oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2019 ( 146 )

2018 ( 320 )

2017 ( 295 )

2016 ( 454 )

Custom range...

Search Results: 1 - 10 of 297452 matches for " J. Spang "
All listed articles are free for downloading (OA Articles)
Page 1 /297452
Display every page Item
La líricopintura. Sobre las interferencias entre lírica y pintura
Kurt Spang
Impossibilia : Revista Internacional de Estudios Literarios , 2012,
Abstract: Este artículo pretende mostrar, estudiando poemas de Rafael Alberti, que las interrelacionesentre las artes pueden ser inmensamente enriquecedoras y poseen la capacidad de incitar no solamente ala creación en general sino a ampliar el abanico de recursos técnicos adaptándose los procedimientos deun arte a los de otro realizándolos, sin embargo, con el substrato que le es propio al arte que los acoge.Así el poema transforma el expediente realizado en el color y la línea pictórica en un recurso verbal. Unode los objetivos de esta colaboración artística puede ser el llamado Gesamtkunstwerk, la obra de artetotal.
Polar stratospheric cloud observations by MIPAS on ENVISAT: detection method, validation and analysis of the northern hemisphere winter 2002/2003
R. Spang,J. J. Remedios,L. J. Kramer,L. R. Poole
Atmospheric Chemistry and Physics Discussions , 2004,
Abstract: The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on ENVISAT has made extensive measurements of polar stratospheric clouds (PSCs) in the northern hemisphere winter 2002/2003. A PSC detection method, based on a ratio of radiances (the cloud index), has been implemented for MIPAS and is validated in this study with respect to ground based lidar and space borne occultation measurements. A very good correspondence in PSC sighting and cloud altitude between MIPAS detections and those of other instruments is found for cloud index values less than four. Comparisons with data from the Stratospheric Aerosol and Gas Experiment (SAGE) III are used to show further that the sensitivity of the MIPAS detection method for this threshold value of cloud index is approximately equivalent to an extinction limit of 10 3 km 1 at 1022 nm, a wavelength used by solar occultation experiments. The MIPAS cloud index data are subsequently used to examine, for the first time with any technique, the evolution of PSCs throughout the Arctic polar vortex up to a latitude of 90° north on a near-daily basis. We find that the winter of 2002/2003 is characterised by three phases of very different PSC activity: First, an unusual, extremely cold phase in the first three weeks of December resulted in high PSC occurrence rates. This was followed by a second phase of only moderate PSC activity from 5–13 January, separated from the first phase by a minor warming event. Finally there was a third phase from February to end of March where only sporadic and mostly weak PSC events took place. The composition of PSCs during the winter period has also been examined, exploiting particularly an infra-red spectral signature which is probably characteristic of NAT. The MIPAS observations show the presence of these particles on a number of occasions in December but very rarely in January. The PSC type differentiation from MIPAS indicates that future comparisons of PSC observations with microphysical and denitrification models might be revealing about aspects of solid particle existence and location.
Stability of Facial Affective Expressions in Schizophrenia
H. Fatouros-Bergman,J. Spang,J. Merten,G. Preisler,A. Werbart
Schizophrenia Research and Treatment , 2012, DOI: 10.1155/2012/867424
Abstract: Thirty-two videorecorded interviews were conducted by two interviewers with eight patients diagnosed with schizophrenia. Each patient was interviewed four times: three weekly interviews by the first interviewer and one additional interview by the second interviewer. 64 selected sequences where the patients were speaking about psychotic experiences were scored for facial affective behaviour with Emotion Facial Action Coding System (EMFACS). In accordance with previous research, the results show that patients diagnosed with schizophrenia express negative facial affectivity. Facial affective behaviour seems not to be dependent on temporality, since within-subjects ANOVA revealed no substantial changes in the amount of affects displayed across the weekly interview occasions. Whereas previous findings found contempt to be the most frequent affect in patients, in the present material disgust was as common, but depended on the interviewer. The results suggest that facial affectivity in these patients is primarily dominated by the negative emotions of disgust and, to a lesser extent, contempt and implies that this seems to be a fairly stable feature.
