No description available.

Polychlorinated biphenyls (PCBs) are toxic and persistent chemicals that have been largely dispersed into the environment. The biological and abiotic transformations of PCBs often generate hydroxylated derivatives, which have been detected in a variety of environmental samples, including animal tissues and feces, water, and sediments. Because of their toxicity and widespread dispersion in the environment, hydroxylated PCBs (OH-PCBs) are today increasingly considered as a new class of environmental contaminants. Although PCBs are known to be susceptible to microbial degradation under both aerobic and anaerobic conditions, bacterial degradation of OH-PCBs has received little attention. The overall objective of this study is therefore to evaluate the transformation of mono-hydroxylated PCBs by the well characterized aerobic PCB-degrading bacterium, Burkholderia xenovorans LB400. In order to achieve our overall objective, a series of model mono-hydroxylated PCBs have been selected and they are used to determine the toxicity of hydroxylated congeners toward the bacterium B. xenovorans LB400. The biodegradation kinetics and metabolic pathways of the selected OH-PCBs by B. xenovorans LB400 are then characterized using GC/MS. To understand further the molecular basis of the metabolism of OH-PCBs by B. xenovorans LB400, gene expression analyses are conducted using reverse-transcription real-time (quantitative) polymerase chain reaction (RT-qPCR) and microarray technology. More formally, the specific aims of the proposed research are stated as follows: (1) To evaluate the toxicity of selected mono-hydroxylated derivatives of lesser-chlorinated PCBs toward the bacterium B. xenovorans LB400. (2) To assess the degradation of the selected OH-PCBs by B. xenovorans LB400. (3) To gain further understanding of the molecular bases of the metabolism of the selected OH-PCBs by B. xenovorans LB400. Three hydroxylated derivatives of 4-chlorobiphenyl and 2,5-dichlorobiphenyl, including 2'-hydroxy-, 3'-hydroxy-, and 4'-hydroxy- congeners, were significantly transformed by Burkholderia xenovorans LB400 when the bacterium was growing on biphenyl (biphenyl pathway-inducing conditions). On the contrary, only 2'-OH-4-chlorobiphenyl and 2'-OH-2,5-dichlorobiphenyl were transformed by the bacterium growing on succinate (conditions non-inductive of the biphenyl pathway). Gene expression analyses showed that only exposure to 2'-OH-4-chlorobiphenyl and 2'-OH-2,5-dichlorobiphneyl resulted in induction of key genes of the biphenyl pathway, when cells grown on succinate. These observations suggest that 2'OH-PCBs were capable of inducing the genes of biphenyl pathway. These results provide the first evidence that bacteria are able to cometabolize PCB derivatives hydroxylated on the non-chlorinated ring. Genome-wide transcriptional analyses using microarrays showed that 134 genes were differentially expressed in cells exposed to biphenyl, 2,5-dichlorobiphenyl, and 2'-OH-2,5-dichlorobiphneyl as compared to non-exposed cells. A significant proportion of differentially expressed genes were simultaneously expressed or down regulated by exposure to the three target compounds i.e., biphenyl, 2,5-DCB, and 2'-OH-2,5-DCB, which suggests that these structurally similar compounds induce similar transcriptional response of B.xenovorans LB400. Results of this study may have important implications for the natural attenuation of PCBs and fate of OH-PCBs in the environment. The recalcitrance to biodegradation and the high toxicity of some OH-PCBs may provide a partial explanation for the persistence of PCBs in the environment. / Civil Engineering

No description available.

Yes / Cyber security has become a matter of a global interest, and several attacks target industrial companies and governmental organizations. The advanced persistent threats (APTs) have emerged as a new and complex version of multi-stage attacks (MSAs), targeting selected companies and organizations. Current APT detection systems focus on raising the detection alerts rather than predicting APTs. Forecasting the APT stages not only reveals the APT life cycle in its early stages but also helps to understand the attacker's strategies and aims. This paper proposes a novel intrusion detection system for APT detection and prediction. This system undergoes two main phases; the first one achieves the attack scenario reconstruction. This phase has a correlation framework to link the elementary alerts that belong to the same APT campaign. The correlation is based on matching the attributes of the elementary alerts that are generated over a configurable time window. The second phase of the proposed system is the attack decoding. This phase utilizes the hidden Markov model (HMM) to determine the most likely sequence of APT stages for a given sequence of correlated alerts. Moreover, a prediction algorithm is developed to predict the next step of the APT campaign after computing the probability of each APT stage to be the next step of the attacker. The proposed approach estimates the sequence of APT stages with a prediction accuracy of at least 91.80%. In addition, it predicts the next step of the APT campaign with an accuracy of 66.50%, 92.70%, and 100% based on two, three, and four correlated alerts, respectively. / The Gulf Science, Innovation and Knowledge Economy Programme of the U.K. Government under UK-Gulf Institutional Link Grant IL 279339985 and in part by the Engineering and Physical Sciences Research Council (EPSRC), U.K., under Grant EP/R006385/1.

