RESUMEN

Introduction: Imbalances in gut microbes have been implied in many human diseases, including colorectal cancer (CRC), inflammatory bowel disease, type 2 diabetes, obesity, autism, and Alzheimer's disease. Compared with other human diseases, CRC is a gastrointestinal malignancy with high mortality and a high probability of metastasis. However, current studies mainly focus on the prediction of colorectal cancer while neglecting the more serious malignancy of metastatic colorectal cancer (mCRC). In addition, high dimensionality and small samples lead to the complexity of gut microbial data, which increases the difficulty of traditional machine learning models. Methods: To address these challenges, we collected and processed 16S rRNA data and calculated abundance data from patients with non-metastatic colorectal cancer (non-mCRC) and mCRC. Different from the traditional health-disease classification strategy, we adopted a novel disease-disease classification strategy and proposed a microbiome-based multi-view convolutional variational information bottleneck (MV-CVIB). Results: The experimental results show that MV-CVIB can effectively predict mCRC. This model can achieve AUC values above 0.9 compared to other state-of-the-art models. Not only that, MV-CVIB also achieved satisfactory predictive performance on multiple published CRC gut microbiome datasets. Discussion: Finally, multiple gut microbiota analyses were used to elucidate communities and differences between mCRC and non-mCRC, and the metastatic properties of CRC were assessed by patient age and microbiota expression.

RESUMEN

The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for one class of therapeutic peptides. It is worth noting that currently no predictive method considers sequence length information as a distinct feature of therapeutic peptides. In this article, a novel deep learning approach with matrix factorization for predicting therapeutic peptides (DeepTPpred) by integrating length information are proposed. The matrix factorization layer can learn the potential features of the encoded sequence through the mechanism of first compression and then restoration. And the length features of the sequence of therapeutic peptides are embedded with encoded amino acid sequences. To automatically learn therapeutic peptide predictions, these latent features are input into the neural networks with self-attention mechanism. On eight therapeutic peptide datasets, DeepTPpred achieved excellent prediction results. Based on these datasets, we first integrated eight datasets to obtain a full therapeutic peptide integration dataset. Then, we obtained two functional integration datasets based on the functional similarity of the peptides. Finally, we also conduct experiments on the latest versions of the ACP and CPP datasets. Overall, the experimental results show that our work is effective for the identification of therapeutic peptides.

Asunto(s)

RESUMEN

Iris tectorum Maxim. is an important plant that plays a very crucial role in the ecological welfare of wetlands. In this study, the effects of different intensities of UV-B radiation on the growth, photosynthetic pigment content, chlorophyll fluorescence characteristics, chloroplast ultrastructure, and gas exchange parameters of Iris tectorum Maxim. were studied. The results showed that enhanced UV-B radiation had a significant influence on the above-mentioned parameters of iris. Compared with the control, enhanced UV-B radiation caused certain damage to the leaf appearance. With the increasing intensity of radiation, the apparent damage degree became more serious. Enhanced UV-B radiation significantly decreased leaf chlorophyll contents, and the effect accumulated with the exposure time. Enhanced UV-B radiation increased Fo, significantly increased the non-photochemical quenching coefficient NPQ, reduced PSII and Qp, and significantly decreased the Fm, Fv/Fm, and Fv/Fo in leaves. The effect of UV-B radiation on PSII destruction of Iris tectorum Maxim. increased as the radiation intensity increased and the exposure time prolonged. The chloroplast structure was damaged under the enhanced UV-B radiation. More specifically, thylakoid lamellae were distorted, swelling and even blurred, and a large number of starch granules appeared. The effect of the high intensity of radiation on chloroplast ultrastructure was greater than that of lower intensity. Enhanced UV-B radiation reduced significantly the net photosynthetic rate, stomatal conductance, and transpiration rate, and the degree of degradation increased with the increasing irradiation intensity. However, the intercellular CO2 content increased, which suggests that the main reason for the decrease of photosynthetic rate was the non-stomatal factors.

Asunto(s)

RESUMEN

The abuse of traditional antibiotics has led to an increase in the resistance of bacteria and viruses. Similar to the function of antibacterial peptides, bacteriocins are more common as a kind of peptides produced by bacteria that have bactericidal or bacterial effects. More importantly, the marine environment is one of the most abundant resources for extracting marine microbial bacteriocins (MMBs). Identifying bacteriocins from marine microorganisms is a common goal for the development of new drugs. Effective use of MMBs will greatly alleviate the current antibiotic abuse problem. In this work, deep learning is used to identify meaningful MMBs. We propose a random multi-scale convolutional neural network method. In the scale setting, we set a random model to update the scale value randomly. The scale selection method can reduce the contingency caused by artificial setting under certain conditions, thereby making the method more extensive. The results show that the classification performance of the proposed method is better than the state-of-the-art classification methods. In addition, some potential MMBs are predicted, and some different sequence analyses are performed on these candidates. It is worth mentioning that after sequence analysis, the HNH endonucleases of different marine bacteria are considered as potential bacteriocins.

Asunto(s)