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It is proposed to install an experimental setup in the fixed-target hall of the Nuclotron with the final goal to perform a research program focused on the production of strange matter in heavyion collisions at beam energies between 2 and 6 A GeV. The basic setup will comprise a large acceptance dipole magnet with inner tracking detector modules based on double-sided Silicon micro-strip sensors and GEMs. The outer tracking will be based on the drift chambers and straw tube detector. Particle identification will be based on the time-of-flight measurements. This setup will be sufficient perform a comprehensive study of strangeness production in heavy-ion collisions, including multi-strange hyperons, multi-strange hypernuclei, and exotic multi-strange heavy objects. These pioneering measurements would provide the first data on the production of these particles in heavy-ion collisions at Nuclotron beam energies, and would open an avenue to explore the third (strangeness) axis of the nuclear chart. The extension of the experimental program is related with the study of in-medium effects for vector mesons decaying in hadronic modes. The studies of the NN and NA reactions for the reference is assumed.
Correlative dynamic imaging of cellular landmarks, such as nuclei and nucleoli, cell membranes, nuclear envelope and lipid droplets is critical for systems cell biology and drug discovery, but challenging to achieve with molecular labels. Virtual staining of label-free images with deep neural networks is an emerging solution for correlative dynamic imaging. Multiplexed imaging of cellular landmarks from scattered light and subsequent demultiplexing with virtual staining leaves the light spectrum for imaging additional molecular reporters, photomanipulation, or other tasks. Current approaches for virtual staining of landmark organelles are fragile in the presence of nuisance variations in imaging, culture conditions, and cell types. We report training protocols for virtual staining of nuclei and membranes robust to variations in imaging parameters, cell states, and cell types. We describe a flexible and scalable convolutional architecture, UNeXt2, for supervised training and self-supervised pre-training. The strategies we report here enable robust virtual staining of nuclei and cell membranes in multiple cell types, including human cell lines, neuromasts of zebrafish and stem cell (iPSC)-derived neurons, across a range of imaging conditions. We assess the models by comparing the intensity, segmentations, and application-specific measurements obtained from virtually stained and experimentally stained nuclei and cell membranes. The models rescue missing labels, non-uniform expression of labels, and photobleaching. We share three pre-trained models (VSCyto3D, VSNeuromast, and VSCyto2D) and a PyTorch-based pipeline (VisCy) for training, inference, and deployment that leverages current community standards for image data and metadata.
The search for short-lived particles is usually the final stage in the chain of event reconstruction and precedes event selection when operating in online mode or physics analysis when operating in offline mode. Most often such short-lived particles are neutral and their search and reconstruction is carried out using their daughter charged particles resulting from their decay.
The use of the missing mass method makes it possible to find and analyze also decays of charged short-lived particles, when one of the daughter particles is neutral and is not registered in the detector system. One of the most known examples of such decays is the decay Σ− → nπ−.
In this paper, we discuss in detail the missing mass method, which was implemented as part of the KF Particle Finder package for the search and analysis of short-lived particles, and describe the use of the method in the STAR experiment (BNL, USA).
The method was used to search for pion (π± → μ±ν) and kaon (K± → μ±ν and K± → π±π0) decays online on the HLT farm in the express production chain. An important feature of the express production chain in the STAR experiment is that it allows one to start calibration, production, and analysis of the data immediately after receiving them.
Here, the particular features and results of the real-time application of the method within the express processing of data obtained in the BES-II program at a beam energy of 3.85 GeV/n when working with a fixed target are presented and discussed.
Dynamic imaging of landmark organelles, such as nuclei, cell membrane, nuclear envelope, and lipid droplets enables image-based phenotyping of functional states of cells. Multispectral fluorescent imaging of landmark organelles requires labor-intensive labeling, limits throughput, and compromises cell health. Virtual staining of label-free images with deep neural networks is an emerging solution for this problem. Multiplexed imaging of cellular landmarks from scattered light and subsequent demultiplexing with virtual staining saves the light spectrum for imaging additional molecular reporters, photomanipulation, or other tasks. Published approaches for virtual staining of landmark organelles are fragile in the presence of nuisance variations in imaging, culture conditions, and cell types. This paper reports model training protocols for virtual staining of nuclei and membranes robust to cell types, cell states, and imaging parameters. We developed a flexible and scalable convolutional architecture, named UNeXt2, for supervised training and self-supervised pre-training. The strategies we report here enable robust virtual staining of nuclei and cell membranes in multiple cell types, including neuromasts of zebrafish, across a range of imaging conditions. We assess the models by comparing the intensity, segmentations, and application-specific measurements obtained from virtually stained and experimentally stained nuclei and membranes. The models rescue the missing label, non-uniform expression of labels, and photobleaching. We share three pre-trained models, named VSCyto3D, VSCyto2D, and VSNeuromast, as well as VisCy, a PyTorch-based pipeline for training, inference, and deployment that leverages the modern OME-Zarr format.
Dynamic imaging of landmark organelles, such as nuclei, cell membrane, nuclear envelope, and lipid droplets enables image-based phenotyping of functional states of cells. Multispectral fluorescent imaging of landmark organelles requires labor-intensive labeling, limits throughput, and compromises cell health. Virtual staining of label-free images with deep neural networks is an emerging solution for this problem. Multiplexed imaging of cellular landmarks from scattered light and subsequent demultiplexing with virtual staining saves the light spectrum for imaging additional molecular reporters, photomanipulation, or other tasks. Published approaches for virtual staining of landmark organelles are fragile in the presence of nuisance variations in imaging, culture conditions, and cell types. This paper reports model training protocols for virtual staining of nuclei and membranes robust to label-free imaging parameters, cell states, and cell types. We developed a flexible and scalable convolutional architecture, named UNeXt2, for supervised training and self-supervised pre-training. The strategies we report here enable robust virtual staining of nuclei and cell membranes in multiple cell types, including neuromasts of zebrafish, across a range of imaging conditions. We assess the models by comparing the intensity, segmentations, and application-specific measurements obtained from virtually stained and experimentally stained nuclei and membranes. The models rescue the missing label, non-uniform expression of labels, and photobleaching. We share three pre-trained models, named VSCyto3D, VSCyto2D, and VSNeuromast, as well as VisCy, a PyTorch-based pipeline for training, inference, and deployment that leverages the modern OME-Zarr format.