Forensic science is more than a test result
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A mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations
Police forensic investigations are not immune to our society’s ubiquitous search for better predictive ability. In the particular and very topical case of Traumatic Brain Injury (TBI), police forensic investigations aim at evaluating whether a given impact or assault scenario led to the clinically observed TBI. This question is traditionally answered by means of forensic biomechanics and neurosurgical expertise which cannot provide a fully objective probabilistic measure. To this end, we propose here a numerical framework-based solution coupling biomechanical simulations of a variety of injurious impacts to machine learning training of police reports provided by the UK’s Thames Valley Police and the National Crime Agency’s National Injury Database. In this approach, the biomechanical predictions of mechanical metrics such as strain and stress distributions are interpreted by the machine learning model by additionally considering assault specific metadata to predict brain injury outcomes. The framework, only taking as input information typically available in police reports, reaches prediction accuracies exceeding 94% for skull fracture, 79% for loss of consciousness and intracranial haemorrhage, and is able to identify the best predictive features for each targeted injury. Overall, the proposed framework offers new avenues for the prediction, directly from police reports, of any TBI related symptom as required by forensic law enforcement investigations.
Trust in scientists and their role in society across 68 countries
Science is crucial for evidence-based decision-making. Public trust in scientists can help decision makers act on the basis of the best available evidence, especially during crises. However, in recent years the epistemic authority of science has been challenged, causing concerns about low public trust in scientists. We interrogated these concerns with a preregistered 68-country survey of 71,922 respondents and found that in most countries, most people trust scientists and agree that scientists should engage more in society and policymaking. We found variations between and within countries, which we explain with individual- and country-level variables, including political orientation. While there is no widespread lack of trust in scientists, we cannot discount the concern that lack of trust in scientists by even a small minority may affect considerations of scientific evidence in policymaking. These findings have implications for scientists and policymakers seeking to maintain and increase trust in scientists.
A manifesto for a globally diverse, equitable, and inclusive open science
The field of psychology has rapidly transformed its open science practices in recent years. Yet there has been limited progress in integrating principles of diversity, equity and inclusion. In this Perspective, we raise the spectre of Questionable Generalisability Practices and the issue of MASKing (Making Assumptions based on Skewed Knowledge), calling for more responsible practices in generalising study findings and co-authorship to promote global equity in knowledge production. To drive change, researchers must target all four key components of the research process: design, reporting, generalisation, and evaluation. Additionally, macro-level geopolitical factors must be considered to move towards a robust behavioural science that is truly inclusive, representing the voices and experiences of the majority world (i.e., low-and-middle-income countries).
Evolution and impact of the science of science: from theoretical analysis to digital-AI driven research
The Science of Science (SoS) examines the mechanisms driving the development and societal role of science, evolving from its sociological roots into a data-driven discipline. This paper traces the progression of SoS from its early focus on the social functions of science to the current era, characterized by large-scale quantitative analysis and AI-driven methodologies. Scientometrics, a key branch of SoS, has utilized statistical methods and citation analysis to understand scientific growth and knowledge diffusion. With the rise of big data and complex network theory, SoS has transitioned toward more refined analyses, leveraging artificial intelligence (AI) for predictive modeling, sentiment annotation, and entity extraction. This paper explores the application of AI in SoS, highlighting its role as a surrogate, quant, and arbiter in advancing data processing, data analysis and peer review. The integration of AI has ushered in a new paradigm for SoS, enhancing its predictive accuracy and providing deeper insights into the internal dynamics of science and its impact on society.
Environmental public health research at the U.S. Environmental Protection Agency: A blueprint for exposure science in a connected world
Exposure science plays an essential role in the U.S. Environmental Protection Agency’s (U.S. EPA) mission to protect human health and the environment. The U.S. EPA’s Center for Public Health and Environmental Assessment (CPHEA) within the Office of Research and Development (ORD) provides the exposure science needed to characterize the multifaceted relationships between people and their surroundings in support of national, regional, local and individual-level actions. Furthermore, exposure science research must position its enterprise to tackle the most pressing public health challenges in an ever-changing environment. These challenges include understanding and confronting complex human disease etiologies, disparities in the social environment, and system-level changes in the physical environment. Solutions will sustainably balance and optimize the health of people, animals, and ecosystems. Our objectives for this paper are to review the role of CPHEA exposure science research in various recent decision-making contexts, to present current challenges facing U.S. EPA and the larger exposure science field, and to provide illustrative case examples where CPHEA exposure science is demonstrating the latest methodologies at the intersection of these two motivations. This blueprint provides a foundation for applying exposomic tools and approaches to holistically understand real-world exposures so optimal environmental public health protective actions can be realized within the broader context of a One Health framework.
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