Area summation in human vision at and above detection threshold

The initial image-processing stages of visual cortex are well suited to a local (patchwise) analysis of the viewed scene. But the world's structures extend over space as textures and surfaces, suggesting the need for spatial integration. Most models of contrast vision fall shy of this process because (i) the weak area summation at detection threshold is attributed to probability summation (PS) and (ii) there is little or no advantage of area well above threshold. Both of these views are challenged here. First, it is shown that results at threshold are consistent with linear summation of contrast following retinal inhomogeneity, spatial filtering, nonlinear contrast transduction and multiple sources of additive Gaussian noise. We suggest that the suprathreshold loss of the area advantage in previous studies is due to a concomitant increase in suppression from the pedestal. To overcome this confound, a novel stimulus class is designed where: (i) the observer operates on a constant retinal area, (ii) the target area is controlled within this summation field, and (iii) the pedestal is fixed in size. Using this arrangement, substantial summation is found along the entire masking function, including the region of facilitation. Our analysis shows that PS and uncertainty cannot account for the results, and that suprathreshold summation of contrast extends over at least seven target cycles of grating. © 2007 The Royal Society.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Intermediate pyrolysis studies of aquatic biomass and potential applications in the BtVB-process

Aquatic biomass is seen as one of the major feedstocks to overcome difficulties associated with 1st generation biofuels, such as competition with food production, change of land use and further environmental issues. Although, this finding is widely accepted only little work has been carried out to investigate thermo-chemical conversion of algal specimen to produce biofuels, power and heat. This work aims at contributing fundamental knowledge for thermo-chemical processing of aquatic biomass via intermediate pyrolysis. Therefore, it was necessary to install and commission an analytical pyrolysis apparatus which facilitates intermediate pyrolysis process conditions as well as subsequent separation and detection of pyrolysates (Py- GC/MS). In addition, a methodology was established to analyse aquatic biomass under intermediate conditions by Thermo-Gravimetric Analysis (TGA). Several microalgae (e.g. Chlamydomonas reinhardtii, Chlorella vulgaris) and macroalgae specimen (e.g. Fucus vesiculosus) from main algal divisions and various natural habitats (fresh and saline water, temperate and polar climates) were chosen and their thermal degradation under intermediate pyrolysis conditions was studied. In addition, it was of interest to examine the contribution of biochemical constituents of algal biomass onto the chemical compounds contained in pyrolysates. Therefore, lipid and protein fractions were extracted from microalgae biomass and analysed separately. Furthermore, investigations of residual algal materials obtained by extraction of high valuable compounds (e.g. lipids, proteins, enzymes) were included to evaluate their potential for intermediate pyrolysis processing. On basis of these thermal degradation studies, possible applications of algal biomass and from there derived materials in the Bio-thermal Valorisation of Biomass-process (BtVB-process) are presented. It was of interest to evaluate the combination of the production of high valuable products and bioenergy generation derived by micro- and macro algal biomass.

Detection of Logical-and-Probabilistic Correlation in Time Series

An application of the heterogeneous variables system prediction method to solving the time series analysis problem with respect to the sample size is considered in this work. It is created a logical-and-probabilistic correlation from the logical decision function class. Two ways is considered. When the information about event is kept safe in the process, and when it is kept safe in depending process.

Parametric Identification and Diagnosis of Integrated Navigation Systems in Bench Test Process

Growth of complexity and functional importance of integrated navigation systems (INS) leads to high losses at the equipment refusals. The paper is devoted to the INS diagnosis system development, allowing identifying the cause of malfunction. The proposed solutions permit taking into account any changes in sensors dynamic and accuracy characteristics by means of the appropriate error models coefficients. Under actual conditions of INS operation, the determination of current values of the sensor models and estimation filter parameters rely on identification procedures. The results of full-scale experiments are given, which corroborate the expediency of INS error models parametric identification in bench test process.

Probabilistic multiple model neural network based leak detection system:experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.

