Birds exhibit a variety of flight styles, primarily classified as flapping, which is characterized by rapid up-and-down wing movements, and soaring, which involves gliding with wings outstretched. Each species usually performs specific flight styles, and this has been argued in terms of morphological and physiological adaptation. However, it remains a challenge to evaluate the contribution of each factor to the difference in flight styles. In this study, using phenotypic data from 635 migratory bird species, such as body mass, wing length, and breeding periods, we quantified the relative importance of each feature using Feature Importance and SHAP values, and used them to construct weighted L1 distance matrices and construct NJ trees. Comparison with traditional phylogenetic logistic regression revealed similarity in top-ranked features, but also differences in overall weight distributions and clustering patterns in NJ trees. Our results highlight the complexity of constructing a biologically useful distance matrix from correlated phenotypic features, while the complementary nature of these weighting methods suggests the potential utility of multi-faceted approaches to assessing feature contributions.

Testicular cancer that metastasizes to retroperitoneal lymph nodes is typically treated with chemotherapy, followed by post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND). A significant concern is that approximately 50% of patients undergoing PC-RPLND have necrotic tissue in the resected specimens, indicating potential overtreatment. In this study, we propose a U-Net-based classification model to distinguish between necrosis and residual teratoma prior to surgery, aiming to reduce unnecessary procedures. The U-Net-based classifier achieves an area under the curve (AUC) of 0.856 and demonstrates superior performance compared to a ResNet50 classifier when results are shown in scatterplots with the results given by Logistic Regression using clinical variables. These plots highlight that the U-Net-based model more accurately identifies benign tissues, supporting clinical decision-making and potentially minimizing unnecessary surgeries in testicular cancer patients.

The application of deep learning to the microscopic Extended Depth of Field (EDoF) images generation is on the rise as an alternative to the traditional time-consuming method. In this research, the packing mechanism from the self-driving car field is incorporated into the cytology images problem. Additionally, color transfer algorithm was selected as a post-processing method. We proposed novel models for both grayscale and RGB images. The evaluation of the model is then compared with the state-of-the-art and significant improvement was discovered with the metrics of Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal-to-Noise-Ratio (PSNR), and Structural Similarity Index (SSIM).

Molecular dynamics (MD) simulations yield varied results based on simulation conditions; therefore, the results must be compared across different conditions. Previous studies have introduced measures to compare two protein conformational ensembles, each containing multiple protein structures, generated via MD simulations. However, existing brute-force algorithms for computing measures, such as the minimum root mean square deviation (RMSD) and average minimum RMSD, require O(nNM)-time, where n denotes the protein length and N and M are the number of structures in each ensemble. This time complexity can be prohibitively slow when comparing two conformational ensembles generated via long MD simulations. We propose three faster heuristic methods—single-direction method, dual-direction method, and all-direction method—for computing the minimum RMSD and average minimum RMSD in O(n(N+M))-time using the SMAWK algorithm. Experiments on an MD simulation dataset with (N, M) =(10,000, 10,000) demonstrated that the all-direction method was the most accurate, while the single-direction method was the fastest among the proposed methods. The all-direction method achieved average approximation ratios of 1.058 and 1.174 for the minimum RMSD and average minimum RMSD, respectively. The mean absolute error for the minimum RMSD using the all-direction method was approximately three times smaller, and that for the average minimum RMSD was approximately 1.6 times smaller compared with that of the single-direction method. The single-direction method was approximately 1,980 times faster than the existing brute-force algorithm in computing the minimum RMSD and average minimum RMSD.