Fourmis des bois en Suisse: état de la situation et perspectives de monitoring

The authors summarise their state of knowledge about wood ants and their role in the forest ecosystem. They also describe the situation in Switzerland, their past development and their conservation status. In several re- gions, mainly in the Plateau, wood ants seem to diminish, despite their total protection since 1966. The reasons for this regression are not well known, but the fragmentation of forest habitats in the Plateau region and direct damage to ant nests seem to play a certain role. A new project in which the development of wood ant nests is monitored in Swiss forest reserves (Formica-Forêts-CH) was recently started in the Swiss national park. It is to be extended, in collaboration with the forest services, over the whole of Switzerland.

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.

Latent tuberculosis: which test in which situation?

Detection of latent tuberculosis infection (LTBI) is a cost-effective procedure in patients at high risk of developing tuberculosis later and who could benefit from preventive treatment. The commonest situation where screening is indicated is the search for infected contacts of an index case with pulmonary tuberculosis. As a screening procedure the current tendency is to replace the time-honoured tuberculin skin test by one of the new blood tests measuring the release of interferon gamma by sensitised T lymphocytes after stimulation by specific peptides from M. tuberculosis. The main advantage of the new tests is the absence of interference with BCG and non-tuberculous mycobacteria, which confers high specificity on the test. This allows a more selective choice of persons for whom preventive treatment is indicated. Some controversial issues remain, such as sensitivity in children and immunocompromised subjects, the predictive value of the blood test and interpretation of possible changes in test results over time. The technical aspects required for performance of the tests must be considered.

Probabilistic evidential assessment of gunshot residue particle evidence (Part I): likelihood ratio calculation and case pre-assessment using Bayesian networks

Well developed experimental procedures currently exist for retrieving and analyzing particle evidence from hands of individuals suspected of being associated with the discharge of a firearm. Although analytical approaches (e.g. automated Scanning Electron Microscopy with Energy Dispersive X-ray (SEM-EDS) microanalysis) allow the determination of the presence of elements typically found in gunshot residue (GSR) particles, such analyses provide no information about a given particle's actual source. Possible origins for which scientists may need to account for are a primary exposure to the discharge of a firearm or a secondary transfer due to a contaminated environment. In order to approach such sources of uncertainty in the context of evidential assessment, this paper studies the construction and practical implementation of graphical probability models (i.e. Bayesian networks). These can assist forensic scientists in making the issue tractable within a probabilistic perspective. The proposed models focus on likelihood ratio calculations at various levels of detail as well as case pre-assessment.