Statistical analysis of spectral data: A methodology for designing an intelligent monitoring system for the diabetic foot

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Influence of surface chemistry of mesoporous alumina with wide pore distribution on controlled drug release

The crucial role of the drug carrier surface chemical moeities on the uptake and in vitro release of drug is discussed here in a systematic manner. Mesoporous alumina with a wide pore size distribution (2-7 nm) functionalized with various hydrophilic and hydrophobic surface chemical groups was employed as the carrier for delivery of the model drug ibuprofen. Surface functionalization with hydrophobic groups resulted in low degree of drug loading (approximately 20%) and fast rate of release (85% over a period of 5 h) whereas hydrophilic groups resulted in a significantly higher drug payloads (21%-45%) and slower rate of release (12%-40% over a period of 5 h). Depending on the chemical moiety, the diffusion controlled (proportional to time(-0.5)) drug release was additionally observed to be dependent on the mode of arrangement of the functional groups on the alumina surface as well as on the pore characteristics of the matrix. For all mesoporous alumina systems the drug dosages were far lower than the maximum recommended therapeutic dosages (MRTD) for oral delivery. We envisage that the present study would aid in the design of delivery systems capable of sustained release of multiple drugs.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

RTK-based GNSS drifters for monitoring in estuaries and rivers: Field experimental results

This paper presents a novel RTK-based GNSS Lagrangian drifter system that is capable of monitoring water velocity, turbulence and dispersion coefficients of river and estuarine. The Lagrangian drifters use the dual-frequency real time kinematic (RTK) technique for both position and velocity estimations. The capsule is designed to meet the requirements such as minimizing height, diameter, minimizing the direct wind drag, positive buoyancy for satellite signal reception and stability, and waterproof housing for electronic components, such as GNSS receiver and computing board. The collected GNSS data are processed with post-processing RTK software. Several experiments have been carried out in two rivers in Brisbane and Sunshine Coast in Queensland. Results show that the high accuracy GNSS-drifters can be used to measure dispersion coefficient resulting from sub-tidal velocity fluctuations in shallow tidal water. In addition, the RTK-GNSS drifters respond well to vertical motion and thus could be applicable to flood monitoring.

Thrombophilia and direct thrombin inhibitor lepirudin clinical and monitoring aspects

Thrombophilia (TF) predisposes both to venous and arterial thrombosis at a young age. TF may also impact the thrombosis or stenosis of hemodialysis (HD) vascular access in patients with end-stage renal disease (ESRD). When involved in severe thrombosis TF may associate with inappropriate response to anticoagulation. Lepirudin, a potent direct thrombin inhibitor (DTI), indicated for heparin-induced thrombocytopenia-related thrombosis, could offer a treatment alternative in TF. Monitoring of narrow-ranged lepirudin demands new insights also in laboratory. The above issues constitute the targets in this thesis. We evaluated the prevalence of TF in patients with ESRD and its impact upon thrombosis- or stenosis-free survival of the vascular access. Altogether 237 ESRD patients were prospectively screened for TF and thrombogenic risk factors prior to HD access surgery in 2002-2004 (mean follow-up of 3.6 years). TF was evident in 43 (18%) of the ESRD patients, more often in males (23 vs. 9%, p=0.009). Known gene mutations of FV Leiden and FII G20210A occurred in 4%. Vascular access sufficiently matured in 226 (95%). The 1-year thrombosis- and stenosis-free access survival was 72%. Female gender (hazards ratio, HR, 2.5; 95% CI 1.6-3.9) and TF (HR 1.9, 95% CI 1.1-3.3) were independent risk factors for the shortened thrombosis- and stenosis-free survival. Additionally, TF or thrombogenic background was found in relatively young patients having severe thrombosis either in hepatic veins (Budd-Chiari syndrome, BCS, one patient) or inoperable critical limb ischemia (CLI, six patients). Lepirudin was evaluated in an off-label setting in the severe thrombosis after inefficacious traditional anticoagulation without other treatment options except severe invasive procedures, such as lower extremity amputation. Lepirudin treatments were repeatedly monitored clinically and with laboratory assessments (e.g. activated partial thromboplastin time, APTT). Our preliminary studies with lepirudin in thrombotic calamities appeared safe, and no bleeds occurred. An effective DTI lepirudin calmed thrombosis as all patients gradually recovered. Only one limb amputation was performed 3 years later during the follow-up (mean 4 years). Furthermore, we aimed to overcome the limitations of APTT and confounding effects of warfarin (INR of 1.5-3.9) and lupus anticoagulant (LA). Lepirudin responses were assessed in vitro by five specific laboratory methods. Ecarin chromogenic assay (ECA) or anti-Factor IIa (anti-FIIa) correlated precisely (r=0.99) with each other and with spiked lepirudin in all plasma pools: normal, warfarin, and LA-containing plasma. In contrast, in the presence of warfarin and LA both APTT and prothrombinase-induced clotting time (PiCT®) were limited by non-linear and imprecise dose responses. As a global coagulation test APTT is useful in parallel to the precise chromogenic methods ECA or Anti-FIIa in challenging clinical situations. Lepirudin treatment requires multidisciplinary approach to ensure appropriate patient selection, interpretation of laboratory monitoring, and treatment safety. TF seemed to be associated with complicated thrombotic events, in venous (BCS), arterial (CLI), and vascular access systems. TF screening should be aimed to patients with repeated access complications or prior unprovoked thromboembolic events. Lepirudin inhibits free and clot-bound thrombin which heparin fails to inhibit. Lepirudin seems to offer a potent and safe option for treatment of severe thrombosis. Multi-centered randomized trials are necessary to assess the possible management of complicated thrombotic events with DTIs like lepirudin and seek prevention options against access complications.

