Short term variability in urinary bisphenol A in Australian children

Used frequently in food contact materials, bisphenol A (BPA) has been studied extensively in recent years, and ubiquitous exposure in the general population has been demonstrated worldwide. Characterising within- and between-individual variability of BPA concentrations is important for characterising exposure in biomonitoring studies, and this has been investigated previously in adults, but not in children. The aim of this study was to characterise the short-term variability of BPA in spot urine samples in young children. Children aged ≥2-<4 years (n = 25) were recruited from an existing cohort in Queensland Australia, and donated four spot urine samples each over a two day period. Samples were analysed for total BPA using isotope dilution online solid phase extraction-liquid chromatography-tandem mass spectrometry, and concentrations ranged from 0.53–74.5 ng/ml, with geometric mean and standard deviation of 2.70 ng/ml and 2.94 ng/ml, respectively. Sex and time of sample collection were not significant predictors of BPA concentration. The between-individual variability was approximately equal to the within-individual variability (ICC = 0.51), and this ICC is somewhat higher than previously reported literature values. This may be the result of physiological or behavioural differences between children and adults or of the relatively short exposure window assessed. Using a bootstrapping methodology, a single sample resulted in correct tertile classification approximately 70% of the time. This study suggests that single spot samples obtained from young children provide a reliable characterization of absolute and relative exposure over the short time window studied, but this may not hold true over longer timeframes.

Automatic image scaling for place recognition in changing environments

Robustness to variations in environmental conditions and camera viewpoint is essential for long-term place recognition, navigation and SLAM. Existing systems typically solve either of these problems, but invariance to both remains a challenge. This paper presents a training-free approach to lateral viewpoint- and condition-invariant, vision-based place recognition. Our successive frame patch-tracking technique infers average scene depth along traverses and automatically rescales views of the same place at different depths to increase their similarity. We combine our system with the condition-invariant SMART algorithm and demonstrate place recognition between day and night, across entire 4-lane-plus-median-strip roads, where current algorithms fail.

Active learning: A step towards automating medical concept extraction

Objective This paper presents an automatic active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort, and (2) the robustness of incremental active learning framework across different selection criteria and datasets is determined. Materials and methods The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional Random Fields as the supervised method, and least confidence and information density as two selection criteria for active learning framework were used. The effect of incremental learning vs. standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. Two clinical datasets were used for evaluation: the i2b2/VA 2010 NLP challenge and the ShARe/CLEF 2013 eHealth Evaluation Lab. Results The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared to the Random sampling baseline, the saving is at least doubled. Discussion Incremental active learning guarantees robustness across all selection criteria and datasets. The reduction of annotation effort is always above random sampling and longest sequence baselines. Conclusion Incremental active learning is a promising approach for building effective and robust medical concept extraction models, while significantly reducing the burden of manual annotation.

Automatic clustering and population analysis of white matter tracts using maximum density paths

We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.

Incorporating visual information for spoken term detection

Spoken term detection (STD) is the task of looking up a spoken term in a large volume of speech segments. In order to provide fast search, speech segments are first indexed into an intermediate representation using speech recognition engines which provide multiple hypotheses for each speech segment. Approximate matching techniques are usually applied at the search stage to compensate the poor performance of automatic speech recognition engines during indexing. Recently, using visual information in addition to audio information has been shown to improve phone recognition performance, particularly in noisy environments. In this paper, we will make use of visual information in the form of lip movements of the speaker in indexing stage and will investigate its effect on STD performance. Particularly, we will investigate if gains in phone recognition accuracy will carry through the approximate matching stage to provide similar gains in the final audio-visual STD system over a traditional audio only approach. We will also investigate the effect of using visual information on STD performance in different noise environments.