Stability of Facial Affective Expressions in Schizophrenia
H. Fatouros-Bergman,J. Spang,J. Merten,G. Preisler,A. Werbart
Schizophrenia Research and Treatment , 2012, DOI: 10.1155/2012/867424
Abstract: Thirty-two videorecorded interviews were conducted by two interviewers with eight patients diagnosed with schizophrenia. Each patient was interviewed four times: three weekly interviews by the first interviewer and one additional interview by the second interviewer. 64 selected sequences where the patients were speaking about psychotic experiences were scored for facial affective behaviour with Emotion Facial Action Coding System (EMFACS). In accordance with previous research, the results show that patients diagnosed with schizophrenia express negative facial affectivity. Facial affective behaviour seems not to be dependent on temporality, since within-subjects ANOVA revealed no substantial changes in the amount of affects displayed across the weekly interview occasions. Whereas previous findings found contempt to be the most frequent affect in patients, in the present material disgust was as common, but depended on the interviewer. The results suggest that facial affectivity in these patients is primarily dominated by the negative emotions of disgust and, to a lesser extent, contempt and implies that this seems to be a fairly stable feature. 1. Introduction Studies of facial behaviour have shown a clear reduction of facial expressiveness and of facial affective expressiveness in patients diagnosed with schizophrenia [1–11]. The reduction of facial expressiveness is especially prominent in the upper face [12] and has also been observed while patients are confronted with emotional stimuli [6, 10, 12, 13] as well as when they are imitating emotional stimuli [14]. Interestingly, most findings point at the conclusion that reduction of facial expressiveness is not correlated with impaired emotional experience [8, 10, 11]. The patients’ capacity for emotional recognition is also reduced [11], a very robust finding according to a recent review [15]. However, facial emotional expressiveness is also reduced in patients with depression [6, 12], but patients with schizophrenia are still distinguished in this respect from patients with depression, Parkinson’s disease, and right hemisphere brain damage [10] since their diminished expressiveness is more prominent. Furthermore, patients with schizophrenia have been found to limit their facial affective repertoire to mainly negative affective expressions [7, 11, 16], an observation that may be present even before the clinical onset of psychosis [17]. Contempt was found to be the most frequent affect shown by these patients [5, 7, 18] and they showed significantly less happiness compared to healthy subjects [5]. Healthy
ReseqChip: Automated integration of multiple local context probe data from the MitoChip array in mitochondrial DNA sequence assembly
Marian Thieme, Claudio Lottaz, Harald Niederst?tter, Walther Parson, Rainer Spang, Peter J Oefner
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-440
Abstract: We provide ReseqChip, a free software that automates the process of resequencing mtDNA using multiple local context probes on the MitoChip v2.0. ReseqChip significantly improves base call rate and sequence accuracy. ReseqChip is available at http:/ / code.open-bio.org/ svnweb/ index.cgi/ bioperl/ browse/ bioperl-live/ trunk/ Bio/ Microarray/ Tools/ webcite.ReseqChip allows for the automated consolidation of base calls from alternative local mt genome context probes. It thereby improves the accuracy of resequencing, while reducing the number of non-called bases.The human mitochondrial (mt) DNA is a double-stranded circular molecule of 16,569 base pairs (bp) and consists of two parts: The non-coding displacement loop, also referred to as the control region, and the coding region. The control region is 1,124 bp in size and encompasses the nucleotide positions (nps) 16,024 to 576. It contains transcription and replication elements. The hypervariable segments HVS I (nps 16,024-16,383) and HVS II (nps 57-372) within the control region are hotspots for mtDNA alterations. The mutation rate of the hypervariable segments is tenfold higher than that of the coding region [1], whose mutation rate is already 10 times higher than that of nuclear genomic DNA because of the lack of protective histones, inefficient DNA repair systems and continuous exposure to mutagenic effects of oxygen radicals generated by oxidative phosphorylation [2]. The mtDNA coding region, on the other hand, contains 37 genes coding for two ribosomal RNAs and 22 transfer RNAs, which are required for intramitochondrial translation, as well as 13 polypeptides, which are components of the respiratory chain enzyme complexes in the inner membrane of the mitochondria that are essential in the energy production of the human cell. While most human cells contain two copies of nuclear DNA, they may possess up to 100,000 copies of mtDNA [3]. The majority of these copies are identical or homoplasmic immediately after bir
