Single breath-hold 3D measurement of left atrial volume using compressed sensing cardiovascular magnetic resonance and a non-model-based reconstruction approach.

BACKGROUND: Left atrial (LA) dilatation is associated with a large variety of cardiac diseases. Current cardiovascular magnetic resonance (CMR) strategies to measure LA volumes are based on multi-breath-hold multi-slice acquisitions, which are time-consuming and susceptible to misregistration. AIM: To develop a time-efficient single breath-hold 3D CMR acquisition and reconstruction method to precisely measure LA volumes and function. METHODS: A highly accelerated compressed-sensing multi-slice cine sequence (CS-cineCMR) was combined with a non-model-based 3D reconstruction method to measure LA volumes with high temporal and spatial resolution during a single breath-hold. This approach was validated in LA phantoms of different shapes and applied in 3 patients. In addition, the influence of slice orientations on accuracy was evaluated in the LA phantoms for the new approach in comparison with a conventional model-based biplane area-length reconstruction. As a reference in patients, a self-navigated high-resolution whole-heart 3D dataset (3D-HR-CMR) was acquired during mid-diastole to yield accurate LA volumes. RESULTS: Phantom studies. LA volumes were accurately measured by CS-cineCMR with a mean difference of -4.73 ± 1.75 ml (-8.67 ± 3.54%, r2 = 0.94). For the new method the calculated volumes were not significantly different when different orientations of the CS-cineCMR slices were applied to cover the LA phantoms. Long-axis "aligned" vs "not aligned" with the phantom long-axis yielded similar differences vs the reference volume (-4.87 ± 1.73 ml vs. -4.45 ± 1.97 ml, p = 0.67) and short-axis "perpendicular" vs. "not-perpendicular" with the LA long-axis (-4.72 ± 1.66 ml vs. -4.75 ± 2.13 ml; p = 0.98). The conventional bi-plane area-length method was susceptible for slice orientations (p = 0.0085 for the interaction of "slice orientation" and "reconstruction technique", 2-way ANOVA for repeated measures). To use the 3D-HR-CMR as the reference for LA volumes in patients, it was validated in the LA phantoms (mean difference: -1.37 ± 1.35 ml, -2.38 ± 2.44%, r2 = 0.97). Patient study: The CS-cineCMR LA volumes of the mid-diastolic frame matched closely with the reference LA volume (measured by 3D-HR-CMR) with a difference of -2.66 ± 6.5 ml (3.0% underestimation; true LA volumes: 63 ml, 62 ml, and 395 ml). Finally, a high intra- and inter-observer agreement for maximal and minimal LA volume measurement is also shown. CONCLUSIONS: The proposed method combines a highly accelerated single-breathhold compressed-sensing multi-slice CMR technique with a non-model-based 3D reconstruction to accurately and reproducibly measure LA volumes and function.

Forecasting a language shift based on cellular automata

Language extinction as a consequence of language shifts is a widespread social phenomenon that affects several million people all over the world today. An important task for social sciences research should therefore be to gain an understanding of language shifts, especially as a way of forecasting the extinction or survival of threatened languages, i.e., determining whether or not the subordinate language will survive in communities with a dominant and a subordinate language. In general, modeling is usually a very difficult task in the social sciences, particularly when it comes to forecasting the values of variables. However, the cellular automata theory can help us overcome this traditional difficulty. The purpose of this article is to investigate language shifts in the speech behavior of individuals using the methodology of the cellular automata theory. The findings on the dynamics of social impacts in the field of social psychology and the empirical data from language surveys on the use of Catalan in Valencia allowed us to define a cellular automaton and carry out a set of simulations using that automaton. The simulation results highlighted the key factors in the progression or reversal of a language shift and the use of these factors allowed us to forecast the future of a threatened language in a bilingual community.

Atypical language organization in temporal lobe epilepsy revealed by a passive semantic paradigm

