Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy

To maintain the pace of development set by Moore's law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

AGV Safety Areas Obstacle Detection and Interaction with Point Cloud Derived Map

This thesis project aims to the development of an algorithm for the obstacle detection and the interaction between the safety areas of an Automated Guided Vehicles (AGV) and a Point Cloud derived map inside the context of a CAD software. The first part of the project focuses on the implementation of an algorithm for the clipping of general polygons, with which has been possible to: construct the safety areas polygon, derive the sweep of this areas along the navigation path performing a union and detect the intersections with line or polygon representing the obstacles. The second part is about the construction of a map in terms of geometric entities (lines and polygons) starting from a point cloud given by the 3D scan of the environment. The point cloud is processed using: filters, clustering algorithms and concave/convex hull derived algorithms in order to extract line and polygon entities representing obstacles. Finally, the last part aims to use the a priori knowledge of possible obstacle detections on a given segment, to predict the behavior of the AGV and use this prediction to optimize the choice of the vehicle's assigned velocity in that segment, minimizing the travel time.

Detection of metallo-'beta'-lactamases-encoding genes in environmental isolates of Aeromonas hydrophila and Aeromonas jandaei

Aims: To determine the prevalence and expression of metallo-beta-lactamases (MBL)-encoding genes in Aeromonas species recovered from natural water reservoirs in southeastern Brazil. Methods and Results: Eighty-seven Aeromonas isolates belonging to Aeromonas hydrophila (n = 41) and Aer. jandaei (n = 46) species were tested for MBL production by the combined disk test using imipenem and meropenem disks as substrates and EDTA or thioglycolic acid as inhibitors. The presence of MBL genes was investigated by PCR and sequencing using new consensus primer pairs designed in this study. The cphA gene was found in 97.6% and 100% of Aer. hydrophila and Aer. jandaei isolates, respectively, whereas the acquired MBL genes bla(IMP), bla(VIM) and bla(SPM-1) were not detected. On the other hand, production of MBL activity was detectable in 87.8% and 10.9% of the cphA-positive Aer. hydrophila and Aer. jandaei isolates respectively. Conclusions: Our results indicate that cphA seems to be intrinsic in the environmental isolates of Aer. hydrophila and Aer. jandaei in southeastern Brazil, although, based on the combined disk test, not all of them are apparently able to express the enzymatic activity. Significance and Impact of the Study: These data confirm the presence of MBL-producing Aeromonas species in natural water reservoirs. Risk of water-borne diseases owing to domestic and industrial uses of freshwater should be re-examined from the increase of bacterial resistance point of view

Ventilation behavior in trained and untrained men during incremental test: evidence of one metabolic transition point

This study aimed to describe and compare the ventilation behavior during an incremental test utilizing three mathematical models and to compare the feature of ventilation curve fitted by the best mathematical model between aerobically trained (TR) and untrained ( UT) men. Thirty five subjects underwent a treadmill test with 1 km.h(-1) increases every minute until exhaustion. Ventilation averages of 20 seconds were plotted against time and fitted by: bi-segmental regression model (2SRM); three-segmental regression model (3SRM); and growth exponential model (GEM). Residual sum of squares (RSS) and mean square error (MSE) were calculated for each model. The correlations between peak VO2 (VO2PEAK), peak speed (Speed(PEAK)), ventilatory threshold identified by the best model (VT2SRM) and the first derivative calculated for workloads below (moderate intensity) and above (heavy intensity) VT2SRM were calculated. The RSS and MSE for GEM were significantly higher (p < 0.01) than for 2SRM and 3SRM in pooled data and in UT, but no significant difference was observed among the mathematical models in TR. In the pooled data, the first derivative of moderate intensities showed significant negative correlations with VT2SRM (r = -0.58; p < 0.01) and Speed(PEAK) (r = -0.46; p < 0.05) while the first derivative of heavy intensities showed significant negative correlation with VT2SRM (r = -0.43; p < 0.05). In UT group the first derivative of moderate intensities showed significant negative correlations with VT2SRM (r = -0.65; p < 0.05) and Speed(PEAK) (r = -0.61; p < 0.05), while the first derivative of heavy intensities showed significant negative correlation with VT2SRM (r= -0.73; p < 0.01), Speed(PEAK) (r = -0.73; p < 0.01) and VO2PEAK (r = -0.61; p < 0.05) in TR group. The ventilation behavior during incremental treadmill test tends to show only one threshold. UT subjects showed a slower ventilation increase during moderate intensities while TR subjects showed a slower ventilation increase during heavy intensities.

Border detection in complex networks

One important issue implied by the finite nature of real-world networks regards the identification of their more external (border) and internal nodes. The present work proposes a formal and objective definition of these properties, founded on the recently introduced concept of node diversity. It is shown that this feature does not exhibit any relevant correlation with several well-established complex networks measurements. A methodology for the identification of the borders of complex networks is described and illustrated with respect to theoretical (geographical and knitted networks) as well as real-world networks (urban and word association networks), yielding interesting results and insights in both cases.

