Quantitative Assessment of Handwriting Evidence: The Value of the Shape of the Letter "A"

To provide a quantitative support to the handwriting evidence evaluation, a new method was developed through the computation of a likelihood ratio based on a Bayesian approach. In the present paper, the methodology is briefly described and applied to data collected within a simulated case of a threatening letter. Fourier descriptors are used to characterise the shape of loops of handwritten characters "a" of the true writer of the threatening letter, and: 1) with reference characters "a" of the true writer of the threatening letter, and then 2) with characters "a" of a writer who did not write the threatening letter. The findings support that the probabilistic methodology correctly supports either the hypothesis of authorship or the alternative hypothesis. Further developments will enable the handwriting examiner to use this methodology as a helpful assistance to assess the strength of evidence in handwriting casework.

History of diabetes and risk of head and neck cancer: a pooled analysis from the international head and neck cancer epidemiology consortium.

BACKGROUND: A history of diabetes is associated with an increased risk of several types of cancers. Whether diabetes is a risk factor for head and neck cancer (HNC) has received little attention. METHODS: We pooled data from 12 case-control studies including 6,448 cases and 13,747 controls, and estimated odds ratios (OR) and 95% confidence intervals (CI) for the associations between diabetes and HNC, adjusted for age, education level, sex, race/ethnicity, study center, cigarette smoking, alcohol use and body mass index (BMI). RESULTS: We observed a weak association between diabetes and the incidence of HNC overall (OR, 1.09; 95% CI, 0.95-1.24). However, we observed a modest association among never smokers (OR, 1.59; 95% CI, 1.22-2.07), and no association among ever smokers (OR, 0.96; 95% CI, 0.83-1.11); likelihood ratio test for interaction p=0.001. CONCLUSIONS: A history of diabetes was weakly associated with HNC overall, but we observed evidence of effect modification by smoking status, with a positive association among those who never smoked cigarettes. Impact: This study suggests that glucose metabolism abnormalities may be a HNC risk factor in subgroups of the population. Prospective studies incorporating biomarkers are needed to improve our understanding of the relationship between diabetes and HNC risk, possibly providing new strategies in the prevention of HNC.

Elevated inflammatory markers combined with positive pneumococcal urinary antigen are a good predictor of pneumococcal community-acquired pneumonia in children.

BACKGROUND: Our objective was to evaluate procalcitonin (PCT) and C-reactive protein (CRP) as predictors of a pneumococcal etiology in community-acquired pneumonia (CAP) in hospitalized children. METHODS: Children requiring hospitalization for CAP were prospectively enrolled. The following indices were determined: antibodies against pneumococcal surface proteins (anti-PLY, pneumococcal histidine triad D, pneumococcal histidine triad E, LytB and pneumococcal choline-binding protein A), viral serology, nasopharyngeal cultures and polymerase chain reaction for 13 respiratory viruses, blood pneumococcal polymerase chain reaction, pneumococcal urinary antigen, PCT and CRP. Presumed pneumococcal CAP (P-CAP) was defined as a positive blood culture or polymerase chain reaction for Streptococcus pneumoniae or as a pneumococcal surface protein seroresponse (≥2-fold increase). RESULTS: Seventy-five patients were included from which 37 (49%) met the criteria of P-CAP. Elevated PCT and CRP values were strongly associated with P-CAP with odds ratios of 23 (95% confidence interval: 5-117) for PCT and 19 (95% confidence interval: 5-75) for CRP in multivariate analysis. The sensitivity was 94.4% for PCT (cutoff: 1.5 ng/mL) and 91.9% for CRP (cutoff: 100 mg/L). A value≤0.5 ng/mL of PCT ruled out P-CAP in >90% of cases (negative likelihood ratio: 0.08). Conversely, a PCT value≥1.5 ng/mL associated with a positive pneumococcal urinary antigen had a diagnostic probability for P-CAP of almost 80% (positive likelihood ratio: 4.59). CONCLUSIONS: PCT and CRP are reliable predictors of P-CAP. Low cutoff values of PCT allow identification of children at low risk of P-CAP. The association of elevated PCT or CRP with a positive pneumococcal urinary antigen is a strong predictor of P-CAP.

