Different Amyloid-β Self-Assemblies Have Distinct Effects on Intracellular Tau Aggregation.

Alzheimer's disease (AD) pathology is characterized by the aggregation of beta-amyloid (Aβ) and tau in the form of amyloid plaques and neurofibrillary tangles in the brain. It has been found that a synergistic relationship between these two proteins may contribute to their roles in disease progression. However, how Aβ and tau interact has not been fully characterized. Here, we analyze how tau seeding or aggregation is influenced by different Aβ self-assemblies (fibrils and oligomers). Our cellular assays utilizing tau biosensor cells show that transduction of Aβ oligomers into the cells greatly enhances seeded tau aggregation in a concentration-dependent manner. In contrast, transduced Aβ fibrils slightly reduce tau seeding while untransduced Aβ fibrils promote it. We also observe that the transduction of α-synuclein fibrils, another amyloid protein, has no effect on tau seeding. The enhancement of tau seeding by Aβ oligomers was confirmed using tau fibril seeds derived from both recombinant tau and PS19 mouse brain extracts containing human tau. Our findings highlight the importance of considering the specific form and cellular location of Aβ self-assembly when studying the relationship between Aβ and tau in future AD therapeutic development

Perturbation theory for O(3) topological charge correlators

To check the consistency of positivity requirements for the two-point correlation function of the topological charge density, which were identified in a previous paper, we are computing perturbatively this two-point correlation function in the two-dimensional O(3) model. We find that at the one-loop level these requirements are fulfilled.Comment: v1: 27 pages, 7 figures; v2: 28 pages, 8 figures, matches published versio

Escherichia coli K1 RS218 Interacts with Human Brain Microvascular Endothelial Cells via Type 1 Fimbria Bacteria in the Fimbriated State

Escherichia coli K1 is a major gram-negative organism causing neonatal meningitis. E. coli K1 binding to and invasion of human brain microvascular endothelial cells (HBMEC) are a prerequisite for E. coli penetration into the central nervous system in vivo. In the present study, we showed using DNA microarray analysis that E. coli K1 associated with HBMEC expressed significantly higher levels of the fim genes compared to nonassociated bacteria. We also showed that E. coli K1 binding to and invasion of HBMEC were significantly decreased with its fimH deletion mutant and type 1 fimbria locked-off mutant, while they were significantly increased with its type 1 fimbria locked-on mutant. E. coli K1 strains associated with HBMEC were predominantly type 1 fimbria phase-on (i.e., fimbriated) bacteria. Taken together, we showed for the first time that type 1 fimbriae play an important role in E. coli K1 binding to and invasion of HBMEC and that type 1 fimbria phase-on E. coli is the major population interacting with HBMEC

Bundling and Competition for Slots

We study competition among upstream firms when each of them sells a portfolio of distinct products and the downstream has a limited number of slots (or shelf space). In this situation, we study how bundling a¤ects competition for slots. When the downstream has k number of slots, social e¢ ciency requires that it purchases the best k products among all upstream firms' products. We find that under bundling, the outcome is always socially efficient but under individual sale, the outcome is not necessarily efficient. Under individual sale, each upstream firm faces a trade-off between quantity and rent extraction due to the coexistence of the internal competition (i.e. competition among its own products) and the external competition (i.e. competition from other firms' products), which can create inefficiency. On the contrary, bundling removes the internal competition and the external competition among bundles makes it optimal for each upstream firm to sell only the products belonging to the best k. This unambiguous welfare-enhancing e¤ect of bundling is novel

Four-loop free energy for the 2D O(n) nonlinear sigma-model with 0-loop and 1-loop Symanzik improved actions

We calculate up to four loops the free energy of the two-dimensional (2D) O(n) nonlinear sigma-model regularized on the lattice with the 0-loop and 1-loop Symanzik improved actions. An effective coupling constant based on this calculation is defined.Comment: 26 pages, Revtex. More details about the calculation procedur

eulerForce: Force-directed Layout for Euler Diagrams

Euler diagrams use closed curves to represent sets and their relationships. They facilitate set analysis, as humans tend to perceive distinct regions when closed curves are drawn on a plane. However, current automatic methods often produce diagrams with irregular, non-smooth curves that are not easily distinguishable. Other methods restrict the shape of the curve to for instance a circle, but such methods cannot draw an Euler diagram with exactly the required curve intersections for any set relations. In this paper, we present eulerForce, as the first method to adopt a force-directed approach to improve the layout and the curves of Euler diagrams generated by current methods. The layouts are improved in quick time. Our evaluation of eulerForce indicates the benefits of a force-directed approach to generate comprehensible Euler diagrams for any set relations in relatively fast time

Adaptation and learning over networks for nonlinear system modeling

In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environment receive measurements in a streaming fashion, and they are required to estimate a common (nonlinear) model by alternating local computations and communications with their neighbors. We focus on the important distinction between single-task problems, where the underlying model is common to all agents, and multitask problems, where each agent might converge to a different model due to, e.g., spatial dependencies or other factors. Currently, most of the literature on distributed learning in the nonlinear case has focused on the single-task case, which may be a strong limitation in real-world scenarios. After introducing the problem and reviewing the existing approaches, we describe a simple kernel-based algorithm tailored for the multitask case. We evaluate the proposal on a simulated benchmark task, and we conclude by detailing currently open problems and lines of research.Comment: To be published as a chapter in `Adaptive Learning Methods for Nonlinear System Modeling', Elsevier Publishing, Eds. D. Comminiello and J.C. Principe (2018

Superlensing properties of one-dimensional dielectric photonic crystals

We present the experimental observation of the superlensing effect in a slab of a one-dimensional photonic crystal made of tilted dielectric elements. We show that this flat lens can achieve subwavelength resolution in different frequency bands. We also demonstrate that the introduction of a proper corrugation on the lens surface can dramatically improve both the transmission and the resolution of the imaged signal.Comment: 9 pages, 9 figure