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Start Thinking in Systems / Berthold Kracke
Buying into Fraud – German Retail Investors and the Wirecard Scandal / Konstantin Bräuer, Andreas Hackethal, Guido Lenz, Thomas Pauls
Insights from Explainable Interactive Machine Learning in the Age of COVID-19 / Oliver Hinz, Nicolas Pfeuffer, Wolfgang Stammer, Patrick Schramowski, Benjamin M. Abdel-Karim, Andreas Bucher, Christian Hügel, Gernot Rohde, Kristian Kersting
The Customer Determines the Success or Failure of the Company : interview with Philipp Schmitt
The ongoing digitalization of educational resources and the use of the internet lead to a steady increase of potentially available learning media. However, many of the media which are used for educational purposes have not been designed specifically for teaching and learning. Usually, linguistic criteria of readability and comprehensibility as well as content-related criteria are used independently to assess and compare the quality of educational media. This also holds true for educational media used in economics. This article aims to improve the analysis of textual learning media used in economic education by drawing on threshold concepts. Threshold concepts are key terms in knowledge acquisition within a domain. From a linguistic perspective, however, threshold concepts are instances of specialized vocabularies, exhibiting particular linguistic features. In three kinds of (German) resources, namely in textbooks, in newspapers, and on Wikipedia, we investigate the distributive profiles of 63 threshold concepts identified in economics education (which have been collected from threshold concept research). We looked at the threshold concepts' frequency distribution, their compound distribution, and their network structure within the three kinds of resources. The two main findings of our analysis show that firstly, the three kinds of resources can indeed be distinguished in terms of their threshold concepts' profiles. Secondly, Wikipedia definitely shows stronger associative connections between economic threshold concepts than the other sources. We discuss the findings in relation to adequate media use for teaching and learning—not only in economic education.
Introduction Occurrence of inaccurate or delayed diagnoses is a significant concern in patient care, particularly in emergency medicine, where decision making is often constrained by high throughput and inaccurate admission diagnoses. Artificial intelligence-based diagnostic decision support system have been developed to enhance clinical performance by suggesting differential diagnoses to a given case, based on an integrated medical knowledge base and machine learning techniques. The purpose of the study is to evaluate the diagnostic accuracy of Ada, an app-based diagnostic tool and the impact on patient outcome.
Methods and analysis The eRadaR trial is a prospective, double-blinded study with patients presenting to the emergency room (ER) with abdominal pain. At initial contact in the ER, a structured interview will be performed using the Ada-App and both, patients and attending physicians, will be blinded to the proposed diagnosis lists until trial completion. Throughout the study, clinical data relating to diagnostic findings and types of therapy will be obtained and the follow-up until day 90 will comprise occurrence of complications and overall survival of patients. The primary efficacy of the trial is defined by the percentage of correct diagnoses suggested by Ada compared with the final discharge diagnosis. Further, accuracy and timing of diagnosis will be compared with decision making of classical doctor–patient interaction. Secondary objectives are complications, length of hospital stay and overall survival.
Ethics and dissemination Ethical approval was received by the independent ethics committee (IEC) of the Goethe-University Frankfurt on 9 April 2020 including the patient information material and informed consent form. All protocol amendments must be reported to and adapted by the IEC. The results from this study will be submitted to peer-reviewed journals and reported at suitable national and international meetings.
Trial registration number DRKS00019098.
The Kinase Chemogenomic Set (KCGS): an open science resource for kinase vulnerability identification
(2021)
We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS), current Version 1.0, is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.
Drawing on the role of teachers for peer ecologies, we investigated whether students favored ethnically homogenous over ethnically diverse relationships, depending on classroom diversity and perceived teacher care. We specifically studied students’ intra- and interethnic relationships in classrooms with different ethnic compositions, accounting for homogeneous subgroups forming on the basis of ethnicity and gender diversity (i.e., ethnic-demographic faultlines). Based on multilevel social network analyses of dyadic networks between 1299 early adolescents in 70 German fourth grade classrooms, the results indicated strong ethnic homophily, particularly driven by German students who favored ethnically homogenous dyads over mixed dyads. As anticipated, the results showed that there was more in-group bias if perceived teacher care was low rather than high. Moreover, stronger faultlines were associated with stronger in-group bias; however, this relation was moderated by teacher care: If students perceived high teacher care, they showed a higher preference for mixed-ethnic dyads, even in classrooms with strong faultlines. These findings highlight the central role of teachers as agents of positive diversity management and the need to consider contextual classroom factors other than ethnic diversity when investigating intergroup relations in schools.
