The antioxidant activities of 18 typical phenolic acids were investigated using 2, 2'-diphenyl-1-picrylhydrazyl (DPPH) and ferric ion reducing antioxidant power (FRAP) assays. Five thermodynamic ...parameters involving hydrogen atom transfer (HAT), single-electron transfer followed by proton transfer (SET-PT), and sequential proton-loss electron transfer (SPLET) mechanisms were calculated using density functional theory with the B3LYP/UB3LYP functional and 6-311++G (d, p) basis set and compared in the phenolic acids. Based on the same substituents on the benzene ring, -CH
COOH and -CH = CHCOOH can enhance the antioxidant activities of phenolic acids, compared with -COOH. Methoxyl (-OCH
) and phenolic hydroxyl (-OH) groups can also promote the antioxidant activities of phenolic acids. These results relate to the O-H bond dissociation enthalpy of the phenolic hydroxyl group in phenolic acids and the values of proton affinity and electron transfer enthalpy (ETE) involved in the electron donation ability of functional groups. In addition, we speculated that HAT, SET-PT, and SPLET mechanisms may occur in the DPPH reaction system. Whereas SPLET was the main reaction mechanism in the FRAP system, because, except for 4-hydroxyphenyl acid, the ETE values of the phenolic acids in water were consistent with the experimental results.
Abstract
DNA N4-methylcytosine (4mC) is an important epigenetic modification that plays a vital role in regulating DNA replication and expression. However, it is challenging to detect 4mC sites ...through experimental methods, which are time-consuming and costly. Thus, computational tools that can identify 4mC sites would be very useful for understanding the mechanism of this important type of DNA modification. Several machine learning-based 4mC predictors have been proposed in the past 3 years, although their performance is unsatisfactory. Deep learning is a promising technique for the development of more accurate 4mC site predictions. In this work, we propose a deep learning-based approach, called DeepTorrent, for improved prediction of 4mC sites from DNA sequences. It combines four different feature encoding schemes to encode raw DNA sequences and employs multi-layer convolutional neural networks with an inception module integrated with bidirectional long short-term memory to effectively learn the higher-order feature representations. Dimension reduction and concatenated feature maps from the filters of different sizes are then applied to the inception module. In addition, an attention mechanism and transfer learning techniques are also employed to train the robust predictor. Extensive benchmarking experiments demonstrate that DeepTorrent significantly improves the performance of 4mC site prediction compared with several state-of-the-art methods.
This study proposes a type of trabecular–honeycomb biomimetic structures with high-efficiency energy-absorbing abilities inspired by beetle elytra. Because the trabecular structure is distributed at ...the ends of the honeycomb walls, the proposed structure is named an end-trabecular beetle elytron plate crash box, or EBEP crash box for simplification. A comparison between the EBEP crash box and conventional crash box (a buffering structure generally used in modern devices and vehicles) is conducted using compression experiments and finite element method. We present the following results. (1) In contrast to the fluctuation stage with a low force in a conventional crash box, the force–displacement curve of the EBEP crash box possesses a rising stage and an approximate plateau with a higher force; as a result, the absorbing energy ability and compression force efficiency are 5 and 2.6 times greater than those of a conventional crash box, respectively. (2) Experimental and numerical comparisons reveal that there is cracking failure in the conventional crash box; however, the coordinated and uniform S-typed laminated compression deformation is developed in the EBEP crash box. (3) The influences of the amplitude (
A
) of the sine wave deformation line on the peak force and the compression force efficiency of the EBEP crash box are investigated, thereby providing a feasible method for adjusting the peak force according to different engineering requirements. These results provide new inspiration for applying EBEP crash boxes and exploiting new buffering structures and materials in the energy-absorbing field.
► The optimal design in the forewings of two beetles species is reported. ► Extensive review of the work related to the structure of the beetle forewing is presented. ► A sandwich structure based on ...beetle forewing configuration is proposed. ► A new design route to develop biomimetic composite materials is presented.
