MOTIVATION: As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance. RESULTS: On both the cross-fold and cross-dataset validation, our method outperforms the state-of-the-art methods. Notably, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test problem comparing with state-of-art Deep Learning method. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model ESM-1b, which was pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that the Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. AlphaFold prediction, further motif analysis, and docking experiments further demonstrate that AcrNET can capture the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly. AVAILABILITY AND IMPLEMENTATION: Web server: https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/. Training code and pre-trained model are available at.

MOTIVATION: We have entered the multi-omics era and can measure cells from different aspects. Hence, we can get a more comprehensive view by integrating or matching data from different spaces corresponding to the same object. However, it is particularly challenging in the single-cell multi-omics scenario because such data are very sparse with extremely high dimensions. Though some techniques can be used to measure scATAC-seq and scRNA-seq simultaneously, the data are usually highly noisy due to the limitations of the experimental environment. RESULTS: To promote single-cell multi-omics research, we overcome the above challenges, proposing a novel framework, contrastive cycle adversarial autoencoders, which can align and integrate single-cell RNA-seq data and single-cell ATAC-seq data. Con-AAE can efficiently map the above data with high sparsity and noise from different spaces to a coordinated subspace, where alignment and integration tasks can be easier. We demonstrate its advantages on several datasets. AVAILABILITY AND IMPLEMENTATION: Zenodo link: https://zenodo.org/badge/latestdoi/368779433. github: https://github.com/kakarotcq/Con-AAE.

The address space layout randomization (ASLR) has been widely deployed on modern operating systems against code reuse attacks (CRAs), such as return-oriented programming (ROP) and jump-oriented programming (JOP). However, porting ASLR to resource-constrained IoT devices is a great challenge due to the limited memory space for randomization. We propose a function-based ASLR scheme (fASLR) for IoT runtime security utilizing the ARM TrustZone-M technology and the memory protection unit (MPU) supported by ARM Cortex-M processors. fASLR loads a function from the flash and randomizes its base address in a randomization region in RAM when the function is being called. We design novel mechanisms on cleaning up finished functions from the RAM and memory addressing to tackle the complexity of function relocation and randomization. Optimizations are applied to effectively reduce overhead introduced by runtime memory management. We also formally prove that user applications will run correctly with fASLR enabled. Compared with the related work, a prominent advantage of fASLR is that fASLR can run an application even if the application code cannot be completely loaded into RAM for execution. We test fASLR with 21 applications. The experimental results show that fASLR achieves a high randomization entropy and incurs a runtime overhead of less than 10%.