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September 20, 2013

Sarah Guarino

- Sep. 20th 2013 6:52 am PT

@sarahg1113

In earlier versions of iOS, the system would notify users of available App Store updates with a badge on the icon (seen above).

You had to open up the App Store, press the Update button, then press Update All. It was rather annoying to keep up with all the apps that needed updating. Sometimes by the time you finished updating, more apps would need updating.

Now with iOS 7, the apps will now automatically update. To make sure that the apps are set to automatically update, go into Settings and scroll until you see iTunes and App Store.

Apple TV

Tap on iTunes App Store. Then scroll all the way down towards the bottom until see Automatic Downloads.

To turn on automatic app updates, tap in the white oval next to Updates. The apps will now update automatically. Apple even asks you how often you want the apps to automatically update, whether to occur only when you’re on Wi-fi or if it can occur when you’re on a Cellular network. This is great because not everyone might have a data plan that can support automatically updating the apps on a Cellular connection.

If you have automatic app updates turned on, and happen to be curious as to which apps updated, you have two places to find the same information. If you go to the App Store and press on Updates, it will show you when apps last updated. Also, Notification Center can even show you (in the “All” tab) which apps were automatically updated.

How To

A collection of tutorials from the 9to5Mac team helping you fix and get the most out of your Mac and iOS devices.

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Sarah Guarino

@sarahg1113

MacBook Air

iPhone 4S

Apple updates MacBook Pro with Touch bar

Visualizing 11" iPad Pro and Apple Watch Series 4

Kuo: 11" iPad, new Mac mini, 1.57" + 1.78" Apple Watch

Sonos speakers now work with AirPlay 2

- 5 years ago
Reply

Great feature but I have a question thou. I have my account on which I buy my apps – so everything’s cool. But what if I download an app from my friends’ account and log-in back to mine acc. Won’t it still try to update the app – battery life getting worse (I know it’s not possible to update that app until I give it my friends’ login info)?

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Identification and characterization of SEs in skeletal muscle differentiation. ( A ) Illustration of the computational pipeline to identify TEs and SEs in MB and MT. ( B ) SEs were identified in a stage-specific manner in MB or MT utilizing ROSE, a program that stitches and ranks enhancers. When the background subtracted H3K27ac signal for enhancers was plotted in a ranked order, a clear geometric inflection point was revealed on the curve, with the ones above this point being super-enhancers (SEs), and the rest typical enhancers (TEs). ( C and D ) Shifted landscapes of SEs and SE constituents from MB to MT. A high portion was lost or gained during the differentiation. ( E L ) Increased ChIP-seq intensity of enhancer marks (e.g. H3K27ac, H3K4me1, H3K4me2, H3K18ac, H3K9ac and H4K12ac), and indicators of active transcription (e.g. Pol II and H3K36me3) was observed on SE than TE constituents. *** < 0.001. ( M and N ) Gene Ontology (GO) analysis of molecular functions for SE associated genes revealed enrichment of transcription regulator/factor activity in both MB and MT. The y-axis shows the top 10 enriched GO terms and the x-axis shows the enrichment significance -values. ( O ) The number of SEs lost, maintained or gained as cells progressed from MB to MT. ( P ) GO analysis of the genes (top five) associated with the above categories of SEs. ( Q ) The differential expression patterns of the above genes were examined by RNA-seq collected from MB or MT at various time points in differentiation medium (DM). ( R ) H3K27ac ChIP-seq signals revealed SE landscape shifts with the progression of differentiation program from −24, 0, 6, 12, 24 and 72 hr in DM. The Pearson correlation coefficient (PCC) values are labeled on each grid to indicate the degree of correlation between the two consecutive stages. ( S ) Increasing H3K27ac density was observed for the SE (red bar) associated with gene during the differentiation. ( T ) Decreasing H3K27ac density was observed for the SE associated with gene during the differentiation.

