Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet

Rotation invariance is important for an iris recognition system since changes of head orientation and binocular vergence may cause eye rotation. The conventional methods of iris recognition cannot achieve true rotation invariance. They only achieve approximate rotation invariance by rotating the feature vector before matching or unwrapping the iris ring at different initial angles. In these methods, the complexity of the method is increased, and when the rotation scale is beyond the certain scope, the error rates of these methods may substantially increase. In order to solve this problem, a new rotation invariant approach for iris feature extraction based on the non-separable wavelet is proposed in this paper. Firstly, a bank of non-separable orthogonal wavelet filters is used to capture characteristics of the iris. Secondly, a method of Markov random fields is used to capture rotation invariant iris feature. Finally, two-class kernel Fisher classifiers are adopted for classification. Experimental results on public iris databases show that the proposed approach has a low error rate and achieves true rotation invariance. © 2010.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

Background: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions. Results: We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods. Conclusions: The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.

Rigid and non-rigid point-based medical image registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Rigid and Non-rigid Point-based Medical Image Registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Can a Broader Education Narrow the Gap? Evidence on Non-Academic Features of Schooling

Empirical studies of education programs and systems, by nature, rely upon use of student outcomes that are measurable. Often, these come in the form of test scores. However, in light of growing evidence about the long-run importance of other student skills and behaviors, the time has come for a broader approach to evaluating education. This dissertation undertakes experimental, quasi-experimental, and descriptive analyses to examine social, behavioral, and health-related mechanisms of the educational process. My overarching research question is simply, which inside- and outside-the-classroom features of schools and educational interventions are most beneficial to students in the long term? Furthermore, how can we apply this evidence toward informing policy that could effectively reduce stark social, educational, and economic inequalities?

The first study of three assesses mechanisms by which the Fast Track project, a randomized intervention in the early 1990s for high-risk children in four communities (Durham, NC; Nashville, TN; rural PA; and Seattle, WA), reduced delinquency, arrests, and health and mental health service utilization in adolescence through young adulthood (ages 12-20). A decomposition of treatment effects indicates that about a third of Fast Track’s impact on later crime outcomes can be accounted for by improvements in social and self-regulation skills during childhood (ages 6-11), such as prosocial behavior, emotion regulation and problem solving. These skills proved less valuable for the prevention of mental and physical health problems.

The second study contributes new evidence on how non-instructional investments – such as increased spending on school social workers, guidance counselors, and health services – affect multiple aspects of student performance and well-being. Merging several administrative data sources spanning the 1996-2013 school years in North Carolina, I use an instrumental variables approach to estimate the extent to which local expenditure shifts affect students’ academic and behavioral outcomes. My findings indicate that exogenous increases in spending on non-instructional services not only reduce student absenteeism and disciplinary problems (important predictors of long-term outcomes) but also significantly raise student achievement, in similar magnitude to corresponding increases in instructional spending. Furthermore, subgroup analyses suggest that investments in student support personnel such as social workers, health services, and guidance counselors, in schools with concentrated low-income student populations could go a long way toward closing socioeconomic achievement gaps.

The third study examines individual pathways that lead to high school graduation or dropout. It employs a variety of machine learning techniques, including decision trees, random forests with bagging and boosting, and support vector machines, to predict student dropout using longitudinal administrative data from North Carolina. I consider a large set of predictor measures from grades three through eight including academic achievement, behavioral indicators, and background characteristics. My findings indicate that the most important predictors include eighth grade absences, math scores, and age-for-grade as well as early reading scores. Support vector classification (with a high cost parameter and low gamma parameter) predicts high school dropout with the highest overall validity in the testing dataset at 90.1 percent followed by decision trees with boosting and interaction terms at 89.5 percent.

