Speech perception and cortical auditory evoked potentials in cochlear implant users with auditory neuropathy spectrum disorders

Objective: To characterize the PI component of long latency auditory evoked potentials (LLAEPs) in cochlear implant users with auditory neuropathy spectrum disorder (ANSD) and determine firstly whether they correlate with speech perception performance and secondly whether they correlate with other variables related to cochlear implant use. Methods: This study was conducted at the Center for Audiological Research at the University of Sao Paulo. The sample included 14 pediatric (4-11 years of age) cochlear implant users with ANSD, of both sexes, with profound prelingual hearing loss. Patients with hypoplasia or agenesis of the auditory nerve were excluded from the study. LLAEPs produced in response to speech stimuli were recorded using a Smart EP USB Jr. system. The subjects' speech perception was evaluated using tests 5 and 6 of the Glendonald Auditory Screening Procedure (GASP). Results: The P-1 component was detected in 12/14 (85.7%) children with ANSD. Latency of the P-1 component correlated with duration of sensorial hearing deprivation (*p = 0.007, r = 0.7278), but not with duration of cochlear implant use. An analysis of groups assigned according to GASP performance (k-means clustering) revealed that aspects of prior central auditory system development reflected in the P-1 component are related to behavioral auditory skills. Conclusions: In children with ANSD using cochlear implants, the P-1 component can serve as a marker of central auditory cortical development and a predictor of the implanted child's speech perception performance. (c) 2012 Elsevier Ireland Ltd. All rights reserved.

Dynamic texture segmentation based on deterministic partially self-avoiding walks

Recently there has been a considerable interest in dynamic textures due to the explosive growth of multimedia databases. In addition, dynamic texture appears in a wide range of videos, which makes it very important in applications concerning to model physical phenomena. Thus, dynamic textures have emerged as a new field of investigation that extends the static or spatial textures to the spatio-temporal domain. In this paper, we propose a novel approach for dynamic texture segmentation based on automata theory and k-means algorithm. In this approach, a feature vector is extracted for each pixel by applying deterministic partially self-avoiding walks on three orthogonal planes of the video. Then, these feature vectors are clustered by the well-known k-means algorithm. Although the k-means algorithm has shown interesting results, it only ensures its convergence to a local minimum, which affects the final result of segmentation. In order to overcome this drawback, we compare six methods of initialization of the k-means. The experimental results have demonstrated the effectiveness of our proposed approach compared to the state-of-the-art segmentation methods.

Segmentazione automatica di regioni in immagini istologiche

L’analisi istologica riveste un ruolo fondamentale per la pianificazione di eventuali terapie mediche o chirurgiche, fornendo diagnosi sulla base dell’analisi di tessuti, o cellule, prelevati con biopsie o durante operazioni. Se fino ad alcuni anni fa l’analisi veniva fatta direttamente al microscopio, la sempre maggiore diffusione di fotocamere digitali accoppiate consente di operare anche su immagini digitali. Il presente lavoro di tesi ha riguardato lo studio e l’implementazione di un opportuno metodo di segmentazione automatica di immagini istopatologiche, avendo come riferimento esclusivamente ciò che viene visivamente percepito dall’operatore. L’obiettivo è stato quello di costituire uno strumento software semplice da utilizzare ed in grado di assistere l’istopatologo nell’identificazione di regioni percettivamente simili, presenti all’interno dell’immagine istologica, al fine di considerarle per una successiva analisi, oppure di escluderle. Il metodo sviluppato permette di analizzare una ampia varietà di immagini istologiche e di classificarne le regioni esclusivamente in base alla percezione visiva e senza sfruttare alcuna conoscenza a priori riguardante il tessuto biologico analizzato. Nella Tesi viene spiegato il procedimento logico seguito per la progettazione e la realizzazione dell’algoritmo, che ha portato all’adozione dello spazio colore Lab come dominio su cu cui calcolare gli istogrammi. Inoltre, si descrive come un metodo di classificazione non supervisionata utilizzi questi istogrammi per pervenire alla segmentazione delle immagini in classi corrispondenti alla percezione visiva dell’utente. Al fine di valutare l’efficacia dell’algoritmo è stato messo a punto un protocollo ed un sistema di validazione, che ha coinvolto 7 utenti, basato su un data set di 39 immagini, che comprendono una ampia varietà di tessuti biologici acquisiti da diversi dispositivi e a diversi ingrandimenti. Gli esperimenti confermano l’efficacia dell’algoritmo nella maggior parte dei casi, mettendo altresì in evidenza quelle tipologie di immagini in cui le prestazioni risultano non pienamente soddisfacenti.

