JIP1 Regulates Axonal Transport of APP and Autophagosomes via Coordination of Kinesin and Dynein Motors

Neurons are specialized cells that extend polarized processes called dendrites and axons in order to maintain synaptic connections over long distances. Consequently, neuronal homeostasis requires axonal transport of organelles, such as mitochondria, synaptic vesicles, and autophagosomes. The microtubule-based motors responsible for long-distance fast axonal transport are the anterograde kinesin motors and the retrograde dynein motors. Two cargos that exhibit robust axonal transport characterized by high speeds with few directional switches are APP- (amyloid precursor protein) positive vesicles and autophagosomes. While APP-positive vesicles transport occurs in both anterograde and retrograde directions, autophagosomes move unidirectionally in the retrograde direction. Here, we demonstrate that processive transport of both these cargos requires coordination of opposing motor activity by the scaffolding protein JIP1 (c-jun N-terminal kinase- interacting protein). We identify novel interactions between JIP1 and kinesin heavy chain (KHC), which are sufficient to relieve KHC autoinhibition and activate motor function in single molecule assays. In addition, the direct binding of the dynactin subunit p150Glued to JIP1 competitively inhibits KHC activation in vitro and disrupts the transport of APP in neurons. Together with coimmunoprecipitation results, these experiments support a model whereby JIP1 coordinates transport by switching between anterograde and retrograde motile complexes. Furthermore, we find that mutations in the JNK-dependent phosphorylation site S421 in JIP1 alter both KHC activation in vitro and the directionality of APP and autophagosome transport in neurons. In knockdown and rescue experiments, the phosphomimetic JIP1-S421D promotes anterograde APP transport and disrupts retrograde autophagosome transport while the phosphodeficient JIP1-S421A promotes retrograde APP transport and rescues retrograde autophagosome transport. Thus, post-translational modification of a scaffolding protein can serve as a molecular switch that coordinates opposing motor activity in order to regulate the direction of vesicular transport of various organelles in the axon

The Mechanical Performance and Microstructure Development of Laser Beam Welds and Post Weld Heat Treatment of Ti 1023 Alloy

Titanium alloys are widely used in the aerospace industry. For example, most components of the Boeing 777 landing gear are made of the Ti-10V-2Fe-3Al (Ti1023) alloy due to its lighweight and superior mechanical strength. In addition, because Titanium is chemicaly active element, when jointing the Ti alloy components, the laser beam welding (LBW) has been selected for avoiding the eventual contaminations and narrowing the heat affected zone by the presence of its protected atmosphere and low heat input. However, there is a lack of reported research results about welded Ti1023 alloy, particularly laser beam welding. Therefore, the microstructure and mechanical properties of LBW Ti1023 were first investigated in this study.Three zones were formed on the LBW Ti1023: base material (BM), heat affected zone (HAZ), and fusion zone (FZ). The bimodal (spherical and lath) distribution of primary α phase dispersed in a matrix of β phase was observed on the BM and HAZ. However, only β phase was observed in the fusion zone (FZ). This whole β phase structure was formed due to two steps: melting and retaining. The melting temperature was higher than β-transus temperature and transformed the α phase into β phase. Then, fast cooling can avoid α martensite formed and retain the β phase. In addition to the microstructure, the experimental results from the hardness and tensile tests showed lower properties in the FZ. Moreover, the residual stress of BM and FZ were measured separately in this study.During analysis of the entire manufacturing process, post welding heat treatment (PWHT) was applied to improve mechanical properties of the welded components. Two subgroups of the heat treatment conditions were set and investigated. The experiment results of both subgroups presented a different extent of strength improvement. The heat treatment condition of one subgroup is annealing+aging. The hardness of all three zones (base material, heat affacted zone, and fusion zone) in this subgroup increased and are almost equal to each other due to their structure consisted of primary α (αp) and secondary α (αs) phase. From the hardness profile and tensile test results, the optimization heat treatment condition of this subgroup is chosen as 750oC annealing for 1 hour and following by water quenching, then through 500oC aging for 4 hours and following by air cooling. Another subgroup heat treatment condition consists only of aging. The FZ in this subgroup showed the highest hardness because only αs was observed. Also, the optimization heat treatment condition is chosen as 500oC aging for 4 hours and following by air cooling

Scaling invariance of spatial autocorrelation in urban built-up area

City is proved to be a scale-free phenomenon, and spatial autocorrelation is often employed to analyze spatial redundancy of cities. Unfortunately, spatial analysis results deviated practical requirement in many cases due to fractal nature of cities. This paper is devoted to revealing the internal relationship between the scale dependence of Moran's I and fractal scaling. Mathematical reasoning and empirical analysis are employed to derive and test the model on the scale dependence of spatial autocorrelation. The data extraction way for fractal dimension estimation is box-counting method, and parameter estimation relies on the least squares regression. In light of the locality postulate of spatial correlation and the idea of multifractals, a power law model on Moran's I changing with measurement scale is derived from the principle of recursive subdivision of space. The power exponent is proved to be a function of fractal dimension. This suggests that the numerical relationship between Moran's I and fractal dimension can be established through the scaling process of granularity. An empirical analysis is made to testify the theoretical model. It can be concluded that spatial autocorrelation of urban built-up area has no characteristic scale in many cases, and urban spatial analysis need new thinking.Comment: 25 pages, 6 figures, 5 table

Self-Learning Monte Carlo Method

Monte Carlo simulation is an unbiased numerical tool for studying classical and quantum many-body systems. One of its bottlenecks is the lack of general and efficient update algorithm for large size systems close to phase transition or with strong frustrations, for which local updates perform badly. In this work, we propose a new general-purpose Monte Carlo method, dubbed self-learning Monte Carlo (SLMC), in which an efficient update algorithm is first learned from the training data generated in trial simulations and then used to speed up the actual simulation. We demonstrate the efficiency of SLMC in a spin model at the phase transition point, achieving a 10-20 times speedup.Comment: add more refs and correct some typo