Testing and Modeling of MR Damper and Its Application to SDOF Systems Using Integral Backstepping Technique

Magnetorheological dampers are intrinsically nonlinear devices, which make the modeling and design of a suitable control algorithm an interesting and challenging task. To evaluate the potential of magnetorheological (MR) dampers in control applications and to take full advantages of its unique features, a mathematical model to accurately reproduce its dynamic behavior has to be developed and then a proper control strategy has to be taken that is implementable and can fully utilize their capabilities as a semi-active control device. The present paper focuses on both the aspects. First, the paper reports the testing of a magnetorheological damper with an universal testing machine, for a set of frequency, amplitude, and current. A modified Bouc-Wen model considering the amplitude and input current dependence of the damper parameters has been proposed. It has been shown that the damper response can be satisfactorily predicted with this model. Second, a backstepping based nonlinear current monitoring of magnetorheological dampers for semi-active control of structures under earthquakes has been developed. It provides a stable nonlinear magnetorheological damper current monitoring directly based on system feedback such that current change in magnetorheological damper is gradual. Unlike other MR damper control techniques available in literature, the main advantage of the proposed technique lies in its current input prediction directly based on system feedback and smooth update of input current. Furthermore, while developing the proposed semi-active algorithm, the dynamics of the supplied and commanded current to the damper has been considered. The efficiency of the proposed technique has been shown taking a base isolated three story building under a set of seismic excitation. Comparison with widely used clipped-optimal strategy has also been shown.

Fuzzy-logic-based health monitoring and residual-life prediction for composite helicopter rotor

A health-monitoring and life-estimation strategy for composite rotor blades is developed in this work. The cross-sectional stiffness reduction obtained by physics-based models is expressed as a function of the life of the structure using a recent phenomenological damage model. This stiffness reduction is further used to study the behavior of measurable system parameters such as blade deflections, loads, and strains of a composite rotor blade in static analysis and forward flight. The simulated measurements are obtained using an aeroelastic analysis of the composite rotor blade based on the finite element in space and time with physics-based damage modes that are then linked to the life consumption of the blade. The model-based measurements are contaminated with noise to simulate real data. Genetic fuzzy systems are developed for global online prediction of physical damage and life consumption using displacement- and force-based measurement deviations between damaged and undamaged conditions. Furthermore, local online prediction of physical damage and life consumption is done using strains measured along the blade length. It is observed that the life consumption in the matrix-cracking zone is about 12-15% and life consumption in debonding/delamination zone is about 45-55% of the total life of the blade. It is also observed that the success rate of the genetic fuzzy systems depends upon the number of measurements, type of measurements and training, and the testing noise level. The genetic fuzzy systems work quite well with noisy data and are recommended for online structural health monitoring of composite helicopter rotor blades.

Methodological perspectives for register-based health system performance assessment : Developing a hip fracture monitoring system in Finland

The resources of health systems are limited. There is a need for information concerning the performance of the health system for the purposes of decision-making. This study is about utilization of administrative registers in the context of health system performance evaluation. In order to address this issue, a multidisciplinary methodological framework for register-based data analysis is defined. Because the fixed structure of register-based data indirectly determines constraints on the theoretical constructs, it is essential to elaborate the whole analytic process with respect to the data. The fundamental methodological concepts and theories are synthesized into a data sensitive approach which helps to understand and overcome the problems that are likely to be encountered during a register-based data analyzing process. A pragmatically useful health system performance monitoring should produce valid information about the volume of the problems, about the use of services and about the effectiveness of provided services. A conceptual model for hip fracture performance assessment is constructed and the validity of Finnish registers as a data source for the purposes of performance assessment of hip fracture treatment is confirmed. Solutions to several pragmatic problems related to the development of a register-based hip fracture incidence surveillance system are proposed. The monitoring of effectiveness of treatment is shown to be possible in terms of care episodes. Finally, an example on the justification of a more detailed performance indicator to be used in the profiling of providers is given. In conclusion, it is possible to produce useful and valid information on health system performance by using Finnish register-based data. However, that seems to be far more complicated than is typically assumed. The perspectives given in this study introduce a necessary basis for further work and help in the routine implementation of a hip fracture monitoring system in Finland.