Background Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy in adults and can be successfully cured by surgery. One of the main complications of this surgery however is a decline in language abilities. The magnitude of this decline is related to the degree of language lateralization to the left hemisphere. Most fMRI paradigms used to determine language dominance in epileptic populations have used active language tasks. Sometimes, these paradigms are too complex and may result in patient underperformance. Only a few studies have used purely passive tasks, such as listening to standard speech. Methods In the present study we characterized language lateralization in patients with MTLE using a rapid and passive semantic language task. We used functional magnetic resonance imaging (fMRI) to study 23 patients [12 with Left (LMTLE), 11 with Right mesial temporal lobe epilepsy (RMTLE)] and 19 healthy right-handed controls using a 6 minute long semantic task in which subjects passively listened to groups of sentences (SEN) and pseudo sentences (PSEN). A lateralization index (LI) was computed using a priori regions of interest of the temporal lobe. Results The LI for the significant contrasts produced activations for all participants in both temporal lobes. 81.8% of RMTLE patients and 79% of healthy individuals had a bilateral language representation for this particular task. However, 50% of LMTLE patients presented an atypical right hemispheric dominance in the LI. More importantly, the degree of right lateralization in LMTLE patients was correlated with the age of epilepsy onset. Conclusions The simple, rapid, non-collaboration dependent, passive task described in this study, produces a robust activation in the temporal lobe in both patients and controls and is capable of illustrating a pattern of atypical language organization for LMTLE patients. Furthermore, we observed that the atypical right-lateralization patterns in LMTLE patients was associated to earlier age at epilepsy onset. These results are in line with the idea that early onset of epileptic activity is associated to larger neuroplastic changes.

Why and how would we implement a lung cancer screening program?

For decades, lung cancer has been the most common cancer in terms of both incidence and mortality. There has been very little improvement in the prognosis of lung cancer. Early treatment following early diagnosis is considered to have potential for development. The National Lung Screening Trial (NLST), a large, well-designed randomized controlled trial, evaluated low-dose computed tomography (LDCT) as a screening tool for lung cancer. Compared with chest X-ray, annual LDCT screening reduced death from lung cancer and overall mortality by 20 and 6.7 %, respectively, in high-risk people aged 55-74 years. Several smaller trials of LDCT screening are under way, but none are sufficiently powered to detect a 20 % reduction in lung cancer death. Thus, it is very unlikely that the NLST results will be replicated. In addition, the NLST raises several issues related to screening, such as the high false-positive rate, overdiagnosis and cost. Healthcare providers and systems are now left with the question of whether the available findings should be translated into practice. We present the main reasons for implementing lung cancer screening in high-risk adults and discuss the main issues related to lung cancer screening. We stress the importance of eligibility criteria, smoking cessation programs, primary care physicians, and informed-decision making should lung cancer screening be implemented. Seven years ago, we were waiting for the results of trials. Such evidence is now available. Similar to almost all other cancer screens, uncertainties exist and persist even after recent scientific efforts and data. We believe that by staying within the characteristics of the original trial and appropriately sharing the evidence as well as the uncertainties, it is reasonable to implement a LDCT lung cancer screening program for smokers and former smokers.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Connectivity and tissue microstructural alterations in right and left temporal lobe epilepsy revealed by diffusion spectrum imaging.

Focal epilepsy is increasingly recognized as the result of an altered brain network, both on the structural and functional levels and the characterization of these widespread brain alterations is crucial for our understanding of the clinical manifestation of seizure and cognitive deficits as well as for the management of candidates to epilepsy surgery. Tractography based on Diffusion Tensor Imaging allows non-invasive mapping of white matter tracts in vivo. Recently, diffusion spectrum imaging (DSI), based on an increased number of diffusion directions and intensities, has improved the sensitivity of tractography, notably with respect to the problem of fiber crossing and recent developments allow acquisition times compatible with clinical application. We used DSI and parcellation of the gray matter in regions of interest to build whole-brain connectivity matrices describing the mutual connections between cortical and subcortical regions in patients with focal epilepsy and healthy controls. In addition, the high angular and radial resolution of DSI allowed us to evaluate also some of the biophysical compartment models, to better understand the cause of the changes in diffusion anisotropy. Global connectivity, hub architecture and regional connectivity patterns were altered in TLE patients and showed different characteristics in RTLE vs LTLE with stronger abnormalities in RTLE. The microstructural analysis suggested that disturbed axonal density contributed more than fiber orientation to the connectivity changes affecting the temporal lobes whereas fiber orientation changes were more involved in extratemporal lobe changes. Our study provides further structural evidence that RTLE and LTLE are not symmetrical entities and DSI-based imaging could help investigate the microstructural correlate of these imaging abnormalities.