Extraction methods in soil phosphorus characterisation : Limitations and applications

The quantification and characterisation of soil phosphorus (P) is of agricultural and environmental importance and different extraction methods are widely used to asses the bioavailability of P and to characterize soil P reserves. However, the large variety of extractants, pre-treatments and sample preparation procedures complicate the comparison of published results. In order to improve our understanding of the behaviour and cycling of P in soil, it is crucial to know the scientific relevance of the methods used for various purposes. The knowledge of the factors affecting the analytical outcome is a prerequisite for justified interpretation of the results. The aim of this thesis was to study the effects of sample preparation procedures on soil P and to determine the dependence of the recovered P pool on the chemical nature of extractants. Sampling is a critical step in soil testing and sampling strategy is dependent on the land-use history and the purpose of sampling. This study revealed that pre-treatments changed soil properties and air-drying was found to affect soil P, particularly extractable organic P, by disrupting organic matter. This was evidenced by an increase in the water-extractable small-sized (<0.2 µm) P that, at least partly, took place at the expense of the large-sized (>0.2 µm) P. However, freezing induced only insignificant changes and thus, freezing can be taken to be a suitable method for storing soils from the boreal zone that naturally undergo periodic freezing. The results demonstrated that chemical nature of the extractant affects its sensitivity to detect changes in soil P solubility. Buffered extractants obscured the alterations in P solubility induced by pH changes; however, water extraction, though sensitive to physicochemical changes, can be used to reveal short term changes in soil P solubility. As for the organic P, the analysis was found to be sensitive to the sample preparation procedures: filtering may leave a large proportion of extractable organic P undetected, whereas the outcome of centrifugation was found to be affected by the ionic strength of the extractant. Widely used sequential fractionation procedures proved to be able to detect land-use -derived differences in the distribution of P among fractions of different solubilities. However, interpretation of the results from extraction experiments requires better understanding of the biogeochemical function of the recovered P fraction in the P cycle in differently managed soils under dissimilar climatic conditions.

Methods for Answer Extraction in Textual Question Answering

In this thesis we present and evaluate two pattern matching based methods for answer extraction in textual question answering systems. A textual question answering system is a system that seeks answers to natural language questions from unstructured text. Textual question answering systems are an important research problem because as the amount of natural language text in digital format grows all the time, the need for novel methods for pinpointing important knowledge from the vast textual databases becomes more and more urgent. We concentrate on developing methods for the automatic creation of answer extraction patterns. A new type of extraction pattern is developed also. The pattern matching based approach chosen is interesting because of its language and application independence. The answer extraction methods are developed in the framework of our own question answering system. Publicly available datasets in English are used as training and evaluation data for the methods. The techniques developed are based on the well known methods of sequence alignment and hierarchical clustering. The similarity metric used is based on edit distance. The main conclusions of the research are that answer extraction patterns consisting of the most important words of the question and of the following information extracted from the answer context: plain words, part-of-speech tags, punctuation marks and capitalization patterns, can be used in the answer extraction module of a question answering system. This type of patterns and the two new methods for generating answer extraction patterns provide average results when compared to those produced by other systems using the same dataset. However, most answer extraction methods in the question answering systems tested with the same dataset are both hand crafted and based on a system-specific and fine-grained question classification. The the new methods developed in this thesis require no manual creation of answer extraction patterns. As a source of knowledge, they require a dataset of sample questions and answers, as well as a set of text documents that contain answers to most of the questions. The question classification used in the training data is a standard one and provided already in the publicly available data.

The Effect of Intrauterine Growth Restriction on Long-Term Outcome in Very or Extremely Low Birth Weight Infants

The project consisted of two long-term follow-up studies of preterm children addressing the question whether intrauterine growth restriction affects the outcome. Assessment at 5 years of age of 203 children with a birth weight less than 1000 g born in Finland in 1996-1997 showed that 9% of the children had cognitive impairment, 14% cerebral palsy, and 4% needed a hearing aid. The intelligence quotient was lower (p<0.05) than the reference value. Thus, 20% exhibited major, 19% minor disabilities, and 61% had no functional abnormalities. Being small for gestational age (SGA) was associated with sub-optimal growth later. In children born before 27 gestational weeks, the SGA had more neuropsychological disabilities than those appropriate for gestational age (AGA). In another cohort with birth weight less than 1500 g assessed at 5 years of age, echocardiography showed a thickened interventricular septum and a decreased left ventricular end-diastolic diameter in both SGA and AGA born children. They also had a higher systolic blood pressure than the reference. Laser-Doppler flowmetry showed different endothelium-dependent and -independent vasodilation responses in the AGA children compared to those of the controls. SGA was not associated with cardio-vascular abnormalities. Auditory event-related potentials (AERPs) were recorded using an oddball paradigm with frequency deviants (standard tone 500 Hz and deviant 750-Hz with 10% probability). At term, the P350 was smaller in SGA and AGA infants than in controls. At 12 months, the automatic change detection peak (mismatch negativity, MMN) was observed in the controls. However, the pre-term infants had a difference positivity that correlated with their neurodevelopment scores. At 5 years of age, the P1-deflection, which reflects primary auditory processing, was smaller, and the MMN larger in the preterm than in the control children. Even with a challenging paradigm or a distraction paradigm, P1 was smaller in the preterm than in the control children. The SGA and AGA children showed similar AERP responses. Prematurity is a major risk factor for abnormal brain development. Preterm children showed signs of cardiovascular abnormality suggesting that prematurity per se may carry a risk for later morbidity. The small positive amplitudes in AERPs suggest persisting altered auditory processing in the preterm in-fants.

