ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization.

Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments. Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/. Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.

ACP-Dnnel: anti-coronavirus peptides' prediction based on deep neural network ensemble learning.

The ongoing COVID-19 pandemic has caused dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs' identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides' candidates. In this study, we ensemble several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pre-trained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset. We followed Chou's 5-step rules. (1) we constructed the benchmark datasets data1, data2, and data3 for training and testing, and introduced the independent validation dataset ACVP-M; (2) we analyzed the peptides sequence composition feature of the benchmark dataset; (3) we constructed the ACP-Dnnel model with deep convolutional neural network (DCNN) merged the bi-directional long short-term memory (BiLSTM) as the base model for pre-training to extract the features embedded in the benchmark dataset, and then, nine classification algorithms were introduced to ensemble together for classification prediction and voting together; (4) tenfold cross-validation was introduced during the training process, and the final model performance was evaluated; (5) finally, we constructed a user-friendly web server accessible to the public at http://150.158.148.228:5000/ . The highest accuracy (ACC) of ACP-Dnnel reaches 97%, and the Matthew's correlation coefficient (MCC) value exceeds 0.9. On three different datasets, its average accuracy is 96.0%. After the latest independent dataset validation, ACP-Dnnel improved at MCC, SP, and ACC values 6.2%, 7.5% and 6.3% greater, respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides' prediction and it is available at http://150.158.148.228:5000/ .

The Relationship Between Being Bullied and Addictive Internet Use Among Chinese Rural Adolescents: The Mediating Effect of Adult Attachment.

Internet addiction among adolescents is an emerging public health issue. The aim of this study was to examine the relationship between being bullied and internet addiction, and further to test the mediating effect of adult attachments on this relationship among Chinese rural adolescents. A total of 1,270 adolescents from three high schools in Henan province were recruited as subjects. Internet addiction was measured by Young's internet addiction scale. This study showed that the prevalence of being bullied and addictive internet use among the recruited Chinese rural adolescents were 41.97% and 11.34%, respectively. Being bullied was significantly associated with internet addition. Adult attachment was found to mediate the association between being bullied and internet addiction. Higher anxiety attachment and lower close-dependent attachment partially explained the higher risk of internet addiction among the Chinese rural adolescents being bullied. This study suggests that the experience of being bullied can increase the risk of addictive internet use and that this risk can be mediated through adult attachments. This discovery supports the adoption of effective prevention programs to decrease school bullying and to improve adult attachments for adolescents in China and other countries.

Molecular Mechanisms Underlying the Biosynthesis of Melatonin and Its Isomer in Mulberry.

Mulberry (Morus alba L.) leaves and fruit are traditional Chinese medicinal materials with anti-inflammatory, immune regulatory, antiviral and anti-diabetic properties. Melatonin performs important roles in the regulation of circadian rhythms and immune activities. We detected, identified and quantitatively analyzed the melatonin contents in leaves and mature fruit from different mulberry varieties. Melatonin and three novel isoforms were found in the Morus plants. Therefore, we conducted an expression analysis of melatonin and its isomer biosynthetic genes and in vitro enzymatic synthesis of melatonin and its isomer to clarify their biosynthetic pathway in mulberry leaves. MaASMT4 and MaASMT20, belonging to class II of the ASMT gene family, were expressed selectively in mulberry leaves, and two recombinant proteins that they expressed catalyzed the conversion of N-acetylserotonin to melatonin and one of three isomers in vitro. Unlike the ASMTs of Arabidopsis and rice, members of the three ASMT gene families in mulberry can catalyze the conversion of N-acetylserotonin to melatonin. This study provides new insights into the molecular mechanisms underlying melatonin and its isomers biosynthesis and expands our knowledge of melatonin isomer biosynthesis.

Genome-wide identification and characterization of genes involved in melatonin biosynthesis in Morus notabilis (wild mulberry).