Yes / As one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale. APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss. The accurate detection and prediction of APT is an ongoing challenge. This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way. The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. MLAPT is experimentally evaluated and the presented system is able to predict APT in its early steps with a prediction accuracy of 84.8%.

The author proposes a novel functional regression method for parameter estimation and adaptive control in this dissertation. In the functional regression method, the regressors and a signal which contains the information of the unknown parameters are either determined from raw measurements or calculated as the functions of the measurements. The novel feature of the method is that the algorithm maps the regressors to the functionals which are represented in terms of customized test functions. The functionals are updated continuously by the evolution laws, and only an infinite number of variables are needed to compute the functionals. These functionals are organized as the entries of a matrix, and the parameter estimates are obtained using either the generalized inverse method or the transpose method. It is shown that the schemes of some conventional adaptive methods are recaptured if certain test function designs are employed. It is proved that the functional regression method guarantees asymptotic convergence of the parameter estimation error to the origin, if the system is persistently excited. More importantly, in contrast to the conventional schemes, the parameter estimation error may be expected to converge to the origin even when the system is not persistently excited. The novel adaptive method are also applied to the Model Reference Adaptive Controller (MRAC) and adaptive observer. It is shown that the functional regression method ensures asymptotic stability of the closed loop systems. Additionally, the studies indicate that the transient performance of the closed loop systems is improved compared to that of the schemes using the conventional adaptive methods. Besides, it is possible to analyze the transient responses a priori of the closed loop systems with the functional regression method. The simulations verify the theoretical analyses and exhibit the improved transient and steady state performances of the closed loop systems. / Ph. D.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第25087号 / 理博第4994号 / 新制||理||1713(附属図書館) / 京都大学大学院理学研究科数学・数理解析専攻 / (主査)教授 平岡 裕章, 教授 COLLINSBenoit Vincent Pierre, 教授 坂上 貴之 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(理学) / 甲第25104号 / 理博第5011号 / 新制||理||1715(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)准教授 森前 智行, 教授 高橋 義朗, 准教授 戸塚 圭介 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Mudanças de padrão são variações nos dados da série temporal. Tais mudanças podem representar transições que ocorrem entre estados. A análise de dados topológicos (TDA) permite uma caracterização de dados de séries temporais obtidos a partir de sistemas dinâmicos complexos. Neste trabalho, apresentamos uma técnica de detecção de mudança de padrão baseada em TDA. Especificamente, a partir de uma determinada série temporal, dividimos o sinal em janelas deslizantes sem sobreposição e para cada janela calculamos a homologia persistente, ou seja, o barcode associado. A partir desse barcode, o intervalo médio e a entropia persistente são calculados e plotados em relação à duração do sinal. Resultados experimentais em conjuntos de dados reais e artificiais mostram bons resultados do método proposto: 1) Detecta mudança de padrões identificando a mudança no intervalo médio e calculando a entropia persistente para os barcodes gerados pelo conjunto de dados de entrada. 2) Mostra qualitativamente quão sensível é a escolha do método de filtragem para evidenciar características topológicas do espaço original sob exame. Isto é conseguido usando duas filtragens: uma filtragem métrica e uma do tipo lower-star. 3) Variando o tamanho da janela, o método pode caracterizar a presença de estruturas locais do conjunto de dados, como o período de convulsão nos sinais EEG. 4) O método proposto é capaz de caracterizar a complexidade pela medida de entropia persistente dos barcodes, uma medida de entropia baseada na definição de entropia de Shannon. Além disso, neste trabalho, mostramos a evidência de mudanças de complexidade associadas a um período de convulsão de um sinal de EEG / Pattern changings are variations in time series data. Such changes may represent transitions that occur between states. Topological data analysis (TDA) allows characterization of time-series data obtained from complex dynamical systems. In this work, we present a pattern changing detection technique based on TDA. Specifically, starting from a given time series, we divide the signal in slicing windows with no overlapping and for each window we calculate the persistent homology, i.e., the associated barcode. From the barcode the average interval size and persistent entropy are calculated and plotted against the signal duration. Experimental results on artificial and real data sets show good results of the proposed method: 1) It detects pattern changing by identifying the change in the average interval size and calculated persistent entropy for the barcodes generated by the input data set. 2) It shows qualitatively how sensible the choice of filtration method is to evidence topological features of the original space under examination. This is accomplished by using two filtrations: a metric and a lower-star filtration. 3) By varying the slice window size, the method can characterize the presence of local structures of the data set such as the seizure period in EEG signals. 4) The proposed method can characterize complexity by the measure persistent entropy for barcodes, an entropy measure based on Shannon´s entropy definition. Moreover, in this work, we show the evidence of complexity changes associated with a seizure period of an EEG signal

