Are Greenhouse Gas Signals of Northern Hemisphere winter extra-tropical cyclone activity dependent on the identification and tracking algorithm?

For Northern Hemisphere extra-tropical cyclone activity, the dependency of a potential anthropogenic climate change signal on the identification method applied is analysed. This study investigates the impact of the used algorithm on the changing signal, not the robustness of the climate change signal itself. Using one single transient AOGCM simulation as standard input for eleven state-of-the-art identification methods, the patterns of model simulated present day climatologies are found to be close to those computed from re-analysis, independent of the method applied. Although differences in the total number of cyclones identified exist, the climate change signals (IPCC SRES A1B) in the model run considered are largely similar between methods for all cyclones. Taking into account all tracks, decreasing numbers are found in the Mediterranean, the Arctic in the Barents and Greenland Seas, the mid-latitude Pacific and North America. Changing patterns are even more similar, if only the most severe systems are considered: the methods reveal a coherent statistically significant increase in frequency over the eastern North Atlantic and North Pacific. We found that the differences between the methods considered are largely due to the different role of weaker systems in the specific methods.

The Quiet Eye and Information Processing: Facilitation of Stimulus Identification and Response Selection

Background: A prerequisite for high performance in motor tasks is the acquisition of egocentric sensory information that must be translated into motor actions. A phenomenon that supports this process is the Quiet Eye (QE) defined as long final fixation before movement initiation. It is assumed that the QE facilitates information processing, particularly regarding movement parameterization. Aims: The question remains whether this facilitation also holds for the information-processing stage of response selection and – related to perception crucial – stage of stimulus identification. Method: In two experiments with sport science students, performance-enhancing effects of experimentally manipulated QE durations were tested as a function of target position predictability and target visibility, thereby selectively manipulating response selection and stimulus identification demands, respectively. Results: The results support the hypothesis of facilitated information processing through long QE durations since in both experiments performance-enhancing effects of long QE durations were found under increased processing demands only. In Experiment 1, QE duration affected performance only if the target position was not predictable and positional information had to be processed over the QE period. In Experiment 2, in a full vs. no target visibility comparison with saccades to the upcoming target position induced by flicker cues, the functionality of a long QE duration depended on the visual stimulus identification period as soon as the interval falls below a certain threshold. Conclusions: The results corroborate earlier findings that QE efficiency depends on demands put on the visuomotor system, thereby furthering the assumption that the phenomenon supports the processes of sensorimotor integration.

Computational Methods for Identification of Vibrating Structures

System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system" [1]. In the context of civil engineering, the system refers to a large scale structure such as a building, bridge, or an offshore structure, and identification mostly involves the determination of modal parameters (the natural frequencies, damping ratios, and mode shapes). This paper presents some modal identification results obtained using a state-of-the-art time domain system identification method (data-driven stochastic subspace algorithms [2]) applied to the output-only data measured in a steel arch bridge. First, a three dimensional finite element model was developed for the numerical analysis of the structure using ANSYS. Modal analysis was carried out and modal parameters were extracted in the frequency range of interest, 0-10 Hz. The results obtained from the finite element modal analysis were used to determine the location of the sensors. After that, ambient vibration tests were conducted during April 23-24, 2009. The response of the structure was measured using eight accelerometers. Two stations of three sensors were formed (triaxial stations). These sensors were held stationary for reference during the test. The two remaining sensors were placed at the different measurement points along the bridge deck, in which only vertical and transversal measurements were conducted (biaxial stations). Point estimate and interval estimate have been carried out in the state space model using these ambient vibration measurements. In the case of parametric models (like state space), the dynamic behaviour of a system is described using mathematical models. Then, mathematical relationships can be established between modal parameters and estimated point parameters (thus, it is common to use experimental modal analysis as a synonym for system identification). Stable modal parameters are found using a stabilization diagram. Furthermore, this paper proposes a method for assessing the precision of estimates of the parameters of state-space models (confidence interval). This approach employs the nonparametric bootstrap procedure [3] and is applied to subspace parameter estimation algorithm. Using bootstrap results, a plot similar to a stabilization diagram is developed. These graphics differentiate system modes from spurious noise modes for a given order system. Additionally, using the modal assurance criterion, the experimental modes obtained have been compared with those evaluated from a finite element analysis. A quite good agreement between numerical and experimental results is observed.

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Empirical evaluation of alternative approaches in identifying crash hot spots

This study proposes a framework of a model-based hot spot identification method by applying full Bayes (FB) technique. In comparison with the state-of-the-art approach [i.e., empirical Bayes method (EB)], the advantage of the FB method is the capability to seamlessly integrate prior information and all available data into posterior distributions on which various ranking criteria could be based. With intersection crash data collected in Singapore, an empirical analysis was conducted to evaluate the following six approaches for hot spot identification: (a) naive ranking using raw crash data, (b) standard EB ranking, (c) FB ranking using a Poisson-gamma model, (d) FB ranking using a Poisson-lognormal model, (e) FB ranking using a hierarchical Poisson model, and (f) FB ranking using a hierarchical Poisson (AR-1) model. The results show that (a) when using the expected crash rate-related decision parameters, all model-based approaches perform significantly better in safety ranking than does the naive ranking method, and (b) the FB approach using hierarchical models significantly outperforms the standard EB approach in correctly identifying hazardous sites.

