University of Fribourg

About: University of Fribourg is a education organization based out in Fribourg, Freiburg, Switzerland. It is known for research contribution in the topics: Population & Glacier. The organization has 6040 authors who have published 14975 publications receiving 542500 citations. The organization is also known as: UNIFR & Universität Freiburg.

De novo lipogenesis in metabolic homeostasis: More friend than foe?

Abstract: Background An acute surplus of carbohydrates, and other substrates, can be converted and safely stored as lipids in adipocytes via de novo lipogenesis (DNL). However, in obesity, a condition characterized by chronic positive energy balance, DNL in non-adipose tissues may lead to ectopic lipid accumulation leading to lipotoxicity and metabolic stress. Indeed, DNL is dynamically recruited in liver during the development of fatty liver disease, where DNL is an important source of lipids. Nonetheless, a number of evidences indicates that DNL is an inefficient road for calorie to lipid conversion and that DNL may play an important role in sustaining metabolic homeostasis. Scope of review In this manuscript, we discuss the role of DNL as source of lipids during obesity, the energetic efficiency of this pathway in converting extra calories to lipids, and the function of DNL as a pathway supporting metabolic homeostasis. Major conclusion We conclude that inhibition of DNL in obese subjects, unless coupled with a correction of the chronic positive energy balance, may further promote lipotoxicity and metabolic stress. On the contrary, strategies aimed at specifically activating DNL in adipose tissue could support metabolic homeostasis in obese subjects by a number of mechanisms, which are discussed in this manuscript.

Ranking in evolving complex networks

Abstract: Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Well-established ranking algorithms (such as the popular Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. The recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of real network traffic, prediction of future links, and identification of highly-significant nodes.

Dynamic cluster-based over-demand prediction in bike sharing systems

Abstract: Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly.

Specification of Distinct Dopaminergic Neural Pathways: Roles of the Eph Family Receptor EphB1 and Ligand Ephrin-B2

Abstract: Dopaminergic neurons in the substantia nigra and ventral tegmental area project to the caudate putamen and nucleus accumbens/olfactory tubercle, respectively, constituting mesostriatal and mesolimbic pathways. The molecular signals that confer target specificity of different dopaminergic neurons are not known. We now report that EphB1 and ephrin-B2, a receptor and ligand of the Eph family, are candidate guidance molecules for the development of these distinct pathways. EphB1 and ephrin-B2 are expressed in complementary patterns in the midbrain dopaminergic neurons and their targets, and the ligand specifically inhibits the growth of neurites and induces the cell loss of substantia nigra, but not ventral tegmental, dopaminergic neurons. These studies suggest that the ligand–receptor pair may contribute to the establishment of distinct neural pathways by selectively inhibiting the neurite outgrowth and cell survival of mistargeted neurons. In addition, we show that ephrin-B2 expression is upregulated by cocaine and amphetamine in adult mice, suggesting that ephrin-B2/EphB1 interaction may play a role in drug-induced plasticity in adults as well.

Horizons in the evolution of aging

Abstract: Between the 1930s and 50s, evolutionary biologists developed a successful theory of why organisms age, firmly rooted in population genetic principles. By the 1980s the evolution of aging had a secure experimental basis. Since the force of selection declines with age, aging evolves due to mutation accumulation or a benefit to fitness early in life. Here we review major insights and challenges that have emerged over the last 35 years: selection does not always necessarily decline with age; higher extrinsic (i.e., environmentally caused) mortality does not always accelerate aging; conserved pathways control aging rate; senescence patterns are more diverse than previously thought; aging is not universal; trade-offs involving lifespan can be ‘broken’; aging might be ‘druggable’; and human life expectancy continues to rise but compressing late-life morbidity remains a pressing challenge.