870 resultados para Classification of sciences
Resumo:
Tropical cyclogenesis is generally considered to occur in regions devoid of baroclinic structures; however, an appreciable number of tropical cyclones (TCs) form in baroclinic environments each year. A global climatology of these baroclinically influenced TC developments is presented in this study. An objective classification strategy is developed that focuses on the characteristics of the environmental state rather than on properties of the vortex, thus allowing for a pointwise “development pathway” classification of reanalysis data. The resulting climatology shows that variability within basins arises primarily as a result of local surface thermal contrasts and the positions of time-mean features on the subtropical tropopause. The pathway analyses are sampled to generate a global climatology of 1948–2010 TC developments classified by baroclinic influence: nonbaroclinic (70%), low-level baroclinic (9%), trough induced (5%), weak tropical transition (11%), and strong tropical transition (5%). All basins other than the North Atlantic are dominated by nonbaroclinic events; however, there is extensive interbasin variability in secondary development pathways. Within each basin, subregions and time periods are identified in which the relative importance of the development pathways also differs. The efficiency of tropical cyclogenesis is found to be highly dependent on development pathway. The peak efficiency defined in the classification subspace straddles the nonbaroclinic/trough-induced boundary, suggesting that the optimal environment for TC development includes a baroclinic contribution from an upper-level disturbance. By assessing the global distribution of baroclinically influenced TC formations, this study identifies regions and pathways whose further study could yield improvements in our understanding of this important subset of TC developments.
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The delineation of shifting cultivation landscapes using remote sensing in mountainous regions is challenging. On the one hand, there are difficulties related to the distinction of forest and fallow forest classes as occurring in a shifting cultivation landscape in mountainous regions. On the other hand, the dynamic nature of the shifting cultivation system poses problems to the delineation of landscapes where shifting cultivation occurs. We present a two-step approach based on an object-oriented classification of Advanced Land Observing Satellite, Advanced Visible and Near-Infrared Spectrometer (ALOS AVNIR) and Panchromatic Remote-sensing Instrument for Stereo Mapping (ALOS PRISM) data and landscape metrics. When including texture measures in the object-oriented classification, the accuracy of forest and fallow forest classes could be increased substantially. Based on such a classification, landscape metrics in the form of land cover class ratios enabled the identification of crop-fallow rotation characteristics of the shifting cultivation land use practice. By classifying and combining these landscape metrics, shifting cultivation landscapes could be delineated using a single land cover dataset.
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1 Natural soil profiles may be interpreted as an arrangement of parts which are characterized by properties like hydraulic conductivity and water retention function. These parts form a complicated structure. Characterizing the soil structure is fundamental in subsurface hydrology because it has a crucial influence on flow and transport and defines the patterns of many ecological processes. We applied an image analysis method for recognition and classification of visual soil attributes in order to model flow and transport through a man-made soil profile. Modeled and measured saturation-dependent effective parameters were compared. We found that characterizing and describing conductivity patterns in soils with sharp conductivity contrasts is feasible. Differently, solving flow and transport on the basis of these conductivity maps is difficult and, in general, requires special care for representation of small-scale processes.
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The sensory neurons (photoreceptors) in the visual system of Hermissenda are one site of plasticity produced by Pavlovian conditioning. A second site of plasticity produced by conditioning is the type I interneurons in the cerebropleural ganglia. Both photoreceptors and statocyst hair cells of the graviceptive system form monosynaptic connections with identified type I interneurons. Two proposed neurotransmitters in the graviceptive system, serotonin (5-HT) and gamma-aminobutyric acid (GABA), have been shown to modify synaptic strength and intrinsic neuronal excitability in identified photoreceptors. However, the potential role of 5-HT and GABA in plasticity of type I interneurons has not been investigated. Here we show that 5-HT increased the peak amplitude of light-evoked complex excitatory postsynaptic potentials (EPSPs), enhanced intrinsic excitability, and increased spike activity of identified type I(e(A)) interneurons. In contrast, 5-HT decreased spike activity and intrinsic excitability of type I(e(B)) interneurons. The classification of two categories of type I(e) interneurons was also supported by the observation that 5-HT produced opposite effects on whole cell steady-state outward currents in type I(e) interneurons. Serotonin produced a reduction in the amplitude of light-evoked complex inhibitory PSPs (IPSPs), increased spontaneous spike activity, decreased intrinsic excitability, and depolarized the resting membrane potential of identified type I(i) interneurons. In contrast to the effects of 5-HT, GABA produced inhibition in both types of I(e) interneurons and type I(i) interneurons. These results show that 5-HT and GABA can modulate the intrinsic excitability of type I interneurons independent of the presynaptic effects of the same transmitters on excitability and synaptic efficacy of photoreceptors.