On Handling Replay Attacks in Intrusion Detection Systems

We propose a method for detecting and analyzing the so-called replay attacks in intrusion detection systems, when an intruder contributes a small amount of hostile actions to a recorded session of a legitimate user or process, and replays this session back to the system. The proposed approach can be applied if an automata-based model is used to describe behavior of active entities in a computer system.

Protein lipoxidation:detection strategies and challenges

Enzymatic and non-enzymatic lipid metabolism can give rise to reactive species that may covalently modify cellular or plasma proteins through a process known as lipoxidation. Under basal conditions, protein lipoxidation can contribute to normal cell homeostasis and participate in signaling or adaptive mechanisms, as exemplified by lipoxidation of Ras proteins or of the cytoskeletal protein vimentin, both of which behave as sensors of electrophilic species. Nevertheless, increased lipoxidation under pathological conditions may lead to deleterious effects on protein structure or aggregation. This can result in impaired degradation and accumulation of abnormally folded proteins contributing to pathophysiology, as may occur in neurodegenerative diseases. Identification of the protein targets of lipoxidation and its functional consequences under pathophysiological situations can unveil the modification patterns associated with the various outcomes, as well as preventive strategies or potential therapeutic targets. Given the wide structural variability of lipid moieties involved in lipoxidation, highly sensitive and specific methods for its detection are required. Derivatization of reactive carbonyl species is instrumental in the detection of adducts retaining carbonyl groups. In addition, use of tagged derivatives of electrophilic lipids enables enrichment of lipoxidized proteins or peptides. Ultimate confirmation of lipoxidation requires high resolution mass spectrometry approaches to unequivocally identify the adduct and the targeted residue. Moreover, rigorous validation of the targets identified and assessment of the functional consequences of these modifications are essential. Here we present an update on methods to approach the complex field of lipoxidation along with validation strategies and functional assays illustrated with well-studied lipoxidation targets.

Detection and quantification of protein oxidation in sarcopenic models:a mass spectrometry study

Oxidised biomolecules in aged tissue could potentially be used as biomarkers for age-related diseases; however, it is still unclear whether they causatively contribute to ageing or are consequences of the ageing process. To assess the potential of using protein oxidation as markers of ageing, mass spectrometry (MS) was employed for the identification and quantification of oxidative modifications in obese (ob/ob) mice. Lean muscle mass and strength is reduced in obesity, representing a sarcopenic model in which the levels of oxidation can be evaluated for different muscular systems including calcium homeostasis, metabolism and contractility. Several oxidised residues were identified by tandem MS (MS/MS) in both muscle homogenate and isolated sarcoplasmic reticulum (SR), an organelle that regulates intracellular calcium levels in muscle. These modifications include oxidation of methionine, cysteine, tyrosine, and tryptophan in several proteins such as sarcoplasmic reticulum calcium ATPase (SERCA), glycogen phosphorylase, and myosin. Once modifications had been identified, multiple reaction monitoring MS (MRM) was used to quantify the percentage modification of oxidised residues within the samples. Preliminary data suggests proteins in ob/ob mice are more oxidised than the controls. For example SERCA, which constitutes 60-70% of the SR, had approximately a 2-fold increase in cysteine trioxidation of Cys561 in the obese model when compared to the control. Other obese muscle proteins have also shown a similar increase in oxidation for various residues. Further analysis with complex protein mixtures will determine the potential diagnostic use of MRM experiments for analysing protein oxidation in small biological samples such as muscle needle biopsies.