IntelliChair: a non-intrusive sitting posture and sitting activity recognition system

Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to further analyses human sitting activity. The aim of this research is to use non-intrusive low cost pressure sensor embedded chair system to recognize a subject’s activity by using their detected postures. There are three steps for this research, the first step is to find a hardware solution for low cost sitting posture detection, second step is to find a suitable strategy of sitting posture detection and the last step is to correlate the time-ordered sitting posture sequences with sitting activity. The author initiated a prototype type of sensing system called IntelliChair for sitting posture detection. Two experiments are proceeded in order to determine the hardware architecture of IntelliChair system. The prototype looks at the sensor selection and integration of various sensor and indicates the best for a low cost, non-intrusive system. Subsequently, this research implements signal process theory to explore the frequency feature of sitting posture, for the purpose of determining a suitable sampling rate for IntelliChair system. For second and third step, ten subjects are recruited for the sitting posture data and sitting activity data collection. The former dataset is collected byasking subjects to perform certain pre-defined sitting postures on IntelliChair and it is used for posture recognition experiment. The latter dataset is collected by asking the subjects to perform their normal sitting activity routine on IntelliChair for four hours, and the dataset is used for activity modelling and recognition experiment. For the posture recognition experiment, two Support Vector Machine (SVM) based classifiers are trained (one for spine postures and the other one for leg postures), and their performance evaluated. Hidden Markov Model is utilized for sitting activity modelling and recognition in order to establish the selected sitting activities from sitting posture sequences.2. After experimenting with possible sensors, Force Sensing Resistor (FSR) is selected as the pressure sensing unit for IntelliChair. Eight FSRs are mounted on the seat and back of a chair to gather haptic (i.e., touch-based) posture information. Furthermore, the research explores the possibility of using alternative non-intrusive sensing technology (i.e. vision based Kinect Sensor from Microsoft) and find out the Kinect sensor is not reliable for sitting posture detection due to the joint drifting problem. A suitable sampling rate for IntelliChair is determined according to the experiment result which is 6 Hz. The posture classification performance shows that the SVM based classifier is robust to “familiar” subject data (accuracy is 99.8% with spine postures and 99.9% with leg postures). When dealing with “unfamiliar” subject data, the accuracy is 80.7% for spine posture classification and 42.3% for leg posture classification. The result of activity recognition achieves 41.27% accuracy among four selected activities (i.e. relax, play game, working with PC and watching video). The result of this thesis shows that different individual body characteristics and sitting habits influence both sitting posture and sitting activity recognition. In this case, it suggests that IntelliChair is suitable for individual usage but a training stage is required.

CHARACTERIZATION OF TOBACCO MOSAIC VIRUS DIRECTED REPROGRAMMING OF PHLOEM GENE EXPRESSION TO PROMOTE VIRAL PHLOEM LOADING AND SYSTMEIC MOVEMENT

Vascular phloem loading has long been recognized as an essential step in the establishment of a systemic virus infection. Yet little is known about this process and the mechanisms that control it. In this study, an interaction between the replication protein of Tobacco mosaic virus (TMV) and phloem specific auxin/indole acetic acid (Aux/IAA) transcriptional regulators was found to modulate virus phloem loading. Promoter expression studies show TMV 126/183 kDa interacting Aux/IAAs predominantly express and accumulate within the nuclei of phloem companion cells (CC). Furthermore, CC Aux/IAA nuclear localization is disrupted upon infection with an interacting virus but not during infection with a non-interacting virus. In situ analysis of virus spread shows the inability of TMV variants to disrupt Aux/IAA CC nuclear localization correlates with a reduced ability to load into the vascular tissue. Subsequent systemic movement assays also demonstrate that a virus capable of disrupting Aux/IAA localization is significantly more competitive at systemic movement than a non-interacting virus. Similarly, CC expression and over-accumulation of a degradation-resistant-interacting Aux/IAA protein was found to selectively inhibit TMV accumulation and phloem loading. Transcriptional expression studies demonstrate a role for interacting Aux/IAA proteins in the regulation of salicylic acid and jasmonic acid dependent host defense responses as well as virus specific movement factors including pectin methylesterase that are involved in regulating plasmodesmata size exclusion limits and promoting virus cell-to-cell movement. Further characterization of the phloem environment was done using two phloem specific promoters (pSUC2 and pSULTR2;2) to generate epitope-tagged polysomal-RNA complexes. Immuno-purification using the epitope tag allowed us to obtain mRNAs bound to polysomes (the translatome) specifically in phloem tissue. We found the phloem translatome is uniquely altered during TMV infection with 90% and 88% of genes down regulated in the pSUC2 and pSULTR2;2 phloem translatomes, compared to 31% of genes down regulated in the whole plant p35S translatome. Transcripts down regulated in phloem include genes involved in callose deposition at plasmodesmata, host defense responses, and RNA silencing. Combined, these findings indicate TMV reprograms gene expression within the vascular phloem as a means to enhance phloem loading and systemic spread.

