A note on the stop-loss preservin property of Wang's premium principle.

A desirable property for a premium principle is that it preserves stop-loss order. In this paper, we present a simple proof for the stop-loss preserving property of Wang's class of premium principles, in the case that the distribution functions involved have only finitely many crossing points.Principles; Distribution; Functions;

Multiscale entropy-based analyses of soil transect data

A deeper understanding of the spatial variability of soil properties and the relationships between them is needed to scale up measured soil properties and to model soil processes. The object of this study was to describe the spatial scaling properties of a set of soil physical properties measured on a common 1024-m transect across arable fields at Silsoe in Bedfordshire, east-central England. Properties studied were volumetric water content ({theta}), total porosity ({pi}), pH, and N2O flux. We applied entropy as a means of quantifying the scaling behavior of each transect. Finally, we examined the spatial intrascaling behavior of the correlations between {theta} and the other soil variables. Relative entropies and increments in relative entropy calculated for {theta}, {pi}, and pH showed maximum structure at the 128-m scale, while N2O flux presented a more complex scale dependency at large and small scales. The intrascale-dependent correlation between {theta} and {pi} was negative at small scales up to 8 m. The rest of the intrascale-dependent correlation functions between {theta} with N2O fluxes and pH were in agreement with previous studies. These techniques allow research on scale effects localized in scale and provide the information that is complementary to the information about scale dependencies found across a range of scale

In vitro production of bovine embryos derived from individual donors in the Corral® dish

Background: Since the identity of the embryo is of outmost importance during commercial in vitro embryo production, bovine oocytes and embryos have to be cultured strictly per donor. Due to the rather low yield of oocytes collected after ovum pick-up (OPU) per individual cow, oocyte maturation and embryo culture take place in small groups, which is often associated with inferior embryo development. The objective of this study was to improve embryonic development in small donor groups by using the Corral (R) dish. This commercial dish is designed for human embryo production. It contains two central wells that are divided into quadrants by a semi-permeable wall. In human embryo culture, one embryo is placed per quadrant, allowing individual follow-up while embryos are exposed to a common medium. In our study, small groups of oocytes and subsequently embryos of different bovine donors were placed in the Corral (R) dish, each donor group in a separate quadrant. Results: In two experiments, the Corral (R) dish was evaluated during in vitro maturation (IVM) and/or in vitro culture (IVC) by grouping oocytes and embryos of individual bovine donors per quadrant. At day 7, a significantly higher blastocyst rate was noted in the Corral (R) dish used during IVM and IVC than when only used during IVM (12.9% +/- 2.10 versus 22.8% +/- 2.67) (P < 0.05). However, no significant differences in blastocyst yield were observed anymore between treatment groups at day 8 post insemination. Conclusions: In the present study, the Corral (R) dish was used for in vitro embryo production (IVP) in cattle; allowing to allocate oocytes and/or embryos per donor. As fresh embryo transfers on day 7 have higher pregnancy outcomes, the Corral (R) dish offers an added value for commercial OPU/IVP, since a higher blastocyst development at day 7 is obtained when the Corral (R) dish is used during IVM and IVC

δ′\delta'-Function Perturbations and Neumann Boundary-Conditions by Path Integration

$\delta'$-function perturbations and Neumann boundary conditions are incorporated into the path integral formalism. The starting point is the consideration of the path integral representation for the one dimensional Dirac particle together with a relativistic point interaction. The non-relativistic limit yields either a usual $\delta$-function or a $\delta'$-function perturbation; making their strengths infinitely repulsive one obtains Dirichlet, respectively Neumann boundary conditions in the path integral.Comment: nine pages, plain Tex with AmSTeX macro-packag

Multi-task learning for subthalamic nucleus identification in deep brain stimulation

Deep brain stimulation (DBS) of Subthalamic nucleus (STN) is the most successful treatment for advanced Parkinson’s disease. Localization of the STN through Microelectrode recordings (MER) is a key step during the surgery. However, it is a complex task even for a skilled neurosurgeon. Different researchers have developed methodologies for processing and classification of MER signals to locate the STN. Previous works employ the classical paradigm of supervised classification, assuming independence between patients. The aim of this paper is to introduce a patient-dependent learning scenario, where the predictive ability for STN identification at the level of a particular patient, can be used to improve the accuracy for STN identification in other patients. Our inspiration is the multi-task learning framework, that has been receiving increasing interest within the machine learning community in the last few years. To this end, we employ the multi-task Gaussian processes framework that exhibits state of the art performance in multi-task learning problems. In our context, we assume that each patient undergoing DBS is a different task, and we refer to the method as multi-patient learning. We show that the multi-patient learning framework improves the accuracy in the identification of STN in a range from 4.1 to 7.7%, compared to the usual patient-independent setup, for two different datasets. Given that MER are non stationary and noisy signals. Traditional approaches in machine learning fail to recognize accurately the STN during DBS. By contrast in our proposed method, we properly exploit correlations between patients with similar diseases, obtaining an additional information. This information allows to improve the accuracy not only for locating STN for DBS but also for other biomedical signal classification problems

