864 resultados para Criminal Profiling
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Personality disorders are associated with criminality and antisocial and borderline personalities as strong predictors of violence. Nevertheless antisocial patients show more instrumental violence, while borderline patients more emotional violence. We surveilled medical records of a personality disorder facility, searching data of aggression and crimes against property among 11 patients with antisocial personality disorder and 19 borderline personality disorder. We found that there are differences regarding engagement in violence and lawbreaking according to the personality disorder: antisocial patients statistically engage more in crimes against property than the borderline patients, and more in this kind of crime than in aggression, whilst borderline patients show a tendency to engage more in episodes of aggression and physical violence than antisocial patients, and less in crimes against property. We conclude that the distinct personality leads to a distinct pattern of crimes and violence: antisocial patients are c old and get more involved in crimes requiring more detailed planning, whilst borderline patients are impulsive and engage in explosive episodes of physical violence. Further studies on the association among personality disorder, behavior pattern and violence type may be useful for both treatment and criminal profiling. (C) 2008 Elsevier Ireland Ltd. All rights reserved.
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Réalisé sous la co-direction de Pierre Tremblay
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Projeto de Graduação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de licenciado em Criminologia
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Almost 30 years ago, Bayesian networks (BNs) were developed in the field of artificial intelligence as a framework that should assist researchers and practitioners in applying the theory of probability to inference problems of more substantive size and, thus, to more realistic and practical problems. Since the late 1980s, Bayesian networks have also attracted researchers in forensic science and this tendency has considerably intensified throughout the last decade. This review article provides an overview of the scientific literature that describes research on Bayesian networks as a tool that can be used to study, develop and implement probabilistic procedures for evaluating the probative value of particular items of scientific evidence in forensic science. Primary attention is drawn here to evaluative issues that pertain to forensic DNA profiling evidence because this is one of the main categories of evidence whose assessment has been studied through Bayesian networks. The scope of topics is large and includes almost any aspect that relates to forensic DNA profiling. Typical examples are inference of source (or, 'criminal identification'), relatedness testing, database searching and special trace evidence evaluation (such as mixed DNA stains or stains with low quantities of DNA). The perspective of the review presented here is not exclusively restricted to DNA evidence, but also includes relevant references and discussion on both, the concept of Bayesian networks as well as its general usage in legal sciences as one among several different graphical approaches to evidence evaluation.
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False identity documents represent a serious threat through their production and use in organized crime and by terrorist organizations. The present-day fight against this criminal problem and threats to national security does not appropriately address the organized nature of this criminal activity, treating each fraudulent document on its own during investigation and the judicial process, which causes linkage blindness and restrains the analysis capacity. Given the drawbacks of this case-by-case approach, this article proposes an original model in which false identity documents are used to inform a systematic forensic intelligence process. The process aims to detect links, patterns, and tendencies among false identity documents in order to support strategic and tactical decision making, thus sustaining a proactive intelligence-led approach to fighting identity document fraud and the associated organized criminality. This article formalizes both the model and the process, using practical applications to illustrate its powerful capabilities. This model has a general application and can be transposed to other fields of forensic science facing similar difficulties.
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Illicit drug analyses usually focus on the identification and quantitation of questioned material to support the judicial process. In parallel, more and more laboratories develop physical and chemical profiling methods in a forensic intelligence perspective. The analysis of large databases resulting from this approach enables not only to draw tactical and operational intelligence, but may also contribute to the strategic overview of drugs markets. In Western Switzerland, the chemical analysis of illicit drug seizures is centralised in a laboratory hosted by the University of Lausanne. For over 8 years, this laboratory has analysed 5875 cocaine and 2728 heroin specimens, coming from respectively 1138 and 614 seizures operated by police and border guards or customs. Chemical (major and minor alkaloids, purity, cutting agents, chemical class), physical (packaging and appearance) as well as circumstantial (criminal case number, mass of drug seized, date and place of seizure) information are collated in a dedicated database for each specimen. The study capitalises on this extended database and defines several indicators to characterise the structure of drugs markets, to follow-up on their evolution and to compare cocaine and heroin markets. Relational, spatial, temporal and quantitative analyses of data reveal the emergence and importance of distribution networks. They enable to evaluate the cross-jurisdictional character of drug trafficking and the observation time of drug batches, as well as the quantity of drugs entering the market every year. Results highlight the stable nature of drugs markets over the years despite the very dynamic flows of distribution and consumption. This research work illustrates how the systematic analysis of forensic data may elicit knowledge on criminal activities at a strategic level. In combination with information from other sources, such knowledge can help to devise intelligence-based preventive and repressive measures and to discuss the impact of countermeasures.
