Data-driven design: The new challenges of digitalization on product design and development

Digitalization and the momentous role being assumed by data are commonly viewed as pervasive phenomena whose impact is felt in all aspects of society and the economy. Design activity is by no means immune from this trend, and the relationship between digitalization and design is decades old. However, what is the current impact of this 'data revolution' on design? How will the design activity change? What are the resulting research questions of interest to academics? What are the main challenges for firms and for educational institutions having to cope with this change? The paper provides a comprehensive conceptual framework, based on recent literature and anecdotal evidence from the industry. It identifies three main streams: namely the consequences on designers, the consequences on design processes and the role of methods for data analytics. In turn, these three streams lead to implications at individual, organizational and managerial level, and several questions arise worthy of defining future research agendas. Moreover, the paper introduces relational diagrams depicting the interactions between the objects and the actors involved in the design process and suggests that what is occurring is by no means a simple evolution but a paradigmatic shift in the way artefacts are designed

Genetic Classification of Populations using Supervised Learning

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case--control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed \emph{unsupervised}. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.Comment: Accepted PLOS On

Pharmacogenomic testing and outcome among depressed patients in a tertiary care outpatient psychiatric consultation practice

The authors tested the hypothesis that pharmacogenomic genotype knowledge is associated with better clinical and cost outcomes in depressed patients, after controlling for other factors that might differentiate tested and non-tested patients. Medical records of 251 patients, seen in the Mayo Clinic Rochester outpatient psychiatric practice, who had patient health questionnaire-9 (PHQ-9) scores before and after consultation, were reviewed. Comparisons of differences in pre-consultation and post-consultation depression scores and slopes between tested and non-tested patients and between genotype categories of tested patients, were evaluated, along with healthcare cost and utilization comparisons between tested and non-tested patients, using Kruskal–Wallis tests, Wilcoxon rank-sum tests and group mean comparisons, controlling for significant univariate demographic and clinical differences. Tested patients had significantly higher depression diagnosis frequency, baseline PHQ-9 scores, family history of depression, psychiatric hospitalization history, and higher numbers of antidepressant, mood stabilizer and antipsychotic medication trials. After controlling for these differences, there were no differences between tested and non-tested patients in post-baseline depression scores or slopes for CYP genotype categories. For patients with 5-HTTLPR testing, there was significantly more depression score improvement for patients with the long/long genotype at time 4 (N=55, χ2-value=8.0492, P=0.018) and at time 5 (N=44, χ2-value=6.1492, P=0.046). For a subgroup (n=46) with ⩾two pre- and ⩾two post-baseline PHQ-9 scores, the mean difference between pre-baseline and post-baseline PHQ-9 score slopes for tested patients was −0.08 (median −0.01; range −1.20 to 0.15) compared with 0.13 (median 0.02; range −0.18 to 2.16) for non-tested patients (P=0.03). Among genotype categories, mean differences between pre-consultation and post-consultation slopes were significantly better for poor CYP2D6 metabolizers than intermediate or extensive metabolizers (P=0.04); there was a trend for slope differences to be better for 5-HTTLPR long/long genotype patients (P=0.06). Subsets of local tested and consultant-adjusted non-tested controls (n=19), who had 8 years of longitudinal care within the health system, had similar overall mean healthcare costs before and after testing; however, tested patients on average had significantly fewer time-adjusted post-baseline psychiatric admissions (0.8 vs 3.8, P=0.04) and fewer time-adjusted psychiatric consultations and comprehensive mental health-specialty evaluations (4.2 vs 9.9, P=0.03). Prospective study is indicated as to whether and how pharmacogenomic testing in a psychiatric consultation practice may improve clinical and cost outcomes

A decision support model to assess technological paradigms

Envisioning the emergence of a new technological paradigm involves several issues, spanning from strategic assessments and managerial actions to design decisions and technology related choices. The present study focuses on this latter perspective, by proposing a model that estimates the success probability of innovative products as a function of design actions. This focus on the design decisions that underlie radical shifts is not conflicting, but complementary to the more traditional perspectives of forecasting that consider environmental variables or process management factors. The model is based on a database of past successful and unsuccessful innovations, which are used to build a logistic regression model, whose evidences can assist both designers and managers. The former get advice on how specific design choices affect product perception and innovation adoption, while the latter are supported in identifying the most promising projects. The model is illustrated through two cases of digital products

