Electrochemical sensing strategies for the detection of interactions between biological macromolecules

Electrochemical biosensors provide an attractive means to analyze the content of a biological sample due to the direct conversion of a biological event to an electronic signal, enabling the development of cheap, small, portable and simple devices, that allow multiplex and real-time detection. At the same time nanobiotechnology is drastically revolutionizing the biosensors development and different transduction strategies exploit concepts developed in these field to simplify the analysis operations for operators and end users, offering higher specificity, higher sensitivity, higher operational stability, integrated sample treatments and shorter analysis time. The aim of this PhD work has been the application of nanobiotechnological strategies to electrochemical biosensors for the detection of biological macromolecules. Specifically, one project was focused on the application of a DNA nanotechnology called hybridization chain reaction (HCR), to amplify the hybridization signal in an electrochemical DNA biosensor. Another project on which the research activity was focused concerns the development of an electrochemical biosensor based on a biological model membrane anchored to a solid surface (tBLM), for the recognition of interactions between the lipid membrane and different types of target molecules.

Automatic induction of lexical features

This thesis concerns artificially intelligent natural language processing systems that are capable of learning the properties of lexical items (properties like verbal valency or inflectional class membership) autonomously while they are fulfilling their tasks for which they have been deployed in the first place. Many of these tasks require a deep analysis of language input, which can be characterized as a mapping of utterances in a given input C to a set S of linguistically motivated structures with the help of linguistic information encoded in a grammar G and a lexicon L: G + L + C → S (1) The idea that underlies intelligent lexical acquisition systems is to modify this schematic formula in such a way that the system is able to exploit the information encoded in S to create a new, improved version of the lexicon: G + L + S → L' (2) Moreover, the thesis claims that a system can only be considered intelligent if it does not just make maximum usage of the learning opportunities in C, but if it is also able to revise falsely acquired lexical knowledge. So, one of the central elements in this work is the formulation of a couple of criteria for intelligent lexical acquisition systems subsumed under one paradigm: the Learn-Alpha design rule. The thesis describes the design and quality of a prototype for such a system, whose acquisition components have been developed from scratch and built on top of one of the state-of-the-art Head-driven Phrase Structure Grammar (HPSG) processing systems. The quality of this prototype is investigated in a series of experiments, in which the system is fed with extracts of a large English corpus. While the idea of using machine-readable language input to automatically acquire lexical knowledge is not new, we are not aware of a system that fulfills Learn-Alpha and is able to deal with large corpora. To instance four major challenges of constructing such a system, it should be mentioned that a) the high number of possible structural descriptions caused by highly underspeci ed lexical entries demands for a parser with a very effective ambiguity management system, b) the automatic construction of concise lexical entries out of a bulk of observed lexical facts requires a special technique of data alignment, c) the reliability of these entries depends on the system's decision on whether it has seen 'enough' input and d) general properties of language might render some lexical features indeterminable if the system tries to acquire them with a too high precision. The cornerstone of this dissertation is the motivation and development of a general theory of automatic lexical acquisition that is applicable to every language and independent of any particular theory of grammar or lexicon. This work is divided into five chapters. The introductory chapter first contrasts three different and mutually incompatible approaches to (artificial) lexical acquisition: cue-based queries, head-lexicalized probabilistic context free grammars and learning by unification. Then the postulation of the Learn-Alpha design rule is presented. The second chapter outlines the theory that underlies Learn-Alpha and exposes all the related notions and concepts required for a proper understanding of artificial lexical acquisition. Chapter 3 develops the prototyped acquisition method, called ANALYZE-LEARN-REDUCE, a framework which implements Learn-Alpha. The fourth chapter presents the design and results of a bootstrapping experiment conducted on this prototype: lexeme detection, learning of verbal valency, categorization into nominal count/mass classes, selection of prepositions and sentential complements, among others. The thesis concludes with a review of the conclusions and motivation for further improvements as well as proposals for future research on the automatic induction of lexical features.

