Bayesian networks and probabilistic reasoning about scientific evidence when there is a lack of data

Quantitative association between lead exposure and amyotrophic lateral sclerosis: a Bayesian network-based predictive study

BACKGROUND

Environmental lead (Pb) exposure have been suggested as a causative factor for amyotrophic lateral sclerosis (ALS). However, the role of Pb content of human body in ALS outcomes has not been quantified clearly. The purpose of this study was to apply Bayesian networks to forecast the risk of Pb exposure on the disease occurrence.

METHODS

We retrospectively collected medical records of ALS inpatients who underwent blood Pb testing, while matched controlled inpatients on age, gender, hospital ward and admission time according to the radio of 1:9. Tree Augmented Naïve Bayes (TAN), a semi-naïve Bayes classifier, was established to predict probability of ALS or controls with risk factors.

RESULTS

A total of 140 inpatients were included in this study. The whole blood Pb levels of ALS patients (57.00 μg/L) were more than twice as high as the controls (27.71 μg/L). Using the blood Pb concentrations to calculate probability of ALS, TAN produced the total coincidence rate of 90.00%. The specificity, sensitivity of Pb for ALS prediction was 0.79, or 0.74, respectively.

CONCLUSION

Therefore, these results provided quantitative evidence that Pb exposure may contribute to the development of ALS. Bayesian networks may be used to predict the ALS early onset with blood Pb levels.

A collection of idioms for modeling activity level evaluations in forensic science

This paper presents a collection of idioms that is useful for modeling activity level evaluations in forensic science using Bayesian networks. The idioms are categorized into five groups: cause-consequence idioms, narrative idioms, synthesis idioms, hypothesis-conditioning idioms, and evidence-conditioning idioms. Each category represents a specific modeling objective. Furthermore, we support the use of an idiom-based approach and emphasize the relevance of our collection by combining several of the presented idioms to create a more comprehensive template model. This model can be used in cases involving transfer evidence and disputes over the actor and/or activity. Additionally, we cite literature that employs idioms in template models or case-specific models, providing the reader with examples of their use in forensic casework.

Bayesian-Based Approaches to Exploring the Long-Term Alteration in Trace Metals of Surface Water and Its Driving Forces

Trace metal pollution poses a serious threat to the aquatic ecosystem. Therefore, characterizing the long-term environmental behavior of trace metals and their driving forces is essential for guiding water quality management. Based on a long-term data set from 1990 to 2019, this study systematically conducted the spatiotemporal trend assessment, influential factor analysis, and source apportionment of trace elements in the rivers of the German Elbe River basin. Results show that the mean concentrations of the given elements in the last 30 years were found in the order of Fe (1179.5 ± 1221 μg·L^-1) ≫ Mn (209.6 ± 181.7 μg·L^-1) ≫ Zn (52.5 ± 166.2 μg·L^-1) ≫ Cu (5.3 ± 5.5 μg·L^-1) > Ni (4.4 ± 8.3 μg·L^-1) > Pb (3.3 ± 4.4 μg·L^-1) > As (2.9 ± 2.3 μg·L^-1) > Cr (1.8 ± 2.4 μg·L^-1) ≫ Cd (0.3 ± 1.1 μg·L^-1) > Hg (0.05 ± 0.12 μg·L^-1). Wavelet analyses show that river flow regimes and flooding dominated the periodic variations in metal pollution. Bayesian network suggests that the hydrochemical factors (i.e., TOC, TP, TN, pH, and EC) chemically influenced the metal mobility between water and sediments. Furthermore, the source apportionment computed by the Bayesian multivariate receptor model shows that the given element contamination was typically attributed to the geogenic sources (17.5, 95% confidence interval: 13.1-17.6%), urban and industrial sources (22.1, 18.0-27.2%), arable soil erosion (24.2, 16.4-31.5%), and historical anthropogenic activities (35.2, 32.8-43.3%). The results provided herein reveal that both the hydrochemical influence on metal mobility and the chronic disturbance from anthropogenic activities caused the long-term variation in trace metal pollution.

