pLMFPPred: a novel approach for accurate prediction of functional peptides integrating embedding from pre-trained protein language model and imbalanced learning

Functional peptides have the potential to treat a variety of diseases. Their good therapeutic efficacy and low toxicity make them ideal therapeutic agents. Artificial intelligence-based computational strategies can help quickly identify new functional peptides from collections of protein sequences and discover their different functions.Using protein language model-based embeddings (ESM-2), we developed a tool called pLMFPPred (Protein Language Model-based Functional Peptide Predictor) for predicting functional peptides and identifying toxic peptides. We also introduced SMOTE-TOMEK data synthesis sampling and Shapley value-based feature selection techniques to relieve data imbalance issues and reduce computational costs. On a validated independent test set, pLMFPPred achieved accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score values of 0.974, 0.99, and 0.974, respectively. Comparative experiments show that pLMFPPred outperforms current methods for predicting functional peptides.The experimental results suggest that the proposed method (pLMFPPred) can provide better performance in terms of Accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score than existing methods. pLMFPPred has achieved good performance in predicting functional peptides and represents a new computational method for predicting functional peptides.Comment: 20 pages, 5 figures,under revie

An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks

In energy-constrained wireless sensor networks (WSNs), the design of an energy-efficient smart strategy is a key to extend the network lifetime, but the unbalance of energy consumption and node load severely restrict the long-term operation of the network. To address these issues, a novel routing algorithm which considers both energy saving and load balancing is proposed in this paper. First of all, the transmission energy consumption, node residual energy and path hops are considered to create the link cost, and then a minimum routing graph is generated based on the link cost. Finally, in order to ensure the balance of traffic and residual energy of each node in the network, an “edge-cutting” strategy is proposed to optimize the minimum routing graph and turn it into a minimum routing tree. The simulation results show that, the proposed algorithm not only can balance the network load and prolong the lifetime of network, but meet the needs of delay and packet loss rate

Crystallization Behavior and Physical Properties of Monoglycerides-Based Oleogels as Function of Oleogelator Concentration

Oleogels have been shown as a promising replacer of hydrogenated vegetable oil. Fatty acid glycerides, including some typical mono- and di-glycerides, were used to form oleogels. The concentration effects of fatty acid glycerides on the crystallization behavior and physical properties of oleogels were investigated by using different analysis techniques. The results showed that all the oleogels formed by saturated fatty acid glycerides (glyceryl monostearate (GMS), glyceryl monolaurate (GML), glycerol monocaprylate (GMC)) exhibited a solid-like behavior and were thermally reversible systems, while a higher amount of unsaturated fatty acid glycerides (monoolein (GMO), diolein (GDO)) were needed to form oleogels. The onset gelation concentration of GMS and GMC was found to be 2 wt% (w/w), while that of GML was 4 wt% by the inverted tube method. The crystallization results illustrated that the GMS and GMC formed small needle-like crystals with the presence of β and β′ crystals, while GML formed large flake-like crystals with α crystals in oleogels, and faster cooling rates caused smaller crystals. GMS- and GMC-based oleogels had higher crystallinity, resulting in higher thermal stability and better mechanical properties than GML-based ones at the same monoglyceride (MAG) level. With the increasing MAG content, the oleogels showed a more compact three-dimensional network leading to higher mechanical properties and better thermal stability and resistance to deformations. Hence, MAG-based oleogels, especially GMC ones with medium chain fatty acid, could be a promising replacer for hydrogenation vegetable oils

Joint Energy Supply and Routing Path Selection for Rechargeable Wireless Sensor Networks

The topic of network lifetime has been attracting much research attention because of its importance in prolonging the standing operation of battery-restricted wireless sensor networks, and the rechargeable wireless sensor network has emerged as a promising solution. In this paper, we propose a joint energy supply and routing path selection algorithm to extend the network lifetime based on an initiative power supply. We develop a two-stage energy replenishment strategy to supplement the energy consumption of nodes as much as possible. Furthermore, the influence of charging factors on the selection of next-hop nodes in data routing is considered. The simulation results show that our algorithm effectively prolong the network lifetime, and different demands of network delay and energy consumption can be obtained by dynamically adjusting parameters