Close Encounters of the Third Domain: The Emerging Genomic View of Archaeal Diversity and Evolution
Anja Spang,Joran Martijn,Jimmy H. Saw,Anders E. Lind,Lionel Guy,Thijs J. G. Ettema
Archaea , 2013, DOI: 10.1155/2013/202358
Abstract: The Archaea represent the so-called Third Domain of life, which has evolved in parallel with the Bacteria and which is implicated to have played a pivotal role in the emergence of the eukaryotic domain of life. Recent progress in genomic sequencing technologies and cultivation-independent methods has started to unearth a plethora of data of novel, uncultivated archaeal lineages. Here, we review how the availability of such genomic data has revealed several important insights into the diversity, ecological relevance, metabolic capacity, and the origin and evolution of the archaeal domain of life. 1. Introduction The description of the three (cellular) domains of life—Eukarya, Bacteria, and Archaea—by Carl Woese and George Fox [1] represents a milestone in the modern era of microbiology. In particular, using phylogenetic reconstructions of the small-subunit (16S or 18S) ribosomal RNA gene, Woese discovered that microscopically indistinguishable prokaryotes are not a homogeneous assemblage but are comprised of two fundamentally different groups of organisms: Eubacteria (later Bacteria) on one side and an additional life form referred to as Archaebacteria (later Archaea) on the other side [1]. Though not immediately accepted by the scientific community, this finding was early on supported by Wolfram Zillig through his studies on DNA-dependent RNA polymerases, as well as by Otto Kandler investigating “bacterial” cell walls [2]. Indeed, a subset of prokaryotic organisms subsequently assigned to Archaea was found to harbor DNA-dependent RNA polymerases that bore more similarity to those of eukaryotes, and to contain proteinaceous cell walls that lack peptidoglycan as well as cell membranes composed of L-glycerol ether lipids with isoprenoid chains instead of D-glycerol ester lipids with fatty acid chains [3–6]. Since then, further investigation of cellular characteristics of archaea has revealed that this domain of life contains eukaryotic-like information-processing machineries [7–14]. These findings were later supported by genome sequences and comparative analyses of genes coding for replication, transcription, and translation machineries as well as by protein crystal structures [15–21]. Additionally, some archaeal lineages were shown to contain homologs of eukaryotic cell division and cytoskeleton genes as well as histones and seem to express a chromatin architecture similar to eukaryotes [22–28]. In contrast to information-processing and cell division genes, archaeal operational systems (energy metabolism, biosynthesis pathways, and regulation) often
Microarray Based Diagnosis Profits from Better Documentation of Gene Expression Signatures
Dennis Kostka ,Rainer Spang
PLOS Computational Biology , 2008, DOI: 10.1371/journal.pcbi.0040022
Abstract: Microarray gene expression signatures hold great promise to improve diagnosis and prognosis of disease. However, current documentation standards of such signatures do not allow for an unambiguous application to study-external patients. This hinders independent evaluation, effectively delaying the use of signatures in clinical practice. Data from eight publicly available clinical microarray studies were analyzed and the consistency of study-internal with study-external diagnoses was evaluated. Study-external classifications were based on documented information only. Documenting a signature is conceptually different from reporting a list of genes. We show that even the exact quantitative specification of a classification rule alone does not define a signature unambiguously. We found that discrepancy between study-internal and study-external diagnoses can be as frequent as 30% (worst case) and 18% (median). By using the proposed documentation by value strategy, which documents quantitative preprocessing information, the median discrepancy was reduced to 1%. The process of evaluating microarray gene expression diagnostic signatures and bringing them to clinical practice can be substantially improved and made more reliable by better documentation of the signatures.