Based on studies of the forewings of two beetles, Allomyrina dichotoma and Prosopocoilus inclinatus, this paper reviews and identifies the potential benefits of studying the structure of the beetle forewing and the associated development of lightweight biomimetic composite materials. The forewings of both beetle species consist of an integrated border frame structure and a large center part with distributed trabecular supports in the hollow core. The forewings of the male A. dichotoma are constructed to reflect a lightweight honeycomb design. However, the forewings of P. inclinatus are a durable structure. The biological significance of these structures is also discussed. This work proposes an integrated honeycomb structure inspired by the beetle forewing. A series of biological models are also proposed for lightweight integrated honeycomb structures and durable sandwich structures with a trabecular core, which are intended to establish a new direction in the development of biomimetic composite materials.
In pursuit of the development of lightweight biomimetic functional–structural materials, this study investigated the flexural properties and failure characteristics of end-trabecular beetle elytron ...plates (EBEPs) as well as the flexural mechanism and the role of the trabeculae. The results were as follows: (1) The EBEP specimens showed better ductility performance after the peak load was reached, and their specific elastic strength and specific flexural strength were similar to those of honeycomb plates (HPs). In an EBEP before failure, the lower skin in the same location as the load was significantly stretched, and the trabeculae in the core showed two failure modes: destruction by means of slant cracks and vertical cracks. (2) The failure mechanism of the trabeculae in an EBEP was investigated by qualitatively analyzing the load and deformation of the parts adjacent and nonadjacent to the loading point. From the macro point of view, the cores of EBEP and HP are continuous. These cores can not only bear tension with lower skins, but also divide upper skins into much smaller parts and play a role as reinforcing ribs. The equivalent trabeculae in EBEP are closed ended, the honeycomb walls are narrow, and these two parts can support and constrain each other.
Abstract
Motivation
Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated ...by proteases plays a central role in the ‘life and death’ cellular processes, many of the corresponding substrates and their cleavage sites were not found yet. Availability of accurate predictors of the substrates and cleavage sites would facilitate understanding of proteases’ functions and physiological roles. Deep learning is a promising approach for the development of accurate predictors of substrate cleavage events.
Results
We propose DeepCleave, the first deep learning-based predictor of protease-specific substrates and cleavage sites. DeepCleave uses protein substrate sequence data as input and employs convolutional neural networks with transfer learning to train accurate predictive models. High predictive performance of our models stems from the use of high-quality cleavage site features extracted from the substrate sequences through the deep learning process, and the application of transfer learning, multiple kernels and attention layer in the design of the deep network. Empirical tests against several related state-of-the-art methods demonstrate that DeepCleave outperforms these methods in predicting caspase and matrix metalloprotease substrate-cleavage sites.
Availability and implementation
The DeepCleave webserver and source code are freely available at http://deepcleave.erc.monash.edu/.
Supplementary information
Supplementary data are available at Bioinformatics online.
To investigate the characteristics of compression, buffering and energy dissipation in beetle elytron plates (BEPs), compression experiments were performed on BEPs and honeycomb plates (HPs) with the ...same wall thickness in different core structures and using different molding methods. The results are as follows: 1) The compressive strength and energy dissipation capacity in the BEP are 2.44 and 5.0 times those in the HP, respectively, when the plates are prepared using the full integrated method (FIM). 2) The buckling stress is directly proportional to the square of the wall thickness (t). Thus, for core structures with equal wall thicknesses, although the core volume of the BEP is 42 percent greater than that of the HP, the mechanical properties of the BEP are several times higher than those of the HP. 3) It is also proven that even when the single integrated method (SIM) is used to prepare BEPs, the properties discussed above remain superior to those of HPs by a factor of several; this finding lays the foundation for accelerating the commercialization of BEPs based on modern manufacturing processes.