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Monte Carlo simulation is a powerful method in many natural and social sciences. But what sort of method is it? And where does its power come from? Are Monte Carlo simulations experiments, theories or something else? The aim of this talk is to answer these questions and to explain the power of Monte Carlo simulations. I provide a classification of Monte Carlo techniques and defend the claim that Monte Carlo simulation is a sort of inference.
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A graphing method was developed and tested to estimate gestational ages pre-and postnatally in a consistent manner for epidemiological research and clinical purposes on feti/infants of women with few consistent prenatal estimators of gestational age. Each patient's available data was plotted on a single page graph to give a comprehensive overview of that patient. A hierarchical classification of gestational age determination was then applied in a systematic manner, and reasonable gestational age estimates were produced. The method was tested for validity and reliability on 50 women who had known dates for their last menstrual period or dates of conception, and multiple ultrasound examinations and other gestational age estimating measures. The feasibility of the procedure was then tested on 1223 low income women with few gestational age estimators. The graphing method proved to have high inter- and intrarater reliability. It was quick, easy to use, inexpensive, and did not require special equipment. The graphing method estimate of gestational age for each infant was tested against the last menstrual period gestational age estimate using paired t-Tests, F tests and the Kolmogorov-Smirnov test of similar populations, producing a 98 percent probability or better that the means and data populations were the same. Less than 5 percent of the infants' gestational ages were misclassified using the graphing method, much lower than the amount of misclassification produced by ultrasound or neonatal examination estimates. ^
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Gravity wants to pull an ice sheet to the center of the Earth, but cannot because the Earth's crust is in the way, so ice is pushed out sideways instead. Or is it? The ice sheet "sees" nothing preventing it from spreading out except air, which is much less massive than ice. Therefore, does not ice rush forward to fill this relative vacuum; does not the relative vacuum suck ice into it, because Nature abhors a vacuum? If so, the ice sheet is not only pulled downward by gravity, it is also pulled outward by the relative vacuum. This pulling outward will be most rapid where the ice sheet encounters least resistance. The least resistance exists along the bed of ice streams, where ice-bed coupling is reduced by a basal water layer, especially if the ice stream becomes afloat and the floating part is relatively unconfined around its perimeter and unpinned to the sea floor. Ice streams are therefore fast currents of ice that develop near the margins of an ice sheet where these conditions exist. Because of these conditions, ice streams pull ice out of ice sheets and have pulling power equal to the longitudinal gravitational pulling force multiplied by the ice-stream velocity. These boundary conditions beneath and beyond ice streams can be quantified by a basal buoyancy factor that provides a life-cycle classification of ice streams into inception, growth, mature, declining and terminal stages, during which ice streams disintegrate the ice sheet. Surface profiles of ice streams are diagnostic of the stage in a life cycle and, hence, of the vitality of the ice sheet.
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This paper is the maritime and sub–Antarctic contribution to the Scientific Committee for Antarctic Research (SCAR) Past Antarctic Ice Sheet Dynamics (PAIS) community Antarctic Ice Sheet reconstruction. The overarching aim for all sectors of Antarctica was to reconstruct the Last Glacial Maximum (LGM) ice sheet extent and thickness, and map the subsequent deglaciation in a series of 5000 year time slices. However, our review of the literature found surprisingly few high quality chronological constraints on changing glacier extents on these timescales in the maritime and sub–Antarctic sector. Therefore, in this paper we focus on an assessment of the terrestrial and offshore evidence for the LGM ice extent, establishing minimum ages for the onset of deglaciation, and separating evidence of deglaciation from LGM limits from those associated with later Holocene glacier fluctuations. Evidence included geomorphological descriptions of glacial landscapes, radiocarbon dated basal peat and lake sediment deposits, cosmogenic isotope ages of glacial features and molecular biological data. We propose a classification of the glacial history of the maritime and sub–Antarctic islands based on this assembled evidence. These include: (Type I) islands which accumulated little or no LGM ice; (Type II) islands with a limited LGM ice extent but evidence of extensive earlier continental shelf glaciations; (Type III) seamounts and volcanoes unlikely to have accumulated significant LGM ice cover; (Type IV) islands on shallow shelves with both terrestrial and submarine evidence of LGM (and/or earlier) ice expansion; (Type V) Islands north of the Antarctic Polar Front with terrestrial evidence of LGM ice expansion; and (Type VI) islands with no data. Finally, we review the climatological and geomorphological settings that separate the glaciological history of the islands within this classification scheme.
