Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance

Preventable hospitalization and access to primary health care in an area of Southern Italy

<p>Abstract</p> <p>Background</p> <p>Ambulatory care-sensitive conditions (ACSC), such as hypertension, diabetes, chronic heart failure, chronic obstructive pulmonary disease and asthma, are conditions that can be managed with timely and effective outpatient care reducing the need of hospitalization. Avoidable hospitalizations for ACSC have been used to assess access, quality and performance of the primary care delivery system. The aims of this study were to quantify the proportion of avoidable hospital admissions for ACSCs, to identify the related patient's socio-demographic profile and health conditions, to assess the relationship between the primary care access characteristics and preventable hospitalizations, and the usefulness of avoidable hospitalizations for ACSCs to monitor the effectiveness of primary health care.</p> <p>Methods</p> <p>A random sample of 520 medical records of patients admitted to medical wards (Cardiology, Internal Medicine, Pneumology, Geriatrics) of a non-teaching acute care 717-bed hospital located in Catanzaro (Italy) were reviewed.</p> <p>Results</p> <p>A total of 31.5% of the hospitalizations in the sample were judged to be preventable. Of these, 40% were for congestive heart failure, 23.2% for chronic obstructive pulmonary disease, 13.5% for angina without procedure, 8.4% for hypertension, and 7.1% for bacterial pneumonia. Preventable hospitalizations were significantly associated to age and sex since they were higher in older patients and in males. The proportion of patients who had a preventable hospitalization significantly increased with regard to the number of hospital admissions in the previous year and to the number of patients for each primary care physician (PCP), with lower number of PCP accesses and PCP medical visits in the previous year, with less satisfaction about PCP health services, and, finally, with worse self-reported health status and shorter length of hospital stay.</p> <p>Conclusion</p> <p>The findings from this study add to the evidence and the urgency of developing and implementing effective interventions to improve delivery of health care at the community level and provided support to the usefulness of avoidable hospitalizations for ACSCs to monitor this process.</p

Different approaches for dealing with rare variants in family-based genetic studies: application of a Genetic Analysis Workshop 17 problem

Rare variants are becoming the new candidates in the search for genetic variants that predispose individuals to a phenotype of interest. Their low prevalence in a population requires the development of dedicated detection and analytical methods. A family-based approach could greatly enhance their detection and interpretation because rare variants are nearly family specific. In this report, we test several distinct approaches for analyzing the information provided by rare and common variants and how they can be effectively used to pinpoint putative candidate genes for follow-up studies. The analyses were performed on the mini-exome data set provided by Genetic Analysis Workshop 17. Eight approaches were tested, four using the trait’s heritability estimates and four using QTDT models. These methods had their sensitivity, specificity, and positive and negative predictive values compared in light of the simulation parameters. Our results highlight important limitations of current methods to deal with rare and common variants, all methods presented a reduced specificity and, consequently, prone to false positive associations. Methods analyzing common variants information showed an enhanced sensibility when compared to rare variants methods. Furthermore, our limited knowledge of the use of biological databases for gene annotations, possibly for use as covariates in regression models, imposes a barrier to further research

The combined effect of the T2DM susceptibility genes is an important risk factor for T2DM in non-obese Japanese: a population based case-control study

<p>Abstract</p> <p>Background</p> <p>Type 2 diabetes mellitus (T2DM) is a complex endocrine and metabolic disorder. Recently, several genome-wide association studies (GWAS) have identified many novel susceptibility loci for T2DM, and indicated that there are common genetic causes contributing to the susceptibility to T2DM in multiple populations worldwide. In addition, clinical and epidemiological studies have indicated that obesity is a major risk factor for T2DM. However, the prevalence of obesity varies among the various ethnic groups. We aimed to determine the combined effects of these susceptibility loci and obesity/overweight for development of T2DM in the Japanese.</p> <p>Methods</p> <p>Single nucleotide polymorphisms (SNPs) in or near 17 susceptibility loci for T2DM, identified through GWAS in Caucasian and Asian populations, were genotyped in 333 cases with T2DM and 417 control subjects.</p> <p>Results</p> <p>We confirmed that the cumulative number of risk alleles based on 17 susceptibility loci for T2DM was an important risk factor in the development of T2DM in Japanese population (<it>P </it>< 0.0001), although the effect of each risk allele was relatively small. In addition, the significant association between an increased number of risk alleles and an increased risk of T2DM was observed in the non-obese group (<it>P </it>< 0.0001 for trend), but not in the obese/overweight group (<it>P </it>= 0.88 for trend).</p> <p>Conclusions</p> <p>Our findings indicate that there is an etiological heterogeneity of T2DM between obese/overweight and non-obese subjects.</p

