Integrating Smart Resources in ROS-based systems to distribute services

[EN] Mobile robots execute complexes tasks that involve the management of several embedded sensors and actuators. Therefore, in many cases, a robot is characterized as an intelligent distributed system formed with a central unit, which manages the on-board embedded devices and distributes the tasks execution. Embedded devices are also evolving to more complex systems. These systems are developed not only for executing simple tasks but also for offering some advanced mechanisms. Thus, complex data processing, adaptive execution, or fault-tolerance routines are some common system features. The Smart Resource topology has been developed in order to manage these embedded systems. This topology offers high-level routines that rely on a certain physical hardware execution. Therefore, Smart Resources are defined as distributed services providers, which operates within some context and quality requirements. Provided services can adapt its execution in order accomplish the set requirements and maximize the system performance. How to improve the versatility of the Smart Resources by making their services compatibles with the Robot Operating System (ROS) is addressed along this work. This solution integrates all the execution mechanisms provided by ROS with the service distribution, adaptive execution, and fault-tolerance routines offered by the Smart Resources. This integration is tested through a set of experiments using the Turtlebot robot platform and a simulated version of it. In both approaches ROS mechanisms are used to access the Smart Resource Services. Finally, obtained results are used to characterize the performance of this proposal.Work supported by the Spanish Science and Innovation Ministry MICINN: CICYT project M2C2: "Codiseno de sistemas de control con criticidad mixta basado en misiones" TIN2014-56158-C4-4-P and PAID (Polytechnic University of Valencia): UPV-PAID-FPI-2013.Munera-Sánchez, E.; Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2017). Integrating Smart Resources in ROS-based systems to distribute services. Advances in Distributed Computing and Artificial Intelligence Journal. 6(1):13-19. https://doi.org/10.14201/ADCAIJ2017611319S13196

2002 Bluestone

The Bluestone is the yearbook of James Madison University.https://commons.lib.jmu.edu/allyearbooks/1095/thumbnail.jp

Trial efficacy vs real world effectiveness in first line treatment of multiple myeloma

Background: Large randomized clinical trials (RCT) are the foundation of the registration of newly developed drugs. A potential problem with RCTs is that the inclusion/exclusion criteria will make the population different from the actual population treated in real life. Hence, it is important to understand how the results from the RCT can be generalized to a general population. Aims: The primary aim of the present study was to assess the generalizability of the large 1st line RCTs in Multiple Myeloma (MM) to the Nordic setting and to understand potential difference and magnitude in outcomes between RCTs and patients treated in standard care in the Nordics. Methods: A retrospective analysis was performed on an incident cohort of 2960 MM-patients from 24 hospitals in Denmark, Finland, Norway and Sweden. The database contained information on patient baseline characteristics, treatments and outcomes. Data from relevant 1st line MM RCTs was selected from the treatment MP (Waage, A., et al., Blood. 2010], MPT (Waage, A., et al., Blood. 2010) and VMP (San Miguel, J.F., et al., N Engl J Med, 2008) and baseline characteristics were compared to newly diagnosed Nordic MM treated patients. Potential difference in response and overall survival (OS) was estimated by adjusting the RWE population to the RCT population using matching adjusted indirect comparisons. Patients were matched on age (median approximated to mean), gender, calcium, beta2-microglobulin and ISS score 3. These variables were selected because they were reported in all trials and have previously been identified as having prognostic value. Results: Patients in the Nordic database treated with MP (n=880) had a response rate of (PD, NR, PR, VGPR, ≥nCR) of (13%, 39%, 38%, 6%, 4%). After matching (n=347), the response rate was slightly worse (12%, 43%, 36%, 6%, 3%). This can be compared to the response rate from the RCT of (7%, 53%, 33%, 3%, 4%). OS for Nordic MP treated patients was 2.67 years (2.25-3.17). After matching the OS was 3.37 years (2.86-3.96) and this can be compared to the trial with OS 2.40 years (2.23-2.66). Patients treated with MPT (n=283) in the Nordic countries had a response rate of (5%, 14%, 52%, 20%, 9%). After matching (n=179) the response rate was slightly changed to (6%, 20%, 50%, 13% 11%). The corresponding RCT response results were 14%, 29%, 34%, 10%, and 13% respectively. OS for Nordic MPT treated patients was 4.15 years (3.73- 4.74). After matching the OS was 4.28 years (3.98-NA) years and compared to 2.42 years (2.08-3.17) OS observed in the corresponding trial. Patients treated with VMP (n=59) in the Nordic countries had a response rate of (4%, 5%, 40%, 18%, 33%). After matching (n=31) the response rate was improved to (8%, 11%, 28%, 8%, 45%). This corresponding response rates shown in the trial are 1%, 23%, 33%, 8%, and 33% respectively. OS for Nordic MP treated patients was 4.86 years (3.79-NA). After matching the OS was 4.86 years (4.86-NA) and this can be compared to the trial with OS 4.70 years. Summary and Conclusions: Surprisingly Nordic treated MM patients do very well compared to, and even better than, patients treated in RCTs. Since the OS for all tested treatments improves after matching to the RCT baseline characteristics, patients recruited to the RCTs seems to be a bit better than ordinary Nordic patents. The database used in the present study, and the used method, can be valuable for generalizing the results to the Nordic setting and estimating potential difference for future RCTs and Nordic MM treated patients. Future research should include different data cuts to see whether the analyses are biased by differences subsequent treatments applied in RCTs and clinical practice