Characterization of hydrologic parameters and processes in shrink-swell clay soils

Vertisols and other vertic-intergrade soils are found all over the globe, including many agricultural and urban areas. These soils are characterized by their cyclical shrinking and swelling behaviors, where bulk density and porosity distribution both vary as functions of time and/or soil moisture. In turn, alterations in physical soil parameters become manifest as crack networks, which open during the shrinkage phase and re-seal as the soil swells. As a consequence, when present these soils can significantly impact agriculture and infrastructure, and can act as a dominant control on the local hydrology. Therefore, understanding how water moves through and interacts with these soils is of utmost importance to ensure their proper utilization and management. This study had three objectives: 1) to identify the role of initial soil moisture on infiltration in a rigid (non-swelling) soil; 2) to quantify variation in the soil's hydraulic properties, using single-ring infiltration measurements taken in a vertic soil over a 1.5 year period; and 3) to build on these concepts to examine data from a set of instrumented field plots in a vertic soil located in the Secano Interior region of Chile. Significant findings of the study include 1) the development of a new formulation to describe a soil's wetting front potential (a measure of capillary pull) in terms of initial degree of saturation; 2) the creation of a simple correction to approximate the decrease of wetting front potential in wetter soils, thereby improving the accuracy of the traditional Green and Ampt sorptivity model in such conditions; 3) an equation to accurately estimate hydraulic parameters from short-duration infiltration tests, when steady-state conditions have not been realized; 4) a new theoretical model to estimate crack porosity as a function of soil moisture, which was developed based on the soil shrinkage curve; and 5) modification of the traditional two-term Philip infiltration model for use in vertic soils. The field study also showed that crack networks cause highly complex and non-linear wetting of the soil profile, with water simultaneously infiltrating from the soil surface and from the soil-crack interfaces; that cracks can seal at the surface while remaining open and hydraulically active below the surface, which indicates that surface-based monitoring alone may not be sufficient to predict water movement and soil response in vertic soils; and that the transition between infiltration and runoff may be strongly correlated with the cumulative amount of net precipitation that had reached the soil surface, so that cumulative amount of precipitation has the potential to be a simple yet accurate metric to predict runoff in vertic soils. These findings offer improved understanding of soil-water interactions in vertic soils, and reveal that very simple concepts underlie seemingly complex systems. As a result, the concepts and formulations developed in this study should allow for straightforward integration into other studies and models

Current Understanding of the Impact of Childhood Obesity on the Foot and Lower Limb

Childhood obesity has emerged in recent years as a major public health problem. As this continues to concern across local, national and international populations, and as our understanding of obesity advances, access to multi-disciplinary care and understanding of the complications is warranted. Recent findings have suggested that the musculoskeletal system is one of the multiple body systems compromised by obesity and that aberrant biomechanical function may be a precursor to the onset of musculoskeletal symptoms. This review will consider childhood obesity and its impact on the paediatric foot and lower limb through examination of literature on foot structure and biomechanics of gait. An overview of evidence-based management is out with the context of this review, however some recommendations for clinical practice will be proposed

Talking SMAAC: A New Tool to Measure Soil Respiration and Microbial Activity

Soil respiration measurements are widely used to quantify carbon fluxes and ascertain soil biological properties related to soil microbial ecology and soil health, yet current methods to measure soil respiration either require expensive equipment or use discrete spot measurements that may have limited accuracy, and neglect underlying response dynamics. To overcome these drawbacks, we developed an inexpensive setup for measuring CO2 called the soil microbial activity assessment contraption (SMAAC). We then compared the SMAAC with a commercial infrared gas analyzer (IRGA) unit by analyzing a soil that had been subjected to two different management practices: grass buffer vs. row crop cultivation with tillage. These comparisons were done using three configurations that detected (1) in situ soil respiration, (2) CO2 burst tests, and (3) substrate induced respiration (SIR), a measure of active microbial biomass. The SMAAC provided consistent readings with the commercial IRGA unit for all three configurations tested, showing that the SMAAC can perform well as an inexpensive yet accurate tool for measuring soil respiration and microbial activity

Distribution of Metals in the Termite Tumulitermes tumuli (Froggatt): Two Types of Malpighian Tubule Concretion Host Zn and Ca Mutually Exclusively

