Citrullination in the periodontium-a possible link between periodontitis and rheumatoid arthritis

OBJECTIVES The aim of the present study was to assess human and bacterial peptidylarginine deiminase (PAD) activity in the gingival crevicular fluid (GCF) in the context of serum levels of antibodies against citrullinated epitopes in rheumatoid arthritis and periodontitis. MATERIALS AND METHODS Human PAD and Porphyromonas gingivalis-derived enzyme (PPAD) activities were measured in the GCF of 52 rheumatoid arthritis (RA) patients (48 with periodontitis and 4 without) and 44 non-RA controls (28 with periodontitis and 16 without). Serum antibodies against citrullinated epitopes were measured by ELISA. Bacteria being associated with periodontitis were determined by nucleic-acid-based methods. RESULTS Citrullination was present in 26 (50 %) RA patients and 23 (48 %) controls. PAD and PPAD activities were detected in 36 (69 %) and 30 (58 %) RA patients, respectively, and in 30 (68 %) and 21 (50 %) controls, respectively. PPAD activity was higher in RA and non-RA patients with periodontitis than in those without (p = 0.038; p = 0.004), and was detected in 35 of 59 P. gingivalis-positive samples, and in 16 of 37 P. gingivalis-negative samples in association with high antibody levels against that species. CONCLUSIONS PAD and PPAD activities within the periodontium are elevated in RA and non-RA patients with periodontitis. PPAD secreted by P. gingivalis residing in epithelial cells may exert its citrullinating activity in distant regions of the periodontium or even distant tissues. CLINICAL RELEVANCE In periodontitis, the citrullination of proteins/peptides by human and bacterial peptidylarginine deiminases may generate antibodies after breaching immunotolerance in susceptible individuals.

Fine-grained indoor positioning and tracking systems

Indoor positioning has attracted considerable attention for decades due to the increasing demands for location based services. In the past years, although numerous methods have been proposed for indoor positioning, it is still challenging to find a convincing solution that combines high positioning accuracy and ease of deployment. Radio-based indoor positioning has emerged as a dominant method due to its ubiquitousness, especially for WiFi. RSSI (Received Signal Strength Indicator) has been investigated in the area of indoor positioning for decades. However, it is prone to multipath propagation and hence fingerprinting has become the most commonly used method for indoor positioning using RSSI. The drawback of fingerprinting is that it requires intensive labour efforts to calibrate the radio map prior to experiments, which makes the deployment of the positioning system very time consuming. Using time information as another way for radio-based indoor positioning is challenged by time synchronization among anchor nodes and timestamp accuracy. Besides radio-based positioning methods, intensive research has been conducted to make use of inertial sensors for indoor tracking due to the fast developments of smartphones. However, these methods are normally prone to accumulative errors and might not be available for some applications, such as passive positioning. This thesis focuses on network-based indoor positioning and tracking systems, mainly for passive positioning, which does not require the participation of targets in the positioning process. To achieve high positioning accuracy, we work on some information of radio signals from physical-layer processing, such as timestamps and channel information. The contributions in this thesis can be divided into two parts: time-based positioning and channel information based positioning. First, for time-based indoor positioning (especially for narrow-band signals), we address challenges for compensating synchronization offsets among anchor nodes, designing timestamps with high resolution, and developing accurate positioning methods. Second, we work on range-based positioning methods with channel information to passively locate and track WiFi targets. Targeting less efforts for deployment, we work on range-based methods, which require much less calibration efforts than fingerprinting. By designing some novel enhanced methods for both ranging and positioning (including trilateration for stationary targets and particle filter for mobile targets), we are able to locate WiFi targets with high accuracy solely relying on radio signals and our proposed enhanced particle filter significantly outperforms the other commonly used range-based positioning algorithms, e.g., a traditional particle filter, extended Kalman filter and trilateration algorithms. In addition to using radio signals for passive positioning, we propose a second enhanced particle filter for active positioning to fuse inertial sensor and channel information to track indoor targets, which achieves higher tracking accuracy than tracking methods solely relying on either radio signals or inertial sensors.

How specific is apprenticeship training? Evidence from inter-firm and occupational mobility after graduation

Do apprenticeships convey mainly general or also firm- and occupation-specific human capital? Specific human capital may allow for specialization gains, but may also lead to allocative inefficiency due to mobility barriers. We analyse the case of Switzerland, which combines a comprehensive, high-quality apprenticeship system with a lightly regulated labour market. To assess human capital transferability after standardized firm-based apprenticeship training, we analyse inter-firm and occupational mobility and their effects on post-training wages. Using a longitudinal data set based on the PISA 2000 survey, we find high inter-firm and low occupational mobility within one year after graduation. Accounting for endogenous changes, we find a negative effect of occupation changes on wages, but no significant wage effect for firm changes. This indicates that occupation-specific human capital is an important component of apprenticeship training and that skills are highly transferable within an occupational field.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranging and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1.3m for mean accuracy and 2.2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

A Multiplex Real-Time PCR with High Resolution Melting Analysis for the Characterization of Antimicrobial Resistance in Neisseria gonorrhoeae.

Resistance to antibiotics used against Neisseria gonorrhoeae infections is a major public health concern. Antimicrobial resistance (AMR) testing relies on time-consuming culture-based methods. Development of rapid molecular tests for detecting AMR determinants could provide valuable tools for surveillance, epidemiological studies and to inform individual case management. We developed a fast (<1.5 hrs) SYBR-green based real-time PCR method with high resolution melting (HRM) analysis. One triplex and three duplex reactions included two sequences for N. gonorrhoeae identification and seven determinants of resistance to extended-spectrum cephalosporins (ESCs), azithromycin, ciprofloxacin, and spectinomycin. The method was validated by testing 39 previously fully-characterized N. gonorrhoeae strains, 19 commensal Neisseria spp., and an additional panel of 193 gonococcal isolates. Results were compared with culture-based AMR determination. The assay correctly identified N. gonorrhoeae and the presence or absence of the seven AMR determinants. There was some cross-reactivity with non-gonococcal Neisseria species and the detection limit was 10(3)-10(4) gDNA copies/reaction. Overall, the platform accurately detected resistance to ciprofloxacin (sensitivity and specificity, 100%), ceftriaxone (sensitivity 100%, specificity 90%), cefixime (sensitivity 92%, specificity 94%), azithromycin and spectinomycin (both sensitivity and specificity, 100%). In conclusion, our methodology accurately detects mutations generating resistance to antibiotics used to treat gonorrhea. Low assay sensitivity prevents direct diagnostic testing of clinical specimens but this method can be used to screen collections of gonococcal isolates for AMR more quickly than with current culture-based AMR testing.

Associating optical measurements and estimating orbits of geocentric objects through population-based meta-heuristic methods

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this context both, the correct associations among the observations and the orbits of the objects have to be determined. The complexity of the MTT problem is defined by its dimension S. The number S corresponds to the number of fences involved in the problem. Each fence consists of a set of observations where each observation belongs to a different object. The S ≥ 3 MTT problem is an NP-hard combinatorial optimization problem. There are two general ways to solve this. One way is to seek the optimum solution, this can be achieved by applying a branch-and- bound algorithm. When using these algorithms the problem has to be greatly simplified to keep the computational cost at a reasonable level. Another option is to approximate the solution by using meta-heuristic methods. These methods aim to efficiently explore the different possible combinations so that a reasonable result can be obtained with a reasonable computational effort. To this end several population-based meta-heuristic methods are implemented and tested on simulated optical measurements. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.