Improved fault diagnosis in multivariate systems using regression-based reconstruction

Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and isolating fault conditions, which reveals that the existing work suffers from inherent limitations if complex fault senarios arise. Based on the assumption that the fault signature is deterministic while the monitored variables are stochastic, the paper introduces a regression-based reconstruction technique to overcome these limitations. The utility of the proposed fault identification and isolation method is shown using a simulation example and the analysis of experimental data from an industrial reactive distillation unit.

Application of connectivity mapping in predictive toxicology based on gene-expression similarity.

Connectivity mapping is the process of establishing connections between different biological states using gene-expression profiles or signatures. There are a number of applications but in toxicology the most pertinent is for understanding mechanisms of toxicity. In its essence the process involves comparing a query gene signature generated as a result of exposure of a biological system to a chemical to those in a database that have been previously derived. In the ideal situation the query gene-expression signature is characteristic of the event and will be matched to similar events in the database. Key criteria are therefore the means of choosing the signature to be matched and the means by which the match is made. In this article we explore these concepts with examples applicable to toxicology.

Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping.

Connectivity mapping is a recently developed technique for discovering the underlying connections between different biological states based on gene-expression similarities. The sscMap method has been shown to provide enhanced sensitivity in mapping meaningful connections leading to testable biological hypotheses and in identifying drug candidates with particular pharmacological and/or toxicological properties. Challenges remain, however, as to how to prioritise the large number of discovered connections in an unbiased manner such that the success rate of any following-up investigation can be maximised. We introduce a new concept, gene-signature perturbation, which aims to test whether an identified connection is stable enough against systematic minor changes (perturbation) to the gene-signature. We applied the perturbation method to three independent datasets obtained from the GEO database: acute myeloid leukemia (AML), cervical cancer, and breast cancer treated with letrozole. We demonstrate that the perturbation approach helps to identify meaningful biological connections which suggest the most relevant candidate drugs. In the case of AML, we found that the prevalent compounds were retinoic acids and PPAR activators. For cervical cancer, our results suggested that potential drugs are likely to involve the EGFR pathway; and with the breast cancer dataset, we identified candidates that are involved in prostaglandin inhibition. Thus the gene-signature perturbation approach added real values to the whole connectivity mapping process, allowing for increased specificity in the identification of possible therapeutic candidates.

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Rule-Based Intrusion Detection System for SCADA Networks

Increased complexity and interconnectivity of Supervisory Control and Data Acquisition (SCADA) systems in Smart Grids potentially means greater susceptibility to malicious attackers. SCADA systems with legacy communication infrastructure have inherent cyber-security vulnerabilities as these systems were originally designed with little consideration of cyber threats. In order to improve cyber-security of SCADA networks, this paper presents a rule-based Intrusion Detection System (IDS) using a Deep Packet Inspection (DPI) method, which includes signature-based and model-based approaches tailored for SCADA systems. The proposed signature-based rules can accurately detect several known suspicious or malicious attacks. In addition, model-based detection is proposed as a complementary method to detect unknown attacks. Finally, proposed intrusion detection approaches for SCADA networks are implemented and verified using a ruled based method.

Intrusion Detection System for IEC 60870-5-104 Based SCADA Networks

Increased complexity and interconnectivity of Supervisory Control and Data Acquisition (SCADA) systems in Smart Grids potentially means greater susceptibility to malicious attackers. SCADA systems with legacy communication infrastructure have inherent cyber-security vulnerabilities as these systems were originally designed with little consideration of cyber threats. In order to improve cyber-security of SCADA networks, this paper presents a rule-based Intrusion Detection System (IDS) using a Deep Packet Inspection (DPI) method, which includes signature-based and model-based approaches tailored for SCADA systems. The proposed signature-based rules can accurately detect several known suspicious or malicious attacks. In addition, model-based detection is proposed as a complementary method to detect unknown attacks. Finally, proposed intrusion detection approaches for SCADA networks are implemented and verified via Snort rules.

