Bayesian networks for evaluating forensic DNA profiling evidence: a review and guide to literature

Almost 30 years ago, Bayesian networks (BNs) were developed in the field of artificial intelligence as a framework that should assist researchers and practitioners in applying the theory of probability to inference problems of more substantive size and, thus, to more realistic and practical problems. Since the late 1980s, Bayesian networks have also attracted researchers in forensic science and this tendency has considerably intensified throughout the last decade. This review article provides an overview of the scientific literature that describes research on Bayesian networks as a tool that can be used to study, develop and implement probabilistic procedures for evaluating the probative value of particular items of scientific evidence in forensic science. Primary attention is drawn here to evaluative issues that pertain to forensic DNA profiling evidence because this is one of the main categories of evidence whose assessment has been studied through Bayesian networks. The scope of topics is large and includes almost any aspect that relates to forensic DNA profiling. Typical examples are inference of source (or, 'criminal identification'), relatedness testing, database searching and special trace evidence evaluation (such as mixed DNA stains or stains with low quantities of DNA). The perspective of the review presented here is not exclusively restricted to DNA evidence, but also includes relevant references and discussion on both, the concept of Bayesian networks as well as its general usage in legal sciences as one among several different graphical approaches to evidence evaluation.

First consumption ever of multiple substances: applying an expert-based taxonomy to a Swiss national sample of adolescents.

The use of multiple legal and illegal substances by adolescents is a growing concern in all countries, but since no consensus about a taxonomy did emerge yet, it is difficult to understand the different patterns of consumption and to implement tailored prevention and treatment programs directed towards specific subgroups of the adolescent population. Using data from a Swiss survey on adolescent health, we analyzed the age at which ten legal and illegal substances were consumed for the first time ever by applying a method combining the strength of both automatic clustering and use of substance experts. Results were then compared to 30 socio-economic factors to establish the usefulness of and to validate our taxonomy. We also analyzed the succession of substance first use for each group. The final taxonomy consists of eight groups ranging from non-consumers to heavy drug addicts. All but four socio-economic factors were significantly associated with the taxonomy, the strongest associations being observed with health, behavior, and sexuality factors. Numerous factors influence adolescents in their decision to first try substances or to use them on a regular basis, and no factor alone can be considered as an absolute marker of problematic behavior regarding substance use. Different processes of experimentation with substances are associated with different behaviors, therefore focusing on only one substance or only one factor is not efficient. Prevention and treatment programs can then be tailored to address specific issues related to different youth subgroups.

Identification of particular groups of microRNAs that positively or negatively impact on beta cell function in obese models of type 2 diabetes.

AIMS/HYPOTHESIS: MicroRNAs are key regulators of gene expression involved in health and disease. The goal of our study was to investigate the global changes in beta cell microRNA expression occurring in two models of obesity-associated type 2 diabetes and to assess their potential contribution to the development of the disease. METHODS: MicroRNA profiling of pancreatic islets isolated from prediabetic and diabetic db/db mice and from mice fed a high-fat diet was performed by microarray. The functional impact of the changes in microRNA expression was assessed by reproducing them in vitro in primary rat and human beta cells. RESULTS: MicroRNAs differentially expressed in both models of obesity-associated type 2 diabetes fall into two distinct categories. A group including miR-132, miR-184 and miR-338-3p displays expression changes occurring long before the onset of diabetes. Functional studies indicate that these expression changes have positive effects on beta cell activities and mass. In contrast, modifications in the levels of miR-34a, miR-146a, miR-199a-3p, miR-203, miR-210 and miR-383 primarily occur in diabetic mice and result in increased beta cell apoptosis. These results indicate that obesity and insulin resistance trigger adaptations in the levels of particular microRNAs to allow sustained beta cell function, and that additional microRNA deregulation negatively impacting on insulin-secreting cells may cause beta cell demise and diabetes manifestation. CONCLUSIONS/INTERPRETATION: We propose that maintenance of blood glucose homeostasis or progression toward glucose intolerance and type 2 diabetes may be determined by the balance between expression changes of particular microRNAs.

A new rating scale to evaluate the potential benefit of therapeutic drug monitoring

