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Die Universitätsbibliothek Leipzig (UBL) steht zur Sächsischen Akademie der Wissenschaften in einer speziellen Beziehung: Sie ist deren Archivbibliothek. Außerdem versorgt sie natürlich die Wissenschaftler der Akademie mit der von ihnen gewünschten wissenschaftlichen Literatur. Seit dem 19. Jahrhundert – dem Jahrhundert der Gründung der Sächsischen Akademie der Wissenschaften zu Leipzig – hat die Universitätsbibliothek Leipzig eine große Zahl an Schätzen des Weltschrifterbes erhalten, die sie bis heute bewahrt. Nun kommen im 21. Jahrhundert neue technische Möglichkeiten hinzu, und eine kleine Revolution ist perfekt: Alte Texte können in neuen Medien präsentiert bzw. veröffentlicht werden, die Erschließungsleistung der Bibliothekare kann unmittelbar für die Forschung bereitgestellt werden, darüber hinaus sind die Originale im Tresor der Bibliotheca Albertina gerade durch ihre erheblich verbesserte Zugänglichkeit über digitale Sekundärformen besser geschützt. Die laufenden Projekte der Universitätsbibliothek kann man seit Anfang 2008 auf der Homepage der UBL (www.ub.uni-leipzig.de) eigens aufgelistet finden (s. dort unter ›Projekte‹). Sie lassen sich in vier Gruppen gliedern und sollen im Folgenden kurz erläutert werden. Neuere Projektvorhaben werde ich im Anschluss daran erläutern. Die vier thematisch-kulturellen Gruppen, innerhalb deren Katalogisierungs-, Erschließungs- und Forschungsleistungen an der Universitätsbibliothek Leipzig erbracht werden, sind 1. Texte der Antike, 2. Texte und Textträger des Mittelalters, 3. Texte aus dem orientalischen Kulturraum und 4. Quellentexte zur Wissenschaftsgeschichte der Neuzeit.
In this study we show how size-resolved measurements of aerosol particles and cloud condensation nuclei (CCN) can be used to characterize the supersaturation of water vapor in a cloud. The method was developed and applied for the investigation of a cloud event during the ACRIDICON-Zugspitze campaign (17 September to 4 October 2012) at the high-alpine research station Schneefernerhaus (German Alps, 2650 m a.s.l.). Number size distributions of total and interstitial aerosol particles were measured with a scanning mobility particle sizer (SMPS), and size-resolved CCN efficiency spectra were recorded with a CCN counter system operated at different supersaturation levels.
During the evolution of a cloud, aerosol particles are exposed to different supersaturation levels. We outline and compare different estimates for the lower and upper bounds (Slow, Shigh) and the average value (Savg) of peak supersaturation encountered by the particles in the cloud. For the investigated cloud event, we derived Slow ≈ 0.19–0.25%, Shigh ≈ 0.90–1.64% and Savg ≈ 0.38–0.84%. Estimates of Slow, Shigh and Savg based on aerosol size distribution data require specific knowledge or assumptions of aerosol hygroscopicity, which are not required for the derivation of Slow and Savg from the size-resolved CCN efficiency spectra.
In this study we show how size-resolved measurements of aerosol particles and cloud condensation nuclei (CCN) can be used to characterize the supersaturation of water vapor in a cloud. The method was developed and applied during the ACRIDICON-Zugspitze campaign (17 September to 4 October 2012) at the high-Alpine research station Schneefernerhaus (German Alps, 2650 m a.s.l.). Number size distributions of total and interstitial aerosol particles were measured with a scanning mobility particle sizer (SMPS), and size-resolved CCN efficiency spectra were recorded with a CCN counter system operated at different supersaturation levels.
During the evolution of a cloud, aerosol particles are exposed to different supersaturation levels. We outline and compare different estimates for the lower and upper bounds (Slow, Shigh) and the average value (Savg) of peak supersaturation encountered by the particles in the cloud. A major advantage of the derivation of Slow and Savg from size-resolved CCN efficiency spectra is that it does not require the specific knowledge or assumptions about aerosol hygroscopicity that are needed to derive estimates of Slow, Shigh, and Savg from aerosol size distribution data. For the investigated cloud event, we derived Slow ≈ 0.07–0.25%, Shigh ≈ 0.86–1.31% and Savg ≈ 0.42–0.68%.
