Motivated by McLean and Pontiff (2016), we study the pre- and post-publication return predictability of 241 cross-sectional anomalies in 39 stock markets. We find, based on more than two million ...anomaly country-months, that the United States is the only country with a reliable post-publication decline in long-short returns. Collectively, our meta-analysis of return predictors suggests that barriers to arbitrage trading can create segmented markets and that anomalies tend to represent mispricing instead of data mining.
Biologics for tendon repair Docheva, Denitsa; Müller, Sebastian A.; Majewski, Martin ...
Advanced drug delivery reviews,
04/2015, Letnik:
84
Journal Article
Recenzirano
Odprti dostop
Tendon injuries are common and present a clinical challenge to orthopedic surgery mainly because these injuries often respond poorly to treatment and require prolonged rehabilitation. Therapeutic ...options used to repair ruptured tendons have consisted of suture, autografts, allografts, and synthetic prostheses. To date, none of these alternatives has provided a successful long-term solution, and often the restored tendons do not recover their complete strength and functionality. Unfortunately, our understanding of tendon biology lags far behind that of other musculoskeletal tissues, thus impeding the development of new treatment options for tendon conditions. Hence, in this review, after introducing the clinical significance of tendon diseases and the present understanding of tendon biology, we describe and critically assess the current strategies for enhancing tendon repair by biological means. These consist mainly of applying growth factors, stem cells, natural biomaterials and genes, alone or in combination, to the site of tendon damage. A deeper understanding of how tendon tissue and cells operate, combined with practical applications of modern molecular and cellular tools could provide the long awaited breakthrough in designing effective tendon-specific therapeutics and overall improvement of tendon disease management.
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Centromeric chromatin undergoes major changes in composition and architecture during each cell cycle. These changes in specialized chromatin facilitate kinetochore formation in mitosis to ensure ...proper chromosome segregation. Thus, proper orchestration of centromeric chromatin dynamics during interphase, including replication in S phase, is crucial. We provide the current view concerning the centromeric architecture associated with satellite repeat sequences in mammals and its dynamics during the cell cycle. We summarize the contributions of deposited histone variants and their chaperones, other centromeric components - including proteins and their post-translational modifications, and RNAs - and we link the expression and deposition timing of each component during the cell cycle. Because neocentromeres occur at ectopic sites, we highlight how cell cycle processes can go wrong, leading to neocentromere formation and potentially disease.
Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines a ...well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. It is validated against the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. It predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reproduction number R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions.
Media Makes Momentum Hillert, Alexander; Jacobs, Heiko; Müller, Sebastian
The Review of financial studies,
12/2014, Letnik:
27, Številka:
12
Journal Article
Recenzirano
Relying on 2.2 million articles from forty-five national and local U.S. newspapers between 1989 and 2010, we find that firms particularly covered by the media exhibit, ceteris paribus, significantly ...stronger momentum. The effect depends on article tone, reverses in the long run, is more pronounced for stocks with high uncertainty, and is stronger in states with high investor individualism. Our findings suggest that media coverage can exacerbate investor biases, leading return predictability to be strongest for firms in the spotlight of public attention. These results collectively lend credibility to an overreaction-based explanation for the momentum effect.
Chemistry and biology of ferritin Plays, Marina; Müller, Sebastian; Rodriguez, Raphaël
Metallomics,
05/2021, Letnik:
13, Številka:
5
Journal Article
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Odprti dostop
Iron is an essential element required by cells and has been described as a key player in ferroptosis. Ferritin operates as a fundamental iron storage protein in cells forming multimeric assemblies ...with crystalline iron cores. We discuss the latest findings on ferritin structure and activity and its link to cell metabolism and ferroptosis. The chemistry of iron, including its oxidation states, is important for its biological functions, its reactivity, and the biology of ferritin. Ferritin can be localized in different cellular compartments and secreted by cells with a variety of functions depending on its spatial context. Here, we discuss how cellular ferritin localization is tightly linked to its function in a tissue-specific manner, and how impairment of iron homeostasis is implicated in diseases, including cancer and coronavirus disease 2019. Ferritin is a potential biomarker and we discuss latest research where it has been employed for imaging purposes and drug delivery.
