a morally admirable and a virtuous political life. Failure to appreciate this possibility causes narrations of crisis to misconstrue the moral messiness of politics in ways that lead us to misunderstand how we should respond to disenchantment. Specifically, I suggest that: (i) we think that there is a moral crisis in politics because we have an unsatisfactorily idealistic understanding of political integrity in the first place; and (ii) it is a mistake to imagine that the moral purification of politics is possible or desirable. Put simply, our crisis is not moral per se but primarily philosophical in nature: it relates to the very concepts we employ—the qualities of character and context we presuppose whilst pondering over political integrity

Modeling Peripheral Olfactory Coding in Drosophila Larvae

The Drosophila larva possesses just 21 unique and identifiable pairs of olfactory sensory neurons (OSNs), enabling investigation of the contribution of individual OSN classes to the peripheral olfactory code. We combined electrophysiological and computational modeling to explore the nature of the peripheral olfactory code in situ. We recorded firing responses of 19/21 OSNs to a panel of 19 odors. This was achieved by creating larvae expressing just one functioning class of odorant receptor, and hence OSN. Odor response profiles of each OSN class were highly specific and unique. However many OSN-odor pairs yielded variable responses, some of which were statistically indistinguishable from background activity. We used these electrophysiological data, incorporating both responses and spontaneous firing activity, to develop a Bayesian decoding model of olfactory processing. The model was able to accurately predict odor identity from raw OSN responses; prediction accuracy ranged from 12%–77% (mean for all odors 45.2%) but was always significantly above chance (5.6%). However, there was no correlation between prediction accuracy for a given odor and the strength of responses of wild-type larvae to the same odor in a behavioral assay. We also used the model to predict the ability of the code to discriminate between pairs of odors. Some of these predictions were supported in a behavioral discrimination (masking) assay but others were not. We conclude that our model of the peripheral code represents basic features of odor detection and discrimination, yielding insights into the information available to higher processing structures in the brain

An Inhibitory Sex Pheromone Tastes Bitter for Drosophila Males

Sexual behavior requires animals to distinguish between the sexes and to respond appropriately to each of them. In Drosophila melanogaster, as in many insects, cuticular hydrocarbons are thought to be involved in sex recognition and in mating behavior, but there is no direct neuronal evidence of their pheromonal effect. Using behavioral and electrophysiological measures of responses to natural and synthetic compounds, we show that Z-7-tricosene, a Drosophila male cuticular hydrocarbon, acts as a sex pheromone and inhibits male-male courtship. These data provide the first direct demonstration that an insect cuticular hydrocarbon is detected as a sex pheromone. Intriguingly, we show that a particular type of gustatory neurons of the labial palps respond both to Z-7-tricosene and to bitter stimuli. Cross-adaptation between Z-7-tricosene and bitter stimuli further indicates that these two very different substances are processed by the same neural pathways. Furthermore, the two substances induced similar behavioral responses both in courtship and feeding tests. We conclude that the inhibitory pheromone tastes bitter to the fly