Spatial analysis of corresponding fingerprint features from match and close non-match populations

The development of statistical models for forensic fingerprint identification purposes has been the subject of increasing research attention in recent years. This can be partly seen as a response to a number of commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. In addition, key forensic identification bodies such as ENFSI [1] and IAI [2] have recently endorsed and acknowledged the potential benefits of using statistical models as an important tool in support of the fingerprint identification process within the ACE-V framework. In this paper, we introduce a new Likelihood Ratio (LR) model based on Support Vector Machines (SVMs) trained with features discovered via morphometric and spatial analyses of corresponding minutiae configurations for both match and close non-match populations often found in AFIS candidate lists. Computed LR values are derived from a probabilistic framework based on SVMs that discover the intrinsic spatial differences of match and close non-match populations. Lastly, experimentation performed on a set of over 120,000 publicly available fingerprint images (mostly sourced from the National Institute of Standards and Technology (NIST) datasets) and a distortion set of approximately 40,000 images, is presented, illustrating that the proposed LR model is reliably guiding towards the right proposition in the identification assessment of match and close non-match populations. Results further indicate that the proposed model is a promising tool for fingerprint practitioners to use for analysing the spatial consistency of corresponding minutiae configurations.

Integration of Pore Features into the Evaluation of Fingerprint Evidence

Fingerprint practitioners rely on level 3 features to make decisions in relation to the source of an unknown friction ridge skin impression. This research proposes to assess the strength of evidence associated with pores when shown in (dis)agreement between a mark and a reference print. Based upon an algorithm designed to automatically detect pores, a metric is defined in order to compare different impressions. From this metric, the weight of the findings is quantified using a likelihood ratio. The results obtained on four configurations and 54 donors show the significant contribution of the pore features and translate into statistical terms what latent fingerprint examiners have developed holistically through experience. The system provides LRs that are indicative of the true state under both the prosecution and the defense propositions. Not only such a system brings transparency regarding the weight to assign to such features, but also forces a discussion in relation to the risks of such a model to mislead.

Diagnostic performance of Fluorine-18-Fluorodeoxyglucose positron emission tomography in the assessment of pleural abnormalities in cancer patients: A systematic review and a meta-analysis.

OBJECTIVE: To systematically review and meta-analyze published data about the diagnostic performance of Fluorine-18-Fluorodeoxyglucose ((18)F-FDG) positron emission tomography (PET) and PET/computed tomography (PET/CT) in the assessment of pleural abnormalities in cancer patients. METHODS: A comprehensive literature search of studies published through June 2013 regarding the role of (18)F-FDG-PET and PET/CT in evaluating pleural abnormalities in cancer patients was performed. All retrieved studies were reviewed and qualitatively analyzed. Pooled sensitivity, specificity, positive and negative likelihood ratio (LR+ and LR-) and diagnostic odd ratio (DOR) of (18)F-FDG-PET or PET/CT on a per patient-based analysis were calculated. The area under the summary ROC curve (AUC) was calculated to measure the accuracy of these methods in the assessment of pleural abnormalities. Sub-analyses considering (18)F-FDG-PET/CT and patients with lung cancer only were carried out. RESULTS: Eight studies comprising 360 cancer patients (323 with lung cancer) were included. The meta-analysis of these selected studies provided the following results: sensitivity 86% [95% confidence interval (95%CI): 80-91%], specificity 80% [95%CI: 73-85%], LR+ 3.7 [95%CI: 2.8-4.9], LR- 0.18 [95%CI: 0.09-0.34], DOR 27 [95%CI: 13-56]. The AUC was 0.907. No significant improvement considering PET/CT studies only and patients with lung cancer was found. CONCLUSIONS: (18)F-FDG-PET and PET/CT demonstrated to be useful diagnostic imaging methods in the assessment of pleural abnormalities in cancer patients, nevertheless possible sources of false-negative and false-positive results should be kept in mind. The literature focusing on the use of (18)F-FDG-PET and PET/CT in this setting remains still limited and prospective studies are needed.

