Immune Complexes in Juvenile Idiopathic Arthritis

Abstract for invited review in Molecular Mechanisms of Immune Complex Pathophysiology thematic issue to be published in Frontiers in Immunology. Immune Complexes(IC)in Juvenile Idiopathic Arthritis (JIA) Terry L. Moore, MD, FAAP, FACR, MACR Professor of Internal Medicine,Pediatrics, and Molecular Biology and Immunology Director of Adult and Pediatric Rheumatology Saint Louis University School of Medicine Saint Louis, Missouri 631`04,USA Juvenile idiopathic arthritis (JIA) reflects a group of clinically heterogeneous, autoimmune disorders in children characterized by chronic arthritis and hallmarked by elevated levels of circulating immune complexes (CICs) and associated complement activation by-products in their sera. ICs have been detected in patients’ sera with JIA utilizing a variety of methods, including the anti-human IgM affinity column,C1q solid phase assay, polyethylene glycol precipitation, Staphylococcal Protein A separation method, anti-C1q/C3 affinity columns, and FcγRIII affinity method. As many as 75% of JIA patients have had IC detected in their sera. The CIC proteome in JIA patients has been examined to elucidate disease-associated proteins that are expressed in active disease. Evaluation of these IC s have shown the presence of multiple peptide fragments by SDS-PAGE and 2-DE. Subsequently, all isotypes of rheumatoid factor (RF), isotypes of anti-cyclic citrullinated (CCP) peptide antibodies, IgG, C1q, C4, C3, and the membrane attack complex (MAC) were detected in these IC. Complement activation and levels of IC correlate with disease activity in JIA, indicating their role in the pathophysiology of the disease. This review will summarize the existing literature and discuss the role of possible protein modification that participates in the generation of immune response. We will address the possible role of these events in the development of ectopic germinal centers that become the secondary site of plasma cell development in JIA. We will further address possible therapeutic modalities that could be instituted as a result of the information gathered by the presence of ICs in JIA

Can a Lattice String Have a Vanishing Cosmological Constant?

We prove that a class of one-loop partition functions found by Dienes, giving rise to a vanishing cosmological constant to one-loop, cannot be realized by a consistent lattice string. The construction of non-supersymmetric string with a vanishing cosmological constant therefore remains as elusive as ever. We also discuss a new test that any one-loop partition function for a lattice string must satisfy.Comment: 14 page

Avionics-based GNSS integrity augmentation for unmanned aerial systems sense-and-avoid

This paper investigates the synergies between a GNSS Avionics Based Integrity Augmentation (ABIA) system and a novel Unmanned Aerial System (UAS) Sense-and-Avoid (SAA) architecture for cooperative and non-cooperative scenarios. The integration of ABIA with SAA has the potential to provide an integrity-augmented SAA solution that will allow the safe and unrestricted access of UAS to commercial airspace. The candidate SAA system uses Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) for the cooperative case. In the non-cooperative scenario, the system employs navigation-based image stabilization with image morphology operations and a multi-branch Viterbi filter for obstacle detection, which allows heading estimation. The system utilizes a Track-to-Track (T3) algorithm for data fusion that allows combining data from different tracks obtained with FLS and/or ADS-B depending on the scenario. Successively, it utilizes an Interacting Multiple Model (IMM) algorithm to estimate the state vector allowing a prediction of the intruder trajectory over a specified time horizon. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuver is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole SAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Various simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented SAA (IAS) architecture. The selected host platform was the AEROSONDE Unmanned Aerial Vehicle (UAV) and the simulation cases addressed a variety of cooperative and non-cooperative scenarios in a representative cross-section of the AEROSONDE operational flight envelope. The simulation results show that the proposed IAS architecture is an excellent candidate to perform high-integrity Collision Detection and Resolution (CD&R) utilizing GNSS as the primary source of navigation data, providing solid foundation for future research and developments in this domain

Particle filter for context sensitive indoor pedestrian navigation

Novel particle filter design combines foot mounted inertial (IMU), bluetooth low energy (BLE) and ultra-wideband (UWB) technologies along with map matching into a seamless integrated navigation system for indoors. The system was evaluated by 10 test walks along an 80m long indoor track including stairs and running. 95% of the time the average error for a particle was below 3.1 m with filter completion success rate of 90%. Furthermore, a system without UWB using only IMU, BLE and map matching achieved an average error for a particle to be below 3.6 m with filter completion success rate of 70%. The selected technologies and sensors are affordable and easily deployable. Inertial measurement unit's characteristics complement the disadvantages in the rf technologies and vice versa. The code for the rover can be implemented on a modern mobile device together with a foot mounted IMU

