Optimization and Applications of Fluorescence anisotropy assays and Fluorescence Resonance Energy Transfer Measurements

Calmodulin (CaM) is a calcium signaling protein that activates over hundred of targets including PMCA. This dissertation mainly focuses on optimizing and applications of fluorescence anisotropy (FA) and FRET experiments for CaM-target interactions. First we evaluated the extent of interaction of fluorophores with CaM upon conjugation. In this study, three dyes were tested for influences of their charges on interaction with CaM. We employed time-resolved and steady state fluoresce anisotropy as well as fluorescence quenching experiments to study these interactions. The positively charged dye turns out to strongly interact with CaM than neutral and negatively charged dyes. Secondly, FA based assays for direct determination of affinities of CaM-target interactions are developed and the results are consistent with previously reported values. Finally, a FRET based methods are used to study the mechanism of activation of PMCA by CaM and found that the results are consistent with previously reported three-state model

Non-intrusive load monitoring under residential solar power influx

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) - USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day’s total load that can be shed

Title-molecular diagnostics of dystrophinopathies in Sri Lanka towards phenotype predictions: an insight from a South Asian resource limited setting

Background: The phenotype of Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) patients is determined by the type of DMD gene variation, its location, effect on reading frame, and its size. The primary objective of this investigation was to determine the frequency and distribution of DMD gene variants (deletions/duplications) in Sri Lanka through the utilization of a combined approach involving multiplex polymerase chain reaction (mPCR) followed by Multiplex Ligation Dependent Probe Amplification (MLPA) and compare to the international literature. The current consensus is that MLPA is a labor efficient yet expensive technique for identifying deletions and duplications in the DMD gene. Methodology: Genetic analysis was performed in a cohort of 236 clinically suspected pediatric and adult myopathy patients in Sri Lanka, using mPCR and MLPA. A comparative analysis was conducted between our findings and literature data. Results: In the entire patient cohort (n = 236), mPCR solely was able to identify deletions in the DMD gene in 131/236 patients (DMD-120, BMD-11). In the same cohort, MLPA confirmed deletions in 149/236 patients [DMD-138, BMD -11]. These findings suggest that mPCR has a detection rate of 95% (131/138) among all patients who received a diagnosis. The distal and proximal deletion hotspots for DMD were exons 45–55 and 6–15. Exon 45–60 identified as a novel in-frame variation hotspot. Exon 45–59 was a hotspot for BMD deletions. Comparisons with the international literature show significant variations observed in deletion and duplication frequencies in DMD gene across different populations. Conclusion: DMD gene deletions and duplications are concentrated in exons 45–55 and 2–20 respectively, which match global variation hotspots. Disparities in deletion and duplication frequencies were observed when comparing our data to other Asian and Western populations. Identified a 95% deletion detection rate for mPCR, making it a viable initial molecular diagnostic approach for low-resource countries where MLPA could be used to evaluate negative mPCR cases and cases with ambiguous mutation borders. Our findings may have important implications in the early identification of DMD with limited resources in Sri Lanka and to develop tailored molecular diagnostic algorithms that are regional and population specific and easily implemented in resource limited settings

Real-time non-intrusive appliance load monitoring under supply voltage fluctuations

This paper presents a complete real-time implementation of a Non-Intrusive Appliance Load Monitoring (NIALM) system that, is robust under residential voltage level fluctuations. Existing NIALM techniques rely on multiple measurements taken at high sampling rates, but, only have been proven in simulated environments without even considering the effect of residential voltage level fluctuations - which is a severe problem in power systems of most developing countries like Sri Lanka. In contrast, through the NIALM method proposed in this paper, accurate load monitoring results were obtained in realtime using only smart meter measurements taken at a low sampling rate from a real appliance setup under residential voltage level fluctuations. In the proposed NIALM method, initially in the learning phase, a properly constructed MATLABTM Graphical User Interface (GUI) was used to acquire signals of each appliance active power consumption and voltage levels. Then, obtained active power measurements were separated into subspace components (SCs) via the Karhunen Loeve' Expansion (KLE) while also taking the voltage variations into account. Using those SCs, a unique information rich appliance level signature database was constructed and it was then used to obtain the signatures for all possible device combinations. Next, a separate GUI was designed to identify the turned ON appliance combination in the current time window using the pre-constructed signature databases, after reading the total residential active power consumption and the supply voltage. To validate the proposed real-time NIALM implementation, data from a laboratory arrangement consisting of ten household appliances was used. From the results, it was found that the proposed method is capable of accurately identifying the turned on appliances even under severe residential supply voltage level fluctuations