Rad54: the Swiss Army knife of homologous recombination?

Homologous recombination (HR) is a ubiquitous cellular pathway that mediates transfer of genetic information between homologous or near homologous (homeologous) DNA sequences. During meiosis it ensures proper chromosome segregation in the first division. Moreover, HR is critical for the tolerance and repair of DNA damage, as well as in the recovery of stalled and broken replication forks. Together these functions preserve genomic stability and assure high fidelity transmission of the genetic material in the mitotic and meiotic cell divisions. This review will focus on the Rad54 protein, a member of the Snf2-family of SF2 helicases, which translocates on dsDNA but does not display strand displacement activity typical for a helicase. A wealth of genetic, cytological, biochemical and structural data suggests that Rad54 is a core factor of HR, possibly acting at multiple stages during HR in concert with the central homologous pairing protein Rad51

CIBZ, a Novel BTB Domain-Containing Protein, Is Involved in Mouse Spinal Cord Injury via Mitochondrial Pathway Independent of p53 Gene

Spinal cord injury (SCI) induces both primary uncontrollable mechanical injury and secondary controllable degeneration, which further results in the activation of cell death cascades that mediate delayed tissue damage. To alleviate its impairments and seek for an effective remedy, mRNA differential display was used to investigate gene mRNA expression profiling in mice following SCI. A specific Zinc finger and BTB domain-containing protein, CIBZ, was discovered to implicate in the SCI process for the first time. Further researches indicated that CIBZ was extensively distributed in various tissues, and the expression level was highest in muscle, followed by spinal cord, large intestine, kidney, spleen, thymus, lung, cerebrum, stomach, ovary and heart, respectively. After injury, the CIBZ expression decreased dramatically and reached the lowest level at 8 h, but it gradually increased to the maximal level at 7 d. Caspase-3 and C-terminal-binding protein (CtBP), two CIBZ-related proteins, showed similar tendency. Interestingly, p53 expression remained constant in all groups. Via flow cytometry (FCM) analysis, it was found that the cell death rate in SCI group markedly increased and reached the highest value 1 d after surgery and the mitochondrial transmembrane potential (ΔΨm) at 1 d was the lowest in all groups. Taken together, it is suggested that: (i) in the presence of CtBP, CIBZ gene is involved in secondary injury process and trigger the activation of apoptotic caspase-3 and bax genes independent of p53; (ii) abrupt down-regulation of CtBP at 8 h is a sign of mitochondria dysfunction and the onset of cell death; (iii) it could be used as an inhibitor or target drug of caspase-3 gene to improve spinal cord function

DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection

Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework has proved to be effective in solving this challenge. However, previous works based on S-T only empirically applied constraints on normal data and fused multi-level information. In this study, we propose an improved model called DeSTSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework. First, to strengthen the constraints on anomalous data, we introduce a denoising procedure that allows the student network to learn more robust representations. From synthetically corrupted normal images, we train the student network to match the teacher network feature of the same images without corruption. Second, to fuse the multi-level S-T features adaptively, we train a segmentation network with rich supervision from synthetic anomaly masks, achieving a substantial performance improvement. Experiments on the industrial inspection benchmark dataset demonstrate that our method achieves state-of-the-art performance, 98.6% on image-level ROC, 75.8% on pixel-level average precision, and 76.4% on instance-level average precision

Preparation and Application of Sustained-Release Potassium Ferrate(VI)

In this study, a composite system for the sustained release of potassium ferrate(VI) (sustained-release K 2 FeO 4 ) was prepared and applied for water treatment. The objective of this research was to maximize the effectiveness of K 2 FeO 4 for water treatment by enhancing its stability using diatomite. The sustained-release K 2 FeO 4 was characterized using X-ray diffraction, scanning electron microscopy, and Fourier transform infrared spectroscopy. The results indicated that no new crystal phase was formed during the preparation and some K 2 FeO 4 crystals entered the pores of the diatomite. From K 2 FeO 4 release experiments, we found that the decomposition rate of K 2 FeO 4 was obviously decreased, which greatly improved the contact rate between released K 2 FeO 4 and pollutants. Via degradation of methyl orange, which was used as a model pollutant, the influential factor of K 2 FeO 4 content within the complete sustained-release K 2 FeO 4 system was studied. The optimal K 2 FeO 4 content within the sustained-release K 2 FeO 4 system was approximately 70%. In natural water samples, sustained-release K 2 FeO 4 at a dosage of 0.06 g/L and with a reaction time of 20 minutes removed 36.84% of soluble microbial products and 17.03% of simple aromatic proteins, and these removal rates were better than those observed after traditional chlorine disinfection