Ozone-initiated particle formation, particle aging, and precursors in a laser printer

An increasing number of researchers have hypothesized that ozone may be involved in the particle formation processes that occur during printing, however no studies have investigated this further. In the current study, this hypothesis was tested in a chamber study by adding supplemental ozone to the chamber after a print job without measurable ozone emissions. Subsequent particle number concentration and size distribution measurements showed that new particles were formed minutes after the addition of ozone. The results demonstrated that ozone did react with printer-generated volatile organic compounds (VOCs) to form secondary organic aerosols (SOAs). The hypothesis was further confirmed by the observation of correlations among VOCs, ozone, and particles concentrations during a print job with measurable ozone emissions. The potential particle precursors were identified by a number of furnace tests, which suggested that squalene and styrene were the most likely SOA precursors with respect to ozone. Overall, this study significantly improved scientific understanding of the formation mechanisms of printer-generated particles, and highlighted the possible SOA formation potential of unsaturated nonterpene organic compounds by ozone-initiated reactions in the indoor environment. © 2011 American Chemical Society.

Privacy of RFID - models and protocols

Radio Frequency Identification is a wireless identification method that utilizes the reception of electromagnetic radio waves. This research has proposed a novel model to allow for an in-depth security analysis of current protocols and developed new flexible protocols that can be adapted to offer either stronger security or better efficiency.

Identifying the spatial scale of land use that most strongly influences overall river ecosystem health score

Catchment and riparian degradation has resulted in declining ecosystem health of streams worldwide. With restoration a priority in many regions, there is an increasing interest in the scale at which land use influences stream ecosystem health. Our goal was to use a substantial data set collected as part of a monitoring program (the Southeast Queensland, Australia, Ecological Health Monitoring Program data set, collected at 116 sites over six years) to identify the spatial scale of land use, or the combination of spatial scales, that most strongly influences overall ecosystem health. In addition, we aimed to determine whether the most influential scale differed for different aspects of ecosystem health. We used linear-mixed models and a Bayesian model-averaging approach to generate models for the overall aggregated ecosystem health score and for each of the five component indicators (fish, macroinvertebrates, water quality, nutrients, and ecosystem processes) that make up the score. Dense forest close to the survey site, mid-dense forest in the hydrologically active nearstream areas of the catchment, urbanization in the riparian buffer, and tree cover at the reach scale were all significant in explaining ecosystem health, suggesting an overriding influence of forest cover, particularly close to the stream. Season and antecedent rainfall were also important explanatory variables, with some land-use variables showing significant seasonal interactions. There were also differential influences of land use for each of the component indicators. Our approach is useful given that restoring general ecosystem health is the focus of many stream restoration projects; it allowed us to predict the scale and catchment position of restoration that would result in the greatest improvement of ecosystem health in the regions streams and rivers. The models we generated suggested that good ecosystem health can be maintained in catchments where 80% of hydrologically active areas in close proximity to the stream have mid-dense forest cover and moderate health can be obtained with 60% cover.

Virtual 3D models of insects for accelerated quarantine control

We learn from the past that invasive species have caused tremendous damage to native species and serious disruption to agricultural industries. It is crucial for us to prevent this in the future. The first step of this process is to identify correctly an invasive species from native ones. Current identification methods, relying on mainly 2D images, can result in low accuracy and be time consuming. Such methods provide little help to a quarantine officer who has time constraints to response when on duty. To deal with this problem, we propose new solutions using 3D virtual models of insects. We explain how working with insects in the 3D domain can be much better than the 2D domain. We also describe how to create true-color 3D models of insects using an image-based 3D reconstruction method. This method is ideal for quarantine control and inspection tasks that involve the verification of a physical specimen against known invasive species. Finally we show that these insect models provide valuable material for other applications such as research, education, arts and entertainment. © 2013 IEEE.

