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Ergodic subspace analysis
(2020)
Properties of psychological variables at the mean or variance level can differ between persons and within persons across multiple time points. For example, cross-sectional findings between persons of different ages do not necessarily reflect the development of a single person over time. Recently, there has been an increased interest in the difference between covariance structures, expressed by covariance matrices, that evolve between persons and within a single person over multiple time points. If these structures are identical at the population level, the structure is called ergodic. However, recent data confirms that ergodicity is not generally given, particularly not for cognitive variables. For example, the <i>g</i> factor that is dominant for cognitive abilities between persons seems to explain far less variance when concentrating on a single person’s data. However, other subdimensions of cognitive abilities seem to appear both between and within persons; that is, there seems to be a lower-dimensional subspace of cognitive abilities in which cognitive abilities are in fact ergodic. In this article, we present ergodic subspace analysis (ESA), a mathematical method to identify, for a given set of variables, which subspace is most important within persons, which is most important between person, and which is ergodic. Similar to the common spatial patterns method, the ESA method first whitens a joint distribution from both the between and the within variance structure and then performs a principle component analysis (PCA) on the between distribution, which then automatically acts as an inverse PCA on the within distribution. The difference of the eigenvalues allows a separation of the rotated dimensions into the three subspaces corresponding to within, between, and ergodic substructures. We apply the method to simulated data and to data from the COGITO study to exemplify its usage.
In Österreich kategorisiert ab 2019 ein Algorithmus arbeitslose Personen nach ihren Chancen auf dem Arbeitsmarkt. Die Software trennt in drei Personengruppen: Arbeitssuchende mit guten, mittleren und schlechten Perspektiven, einen Arbeitsplatz zu finden. Auf dieser Basis will der Arbeitsmarktservice Österreich (AMS) seine Ressourcen ab 2020 überwiegend auf Personen der mittleren Gruppe konzentrieren. Dort seien sie am effektivsten eingesetzt. Die "Arbeitsmarktintegrationschancen" von Frauen bewertet der Algorithmus pauschal negativ. Zudem führen betreuungspflichtige Kinder zu einer schlechten Einstufung – allerdings nur für Frauen. Bei Männern, so begründen die Entwickler, habe eine Betreuungspflicht statistisch gesehen keine negativen Auswirkungen auf die Arbeitsmarktchancen.
Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs).
Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups.
Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier applied to the data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal.
Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697)
Freund oder Feind?
(2019)
Bemühen wir uns um einen nüchternen Blick auf die "Fakten". Ein Hochschulprofessor betritt von Protesten begleitet einen Hörsaal, um seine Vorlesung zu halten. Aufgrund lauter Beschimpfungen und Störungen kann er diese Vorlesung nicht halten und verlässt den Campus schließlich zwei Stunden später unter Polizeischutz. Es handelt sich nicht um irgendeinen Professor, sondern um den Mann, der eine Partei gründete, vordergründig, um den Austritt Deutschlands aus der Eurozone zu erreichen und der auf der Pegida-Welle reitend eine rechtspopulistische Partei hervorbrachte, die ihre Umfragewerte von Unzufriedenheit und Enttäuschung nährt. Seit 2015 gehört er dieser Partei nicht mehr an. Samthandschuhe hat Bernd Lucke deswegen noch lange nicht verdient. Wie weit sollte aber der grundsätzlich berechtigte Protest gegen Lucke gehen?
Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs).
Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups.
Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier built based upon data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal.
Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697).
Schneller als erwartet fängt Donald Trump an, seine Versprechungen, mit denen er sich die Stimmen der radikalen Rechten im Wahlkampf erkauft hat, einzulösen. Und er scheint die Möglichkeit der Befriedung der Ultra-Rechten gefunden zu haben: die Nominierung eines neuen Richters am Obersten Gerichtshof der Vereinigten Staaten, dem Supreme Court.
Antisynthetase syndrome (ASSD) is a rare clinical condition that is characterized by the occurrence of a classic clinical triad, encompassing myositis, arthritis, and interstitial lung disease (ILD), along with specific autoantibodies that are addressed to different aminoacyl tRNA synthetases (ARS). Until now, it has been unknown whether the presence of a different ARS might affect the clinical presentation, evolution, and outcome of ASSD. In this study, we retrospectively recorded the time of onset, characteristics, clustering of triad findings, and survival of 828 ASSD patients (593 anti-Jo1, 95 anti-PL7, 84 anti-PL12, 38 anti-EJ, and 18 anti-OJ), referring to AENEAS (American and European NEtwork of Antisynthetase Syndrome) collaborative group’s cohort. Comparisons were performed first between all ARS cases and then, in the case of significance, while using anti-Jo1 positive patients as the reference group. The characteristics of triad findings were similar and the onset mainly began with a single triad finding in all groups despite some differences in overall prevalence. The “ex-novo” occurrence of triad findings was only reduced in the anti-PL12-positive cohort, however, it occurred in a clinically relevant percentage of patients (30%). Moreover, survival was not influenced by the underlying anti-aminoacyl tRNA synthetase antibodies’ positivity, which confirmed that antisynthetase syndrome is a heterogeneous condition and that antibody specificity only partially influences the clinical presentation and evolution of this condition.
