Forensic dentistry now and in the future

Forensic dentistry (odontology) deals with the examination, handling and presentation of dental evidence for the legal system. In the UK this work mainly involves criminal cases but in many other countries its remit also extends to civil litigation. There are four main aspects to forensic dentistry: single body identification, Disaster Victim Identification (DVI), age estimation and bite mark identification and analysis. This article provides a brief introduction to the topics and discusses potential future developments that aim to reduce the subjectivity in the analysis process and simplify presentation of evidence to non-dental parties. CPD/Clinical Relevance: This article highlights ways that dental practitioners can assist legal investigations and, in particular, forensic dentists

To be financed or not : the role of patents for venture capital financing

This paper investigates how patent applications and grants held by new ventures improve their ability to attract venture capital (VC) financing. We argue that investors are faced with considerable uncertainty and therefore rely on patents as signals when trying to assess the prospects of potential portfolio companies. For a sample of VC-seeking German and British biotechnology companies we have identified all patents filed at the European Patent Office (EPO). Applying hazard rate analysis, we find that in the presence of patent applications, VC financing occurs earlier. Our results also show that VCs pay attention to patent quality, financing those ventures faster which later turn out to have high-quality patents. Patent oppositions increase the likelihood of receiving VC, but ultimate grant decisions do not spur VC financing, presumably because they are anticipated. Our empirical results and interviews with VCs suggest that the process of patenting generates signals which help to overcome the liabilities of newness faced by new ventures

Sequence-selective detection of double-stranded DNA sequences using pyrrole-imidazole polyamide microarrays

We describe a microarray format that can detect double-stranded DNA sequences with a high degree of sequence selectivity. Cyclooctyne-derivatized pyrrole-imidazole polyamides were immobilized on azide-modified glass substrates using microcontact printing and a strain-promoted azide-alkyne cycloaddition (SPAAC) reaction. These polyamide-immobilized substrates selectively detected a seven-base-pair binding site incorporated within a double-stranded oligodeoxyribonucleotide sequence even in the presence of an excess of a sequence with a single-base-pair mismatc

Control of exhaust emissions using piston coating on two-stroke SI engines with gasoline blends

An increase in fuel utilization to internal combustion engines, variation in gasoline price, reduction of the fossil fuels and natural resources, needs less carbon content in fuel to find an alternative fuel. This paper presents a comparative study of various gasoline blends in a single-cylinder two-stroke SI engine. The present experimental investigation with gasoline blends of butanol and propanol and magnesium partially stabilized zirconium (Mg-PSZ) as thermal barrier coating on piston crown of 100 µm. The samples of gasoline blends were blended with petrol in 1:4 ratios: 20 % of butanol and 80 % of gasoline; 20 % of propanol and 80 % of gasoline. In this work, the following engine characteristics of brake thermal efficiency (BTH), specific fuel consumption (SFC), HC, and CO emissions were measured for both coated and non-coated pistons. Experiments have shown that the thermal efficiency is increased by 2.2 % at P20. The specific fuel consumption is minimized by 2.2 % at P20. Exhaust emissions are minimized by 2.0 % of HC and 2.4 % of CO at B20. The results strongly indicate that the combination of thermal barrier coatings and gasoline blends can improve engine performance and reduce exhaust emissions

Control of exhaust emissions using piston coating on two-stroke SI engines with gasoline blends

An increase in fuel utilization to internal combustion engines, variation in gasoline price, reduction of the fossil fuels and natural resources, needs less carbon content in fuel to find an alternative fuel. This paper presents a comparative study of various gasoline blends in a single-cylinder two-stroke SI engine. The present experimental investigation with gasoline blends of butanol and propanol and magnesium partially stabilized zirconium (Mg-PSZ) as thermal barrier coating on piston crown of 100 µm. The samples of gasoline blends were blended with petrol in 1:4 ratios: 20 % of butanol and 80 % of gasoline; 20 % of propanol and 80 % of gasoline. In this work, the following engine characteristics of brake thermal efficiency (BTH), specific fuel consumption (SFC), HC, and CO emissions were measured for both coated and non-coated pistons. Experiments have shown that the thermal efficiency is increased by 2.2 % at P20. The specific fuel consumption is minimized by 2.2 % at P20. Exhaust emissions are minimized by 2.0 % of HC and 2.4 % of CO at B20. The results strongly indicate that the combination of thermal barrier coatings and gasoline blends can improve engine performance and reduce exhaust emissions

White matter diffusion estimates in obsessive-compulsive disorder across 1,653 individuals: Machine learning findings from the ENIGMA OCD Working Group

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1,336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) “OCD vs. healthy controls'' (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) “unmedicated OCD vs. healthy controls” (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) “medicated OCD vs. unmedicated OCD” (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6–79.1 in adults; 35.9–63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen’s d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen’s d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level

A comparison of DNA sequencing and gene expression profiling to assist tissue of origin diagnosis in cancer of unknown primary.

Cancer of unknown primary (CUP) is a syndrome defined by clinical absence of a primary cancer after standardised investigations. Gene expression profiling (GEP) and DNA sequencing have been used to predict primary tissue of origin (TOO) in CUP and find molecularly guided treatments; however, a detailed comparison of the diagnostic yield from these two tests has not been described. Here, we compared the diagnostic utility of RNA and DNA tests in 215 CUP patients (82% received both tests) in a prospective Australian study. Based on retrospective assessment of clinicopathological data, 77% (166/215) of CUPs had insufficient evidence to support TOO diagnosis (clinicopathology unresolved). The remainder had either a latent primary diagnosis (10%) or clinicopathological evidence to support a likely TOO diagnosis (13%) (clinicopathology resolved). We applied a microarray (CUPGuide) or custom NanoString 18-class GEP test to 191 CUPs with an accuracy of 91.5% in known metastatic cancers for high-medium confidence predictions. Classification performance was similar in clinicopathology-resolved CUPs - 80% had high-medium predictions and 94% were concordant with pathology. Notably, only 56% of the clinicopathology-unresolved CUPs had high-medium confidence GEP predictions. Diagnostic DNA features were interrogated in 201 CUP tumours guided by the cancer type specificity of mutations observed across 22 cancer types from the AACR Project GENIE database (77,058 tumours) as well as mutational signatures (e.g. smoking). Among the clinicopathology-unresolved CUPs, mutations and mutational signatures provided additional diagnostic evidence in 31% of cases. GEP classification was useful in only 13% of cases and oncoviral detection in 4%. Among CUPs where genomics informed TOO, lung and biliary cancers were the most frequently identified types, while kidney tumours were another identifiable subset. In conclusion, DNA and RNA profiling supported an unconfirmed TOO diagnosis in one-third of CUPs otherwise unresolved by clinicopathology assessment alone. DNA mutation profiling was the more diagnostically informative assay. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland