Duplex-PCR assay for the detection of adenovirus and respiratory syncytial virus in nasopharyngeal samples

Human adenovirus (HAdV) and human respiratory syncytial virus (HRSV) are important etiologic agents of acute respiratory infections. In this study, a duplex polymerase chain reaction (PCR) assay was developed for the simultaneous detection of HAdV and HRSV in clinical samples. Sixty previously screened nasopharyngeal aspirates were used: 20 HAdV-positive, 20 HRSV-positive and 20 double-negative controls. Eight samples were positive for both viruses. The duplex PCR assay proved to be as sensitive and specific as single-target assays and also detected the mixed infections with certainty. The identification of both viruses in a single reaction offers a reduction in both cost and laboratory diagnostic time.

Detection of ingested cocaine-filled packets: comparison of filtered back projection CT with adaptive statistical iterative reconstructed images

Purpose: To evaluate the diagnostic value and image quality of CT with filtered back projection (FBP) compared with adaptive statistical iterative reconstructed images (ASIR) in body stuffers with ingested cocaine-filled packets.Methods and Materials: Twenty-nine body stuffers (mean age 31.9 years, 3 women) suspected for ingestion of cocaine-filled packets underwent routine-dose 64-row multidetector CT with FBP (120kV, pitch 1.375, 100-300 mA and automatic tube current modulation (auto mA), rotation time 0.7sec, collimation 2.5mm), secondarily reconstructed with 30 % and 60 % ASIR. In 13 (44.83%) out of the body stuffers cocaine-filled packets were detected, confirmed by exact analysis of the faecal content including verification of the number (range 1-25). Three radiologists independently and blindly evaluated anonymous CT examinations (29 FBP-CT and 68 ASIR-CT) for the presence and number of cocaine-filled packets indicating observers' confidence, and graded them for diagnostic quality, image noise, and sharpness. Sensitivity, specificity, area under the receiver operating curve (ROC) Az and interobserver agreement between the 3 radiologists for FBP-CT and ASIR-CT were calculated.Results: The increase of the percentage of ASIR significantly diminished the objective image noise (p<0.001). Overall sensitivity and specificity for the detection of the cocaine-filled packets were 87.72% and 76.15%, respectively. The difference of ROC area Az between the different reconstruction techniques was significant (p= 0.0101), that is 0.938 for FBP-CT, 0.916 for 30 % ASIR-CT, and 0.894 for 60 % ASIR-CT.Conclusion: Despite the evident image noise reduction obtained by ASIR, the diagnostic value for detecting cocaine-filled packets decreases, depending on the applied ASIR percentage.

Development and Preliminary Evaluation of a Rapid Oligochromatographic Assay for Specific Detection of New Human Influenza A H1N1 Virus

A new oligochromatographic assay, Speed-Oligo Novel Influenza A H1N1, was designed and optimized for the specific detection of the 2009 influenza A H1N1 virus. The assay is based on a PCR method coupled to detection of PCR products by means of a dipstick device. The target sequence is a 103-bp fragment within the hemagglutinin gene. The analytical sensitivity of the new assay was measured with serial dilutions of a plasmid that contained the target sequence, and we determined that down to one copy per reaction of the plasmid was reliably detected. Diagnostic performance was assessed with 103 RNAs from suspected cases (40 positive and 63 negative results) previously analyzed with a reference real-time PCR technique. All positive cases were confirmed, and no false-positive results were detected with the new assay. No cross-reactions were observed when other viral strains or clinical samples with other respiratory viruses were tested. According to these results, this new assay has 100% sensitivity and specificity. The turnaround time for the whole procedure was 140 min. The assay may be especially useful for the specific detection of 2009 H1N1 virus in laboratories not equipped with real-time PCR instruments

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Digital beamforming and moving target indication for future spaceborne synthetic aperture radar

