Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis with TIMP

Fluorescence Lifetime Imaging Microscopy (FLIM) allows fluorescence lifetime images of biological objects to be collected at 250 nm spatial resolution and at (sub-)nanosecond temporal resolution. Often n_comp kinetic processes underlie the observed fluorescence at all locations, but the intensity of the fluorescence associated with each process varies per-location, i.e., per-pixel imaged. Then the statistical challenge is global analysis of the image: use of the fluorescence decay in time at all locations to estimate the n_comp lifetimes associated with the kinetic processes, as well as the amplitude of each kinetic process at each location. Given that typical FLIM images represent on the order of 10^2 timepoints and 10^3 locations, meeting this challenge is computationally intensive. Here the utility of the TIMP package for R to solve parameter estimation problems arising in FLIM image analysis is demonstrated. Case studies on simulated and real data evidence the applicability of the partitioned variable projection algorithm implemented in TIMP to the problem domain, and showcase options included in the package for the visual validation of models for FLIM data.

RuPersonaChat: a dialog corpus for personalizing conversational agents

Personalization is one of the keyways to improve the performance of conversational agents. It improves the quality of user interaction with a conversational agent and increases user satisfaction by increasing the consistency and specificity of responses. The dialogue with the agent becomes more consistent, the inconsistency of responses is reduced, and the responses become more specific and interesting. Training and testing personalized conversational agents requires specific datasets containing facts about a persona and texts of persona’s dialogues where replicas use those facts. There are several datasets in English and Chinese containing an average of five facts about a persona where the dialogues are composed by crowdsourcing users who repeatedly imitate different personas. This paper proposes a methodology for collecting an original dataset containing an extended set of facts about a persona and natural dialogues between personas. The new RuPersonaChat dataset is based on three different recording scenarios: an interview, a short conversation, and a long conversation. This is the first dataset for dialogue agent personalization collected which includes both natural dialogues and extended persona’s descriptions. Additionally, in the dataset, the persona’s replicas are annotated with the facts about the persona from which they are generated. The methodology for collecting an original corpus of test data proposed in this paper allows for testing language models for various tasks within the framework of personalized dialogue agent development. The collected dataset includes 139 dialogues and 2608 replicas. This dataset was used to test answer and question generation models and the best results were obtained using the Gpt3-large model (perplexity is equal to 15.7). The dataset can be used to test the personalized dialogue agents’ ability to talk about themselves to the interlocutor, to communicate with the interlocutor utilizing phatic speech and taking into account the extended context when communicating with the user

FRET Study of Membrane Proteins: Simulation-Based Fitting for Analysis of Membrane Protein Embedment and Association

A new formalism for the simultaneous determination of the membrane embedment and aggregation of membrane proteins is developed. This method is based on steady-state Förster (or fluorescence) resonance energy transfer (FRET) experiments on site-directed fluorescence labeled proteins in combination with global data analysis utilizing simulation-based fitting. The simulation of FRET was validated by a comparison with a known analytical solution for energy transfer in idealized membrane systems. The applicability of the simulation-based fitting approach was verified on simulated FRET data and then applied to determine the structural properties of the well-known major coat protein from bacteriophage M13 reconstituted into unilamellar DOPC/DOPG (4:1 mol/mol) vesicles. For our purpose, the cysteine mutants Y24C, G38C, and T46C of this protein were produced and specifically labeled with the fluorescence label AEDANS. The energy transfer data from the natural tryptophan at position 26, which is used as a donor, to AEDANS were analyzed assuming a helix model for the transmembrane domain of the protein. As a result of the FRET data analysis, the topology and bilayer embedment of this domain were quantitatively characterized. The resulting tilt of the transmembrane helix of the protein is 18 ± 2°. The tryptophan is located at a distance of 8.5 ± 0.5 Å from the membrane center. No specific aggregation of the protein was found. The methodology developed here is not limited to M13 major coat protein and can be used in principle to study the bilayer embedment of any small protein with a single transmembrane domain