Development of a National Core Dataset for Preoperative Assessment

Objective:To define a core dataset for preoperative assessment to leverage uniform data collection in this domain. This uniformity is a prerequisite for data exchange between care providers and semantic interoperability between health record systems. Methods: To design this core dataset a combination of literature review and expert consensus meetings were used. In the first meeting a working definition for “core dataset” was specified. Subgroups were formed to address major headings of the core dataset. In the following eight meetings data items for each subheading were discussed. The items in the resulting draft of the dataset were compared to those retrieved from an earlier literature review study. In the last two expert meetings modifications of the dataset were performed based on the result of this literature study. Results: Based on expert consensus a draft dataset including 82 data items was designed. Seventy-six percent of data items in the draft dataset were covered by the literature study. Nine data items were modified in the draft and 14 data items were added to the dataset based on input from the literature review. The final dataset of 93 data items covers patient history, physical examination, supplementary examination and consultation, and final judgment. Conclusions: This preoperative-assessment dataset was defined based on expert con - sensus and literature review. Both methods proved to be valuable and complementary. This dataset opens the door for creating standardized approaches in data collection in the preoperative assessment field which will facilitate interoperability between different electronic health records and different users

Some kinds of the controllable problems for fuzzy control dynamic systems

In this work, we have discussed the fuzzy solutions for fuzzy controllable problem, fuzzy feedback problem, and fuzzy global controllable (GC) problems. We use the method of successive approximations under the generalized Lipschitz condition for the local existence and furthermore, we have described the contraction principle under suitable conditions for global existence and uniqueness of fuzzy solutions. We have too the GC results for fuzzy systems. Some examples and computer simulation illustrating our approach are also given for these controllable problems

Narrow genetic basis for the Australian dingo confirmed through analysis of paternal ancestry

The dingo (Canis lupus dingo) is an iconic animal in the native culture of Australia, but archaeological and molecular records indicate a relatively recent history on the continent. Studies of mitochondrial DNA (mtDNA) imply that the current dingo population was founded by a small population of already tamed dogs from Southeast Asia. However, the maternal genetic data might give a unilateral picture, and the gene pool has yet to be screened for paternal ancestry. We sequenced 14,437 bp of the Y-chromosome (Y-chr) from two dingoes and one New Guinea Singing Dog (NGSD). This positioned dingo and NGSD within the domestic dog Y-chr phylogeny, and produced one haplotype not detected before. With this data, we characterized 47 male dingoes in 30 Y-chr single-nucleotide polymorphism sites using protease-mediated allele-specific extension technology. Only two haplotypes, H3 and H60, were found among the dingoes, at frequencies of 68.1 and 31.9 %, respectively, compared to 27 haplotypes previously established in the domestic dog. While H3 is common among Southeast Asian dogs, H60 was specifically found in dingoes and the NGSD, but was related to Southeast Asian dog Y-chr haplotypes. H3 and H60 were observed exclusively in the western and eastern parts of Australia, respectively, but had a common range in Southeast. Thus, the Y-chr diversity was very low, similar to previous observations for d-loop mtDNA. Overall genetic evidence suggests a very restricted introduction of the first dingoes into Australia, possibly from New Guinea. This study further confirms the dingo as an isolated feral dog. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10709-012-9658-5) contains supplementary material, which is available to authorized users

A novel method for the analysis of particle coating behaviour via contact spreading in a tumbling drum: Effect of coating liquid viscosity

Spray coating is a common method of distributing liquids over powders, especially in the pharmaceutical, detergent and food industries. During this process, liquid drops are deposited on the surface of particles. Liquid is then transferred between particles via particle collisions, in a process called liquid contact spreading. This contact spreading process facilitates inter-particle coating, in which wetting, de-wetting, mixing and drying are occurring simultaneously. This work presents the first experimental study of the mechanism of liquid contact spreading. In this work, a novel experimental method has been developed to investigate the mechanism of contact spreading, incorporating a newly developed image analysis technique, based on colourimetric measurements, to quantitatively determine coating behaviour via contact spreading. Here, experiments designed to isolate the contact spreading coating mechanism were performed in a tumbling drum using a model material system; alumina particles and dyed polyethylene glycol solutions of varying viscosities. The coating uniformity was quantified by the variation in inter-particle coating; the coefficient of variation (CoV). For all systems, the uniformity of the coating increased with time until the CoV decreased to an asymptotic value. The rate of the decrease in the CoV was successfully fitted using an exponential decay function. The viscosity of the coating solution had a significant effect on the rate of liquid transfer; the lower the viscosity the faster the contact spreading process. This effect is attributed to differences in the formation and stability of liquid bridges between the particles, influencing the extent of liquid transfer. The results also show that in most cases examined here, viscous forces play a main role in the contact spreading process, and the contribution of capillary forces are minimal. This understanding could assist the design and scale up for the wet coating processes