Modelling the brightness increase signature due to asteroid collisions

We have developed a model to predict the post-collision brightness increase of sub-catastrophic collisions between asteroids and to evaluate the likelihood of a survey detecting these events. It is based on the cratering scaling laws of Holsapple and Housen (2007) and models the ejecta expansion following an impact as occurring in discrete shells each with their own velocity. We estimate the magnitude change between a series of target/impactor pairs, as- suming it is given by the increase in reflecting surface area within a photometric aperture due to the resulting ejecta. As expected the photometric signal increases with impactor size, but we find also that the photometric signature decreases rapidly as the target aster- oid diameter increases, due to gravitational fallback. We have used the model results to make an estimate of the impactor diameter for the (596) Scheila collision of D = 49 − 65m depending on the impactor taxonomy, which is broadly consistent with previous estimates. We varied both the strength regime (highly porous and sand/cohesive soil) and the tax- onomic type (S-, C- and D-type) to examine the effect on the magnitude change, finding that it is significant at early stages but has only a small effect on the overall lifetime of the photometric signal. Combining the results of this model with the collision frequency estimates of Bottke et al. (2005), we find that low-cadence surveys of ∼one visit per luna- tion will be insensitive to impacts on asteroids with D < 20km if relying on photometric detections.

The signature of fine scale local adaptation in Atlantic salmon revealed from common garden experiments in nature

Understanding the extent, scale and genetic basis of local adaptation is important for conservation and management. Its relevance in salmonids at microgeographic scales, where dispersal (and hence potential gene flow) can be substantial, has however been questioned. Here we compare the fitness of communally-reared offspring of local and foreign Atlantic salmon Salmo salar from adjacent Irish rivers and reciprocal F1 hybrid crosses between them, in the wild ‘home’ environment of the local population. Experimental groups did not differ in wild smolt output but a catastrophic flood event may have limited our ability to detect freshwater performance differences, which were evident in a previous study. Foreign parr exhibited higher, and hybrids intermediate, emigration rates from the natal stream relative to local parr, consistent with genetically-based behavioural differences. Adult return rates were lower for the foreign compared to the local group. Overall lifetime success of foreigners and hybrids relative to locals was estimated at 31% and 40% (mean of both hybrid groups), respectively. The results imply a genetic basis to fitness differences among populations separated by only 50km, driven largely by variation in smolt to adult return rates. Hence even if supplementary stocking programs obtain broodstock from neighbouring rivers, the risk of extrinsic outbreeding depression may be high.

Electron-impact excitation of Xe 26+ and its resultant spectral signature

We have carried out a 129 close-coupling level Dirac-Coulomb R-matrix calculation for the electron-impact excitation of Ni-like Xe. We have utilized this data to generate the spectral signature of Xe26+ in terms of feature photon-emissivity coefficients (F-PεCs). We have compared these F-PεCs with those generated using semi-relativistic plane-wave Born excitation data, which forms the heavy species baseline for the Atomic Data and Analysis Structure (ADAS), We find that the Born-based F-PεCs give a reasonable qualitative description of the spectral signature but that, quantitatively, the R-matrix-based F-PεCs differ by up to a factor of 2. The spectral signature of heavy species is key to diagnosing hot plasmas such as will be found in the International Thermonuclear Experimental Reactor.

Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids

This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.

A novel gene expression signature in peripheral blood mononuclear cells for early detection of colorectal cancer.

BACKGROUND: Early detection and treatment of colorectal adenomatous polyps (AP) and colorectal cancer (CRC) is associated with decreased mortality for CRC. However, accurate, non-invasive and compliant tests to screen for AP and early stages of CRC are not yet available. A blood-based screening test is highly attractive due to limited invasiveness and high acceptance rate among patients. AIM: To demonstrate whether gene expression signatures in the peripheral blood mononuclear cells (PBMC) were able to detect the presence of AP and early stages CRC. METHODS: A total of 85 PBMC samples derived from colonoscopy-verified subjects without lesion (controls) (n = 41), with AP (n = 21) or with CRC (n = 23) were used as training sets. A 42-gene panel for CRC and AP discrimination, including genes identified by Digital Gene Expression-tag profiling of PBMC, and genes previously characterised and reported in the literature, was validated on the training set by qPCR. Logistic regression analysis followed by bootstrap validation determined CRC- and AP-specific classifiers, which discriminate patients with CRC and AP from controls. RESULTS: The CRC and AP classifiers were able to detect CRC with a sensitivity of 78% and AP with a sensitivity of 46% respectively. Both classifiers had a specificity of 92% with very low false-positive detection when applied on subjects with inflammatory bowel disease (n = 23) or tumours other than CRC (n = 14). CONCLUSION: This pilot study demonstrates the potential of developing a minimally invasive, accurate test to screen patients at average risk for colorectal cancer, based on gene expression analysis of peripheral blood mononuclear cells obtained from a simple blood sample.

An atlas of human gene expression from massively parallel signature sequencing (MPSS).