Paying the extinction debt: Woodland birds in the Mount Lofty Ranges, South Australia

Approximately 90% of the original woodlands of the Mount Lofty Ranges of South Australia has been cleared, modified or fragmented, most severely in the last 60 years, and affecting the avifauna dependent on native vegetation. This study identifies which woodland-dependent species are still declining in two different habitats, Pink GumBlue Gum woodland and Stringybark woodland. We analyse the Mount Lofty Ranges Woodland Bird Long-Term Monitoring Dataset for 1999-2007, to look for changes in abundance of 59 species. We use logistic regression of prevalence on lists in a Bayesian framework, and List Length Analysis to control for variation in detectability. Compared with Reporting Rate Analysis, a more traditional approach, List Length Analysis provides tighter confidence intervals by accounting for changing detectability. Several common species were declining significantly. Increasers were generally large-bodied generalists. Many birds have already disappeared from this modified and naturally isolated woodland island, and our results suggest that more specialist insectivores are likely to follow. The Mount Lofty Ranges can be regarded as a 'canary landscape' for temperate woodlands elsewhere in Australia without immediate action their bird communities are likely to follow the trajectory of the Mount Lofty Ranges avifauna. Alternatively, with extensive habitat restoration and management, we could avoid paying the extinction debt. © Royal Australasian Ornithologists Union 2011.

Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P<1.09 × 10−9) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N=1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.

Mass spectrometry and n-in-one analytics in early drug discovery: Combinatorial chemistry libraries, lipophilicity and absorption screening

This thesis describes current and past n-in-one methods and presents three early experimental studies using mass spectrometry and the triple quadrupole instrument on the application of n-in-one in drug discovery. N-in-one strategy pools and mix samples in drug discovery prior to measurement or analysis. This allows the most promising compounds to be rapidly identified and then analysed. Nowadays properties of drugs are characterised earlier and in parallel with pharmacological efficacy. Studies presented here use in vitro methods as caco-2 cells and immobilized artificial membrane chromatography for drug absorption and lipophilicity measurements. The high sensitivity and selectivity of liquid chromatography mass spectrometry are especially important for new analytical methods using n-in-one. In the first study, the fragmentation patterns of ten nitrophenoxy benzoate compounds, serial homology, were characterised and the presence of the compounds was determined in a combinatorial library. The influence of one or two nitro substituents and the alkyl chain length of methyl to pentyl on collision-induced fragmentation was studied, and interesting structurefragmentation relationships were detected. Two nitro group compounds increased fragmentation compared to one nitro group, whereas less fragmentation was noted in molecules with a longer alkyl chain. The most abundant product ions were nitrophenoxy ions, which were also tested in the precursor ion screening of the combinatorial library. In the second study, the immobilized artificial membrane chromatographic method was transferred from ultraviolet detection to mass spectrometric analysis and a new method was developed. Mass spectra were scanned and the chromatographic retention of compounds was analysed using extract ion chromatograms. When changing detectors and buffers and including n-in-one in the method, the results showed good correlation. Finally, the results demonstrated that mass spectrometric detection with gradient elution can provide a rapid and convenient n-in-one method for ranking the lipophilic properties of several structurally diverse compounds simultaneously. In the final study, a new method was developed for caco-2 samples. Compounds were separated by liquid chromatography and quantified by selected reaction monitoring using mass spectrometry. This method was used for caco-2 samples, where absorption of ten chemically and physiologically different compounds was screened using both single and nin- one approaches. These three studies used mass spectrometry for compound identification, method transfer and quantitation in the area of mixture analysis. Different mass spectrometric scanning modes for the triple quadrupole instrument were used in each method. Early drug discovery with n-in-one is area where mass spectrometric analysis, its possibilities and proper use, is especially important.

一种基于比例反馈控制原理的动载荷时域反演方法

通过借鉴系统控制论中的比例反馈控制原理，提出了一种新的结构动载荷时域反演方法。该方法在原开环系统的输出与结构模型之间连接一个虚拟的比例反馈增益，使得原来的开环系统成为一个虚拟的闭环反馈控制系统，系统控制信号为实测的结构加速度响应。反馈控制器将系统输出与控制信号之间的差值进行放大后作为反馈不断输入到结构模型中，直到差值趋于稳定，此时该差值与反馈增益的乘积经过高通滤波后即得到所反演的动态载荷。该方法将载荷反演问题的求解转化为正问题中的结构瞬态响应求解，采用一般的数值解法如New-mark法即可实现，因此计算比较简便迅速。该方法仅需要测量结构的加速度响应即可进行反演，便于实际应用，而且并不十分依赖于真实的初始条件，由于不存在误差累积的现象，反演结果具有较好的稳定性。最后，通过海洋平台结构冰载荷反演的模型实验和数值仿真证明了该方法的有效性。

Phylogeography and non-invasive landscape genetics of the European pine marten ("martes martes" L. 1758) : insights into ancient and contemporary processes shaping genetic variation

283 p. : graf., map.

Molecular Techniques for Dicistrovirus Detection without RNA Extraction or Purification

Dicistroviridae is a new family of small, nonenveloped, and +ssRNA viruses pathogenic to both beneficial arthropods and insect pests as well. Triatoma virus (TrV), a dicistrovirus, is a pathogen of Triatoma infestans (Hemiptera: Reduviidae), one of the main vectors of Chagas disease. In this work, we report a single-step method to identify TrV, a dicistrovirus, isolated from fecal samples of triatomines. The identification method proved to be quite sensitive, even without the extraction and purification of RNA virus.