Automated derivation of the adjoint of high-level transient finite element programs

In this paper we demonstrate a new technique for deriving discrete adjoint and tangent linear models of finite element models. The technique is significantly more efficient and automatic than standard algorithmic differentiation techniques. The approach relies on a high-level symbolic representation of the forward problem. In contrast to developing a model directly in Fortran or C++, high-level systems allow the developer to express the variational problems to be solved in near-mathematical notation. As such, these systems have a key advantage: since the mathematical structure of the problem is preserved, they are more amenable to automated analysis and manipulation. The framework introduced here is implemented in a freely available software package named dolfin-adjoint, based on the FEniCS Project. Our approach to automated adjoint derivation relies on run-time annotation of the temporal structure of the model, and employs the FEniCS finite element form compiler to automatically generate the low-level code for the derived models. The approach requires only trivial changes to a large class of forward models, including complicated time-dependent nonlinear models. The adjoint model automatically employs optimal checkpointing schemes to mitigate storage requirements for nonlinear models, without any user management or intervention. Furthermore, both the tangent linear and adjoint models naturally work in parallel, without any need to differentiate through calls to MPI or to parse OpenMP directives. The generality, applicability and efficiency of the approach are demonstrated with examples from a wide range of scientific applications

Following wrong suggestions: self-blame in human and computer scenarios

This paper investigates the specific experience of following a suggestion by an intelligent machine that has a wrong outcome and the emotions people feel. By adopting a typical task employed in studies on decision-making, we presented participants with two scenarios in which they follow a suggestion and have a wrong outcome by either an expert human being or an intelligent machine. We found a significant decrease in the perceived responsibility on the wrong choice when the machine offers the suggestion. At present, few studies have investigated the negative emotions that could arise from a bad outcome after following the suggestion given by an intelligent system, and how to cope with the potential distrust that could affect the long-term use of the system and the cooperation. This preliminary research has implications in the study of cooperation and decision making with intelligent machines. Further research may address how to offer the suggestion in order to better cope with user's self-blame.Comment: To be published in the Proceedings of IFIP Conference on Human-Computer Interaction (INTERACT)201

The receptors for gibbon ape leukemia virus and amphotropic murine leukemia virus are not downregulated in productively infected cells

<p>Abstract</p> <p>Background</p> <p>Over the last several decades it has been noted, using a variety of different methods, that cells infected by a specific gammaretrovirus are resistant to infection by other retroviruses that employ the same receptor; a phenomenon termed receptor interference. Receptor masking is thought to provide an earlier means of blocking superinfection, whereas receptor down regulation is generally considered to occur in chronically infected cells.</p> <p>Results</p> <p>We used replication-competent GFP-expressing viruses containing either an amphotropic murine leukemia virus (A-MLV) or the gibbon ape leukemia virus (GALV) envelope. We also constructed similar viruses containing fluorescence-labeled Gag proteins for the detection of viral particles. Using this repertoire of reagents together with a wide range of antibodies, we were able to determine the presence and availability of viral receptors, and detect viral envelope proteins and particles presence on the cell surface of chronically infected cells.</p> <p>Conclusions</p> <p>A-MLV or GALV receptors remain on the surface of chronically infected cells and are detectable by respective antibodies, indicating that these receptors are not downregulated in these infected cells as previously proposed. We were also able to detect viral envelope proteins on the infected cell surface and infected cells are unable to bind soluble A-MLV or GALV envelopes indicating that receptor binding sites are masked by endogenously expressed A-MLV or GALV viral envelope. However, receptor masking does not completely prevent A-MLV or GALV superinfection.</p

3D Reconstruction for Partial Data Electrical Impedance Tomography Using a Sparsity Prior

In electrical impedance tomography the electrical conductivity inside a physical body is computed from electro-static boundary measurements. The focus of this paper is to extend recent result for the 2D problem to 3D. Prior information about the sparsity and spatial distribution of the conductivity is used to improve reconstructions for the partial data problem with Cauchy data measured only on a subset of the boundary. A sparsity prior is enforced using the $\ell_1$ norm in the penalty term of a Tikhonov functional, and spatial prior information is incorporated by applying a spatially distributed regularization parameter. The optimization problem is solved numerically using a generalized conditional gradient method with soft thresholding. Numerical examples show the effectiveness of the suggested method even for the partial data problem with measurements affected by noise.Comment: 10 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:1405.655

The Host Range of Gammaretroviruses and Gammaretroviral Vectors Includes Post-Mitotic Neural Cells

Gammaretroviruses and gammaretroviral vectors, in contrast to lentiviruses and lentiviral vectors, are reported to be restricted in their ability to infect growth-arrested cells. The block to this restriction has never been clearly defined. The original assessment of the inability of gammaretroviruses and gammaretroviral vectors to infect growth-arrested cells was carried out using established cell lines that had been growth-arrested by chemical means, and has been generalized to neurons, which are post-mitotic. We re-examined the capability of gammaretroviruses and their derived vectors to efficiently infect terminally differentiated neuroendocrine cells and primary cortical neurons, a target of both experimental and therapeutic interest.Using GFP expression as a marker for infection, we determined that both growth-arrested (NGF-differentiated) rat pheochromocytoma cells (PC12 cells) and primary rat cortical neurons could be efficiently transduced, and maintained long-term protein expression, after exposure to murine leukemia virus (MLV) and MLV-based retroviral vectors. Terminally differentiated PC12 cells transduced with a gammaretroviral vector encoding the anti-apoptotic protein Bcl-xL were protected from cell death induced by withdrawal of nerve growth factor (NGF), demonstrating gammaretroviral vector-mediated delivery and expression of genes at levels sufficient for therapeutic effect in non-dividing cells. Post-mitotic rat cortical neurons were also shown to be susceptible to transduction by murine replication-competent gammaretroviruses and gammaretroviral vectors.These findings suggest that the host range of gammaretroviruses includes post-mitotic and other growth-arrested cells in mammals, and have implications for re-direction of gammaretroviral gene therapy to neurological disease

An incremental approach to automated protein localisation

Tscherepanow M, Jensen N, Kummert F. An incremental approach to automated protein localisation. BMC Bioinformatics. 2008;9(1): 445.Background: The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches – i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards – has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected. Results: We introduce a novel approach to automated protein localisation in living cells. In contrast to well-known techniques, the protein localisation technique presented in this article aims at combining the two types of approaches described above: After an automatic identification of unknown protein locations, a potential user is enabled to incorporate them into the pre-trained system. An incremental neural network allows the classification of a fixed set of protein location as well as the detection, clustering and incorporation of additional patterns that occur during an experiment. Here, the proposed technique achieves promising results with respect to both tasks. In addition, the protein localisation procedure has been adapted to an existing cell recognition approach. Therefore, it is especially well-suited for high-throughput investigations where user interactions have to be avoided. Conclusion: We have shown that several aspects required for developing an automatic protein localisation technique – namely the recognition of cells, the classification of protein distribution patterns into a set of learnt protein locations, and the detection and learning of new locations – can be combined successfully. So, the proposed method constitutes a crucial step to render image-based protein localisation techniques amenable to large-scale experiments