We have used massively parallel signature sequencing (MPSS) to sample the transcriptomes of 32 normal human tissues to an unprecedented depth, thus documenting the patterns of expression of almost 20,000 genes with high sensitivity and specificity. The data confirm the widely held belief that differences in gene expression between cell and tissue types are largely determined by transcripts derived from a limited number of tissue-specific genes, rather than by combinations of more promiscuously expressed genes. Expression of a little more than half of all known human genes seems to account for both the common requirements and the specific functions of the tissues sampled. A classification of tissues based on patterns of gene expression largely reproduces classifications based on anatomical and biochemical properties. The unbiased sampling of the human transcriptome achieved by MPSS supports the idea that most human genes have been mapped, if not functionally characterized. This data set should prove useful for the identification of tissue-specific genes, for the study of global changes induced by pathological conditions, and for the definition of a minimal set of genes necessary for basic cell maintenance. The data are available on the Web at http://mpss.licr.org and http://sgb.lynxgen.com.

Continental margin architecture : the palynological signature of glacioeustasy /

Palynomorphs from two siliciclastic margins were examined to gain insights into continental margin architecture. Sea level change is thought to be one of the primary controls on continental margin architecture. Because Late Neogene glacioeustasy has been well studied marine sediments deposited during the Late Neogene were examined to test this concept. Cores from the outer shelf and upper slope were taken from the New Jersey margin in the western North Atlantic Ocean and from the Sunda Shelf margin in the South China Sea. Continental margin architecture is often described in a sequence stratigraphic context. One of the main goals of both coring projects was to test the theoretical sequence stratigraphic models developed by a research group at Exxon (e.g. Wilgus et al., 1988). Palynomorphs provide one of the few methods of inferring continental margin architecture in monotonous, siliciclastic marine sediments where calcareous sediments are rare (e.g. New Jersey margin). In this study theoretical models of the palynological signature expected in sediment packages deposited during the various increments of a glacioeustatic cycle were designed. These models were based on the modem palynomorph trends and taphonomic factors thought to control palynomorph distribution. Both terrestrial (pollen and spores) and marine (dinocysts) palynomorphs were examined. The palynological model was then compared with New Jersey margin and Sunda Shelf margin sediments. The predicted palynological trends provided a means of identifying a complete cycle of glacioeustatic change (Oxygen Isotope Stage 5e to present) in the uppermost 80 meters of sediment on the slope at the New Jersey margin. Sediment availability, not sea meters of sediment on the slope at the New Jersey margin. Sediment availability, not sea level change, is thought to be the major factor controlling margin architecture during the late Pleistocene here at the upper slope. This is likely a function of the glacial scouring of the continents which significantly increases sediment availability during glacial stages. The subaerially exposed continental shelf during the lowstand periods would have been subject to significant amounts of erosion fi:om the proglacial rivers flowing fi-om the southern regions of the ice-sheet. The slope site is non-depositional today and was also non-depositional during the last full interglacial period. The palynomorph data obtained fi-om the South China Sea indicate that the major difference between the New Jersey Margin sites and the Sunda Shelf margin sites is the variation in sediment supply and the rate of sediment accumulation. There was significantly less variation in sediment supply between glacial and interglacial periods and less overall sediment accumulation at the Sunda Shelf margin. The data presented here indicate that under certain conditions the theoretical palynological models allow the identification of individual sequence stratigraphic units and therefore, allow inferences regarding continental margin architecture. The major condition required in this approach is that a complete and reliable database of the contemporaneous palynomorphs be available.

Certificate to Announce Thomas Henderson Captain of a Company of the Northern Regiment

Certificate (vellum) to announce that Thomas Henderson is to be Captain of a company of the Northern Regiment of Local Militia in the County of Northumberland, England. The document is stained, faded and contains holes. The text is slightly affected. The signature on this document has been cut away, Sept. 4, 1813.

An efficient scheme for the evaluation of coding performance of high speed links incorporating joint error behaviour based on Monte Carlo Method

While channel coding is a standard method of improving a system’s energy efficiency in digital communications, its practice does not extend to high-speed links. Increasing demands in network speeds are placing a large burden on the energy efficiency of high-speed links and render the benefit of channel coding for these systems a timely subject. The low error rates of interest and the presence of residual intersymbol interference (ISI) caused by hardware constraints impede the analysis and simulation of coded high-speed links. Focusing on the residual ISI and combined noise as the dominant error mechanisms, this paper analyses error correlation through concepts of error region, channel signature, and correlation distance. This framework provides a deeper insight into joint error behaviours in high-speed links, extends the range of statistical simulation for coded high-speed links, and provides a case against the use of biased Monte Carlo methods in this setting