Melatonin is recognized as an important regulator for human health and widely distributed in many plant species, including mulberry (Morus L.). Previous studies suggested mulberry contains high melatonin content, but the molecular mechanisms underlying melatonin biosynthesis in mulberry remain unclear. Here, 37 genes involved in melatonin biosynthesis were identified in mulberry genome, including a tryptophan decarboxylase gene (MnTDC), seven tryptophan 5-hydroxylase genes (MnT5Hs), six serotonin N-acetyltransferase genes (MnSNATs), 20 N-acetylserotonin methyltransferase genes (MnASMTs) and three caffeic acid 3-O-methyltransferase genes (MnCOMTs). Expression analysis showed that MnTDC, MnT5H2, MnSNAT5, MnASMT12 and MnCOMT1 from these genes had highest expression levels within their corresponding families. In vitro enzymatic assays indicated that MnTDC, MnT5H2, MnSNAT5, MnASMT12 and MnCOMT1 play important roles in melatonin biosynthesis. Multiple different pathways for melatonin biosynthesis in mulberry were discovered. In addition, mulberry ASMT showed distinct roles with those of ASTMs in Arabidopsis and rice. The class I ASMT, MnASMT12, and the class III ASMT, MnASMT20, catalyzed the conversion of N-acetylserotonin to melatonin and serotonin to 5-methoxytryptamine. Furthermore, the class II ASMT, MnASMT16, only catalyzed the conversion of N-acetylserotonin to melatonin. This study improved our knowledge on melatonin biosynthesis in mulberry and expands the repertoire of melatonin biosynthesis pathways in plants.

Heterotrimeric G-protein γ subunits regulate ABA signaling in response to drought through interacting with PP2Cs and SnRK2s in mulberry (Morus alba L.).

ABA signaling plays a central role in regulating plants respond to drought. Although much progress has been made in understanding the functions of ABA signaling in drought response, very little information is available regarding woody plants. In this study, the components of ABA signaling pathway were identified in mulberry which has excellent adaptation to drought, including three PYLs, two PP2Cs, two SnRK2s, four ABFs, and an ABA responsive gene MaRD29B. The gene expression of ABA signaling components exhibited significant response to ABA and drought, and their roles in drought response were revealed using a transient transformation system in mulberry seedlings. We discovered the ABA signaling components, MaABI1/2 and MaSnRK2.1/2.4, could directly interact with G-protein γ subunits, MaGγ1 and MaGγ2, which indicated that G-protein γ subunits may mediate the signal crosstalk between G-proteins and ABA signaling. Transient activation assay in tobacco and RNAi silencing assay in mulberry further demonstrated that MaGγ1 and MaGγ2 regulated drought response by enhancing ABA signaling. This study expands the repertoire of ABA signaling controlling drought responses in plants and provides the direct evidence about the crosstalk between ABA signaling and G-proteins for the first time.

Depressive symptoms and health service utilization among Chinese middle-aged and older adults: a national population-based longitudinal survey.

OBJECTIVES: This study aimed to (1) examine the cross-sectional and longitudinal relationships between depressive symptoms and health services utilization among Chinese middle-aged and older adults; and (2) evaluate whether there exists a rural-urban difference in such relationships. METHODS: Data was obtained from China Health and Retirement Longitudinal Study (CHARLS) in 2013 and 2015, a nationally representative survey of 13,551 adults aged 45 years and above in China. RESULTS: Depressive symptoms were positively associated with a greater likelihood of outpatient and inpatient health services utilization. This association was consistent across rural and urban settings, indicating the robustness of such findings across geographic areas. CONCLUSIONS: Findings indicate that depressive symptoms are significantly associated with both in-patient and out-patient health service utilization among Chinese adults. Screening for depressive symptoms needs to be incorporated in these care settings in China.

Soil aggregates stability and storage of soil organic carbon respond to cropping systems on Black Soils of Northeast China.

