scholarly journals Applications of Metabolomics in Forensic Toxicology and Forensic Medicine

2021 ◽  
Vol 22 (6) ◽  
pp. 3010
Author(s):  
Michal Szeremeta ◽  
Karolina Pietrowska ◽  
Anna Niemcunowicz-Janica ◽  
Adam Kretowski ◽  
Michal Ciborowski
Keyword(s):  
Machine Learning ◽  
Multivariate Statistics ◽  
High Throughput ◽  
Forensic Medicine ◽  
Forensic Toxicology ◽  
Metabolic Changes ◽  
Untargeted Metabolomics ◽  
Criminal Cases ◽  
Statistical Approaches

Forensic toxicology and forensic medicine are unique among all other medical fields because of their essential legal impact, especially in civil and criminal cases. New high-throughput technologies, borrowed from chemistry and physics, have proven that metabolomics, the youngest of the “omics sciences”, could be one of the most powerful tools for monitoring changes in forensic disciplines. Metabolomics is a particular method that allows for the measurement of metabolic changes in a multicellular system using two different approaches: targeted and untargeted. Targeted studies are focused on a known number of defined metabolites. Untargeted metabolomics aims to capture all metabolites present in a sample. Different statistical approaches (e.g., uni- or multivariate statistics, machine learning) can be applied to extract useful and important information in both cases. This review aims to describe the role of metabolomics in forensic toxicology and in forensic medicine.

scholarly journals To the question of «competence» in forensic medicine and forensic medical examination

2019 ◽  
Vol 27 (1) ◽  
pp. 107-113
Author(s):  
Igor` B. Boyko
Keyword(s):  
Law Enforcement ◽  
Forensic Medicine ◽  
Scientific Discipline ◽  
Common Type ◽  
Negative Consequences ◽  
Criminal Cases ◽  
Medical Field ◽  
Forensic Examination ◽  
The Subject

In the article a problem concerning the concept of competence in application to forensic medicine as an educational discipline and as forensic examination, which is the most common type of legal enquiry, is first considered. Modern concepts of the role of law in the forensic medicine, of the subject of this medical field and of possibilities of forensic examination are given. The author comes to the conclusion that, unfortunately, nowadays the question of forensic medicine still belongs to the category of debatable questions. This fact does not permit to agree with a widely spread opinion stating that forensic medicine has completed its formation as a scientific discipline, besides, this opinion has negative consequences for the activity of law enforcement authorities first of all the activity associated with investigation of criminal cases, consideration of civil matters, cases of administrative violations, since the competence of forensic medicine in itself determines the competence of specialists in the field of forensic medicine. And, as it is known, the latter often determines the court judgment.

A Good Sentencing Precedent is Hard to Find

2020 ◽  
Vol 32 (3) ◽  
pp. 138-144
Author(s):  
Brian A. Jacobs
Keyword(s):  
United States ◽  
Significant Role ◽  
College Admissions ◽  
The Body ◽  
Federal Law ◽  
Criminal Cases ◽  
Federal Sentencing

In federal criminal cases, federal law requires that judges consider the sentences other courts have imposed in factually similar matters. Courts and parties, however, face significant challenges in finding applicable sentencing precedents because judges do not typically issue written sentencing opinions, and transcripts of sentencings are not readily available in advanced searchable databases. At the same time, particularly since the Supreme Court’s 2005 decision in United States v. Booker, sentencing precedent has come to play a significant role in federal sentencing proceedings. By way of example, this article discusses recent cases involving defendants with gambling addictions, and recent cases involving college admissions or testing fraud. The article explores the ways the parties in those cases have used sentencing precedent in their advocacy, as well as the ways the courts involved have used sentencing precedent to justify their decisions. Given the important role of sentencing precedent in federal criminal cases, the article finally looks at ways in which the body of sentencing law could be made more readily available to parties and courts alike.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Inside the Enemy's Computer

2017 ◽  
Author(s):  
Clement Guitton
Keyword(s):  
National Security ◽  
Technical Problem ◽  
Technical Information ◽  
Cyber Attack ◽  
Criminal Cases ◽  
Private Companies ◽  
Political Issues ◽  
Current Thinking ◽  
Standards Of Evidence

Attribution — tracing those responsible for a cyber attack — is of primary importance when classifying it as a criminal act, an act of war, or an act of terrorism. Three assumptions dominate current thinking: attribution is a technical problem; it is unsolvable; and it is unique. Approaching attribution as a problem forces us to consider it either as solved or unsolved. Yet attribution is far more nuanced, and is best approached as a process in constant flux, driven by judicial and political pressures. In the criminal context, courts must assess the guilt of criminals, mainly based on technical evidence. In the national security context, decision-makers must analyze unreliable and mainly non-technical information in order to identify an enemy of the state. Attribution in both contexts is political: in criminal cases, laws reflect society’s prevailing norms and powers; in national security cases, attribution reflects a state’s will to maintain, increase or assert its power. However, both processes differ on many levels. The constraints, which reflect common aspects of many other political issues, constitute the structure of the book: the need for judgment calls, the role of private companies, the standards of evidence, the role of time, and the plausible deniability of attacks.

Role of machine learning algorithms over heart diseases prediction

2020 ◽  
Author(s):  
Siva Kumar Jonnavithula ◽  
Abhilash Kumar Jha ◽  
Modepalli Kavitha ◽  
Singaraju Srinivasulu
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Accelerating organic solar cell material's discovery: high-throughput screening and big data

2021 ◽  
Author(s):  
Xabier Rodríguez-Martínez ◽  
Enrique Pascual-San-José ◽  
Mariano Campoy-Quiles
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Organic Solar Cell ◽  
State Of The Art ◽  
Review Article ◽  
Machine Learning Algorithms ◽  
Device Optimization

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

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