Uma abordagem para derivar modelos de requisitos a partir de mecanismos de reconhecimento de voz

A elicitação de requisitos é uma das primeiras actividades do processo de Engenharia de Requisitos. Através desta etapa é possível capturar e estruturar os requisitos dos stakeholders e do sistema a ser implementado. Tipicamente esta estruturação é representada através de notação gráfica com o auxílio de ferramentas CASE. Estas ferramentas tornam esta actividade exclusiva, em termos de acessibilidade, a engenheiros sem deficiências físicas devido à complexidade das funcionalidades oferecidas pelas mesmas que permitem construir estes modelos. Nesta dissertação de mestrado é proposto desenvolver uma abordagem com suporte de uma ferramenta para melhorar a acessibilidade, e consequentemente,integrar um engenheiro de requisitos com limitações físicas na actividade de elicitação de requisitos. A ferramenta também possibilita uma alternativa para os stakeholders em geral para produzir modelos sem usar as mãos quando for mais conveniente. Esta abordagem propõe usar mecanismos de voz na geração de modelos de requisitos, mais concretamente modelos de requisitos orientados a objectivos, modelos de objectos e modelos de features usando técnicas de Model-Driven Development (MDD) (e.g., metamodelos). O stakeholder assim irá capturar os seus requisitos através de mecanismos de reconhecimento de voz, sendo automaticamente realizada uma transformação do seu discurso para um modelo KAOS, para um modelo conceptual ou para um modelo de features.

Família de DSLs para integrated modular avionics

Na atualidade existe a necessidade de produzir novos aviões de forma rápida, eficiente e económica com o objetivo de abrir novas rotas de voo, expansão das já existentes e substituição de aviões em fim de vida útil. Neste contexto, e sem nunca deixar de cumprir os apertados requisitos do domínio que incluem a exigência de elevada qualidade, a indústria adotou a arquitetura IMA que permite executar várias aplicações aviónicas num único sistema de computação partilhado. Com esta arquitetura, o desenvolvimento do software para as aeronaves ganhou uma elevada relevância, sendo necessário gerar código automaticamente, reutilizar código já testado proveniente de outras aplicações anteriormente desenvolvidas e garantir o mais cedo possível que o software desenvolvido se encontra de acordo com os standards. Apesar da complexidade do domínio, desenvolveu-se uma ferramenta que permite produzir a estrutura do código de novas aplicações para a aviónica. Aferramenta lida com a variabilidade das diversas linhas de produto e reduz o tempo de desenvolvimento. Uma DSL poderia ser uma solução apropriada, pois permite cobrir diversos requisitos exigidos, no entanto, esta solução não é exequível porque seria necessário produzir uma linguagem para cada configuração de software aviónico diferente se pretendêssemos tirar partido da especificidade. Com esta dissertação, solucionou-se esta dificuldade com recurso à noção de família de DSLs. Trata-se de um conjunto de linguagens para um domínio específico, que apresentam um conjunto comum de conceitos chave, mas que adaptam alguns desses conceitos para cumprir a variabilidade dos requisitos. Utilizou-se a abordagem MDD para desenvolver um gerador automático de DSLs que é capaz de produzir a linguagem desejada de acordo com a configuração de software pretendida para a partição pertencente a um módulo aviónico. As linguagens geradas apresentam um nível de usabilidade adequada para o domínio, bem como têm a capacidade de validar as construções efetuadas usando a DSL e produzir os artefactos pretendidos.

