Study protocol for a cluster-randomized controlled trial of an NCD access to medicines initative: Evaluation of Novartis Access in Kenya

INTRODUCTION: Novartis recently launched Novartis Access, an initiative to provide a basket of reduced price medicines for non-communicable diseases (NCDs) to be sold through the public and private nonprofit sectors in programme countries. This study will evaluate the impact of Novartis Access on the availability and price of NCD medicines at health facilities and households in Kenya, the first country to receive the programme. METHODS: This study will be a cluster randomised controlled trial. 8 counties in Kenya will be randomly assigned to the intervention or control group using a covariate constrained randomisation method to maximise balance on demographic and health characteristics. In intervention counties, public and private non-profit health facilities will be able to order Novartis Access NCD medicines from the Mission for Essential Drugs and Supplies (MEDS). Data will be collected from a random sample of 384 health facilities and 800 households at baseline, midline after 1-year of intervention, and end-line after 2 years. Quarterly surveillance data will also be collected from health facilities and a subsample of households through phone-based interviews. Households will be eligible if at least one resident has been previously diagnosed and prescribed a medicine for an NCD addressed by Novartis Access, including hypertension and diabetes. The primary outcomes will be availability and price of NCD medicines at health facilities, and availability, price, and expenditures on NCD medicines at households. Impacts will be estimated using intention-to-treat analysis. ETHICS AND DISSEMINATION: This protocol was approved by the Institutional Review Boards at Strathmore University and at Boston University. Informed consent will be obtained from all participants at the start of the trial. The findings of the trial will be disseminated through peer-reviewed journals, international conferences, and meetings and events organised with local stakeholders

Vibration Analysis Via Neural Network Inverse Models to Determine Aircraft Engine Unbalance Condition

This paper describes the use of artificial neural networks (ANNs) with the vibration data from real flight tests for detecting engine health condition - mass imbalance herein. Order-tracking data, calculated from time series is used as the input to the neural networks to determine the amount and location of mass imbalance on aircraft engines. Several neural network methods, including multilayer perceptron (MLP), extended Kalman filter (EKF) and support vector machines (SVMs) are used in the neural network inverse model for the performance comparison. The promising performances are presented at the end

Aircraft Cabin Noise Minimization Via Neural Network Inverse Model

This paper describes research to investigate an artificial neural network (ANN) approach to minimize aircraft cabin noise in flight. The ANN approach is shown to be able to accurately model the non-linear relationships between engine unbalance, airframe vibration, and cabin noise to overcome limitations associated with traditional linear influence coefficient methods. ANN system inverse models are developed using engine test-stand vibration data and on-airplane vibration and noise data supplemented with influence coefficient empirical data. The inverse models are able to determine balance solutions that satisfy cabin noise specifications. The accuracy of the ANN model with respect to the real system is determined by the quantity and quality of test stand and operational aircraft data. This data-driven approach is particularly appealing for implementation on future systems that include continuous monitoring processes able to capture data while in operation

Query-Based Learning for Aerospace Applications

Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper investigates the use of both inversion techniques for QBL learning, and introduces an original heuristic to select the inversion target values for continuous network inversion method. Efficiency and generalization was further enhanced by employing node decoupled extended Kalman filter (NDEKF) training and a causality index (CI) as a means to reduce the input search dimensionality. The benefits of the overall QBL approach are experimentally demonstrated in two aerospace applications: a classification problem with large input space and a control distribution problem

Adaptive Multi-Vehicle Area Coverage Optimization System and Method

A mission planning system for determining an optimum use of a plurality of vehicles in searching a predefined geographic area (PGA). A discretizer subsystem may be used for sensing the capabilities of each vehicle to produce a point set defining a number of points within the PGA that the vehicles must traverse to completely search the PGA. A task allocator subsystem may determine an optimum division of the PGA into different subregions to be handled by specific ones of the vehicles, thus to minimize an overall time needed to search the PGA. A path optimizer subsystem may determine an optimum path through a particular vehicle\u27s assigned subregion to minimize the time needed for each specific vehicle to traverse its associated subregion

Fluoropyrimidine sensitivity of human MCF-7 breast cancer cells stably transfected with human uridinehosphorylase

The relationship between uridine phosphorylase (UP) expression level in cancer cells and the tumour sensitivity to fluoropyrimidines is unclear. In this study, we found that UP overexpression by gene transfer, and the subsequent efficient metabolic activation of 5-fluorouracil (5-FU) by the ribonucleotide pathway, does not increase the fluoropyrimidine sensitivity of MCF-7 human cancer cells. © 2001 Cancer Research Campaign http://www.bjcancer.co

Light and nitrogen nutrition regulate apical control in Rosa hybrida L.

Apical control is defined as the inhibition of basal axillary bud outgrowth by an upper actively growing axillary axis, whose regulation is poorly understood yet differs markedly from the better-known apical dominance. We studied the regulation of apical control by environmental factors in decapitated Rosa hybrida in order to remove the apical hormonal influence and nutrient sink. In this plant model, all the buds along the main axis have a similar morphology and are able to burst in vitro. We concentrated on the involvement of light intensity and nitrate nutrition on bud break and axillary bud elongation in the primary axis pruned above the fifth leaf of each rose bush. We observed that apical control took place in low light (92 μmol m−2 s−1), where only the 2-apical buds grew out, both in low (0.25 mM) and high (12.25 mM) nitrate. In contrast, in high light (453 μmol m−2 s−1), the apical control only operates in low nitrate while all the buds along the stem grew out when the plant was supplied with a high level of nitrate. We found a decreasing photosynthetic activity from the top to the base of the plant concomitant with a light gradient along the stem. The quantity of sucrose, fructose, glucose and starch are higher in high light conditions in leaves and stem. The expression of the sucrose transporter RhSUC2 was higher in internodes and buds in this lighting condition, suggesting an increased capacity for sucrose transport. We propose that light intensity and nitrogen availability both contribute to the establishment of apical control

Informação tecnológica sobre cana-de-açúcar na internet.

O Brasil é o maior produtor mundial de cana-de-açúcar e de etanol1. A cana-de-açúcar é a maior fonte de energia renovável do País, com 15,9% de participação na matriz energética atual, superando pela primeira vez a oferta de energia hidrelétrica (14,8%), considerando-se o etanol combustível e a cogeração de eletricidade a partir do bagaço2. Sua área plantada passa de 7 milhões de hectares; e sua produção gira em torno de 490 milhões de toneladas na safra 2007/083

Vehicle Base Station

A system to load and unload material from a vehicle comprises a vehicle base station and an assembly to autonomously load and unload material from the vehicle