Towards the Construction of a Multi-agent Approach for Discovering the Meaning of Natural Language Collaborative Conversations

Peer-reviewed

A Case-based reasoning approach to validate grammatical gender and number agreement in Spanish language

Across Latin America 420 indigenous languages are spoken. Spanish is considered a second language in indigenous communities and is progressively introduced in education. However, most of the tools to support teaching processes of a second language have been developed for the most common languages such as English, French, German, Italian, etc. As a result, only a small amount of learning objects and authoring tools have been developed for indigenous people considering the specific needs of their population. This paper introduces Multilingual–Tiny as a web authoring tool to support the virtual experience of indigenous students and teachers when they are creating learning objects in indigenous languages or in Spanish language, in particular, when they have to deal with the grammatical structures of Spanish. Multilingual–Tiny has a module based on the Case-based Reasoning technique to provide recommendations in real time when teachers and students write texts in Spanish. An experiment was performed in order to compare some local similarity functions to retrieve cases from the case library taking into account the grammatical structures. As a result we found the similarity function with the best performance

Teclat virtual amb predicció del llenguatge

L'objectiu de la realització d'aquest treball és la creació d'un teclat virtual destinat a ajudar a persones amb mobilitat reduïda, que no poden utilitzar el teclat físic de l'ordinador, a escriure intentant aconseguir una velocitat d'escriptura raonable per a textos de qualsevol mida. Per aconseguir aquesta velocitat d'escriptura raonable s'ha implementat un sistema de predicció del llenguatge que té dos aspectes. D'una banda es prediuen paraules segons la seva freqüència d'ús en un determinat diccionari i, d'altra banda, es prediuen paraules seguint les regles d'escriptura de la gramàtica catalana. Un altre aspecte important era que el programa creat es pogués utilitzar en diferents sistemes operatius ja que només hi havia versions específiques per a cada un d'ells. El programa creat es pot executar en els sistemes operatius Windows XP, Mac OS i Ubuntu Linux. El programa creat pretén ser una base per a posteriors millores i ampliacions en diferents parts del seu conjunt. No obstant això, com a resultat s'ha obtingut un programa que permet escriure raonablement ràpid i permet a l'usuari gestionar diccionaris i els dos tipus de predicció que s'han implementat.

Language recovery and evidence of residual deficits after nonthalamic subcortical stroke: A 1 year follow-up study

A variety of language disturbances including aphasia have been described after subcortical stroke but less is known about the factors that influence the long-term recovery of stroke-induced language dysfunction. We prospectively examined the role of the affected hemisphere and the lesion site in the occurrence and recovery of language deficits in nonthalamic subcortical stroke. Forty patients with unilateral basal gangliastroke underwent language assessment within 1 week, 3 months and 1 year after stroke. Disturbances in at least one language domain were observed in 35 patients during the first week post stroke including aphasia diagnosed in 11 patients. Importantly, the appearance of deficits after stroke onset and the improvement of language function were not determined by the site of subcortical lesion, but instead were critically influenced by the affected hemisphere. In fact, the language impairments following left and right basal ganglia stroke mirrored the language dysfunction observed after cortical lesions in the same hemisphere. A significant overall language improvement was observed at 3 months after stroke, although residual deficits in languageexecutive function were the most commonly observed impairment at 1 year follow-up. Although a substantial improvement of language function can be expected after nonthalamic subcortical stroke, our findings suggest that language recovery may not be fully achieved at 1 year post

New computer software for defining the three-dimensional geometry for a centrifu- gal compressor impeller

In this thesis, a computer software for defining the geometry for a centrifugal compressor impeller is designed and implemented. The project is done under the supervision of Laboratory of Fluid Dynamics in Lappeenranta University of Technology. This thesis is similar to the thesis written by Tomi Putus (2009) in which a centrifugal compressor impeller flow channel is researched and commonly used design practices are reviewed. Putus wrote a computer software which can be used to define impeller’s three-dimensional geometry based on the basic geometrical dimensions given by a preliminary design. The software designed in this thesis is almost similar but it uses a different programming language (C++) and a different way to define the shape of the impeller meridional projection.

