A juvenile recruitment prediction model for Ruditapes decussatus (L.) (Bivalvia : Mollusca)

Despite the importance of larval abundance in determining the recruitment of benthic marine invertebrates and as a major factor in marine benthic community structure, relating planktonic larval abundance with post-settlement post-larvae and juveniles in the benthos is difficult. It is hampered by several methodological difficulties, including sampling frequency, ability to follow larval and post-larval or juvenile cohorts, and ability to calculate growth and mortality rates. In our work, an intensive sampling strategy was used. Larvae in the plankton were collected at weekly intervals, while post-larvae that settled into collectors were analysed fortnightly. Planktonic larval and benthic post-larval/juvenile cohorts were determined, and growth and mortality rates calculated. Integration of all equations allowed the development of a theoretical formulation that, based on the abundance and planktonic larval duration, permits an estimation of the future abundance of post-larvae/juveniles during the first year of benthic life. The model can be applied to a sample in which it was necessary only to measure larval length.

The influence of multimorbidity on hospital, intensive care, and emergency department use at the end of life for patients with chronic conditions

Thesis (Master's)--University of Washington, 2016-03

Optimal and Robust Designs of Step-stress Accelerated Life Testing Experiments for Proportional Hazards Models

Accelerated life testing (ALT) is widely used to obtain reliability information about a product within a limited time frame. The Cox s proportional hazards (PH) model is often utilized for reliability prediction. My master thesis research focuses on designing accelerated life testing experiments for reliability estimation. We consider multiple step-stress ALT plans with censoring. The optimal stress levels and times of changing the stress levels are investigated. We discuss the optimal designs under three optimality criteria. They are D-, A- and Q-optimal designs. We note that the classical designs are optimal only if the model assumed is correct. Due to the nature of prediction made from ALT experimental data, attained under the stress levels higher than the normal condition, extrapolation is encountered. In such case, the assumed model cannot be tested. Therefore, for possible imprecision in the assumed PH model, the method of construction for robust designs is also explored.

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.

Computational models for the prediction of severe thunderstorms over east indian region

Thunderstorm is one of the most spectacular weather phenomena in the atmosphere. Many parts over the Indian region experience thunderstorms at higher frequency during pre-monsoon months (March- May), when the atmosphere is highly unstable because of high temperatures prevailing at lower levels. Most dominant feature of the weather during the pre-monsoon season over the eastern Indo-Gangetic plain and northeast India is the outburst of severe local convective storms, commonly known as ‘Nor’wester’ or ‘Kalbaishakhi’. The severe thunderstorms associated with thunder, squall line, lightning and hail cause extensive losses in agriculture, damage to structure and also loss of life. The casualty due to lightning associated with thunderstorms in this region is the highest in the world. The highest numbers of aviation hazards are reported during occurrence of these thunderstorms. In India, 72% of tornadoes are associated with this thunderstorm.

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Intrinsic disorder prediction from the analysis of multiple protein fold recognition models

Motivation: Intrinsic protein disorder is functionally implicated in numerous biological roles and is, therefore, ubiquitous in proteins from all three kingdoms of life. Determining the disordered regions in proteins presents a challenge for experimental methods and so recently there has been much focus on the development of improved predictive methods. In this article, a novel technique for disorder prediction, called DISOclust, is described, which is based on the analysis of multiple protein fold recognition models. The DISOclust method is rigorously benchmarked against the top.ve methods from the CASP7 experiment. In addition, the optimal consensus of the tested methods is determined and the added value from each method is quantified. Results: The DISOclust method is shown to add the most value to a simple consensus of methods, even in the absence of target sequence homology to known structures. A simple consensus of methods that includes DISOclust can significantly outperform all of the previous individual methods tested.

Life cycle GHG assessment of fossil fuel power plants with carbon capture and storage

The evaluation of life cycle greenhouse gas emissions from power generation with carbon capture and storage (CCS) is a critical factor in energy and policy analysis. The current paper examines life cycle emissions from three types of fossil-fuel-based power plants, namely supercritical pulverized coal (super-PC), natural gas combined cycle (NGCC) and integrated gasification combined cycle (IGCC), with and without CCS. Results show that, for a 90% CO2 capture efficiency, life cycle GHG emissions are reduced by 75-84% depending on what technology is used. With GHG emissions less than 170 g/kWh, IGCC technology is found to be favorable to NGCC with CCS. Sensitivity analysis reveals that, for coal power plants, varying the CO2 capture efficiency and the coal transport distance has a more pronounced effect on life cycle GHG emissions than changing the length of CO2 transport pipeline. Finally, it is concluded from the current study that while the global warming potential is reduced when MEA-based CO2 capture is employed, the increase in other air pollutants such as NOx and NH3 leads to higher eutrophication and acidification potentials.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Automated tertiary structure prediction with accurate local model quality assessment using the IntFOLD-TS method

