Geostatistical modelling of a coal seam for resource risk assessment

The evaluation of a coal seam for profitable extraction requires the estimation of its thickness and quality characteristics together with the spatial variability of these variables. In many cases the only data available for the estimation are from a limited number of exploration and feasibility drill holes. Spatial variability can be quantified by geostatistical modelling, which provides the basis for estimation (kriging). In cases where the spatial variability of the seam thickness and quality characteristics has a significant impact on how the coal is extracted and stored, geostatistical simulation may be preferable to geostatistical kriging methods. The aim of this paper is to present an improved approach to resource risk assessment by propagating the uncertainty in semi-variogram model parameters into the spatial variability of coal variables. We show that a more realistic assessment of risk is obtained when the uncertainty of semi-variogram model parameters is taken into account. The methodology is illustrated with a coal seam from North-western Spain. © 2012 Elsevier B.V.E. Pardo-Igúzquiza, P.A. Dowd, J.M. Baltuille, M. Chica-Olm

Multiple cut-off grade optimization by genetic algorithms and comparison with grid search method and dynamic programming

Optimization of cut-off grades is a fundamental issue for mineral deposits. Determination of optimum cut-off grades, instead of application of a static cut-off grade for the life of a mine, maximizes the net present value. The authors describe the general problem of cut-off grade optimization for multi-mineral deposits and outline the use of genetic algorithms, the grid search method, and dynamic programming for optimal cut-off grade schedules for deposits with up to three constituent minerals. The methods are compared by assessing the results of the implications involved in using them.E. Cetin and P.A. Dow

Comparison of inference methods for estimating semivariogram model parameters and their uncertainty: The case of small data sets

The semivariogram model is the fundamental component in all geostatistical applications and its inference is an issue of significant practical interest. The semivariogram model is defined by a mathematical function, the parameters of which are usually estimated from the experimental data. There are important application areas in which small data sets are the norm; rainfall estimation from rain gauge data and transmissivity estimation from pumping test data are two examples from, respectively, surface and subsurface hydrology. Thus a benchmark problem in geostatistics is deciding on the most appropriate method for the inference of the semivariogram model.The various methods for semivariogram inference can be classified as indirect methods, in which there is an intermediate step of calculating the experimental semivariogram, and direct approaches that obtain the model parameter values directly as the values that minimize some objective function.To avoid subjectivity in fitting models to experimental semivariograms, ordinary least squares (OLS), weighted least squares (WLS) and generalized least squares (GLS) are often used. Uncertainty evaluation in indirect methods is done using computationally intensive resampling procedures such as the bootstrap method.Direct methods include parametric methods, such as maximum likelihood (ML) and maximum likelihood cross-validation (MLCV), and non-parametric methods, such as minimization of cross-validation statistics (CV).The bases for comparing the previous methods are the sampling distribution of the various parameters and the "goodness" of the uncertainty evaluation in a sense that we define. The final questions to be answered are (1) which is the best method for estimating each of the semivariogram parameters? (2) Which is the best method for assessing the uncertainty of each of the parameters? (3) Which method best selects the functional form of the semivariogram from among a set of options? and (4) which is the best method that jointly addresses all the previous questions? © 2012 Elsevier Ltd.Eulogio Pardo-Igúzquiza, Peter A.Dow

A review of fractals in karst

Many features of a karst massif can either be modelled using fractal geometry or have a fractal distribution. For the exokarst, typical examples include the geometry of the landscape and the spatial location and size-distribution of karst depressions. Typical examples for the endokarst are the geometry of the three-dimensional network of karst conduits and the length distribution of caves. In addition, the hydrogeological parameters of the karst massif, such as hydraulic conductivity, and karst spring hydrographs may also exhibit fractal behaviour. In this work we review the karst features that exhibit fractal behaviour, we review the literature in which they are described, and we propose hypotheses and conjectures about the origin of such behaviour. From the review and analysis, we conclude that fractal behaviour is exhibited at all scales in karst systems.Eulogio Pardo-Igúzquiza, Peter A. Dowd, Juan J. Durán, and Pedro Robledo-Ardil

Light-intensity physical activity is associated with adiposity in adolescent females

