Aggregate fluctuations and the network structure of intersectoral trade

This paper analyzes the flow of intermediate inputs across sectors by adopting a network perspective on sectoral interactions. I apply these tools to show how fluctuationsin aggregate economic activity can be obtained from independent shocks to individualsectors. First, I characterize the network structure of input trade in the U.S. On thedemand side, a typical sector relies on a small number of key inputs and sectors arehomogeneous in this respect. However, in their role as input-suppliers sectors do differ:many specialized input suppliers coexist alongside general purpose sectors functioningas hubs to the economy. I then develop a model of intersectoral linkages that can reproduce these connectivity features. In a standard multisector setup, I use this modelto provide analytical expressions linking aggregate volatility to the network structureof input trade. I show that the presence of sectoral hubs - by coupling productiondecisions across sectors - leads to fluctuations in aggregates.

Informing the judgments of fingerprint analysts using quality metric and statistical assessment tools

The aim of this research was to evaluate how fingerprint analysts would incorporate information from newly developed tools into their decision making processes. Specifically, we assessed effects using the following: (1) a quality tool to aid in the assessment of the clarity of the friction ridge details, (2) a statistical tool to provide likelihood ratios representing the strength of the corresponding features between compared fingerprints, and (3) consensus information from a group of trained fingerprint experts. The measured variables for the effect on examiner performance were the accuracy and reproducibility of the conclusions against the ground truth (including the impact on error rates) and the analyst accuracy and variation for feature selection and comparison.¦The results showed that participants using the consensus information from other fingerprint experts demonstrated more consistency and accuracy in minutiae selection. They also demonstrated higher accuracy, sensitivity, and specificity in the decisions reported. The quality tool also affected minutiae selection (which, in turn, had limited influence on the reported decisions); the statistical tool did not appear to influence the reported decisions.

The evolution of recent research on Catalan literature through the production of PhD theses: a bibliometric and social network analysis

Introduction. This paper studies the situation of research on Catalan literature between 1976 and 2003 by carrying out a bibliometric and social network analysis of PhD theses defended in Spain. It has a dual aim: to present interesting results for the discipline and to demonstrate the methodological efficacy of scientometric tools in the humanities, a field in which they are often neglected due to the difficulty of gathering data. Method. The analysis was performed on 151 records obtained from the TESEO database of PhD theses. The quantitative estimates include the use of the UCINET and Pajek software packages. Authority control was performed on the records. Analysis. Descriptive statistics were used to describe the sample and the distribution of responses to each question. Sex differences on key questions were analysed using the Chi-squared test. Results. The value of the figures obtained is demonstrated. The information obtained on the topic and the periods studied in the theses, and on the actors involved (doctoral students, thesis supervisors and members of defence committees), provide important insights into the mechanisms of humanities disciplines. The main research tendencies of Catalan literature are identified. It is observed that the composition of members of the thesis defence committees follows Lotka's Law. Conclusions. Bibliometric analysis and social network analysis may be especially useful in the humanities and in other fields which are lacking in scientometric data in comparison with the experimental sciences.

Forensic intelligence for medicine anti-counterfeiting

Medicine counterfeiting is a crime that has increased in recent years and now involves the whole world. Health and economic repercussions have led pharmaceutical industries and agencies to develop many measures to protect genuine medicines and differentiate them from counterfeits. Detecting counterfeit is chemically relatively simple for the specialists, but much more information can be gained from the analyses in a forensic intelligence perspective. Analytical data can feed criminal investigation and law enforcement by detecting and understanding the criminal phenomenon. Profiling seizures using chemical and packaging data constitutes a strong way to detect organised production and industrialised forms of criminality, and is the focus of this paper. Thirty-three seizures of a commonly counterfeited type of capsule have been studied. The results of the packaging and chemical analyses were gathered within an organised database. Strong linkage was found between the seizures at the different production steps, indicating the presence of a main counterfeit network dominating the market. The interpretation of the links with circumstantial data provided information about the production and the distribution of counterfeits coming from this network. This forensic intelligence perspective has the potential to be generalised to other types of products. This may be the only reliable approach to help the understanding of the organised crime phenomenon behind counterfeiting and to enable efficient strategic and operational decision making in an attempt to dismantle counterfeit network.

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools.

BACKGROUND: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS: We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.