Geochemical, sedimentological and microbial diversity in two thermokarst lakes of far Eastern Siberia

Thermokarst lakes are important conduits for organic carbon sequestration, soil organic matter (soil-OM) decomposition and release of atmospheric greenhouse gases in the Arctic. They can be classified as either floating-ice lakes, which sustain a zone of unfrozen sediment (talik) at the lakebed year-round, or as bedfast-ice lakes, which freeze all the way to the lakebed in winter. Another key characteristic of thermokarst lakes are their eroding shorelines, depending on the surrounding landscape, they can play a major role in supplying the lakebeds with sediment and OM. These differences in winter ice regime and eroding shorelines are key factors which determine the quantity and quality of OM in thermokarst lake sediments. We used an array of physical, geochemical, and microbiological tools to identify the differences in the environmental conditions, sedimentary characteristics, carbon stocks and microbial community compositions in the sediments of a bedfast-ice and a floating-ice lake in Far East Siberia with different eroding shorelines. Our data show strong differences across most of the measured parameters between the two lakes. For example, the floating-ice lake contains considerably lower amounts of sediment organic matter and dissolved organic carbon, both of which also appear to be more degraded in comparison to the bedfast-ice lake, based on their stable carbon isotope composition (δ13C). We also document clear differences in the microbial community composition, for both archaea and bacteria. We identified the lake water depth (bedfast-ice vs. floating-ice) and shoreline erosion to be the two most likely main drivers of the sedimentary, microbial and biogeochemical diversity in thermokarst lakes. With ongoing climate warming, it is likely that an increasing number of lakes will shift from a bedfast- to a floating-ice state, and that increasing levels of shoreline erosion will supply the lakes with sediments. Yet, still little is known about the physical, biogeochemical and microbial differences in the sediments of these lake types and how different eroding shorelines impact these lake system

Spatial variation and socio-economic determinants of Plasmodium falciparum infection in northeastern Tanzania

<p>Abstract</p> <p>Background</p> <p>Malaria due to <it>Plasmodium falciparum </it>is the leading cause of morbidity and mortality in Tanzania. According to health statistics, malaria accounts for about 30% and 15% of hospital admissions and deaths, respectively. The risk of <it>P. falciparum </it>infection varies across the country. This study describes the spatial variation and socio-economic determinants of <it>P. falciparum </it>infection in northeastern Tanzania.</p> <p>Methods</p> <p>The study was conducted in 14 villages located in highland, lowland and urban areas of Korogwe district. Four cross-sectional malaria surveys involving individuals aged 0-19 years were conducted during short (Nov-Dec) and long (May-Jun) rainy seasons from November 2005 to June 2007. Household socio-economic status (SES) data were collected between Jan-April 2006 and household's geographical positions were collected using hand-held geographical positioning system (GPS) unit. The effects of risk factors were determined using generalized estimating equation and spatial risk of <it>P. falciparum </it>infection was modelled using a kernel (non-parametric) method.</p> <p>Results</p> <p>There was a significant spatial variation of <it>P. falciparum </it>infection, and urban areas were at lower risk. Adjusting for covariates, high risk of <it>P. falciparum </it>infection was identified in rural areas of lowland and highland. Bed net coverage levels were independently associated with reduced risk of <it>P. falciparum </it>by 19.1% (95%CI: 8.9-28.2, p < 0.001) and by 39.3% (95%CI: 28.9-48.2, p < 0.001) in households with low and high coverage, respectively, compared to those without bed nets. Households with moderate and lower SES had risk of infection higher than 60% compared to those with higher SES; while inhabitants of houses built of mud walls were at 15.5% (95%CI: 0.1 - 33.3, p < 0.048) higher risk compared to those living in houses built by bricks. Individuals in houses with thatched roof had an excess risk of 17.3% (95%CI: 4.1 - 32.2, p < 0.009) compared to those living in houses roofed with iron sheet.</p> <p>Conclusions</p> <p>There was high spatial variation of risk of <it>P. falciparum </it>infection and urban area was at the lowest risk. High bed net coverage, better SES and good housing were among the important risk factors associated with low risk of <it>P. falciparum </it>infection.</p

Developing a new Bayesian Risk Index for risk evaluation of soil contamination

Supplementary data associated with this article can befound in the online version, at http://dx.doi.org/10.1016/j.scitotenv.2017.06.068. These data include the Google map of the most important areas described in this article.Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly derived from the metallurgical industry, contribute to soil contamination by PTEs.Carlos Sierra obtained a grant from the “Severo Ochoa (BP10-112)” Programme(Ficyt, Asturias,Spain).info:eu-repo/semantics/publishedVersio