Robust Whisper Activity Detection Using Long-Term Log Energy Variation of Sub-Band Signal

The goal in the whisper activity detection (WAD) is to find the whispered speech segments in a given noisy recording of whispered speech. Since whispering lacks the periodic glottal excitation, it resembles an unvoiced speech. This noise-like nature of the whispered speech makes WAD a more challenging task compared to a typical voice activity detection (VAD) problem. In this paper, we propose a feature based on the long term variation of the logarithm of the short-time sub-band signal energy for WAD. We also propose an automatic sub-band selection algorithm to maximally discriminate noisy whisper from noise. Experiments with eight noise types in four different signal-to-noise ratio (SNR) conditions show that, for most of the noises, the performance of the proposed WAD scheme is significantly better than that of the existing VAD schemes and whisper detection schemes when used for WAD.

Semi-automatic generation of learning domain modules for technology supported learning systems

In a time when Technology Supported Learning Systems are being widely used, there is a lack of tools that allows their development in an automatic or semi-automatic way. Technology Supported Learning Systems require an appropriate Domain Module, ie. the pedagogical representation of the domain to be mastered, in order to be effective. However, content authoring is a time and effort consuming task, therefore, efforts in automatising the Domain Module acquisition are necessary.Traditionally, textbooks have been used as the main mechanism to maintain and transmit the knowledge of a certain subject or domain. Textbooks have been authored by domain experts who have organised the contents in a means that facilitate understanding and learning, considering pedagogical issues.Given that textbooks are appropriate sources of information, they can be used to facilitate the development of the Domain Module allowing the identification of the topics to be mastered and the pedagogical relationships among them, as well as the extraction of Learning Objects, ie. meaningful fragments of the textbook with educational purpose.Consequently, in this work DOM-Sortze, a framework for the semi-automatic construction of Domain Modules from electronic textbooks, has been developed. DOM-Sortze uses NLP techniques, heuristic reasoning and ontologies to fulfill its work. DOM-Sortze has been designed and developed with the aim of automatising the development of the Domain Module, regardless of the subject, promoting the knowledge reuse and facilitating the collaboration of the users during the process.

Query-by-Example Spoken Term Detection ALBAYZIN 2012 evaluation: overview, systems, results, and discussion

Query-by-Example Spoken Term Detection (QbE STD) aims at retrieving data from a speech data repository given an acoustic query containing the term of interest as input. Nowadays, it has been receiving much interest due to the high volume of information stored in audio or audiovisual format. QbE STD differs from automatic speech recognition (ASR) and keyword spotting (KWS)/spoken term detection (STD) since ASR is interested in all the terms/words that appear in the speech signal and KWS/STD relies on a textual transcription of the search term to retrieve the speech data. This paper presents the systems submitted to the ALBAYZIN 2012 QbE STD evaluation held as a part of ALBAYZIN 2012 evaluation campaign within the context of the IberSPEECH 2012 Conference(a). The evaluation consists of retrieving the speech files that contain the input queries, indicating their start and end timestamps within the appropriate speech file. Evaluation is conducted on a Spanish spontaneous speech database containing a set of talks from MAVIR workshops(b), which amount at about 7 h of speech in total. We present the database metric systems submitted along with all results and some discussion. Four different research groups took part in the evaluation. Evaluation results show the difficulty of this task and the limited performance indicates there is still a lot of room for improvement. The best result is achieved by a dynamic time warping-based search over Gaussian posteriorgrams/posterior phoneme probabilities. This paper also compares the systems aiming at establishing the best technique dealing with that difficult task and looking for defining promising directions for this relatively novel task.

