Spectroscopic Investigation of Tooth Caries and Demineralization

Dental caries persists to be the most predominant oral disease in spite of remarkable progress made during the past half- century to reduce its prevalence. Early diagnosis of carious lesions is an important factor in the prevention and management of dental caries. Conventional procedures for caries detection involve visual-tactile and radiographic examination, which is considered as “gold standard”. These techniques are subjective and are unable to detect the lesions until they are well advanced and involve about one-third of the thickness of enamel. Therefore, all these factors necessitate the need for the development of new techniques for early diagnosis of carious lesions. Researchers have been trying to develop various instruments based on optical spectroscopic techniques for detection of dental caries during the last two decades. These optical spectroscopic techniques facilitate noninvasive and real-time tissue characterization with reduced radiation exposure to patient, thereby improving the management of dental caries. Nonetheless, a costeffective optical system with adequate sensitivity and specificity for clinical use is still not realized and development of such a system is a challenging task.Two key techniques based on the optical properties of dental hard tissues are discussed in this current thesis, namely laser-induced fluorescence (LIF) and diffuse reflectance (DR) spectroscopy for detection of tooth caries and demineralization. The work described in this thesis is mainly of applied nature, focusing on the analysis of data from in vitro tooth samples and extending these results to diagnose dental caries in a clinical environment. The work mainly aims to improve and contribute to the contemporary research on fluorescence and diffuse reflectance for discriminating different stages of carious lesions. Towards this, a portable and compact laser-induced fluorescence and reflectance spectroscopic system (LIFRS) was developed for point monitoring of fluorescence and diffuse reflectance spectra from tooth samples. The LIFRS system uses either a 337 nm nitrogen laser or a 404 nm diode laser for the excitation of tooth autofluorescence and a white light source (tungsten halogen lamp) for measuring diffuse reflectance.Extensive in vitro studies were carried out on extracted tooth samples to test the applicability of LIFRS system for detecting dental caries, before being tested in a clinical environment. Both LIF and DR studies were performed for diagnosis of dental caries, but special emphasis was given for early detection and also to discriminate between different stages of carious lesions. Further the potential of LIFRS system in detecting demineralization and remineralization were also assessed.In the clinical trial on 105 patients, fluorescence reference standard (FRS) criteria was developed based on LIF spectral ratios (F500/F635 and F500/F680) to discriminate different stages of caries and for early detection of dental caries. The FRS ratio scatter plots developed showed better sensitivity and specificity as compared to clinical and radiographic examination, and the results were validated with the blindtests. Moreover, the LIF spectra were analyzed by curve-fitting using Gaussian spectral functions and the derived curve-fitted parameters such as peak position, Gaussian curve area, amplitude and width were found to be useful for distinguishing different stages of caries. In DR studies, a novel method was established based on DR ratios (R500/R700, R600/R700 and R650/R700) to detect dental caries with improved accuracy. Further the diagnostic accuracy of LIFRS system was evaluated in terms of sensitivity, specificity and area under the ROC curve. On the basis of these results, the LIFRS system was found useful as a valuable adjunct to the clinicians for detecting carious lesions.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Chemical Evaluation of Selected Organics and Trace Metals in the Mangrove Macroflora Of Cochin

Mangroves are considered to play a significant role in global carbon cycling. Themangrove forests would fix CO2 by photosynthesis into mangrove lumber and thus decrease the possibility of a catastrophic series of events - global warming by atmospheric CO2, melting of the polar ice caps, and inundation of the great coastal cities of the world. The leaf litter and roots are the main contributors to mangrove sediments, though algal production and allochthonous detritus can also be trapped (Kristensen et al, 2008) by mangroves due to their high organic matter content and reducing nature are excellent metal retainers. Environmental pollution due to metals is of major concern. This is due to the basic fact that metals are not biodegradable or perishable the way most organic pollutants are. While most organic toxicants can be destroyed by combustion and converted into compounds such as C0, C02, SOX, NOX, metals can't be destroyed. At the most the valance and physical form of metals may change. Concentration of metals present naturally in air, water and soil is very low. Metals released into the environment through anthropogenic activities such as burning of fossils fuels, discharge of industrial effluents, mining, dumping of sewage etc leads to the development of higher than tolerable or toxic levels of metals in the environment leading to metal pollution. Of course, a large number of heavy metals such as Fe, Mn, Cu, Ni, Zn, Co, Cr, Mo, and V are essential to plants and animals and deficiency of these metals may lead to diseases, but at higher levels, it would lead to metal toxicity. Almost all industrial processes and urban activities involve release of at least trace quantities of half a dozen metals in different forms. Heavy metal pollution in the environment can remain dormant for a long time and surface with a vengeance. Once an area gets toxified with metals, it is almost impossible to detoxify it. The symptoms of metal toxicity are often quite similar to the symptoms of other common diseases such as respiratory problems, digestive disorders, skin diseases, hypertension, diabetes, jaundice etc making it all the more difficult to diagnose metal poisoning. For example the Minamata disease caused by mercury pollution in addition to affecting the nervous system can disturb liver function and cause diabetes and hypertension. The damage caused by heavy metals does not end up with the affected person. The harmful effects can be transferred to the person's progenies. Ironically heavy metal pollution is a direct offshoot of our increasing ability to mass produce metals and use them in all spheres of existence. Along with conventional physico- chemical methods, biosystem approachment is also being constantly used for combating metal pollution

The diagnosis of severe thunderstorms with high-resolution WRF model

Thunderstorm, resulting from vigorous convective activity, is one of the most spectacular weather phenomena in the atmosphere. A common feature of the weather during the pre-monsoon season over the Indo-Gangetic Plain and northeast India is the outburst of severe local convective storms, commonly known as ‘Nor’westers’(as they move from northwest to southeast). The severe thunderstorms associated with thunder, squall lines, lightning and hail cause extensive losses in agricultural, damage to structure and also loss of life. In this paper, sensitivity experiments have been conducted with the Non-hydrostatic Mesoscale Model (NMM) to test the impact of three microphysical schemes in capturing the severe thunderstorm event occurred over Kolkata on 15 May 2009. The results show that the WRF-NMM model with Ferrier microphysical scheme appears to reproduce the cloud and precipitation processes more realistically than other schemes. Also, we have made an attempt to diagnose four severe thunderstorms that occurred during pre-monsoon seasons of 2006, 2007 and 2008 through the simulated radar reflectivity fields from NMM model with Ferrier microphysics scheme and validated the model results with Kolkata Doppler Weather Radar (DWR) observations. Composite radar reflectivity simulated by WRF-NMM model clearly shows the severe thunderstorm movement as observed by DWR imageries, but failed to capture the intensity as in observations. The results of these analyses demonstrated the capability of high resolution WRF-NMM model in the simulation of severe thunderstorm events and determined that the 3 km model improve upon current abilities when it comes to simulating severe thunderstorms over east Indian region