Positional Cloning of a Type 2 Diabetes Quantitative Trait Locus; Tomosyn-2, a Negative Regulator of Insulin Secretion

We previously mapped a type 2 diabetes (T2D) locus on chromosome 16 (Chr 16) in an F2 intercross from the BTBR T (+) tf (BTBR) Lepob/ob and C57BL/6 (B6) Lepob/ob mouse strains. Introgression of BTBR Chr 16 into B6 mice resulted in a consomic mouse with reduced fasting plasma insulin and elevated glucose levels. We derived a panel of sub-congenic mice and narrowed the diabetes susceptibility locus to a 1.6 Mb region. Introgression of this 1.6 Mb fragment of the BTBR Chr 16 into lean B6 mice (B6.16BT36–38) replicated the phenotypes of the consomic mice. Pancreatic islets from the B6.16BT36–38 mice were defective in the second phase of the insulin secretion, suggesting that the 1.6 Mb region encodes a regulator of insulin secretion. Within this region, syntaxin-binding protein 5-like (Stxbp5l) or tomosyn-2 was the only gene with an expression difference and a non-synonymous coding single nucleotide polymorphism (SNP) between the B6 and BTBR alleles. Overexpression of the b-tomosyn-2 isoform in the pancreatic β-cell line, INS1 (832/13), resulted in an inhibition of insulin secretion in response to 3 mM 8-bromo cAMP at 7 mM glucose. In vitro binding experiments showed that tomosyn-2 binds recombinant syntaxin-1A and syntaxin-4, key proteins that are involved in insulin secretion via formation of the SNARE complex. The B6 form of tomosyn-2 is more susceptible to proteasomal degradation than the BTBR form, establishing a functional role for the coding SNP in tomosyn-2. We conclude that tomosyn-2 is the major gene responsible for the T2D Chr 16 quantitative trait locus (QTL) we mapped in our mouse cross. Our findings suggest that tomosyn-2 is a key negative regulator of insulin secretion

Phase II trial of imatinib mesylate in patients with metastatic melanoma

Metastatic melanoma cells express a number of protein tyrosine kinases (PTKs) that are considered to be targets for imatinib. We conducted a phase II trial of imatinib in patients with metastatic melanoma expressing at least one of these PTKs. Twenty-one patients whose tumours expressed at least one PTK (c-kit, platelet-derived growth factor receptors, c-abl, or abl-related gene) were treated with 400 mg of imatinib twice daily. One patient with metastatic acral lentiginous melanoma, containing the highest c-kit expression among all patients, had dramatic improvement on positron emission tomographic scan at 6 weeks and had a partial response lasting 12.8 months. The responder had a substantial increase in tumour and endothelial cell apoptosis at 2 weeks of treatment. Imatinib was fairly well tolerated: no patient required treatment discontinuation because of toxicity. Fatigue and oedema were the only grade 3 or 4 toxicities that occurred in more than 10% of the patients. Imatinib at the studied dose had minimal clinical efficacy as a single-agent therapy for metastatic melanoma. However, based on the characteristics of the responding tumour in our study, clinical activity of imatinib, specifically in patients with melanoma with certain c-kit aberrations, should be examined

Genetic Analysis of Genome-Scale Recombination Rate Evolution in House Mice

The rate of meiotic recombination varies markedly between species and among individuals. Classical genetic experiments demonstrated a heritable component to population variation in recombination rate, and specific sequence variants that contribute to recombination rate differences between individuals have recently been identified. Despite these advances, the genetic basis of species divergence in recombination rate remains unexplored. Using a cytological assay that allows direct in situ imaging of recombination events in spermatocytes, we report a large (∼30%) difference in global recombination rate between males of two closely related house mouse subspecies (Mus musculus musculus and M. m. castaneus). To characterize the genetic basis of this recombination rate divergence, we generated an F2 panel of inter-subspecific hybrid males (n = 276) from an intercross between wild-derived inbred strains CAST/EiJ (M. m. castaneus) and PWD/PhJ (M. m. musculus). We uncover considerable heritable variation for recombination rate among males from this mapping population. Much of the F2 variance for recombination rate and a substantial portion of the difference in recombination rate between the parental strains is explained by eight moderate- to large-effect quantitative trait loci, including two transgressive loci on the X chromosome. In contrast to the rapid evolution observed in males, female CAST/EiJ and PWD/PhJ animals show minimal divergence in recombination rate (∼5%). The existence of loci on the X chromosome suggests a genetic mechanism to explain this male-biased evolution. Our results provide an initial map of the genetic changes underlying subspecies differences in genome-scale recombination rate and underscore the power of the house mouse system for understanding the evolution of this trait