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El objetivo de la presente investigación consiste en describir las características de un asesino en serie colombiano desde la perspectiva psicodinámica. En este sentido, el abordaje teórico realizado en este trabajo se compone inicialmente de una concepción de asesinos en serie, posteriormente se hace una revisión acerca de las bases biológicas y los factores sociales del homicida serial, igualmente, se explican tres teorías psicodinámicas a trabajar (Sigmund Freud y Erick Erickson). Finalmente, se hace mención dentro de la investigación a la comparación casuística de los asesinos en serie, teniendo en cuenta a cuatro asesinos en serie mediante el abordaje psicodinámico. Por otra parte, a nivel metodológico, el tipo de estudio realizado es descriptivo con un corte cualitativo y un diseño no experimental, basado en la revisión de fuentes bibliográficas. Como producto se pretende hacer una aproximación al perfil correspondiente de la personalidad de un asesino en serie colombiano mediante las teorías psicodinámicas.
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Includes bibliography
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This phenomenological study explored Black male law enforcement officers’ perspectives of how racial profiling shaped their decisions to explore and commit to a law enforcement career. Criterion and snow ball sampling was used to obtain the 17 participants for this study. Super’s (1990) archway model was used as the theoretical framework. The archway model “is designed to bring out the segmented but unified and developmental nature of career development, to highlight the segments, and to make their origin clear” (Super, 1990, p. 201). Interview data were analyzed using inductive, deductive, and comparative analyses. Three themes emerged from the inductive analysis of the data: (a) color and/or race does matter, (b) putting on the badge, and (c) too black to be blue and too blue to be black. The deductive analysis used a priori coding that was based on Super’s (1990) archway model. The deductive analysis revealed the participants’ career exploration was influenced by their knowledge of racial profiling and how others view them. The comparative analysis between the inductive themes and deductive findings found the theme “color and/or race does matter” was present in the relationships between and within all segments of Super’s (1990) model. The comparative analysis also revealed an expanded notion of self-concept for Black males – marginalized and/or oppressed individuals. Self-concepts, “such as self-efficacy, self-esteem, and role self-concepts, being combinations of traits ascribed to oneself” (Super, 1990, p. 202) do not completely address the self-concept of marginalized and/or oppressed individuals. The self-concept of marginalized and/or oppressed individuals is self-efficacy, self-esteem, traits ascribed to oneself expanded by their awareness of how others view them. (DuBois, 1995; Freire, 1970; Sheared, 1990; Super, 1990; Young, 1990). Ultimately, self-concept is utilized to make career and life decisions. Current human resource policies and practices do not take into consideration that negative police contact could be the result of racial profiling. Current human resource hiring guidelines penalize individuals who have had negative police contact. Therefore, racial profiling is a discriminatory act that can effectively circumvent U.S. Equal Employment Opportunities Commission laws and serve as a boundary mechanism to employment (Rocco & Gallagher, 2004).
