Radiological Significance of Ligamentum Flavum Hypertrophy in the Occurrence of Redundant Nerve Roots of Central Lumbar Spinal Stenosis

Objective: There were previous reports of redundant nerve roots (RNRs) focused on their clinical significance and pathogenesis. In this study, we investigated the significant radiologic findings that correlate with RNRs occurrence. These relations would provide an advanced clue for clinical significance and pathogenesis of RNRs. Methods: Retrospective research was performed with data from 126 patients who underwent surgery for central lumbar spinal stenosis (LSS). Finally, 106 patients with common denominators (inter-observer accuracy : 84%) were included on this study. We divided the patients into two groups by MRI, patients with RNRs and those with no RNRs (NRNRs). Comparative analyses were performed with clinical and radiologic parameters. Results: RNRs were found in 45 patients (42%) with central LSS. There were no statistically significant differences between the two groups in severity of symptoms. On the other hand, we found statistically significant differences in duration of symptom and number of level included (p<0.05). In the maximal stenotic level, ligamentum flavum (LF) thickness, LF cross-sectional area (CSA), dural sac CSA, and segmental angulation are significantly different in RNRs group compared to NRNRs group (p<0.05). Conclusion: RNRs patients showed clinically longer duration of symptoms and multiple levels included. We also confirmed that wide segmental angulation and LF hypertrophy play a major role of the development of RNRs in central LSS. Together, our results suggest that wide motion in long period contribute to LF hypertrophy, and it might be the key factor of RNRs formation in central LSS

Visual Tracking Using Wang–Landau Reinforcement Sampler

In this study, we present a novel tracking system, in which the tracking accuracy can be considerably enhanced by state prediction. Accordingly, we present a new Q-learning-based reinforcement method, augmented by Wang&ndash;Landau sampling. In the proposed method, reinforcement learning is used to predict a target configuration for the subsequent frame, while Wang&ndash;Landau sampler balances the exploitation and exploration degrees of the prediction. Our method can adapt to control the randomness of policy, using statistics on the number of visits in a particular state. Thus, our method considerably enhances conventional Q-learning algorithm performance, which also enhances visual tracking performance. Numerical results demonstrate that our method substantially outperforms other state-of-the-art visual trackers and runs in realtime because our method contains no complicated deep neural network architectures

AttSec: protein secondary structure prediction by capturing local patterns from attention map

Abstract Background Protein secondary structures that link simple 1D sequences to complex 3D structures can be used as good features for describing the local properties of protein, but also can serve as key features for predicting the complex 3D structures of protein. Thus, it is very important to accurately predict the secondary structure of the protein, which contains a local structural property assigned by the pattern of hydrogen bonds formed between amino acids. In this study, we accurately predict protein secondary structure by capturing the local patterns of protein. For this objective, we present a novel prediction model, AttSec, based on transformer architecture. In particular, AttSec extracts self-attention maps corresponding to pairwise features between amino acid embeddings and passes them through 2D convolution blocks to capture local patterns. In addition, instead of using additional evolutionary information, it uses protein embedding as an input, which is generated by a language model. Results For the ProteinNet DSSP8 dataset, our model showed 11.8% better performance on the entire evaluation datasets compared with other no-evolutionary-information-based models. For the NetSurfP-2.0 DSSP8 dataset, it showed 1.2% better performance on average. There was an average performance improvement of 9.0% for the ProteinNet DSSP3 dataset and an average of 0.7% for the NetSurfP-2.0 DSSP3 dataset. Conclusion We accurately predict protein secondary structure by capturing the local patterns of protein. For this objective, we present a novel prediction model, AttSec, based on transformer architecture. Although there was no dramatic accuracy improvement compared with other models, the improvement on DSSP8 was greater than that on DSSP3. This result implies that using our proposed pairwise feature could have a remarkable effect for several challenging tasks that require finely subdivided classification. Github package URL is https://github.com/youjin-DDAI/AttSec

Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting

In this paper, we propose a novel visual tracking method for unmanned aerial vehicles (UAVs) in aerial scenery. To track the UAVs robustly, we present a new object proposal method that can accurately determine the object regions that are likely to exist. The proposed object proposal method is robust to small objects and severe background clutter. For this, we vote on candidate areas of the object and increase or decrease the weight of the area accordingly. Thus, the method can accurately propose the object areas that can be used to track small-sized UAVs with the assumption that their motion is smooth over time. Experimental results verify that UAVs are accurately tracked even when they are very small and the background is complex. The proposed method qualitatively and quantitatively delivers state-of-the-art performance in comparison with conventional object proposal-based methods