Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

Currently, Graph Neural Networks have been extended to the field of speech signal processing. It is the more compact and flexible way to represent speech sequences by graphs. However, the structures of the relationships in recent studies are tend to be relatively uncomplicated. Moreover, the graph convolution module exhibits limitations that impede its adaptability to intricate application scenarios. In this study, we establish the speech-graph using feature similarity and introduce a novel architecture for graph neural network that leverages an LSTM aggregator and weighted pooling. The unweighted accuracy of 65.39% and the weighted accuracy of 71.83% are obtained on the IEMOCAP dataset, achieving the performance comparable to or better than existing graph baselines. This method can improve the interpretability of the model to some extent, and identify speech emotion features effectively.

In the multiple unmanned aerial vehicle (UAV) mobile edge computing (MEC) systems, the cooperative computation among multiple UAVs can improve the overall computation service capability. Multi-UAV MEC systems can meet the quality of service requirements for computation intensive applications of ground terminals (GTs) in complex field environments, emergency disaster relief and other special scenarios. In this paper, a multi-UAV cooperative computation framework is proposed while taking the GT movement and random arrival of computation tasks into consideration. A long-term optimization problem is formulated for the joint optimization of UAV trajectory and resource allocation, subject to minimizing the total GT computation task completion time and the total system energy consumption. To solve this problem, a joint multiple time-scale optimization algorithm is proposed. In particular, the optimization problem is decomposed into a long time-scale multi-UAV trajectory planning subproblem and a short time-scale resource allocation subproblem. The proximal policy optimization algorithm is invoked to solve the long time-scale subproblem. The greedy algorithm and the successive convex approximation (SCA) method are employed to solve the short time-scale subproblem. Finally, a joint multiple time-scale optimization algorithm with a two-layer loop structure is proposed. Simulation results show that: 1) the proposed multi-UAV cooperative computation MEC system outperforms the conventional MEC system without collaboration among UAVs; and 2) the proposed algorithm can quickly adapt to different degrees of environmental dynamics and outperforms the benchmark algorithm for different network sizes, task requirements, and available resources.

Antimicrobial peptides (AMPs) are a promising alternative for combating bacterial drug resistance. While current computer prediction models excel at binary classification of AMPs based on sequences, there is a lack of regression methods to accurately quantify AMP activity against specific bacteria, making the identification of highly potent AMPs a challenge. Here, we present a deep learning method, BERT-AmPEP60, based on the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) architecture to extract embedding features from input sequences. Using the transfer learning strategy, we built regression models to predict the minimum inhibitory concentration (MIC) of peptides for Escherichia coli (EC) and Staphylococcus aureus (SA). In five independent experiments with 10% leave-out sequences as the test sets, the optimal EC and SA models outperformed the state-of-the-art regression method and traditional machine learning methods, achieving an average mean squared error of 0.2664 and 0.3032 (log μM), respectively. They also showed a Pearson correlation coefficient of 0.7955 and 0.7530, and a Kendall correlation coefficient of 0.5797 and 0.5222, respectively. Our models outperformed existing deep learning and machine learning methods that rely on conventional sequence features. This work underscores the effectiveness of utilizing BERT with transfer learning for training quantitative AMP prediction models specific for different bacterial species. The web server of BERT-AmPEP60 can be found at https://app.cbbio.online/ampep/home. To facilitate development, the program source codes are available at https://github.com/janecai0714/AMP_regression_EC_SA.

Land cover change detection (LCCD) is a crucial research topic for rational planning of land use and facilitation of sustainable land resource growth. However, due to the complexity of LCCD tasks, integrating global and local features and fusing contextual information from remote sensing features are essential. Recently, with the advent of Mamba, which maintains linear time complexity and high efficiency in processing long-range data, it offers a new solution to address feature-fusion challenges in LCCD. Therefore, a novel Visual State Space Model (SSM) for Land Cover Change Detection (LCCDMamba) is proposed, which uses Siam-VMamba as a backbone to extract multidimensional land cover features. To fuse the change information across difference temporal, Multi-scale Information Spatio-temporal Fusion (MISF) module is designed to aggregate difference information from bi-temporal features. The proposed MISF comprises Multi-Scale Feature Aggregation (MSFA) which utilizes strip convolution to aggregate multi-scale local change information of bi-temporal land cover features, and Residual with SS2D (RSS) which employs residual structure with SS2D to capture global feature differences of bi-temporal land cover features. To enhance the correlation of change features across different dimensions, in the Decoder, we design a Dual Token Modeling SSM (DTMS) through two token modeling approaches. This preserves high-dimensional semantic features and thus ensures that the multi-scale change information across various dimensions will not be lost during feature restoration. Experiments have been conducted on WHU-CD, LEVIR-CD, and GVLM datasets, and the results demonstrate that LCCDMamba achieves F1 scores of 94.18%, 91.68%, and 87.14%, respectively, outperforming all the models compared.

Background: Valuable findings can be obtained through data mining in patients’ online reviews. Also identifying healthcare needs from the patient’s perspective can more accurately improve the quality of care and the experience of the visit. Thereby avoiding unnecessary waste of health care resources. The large language model (LLM) can be a promising tool due to research that demonstrates its outstanding performance and potential in directions such as data mining, healthcare management, and more. Objective: We aim to propose a methodology to address this problem, specifically, the recent breakthrough of LLM can be leveraged for effectively understanding healthcare needs from patient experience reviews. Methods: We used 504,198 reviews collected from a large online medical platform, haodf.com. We used the reviews to create Aspect Based Sentiment Analysis (ABSA) templates, which categorized patient reviews into three categories, reflecting the areas of concern of patients. With the introduction of thought chains, we embedded ABSA templates into the prompts for ChatGPT, which was then used to identify patient needs. Results: Our method has a weighted total precision of 0.944, which was outstanding compared to the direct narrative tasks in ChatGPT-4o, which have a weighted total precision of 0.890. Weighted total recall and F1 scores also reached 0.884 and 0.912 respectively, surpassing the 0.802 and 0.843 scores for “direct narratives in ChatGPT.” Finally, the accuracy of the three sampling methods was 91.8%, 91.7%, and 91.2%, with an average accuracy of over 91.5%. Conclusions: Combining ChatGPT with ABSA templates can achieve satisfactory results in analyzing patient reviews. As our work applies to other LLMs, we shed light on understanding the demands of patients and health consumers with novel models, which can contribute to the agenda of enhancing patient experience and better healthcare resource allocations effectively.