GNSS avionics-based integrity augmentation for RPAS detect-and-avoid applications

Taking the move from our recent research on GNSS Avionics Based Integrity Augmentation (ABIA), this article investigates the synergies of ABIA with a novel Detect-and-Avoid (DAA) architecture for Remotely Piloted Aircraft System (RPAS). Based on simulation and experimental data collected on a variety of manned and unmanned aircraft, it was observed that the integration of ABIA with DAA has the potential to provide an integrity-augmented DAA for both cooperative and non-cooperative applications. The candidate DAA system uses various Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) in addition to TCAS/ASAS for the cooperative case. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuvre is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of RPAS platform dynamics and GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole DAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Cooperative and non-cooperative simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented DAA (IAS) architecture. The selected host platform was the AEROSONDE RPAS and the simulation cases were performed in a representative cross-section of the RPAS operational flight envelope. The simulation results show that the proposed IAS architecture is capable of performing high-integrity conflict detection and resolution when GNSS is the primary source of navigation data

GNSS avionics-based integrity augmentation for RPAS detect-and-avoid applications

Taking the move from our recent research on GNSS Avionics Based Integrity Augmentation (ABIA), this article investigates the synergies of ABIA with a novel Detect-and-Avoid (DAA) architecture for Remotely Piloted Aircraft System (RPAS). Based on simulation and experimental data collected on a variety of manned and unmanned aircraft, it was observed that the integration of ABIA with DAA has the potential to provide an integrity-augmented DAA for both cooperative and non-cooperative applications. The candidate DAA system uses various Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) in addition to TCAS/ASAS for the cooperative case. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuvre is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of RPAS platform dynamics and GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole DAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Cooperative and non-cooperative simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented DAA (IAS) architecture. The selected host platform was the AEROSONDE RPAS and the simulation cases were performed in a representative cross-section of the RPAS operational flight envelope. The simulation results show that the proposed IAS architecture is capable of performing high-integrity conflict detection and resolution when GNSS is the primary source of navigation data

Adaptive real-time dual-mode filter design for seamless pedestrian navigation

Seamless navigation requires that the mobile device is capable of offering a position solution both indoors and outdoors. Novel seamless navigation system design was implemented and tested to achieve this aim. The design consists of general navigation system framework blocks and of the necessary interface agreements between the blocks. This approach enables plug-and-play style design of modules. The implementation used four preselected key technologies. Microstrain 3DM-GX4-45 foot-mounted inertial measurement unit sensor data was fused together with the u-blox GNSS receiver positions outdoors. Context sensitive inference engine enabled the fusion of position updates indoors from the Decawave TREK1000 Ultra WideBand ranging kit and from the 6 Kontakt.io/Raspberry Pi anchor-based Bluetooth low energy fingeprinting system. Novel dual-mode filter design uses a particle filter and the pentagon buffer enhanced Kalman filter in the position solution derivation. Depending on the map and the walls in the environment and on the quality of position updates, the implemented control logic employs the most fit filter for the current context. Computational power is now focussed, when particle filter is needed. The novel pentagon buffer enhanced Kalman filter is 10 times faster, allowing power saving when situation is not too critical. Moreover, the buffer provides position updates by interacting with the map and helps to correct the position solution. The navigation system is seamless according to the tests conducted around and within the Nottingham Geospatial building. No user input is needed for smooth transition from outdoors to indoors and vice versa. The system achieves an accuracy of 2.35m outdoors and 1.4 m indoors (95% of error). Inertial system availability was continuous, while GNSS was available outdoors and BLE and UWB indoors

Assessing GNSS integrity augmentation techniques in UAV sense-and-avoid architectures