Neural correlates of language learning in adults

The human language-learning ability persists throughout life, indicating considerable flexibility at the cognitive and neural level. This ability spans from expanding the vocabulary in the mother tongue to acquisition of a new language with its lexicon and grammar. The present thesis consists of five studies that tap both of these aspects of adult language learning by using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) during language processing and language learning tasks. The thesis shows that learning novel phonological word forms, either in the native tongue or when exposed to a foreign phonology, activates the brain in similar ways. The results also show that novel native words readily become integrated in the mental lexicon. Several studies in the thesis highlight the left temporal cortex as an important brain region in learning and accessing phonological forms. Incidental learning of foreign phonological word forms was reflected in functionally distinct temporal lobe areas that, respectively, reflected short-term memory processes and more stable learning that persisted to the next day. In a study where explicitly trained items were tracked for ten months, it was found that enhanced naming-related temporal and frontal activation one week after learning was predictive of good long-term memory. The results suggest that memory maintenance is an active process that depends on mechanisms of reconsolidation, and that these process vary considerably between individuals. The thesis put special emphasis on studying language learning in the context of language production. The neural foundation of language production has been studied considerably less than that of perceptive language, especially on the sentence level. A well-known paradigm in language production studies is picture naming, also used as a clinical tool in neuropsychology. This thesis shows that accessing the meaning and phonological form of a depicted object are subserved by different neural implementations. Moreover, a comparison between action and object naming from identical images indicated that the grammatical class of the retrieved word (verb, noun) is less important than the visual content of the image. In the present thesis, the picture naming was further modified into a novel paradigm in order to probe sentence-level speech production in a newly learned miniature language. Neural activity related to grammatical processing did not differ between the novel language and the mother tongue, but stronger neural activation for the novel language was observed during the planning of the upcoming output, likely related to more demanding lexical retrieval and short-term memory. In sum, the thesis aimed at examining language learning by combining different linguistic domains, such as phonology, semantics, and grammar, in a dynamic description of language processing in the human brain.

MODELING A COMPUTER APPLICATION FOR MANAGEMENT OF MAINTENANCE ACTIVITIES OF UNPAVED ROADS

ABSTRACTThis study presents a contribution to the modeling of a computer application employing a method of serviceability performance for unpaved roads, aiming the management of maintenance/restoration activities of the primary surface layer. The proposed methodology consisted of field inspections during dry (April to September) and rainy (October to March) periods, during which objective evaluations were performed to survey of defects and their densities and degrees of severity. To aid the functional classification of analyzed road sections and the determination of the defect with major influence on the serviceability of these roads, the method of serviceability performance proposed by Silva (2009)was implemented in the Visual Basic for Applications (VBA) language in Microsoft Excel software. With the use of the computer application proposed it was possible to identify among the defects analyzed in field, through the index of serviceability of the sampling unit per defect type (ISUdef), which one had the greatest influence on determining the relative serviceability index per road section (IST). The results allow us to conclude that the computer application Road achieved satisfactory results, since the objective evaluation criteria applied to road sections denotes consistency regarding their serviceability.

High-speed solid-rotor induction machine – calculation program

The aim of this thesis is to utilize the technology developed at LUT and to provide an easy tool for high-speed solid-rotor induction machine preliminary design. Computer aided design tool MathCAD has been chosen as the environment for realizing the calculation program. Four versions of the design program have been made depending on the motor rotor type. The first rotor type is an axially slitted solid-rotor with steel end rings. The next one is an axially slitted solid-rotor with copper end rings. The third machine type is a solid rotor with deep, rectangular copper bars and end rings (squirrel cage). And the last one is a solid-rotor with round copper bars and end rings (squirrel cage). Each type of rotor has its own specialties but a general thread of design is common. This paper follows the structure of the calculating program and explains some features and formulas. The attention is concentrated on the difference between laminated and solid-rotor machine design principles. There is no deep analysis of the calculation ways are presented. References for all solution methods appearing during the design procedure are given for more detailed studying. This thesis pays respect to the latest innovations in solid-rotor machines theory. Rotor ends’ analytical calculation follows the latest knowledge in this field. Correction factor for adjusting the rotor impedance is implemented. The purpose of the created design program is to calculate the preliminary dimensions of the machine according to initial data. Obtained results are not recommended for exact machine development. Further more detailed design should be done in a finite element method application. Hence, this thesis is a practical tool for the prior evaluating of the high-speed machine with different solid-rotor types parameters.

Aging based maintenance and reinvestment scheduling of electric distribution

The maintenance of electric distribution network is a topical question for distribution system operators because of increasing significance of failure costs. In this dissertation the maintenance practices of the distribution system operators are analyzed and a theory for scheduling maintenance activities and reinvestment of distribution components is created. The scheduling is based on the deterioration of components and the increasing failure rates due to aging. The dynamic programming algorithm is used as a solving method to maintenance problem which is caused by the increasing failure rates of the network. The other impacts of network maintenance like environmental and regulation reasons are not included to the scope of this thesis. Further the tree trimming of the corridors and the major disturbance of the network are not included to the problem optimized in this thesis. For optimizing, four dynamic programming models are presented and the models are tested. Programming is made in VBA-language to the computer. For testing two different kinds of test networks are used. Because electric distribution system operators want to operate with bigger component groups, optimal timing for component groups is also analyzed. A maintenance software package is created to apply the presented theories in practice. An overview of the program is presented.