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Subcellular localization plays important roles in protein’s functioning. In this paper, we developed a hidden Markov model to detect de novo signals in protein sequences that target at a particular cellular location: plasmodesmata. We also developed a support vector machine to classify plasmodesmata located proteins (PDLPs) in Arabidopsis, and devised a decision-tree approach to combine the SVM and HMM for better classification performance. The methods achieved high performance with ROC score 0.99 in cross-validation test on a set of 360 type I transmembrane proteins in Arabidopsis. The predicted PD targeting signals in one PDLP have been experimentally verified. 

Plasmodesmata are membrane-lined cytoplasmic passageways that facilitate the movement of nutrients and various types of molecules between cells in the plant. They are highly dynamic channels, opening or closing in response to physiological and developmental stimuli or environmental challenges such as biotic and abiotic stresses. Accumulating evidence supports the idea that such dynamic controls occur through integrative cellular mechanisms. Currently, a few fluorescence-based methods are available that allow monitoring changes in molecular movement through plasmodesmata. In this chapter, following a brief introduction to those methods, we provide a detailed step-by-step protocol for the Drop-ANd-See (DANS) assay, which is advantageous when it is desirable to measure plasmodesmal permeability non-invasively, in situ and in real-time. We discuss the experimental conditions one should consider to produce reliable and reproducible DANS results along with troubleshooting ideas. 

Abstract We present a background model for dark matter searches using an array of NaI(Tl) crystals in the COSINE-100 experiment that is located in the Yangyang underground laboratory. The model includes background contributions from both internal and external sources, including cosmogenic radionuclides and surface $$^{210}$$ 210 Pb contamination. To build the model in the low energy region, with a threshold of 1 keV, we used a depth profile of $$^{210}$$ 210 Pb contamination in the surface of the NaI(Tl) crystals determined in a comparison between measured and simulated spectra. We also considered the effect of the energy scale errors propagated from the statistical uncertainties and the nonlinear detector response at low energies. The 1.7 years COSINE-100 data taken between October 21, 2016 and July 18, 2018 were used for this analysis. Our Monte Carlo simulation provides a non-Gaussian peak around 50 keV originating from beta decays of bulk $$^{210}$$ 210 Pb in a good agreement with the measured background. This model estimates that the activities of bulk $$^{210}$$ 210 Pb and $$^{3}$$ 3 H are dominating the background rate that amounts to an average level of $$2.85\pm 0.15$$ 2.85 ± 0.15  counts/day/keV/kg in the energy region of (1–6) keV, using COSINE-100 data with a total exposure of 97.7 kg $$\cdot $$ · years. 

We report the identification of metastable isomeric states of$$^{228}$$${}^{228}$Ac at 6.28 keV, 6.67 keV and 20.19 keV, with lifetimes of an order of 100 ns. These states are produced by the$$\beta $$$\beta$-decay of$$^{228}$$${}^{228}$Ra, a component of the$$^{232}$$${}^{232}$Th decay chain, with$$\beta $$$\beta$Q-values of 39.52 keV, 39.13 keV and 25.61 keV, respectively. Due to the low Q-value of$$^{228}$$${}^{228}$Ra as well as the relative abundance of$$^{232}$$${}^{232}$Th and their progeny in low background experiments, these observations potentially impact the low-energy background modeling of dark matter search experiments.