Mass spectrometry and n-in-one analytics in early drug discovery: Combinatorial chemistry libraries, lipophilicity and absorption screening

This thesis describes current and past n-in-one methods and presents three early experimental studies using mass spectrometry and the triple quadrupole instrument on the application of n-in-one in drug discovery. N-in-one strategy pools and mix samples in drug discovery prior to measurement or analysis. This allows the most promising compounds to be rapidly identified and then analysed. Nowadays properties of drugs are characterised earlier and in parallel with pharmacological efficacy. Studies presented here use in vitro methods as caco-2 cells and immobilized artificial membrane chromatography for drug absorption and lipophilicity measurements. The high sensitivity and selectivity of liquid chromatography mass spectrometry are especially important for new analytical methods using n-in-one. In the first study, the fragmentation patterns of ten nitrophenoxy benzoate compounds, serial homology, were characterised and the presence of the compounds was determined in a combinatorial library. The influence of one or two nitro substituents and the alkyl chain length of methyl to pentyl on collision-induced fragmentation was studied, and interesting structurefragmentation relationships were detected. Two nitro group compounds increased fragmentation compared to one nitro group, whereas less fragmentation was noted in molecules with a longer alkyl chain. The most abundant product ions were nitrophenoxy ions, which were also tested in the precursor ion screening of the combinatorial library. In the second study, the immobilized artificial membrane chromatographic method was transferred from ultraviolet detection to mass spectrometric analysis and a new method was developed. Mass spectra were scanned and the chromatographic retention of compounds was analysed using extract ion chromatograms. When changing detectors and buffers and including n-in-one in the method, the results showed good correlation. Finally, the results demonstrated that mass spectrometric detection with gradient elution can provide a rapid and convenient n-in-one method for ranking the lipophilic properties of several structurally diverse compounds simultaneously. In the final study, a new method was developed for caco-2 samples. Compounds were separated by liquid chromatography and quantified by selected reaction monitoring using mass spectrometry. This method was used for caco-2 samples, where absorption of ten chemically and physiologically different compounds was screened using both single and nin- one approaches. These three studies used mass spectrometry for compound identification, method transfer and quantitation in the area of mixture analysis. Different mass spectrometric scanning modes for the triple quadrupole instrument were used in each method. Early drug discovery with n-in-one is area where mass spectrometric analysis, its possibilities and proper use, is especially important.

Dual-Drug Delivery System Based on In Situ Gel-Forming Nanosuspension of Forskolin to Enhance Antiglaucoma Efficacy

The present study was designed to improve the bioavailability of forskolin by the influence of precorneal residence time and dissolution characteristics. Nanosizing is an advanced approach to overcome the issue of poor aqueous solubility of active pharmaceutical ingredients. Forskolin nanocrystals have been successfully manufactured and stabilized by poloxamer 407. These nanocrystals have been characterized in terms of particle size by scanning electron microscopy and dynamic light scattering. By formulating Noveon AA-1 polycarbophil/poloxamer 407 platforms, at specific concentrations, it was possible to obtain a pH and thermoreversible gel with a pH(gel)/T-gel close to eye pH/temperature. The addition of forskolin nanocrystals did not alter the gelation properties of Noveon AA-1 polycarbophil/poloxamer 407 and nanocrystal properties of forskolin. The formulation was stable over a period of 6 months at room temperature. In vitro release experiments indicated that the optimized platform was able to prolong and control forskolin release for more than 5 h. The in vivo studies on dexamethasone-induced glaucomatous rabbits indicated that the intraocular pressure lowering efficacy for nanosuspension/hydrogel systems was 31% and lasted for 12 h, which is significantly better than the effect of traditional eye suspension (18%, 4-6 h). Hence, our investigations successfully prove that the pH and thermoreversible polymeric in situ gel-forming nanosuspension with ability of controlled drug release exhibits a greater potential for glaucoma therapy.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Steric and rotational constraints in the X-ray structure of the animalarial drug amodiaquine

In the crystal structure of the antimalarial drug amodiaquine, the bonds linking the quinoline and the phenyl groups show partial double-bond character. The partial double-bond character of the two exocyclic bonds, together with stereochemical constraints, reduce flexibility of the two ring systems of the molecule. The dihedral angle between the two ring planes is lowest compared to those in the antileukaemic drug amsacrine and its derivatives. CPK-modelling studies suggest the way amodiaquine can bind to DNA. Stacking interaction between the quinoline and phenyl groups of independent molecules and the hydrogen-bond network stabilize the crystal structure.

Ring Resonators and Micro Fluidics: Novel Design for Drug/Disease Detection

MEMS systems are technologically developed from integrated circuit industry to create miniature sensors and actuators. Originally these semiconductor processes and materials were used to build electrical and mechanical systems, but expanded to include biological, optical fluidic magnetic and other systems 12]. Here a novel approach is suggested where in two different fields are integrated via moems, micro fluidics and ring resonators. It is well known at any preliminary stage of disease onset, many physiological changes occur in the body fluids like saliva, blood, urine etc. The drawback till now was that current calibrations are not sensitive enough to detect the minor physiological changes. This is overcome using optical detector techniques 1]. The basic concepts of ring resonators, with slight variations can be used for optical detection of these minute disease markers. A well known fact of ring resonators is that a change in refractive index will trigger a shift in the resonant wavelength 5]. The trigger for the wavelength shift in the case discussed will be the presence of disease agents. To trap the disease agents specific antibody has to be used (e. g. BSA).