Outcomes and cost effectiveness of treating type 2 diabetes with a nurse case manager following treatment algorithms versus primary care physicians

Background. Diabetes places a significant burden on the health care system. Reduction in blood glucose levels (HbA1c) reduces the risk of complications; however, little is known about the impact of disease management programs on medical costs for patients with diabetes. In 2001, economic costs associated with diabetes totaled $100 billion, and indirect costs totaled $54 billion. ^ Objective. To compare outcomes of nurse case management by treatment algorithms with conventional primary care for glycemic control and cardiovascular risk factors in type 2 diabetic patients in a low-income Mexican American community-based setting, and to compare the cost effectiveness of the two programs. Patient compliance was also assessed. ^ Research design and methods. An observational group-comparison to evaluate a treatment intervention for type 2 diabetes management was implemented at three out-patient health facilities in San Antonio, Texas. All eligible type 2 diabetic patients attending the clinics during 1994–1996 became part of the study. Data were obtained from the study database, medical records, hospital accounting, and pharmacy cost lists, and entered into a computerized database. Three groups were compared: a Community Clinic Nurse Case Manager (CC-TA) following treatment algorithms, a University Clinic Nurse Case Manager (UC-TA) following treatment algorithms, and Primary Care Physicians (PCP) following conventional care practices at a Family Practice Clinic. The algorithms provided a disease management model specifically for hyperglycemia, dyslipidemia, hypertension, and microalbuminuria that progressively moved the patient toward ideal goals through adjustments in medication, self-monitoring of blood glucose, meal planning, and reinforcement of diet and exercise. Cost effectiveness of hemoglobin AI, final endpoints was compared. ^ Results. There were 358 patients analyzed: 106 patients in CC-TA, 170 patients in UC-TA, and 82 patients in PCP groups. Change in hemoglobin A1c (HbA1c) was the primary outcome measured. HbA1c results were presented at baseline, 6 and 12 months for CC-TA (10.4%, 7.1%, 7.3%), UC-TA (10.5%, 7.1%, 7.2%), and PCP (10.0%, 8.5%, 8.7%). Mean patient compliance was 81%. Levels of cost effectiveness were significantly different between clinics. ^ Conclusion. Nurse case management with treatment algorithms significantly improved glycemic control in patients with type 2 diabetes, and was more cost effective. ^

A full pedigree based method for the statistical assessment of genetic anticipation

Genetic anticipation is defined as a decrease in age of onset or increase in severity as the disorder is transmitted through subsequent generations. Anticipation has been noted in the literature for over a century. Recently, anticipation in several diseases including Huntington's Disease, Myotonic Dystrophy and Fragile X Syndrome were shown to be caused by expansion of triplet repeats. Anticipation effects have also been observed in numerous mental disorders (e.g. Schizophrenia, Bipolar Disorder), cancers (Li-Fraumeni Syndrome, Leukemia) and other complex diseases. ^ Several statistical methods have been applied to determine whether anticipation is a true phenomenon in a particular disorder, including standard statistical tests and newly developed affected parent/affected child pair methods. These methods have been shown to be inappropriate for assessing anticipation for a variety of reasons, including familial correlation and low power. Therefore, we have developed family-based likelihood modeling approaches to model the underlying transmission of the disease gene and penetrance function and hence detect anticipation. These methods can be applied in extended families, thus improving the power to detect anticipation compared with existing methods based only upon parents and children. The first method we have proposed is based on the regressive logistic hazard model. This approach models anticipation by a generational covariate. The second method allows alleles to mutate as they are transmitted from parents to offspring and is appropriate for modeling the known triplet repeat diseases in which the disease alleles can become more deleterious as they are transmitted across generations. ^ To evaluate the new methods, we performed extensive simulation studies for data simulated under different conditions to evaluate the effectiveness of the algorithms to detect genetic anticipation. Results from analysis by the first method yielded empirical power greater than 87% based on the 5% type I error critical value identified in each simulation depending on the method of data generation and current age criteria. Analysis by the second method was not possible due to the current formulation of the software. The application of this method to Huntington's Disease and Li-Fraumeni Syndrome data sets revealed evidence for a generation effect in both cases. ^

