Phylogenetic analysis of plastid and nuclear DNA sequences reveals a rapid late Miocene radiation of the temperate bamboo tribe Arundinarieae (Poaceae, Bambusoideae)

Background: Arundinarieae are a large tribe of temperate woody bamboos for which phylogenetics are poorly understood because of limited taxon sampling and lack of informative characters. Aims: This study assessed phylogenetic relationships, origins and classification of Arundinarieae. Methods: DNA sequences (plastid trnL-F; nuclear ITS) were used for parsimony and Bayesian inference including 41 woody bamboo taxa. Divergence dates were estimated using a relaxed Bayesian clock. Results: Arundinarieae were monophyletic but their molecular divergence was low compared to the tropical Bambuseae. Ancestors of the Arundinarieae lineage were estimated to have diverged from the other bamboos 23 (15-30) million years ago (Mya). However, the Arundinarieae radiation occurred 10 (6-16) Mya compared to 18 (11-25) Mya for the tropical Bambuseae. Some groups could be defined within Arundinarieae, but they do not correspond to recognised subtribes such as Arundinariinae or Shibataeinae. Conclusions: Arundinarieae are a relatively ancient bambusoid lineage that underwent a rapid radiation in the late Miocene. The radiation coincides with the continental collision of the Indo-Australian and Eurasian Plates. Arundinarieae are distributed primarily in East Asia and the Himalayas to northern Southeast Asia. It is unknown whether they were present in Asia long before their radiation, but we believe recent dispersal is a more likely scenario. Keywords: Arundinarieae; Bambuseae; internal transcribed spacer (ITS); molecular clock; phylogenetics; radiation; temperate bamboos; Thamnocalaminae; trnL-F

Optinen merkintunnistus satamaliikenteessä ja junavaunujen tunnistamisessa

Optisella merkintunnistuksella on tärkeä rooli nykypäivän automaatiossa. Optisen merkintunnistuksen eri sovellusalueet vaihtelevat dokumenttien tekstin tunnistamisesta ajoneuvojen tunnistamiseen ja erilaisten tuotanto- ja kokoonpanolinjojen automaatioon ja laadun tarkkailuun. Tässä työssä keskitytään optisen merkintunnistuksen käyttöön satamaliikenteessä. Työ jakaantuu kahteen osaan. Ensimmäisessä osassa esitellään satamien kannalta kaksi yleisintä ja samalla tärkeintä optisen merkintunnistuksen sovellusaluetta: rekisterikilpien tunnistus ja konttien tunnistus. Työn jälkimmäinen osa käsittelee junavaunujen tunnistamista optisen merkintunnistuksen avulla. Satamissa toimiva vaunukalusto ja niissä esiintyvät tunnisteet esitellään. Vaunujen tunnistamisen toteuttava konenäköjärjestelmä, sen vaativat laitteet sekä kuvankäsittelyn ja kuva-analyysin vaiheet käydään läpi. Kuva-analyysion jaettu työssä neljään päävaiheeseen: esikäsittely, segmentointi, piirreirrotus ja luokittelu. Kustakin vaiheesta esitetään useita eri menetelmiä, joiden käyttökelpoisuutta esitettyyn ongelmaan arvioidaan työn lopussa.

How Reliable Is Ki-67 Immunohistochemistry in Grade 2 Breast Carcinomas? A QA Study of the Swiss Working Group of Breast- and Gynecopathologists.

Adjuvant chemotherapy decisions in breast cancer are increasingly based on the pathologist's assessment of tumor proliferation. The Swiss Working Group of Gyneco- and Breast Pathologists has surveyed inter- and intraobserver consistency of Ki-67-based proliferative fraction in breast carcinomas. METHODS: Five pathologists evaluated MIB-1-labeling index (LI) in ten breast carcinomas (G1, G2, G3) by counting and eyeballing. In the same way, 15 pathologists all over Switzerland then assessed MIB-1-LI on three G2 carcinomas, in self-selected or pre-defined areas of the tumors, comparing centrally immunostained slides with slides immunostained in the different laboratoires. To study intra-observer variability, the same tumors were re-examined 4 months later. RESULTS: The Kappa values for the first series of ten carcinomas of various degrees of differentiation showed good to very good agreement for MIB-1-LI (Kappa 0.56-0.72). However, we found very high inter-observer variabilities (Kappa 0.04-0.14) in the read-outs of the G2 carcinomas. It was not possible to explain the inconsistencies exclusively by any of the following factors: (i) pathologists' divergent definitions of what counts as a positive nucleus (ii) the mode of assessment (counting vs. eyeballing), (iii) immunostaining technique, and (iv) the selection of the tumor area in which to count. Despite intensive confrontation of all participating pathologists with the problem, inter-observer agreement did not improve when the same slides were re-examined 4 months later (Kappa 0.01-0.04) and intra-observer agreement was likewise poor (Kappa 0.00-0.35). CONCLUSION: Assessment of mid-range Ki-67-LI suffers from high inter- and intra-observer variability. Oncologists should be aware of this caveat when using Ki-67-LI as a basis for treatment decisions in moderately differentiated breast carcinomas.