A DSL for querying building data streams of energy, eeather and occupation data

The superfluous consumption of energy is faced by the modern society as a Socio-Economical and Environmental problem of the present days. This situation is worsening given that it is becoming clear that the tendency is to increase energy price every year. It is also noticeable that people, not necessarily proficient in technology, are not able to know where savings can be achieved, due to the absence of accessible awareness mechanisms. One of the home user concerns is to balance the need of reducing energy consumption, while producing the same activity with all the comfort and work efficiency. The common techniques to reduce the consumption are to use a less wasteful equipment, altering the equipment program to a more economical one or disconnecting appliances that are not necessary at the moment. However, there is no direct feedback from this performed actions, which leads to the situation where the user is not aware of the influence that these techniques have in the electrical bill. With the intension to give some control over the home consumption, Energy Management Systems (EMS) were developed. These systems allow the access to the consumption information and help understanding the energy waste. However, some studies have proven that these systems have a clear mismatch between the information that is presented and the one the user finds useful for his daily life, leading to demotivation of use. In order to create a solution more oriented towards the user’s demands, a specially tailored language (DSL) was implemented. This solution allows the user to acquire the information he considers useful, through the construction of questions about his energy consumption. The development of this language, following the Model Driven Development (MDD) approach, took into consideration the ideas of facility managers and home users in the phases of design and validation. These opinions were gathered through meetings with experts and a survey, which was conducted to the purpose of collecting statistics about what home users want to know.

A domain specific language for domotic systems

To cope with modernity, the interesting of having a fully automated house has been increasing over the years, as technology evolves and as our lives become more stressful and overloaded. An automation system provides a way to simplify some daily tasks, allowing us to have more spare time to perform activities where we are really needed. There are some systems in this domain that try to implement these characteristics, but this kind of technology is at its early stages of evolution being that it is still far away of empowering the user with the desired control over a habitation. The reason is that the mentioned systems miss some important features such as adaptability, extension and evolution. These systems, developed from a bottom-up approach, are often tailored for programmers and domain experts, discarding most of the times the end users that remain with unfinished interfaces or products that they have difficulty to control. Moreover, complex behaviors are avoided, since they are extremely difficult to implement mostly due to the necessity of handling priorities, conflicts and device calibration. Besides, these solutions are only reachable at very high costs, yet they still have the limitation of being difficult to configure by non-technical people once in runtime operation. As a result, it is necessary to create a tool that allows the execution of several automated actions, with an interface that is easy to use but at the same time supports all the main features of this domain. It is also desirable that this tool is independent of the hardware so it can be reused, thus a Model Driven Development approach (MDD) is the ideal option, as it is a method that follows those principles. Since the automation domain has some very specific concepts, the use of models should be combined with a Domain Specific Language (DSL). With these two methods, it is possible to create a solution that is adapted to the end users, but also to domain experts and programmers due to the several levels of abstraction that can be added to diminish the complexity of use. The aim of this thesis is to design a Domain Specific Language (DSL) that uses the Model Driven Development approach (MDD), with the purpose of supporting Home Automation (HA) concepts. In this implementation, the development of simple and complex scenarios should be supported and will be one of the most important concerns. This DSL should also support other significant features in this domain, such as the ability to schedule tasks, which is something that is limited in the current existing solutions.

Biological markers in noninvasive brain stimulation trials in major depressive disorder: a systematic review

Objectives: The therapeutic effects of transcranial magnetic stimulation (TMS) and transcranial direct current stimulation in patients with major depression have shown promising results; however, there is a lack of mechanistic studies using biological markers (BMs) as an outcome. Therefore, our aim was to review noninvasive brain stimulation trials in depression using BMs. Methods: The following databases were used for our systematic review: MEDLINE, Web of Science, Cochrane, and SCIELO. We examined articles published before November 2012 that used TMS and transcranial direct current stimulation as an intervention for depression and had BM as an outcome measure. The search was limited to human studies written in English. Results: Of 1234 potential articles, 52 articles were included. Only studies using TMS were found. Biological markers included immune and endocrine serum markers, neuroimaging techniques, and electrophysiological outcomes. In 12 articles (21.4%), end point BM measurements were not significantly associated with clinical outcomes. All studies reached significant results in the main clinical rating scales. Biological marker outcomes were used as predictors of response, to understand mechanisms of TMS, and as a surrogate of safety. Conclusions: Functional magnetic resonance imaging, single-photon emission computed tomography, positron emission tomography, magnetic resonance spectroscopy, cortical excitability, and brain-derived neurotrophic factor consistently showed positive results. Brain-derived neurotrophic factor was the best predictor of patients’ likeliness to respond. These initial results are promising; however, all studies investigating BMs are small, used heterogeneous samples, and did not take into account confounders such as age, sex, or family history. Based on our findings, we recommend further studies to validate BMs in noninvasive brain stimulation trials in MDD.