Ict security: Defence strategies against targeted attacks

L’avanzare delle tecnologie ICT e l’abbattimento dei costi di produzione hanno portato ad un aumento notevole della criminalità informatica. Tuttavia il cambiamento non è stato solamente quantitativo, infatti si può assistere ad un paradigm-shift degli attacchi informatici da completamente opportunistici, ovvero senza un target specifico, ad attacchi mirati aventi come obiettivo una particolare persona, impresa o nazione. Lo scopo della mia tesi è quello di analizzare modelli e tassonomie sia di attacco che di difesa, per poi valutare una effettiva strategia di difesa contro gli attacchi mirati. Il lavoro è stato svolto in un contesto aziendale come parte di un tirocinio. Come incipit, ho effettuato un attacco mirato contro l’azienda in questione per valutare la validità dei sistemi di difesa. L’attacco ha avuto successo, dimostrando l’inefficacia di moderni sistemi di difesa. Analizzando i motivi del fallimento nel rilevare l’attacco, sono giunto a formulare una strategia di difesa contro attacchi mirati sotto forma di servizio piuttosto che di prodotto. La mia proposta è un framework concettuale, chiamato WASTE (Warning Automatic System for Targeted Events) il cui scopo è fornire warnings ad un team di analisti a partire da eventi non sospetti, ed un business process che ho nominato HAZARD (Hacking Approach for Zealot Attack Response and Detection), che modella il servizio completo di difesa contro i targeted attack. Infine ho applicato il processo all’interno dell’azienda per mitigare minacce ed attacchi informatici.

Target Tracking in UWB Multistatic Radars

Detection, localization and tracking of non-collaborative objects moving inside an area is of great interest to many surveillance applications. An ultra- wideband (UWB) multistatic radar is considered as a good infrastructure for such anti-intruder systems, due to the high range resolution provided by the UWB impulse-radio and the spatial diversity achieved with a multistatic configuration. Detection of targets, which are typically human beings, is a challenging task due to reflections from unwanted objects in the area, shadowing, antenna cross-talks, low transmit power, and the blind zones arised from intrinsic peculiarities of UWB multistatic radars. Hence, we propose more effective detection, localization, as well as clutter removal techniques for these systems. However, the majority of the thesis effort is devoted to the tracking phase, which is an essential part for improving the localization accuracy, predicting the target position and filling out the missed detections. Since UWB radars are not linear Gaussian systems, the widely used tracking filters, such as the Kalman filter, are not expected to provide a satisfactory performance. Thus, we propose the Bayesian filter as an appropriate candidate for UWB radars. In particular, we develop tracking algorithms based on particle filtering, which is the most common approximation of Bayesian filtering, for both single and multiple target scenarios. Also, we propose some effective detection and tracking algorithms based on image processing tools. We evaluate the performance of our proposed approaches by numerical simulations. Moreover, we provide experimental results by channel measurements for tracking a person walking in an indoor area, with the presence of a significant clutter. We discuss the existing practical issues and address them by proposing more robust algorithms.

Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data

Data sets describing the state of the earth's atmosphere are of great importance in the atmospheric sciences. Over the last decades, the quality and sheer amount of the available data increased significantly, resulting in a rising demand for new tools capable of handling and analysing these large, multidimensional sets of atmospheric data. The interdisciplinary work presented in this thesis covers the development and the application of practical software tools and efficient algorithms from the field of computer science, aiming at the goal of enabling atmospheric scientists to analyse and to gain new insights from these large data sets. For this purpose, our tools combine novel techniques with well-established methods from different areas such as scientific visualization and data segmentation. In this thesis, three practical tools are presented. Two of these tools are software systems (Insight and IWAL) for different types of processing and interactive visualization of data, the third tool is an efficient algorithm for data segmentation implemented as part of Insight.Insight is a toolkit for the interactive, three-dimensional visualization and processing of large sets of atmospheric data, originally developed as a testing environment for the novel segmentation algorithm. It provides a dynamic system for combining at runtime data from different sources, a variety of different data processing algorithms, and several visualization techniques. Its modular architecture and flexible scripting support led to additional applications of the software, from which two examples are presented: the usage of Insight as a WMS (web map service) server, and the automatic production of a sequence of images for the visualization of cyclone simulations. The core application of Insight is the provision of the novel segmentation algorithm for the efficient detection and tracking of 3D features in large sets of atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. Data segmentation usually leads to a significant reduction of the size of the considered data. This enables a practical visualization of the data, statistical analyses of the features and their events, and the manual or automatic detection of interesting situations for subsequent detailed investigation. The concepts of the novel algorithm, its technical realization, and several extensions for avoiding under- and over-segmentation are discussed. As example applications, this thesis covers the setup and the results of the segmentation of upper-tropospheric jet streams and cyclones as full 3D objects. Finally, IWAL is presented, which is a web application for providing an easy interactive access to meteorological data visualizations, primarily aimed at students. As a web application, the needs to retrieve all input data sets and to install and handle complex visualization tools on a local machine are avoided. The main challenge in the provision of customizable visualizations to large numbers of simultaneous users was to find an acceptable trade-off between the available visualization options and the performance of the application. Besides the implementational details, benchmarks and the results of a user survey are presented.

