Outcomes Associated With Microalbuminuria: Effect Modificatin By Chronic Kidney Disease.

OBJECTIVES: To compare association of microalbuminuria with outcomes in patients with different comorbidities. BACKGROUND: The risk of adverse outcomes associated with proteinuria has been found to be linearly decreasing with even low-normal levels of microalbuminuria. It is unclear if comorbid conditions change these associations. METHODS: We examined the association of urine microalbumin-creatinine ratio (UACR) with mortality and the slopes of estimated glomerular filtration rate (eGFR) in a nationally representative cohort of 298,875 US veterans. Associations of UACR with all-cause mortality overall and in subgroups of patients with and without diabetes, hypertension, cardiovascular disease, congestive heart failure and advanced CKD were examined in Cox models, and with the slopes of eGFR in linear and logistic regression models. RESULTS: Very low levels of UACR were linearly associated with decreased mortality and less progression of CKD overall: adjusted mortality hazard ratio and estimated glomerular filtration rate slope (95%CI) associated with UACR =200, compared to <5 mcg/mg were 1.53 (1.38-1.69, p<0.001) and -1.59 (-1.83, -1.35, p<0.001). Similar linearity was present in all examined subgroups, except in patients with CKD in whom a U-shaped association was present and in whom a UACR of 10-19 was associated with the best outcomes. CONCLUSIONS: The association of UACR with mortality and with progressive CKD is modified in patients with CKD, who experience higher mortality and worse progression of CKD with the lowest levels of UACR. Proteinuria-lowering interventions in patients with advanced CKD should be implemented cautiously, considering the potential for adverse outcomes

TSK Inference with Sparse Rule Bases

The Mamdani and TSK fuzzy models are fuzzy inference engines which have been most widely applied in real-world problems. Compared to the Mamdani approach, the TSK approach is more convenient when the crisp outputs are required. Common to both approaches, when a given observation does not overlap with any rule antecedent in the rule base (which usually termed as a sparse rule base), no rule can be fired, and thus no result can be generated. Fuzzy rule interpolation was proposed to address such issue. Although a number of important fuzzy rule interpolation approaches have been proposed in the literature, all of them were developed for Mamdani inference approach, which leads to the fuzzy outputs. This paper extends the traditional TSK fuzzy inference approach to allow inferences on sparse TSK fuzzy rule bases with crisp outputs directly generated. This extension firstly calculates the similarity degrees between a given observation and every individual rule in the rule base, such that the similarity degrees between the observation and all rule antecedents are greater than 0 even when they do not overlap. Then the TSK fuzzy model is extended using the generated matching degrees to derive crisp inference results. The experimentation shows the promising of the approach in enhancing the TSK inference engine when the knowledge represented in the rule base is not complete

Slicing Strategies for the Generalised Type-2 Mamdani Fuzzy Inferencing System

The final publication is available at Springer via http://dx.doi.org/[insert DOI]".As a three-dimensional object, there are a number of ways of slicing a generalised type-2 fuzzy set. In the context of the Mamdani Fuzzy Inferencing System, this paper concerns three accepted slicing strategies, the vertical slice, the wavy slice, and the horizontal slice or alpha -plane. Two ways of de ning the generalised type-2 fuzzy set, vertical slices and wavy slices, are presented. Fuzzi cation and inferencing is presented in terms of vertical slices. After that, the application of all three slicing strategies to defuzzi cation is described, and their strengths and weaknesses assessed

Ultrasonic Flaw Classification — An Expert System Approach

An expert system, FLEX, for classifying isolated flaws as either crack-like or volumetric has been under development at the Center for NDE, Iowa State University. Previously, we have described the overall design of the system [1], which is composed of two cooperating systems FEAP and FLAP. The feature processing (FEAP) system is designed to extract fundamental features in the ultrasonic signals that are indicative of cracks or volumetric flaws. The flaw processing (FLAP) system then uses the existence (or non-existence) of these features to classify the flaw. FLAP is structured as a classical rule-based expert system and has also been described previously [2]. Here, we will present the major elements of FEAP and the design philosophy that has gone into its construction. A more detailed account of FEAP is given in the thesis of Christensen [3].</p