Sigma models for genuinely non-geometric backgrounds

The existence of genuinely non-geometric backgrounds, i.e. ones without geometric dual, is an important question in string theory. In this paper we examine this question from a sigma model perspective. First we construct a particular class of Courant algebroids as protobialgebroids with all types of geometric and non-geometric fluxes. For such structures we apply the mathematical result that any Courant algebroid gives rise to a 3D topological sigma model of the AKSZ type and we discuss the corresponding 2D field theories. It is found that these models are always geometric, even when both 2-form and 2-vector fields are neither vanishing nor inverse of one another. Taking a further step, we suggest an extended class of 3D sigma models, whose world volume is embedded in phase space, which allow for genuinely non-geometric backgrounds. Adopting the doubled formalism such models can be related to double field theory, albeit from a world sheet perspective.

The Repellent DEET Potentiates Carbamate Effects via Insect Muscarinic Receptor Interactions: An Alternative Strategy to Control Insect Vector-Borne Diseases.

International audience

A non-enteric adenovirus A12 gastroenteritis outbreak in Rio de Janeiro, Brazil

A gastroenteritis outbreak that occurred in 2013 in a low-income community in Rio de Janeiro was investigated for the presence of enteric viruses, including species A rotavirus (RVA), norovirus (NoV), astrovirus (HAstV), bocavirus (HBoV), aichivirus (AiV), and adenovirus (HAdV). Five of nine stool samples (83%) from patients were positive for HAdV, and no other enteric viruses were detected. Polymerase chain reaction products were sequenced and subjected to phylogenetic analysis, which revealed four strains and one strain of non-enteric HAdV-A12 and HAdV-F41, respectively. The HAdV-A12 nucleotide sequences shared 100% nucleotide similarity. Viral load was assessed using a TaqMan real-time PCR assay. Stool samples that were positive for HAdV-A12 had high viral loads (mean 1.9 X 107 DNA copies/g stool). All four patients with HAdV-A12 were < 25 months of age and had symptoms of fever and diarrhoea. Evaluation of enteric virus outbreaks allows the characterisation of novel or unique diarrhoea-associated viruses in regions where RVA vaccination is routinely performed.

Anomalies immunitaires chez les enfants exposés au VIH mais non infectés.