Dual Convolutional Neural Networks With Attention Mechanisms Based Method for Predicting Disease-Related lncRNA Genes

A lot of studies indicated that aberrant expression of long non-coding RNA genes (lncRNAs) is closely related to human diseases. Identifying disease-related lncRNAs (disease lncRNAs) is critical for understanding the pathogenesis and etiology of diseases. Most of the previous methods focus on prioritizing the potential disease lncRNAs based on shallow learning methods. The methods fail to extract the deep and complex feature representations of lncRNA-disease associations. Furthermore, nearly all the methods ignore the discriminative contributions of the similarity, association, and interaction relationships among lncRNAs, disease, and miRNAs for the association prediction. A dual convolutional neural networks with attention mechanisms based method is presented for predicting the candidate disease lncRNAs, and it is referred to as CNNLDA. CNNLDA deeply integrates the multiple source data like the lncRNA similarities, the disease similarities, the lncRNA-disease associations, the lncRNA-miRNA interactions, and the miRNA-disease associations. The diverse biological premises about lncRNAs, miRNAs, and diseases are combined to construct the feature matrix from the biological perspectives. A novel framework based on the dual convolutional neural networks is developed to learn the global and attention representations of the lncRNA-disease associations. The left part of the framework exploits the various information contained by the feature matrix to learn the global representation of lncRNA-disease associations. The different connection relationships among the lncRNA, miRNA, and disease nodes and the different features of these nodes have the discriminative contributions for the association prediction. Hence we present the attention mechanisms from the relationship level and the feature level respectively, and the right part of the framework learns the attention representation of associations. The experimental results based on the cross validation indicate that CNNLDA yields superior performance than several state-of-the-art methods. Case studies on stomach cancer, lung cancer, and colon cancer further demonstrate CNNLDA's ability to discover the potential disease lncRNAs

Mining disease genes using integrated protein–protein interaction and gene–gene co-regulation information

AbstractIn humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein–protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene–gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining

Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects

Background: Inferring drug-related side effects is beneficial for reducing drug development cost and time. Current computational prediction methods have concentrated on graph reasoning over heterogeneous graphs comprising the drug and side effect nodes. However, the various topologies and node attributes within multiple drug–side effect heterogeneous graphs have not been completely exploited.Methods: We proposed a new drug-side effect association prediction method, GGSC, to deeply integrate the diverse topologies and attributes from multiple heterogeneous graphs and the self-calibration attributes of each drug-side effect node pair. First, we created two heterogeneous graphs comprising the drug and side effect nodes and their related similarity and association connections. Since each heterogeneous graph has its specific topology and node attributes, a node feature learning strategy was designed and the learning for each graph was enhanced from a graph generative and adversarial perspective. We constructed a generator based on a graph convolutional autoencoder to encode the topological structure and node attributes from the whole heterogeneous graph and then generate the node features embedding the graph topology. A discriminator based on multilayer perceptron was designed to distinguish the generated topological features from the original ones. We also designed representation-level attention to discriminate the contributions of topological representations from multiple heterogeneous graphs and adaptively fused them. Finally, we constructed a self-calibration module based on convolutional neural networks to guide pairwise attribute learning through the features of the small latent space.Results: The comparison experiment results showed that GGSC had higher prediction performance than several state-of-the-art prediction methods. The ablation experiments demonstrated the effectiveness of topological enhancement learning, representation-level attention, and self-calibrated pairwise attribute learning. In addition, case studies over five drugs demonstrated GGSC’s ability in discovering the potential drug-related side effect candidates.Conclusion: We proposed a drug-side effect association prediction method, and the method is beneficial for screening the reliable association candidates for the biologists to discover the actual associations