Non Invasive Tools for Early Detection of Autism Spectrum Disorders

Autism Spectrum Disorders (ASDs) describe a set of neurodevelopmental disorders. ASD represents a significant public health problem. Currently, ASDs are not diagnosed before the 2nd year of life but an early identification of ASDs would be crucial as interventions are much more effective than specific therapies starting in later childhood. To this aim, cheap an contact-less automatic approaches recently aroused great clinical interest. Among them, the cry and the movements of the newborn, both involving the central nervous system, are proposed as possible indicators of neurological disorders. This PhD work is a first step towards solving this challenging problem. An integrated system is presented enabling the recording of audio (crying) and video (movements) data of the newborn, their automatic analysis with innovative techniques for the extraction of clinically relevant parameters and their classification with data mining techniques. New robust algorithms were developed for the selection of the voiced parts of the cry signal, the estimation of acoustic parameters based on the wavelet transform and the analysis of the infant’s general movements (GMs) through a new body model for segmentation and 2D reconstruction. In addition to a thorough literature review this thesis presents the state of the art on these topics that shows that no studies exist concerning normative ranges for newborn infant cry in the first 6 months of life nor the correlation between cry and movements. Through the new automatic methods a population of control infants (“low-risk”, LR) was compared to a group of “high-risk” (HR) infants, i.e. siblings of children already diagnosed with ASD. A subset of LR infants clinically diagnosed as newborns with Typical Development (TD) and one affected by ASD were compared. The results show that the selected acoustic parameters allow good differentiation between the two groups. This result provides new perspectives both diagnostic and therapeutic.

UWB radar sensor networks: Detection algorithms design and experimental analysis

In the last years radar sensor networks for localization and tracking in indoor environment have generated more and more interest, especially for anti-intrusion security systems. These networks often use Ultra Wide Band (UWB) technology, which consists in sending very short (few nanoseconds) impulse signals. This approach guarantees high resolution and accuracy and also other advantages such as low price, low power consumption and narrow-band interference (jamming) robustness. In this thesis the overall data processing (done in MATLAB environment) is discussed, starting from experimental measures from sensor devices, ending with the 2D visualization of targets movements over time and focusing mainly on detection and localization algorithms. Moreover, two different scenarios and both single and multiple target tracking are analyzed.

A Fully Automatic Calibration Framework for Navigated Ultrasound Imaging

Navigated ultrasound (US) imaging is used for the intra-operative acquisition of 3D image data during imageguided surgery. The presented approach includes the design of a compact and easy to use US calibration device and its integration into a software application for navigated liver surgery. User interaction during the calibration process is minimized through automatic detection of the calibration process followed by automatic image segmentation, calculation of the calibration transform and validation of the obtained result. This leads to a fast, interaction-free and fully automatic calibration procedure enabling intra-operative

High-accuracy patient-to-image registration for the facilitation of image-guided robotic microsurgery on the head

Image-guided microsurgery requires accuracies an order of magnitude higher than today's navigation systems provide. A critical step toward the achievement of such low-error requirements is a highly accurate and verified patient-to-image registration. With the aim of reducing target registration error to a level that would facilitate the use of image-guided robotic microsurgery on the rigid anatomy of the head, we have developed a semiautomatic fiducial detection technique. Automatic force-controlled localization of fiducials on the patient is achieved through the implementation of a robotic-controlled tactile search within the head of a standard surgical screw. Precise detection of the corresponding fiducials in the image data is realized using an automated model-based matching algorithm on high-resolution, isometric cone beam CT images. Verification of the registration technique on phantoms demonstrated that through the elimination of user variability, clinically relevant target registration errors of approximately 0.1 mm could be achieved.