A promising exponentially-fitted two-derivative Runge–Kutta–Nyström method for solving y′′=f(x,y): Application to Verhulst logistic growth model

Explicit exponentially-fitted two-derivative Runge–Kutta–Nyström method with single -function and multiple third derivatives is proposed for solving special type of second-order ordinary differential equations with exponential solutions. B-series and rooted tree theory for the proposed method are developed for the derivation of order conditions. Then, we build frequency-dependent coefficients for the proposed method by integrating the second-order initial value problem exactly with solution in the linear composition of set functions and with . An exponentially-fitted two-derivative Runge–Kutta–Nyström method with three stages fifth order is derived. Linear stability and stability region of the proposed method are analyzed. The numerical tests show that the proposed method is more effective than other existing methods with similar algebraic order in the integration of special type of second-order ordinary differential equations with exponential solutions. Also, the proposed method is used to solve a famous application problem, Verhulst logistic growth model and the result shows the proposed method still works effectively for solving this model

Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting

Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF) approach, trained by the polynomial model tree (POLYMOT) learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting

Introducing standard protocol for enrichment of Artemia urmiana nauplii with Canola oil

This research was performed to introduce a standard protocol for enrichment of Artemia urmian with Canola oil. Artemia urmiana nauplii were enriched at three densities (50000, 100000 and 200000 nauplii L^-1) and three concentrations of Canola oil (0.1, 0.2 and 0.3 g L^-1). Their effects were evaluated on survival, total length and profile of fatty acids at 6, 9, 12, 15 and 18 hours after the onset of enrichment. Cysts of A.urmiana were hatched according to the standard method. A.urmiana nauplii were stocked at above densities in 7 L cylindrical containers. Canola oil emulsion was added at concentrations of 0.1, 0.2 and 0.3 g L^-1 at the beginning and 12 hours after the onset of enrichment. The results of analysis showed that enrichment of A.urmiana with 0.3 g L^ -1 Canola oil at 100000 nauplii L^-1 for 18 hours was considered as the best treatment. Artemia nauplii enriched in this treatment had significantly higher levels of (n-3) PUFA and survival and minimum total length comparing to other treatments. The treatment had significantly higher levels of (n-6) PUFA than all treatments except treatment with a density of 50,000 nauplii L^-1 with 0.1 g L^-1 Canola oil for 18 hours

A subcutaneous adipose tissue-liver signalling axis controls hepatic gluconeogenesis.

The search for effective treatments for obesity and its comorbidities is of prime importance. We previously identified IKK-ε and TBK1 as promising therapeutic targets for the treatment of obesity and associated insulin resistance. Here we show that acute inhibition of IKK-ε and TBK1 with amlexanox treatment increases cAMP levels in subcutaneous adipose depots of obese mice, promoting the synthesis and secretion of the cytokine IL-6 from adipocytes and preadipocytes, but not from macrophages. IL-6, in turn, stimulates the phosphorylation of hepatic Stat3 to suppress expression of genes involved in gluconeogenesis, in the process improving glucose handling in obese mice. Preliminary data in a small cohort of obese patients show a similar association. These data support an important role for a subcutaneous adipose tissue-liver axis in mediating the acute metabolic benefits of amlexanox on glucose metabolism, and point to a new therapeutic pathway for type 2 diabetes

Sampling constrained probability distributions using Spherical Augmentation

Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained domains could be quite challenging for commonly used sampling algorithms. In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space. By moving freely on the surface of this sphere, sampling algorithms handle constraints implicitly and generate proposals that remain within boundaries when mapped back to the original space. Our proposed method, called {Spherical Augmentation}, provides a mathematically natural and computationally efficient framework for sampling from constrained probability distributions. We show the advantages of our method over state-of-the-art sampling algorithms, such as exact Hamiltonian Monte Carlo, using several examples including truncated Gaussian distributions, Bayesian Lasso, Bayesian bridge regression, reconstruction of quantized stationary Gaussian process, and LDA for topic modeling.Comment: 41 pages, 13 figure