Aims: Therapeutic Drug Monitoring (TDM) is an established tool to optimize thepharmacotherapy with immunosupressants, antibiotics, antiretroviral agents, anticonvulsantsand psychotropic drugs. The TDM expert group of the Association ofNeuropsychopharmacolgy and Pharmacopsychiatry recommended clinical guidelinesfor TDM of psychotropic drugs in 2004 and in 2011. They allocate 4 levelsof recommendation based on studies reporting plasma concentrations and clinicaloutcomes. To evaluate the additional benefit for drugs without direct evidence forTDM and to verify the recommendation levels of the expert group the authorsbuilt a new rating scale. Methods: This rating scale included 28 items and wasdivided in 5 categories: Efficacy, toxicity, pharmacokinetics, patient characteristicsand cost effectiveness. A literature search was performed for 10 antidepressants,10 antipsychotics, 8 drugs used in the treatment of substance related disordersand lithium, thereafter, a comparison with the assessment of the TDMexpert group was carried out. Results: The antidepressants as well as the antipsychoticsshowed a high and significant correlation with the recommendations inthe consensus guidelines. However, meanderings could be detected for the drugsused in the therapy of substance related disorders, for which TDM is mostly notestablished yet. The result of the antidepressants and antipsychotics permits aclassification of the reachable points; upper 13 - TDM strongly recommended10 to 13 - TDM recommended, 8 to 10 - TDM useful and below 8 - TDMpotentially useful. Conclusion: These results suggest this rating scale is sensitiveto detect the appropriateness of TDM for drug treatment. For those drugs TDM isnot established a more objective estimation is possible, thus the scoring helps tofocus on the most likely drugs to require TDM.

Evolving from inferences to decisions in the interpretation of scientific evidence

Le travail d'un(e) expert(e) en science forensique exige que ce dernier (cette dernière) prenne une série de décisions. Ces décisions sont difficiles parce qu'elles doivent être prises dans l'inévitable présence d'incertitude, dans le contexte unique des circonstances qui entourent la décision, et, parfois, parce qu'elles sont complexes suite à de nombreuse variables aléatoires et dépendantes les unes des autres. Etant donné que ces décisions peuvent aboutir à des conséquences sérieuses dans l'administration de la justice, la prise de décisions en science forensique devrait être soutenue par un cadre robuste qui fait des inférences en présence d'incertitudes et des décisions sur la base de ces inférences. L'objectif de cette thèse est de répondre à ce besoin en présentant un cadre théorique pour faire des choix rationnels dans des problèmes de décisions rencontrés par les experts dans un laboratoire de science forensique. L'inférence et la théorie de la décision bayésienne satisfont les conditions nécessaires pour un tel cadre théorique. Pour atteindre son objectif, cette thèse consiste de trois propositions, recommandant l'utilisation (1) de la théorie de la décision, (2) des réseaux bayésiens, et (3) des réseaux bayésiens de décision pour gérer des problèmes d'inférence et de décision forensiques. Les résultats présentent un cadre uniforme et cohérent pour faire des inférences et des décisions en science forensique qui utilise les concepts théoriques ci-dessus. Ils décrivent comment organiser chaque type de problème en le décomposant dans ses différents éléments, et comment trouver le meilleur plan d'action en faisant la distinction entre des problèmes de décision en une étape et des problèmes de décision en deux étapes et en y appliquant le principe de la maximisation de l'utilité espérée. Pour illustrer l'application de ce cadre à des problèmes rencontrés par les experts dans un laboratoire de science forensique, des études de cas théoriques appliquent la théorie de la décision, les réseaux bayésiens et les réseaux bayésiens de décision à une sélection de différents types de problèmes d'inférence et de décision impliquant différentes catégories de traces. Deux études du problème des deux traces illustrent comment la construction de réseaux bayésiens permet de gérer des problèmes d'inférence complexes, et ainsi surmonter l'obstacle de la complexité qui peut être présent dans des problèmes de décision. Trois études-une sur ce qu'il faut conclure d'une recherche dans une banque de données qui fournit exactement une correspondance, une sur quel génotype il faut rechercher dans une banque de données sur la base des observations faites sur des résultats de profilage d'ADN, et une sur s'il faut soumettre une trace digitale à un processus qui compare la trace avec des empreintes de sources potentielles-expliquent l'application de la théorie de la décision et des réseaux bayésiens de décision à chacune de ces décisions. Les résultats des études des cas théoriques soutiennent les trois propositions avancées dans cette thèse. Ainsi, cette thèse présente un cadre uniforme pour organiser et trouver le plan d'action le plus rationnel dans des problèmes de décisions rencontrés par les experts dans un laboratoire de science forensique. Le cadre proposé est un outil interactif et exploratoire qui permet de mieux comprendre un problème de décision afin que cette compréhension puisse aboutir à des choix qui sont mieux informés. - Forensic science casework involves making a sériés of choices. The difficulty in making these choices lies in the inévitable presence of uncertainty, the unique context of circumstances surrounding each décision and, in some cases, the complexity due to numerous, interrelated random variables. Given that these décisions can lead to serious conséquences in the admin-istration of justice, forensic décision making should be supported by a robust framework that makes inferences under uncertainty and décisions based on these inferences. The objective of this thesis is to respond to this need by presenting a framework for making rational choices in décision problems encountered by scientists in forensic science laboratories. Bayesian inference and décision theory meets the requirements for such a framework. To attain its objective, this thesis consists of three propositions, advocating the use of (1) décision theory, (2) Bayesian networks, and (3) influence diagrams for handling forensic inference and décision problems. The results present a uniform and coherent framework for making inferences and décisions in forensic science using the above theoretical concepts. They describe how to organize each type of problem by breaking it down into its différent elements, and how to find the most rational course of action by distinguishing between one-stage and two-stage décision problems and applying the principle of expected utility maximization. To illustrate the framework's application to the problems encountered by scientists in forensic science laboratories, theoretical case studies apply décision theory, Bayesian net-works and influence diagrams to a selection of différent types of inference and décision problems dealing with différent catégories of trace evidence. Two studies of the two-trace problem illustrate how the construction of Bayesian networks can handle complex inference problems, and thus overcome the hurdle of complexity that can be present in décision prob-lems. Three studies-one on what to conclude when a database search provides exactly one hit, one on what genotype to search for in a database based on the observations made on DNA typing results, and one on whether to submit a fingermark to the process of comparing it with prints of its potential sources-explain the application of décision theory and influ¬ence diagrams to each of these décisions. The results of the theoretical case studies support the thesis's three propositions. Hence, this thesis présents a uniform framework for organizing and finding the most rational course of action in décision problems encountered by scientists in forensic science laboratories. The proposed framework is an interactive and exploratory tool for better understanding a décision problem so that this understanding may lead to better informed choices.