Improving the scientific reliability of biological detection of explosives by Canis familiaris: Active odour signatures and their implications

The use of canines as a method of detection of explosives is well established worldwide and those applying this technology range from police forces and law enforcement to humanitarian agencies in the developing world. Despite the recent surge in publication of novel instrumental sensors for explosives detection, canines are still regarded by many to be the most effective real-time field method of explosives detection. However, unlike instrumental methods, currently it is difficult to determine detection levels, perform calibration of the canines' ability or produce scientifically valid quality control checks. Accordingly, amongst increasingly strict requirements regarding forensic evidence admission such as Frye and Daubert, there is a need for better scientific understanding of the process of canine detection. ^ When translated to the field of canine detection, just like any instrumental technique, peer reviewed publication of the reliability, success and error rates, is required for admissibility. Commonly training is focussed towards high explosives such as TNT and Composition 4, and the low explosives such as Black and Smokeless Powders are added often only for completeness. ^ Headspace analyses of explosive samples, performed by Solid Phase Microextraction (SPME) paired with Gas Chromatography - Mass Spectrometry (GC-MS), and Gas Chromatography - Electron Capture Detection (GC-ECD) was conducted, highlighting common odour chemicals. The odour chemicals detected were then presented to previously trained and certified explosives detection canines, and the activity/inactivity of the odour determined through field trials and experiments. ^ It was demonstrated that TNT and cast explosives share a common odour signature, and the same may be said for plasticized explosives such as Composition C-4 and Deta Sheet. Conversely, smokeless powders were demonstrated not to share common odours. An evaluation of the effectiveness of commercially available pseudo aids reported limited success. The implications of the explosive odour studies upon canine training then led to the development of novel inert training aids based upon the active odours determined. ^

Functionalized carbon micro/nanostructures for biomolecular detection

Advancements in the micro-and nano-scale fabrication techniques have opened up new avenues for the development of portable, scalable and easier-to-use biosensors. Over the last few years, electrodes made of carbon have been widely used as sensing units in biosensors due to their attractive physiochemical properties. The aim of this research is to investigate different strategies to develop functionalized high surface carbon micro/nano-structures for electrochemical and biosensing devices. High aspect ratio three-dimensional carbon microarrays were fabricated via carbon microelectromechanical systems (C-MEMS) technique, which is based on pyrolyzing pre-patterned organic photoresist polymers. To further increase the surface area of the carbon microstructures, surface porosity was introduced by two strategies, i.e. (i) using F127 as porogen and (ii) oxygen reactive ion etch (RIE) treatment. Electrochemical characterization showed that porous carbon thin film electrodes prepared by using F127 as porogen had an effective surface area (Aeff 185%) compared to the conventional carbon electrode. To achieve enhanced electrochemical sensitivity for C-MEMS based functional devices, graphene was conformally coated onto high aspect ratio three-dimensional (3D) carbon micropillar arrays using electrostatic spray deposition (ESD) technique. The amperometric response of graphene/carbon micropillar electrode arrays exhibited higher electrochemical activity, improved charge transfer and a linear response towards H2O2 detection between 250&mgr;M to 5.5mM. Furthermore, carbon structures with dimensions from 50 nano-to micrometer level have been fabricated by pyrolyzing photo-nanoimprint lithography patterned organic resist polymer. Microstructure, elemental composition and resistivity characterization of the carbon nanostructures produced by this process were very similar to conventional photoresist derived carbon. Surface functionalization of the carbon nanostructures was performed using direct amination technique. Considering the need for requisite functional groups to covalently attach bioreceptors on the carbon surface for biomolecule detection, different oxidation techniques were compared to study the types of carbon-oxygen groups formed on the surface and their percentages with respect to different oxidation pretreatment times. Finally, a label-free detection strategy using signaling aptamer/protein binding complex for platelet-derived growth factor oncoprotein detection on functionalized three-dimensional carbon microarrays platform was demonstrated. The sensor showed near linear relationship between the relative fluorescence difference and protein concentration even in the sub-nanomolar range with an excellent detection limit of 5 pmol.