Analisi di dati citofluorimetrici con tecniche di Data Mining

Il citofluorimetro è uno strumento impiegato in biologia genetica per analizzare dei campioni cellulari: esso, analizza individualmente le cellule contenute in un campione ed estrae, per ciascuna cellula, una serie di proprietà fisiche, feature, che la descrivono. L’obiettivo di questo lavoro è mettere a punto una metodologia integrata che utilizzi tali informazioni modellando, automatizzando ed estendendo alcune procedure che vengono eseguite oggi manualmente dagli esperti del dominio nell’analisi di alcuni parametri dell’eiaculato. Questo richiede lo sviluppo di tecniche biochimiche per la marcatura delle cellule e tecniche informatiche per analizzare il dato. Il primo passo prevede la realizzazione di un classificatore che, sulla base delle feature delle cellule, classifichi e quindi consenta di isolare le cellule di interesse per un particolare esame. Il secondo prevede l'analisi delle cellule di interesse, estraendo delle feature aggregate che possono essere indicatrici di certe patologie. Il requisito è la generazione di un report esplicativo che illustri, nella maniera più opportuna, le conclusioni raggiunte e che possa fungere da sistema di supporto alle decisioni del medico/biologo.

Untersuchungen von Aerosolpartikeln und Wolkenresidualpartikeln mittels Einzelpartikel-Massenspektrometrie und optischen Methoden

Atmosphärische Partikel beeinflussen das Klima durch Prozesse wie Streuung, Reflexion und Absorption. Zusätzlich fungiert ein Teil der Aerosolpartikel als Wolkenkondensationskeime (CCN), die sich auf die optischen Eigenschaften sowie die Rückstreukraft der Wolken und folglich den Strahlungshaushalt auswirken. Ob ein Aerosolpartikel Eigenschaften eines Wolkenkondensationskeims aufweist, ist vor allem von der Partikelgröße sowie der chemischen Zusammensetzung abhängig. Daher wurde die Methode der Einzelpartikel-Laserablations-Massenspektrometrie angewandt, die eine größenaufgelöste chemische Analyse von Einzelpartikeln erlaubt und zum Verständnis der ablaufenden multiphasenchemischen Prozesse innerhalb der Wolke beitragen soll.rnIm Rahmen dieser Arbeit wurde zur Charakterisierung von atmosphärischem Aerosol sowie von Wolkenresidualpartikel das Einzelpartikel-Massenspektrometer ALABAMA (Aircraft-based Laser Ablation Aerosol Mass Spectrometer) verwendet. Zusätzlich wurde zur Analyse der Partikelgröße sowie der Anzahlkonzentration ein optischer Partikelzähler betrieben. rnZur Bestimmung einer geeigneten Auswertemethode, die die Einzelpartikelmassenspektren automatisch in Gruppen ähnlich aussehender Spektren sortieren soll, wurden die beiden Algorithmen k-means und fuzzy c-means auf ihrer Richtigkeit überprüft. Es stellte sich heraus, dass beide Algorithmen keine fehlerfreien Ergebnisse lieferten, was u.a. von den Startbedingungen abhängig ist. Der fuzzy c-means lieferte jedoch zuverlässigere Ergebnisse. Darüber hinaus wurden die Massenspektren anhand auftretender charakteristischer chemischer Merkmale (Nitrat, Sulfat, Metalle) analysiert.rnIm Herbst 2010 fand die Feldkampagne HCCT (Hill Cap Cloud Thuringia) im Thüringer Wald statt, bei der die Veränderung von Aerosolpartikeln beim Passieren einer orographischen Wolke sowie ablaufende Prozesse innerhalb der Wolke untersucht wurden. Ein Vergleich der chemischen Zusammensetzung von Hintergrundaerosol und Wolkenresidualpartikeln zeigte, dass die relativen Anteile von Massenspektren der Partikeltypen Ruß und Amine für Wolkenresidualpartikel erhöht waren. Dies lässt sich durch eine gute CCN-Aktivität der intern gemischten Rußpartikel mit Nitrat und Sulfat bzw. auf einen begünstigten Übergang der Aminverbindungen aus der Gas- in die Partikelphase bei hohen relativen Luftfeuchten und tiefen Temperaturen erklären. Darüber hinaus stellte sich heraus, dass bereits mehr als 99% der Partikel des Hintergrundaerosols intern mit Nitrat und/oder Sulfat gemischt waren. Eine detaillierte Analyse des Mischungszustands der Aerosolpartikel zeigte, dass sich sowohl der Nitratgehalt als auch der Sulfatgehalt der Partikel beim Passieren der Wolke erhöhte. rn