High-resolution automatic water quality monitoring systems applied to catchment and reservoir monitoring

Automatic recording instruments provide the ideal means of recording the responses of rivers, lakes and reservoirs to short-term changes in the weather. As part of the project ‘Using Automatic Monitoring and Dynamic Modelling for the Active Management of Lakes and Reservoirs', a family of three automatic monitoring stations were designed by engineers at the Centre for Ecology and Hydrology in Windermere to monitor such responses. In this article, the authors describe this instrument network in some detail and present case studies that illustrate the value of high resolution automatic monitoring in both catchment and reservoir applications.

Investigation of multilingual deep neural networks for spoken term detection

The development of high-performance speech processing systems for low-resource languages is a challenging area. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to use bottleneck features, or hybrid systems, trained on multilingual data for speech-to-text (STT) systems. This paper presents an investigation into the application of these multilingual approaches to spoken term detection. Experiments were run using the IARPA Babel limited language pack corpora (∼10 hours/language) with 4 languages for initial multilingual system development and an additional held-out target language. STT gains achieved through using multilingual bottleneck features in a Tandem configuration are shown to also apply to keyword search (KWS). Further improvements in both STT and KWS were observed by incorporating language questions into the Tandem GMM-HMM decision trees for the training set languages. Adapted hybrid systems performed slightly worse on average than the adapted Tandem systems. A language independent acoustic model test on the target language showed that retraining or adapting of the acoustic models to the target language is currently minimally needed to achieve reasonable performance. © 2013 IEEE.

Electrochemiluminescent detection based on solid-phase extraction at tris(2,2 '-bipyridyl)ruthenium(II)-modified ceramic carbon electrode

A sensitive electrochemiluminescent detection scheme by solid-phase extraction at Ru(bpy)(3)(2+)-modified ceramic carbon electrodes (CCEs) was developed. The as-prepared Ru(bpy)(3)(2+)-modified CCEs show much better long-term stability than other Nafion-based Ru(bpy)(3)(2+)-modified electrodes and enjoy the inherent advantages of CCEs. The log-log calibration plot for dioxopromethazine is linear from 1.0 x 10(-9) to 1.0 x 10(-4) mol L-1 using the new detection scheme. The detection limit is 6.6 x 10(-10) mol L-1 at a signal-to-noise ratio of 3. The new scheme improves the sensitivity by similar to 3 orders of magnitude, which is the most sensitive Ru(bpy)(3)(2+) ECL method. The scheme allows the detection of dioxopromethazine in a urine sample within 3 min. Since Ru(bpy)(3)(2+) ECL is a powerful technique for determination of numerous amine-containing substances, the new detection scheme holds great promise in measurement of free concentrations, investigation of protein-drug interactions and DNA-drug interactions, pharmaceutical analysis, and so on.

Extraction and separation of heavy rare earth (III) with Extraction Resin containing di(2,4,4-trimethyl pentyl) phosphinic acid (Cyanex 272)

Extraction resins, of the type of;levextrel, (which is a collective term for styrene/divinylbenzene based copolymers of predominantly macroporous structure that contain a selective extractant) are important for the recovery and separation of metal ions, as they combine features of solvent extraction and ion exchange resins. This paper presents the results of the adsorption of heavy rare earth ions (Ho(III), Er(III), Tm(III), Yb(III), Lu(III) and Y(III)) from hydrochloric acid solutions at 0.2 mol/L ionic strength and 50 degrees C by the extraction resin containing di (2,4,4-trimethyl pentyl) phosphinic acid (Cyanex 272) and the chromatographic separation of (Er(III), Tm(III) and Yb(III)). Technological separation products, with purity and yield of Tm2O3 >99.97%, >80%, Er2O3 >99.9%, >94% and Yb2O3 >99.8%, >80% respectively, have been obtained from a feed having the composition Tm2O3 60%, Er2O3 10%, and Yb2O3 3%, the others 27%. The distribution coefficients, extraction equilibrium constants and separation factors have been determined as a function of acidity, loading of the resin and rare earths, flow rates and column ratios. The resolutions and efficiencies of separation of Er/Tm/Yb each other have been calculated. The stoichiometry of the extraction of rare earth ions has been suggested as well.