3D Microfluidic model for evaluating immunotherapy efficacy by tracking dendritic cell behaviour toward tumor cells

Immunotherapy efficacy relies on the crosstalk within the tumor microenvironment between cancer and dendritic cells (DCs) resulting in the induction of a potent and effective antitumor response. DCs have the specific role of recognizing cancer cells, taking up tumor antigens (Ags) and then migrating to lymph nodes for Ag (cross)-presentation to naïve T cells. Interferon-α-conditioned DCs (IFN-DCs) exhibit marked phagocytic activity and the special ability of inducing Ag-specific T-cell response. Here, we have developed a novel microfluidic platform recreating tightly interconnected cancer and immune systems with specific 3D environmental properties, for tracking human DC behaviour toward tumor cells. By combining our microfluidic platform with advanced microscopy and a revised cell tracking analysis algorithm, it was possible to evaluate the guided efficient motion of IFN-DCs toward drug-treated cancer cells and the succeeding phagocytosis events. Overall, this platform allowed the dissection of IFN-DC-cancer cell interactions within 3D tumor spaces, with the discovery of major underlying factors such as CXCR4 involvement and underscored its potential as an innovative tool to assess the efficacy of immunotherapeutic approaches

Reducing the overhead of an MPI application-level migration approach

[Abstract] Process migration provides many benefits for parallel environments including dynamic load balance, data access locality, or fault tolerance. This work proposes a solution that reduces the memory and I/O overhead in an application-level checkpoint-based migration approach. The proposal splits the checkpoint files in order to overlap the writing of the state in the terminating processes with the read and restarting operation in the newly spawned processes. It has been tested using the MPI NAS Parallel Benchmarks, showing encouraging results, both in terms of memory consumption and I/O migration times.Ministerio de Economía y Competitividad; TIN2013-42148-PGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013/05

Session 5: Development, Neuroscience and Evolutionary Psychology

Proceedings of the Pittsburgh Workshop in History and Philosophy of Biology, Center for Philosophy of Science, University of Pittsburgh, March 23-24 2001 Session 5: Development, Neuroscience and Evolutionary Psycholog

ImageJ2: ImageJ for the next generation of scientific image data

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs

Genetic Programming for Smart Phone Personalisation

Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure

ATCA: Its Performance and Application for Real Time Systems

The Advanced Telecom Computing Architecture (ATCA), describes a high bandwidth, high connectivity, chassis based architecture designed principally to appeal to the telecommunications industry. The object of the exercise was to closely connect compute engines within the chassis to multiple user services brought in at the front panel. This maps closely to the needs of real time systems and the main points of the architecture are reviewed and discussed in that light. The performance of an ATCA backplane has been tested and measured using a Backplane Tester developed within a 10 Gb/s Ethernet switch project that was an early adopter of the ATCA standard. Some results from these tests are presented

Adaptive tracking of people and vehicles using mobile platforms

Tracking algorithms have important applications in detection of humans and vehicles for border security and other areas. For large-scale deployment of such algorithms, it is critical to provide methods for their cost- and energy-efficient realization. To this end, commodity mobile devices have significant potential for use as prototyping and testing platforms due to their low cost, widespread availability, and integration of advanced communications, sensing, and processing features. Prototypes developed on mobile platforms can be tested, fine-tuned, and demonstrated in the field and then provide reference implementations for application-specific disposable sensor node implementations that are targeted for deployment. In this paper, we develop a novel, adaptive tracking system that is optimized for energy-efficient, real-time operation on off-the-shelf mobile platforms. Our tracking system applies principles of dynamic data-driven application systems (DDDAS) to periodically monitor system operating characteristics and apply these measurements to dynamically adapt the specific classifier configurations that the system employs. Our resulting adaptive approach enables powerful optimization of trade-offs among energy consumption, real-time performance, and tracking accuracy based on time-varying changes in operational characteristics. Through experiments employing an Android-based tablet platform, we demonstrate the efficiency of our proposed tracking system design for multimode detection of human and vehicle targets.publishedVersionPeer reviewe

Plasticity of Cell Migration in Vivo and in Silico

Cell migration results from stepwise mechanical and chemical interactions between cells and their extracellular environment. Mechanistic principles that determine single-cell and collective migration modes and their interconversions depend upon the polarization, adhesion, deformability, contractility, and proteolytic ability of cells. Cellular determinants of cell migration respond to extracellular cues, including tissue composition, topography, alignment, and tissue-associated growth factors and cytokines. Both cellular determinants and tissue determinants are interdependent; undergo reciprocal adjustment; and jointly impact cell decision making, navigation, and migration outcome in complex environments. We here review the variability, decision making, and adaptation of cell migration approached by live-cell, in vivo, and in silico strategies, with a focus on cell movements in morphogenesis, repair, immune surveillance, and cancer metastasi