A new classification of the Turkish terranes and sutures and its implication for the paleotectonic history of the region

The Turkish part of the Tethyan realm is represented by a series of terranes juxtaposed through Alpine convergent movements and separated by complex suture zones. Different terranes can be defined and characterized by their dominant geological background. The Pontides domain represents a segment of the former active margin of Eurasia, where back-arc basins opened in the Triassic and separated the Sakarya terrane from neighbouring regions. Sakarya was re-accreted to Laurasia through the Balkanic mid-Cretaceous orogenic event that also affected the Rhodope and Strandja zones. The whole region from the Balkans to the Caucasus was then affected by a reversal of subduction and creation of a Late Cretaceous arc before collision with the Anatolian domain in the Eocene. If the Anatolian terrane underwent an evolution similar to Sakarya during the Late Paleozoic and Early Triassic times, both terranes had a diverging history during and after the Eo-Cimmerian collision. North of Sakarya, the Küre back-arc was closed during the Jurassic, whereas north of the Anatolian domain, the back-arc type oceans did not close before the Late Cretaceous. During the Cretaceous, both domains were affected by ophiolite obduction, but in very different ways: north directed diachronous Middle to Late Cretaceous mélange obduction on the Jurassic Sakarya passive margin; Senonian synchronous southward obduction on the Triassic passive margin of Anatolia. From this, it appears that the Izmir-Ankara suture, currently separating both terranes, is composite, and that the passive margin of Sakarya is not the conjugate margin of Anatolia. To the south, the Cimmerian Taurus domain together with the Beydağları domain (part of the larger Greater Apulian terrane), were detached from north Gondwana in the Permian during the opening of the Neotethys (East-Mediterranean basin). The drifting Cimmerian blocks entered into a soft collision with the Anatolian and related terranes in the Eo-Cimmerian orogenic phase (Late Triassic), thus suturing the Paleotethys. At that time, the Taurus plate developed foreland-type basins, filled with flysch-molasse deposits that locally overstepped the lower plate Taurus terrane and were deposited in the opening Neotethys to the south. These olistostromal deposits are characterized by pelagic Carboniferous and Permian material from the Paleotethys suture zone found in the Mersin mélange. The latter, as well as the Antalya and Mamonia domains are represented by a series of exotic units now found south of the main Taurus range. Part of the Mersin exotic material was clearly derived from the former north Anatolian passive margin (Huğlu-type series) and re-displaced during the Paleogene. This led us to propose a plate tectonic model where the Anatolian ophiolitic front is linked up with the Samail/Baër-Bassit obduction front found along the Arabian margin. The obduction front was indented by the Anatolian promontory whose eastern end was partially subducted. Continued slab roll-back of the Neotethys allowed Anatolian exotics to continue their course southwestward until their emplacement along the Taurus southern margin (Mersin) and up to the Beydağları promontory (Antaya-Mamonia) in the latest Cretaceous-Paleocene. The supra-subduction ocean opening at the back of the obduction front (Troodos-type Ocean) was finally closed by Eocene north-south shortening between Africa and Eurasia. This brought close to each other Cretaceous ophiolites derived from the north of Anatolia and those obducted on the Arabian promontory. The latter were sealed by a Maastrichtian platform, and locally never affected by Alpine tectonism, whereas those located on the eastern Anatolian plate are strongly deformed and metamorphosed, and affected by Eocene arc magmatism. These observations help to reconstruct the larger frame of the central Tethyan realm geodynamic evolution.

Enchondromatosis revisited: New classification with molecular basis.

The so-called "enchondromatoses" are skeletal disorders defined by the presence of ectopic cartilaginous tissue within bone tissue. The clinical and radiographic features of the different enchondromatoses are distinct, and grouping them does not reflect a common pathogenesis but simply a similar radiographic appearance and thus the need for a differential diagnosis. Recent advances in the understanding of their molecular and cellular bases confirm the heterogeneous nature of the different enchondromatoses. Some, like Ollier disease, Maffucci disease, metaphyseal chondromatosis with hydroxyglutaric aciduria, and metachondromatosis are produced by a dysregulation of chondrocyte proliferation, while others (such as spondyloenchondrodysplasia or dysspondyloenchondromatosis) are caused by defects in structure or metabolism of cartilage or bone matrix. In other forms (e.g., the dominantly inherited genochondromatoses), the basic defect remains to be determined. The classification, proposed by Spranger and associates in 1978 and tentatively revised twice, was based on the radiographic appearance, the anatomic sites involved, and the mode of inheritance. The new classification proposed here integrates the molecular genetic advances and delineates phenotypic families based on the molecular defects. Reference radiographs are provided to help in the diagnosis of the well-defined forms. In spite of advances, many cases remain difficult to diagnose and classify, implying that more variants remain to be defined at both the clinical and molecular levels. © 2012 Wiley Periodicals, Inc.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

Classification of indian classical vocal styles from melodic contours

A prominent categorization of Indian classical music is the Hindustani and Carnatic traditions, the two styleshaving evolved under distinctly different historical andcultural influences. Both styles are grounded in the melodicand rhythmic framework of raga and tala. The styles differ along dimensions such as instrumentation,aesthetics and voice production. In particular, Carnatic music is perceived as being more ornamented. The hypothesisthat style distinctions are embedded in the melodic contour is validated via subjective classification tests. Melodic features representing the distinctive characteristicsare extracted from the audio. Previous work based on the extent of stable pitch regions is supported by measurements of musicians’ annotations of stable notes. Further, a new feature is introduced that captures thepresence of specific pitch modulations characteristic ofornamentation in Indian classical music. The combined features show high classification accuracy on a database of vocal music of prominent artistes. The misclassifications are seen to match actual listener confusions.

Validation of tissue modelization and classification techniques in T1-weighted MR brain images

We propose a deep study on tissue modelization andclassification Techniques on T1-weighted MR images. Threeapproaches have been taken into account to perform thisvalidation study. Two of them are based on FiniteGaussian Mixture (FGM) model. The first one consists onlyin pure gaussian distributions (FGM-EM). The second oneuses a different model for partial volume (PV) (FGM-GA).The third one is based on a Hidden Markov Random Field(HMRF) model. All methods have been tested on a DigitalBrain Phantom image considered as the ground truth. Noiseand intensity non-uniformities have been added tosimulate real image conditions. Also the effect of ananisotropic filter is considered. Results demonstratethat methods relying in both intensity and spatialinformation are in general more robust to noise andinhomogeneities. However, in some cases there is nosignificant differences between all presented methods.

Missouri River Studies: Alluvial Morphology and Engineering Soil Classification, HR-1, Progress Report, 1960

The primary purposes of this investigation are: 1) To delineate flood plain deposits with different geologic and engineering properties. 2) To provide basic data necessary for any attempt at stabilizing flood plain deposits. The alluvial valley of the Missouri River adjacent to Iowa was chosen as the logical place to begin this study. The river forms the western boundary of the state for an airline distance of approximately 139 miles; and the flood plain varies from a maximum width of approximately 18 miles (Plates 2 and 3, Sheets 75 and 75L) to approximately 4 miles near Crescent, Iowa (Plate 8, Sheet 66). The area studied includes parts of Woodbury, Monona, Harrison, Pottawattamie, Mills, and Fremont counties in Iowa and parts of Dakota, Thurston, Burt, Washington, Douglas, Sarpy, Cass and Otoe counties in Nebraska. Plate l is an index map of the area under consideration.