Interpretation: Printed Document Examination and Evidence

The literature dealing with the interpretation of results of examinations performed on "printed" documents is very limited. The absence of published literature reflects the absence of formal guidelines to help scientists assess the relationship between a questioned document and a particular printing technology. Generally, every printout, independent of the printing technology, may bear traces induced by characteristics of manufacture and/or acquired features of the printing device. A logical approach to help the scientist in the formal interpretation of such findings involves the consideration of a likelihood ratio. Three examples aim to show the application of this approach.

Estimating the time since discharge of spent cartridges: a logical approach fro interpreting the evidence

Estimating the time since discharge of a spent cartridge or a firearm can be useful in criminal situa-tions involving firearms. The analysis of volatile gunshot residue remaining after shooting using solid-phase microextraction (SPME) followed by gas chromatography (GC) was proposed to meet this objective. However, current interpretative models suffer from several conceptual drawbacks which render them inadequate to assess the evidential value of a given measurement. This paper aims to fill this gap by proposing a logical approach based on the assessment of likelihood ratios. A probabilistic model was thus developed and applied to a hypothetical scenario where alternative hy-potheses about the discharge time of a spent cartridge found on a crime scene were forwarded. In order to estimate the parameters required to implement this solution, a non-linear regression model was proposed and applied to real published data. The proposed approach proved to be a valuable method for interpreting aging-related data.

Surface granularity as a discriminating feature of illicit tablets

In this paper we propose an innovative methodology for automated profiling of illicit tablets bytheir surface granularity; a feature previously unexamined for this purpose. We make use of the tinyinconsistencies at the tablet surface, referred to as speckles, to generate a quantitative granularity profileof tablets. Euclidian distance is used as a measurement of (dis)similarity between granularity profiles.The frequency of observed distances is then modelled by kernel density estimation in order to generalizethe observations and to calculate likelihood ratios (LRs). The resulting LRs are used to evaluate thepotential of granularity profiles to differentiate between same-batch and different-batches tablets.Furthermore, we use the LRs as a similarity metric to refine database queries. We are able to derivereliable LRs within a scope that represent the true evidential value of the granularity feature. Thesemetrics are used to refine candidate hit-lists form a database containing physical features of illicittablets. We observe improved or identical ranking of candidate tablets in 87.5% of cases when granularityis considered.

Recent misconceptions about the "database search problem": a probabilistic analysis using Bayesian networks

This paper analyses and discusses arguments that emerge from a recent discussion about the proper assessment of the evidential value of correspondences observed between the characteristics of a crime stain and those of a sample from a suspect when (i) this latter individual is found as a result of a database search and (ii) remaining database members are excluded as potential sources (because of different analytical characteristics). Using a graphical probability approach (i.e., Bayesian networks), the paper here intends to clarify that there is no need to (i) introduce a correction factor equal to the size of the searched database (i.e., to reduce a likelihood ratio), nor to (ii) adopt a propositional level not directly related to the suspect matching the crime stain (i.e., a proposition of the kind 'some person in (outside) the database is the source of the crime stain' rather than 'the suspect (some other person) is the source of the crime stain'). The present research thus confirms existing literature on the topic that has repeatedly demonstrated that the latter two requirements (i) and (ii) should not be a cause of concern.

