Corneal recovery after uncomplicated and complicated PRK and LASIK

Refractive errors, especially myopia, seem to increase worldwide. Concurrently, the number of surgical refractive corrections has increased rapidly, with several million procedures performed annually. However, excimer laser surgery was introduced after a limited number of studies done with animals and to date there still are only few long-term follow-up studies of the results. The present thesis aims to evaluate the safety and functional outcome of, as well as to quantify the cellular changes and remodelling in the human cornea after, photorefractive keratectomy (PRK) and laser assisted in situ keratomileusis (LASIK). These procedures are the two most common laser surgical refractive methods. In Study I, myopic ophthalmic residents at Helsinki University Eye Hospital underwent a refractive correction by PRK. Five patients were followed up for 6 months to assess their subjective experience in hospital work and their performance in car driving simulator and in other visuomotor functions. Corneal morphological changes were assessed by in vivo confocal microscopy (ivCM). Study II comprised 14 patients who had undergone a PRK operation in 1993-1994. Visual acuity was examined and ivCM examinations performed 5 years postoperatively. In Study III 15 patients received LASIK refractive correction for moderate to high myopia (-6 - -12 D). Their corneal recovery was followed by ivCM for 2 years. Diffuse lamellar keratitis (DLK) is a common but variable complication of LASIK. Yet, its aetiology remains unknown. In Study IV we examined six patients who had developed DLK as a consequence of formation of an intraoperative or post-LASIK epithelial defect, to assess the corneal and conjunctival inflammatory reaction. In the whole series, the mean refractive correction was -6.46 diopters. The best spectacle corrected visual acuity (BSCVA) improved in 30 % of patients, whereas in four patients BSCVA decreased slightly. The mean achieved refraction was 0.35 D undercorrected. After PRK, the stromal scar formation was highest at 2 to 3 months postoperatively and subsequently decreased. At 5 years increased reflectivity in the subepithelial stroma was observed in all patients. Interestingly, no Bowman s layer was detected in any patient. Subbasal nerve fiber bundle(snfb) regeneration could be observed already at 2 months in 2 patients after PRK. After 5 years, all corneas presented with snfb, the density of which, however, was still lower than in control corneas. LASIK induced a hypocellular area on both sides of the flap interface. A decrease of the most anterior keratocyte density was also observed. In the corneas that developed DLK, inflammatory cell-type objects were present in the flap interface in half of the patients. The other patients presented only with keratocyte activation and highly reflective extracellular matrix. These changes resolved completely with medication and time. Snfb regeneration was first detected at one month post-LASIK, but still after two years the density of snfb, however, was only 64 % of the preoperative values. The performance of ophthalmological examinations and microsurgery without spectacles was easier postoperatively, which was appreciated by the residents. Both PRK and LASIK showed moderate to good accuracy and high safety. In terms of visual perception and subjective evaluation, few patients stated any complaints in the whole series of studies. Instead, the majority of patients experienced a marked improvement in everyday life and work performance. PRK and LASIK have shown similar results, with good long term morphological healing. It seems evident that, even without the benefit of over-20-year follow-up results, these procedures are sufficiently safe and accurate for refractive corrections and corneal reshaping.

Natural recovery from cannabis use in people with psychosis: A qualitative study

- Objective There is rapidly growing evidence of natural recovery from cannabis use in people with psychosis, but little is known about how it occurs. This qualitative study explores what factors influence the decision to cease cannabis use, maintain cessation, and prevent relapse. - Methods Ten people with early psychosis and lifetime cannabis misuse, who had been abstinent for at least a month, were recruited from public adult mental health services. These six men and four women participated in a semi-structured qualitative interview assessing reasons for addressing cannabis use, effective change strategies, lapse contexts, and methods used to regain control. Interpretative phenomenological analysis was used to identify themes in their responses. - Results Participants had a mean age of 23 years (SD = 3.7), started using cannabis at age 13.7 (SD = 1.6), began daily use at 17 (SD = 3.1), and had abstained from cannabis for 7.9 months (SD = 5.4). Awareness of the negative impact of substance use across multiple domains and the presence of social support for cannabis cessation were seen as vital to sustained success, as was utilization of a combination of coping strategies. The ability to address pressure from substance-using peers was commonly mentioned. - Conclusions Maximally effective treatment may need to focus on eliciting a range of benefits of cessation and control strategies and on maximizing both support for change and resistance to peer pressure. Further research might focus on comparing perceived effective strategies between individuals who obtain sustained cessation versus those who relapse.

