The development of intelligent methods capable of predicting protein-ligand binding sites has become a popular research field. Recently, deep learning based methods have been proposed as a promising solution for this task. However, some limitations still exist. For example, the network structure is not optimized for predicting protein binding pockets, which limits the model's capabilities. To address the aforementioned challenges, a novel method called CATransUnetLPB is proposed, in which a new network structure named CATransUnet is designed. The proposed CATransUnet combines CNN and Transformer models to accurately segment binding pocket regions from protein 3D structures. It outperforms existing representative methods on three test sets, demonstrating the effectiveness of optimizing the deep network model for detecting protein ligand binding pockets. Furthermore, we conduct thorough analysis on applying data augmentation to protein data structure and confirm that such technique can enhance the model's generalization ability, thereby ensuring good performance on new protein structures. Moreover, experiments show that the predicted binding pockets from our model can complement the results obtained from other methods. This suggests that integrating our method with existing approaches could further improve the prediction of protein-ligand binding pockets.

Traditional biological experimental methods to determine enzyme properties are time-consuming and costly, leading to an increasing interest in computational models for enzyme function prediction. However, the existing computational methods are insufficient and inefficient to exploit enzyme structure. In this work, we introduce SEFP, a novel method leveraging enzyme point clouds for enzyme function prediction. The structure encoder of SEFP uses a tailored enzyme point cloud network to analyze the three-dimensional arrangement of atoms within the enzyme, integrating hierarchical residue global features through a residue feature adapter to extract detailed enzyme point features. Additionally, the Bio-BCS residue feature encoder extracts enzyme residue features with channel and spatial weights using a specially designed attention mechanism. Finally, SEFP fuses point and residue features to generate the final prediction results. Comparative evaluations show that SEFP outperforms various recent computational methods, demonstrating superior performance. On the RSCB enzyme structure dataset, SEFP achieves an f1-score of 95.85, outperforming two representative structure-based methods, EnzyNet and DeepFri. On the HECNet dataset, SEFP maintains its superiority over all comparison sequence-based methods, yielding an f1-score of 94.29. Ablation studies are conducted to confirm the effectiveness of individual modules within SEFP. These findings underscore the potential of SEFP for reliable and precise enzyme function prediction, offering advancements in bioinformatics and computational biology.

Big Data systems are typically implemented in object-oriented languages such as Java and Scala due to the quick development cycle they provide. These systems are executed on top of a managed runtime such as the Java Virtual Machine (JVM), which requires each data item to be represented as an object before it can be processed. This representation is the direct cause of many kinds of severe inefficiencies.

A calling context is an important piece of information used widely to help developers understand program executions (e.g., for debugging). While calling contexts offer useful control information, information regarding data involved in a bug (e.g., what data structure holds a leaking object), in many cases, can bring developers closer to the bug's root cause. Such data information, often exhibited as heap reference paths, has already been needed by many tools.

For existing mainstream visual object counting (VOC) methods, training data insufficiency will lead to significant performance degradation. To address this challenge, we propose a novel sparsity-constrained example-based VOC method. Given a test image, its counts are estimated by integrating over its density map, and our method will predict such density map based on patch using training examples. Specifically, image patches and their counterpart density maps generated from annotated training images share similar local geometry on manifolds. Such local geometry can be captured by locally linear embedding (LLE) only when data are well-sampled. However, training data are poorly sampled due to their insufficiency. To handle this problem, we impose sparsity on the local optimization based on LLE, where the chosen examples favor the similar structure of input patches. Extensive experiments on public datasets demonstrate the effectiveness and competitiveness of our method by using simple features and a few training images.