Monoculture and improper management may reduce soil fertility and deteriorate soil structure in Black soils (Mollisols) of Northeast China. The experiment was carried out from 2015 to 2016 in Black Soils comprising five cropping systems: continuous corn (CC), soybean-corn rotation (SC), corn-soybean rotation (CS), fallow-corn (FC), and fallow-soybean (FS). Our results showed that CS and FS treatments significantly increased mean weight diameter (MWD) and fractal dimension (D) in mechanical stability aggregates (MSAs), and increased MWD and geometric mean diameter (GMD) in water-stable aggregates (WSAs) compared with CC treatment. These two treatments were also significantly increased water-stable aggregates stability rate (WSAR), but decreased percentage of aggregates destruction (PAD) than CC treatment. Meanwhile, CS and FS treatments exhibited a higher carbon accumulation than CC treatment in bulk soils. Soil organic carbon (SOC) concentration in WSA0.106-0.25,WSA2-5 mm and WSA0.5-1 mm had a dominant effect on aggregate stability. Simutaneously, SOC in WSA>5 mm affected SOC concentration in bulk soils. As a whole, the CS and FS treatments can increase the percentage of macro-aggregates, enhance aggregate stability, as well as increase SOC concentration in bulk soils and all soil aggregate sizes.

Tungsten Carbide-Cobalt Nanoparticles Induce Reactive Oxygen Species, AKT, ERK, AP-1, NF-κB, VEGF, and Angiogenesis.

Powder mixtures of tungsten carbide and metallic cobalt (WC-Co) are widely used in various products. Nanoparticles are engineered structures with at least one dimension of 100 nm or smaller. WC-Co is known to be associated with lung injury and diseases. Angiogenesis is a key process during vasculature, carcinogenesis, recovery of injury, and inflammatory diseases. However, the cellular effects of WC-Co nanoparticles on angiogenesis remain to be elucidated. In this study, we investigated angiogenic response and relative mechanisms after exposure to WC-Co nanoparticles. Our results showed that WC-Co nanoparticles at 5 µg/cm(2) induced ROS production which activated AKT and ERK1/2 signaling pathways in lung epithelial cells by reactive oxygen species (ROS) staining and immunoblotting; WC-Co treatment also increased transcriptional activation of AP-1, NF-κB, and VEGF by reporter assay. Further studies demonstrated that ROS are upstream molecules of AKT and ERK signaling pathways; the activation of AP-1, NF-κB, and VEGF was through ROS generation, AKT and ERK1/2 activation. In addition, WC-Co nanoparticles affected the cells to induce angiogenesis by chicken chorioallantoic membrane (CAM) assay. These results illustrate that exposure to WC-Co nanoparticles induces angiogenic response by activating ROS, AKT, and ERK1/2 signaling pathways and the downstream molecules and elucidate the potential molecular mechanisms during this process. This information may be useful for preventing potential damage from nanoparticle exposure in the future.

Cadmium increases HIF-1 and VEGF expression through ROS, ERK, and AKT signaling pathways and induces malignant transformation of human bronchial epithelial cells.

Cadmium is categorized as a human carcinogen especially involved in lung cancers. Angiogenesis is considered a fundamental requirement for tumorigenesis, but the mechanisms underlying the tumor angiogenesis induced by cadmium are poorly understood. Using in vitro and in vivo models, we investigated the angiogenic mechanisms of cadmium in human bronchial epithelial cells and tumor formation. Our results demonstrated that cadmium (CdCl(2)) activated extracellular signal-regulated kinases (ERK) and AKT signaling and elevated the expression of a key downstream proangiogenic molecule hypoxia-inducible factor-1 (HIF-1) in immortalized human lung epithelial BEAS-2B cells. Cadmium also induced reactive oxygen species (ROS) production, which could be inhibited by ROS scavengers, catalase and diphenyleneiodonium chloride. Inhibition of ROS generation also attenuated ERK, AKT, p70S6K1 activation, and HIF-1α expression. Similar results were obtained in normal human bronchial epithelial (NHBE) cells, showing that cadmium induced HIF-1 expression via ROS/ERK/AKT signaling pathway. Furthermore, cadmium induced vascular endothelial growth factor expression and transcriptional activation through ROS, ERK, and AKT pathways. Finally, cadmium transformed human bronchial epithelial cells in culture; the transformed cells induced tube formation in vitro, angiogenesis on chicken chorioallantoic membrane, and formed tumors in nude mice. Taken together, the results of this study provide explanation for the role and molecular mechanisms of cadmium in promoting angiogenesis in lung epithelial cells and malignant transformation and will be helpful for improved occupational protection, prevention, as well as chemotherapy of human lung cancers caused by heavy metal cadmium.