Autor: Stomma, Piotr - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Antimicrobial resistance in diverse urban microbiomes : uncovering patterns and predictive markers
Autorzy:: Rudnicki, Witold R.
Stomma, Piotr
Łabaj, Paweł
Lesiński, Wojciech
Brizola Toscan, Rodolfo
Subramanian, Balakrishnan
Opis:: Antimicrobial resistance (AMR) is a growing global health concern, driven by urbanization and anthropogenic activities. This study investigated AMR distribution and dynamics across microbiomes from six U.S. cities, focusing on resistomes, viromes, and mobile genetic elements (MGEs). Using metagenomic data from the CAMDA 2023 challenge, we applied tools such as AMR++, Bowtie, AMRFinderPlus, and RGI for resistome profiling, along with clustering, normalization, and machine learning techniques to identify predictive markers. AMR++ and Bowtie outperformed other tools in detecting diverse AMR markers, with binary normalization improving classification accuracy. MGEs were found to play a critical role in AMR dissemination, with 394 genes shared across all cities. Removal of MGE-associated AMR genes altered resistome profiles and reduced model performance. The findings reveal a heterogeneous AMR landscape in urban microbiomes, particularly in New York City, which showed the highest resistome diversity. These results underscore the importance of MGEs in AMR profiling and provide valuable insights for designing targeted strategies to address AMR in urban settings.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 2.

Tytuł:: An intelligent approach to short-term wind power prediction using deep neural networks
Autorzy:: Niksa-Rynkiewicz, Tacjana
Stomma, Piotr
Witkowska, Anna
Rutkowska, Danuta
Słowik, Adam
Cpałka, Krzysztof
Jaworek-Korjakowska, Joanna
Kolendo, Piotr
Tematy:: renewable energy
wind energy
wind power
wind turbine
short-term wind power prediction
deep learning
convolutional neural networks
gated recurrent unit
hierarchical multilayer perceptron
deep neural networks; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/23944826.pdf Link otwiera się w nowym oknie
Opis:: In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.
Dostawca treści:: Biblioteka Nauki

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