Transcription factors can serve as links between tumor microenvironment signaling and oncogenesis. Interferon regulatory factor 9 (IRF9) is recruited and expressed upon interferon stimulation and is dependent on cofactors that exert in tumor-suppressing or oncogenic functions via the JAK-STAT pathway. IRF9 is frequently overexpressed in human lung cancer and is associated with decreased patient survival; however, the underlying mechanisms remain to be elucidated. Here, we used stably transduced lung adenocarcinoma cell lines (A549 and A427) to overexpress or knockdown IRF9. Overexpression led to increased oncogenic behavior in vitro, including enhanced proliferation and migration, whereas knockdown reduced these effects. These findings were confirmed in vivo using lung tumor xenografts in nude mice, and effects on both tumor growth and tumor mass were observed. Using RNA sequencing, we identified versican (VCAN) as a novel downstream target of IRF9. Indeed, IRF9 and VCAN expression levels were found to be correlated. We showed for the first time that IRF9 binds at a newly identified response element in the promoter region of VCAN to regulate its transcription. Using an siRNA approach, VCAN was found to enable the oncogenic properties (proliferation and migration) of IRF9 transduced cells, perhaps with CDKN1A involvement. The targeted inhibition of IRF9 in lung cancer could therefore be used as a new treatment option without multimodal interference in microenvironment JAK-STAT signaling.
Inflammation is a crucial part of immune responses towards invading pathogens or tissue damage. While inflammatory reactions are aimed at removing the triggering stimulus, it is important that these processes are terminated in a coordinate manner to prevent excessive tissue damage due to the highly reactive inflammatory environment. Initiation of inflammatory responses was proposed to be regulated predominantly at a transcriptional level, whereas post-transcriptional modes of regulation appear to be crucial for resolution of inflammation. The RNA-binding protein tristetraprolin (TTP) interacts with AU-rich elements in the 3′ untranslated region of mRNAs, recruits deadenylase complexes and thereby facilitates degradation of its targets. As TTP regulates the mRNA stability of numerous inflammatory mediators, it was put forward as a crucial post-transcriptional regulator of inflammation. Here, we summarize the current understanding of the function of TTP with a specific focus on its role in adding to resolution of inflammation.
Protein ubiquitination is a post-translational modification that typically involves the conjugation of ubiquitin to substrate proteins via a three-enzyme cascade and regulates a wide variety of cellular processes. Recent studies have revealed that SidE family of Legionella effectors such as SdeA catalyzes novel phosphoribosyl-linked ubiquitination (PR-ubiquitination) of serines in host substrate proteins utilizing NAD+, without the need of E2, E3. The catalytic core of SdeA comprises a mono-ADP-ribosyltransferase (mART) domain that functions to ADP-ribosylate ubiquitin, and a phosphodiesterase (PDE) domain that processes ADP-ribosylated ubiquitin and transfers the resulting phosphoribosylated ubiquitin to serines of substrates.
To date, extensive efforts have been made to study the function of SdeA and mechanism of SdeA mediated PR-ubiquitination, however, the cellular effects of this novel ubiquitination and phosphoribosylation of ubiquitin remained poorly understood. In our study, using biochemical and cell biological approaches, we explored the biological effect of phosphoribosylation of ubiquitin caused by SdeA in cells. We found that phosphoribosylated ubiquitin is not available for conventional ubiquitination, thereby phosphoribosylation of ubiquitin impairs numerous classical ubiquitination related cellular processes including mitophagy, TNF-α signaling and proteasomal degradation.
