Picasso Images ~UPD~ Download

However, I'm seeing a noticeable performance penalty when loading images with Picasso into the ImageViews of my layout. There is a visible "blip" between the layout being rendered and the bitmaps becoming visible (this blip disappears once the images are cached). With my HG library, which is basically just BitmapFactory.decodeResource with some cache coding around a sparse array of SoftReferences (this is old, as I said), loading for the same view is seamless and appears to be instantaneous.

Obviously, there are big differences in how I normally load the images and the asynch loading in Picasso, but is this really the expected behavior? This would seem to make Picasso ill-suited for the loading of local drawables into the UI, which I find rather surprising. I load images with the very simple:

Picasso Images Download

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Picasso optimize the image automatically loading a smaller version of the image into the ImageView, but if the source image is "big", the load will become slow so i recommend to you optimize the source images.

At the start of the Spanish Civil War in 1936, Picasso was 54 years of age. Soon after hostilities began, the Republicans appointed him "director of the Prado, albeit in absentia", and "he took his duties very seriously", according to John Richardson, supplying the funds to evacuate the museum's collection to Geneva.[81] The war provided the impetus for Picasso's first overtly political work. He expressed anger and condemnation of Francisco Franco and fascists in The Dream and Lie of Franco (1937), which was produced "specifically for propagandistic and fundraising purposes".[82] This surreal fusion of words and images was intended to be sold as a series of postcards to raise funds for the Spanish Republican cause.[82][83]

Ultra-multiplexed fluorescence imaging requires the use of spectrally overlapping fluorophores to label proteins and then to unmix the images of the fluorophores. However, doing this remains a challenge, especially in highly heterogeneous specimens, such as the brain, owing to the high degree of variation in the emission spectra of fluorophores in such specimens. Here, we propose PICASSO, which enables more than 15-color imaging of spatially overlapping proteins in a single imaging round without using any reference emission spectra. PICASSO requires an equal number of images and fluorophores, which enables such advanced multiplexed imaging, even with bandpass filter-based microscopy. We show that PICASSO can be used to achieve strong multiplexing capability in diverse applications. By combining PICASSO with cyclic immunofluorescence staining, we achieve 45-color imaging of the mouse brain in three cycles. PICASSO provides a tool for multiplexed imaging with high accessibility and accuracy for a broad range of researchers.

To address this problem, a different approach has been developed that does not require reference spectra measurement. This approach, termed blind unmixing, compensates for the lack of prior knowledge of the emission spectra through unsupervised learning, either by finding a low-rank representation of mixed images (e.g., via non-negative matrix factorization (NMF))8,9,10 or by clustering11. The former approach accurately unmixes images when a sufficiently large number of input images are provided through fluorescence lifetime imaging10. However, only partial success has been demonstrated in unmixing spatially overlapping proteins via conventional microscopy using a spectral detector (see Supplementary Fig. 3 for our NMF results)8,9. The latter approach uses unsupervised machine learning to classify pixels to the nearest cluster11. However, in this approach, pixels expressing more than one protein are classified into another cluster, and the ratio of the expression levels of the proteins is not measured11. In addition to these two approaches, an alternative approach has also been demonstrated that uses fluorophores with low cross-channel bleed-through and then unmixing their signals via orthogonalization12. However, the use of fluorophores with low cross-channel bleed-through limits the number of fluorophores that can be simultaneously used with one excitation laser; it would be challenging to achieve higher-level multiplexing with this approach.

Therefore, we propose a non-reference-based unmixing technique called PICASSO (Process of ultra-multiplexed Imaging of biomoleCules viA the unmixing of the Signals of Spectrally Overlapping fluorophores), which can blindly unmix images without reference emission spectra, enabling multiplexed imaging of 15 proteins in the brain in a single staining and imaging round. We devised a strategy based on information theory; unmixing is performed by iteratively minimizing the mutual information between mixed images. This allows us to get away with the assumption that the spatial distribution of different proteins is mutually exclusive, therefore enabling accurate information unmixing. By combining PICASSO with an antibody complex formation technique, we demonstrate 15-color multiplexed imaging of a mouse brain in a single staining and imaging round. We also show that PICASSO can be used for multiplexed 3D imaging, large-area imaging, mRNA imaging, super-resolution imaging through tissue expansion, tissue clearing, and the multiplexed imaging of clinical specimens. Since PICASSO can improve the multiplexing capability of cyclic immunofluorescence techniques by letting them use more fluorophores in one cycle, we can achieve 45-color multiplexed imaging of the mouse brain in only three staining and imaging cycles through Cyclic-PICASSO. Lastly, we show that PICASSO can be implemented with bandpass filter-based microscopy because it only requires the number of image acquisitions equal to the number of fluorophores.