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Witches' broom disease (WBD), caused by the hemibiotrophic fungus Moniliophthora perniciosa, is one of the most devastating diseases of Theobroma cacao, the chocolate tree. In contrast to other hemibiotrophic interactions, the WBD biotrophic stage lasts for months and is responsible for the most distinctive symptoms of the disease, which comprise drastic morphological changes in the infected shoots. Here, we used the dual RNA-seq approach to simultaneously assess the transcriptomes of cacao and M. perniciosa during their peculiar biotrophic interaction. Infection with M. perniciosa triggers massive metabolic reprogramming in the diseased tissues. Although apparently vigorous, the infected shoots are energetically expensive structures characterized by the induction of ineffective defense responses and by a clear carbon deprivation signature. Remarkably, the infection culminates in the establishment of a senescence process in the host, which signals the end of the WBD biotrophic stage. We analyzed the pathogen's transcriptome in unprecedented detail and thereby characterized the fungal nutritional and infection strategies during WBD and identified putative virulence effectors. Interestingly, M. perniciosa biotrophic mycelia develop as long-term parasites that orchestrate changes in plant metabolism to increase the availability of soluble nutrients before plant death. Collectively, our results provide unique insight into an intriguing tropical disease and advance our understanding of the development of (hemi)biotrophic plant-pathogen interactions.
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Background: Cutaneous mycoses are common human infections among healthy and immunocompromised hosts, and the anthropophilic fungus Trichophyton rubrum is the most prevalent microorganism isolated from such clinical cases worldwide. The aim of this study was to determine the transcriptional profile of T. rubrum exposed to various stimuli in order to obtain insights into the responses of this pathogen to different environmental challenges. Therefore, we generated an expressed sequence tag (EST) collection by constructing one cDNA library and nine suppression subtractive hybridization libraries. Results: The 1388 unigenes identified in this study were functionally classified based on the Munich Information Center for Protein Sequences (MIPS) categories. The identified proteins were involved in transcriptional regulation, cellular defense and stress, protein degradation, signaling, transport, and secretion, among other functions. Analysis of these unigenes revealed 575 T. rubrum sequences that had not been previously deposited in public databases. Conclusion: In this study, we identified novel T. rubrum genes that will be useful for ORF prediction in genome sequencing and facilitating functional genome analysis. Annotation of these expressed genes revealed metabolic adaptations of T. rubrum to carbon sources, ambient pH shifts, and various antifungal drugs used in medical practice. Furthermore, challenging T. rubrum with cytotoxic drugs and ambient pH shifts extended our understanding of the molecular events possibly involved in the infectious process and resistance to antifungal drugs.
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The mating sign that each drone leaves when mating with a queen essentially consists of mucus gland proteins. We employed a Representational Difference Analysis (RDA) methodology to identify genes that are differentially expressed in mucus glands during sexual maturation of drones. The RDA library for mucus glands of newly emerged drones was more complex than that of 8 day-old drones, with matches to 20 predicted genes. Another 26 reads matched to the Apis genome but not to any predicted gene. Since these ESTs were located within ORFs they may represent novel honey bee genes, possibly fast evolving mucus gland proteins. In the RDA library for mucus glands of 8 day-old drones, most reads corresponded to a capsid protein of deformed wing virus, indicating high viral loads in these glands. The expression of two genes encoding venom allergens, acid phosphatase-1 and hyaluronidase, in drone mucus glands argues for their homology with the female venom glands, both associated with the reproductive system.
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Background: High-throughput molecular approaches for gene expression profiling, such as Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS) or Sequencing-by-Synthesis (SBS) represent powerful techniques that provide global transcription profiles of different cell types through sequencing of short fragments of transcripts, denominated sequence tags. These techniques have improved our understanding about the relationships between these expression profiles and cellular phenotypes. Despite this, more reliable datasets are still necessary. In this work, we present a web-based tool named S3T: Score System for Sequence Tags, to index sequenced tags in accordance with their reliability. This is made through a series of evaluations based on a defined rule set. S3T allows the identification/selection of tags, considered more reliable for further gene expression analysis. Results: This methodology was applied to a public SAGE dataset. In order to compare data before and after filtering, a hierarchical clustering analysis was performed in samples from the same type of tissue, in distinct biological conditions, using these two datasets. Our results provide evidences suggesting that it is possible to find more congruous clusters after using S3T scoring system. Conclusion: These results substantiate the proposed application to generate more reliable data. This is a significant contribution for determination of global gene expression profiles. The library analysis with S3T is freely available at http://gdm.fmrp.usp.br/s3t/.S3T source code and datasets can also be downloaded from the aforementioned website.