The ciliary smooth muscle electrotransfer: basic principles and potential for sustained intraocular production of therapeutic proteins.

BACKGROUND: We have developed a nonviral gene therapy method based on the electrotransfer of plasmid in the ciliary muscle. These easily accessible smooth muscle cells could be turned into a biofactory for any therapeutic proteins to be secreted in a sustained manner in the ocular media. METHODS: Electrical conditions, design of electrodes, plasmid formulation, method and number of injections were optimized in vivo in the rat by localizing β-galactosidase expression and quantifying reporter (luciferase) and therapeutic (anti-tumor necrosis factor) proteins secretion in the ocular media. Anatomical measurements were performed via human magnetic resonance imaging to design a human eye-sized prototype that was tested in the rabbit. RESULTS: In the rat, transscleral injection of 30 µg of plasmid diluted in half saline (77 mM NaCl) followed by application of eight square-wave electrical pulses (15 V, 10 ms, 5.3 Hz) using two platinum/iridium electrodes, an internal wire and an external sheet, delivered plasmid efficiently to the ciliary muscle fibers. Gene transfer resulted in a long-lasting (at least 5 months) and plasmid dose-/injection number- dependent secretion of different molecular weight proteins mainly in the vitreous, without any systemic exposure. Because ciliary muscle anatomical measurements remained constant among ages in adult humans, an integrated device comprising needle-electrodes was designed and manufactured. Its usefulness was validated in the rabbit. CONCLUSIONS: Plasmid electrotransfer to the ciliary muscle with a suitable medical device represents a promising local and sustained protein delivery system for treating posterior segment diseases, avoiding repeated intraocular injections.

From evidence to clinical practice: effective implementation of therapeutic hypothermia to improve patient outcome after cardiac arrest.

OBJECTIVES: Therapeutic hypothermia has been recommended for postcardiac arrest coma due to ventricular fibrillation. However, no studies have evaluated whether therapeutic hypothermia could be effectively implemented in intensive care practice and whether it would improve the outcome of all comatose patients with cardiac arrest, including those with shock or with cardiac arrest due to nonventricular fibrillation rhythms. DESIGN: Retrospective study. SETTING: Fourteen-bed medical intensive care unit in a university hospital. PATIENTS: Patients were 109 comatose patients with out-of-hospital cardiac arrest due to ventricular fibrillation and nonventricular fibrillation rhythms (asystole/pulseless electrical activity). INTERVENTIONS: We analyzed 55 consecutive patients (June 2002 to December 2004) treated with therapeutic hypothermia (to a central target temperature of 33 degrees C, using external cooling). Fifty-four consecutive patients (June 1999 to May 2002) treated with standard resuscitation served as controls. Efficacy, safety, and outcome at hospital discharge were assessed. Good outcome was defined as Glasgow-Pittsburgh Cerebral Performance category 1 or 2. MEASUREMENTS AND MAIN RESULTS: In patients treated with therapeutic hypothermia, the median time to reach the target temperature was 5 hrs, with a progressive reduction over the 18 months of data collection. Therapeutic hypothermia had a major positive impact on the outcome of patients with cardiac arrest due to ventricular fibrillation (good outcome in 24 of 43 patients [55.8%] of the therapeutic hypothermia group vs. 11 of 43 patients [25.6%] of the standard resuscitation group, p = .004). The benefit of therapeutic hypothermia was also maintained in patients with shock (good outcome in five of 17 patients of the therapeutic hypothermia group vs. zero of 14 of the standard resuscitation group, p = .027). The outcome after cardiac arrest due to nonventricular fibrillation rhythms was poor and did not differ significantly between the two groups. Therapeutic hypothermia was of particular benefit in patients with short duration of cardiac arrest (<30 mins). CONCLUSIONS: Therapeutic hypothermia for the treatment of postcardiac arrest coma can be successfully implemented in intensive care practice with a major benefit on patient outcome, which appeared to be related to the type and the duration of initial cardiac arrest and seemed maintained in patients with shock.

