Learning the nonlinear interactions from particle trajectories

Nonlinear interaction of membrane proteins with cytoskeleton and membrane leads to non-Gaussian structure of their displacement probability distribution. We propose a novel statistical analysis technique for learning the characteristics of the nonlinear potential from the cumulants of the displacement distribution. The efficiency of the approach is demonstrated on the analysis of kurtosis of the displacement distribution of the particle traveling on a membrane in a cage-type potential. Results of numerical simulations are supported by analytical predictions. We show that the approach allows robust identification of the potential for the much lower temporal resolution compare with the mean square displacement analysis.Comment: 5 pages, 4 figure

FIRE-PROTECTION WITH ALKALI-ACTIVATED CEMENT BINDER

Fire resistance of unprotected steel structures is very low and steel elements must be protected from fire. One possibility is to create a protective layer of a cement-based material. Most types of cement have a low resistance to high temperatures, reducing mechanical properties. In flammability tests, cement activated with alkaline compounds showed better properties compared to conventional types of cement. This paper represents the determination of the properties of two H-Cement mortars with experlite or fireclay sand. Experiments carried out in a small kiln simulating a 1D load showed differences between elements in terms of heat transfer to the tested elements. The calculation model created to predict the course of the experiments has been validated and the unknown properties of the material have been calculated based on the data collected. The samples were tested in a small fire furnace. Finally, the thermal conductivity pattern was determined depending on the temperature

Symmetries of finite Heisenberg groups for k-partite systems

Symmetries of finite Heisenberg groups represent an important tool for the study of deeper structure of finite-dimensional quantum mechanics. This short contribution presents extension of previous investigations to composite quantum systems comprised of k subsystems which are described with position and momentum variables in Z_{n_i}, i=1,...,k. Their Hilbert spaces are given by k-fold tensor products of Hilbert spaces of dimensions n_1,...,n_k. Symmetry group of the corresponding finite Heisenberg group is given by the quotient group of a certain normalizer. We provide the description of the symmetry groups for arbitrary multipartite cases. The new class of symmetry groups represents very specific generalization of finite symplectic groups over modular rings.Comment: 6 pages, to appear in Proceedings of QTS7 "Quantum Theory and Symmetries 7", Prague, August 7-13, 201

PACCMIT/PACCMIT-CDS: identifying microRNA targets in 3′ UTRs and coding sequences

The purpose of the proposed web server, publicly available at http://paccmit.epfl.ch, is to provide a user-friendly interface to two algorithms for predicting messenger RNA (mRNA) molecules regulated by microRNAs: (i) PACCMIT (Prediction of ACcessible and/or Conserved MIcroRNA Targets), which identifies primarily mRNA transcripts targeted in their 3′ untranslated regions (3′ UTRs), and (ii) PACCMIT-CDS, designed to find mRNAs targeted within their coding sequences (CDSs). While PACCMIT belongs among the accurate algorithms for predicting conserved microRNA targets in the 3′ UTRs, the main contribution of the web server is 2-fold: PACCMIT provides an accurate tool for predicting targets also of weakly conserved or non-conserved microRNAs, whereas PACCMIT-CDS addresses the lack of similar portals adapted specifically for targets in CDS. The web server asks the user for microRNAs and mRNAs to be analyzed, accesses the precomputed P-values for all microRNA–mRNA pairs from a database for all mRNAs and microRNAs in a given species, ranks the predicted microRNA–mRNA pairs, evaluates their significance according to the false discovery rate and finally displays the predictions in a tabular form. The results are also available for download in several standard formats

Automatic Fungi Recognition: Deep Learning Meets Mycology

The article presents an AI-based fungi species recognition system for a citizen-science community. The system’s real-time identification too — FungiVision — with a mobile application front-end, led to increased public interest in fungi, quadrupling the number of citizens collecting data. FungiVision, deployed with a human-in-the-loop, reaches nearly 93% accuracy. Using the collected data, we developed a novel fine-grained classification dataset — Danish Fungi 2020 (DF20) — with several unique characteristics: species-level labels, a small number of errors, and rich observation metadata. The dataset enables the testing of the ability to improve classification using metadata, e.g., time, location, habitat and substrate, facilitates classifier calibration testing and finally allows the study of the impact of the device settings on the classification performance. The continual flow of labelled data supports improvements of the online recognition system. Finally, we present a novel method for the fungi recognition service, based on a Vision Transformer architecture. Trained on DF20 and exploiting available metadata, it achieves a recognition error that is 46.75% lower than the current system. By providing a stream of labeled data in one direction, and an accuracy increase in the other, the collaboration creates a virtuous cycle helping both communities

