Cell-penetrating peptides (CPPs) are naturally able to cross the lipid bilayer membrane that protects cells. These peptides share common structural and physicochemical properties and show different pharmaceutical applications, among which drug delivery is the most important. Due to their ability to cross the membranes by pulling high-molecular-weight polar molecules, they are termed Trojan horses. In this study, we proposed a machine learning (ML)-based framework named BChemRF-CPPred (beyondchemicalrules-basedframework forCPP prediction) that uses an artificial neural network, a support vector machine, and a Gaussian process classifier to differentiate CPPs from non-CPPs, using structure- and sequence-based descriptors extracted from PDB and FASTA formats. The performance of our algorithm was evaluated by tenfold cross-validation and compared with those of previously reported prediction tools using an independent dataset. The BChemRF-CPPred satisfactorily identified CPP-like structures using natural and synthetic modified peptide libraries and also obtained better performance than those of previously reported ML-based algorithms, reaching the independent test accuracy of 90.66% (AUC = 0.9365) for PDB, and an accuracy of 86.5% (AUC = 0.9216) for FASTA input. Moreover, our analyses of the CPP chemical space demonstrated that these peptides break some molecular rules related to the prediction of permeability of therapeutic molecules in cell membranes. This is the first comprehensive analysis to predict synthetic and natural CPP structures and to evaluate their chemical space using an ML-based framework. Our algorithm is freely available for academic use at http://comptools.linc.ufpa.br/BChemRF-CPPred.

This study evaluates different machine learning algorithms in detecting and identifying drones using radar data from a 60 GHz millimeter-wave sensor. These signals were collected from a bionic bird and two drones, namely DJI Mavic and DJI Phantom 3 Pro, which were represented in complex form to preserve amplitude and phase information. The first benchmarks used four algorithms, namely long short-term memory (LSTM), gated recurrent unit (GRU), one-dimensional convolutional neural network (Conv1D), and Transformer, and they were benchmarked for robustness under noisy conditions, including artificial noise types like white noise, Pareto noise, impulsive noise, and multipath interference. As expected, Transformer outperformed other algorithms in terms of accuracy, even on noisy data; however, in certain noise contexts, particularly Pareto noise, it showed weaknesses. For this purpose, we propose Multimodal Transformer, which incorporates more statistical features—skewness and kurtosis—in addition to amplitude and phase data. This resulted in a improvement in detection accuracy, even under difficult noise conditions. Our results demonstrate the importance of noise in processing radar signals and the benefits afforded by a multimodal presentation of data in detecting unmanned aerial vehicle and birds. This study sets up a benchmark for state-of-the-art machine learning methodologies for radar-based detection systems, providing valuable insight into methods of increasing the robustness of algorithms to environmental noise.

Structural Health Monitoring (SHM) is a skillful strategy for preserving different kinds of structures in short-and long-term. This work describes the effects of environmental variability and operational conditions (e.g., temperature, relative humidity, wind speed and operational load) on real structures, as well as measures efficiency in data normalization by applying state-of-the-art algorithms that carry out nonlinear principal component analysis (PCA), which are Autoassociative Neural Network (AANN), Kernel PCA (KPCA) and Greedy KPCA (GKPCA), in contrast to the respective linear version, the PCA algorithm. The results allow us to conclude that KPCA and GKPCA are the most efficient algorithms in the tasks of learning structure normal conditions and classification against the other techniques in terms of false positive and negative indications of damage, suggesting applicability in real monitoring scenarios.

The newly proposed multimodal transformer architecture offers a new paradigm for UAV detection and aerial object recognition. It introduces an innovative way of feeding multiple data streams, such as audio, infrared video, RGB video, and radar, into the architecture for processing, using independent modalities. The unique features of each modality are attached and processed together in the architecture, where the features are then exposed to the multimodal transformer for classification. Thus, all complementary information can be pooled within the integration framework to allow the model discrimination of any drone target under outdoor conditions from other aerial objects such as birds, helicopters, and airplanes. These methodologies are expected to outperform traditional single-modality systems by improving detection accuracy through class balancing and addressing modality-specific limitations. The proposed model has been further tested through various experiments to evaluate its robustness under conditions such as missing entries, corrupted data, and synthetic inputs. The results suggest that it has strong potential to serve as a benchmark in UAV detection. Thus, this work takes part of an emerging body of sensor fusion and deep learning-related research, demonstrating the potential of multimodal data in real-world detection problems.

Peptide chemical space can be represented by interpretable numerical and binary features, as well as by learned vector representations (embeddings) generated by encoders.