Impact of resistance exercise on ribosome biogenesis is acutely regulated by post-exercise recovery strategies

Muscle hypertrophy occurs following increased protein synthesis, which requires activation of the ribosomal complex. Additionally, increased translational capacity via elevated ribosomal RNA (rRNA) synthesis has also been implicated in resistance training-induced skeletal muscle hypertrophy. The time course of ribosome biogenesis following resistance exercise (RE) and the impact exerted by differing recovery strategies remains unknown. In the present study, the activation of transcriptional regulators, the expression levels of pre-rRNA, and mature rRNA components were measured through 48 h after a single-bout RE. In addition, the effects of either low-intensity cycling (active recovery, ACT) or a cold-water immersion (CWI) recovery strategy were compared. Nine male subjects performed two bouts of high-load RE randomized to be followed by 10 min of either ACT or CWI. Muscle biopsies were collected before RE and at 2, 24, and 48 h after RE. RE increased the phosphorylation of the p38-MNK1-eIF4E axis, an effect only evident with ACT recovery. Downstream, cyclin D1 protein, total eIF4E, upstream binding factor 1 (UBF1), and c-Myc proteins were all increased only after RE with ACT. This corresponded with elevated abundance of the pre-rRNAs (45S, ITS-28S, ITS-5.8S, and ETS-18S) from 24 h after RE with ACT. In conclusion, coordinated upstream signaling and activation of transcriptional factors stimulated pre-rRNA expression after RE. CWI, as a recovery strategy, markedly blunted these events, suggesting that suppressed ribosome biogenesis may be one factor contributing to the impaired hypertrophic response observed when CWI is used regularly after exercise.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

An optimal dynamic inversion-based neuro-adaptive approach for treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

An adaptive decomposition method for video

Scalable video coding (SVC) is an emerging standard built on the success of advanced video coding standard (H.264/AVC) by the Joint video team (JVT). Motion compensated temporal filtering (MCTF) and Closed loop hierarchical B pictures (CHBP) are two important coding methods proposed during initial stages of standardization. Either of the coding methods, MCTF/CHBP performs better depending upon noise content and characteristics of the sequence. This work identifies other characteristics of the sequences for which performance of MCTF is superior to that of CHBP and presents a method to adaptively select either of MCTF and CHBP coding methods at the GOP level. This method, referred as "Adaptive Decomposition" is shown to provide better R-D performance than of that by using MCTF or CRBP only. Further this method is extended to non-scalable coders.

Impeding the adaptive response of prostate cancer to androgen targeted therapies with relaxin receptor antagonist

Androgen deprivation and androgen targeted therapies (ATT) are established treatments for prostate cancer (PCa). Although initially effective, ATT induces an adaptive response that leads to treatment resistance. Increased expression of relaxin-2 (RLN2) is an important alteration in the adaptive response. RLN2 has a well described role in PCa cell proliferation, adhesion and tumour growth. The objectives of this study were to develop cell models for studies of RLN2 signalling and to implement in vitro assays for evaluating the therapeutic properties of the unique RLN2 receptor (RXFP1) antagonist

Nonlinear structural dynamical system identification using adaptive particle filters

The problem of identifying parameters of nonlinear vibrating systems using spatially incomplete, noisy, time-domain measurements is considered. The problem is formulated within the framework of dynamic state estimation formalisms that employ particle filters. The parameters of the system, which are to be identified, are treated as a set of random variables with finite number of discrete states. The study develops a procedure that combines a bank of self-learning particle filters with a global iteration strategy to estimate the probability distribution of the system parameters to be identified. Individual particle filters are based on the sequential importance sampling filter algorithm that is readily available in the existing literature. The paper develops the requisite recursive formulary for evaluating the evolution of weights associated with system parameter states. The correctness of the formulations developed is demonstrated first by applying the proposed procedure to a few linear vibrating systems for which an alternative solution using adaptive Kalman filter method is possible. Subsequently, illustrative examples on three nonlinear vibrating systems, using synthetic vibration data, are presented to reveal the correct functioning of the method. (c) 2007 Elsevier Ltd. All rights reserved.