Identification of characteristic volatile organic compounds released during the decomposition process of human remains and analogues

The manner in which remains decompose has been and is currently being researched around the world, yet little is still known about the generated scent of death. In fact, it was not until the Casey Anthony trial that research on the odor released from decomposing remains, and the compounds that it is comprised of, was brought to light. The Anthony trial marked the first admission of human decomposition odor as forensic evidence into the court of law; however, it was not "ready for prime time" as the scientific research on the scent of death is still in its infancy. This research employed the use of solid-phase microextraction (SPME) with gas chromatography-mass spectrometry (GC-MS) to identify the volatile organic compounds (VOCs) released from decomposing remains and to assess the impact that different environmental conditions had on the scent of death. Using human cadaver analogues, it was discovered that the environment in which the remains were exposed to dramatically affected the odors released by either modifying the compounds that it was comprised of or by enhancing/hindering the amount that was liberated. In addition, the VOCs released during the different stages of the decomposition process for both human remains and analogues were evaluated. Statistical analysis showed correlations between the stage of decay and the VOCs generated, such that each phase of decomposition was distinguishable based upon the type and abundance of compounds that comprised the odor. This study has provided new insight into the scent of death and the factors that can dramatically affect it, specifically, frozen, aquatic, and soil environments. Moreover, the results revealed that different stages of decomposition were distinguishable based upon the type and total mass of each compound present. Thus, based upon these findings, it is suggested that the training aids that are employed for human remains detection (HRD) canines should 1) be characteristic of remains that have undergone decomposition in different environmental settings, and 2) represent each stage of decay, to ensure that the HRD canines have been trained to the various odors that they are likely to encounter in an operational situation.

The Effect of Sample and Sample Matrix on DNA Processing: Mechanisms for the Detection and Management of Inhibition in Forensic Samples

The presence of inhibitory substances in biological forensic samples has, and continues to affect the quality of the data generated following DNA typing processes. Although the chemistries used during the procedures have been enhanced to mitigate the effects of these deleterious compounds, some challenges remain. Inhibitors can be components of the samples, the substrate where samples were deposited or chemical(s) associated to the DNA purification step. Therefore, a thorough understanding of the extraction processes and their ability to handle the various types of inhibitory substances can help define the best analytical processing for any given sample. A series of experiments were conducted to establish the inhibition tolerance of quantification and amplification kits using common inhibitory substances in order to determine if current laboratory practices are optimal for identifying potential problems associated with inhibition. DART mass spectrometry was used to determine the amount of inhibitor carryover after sample purification, its correlation to the initial inhibitor input in the sample and the overall effect in the results. Finally, a novel alternative at gathering investigative leads from samples that would otherwise be ineffective for DNA typing due to the large amounts of inhibitory substances and/or environmental degradation was tested. This included generating data associated with microbial peak signatures to identify locations of clandestine human graves. Results demonstrate that the current methods for assessing inhibition are not necessarily accurate, as samples that appear inhibited in the quantification process can yield full DNA profiles, while those that do not indicate inhibition may suffer from lowered amplification efficiency or PCR artifacts. The extraction methods tested were able to remove >90% of the inhibitors from all samples with the exception of phenol, which was present in variable amounts whenever the organic extraction approach was utilized. Although the results attained suggested that most inhibitors produce minimal effect on downstream applications, analysts should practice caution when selecting the best extraction method for particular samples, as casework DNA samples are often present in small quantities and can contain an overwhelming amount of inhibitory substances.

Tsunami simulation and detection using global navigation satellite system reflectometry