Figure 1.
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Identification and characterization of SEs in skeletal muscle differentiation. ( A ) Illustration of the computational pipeline to identify TEs and SEs in MB and MT. ( B ) SEs were identified in a stage-specific manner in MB or MT utilizing ROSE, a program that stitches and ranks enhancers. When the background subtracted H3K27ac signal for enhancers was plotted in a ranked order, a clear geometric inflection point was revealed on the curve, with the ones above this point being super-enhancers (SEs), and the rest typical enhancers (TEs). ( C and D ) Shifted landscapes of SEs and SE constituents from MB to MT. A high portion was lost or gained during the differentiation. ( E L ) Increased ChIP-seq intensity of enhancer marks (e.g. H3K27ac, H3K4me1, H3K4me2, H3K18ac, H3K9ac and H4K12ac), and indicators of active transcription (e.g. Pol II and H3K36me3) was observed on SE than TE constituents. *** < 0.001. ( M and N ) Gene Ontology (GO) analysis of molecular functions for SE associated genes revealed enrichment of transcription regulator/factor activity in both MB and MT. The y-axis shows the top 10 enriched GO terms and the x-axis shows the enrichment significance -values. ( O ) The number of SEs lost, maintained or gained as cells progressed from MB to MT. ( P ) GO analysis of the genes (top five) associated with the above categories of SEs. ( Q ) The differential expression patterns of the above genes were examined by RNA-seq collected from MB or MT at various time points in differentiation medium (DM). ( R ) H3K27ac ChIP-seq signals revealed SE landscape shifts with the progression of differentiation program from −24, 0, 6, 12, 24 and 72 hr in DM. The Pearson correlation coefficient (PCC) values are labeled on each grid to indicate the degree of correlation between the two consecutive stages. ( S ) Increasing H3K27ac density was observed for the SE (red bar) associated with gene during the differentiation. ( T ) Decreasing H3K27ac density was observed for the SE associated with gene during the differentiation.

Figure 2.
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Identification of TF assembly and TF hotspots. ( A ) prediction of TF motifs within SE constituents in MB and MT identified key regulators in the respective cell stage. ( B ) Compared with TEs, a larger percentage of SEs are occupied by the indicated TFs by analyzing available ChIP-seq datasets. ( C and D ) TFs showed higher read density at SEs than TEs in both MB and MT. ChIP-seq read density at each SE or TE constituents extended by 100 bp on both sides was calculated. Fold-enrichment below each plot was calculated as the ratio of the mean value between the read density at SE and TE constituents. * < 0.05, ** < 0.01 and *** < 0.001. ( E ) Genome browser snapshot to show co-localization of TFs on the two SEs (red bars) associated with MyoD locus. ( F ) Analysis for TF co-association in MT. A combinational module was identified in the square. The color code indicates the PCC between two TFs at their binding sites. ( G ) Metagene plots were generated to show the TFs cobound to proximal genomic loci. ( H and I ) Enhancer activity elevates with the increasing number of binding TFs as measured by H3K27ac signal or Pol II read density. ( J ) SEs are more enriched with TF hotspots compared with TEs. Y-axis represents the percentage of SEs or TEs harboring at least one hotspot (Hotspot+) or no hotspot (Hotspot−). The number of hotspots per enhancer constituent was calculated (Table on top). ( K and L ) Hotspot regions showed significantly higher level of H3K27ac or Pol II density compared to non-hotspot genomic loci. *** < 0.001.

Figure 2.
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Identification of TF assembly and TF hotspots. ( A ) prediction of TF motifs within SE constituents in MB and MT identified key regulators in the respective cell stage. ( B ) Compared with TEs, a larger percentage of SEs are occupied by the indicated TFs by analyzing available ChIP-seq datasets. ( C and D ) TFs showed higher read density at SEs than TEs in both MB and MT. ChIP-seq read density at each SE or TE constituents extended by 100 bp on both sides was calculated. Fold-enrichment below each plot was calculated as the ratio of the mean value between the read density at SE and TE constituents. * < 0.05, ** < 0.01 and *** < 0.001. ( E ) Genome browser snapshot to show co-localization of TFs on the two SEs (red bars) associated with MyoD locus. ( F ) Analysis for TF co-association in MT. A combinational module was identified in the square. The color code indicates the PCC between two TFs at their binding sites. ( G ) Metagene plots were generated to show the TFs cobound to proximal genomic loci. ( H and I ) Enhancer activity elevates with the increasing number of binding TFs as measured by H3K27ac signal or Pol II read density. ( J ) SEs are more enriched with TF hotspots compared with TEs. Y-axis represents the percentage of SEs or TEs harboring at least one hotspot (Hotspot+) or no hotspot (Hotspot−). The number of hotspots per enhancer constituent was calculated (Table on top). ( K and L ) Hotspot regions showed significantly higher level of H3K27ac or Pol II density compared to non-hotspot genomic loci. *** < 0.001.

