Restraint of the Wallenda/DLK MAP Kinase cascade by the Kinesin-3 motor regulates the assembly of synapses

Synaptic connections are fundamental units of neuronal communication in the brain. They are composed of precisely opposed pre- and postsynaptic specializations, and these structures are dynamically regulated to adapt to changing needs of neuronal circuits. While mechanisms that regulate the postsynaptic composition of synapses are highly studied, less is known about presynaptic regulation. Within presynaptic terminals, synapse assembly requires the formation of active zones (AZs) and synaptic vesicle (SV) release machinery at synapses. An important role in presynaptic assembly has been assigned to a kinesin-3 family member, Unc-104/Imac/KIF1A. Unc-104/Imac/KIF1A is required for the delivery of synaptic components and SVs to nascent synapses. However, its distinct synaptic phenotype from other kinesins and the complexity of the phenotype is not well understood. This thesis work describes how the synaptic defects of Drosophila unc-104 mutants can be rescued by inhibiting the Wallenda (Wnd)/DLK MAP kinase signaling pathway. This pathway has been previously identified as a regulator of axonal damage signaling and presynaptic terminal morphology. The accessible genetic tools in Drosophila (reviewed in Chapter II) allow for characterization of the mechanistic relationship between Wnd/DLK and Unc-104. Wnd/DLK signaling becomes activated in unc-104 mutants, and inhibits synapse formation independently of Unc-104’s transport functions by controlling the levels and timing of the expression of AZ and SV components (Chapter III). In order to understand the activation mechanism of Wnd signaling, multiple possibilities have been examined (Chapter IV). Cumulative findings lead to a model that accumulated presynaptic proteins in the cell body of unc-104 mutants triggers the Wnd signaling pathway, which then down-regulates presynaptic protein levels. In this fashion Wnd signaling may function as a stress response pathway that regulates the expression level of synaptic proteins according to their ability to be transported in axons. This model also raises an interesting possibility that DLK activation may contribute to synapse malfunction and loss in the aged or diseased nervous system.PHDMolecular, Cellular, and Developmental BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137169/1/jiaxing_1.pd

Anomalous diffusion of optical vortices in random wavefields

We investigate the dynamic behavior of optical vortices, or phase singularities, in random wavefields and demonstrate the direct experimental observation of the anomalous diffusion of optical vortices. The observed subdiffusion of optical vortices show excellent agreement with the fractional Brownian motion, a Gaussian process. Paradoxically, the vortex displacements are observed exhibiting a non-Gaussian heavy-tailed distribution. We also tune the extent of subdiffusion and non-Gaussianity of optical vortex by varying the viscoelasticity of light scattering media. This complex motion of optical vortices is reminiscent of particles in viscoelastic environments suggesting a vortex tracking based microrheology approach. The fractional Brownian yet non-Gaussian subdiffusion of optical vortices may not only offer insights into the dynamics of phase singularities, but also contribute to the understanding certain general physics, including vortex diffusion in fluids and the decoupling between Brownian and Gaussian

Reverse Auction Bidding Further Elements to the Game Theory

Reverse Auction Bidding systems are increasingly used by some large corporations for the supply of buildings, an example is the major firm Target. The belief is that the Reverse Auction Bidding system improves the efficiency of the bidding system and leads to cost savings during the construction process. Neither statement has been shown to be correct at this time. A game theory was developed for the Reverse Auction Bidding system; this theory postulated that two sub-games exist within the overall Reverse Auction Bidding game. The first sub-game is between the purchaser and the set of bidders. The purchaser is presented with a group of lowest prices that under the rules of the game must be accepted. This group of prices has been shown to have a non-normal distribution in prior research at TAMU. If economic efficiency was to be maintained by the bidding system, one would expect a normal distribution with a tight range on the standard deviation, which does not occur. The second sub-game is between the bidders, who make use of the non-normal aspects of price group to maximize individual returns. All things being equal and given the intent of the game, the purchaser would expect the bidders return to be normally distributed with a small standard deviation representing a tight control on price, which has never been observed in game play. Three types of bidders have been postulated for the set, the first is an economically efficient bidder, an economically inefficient bidder, and a middle of the road bidder. This study aims to compare statistically the difference between economically efficient bidders, Type ξ bidder, and economically inefficient bidders, Type Ϛ bidder, in terms of the statistical properties of the return data. The central hypothesis is that a statistically evident bias exists between the average return generated by the Type ξ bidder and the Type Ϛ bidder. The addition of the two distributions along with the average return generated by a Type ϕ bidder results in the observed distribution for the group, L. The secondary hypothesis is that Type ξ bidders minimize the price reduction for each bid. The first hypothesis is true, the Type ξ bidder earn on average twice the returns of the Type Ϛ bidder. The second hypothesis is not true, the Type ξ bidder as a set do not attempt to minimize the bid differentials. Further research is suggested on the statistical properties of the bid differentials as more games are played at TAMU