Selecting normalization genes for small diagnostic microarrays
Jochen Jaeger, Rainer Spang
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-388
Abstract: In this paper we point out the differences of normalizing large microarrays and small diagnostic microarrays. We suggest to include additional normalization genes on the small diagnostic microarrays and propose two strategies for selecting them from genomewide microarray studies. The first is a data driven univariate selection of normalization genes. The second is multivariate and based on finding a balanced diagnostic signature. Finally, we compare both methods to standard normalization protocols known from large microarrays.Not including additional genes for normalization on small microarrays leads to a loss of diagnostic information. Using house keeping genes from the literature for normalization fails to work for certain datasets. While a data driven selection of additional normalization genes works well, the best results were obtained using a balanced signature.Several publications have suggested the use of cDNA-microarrays for clinical diagnosis [1-4]. While today's microarrays can cover up to 50,000 genes, only a small percentage of them is needed for diagnosis. Most diagnostic microarray datasets can achieve optimal classification with no more than 5–50 discriminative genes [5-7]. This opens new possibilities for the design of small diagnostic microarrays used for gene expression based diagnosis.To design such disease specific, small custom arrays differential genes are identified from a large set of potential candidate genes using genome wide expression profiling. Then, only these differential genes are put onto a small custom microarray [8]. Throughout this paper, we refer to diagnostic microarrays as small, custom microarrays for diagnostic purpose holding only few genes and large microarrays as genomewide gene expression microarrays, holding tens of thousands of genes.With the concept of diagnostic microarrays new problems arise. A first important step in microarray analysis is normalization. The overall intensity of microarrays can vary in a large datas
stam – a Bioconductor compliant R package for structured analysis of microarray data
Claudio Lottaz, Rainer Spang
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-211
Abstract: We present a computational tool for semi-supervised molecular disease entity detection. It automatically discovers molecular heterogeneities in phenotypically defined disease entities and suggests alternative molecular sub-entities of clinical phenotypes. This is done using both gene expression data and functional gene annotations.We provide stam, a Bioconductor compliant software package for the statistical programming environment R. We demonstrate that our tool detects gene expression patterns, which are characteristic for only a subset of patients from an established disease entity. We call such expression patterns molecular symptoms. Furthermore, stam finds novel sub-group stratifications of patients according to the absence or presence of molecular symptoms.Our software is easy to install and can be applied to a wide range of datasets. It provides the potential to reveal so far indistinguishable patient sub-groups of clinical relevance.Microarray analysis is among the most promising clinical applications of modern genomics. It opens perspectives for more reliable and efficient diagnosis of established tumor entities [1,2], risk group determination [3,4], and the prediction of response to treatment [5]. In the supervised setting, various software tools implementing algorithms from statistical learning theory are available and have been evaluated in the context of microarray data (e.g. [6-10]).All these methods aim for reproducing or predicting predefined clinical phenotypes. However, often clinical phenotypes will not be homogeneous from a molecular point of view. For example, when distinguishing between recurrent and non-recurrent disease, it is of course possible that recurrence has various molecular backgrounds. If this is the case, one will expect different molecular changes in different patients, and purely supervised analysis is unsatisfactory.In several studies, unsupervised clustering algorithms have been applied to patient profiles, with the aim to defi
Inferring cellular networks – a review
Markowetz Florian,Spang Rainer
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-s6-s5
Abstract: In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.
Page 1 /297452
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.