Studio e sviluppo di un metodo di detezione della camera atriale in ecografia intracardiaca

Il lavoro di tesi si è svolto in collaborazione con il laboratorio di elettrofisiologia, Unità Operativa di Cardiologia, Dipartimento Cardiovascolare, dell’ospedale “S. Maria delle Croci” di Ravenna, Azienda Unità Sanitaria Locale della Romagna, ed ha come obiettivo lo sviluppo di un metodo per l’individuazione dell’atrio sinistro in sequenze di immagini ecografiche intracardiache acquisite durante procedure di ablazione cardiaca transcatetere per il trattamento della fibrillazione atriale. La localizzazione della parete posteriore dell'atrio sinistro in immagini ecocardiografiche intracardiache risulta fondamentale qualora si voglia monitorare la posizione dell'esofago rispetto alla parete stessa per ridurre il rischio di formazione della fistola atrio esofagea. Le immagini derivanti da ecografia intracardiaca sono state acquisite durante la procedura di ablazione cardiaca ed esportate direttamente dall’ecografo in formato Audio Video Interleave (AVI). L’estrazione dei singoli frames è stata eseguita implementando un apposito programma in Matlab, ottenendo così il set di dati su cui implementare il metodo di individuazione della parete atriale. A causa dell’eccessivo rumore presente in alcuni set di dati all’interno della camera atriale, sono stati sviluppati due differenti metodi per il tracciamento automatico del contorno della parete dell’atrio sinistro. Il primo, utilizzato per le immagini più “pulite”, si basa sull’utilizzo del modello Chan-Vese, un metodo di segmentazione level-set region-based, mentre il secondo, efficace in presenza di rumore, sfrutta il metodo di clustering K-means. Entrambi i metodi prevedono l’individuazione automatica dell’atrio, senza che il clinico fornisca informazioni in merito alla posizione dello stesso, e l’utilizzo di operatori morfologici per l’eliminazione di regioni spurie. I risultati così ottenuti sono stati valutati qualitativamente, sovrapponendo il contorno individuato all'immagine ecografica e valutando la bontà del tracciamento. Inoltre per due set di dati, segmentati con i due diversi metodi, è stata eseguita una valutazione quantitativa confrontatoli con il risultato del tracciamento manuale eseguito dal clinico.

Punti impropri nella robotica mobile

Negli ultimi decenni molti autori hanno affrontato varie sfide per quanto riguarda la navigazione autonoma di robot e sono state proposte diverse soluzioni per superare le difficoltà di piattaforme di navigazioni intelligenti. Con questo elaborato vogliamo ricercare gli obiettivi principali della navigazione di robot e tra questi andiamo ad approfondire la stima della posa di un robot o di un veicolo autonomo. La maggior parte dei metodi proposti si basa sul rilevamento del punto di fuga che ricopre un ruolo importante in questo campo. Abbiamo analizzato alcune tecniche che stimassero la posizione del robot in primo luogo nell’ambiente interno e presentiamo in particolare un metodo che risale al punto di fuga basato sulla trasformata di Hough e sul raggruppamento K-means. In secondo luogo presentiamo una descrizione generale di alcuni aspetti della navigazione su strade e su ambienti pedonali.