During the last decade the interest on space-borne Synthetic Aperture Radars (SAR) for remote sensing applications has grown as testified by the number of recent and forthcoming missions as TerraSAR-X, RADARSAT-2, COSMO-kyMed, TanDEM-X and the Spanish SEOSAR/PAZ. In this sense, this thesis proposes to study and analyze the performance of the state-of-the-Art space-borne SAR systems, with modes able to provide Moving Target Indication capabilities (MTI), i.e. moving object detection and estimation. The research will focus on the MTI processing techniques as well as the architecture and/ or configuration of the SAR instrument, setting the limitations of the current systems with MTI capabilities, and proposing efficient solutions for the future missions. Two European projects, to which the Universitat Politècnica de Catalunya provides support, are an excellent framework for the research activities suggested in this thesis. NEWA project proposes a potential European space-borne radar system with MTI capabilities in order to fulfill the upcoming European security policies. This thesis will critically review the state-of-the-Art MTI processing techniques as well as the readiness and maturity level of the developed capabilities. For each one of the techniques a performance analysis will be carried out based on the available technologies, deriving a roadmap and identifying the different technological gaps. In line with this study a simulator tool will be developed in order to validate and evaluate different MTI techniques in the basis of a flexible space-borne radar configuration. The calibration of a SAR system is mandatory for the accurate formation of the SAR images and turns to be critical in the advanced operation modes as MTI. In this sense, the SEOSAR/PAZ project proposes the study and estimation of the radiometric budget. This thesis will also focus on an exhaustive analysis of the radiometric budget considering the current calibration concepts and their possible limitations. In the framework of this project a key point will be the study of the Dual Receive Antenna (DRA) mode, which provides MTI capabilities to the mission. An additional aspect under study is the applicability of the Digital Beamforming on multichannel and/or multistatic radar platforms, which conform potential solutions for the NEWA project with the aim to fully exploit its capability jointly with MTI techniques.

Use of sodC versus ctrA for real-time polymerase chain reaction-based detection of Neisseria meningitidis in sterile body fluids

We evaluated the use of a newly described sodC-based real-time-polymerase chain reaction (RT-PCR) assay for detecting Neisseria meningitidis in normally sterile sites, such as cerebrospinal fluid and serum. The sodC-based RT-PCR assay has an advantage over ctrA for detecting nongroupable N. meningitidis isolates, which are commonly present in asymptomatic pharyngeal carriage. However, in our study, sodC-based RT-PCR was 7.5% less sensitive than ctrA. Given the public health impact of possible false-negative results due to the use of the sodC target gene alone, sodC-based RT-PCR for the diagnosis of meningococcal meningitis should be used with caution.

Optimisation of an asymmetric polymerase chain reaction assay for the amplification of single-stranded DNA from Wuchereria bancrofti for electrochemical detection

Single-stranded DNA (ssDNA) is a prerequisite for electrochemical sensor-based detection of parasite DNA and other diagnostic applications. To achieve this detection, an asymmetric polymerase chain reaction method was optimised. This method facilitates amplification of ssDNA from the human lymphatic filarial parasite Wuchereria bancrofti. This procedure produced ssDNA fragments of 188 bp in a single step when primer pairs (forward and reverse) were used at a 100:1 molar ratio in the presence of double-stranded template DNA. The ssDNA thus produced was suitable for immobilisation as probe onto the surface of an Indium tin oxide electrode and hybridisation in a system for sequence-specific electrochemical detection of W. bancrofti. The hybridisation of the ssDNA probe and target ssDNA led to considerable decreases in both the anodic and the cathodic currents of the system's redox couple compared with the unhybridised DNA and could be detected via cyclic voltammetry. This method is reproducible and avoids many of the difficulties encountered by conventional methods of filarial parasite DNA detection; thus, it has potential in xenomonitoring.

Optimisation of a quantitative polymerase chain reaction-based strategy for the detection and quantification of human herpesvirus 6 DNA in patients undergoing allogeneic haematopoietic stem cell transplantation

Human herpesvirus 6 (HHV-6) may cause severe complications after haematopoietic stem cell transplantation (HSCT). Monitoring this virus and providing precise, rapid and early diagnosis of related clinical diseases, constitute essential measures to improve outcomes. A prospective survey on the incidence and clinical features of HHV-6 infections after HSCT has not yet been conducted in Brazilian patients and the impact of this infection on HSCT outcome remains unclear. A rapid test based on real-time quantitative polymerase chain reaction (qPCR) has been optimised to screen and quantify clinical samples for HHV-6. The detection step was based on reaction with TaqMan® hydrolysis probes. A set of previously described primers and probes have been tested to evaluate efficiency, sensitivity and reproducibility. The target efficiency range was 91.4% with linearity ranging from 10-106 copies/reaction and a limit of detection of five copies/reaction or 250 copies/mL of plasma. The qPCR assay developed in the present study was simple, rapid and sensitive, allowing the detection of a wide range of HHV-6 loads. In conclusion, this test may be useful as a practical tool to help elucidate the clinical relevance of HHV-6 infection and reactivation in different scenarios and to determine the need for surveillance.