Wireless capsule endoscopy (WCE) is an innovative solution for gastrointestinal disease detection. The image quality of WCE is not satisfactory for medical applications since some of them are dark or hazy. For the purpose of improving WCE image quality, we take a new way to establish a parametric image generation model (called WCE hazy model) between the captured image and the ideal image by considering adverse effects due to inhomogeneous lighting, unfocused and light reflection. Some experiments have been carried out to validate this model. Accordingly, the retinex theory and dark-channel prior have been adopted to estimate the model parameters adaptively. Hence, the WCE hazy image restoration is achieved by the inverse process of the hazy model. Intensive experiments have been conducted with hazy WCE images of the testers. Experimental results using the subjective and objective performance measures further verify the effectiveness of proposed method.

In this paper, we propose a face-hallucination method, namely face hallucination based on sparse local-pixel structure. In our framework, a high resolution (HR) face is estimated from a single frame low resolution (LR) face with the help of the facial dataset. Unlike many existing face-hallucination methods such as the from local-pixel structure to global image super-resolution method (LPS-GIS) and the super-resolution through neighbor embedding, where the prior models are learned by employing the least-square methods, our framework aims to shape the prior model using sparse representation. Then this learned prior model is employed to guide the reconstruction process. Experiments show that our framework is very flexible, and achieves a competitive or even superior performance in terms of both reconstruction error and visual quality. Our method still exhibits an impressive ability to generate plausible HR facial images based on their sparse local structures.

Data generated from microarray experiments often suffer from missing values. As most downstream analyses need full matrices as input, these missing values have to be estimated. Bayesian principal component analysis (BPCA) is a well-known microarray missing value estimation method, but its performance is not satisfactory on datasets with strong local similarity structure. A bicluster-based BPCA (bi-BPCA) method is proposed in this paper to fully exploit local structure of the matrix. In a bicluster, the most correlated genes and experimental conditions with the missing entry are identified, and BPCA is conducted on these biclusters to estimate the missing values. An automatic parameter learning scheme is also developed to obtain optimal parameters. Experimental results on four real microarray matrices indicate that bi-BPCA obtains the lowest normalized root-mean-square error on 82.14% of all missing rates.

In this paper, a novel wood identification approach based on the two-directional two-dimensional PCA ((2D)2PCA) method is proposed in contrast to the principal component analysis(PCA), two-dimensional PCA(2DPCA), column-directional 2DPCA(c2DPCA). PCA is a classical technique used to find patterns in high dimensional data. Wood identification based on PCA must transform 2D image matrix into 1D vector, and then calculate principal components from these 1D vectors. 2DPCA, c2DPCA and (2D)2PCA methods are based on 2D image matrix as opposed to classical PCA. All these wood identification methods involve seven identical steps: (1) calculating sample's mean; (2) demeaning all images; (3) calculating the demeaned sample's covariance matrix; (4) eigenvalues and eigenvectors decomposition of covariance matrix; (5) eigenvectors selection according to the largest eigenvalues to construct feature space; (6) extracting features by projecting image onto feature space; (7) classifying by the nearest neighbor classifier with Euclidean distance in feature space. Experiments using PCA, 2DPCA, c2DPCA, (2D)2PCA methods are involved in this paper. By comparing PCA, 2DPCA and c2DPCA in these experiments, it is revealed that (2D)2PCA is a more efficient method in wood identification.

This paper describes an efficient approach to scene-break detection, which can detect cuts, dissolves, and wipes reliably and effectively by means of temporally linear prediction models. In our algorithm, two linear prediction models are adopted to predict a current frame: one for dissolves, and the other for stationary scenes. The predicted frames, derived based on the two models, are compared with the original frames, and cuts and dissolves are then determined based on Bayesian cost functions. For the detection, our algorithm requires the setting of a single threshold only. In wipe detection, our linear prediction models are employed to detect areas of change between two successive frames. By accumulating the changed areas and the overlap of the changed areas over the successive frames, wipes of an arbitrary shape and direction are detected. Experimental results show that our algorithm can achieve a high level of precision even if a video contains object motion and camera motion. The detection time required to analyze a 38-min video is no more than several seconds.