Fast Algorithms Using Minimal Data Structures for Common Topological Relationships in Large, Irregularly-spaced Topographic Data Sets

Digital terrain models (DTM) typically contain large numbers of postings, from hundreds of thousands to billions. Many algorithms that run on DTMs require topological knowledge of the postings, such as finding nearest neighbors, finding the posting closest to a chosen location, etc. If the postings are arranged irregu- larly, topological information is costly to compute and to store. This paper offers a practical approach to organizing and searching irregularly-space data sets by presenting a collection of efficient algorithms (O(N),O(lgN)) that compute important topological relationships with only a simple supporting data structure. These relationships include finding the postings within a window, locating the posting nearest a point of interest, finding the neighborhood of postings nearest a point of interest, and ordering the neighborhood counter-clockwise. These algorithms depend only on two sorted arrays of two-element tuples, holding a planimetric coordinate and an integer identification number indicating which posting the coordinate belongs to. There is one array for each planimetric coordinate (eastings and northings). These two arrays cost minimal overhead to create and store but permit the data to remain arranged irregularly.

Genome-wide algorithm for detecting CNV associations with diseases

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). The quality of the inferences about copy number can be affected by many factors including batch effects, DNA sample preparation, signal processing, and analytical approach. Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP genotyping data. However, these algorithms lack specificity to detect small CNVs due to the high false positive rate when calling CNVs based on the intensity values. Association tests based on detected CNVs therefore lack power even if the CNVs affecting disease risk are common. In this research, by combining an existing Hidden Markov Model (HMM) and the logistic regression model, a new genome-wide logistic regression algorithm was developed to detect CNV associations with diseases. We showed that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than an existing popular algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.^

Molecular and evolutionary genetics of the X -linked visual pigment genes in humans and New World monkeys

Normal humans have one red and at least one green visual pigment genes. These genes are tightly linked as tandem repeats on the X chromosome and each of them has six exons. There is only one X-linked visual pigment gene in New World monkeys (NWMs) but the locus has three polymorphic alleles encoding red, yellow and green visual pigments, respectively. The spectral properties of the squirrel monkey and the marmoset (both NWMs) have been studied and partial sequences of the three alleles are available. To study the evolutionary history of these X-linked opsin genes in humans and NWMs, coding and intron sequences of the three squirrel monkey alleles and the three marmoset alleles were amplified by PCR followed by subcloning and sequencing. Introns 2 and 4 of the human red and green pigment genes were also sequenced. The results obtained are as follows: (1) The sequences of introns 2 and 4 of the human red and green opsin genes are significantly more similar between the two genes than are coding sequences, contrary to the usual situation where coding regions are better conserved in evolution than are introns. The high similarities in the two introns are probably due to recent gene conversion events during evolution of the human lineage. (2) Phylogenetic analysis of both intron and exon sequences indicates that the phylogenetic tree of the available primate opsin genes is the same as the species tree. The two human genes were derived from a gene duplication event after the divergence of the human and NWM lineages. The three alleles in each of the two NWM species diverged after the split of the two NWMs but have persisted in the population for at least 5 million years. (3) Allelic gene conversion might have occurred between the three squirrel monkey alleles. (4) A model of additive effect of hydroxyl-bearing amino acids on spectral tuning is proposed by treating some unknown variables as groups. Under the assumption that some residues have no effect, it is found that at least five amino acid residues, at positions 178 (3 nm), 180 (5 nm), 230 ($-$4 nm), 277 (9 nm) and 285 (13 nm), have linear spectral tuning effects. (5) Adaptive evolution of the opsin genes to different spectral peaks was observed at four residues that are important for spectral tuning. ^

The CoastColour Round Robin datasets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

The CoastColour project Round Robin (CCRR) project (http://www.coastcolour.org) funded by the European Space Agency (ESA) was designed to bring together a variety of reference datasets and to use these to test algorithms and assess their accuracy for retrieving water quality parameters. This information was then developed to help end-users of remote sensing products to select the most accurate algorithms for their coastal region. To facilitate this, an inter-comparison of the performance of algorithms for the retrieval of in-water properties over coastal waters was carried out. The comparison used three types of datasets on which ocean colour algorithms were tested. The description and comparison of the three datasets are the focus of this paper, and include the Medium Resolution Imaging Spectrometer (MERIS) Level 2 match-ups, in situ reflectance measurements and data generated by a radiative transfer model (HydroLight). The datasets mainly consisted of 6,484 marine reflectance associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: Total Suspended Matter (TSM) and Chlorophyll-a (CHL) concentrations, and the absorption of Coloured Dissolved Organic Matter (CDOM). Inherent optical properties were also provided in the simulated datasets (5,000 simulations) and from 3,054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three datasets are compared. Match-up and in situ sites where deviations occur are identified. The distribution of the three reflectance datasets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.