The IntFOLD-TS method was developed according to the guiding principle that the model quality assessment would be the most critical stage for our template based modelling pipeline. Thus, the IntFOLD-TS method firstly generates numerous alternative models, using in-house versions of several different sequence-structure alignment methods, which are then ranked in terms of global quality using our top performing quality assessment method – ModFOLDclust2. In addition to the predicted global quality scores, the predictions of local errors are also provided in the resulting coordinate files, using scores that represent the predicted deviation of each residue in the model from the equivalent residue in the native structure. The IntFOLD-TS method was found to generate high quality 3D models for many of the CASP9 targets, whilst also providing highly accurate predictions of their per-residue errors. This important information may help to make the 3D models that are produced by the IntFOLD-TS method more useful for guiding future experimental work

Longitudinal investigation of the role of temperament and stressful life events in childhood anxiety

The current study investigated the longitudinal relationships between BI, life events, and anxiety in a sample of 102 behaviourally inhibited (BI) and 100 uninhibited (BUI) children aged 3 to 4 years. Children’s parents completed questionnaires on BI, stressful life events, and anxiety symptoms, and were administered a diagnostic interview three times in a 5-year period. In line with our hypotheses, negative life events, and negative behaviour- dependent life events (i.e. life events that are related to the children’s own behaviours) in particular, and the impact of negative life events, were predictive of increases in subsequent anxiety symptoms, the likelihood of having an anxiety disorder, and increased number of anxiety diagnoses over the five year follow-up period. Experiencing more positive, behaviour-independent life events decreased the risk of being diagnosed with an anxiety disorder. Furthermore, differences were found in life events between BI and BUI children. That is, BI children experienced fewer positive and specifically positive behaviour-dependent life events, and the impact of these positive life events was also lower in BI children than in BUI children. However, BI did not interact with life events in the prediction of anxiety problems as hypothesized. Therefore, this study seems to indicate that BI and life events act as additive risk factors in the development of anxiety problems.

IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences

IntFOLD is an independent web server that integrates our leading methods for structure and function prediction. The server provides a simple unified interface that aims to make complex protein modelling data more accessible to life scientists. The server web interface is designed to be intuitive and integrates a complex set of quantitative data, so that 3D modelling results can be viewed on a single page and interpreted by non-expert modellers at a glance. The only required input to the server is an amino acid sequence for the target protein. Here we describe major performance and user interface updates to the server, which comprises an integrated pipeline of methods for: tertiary structure prediction, global and local 3D model quality assessment, disorder prediction, structural domain prediction, function prediction and modelling of protein-ligand interactions. The server has been independently validated during numerous CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiments, as well as being continuously evaluated by the CAMEO (Continuous Automated Model Evaluation) project. The IntFOLD server is available at: http://www.reading.ac.uk/bioinf/IntFOLD/

Evidence for interplay between genes and parenting on infant temperament in the first year of life: monoamine oxidase A polymorphism moderates effects of maternal sensitivity on infant anger proneness

Background The low expression polymorphism of the MAOA gene in interaction with adverse environments (G × E) is associated with antisocial behaviour disorders. These have their origins in early life, but it is not known whether MAOA G × E occurs in infants. We therefore examined whether MAOA G × E predicts infant anger proneness, a temperamental dimension associated with later antisocial behaviour disorders. In contrast to previous studies, we examined MAOA G × E prospectively using an observational measure of a key aspect of the infant environment, maternal sensitivity, at a specified developmental time point. Methods In a stratified epidemiological cohort recruited during pregnancy, we ascertained MAOA status (low vs. high expression alleles) from the saliva of 193 infants, and examined specific predictions to maternal report of infant temperament at 14 months from maternal sensitivity assessed at 29 weeks of age. Results Analyses, weighted to provide general population estimates, indicated a robust interaction between MAOA status and maternal sensitivity in the prediction of infant anger proneness (p = .003) which became stronger once possible confounders for maternal sensitivity were included in the model (p = .0001). The interaction terms were similar in males (p = .010) and females (p = .016), but the effects were different as a consequence of an additional sex of infant by maternal sensitivity interaction. Conclusions This prospective study provides the first evidence of moderation by the MAOA gene of effects of parenting on infant anger proneness, an important early risk for the development of disruptive and aggressive behaviour disorders.

Variability in life-history and ecological traits is a buffer against extinction in mammals

Anthropogenic degradation of the world's ecosystems is leading to a widespread and accelerating loss of biodiversity. However, not all species respond equally to existing threats, raising the question: what makes a species more vulnerable to extinction? We propose that higher intraspecific variability may reduce the risk of extinction, as different individuals and populations within a species may respond differently to occurring threats. Supporting this prediction, our results show that mammalian species with more variable adult body masses, litter sizes, sexual maturity ages and population densities are less vulnerable to extinction. Our findings reveal the role of local variation among populations, particularly of large mammals, as a buffering mechanism against extinction, and emphasise the importance of considering trait variation in comparative analyses and conservation management.