MOTIVATION: Exploring the association between drugs and targets is essential for drug discovery and repurposing. Comparing with the traditional methods that regard the exploration as a binary classification task, predicting the drug-target binding affinity can provide more specific information. Many studies work based on the assumption that similar drugs may interact with the same target. These methods constructed a symmetric graph according to the undirected drug similarity or target similarity. Although these similarities can measure the difference between two molecules, it is unable to analyze the inclusion relationship of their substructure. For example, if drug A contains all the substructures of drug B, then in the message-passing mechanism of the graph neural network, drug A should acquire all the properties of drug B, while drug B should only obtain some of the properties of A. RESULTS: To this end, we proposed a structure-inclusive similarity (SIS) which measures the similarity of two drugs by considering the inclusion relationship of their substructures. Based on SIS, we constructed a drug graph and a target graph, respectively, and predicted the binding affinities between drugs and targets by a graph convolutional network-based model. Experimental results show that considering the inclusion relationship of the substructure of two molecules can effectively improve the accuracy of the prediction model. The performance of our SIS-based prediction method outperforms several state-of-the-art methods for drug-target binding affinity prediction. The case studies demonstrate that our model is a practical tool to predict the binding affinity between drugs and targets. AVAILABILITY AND IMPLEMENTATION: Source codes and data are available at https://github.com/HuangStomach/SISDTA.

Exploring drug-protein interactions (DPIs) through computational methods can effectively reduce the workload and the cost of DPI identification. Previous works try to predict DPIs by integrating and analyzing the unique features of drugs and proteins. They cannot adequately analyze the consistency between the drug features and the protein features due to their different semantics. However, the consistency of their features, such as the correlation originating from their sharing diseases, may reveal some potential DPIs. Here we propose a deep neural network-based co-coding method (DNNCC for short) to predict novel DPIs. DNNCC projects the original features of drugs and proteins to a common embedding space through a co-coding strategy. In this way, the embedding features of drugs and proteins have the same semantics. Therefore, the prediction module can discover the unknown DPIs by exploring the feature consistency between drugs and proteins. The experimental results indicate that the performance of DNNCC is significantly superior to five state-of-the-art DPI prediction methods under several evaluation metrics. The superiority of integrating and analyzing the common features of drugs and proteins is proved by the ablation experiments. The novel DPIs predicted by DNNCC verify that DNNCC is a powerful prior tool that can effectively discover potential DPIs.

Many powerful computational methods based on graph neural networks (GNNs) have been proposed to predict drug-protein interactions (DPIs). It can effectively reduce laboratory workload and the cost of drug discovery and drug repurposing. However, many clinical functions of drugs and proteins are unknown due to their unobserved indications. Therefore, it is difficult to establish a reliable drug-protein heterogeneous network that can describe the relationships between drugs and proteins based on the available information. To solve this problem, we propose a DPI prediction method that can self-adaptively adjust the topological structure of the heterogeneous networks, and name it SATS. SATS establishes a representation learning module based on graph attention network to carry out the drug-protein heterogeneous network. It can self-adaptively learn the relationships among the nodes based on their attributes and adjust the topological structure of the network according to the training loss of the model. Finally, SATS predicts the interaction propensity between drugs and proteins based on their embeddings. The experimental results show that SATS can effectively improve the topological structure of the network. The performance of SATS outperforms several state-of-the-art DPI prediction methods under various evaluation metrics. These prove that SATS is useful to deal with incomplete data and unreliable networks. The case studies on the top section of the prediction results further demonstrate that SATS is powerful for discovering novel DPIs.

A novel laser printing files detection method is proposed in this paper to solve the problem of low efficiency and difficulty in traditional detection. The new method is based on improved scale-invariant feature transform (SIFT) feature and histogram feature. Firstly, analyze the graphical features of different laser printing files. Different files have different printing texture features in valid data area. So segment the valid data area to remove the interference of background. Secondly, extract the histogram feature of the same character in the printing file. Normalize the histogram and then calculate the Bhattacharyya coefficient between the detected file and the original file to determine whether the detected file is right or fake. At the same time, calculate the SIFT features and match the detected file and the original file. To focus on the letter or character region, the SIFT features which are out of contour are deleted. Finally, the results of the two different methods are both used as the result of the identification. When any of the result is fake, the end result will be fake. In the self-built database experiment, in different printing files from different printers, the inkjet areas possess different image features. When scanning different files using 600 dpi, the detect accuracy is higher than 97%. This method was able to meet the reliability requirements of law.

Software transactional memory offers an appealing alternative to locks by improving programmability, reliability, and scalability. However, existing STMs are impractical because they add high instrumentation costs and often provide weak progress guarantees and/or semantics. This paper introduces a novel STM called LarkTM that provides three significant features. (1) Its instrumentation adds low overhead except when accesses actually conflict, enabling low single-thread overhead and scaling well on low-contention workloads. (2) It uses eager concurrency control mechanisms, yet naturally supports flexible conflict resolution, enabling strong progress guarantees. (3) It naturally provides strong atomicity semantics at low cost. LarkTM's design works well for low-contention workloads, but adds significant overhead under higher contention, so we design an adaptive version of LarkTM that uses alternative concurrency control for high-contention objects. An implementation and evaluation in a Java virtual machine show that the basic and adaptive versions of LarkTM not only provide low single-thread overhead, but their multithreaded performance compares favorably with existing high-performance STMs.

Atomicity is a key correctness property that allows programmers to reason about code regions in isolation. However, programs often fail to enforce atomicity correctly, leading to atomicity violations that are difficult to detect. Dynamic program analysis can detect atomicity violations based on an atomicity specification, but existing approaches slow programs substantially.

Parallel programming is essential for reaping the benefits of parallel hardware, but it is notoriously difficult to develop and debug reliable, scalable software systems. One key challenge is that modern languages and systems provide poor support for ensuring concurrency correctness properties - atomicity, sequential consistency, and multithreaded determinism - because all existing approaches are impractical. Dynamic, software-based approaches slow programs by up to an order of magnitude because capturing and controlling cross-thread dependences (i.e., conflicting accesses to shared memory) requires synchronization at virtually every access to potentially shared memory.

State-of-the-art dynamic bug detectors such as data race and memory leak detectors report program locations that are likely causes of bugs. However, programmers need more than static program locations to understand the behavior of increasingly complex and concurrent software. Dynamic calling context provides additional information, but it is expensive to record calling context frequently, e.g., at every read and write. Context-sensitive dynamic analyses can build and maintain a calling context tree (CCT) to track calling context--but in order to reuse existing nodes, CCT-based approaches require an expensive lookup.