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193. First Report of Globisporangium (Pythium) mastophorum causing Damping-off / Root Rot on Parsley in SloveniaJanja Zajc, Eva Kovačec, Urša Prislan, Aleksandra Podboj Ronta, Metka Žerjav, Hans-Josef Schroers, 2024, drugi znanstveni članki Ključne besede: damping-off / Root Rot, parsley, pathogen detection, common bean, phatoggenecity
Objavljeno v DiRROS: 23.10.2024; Ogledov: 53; Prenosov: 18
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196. Poročilo o preskusu št.: LVG 2024-157 : vzorec št. 2024/00650Nikica Ogris, Patricija Podkrajšek, Zina Devetak, Špela Hočevar, Barbara Piškur, 2024, izvedensko mnenje, arbitražna odločba Ključne besede: varstvo gozdov, morfološke analize, Pseudocercospora pini-densiflorae, rdeči bor, bolezen iglic
Objavljeno v DiRROS: 23.10.2024; Ogledov: 47; Prenosov: 0
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197. Poročilo o preskusu št.: LVG 2024-156 : vzorec št. 2024/00627Barbara Piškur, Patricija Podkrajšek, Zina Devetak, Nikica Ogris, 2024, izvedensko mnenje, arbitražna odločba Ključne besede: varstvo gozdov, morfološke analize, Pseudocercospora pini-densiflorae, rdeči bor, bolezen iglic
Objavljeno v DiRROS: 23.10.2024; Ogledov: 42; Prenosov: 0
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199. Poročilo o preskusu št.: LVG 2024-153 : vzorec št. 2024/00608Barbara Piškur, Zina Devetak, Patricija Podkrajšek, Špela Hočevar, Nikica Ogris, 2024, izvedensko mnenje, arbitražna odločba Ključne besede: varstvo gozdov, morfološke analize, Pseudocercospora pini-densiflorae, rdeči bor, bolezen iglic
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200. Knots and $\theta$-curves identification in polymeric chains and native proteins using neural networksFernando Bruno da Silva, Boštjan Gabrovšek, Marta Korpacz, Kamil Luczkiewicz, Szymon Niewieczerzal, Maciej Sikora, Joanna I. Sulkowska, 2024, izvirni znanstveni članek Povzetek: Entanglement in proteins is a fascinating structural motif that is neither easy to detect via traditional methods nor fully understood. Recent advancements in AI-driven models have predicted that millions of proteins could potentially have a nontrivial topology. Herein, we have shown that long short-term memory (LSTM)-based neural networks (NN) architecture can be applied to detect, classify, and predict entanglement not only in closed polymeric chains but also in polymers and protein-like structures with open knots, actual protein configurations, and also $\theta$-curves motifs. The analysis revealed that the LSTM model can predict classes (up to the $6_1$ knot) accurately for closed knots and open polymeric chains, resembling real proteins. In the case of open knots formed by protein-like structures, the model displays robust prediction capabilities with an accuracy of 99%. Moreover, the LSTM model with proper features, tested on hundreds of thousands of knotted and unknotted protein structures with different architectures predicted by AlphaFold 2, can distinguish between the trivial and nontrivial topology of the native state of the protein with an accuracy of 93%.
Ključne besede: machine learning, topology, protein databases, entanglements, open knots, closed knots
Objavljeno v DiRROS: 23.10.2024; Ogledov: 55; Prenosov: 26
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