COPA syndrome is a newly discovered hereditary immunodeficiency affecting the lung, kidneys, and joints. The mutated gene encodes the α subunit of the coatomer complex I, a protein transporter from the Golgi back to the endoplasmic reticulum. The impaired return of proteins leads to intracellular stress. The syndrome is an autoimmune and autoinflammatory disease that can be grouped among the interferonopathies. The knowledge about COPA syndrome and its treatment is still limited. In this paper, we describe an additional patient, a 15-year-old girl with rheumatoid factor-positive polyarthritis and rheumatoid nodules since the age of 2, who developed interstitial lung disease. The detected mutation c.698G>A was causing the disease. The patient presented with symmetric polyarthritis on wrists, fingers, and hip and ankle joints, with significant functional impairment, and high disease activity. Laboratory parameters demonstrated chronic inflammation, hypergammaglobulinemia, high titre ANA (antinuclear antibodies) and CCP (anti-citrullinated protein) antibodies, and rheumatoid factors. Therapies with various DMARDs (Disease Modifying Anti-Rheumatic Drugs) and biologicals failed. Upon baricitinib application, the clinical activity decreased dramatically with disappearance of joint pain and morning stiffness and significant decrease of joint swelling. A low disease activity was reached after 12 months, with complete disappearance of rheumatoid nodules. In contrast to IL-1 (interleukin-1), IL-6, and TNF (tumor necrosis factor) inhibitors, baricitinib was very successful, probably because baricitinib acts as a JAK-1/2 (janus kinase-1/2) inhibitor in the IFNα/β (inteferone α/β) pathway. A relatively higher dose in children is necessary. COPA syndrome represents a novel disorder of intracellular transport. Reviewing published literature on COPA syndrome, in addition to our patient, there were 31 cases further described.
Chimeric antigen receptor (CAR) T cells are a novel class of anti-cancer therapy in which autologous or allogeneic T cells are engineered to express a CAR targeting a membrane antigen. In Europe, tisagenlecleucel (Kymriah™) is approved for the treatment of refractory/relapsed acute lymphoblastic leukemia in children and young adults as well as relapsed/refractory diffuse large B-cell lymphoma, while axicabtagene ciloleucel (Yescarta™) is approved for the treatment of relapsed/refractory high-grade B-cell lymphoma and primary mediastinal B-cell lymphoma. Both agents are genetically engineered autologous T cells targeting CD19. These practical recommendations, prepared under the auspices of the European Society of Blood and Marrow Transplantation, relate to patient care and supply chain management under the following headings: patient eligibility, screening laboratory tests and imaging and work-up prior to leukapheresis, how to perform leukapheresis, bridging therapy, lymphodepleting conditioning, product receipt and thawing, infusion of CAR T cells, short-term complications including cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome, antibiotic prophylaxis, medium-term complications including cytopenias and B-cell aplasia, nursing and psychological support for patients, long-term follow-up, post-authorization safety surveillance, and regulatory issues. These recommendations are not prescriptive and are intended as guidance in the use of this novel therapeutic class.
In this paper, we developed a method to extract item-level response times from log data that are available in computer-based assessments (CBA) and paper-based assessments (PBA) with digital pens. Based on response times that were extracted using only time differences between responses, we used the bivariate generalized linear IRT model framework (B-GLIRT, [1]) to investigate response times as indicators for response processes. A parameterization that includes an interaction between the latent speed factor and the latent ability factor in the cross-relation function was found to fit the data best in CBA and PBA. Data were collected with a within-subject design in a national add-on study to PISA 2012 administering two clusters of PISA 2009 reading units. After investigating the invariance of the measurement models for ability and speed between boys and girls, we found the expected gender effect in reading ability to coincide with a gender effect in speed in CBA. Taking this result as indication for the validity of the time measures extracted from time differences between responses, we analyzed the PBA data and found the same gender effects for ability and speed. Analyzing PBA and CBA data together we identified the ability mode effect as the latent difference between reading measured in CBA and PBA. Similar to the gender effect the mode effect in ability was observed together with a difference in the latent speed between modes. However, while the relationship between speed and ability is identical for boys and girls we found hints for mode differences in the estimated parameters of the cross-relation function used in the B-GLIRT model.