Augmenting Data with Generative Adversarial Networks to Improve Machine Learning-Based Fraud Detection

While current machine learning methods can detect financial fraud more effectively, they suffer from a common problem: dataset imbalance, i.e. there are substantially more non-fraud than fraud cases. In this paper, we propose the application of generative adversarial networks (GANs) to generate synthetic fraud cases on a dataset of public firms convicted by the United States Securities and Exchange Commission for accounting malpractice. This approach aims to increase the prediction accuracy of a downstream logit, support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) classifier by training on a more well-balanced dataset. While the results indicate that a state-of-the-art machine learning model like XGBoost can outperform previous fraud detection models on the same data, generating synthetic fraud cases before applying a machine learning model does not improve performance

Toward a Metric Catalog for Large-Scale Agile Development

Nowadays, organizations use agile software development to remain competitive in their frequently changing business environment. Inspired by the success of agile methods on a small scale, organizations have started to apply them in larger contexts. However, the limited scalability of agile methods is a problem. Metrics can be a success factor for achieving agility at scale, thus adopting them is promising. Most scaling agile frameworks provide few recommendations regarding metrics. Likewise, research on metrics in large-scale agile development lacks concrete guidance for metrics or their organization-specific adoption. To fill this gap, we propose two artifacts. We present the design of a minimalistic metric management fact sheet (MMFS) for large-scale agile development to support practitioners in using metrics in their organization-specific development environment. Furthermore, the MMFS is the basis for our metric catalog documenting 196 metrics identified in an expert study to provide a comprehensive metric set for scaling agile environments

Enzyme-Responsive Nanoparticles and Coatings Made from Alginate/Peptide Ciprofloxacin Conjugates as Drug Release System

Infection-controlled release of antibacterial agents is of great importance, particularly for the control of peri-implant infections in the postoperative phase. Polymers containing antibiotics bound via enzymatically cleavable linkers could provide access to drug release systems that could accomplish this. Dispersions of nanogels were prepared by ionotropic gelation of alginate with poly-L-lysine, which was conjugated with ciprofloxacin as model drug via a copper-free 1,3-dipolar cy-cloaddition (click reaction). The nanogels are stable in dispersion and form films which are stable in aqueous environments. However, both the nanogels and the layers are degraded in the presence of an enzyme and the ciprofloxacin is released. The efficacy of the released drug against Staphylococcus aureus is negatively affected by the residues of the linker. Both the acyl modification of the amine nitrogen in ciprofloxacin and the sterically very demanding linker group with three annel-lated rings could be responsible for this. However the basic feasibility of the principle for enzyme-triggered release of drugs was successfully demonstrated

Scattered Light Imaging: Resolving the substructure of nerve fiber crossings in whole brain sections with micrometer resolution

For developing a detailed network model of the brain based on image reconstructions, it is necessary to spatially resolve crossing nerve fibers. The accuracy hereby depends on many factors, including the spatial resolution of the imaging technique. 3D Polarized Light Imaging (3D-PLI) allows the three-dimensional reconstruction of nerve fiber tracts in whole brain sections with micrometer in-plane resolution, but leaves uncertainties in pixels containing crossing fibers. Here we introduce Scattered Light Imaging (SLI) to resolve the substructure of nerve fiber crossings. The measurement is performed on the same unstained histological brain sections as in 3D-PLI. By illuminating the brain sections from different angles and measuring the transmitted (scattered) light under normal incidence, SLI provides information about the underlying nerve fiber structure. A fully automated evaluation of the resulting light intensity profiles has been developed, allowing the user to extract various characteristics, like the individual directions of in-plane crossing nerve fibers, for each image pixel at once. We validate the reconstructed nerve fiber directions against results from previous simulation studies, scatterometry measurements, and fiber directions obtained from 3D-PLI. We demonstrate in different brain samples (human optic tracts, vervet monkey brain, rat brain) that the 2D fiber directions can be reliably reconstructed for up to three crossing nerve fiber bundles in each image pixel with an in-plane resolution of up to 6.5 $\mu$m. We show that SLI also yields reliable fiber directions in brain regions with low 3D-PLI signals coming from regions with a low density of myelinated nerve fibers or out-of-plane fibers. In combination with 3D-PLI, the technique can be used for a full reconstruction of the three-dimensional nerve fiber architecture in the brain.Comment: 30 pages, 16 figure

Sequence features associated with microRNA strand selection in humans and flies

<p>Abstract</p> <p>Background</p> <p>During microRNA (miRNA) maturation in humans and flies, Drosha and Dicer cut the precursor transcript, thereby producing a short RNA duplex. One strand of this duplex becomes a functional component of the RNA-Induced Silencing Complex (RISC), while the other is eliminated. While thermodynamic asymmetry of the duplex ends appears to play a decisive role in the strand selection process, the details of the selection mechanism are not yet understood.</p> <p>Results</p> <p>Here, we assess miRNA strand selection bias in humans and fruit flies by analyzing the sequence composition and relative expression levels of the two strands of the precursor duplex in these species. We find that the sequence elements associated with preferential miRNA strand selection and/or rejection differ between the two species. Further, we identify another feature that distinguishes human and fly miRNA processing machinery: the relative accuracy of the Drosha and Dicer enzymes.</p> <p>Conclusion</p> <p>Our result provides clues to the mechanistic aspects of miRNA strand selection in humans and other mammals. Further, it indicates that human and fly miRNA processing pathways are more distinct than currently recognized. Finally, the observed strand selection determinants are instrumental in the rational design of efficient miRNA-based expression regulators.</p

Comprehensive survey of human brain microRNA by deep sequencing

<p>Abstract</p> <p>Background</p> <p>MicroRNA (miRNA) play an important role in gene expression regulation. At present, the number of annotated miRNA continues to grow rapidly, in part due to advances of high-throughput sequencing techniques. Here, we use deep sequencing to characterize a population of small RNA expressed in human and rhesus macaques brain cortex.</p> <p>Results</p> <p>Based on a total of more than 150 million sequence reads we identify 197 putative novel miRNA, in humans and rhesus macaques, that are highly conserved among mammals. These putative miRNA have significant excess of conserved target sites in genes' 3'UTRs, supporting their functional role in gene regulation. Additionally, in humans and rhesus macaques respectively, we identify 41 and 22 conserved putative miRNA originating from non-coding RNA (ncRNA) transcripts. While some of these molecules might function as conventional miRNA, others might be harmful and result in target avoidance.</p> <p>Conclusions</p> <p>Here, we further extend the repertoire of conserved human and rhesus macaque miRNA. Even though our study is based on a single tissue, the coverage depth of our study allows identification of functional miRNA present in brain tissue at background expression levels. Therefore, our study might cover large proportion of the yet unannotated conserved miRNA present in the human genome.</p

Estimating accuracy of RNA-Seq and microarrays with proteomics

<p>Abstract</p> <p>Background</p> <p>Microarrays revolutionized biological research by enabling gene expression comparisons on a transcriptome-wide scale. Microarrays, however, do not estimate absolute expression level accurately. At present, high throughput sequencing is emerging as an alternative methodology for transcriptome studies. Although free of many limitations imposed by microarray design, its potential to estimate absolute transcript levels is unknown.</p> <p>Results</p> <p>In this study, we evaluate relative accuracy of microarrays and transcriptome sequencing (RNA-Seq) using third methodology: proteomics. We find that RNA-Seq provides a better estimate of absolute expression levels.</p> <p>Conclusion</p> <p>Our result shows that in terms of overall technical performance, RNA-Seq is the technique of choice for studies that require accurate estimation of absolute transcript levels.</p