Accurate prediction of bacterial virulence factors (VFs) is crucial for combating infectious diseases, yet traditional methods often fail to capture their complex sequence properties. We address this challenge by leveraging deep, context-aware representations from large-scale protein language models (PLMs). Our framework begins with a systematic engineering of features from ESM-2 and ProtT5, which confirmed their complementary nature but also revealed that simple concatenation is a suboptimal fusion strategy due to a "feature overshadowing" effect. To overcome this, we developed two novel architectures: VF-Iter, for robust feature enhancement via iterative low-rank updates, and the Dual-Path Feature Fusion (DPF) network, for intelligently integrating the complementary embeddings. The construction of our final model, VF-Fuse, involved a two-stage process. First, we selected four powerful and diverse base models representing our distinct feature strategies (ESM-2 only, ProtT5 only, simple concatenation, and DPF). Second, we empirically determined the best method for combining their predictions by benchmarking 15 ensemble techniques, from which Majority Voting emerged as the superior choice. On the independent test set, VF-Fuse establishes a new state of the art, achieving a superior F1-Score of 87.15% and a Matthews Correlation Coefficient of 73.61%. This F1-Score marks a significant 3.3% improvement over the previous best method, driven by an excellent balance between a high Sensitivity of 90.1% and a strong Specificity of 83.33%. Crucially, in-depth interpretability analyses validated our architectural design, demonstrating how the DPF model learns to intelligently route complementary features to specialized pathways.

LncRNA-protein interactions are ubiquitous in organisms and play a crucial role in a variety of biological processes and complex diseases. Many computational methods have been reported for lncRNA-protein interaction prediction. However, the experimental techniques to detect lncRNA-protein interactions are laborious and time-consuming. Therefore, to address this challenge, this paper proposes a reweighting boosting feature selection (RBFS) method model to select key features. Specially, a reweighted apporach can adjust the contribution of each observational samples to learning model fitting; let higher weights are given more influence samples than those with lower weights. Feature selection with boosting can efficiently rank to iterate over important features to obtain the optimal feature subset. Besides, in the experiments, the RBFS method is applied to the prediction of lncRNA-protein interactions. The experimental results demonstrate that our method achieves higher accuracy and less redundancy with fewer features.

Future mobile communication systems are expected to integrate the satellite technology to enhance the capability, reliability, and coverage area of the resilient network. Moreover, D2D communication is an effective technique that uses the same resources as allocated to cellular users and provides ultralow latency. The co-operative network optimization to maximize capacity, the number of admitted users, and allocate optimum power have not addressed from the perspective of D2D and cognitive satellite network integration for future networks. In this paper, we formulated a novel optimization problem that allocates optimum power, maximize capacity, and admitted users/IoTs by taking advantage of LEO earth orbit satellites (mega-constellation) and terrestrial-D2D mode. Besides, the algorithm defines optimum policy in mode selection, i.e., cellular, D2D, and satellite. The problem formulated is the Mix integer Non-Linear Problem (MINLP), which is NP-hard. The relaxation of the formulated problem is then proposed, which guarantees convergence. Our algorithm named as the adaptive terrestrial-satellite-power sentient network (ATSPSN) is then solved by GAMS branch-cut and Non-orthogonal Mesh Adaptive (NOMAD). The results show that ATSPSN-GAMS branch-cut with D2D-satellite aided network enhances the performance in terms of capacity, admitted users, and energy efficiency without compromising on link budget cost in comparison with ATSPSN NOMAD, GAMS branch bound and conventional terrestrial network.

BACKGROUND: Shotgun metagenomics based on untargeted sequencing can explore the taxonomic profile and the function of unknown microorganisms in samples, and complement the shortage of amplicon sequencing. Binning assembled sequences into individual groups, which represent microbial genomes, is the key step and a major challenge in metagenomic research. Both supervised and unsupervised machine learning methods have been employed in binning. Genome binning belonging to unsupervised method clusters contigs into individual genome bins by machine learning methods without the assistance of any reference databases. So far a lot of genome binning tools have emerged. Evaluating these genome tools is of great significance to microbiological research. In this study, we evaluate 15 genome binning tools containing 12 original binning tools and 3 refining binning tools by comparing the performance of these tools on chicken gut metagenomic datasets and the first CAMI challenge datasets. RESULTS: For chicken gut metagenomic datasets, original genome binner MetaBat, Groopm2 and Autometa performed better than other original binner, and MetaWrap combined the binning results of them generated the most high-quality genome bins. For CAMI datasets, Groopm2 achieved the highest purity (> 0.9) with good completeness (> 0.8), and reconstructed the most high-quality genome bins among original genome binners. Compared with Groopm2, MetaBat2 had similar performance with higher completeness and lower purity. Genome refining binners DASTool predicated the most high-quality genome bins among all genomes binners. Most genome binner performed well for unique strains. Nonetheless, reconstructing common strains still is a substantial challenge for all genome binner. CONCLUSIONS: In conclusion, we tested a set of currently available, state-of-the-art metagenomics hybrid binning tools and provided a guide for selecting tools for metagenomic binning by comparing range of purity, completeness, adjusted rand index, and the number of high-quality reconstructed bins. Furthermore, available information for future binning strategy were concluded.