Assessing cardiorespiratory capacity in older adults with major depression and Alzheimer disease

ABSTRACT Objective To assess cardiorespiratory capacity through subjective and objective tests in older adults diagnosed with major depression (MDD), Alzheimer disease (AD) and healthy older adults. Methods Fifty seven subjects (72 ± 7.9 years) were divided into three groups: MDD (n = 20), AD (n = 17) and Healthy (n = 20). The subjects answered Hamilton Scale (HAM-D), Mini-Mental State Examination (MMSE), Veterans Specific Activity Questionnaire (VSAQ) and 2-minute Step test. Results MDD and AD showed lower scores than healthy group for Nomogram VSAQ (p < 0.001) and 2-minute Step (p = 0.009; p = 0.008, respectively). Adjusted for age and educational level, no differences among groups were observed for Step (MDD, p = 0.097; AD, p = 0.102). AD group did not present differences to healthy group for Step, when adjusting for MMSE (p = 0.261). Conclusions Despite the lower cardiorespiratory fitness of elderly patients with DM and DA have been found in both evaluations, the results should be viewed with caution, since the tests showed low correlation and different risk classifications of functional loss. In addition, age, level educational and cognitive performance are variables that can influence the performance objective evaluation.

Prenatal dysthymia versus major depression effects on maternal cortisol and fetal growth

To determine differences between pregnant women diagnosed with Dysthymia versus Major Depression, depressed pregnant women (N=102) were divided by their diagnosis into Dysthymic (N=48) and Major Depression (N=54) groups and compared on self-report measures (depression, anxiety, anger, daily hassles and behavioral inhibition), on stress hormone levels (cortisol and norepinephrine), and on fetal measurements. The Major Depression group had more self-reported symptoms. However, the Dysthymic group had higher prenatal cortisol levels and lower fetal growth measurements (estimated weight, femur length, abdominal circumference) as measured at their first ultrasound (M=18 weeks gestation). Thus, depressed pregnant women with Dysthymia and Major Depression appeared to have different prenatal symptoms.

Association of serum homocysteine with major depressive disorder: results from a large population-based study.

BACKGROUND: Studies on the association between homocysteine levels and depression have shown conflicting results. To examine the association between serum total homocysteine (tHcy) levels and major depressive disorder (MDD) in a large community sample with an extended age range. METHODS: A total of 3392 men and women aged 35-66 years participating in the CoLaus study and its psychiatric arm (PsyCoLaus) were included in the analyses. High tHcy measured from fasting blood samples was defined as a concentration ≥15μmol/L. MDD was assessed using the semi-structured Diagnostic Interview for Genetics Studies. RESULTS: In multivariate analyses, elevated tHcy levels were associated with greater odds of meeting the diagnostic criteria for lifetime MDD among men (OR=1.71; 95% CI, 1.18-2.50). This was particularly the case for remitted MDD. Among women, there was no significant association between tHcy levels and MDD and the association tended to be in the opposite direction (OR=0.61; 95% CI, 0.34-1.08). CONCLUSIONS: In this large population-based study, elevated tHcy concentrations are associated with lifetime MDD and particularly with remitted MDD among men.

A mega-analysis of genome-wide association studies for major depressive disorder.

Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P<0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P<5 × 10(-8)), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083-53 822 102, minimum P=5.9 × 10(-9) at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.

Parent-child agreement and prevalence estimates of diagnoses in childhood: direct interview versus family history method.