In this thesis, research for tsunami remote sensing using the Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler maps (DDMs) is presented. Firstly, a process for simulating GNSS-R DDMs of a tsunami-dominated sea sur- face is described. In this method, the bistatic scattering Zavorotny-Voronovich (Z-V) model, the sea surface mean square slope model of Cox and Munk, and the tsunami- induced wind perturbation model are employed. The feasibility of the Cox and Munk model under a tsunami scenario is examined by comparing the Cox and Munk model- based scattering coefficient with the Jason-1 measurement. A good consistency be- tween these two results is obtained with a correlation coefficient of 0.93. After con- firming the applicability of the Cox and Munk model for a tsunami-dominated sea, this work provides the simulations of the scattering coefficient distribution and the corresponding DDMs of a fixed region of interest before and during the tsunami. Fur- thermore, by subtracting the simulation results that are free of tsunami from those with presence of tsunami, the tsunami-induced variations in scattering coefficients and DDMs can be clearly observed. Secondly, a scheme to detect tsunamis and estimate tsunami parameters from such tsunami-dominant sea surface DDMs is developed. As a first step, a procedure to de- termine tsunami-induced sea surface height anomalies (SSHAs) from DDMs is demon- strated and a tsunami detection precept is proposed. Subsequently, the tsunami parameters (wave amplitude, direction and speed of propagation, wavelength, and the tsunami source location) are estimated based upon the detected tsunami-induced SSHAs. In application, the sea surface scattering coefficients are unambiguously re- trieved by employing the spatial integration approach (SIA) and the dual-antenna technique. Next, the effective wind speed distribution can be restored from the scat- tering coefficients. Assuming all DDMs are of a tsunami-dominated sea surface, the tsunami-induced SSHAs can be derived with the knowledge of background wind speed distribution. In addition, the SSHA distribution resulting from the tsunami-free DDM (which is supposed to be zero) is considered as an error map introduced during the overall retrieving stage and is utilized to mitigate such errors from influencing sub- sequent SSHA results. In particular, a tsunami detection procedure is conducted to judge the SSHAs to be truly tsunami-induced or not through a fitting process, which makes it possible to decrease the false alarm. After this step, tsunami parameter estimation is proceeded based upon the fitted results in the former tsunami detec- tion procedure. Moreover, an additional method is proposed for estimating tsunami propagation velocity and is believed to be more desirable in real-world scenarios. The above-mentioned tsunami-dominated sea surface DDM simulation, tsunami detection precept and parameter estimation have been tested with simulated data based on the 2004 Sumatra-Andaman tsunami event.

Sparse Sample Detection Using Magnetic Bead Manipulation on a Digital Microfluidic Device

This thesis demonstrates a new way to achieve sparse biological sample detection, which uses magnetic bead manipulation on a digital microfluidic device. Sparse sample detection was made possible through two steps: sparse sample capture and fluorescent signal detection. For the first step, the immunological reaction between antibody and antigen enables the binding between target cells and antibody-­‐‑ coated magnetic beads, hence achieving sample capture. For the second step, fluorescent detection is achieved via fluorescent signal measurement and magnetic bead manipulation. In those two steps, a total of three functions need to work together, namely magnetic beads manipulation, fluorescent signal measurement and immunological binding. The first function is magnetic bead manipulation, and it uses the structure of current-­‐‑carrying wires embedded in the actuation electrode of an electrowetting-­‐‑on-­‐‑dielectric (EWD) device. The current wire structure serves as a microelectromagnet, which is capable of segregating and separating magnetic beads. The device can achieve high segregation efficiency when the wire spacing is 50µμm, and it is also capable of separating two kinds of magnetic beads within a 65µμm distance. The device ensures that the magnetic bead manipulation and the EWD function can be operated simultaneously without introducing additional steps in the fabrication process. Half circle shaped current wires were designed in later devices to concentrate magnetic beads in order to increase the SNR of sample detection. The second function is immunological binding. Immunological reaction kits were selected in order to ensure the compatibility of target cells, magnetic bead function and EWD function. The magnetic bead choice ensures the binding efficiency and survivability of target cells. The magnetic bead selection and binding mechanism used in this work can be applied to a wide variety of samples with a simple switch of the type of antibody. The last function is fluorescent measurement. Fluorescent measurement of sparse samples is made possible of using fluorescent stains and a method to increase SNR. The improved SNR is achieved by target cell concentration and reduced sensing area. Theoretical limitations of the entire sparse sample detection system is as low as 1 Colony Forming Unit/mL (CFU/mL).