Danish Fungi 2020 - Not Just Another Image Recognition Dataset

We introduce a novel fine-grained dataset and bench-mark, the Danish Fungi 2020 (DF20). The dataset, constructed from observations submitted to the Atlas of Danish Fungi, is unique in its taxonomy-accurate class labels, small number of errors, highly unbalanced long-tailed class distribution, rich observation metadata, and well-defined class hierarchy. DF20 has zero overlap with ImageNet, al-lowing unbiased comparison of models fine-tuned from publicly available ImageNet checkpoints. The proposed evaluation protocol enables testing the ability to improve classification using metadata - e.g. precise geographic location, habitat, and substrate, facilitates classifier calibration testing, and finally allows to study the impact of the device settings on the classification performance. Experiments using Convolutional Neural Networks (CNN) and the recent Vision Transformers (ViT) show that DF20 presents a challenging task. Interestingly, ViT achieves results su-perior to CNN baselines with 80.45% accuracy and 0.743 macro F1 score, reducing the CNN error by 9% and 12% respectively. A simple procedure for including metadata into the decision process improves the classification accuracy by more than 2.95 percentage points, reducing the error rate by 15%. The source code for all methods and experiments is available at https://sites.google.com/view/danish-fungi-dataset

A nucleotide-level coarse-grained model of RNA

We present a new, nucleotide-level model for RNA, oxRNA, based on the coarse-graining methodology recently developed for the oxDNA model of DNA. The model is designed to reproduce structural, mechanical, and thermodynamic properties of RNA, and the coarse-graining level aims to retain the relevant physics for RNA hybridization and the structure of single- and double-stranded RNA. In order to explore its strengths and weaknesses, we test the model in a range of nanotechnological and biological settings. Applications explored include the folding thermodynamics of a pseudoknot, the formation of a kissing loop complex, the structure of a hexagonal RNA nanoring, and the unzipping of a hairpin motif. We argue that the model can be used for efficient simulations of the structure of systems with thousands of base pairs, and for the assembly of systems of up to hundreds of base pairs. The source code implementing the model is released for public use

DNA hairpins destabilize duplexes primarily by promoting melting rather than by inhibiting hybridization

The effect of secondary structure on DNA duplex formation is poorly understood. Using oxDNA, a nucleotide level coarse-grained model of DNA, we study how hairpins influence the rate and reaction pathways of DNA hybridzation. We compare to experimental systems studied by Gao et al. (1) and find that 3-base pair hairpins reduce the hybridization rate by a factor of 2, and 4-base pair hairpins by a factor of 10, compared to DNA with limited secondary structure, which is in good agreement with experiments. By contrast, melting rates are accelerated by factors of ∼100 and ∼2000. This surprisingly large speed-up occurs because hairpins form during the melting process, and significantly lower the free energy barrier for dissociation. These results should assist experimentalists in designing sequences to be used in DNA nanotechnology, by putting limits on the suppression of hybridization reaction rates through the use of hairpins and offering the possibility of deliberately increasing dissociation rates by incorporating hairpins into single strands.The effect of secondary structure on DNA duplex formation is poorly understood. Using oxDNA, a nucleotide level coarse-grained model of DNA, we study how hairpins influence the rate and reaction pathways of DNA hybridzation. We compare to experimental systems studied by Gao et al. (1) and find that 3-base pair hairpins reduce the hybridization rate by a factor of 2, and 4-base pair hairpins by a factor of 10, compared to DNA with limited secondary structure, which is in good agreement with experiments. By contrast, melting rates are accelerated by factors of similar to 100 and similar to 2000. This surprisingly large speed-up occurs because hairpins form during the melting process, and significantly lower the free energy barrier for dissociation. These results should assist experimentalists in designing sequences to be used in DNA nanotechnology, by putting limits on the suppression of hybridization reaction rates through the use of hairpins and offering the possibility of deliberately increasing dissociation rates by incorporating hairpins into single strands