Feature selection environment for genomic applications

Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e. g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application. Results: The intent of this work is to provide an open-source multiplataform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs. A feature selection approach for growing genetic networks from seed genes ( targets or predictors) is also implemented in the system. Conclusion: The proposed feature selection environment allows data analysis using several algorithms, criterion functions and graphic visualization tools. Our experiments have shown the software effectiveness in two distinct types of biological problems. Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.

Cloud point extraction to avoid interferences by structured background on nickel determination in plant materials by FAAS

An analytical procedure based on microwave-assisted digestion with diluted acid and a double cloud point extraction is proposed for nickel determination in plant materials by flame atomic absorption spectrometry. Extraction in micellar medium was successfully applied for sample clean up, aiming to remove organic species containing phosphorous that caused spectral interferences by structured background attributed to the formation of PO species in the flame. Cloud point extraction of nickel complexes formed with 1,2-thiazolylazo-2-naphthol was explored for pre-concentration, with enrichment factor estimated as 30, detection limit of 5 mu g L(-1) (99.7% confidence level) and linear response up to 80 mu g L(-1). The accuracy of the procedure was evaluated by nickel determinations in reference materials and the results agreed with the certified values at the 95% confidence level.

Simultaneous preconcentration of copper, zinc, cadmium, and nickel in water samples by cloud point extraction using 4-(2-pyridylazo)-resorcinol and their determination by inductively coupled plasma optic emission spectrometry

A procedure for simultaneous separation/preconcentration of copper. zinc, cadmium, and nickel in water samples, based on cloud point extraction (CPE) as a prior step to their determination by inductively coupled plasma optic emission spectrometry (ICP-OES), has been developed. The analytes reacted with 4-(2-pyridylazo)-resorcinol (PAR) at pH 5 to form hydrophobic chelates, which were separated and preconcentrated in a surfactant-rich phase of octylphenoxypolyethoxyethanol (Triton X-I 14). The parameters affecting the extraction efficiency of the proposed method, such as sample pH, complexing agent concentration, buffer amount, surfactant concentration, temperature, kinetics of complexation reaction, and incubation time were optimized and their respective values were 5, 0.6 mmol L(-1). 0.3 mL, 0.15% (w/v), 50 degrees C, 40 min, and 10 min for 15 mL of preconcentrated solution. The method presented precision (R.S.D.) between 1.3% and 2.6% (n = 9). The concentration factors with and without dilution of the surfactant-rich phase for the analytes ranged from 9.4 to 10.1 and from 94.0 to 100.1, respectively. The limits of detection (L.O.D.) obtained for copper, zinc, cadmium, and nickel were 1.2, 1.1, 1.0. and 6.3 mu g L(-1), respectively. The accuracy of the procedure was evaluated through recovery experiments on aqueous samples. (C) 2009 Published by Elsevier B.V.

Cloud point extraction and concentration of carbaryl from natural waters

An improved procedure is proposed for determination of the pesticide carbaryl in natural waters based on double cloud point extraction. The clean up step was carried out only with Triton X-114 in alkaline medium in order to avoid the use of toxic organic solvents as well as to minimise waste generation. Cloud point preconcentration of the product of the reaction of the analyte with p-aminophenol and cetyltrimethylammonium bromide was explored to increase sensitivity and improve the detection limit. Linear response was achieved within 10 and 500 mu g L-1 and the apparent molar absorptivity was estimated as 4.6 x 105 L mol-1 cm-1. The detection limit was estimated as 7 mu g L-1 at the 99.7% confidence level and the coefficient of variation was 3.4% (n = 8). Recoveries within 91 and 99% were estimated for carbaryl spiked water samples. The results obtained for natural water samples were in agreement with those achieved by the batch of spectrophotometric procedure at the 95% confidence level. The proposed procedure is then a simple, fast, inexpensive and greener alternative for carbaryl determination.

Simultaneous flow injection preconcentration of lead and cadmium using cloud point extraction and determination by atomic absorption spectrometry

A flow injection (FI) micelle-mediated separation/preconcentration procedure for the determination of lead and cadmium by flame atomic absorption spectrometry (FAAS) has been proposed. The analytes reacted with 1-(2-thiazolylazo)-2-naphthol (TAN) to form hydrophobic chelates, which were extracted into the micelles of 0.05% (w/v) Triton X-114 in a solution buffered at pH 8.4. In the preconcentration stage, the micellar solution was continuously injected into a flow system with four mini-columns packed with cotton, glass wool. or TNT compresses for phase separation. The analytes-containing micelles were eluted from the mini-columns by a stream of 3 mol L(-1) HCl solution and the analytes were determined by FAAS. Chemical and flow variables affecting the preconcentration of the analytes were studied. For 15 mL. of preconcentrated solution, the enhancement factors varied between 15.1 and 20.3, the limits of detection were approximately 4.5 and 0.75 mu g L(-1) for lead and cadmium, respectively. For a solution containing 100 and 10 mu g L(-1) of lead and cadmium, respectively, the R.S.D. values varied from 1.6 to 3.2% (n = 7). The accuracy of the preconcentration system was evaluated by recovery measurements on spiked water samples. The method was susceptible to matrix effects, but these interferences were minimized by adding barium ions as masking agent in the sample solutions, and recoveries from spiked sample varied in the range of 95.1-107.3%. (C) 2008 Elsevier B.V. All rights reserved.