The PICASSO unmixing algorithm takes N channel mixed images of N fluorophores as the input and obtains the unmixed images by iteratively subtracting scaled images from one another to minimize the mutual information (MI) between them. The main assumption underlying PICASSO is that spectral mixing results in an increase of the MI between multiple channels, meaning that the unmixed images can therefore be recovered through MI minimization. We first confirmed this assumption in a simple setting by using two spectrally overlapping fluorophores and two specific detection channels. We set the two detection channels such that the first contained the signal of only the first fluorophore while the second contained the signals of both fluorophores (Fig. 1d). We can therefore express the relationship between the images as follows:

The poor performance of linear unmixing was also confirmed in images. Figure 2e shows ground-truth images and both the linear unmixing and PICASSO unmixing results. We performed the linear unmixing twice with different reference spectra, the reported emission spectra shown in Fig. 2a and the average of the measured emission spectra for each fluorophore. We measured transverse intensity profiles along the white arrows in Fig. 2e and compared the profiles between ground-truth, linear unmixing with reported spectra, linear unmixing with measured spectra, and PICASSO. As shown in Fig. 2f, PICASSO (yellow line) exhibited a high level of agreement with ground-truth (black line) while linear unmixing (blue and magenta lines) showed a lower level of agreement due to the variations in the emission spectra.

We also validated the unmixing performance of PICASSO in imaging three spatially overlapping proteins. A mouse brain slice was stained with three preformed antibody complexes against PV, NeuN, and GFAP, conjugated with CF488A, ATTO514, and ATTO532, respectively. The slice was simultaneously stained with guinea pig anti-NeuN antibody and mouse anti-GFAP antibody, and then with CF660R-conjugated secondary antibody against guinea pig and CF405S-conjugated secondary antibody against mouse. By using a 488-nm excitation laser, we acquired three mixed images of PV, NeuN, and GFAP at three detection ranges as shown in IMG1, IMG2, and IMG3 (first raw of Fig. 3k; see Fig. 3a for the detection channels used). We then unmixed the mixed images via PICASSO (second raw of Fig. 3k). We also acquired ground-truth images of NeuN and GFAP by using 405 and 640-nm lasers (Fig. 3l). The unmixed images matched with the ground-truth images.

In all forms of unmixing techniques, including both reference-based and non-reference-based techniques, Poisson noises contained in the mixed images are not properly unmixed, thereby limiting the perceptual quality of the unmixed images15. Fortunately, we confirmed that the noise in the unmixed images can be reduced by averaging mixed images (Supplementary Fig. 15). Acquiring images of the same field of view twice and averaging the two images resulted in images with reduced noise.

PICASSO is a versatile tool for the multiplexed biomolecule imaging of cultured cells, tissue slices, and clinical specimens. It is suitable for high-throughput protein imaging, as antibodies can be simultaneously applied to specimens during the staining process and a minimal number of collected images are required. In addition, PICASSO does not require complicated optics or spectral detectors; it can instead be implemented with a simple imaging system consisting of objectives, light sources such as a lamp or LED, a camera, and excitation/emission bandpass filters (Supplementary Fig. 30). For more than 10-color imaging, custom multi-band pass filters could be used for such microscopy setups. PICASSO can be combined with live imaging3, tissue clearing22,30, or tissue expansion21,31,32,33 techniques to achieve multiplexed 3D super-resolution imaging or multiplexed whole-organ imaging. PICASSO can also be combined with cyclic immunofluorescence imaging techniques based on various mechanisms, such as cyclic DNA hybridization, photo-bleaching of fluorophores, chemical bleaching of fluorophores, and antibody stripping, without any significant change34,35,36,37,38,39,40,41,42. Once combined, fewer cycles are then required to image a given number of proteins, greatly reducing the time and complexity of the whole imaging process. As PICASSO is a strategy for distinguishing fluorophores, it could also be used to improve the multiplexing capabilities of mRNA imaging43,44,45,46, bioassays47, and cell tracing48. It could also be combined with fluorescent barcoding techniques to increase the amount of information that a single barcode can encode49,50,51,52. 17dc91bb1f