The precise temporal regulation of the functions of bacterial effectors during Legionella infection by other effectors with antagonizing activities has been well studied so far. Not surprisingly, PR-ubiquitination catalyzed by SidE family effecters is tightly controlled as well, it has been long known that effector SidJ counteracts the toxicity of SdeA to yeast cells. Interestingly, in an experiment for verifying the activity of SidJ, we found that Legionella lysate lacking SidJ was still able to remove ubiquitin from PR-ubiquitinated substrates. Using biochemical approach we identified DupA and DupB, two Legionella bacterial effectors that specifically reverse the novel serine PR-ubiquitination catalyzed by SdeA. We found that DupA and DupB possess a highly homologous PDE domain that removes ubiquitin from PR-ubiquitinated substrates by cleaving the phosphodiester bond between the phosphoribosylated-ubiquitin and serines of substrates. Catalytically deficient mutant DupA H67A strongly binds to PR-ubiquitinated proteins but not capable of cleaving PR-ubiquitin, using it as a trapping bait we identified over 180 substrates of PR-ubiquitination, including a number of ER and Golgi proteins.
In particular, we found that exogenously expressed SdeA localizes to the Golgi apparatus via its C-terminal region and disrupts the Golgi. We validated the identified potential substrates of SidE effectors and found that SdeA modifies Golgi tethering proteins GRASP55 and GRASP65. Using mass spectrometry analyses we identified four serine targets (S3, S408, S409, S449) of GRASP55 PR-ubiquitinated by SdeA in vitro. Ubiquitination of GRASP55 serine mutant in cells co-expressing SdeA or infected with Legionella was markedly decreased, compared with that of the wild-type GRASP55. In addition, with co-immunoprecipitation analyses we found that SdeA-catalyzed ubiquitination regulates the function of GRASP55. PR-ubiquitinated GRASP55 exhibited reduced self-interaction compared to unmodified GRASP55, expression of GRASP55 serine mutant in cells in part rescued Golgi damage caused by SdeA. Furthermore, our study reveals that Golgi structure disruption caused by SdeA does not result in the recruitment of Golgi membranes to the Legionella-containing vacuoles. Instead, it affects cellular secretory pathway including cytokine secretion in cells.
Taken all together, this work expands the understanding of this unconventional PR-ubiquitination catalyzed by Legionella effectors and sheds light on the functions of PR-ubiquitination by which Legionella regulates the Golgi function and secretion pathway during bacterial infection.
Ziel der Studie: Eine psychische Komorbidität spielt im Kontext mit weiteren persönlichen, sozialen und beruflichen Faktoren bei der Ermittlung des spezifischen Rehabilitationsbedarfs der Patienten in Deutschland eine immer bedeutendere Rolle. Um die Zuweisung von Patienten zu einer Rehabilitationsform besser ausdifferenzieren zu können, soll im Rahmen dieser retrospektiven Analyse ermittelt werden, von welchem der beiden untersuchten Rehabilitationskonzepte (OR/VMO) Patienten mit psychischer Komorbidität unter Berücksichtigung von Geschlecht, Erwerbsstatus und orthopädischer Hauptdiagnose stärker profitieren.
Methodik: Mittels der Screening-Fragebögen HADS-A, HADS-D, SIMBO und BPI sowie eines Klinikfragebogens zu Beginn der Rehabilitation wurden Angaben von 913 Probanden (529 m/384 w) ausgewertet. Hiervon wurden 43 % der OR und 57 % der VMO zugewiesen. So wurde die Häufigkeitsverteilung der Faktoren psychische Komorbidität, Geschlecht, Erwerbsstatus und orthopädische Hauptdiagnose festgestellt. Mittels HADS wurde am Ende der Therapie der Benefit durch Vergleich der Scorewert-Mediane ermittelt.
Ergebnisse: Häufigkeitsverteilungen und die Entwicklung der HADS-Scores zeigen, dass die im Vorfeld erfolgte Einteilung gemäß psychischer Komorbidität korrekt war. Frauen waren häufiger von einer psychischen Komorbidität betroffen und erzielten in der VMO größere Erfolge. Bezüglich der orthopädischen Hauptdiagnose ergab sich eine hohe Prävalenz von HWS- und LWS-Beschwerden. Beim Erwerbsstatus (Arbeits(un)fähigkeit, Arbeitslosigkeit, berufliche Problemlage) zeigte sich ein diffuseres Bild, das keine generalisierende Aussage bezüglich der arbeitsweltbezogenen Faktoren zulässt.