Figure 3.
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FoxO3 and MyoD coordinate the genome-wide activation of SE hotspots during myoblast (MB) differentiation. ( A ) MyoD deletion decreased the activity at MyoD-bound hotspots within SEs compared to the wild-type (WT) control. H3K27ac ChIP-seq signal in WT or KO surrounding the aligned MyoD peak center in WT SE hotspots was calculated to measure the enhancer activity. ( B ) FoxO3 and some known co-factors of MyoD bind to MyoD peaks in SE hotspots in MT. ( C ) Genomic loci within SEs bound by both FoxO3 and MyoD displayed the highest H3K27ac density compared with FoxO3 or MyoD only sites in MT. *** < 0.001. ( D ) PCC between H3K27ac or Pol II and TF ChIP-seq density revealed that the FoxO3 binding displayed the highest correlation with SE activity among the TFs. ( E ) A total of 96% of FoxO3 peaks within SE hotspots overlap with MyoD binding. ( F ) On the FoxO3-MyoD co-bound enhancer hotspots, a much higher percentage of MyoD binding E-box motif (5’-CAGGTG-3’) than FoxO3 binding Fork head motif (5’-GTAAACA-3’) was detected. ( G ) Schematic illustration of MyoD recruitment of FoxO3 to the hotspot region. ( H ) Snapshots of examples of MyoD/FoxO3 co-bound hotspots (red bar) and their associated genes. ( I ) Sequential ChIP showed both MyoD and FoxO3 bound on the same region in one hotspot associated with , or two hotspots associated with gene. ( J and K ) Decreased FoxO3 or H3K27ac binding on six selected examples of hotspots associated with and was detected in MyoD knockout (KO) cell lines #1 and #2 as compared to WT control. ( L and M ) Knockdown of FoxO3 by shRNA decreased MyoD or H3K27ac binding on the above SEs. ( N ) Knockdown of either MyoD or FoxO3 by siRNA reduced the expression of the above genes. ( O ) ChIP-PCR was performed to detect the enrichment of FoxO3 on the above hotspots in the differentiating myotubes (MTs) at −24, 24, 48 and 72 hr. ( P ) The expression of MyoD and FoxO3 in differentiating C2C12 was detected by western blotting using α-Tubulin as loading control. ( Q ) ChIP-PCR was performed to detect the enrichment of MyoD on the above hotspots at DM −24, 24, 48 and 72 hr. All qRT-PCR and ChIP-PCR data were normalized to mRNA and ChIP input respectively and represent the average of three independent experiments ±s.d. * < 0.05, ** < 0.01 and *** < 0.001.