New perspectives in the use of ink evidence in forensic science : part III : operational applications and evaluation

The research reported in this series of article aimed at (1) automating the search of questioned ink specimens in ink reference collections and (2) at evaluating the strength of ink evidence in a transparent and balanced manner. These aims require that ink samples are analysed in an accurate and reproducible way and that they are compared in an objective and automated way. This latter requirement is due to the large number of comparisons that are necessary in both scenarios. A research programme was designed to (a) develop a standard methodology for analysing ink samples in a reproducible way, (b) comparing automatically and objectively ink samples and (c) evaluate the proposed methodology in forensic contexts. This report focuses on the last of the three stages of the research programme. The calibration and acquisition process and the mathematical comparison algorithms were described in previous papers [C. Neumann, P. Margot, New perspectives in the use of ink evidence in forensic science-Part I: Development of a quality assurance process for forensic ink analysis by HPTLC, Forensic Sci. Int. 185 (2009) 29-37; C. Neumann, P. Margot, New perspectives in the use of ink evidence in forensic science-Part II: Development and testing of mathematical algorithms for the automatic comparison of ink samples analysed by HPTLC, Forensic Sci. Int. 185 (2009) 38-50]. In this paper, the benefits and challenges of the proposed concepts are tested in two forensic contexts: (1) ink identification and (2) ink evidential value assessment. The results show that different algorithms are better suited for different tasks. This research shows that it is possible to build digital ink libraries using the most commonly used ink analytical technique, i.e. high-performance thin layer chromatography, despite its reputation of lacking reproducibility. More importantly, it is possible to assign evidential value to ink evidence in a transparent way using a probabilistic model. It is therefore possible to move away from the traditional subjective approach, which is entirely based on experts' opinion, and which is usually not very informative. While there is room for the improvement, this report demonstrates the significant gains obtained over the traditional subjective approach for the search of ink specimens in ink databases, and the interpretation of their evidential value.

The systematic profiling of false identity documents: method validation and performance evaluation using seizures known to originate from common and different sources

False identity documents constitute a potential powerful source of forensic intelligence because they are essential elements of transnational crime and provide cover for organized crime. In previous work, a systematic profiling method using false documents' visual features has been built within a forensic intelligence model. In the current study, the comparison process and metrics lying at the heart of this profiling method are described and evaluated. This evaluation takes advantage of 347 false identity documents of four different types seized in two countries whose sources were known to be common or different (following police investigations and dismantling of counterfeit factories). Intra-source and inter-sources variations were evaluated through the computation of more than 7500 similarity scores. The profiling method could thus be validated and its performance assessed using two complementary approaches to measuring type I and type II error rates: a binary classification and the computation of likelihood ratios. Very low error rates were measured across the four document types, demonstrating the validity and robustness of the method to link documents to a common source or to differentiate them. These results pave the way for an operational implementation of a systematic profiling process integrated in a developed forensic intelligence model.

The pulmonary embolism rule-out criteria (PERC) rule does not safely exclude pulmonary embolism.

Background: The Pulmonary Embolism Rule-out Criteria (PERC) rule is a clinical diagnostic rule designed to exclude pulmonary embolism (PE) without further testing. We sought to externally validate the diagnostic performance of the PERC rule alone and combined with clinical probability assessment based on the revised Geneva score. Methods: The PERC rule was applied retrospectively to consecutive patients who presented with a clinical suspicion of PE to six emergency departments, and who were enrolled in a randomized trial of PE diagnosis. Patients who met all eight PERC criteria [PERC(-)] were considered to be at a very low risk for PE. We calculated the prevalence of PE among PERC(-) patients according to their clinical pretest probability of PE. We estimated the negative likelihood ratio of the PERC rule to predict PE. Results: Among 1675 patients, the prevalence of PE was 21.3%. Overall, 13.2% of patients were PERC(-). The prevalence of PE was 5.4% [95% confidence interval (CI): 3.1-9.3%] among PERC(-) patients overall and 6.4% (95% CI: 3.7-10.8%) among those PERC(-) patients with a low clinical pretest probability of PE. The PERC rule had a negative likelihood ratio of 0.70 (95% CI: 0.67-0.73) for predicting PE overall, and 0.63 (95% CI: 0.38-1.06) in low-risk patients. Conclusions: Our results suggest that the PERC rule alone or even when combined with the revised Geneva score cannot safely identify very low risk patients in whom PE can be ruled out without additional testing, at least in populations with a relatively high prevalence of PE.