peer-reviewedIntroduction: Sedentary behavior (SB) research has relied on accelerometer thresholds to distinguish between sitting/lying time (SLT) and light-intensity physical activity (LIPA). Such methods may misclassify SLT, standing time (StT), and LIPA. This study examines the association between directly measured SB, physical activity (PA), and adiposity in an adolescent female sample. Methods: Female adolescents (n = 195; mean age, 15.7 yr (SD, 0.9)) had body mass index (BMI) (median, 21.7 kg.m(-2) (interquartile range, 5.2 kg.m(-2))) and four-site sum of skinfolds (median, 62.0 mm; interquartile range, 37.1 mm) measured and wore an activPAL (TM) activity monitor for 7 d. SLT, StT, breaks in SLT, and bouts of SLT = 30 min were determined from activPAL outputs. A threshold of 2997 counts per 15 s determined moderate-to-vigorous PA. All remaining time was quantified as LIPA. Mixed linear regression models examined associations between PA variables, SB variables, and adiposity. Results: Participants spent a mean of 65.3% (SD, 7.1) of the waking day in SLT, 23.0% (SD, 5.3) in StT, 5.6% (SD, 1.5) in LIPA, and 6.1% (SD, 2.4) in moderate-to-vigorous PA. Significant effects for the percentage of LIPA (which excluded StT) with both BMI (beta = -4.38, P = 0.0006) and sum of skinfolds (beta = -4.05, P = 0.006) were identified. Significant effects for breaks in SLT with BMI (beta = -0.30, P = 0.04) were also observed. No additional significant associations were found between activity measures and adiposity. Conclusions: Increased LIPA (excluding StT) and breaks in SLT were negatively associated with adiposity in this sample, independent of age. Interventional work should examine whether reducing SLT through breaks and increasing LIPA may prevent increases in adiposity in adolescent females.ACCEPTEDpeer-reviewe

Decreasing seasonal cycle amplitude of methane in the northern high latitudes being driven by lower-latitude changes in emissions and transport

Atmospheric methane (CH4) concentrations are rising, which are expected to lead to a corresponding increase in the global seasonal cycle amplitude (SCA) – the difference between its seasonal maximum and minimum values. The reaction between CH4 and its main sink, OH, is dependent on the amount of CH4 and OH in the atmosphere. The concentration of OH varies seasonally, and due to the increasing burden of CH4 in the atmosphere, it is expected that the SCA of CH4 will increase due to the increased removal of CH4 through a reaction with OH in the atmosphere. Spatially varying changes in the SCA could indicate long-term persistent variations in the seasonal sources and sinks, but such SCA changes have not been investigated. Here we use surface flask measurements and a 3D chemical transport model (TOMCAT) to diagnose changes in the SCA of atmospheric CH4 between 1995–2020 and attribute the changes regionally to contributions from different sectors. We find that the observed SCA decreased by 4 ppb (7.6 %) in the northern high latitudes (NHLs; 60–90∘ N), while the SCA increased globally by 2.5 ppb (6.5 %) during this time period. TOMCAT reproduces the change in the SCA at observation sites across the globe. Therefore, we use it to attribute regions which are contributing to the changes in the NHL SCA, which shows an unexpected change in the SCA that differs from the rest of the world. We find that well-mixed background CH4, likely from emissions originating in, and transported from, more southerly latitudes has the largest impact on the decreasing SCA in the NHLs (56.5 % of total contribution to NHLs). In addition to the background CH4, recent emissions from Canada, the Middle East, and Europe contribute 16.9 %, 12.1 %, and 8.4 %, respectively, to the total change in the SCA in the NHLs. The remaining contributions are due to changes in emissions and transport from other regions. The three largest regional contributions are driven by increases in summer emissions from the Boreal Plains in Canada, decreases in winter emissions across Europe, and a combination of increases in summer emissions and decreases in winter emissions over the Arabian Peninsula and Caspian Sea in the Middle East. These results highlight that changes in the observed seasonal cycle can be an indicator of changing emission regimes in local and non-local regions, particularly in the NHL, where the change is counterintuitive.</p

Pyrosequencing of 16S rRNA genes in fecal samples reveals high diversity of hindgut microflora in horses and potential links to chronic laminitis

BACKGROUND: The nutrition and health of horses is closely tied to their gastrointestinal microflora. Gut bacteria break down plant structural carbohydrates and produce volatile fatty acids, which are a major source of energy for horses. Bacterial communities are also essential for maintaining gut homeostasis and have been hypothesized to contribute to various diseases including laminitis. We performed pyrosequencing of 16S rRNA bacterial genes isolated from fecal material to characterize hindgut bacterial communities in healthy horses and those with chronic laminitis. RESULTS: Fecal samples were collected from 10 normal horses and 8 horses with chronic laminitis. Genomic DNA was extracted and the V4-V5 segment of the 16S rRNA gene was PCR amplified and sequenced on the 454 platform generating a mean of 2,425 reads per sample after quality trimming. The bacterial communities were dominated by Firmicutes (69.21% control, 56.72% laminitis) and Verrucomicrobia (18.13% control, 27.63% laminitis), followed by Bacteroidetes, Proteobacteria, and Spirochaetes. We observed more OTUs per individual in the laminitis group than the control group (419.6 and 355.2, respectively, P = 0.019) along with a difference in the abundance of two unassigned Clostridiales genera (P = 0.03 and P = 0.01). The most abundant bacteria were Streptococcus spp., Clostridium spp., and Treponema spp.; along with unassigned genera from Subdivision 5 of Verrucomicrobia, Ruminococcaceae, and Clostridiaceae, which together constituted ~ 80% of all OTUs. There was a high level of individual variation across all taxonomic ranks. CONCLUSIONS: Our exploration of the equine fecal microflora revealed higher bacterial diversity in horses with chronic laminitis and identification of two Clostridiales genera that differed in abundance from control horses. There was large individual variation in bacterial communities that was not explained in our study. The core hindgut microflora was dominated by Streptococcus spp., several cellulytic genera, and a large proportion of uncharacterized OTUs that warrant further investigation regarding their function. Our data provide a foundation for future investigations of hindgut bacterial factors that may influence the development and progression of chronic laminitis

