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Hepatic encephalopathy (HE) represents a dysfunctional gut-liver-brain axis in cirrhosis which can negatively impact outcomes. This altered gut-brain relationship has been treated using gut-selective antibiotics such as rifaximin, that improve cognitive function in HE, especially its subclinical form, minimal HE (MHE). However, the precise mechanism of the action of rifaximin in MHE is unclear. We hypothesized that modulation of gut microbiota and their end-products by rifaximin would affect the gut-brain axis and improve cognitive performance in cirrhosis. Aim To perform a systems biology analysis of the microbiome, metabolome and cognitive change after rifaximin in MHE. Twenty cirrhotics with MHE underwent cognitive testing, endotoxin analysis, urine/serum metabolomics (GC and LC-MS) and fecal microbiome assessment (multi-tagged pyrosequencing) at baseline and 8 weeks post-rifaximin 550 mg BID. Changes in cognition, endotoxin, serum/urine metabolites (and microbiome were analyzed using recommended systems biology techniques. Specifically, correlation networks between microbiota and metabolome were analyzed before and after rifaximin. There was a significant improvement in cognition(six of seven tests improved, p<0.01) and endotoxemia (0.55 to 0.48 Eu/ml, p = 0.02) after rifaximin. There was a significant increase in serum saturated (myristic, caprylic, palmitic, palmitoleic, oleic and eicosanoic) and unsaturated (linoleic, linolenic, gamma-linolenic and arachnidonic) fatty acids post-rifaximin. No significant microbial change apart from a modest decrease in Veillonellaceae and increase in Eubacteriaceae was observed. Rifaximin resulted in a significant reduction in network connectivity and clustering on the correlation networks. The networks centered on Enterobacteriaceae, Porphyromonadaceae and Bacteroidaceae indicated a shift from pathogenic to beneficial metabolite linkages and better cognition while those centered on autochthonous taxa remained similar. Rifaximin is associated with improved cognitive function and endotoxemia in MHE, which is accompanied by alteration of gut bacterial linkages with metabolites without significant change in microbial abundance. ClinicalTrials.gov NCT01069133.

A continuous spatio-temporal database of accurate soil moisture (SM) measurements is an important asset for agricultural activities, hydrologic studies, and environmental monitoring. The Advanced Microwave Scanning Radiometer 2 (AMSR2), which was launched in May 2012, has been providing SM data globally with a revisit period of two days. It is imperative to assess the quality of this data before performing any application. Since resources of accurate SM measurements are very limited in Puerto Rico, this research will assess the quality of the AMSR2 data by comparing it with ground-based measurements, as well as perform a downscaling technique to provide a better description of how the sensor perceives the surface soil moisture as it passes over the island. The comparison consisted of the evaluation of the mean error, root mean squared error, and the correlation coefficient. Two downscaling techniques were used, and their performances were studied. The results revealed that AMSR2 products tend to underestimate soil moisture. This is due to the extreme heterogeneous distributions of elevations, vegetation densities, soil types, and weather events on the island. This research provides a comprehensive study on the accuracy and potential of the AMSR2 products over Puerto Rico. Further studies are recommended to improve the AMSR2 products.

A novel fluorescent surrogate based immunosensor is presented for use in remote detection of Escherichia coli O157:H7. A total of 3704 confirmed E. coli O157:H7 cases with a hospitalization rate of 46.5 % were reported by the Centers for Disease Control and Prevention in 2008. Rapid detection would aid in both prevention and water supply security. This report describes the successful generation of a sensor comprised of fluorescent surrogates with attachment to covalently immobilized antibodies. Fluorescence signal loss was seen upon live E. coli O157:H7 challenge. The 'acceleration' in signal decay detected a spiking event after 7 minutes with a detection threshold of 100 colony forming units per milliliter. The sensor surface was stable for 300 sampling times over a 5 hour period. This sensor design is adaptable for integration into remote sensing type systems due to the small size, low power requirements and stability. [PUBLICATION ABSTRACT]

Soil moisture is vital to understanding many natural systems such as hydrology, climate and weather, erosion, and biology. Current remote sensing provides soil moisture data with a resolution on the scale of tens of kilometers, due to the current constraints of microwave antennae technology. In this study, we present a machine-learning technique based on rule transference that allows us to use a low-resolution but high-accuracy product, obtained through multiple proxies, to produce a high-resolution model of Earth's soil moisture. The low-resolution, high-accuracy microwave product is utilized as a dependent variable in rule-building only. This algorithm is simple, utilizes public data, and overcomes many local issues inherent in other techniques, such as topographic, biographic, temporal, and climatic variations. The final result demonstrates close parity with high-resolution airborne L-band radiometric data. Keywords: Downscaling, soil moisture, random forests

The gut microbiota constitutes one of the most complex mammalian microbial communities and has a major impact on host health status. The goal of this study was to analyze and compare the gut microbiomes of pigs, mice, humans and humanized mice to determine whether the microbial communities from the feces of pigs or mice are more similar to the human fecal microbiome. We also compared the microbial communities from the luminal contents of the pig and mouse gut with the communities from the mucosa to test the hypothesis that the microbiota from the luminal contents will be more diverse and can predict the mucosal community composition. Microbiome data from the V1 and V2 regions of the 16S ribosomal RNA genes were analyzed using the QIIME pipeline in order to determine the structure of the gut microbiota. Metastats and correlation network analysis were used to further investigate community differences. Obesity in pigs caused alterations in the structure of the luminal microbiome, but not the mucosal microbiome. In both mice and pigs, the bacteria that comprised the microbial communities of the mucosa and luminal contents are different within the same individual. Comparison of the microbiota from feces of pigs, humans, mice and humanized mice via PCO of unweighted UniFrac values suggests that the structure of transplanted gut microbiotas may no longer represent the original host due to the absence of appropriate ecological niches. Analyzing networks constructed using pairwise Spearman’s correlations indicates statistically significant feature pair differences for the microbial communities from lean and obese pigs and humans. These results highlight the impact of diet on the genetic diversity of the microbiota in the mammalian gut and indicate that network analysis is a novel and promising methodology that can elucidate patterns in complex biological data.