Coronary microembolization (CME), a common reason for periprocedural myocardial infarction (PMI), bears very important prognostic implications. However, the molecular mechanisms related to CME remain ...largely elusive. Statins have been shown to prevent PMI, but the underlying mechanism has not been identified. Here, we examine whether the NLRP3 inflammasome contributes to CME-induced cardiac injury and investigate the effects of statin therapy on CME. In vivo study, mice with CME were treated with 40 mg/kg/d rosuvastatin (RVS) orally or a selective NLRP3 inflammasome inhibitor MCC950 intraperitoneally (20 mg/kg/d). Mice treated with MCC950 and RVS showed improved cardiac contractile function and morphological changes, diminished fibrosis and microinfarct size, and reduced serum lactate dehydrogenase (LDH) level. Mechanistically, RVS decreased the expression of NLRP3, caspase-1, interleukin-1β, and Gasdermin D N-terminal domains. Proteomics analysis revealed that RVS restored the energy metabolism and oxidative phosphorylation in CME. Furthermore, reduced reactive oxygen species (ROS) level and alleviated mitochondrial damage were observed in RVS-treated mice. In vitro study, RVS inhibited the activation of NLRP3 inflammasome induced by tumor necrosis factor α plus hypoxia in H9c2 cells. Meanwhile, the pyroptosis was also suppressed by RVS, indicated by the increased cell viability, decreased LDH and propidium iodide uptake in H9c2 cells. RVS also reduced the level of mitochondrial ROS generation in vitro. Our results indicate the NLRP3 inflammasome-dependent cardiac pyroptosis plays an important role in CME-induced cardiac injury and its inhibitor exerts cardioprotective effect following CME. We also uncover the anti-pyroptosis role of RVS in CME, which is associated with regulating mitochondrial ROS.
Autophagy-related gene-5 (ATG-5) is one of the key regulators of autophagic cell death. It has been widely regarded as a protective molecular mechanism for tumor cells during the course of ...chemotherapy. In the present study, we investigated the expression pattern of ATG-5 and multidrug resistance-associated protein-1 (MRP-1) in 135 gastric cancers (GC) patients who were treated with epirubicin, cisplatin and 5-FU adjuvant chemotherapy (ECF) following surgical resection and explored their potential clinical significance. We found that both ATG-5 (77.78%) and MRP-1 (79.26%) were highly expressed in GC patients. ATG-5 expression was significantly associated with depth of wall invasion, TNM stages and distant metastasis of GC (P<0.05), whereas MRP-1 expression was significantly linked with tumor size, depth of wall invasion, lymph node metastasis, TNM stages and differentiation status (P<0.05). ATG-5 expression was positively correlated with MRP-1 (rp = 0.616, P<0.01). Increased expression of ATG-5 and MPR-1 was significantly correlated with poor overall survival (OS; P<0.01) and disease free survival (DFS; P<0.01) of our GC cohort. Furthermore, we demonstrated that ATG-5 was involved in drug resistant of GC cells, which was mainly through regulating autophagy. Our data suggest that upregulated expression of ATG-5, an important molecular feature of protective autophagy, is associated with chemoresistance in GC. Expression of ATG-5 and MRP-1 may be independent prognostic markers for GC treatment.
Abstract
Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with ...important roles in initiating gene transcription. Therefore, solving promoter-identification problems has important implications for improving the understanding of their functions. To this end, computational methods targeting promoter classification have been established; however, their performance remains unsatisfactory. In this study, we present a novel stacked-ensemble approach (termed SELECTOR) for identifying both promoters and their respective classification. SELECTOR combined the composition of k-spaced nucleic acid pairs, parallel correlation pseudo-dinucleotide composition, position-specific trinucleotide propensity based on single-strand, and DNA strand features and using five popular tree-based ensemble learning algorithms to build a stacked model. Both 5-fold cross-validation tests using benchmark datasets and independent tests using the newly collected independent test dataset showed that SELECTOR outperformed state-of-the-art methods in both general and specific types of promoter prediction in Escherichia coli. Furthermore, this novel framework provides essential interpretations that aid understanding of model success by leveraging the powerful Shapley Additive exPlanation algorithm, thereby highlighting the most important features relevant for predicting both general and specific types of promoters and overcoming the limitations of existing ‘Black-box’ approaches that are unable to reveal causal relationships from large amounts of initially encoded features.
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