Investigation of the tetraquark states QqQˉqˉQq\bar{Q} \bar{q} in the improved chromomagnetic interaction model

In the framework of the improved chromomagnetic interaction model, we complete a systematic study of the $S$-wave tetraquark states $Qq\bar{Q}\bar{q}$ ($Q=c,b$, and $q=u,d,s$) with different quantum numbers, $J^{PC}=0^{+(+)}$, $1^{+(\pm)}$, and $2^{+(+)}$. The mass spectra of tetraquark states are predicted and the possible decay channels are analyzed by considering both the angular momentum and $\mathcal{C}$-parity conservation. The recently observed hidden-charm tetraquark states with strangeness, such as $Z_{cs}(3985)^-$, $X(3960)$, and $Z_{cs}(4220)^+$, can be well explained in our model. Besides, based on the wave function of each tetraquark state, we find that the low-lying states of each $Qq\bar{Q}\bar{q}$ configuration have a large overlap to the $Q\bar Q$ and $q\bar q$ meson basis, instead of $Q\bar q$ and $q\bar Q$ meson basis. This indicates one can search these tetraquark states in future experiments via the channel of $Q\bar Q$ and $q\bar q$ mesons.Comment: 11 pages, 9 figures, and 4 tables; accepted for publication in Chinese Physics

Identification of suitable reference genes for miRNA quantitation in bumblebee (Hymenoptera: Apidae) response to reproduction

International audienceAbstractThe precise quantification of microRNAs (miRNAs) expression level is a critical factor in mastering its functions. We evaluate the suitability of two common genes and ten miRNAs as normalizers for miRNA quantification in the head and ovary at different reproductive status of bumblebees, Bombus lantschouensis by using four different algorithms and one consensus rank approach. For the head and ovary combination, miR-275 was the best candidate. For different tissues, miR-275 was the most stable candidate in the head, while the candidate for the ovary was miR-277. To test the best candidate accuracy, miR-315 was demonstrated to be downregulated based on miR-275 normalization in ovipositor bumblebees. The miR-275 and miR-277 combination is identified to be the most reliable and suitable reference genes for the head and ovary of bumblebees

Scaling Analysis of the Tensile Strength of Bamboo Fibers Using Weibull Statistics

This study demonstrates the effect of weak-link scaling on the tensile strength of bamboo fibers. The proposed model considers the random nature of fiber strength, which is reflected by using a two-parameter Weibull distribution function. Tension tests were performed on samples that could be scaled in length. The size effects in fiber length on the strength were analyzed based on Weibull statistics. The results verify the use of Weibull parameters from specimen testing for predicting the strength distributions of fibers of longer gauge lengths

Deep Spatio-temporal Learning Model for Air Quality Forecasting

In recent years, air pollution has seriously affected people’s production and life, so the air prediction has become a research hotspot in recent years. When analyzing air data, it is found that this type of data has not only temporal correlation, but also spatial correlation. For these temporal and spatial characteristics, this paper studies deep spatio-temporal learning method to global prediction. The purpose is to learn the evolution rule behind the spatio-temporal sequence, and give an estimation for future state. To be specific, we propose two novel forecasting models based on video processing technology: Spatio-temporal Orthogonal Cube model (STOR-cube) and Spatio-temporal Dynamic Advection model (ST-DA), which effectively capture the spatio-temporal correlation and accurately predict the long-term air quality. STOR-cube contains three branches, i.e., a spatial branch for capturing moving objects, a temporal branch for processing motion, and an output branch for coupling the first two mutually orthogonal branches to generate a prediction frame. ST-DA constructs a spatio-temporal reasoning network to learn the characteristics of the spatio-temporal domain, and its impact on the future is explicitly modeled by pixel motion. Experiments results on the real-world datasets demonstrate our proposed approach significantly outperforms the state-of-the-art ones. Moreover, our model can be extended to other spatio-temporal data prediction tasks