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AIMS To assess whether in oligoastrocytomas ATRX deficiency - as a surrogate of the alternative lengthening of telomeres (ALT) pathway - has a role in predicting the presence or absence of loss of heterozygosity of 1p and 19q (LOH), the genetic signature of oligodendroglial differentiation and a favourable prognostic marker. METHODS AND RESULTS A series of 54 oligoastrocytomas were investigated by immunohistochemistry as well as microsatellite analysis for LOH 1p19q. Genetic findings were correlated with morphological assessment. CONCLUSIONS ATRX deficiency was mutually exclusive with LOH. Conversely, ATRX-proficient tumours immunoreactive for R132H-mutant isocitrate dehydrogenase 1 (IDH1) showed a high rate (85%) of LOH. A more oligodendroglioma-like morphology was associated with a higher rate of LOH even in the morphologically ambiguous group of oligoastrocytomas. Our findings support the concept that oligoastrocytomas represent a morphological grey zone rather than a group of truly "mixed" or "intermediate" tumours. More precise classification of diffuse gliomas may also improve grading of borderline cases. We propose an immunohistochemical algorithm for classification of morphologically ambiguous diffuse gliomas. This article is protected by copyright. All rights reserved.
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The Semail ophiolite in Oman is capped by up to 2 km of basaltic-andesitic lavas that host copper-dominant, Cyprus-type, volcanogenic massive sulfide (VMS) deposits. This study identifies multiple volcanostratigraphic horizons on which the deposits are situated, based on characterization of footwall and hanging-wall lavas from 16 deposits or deposit clusters. Comparison of field and petrographic features, compositions of igneous clinopyroxenes, and whole-rock geochemical signatures permits classification of the lavas within a modified version of the established regional volcanostratigraphy. Four extrusive units host deposits: Geotimes (earliest), Lasail, Alley, and Boninitic Alley (latest). The latter was previously known only at few localities, but this study reveals its regional extent and significance as a host for VMS deposits. The Geotimes and Lasail units represent Late Cretaceous, ocean spreading ridge and related off-axis volcanic environments, respectively. The Alley and Boninitic Alley units represent younger, subduction-related volcanism prior to Coniacian-Santonian obduction of the ophiolite. Our results show that VMS deposits occur on or near the Geotimes/Lasail and Geotimes/Alley contacts as well as entirely within the Geotimes, Lasail, Alley, and Boninitic Alley units. Highest Cu grades tend to occur in deposits lying on or within the Geotimes, whereas highest Au grades occur in deposits within the Boninitic Alley. In contrast to earlier studies, we conclude that essentially every horizon marking a hiatus in lava deposition in the Semail ophiolite, i.e., contacts between the four major eruptive units, and umbers and sedimentary chert layers within the units, has exploration potential for Cu-Au VMS deposits.
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PURPOSE To observe changes in fundus autofluorescence 2 years after implantation of blue light-filtering (yellow-tinted) and ultraviolet light-filtering (colorless) intraocular lenses (IOLs). SETTING Department of Ophthalmology and Visual Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan, and the Department of Ophthalmology, University of Bern, Bern, Switzerland. DESIGN Prospective comparative observational study. METHODS Patients were enrolled who had cataract surgery with implantation of a yellow-tinted or colorless IOL and for whom images were obtained on which the fundus autofluorescence was measurable using the Heidelberg Retina Angiogram 2 postoperatively. The fundus autofluorescence in the images was classified into 8 abnormal patterns based on the classification of the International Fundus Autofluorescence Classification Group, The presence of normal fundus autofluorescence, geographic atrophy, and wet age-related macular degeneration (AMD) also was recorded. The fundus findings at baseline and 2 years postoperatively were compared. RESULTS Fifty-two eyes with a yellow-tinted IOL and 79 eyes with a colorless IOL were included. Abnormal fundus autofluorescence did not develop or increase in the yellow-tinted IOL group; however, progressive abnormal fundus autofluorescence developed or increased in 12 eyes (15.2%) in the colorless IOL group (P = .0016). New drusen, geographic atrophy, and choroidal neovascularization were observed mainly in the colorless IOL group. The incidence of AMD was statistically significantly higher in the colorless IOL group (P = .042). CONCLUSIONS Two years after cataract surgery, significant differences were seen in the progression of abnormal fundus autofluorescence between the 2 groups. The incidence of AMD was lower in eyes with a yellow-tinted IOL. FINANCIAL DISCLOSURE No author has a financial or proprietary interest in any material or method mentioned.
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The classification of neuroendocrine neoplasms (NENs) has been evolving steadily over the last decades. Important prognostic factors of NENs are their proliferative activity and presence/absence of necrosis. These factors are reported in NENs of all body sites; however, the terminology as well as the exact rules of classification differ according to the location of the primary tumor. Only in gastroenteropancreatic (GEP) NENs a formal grading is performed. This grading is based on proliferation assessed by the mitotic count and/or Ki-67 proliferation index. In the lung, NEN grading is an intrinsic part of the tumor designation with typical carcinoids corresponding to neuroendocrine tumor (NET) G1 and atypical carcinoids to NET G2; however, the presence or absence of necrotic foci is as important as proliferation for the differentiation between typical and atypical carcinoids. Immunohistochemical markers can be used to demonstrate neuroendocrine differentiation. Synaptophysin and chromogranin A are, to date, the most reliable and most commonly used for this purpose. Beyond this, other markers can be helpful, for example in the situation of a NET metastasis of unknown primary, where a hormonal profile or a panel of transcription factors can give hints to the primary site. Many immunohistochemical markers have been shown to correlate with prognosis but are not used in clinical practice, for example cytokeratin 19 and KIT expression in pancreatic NETs. There is no predictive biomarker in use, with the exception of somatostatin receptor (SSTR) 2 expression for predicting the amenability of a tumor to in vivo SSTR targeting for imaging or therapy.