Quantitative photoacoustic tomography from boundary pressure measurements: noniterative recovery of optical absorption coefficient from the reconstructed absorbed energy map

We describe a noniterative method for recovering optical absorption coefficient distribution from the absorbed energy map reconstructed using simulated and noisy boundary pressure measurements. The source reconstruction problem is first solved for the absorbed energy map corresponding to single- and multiple-source illuminations from the side of the imaging plane. It is shown that the absorbed energy map and the absorption coefficient distribution, recovered from the single-source illumination with a large variation in photon flux distribution, have signal-to-noise ratios comparable to those of the reconstructed parameters from a more uniform photon density distribution corresponding to multiple-source illuminations. The absorbed energy map is input as absorption coefficient times photon flux in the time-independent diffusion equation (DE) governing photon transport to recover the photon flux in a single step. The recovered photon flux is used to compute the optical absorption coefficient distribution from the absorbed energy map. In the absence of experimental data, we obtain the boundary measurements through Monte Carlo simulations, and we attempt to address the possible limitations of the DE model in the overall reconstruction procedure.

Adaptive tree multigrids and simplified spherical harmonics approximation in deterministic neutral and charged particle transport

A new deterministic three-dimensional neutral and charged particle transport code, MultiTrans, has been developed. In the novel approach, the adaptive tree multigrid technique is used in conjunction with simplified spherical harmonics approximation of the Boltzmann transport equation. The development of the new radiation transport code started in the framework of the Finnish boron neutron capture therapy (BNCT) project. Since the application of the MultiTrans code to BNCT dose planning problems, the testing and development of the MultiTrans code has continued in conventional radiotherapy and reactor physics applications. In this thesis, an overview of different numerical radiation transport methods is first given. Special features of the simplified spherical harmonics method and the adaptive tree multigrid technique are then reviewed. The usefulness of the new MultiTrans code has been indicated by verifying and validating the code performance for different types of neutral and charged particle transport problems, reported in separate publications.

Resource recovery potential from secondary components of segregated municipal solid wastes

Fermentable components of municipal solid wastes (MSW) such as fruit and vegetable wastes (FVW), leaf litter, paddy straw, cane bagasse, cane trash and paper are generated in large quantities at various pockets of the city. These form potential feedstocks for decentralized biogas plants to be operated in the vicinity. We characterized the fermentation potential of six of the above MSW fractions for their suitability to be converted to biogas and anaerobic compost using the solid-state stratified bed (SSB) process in a laboratory study. FVW and leaf litter (papermulberry leaves) decomposed almost completely while paddy straw, sugarcane trash, sugarcane bagasse and photocopying paper decomposed to a lower extent. In the SSB process between 50-60% of the biological methane potential (BMP) could be realized. Observations revealed that the SSB process needs to be adapted differently for each of the feedstocks to obtain a higher gas recovery. Bagasse produced the largest fraction of anaerobic compost (fermentation residue) and has the potential for reuse in many ways.

A model based factorization approach for dense 3D recovery from monocular video

Feature track matrix factorization based methods have been attractive solutions to the Structure-front-motion (Sfnl) problem. Group motion of the feature points is analyzed to get the 3D information. It is well known that the factorization formulations give rise to rank deficient system of equations. Even when enough constraints exist, the extracted models are sparse due the unavailability of pixel level tracks. Pixel level tracking of 3D surfaces is a difficult problem, particularly when the surface has very little texture as in a human face. Only sparsely located feature points can be tracked and tracking error arc inevitable along rotating lose texture surfaces. However, the 3D models of an object class lie in a subspace of the set of all possible 3D models. We propose a novel solution to the Structure-from-motion problem which utilizes the high-resolution 3D obtained from range scanner to compute a basis for this desired subspace. Adding subspace constraints during factorization also facilitates removal of tracking noise which causes distortions outside the subspace. We demonstrate the effectiveness of our formulation by extracting dense 3D structure of a human face and comparing it with a well known Structure-front-motion algorithm due to Brand.

An agent based adaptive bandwidth allocation scheme for multimedia applications

Bandwidth allocation for multimedia applications in case of network congestion and failure poses technical challenges due to bursty and delay sensitive nature of the applications. The growth of multimedia services on Internet and the development of agent technology have made us to investigate new techniques for resolving the bandwidth issues in multimedia communications. Agent technology is emerging as a flexible promising solution for network resource management and QoS (Quality of Service) control in a distributed environment. In this paper, we propose an adaptive bandwidth allocation scheme for multimedia applications by deploying the static and mobile agents. It is a run-time allocation scheme that functions at the network nodes. This technique adaptively finds an alternate patchup route for every congested/failed link and reallocates the bandwidth for the affected multimedia applications. The designed method has been tested (analytical and simulation)with various network sizes and conditions. The results are presented to assess the performance and effectiveness of the approach. This work also demonstrates some of the benefits of the agent based schemes in providing flexibility, adaptability, software reusability, and maintainability. (C) 2004 Elsevier Inc. All rights reserved.