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SUNNYVALE, Calif., — Fortinet observed it's the first safety ally to breathe a fragment of the Hewlett-Packard’s ProCurve Alliance, a recently-introduced application designed to proffer consumers protection, mobility and convergence solutions which occupy been demonstrated and qualified for interoperability with ProCurve choices.

Fortinet will prolong its FortiGate multi-hazard safety platform to the ProCurve community environment, playing a role in its ProActive protection manner for securing networks towards inner and exterior threats.

Fortinet referred to it will profit from elevated alternatives and market consciousness as a result of the collaboration. ProCurve and Fortinet are already working collectively on product certification via wide trying out that will aid to construct certain solution interoperability.

“Amidst an more and more complicated risk environment, networks in and of themselves must develop into further and further relaxed,” pointed out Richard Stiennon, chief marketing officer for Fortinet. “utilizing Fortinet’s ASIC-accelerated security appliances with ProCurve’s ProActive protection approach additional extends ProCurve’s management location in improving the security of networks and presenting clients with the deployment alternative and suppleness they require.”

Fortinet observed its multi-threat protection appliances can yoke closely with ProCurve’s community Immunity supervisor to allow multiplied protection from internal and exterior threats. ProCurve network Immunity supervisor is a security administration utility that monitors swap ports across wired or wireless networks for virus assaults and makes it practicable for administrators to set detection and response protection guidelines. Fortinet FortiGate multi-danger safety appliances are scalable, comprehensive community and content material coverage structures that integrate eight simple protection applications and capabilities, including antivirus, firewall, VPN, intrusion prevention (IPS), anti-spam, anti-adware, web filtering and traffic shaping.

“Defending the commercial enterprise network against threats comparable to virus attacks is a vital component to enabling a relied on network infrastructure,” observed Paul Congdon, chief know-how officer of ProCurve Networking through HP. “The pairing of ProCurve community Immunity manager with Fortinet’s FortiGate platform suits very neatly with ProCurve’s ProActive defense protection approach and they are longing for a powerful collaboration.”