A novel approach to characterize clonality and differentiation of human melanoma-specific T cell responses: spontaneous priming and efficient boosting by vaccination.

Despite major progress in T lymphocyte analysis in melanoma patients, TCR repertoire selection and kinetics in response to tumor Ags remain largely unexplored. In this study, using a novel ex vivo molecular-based approach at the single-cell level, we identified a single, naturally primed T cell clone that dominated the human CD8(+) T cell response to the Melan-A/MART-1 Ag. The dominant clone expressed a high-avidity TCR to cognate tumor Ag, efficiently killed tumor cells, and prevailed in the differentiated effector-memory T lymphocyte compartment. TCR sequencing also revealed that this particular clone arose at least 1 year before vaccination, displayed long-term persistence, and efficient homing to metastases. Remarkably, during concomitant vaccination over 3.5 years, the frequency of the pre-existing clone progressively increased, reaching up to 2.5% of the circulating CD8 pool while its effector functions were enhanced. In parallel, the disease stabilized, but subsequently progressed with loss of Melan-A expression by melanoma cells. Collectively, combined ex vivo analysis of T cell differentiation and clonality revealed for the first time a strong expansion of a tumor Ag-specific human T cell clone, comparable to protective virus-specific T cells. The observed successful boosting by peptide vaccination support further development of immunotherapy by including strategies to overcome immune escape.

Graphical probabilistic analysis of the combination of items of evidence

Unlike the evaluation of single items of scientific evidence, the formal study and analysis of the jointevaluation of several distinct items of forensic evidence has to date received some punctual, ratherthan systematic, attention. Questions about the (i) relationships among a set of (usually unobservable)propositions and a set of (observable) items of scientific evidence, (ii) the joint probative valueof a collection of distinct items of evidence as well as (iii) the contribution of each individual itemwithin a given group of pieces of evidence still represent fundamental areas of research. To somedegree, this is remarkable since both, forensic science theory and practice, yet many daily inferencetasks, require the consideration of multiple items if not masses of evidence. A recurrent and particularcomplication that arises in such settings is that the application of probability theory, i.e. the referencemethod for reasoning under uncertainty, becomes increasingly demanding. The present paper takesthis as a starting point and discusses graphical probability models, i.e. Bayesian networks, as frameworkwithin which the joint evaluation of scientific evidence can be approached in some viable way.Based on a review of existing main contributions in this area, the article here aims at presentinginstances of real case studies from the author's institution in order to point out the usefulness andcapacities of Bayesian networks for the probabilistic assessment of the probative value of multipleand interrelated items of evidence. A main emphasis is placed on underlying general patterns of inference,their representation as well as their graphical probabilistic analysis. Attention is also drawnto inferential interactions, such as redundancy, synergy and directional change. These distinguish thejoint evaluation of evidence from assessments of isolated items of evidence. Together, these topicspresent aspects of interest to both, domain experts and recipients of expert information, because theyhave bearing on how multiple items of evidence are meaningfully and appropriately set into context.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.

Probabilistic graphical models to deal with age estimation of living persons

Due to the rise of criminal, civil and administrative judicial situations involving people lacking valid identity documents, age estimation of living persons has become an important operational procedure for numerous forensic and medicolegal services worldwide. The chronological age of a given person is generally estimated from the observed degree of maturity of some selected physical attributes by means of statistical methods. However, their application in the forensic framework suffers from some conceptual and practical drawbacks, as recently claimed in the specialised literature. The aim of this paper is therefore to offer an alternative solution for overcoming these limits, by reiterating the utility of a probabilistic Bayesian approach for age estimation. This approach allows one to deal in a transparent way with the uncertainty surrounding the age estimation process and to produce all the relevant information in the form of posterior probability distribution about the chronological age of the person under investigation. Furthermore, this probability distribution can also be used for evaluating in a coherent way the possibility that the examined individual is younger or older than a given legal age threshold having a particular legal interest. The main novelty introduced by this work is the development of a probabilistic graphical model, i.e. a Bayesian network, for dealing with the problem at hand. The use of this kind of probabilistic tool can significantly facilitate the application of the proposed methodology: examples are presented based on data related to the ossification status of the medial clavicular epiphysis. The reliability and the advantages of this probabilistic tool are presented and discussed.