Computational Study of the Hydration of Sulfuric Acid Dimers: Implications for Acid Dissociation and Aerosol Formation

We have investigated the thermodynamics of sulfuric acid dimer hydration using ab initio quantum mechanical methods. For (H2SO4)2(H2O)n where n = 0−6, we employed high-level ab initio calculations to locate the most stable minima for each cluster size. The results presented herein yield a detailed understanding of the first deprotonation of sulfuric acid as a function of temperature for a system consisting of two sulfuric acid molecules and up to six waters. At 0 K, a cluster of two sulfuric acid molecules and one water remains undissociated. Addition of a second water begins the deprotonation of the first sulfuric acid leading to the di-ionic species (the bisulfate anion HSO4−, the hydronium cation H3O+, an undissociated sulfuric acid molecule, and a water). Upon the addition of a third water molecule, the second sulfuric acid molecule begins to dissociate. For the (H2SO4)2(H2O)3 cluster, the di-ionic cluster is a few kcal mol−1 more stable than the neutral cluster, which is just slightly more stable than the tetra-ionic cluster (two bisulfate anions, two hydronium cations, and one water). With four water molecules, the tetra-ionic cluster, (HSO4−)2(H3O+)2(H2O)2, becomes as favorable as the di-ionic cluster H2SO4(HSO4−)(H3O+)(H2O)3 at 0 K. Increasing the temperature favors the undissociated clusters, and at room temperature we predict that the di-ionic species is slightly more favorable than the neutral cluster once three waters have been added to the cluster. The tetra-ionic species competes with the di-ionic species once five waters have been added to the cluster. The thermodynamics of stepwise hydration of sulfuric acid dimer is similar to that of the monomer; it is favorable up to n = 4−5 at 298 K. A much more thermodynamically favorable pathway forming sulfuric acid dimer hydrates is through the combination of sulfuric acid monomer hydrates, but the low concentration of sulfuric acid relative to water vapor at ambient conditions limits that process.

Estimation of scots pine defoliation by the common pine sawfly (Diprion pini L.) using multi-temporal radar data

In 1998-2001 Finland suffered the most severe insect outbreak ever recorded, over 500,000 hectares. The outbreak was caused by the common pine sawfly (Diprion pini L.). The outbreak has continued in the study area, Palokangas, ever since. To find a good method to monitor this type of outbreaks, the purpose of this study was to examine the efficacy of multi-temporal ERS-2 and ENVISAT SAR imagery for estimating Scots pine (Pinus sylvestris L.) defoliation. Three methods were tested: unsupervised k-means clustering, supervised linear discriminant analysis (LDA) and logistic regression. In addition, I assessed if harvested areas could be differentiated from the defoliated forest using the same methods. Two different speckle filters were used to determine the effect of filtering on the SAR imagery and subsequent results. The logistic regression performed best, producing a classification accuracy of 81.6% (kappa 0.62) with two classes (no defoliation, >20% defoliation). LDA accuracy was with two classes at best 77.7% (kappa 0.54) and k-means 72.8 (0.46). In general, the largest speckle filter, 5 x 5 image window, performed best. When additional classes were added the accuracy was usually degraded on a step-by-step basis. The results were good, but because of the restrictions in the study they should be confirmed with independent data, before full conclusions can be made that results are reliable. The restrictions include the small size field data and, thus, the problems with accuracy assessment (no separate testing data) as well as the lack of meteorological data from the imaging dates.

EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study

In young, first-episode, productive, medication-naive patients with schizophrenia, EEG microstates (building blocks of mentation) tend to be shortened. Koenig et al. [Koenig, T., Lehmann, D., Merlo, M., Kochi, K., Hell, D., Koukkou, M., 1999. A deviant EEG brain microstate in acute, neuroleptic-naïve schizophrenics at rest. European Archives of Psychiatry and Clinical Neuroscience 249, 205–211] suggested that shortening concerned specific microstate classes. Sequence rules (microstate concatenations, syntax) conceivably might also be affected. In 27 patients of the above type and 27 controls, from three centers, multichannel resting EEG was analyzed into microstates using k-means clustering of momentary potential topographies into four microstate classes (A–D). In patients, microstates were shortened in classes B and D (from 80 to 70 ms and from 94 to 82 ms, respectively), occurred more frequently in classes A and C, and covered more time in A and less in B. Topography differed only in class B where LORETA tomography predominantly showed stronger left and anterior activity in patients. Microstate concatenation (syntax) generally were disturbed in patients; specifically, the class sequence A→C→D→A predominated in controls, but was reversed in patients (A→D→C→A). In schizophrenia, information processing in certain classes of mental operations might deviate because of precocious termination. The intermittent occurrence might account for Bleuler's “double bookkeeping.” The disturbed microstate syntax opens a novel physiological comparison of mental operations between patients and controls.