Digital evidence exceptionalism? A review and discussion of conceptual hurdles in digital evidence transformation

Forensic science is currently undergoing a transformation and expansion to include modern types of evidence, such as evidence generated by digital investigations. This development is said to raise a series of challenges, both in operational and conceptual dimensions. This paper reviews and discusses a series of convoluted conceptual hurdles that are encountered in connection with the use of digital evidence as part of evidence and proof processes at trial, in contradistinction to investigative uses of such types of evidence. As a recent example raising such hurdles, we analyse and discuss assertions and proposals made in the article "Digital Evidence Certainty Descriptors (DECDs)" by Graeme Horsman (32 Forensic Science International: Digital Investigation (2020) 200896).

Evaluation of Forensic DNA Traces When Propositions of Interest Relate to Activities: Analysis and Discussion of Recurrent Concerns

When forensic scientists evaluate and report on the probative strength of single DNA traces, they commonly rely on only one number, expressing the rarity of the DNA profile in the population of interest. This is so because the focus is on propositions regarding the source of the recovered trace material, such as "the person of interest is the source of the crime stain." In particular, when the alternative proposition is "an unknown person is the source of the crime stain," one is directed to think about the rarity of the profile. However, in the era of DNA profiling technology capable of producing results from small quantities of trace material (i.e., non-visible staining) that is subject to easy and ubiquitous modes of transfer, the issue of source is becoming less central, to the point that it is often not contested. There is now a shift from the question "whose DNA is this?" to the question "how did it get there?" As a consequence, recipients of expert information are now very much in need of assistance with the evaluation of the meaning and probative strength of DNA profiling results when the competing propositions of interest refer to different activities. This need is widely demonstrated in day-to-day forensic practice and is also voiced in specialized literature. Yet many forensic scientists remain reluctant to assess their results given propositions that relate to different activities. Some scientists consider evaluations beyond the issue of source as being overly speculative, because of the lack of relevant data and knowledge regarding phenomena and mechanisms of transfer, persistence and background of DNA. Similarly, encouragements to deal with these activity issues, expressed in a recently released European guideline on evaluative reporting (Willis et al., 2015), which highlights the need for rethinking current practice, are sometimes viewed skeptically or are not considered feasible. In this discussion paper, we select and discuss recurrent skeptical views brought to our attention, as well as some of the alternative solutions that have been suggested. We will argue that the way forward is to address now, rather than later, the challenges associated with the evaluation of DNA results (from small quantities of trace material) in light of different activities to prevent them being misrepresented in court.

Evaluating forensic biology results given source level propositions

The evaluation of forensic evidence can occur at any level within the hierarchy of propositions depending on the question being asked and the amount and type of information that is taken into account within the evaluation. Commonly DNA evidence is reported given propositions that deal with the sub-source level in the hierarchy, which deals only with the possibility that a nominated individual is a source of DNA in a trace (or contributor to the DNA in the case of a mixed DNA trace). We explore the use of information obtained from examinations, presumptive and discriminating tests for body fluids, DNA concentrations and some case circumstances within a Bayesian network in order to provide assistance to the Courts that have to consider propositions at source level. We use a scenario in which the presence of blood is of interest as an exemplar and consider how DNA profiling results and the potential for laboratory error can be taken into account. We finish with examples of how the results of these reports could be presented in court using either numerical values or verbal descriptions of the results.

