The streamwater microbiome encodes hydrologic data across scales

Many fundamental questions in hydrology remain unanswered due to the limited information that can be extracted from existing data sources. Microbial communities constitute a novel type of environmental data, as they are comprised of many thousands of taxonomically and functionally diverse groups known to respond to both biotic and abiotic environmental factors. As such, these microscale communities reflect a range of macroscale conditions and characteristics, some of which also drive hydrologic regimes. Here, we assess the extent to which streamwater microbial communities (as characterized by 16S gene amplicon sequence abundance) encode information about catchment hydrology across scales. We analyzed 64 summer streamwater DNA samples collected from subcatchments within the Willamette, Deschutes, and John Day river basins in Oregon, USA, which range 0.03–29,000 km2 in area and 343–2334 mm/year of precipitation. We applied information theory to quantify the breadth and depth of information about common hydrologic metrics encoded within microbial taxa. Of the 256 microbial taxa that spanned all three watersheds, we found 9.6 % (24.5/256) of taxa, on average, shared information with a given hydrologic metric, with a median 15.6 % (range = 12.4–49.2 %) reduction in uncertainty of that metric based on knowledge of the microbial biogeography. All of the hydrologic metrics we assessed, including daily discharge at different time lags, mean monthly discharge, and seasonal high and low flow durations were encoded within the microbial community. Summer microbial taxa shared the most information with winter mean flows. Our study demonstrates quantifiable relationships between streamwater microbial taxa and hydrologic metrics at different scales, likely resulting from the integration of multiple overlapping drivers of each. Streamwater microbial communities are rich sources of information that may contribute fresh insight to unresolved hydrologic questions

CHANGES IN SOME BIOPHYSICAL AND BIOCHEMICAL PARAMETERS IN BLOOD AND URINE OF WORKERS CHRONICALLY EXPOSED TO BENZENE

Objective: Benzene may occur naturally as a component of petroleum, or may be manufactured synthetically. It is found in the environment as a contaminant from both human activities and natural processes, posing serious bio-hazards from chronic exposure.Methods: A total of 330 individual were enrolled to study possible health hazards of benzene contamination; 265 males occupationally chronically exposed to low levels of benzene in their daily activity were compared to 65 healthy individuals of the same socio-economic standard. Benzene workers were divided between 45 workers in printing shops, 70 subjects dealing with benzene containing paints (painters), 75 subjects working in professions related to automotive work (autoworkers) and 75 car drivers.Results: benzene itself was not detected in blood or urine of all participants, but the levels of its metabolites; phenol and t,t-muconic acid, were higher in the blood and urine samples in the group of benzene-exposed workers. The results also indicate that individuals in this group are under oxidative stress. However, neither the determined liver function nor the kidney function tests showed significant deviation from controls. However, the results of the biophysical hematological parameters, including the degree of hemolysis, blood viscosity, RBCs aggregation and form factor were significantly deviated from normal.Conclusion: The deviation of the determined biochemical and biophysical parameters from normal may predispose such workers to a variety of health problems. Early correction of the oxidative stress and the hematological parameters and improvement of working conditions are necessary to prevent their progress to more serious health conditions, especially in children and young adolescents working under similar conditions.Running Title : Chronic exposure to benzene in work plac

Technical Support for Geopressured Geothermal Well Activities in Louisiana Annual Report

The rationale previously developed and utilized in interpreting various databases were outlined in the DOE final report titled "Technical Support for Geopressured-Geothermal Well Activities in Louisiana" (August 31, 1981), authored by Fred M. Wrighton, Don G. Bebout, Dale R. Carver, Charles G. Groat, and Adrain E. Johnson, Jr., under Contract No. DE-AS05-78ET27160. Due to the importance of the concepts presented in the previous report for developing an assessment evaluation program, much of the text and illustrations have been repeated for the convenience of the reader. However, the assessment techniques and calculations presented in this report are new. Estimates of the in-place methane resource in the geopressured zone of the U.S. Gulf Coast have varied widely, ranging from less than 1000 trillion cubic feet (TCF) to 49,000 TCF. The most recent assessment for onshore Texas, conducted by Gregory, Dodge, Posey, and Morton in 1981, estimated a total of 690 TCF. Well logs from deep oil and gas wells served as the primary data source for all assessment studies, although the interpretation of these logs varied significantly. In this study focusing on Louisiana, the methods and results were found to compare favorably with those of Gregory and others for Texas. The assessment included the Wilcox, Frio, and Miocene formations. Constructing regional cross-sections across the south Louisiana Gulf Coast was a prime objective to aid in assessing the total solution-methane resource in place. These cross-sections provided point sources of information from key wells and offered insights into the significance of each well through logical correlations with others on the section. Additionally, detailed studies of smaller areas conducted at LSU and other institutions throughout the Gulf Coast were considered, with data from these studies also contributing to the assessment. The total area under consideration in south Louisiana exceeds 18,000 square miles, with geopressured formations including the Wilcox (1692 square miles), Frio (5200 square miles), and Miocene. Parameters considered in this assessment included pressure, sandstone volume, porosity, temperature, and salinity.Bureau of Economic Geolog