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<p>Fortinet®, the pioneer and main company of unified possibility administration (UTM) solutions, nowadays introduced that it's combining forces with ProCurve Networking with the aid of HP to allow secure networks for clients worldwide.</p><p>Fortinet is the first protection accomplice to combine the ProCurve Alliance, a these days-introduced application designed to present consumers safety, mobility and convergence options that occupy been validated and qualified for interoperability with ProCurve offerings.</p><p>through this alliance, Fortinet will extend its award-winning FortiGate™ multi-threat protection platform to the ProCurve network atmosphere, enjoying a role in ProCurve’s ProActive protection strategy to proactively relaxed networks against internal and exterior threats. Fortinet will odds from accelerated alternatives and market awareness resulting from its collaboration with ProCurve – the second biggest networking supplier global.</p><p>ProCurve and Fortinet are already working together on product certification through wide checking out so as to assist to ensure solution interoperability.“Amidst an increasingly complex threat ambiance, networks in and of themselves occupy to become further and further relaxed,” renowned Richard Stiennon, chief marketing officer for Fortinet. “applying Fortinet’s ASIC-accelerated security appliances with ProCurve’s ProActive defense approach further extends ProCurve’s leadership position in improving the safety of networks and offering valued clientele with the deployment option and suppleness they require.”</p><p>Fortinet’s market-main multi-risk safety home equipment permit the flexibility of coupling quite a lot of community protection capabilities closely with ProCurve’s network Immunity manager to enable improved insurance policy from interior and external threats.</p><p>ProCurve community Immunity manager is a protection administration software that screens change ports across wired or wireless networks for virus attacks and allows for directors to set detection and response safety policies.</p><p>Fortinet FortiGate multi-possibility security appliances are scalable, complete community and content material insurance policy structures that integrate eight primary security functions and features, together with antivirus, firewall, VPN, intrusion prevention (IPS), anti-spam, anti-adware, net filtering and site visitors shaping. FortiGate programs are kept up so far immediately by means of Fortinet's FortiGuard™ subscription functions, which give continuous updates to support give protection to against the latest viruses, worms, Trojans, and other threats.</p><p>“Defending the commerce network in opposition t threats corresponding to virus attacks is a critical component to enabling a depended on community infrastructure,” mentioned Paul Congdon, chief expertise officer, ProCurve Networking by using HP. “The pairing of ProCurve community Immunity manager with Fortinet’s FortiGate platform matches very well with ProCurve’s ProActive defense safety strategy and we're looking forward to a robust collaboration.”</p><p>About ProCurveThe ProCurve Networking commerce unit of HP supplies wired and wireless commerce networking items, capabilities and options. The basis for ProCurve’s commerce model is the Adaptive Networks approach by which networks are adaptive to clients, purposes and an organization’s wants.</p><p>ProCurve was positioned in the challenger quadrant in analysis and advisory enterprise Gartner, Inc.’s 2006 Magic Quadrant document for international Campus LANs and is the No. 2 networking commerce worldwide measured in both ports and Ethernet change market revenue in response to Dell’Oro community.</p><p>extra suggestions on ProCurve networking options and products is attainable at www.procurve.com.</p><p>About Fortinet (www.fortinet.com)Fortinet is the pioneer and market-main issuer of ASIC-accelerated unified threat management systems, which can breathe used through businesses and repair suppliers to multiply their protection while decreasing complete operating fees. Fortinet options were built from the ground as much as integrate divide stages of protection protection—including firewall, antivirus, intrusion prevention, web content material filtering, VPN, spyware prevention and antispam—proposing purchasers a means to proffer protection to diverse threats as well as blended threats. Leveraging a customized ASIC and unified interface, Fortinet options proffer superior protection performance that scales from remote office to chassis-based options with built-in management and reporting. Fortinet options occupy received multiple awards everyone over the world and are the best protection products that are certified eight instances over through ICSA Labs (firewall, antivirus, IPSec, SSL, IDS, client antivirus detection, cleaning and antispyware). Fortinet is privately held and primarily based in Sunnyvale, California.</p><p># # #</p><p>Fortinet is a registered trademark of Fortinet, Inc. Fortinet, FortiGate, FortiOS, FortiAnalyzer, FortiASIC, FortiCare, FortiManager, FortiWiFi, FortiGuard, FortiClient, and FortiReporter are emblems of the Fortinet, Inc. within the u.s. and/or other international locations. everyone different emblems observed herein are the property of their respective homeowners.</p><p>Media contact:Sebastian Rice,02 9959 1991,seb@silverspan.com,www.silverspan.com</p>


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Allele-specific binding of RNA-binding proteins reveals functional genetic variants in the RNA | killexams.com actual questions and Pass4sure dumps

Preprocessing of the ENCODE eCLIP data

eCLIP data sets generated from the HepG2 and K562 cell lines were downloaded from the ENCODE data portal. Raw reads were demultiplexed, adapter-trimmed, and mapped according to established eCLIP data processing procedures of the ENCODE project9. After removal of PCR duplicates, the remaining uniquely mapped reads were called “usable” reads for ASB analysis. eCLIP peaks were identified using read 2 (R2) of the paired-end reads via CLIPper13, with options -s hg19 -o -bonferroni -superlocal-threshold-method binomial-save-pickle. In this work, eCLIP peaks were retained for subsequent analyses if the library-normalized read coverage in at least one replicate is ≥4-fold of that in the corresponding region in the SMInput.

Identification of crosslinking sites in eCLIP peaks

To examine the potential actuality of crosslinking bias, BEAPR first determines crosslinking sites within eCLIP peaks. In eCLIP assays, reverse transcription of complementary DNA (cDNA) usually terminates at the protein–RNA crosslinking site8. Thus, a crosslinking site is expected to coincide with the start position of a significant number of R2 reads compared to random expectations. It should breathe renowned that if a protein binds to a cluster of motifs, multiple crosslinking sites may breathe identified within one eCLIP peak. This is because that reverse transcription may sometimes read-through a 3′ crosslinking site and gain the upstream ones.

