The data used here were recorded from amygdala-projecting neurons expressing GCaMP6f in ventral hippocampus. Continued advances in optical imaging technology are greatly expanding the size and depth of neuronal populations that can be visualized. If we can find all split components, we can superimpose all their spatiotemporal activities and run rank-1 NMF to obtain the spatial and temporal activity of the merged neuron. In addition, intra-site variation in total Ca between four replicate trees of the same species were observed possibly because of microsite variability in soil chemistry. the calcium recordings for the mouse dorsal striatum area that were used to make Figure 5) should be made available. The method described here is a significant advancement over those published earlier to estimate the quantity of CaOx in plants in two ways: first, it uses a small quantity of foliar tissue, and second, the same sample following CaOx extraction can be used to analyze other small metabolites (amino acids, polyamines, inorganic ions, etc. Previous work from our laboratory has shown that repeated freezing and thawing of samples is a fast and reliable procedure for extracting soluble nutrients and metabolites (Minocha et al. A Phenomenex Synergi-Hydro-RP 4 µm C18 column, 100 × 4.6 mm id with a Security Guard, 5 µm, 4 × 3 mm id C18 guard column at 25 °C (Phenomenex, Torrance, CA, USA) was used with 25 mM potassium phosphate buffer containing 0.5 mM Tetra-Butyl ammonium Hydrogen Sulfate (THS) and 1% MeOH (pH 2.5) at a flow rate of 1.0 ml min−1; oxalate typically had a retention time of <1.5 min. Over a period of about two hours, the alginate dissolves as sodium alginate to give a very thick slurry. We found that two practical modifications can lead to improved results. [Editors' note: further revisions were requested prior to acceptance, as described below.]. For full access to this pdf, sign in to an existing account, or purchase an annual subscription. This is a clear evidence that the low rank NMF background model used in vanilla CNMF is not enough for modeling the background components in microendoscopic data. We have released our MATLAB implementation of CNMF-E as open-source software (https://github.com/zhoupc/CNMF_E (Zhou, 2017a)). Because of the above inferior results we skip comparisons to the basic CNMF here. Total foliar Ca concentrations of the trees sampled from these sites indicated that soil Ca varied among sites (foliar Ca has a strong relationship with soil Ca). Tests were performed at room temperature with varying particle size and solvent concentration. Colors match the example traces shown in (G), which shows the spatial and temporal components of 10 example neurons detected by both methods. It is always found as a compound such as, in limestone, chalk and marble. Bottom middle: the denoised neural signals. For all samples, a standard curve was repeated after every 20 samples, and check standards were run after every recalibration and after every 10 samples. The spatial components are estimated by regressing the raw video data against these three traces. The variability observed among trees within a species or different species with regard to the concentration of Ca, oxalate and CaOx crystals may be due to the phenology of growth, site chemistry and innate differences among species. We solve this problem by de-correlating their traces (following a similar approach in [Pnevmatikakis et al., 2016]). Thus, it does not have a specific. Our study was aimed at developing a method for extraction and indirect estimation of CaOx in tree leaves. square root of the machine precision) and discuss the heuristics they use to choose when to terminate the iteration early. Top left: the recorded fluorescence data. The manuscript has been improved but there are two small remaining technical points that need to be addressed before acceptance, as outlined below. (E) The spatial and temporal components of 14 example neurons that are only detected by CNMF-E. Given that (a) SVD will always produce a lower reconstruction error than NMF for the same number of components, and that (b) having a high background might allow the neural components to "insulate" themselves from the constraints of nonnegativity by offsetting them from zero, it seems possible that the model also admits solutions that are better from the standpoint of the loss function yet worse from the standpoint of biological plausibility. Constraints on the background term B in Equation (1) are essential to the success of CNMF-E, since clearly, if B is completely unconstrained we could just absorb the observed data Y entirely into B, which would lead to recovery of no neural activity. We did use an in-house ground wood reference sample for quality control and assurance. In this study, solvent extraction of calcium and iron ions has been carried out using different types of acids and bases. (To put it another way, we experimented with many “tweaks” of CNMF that did not work well; this was a highly non-trivial development process.) Given the optimized W^, our estimation of the fluctuating background is B^f=W^X. However, current implementations of the CNMF approach were optimized for 2-photon and light-sheet microscopy, where the background has a simpler spatiotemporal structure. The green dot indicates the pixel of interest. The authors confirmed this information by the use of X-ray diffraction and polarizing microscopy techniques. Scalebars: 10 s. See Video 11 for demixing results. The fact that direct extraction in PCA yields all three fractions of soluble Ca (see Figures S2 and S4 available as Supplementary Data at Tree Physiology Online), along with other nutrients and soluble metabolites, supports integrated or streamlined sample collection and processing. We should clarify one point: it is not quite true that any method can be trivially improved using manual intervention. 10, Extraction of Calcium in the Presence of Iron (II) after;v ' MhsM Cyanide , , ® 38 ' viii. We highlight an example neuron by drawing its ROI to demonstrate the power of CNMF-E in isolating fluorescence signals of neurons from the background fluctuations. On the other hand, insufficient removal of background during the spatial filtering leads to high L(), but the corresponding P() are usually small because most background fluctuations have been removed. Conversely, the calcium traces of the three extra neurons identified by PCA/ICA show noisy signals that are unlikely to be neural responses. The parameter l controls the size of the spatial filter in the initialization step and is chosen as the diameter of a typical neuron in the FOV. This strategy improves the extraction of individual neurons’ traces in the high correlation scenarios and the spatial footprints can be corrected in the following step of updating A^. We have tried to impose these constraints in a tractable manner in this work. Finally, in practice, our results are not sensitive to the selection of the outlier parameter ζ, thus we frequently set it as 10. The raw traces and the filtered traces are shown as well. We then evaluate the quality of initialization using all neurons’ spatial and temporal similarities with their counterparts in the ground truth data. 2) I'm concerned about the semi-manual "interventions" described in subsection “Interventions”. We chose 12 example neurons that were detected by both CNMF-E and PCA/ICA methods and show their spatial and temporal components in Figure 9A–C. However, H2SO4 resulted in higher calcium extraction efficiency as compared to HCl and HNO3. For the example frame in Figure 2A, the true cellular signals are sparse and weak (Figure 2E). If manual interventions are performed, we typically run one last iteration of updating B,A and C sequentially to further refine the results. 2000, Bai et al. Finally, we show that downstream analyses of calcium imaging data can substantially benefit from these improvements. Due to its unconstrained nature, y^i is a noisy estimate of i, plus a constant baseline resulting from inaccurate estimation of 0. Means from significant ANOVA were compared using Tukey’s test (P ≤ 0.05). In a sense, one might imagine that the fully-converged result would not be terribly different from a (sparse, local) singular value decomposition, which is known to not properly segment cells particularly in the presence of temporal correlations among nearby/overlapping cells. Step 4 provides a fast method for correcting abnormal components without redoing the whole analysis. Images were down sampled to 10Hz and processed into TIFFs using Mosaic software. Next we label all connected components in the image and create a mask to select the largest component. Rakesh Minocha, Bradley Chamberlain, Stephanie Long, Swathi A. Turlapati, Gloria Quigley, Extraction and estimation of the quantity of calcium oxalate crystals in the foliage of conifer and hardwood trees, Tree Physiology, Volume 35, Issue 5, May 2015, Pages 574–580, https://doi.org/10.1093/treephys/tpv031.
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