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Recurrent wheezing or asthma is a common problem in children that has increased considerably in prevalence in the past few decades. The causes and underlying mechanisms are poorly understood and it is thought that a numb er of distinct diseases causing similar symptoms are involved. Due to the lack of a biologically founded classification system, children are classified according to their observed disease related features (symptoms, signs, measurements) into phenotypes. The objectives of this PhD project were a) to develop tools for analysing phenotypic variation of a disease, and b) to examine phenotypic variability of wheezing among children by applying these tools to existing epidemiological data. A combination of graphical methods (multivariate co rrespondence analysis) and statistical models (latent variables models) was used. In a first phase, a model for discrete variability (latent class model) was applied to data on symptoms and measurements from an epidemiological study to identify distinct phenotypes of wheezing. In a second phase, the modelling framework was expanded to include continuous variability (e.g. along a severity gradient) and combinations of discrete and continuo us variability (factor models and factor mixture models). The third phase focused on validating the methods using simulation studies. The main body of this thesis consists of 5 articles (3 published, 1 submitted and 1 to be submitted) including applications, methodological contributions and a review. The main findings and contributions were: 1) The application of a latent class model to epidemiological data (symptoms and physiological measurements) yielded plausible pheno types of wheezing with distinguishing characteristics that have previously been used as phenotype defining characteristics. 2) A method was proposed for including responses to conditional questions (e.g. questions on severity or triggers of wheezing are asked only to children with wheeze) in multivariate modelling.ii 3) A panel of clinicians was set up to agree on a plausible model for wheezing diseases. The model can be used to generate datasets for testing the modelling approach. 4) A critical review of methods for defining and validating phenotypes of wheeze in children was conducted. 5) The simulation studies showed that a parsimonious parameterisation of the models is required to identify the true underlying structure of the data. The developed approach can deal with some challenges of real-life cohort data such as variables of mixed mode (continuous and categorical), missing data and conditional questions. If carefully applied, the approach can be used to identify whether the underlying phenotypic variation is discrete (classes), continuous (factors) or a combination of these. These methods could help improve precision of research into causes and mechanisms and contribute to the development of a new classification of wheezing disorders in children and other diseases which are difficult to classify.
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Background The RCSB Protein Data Bank (PDB) provides public access to experimentally determined 3D-structures of biological macromolecules (proteins, peptides and nucleic acids). While various tools are available to explore the PDB, options to access the global structural diversity of the entire PDB and to perceive relationships between PDB structures remain very limited. Methods A 136-dimensional atom pair 3D-fingerprint for proteins (3DP) counting categorized atom pairs at increasing through-space distances was designed to represent the molecular shape of PDB-entries. Nearest neighbor searches examples were reported exemplifying the ability of 3DP-similarity to identify closely related biomolecules from small peptides to enzyme and large multiprotein complexes such as virus particles. The principle component analysis was used to obtain the visualization of PDB in 3DP-space. Results The 3DP property space groups proteins and protein assemblies according to their 3D-shape similarity, yet shows exquisite ability to distinguish between closely related structures. An interactive website called PDB-Explorer is presented featuring a color-coded interactive map of PDB in 3DP-space. Each pixel of the map contains one or more PDB-entries which are directly visualized as ribbon diagrams when the pixel is selected. The PDB-Explorer website allows performing 3DP-nearest neighbor searches of any PDB-entry or of any structure uploaded as protein-type PDB file. All functionalities on the website are implemented in JavaScript in a platform-independent manner and draw data from a server that is updated daily with the latest PDB additions, ensuring complete and up-to-date coverage. The essentially instantaneous 3DP-similarity search with the PDB-Explorer provides results comparable to those of much slower 3D-alignment algorithms, and automatically clusters proteins from the same superfamilies in tight groups. Conclusion A chemical space classification of PDB based on molecular shape was obtained using a new atom-pair 3D-fingerprint for proteins and implemented in a web-based database exploration tool comprising an interactive color-coded map of the PDB chemical space and a nearest neighbor search tool. The PDB-Explorer website is freely available at www.cheminfo.org/pdbexplorer and represents an unprecedented opportunity to interactively visualize and explore the structural diversity of the PDB.
Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
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Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.