Requirements engineering in software product line engineering

The final publication is available at Springer via http://dx.doi.org/10.1007/s00766-013-0189-0Many attempts have been made to increase the productivity and quality of software products based on software reuse. Software product line practice is one such approach, one that focuses on developing a family of products which have a majority of features in common. Hence, there are numerous requirements that are common across the family, but others are unique to individual products. Traditional requirements engineering methods were conceived to deal with single product requirements and are usually not flexible enough to address the needs arising from reusing requirements for a family of products. There is also the additional burden of correctly identifying and engineering both product-line-wide requirements and product-specific requirements as well as evolving them. Therefore, in this special issue, we want to highlight the importance and the role of requirements engineering for product line development as well as to provide insights into the state of the art in the field.Insfrán Pelozo, CE.; Chastek, G.; Donohoe, P.; Sampaio Do Prado Leite, JC. (2014). Requirements engineering in software product line engineering. Requirements Engineering. 19(4):331-332. doi:10.1007/s00766-013-0189-0S331332194Clements P, Northrop LM (2001) Software product lines: practices and patterns. Addison-Wesley, BostonDerakhshanmanesh M, Fox J, Ebert J (2012) Adopting feature-centric reuse of requirements assets: an industrial experience report. First international workshop on requirements engineering practices on software product line engineering, Salvador, BrazilKuloor C, Eberlein A (2002) Requirements engineering for software product lines, proceedings of the 15th international conference on software and systems engineering and their applications (ICSSEA’02), Paris, FranceNorthrop LM, Clements P (2013) A framework for software product line practice. Software engineering institute. http://www.sei.cmu.edu/productlines/tools/framework/index.cfm . Accessed 22 July 2013Yu Y, Lapouchnian A, Liaskos S, Mylopoulos J, Leite JCSP (2008) From Goals to High-Variability Software Design. Foundations of Intelligent Systems, 17th International Symposium Proceedings. ISMIS 2008. Springer Lecture Notes in Computer Science, 4994: 1–1

Specialty Care Use in US Patients with Chronic Diseases

Despite efforts to eliminate health disparities, racial, ethnic, and geographic groups continue lag behind their counterparts in health outcomes in the United States. The purpose of this study is to determine variation in specialty care utilization by chronic disease status. Data were extracted from the Commonwealth Fund 2006 Health Care Quality Survey (n = 2475). A stratified minority sample design was employed to ensure a representative sample. Logistic regression was used in analyses to predict specialty care utilization in the sample. Poor perceived health, minority status, and lack of insurance was associated with reduced specialty care use and chronic disease diagnosis

An inherited duplication at the gene p21 protein-activated Kinase 7 (PAK7) is a risk factor for psychosis

FUNDING Funding for this study was provided by the Wellcome Trust Case Control Consortium 2 project (085475/B/08/Z and 085475/Z/08/Z), the Wellcome Trust (072894/Z/03/Z, 090532/Z/09/Z and 075491/Z/04/B), NIMH grants (MH 41953 and MH083094) and Science Foundation Ireland (08/IN.1/B1916). We acknowledge use of the Trinity Biobank sample from the Irish Blood Transfusion Service; the Trinity Centre for High Performance Computing; British 1958 Birth Cohort DNA collection funded by the Medical Research Council (G0000934) and the Wellcome Trust (068545/Z/02) and of the UK National Blood Service controls funded by the Wellcome Trust. Chris Spencer is supported by a Wellcome Trust Career Development Fellowship (097364/Z/11/Z). Funding to pay the Open Access publication charges for this article was provided by the Wellcome Trust. ACKNOWLEDGEMENTS The authors sincerely thank all patients who contributed to this study and all staff who facilitated their involvement. We thank W. Bodmer and B. Winney for use of the People of the British Isles DNA collection, which was funded by the Wellcome Trust. We thank Akira Sawa and Koko Ishzuki for advice on the PAK7–DISC1 interaction experiment and Jan Korbel for discussions on mechanism of structural variation.Peer reviewedPublisher PD

Exploring gastrointestinal variables affecting drug and formulation behavior: methodologies, challenges and opportunities

Various gastrointestinal (GI) factors affect drug and formulation behavior after oral administration, including GI transfer, motility, pH and GI fluid volume and composition. An in-depth understanding of these physiological and anatomical variables is critical for a continued progress in oral drug development. In this review, different methodologies (invasive versus non-invasive) to explore the impact of physiological variables on formulation behavior in the human GI tract are presented, revealing their strengths and limitations. The techniques mentioned allow for an improved understanding of the role of following GI variables: gastric emptying (magnetic resonance imaging (MRI), scintigraphy, acetaminophen absorption technique, ultrasonography, breath test, intraluminal sampling and telemetry), motility (MRI, small intestinal/colonic manometry and telemetry), GI volume changes (MRI and ultrasonography), temperature (telemetry) and intraluminal pH (intraluminal sampling and telemetry)

Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence

Intelligence is highly heritable(1) and a major determinant of human health and well-being(2). Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.Peer reviewe

Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (OR=1.11, P=5.7×10−15), which persisted after excluding loci implicated in previous studies (OR=1.07, P=1.7 ×10−6). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 ×10−11) and neurobehavioral phenotypes in mouse (OR = 1.18, P= 7.3 ×10−5). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by non-allelic homologous recombination

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders