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Flip-Pal mobile scanner Scan Photos in Place. Anywhere... Anytime

[ Editor’s note : The following guest post from Gary Clark, Founder of PhotoTree.com, discusses how you can create archival quality scans of family photos with the compact yet powerful Flip-Pal mobile scanner ]

Editor’s note

While exhibiting and presenting a session on dating 19th century photographs at the August 2010 Family History Expo in Sandy, Utah, I had the opportunity to take a first-hand look at the Flip-Pal mobile scanner and give it good test.I was very interested in the capabilities of the Flip-Pal mobile scanner, especially for archival-quality scans while traveling.

First, a little background.As the founder of PhotoTree.com, I have digitized over 3,000 19th century photographs using a variety of techniques and products and along the way I have developed some expectations of scanning and copying technologies.

PhotoTree.com publishes an online database of 19th century photographs and has developed processes to date them—both online and using printed publications.Additionally, we perform photo restoration, requiring high-resolution digital copies of the original image.

We digitize photographs either with a high-end flatbed scanner or by copying the image with a high-resolution digital camera, using a copy stand and custom lights.A 12 megapixel Nikon D300 camera with a 60mm macro lens is used.All of this equipment costs more than $2,500 and is very bulky.The flatbed scanner also requires an attached PC, cables, power source, etc.—not a real nimble set up.

When traveling, I usually don’t have the luxury of bringing my scanner or all of my photography equipment along, but I still need to frequently create high-quality scans of newly discovered photographs. Because of this, I have been seeking a high-resolution portable scanning solution.

Back to the Flip-Pal mobile scanner test.During a lull in the second day of the expo I introduced myself to Ben Kimbell in the Flip-Pal mobile scanner booth and explained my activities and scanning needs. Despite my skepticism, the size, ease of use and price seemed very intriguing, if too good to be true—and you know what they say about things being too good to be true.So I gave Ben a challenge and had him scan a really nice 120 year-old cabinet card that was very crisp and had a great tonal range.

Ben scanned the picture at 600 dpi and I took it back to my PC to open with Photoshop.Voila!Most people wouldn’t have been able to tell the difference between the Flip-Pal scan and the master copy I made with my Nikon camera—which was also made at approximately 600 dpi.

When zoomed in as much as 300%, both images exhibited nearly identical sharpness, with differences being mostly a matter of opinion at that point.The scanned image did have some detectable noise and some JPEG compression loss. To be honest, you really had to look for them, and both of those were in acceptable ranges—especially when you compare the JPEG file size of 1.8MB for the Flip-Scan versus the 24MB of the original Photoshop file.The Flip-Pal can also scan at 300 dpi, which we did.The results were very good and ideal for most user applications.

If you don’t want to bloat the code in your email, you can embed all of that CSS in a stylesheet in your HTML email instead. Although embedded CSS in email used to be frowned upon, it’s actually crossover strap sandals Black Lanvin Skze5ao
these days. To embed your background image properties, you first need to target an element in your HTML either directly or by referencing an ID or class:

Then, open a stylesheet in the head of your email using the style tag, reference the HTML element on which you’re including the background image, and add your CSS:

Now, for clients that support embedded CSS, your background image will be visible just as if you had inlined it directly on the element. Plus, your HTML is easier to read and update when needed.

Another well-known and useful technique for including background images is what’s usually known as “bulletproof backgrounds”. This technique was popularized by Stig Morten Myre from Campaign Monitor.

Bulletproof backgrounds combine the attribute method described above with Microsoft’s Vector Markup Language (VML) to ensure that background images are displayed nearly everywhere—especially in Microsoft’s Outlook suite of email clients. Using VML’s v:fill tag, you can include a background image that Outlook will render, then fallback to the background attribute for non-Outlook clients.

Even in its heyday, VML was never well-known. It’s lack of accessible documentation and the various quirks of Outlook can make it problematic to use. Plus, that’s a lot of code to understand and maintain when you need to make updates. Fortunately, Stig and the folks over at Campaign Monitor have released a free tool for generating bulletproof background code. Check it out at backgrounds.cm .

Background images, especially when using inline or embedded CSS, allow for a ton of flexibility and even some more advanced design options like content choreography and image swapping when combined with CSS media queries. A great overview of some of these techniques can be found in Kristian Robinson’s excellent talk, Background Images: Design, Build, and Progressive Enhancement, from Litmus Live 2016, which you can watch below.

Like everything in email design and development, background images have mixed support across email clients. Most clients support one of the techniques described above, the most notable exclusions being earlier versions of Android, some Gmail clients, and some of the webmail clients, which vary greatly depending on which browser is used.

Background image support in desktop email clients

Background image support in mobile email clients

Background image support in webmail clients

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For clients that don’t support background images, you should always include either the bgcolor attribute or background-color property with a color that contrasts the HTML content on top of it to ensure that your message is always readable.

The relationship between the number of expressing cells and the mean is shown in Figure 7 . The two statistics tend to be well-correlated so filtering on either should give roughly similar results.

Spike-in transcripts are highlighted in red.

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In general, we prefer the mean-based filter as it tends to be less aggressive. A gene will be retained as long as it has sufficient expression in any subset of cells. Genes expressed in fewer cells require higher levels of expression in those cells to be retained, but this is not undesirable as it avoids selecting uninformative genes (with low expression in few cells) that contribute little to downstream analyses, e.g., HVG detection or clustering. In contrast, the “at least n ” filter depends heavily on the choice of n . With n = 10, a gene expressed in a subset of 9 cells would be filtered out, regardless of the level of expression in those cells. This may result in the failure to detect rare subpopulations that are present at frequencies below n . While the mean-based filter will retain more outlier-driven genes, this can be handled by choosing methods that are robust to outliers in the downstream analyses.

Thus, we apply the mean-based filter to the data by subsetting the SCESet object as shown below. This removes all rows corresponding to endogenous genes or spike-in transcripts with abundances below the specified threshold.

Using the deconvolution method to deal with zero counts. Read counts are subject to differences in capture efficiency and sequencing depth between cells ( Stegle et al. , 2015 ). Normalization is required to eliminate these cell-specific biases prior to downstream quantitative analyses. This is often done by assuming that most genes are not differentially expressed (DE) between cells. Any systematic difference in count size across the non-DE majority of genes between two cells is assumed to represent bias and is removed by scaling. More specifically, “size factors” are calculated that represent the extent to which counts should be scaled in each library.

Size factors can be computed with several different approaches, e.g., using the estimateSizeFactorsFromMatrix function in the DESeq2 package ( Anders Huber, 2010 ; Love et al. , 2014 ), or with the calcNormFactors function ( Robinson Oshlack, 2010 ) in the platform sandals Blue Lautre Chose pxhbvuuoAU
package. However, single-cell data can be problematic for these bulk data-based methods due to the dominance of low and zero counts. To overcome this, we pool counts from many cells to increase the count size for accurate size factor estimation ( Lun et al. , 2016 ). Pool-based size factors are then “deconvolved” into cell-based factors for cell-specific normalization.

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