Structure-Property Relationship of Amyloidogenic Prion Nanofibrils

The structure and its property for the prion nanofibrils, which exhibit self-assembled steric zipper, amyloid fibrils, are described in this chapter. There is the belief of origin for the infectiousness of the prion can be its molecular structure. It is due to the amyloid toxicity, which is related to its beta sheet rich molecular structure and self-aggregated long fibrils. There is evidence that the difference between PrPc and PrPsc is transitioned beta sheet from alpha helix to self-assemble and then to the amyloidogenic fibrils. Therefore, the scope of this chapter is the amyloidogenic structural characteristics of prion fibrils and its relationship to the property. The molecular structural characteristics can be changed by properties such as affinity, toxicity, infectivity, and so on, so this is a key factor to understand the origin of prion disease and develop the therapeutic strategy. One of the main properties of amyloid fibrils that we want to describe here is mechanical property such as dynamic property and material property for prion nanofibrils. This chapter can shed light on understanding the infectious characteristics of prion and the relationship of its molecular structures

6MapNet: Representing soccer players from tracking data by a triplet network

Although the values of individual soccer players have become astronomical, subjective judgments still play a big part in the player analysis. Recently, there have been new attempts to quantitatively grasp players' styles using video-based event stream data. However, they have some limitations in scalability due to high annotation costs and sparsity of event stream data. In this paper, we build a triplet network named 6MapNet that can effectively capture the movement styles of players using in-game GPS data. Without any annotation of soccer-specific actions, we use players' locations and velocities to generate two types of heatmaps. Our subnetworks then map these heatmap pairs into feature vectors whose similarity corresponds to the actual similarity of playing styles. The experimental results show that players can be accurately identified with only a small number of matches by our method.Comment: 12 pages, 4 figures, In 8th Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA21

Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM

As artificial intelligence spreads out to numerous fields, the application of AI to sports analytics is also in the spotlight. However, one of the major challenges is the difficulty of automated acquisition of continuous movement data during sports matches. In particular, it is a conundrum to reliably track a tiny ball on a wide soccer pitch with obstacles such as occlusion and imitations. Tackling the problem, this paper proposes an inference framework of ball trajectory from player trajectories as a cost-efficient alternative to ball tracking. We combine Set Transformers to get permutation-invariant and equivariant representations of the multi-agent contexts with a hierarchical architecture that intermediately predicts the player ball possession to support the final trajectory inference. Also, we introduce the reality loss term and postprocessing to secure the estimated trajectories to be physically realistic. The experimental results show that our model provides natural and accurate trajectories as well as admissible player ball possession at the same time. Lastly, we suggest several practical applications of our framework including missing trajectory imputation, semi-automated pass annotation, automated zoom-in for match broadcasting, and calculating possession-wise running performance metrics

Discovery of a Supercluster at z ~ 0.91 and Testing the ΛCDM Cosmological Model

The ΛCDM cosmological model successfully reproduces many aspects of the galaxy and structure formation of the universe. However, the growth of large-scale structures (LSSs) in the early universe is not well tested yet with observational data. Here, we have utilized wide and deep optical–near-infrared data in order to search for distant galaxy clusters and superclusters (0.8 < z < 1.2). From the spectroscopic observation with the Inamori Magellan Areal Camera and Spectrograph (IMACS) on the Magellan telescope, three massive clusters at z ~ 0.91 are confirmed in the SSA22 field. Interestingly, all of them have similar redshifts within Δ z ~ 0.01 with velocity dispersions ranging from 470 to 1300 km s−1. Moreover, as the maximum separation is ~15 Mpc, they compose a supercluster at z ~ 0.91, meaning that this is one of the most massive superclusters at this redshift to date. The galaxy density map implies that the confirmed clusters are embedded in a larger structure stretching over ~100 Mpc. ΛCDM models predict about one supercluster like this in our surveyed volume, consistent with our finding so far. However, there are more supercluster candidates in this field, suggesting that additional studies are required to determine if the ΛCDM cosmological model can successfully reproduce the LSSs at high redshift