The formation of acetylcholine receptor clusters visualized with quantum dots

Background: Motor innervation of skeletal muscle leads to the assembly of acetylcholine receptor (AChR) clusters in the postsynaptic membrane at the vertebrate neuromuscular junction (NMJ). Synaptic AChR aggregation, according to the diffusion-mediated trapping hypothesis, involves the establishment of a postsynaptic scaffold that "traps" freely diffusing receptors into forming high-density clusters. Although this hypothesis is widely cited to explain the formation of postsynaptic AChR clusters, direct evidence at molecular level is lacking. Results: Using quantum dots (QDs) and live cell imaging, we provide new measurements supporting the diffusion-trap hypothesis as applied to AChR cluster formation. Consistent with published works, experiments on cultured Xenopus myotomal muscle cells revealed that AChRs at clusters that formed spontaneously (pre-patterned clusters, also called hot spots) and at those induced by nerve-innervation or by growth factor-coated latex beads were very stable whereas diffuse receptors outside these regions were mobile. Moreover, despite the restriction of AChR movement at sites of synaptogenic stimulation, individual receptors away from these domains continued to exhibit free diffusion, indicating that AChR clustering at NMJ does not involve an active attraction of receptors but is passive and diffusion-driven. Conclusion: Single-molecular tracking using QDs has provided direct evidence that the clustering of AChRs in muscle cells in response to synaptogenic stimuli is achieved by two distinct cellular processes: the Brownian motion of receptors in the membrane and their trapping and immobilization at the synaptic specialization. This study also provides a clearer picture of the "trap" that it is not a uniformly sticky area but consists of discrete foci at which AChRs are immobilized

Modelling the impact of social network on energy savings

It is noted that human behaviour changes can have a significant impact on energy consumption, however, qualitative study on such an impact is still very limited, and it is necessary to develop the corresponding mathematical models to describe how much energy savings can be achieved through human engagement. In this paper a mathematical model of human behavioural dynamic interactions on a social network is derived to calculate energy savings. This model consists of a weighted directed network with time evolving information on each node. Energy savings from the whole network is expressed as mathematical expectation from probability theory. This expected energy savings model includes both direct and indirect energy savings of individuals in the network. The savings model is obtained by network weights and modified by the decay of information. Expected energy savings are calculated for cases where individuals in the social network are treated as a single information source or multiple sources. This model is tested on a social network consisting of 40 people. The results show that the strength of relations between individuals is more important to information diffusion than the number of connections individuals have. The expected energy savings of optimally chosen node can be 25.32% more than randomly chosen nodes at the end of the second month for the case of single information source in the network, and 16.96% more than random nodes for the case of multiple information sources. This illustrates that the model presented in this paper can be used to determine which individuals will have the most influence on the social network, which in turn provides a useful guide to identify targeted customers in energy efficiency technology rollout programmes

A Novel Collaborative Self-Supervised Learning Method for Radiomic Data

The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on annotating radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method shows the potential advantage in automatic disease diagnosis with large-scale unlabeled data available.Comment: 14 pages, 7 figure

Asymptotic Behavior of Ext functors for modules of finite complete intersection dimension

Let $R$ be a local ring, and let $M$ and $N$ be finitely generated $R$-modules such that $M$ has finite complete intersection dimension. In this paper we define and study, under certain conditions, a pairing using the modules \Ext_R^i(M,N) which generalizes Buchweitz's notion of the Herbrand diference. We exploit this pairing to examine the number of consecutive vanishing of \Ext_R^i(M,N) needed to ensure that \Ext_R^i(M,N)=0 for all $i\gg 0$. Our results recover and improve on most of the known bounds in the literature, especially when $R$ has dimension at most two

De-Pinning Transition of Bubble Phases in a High Landau Level

While in the lowest Landau level the electron-electron interaction leads to the formation of the Wigner crystal, in higher Landau levels a solid phase with multiple electrons in a lattice site of crystal was predicted, which was called the bubble phase. Reentrant integer quantum Hall states are believed to be the insulating bubble phase pinned by disorder. We carry out nonlinear transport measurements on the reentrant states and study the de-pinning of the bubble phase, which is complementary to previous microwave measurements and provides unique information. In this study, conductivity is directly measured with Corbino geometry. Based on the threshold electric field of de-pinning, a phase diagram of the reentrant state is mapped. We discuss an interaction-driven topological phase transition between the integer quantum Hall state and the reentrant integer quantum Hall state.Comment: 11 pages, 3 figure

Dynamic Potential Intensity: An improved representation of the ocean's impact on tropical cyclones

To incorporate the effects of tropical cyclone (TC)-induced upper ocean mixing and sea surface temperature (SST) cooling on TC intensification, a vertical average of temperature down to a fixed depth was proposed as a replacement for SST within the framework of air-sea coupled Potential Intensity (PI). However, the depth to which TC-induced mixing penetrates may vary substantially with ocean stratification and storm state. To account for these effects, here we develop a “Dynamic Potential Intensity” (DPI) based on considerations of stratified fluid turbulence. For the Argo period 2004–2013 and the three major TC basins of the Northern Hemisphere, we show that the DPI explains 11–32% of the variance in TC intensification, compared to 0–16% using previous methods. The improvement obtained using the DPI is particularly large in the eastern Pacific where the thermocline is shallow and ocean stratification effects are strong.United States. Department of Energy. Office of Science (part of the Regional and Global Climate Modeling Program)Atlantic Oceanographic and Meteorological Laboratory (base funds