Degradation kinetics of veterinary antibiotics and estrogenic hormones in a claypan soil.

Veterinary antibiotics and estrogens are excreted in livestock waste before being applied to agricultural lands as fertilizer, resulting in contamination of soil and adjacent waterways. The objectives of this study were to 1) investigate the degradation kinetics of the VAs sulfamethazine and lincomycin and the estrogens estrone and 17ß-estradiol in soil mesocosms, and 2) assess the effect of the phytochemical DIBOA-Glu, secreted in eastern gamagrass (Tripsacum dactyloides) roots, on antibiotic degradation due to the ability of DIBOA-Glu to facilitate hydrolysis of atrazine in solution assays. Mesocosm soil was a silt loam representing a typical claypan soil in portions of Missouri and the Central United States. Mesocosms (n = 133) were treated with a single target compound (antibiotic concentrations at 125 ng g-1 dw, estrogen concentrations at 1250 ng g-1 dw); a subset of mesocosms treated with antibiotics were also treated with DIBOA-Glu (12,500 ng g-1 dw); all mesocosms were kept at 60% water-filled pore space and incubated at 25 °C in darkness. Randomly chosen mesocosms were destructively sampled in triplicate for up to 96 days. All targeted compounds followed pseudo first-order degradation kinetics in soil. The soil half-life (t0.5) of sulfamethazine ranged between 17.8 and 30.1 d and ranged between 9.37 and 9.90 d for lincomycin. The antibiotics results showed no significant differences in degradation kinetics between treatments with or without DIBOA-Glu. For estrogens, degradation rates of estrone (t0.5 = 4.71-6.08 d) and 17ß-estradiol (t0.5 = 5.59-6.03 d) were very similar; however, results showed that estrone was present as a metabolite in the 17ß-estradiol treated mesocosms and vice-versa within 24 h. The antibiotics results suggest that sulfamethazine has a greater potential to persist in soil than lincomycin. The interconversion of 17ß-estradiol and estrone in soil increased their overall persistence and sustained soil estrogenicity. This study demonstrates the persistence of these compounds in a typical claypan soil representing portions of the Central United States.

Vegetative buffer strips show limited effectiveness for reducing antibiotic transport in surface runoff.

Vegetative buffer strips (VBS) have been demonstrated to effectively reduce loads of sediment, nutrients, and herbicides in surface runoff, but their effectiveness for reducing veterinary antibiotic (VA) loads in runoff has not been well documented. The objective of this study was to determine the effectiveness of VBS vegetation and width on surface runoff loads of the VAs sulfamethazine (SMZ) and lincomycin (LIN). Experimental design of the plots (1.5 × 25 m) was a two-way factorial with four vegetation treatments (tall fescue [Festuca aruninacea Schreb.]; tall fescue with switchgrass [Panicum virgatum L.] hedge; warm-season native grass mix; and continuous fallow control), and four buffer widths (0, 2, 5, and 9 m). Turkey litter spiked with SMZ and LIN was applied to the source area (upper 7 m) of each plot, and runoff was collected at each width. Runoff was generated with a rotating boom simulator. Results showed VA loads in runoff at the 0-m sampler ranged from 3.8 to 5.9% of applied, and overall VA transport in runoff was predominately in the dissolved phase (90% for SMZ and 99% for LIN). Among vegetation treatments, only tall fescue significantly reduced loads of SMZ and LIN compared with the control, with load reductions of ∼30% for both VAs. Estimated field-scale reductions in VA loads showed that source-to-buffer area ratios (SBARs) of 10:1 to 20:1 reduced VA loads by only 7 to 16%. Overall, the grass VBS tested here were less effective at reducing SMZ and LIN loads in surface runoff than has been previously demonstrated for sediment, nutrients, and herbicides.

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.

A First Implementation of Underwater Communications in Raw Water Using the 433 MHz Frequency Combined with a Bowtie Antenna.

In 2016, there were 317 serious water pollution incidents in the UK, with 78,000 locations where businesses discharge controlled quantities of pollutants into rivers; therefore, continuous monitoring is vital. Since 1998, the environment agency has taken over 50 million water samples for water quality monitoring. The Internet of Things has grown phenomenally in recent years, reaching all aspects of our lives, many of these connected devices use wireless sensor networks to relay data to internet-connected nodes, where data can be processed, analyzed and consumed. However, Underwater wireless communications rely mainly on alternative communication methods such as optical and acoustic, with radio frequencies being an under-exploited method. This research presents real world results conducted in the Leeds and Liverpool Canal for the novel use of the 433 MHz radio frequency combined with a bowtie antenna in underwater communications in raw water, achieving distances of 7 m at 1.2 kbps and 5 m at 25 kbps.

REMODEL: Rethinking Deep CNN Models to Detect and Count on a NeuroSynaptic System.

In this work, we perform analysis of detection and counting of cars using a low-power IBM TrueNorth Neurosynaptic System. For our evaluation we looked at a publicly-available dataset that has overhead imagery of cars with context present in the image. The trained neural network for image analysis was deployed on the NS16e system using IBM's EEDN training framework. Through multiple experiments we identify the architectural bottlenecks present in TrueNorth system that does not let us deploy large neural network structures. Following these experiments we propose changes to CNN model to circumvent these architectural bottlenecks. The results of these evaluations have been compared with caffe-based implementations of standard neural networks that were deployed on a Titan-X GPU. Results showed that TrueNorth can detect cars from the dataset with 97.60% accuracy and can be used to accurately count the number of cars in the image with 69.04% accuracy. The car detection accuracy and car count (-/+ 2 error margin) accuracy are comparable to high-precision neural networks like AlexNet, GoogLeNet, and ResCeption, but show a manifold improvement in power consumption.