A minimal model for congestion phenomena on complex networks

We study a minimal model of traffic flows in complex networks, simple enough to get analytical results, but with a very rich phenomenology, presenting continuous, discontinuous as well as hybrid phase transitions between a free-flow phase and a congested phase, critical points and different scaling behaviors in the system size. It consists of random walkers on a queueing network with one-range repulsion, where particles can be destroyed only if they can move. We focus on the dependence on the topology as well as on the level of traffic control. We are able to obtain transition curves and phase diagrams at analytical level for the ensemble of uncorrelated networks and numerically for single instances. We find that traffic control improves global performance, enlarging the free-flow region in parameter space only in heterogeneous networks. Traffic control introduces non-linear effects and, beyond a critical strength, may trigger the appearance of a congested phase in a discontinuous manner. The model also reproduces the cross-over in the scaling of traffic fluctuations empirically observed in the Internet, and moreover, a conserved version can reproduce qualitatively some stylized facts of traffic in transportation networks

The CT20 peptide causes detachment and death of metastatic breast cancer cells by promoting mitochondrial aggregation and cytoskeletal disruption

Metastasis accounts for most deaths from breast cancer, driving the need for new therapeutics that can impede disease progression. Rationally designed peptides that take advantage of cancer-specific differences in cellular physiology are an emerging technology that offer promise as a treatment for metastatic breast cancer. We developed CT20p, a hydrophobic peptide based on the C terminus of Bax that exhibits similarities with antimicrobial peptides, and previously reported that CT20p has unique cytotoxic actions independent of full-length Bax. In this study, we identified the intracellular actions of CT20p which precede cancer cell-specific detachment and death. Previously, we found that CT20p migrated in the heavy membrane fractions of cancer cell lysates. Here, using MDA-MB-231 breast cancer cells, we demonstrated that CT20p localizes to the mitochondria, leading to fusion-like aggregation and mitochondrial membrane hyperpolarization. As a result, the distribution and movement of mitochondria in CT20p-treated MDA-MB-231 cells was markedly impaired, particularly in cell protrusions. In contrast, CT20p did not associate with the mitochondria of normal breast epithelial MCF-10A cells, causing little change in the mitochondrial membrane potential, morphology or localization. In MDA-MB-231 cells, CT20p triggered cell detachment that was preceded by decreased levels of alpha 5 beta 1 integrins and reduced F-actin polymerization. Using folate-targeted nanoparticles to encapsulate and deliver CT20p to murine tumors, we achieved significant tumor regression within days of peptide treatment. These results suggest that CT20p has application in the treatment of metastatic disease as a cancer-specific therapeutic peptide that perturbs mitochondrial morphology and movement ultimately culminating in disruption of the actin cytoskeleton, cell detachment, and loss of cell viability

Comparing Model Representations of Physiological Limits on Transpiration at a Semi-arid Ponderosa Pine Site

Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi-empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi-arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big-leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations

Primary recovery factor as a function of production rate: implications for conventional reservoirs with different drive mechanisms

This study evaluates the dependency of production rate on the recovery of hydrocarbon from conventional reservoirs using MBAL simulator. The results indicated that the recoveries are sensitive to the production rate in almost all hydrocarbon reservoirs. It was also found that the recovery of volumetric gas drive reservoirs is not impacted by the production rate. In fact, any increase in the production rate improves gas recovery in weak and strong water drive reservoirs. Moreover, increasing the production rate in oil reservoirs decreases the recovery with a significant effect observed in the weak water drive reservoirs. The results of this study demonstrate the need for implementing an effective reservoir management in order to obtain a maximum recovery

Theorising Global Governance Inside Out: A Response to Professor Ladeur

Professor Ladeur argues that administrative law’s postmodernism (and by extension Global Administrative Law) necessitates that we move beyond relying on ideas of delegation, account- ability and legitimacy. Global Governance, particularly Global Administrative Law and Global Constitutionalism, should try to adapt and experiment with the changing nature of the postmod- ern legality and support the creation of norms that will adapt to the complexities of globalisation. Ladeur’s contestation, similar to GAL’s propositions, can be challenged. By taking the International Criminal Tribunal for Rwanda, a significant contributor to the field of international criminal law, as an example, it is suggested that the creation of networks that Ladeur makes visible may not account for ‘regulatory capture’. This paper will argue that from the outside, the proliferation of networks may suggest that spontaneous accountability is possible. A closer look, however, drawing on anthropological insights from the ICTR, reveals that international institutions are suscepti- ble to capture by special interests. Furthermore, there are two central themes that animate the response to Professor Ladeur: the political nature of international institutions and the history of international law, and the role of institutions in this history

Neural networks in petroleum geology as interpretation tools

Abstract Three examples of the use of neural networks in analyses of geologic data from hydrocarbon reservoirs are presented. All networks are trained with data originating from clastic reservoirs of Neogene age located in the Croatian part of the Pannonian Basin. Training always included similar reservoir variables, i.e. electric logs (resistivity, spontaneous potential) and lithology determined from cores or logs and described as sandstone or marl, with categorical values in intervals. Selected variables also include hydrocarbon saturation, also represented by a categorical variable, average reservoir porosity calculated from interpreted well logs, and seismic attributes. In all three neural models some of the mentioned inputs were used for analyzing data collected from three different oil fields in the Croatian part of the Pannonian Basin. It is shown that selection of geologically and physically linked variables play a key role in the process of network training, validating and processing. The aim of this study was to establish relationships between log-derived data, core data, and seismic attributes. Three case studies are described in this paper to illustrate the use of neural network prediction of sandstone-marl facies (Case Study # 1, Okoli Field), prediction of carbonate breccia porosity (Case Study # 2, Beničanci Field), and prediction of lithology and saturation (Case Study # 3, Kloštar Field). The results of these studies indicate that this method is capable of providing better understanding of some clastic Neogene reservoirs in the Croatian part of the Pannonian Basin