Indigenous Grasses for Rehabilitating Degraded African Drylands

Drylands provide an important livelihood stream to its inhabitants across the globe through a range of products and ecosystem services. However, these fragile ecosystems are threatened and believed to experience various degrees of land degradation. Estimates of the landmass affected by land degradation in the global drylands range from 10% to 20%, a percentage that is increasing at an annual global rate of 12 million ha of soil lost from desertification and drought. African drylands are especially highly susceptible to severe degradation because of their poor soil structure aggravated by scarce vegetation cover. Causes of degradation in these environments are both natural and anthropogenic in nature. Change in vegetation cover, decline in soil fertility, biodiversity loss and soil erosion demonstrate degradation in African drylands. Grass reseeding using indigenous species is one of the promising sustainable land management strategies to combat degradation in the drylands. Reseeding programmes are aimed at improving vegetation cover and biomass, and they conserve the soil to an extent not possible by grazing and land management alone. Indigenous drought-tolerant grasses notably African foxtail grass (Cenchrus ciliaris), bush rye grass (Enteropogon macrostachyus) and Maasai lovegrass (Eragrostis superba) have produced promising rehabilitation outcomes. Previous studies in African drylands have demonstrated the potential of such indigenous forage grasses in improving both vegetation cover (plant frequency and densities, basal cover) and soil hydrological properties (increased infiltration capacity, reduced runoff and sediment production) as indicators of rehabilitation success. Despite their comparative and widespread success, natural and anthropogenic challenges persist. This makes reseeding programmes a risky and often expensive venture, especially for the resource-poor pastoral communities in African drylands. Despite the risks, grass reseeding using indigenous pastures remains a viable sustainable land management option to combat degradation in African drylands. However, to ensure its continued success in the long term, multifaceted approaches and strategies that will integrate land and water management and seed systems suitable for African drylands need to be developed, strengthened and promoted.Peer reviewe

Shape recognition through multi-level fusion of features and classifiers

Shape recognition is a fundamental problem and a special type of image classification, where each shape is considered as a class. Current approaches to shape recognition mainly focus on designing low-level shape descriptors, and classify them using some machine learning approaches. In order to achieve effective learning of shape features, it is essential to ensure that a comprehensive set of high quality features can be extracted from the original shape data. Thus we have been motivated to develop methods of fusion of features and classifiers for advancing the classification performance. In this paper, we propose a multi-level framework for fusion of features and classifiers in the setting of gran-ular computing. The proposed framework involves creation of diversity among classifiers, through adopting feature selection and fusion to create diverse feature sets and to train diverse classifiers using different learn-Xinming Wang algorithms. The experimental results show that the proposed multi-level framework can effectively create diversity among classifiers leading to considerable advances in the classification performance

No effect of creatine supplementation on oxidative stress and cardiovascular parameters in spontaneously hypertensive rats

Background: Exacerbated oxidative stress is thought to be a mediator of arterial hypertension. It has been postulated that creatine (Cr) could act as an antioxidant agent preventing increased oxidative stress. The aim of this study was to investigate the effects of nine weeks of Cr or placebo supplementation on oxidative stress and cardiovascular parameters in spontaneously hypertensive rats (SHR). Findings: Lipid hydroperoxidation, one important oxidative stress marker, remained unchanged in the coronary artery (Cr: 12.6 +/- 1.5 vs. Pl: 12.2 +/- 1.7 nmol.mg(-1); p = 0.87), heart (Cr: 11.5 +/- 1.8 vs. Pl: 14.6 +/- 1.1 nmol.mg(-1); p = 0.15), plasma (Cr: 67.7 +/- 9.1 vs. Pl: 56.0 +/- 3.2 nmol.mg(-1); p = 0.19), plantaris (Cr: 10.0 +/- 0.8 vs. Pl: 9.0 +/- 0.8 nmol.mg(-1); p = 0.40), and EDL muscle (Cr: 14.9 +/- 1.4 vs. Pl: 17.2 +/- 1.5 nmol.mg(-1); p = 0.30). Additionally, Cr supplementation affected neither arterial blood pressure nor heart structure in SHR (p &gt; 0.05). Conclusions: Using a well-known experimental model of systemic arterial hypertension, this study did not confirm the possible therapeutic effects of Cr supplementation on oxidative stress and cardiovascular dysfunction associated with arterial hypertension.FAPES