134 research outputs found

    Where To Prosecute Cybercrimes

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    Selecting the appropriate venue for a criminal trial has been a matter of constitutional concern since the founding of the country. The issue is thought to be essential to the fair administration of justice and thus public confidence in the criminal justice system. Constitutionally, crimes must be prosecuted in the states and districts in which they were committed. However, the rise of cybercrime has complicated the venue inquiry: cyberspace, the domain of cybercrime, and physical space have become increasingly decoupled. Consequently, under America’s primary but dated cybercrime law, the ideal location for a trial may not be a constitutionally proper venue. This Note explores several possible approaches to permitting cybercrime trials to take place in the locations where they belong, including through an old but recently revisited judicially-created test for venue and through possible legislative reform

    Where To Prosecute Cybercrimes

    Get PDF
    Selecting the appropriate venue for a criminal trial has been a matter of constitutional concern since the founding of the country. The issue is thought to be essential to the fair administration of justice and thus public confidence in the criminal justice system. Constitutionally, crimes must be prosecuted in the states and districts in which they were committed. However, the rise of cybercrime has complicated the venue inquiry: cyberspace, the domain of cybercrime, and physical space have become increasingly decoupled. Consequently, under America’s primary but dated cybercrime law, the ideal location for a trial may not be a constitutionally proper venue. This Note explores several possible approaches to permitting cybercrime trials to take place in the locations where they belong, including through an old but recently revisited judicially-created test for venue and through possible legislative reform

    Treatment of acute diverticulitis laparoscopic lavage vs. resection (DILALA): study protocol for a randomised controlled trial

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    <p>Abstract</p> <p>Background</p> <p>Perforated diverticulitis is a condition associated with substantial morbidity. Recently published reports suggest that laparoscopic lavage has fewer complications and shorter hospital stay. So far no randomised study has published any results.</p> <p>Methods</p> <p>DILALA is a Scandinavian, randomised trial, comparing laparoscopic lavage (LL) to the traditional Hartmann's Procedure (HP). Primary endpoint is the number of re-operations within 12 months. Secondary endpoints consist of mortality, quality of life (QoL), re-admission, health economy assessment and permanent stoma. Patients are included when surgery is required. A laparoscopy is performed and if Hinchey grade III is diagnosed the patient is included and randomised 1:1, to either LL or HP. Patients undergoing LL receive > 3L of saline intraperitoneally, placement of pelvic drain and continued antibiotics. Follow-up is scheduled 6-12 weeks, 6 months and 12 months. A QoL-form is filled out on discharge, 6- and 12 months. Inclusion is set to 80 patients (40+40).</p> <p>Discussion</p> <p>HP is associated with a high rate of complication. Not only does the primary operation entail complications, but also subsequent surgery is associated with a high morbidity. Thus the combined risk of treatment for the patient is high. The aim of the DILALA trial is to evaluate if laparoscopic lavage is a safe, minimally invasive method for patients with perforated diverticulitis Hinchey grade III, resulting in fewer re-operations, decreased morbidity, mortality, costs and increased quality of life.</p> <p>Trial registration</p> <p>British registry (ISRCTN) for clinical trials <a href="http://www.controlled-trials.com/ISRCTN82208287">ISRCTN82208287</a><url>http://www.controlled-trials.com/ISRCTN82208287</url></p

    Financial time series prediction using spiking neural networks

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    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. © 2014 Reid et al

    Detecting Clusters of Mutations

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    Positive selection for protein function can lead to multiple mutations within a small stretch of DNA, i.e., to a cluster of mutations. Recently, Wagner proposed a method to detect such mutation clusters. His method, however, did not take into account that residues with high solvent accessibility are inherently more variable than residues with low solvent accessibility. Here, we propose a new algorithm to detect clustered evolution. Our algorithm controls for different substitution probabilities at buried and exposed sites in the tertiary protein structure, and uses random permutations to calculate accurate P values for inferred clusters. We apply the algorithm to genomes of bacteria, fly, and mammals, and find several clusters of mutations in functionally important regions of proteins. Surprisingly, clustered evolution is a relatively rare phenomenon. Only between 2% and 10% of the genes we analyze contain a statistically significant mutation cluster. We also find that not controlling for solvent accessibility leads to an excess of clusters in terminal and solvent-exposed regions of proteins. Our algorithm provides a novel method to identify functionally relevant divergence between groups of species. Moreover, it could also be useful to detect artifacts in automatically assembled genomes

    Computing with bacterial constituents, cells and populations: from bioputing to bactoputing

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    The relevance of biological materials and processes to computing—aliasbioputing—has been explored for decades. These materials include DNA, RNA and proteins, while the processes include transcription, translation, signal transduction and regulation. Recently, the use of bacteria themselves as living computers has been explored but this use generally falls within the classical paradigm of computing. Computer scientists, however, have a variety of problems to which they seek solutions, while microbiologists are having new insights into the problems bacteria are solving and how they are solving them. Here, we envisage that bacteria might be used for new sorts of computing. These could be based on the capacity of bacteria to grow, move and adapt to a myriad different fickle environments both as individuals and as populations of bacteria plus bacteriophage. New principles might be based on the way that bacteria explore phenotype space via hyperstructure dynamics and the fundamental nature of the cell cycle. This computing might even extend to developing a high level language appropriate to using populations of bacteria and bacteriophage. Here, we offer a speculative tour of what we term bactoputing, namely the use of the natural behaviour of bacteria for calculating

    Deep sequencing of the Mexican avocado transcriptome, an ancient angiosperm with a high content of fatty acids

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    Background: Avocado (Persea americana) is an economically important tropical fruit considered to be a good source of fatty acids. Despite its importance, the molecular and cellular characterization of biochemical and developmental processes in avocado is limited due to the lack of transcriptome and genomic information. Results: The transcriptomes of seeds, roots, stems, leaves, aerial buds and flowers were determined using different sequencing platforms. Additionally, the transcriptomes of three different stages of fruit ripening (pre-climacteric, climacteric and post-climacteric) were also analyzed. The analysis of the RNAseqatlas presented here reveals strong differences in gene expression patterns between different organs, especially between root and flower, but also reveals similarities among the gene expression patterns in other organs, such as stem, leaves and aerial buds (vegetative organs) or seed and fruit (storage organs). Important regulators, functional categories, and differentially expressed genes involved in avocado fruit ripening were identified. Additionally, to demonstrate the utility of the avocado gene expression atlas, we investigated the expression patterns of genes implicated in fatty acid metabolism and fruit ripening. Conclusions: A description of transcriptomic changes occurring during fruit ripening was obtained in Mexican avocado, contributing to a dynamic view of the expression patterns of genes involved in fatty acid biosynthesis and the fruit ripening process
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