TRD-YOLO: A Real-Time, High-Performance Small Traffic Sign Detection Algorithm

Traffic sign detection is an important part of environment-aware technology and has great potential in the field of intelligent transportation. In recent years, deep learning has been widely used in the field of traffic sign detection, achieving excellent performance. Due to the complex traffic environment, recognizing and detecting traffic signs is still a challenging project. In this paper, a model with global feature extraction capabilities and a multi-branch lightweight detection head is proposed to increase the detection accuracy of small traffic signs. First, a global feature extraction module is proposed to enhance the ability of extracting features and capturing the correlation within the features through self-attention mechanism. Second, a new, lightweight parallel decoupled detection head is proposed to suppress redundant features and separate the output of the regression task from the classification task. Finally, we employ a series of data enhancements to enrich the context of the dataset and improve the robustness of the network. We conducted a large number of experiments to verify the effectiveness of the proposed algorithm. The accuracy of the proposed algorithm is 86.3%, the recall is 82.1%, the [email protected] is 86.5% and the [email protected]:0.95 is 65.6% in TT100K dataset, while the number of frames transmitted per second is stable at 73, which meets the requirement of real-time detection

Pathogenic mechanisms and regulatory factors involved in alcoholic liver disease

Abstract Alcoholism is a widespread and damaging behaviour of people throughout the world. Long-term alcohol consumption has resulted in alcoholic liver disease (ALD) being the leading cause of chronic liver disease. Many metabolic enzymes, including alcohol dehydrogenases such as ADH, CYP2E1, and CATacetaldehyde dehydrogenases ALDHsand nonoxidative metabolizing enzymes such as SULT, UGT, and FAEES, are involved in the metabolism of ethanol, the main component in alcoholic beverages. Ethanol consumption changes the functional or expression profiles of various regulatory factors, such as kinases, transcription factors, and microRNAs. Therefore, the underlying mechanisms of ALD are complex, involving inflammation, mitochondrial damage, endoplasmic reticulum stress, nitrification, and oxidative stress. Moreover, recent evidence has demonstrated that the gut-liver axis plays a critical role in ALD pathogenesis. For example, ethanol damages the intestinal barrier, resulting in the release of endotoxins and alterations in intestinal flora content and bile acid metabolism. However, ALD therapies show low effectiveness. Therefore, this review summarizes ethanol metabolism pathways and highly influential pathogenic mechanisms and regulatory factors involved in ALD pathology with the aim of new therapeutic insights

The plant hormone abscisic acid stimulates megakaryocyte differentiation from human iPSCs in vitro

In the clinic, the supply of platelets is frequently insufficient to meet transfusion needs. To address this issue, many scientists have established the derivation of functional platelets from CD34+ cells or human pluripotent stem cells (PSCs). However, the yield of platelets is still far below what is required. Here we found that the plant hormone abscisic acid (ABA) could increase the generation of megakaryocytes (MKs) and platelets from human induced PSCs (hiPSCs). During platelet derivation, ABA treatment promoted the generation of CD34+/CD45+ HPCs and CD41+ MKs on day 14 and then increased CD41+/CD42b+ MKs and platelets on day 19. Moreover, we found ABA-mediated activation of Akt and ERK1/2 signal pathway through receptors LANCL2 and GRP78 in a PKA-dependent manner on CD34+/CD45+ cells. In conclusion, our data suggest that ABA treatment can promote CD34+/CD45+ HPC proliferation and CD41+ MK differentiation

Bibliometric Analysis of Global Research on Tumor Dormancy

Tumor dormancy continues to be a research hotspot with numerous pressing problems that need to be solved. The goal of this study is to perform a bibliometric analysis of pertinent articles published in the twenty-first century. We concentrate on significant keywords, nations, authors, affiliations, journals, and literature in the field of tumor dormancy, which will help researchers to review the results that have been achieved and better understand the directions of future research. We retrieved research articles on tumor dormancy from the Web of Science Core Collection. This study made use of the visualization tools VOSviewer, CiteSpace, and Scimago Graphica, as visualization helps us to uncover the intrinsic connections between information. Research on tumor dormancy has been growing in the 21st century, especially from 2015 to the present. The United States is a leader in many aspects of this research area, such as in the number of publications, the number of partners, the most productive institutions, and the authors working in this field. Harvard University is the institution with the highest number of publications, and Aguirre-Ghiso, Julio A. is the author with the highest number of publications and citations. The keywords that emerged after 2017 were “early dissemination”, “inhibition”, “mechanism”, “bone metastasis”, and “promotion”. We believe that research on tumor dormancy mechanisms and therapy has been, and will continue to be, a major area of interest. The exploration of the tumor dormancy microenvironment and immunotherapeutic treatments for tumor dormancy is likely to represent the most popular future research topics