Schlussfolgerungen: Das Vorliegen einer psychischen Komorbidität stellt einen zielführenden Indikator dar, der als eines der Hauptzuweisungskriterien zur VMO beizubehalten ist. Auch das weibliche Geschlecht in Verbindung mit dem Vorliegen einer psychischen Komorbidität ist als adäquates Kriterium anzusehen. Bezüglich der orthopädischen Hauptdiagnose können insbesondere HWS-Beschwerden als Zuweisungskriterium geeignet sein. Aufgrund der sehr heterogenen Ergebnisse hinsichtlich der Aspekte des Erwerbsstatus lässt sich festhalten, dass diesbezüglich eine Zuweisung zu einem arbeitsweltbezogenen Therapiekonzept (z. B. MBOR) zielführender erscheint.
The aim of this work was to establish a new way of predicting novel dual active compounds by combining classical fingerprint representation with state-of-the-art machine learning algorithms. Advantages and disadvantages of the applied 2D- and 3D-fingerprints were investigated. Further, the impact of various machine learning algorithms was analyzed. The new method developed in this work was used to predict compounds, which inhibit two different targets (LTA4H and sEH) involved in the same disease pattern (inflammation). The development of multitarget drugs has become more important in recent years. Many widespread diseases like metabolic syndrome, or cancer are of a multifactorial nature, which makes them hard to be treated effectively with a single drug. The new in silico method presented in this work can help to accelerate the design and development of multitarget drugs, saving time and efforts.
The nowadays readily available access to a large number of 3D-structures of biological targets and published activity data of millions of synthesized compounds enabled this study and was used as a starting point for this work. Four different data sets were compiled (crystalized ligands from the PDB, active and inactive compounds from ChEMBL23, newly designed compounds using a combinatorial library). Those data sets were collected and processed using an automated KNIME workflow. This automation has the advantage of allowing easy change and update of compound sources and adapted processing ways.
In a next step, the compounds from the compiled data sets were represented using a variety of well-established 2D- and 3D-fingerprints (PLIF, AtomPair, Morgan, FeatMorgan, MACCS). All those fingerprints share the same underlying bit string scheme but vary in the way they describe the molecular structure. Especially the difference between 2D- and 3D-fingerprints was investigated. 2D-fingerprints are solely based on ligand information. 3D-fingerprints, on the other hand, are based on X-ray structure information of protein-ligand complexes. One major difference between 2D- and 3D-fingerprints usage is the need for a 3D-conformation (pose) of the compound in the targets of interest when using 3D-fingerprints. This additional step is time-consuming and brings further uncertainties to the method.
Based on the calculated fingerprints state-of-the-art machine learning algorithms (SVC, RF, XGB and ADA) were used to predict novel dual active compounds. The models were evaluated by 10-fold cross validation and accuracy as the primary measure of model performance was maximized. Second, individual parameters of the four machine learning algorithms were optimized in a grid search to achieve maximal accuracy using the optimized partitioning scheme. Overall accuracies, regardless of fingerprint and machine learning algorithm, are slightly better for LTA4H than for sEH.
The goal to predict dual active compounds was realized by comparing the set of predicted to be active compounds for LTA4H and sEH. For the 3D-fingerprint PLIF the machine learning algorithm Random Forest was chosen, from which compounds for synthesis and testing were selected. Of 115 predicted to be active compounds, six compounds were cherry picked. Two compounds showed very good/moderate dual inhibitory activity. Of the 2D-fingerprints, the AtomPair fingerprint in combination with the machine learning algorithm Random Forest was chosen from which compounds were selected for synthesis and testing. 116 compounds were predicted to be dual active against LTA4H and sEH. One of those compounds showed good dual inhibitory activity.
In this work it was possible to show advantages and disadvantages of using 2D- and 3D-fingerprints in combination with machine learning algorithms. Both strategies (2D: ligand-based, 3D: structure-based) lead to the prediction of novel dual active compounds with moderate to very good inhibitory activity. The method developed in this work is able to predict dual active compounds with very good inhibitory activity and novel (previously unknown) scaffolds inhibiting the targets LTA4H and sEH. This contribution to in silico drug design is promising and can be used for the prediction of novel dual active compounds. Those compounds can further be optimized regarding binding affinity, solubility and further pharmacological and physicochemical properties.