Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a classic density-based clustering method that can identify clusters of arbitrary shapes in noisy datasets. However, DBSCAN requires two input parameters: the neighborhood distance value (Eps) and the minimum number of sample points in its neighborhood (MinPts), to perform clustering on a dataset. The quality of clustering is highly sensitive to these two parameters. To tackle this issue, this paper introduces a parameter-adaptive DBSCAN clustering algorithm based on the Whale Optimization Algorithm (WOA-DBSCAN). The algorithm determines the parameter range based on the dataset distribution and utilizes the silhouette coefficient as the objective function. It iteratively selects the two input parameters of DBSCAN within the parameter range using the WOA. This approach ultimately achieves adaptive clustering of DBSCAN. Experimental results on five typical artificial datasets and six real UCI datasets demonstrate the effectiveness of the proposed WOA-DBSCAN algorithm. Compared with DBSCAN and its related optimization algorithms, WOA-DBSCAN shows significant improvements. The F-values of WOA-DBSCAN increased by 9.8%, 13.2%, and 2% respectively in two-dimensional artificial datasets. Additionally, the accuracy values on low to medium dimensional real datasets increased by 22.3%, 10%, and 23.3%. Hence, WOA-DBSCAN can maintain the clustering ability of DBSCAN while achieving adaptive parameter clustering.

Most membrane-less organelles (MLOs) formed by LLPS contain both nucleic acids and IDR-rich proteins. Currently while IDRs are well-recognized to drive LLPS, nucleic acids are thought to exert non-specific electrostatic/salt effects. TDP-43 functions by binding RNA/ssDNA and its LLPS was characterized without nucleic acids to be driven mainly by PLD-oligomerization, which may further transit into aggregation characteristic of various neurodegenerative diseases. Here by NMR, we discovered unexpectedly for TDP-43 PLD: 1) ssDNAs drive and then dissolve LLPS by multivalently and specifically binding Arg/Lys. 2) LLPS is driven by nucleic-acid-binding coupled with PLD-oligomerization. 3) ATP and nucleic acids universally interplay in modulating LLPS by competing for binding Arg/Lys. However, the unique hydrophobic region within PLD renders LLPS to exaggerate into aggregation. The study not only unveils the first residue-resolution mechanism of the nucleic-acid-driven LLPS of TDP-43 PLD, but also decodes a general principle that not just TDP-43 PLD, all Arg/Lys-containing IDRs are cryptic nucleic-acid-binding domains that may phase separate upon binding nucleic acids. Strikingly, ATP shares a common mechanism with nucleic acids in binding IDRs, thus emerging as a universal mediator for interactions between IDRs and nucleic acids, which may underlie previously-unrecognized roles of ATP at mM in physiology and pathology.

The density-based spatial clustering of applications with noise (DBSCAN) is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points, and it is hard to obtain the best result quickly and accurately with it. To address this issue, a parameter-adaptive DBSCAN clustering algorithm based on the Sparrow Search Algorithm (SSA), referred to as SSA-DBSCAN, is proposed. This method leverages the local fast search ability of SSA, using the optimal number of clusters and the silhouette coefficient of the dataset as the objective functions to iteratively optimize and select the two input parameters of DBSCAN. This avoids the adverse impact of manually inputting parameters, enabling adaptive clustering with DBSCAN. Experiments on typical synthetic datasets, UCI (University of California, Irvine) real-world datasets, and image segmentation tasks have validated the effectiveness of the SSA-DBSCAN algorithm. Comparative analysis with DBSCAN and other related optimization algorithms demonstrates the clustering performance of SSA-DBSCAN.