The integration of Global Navigation Satellite System (GNSS) integrity augmentation functionalities in Unmanned Aerial Vehicles (UAV) Sense-and-Avoid (SAA) architectures has the potential to provide an integrity-augmented SAA solutiThe integration of Global Navigation Satellite System (GNSS) integrity augmentation functionalities in Unmanned Aerial Vehicles (UAV) Sense-and-Avoid (SAA) architectures has the potential to provide an integrity-augmented SAA solution suitable for cooperative and non-cooperative scenarios. In this paper, we evaluate the opportunities offered by this integration, proposing a novel approach that maximizes the synergies between Avionics Based Integrity Augmentation (ABIA) and UAV cooperative/non-cooperative SAA architectures. In the proposed architecture, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area in both cooperative and non-cooperative cases. When the specified thresholds are exceeded, an avoidance manoeuvre is performed by implementing a heading-based Differential Geometry (DG) or Pseudospectral (PS) optimization to generate a set of optimal trajectory solutions free of near mid-air conflicts. The selection of DG or PS is based on the time available to perform the avoidance maneuver as dictated by the relative dynamics of the host and intruder platforms. The trajectory is optimized by implementing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. The optimised avoidance trajectory also considers the constraints imposed by the ABIA in terms of UAV platform dynamics and GNSS constellation satellite elevation angles and thus prevents degradation or loss of navigation data during Track, Decide and Avoid (TDA) processes of a SAA loop. Therefore, real-time trajectory corrections are performed to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place. The performance of this Integrity- Augmented SAA (IAS) architecture was evaluated by simulation case studies involving cooperative and non-cooperative scenarios. The selected host platform was the AEROSONDE UAV and the simulation cases were performed in a representative crosssection of the UAV operational flight envelope. Simulation results demonstrate that the proposed IAS architecture is capable of performing high-integrity conflict detection and resolution when GNSS is considered as the primary source of navigation data

Avionics-based GNSS integrity augmentation for unmanned aerial systems sense-and-avoid

This paper investigates the synergies between a GNSS Avionics Based Integrity Augmentation (ABIA) system and a novel Unmanned Aerial System (UAS) Sense-and-Avoid (SAA) architecture for cooperative and non-cooperative scenarios. The integration of ABIA with SAA has the potential to provide an integrity-augmented SAA solution that will allow the safe and unrestricted access of UAS to commercial airspace. The candidate SAA system uses Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) for the cooperative case. In the non-cooperative scenario, the system employs navigation-based image stabilization with image morphology operations and a multi-branch Viterbi filter for obstacle detection, which allows heading estimation. The system utilizes a Track-to-Track (T3) algorithm for data fusion that allows combining data from different tracks obtained with FLS and/or ADS-B depending on the scenario. Successively, it utilizes an Interacting Multiple Model (IMM) algorithm to estimate the state vector allowing a prediction of the intruder trajectory over a specified time horizon. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuver is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole SAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Various simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented SAA (IAS) architecture. The selected host platform was the AEROSONDE Unmanned Aerial Vehicle (UAV) and the simulation cases addressed a variety of cooperative and non-cooperative scenarios in a representative cross-section of the AEROSONDE operational flight envelope. The simulation results show that the proposed IAS architecture is an excellent candidate to perform high-integrity Collision Detection and Resolution (CD&R) utilizing GNSS as the primary source of navigation data, providing solid foundation for future research and developments in this domain

Avionics-Based GNSS Integrity Augmentation for UAS mission planning and real-time trajectory optimisation

This paper explores the potential of integrating Global Navigation Satellite System (GNSS) Avionics Based Integrity Augmentation (ABIA) functionalities in Unmanned Aerial Systems (UAS) to perform mission planning and real-time trajectory optimisation tasks. In case of mission planning, a pseudo-spectral optimization technique is adopted. For real-time trajectory optimisation a Direct Constrained Optimisation (DCO) method is employed. In this method the aircraft dynamics model is used to generate a number of feasible flight trajectories that also satisfy the GNSS integrity constraints. The feasible trajectories are calculated by initialising the aircraft dynamics model with a manoeuvre identification algorithm. The performance of the proposed GNSS integrity augmentation and trajectory optimisation algorithms was evaluated in representative simulation case studies. Additionally, the ABIA performance was compared with Space-Based and Ground-Based Augmentation Systems (SBAS/GBAS). Simulation results show that the proposed integration scheme is capable of performing safety-critical UAS tasks (CAT III precision approach, UAS Detect-and-Avoid, etc.) when GNSS is used as the primary source of navigation data. There is a synergy with SBAS/GBAS in providing suitable (predictive and reactive) integrity flags in all flight phases. Therefore, the integration of ABIA with SBAS/GBAS is a clear opportunity for future research towards the development of a Space-Ground-Avionics Augmentation Network (SGAAN) for UAS SAA and other safety-critical aviation applications