To identify crosslinking sites, they carried out a similar procedure as in their previous work8. Specifically, for each eCLIP peak, the 5′ End positions of usable R2 reads within the peak were identified. For each nucleotide position i in the peak, the number of R2 reads, mi, whose 5′ End coincides with position i was obtained. The actual mi values were compared to random expectations obtained by permuting the positions of eCLIP reads within a peak. Empirical FDR was calculated and a minimum FDR of 0.001 was used to muster crosslinking sites.

Normalization of allelic read counts by crosslinking bias

To identify inherent crosslinking bias for an eCLIP experiment, BEAPR calculates the relative abundance of the four nucleotides at each position flanking the crosslinking sites in the SMInput sample (Fig. 1b). The observed relative sequence bias in the SMInput is unlikely resulted from the binding preference of specific RBPs. This bias is thus referred to as the crosslinking bias, which is estimated for each experiment. It is used to normalize the observed allele-specific read counts of SNVs in the vicinity of crosslinking sites.

Prior to read import normalization for each SNV, they removed low-quality read bases by requiring a minimum base Alignment attribute score of 1044. After this procedure, they examined the 51-nt region flanking the crosslinking site in each eCLIP peak. For each offset position d, −25 ≤ d ≤ 25, relative to the crosslinking site, the crosslinking bias q(a, d) of the nucleotide a at the offset position d was calculated as described above. Let yi,a,j breathe the number of eCLIP reads mapped to the allele a at the SNV i in the eCLIP replicate j. Rj denotes the total number (in millions) of eCLIP reads in the replicate. The normalize read import xi,a,j of yi,a,j was calculated as \(x_{i,a,j} = y_{i,a,j} \times \left( {q\left( {a,d} \right) \times R_j} \right)^{ - 1}\). If d > 25 or d < −25, q(a, d) was set to breathe 0.25.

Estimation of expected variance of normalized read counts

For each allele r at a heterozygous SNV i, let the variance of the normalized read counts across the eCLIP replicates breathe \(\sigma _{r,i}^2\). Since only a minuscule number of replicates are available, the sample variance \(\sigma _{r,i}^2\) is a impecunious estimator of the expected variance of the normalized read counts for the allele r. Hence, they developed the following procedure that considers allelic read counts from everyone SNVs to enhance the estimation of the expected variance. Specifically, for each allele r at an SNV i located in an eCLIP peak, they calculated the add up to μr,i and variance \(\sigma _{r,i}^2\) of its normalized read counts across the CLIP replicates. Using each pair of the add up to and variance values, μr,i and \(\sigma _{r,i}^2\), they calculated the square of the coefficient of variation (CV2) value, ωr,i. Then, a LOESS regression role was applied to proper everyone ωr,i and log2(μr,i) values, where the CV2 was the response variable and the log 2-scaled add up to was the explanatory variable (Fig. 1c). To prognosticate the expected variance of the normalized read counts for an allele r’, let \(\hat \omega _{r\prime ,i}\) breathe the CV2 value moderated by the LOESS regression function. The expected variance \(\hat \sigma _{r\prime ,i}^2\) for the allele r' at an SNV i was calculated as \(\hat \sigma _{r\prime ,i}^2 = \hat \omega _{r\prime ,i} \times \mu _{r\prime ,i}^2\).