Fusion of laboratory and textual data for investigative bioforensics

Chemical and biological forensic programs focus on the identification of a threat and acquisition of laboratory measurements to determine how a threat agent may have been produced. However, to generate investigative leads, it might also be useful to identify institutions where the same agent has been produced by the same or a very similar process, since the producer of the agent may have learned methods at a university or similar institution. We have developed a Bayesian network framework that fuses hard and soft data sources to assign probability to production practices. It combines the results of laboratory measurements with an automatic text reader to scan scientific literature and rank institutions that had published papers on the agent of interest in order of the probability that the institution has the capability to generate the sample of interest based on laboratory data. We demonstrate the Bayesian network on an example case from microbial forensics, predicting the methods used to produce Bacillus anthracis spores based on mass spectrometric measurements and identifying institutions that have a history of growing Bacillus spores using the same or highly similar methods. We illustrate that the network model can assign a higher posterior probability than expected by random chance to appropriate institutions when trained using only a small set of manually analyzed documents. This is the first example of an automated methodology to integrate experimental and textual data for the purpose of investigative forensics.

Methods for assessing uncertainty in fundamental assumptions and associated models for cancer risk assessment

The distributional approach for uncertainty analysis in cancer risk assessment is reviewed and extended. The method considers a combination of bioassay study results, targeted experiments, and expert judgment regarding biological mechanisms to predict a probability distribution for uncertain cancer risks. Probabilities are assigned to alternative model components, including the determination of human carcinogenicity, mode of action, the dosimetry measure for exposure, the mathematical form of the dose-response relationship, the experimental data set(s) used to fit the relationship, and the formula used for interspecies extrapolation. Alternative software platforms for implementing the method are considered, including Bayesian belief networks (BBNs) that facilitate assignment of prior probabilities, specification of relationships among model components, and identification of all output nodes on the probability tree. The method is demonstrated using the application of Evans, Sielken, and co-workers for predicting cancer risk from formaldehyde inhalation exposure. Uncertainty distributions are derived for maximum likelihood estimate (MLE) and 95th percentile upper confidence limit (UCL) unit cancer risk estimates, and the effects of resolving selected model uncertainties on these distributions are demonstrated, considering both perfect and partial information for these model components. A method for synthesizing the results of multiple mechanistic studies is introduced, considering the assessed sensitivities and selectivities of the studies for their targeted effects. A highly simplified example is presented illustrating assessment of genotoxicity based on studies of DNA damage response caused by naphthalene and its metabolites. The approach can provide a formal mechanism for synthesizing multiple sources of information using a transparent and replicable weight-of-evidence procedure.

Resolving Paternity Relationships Using X-Chromosome STRs and Bayesian Networks

X-chromosomal short tandem repeats (X-STRs) are very useful in complex paternity cases because they are inherited by male and female offspring in different ways. They complement autosomal STRs (as-STRs) allowing higher paternity probabilities to be attained. These probabilities are expressed in a likelihood ratio (LR). The formulae needed to calculate LR depend on the genotype combinations of suspected pedigrees. LR can also be obtained by the use of Bayesian networks (BNs). These are graphical representations of real situations that can be used to easily calculate complex probabilities. In the present work, two BNs are presented, which are designed to derive LRs for half-sisters/half-sisters and mother/daughter/paternal grandmother relationships. These networks were validated against known formulae and show themselves to be useful in other suspect pedigree situations than those for which they were developed. The BNs were applied in two paternity cases. The application of the mother/daughter/paternal grandmother BN highlighted the complementary value of X-STRs to as-STRs. The same case evaluated without the mother underlined that missing information tends to be conservative if the alleged father is the biological father and otherwise nonconservative. The half-sisters case shows a limitation of statistical interpretations in regard to high allelic frequencies.

Value of DNA tests: a decision perspective

Before a Court of Law testifying in DNA-evidence cases, scientists are often challenged with the idea that the more markers (loci) the better, i.e., why does the scientist not use 16 or more markers? This paper introduces a new perspective, decision analysis, to deal with the problem of the number of markers to type in a criminal context. The decision-making process, which plays a key role in the routine work of a forensic scientist, consists of the rational choice, given personal objectives, between two or more possible outcomes when the consequences of the choice are uncertain. Simulated results support the hypothesis that analytical added value does not increase with the number of markers.