The aim of this study was to determine specific distribution of metals in the termite Tumulitermes tumuli (Froggatt) and identify specific organs within the termite that host elevated metals and therefore play an important role in the regulation and transfer of these back into the environment. Like other insects, termites bio-accumulate essential metals to reinforce cuticular structures and utilize storage detoxification for other metals including Ca, P, Mg and K. Previously, Mn and Zn have been found concentrated in mandible tips and are associated with increased hardness whereas Ca, P, Mg and K are accumulated in Malpighian tubules. Using high resolution Particle Induced X-Ray Emission (PIXE) mapping of whole termites and Scanning Electron Microscope (SEM) Energy Dispersive X-ray (EDX) spot analysis, localised accumulations of metals in the termite T. tumuli were identified. Tumulitermes tumuli was found to have proportionally high Mn concentrations in mandible tips. Malpighian tubules had significant enrichment of Zn (1.6%), Mg (4.9%), P (6.8%), Ca (2.7%) and K (2.4%). Synchrotron scanning X-ray Fluorescence Microprobe (XFM) mapping demonstrated two different concretion types defined by the mutually exclusive presence of Ca and Zn. In-situ SEM EDX realisation of these concretions is problematic due to the excitation volume caused by operating conditions required to detect minor amounts of Zn in the presence of significant amounts of Na. For this reason, previous researchers have not demonstrated this surprising finding

Mapping spacetimes with LISA: inspiral of a test-body in a `quasi-Kerr' field

The future LISA detector will constitute the prime instrument for high-precision gravitational wave observations.LISA is expected to provide information for the properties of spacetime in the vicinity of massive black holes which reside in galactic nuclei.Such black holes can capture stellar-mass compact objects, which afterwards slowly inspiral,radiating gravitational waves.The body's orbital motion and the associated waveform carry information about the spacetime metric of the massive black hole,and it is possible to extract this information and experimentally identify (or not!) a Kerr black hole.In this paper we lay the foundations for a practical `spacetime-mapping' framework. Our work is based on the assumption that the massive body is not necessarily a Kerr black hole, and that the vacuum exterior spacetime is stationary axisymmetric,described by a metric which deviates slightly from the Kerr metric. We first provide a simple recipe for building such a `quasi-Kerr' metric by adding to the Kerr metric the deviation in the value of the quadrupole moment. We then study geodesic motion in this metric,focusing on equatorial orbits. We proceed by computing `kludge' waveforms which we compare with their Kerr counterparts. We find that a modest deviation from the Kerr metric is sufficient for producing a significant mismatch between the waveforms, provided we fix the orbital parameters. This result suggests that an attempt to use Kerr waveform templates for studying EMRIs around a non-Kerr object might result in serious loss of signal-to-noise ratio and total number of detected events. The waveform comparisons also unveil a `confusion' problem, that is the possibility of matching a true non-Kerr waveform with a Kerr template of different orbital parameters.Comment: 19 pages, 6 figure

Severity Index for Suspected Arbovirus (SISA) : machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

Funding: This study was supported, in part, by the Department of Defense Global Emerging Infection Surveillance (https://health.mil/Military-Health-Topics/Combat-Support/Armed-Forces-Health-Surveillance-Branch/Global-Emerging-Infections-Surveillance-and-Response) grant (P0220_13_OT) and the Department of Medicine of SUNY Upstate Medical University (http://www.upstate.edu/medicine/). D.F., M.H. and P.H. were supported by the Ben Kean Fellowship from the American Society for Tropical Medicine and Hygeine (https://www.astmh.org/awards-fellowships-medals/benjamin-h-keen-travel-fellowship-in-tropical-medi). S.J.R and A.M.S-I were supported by NSF DEB EEID 1518681, NSF DEB RAPID 1641145 (https://www.nsf.gov/), A.M.S-I was additionally supported by the Prometeo program of the National Secretary of Higher Education, Science, Technology, and Innovation of Ecuador (http://prometeo.educacionsuperior.gob.ec/).Background: Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. Methodology/Principal findings: Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. Conclusions/Significance: Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection.Publisher PDFPeer reviewe