Effect Download |TOP| Background
Lanell Mesina
Hey Squarespacers -- I'm getting tripped up a bit on a mobile customization of a site I'm building. I have "Pan" turned on on my header images to give a little of the parallax effect that squarepace decided to eliminate but everyone still wants. Problem is -- when I get to mobile and am trying to switch out the image it's not letting me. If I turn the pan effect off, I'm golden. Any idea how to just turn that effect off on mobile?
Perhaps I'm a little confused by your question. Viewing your website, I do not see any panning effects that are occurring and I'm not too sure what you're trying to achieve. Are you having a syntax error in the CSS? Are you trying to turn off pan on mobile by just hiding the image that is panning? Have you injected custom code/javascript to help you do this?
I am kinda new to webdesign and webflow. Working on my first client project and am trying to use this kind of a background parallax scrolling effect where the page scrolls over a background image, or even over different once in different sections, which also move up during scroll but slower than the page. Is that possible to do with webflow without coding? If not, I would be happy if the background images staying still but the page scrolling over the background images like on this website:
Thanks for the reply. That is a pretty cool effect in the link you placed in the comment, thanks so much. I will see if I can find something that resembles more the effect on the restaurant website. Still not sure how the scrolling effect works like it is on the website I provided.
I'm trying to get a fixed width white on a gray background (body), but everything is shown gray; the white is ignored. Code is on jsbin. Any ideas? I did this on previous websites, and there everything was peachy. I can't see any difference with what I'm doing here.
Although many people listen to music while performing tasks that require sustained attention, the literature is inconclusive about its effects. The present study examined performance on a sustained-attention task and explored the effect of background music on the prevalence of different attentional states, founded on the non-linear relationship between arousal and performance. Forty students completed a variation of the Psychomotor Vigilance Task-that has long been used to measure sustained attention-in silence and with their self-selected or preferred music in the background. We collected subjective reports of attentional state (specifically mind-wandering, task-focus and external distraction states) as well as reaction time (RT) measures of performance. Results indicated that background music increased the proportion of task-focus states by decreasing mind-wandering states but did not affect external distraction states. Task-focus states were linked to shorter RTs than mind-wandering or external distraction states; however, background music did not reduce RT or variability of RT significantly compared to silence. These findings show for the first time that preferred background music can enhance task-focused attentional states on a low-demanding sustained-attention task and are compatible with arousal mediating the relationship between background music and task-performance.
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But when I add the exported clip to Premeire, the video still has a black background in it. I can get rid of this in Premeire by choosing "screen" as a blend mode, but that seems like an extra step that doesn't make sense here, if the video has an alpha channel?
I know the video has the alpha channel working properly, because at the end of my animated Saber, I keyframe the Opacity of the Saber layer from 100 to 0, and then the video itself with the black background fades away at the end. It's like Saber is adding a black background I can't get rid of?
Because the default is black there's no problem making that one. But for the white one, when I change the background layer(BG) to white it covers the Saber effect. I couldn't figure out why (I'm completely new to Saber). (In the screenshot I hide the BG layer so you can see what it should look like)
Well, you've turned off layer visibility for your BG, so of course it won't show up. The rest is just AE doing its normal thing of using the comp background color/ default black fill when rendering. Similarly, your white fill effect applied to the text probably messes up the transparency of the layers and that's why you're not getting predictable results with your setup. You know, 3D, blending modes and all that can be pretty weird when interacting. I would suggest you at least read the online help on soem of the basics rather than hacking your way through trying to recreate soem tutorial. It seems to me you need to work on your understanding on soem basics first.
In droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events.
Irrespective of the source of background noise, its presence can interfere with analyses. For starters, background noise reduces the separability of cell type clusters as well as the power to pinpoint important (marker) genes via differential expression analysis. Moreover, reads from cell type-specific marker genes spill over to cells of other types, thus yielding novel marker combinations and hence implying the presence of novel cell types [8, 10]. Besides, background noise can also confound differential expression analysis between samples, e.g., when looking for expression changes within a cell type between two conditions. Varying amounts of background noise or differences in the cell type composition between conditions can result in dissimilar background profiles, which might generate false positives when identifying differentially expressed genes. To alleviate such problems during downstream analysis, algorithms to estimate and correct for the amounts of background noise have been developed.
In order to evaluate method performance, one dataset of an even mix between one mouse and one human cell line [3] is commonly used to get an experimentally determined lower bound of background noise levels that is identified as counts covering genes from the other species [4, 8, 11, 12]. Since this dataset is lacking in cell type diversity, it is common to additionally evaluate performance based on other datasets that have a complex cell type mixture and where most cell types have well known profiles with exclusive marker genes. In such studies the performance test is whether the model removes the expression of the exclusive marker genes from the other cell types. In both cases, the feature space of the contamination does not overlap with the endogenous cell feature space. Mouse and human are too diverged, so that mouse reads only map to mouse genes and human reads only to human genes. Similarly, when using marker genes it is assumed that they are exclusively expressed in only one cell type, hence the features that are used for background inference are again not overlapping. However, in reality background noise will mostly induce shifts in expression levels that cannot be described in a binary on or off sense and it remains unclear how background correction will affect those profiles.
Here, we use a mouse kidney dataset representing a complex cell type mixture from three mouse strains of two subspecies, Mus musculus domesticus and M. m. castaneus. From both subspecies, inbred strains were used and thus we can distinguish exogenous and endogenous counts for the same features using known homozygous SNPs [13]. Hence, this dataset serves as a much more realistic experimental standard, providing a ground truth in a complex setting with multiple cell types which allows to analyze the variability, the source and the impact of background noise on single cell analysis. Moreover, this dataset enables us to better benchmark existing background removal methods.
Generation of mouse strain mixture datasets to quantify background noise. A Experimental design (created with BioRender.com). B Strain composition in 5 different replicates, subjected to scRNA-seq (rep1-3) or snRNA-seq (nuc2, nuc3). The replicates rep2 and nuc2 and rep3 and nuc3 were generated from the same samples each. CAST: CAST/EiJ strain; BL6: C57BL/6J strain; SvImJ: 129S1/SvImJ. C Number of homozygous SNPs with a coverage of more than 100 UMIs that distinguish one strain from the other two. D Per cell coverage in M. m. castaneus cells of informative variants that distinguish M. m. castaneus and M. m. domesticus. E Cell type composition per replicate and strain; labels were obtained by reference-based classification using mouse kidney data from Denisenko et al. [14] as reference. F UMAP visualization of M. m. castaneus cells in single-cell replicate 2, colored by assigned cell type. PT, proximal tubule; CD_IC, intercalated cells of collecting duct; CD_PC, principal cells of collecting duct; CD_Trans, transitional cells of collecting duct; CNT, connecting tubule; DCT, distal convoluted tubule; Endo, endothelial; Fib, fibroblasts; aLOH, ascending loop of Henle; dLOH, descending loop of Henle; MC, mesangial cells; Podo, podocytes
The level of background noise is variable across replicates and single cells. A Estimated fraction of background noise per cell. The replicates on the x-axis are ordered by ascending median background noise fraction. B In M. m. castaneus cells both endogenous M. m. castaneus specific alleles (x-axis) and M. m. domesticus specific alleles (y-axis) have coverage in each cell. The detection of M. m. domesticus specific alleles can be seen as background noise originating from cells of a different mouse
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