The Clustering by Fast Search and Find of Density Peaks (DPC) algorithm is a clustering method that automatically identifies clustering centers based on density and relative distance. It has several advantages, including the ability to identify arbitrarily shaped clusters and requiring few input parameters. However, the density measure used in DPC does not consider the spatial distribution characteristics of the sample points in the data set. The clustering performance is suboptimal for datasets with significant differences in cluster density. Additionally, its non-central sample point assignment method is less error-tolerant, which can result in successive assignment errors and a domino effect, ultimately leading to poor clustering accuracy. To address these shortcomings, we propose an improved DPC algorithm based on shared nearest neighbor and multi-cluster fusion (SM-DPC). The local density of sample points is redefined using K-nearest neighbors, which makes the density metric more consistent with the local structural characteristics of the dataset. A two-step allocation strategy for non-central sample points based on shared nearest neighbors is proposed to improve the accuracy of allocation of non-central sample points. A multi-cluster fusion strategy is used to correct the centroid selection bias for datasets where sample points are not uniformly distributed. The experimental results demonstrate that SM-DPC is capable of clustering datasets with arbitrary shape and density distributions effectively. Furthermore, it exhibits superior performance and broader adaptability to different types of datasets compared to DBSCAN, K-means algorithms, and other DPC optimization algorithms.

Autonomous collision avoidance decision making for maritime autonomous surface ships (MASS), as one of the key technologies for MASS autonomous navigation, is a research hotspot focused on by relevant scholars in the field of navigation. In order to guarantee the rationality, efficacy, and credibility of the MASS autonomous collision avoidance scheme, it is essential to design the MASS autonomous collision avoidance algorithm under the stipulations of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). In order to enhance the autonomous collision avoidance decision-making capability of MASS in accordance with the relevant provisions of COLREGs, an improved NSGA-II autonomous collision avoidance decision-making algorithm based on the good point set method (GPS-NSGA-II) is proposed, which incorporates the collision hazard and the path cost of collision avoidance actions. The experimental results in the four simulation scenarios of head-on situation, overtaking situation, crossing situation, and multi-ship encounter situation demonstrate that the MASS autonomous collision avoidance decision making based on the GPS-NSGA-II algorithm under the constraints of COLREGs is capable of providing an effective collision avoidance scheme that meets the requirements of COLREGs in common encounter situations and multi-ship avoidance scenarios promptly, with a promising future application.

Purpose This study assessed the association between diffusion-weighted imaging (DWI) volume and fluid-attenuated inversion recovery vascular hyperintensity (FVH)-DWI mismatch, functional outcome in patients with acute stroke patients receiving endovascular therapy, as well as the value of DWI volume in predicting functional outcome with stroke patients. Methods In 38 stroke patients who received endovascular therapy, FVH-DWI mismatch, DWI volume on admission, DWI volume on follow-up, DWI volume growth, the functional outcome at 3 months [modified Rankin scale (mRS)], and other clinical data were collected. Statistical analysis was performed to compare the associations with the above variables and predict functional outcome after stroke. Results Compared with no FVH-DWI mismatch group (n = 15), FVH-DWI mismatch group (n = 23) had a smaller DWI volume on admission (t = −2.980; P = 0.008), smaller DWI volume on follow-up (t = −2.911; P = 0.009), lower DWI volume growth (t = −2.328; P = 0.031). The 3-month outcome (1.87 ± 0.92) in patients with FVH-DWI mismatch was better than that (2.93 ± 1.62) of patients with no FVH-DWI mismatch (t = −2.307; P = 0.032). Spearman’s rank correlation analysis revealed that FVH-DWI mismatch (r = 0.327; P = 0.045), DWI volume on admission (r = 0.414; P = 0.010), DWI volume on follow-up (r = 0.486; P = 0.002), and DWI volume growth (r = 0.467; P = 0.003) were positively correlated with mRS at 3 months. ROC analysis showed when the optimal cutoff value of DWI volume on admission was 33.50, the sensitivity and specificity for predicting functional outcome was 60 and 95.65%, respectively. Conclusions Evaluating DWI volume on admission, DWI volume on follow-up as well as DWI volume growth comprehensively may be useful in predicting the functional outcome of acute stroke patients after thrombectomy.