Diagnostic information on children is typically elicited from both children and their parents. The aims of the present paper were to: (1) compare prevalence estimates according to maternal reports, paternal reports and direct interviews of children [major depressive disorder (MDD), anxiety and attention-deficit and disruptive behavioural disorders]; (2) assess mother-child, father-child and inter-parental agreement for these disorders; (3) determine the association between several child, parent and familial characteristics and the degree of diagnostic agreement or the likelihood of parental reporting; (4) determine the predictive validity of diagnostic information provided by parents and children. Analyses were based on 235 mother-offspring, 189 father-offspring and 128 mother-father pairs. Diagnostic assessment included the Kiddie-schedule for Affective Disorders and Schizophrenia (K-SADS) (offspring) and the Diagnostic Interview for Genetic Studies (DIGS) (parents and offspring at follow-up) interviews. Parental reports were collected using the Family History - Research Diagnostic Criteria (FH-RDC). Analyses revealed: (1) prevalence estimates for internalizing disorders were generally lower according to parental information than according to the K-SADS; (2) mother-child and father-child agreement was poor and within similar ranges; (3) parents with a history of MDD or attention deficit hyperactivity disorder (ADHD) reported these disorders in their children more frequently; (4) in a sub-sample followed-up into adulthood, diagnoses of MDD, separation anxiety and conduct disorder at baseline concurred with the corresponding lifetime diagnosis at age 19 according to the child rather than according to the parents. In conclusion, our findings support large discrepancies of diagnostic information provided by parents and children with generally lower reporting of internalizing disorders by parents, and differential reporting of depression and ADHD by parental disease status. Follow-up data also supports the validity of information provided by adolescent offspring.

Genome-wide association study of recurrent major depressive disorder in two European case-control cohorts.

Major depressive disorder (MDD) is a highly prevalent disorder with substantial heritability. Heritability has been shown to be substantial and higher in the variant of MDD characterized by recurrent episodes of depression. Genetic studies have thus far failed to identify clear and consistent evidence of genetic risk factors for MDD. We conducted a genome-wide association study (GWAS) in two independent datasets. The first GWAS was performed on 1022 recurrent MDD patients and 1000 controls genotyped on the Illumina 550 platform. The second was conducted on 492 recurrent MDD patients and 1052 controls selected from a population-based collection, genotyped on the Affymetrix 5.0 platform. Neither GWAS identified any SNP that achieved GWAS significance. We obtained imputed genotypes at the Illumina loci for the individuals genotyped on the Affymetrix platform, and performed a meta-analysis of the two GWASs for this common set of approximately half a million SNPs. The meta-analysis did not yield genome-wide significant results either. The results from our study suggest that SNPs with substantial odds ratio are unlikely to exist for MDD, at least in our datasets and among the relatively common SNPs genotyped or tagged by the half-million-loci arrays. Meta-analysis of larger datasets is warranted to identify SNPs with smaller effects or with rarer allele frequencies that contribute to the risk of MDD.

Meta-analysis of genome-wide association data identifies a risk locus for major mood disorders on 3p21.1.

The major mood disorders, which include bipolar disorder and major depressive disorder (MDD), are considered heritable traits, although previous genetic association studies have had limited success in robustly identifying risk loci. We performed a meta-analysis of five case-control cohorts for major mood disorder, including over 13,600 individuals genotyped on high-density SNP arrays. We identified SNPs at 3p21.1 associated with major mood disorders (rs2251219, P = 3.63 x 10(-8); odds ratio = 0.87; 95% confidence interval, 0.83-0.92), with supportive evidence for association observed in two out of three independent replication cohorts. These results provide an example of a shared genetic susceptibility locus for bipolar disorder and MDD.

Depression with atypical features and increase in obesity, body mass index, waist circumference, and fat mass: a prospective, population-based study.

