1,721,012 research outputs found
Probing the access of protons to the K pathway in the Paracoccus d. cytochrome c oxidase
No full textFEBS Journal IF 3.2
Novel tricyclic small molecule inhibitors of Nicotinamide N-methyltransferase for the treatment of metabolic disorders
No full textAbstract
Nicotinamide N-methyltransferase (NNMT) is a metabolic regulator that catalyzes the methylation of nicotinamide (Nam) using the co-factor S-adenosyl-L-methionine to form 1-methyl-nicotinamide (MNA). Overexpression of NNMT and the presence of the active metabolite MNA is associated with a number of diseases including metabolic disorders. We conducted a high-throughput screening campaign that led to the identification of a tricyclic core as a potential NNMT small molecule inhibitor series. Elaborate medicinal chemistry efforts were undertaken and hundreds of analogs were synthesized to understand the structure activity relationship and structure property relationship of this tricyclic series. A lead molecule, JBSNF-000028, was identified that inhibits human and mouse NNMT activity, reduces MNA levels in mouse plasma, liver and adipose tissue, and drives insulin sensitization, glucose modulation and body weight reduction in a diet-induced obese mouse model of diabetes. The co-crystal structure showed that JBSNF-000028 binds below a hairpin structural motif at the nicotinamide pocket and stacks between Tyr-204 (from Hairpin) and Leu-164 (from central domain). JBSNF-000028 was inactive against a broad panel of targets related to metabolism and safety. Interestingly, the improvement in glucose tolerance upon treatment with JBSNF-000028 was also observed in NNMT knockout mice with diet-induced obesity, pointing towards the glucose-normalizing effect that may go beyond NNMT inhibition. JBSNF-000028 can be a potential therapeutic option for metabolic disorders and developmental studies are warranted
Policy makers must adopt agile signal detection tools to strengthen epidemiological surveillance and improve pandemic preparedness
No full textThe SARS-COV2 pandemic has highlighted the urgent need for agile and responsive disease surveillance systems. To strengthen epidemiological surveillance and improve pandemic preparedness, policymakers must adopt real-time signal detection tools that integrate multisource data, including non-traditional health data, advanced analytics, and artificial intelligence (AI). Such approaches enable more efficient monitoring and decision-making through collaborative networks. Expanding these agile tools across Europe under a supranational mandate would enhance public health outcomes, support healthcare system efficiency, and increase business resilience. Leveraging private sector data and ensuring modularity for gradual scale-up are essential to maintaining adaptability and responsiveness.15
Konsortialbericht (Abschluss) 2024
Get PDF1. Derzeitiger Stand von Wissenschaft und Technik - Die COVID-19-Pandemie hat die Entwicklung zahlreicher digitaler Instrumente für die Prognose und Verfolgung des Verlaufs von Epidemien und die Bewertung der Auswirkungen von Maßnahmen sowie der sozioökonomischen Folgen ausgelöst. Ein länderübergreifender, unterschiedliche Datenquellen integrierender und Vorhersagen treffender Ansatz ist derzeit noch nicht verfügbar.
2. Begründung/Zielsetzung der Untersuchung - Im Rahmen des Projekts AIOLOS wurde eine web-basierte multidimensionale Datenplattform entwickelt, die es ermöglicht, frühzeitig Ausbrüche von Infektionserkrankungen der Atemwege zu erkennen (ALERT), die Ausbreitung einer Epidemie und ihre Konsequenzen zu verfolgen (MONITOR) und den Einfluss und die Wirksamkeit verschiedener Interventionen und Maßnahmen zu simulieren, um damit die Entscheidungsfindung auf wissenschaftlicher und politischer Ebene zu unterstützen (DECIDE).
3. Methode - Das AIOLOS Dashboard führt vielseitige Daten unterschiedlicher Quellen aus Deutschland und Frankreich zusammen. Auf diesen Daten aufbauend wurden KI- Modelle entwickelt, um bessere Vorhersagen zum Verlauf des Infektionsgeschehens und zum Einfluss verschiedener Maßnahmen treffen zu können.
4. Ergebnis - Im ALERT Bereich des AIOLOS Dashboard werden Trendanalysen für Zeitreihen unterschiedlicher Indikatoren ausgegeben (z.B.: Abwasserdaten, Social Media Daten). Das Dashboard verfügt ferner über die Modalität, Schwellenwert-basierte Warnungen auszugeben, sowie vor neu auftretenden Virusvarianten zu warnen. Im Bereich MONITOR wurden Modelle zur Extrapolation von Fallzahlen, Hospitalisierungs- und Todesraten entwickelt. Im Bereich DECIDE konnte anhand historischer COVID-19 Daten analysiert werden, wie sich Maßnahmen wie social Distancing und breit angelegte Impfkampagnen auf die Pandemieentwicklung eindämmend ausgewirkt haben, die Übertragbarkeit dieses KI-Modells auf frühe Ausbruchsstadien eines anderen Erregers ist allerdings aufgrund der erregerspezifischen Krankheitsausbreitung unwahrscheinlich.
5. Schlussfolgerung/Anwendungsmöglichkeiten - Der binationale Ansatz hat verdeutlicht, wie stark sich die Datenlage beider Länder unterscheidet und den Bedarf an grenzüberschreitenden Lösungen zur Risikoprävention geschärft. Die sich rasant wandelnden Ausgangslage, bezüglich der Datenverfügbarkeit erschwert zusätzlich die Entwicklung eines stabilen Vorhersagetools. Von großer Bedeutung wäre z.B.: die Einführung eines stetigen und flächendeckenden Abwassermonitoring, um zuverlässige Vorhersagen treffen zu können.
Datei-Upload durch TIB1. Current state of science and technology - The COVID-19 pandemic has triggered the development of numerous digital tools for forecasting and tracking the course of the pandemic and assessing the impact of measures and socio-economic consequences. A cross-national approach that integrates different data sources and makes predictions is not yet available.
2. Justification/objective of the study - In the frame of the AIOLOS project, a web-based multidimensional data platform was developed to enable early detection of outbreaks of respiratory infectious diseases (ALERT), to track the spread of an epidemic and its consequences (MONITOR) and to simulate the impact and effectiveness of different interventions and measures to support decision-making at scientific and policy level (DECIDE).
3. Method - The AIOLOS Dashboard brings together a wide range of data from various sources in Germany and France. Based on this data, AI models were developed in order to make better predictions about the course of the infection and the influence of various measures.
4. Result - The ALERT section of the AIOLOS dashboard displays trend analyses for time series of different indicators (e.g. wastewater data, social media data). The dashboard also has the option of issuing threshold-based warnings and warning of newly occurring virus variants. Within MONITOR, models were developed to extrapolate case numbers, hospitalization and death rates. In the DECIDE area, analysis of historical COVID-19 data showed how measures such as social distancing and broad-based vaccination campaigns have had a containment effect on the development of the pandemic, but the transferability of this AI model to early outbreak stages of another pathogen is unlikely due to the pathogen-specific spread of the disease.
5 Conclusion/possible applications - The binational approach has made it clear how much the data situation differs between the two countries and has sharpened the need for cross-border solutions for risk prevention. The rapidly changing initial situation with regard to data availability also makes the development of a stable forecasting tool more difficult. For example, the introduction of continuous and comprehensive wastewater monitoring would be of great importance in order to be able to make reliable predictions
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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