We derive a trace formula that expresses the level density of chaotic many-body systems as a smooth term plus a sum over contributions associated to solutions of the nonlinear Schrödinger (or ...Gross-Pitaevski) equation. Our formula applies to bosonic systems with discretised positions, such as the Bose-Hubbard model, in the semiclassical limit as well as in the limit where the number of particles is taken to infinity. We use the trace formula to investigate the spectral statistics of these systems, by studying interference between solutions of the nonlinear Schrödinger equation. We show that in the limits taken the statistics of fully chaotic many-particle systems becomes universal and agrees with predictions from the Wigner-Dyson ensembles of random matrix theory. The conditions for Wigner-Dyson statistics involve a gap in the spectrum of the Frobenius-Perron operator, leaving the possibility of different statistics for systems with weaker chaotic properties.
The in silico human surfaceome Bausch-Fluck, Damaris; Goldmann, Ulrich; Müller, Sebastian ...
Proceedings of the National Academy of Sciences - PNAS,
11/2018, Letnik:
115, Številka:
46
Journal Article
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Odprti dostop
Cell-surface proteins are of great biomedical importance, as demonstrated by the fact that 66% of approved human drugs listed in the DrugBank database target a cell-surface protein. Despite this ...biomedical relevance, there has been no comprehensive assessment of the human surfaceome, and only a fraction of the predicted 5,000 human transmembrane proteins have been shown to be located at the plasma membrane. To enable analysis of the human surfaceome, we developed the surfaceome predictor SURFY, based on machine learning. As a training set, we used experimentally verified high-confidence cell-surface proteins from the Cell Surface Protein Atlas (CSPA) and trained a random forest classifier on 131 features per protein and, specifically, per topological domain. SURFY was used to predict a human surfaceome of 2,886 proteins with an accuracy of 93.5%, which shows excellent overlap with known cell-surface protein classes (i.e., receptors). In deposited mRNA data, we found that between 543 and 1,100 surfaceome genes were expressed in cancer cell lines and maximally 1,700 surfaceome genes were expressed in embryonic stem cells and derivative lines. Thus, the surfaceome diversity depends on cell type and appears to be more dynamic than the nonsurface proteome. To make the predicted surfaceome readily accessible to the research community, we provide visualization tools for intuitive interrogation (wlab.ethz.ch/surfaceome). The in silico surfaceome enables the filtering of data generated by multiomics screens and supports the elucidation of the surfaceome nanoscale organization.
Small (s)RNAs play crucial roles in the regulation of gene expression and genome stability across eukaryotes where they direct epigenetic modifications, post-transcriptional gene silencing, and ...defense against both endogenous and exogenous viruses. It is known that Chlamydomonas reinhardtii, a well-studied unicellular green algae species, possesses sRNA-based mechanisms that are distinct from those of land plants. However, definition of sRNA loci and further systematic classification is not yet available for this or any other algae. Here, using data-driven machine learning approaches including Multiple Correspondence Analysis (MCA) and clustering, we have generated a comprehensively annotated and classified sRNA locus map for C. reinhardtii. This map shows some common characteristics with higher plants and animals, but it also reveals distinct features. These results are consistent with the idea that there was diversification in sRNA mechanisms after the evolutionary divergence of algae from higher plant lineages.
•We identify and conceptualize three levels of data-driven inventory management.•We investigate the impact of the levels on the performance in a newsvendor problem.•We present solution approaches ...based on Machine Learning for the newsvendor problem.•We compare our methods to well-established approaches on a real-world data set.•Data-driven methods outperform their model-based counterparts in most cases.
Retailers that offer perishable items are required to make ordering decisions for hundreds of products on a daily basis. This task is non-trivial because the risk of ordering too much or too little is associated with overstocking costs and unsatisfied customers. The well-known newsvendor model captures the essence of this trade-off. Traditionally, this newsvendor problem is solved based on a demand distribution assumption. However, in reality, the true demand distribution is hardly ever known to the decision maker. Instead, large datasets are available that enable the use of empirical distributions. In this paper, we investigate how to exploit this data for making better decisions. We identify three levels on which data can generate value, and we assess their potential. To this end, we present data-driven solution methods based on Machine Learning and Quantile Regression that do not require the assumption of a specific demand distribution. We provide an empirical evaluation of these methods with point-of-sales data for a large German bakery chain. We find that Machine Learning approaches substantially outperform traditional methods if the dataset is large enough. We also find that the benefit of improved forecasting dominates other potential benefits of data-driven solution methods.
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