Bisexuality and Suicide: A Systematic Review of the Current Literature

Introduction: Many studies of lesbian, gay, and bisexual youth have demonstrated that individuals reporting a bisexual orientation have a particularly high risk of suicidal behavior and substance abuse. It has been also suggested that bisexual individuals (both men and women) have higher rates of depression and anxiety compared with homosexual and heterosexual groups. Aim: The aim of the present article was to determine whether or not an association between bisexuality and suicidal behavior exists and to analyze risk factors for suicidal behavior in bisexual individuals. Main Outcome Measures: The combined search strategies yielded a total of 339 records screened from PubMed, Scopus, and Web of Knowledge. Duplicate articles, articles that were not in English, and those that did not analyze bisexuality separately from homosexuality were excluded. A quality assessment was performed for each study included. Methods: A careful systematic review of the literature was conducted investigating the potential bisexuality-suicidal behavior link. A total of 77 articles from peer-reviewed journals were considered, and the most relevant (N=19) were selected for this review. Results: Individuals reporting a bisexual orientation had an increased risk of suicide attempts and ideation compared with their homosexual and heterosexual peers. Risk factors included related victimization, peer judgments, and family rejection. Bisexual individuals also reported higher rates of mental illness and substance abuse. Conclusions: Bisexual individuals may experience more psychological distress and mental health problems than individuals who identify with a homosexual or heterosexual orientation. Clinicians should consider the potential for suicidal behaviors in bisexual individuals and be alert for increased mental health problems and poor social integration. © 2014 International Society for Sexual Medicine

Tryptophan and kynurenine metabolites: Are they related to depression?

Background: Some previous studies found decreased concentrations of L-tryptophan (TRY) and increased L-kynurenine (KYN), or its metabolites, in the body fluids of subjects with major depressive disorder (MDD), sometimes in association with suicidal behavior. Such changes might indicate a shift of TRY away from serotonin production, possibly via the effects of inflammatory peptides which activate indoleamine-2,3-dioxygenase. However, these findings have been inconsistent and require replication. Methods: We used sensitive liquid-chromatography mass spectrometry methods to assay plasma concentrations of TRY, 5-hydroxyindoleacetic acid (5-HIAA), and KYN and its metabolites (anthranilic acid and xanthurenic acid). We compared 49 hospitalized, depressed subjects diagnosed with MDD (n = 37) or bipolar disorder (BD, n = 12), with (n = 22) or without (n = 27) previous suicide attempts, to 78 healthy, ambulatory controls of similar age and sex (total n = 127). Findings: Contrary to expectation, TRY plasma concentrations were higher, KYN plasma concentrations were lower, and their ratio much higher in depressed subjects, with no relationship to suicidal history. Concentrations of 5-HIAA and the kynurenine metabolites did not differ between depressed and healthy subjects. Conclusions: These findings are opposite to expectations and not consistent with a hypothesized increased conversion from TRY to KYN in depressed subjects. In addition, we found no evidence of altered production of serotonin as 5-HIAA concentration was unchanged. None of the observed changes was associated with a history of suicide attempt

The bipolar depression rating scale (BDRS) : its development, validation and utility

Objectives: Unipolar and bipolar depression differ neurobiologically and in clinical presentation. Existing depression rating instruments, used in bipolar depression, fail to capture the necessary phenomenological nuances, as they are based on and skewed towards the characteristics of unipolar depression. Both clinically and in research there is a growing need for a new observer-rated scale that is specifically designed to assess bipolar depression.Methods: An instrument reflecting the characteristics of bipolar depression was drafted by the authors, and administered to 122 participants aged 18&ndash;65 (44 males and 78 females) with a diagnosis of DSM-IV bipolar disorder, who were currently experiencing symptoms of depression. The Bipolar Depression Rating Scale (BDRS) was administered together with the Hamilton Depression Rating Scale (HAM-D), Montgomery Asberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS).Results: The BDRS has strong internal consistency (Cronbach\u27s alpha = 0.917), and robust correlation coefficients with the MADRS (r = 0.906) and HAM-D (r = 0.744), and the mixed subscale correlated with the YMRS (r = 0.757). Exploratory factor analysis showed a three-factor solution gave the best account of the data. These factors corresponded to depression (somatic), depression (psychological) and mixed symptom clusters.Conclusions: This study provides evidence for the validity of the BDRS for the measurement of depression in bipolar disorder. These results suggest good internal validity, provisional evidence of inter-rater reliability and strong correlations with other depression rating scales.<br /