An Interval Intelligent-based Approach for Fault Detection and Modelling

Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Target organ damage: how to detect it and how to treat it?

The early detection of cardiac organ damage in clinical practice is primordial for cardiovascular risk profiling of patients with hypertension. In this respect the determination of microalbuminuria is very appealing because it increasingly appears to be the most cost-effective means to identify cardiovascular and renal complications. Considering the treatment of patients with target organ damage, blockers of the renin-angiotensin system have a key position as they are very effective in regressing left ventricular hypertrophy, lowering urinary albumin excretion and delaying the progression of nephropathy. In high-risk patients with atherosclerosis, the use of a blocker of the renin-angiotensin system is also appealing, and it appears increasingly judicious to combine such a blocker with a calcium antagonist whenever required to control blood pressure.

A new, automated, four-colour interphase FISH approach for the simultaneous detection of specific aneuploidies of diagnostic and prognostic significance in high hyperdiploid ALL

In hyperdiploid acute lymphoblastic leukaemia (ALL), the simultaneous occurrence of specific aneuploidies confers a more favourable outcome than hyperdiploidy alone. Interphase (I) FISH complements conventional cytogenetics (CC) through its sensitivity and ability to detect chromosome aberrations in non-dividing cells. To overcome the limits of manual I-FISH, we developed an automated four-colour I-FISH approach and assessed its ability to detect concurrent aneuploidies in ALL. I-FISH was performed using centromeric probes for chromosomes 4, 6, 10 and 17. Parameters established for automatic nucleus selection and signal detection were evaluated (3 controls). Cut-off values were determined (10 controls, 1000 nuclei/case). Combinations of aneuploidies were considered relevant when each aneuploidy was individually significant. Results obtained in 10 ALL patients (1500 nuclei/patient) were compared with those by CC. Various combinations of aneuploidies were identified. All clones detected by CC were observed by I-FISH. I-FISH revealed numerous additional abnormal clones, ranging between 0.1% and 31.6%, based on the large number of nuclei evaluated. Four-colour automated I-FISH permits the identification of concurrent aneuploidies of prognostic significance in hyperdiploid ALL. Large numbers of cells can be analysed rapidly by this method. Owing to its high sensitivity, the method provides a powerful tool for the detection of small abnormal clones at diagnosis and during follow up. Compared to CC, it generates a more detailed cytogenetic picture, the biological and clinical significance of which merits further evaluation. Once optimised for a given set of probes, the system can be easily adapted for other probe combinations.

Predictability of music descriptor time series and its application to cover song detection

Intuitively, music has both predictable and unpredictable components. In this work we assess this qualitative statement in a quantitative way using common time series models fitted to state-of-the-art music descriptors. These descriptors cover different musical facets and are extracted from a large collection of real audio recordings comprising a variety of musical genres. Our findings show that music descriptor time series exhibit a certain predictability not only for short time intervals, but also for mid-term and relatively long intervals. This fact is observed independently of the descriptor, musical facet and time series model we consider. Moreover, we show that our findings are not only of theoretical relevance but can also have practical impact. To this end we demonstrate that music predictability at relatively long time intervals can be exploited in a real-world application, namely the automatic identification of cover songs (i.e. different renditions or versions of the same musical piece). Importantly, this prediction strategy yields a parameter-free approach for cover song identification that is substantially faster, allows for reduced computational storage and still maintains highly competitive accuracies when compared to state-of-the-art systems.

Direct detection of a BRAF mutation in total RNA from melanoma cells using cantilever arrays.

Malignant melanoma, the deadliest form of skin cancer, is characterized by a predominant mutation in the BRAF gene. Drugs that target tumours carrying this mutation have recently entered the clinic. Accordingly, patients are routinely screened for mutations in this gene to determine whether they can benefit from this type of treatment. The current gold standard for mutation screening uses real-time polymerase chain reaction and sequencing methods. Here we show that an assay based on microcantilever arrays can detect the mutation nanomechanically without amplification in total RNA samples isolated from melanoma cells. The assay is based on a BRAF-specific oligonucleotide probe. We detected mutant BRAF at a concentration of 500 pM in a 50-fold excess of the wild-type sequence. The method was able to distinguish melanoma cells carrying the mutation from wild-type cells using as little as 20 ng µl(-1) of RNA material, without prior PCR amplification and use of labels.