Dynamic Chemical Shift Imaging for Image-Guided Thermal Therapy

Magnetic resonance temperature imaging (MRTI) is recognized as a noninvasive means to provide temperature imaging for guidance in thermal therapies. The most common method of estimating temperature changes in the body using MR is by measuring the water proton resonant frequency (PRF) shift. Calculation of the complex phase difference (CPD) is the method of choice for measuring the PRF indirectly since it facilitates temperature mapping with high spatiotemporal resolution. Chemical shift imaging (CSI) techniques can provide the PRF directly with high sensitivity to temperature changes while minimizing artifacts commonly seen in CPD techniques. However, CSI techniques are currently limited by poor spatiotemporal resolution. This research intends to develop and validate a CSI-based MRTI technique with intentional spectral undersampling which allows relaxed parameters to improve spatiotemporal resolution. An algorithm based on autoregressive moving average (ARMA) modeling is developed and validated to help overcome limitations of Fourier-based analysis allowing highly accurate and precise PRF estimates. From the determined acquisition parameters and ARMA modeling, robust maps of temperature using the k-means algorithm are generated and validated in laser treatments in ex vivo tissue. The use of non-PRF based measurements provided by the technique is also investigated to aid in the validation of thermal damage predicted by an Arrhenius rate dose model.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Measurement of kT splitting scales in W→lv events at sqrt(s)=7 TeV with the ATLAS detector

A measurement of splitting scales, as defined by the kT clustering algorithm, is presented for final states containing a W boson produced in proton-proton collisions at a centre-of-mass energy of 7 TeV. The measurement is based on the full 2010 data sample corresponding to an integrated luminosity of 36 pb(-1) which was collected using the ATLAS detector at the CERN Large Hadron Collider. Cluster splitting scales are measured in events containing W bosons decaying to electrons or muons. The measurement comprises the four hardest splitting scales in a k(T) cluster sequence of the hadronic activity accompanying the W boson, and ratios of these splitting scales. Backgrounds such as multi-jet and top-quark-pair production are subtracted and the results are corrected for detector effects. Predictions from various Monte Carlo event generators at particle level are compared to the data. Overall, reasonable agreement is found with all generators, but larger deviations between the predictions and the data are evident in the soft regions of the splitting scales.

Results of bulk sediment X-ray diffraction analysis and quantification of mineral phases based on the RockJock and on the QUAX quantitative analysis

We have performed quantitative X-ray diffraction (qXRD) analysis of 157 grab or core-top samples from the western Nordic Seas between (WNS) ~57°-75°N and 5° to 45° W. The RockJock Vs6 analysis includes non-clay (20) and clay (10) mineral species in the <2 mm size fraction that sum to 100 weight %. The data matrix was reduced to 9 and 6 variables respectively by excluding minerals with low weight% and by grouping into larger groups, such as the alkali and plagioclase feldspars. Because of its potential dual origins calcite was placed outside of the sum. We initially hypothesized that a combination of regional bedrock outcrops and transport associated with drift-ice, meltwater plumes, and bottom currents would result in 6 clusters defined by "similar" mineral compositions. The hypothesis was tested by use of a fuzzy k-mean clustering algorithm and key minerals were identified by step-wise Discriminant Function Analysis. Key minerals in defining the clusters include quartz, pyroxene, muscovite, and amphibole. With 5 clusters, 87.5% of the observations are correctly classified. The geographic distributions of the five k-mean clusters compares reasonably well with the original hypothesis. The close spatial relationship between bedrock geology and discrete cluster membership stresses the importance of this variable at both the WNS-scale and at a more local scale in NE Greenland.