A method to lower the detection limit for optical beat signals from a frequency-dithered stabilized laser

A narrow absorption feature in an atomic or molecular gas (such as iodine or methane) is used as the frequency reference in many stabilized lasers. As part of the stabilization scheme an optical frequency dither is applied to the laser. In optical heterodyne experiments, this dither is transferred to the RF beat signal, reducing the spectral power density and hence the signal to noise ratio over that in the absence of dither. We removed the dither by mixing the raw beat signal with a dithered local oscillator signal. When the dither waveform is matched to that of the reference laser the output signal from the mixer is rendered dither free. Application of this method to a Winters iodine-stabilized helium-neon laser reduced the bandwidth of the beat signal from 6 MHz to 390 kHz, thereby lowering the detection threshold from 5 pW of laser power to 3 pW. In addition, a simple signal detection model is developed which predicts similar threshold reductions.

PCR bias toward the wild-type k-ras and p53 sequences: Implications for PCR detection of mutations and cancer diagnosis

PCR-based cancer diagnosis requires detection of rare mutations in k-ras, p53 or other genes. The assumption has been that mutant and wild-type sequences amplify with near equal efficiency, so that they are eventually present in proportions representative of the starting material. Work factor IX suggests that this assumption is invalid for one case of near-sequence identity To test the generality of this phenomenon and its relevance to cancer diagnosis, primers distant from point mutations in p53 and k-ras were used to amplify, wild-type and mutant sequences from these genes. A substantial bias against PCR amplification of mutants was observed for two regions of the p53 gene and one region of k-ras. For kras and p53, bias was observed when the wild-type and mutant sequences were amplified separately or when mixed in equal proportions before PCR. Bias was present with proofreading and non-proofreading polymerases. Mutant and wild-type segments of the factor V cystic fibrosis transmembrane conductance regulator and prothrombin genes were amplified and did not exhibit PCR bias. Therefore, the assumption of equal PCR efficiency for point mutant and wild-type sequences is invalid in several systems. Quantitative or diagnostic PCR will require validation for each locus, and enrichment strategies may be needed to optimize detection of mutants.

Post-transplant recurrent hepatitis C: immunohistochemical detection of hepatitis C virus core antigen and possible pathogenic implications

Introduction: The mechanisms by which severe cholestatic hepatitis develops after liver transplantation are not fully understood. Reports on immunohistochemical distribution of hepatitis C virus (HCV) antigens are still scarce, but recently, HCV immunostaining was suggested for early diagnosis of cholestatic forms of recurrent hepatitis C in liver grafts. After purification, Rb246 pab anticore (aa1-68) yielded specific, granular cytoplasmic staining in hepatocytes. Signal amplification through the Envision-Alkaline Phosphatase System avoided endogenous biotin and peroxidase. Aims/Methods: Rb246 was applied to liver samples of explants of 12 transplant recipients, six with the most severe form of post-transplantation recurrence, severe cholestatic hepatitis (group 1) and six with mild recurrence (group 2). We also assessed immuno-reactivity at two time-points post-transplantation (median 4 and 22 months) in both groups. HCV-core Ag was semiquantified from 0 to 3+ in each time point. Serum HCV-RNA was also measured on the different time points by branched DNA. Results: In the early post-transplant time point, one patient had a mild staining (1+), two patients had a moderate staining (2+) and the other three had no staining in group 1, compared with five patients with no staining (0) and one patient with mild staining (1+) in group 2. Late post-transplant liver samples were available in nine patients, and two out of four samples in group 1 showed a mild staining, compared with no staining patients in five patients in group 2. Strikingly, on the explant samples, HCV immunostaining was strongly positive in group 1, and mildly positive in group 2. Two out of five samples showed 3+ staining, and three samples showed 2+ staining in group 1; two out of five samples showed no staining, two samples showed 1+ staining and one sample showed 2+ staining in group 2. Serum HCV-RNA was significantly higher in group 1, on both time-points post-transplantation. HCV-core Ag was not directly associated with serum HCV-RNA on the different time points. Conclusion: These preliminary results suggest that strong HCV immunostaining in the explant is predictive of more severe disease recurrence.