E-learning initiatives in forensic interpretation: report on experiences from current projects and outlook

This paper reports on the purpose, design, methodology and target audience of E-learning courses in forensic interpretation offered by the authors since 2010, including practical experiences made throughout the implementation period of this project. This initiative was motivated by the fact that reporting results of forensic examinations in a logically correct and scientifically rigorous way is a daily challenge for any forensic practitioner. Indeed, interpretation of raw data and communication of findings in both written and oral statements are topics where knowledge and applied skills are needed. Although most forensic scientists hold educational records in traditional sciences, only few actually followed full courses that focussed on interpretation issues. Such courses should include foundational principles and methodology - including elements of forensic statistics - for the evaluation of forensic data in a way that is tailored to meet the needs of the criminal justice system. In order to help bridge this gap, the authors' initiative seeks to offer educational opportunities that allow practitioners to acquire knowledge and competence in the current approaches to the evaluation and interpretation of forensic findings. These cover, among other aspects, probabilistic reasoning (including Bayesian networks and other methods of forensic statistics, tools and software), case pre-assessment, skills in the oral and written communication of uncertainty, and the development of independence and self-confidence to solve practical inference problems. E-learning was chosen as a general format because it helps to form a trans-institutional online-community of practitioners from varying forensic disciplines and workfield experience such as reporting officers, (chief) scientists, forensic coordinators, but also lawyers who all can interact directly from their personal workplaces without consideration of distances, travel expenses or time schedules. In the authors' experience, the proposed learning initiative supports participants in developing their expertise and skills in forensic interpretation, but also offers an opportunity for the associated institutions and the forensic community to reinforce the development of a harmonized view with regard to interpretation across forensic disciplines, laboratories and judicial systems.

Implementing statistical learning methods through Bayesian networks. Part 1: a guide to Bayesian parameter estimation using forensic science data

As a thorough aggregation of probability and graph theory, Bayesian networks currently enjoy widespread interest as a means for studying factors that affect the coherent evaluation of scientific evidence in forensic science. Paper I of this series of papers intends to contribute to the discussion of Bayesian networks as a framework that is helpful for both illustrating and implementing statistical procedures that are commonly employed for the study of uncertainties (e.g. the estimation of unknown quantities). While the respective statistical procedures are widely described in literature, the primary aim of this paper is to offer an essentially non-technical introduction on how interested readers may use these analytical approaches - with the help of Bayesian networks - for processing their own forensic science data. Attention is mainly drawn to the structure and underlying rationale of a series of basic and context-independent network fragments that users may incorporate as building blocs while constructing larger inference models. As an example of how this may be done, the proposed concepts will be used in a second paper (Part II) for specifying graphical probability networks whose purpose is to assist forensic scientists in the evaluation of scientific evidence encountered in the context of forensic document examination (i.e. results of the analysis of black toners present on printed or copied documents).

Artemisia (Asteraceae): Understanding its evolution using cytogenetic and molecular systematic tools, with emphasis on subgenus Dracunculus

The genus Artemisia is one of the largest of the Asteraceae family, with more than 500 species. It is widely distributed mainly across the Northern Hemisphere, being profusely represented in the Old World, with a great centre of diversification in Asia, and also reaching the New World. The evolution of this genus has been deeply studied using different approaches, and polyploidy has been found to perform an important role leading to speciation processes. Karyological, molecular cytogenetic and phylogenetic data have been compiled in the present review to provide a genomic characterization throughout some complexes within the genus.

Pathway databases and tools for their exploitation: benefits, current limitations and challenges

In past years, comprehensive representations of cell signalling pathways have been developed by manual curation from literature, which requires huge effort and would benefit from information stored in databases and from automatic retrieval and integration methods. Once a reconstruction of the network of interactions is achieved, analysis of its structural features and its dynamic behaviour can take place. Mathematical modelling techniques are used to simulate the complex behaviour of cell signalling networks, which ultimately sheds light on the mechanisms leading to complex diseases or helps in the identification of drug targets. A variety of databases containing information on cell signalling pathways have been developed in conjunction with methodologies to access and analyse the data. In principle, the scenario is prepared to make the most of this information for the analysis of the dynamics of signalling pathways. However, are the knowledge repositories of signalling pathways ready to realize the systems biology promise? In this article we aim to initiate this discussion and to provide some insights on this issue.