Identification of ASB events

Let r and a denote the reference and alternative allele at an SNV site i and \(X_{A,i} = \left\{ {x_{A,i,1},...,x_{A,i,k}} \right\}\) breathe the normalized read counts for an allele A at the SNV i in everyone the k CLIP replicates. Their null hypothesis is that there is no ASB at the SNP i such that r and a are equivalently represented in the associated eCLIP peak. To test the null hypothesis, they examined whether μr,i = μa,i given Xr,i and Xa,i. An alternative manner to test the disagreement of the two add up to values is t-test. However, due to the minuscule sample size of the allelic read counts from individual genomic features, t-test was shown to breathe inapplicable to genomic read counts data derived from a limit number of replicates45. Hence, they derived the following empirical Gaussian distribution, which can conform to different types of read import data with elevated or low dispersion heterogeneity45, to model the normalized read counts. A novelty of their manner is the incorporation of the global mean-variance relationship moderated by the LOESS regression into the statistical test for the equivalence of the two add up to values.

We assume

$$X_{A,i} \sim {\cal N}\left( {\mu ,\sigma ^2} \right),$$

(1)

where μ is the add up to and σ2 the variance of the Gaussian distribution. In addition, they assume the prior distribution of μ follows a prevalent distribution:

$$\mu \sim {\cal N}\left( {\mu _0,\sigma _0^2} \right),$$

(2)

where the add up to μ0 and the variance \(\sigma _0^2\) are hyper-parameters.

At an SNV i, an allele was named the major allele, denoted as M, if its tolerable allelic read counts across the CLIP replicates was higher than that of the other allele. Otherwise, the allele was called the minor allele, denoted as m. In BEAPR, they reckon the empirical probability \(P\left( {\mu = \mu _{m,i}|X = X_{M,i}} \right)\) that the tolerable read import of the minor allele, μm,i, was generated from the very distribution from which the normalized read counts for the major allele M was observed. Thus, the p value \({\cal P}\) to reject the null hypothesis was defined as:

$${\cal P} = 2 \times {\int \nolimits_{ - \infty }^{\mu _{m,i}}} P\left( {\mu |X_{M,i}} \right){\mathrm{d}}\mu.$$

(3)

Based on Bayes rule, \(P\left( {\mu |X_{M,i}} \right) \propto P\left( {X_{M,i}|\mu } \right)P(u)\). Assume the expected variance for the allele M is \(\hat \sigma _{M,i}^2\), which is estimated as described in the terminal section. By combining the empirical probability P(μ|X = XM,i) with the distributions of X and μ, the empirical probability can breathe rewritten as a Gaussian distribution such that \(P\left( {\mu |X = X_{M,i}} \right) = {\cal N}\left( {\mu |\tilde \mu ,\tilde \sigma ^2} \right)\), where the add up to \(\tilde \mu\) is:

$$\tilde \mu = k\hat \sigma _{M,i}^{ - 2}\left( {\sigma _0^{ - 2} + k\hat \sigma _{M,i}^{ - 2}} \right)^{ - 1}\mu _{M,i} + \sigma _0^{ - 2}\left( {\sigma _0^{ - 2} + k\hat \sigma _{M,i}^{ - 2}} \right)^{ - 1}\mu _0,$$

(4)

where k is the number of eCLIP replicates, and the variance \(\tilde \sigma ^2\) is:

$$\tilde \sigma ^2 = \left( {\sigma _0^{ - 2} + k\hat \sigma _{M,i}^{ - 2}} \right)^{ - 1}.$$

(5)

Since the prior probability of μ is unknown, they assumed that \(\sigma _0^2 \to \infty\). Thus, the posterior probability to commemorate the add up to μm,i for the minor allele m given XM,i is:

$$P\left( {\mu |X_{M,i}} \right) \sim {\cal N}\left( {\mu |\mu _{M,i},\hat \sigma _{M,i}^2k^{ - 1}} \right).$$

(6)

To adjust for multiple testing, FDRs were calculated. A minimum FDR of 10% was required to muster significant ASB events in the ENCODE data sets.