La thérapie antirétrovirale prévient la transmission mère-enfant du VIH dans plus de 98% des cas lorsqu’administrée pendant la grossesse, le travail et au nouveau-né. L’accessibilité à la thérapie antirétrovirale dans près de 70% des 1,5 millions cas de grossesses VIH+ dans le monde mène à la naissance de plus d’un million d’enfants exposés non infectés chaque année. Le nombre d’enfants exposés non infectés est à la hausse ainsi que les préoccupations concernant leur santé. En effet, plusieurs groupes ont signalé une augmentation de la morbidité et de la mortalité chez les enfants exposés non infectés. L’analyse des données rétrospectives de 705 enfants exposés non infectés de la cohorte mère-enfant du CMIS a révélé qu’à 2 mois d’âge, les enfants nés de mères ayant une charge virale supérieure à 1,000 copies d’ARN / ml avaient une fréquence de lymphocytes B significativement plus élevés par rapport aux enfants exposés non infectés nés de mères ayant une charge virale indétectable. L’objectif de cette étude est de caractériser ces anomalies. Les lymphocytes, provenant du sang de cordon ombilical et de sang veineux obtenu à 6 et 12 mois d’âge, ont été phénotypés par cytométrie en flux à l’aide des marqueurs CD3 / CD10 / CD14 / CD16 / CD19 / CD20 / CD21 / CD27 / IgM pour les lymphocytes B et CD4 / CD8 / CD3 / CCR7 / CD45RA pour les lymphocytes T. De plus, afin d’étudier les capacités fonctionnelles des lymphocytes B CD19+, la réponse antigène-spécifique au vaccin antitétanique a été mesurée par marquage avec des tétramères fluorescents de fragment C du toxoïde tétanique. Nos travaux ont mis en évidence des différences statistiquement significatives entre les enfants exposés non-infectés (ENI) nés de mères avec une charge virale détectable comparativement à ceux nés de mères avec une charge virale indétectable. À la naissance, les enfants ENI nés de mères avec une charge virale détectable avaient significativement moins de lymphocytes B totaux, plus de lymphocytes B mémoires classiques, activés, plasmablastes et lymphocytes T CD8+ mémoires centrales. À 6 mois, ils avaient significativement plus de lymphocytes B naïfs et significativement moins de lymphocytes T CD8+ effecteurs mémoires. À 12 mois d’âge, ils avaient significativement plus de lymphocytes B et T CD8+ totaux; significativement moins de lymphocytes T CD4+ totaux et leurs lymphocytes T affichaient un profil significativement plus activé (plus de cellules mémoires). L’analyse de la réponse antigène-spécifique a révélé une fréquence plus élevé de lymphocytes B mémoires IgM+ suggérant que les enfants nés de mères avec une virémie détectable ont plus de mal à établir une mémoire immunitaire efficace face au vaccin antitétanique. Nos données suggèrent qu’il y a exposition durant le premier trimestre de grossesse à la virémie maternelle et que cette exposition impacte le système immunitaire en développement du fœtus. Les mécanismes sous-jacents causant ces anomalies doivent encore être élucidés et l’épuisement du compartiment T à la naissance et à 6 mois reste à être investigué. Dans un pays industrialisé où l’accès aux soins est facilité, ces anomalies ont des conséquences modérées mais dans des pays à faible et moyen revenu, les conséquences peuvent être beaucoup plus tragiques voir fatales.

Anomalies immunitaires chez les enfants exposés au VIH mais non infectés

La thérapie antirétrovirale prévient la transmission mère-enfant du VIH dans plus de 98% des cas lorsqu’administrée pendant la grossesse, le travail et au nouveau-né. L’accessibilité à la thérapie antirétrovirale dans près de 70% des 1,5 millions cas de grossesses VIH+ dans le monde mène à la naissance de plus d’un million d’enfants exposés non infectés chaque année. Le nombre d’enfants exposés non infectés est à la hausse ainsi que les préoccupations concernant leur santé. En effet, plusieurs groupes ont signalé une augmentation de la morbidité et de la mortalité chez les enfants exposés non infectés. L’analyse des données rétrospectives de 705 enfants exposés non infectés de la cohorte mère-enfant du CMIS a révélé qu’à 2 mois d’âge, les enfants nés de mères ayant une charge virale supérieure à 1,000 copies d’ARN / ml avaient une fréquence de lymphocytes B significativement plus élevés par rapport aux enfants exposés non infectés nés de mères ayant une charge virale indétectable. L’objectif de cette étude est de caractériser ces anomalies. Les lymphocytes, provenant du sang de cordon ombilical et de sang veineux obtenu à 6 et 12 mois d’âge, ont été phénotypés par cytométrie en flux à l’aide des marqueurs CD3 / CD10 / CD14 / CD16 / CD19 / CD20 / CD21 / CD27 / IgM pour les lymphocytes B et CD4 / CD8 / CD3 / CCR7 / CD45RA pour les lymphocytes T. De plus, afin d’étudier les capacités fonctionnelles des lymphocytes B CD19+, la réponse antigène-spécifique au vaccin antitétanique a été mesurée par marquage avec des tétramères fluorescents de fragment C du toxoïde tétanique. Nos travaux ont mis en évidence des différences statistiquement significatives entre les enfants exposés non-infectés (ENI) nés de mères avec une charge virale détectable comparativement à ceux nés de mères avec une charge virale indétectable. À la naissance, les enfants ENI nés de mères avec une charge virale détectable avaient significativement moins de lymphocytes B totaux, plus de lymphocytes B mémoires classiques, activés, plasmablastes et lymphocytes T CD8+ mémoires centrales. À 6 mois, ils avaient significativement plus de lymphocytes B naïfs et significativement moins de lymphocytes T CD8+ effecteurs mémoires. À 12 mois d’âge, ils avaient significativement plus de lymphocytes B et T CD8+ totaux; significativement moins de lymphocytes T CD4+ totaux et leurs lymphocytes T affichaient un profil significativement plus activé (plus de cellules mémoires). L’analyse de la réponse antigène-spécifique a révélé une fréquence plus élevé de lymphocytes B mémoires IgM+ suggérant que les enfants nés de mères avec une virémie détectable ont plus de mal à établir une mémoire immunitaire efficace face au vaccin antitétanique. Nos données suggèrent qu’il y a exposition durant le premier trimestre de grossesse à la virémie maternelle et que cette exposition impacte le système immunitaire en développement du fœtus. Les mécanismes sous-jacents causant ces anomalies doivent encore être élucidés et l’épuisement du compartiment T à la naissance et à 6 mois reste à être investigué. Dans un pays industrialisé où l’accès aux soins est facilité, ces anomalies ont des conséquences modérées mais dans des pays à faible et moyen revenu, les conséquences peuvent être beaucoup plus tragiques voir fatales.