Learning about Bayesian networks for forensic interpretation : An example based on the "the problem of multiple propositions"

Both, Bayesian networks and probabilistic evaluation are gaining more and more widespread use within many professional branches, including forensic science. Notwithstanding, they constitute subtle topics with definitional details that require careful study. While many sophisticated developments of probabilistic approaches to evaluation of forensic findings may readily be found in published literature, there remains a gap with respect to writings that focus on foundational aspects and on how these may be acquired by interested scientists new to these topics. This paper takes this as a starting point to report on the learning about Bayesian networks for likelihood ratio based, probabilistic inference procedures in a class of master students in forensic science. The presentation uses an example that relies on a casework scenario drawn from published literature, involving a questioned signature. A complicating aspect of that case study - proposed to students in a teaching scenario - is due to the need of considering multiple competing propositions, which is an outset that may not readily be approached within a likelihood ratio based framework without drawing attention to some additional technical details. Using generic Bayesian networks fragments from existing literature on the topic, course participants were able to track the probabilistic underpinnings of the proposed scenario correctly both in terms of likelihood ratios and of posterior probabilities. In addition, further study of the example by students allowed them to derive an alternative Bayesian network structure with a computational output that is equivalent to existing probabilistic solutions. This practical experience underlines the potential of Bayesian networks to support and clarify foundational principles of probabilistic procedures for forensic evaluation.

Differences of Functional Connectivity Brain Network in Emotional Judgment

Using combined emotional stimuli, combining photos of faces and recording of voices, we investigated the neural dynamics of emotional judgment using scalp EEG recordings. Stimuli could be either combioned in a congruent, or a non-congruent way.. As many evidences show the major role of alpha in emotional processing, the alpha band was subjected to be analyzed. Analysis was performed by computing the synchronization of the EEGs and the conditions congruent vs. non-congruent were compared using statistical tools. The obtained results demonstrate that scalp EEG ccould be used as a tool to investigate the neural dynamics of emotional valence and discriminate various emotions (angry, happy and neutral stimuli).

Forensic intelligence for medicine anti-counterfeiting

Medicine counterfeiting is a crime that has increased in recent years and now involves the whole world. Health and economic repercussions have led pharmaceutical industries and agencies to develop many measures to protect genuine medicines and differentiate them from counterfeits. Detecting counterfeit is chemically relatively simple for the specialists, but much more information can be gained from the analyses in a forensic intelligence perspective. Analytical data can feed criminal investigation and law enforcement by detecting and understanding the criminal phenomenon. Profiling seizures using chemical and packaging data constitutes a strong way to detect organised production and industrialised forms of criminality, and is the focus of this paper. Thirty-three seizures of a commonly counterfeited type of capsule have been studied. The results of the packaging and chemical analyses were gathered within an organised database. Strong linkage was found between the seizures at the different production steps, indicating the presence of a main counterfeit network dominating the market. The interpretation of the links with circumstantial data provided information about the production and the distribution of counterfeits coming from this network. This forensic intelligence perspective has the potential to be generalised to other types of products. This may be the only reliable approach to help the understanding of the organised crime phenomenon behind counterfeiting and to enable efficient strategic and operational decision making in an attempt to dismantle counterfeit network.

Revisiting sodium and water reabsorption with functional genomics tools.

PURPOSE OF REVIEW: The kidney plays an essential role in maintaining sodium and water balance, thereby controlling the volume and osmolarity of the extracellular body fluids, the blood volume and the blood pressure. The final adjustment of sodium and water reabsorption in the kidney takes place in cells of the distal part of the nephron in which a set of apical and basolateral transporters participate in vectorial sodium and water transport from the tubular lumen to the interstitium and, finally, to the general circulation. According to a current model, the activity and/or cell-surface expression of these transporters is/are under the control of a gene network composed of the hormonally regulated, as well as constitutively expressed, genes. It is proposed that this gene network may include new candidate genes for salt- and water-losing syndromes and for salt-sensitive hypertension. A new generation of functional genomics techniques have recently been applied to the characterization of this gene network. The purpose of this review is to summarize these studies and to discuss the potential of the different techniques for characterization of the renal transcriptome. RECENT FINDINGS: Recently, DNA microarrays and serial analysis of gene expression have been applied to characterize the kidney transcriptome in different in-vivo and in-vitro models. In these studies, a set of new interesting genes potentially involved in the regulation of sodium and water reabsorption by the kidney have been identified and are currently under detailed investigation. SUMMARY: Characterization of the kidney transcriptome is greatly expanding our knowledge of the gene networks involved in multiple kidney functions, including the maintenance of sodium and water homeostasis.

APTIC : a social network to improve the quality of life of members of patients' associations

Peer-reviewed

Versatile iPad forensic acquisition using the Apple Camera Connection Kit

Peer reviewed