[(99m)Tc]Demotate 2 in the detection of sst(2)-positive tumours: a preclinical comparison with [(111)In]DOTA-tate

PURPOSE: The aim of this study was to evaluate [(99m)Tc]Demotate 2 ([(99m)Tc-N(4) (0-1),Asp(0),Tyr(3)]octreotate) as a candidate for in vivo imaging of sst(2)-positive tumours and to compare it with [(111)In]DOTA-tate ([(111)In-DOTA(0),Tyr(3)]octreotate). METHODS: Labelling of Demotate 2 with (99m)Tc was performed at room temperature using SnCl(2) as reductant in the presence of citrate at alkaline pH. Radiochemical analysis involved ITLC and HPLC methods. Peptide conjugate affinities for sst(2) were determined by receptor autoradiography on rat brain cortex sections using [DOTA(0),(125)I-Tyr(3)]octreotate as the radioligand. The affinity profile of Demotate 2 for human sst(1)-sst(5) was studied by receptor autoradiography in cell preparations using the universal somatostatin radioligand [(125)I][Leu(8),(D: )Trp(22),Tyr(25)]somatostatin-28. The internalisation rates of [(99m)Tc]Demotate 2 and [(111)In]DOTA-tate were compared in sst(2)-positive and -negative control cell lines. Biodistribution of radiopeptides was studied in male Lewis rats bearing CA20948 tumours. RESULTS: Peptide conjugates showed selectivity and a high affinity binding for sst(2) (Demotate 2 IC(50)=3.2 nM and DOTA-tate IC(50)=5.4 nM). [(99m)Tc]Demotate 2, like [(111)In]DOTA-tate, internalised rapidly in all sst(2)-positive cells tested, but not in sst(2)-negative control cells. After injection in CA20948 tumour-bearing rats both radiopeptides showed high and specific uptake in the sst(2)-positive organs and in the implanted tumour and rapid excretion from non-target tissues via the kidneys. CONCLUSION: [(99m)Tc]Demotate 2, similarly to the known sst(2)-targeting agent [(111)In]DOTA-tate, showed promising biological qualities for application in the scintigraphy of sst(2)-positive tumours.

New molecular detection tools adapted to emerging rhinoviruses and enteroviruses

Human rhinoviruses (HRV), and to a lesser extent human enteroviruses (HEV), are important respiratory pathogens. Like other RNA viruses, these picornaviruses have an intrinsic propensity to variability. This results in a large number of different serotypes as well as the incessant discovery of new genotypes. This large and growing diversity not only complicates the design of real-time PCR assays but also renders immunofluorescence unfeasible for broad HRV and HEV detection or quantification in cells. In this study, we used the 5' untranslated region, the most conserved part of the genome, as a target for the development of both a real-time PCR assay (Panenterhino/Ge/08) and a peptide nucleic acid-based hybridization oligoprobe (Panenterhino/Ge/08 PNA probe) designed to detect all HRV and HEV species members according to publicly available sequences. The reverse transcription-PCR assay has been validated, using not only plasmid and viral stocks but also quantified RNA transcripts and around 1,000 clinical specimens. These new generic detection PCR assays overcame the variability of circulating strains and lowered the risk of missing emerging and divergent HRV and HEV. An additional real-time PCR assay (Entero/Ge/08) was also designed specifically to provide sensitive and targeted detection of HEV in cerebrospinal fluid. In addition to the generic probe, we developed specific probes for the detection of HRV-A and HRV-B in cells. This investigation provides a comprehensive toolbox for accurate molecular identification of the different HEV and HRV circulating in humans.

PCR and real-time PCR primers developed for detection and identification of Bifidobacterium thermophilum in faeces

BACKGROUND: Culture-independent methods based on the 16S ribosomal RNA molecule are nowadays widely used for assessment of the composition of the intestinal microbiota, in relation to host health or probiotic efficacy. Because Bifidobacterium thermophilum was only recently isolated from human faeces until now, no specific real-time PCR (qPCR) assay has been developed for detection of this species as component of the bifidobacterial community of the human intestinal flora. RESULTS: Design of specific primers and probe was achieved based on comparison of 108 published bifidobacterial 16S rDNA sequences with the recently published sequence of the human faecal isolate B. thermophilum RBL67. Specificity of the primer was tested in silico by similarity search against the sequence database and confirmed experimentally by PCR amplification on 17 Bifidobacterium strains, representing 12 different species, and two Lactobacillus strains. The qPCR assay developed was linear for B. thermophilum RBL67 DNA quantities ranging from 0.02 ng/microl to 200 ng/microl and showed a detection limit of 10(5) cells per gram faeces. The application of this new qPCR assay allowed to detect the presence of B. thermophilum in one sample from a 6-month old breast-fed baby among 17 human faecal samples tested. Additionally, the specific qPCR primers in combination with selective plating experiments led to the isolation of F9K9, a faecal isolate from a 4-month old breast-fed baby. The 16S rDNA sequence of this isolate is 99.93% similar to that of B. thermophilum RBL67 and confirmed the applicability of the new qPCR assay in faecal samples. CONCLUSION: A new B. thermophilum-specific qPCR assay was developed based on species-specific target nucleotides in the 16S rDNA. It can be used to further characterize the composition of the bifidobacterial community in the human gastrointestinal tract. Until recently, B. thermophilum was considered as a species of animal origin, but here we confirm with the application of this new PCR assay the presence of B. thermophilum strains in the human gut.