Zielsetzung: Der Nachweis eines Apallischen Syndroms (APS) ist trotz der diagnostischen Kriterien der Multi Society Task Force on persistent vegetative state (MSTF) selbst für erfahrene Kliniker eine diagnostische Herausforderung. Das Ziel der vorliegenden Arbeit ist, inwieweit etablierte neurologische Untersuchungstechniken die Anwendung der MSTF-Kriterien vereinfachen und so zur Diagnose des APS beitragen. Design: Prospektive diagnostische Studie Patienten: Von initial 24 Patienten mit der Differentialdiagnose eines APS wurden 16 Patienten endgültig in die Studie eingeschlossen und einer prospektiven klinischen Untersuchung unterzogen. Das Studienprotokoll umfasste die Untersuchung der spontanen Motorik sowie Reiz korrelierter motorischen Reaktionen, der Primitivreflexe, Habituation und der Okulomotorik. Ergebnisse: Die Diagnosekriterien der MSTF waren bei allen Patienten nachweisbar, die in die Studie eingeschlossenen wurden. Darüber hinaus wurde durch die Studie weitere, ebenfalls häufig auftretende klinischen Symptome identifiziert, die als Markersymptom für eine APS bewertet wurden: spontane Automatismen (N=12), periodisch-alternierende Augenbewegungen (N=12), startle Reaktion nach externer Reizung (N=10) und Spastik (N=9). Klinische Symptome, die erhaltene Bewusstseinsleistung voraussetzen wie reflektorische Sakkaden, Habituation, der optokinetische Nystagmus und Augenfolgebewegungen oder Symptome, die auf eine schwere Hirnstammschädigung hinweisen wie eine Dezerebrationshaltung, wurden als Ausschlusskriterien eines APS vorgeschlagen. Zusammenfassung: Die Erweiterung der MSTF-Diagnosekriterien um obligatorische und unterstützende Schlüsselsymptome sowie klar definierte Ausschlusskriterien erleichtert die klinische Differentialdiagnose des APS und führt zu einer größeren Diagnosesicherheit des Syndroms. / Objective: Although the Multi Society Task Force (MSTF) on persistent vegetative state (PVS) published diagnostic criteria ten years ago, differentiation of PVS from similar syndromes remains a diagnostic challenge. The aim of our study was the prospective identification of clinically assessable symptoms supplementary to the MSTF criteria which supports or rejects the diagnosis of a PVS and to reevaluate the parameters after 30 month. Design: Prospective diagnostic study Setting: The 90-bed department of Neurology of the University hospital of Berlin. Patients and participants: Out of 24 screened patients with the differential diagnosis PVS, 16 patients were finally included to the study and prospectively assessed by a clinical examination, comprising spontaneous and reflexive motor activities, primitive reflexes, habituation and eye movements. Measurements and results: Mandatory symptoms of the MSTF were found in all 16 patients. In addition, clinical features like spontaneous automatisms (n=12), periodic alternating gaze deviation (n=12), startle reaction to external stimuli (n=10), and spastic muscular tone (n=9) were found frequently and considered supportive for the diagnosis. In contrast to previous observations, periodic alternating eye movements and increased muscular tone were found more frequently in our patients. Symptoms linked to a preserved consciousness like reflexive visually guided saccades, habituation, an optokinetic nystagmus and eye tracking or symptoms indicating a severe functional impairment of the brainstem like a decerebrated posture were proposed as excluding PVS. Conclusion: The application of mandatory and supportive symptoms lead to a further improvement of diagnostic certainty in PVS, particular in patient presenting exceptional clinical phenomena. Clearly defined exclusive criteria prevent from misdiagnosis.