Figure 3.
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FoxO3 and MyoD coordinate the genome-wide activation of SE hotspots during myoblast (MB) differentiation. ( A ) MyoD deletion decreased the activity at MyoD-bound hotspots within SEs compared to the wild-type (WT) control. H3K27ac ChIP-seq signal in WT or KO surrounding the aligned MyoD peak center in WT SE hotspots was calculated to measure the enhancer activity. ( B ) FoxO3 and some known co-factors of MyoD bind to MyoD peaks in SE hotspots in MT. ( C ) Genomic loci within SEs bound by both FoxO3 and MyoD displayed the highest H3K27ac density compared with FoxO3 or MyoD only sites in MT. *** < 0.001. ( D ) PCC between H3K27ac or Pol II and TF ChIP-seq density revealed that the FoxO3 binding displayed the highest correlation with SE activity among the TFs. ( E ) A total of 96% of FoxO3 peaks within SE hotspots overlap with MyoD binding. ( F ) On the FoxO3-MyoD co-bound enhancer hotspots, a much higher percentage of MyoD binding E-box motif (5’-CAGGTG-3’) than FoxO3 binding Fork head motif (5’-GTAAACA-3’) was detected. ( G ) Schematic illustration of MyoD recruitment of FoxO3 to the hotspot region. ( H ) Snapshots of examples of MyoD/FoxO3 co-bound hotspots (red bar) and their associated genes. ( I ) Sequential ChIP showed both MyoD and FoxO3 bound on the same region in one hotspot associated with , or two hotspots associated with gene. ( J and K ) Decreased FoxO3 or H3K27ac binding on six selected examples of hotspots associated with and was detected in MyoD knockout (KO) cell lines #1 and #2 as compared to WT control. ( L and M ) Knockdown of FoxO3 by shRNA decreased MyoD or H3K27ac binding on the above SEs. ( N ) Knockdown of either MyoD or FoxO3 by siRNA reduced the expression of the above genes. ( O ) ChIP-PCR was performed to detect the enrichment of FoxO3 on the above hotspots in the differentiating myotubes (MTs) at −24, 24, 48 and 72 hr. ( P ) The expression of MyoD and FoxO3 in differentiating C2C12 was detected by western blotting using α-Tubulin as loading control. ( Q ) ChIP-PCR was performed to detect the enrichment of MyoD on the above hotspots at DM −24, 24, 48 and 72 hr. All qRT-PCR and ChIP-PCR data were normalized to mRNA and ChIP input respectively and represent the average of three independent experiments ±s.d. * < 0.05, ** < 0.01 and *** < 0.001.

For TF ChIP-seq data, input DNA datasets from GSM915172 and GSM915181 were used as normalization control; for histone mark, p300 and Pol II ChIP-seq data, input DNA datasets from GSE25308 were used; and for H3K27ac ChIP-seq datasets generated by us, two previously published IgG controls (GSM1117984 and GSM1117985) ( 19 ) were used. According to the ChIP-seq guidelines and practices published by ENCODE ( 40 ), both ‘Input DNA’ and ‘IgG control’ are acceptable control samples in analyzing ChIP-seq datasets.

Given the uncovered roles of MyoD/FoxO3 in orchestrating the global hotspot assembly earlier in Figure 3 , we next asked whether they are key to the hotspot interaction and activation on the Myogenin SE. To test the notion, we found H3 or H4 reporter was highly induced in 10T1/2 cells with MyoD overexpression (Figure 6A ), suggesting MyoD is key to their activation. Consistent with what was seen earlier in C2C12 cells, the activation is not through changing FoxO3 levels ( Supplementary Figure S7A ). Interestingly, H1 and H2 reporters showed no such response to MyoD, which is consistent with their lacking of strong MyoD binding from ChIP-seq (Figure 5A ). Expectedly, the activity of H3 or H4 in C2C12 MTs was markedly inhibited by MyoD deletion (Figure 6B ), whereas H1 or H2 activity was unaffected. To further pinpoint the importance of MyoD binding in activating H3, two MyoD binding motifs identified through TRANSFAC database ( 60 ) in H3 ( Supplementary Figure S7B ) were removed individually or combined from the H3 reporter; deletion of # 2 or both but not # 1 abolished MyoD-dependent H3 reporter activation in 10T1/2 cells (Figure 6C ); similarly, the # 2 deletion also inhibited the reporter activity in C2C12 MTs (Figure 6D ), suggesting the essential role of the motif # 2 in mediating MyoD binding to H3 and its subsequent activation. When examining closely, we found indeed motif# 1 is slightly different from the known canonical E-box motif (CANNTG), which may explain its incompetence in mediating MyoD activation. Interestingly, such response to MyoD was not observed when the identified MyoD binding motif was removed from the H4 reporter (Figure 6E and F ), which indicates the binding is not mediated through direct recruitment to this motif. When further examining the role of FoxO3 in controlling the hotspots activation, we found knockdown of FoxO3 in C2C12 cells markedly hampered the activity of H3 (2.3-fold), H2 (2.4-fold) and to a less extent on H4 (1.65-fold) (Figure 6G ); FoxO3 knock-down also prevented the full degree of H3 and H4 activation by MyoD overexpression ( Supplementary Figure S7C ).

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