Building ontologies from folksonomies and linked data: Data structures and Algorithms

We present the data structures and algorithms used in the approach for building domain ontologies from folksonomies and linked data. In this approach we extracts domain terms from folksonomies and enrich them with semantic information from the Linked Open Data cloud. As a result, we obtain a domain ontology that combines the emergent knowledge of social tagging systems with formal knowledge from Ontologies.

Comparison of different stomatal conductance algorithms for ozone flux modelling

A multiplicative and a semi-mechanistic, BWB-type [Ball, J.T., Woodrow, I.E., Berry, J.A., 1987. A model predicting stomatalconductance and its contribution to the control of photosynthesis under different environmental conditions. In: Biggens, J. (Ed.), Progress in Photosynthesis Research, vol. IV. Martinus Nijhoff, Dordrecht, pp. 221–224.] algorithm for calculating stomatalconductance (gs) at the leaf level have been parameterised for two crop and two tree species to test their use in regional scale ozone deposition modelling. The algorithms were tested against measured, site-specific data for durum wheat, grapevine, beech and birch of different European provenances. A direct comparison of both algorithms showed a similar performance in predicting hourly means and daily time-courses of gs, whereas the multiplicative algorithm outperformed the BWB-type algorithm in modelling seasonal time-courses due to the inclusion of a phenology function. The re-parameterisation of the algorithms for local conditions in order to validate ozone deposition modelling on a European scale reveals the higher input requirements of the BWB-type algorithm as compared to the multiplicative algorithm because of the need of the former to model net photosynthesis (An)

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection

Application of the Extended Kalman filter to fuzzy modeling: Algorithms and practical implementation

Modeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows its implementation in-line with the process

High Performance Algorithms and Implementations Using Sparse and Parallelization Techniques on MBS

In this paper we will see how the efficiency of the MBS simulations can be improved in two different ways, by considering both an explicit and implicit semi-recursive formulation. The explicit method is based on a double velocity transformation that involves the solution of a redundant but compatible system of equations. The high computational cost of this operation has been drastically reduced by taking into account the sparsity pattern of the system. Regarding this, the goal of this method is the introduction of MA48, a high performance mathematical library provided by Harwell Subroutine Library. The second method proposed in this paper has the particularity that, depending on the case, between 70 and 85% of the computation time is devoted to the evaluation of forces derivatives with respect to the relative position and velocity vectors. Keeping in mind that evaluating these derivatives can be decomposed into concurrent tasks, the main goal of this paper lies on a successful and straightforward parallel implementation that have led to a substantial improvement with a speedup of 3.2 by keeping all the cores busy in a quad-core processor and distributing the workload between them, achieving on this way a huge time reduction by doing an ideal CPU usage

Automatic lateral control for unmanned vehicles via genetic algorithms

It is known that the techniques under the topic of Soft Computing have a strong capability of learning and cognition, as well as a good tolerance to uncertainty and imprecision. Due to these properties they can be applied successfully to Intelligent Vehicle Systems; ITS is a broad range of technologies and techniques that hold answers to many transportation problems. The unmannedcontrol of the steering wheel of a vehicle is one of the most important challenges facing researchers in this area. This paper presents a method to adjust automatically a fuzzy controller to manage the steering wheel of a mass-produced vehicle; to reach it, information about the car state while a human driver is handling the car is taken and used to adjust, via iterative geneticalgorithms an appropriated fuzzy controller. To evaluate the obtained controllers, it will be considered the performance obtained in the track following task, as well as the smoothness of the driving carried out.

A review of estimation of distribution algorithms in bioinformatics

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain.