Promoting Cardiac Repair through Simple Engineering of Nanoparticles with Exclusive Targeting Capability toward Myocardial Reperfusion Injury by Thermal Resistant Microfluidic Platform

Nanoparticle (NP)-based intravenous administration represents the most convenient cardiac targeting delivery routine, yet, there are still therapeutic issues due to the lack of targeting efficiency and specificity. Active targeting methods using functionalization of ligands onto the NPs' surface may be limited by trivial modification procedures and reduced targeting yield in vivo. Here, a microfluidics assisted single step, green synthesis method is introduced for producing targeting ligands free heart homing NPs in a tailored manner. The generated beta-glucan-based NPs exhibit precise and efficient targeting capability toward Dectin-1(+) monocytes/macrophages, which are confirmed as main pathogenesis mediators for cardiac ischemic/reperfusion (I/R) injury, with a sequentially enhanced cardiac NP accumulation, and this targeting strategy is exclusively suitable for cardiac I/R but not for other cardiovascular diseases, as confirmed both in murine and human model. Comparing to FDA-approved nano-micelles formulation, beta-glucan NPs loaded with NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome inhibitor (CY-09) exhibit better efficiency in ameliorating myocardial injury and heart failure induced by surgically induced I/R. These findings indicate a simple production of targeting-ligand free NPs, and demonstrate their potential therapeutic applications for preclinical I/R-induced cardiac injury amelioration.Peer reviewe

Promoting Cardiac Repair through Simple Engineering of Nanoparticles with Exclusive Targeting Capability toward Myocardial Reperfusion Injury by Thermal Resistant Microfluidic Platform

Nanoparticle (NP)-based intravenous administration represents the most convenient cardiac targeting delivery routine, yet, there are still therapeutic issues due to the lack of targeting efficiency and specificity. Active targeting methods using functionalization of ligands onto the NPs' surface may be limited by trivial modification procedures and reduced targeting yield in vivo. Here, a microfluidics assisted single step, green synthesis method is introduced for producing targeting ligands free heart homing NPs in a tailored manner. The generated beta-glucan-based NPs exhibit precise and efficient targeting capability toward Dectin-1(+) monocytes/macrophages, which are confirmed as main pathogenesis mediators for cardiac ischemic/reperfusion (I/R) injury, with a sequentially enhanced cardiac NP accumulation, and this targeting strategy is exclusively suitable for cardiac I/R but not for other cardiovascular diseases, as confirmed both in murine and human model. Comparing to FDA-approved nano-micelles formulation, beta-glucan NPs loaded with NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome inhibitor (CY-09) exhibit better efficiency in ameliorating myocardial injury and heart failure induced by surgically induced I/R. These findings indicate a simple production of targeting-ligand free NPs, and demonstrate their potential therapeutic applications for preclinical I/R-induced cardiac injury amelioration.Peer reviewe

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

International audienceEvery year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001.Peer reviewe

Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network

In the indoor environment, the activity of the pedestrian can reflect some semantic information. These activities can be used as the landmarks for indoor localization. In this paper, we propose a pedestrian activities recognition method based on a convolutional neural network. A new convolutional neural network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 98% accuracy in about 2 s in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, downstairs and turning. Moreover, we have built a pedestrian activity database, which contains more than 6 GB of data of accelerometers, magnetometers, gyroscopes and barometers collected with various types of smartphones. We will make it public to contribute to academic research

A Robust Crowdsourcing-Based Indoor Localization System

WiFi fingerprinting-based indoor localization has been widely used due to its simplicity and can be implemented on the smartphones. The major drawback of WiFi fingerprinting is that the radio map construction is very labor-intensive and time-consuming. Another drawback of WiFi fingerprinting is the Received Signal Strength (RSS) variance problem, caused by environmental changes and device diversity. RSS variance severely degrades the localization accuracy. In this paper, we propose a robust crowdsourcing-based indoor localization system (RCILS). RCILS can automatically construct the radio map using crowdsourcing data collected by smartphones. RCILS abstracts the indoor map as the semantics graph in which the edges are the possible user paths and the vertexes are the location where users may take special activities. RCILS extracts the activity sequence contained in the trajectories by activity detection and pedestrian dead-reckoning. Based on the semantics graph and activity sequence, crowdsourcing trajectories can be located and a radio map is constructed based on the localization results. For the RSS variance problem, RCILS uses the trajectory fingerprint model for indoor localization. During online localization, RCILS obtains an RSS sequence and realizes localization by matching the RSS sequence with the radio map. To evaluate RCILS, we apply RCILS in an office building. Experiment results demonstrate the efficiency and robustness of RCILS

Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

In the study of the mid-long-term early warning of landslide, the computational efficiency of the prediction model is critical to the timeliness of landslide prevention and control. Accordingly, enhancing the computational efficiency of the prediction model is of practical implication to the mid-long-term prevention and control of landslides. When the Apriori algorithm is adopted to analyze landslide data based on the MapReduce framework, numerous frequent item-sets will be generated, adversely affecting the computational efficiency. To enhance the computational efficiency of the prediction model, the IAprioriMR algorithm is proposed in this paper to enhance the efficiency of the Apriori algorithm based on the MapReduce framework by simplifying operations of the frequent item-sets. The computational efficiencies of the IAprioriMR algorithm and the original AprioriMR algorithm were compared and analyzed in the case of different data quantities and nodes, and then the efficiency of IAprioriMR algorithm was verified to be enhanced to some extent in processing large-scale data. To verify the feasibility of the proposed algorithm, the algorithm was employed in the mid-long-term early warning study of landslides in the Three Parallel Rivers. Under the same conditions, IAprioriMR algorithm of the same rule exhibited higher confidence than FP-Growth algorithm, which implied that IAprioriMR can achieve more accurate landslide prediction. This method is capable of technically supporting the prevention and control of landslides.</jats:p

A Novel Spatial-Temporal Voronoi Diagram-Based Heuristic Approach for Large-Scale Vehicle Routing Optimization with Time Constraints

Vehicle routing optimization (VRO) designs the best routes to reduce travel cost, energy consumption, and carbon emission. Due to non-deterministic polynomial-time hard (NP-hard) complexity, many VROs involved in real-world applications require too much computing effort. Shortening computing time for VRO is a great challenge for state-of-the-art spatial optimization algorithms. From a spatial-temporal perspective, this paper presents a spatial-temporal Voronoi diagram-based heuristic approach for large-scale vehicle routing problems with time windows (VRPTW). Considering time constraints, a spatial-temporal Voronoi distance is derived from the spatial-temporal Voronoi diagram to find near neighbors in the space-time searching context. A Voronoi distance decay strategy that integrates a time warp operation is proposed to accelerate local search procedures. A spatial-temporal feature-guided search is developed to improve unpromising micro route structures. Experiments on VRPTW benchmarks and real-world instances are conducted to verify performance. The results demonstrate that the proposed approach is competitive with state-of-the-art heuristics and achieves high-quality solutions for large-scale instances of VRPTWs in a short time. This novel approach will contribute to spatial decision support community by developing an effective vehicle routing optimization method for large transportation applications in both public and private sectors