Detection of complex point targets with distributed assets in a MIMO radar system

The report explores the problem of detecting complex point target models in a MIMO radar system. A complex point target is a mathematical and statistical model for a radar target that is not resolved in space, but exhibits varying complex reflectivity across the different bistatic view angles. The complex reflectivity can be modeled as a complex stochastic process whose index set is the set of all the bistatic view angles, and the parameters of the stochastic process follow from an analysis of a target model comprising a number of ideal point scatterers randomly located within some radius of the targets center of mass. The proposed complex point targets may be applicable to statistical inference in multistatic or MIMO radar system. Six different target models are summarized here – three 2-dimensional (Gaussian, Uniform Square, and Uniform Circle) and three 3-dimensional (Gaussian, Uniform Cube, and Uniform Sphere). They are assumed to have different distributions on the location of the point scatterers within the target. We develop data models for the received signals from such targets in the MIMO radar system with distributed assets and partially correlated signals, and consider the resulting detection problem which reduces to the familiar Gauss-Gauss detection problem. We illustrate that the target parameter and transmit signal have an influence on the detector performance through target extent and the SNR respectively. A series of the receiver operator characteristic (ROC) curves are generated to notice the impact on the detector for varying SNR. Kullback–Leibler (KL) divergence is applied to obtain the approximate mean difference between density functions the scatterers assume inside the target models to show the change in the performance of the detector with target extent of the point scatterers.

Immuno-detection of anthrose containing tetrasaccharide in the exosporium of Bacillus anthracis and Bacillus cereus strains

AIMS: Bacillus anthracis strains of various origins were analysed with the view to describe intrinsic and persistent structural components of the Bacillus collagen-like protein of anthracis glycoprotein associated anthrose containing tetrasaccharide in the exosporium. METHODS AND RESULTS: The tetrasaccharide consists of three rhamnose residues and an unique monosaccharide--anthrose. As anthrose was not found in spores of related strains of bacteria, we envisioned the detection of B. anthracis spores based on antibodies against anthrose-containing polysaccharides. Carbohydrate-protein conjugates containing the synthetic tetrasaccharide, an anthrose-rhamnose disaccharide or anthrose alone were employed to immunize mice. All three formulations were immunogenic and elicited IgG responses with different fine specificities. All sera and monoclonal antibodies derived from tetrasaccharide immunized mice cross-reacted not only with spore lysates of a panel of virulent B. anthracis strains, but also with some of the B. cereus strains tested. CONCLUSIONS: Our results demonstrate that antibodies to synthetic carbohydrates are useful tools for epitope analyses of complex carbohydrate antigens and for the detection of particular target structures in biological specimens. SIGNIFICANCE AND IMPACT OF THE STUDY: Although not strictly specific for B. anthracis spores, antibodies against the tetrasaccharide may have potential as immuno-capturing components for a highly sensitive spore detection system.

[Field validation of a real-time PCR test for the detection of Mycoplasma hyopneumoniae in nasal swabs of live pigs]

Enzootic pneumonia (EP) of pigs, caused by Mycoplasma hyopneumoniae has been a notifiable disease in Switzerland since May 2003. The diagnosis of EP has been based on multiple methods, including clinical, bacteriological and epidemiological findings as well as pathological examination of lungs (mosaic diagnosis). With the recent development of a real-time PCR (rtPCR) assay with 2 target sequences a new detection method for M. hyopneumoniae became available. This assay was tested for its applicability to nasal swab material from live animals. Pigs from 74 herds (average 10 pigs per herd) were tested. Using the mosaic diagnosis, 22 herds were classified as EP positive and 52 as EP negative. From the 730 collected swab samples we were able to demonstrate that the rtPCR test was 100% specific. In cases of cough the sensitivity on herd level of the rtPCR is 100%. On single animal level and in herds without cough the sensitivity was lower. In such cases, only a positive result would be proof for an infection with M. hyopneumoniae. Our study shows that the rtPCR on nasal swabs from live pigs allows a fast and accurate diagnosis in cases of suspected EP.