Metagenomic detection of viral pathogens in Spanish honeybees: co-infection by Aphid Lethal Paralysis, Israel Acute Paralysis and Lake Sinai Viruses

The situation in Europe concerning honeybees has in recent years become increasingly aggravated with steady decline in populations and/or catastrophic winter losses. This has largely been attributed to the occurrence of a variety of known and "unknown", emerging novel diseases. Previous studies have demonstrated that colonies often can harbour more than one pathogen, making identification of etiological agents with classical methods difficult. By employing an unbiased metagenomic approach, which allows the detection of both unexpected and previously unknown infectious agents, the detection of three viruses, Aphid Lethal Paralysis Virus (ALPV), Israel Acute Paralysis Virus (IAPV), and Lake Sinai Virus (LSV), in honeybees from Spain is reported in this article. The existence of a subgroup of ALPV with the ability to infect bees was only recently reported and this is the first identification of such a strain in Europe. Similarly, LSV appear to be a still unclassified group of viruses with unclear impact on colony health and these viruses have not previously been identified outside of the United States. Furthermore, our study also reveals that these bees carried a plant virus, Turnip Ringspot Virus (TuRSV), potentially serving as important vector organisms. Taken together, these results demonstrate the new possibilities opened up by high-throughput sequencing and metagenomic analysis to study emerging new diseases in domestic and wild animal populations, including honeybees.

Epidemiology and Emergence of Schmallenberg Virus Part 2: Pathogenesis and Risk of Viral Spread

Schmallenberg virus (SBV) is a novel Orthobunyavirus causing mild clinical signs in cows and malformations in aborted and neonatal ruminants in Europe. SBV belongs to the family Bunyaviridae and is transmitted by biting midges. This new virus was identified for the first time in blood samples of cows in the city of Schmallenberg in North-Rhine Westphalia in November 2011. Since then, the virus spread to several European countries. Here, we describe the pathogenesis and the risk of viral spread in the Portuguese territory.

About stabilization of non-minimum phase systems by output feedback

This thesis work has been motivated by an internal benchmark dealing with the output regulation problem of a nonlinear non-minimum phase system in the case of full-state feedback. The system under consideration structurally suffers from finite escape time, and this condition makes the output regulation problem very hard even for very simple steady-state evolution or exosystem dynamics, such as a simple integrator. This situation leads to studying the approaches developed for controlling Non-minimum phase systems and how they affect feedback performances. Despite a lot of frequency domain results, only a few works have been proposed for describing the performance limitations in a state space system representation. In particular, in our opinion, the most relevant research thread exploits the so-called Inner-Outer Decomposition. Such decomposition allows splitting the Non-minimum phase system under consideration into a cascade of two subsystems: a minimum phase system (the outer) that contains all poles of the original system and an all-pass Non-minimum phase system (the inner) that contains all the unavoidable pathologies of the unstable zero dynamics. Such a cascade decomposition was inspiring to start working on functional observers for linear and nonlinear systems. In particular, the idea of a functional observer is to exploit only the measured signals from the system to asymptotically reconstruct a certain function of the system states, without necessarily reconstructing the whole state vector. The feature of asymptotically reconstructing a certain state functional plays an important role in the design of a feedback controller able to stabilize the Non-minimum phase system.