IMPORTANCE: Depression and obesity are 2 prevalent disorders that have been repeatedly shown to be associated. However, the mechanisms and temporal sequence underlying this association are poorly understood. OBJECTIVE: To determine whether the subtypes of major depressive disorder (MDD; melancholic, atypical, combined, or unspecified) are predictive of adiposity in terms of the incidence of obesity and changes in body mass index (calculated as weight in kilograms divided by height in meters squared), waist circumference, and fat mass. DESIGN, SETTING, AND PARTICIPANTS: This prospective population-based cohort study, CoLaus (Cohorte Lausannoise)/PsyCoLaus (Psychiatric arm of the CoLaus Study), with 5.5 years of follow-up included 3054 randomly selected residents (mean age, 49.7 years; 53.1% were women) of the city of Lausanne, Switzerland (according to the civil register), aged 35 to 66 years in 2003, who accepted the physical and psychiatric baseline and physical follow-up evaluations. EXPOSURES: Depression subtypes according to the DSM-IV. Diagnostic criteria at baseline and follow-up, as well as sociodemographic characteristics, lifestyle (alcohol and tobacco use and physical activity), and medication, were elicited using the semistructured Diagnostic Interview for Genetic Studies. MAIN OUTCOMES AND MEASURES: Changes in body mass index, waist circumference, and fat mass during the follow-up period, in percentage of the baseline value, and the incidence of obesity during the follow-up period among nonobese participants at baseline. Weight, height, waist circumference, and body fat (bioimpedance) were measured at baseline and follow-up by trained field interviewers. RESULTS: Only participants with the atypical subtype of MDD at baseline revealed a higher increase in adiposity during follow-up than participants without MDD. The associations between this MDD subtype and body mass index (β = 3.19; 95% CI, 1.50-4.88), incidence of obesity (odds ratio, 3.75; 95% CI, 1.24-11.35), waist circumference in both sexes (β = 2.44; 95% CI, 0.21-4.66), and fat mass in men (β = 16.36; 95% CI, 4.81-27.92) remained significant after adjustments for a wide range of possible cofounding. CONCLUSIONS AND RELEVANCE: The atypical subtype of MDD is a strong predictor of obesity. This emphasizes the need to identify individuals with this subtype of MDD in both clinical and research settings. Therapeutic measures to diminish the consequences of increased appetite during depressive episodes with atypical features are advocated.

Investigating the genetic variation underlying episodicity in major depressive disorder: Suggestive evidence for a bipolar contribution.

BACKGROUND: Highly recurrent major depressive disorder (MDD) has reportedly increased risk of shifting to bipolar disorder; high recurrence frequency has, therefore, featured as evidence of 'soft bipolarity'. We aimed to investigate the genetic underpinnings of total depressive episode count in recurrent MDD. METHODS: Our primary sample included 1966 MDD cases with negative family history of bipolar disorder from the RADIANT studies. Total episode count was adjusted for gender, age, MDD duration, study and center before being tested for association with genotype in two separate genome-wide analyses (GWAS), in the full set and in a subset of 1364 cases with positive family history of MDD (FH+). We also calculated polygenic scores from the Psychiatric Genomics Consortium MDD and bipolar disorder studies. RESULTS: Episodicity (especially intermediate episode counts) was an independent index of MDD familial aggregation, replicating previous reports. The GWAS produced no genome-wide significant findings. The strongest signals were detected in the full set at MAGI1 (p=5.1×10(-7)), previously associated with bipolar disorder, and in the FH+ subset at STIM1 (p=3.9×10(-6) after imputation), a calcium channel signaling gene. However, these findings failed to replicate in an independent Munich cohort. In the full set polygenic profile analyses, MDD polygenes predicted episodicity better than bipolar polygenes; however, in the FH+ subset, both polygenic scores performed similarly. LIMITATIONS: Episode count was self-reported and, therefore, subject to recall bias. CONCLUSIONS: Our findings lend preliminary support to the hypothesis that highly recurrent MDD with FH+ is part of a 'soft bipolar spectrum' but await replication in larger cohorts.

Genome-wide association study of co-occurring anxiety in major depression.

OBJECTIVES: Co-morbidity between depression and anxiety disorders is common. In this study we define a quantitative measure of anxiety by summating four anxiety items from the SCAN interview in a large collection of major depression (MDD) cases to identify genes contributing to this complex phenotype. METHODS: A total of 1522 MDD cases dichotomised according to those with at least one anxiety item scored (n = 1080) and those without anxiety (n = 442) were analysed, and also compared to 1588 healthy controls at a genome-wide level, to identify genes that may contribute to anxiety in MDD. RESULTS: For the quantitative trait, suggestive evidence of association was detected for two SNPs, and for the dichotomous anxiety present/absent ratings for three SNPs at genome-wide level. In the genome-wide analysis of MDD cases with co-morbid anxiety and healthy controls, two SNPs attained P values of < 5 × 10⁻⁶. Analysing candidate genes, P values ≤ 0.0005 were found with three SNPs for the quantitative trait and three SNPs for the dichotomous trait. CONCLUSIONS: This study provides an initial genome-wide assessment of possible genetic contribution to anxiety in MDD. Although suggestive evidence of association was found for several SNPs, our findings suggest that there are no common variants strongly associated with anxious depression.