Long noncoding RNAs in cardiac development and ageing.

A large part of the mammalian genome is transcribed into noncoding RNAs. Long noncoding RNAs (lncRNAs) have emerged as critical epigenetic regulators of gene expression. Distinct molecular mechanisms allow lncRNAs either to activate or to repress gene expression, thereby participating in the regulation of cellular and tissue function. LncRNAs, therefore, have important roles in healthy and diseased hearts, and might be targets for therapeutic intervention. In this Review, we summarize the current knowledge of the roles of lncRNAs in cardiac development and ageing. After describing the definition and classification of lncRNAs, we present an overview of the mechanisms by which lncRNAs regulate gene expression. We discuss the multiple roles of lncRNAs in the heart, and focus on the regulation of embryonic stem cell differentiation, cardiac cell fate and development, and cardiac ageing. We emphasize the importance of chromatin remodelling in this regulation. Finally, we discuss the therapeutic and biomarker potential of lncRNAs.

A reappraisal of 'International Stratigraphic Lexicon' and the need for the creation named units critical catalogues

Analysis of stratigraphic terminology and classification, shows that time-related stratigraphic units, which by definition have a global extent, are the concern of international cornrnissions and committees of the intemational Union of Geological Sciences (IUGS) . In contrast, lithostratigraphic, and other closely related units, are regional in extent and are catalogued in the International Stratigraphic Lexicon (ISL), the last volume of which, was published in 1987. Tlie intemational Commission on Stratigraphy (ICS) is currently attempting to revitalize the publication of ISL, given that the information contained in published volumes has never been updated, and that there has been a significant increase in stratigraphic research in recent decades. The proliferation of named units in the South Pyrenean and Ebro Basin Paleogene is evaluated to illustrate the extent of the problem. Moreover, new approaches to stratigraphic analysis have led to the naming of genetic units according to similar guidelines followed in the naming of descnptive or lithostratigraphic units. This has led to considerable confusion. The proposal to revitalize the ISL is accepted as part of the solution, that should also include the publication of critica1 catalogues, and the creation of norms for genetic unit terminology.

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.

IRM dans le diagnostic des spondyloarthrites axiales: utilité et pièges diagnostiques [MRI of axial spondyloarthritis: diagnostic role and pitfalls].

MRI has become a major tool for the diagnosis of axial spondyloarthritis and provides objective signs based on which therapy can be initiated. In clinical practice, ASAS classification criteria are often applied for the diagnosis of spondyloarthritis at a pre-radiographic stage. However, MRI signs of spondyloarthritis as stated in ASAS criteria lack specificity, and can be encountered in a wide array of diagnoses, in particular degenerative and mechanical conditions. In this article, we will review the role of MRI in the diagnosis and classification of spondyloarthritis, general technical considerations, the elementary MRI signs of axial spondyloarthritis, as well as diagnostic pitfalls. We also provide a practical approach on how to avoid overdiagnosis of spondyloarthritis and to improve the diagnostic value of MRI.

Imaging and Characterisation of Dirt Particles in Pulp and Paper

Dirt counting and dirt particle characterisation of pulp samples is an important part of quality control in pulp and paper production. The need for an automatic image analysis system to consider dirt particle characterisation in various pulp samples is also very critical. However, existent image analysis systems utilise a single threshold to segment the dirt particles in different pulp samples. This limits their precision. Based on evidence, designing an automatic image analysis system that could overcome this deficiency is very useful. In this study, the developed Niblack thresholding method is proposed. The method defines the threshold based on the number of segmented particles. In addition, the Kittler thresholding is utilised. Both of these thresholding methods can determine the dirt count of the different pulp samples accurately as compared to visual inspection and the Digital Optical Measuring and Analysis System (DOMAS). In addition, the minimum resolution needed for acquiring a scanner image is defined. By considering the variation in dirt particle features, the curl shows acceptable difference to discriminate the bark and the fibre bundles in different pulp samples. Three classifiers, called k-Nearest Neighbour, Linear Discriminant Analysis and Multi-layer Perceptron are utilised to categorize the dirt particles. Linear Discriminant Analysis and Multi-layer Perceptron are the most accurate in classifying the segmented dirt particles by the Kittler thresholding with morphological processing. The result shows that the dirt particles are successfully categorized for bark and for fibre bundles.