Charts are used to measure relative success for a large variety of cultural items. Traditional music charts have been shown to follow self-organizing principles with regard to the distribution of item lifetimes, the on-chart residence times. Here we examine if this observation holds also for (a) music streaming charts (b) book best-seller lists and (c) for social network activity charts, such as Twitter hashtags and the number of comments Reddit postings receive. We find that charts based on the active production of items, like commenting, are more likely to be influenced by external factors, in particular by the 24 h day–night cycle. External factors are less important for consumption-based charts (sales, downloads), which can be explained by a generic theory of decision-making. In this view, humans aim to optimize the information content of the internal representation of the outside world, which is logarithmically compressed. Further support for information maximization is argued to arise from the comparison of hourly, daily and weekly charts, which allow to gauge the importance of decision times with respect to the chart compilation period.
Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions: N.A.
Main outcomes and measures: Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.
Scores to identify patients at high risk of progression of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may become instrumental for clinical decision-making and patient management. We used patient data from the multicentre Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) and applied variable selection to develop a simplified scoring system to identify patients at increased risk of critical illness or death. A total of 1946 patients who tested positive for SARS-CoV-2 were included in the initial analysis and assigned to derivation and validation cohorts (n = 1297 and n = 649, respectively). Stability selection from over 100 baseline predictors for the combined endpoint of progression to the critical phase or COVID-19-related death enabled the development of a simplified score consisting of five predictors: C-reactive protein (CRP), age, clinical disease phase (uncomplicated vs. complicated), serum urea, and D-dimer (abbreviated as CAPS-D score). This score yielded an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.77–0.85) in the validation cohort for predicting the combined endpoint within 7 days of diagnosis and 0.81 (95% CI: 0.77–0.85) during full follow-up. We used an additional prospective cohort of 682 patients, diagnosed largely after the “first wave” of the pandemic to validate the predictive accuracy of the score and observed similar results (AUC for the event within 7 days: 0.83 [95% CI: 0.78–0.87]; for full follow-up: 0.82 [95% CI: 0.78–0.86]). An easily applicable score to calculate the risk of COVID-19 progression to critical illness or death was thus established and validated.
During the measurement campaign FROST 2 (FReezing Of duST 2), the Leipzig Aerosol Cloud Interaction Simulator (LACIS) was used to investigate the influence of various surface modifications on the ice nucleating ability of Arizona Test Dust (ATD) particles in the immersion freezing mode. The dust particles were exposed to sulfuric acid vapor, to water vapor with and without the addition of ammonia gas, and heat using a thermodenuder operating at 250 °C. Size selected, quasi monodisperse particles with a mobility diameter of 300 nm were fed into LACIS and droplets grew on these particles such that each droplet contained a single particle. Temperature dependent frozen fractions of these droplets were determined in a temperature range between −40 °C ≤T≤−28 °C. The pure ATD particles nucleated ice over a broad temperature range with their freezing behavior being separated into two freezing branches characterized through different slopes in the frozen fraction vs. temperature curves. Coating the ATD particles with sulfuric acid resulted in the particles' IN potential significantly decreasing in the first freezing branch (T>−35 °C) and a slight increase in the second branch (T≤−35 °C). The addition of water vapor after the sulfuric acid coating caused the disappearance of the first freezing branch and a strong reduction of the IN ability in the second freezing branch. The presence of ammonia gas during water vapor exposure had a negligible effect on the particles' IN ability compared to the effect of water vapor. Heating in the thermodenuder led to a decreased IN ability of the sulfuric acid coated particles for both branches but the additional heat did not or only slightly change the IN ability of the pure ATD and the water vapor exposed sulfuric acid coated particles. In other words, the combination of both sulfuric acid and water vapor being present is a main cause for the ice active surface features of the ATD particles being destroyed. A possible explanation could be the chemical transformation of ice active metal silicates to metal sulfates. The strongly enhanced reaction between sulfuric acid and dust in the presence of water vapor and the resulting significant reductions in IN potential are of importance for atmospheric ice cloud formation. Our findings suggest that the IN concentration can decrease by up to one order of magnitude for the conditions investigated.