Spatial Patterns of Aflatoxin Levels in Relation to Ear-Feeding Insect Damage in Pre-Harvest Corn

Key impediments to increased corn yield and quality in the southeastern US coastal plain region are damage by ear-feeding insects and aflatoxin contamination caused by infection of Aspergillus flavus. Key ear-feeding insects are corn earworm, Helicoverpa zea, fall armyworm, Spodoptera frugiperda, maize weevil, Sitophilus zeamais, and brown stink bug, Euschistus servus. In 2006 and 2007, aflatoxin contamination and insect damage were sampled before harvest in three 0.4-hectare corn fields using a grid sampling method. The feeding damage by each of ear/kernel-feeding insects (i.e., corn earworm/fall armyworm damage on the silk/cob, and discoloration of corn kernels by stink bugs), and maize weevil population were assessed at each grid point with five ears. The spatial distribution pattern of aflatoxin contamination was also assessed using the corn samples collected at each sampling point. Aflatoxin level was correlated to the number of maize weevils and stink bug-discolored kernels, but not closely correlated to either husk coverage or corn earworm damage. Contour maps of the maize weevil populations, stink bug-damaged kernels, and aflatoxin levels exhibited an aggregated distribution pattern with a strong edge effect on all three parameters. The separation of silk- and cob-feeding insects from kernel-feeding insects, as well as chewing (i.e., the corn earworm and maize weevil) and piercing-sucking insects (i.e., the stink bugs) and their damage in relation to aflatoxin accumulation is economically important. Both theoretic and applied ramifications of this study were discussed by proposing a hypothesis on the underlying mechanisms of the aggregated distribution patterns and strong edge effect of insect damage and aflatoxin contamination, and by discussing possible management tactics for aflatoxin reduction by proper management of kernel-feeding insects. Future directions on basic and applied research related to aflatoxin contamination are also discussed

Secondary Structure and Glycosylation of Mucus Glycoproteins by Raman Spectroscopies

The major structural components of protective mucus hydrogels on mucosal surfaces are the secreted polymeric gel-forming mucins. The very high molecular weight and extensive O-glycosylation of gel-forming mucins, which are key to their viscoelastic properties, create problems when studying mucins using conventional biochemical/structural techniques. Thus, key structural information, such as the secondary structure of the various mucin subdomains, and glycosylation patterns along individual molecules, remains to be elucidated. Here, we utilized Raman spectroscopy, Raman optical activity (ROA), circular dichroism (CD), and tip-enhanced Raman spectroscopy (TERS) to study the structure of the secreted polymeric gel-forming mucin MUC5B. ROA indicated that the protein backbone of MUC5B is dominated by unordered conformation, which was found to originate from the heavily glycosylated central mucin domain by isolation of MUC5B O-glycan-rich regions. In sharp contrast, recombinant proteins of the N-terminal region of MUC5B (D1-D2-D′-D3 domains, NT5B), C-terminal region of MUC5B (D4-B-C-CK domains, CT5B) and the Cys-domain (within the central mucin domain of MUC5B) were found to be dominated by the β-sheet. Using these findings, we employed TERS, which combines the chemical specificity of Raman spectroscopy with the spatial resolution of atomic force microscopy to study the secondary structure along 90 nm of an individual MUC5B molecule. Interestingly, the molecule was found to contain a large amount of α-helix/unordered structures and many signatures of glycosylation, pointing to a highly O-glycosylated region on the mucin

Cytomegalovirus antibodies in dried blood spots: a minimally invasive method for assessing stress, immune function, and aging

<p>Abstract</p> <p>Background</p> <p>Cytomegalovirus (CMV) is a prevalent herpesvirus with links to both stress and aging. This paper describes and validates a minimally invasive method for assessing antibodies against CMV in finger stick whole blood spot samples for use as an indirect marker of an aspect of cell-mediated immunity.</p> <p>Results</p> <p>Analysis of CMV in dried blood spot samples (DBS) was based on modifications of a commercially available protocol for quantifying CMV antibodies in serum or plasma. The method was evaluated through analysis of precision, reliability, linearity, and correlation between matched serum and DBS samples collected from 75 volunteers. Correlation between DBS and plasma values was linear and high (Pearson correlation <it>R </it>= .96), and precision, reliability, and linearity of the DBS assay were within acceptable ranges.</p> <p>Conclusions</p> <p>The validity of a DBS assay for CMV antibodies will enable its inclusion in population-based surveys and other studies collecting DBS samples in non-clinical settings, increasing scientific understanding of the interaction of social and biological stress and immune function.</p