Modeling and developing a customer-oriented configuration process in a mass customization environment

In this thesis the main objective is to examine and model configuration system and related processes. When and where configuration information is created in product development process and how it is utilized in order-delivery process? These two processes are the essential part of the whole configuration system from the information point of view. Empirical part of the work was done as a constructive research inside a company that follows a mass customization approach. Data models and documentation are created for different development stages of the configuration system. A base data model already existed for new structures and relations between these structures. This model was used as the basis for the later data modeling work. Data models include different data structures, their key objects and attributes, and relations between. Representation of configuration rules for the to-be configuration system was defined as one of the key focus point. Further, it is examined how the customer needs and requirements information can be integrated into the product development process. Requirements hierarchy and classification system is presented. It is shown how individual requirement specifications can be connected for physical design structure via features by developing the existing base data model further.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Advances in modelling of mechatronic systems: the toolset FASIM_C++ for the simulation of vehicle dynamics

The increasing complexity of controller systems, applied in modern passenger cars, requires adequate simulation tools. The toolset FASIM_C++, described in the following, uses complex vehicle models in three-dimensional vehicle dynamics simulation. The structure of the implemented dynamic models and the generation of the equations of motion applying the method of kinematic differentials is explained briefly. After a short introduction in methods of event handling, several vehicle models and applications like controller development, roll-over simulation and real-time-simulation are explained. Finally some simulation results are presented.

PDM system functions and utilizations analysis to improve efficiency of sheet metal product design and manufacturing

This study will concentrate on Product Data Management (PDM) systems, and sheet metal design features and classification. In this thesis, PDM is seen as an individual system which handles all product-related data and information. The meaning of relevant data is to take the manufacturing process further with fewer errors. The features of sheet metals are giving more information and value to the designed models. The possibility of implementing PDM and sheet metal features recognition are the core of this study. Their integration should make the design process faster and manufacturing-friendly products easier to design. The triangulation method is the basis for this research. The sections of this triangle are: scientific literature review, interview using the Delphi method and the author’s experience and observations. The main key findings of this study are: (1) the area of focus in triangle (the triangle of three different point of views: business, information exchange and technical) depends on the person’s background and their role in the company, (2) the classification in the PDM system (and also in the CAD system) should be done using the materials, tools and machines that are in use in the company and (3) the design process has to be more effective because of the increase of industrial production, sheet metal blank production and the designer’s time spent on actual design and (4) because Design For Manufacture (DFM) integration can be done with CAD-programs, DFM integration with the PDM system should also be possible.

Automatic traffic sign inventory- and condition analysis

This thesis researches automatic traffic sign inventory and condition analysis using machine vision and pattern recognition methods. Automatic traffic sign inventory and condition analysis can be used to more efficient road maintenance, improving the maintenance processes, and to enable intelligent driving systems. Automatic traffic sign detection and classification has been researched before from the viewpoint of self-driving vehicles, driver assistance systems, and the use of signs in mapping services. Machine vision based inventory of traffic signs consists of detection, classification, localization, and condition analysis of traffic signs. The produced machine vision system performance is estimated with three datasets, from which two of have been been collected for this thesis. Based on the experiments almost all traffic signs can be detected, classified, and located and their condition analysed. In future, the inventory system performance has to be verified in challenging conditions and the system has to be pilot tested.

Effect of inhibition of nitric oxide synthase on blood pressure and renal sodium handling in renal denervated rats

The role of sympathetic nerve activity in the changes in arterial blood pressure and renal function caused by the chronic administration of NG-nitro-L-arginine methyl ester (L-NAME), an inhibitor of nitric oxide (NO) synthesis, was examined in sham and bilaterally renal denervated rats. Several studies have demonstrated that sympathetic nerve activity is elevated acutely after L-NAME administration. To evaluate the role of renal nerve activity in L-NAME-induced hypertension, we compared the blood pressure response in four groups (N = 10 each) of male Wistar-Hannover rats weighing 200 to 250 g: 1) sham-operated vehicle-treated, 2) sham-operated L-NAME-treated, 3) denervated vehicle-treated, and 4) denervated L-NAME-treated rats. After renal denervation or sham surgery, one control week was followed by three weeks of oral administration of L-NAME by gavage. Arterial pressure was measured weekly in conscious rats by a tail-cuff method and renal function tests were performed in individual metabolic cages 0, 7, 14 and 21 days after the beginning of L-NAME administration. L-NAME (60 mg kg-1 day-1) progressively increased arterial pressure from 108 ± 6.0 to 149 ± 12 mmHg (P<0.05) in the sham-operated group by the third week of treatment which was accompanied by a fall in creatinine clearance from 336 ± 18 to 222 ± 59 µl min-1 100 g body weight-1 (P<0.05) and a rise in fractional urinary sodium excretion from 0.2 ± 0.04 to 1.62 ± 0.35% (P<0.05) and in sodium post-proximal fractional excretion from 0.54 ± 0.09 to 4.7 ± 0.86% (P<0.05). The development of hypertension was significantly delayed and attenuated in denervated L-NAME-treated rats. This was accompanied by a striking additional increase in fractional renal sodium and potassium excretion from 0.2 ± 0.04 to 4.5 ± 1.6% and from 0.1 ± 0.015 to 1.21 ± 0.37%, respectively, and an enhanced post-proximal sodium excretion compared to the sham-operated group. These differences occurred despite an unchanged creatinine clearance and Na+ filtered load. These results suggest that bilateral renal denervation delayed and attenuated the L-NAME-induced hypertension by promoting an additional decrease in tubule sodium reabsorption in the post-proximal segments of nephrons. Much of the hypertension caused by chronic NO synthesis inhibition is thus dependent on renal nerve activity.

Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.