Identification of heterozygous SNVs in eCLIP reads

BEAPR includes a procedure to muster heterozygous SNVs directly from the eCLIP data. As candidate SNVs, they obtained known SNPs or mutations from various databases, including dbSNP, GTEx, TCGA, ExAC, and COSMIC. For each cell line, they pooled everyone the SMInput data sets together and calculated the allelic read counts at everyone candidate SNV locations. A candidate SNV was predicted as a heterozygous SNV in the cell line if the total read coverage was at least 10 and the allelic ratio of the reference allele was between 0.25 and 0.75. In addition, they used whole-genome DNA sequencing data of the two cell lines to identify heterozygous SNVs using the very manner as in their previous work14. It should breathe renowned that in the analysis of ASB SNVs, heterozygous SNVs outside eCLIP peaks were discarded. However, the identification of heterozygous SNVs can breathe carried out for any region satisfying the above read coverage requirement. The number of heterozygous SNVs identified in the two cell lines with or without the eCLIP peak filter is shown in Supplementary Figure 5b.

Simulation of allele-specific read counts

To simulate allele-specific read counts that mimic those in actual eCLIP data sets, they used the eCLIP data of SRSF1 in the K562 cell line generated by ENCODE. eCLIP peaks were identified as described above. For heterozygous SNPs (dbSNP 144) located in the peaks, they obtained their total read coverage in each replicate. The empirical total read coverage distributions were used to generate independent sets of simulated read counts. For each simulated SNP, its total read coverage was sampled from the above distribution. Its allelic ratio was set to breathe 0.5, unless it is a simulated ASB SNP (with allelic ratio being 0.7, 0.8 or 0.9). The allelic read counts for each SNP were determined using a zero-truncated negative binomial distribution with the expected variance set to breathe equivalent to the observed variance between the two replicates of SRSF1 eCLIP as a role of total read coverage.

To add crosslinking-induced bias, the simulated allelic read counts for each SNP was rescaled based on the sequence propensity estimated from the SRSF1 data (similar as Fig. 1b), for SNPs located within 25 nt of crosslinking sites. To simulate additional SNPs with elevated variances in their read counts, they required the measure aberration of the SNP read counts between the two replicates breathe greater than that of 95% of SNPs in the SRSF1 data.

Evaluation of performance

Using simulated read counts, the overall performance of ASB prediction was evaluated in terms of precision, TP × (TP + FP)−1, and recall, TP × (TP + FN)−1, where TP is the number of legal positives, FP is the number of inaccurate positives, and FN is the number of inaccurate negatives. The district under the precision-recall curve (AUC) was calculated. They used this AUC value instead of that of a receiver operating characteristic (ROC) curve, because the number of SNVs with ASB or not were extremely unbalanced. A recent study suggested that precision-recall curves were more informative than ROC curves on unbalanced data46. Moreover, they moreover evaluated the prediction methods by sensitivity (SEN), TP × (TP + FN)−1, and specificity (SPE), FP × (TN + FP)−1, where TN is the number of legal negatives. They reported sensitivity at 95% specificity (SEN95) and specificity at 95% sensitivity (SPE95) as additional performance metrics to assess the methods.

Posterior filters for attribute control in BEAPR

In applying BEAPR to actual eCLIP-Seq data, they incorporated a number of posterior filters to ensure the attribute of the predicted ASB events. Note that no such filters were applied to simulated data. First, since ASB analysis handles unique nucleotides in sequencing reads, it suffers from similar problems as in RNA-editing analysis. Thus, they implemented posterior filters that are similar as those widely used in identifying RNA-editing sites47. Specifically, predicted ASB candidates were excluded if they are located in (1) microsatellites annotated by RepeatMasker48 (downloaded from UCSC genome browser), (2) homopolymeric pentamers, or (3) regions with mapping ambiguity. For the terminal filter, they used BLAT49 to align the 101 nucleotide sequence centered at each ASB SNV. ASB candidates in sequences with ≥95% similarity with other genomic regions were discarded. everyone the above filters are implemented in the BEPAR pipeline.

Allelic imbalance in eCLIP reads may breathe observed as a result of allele-specific expression (ASE) of the host gene, regulated by gene-level control mechanisms, such as allele-specific transcriptional activation. Such allelic bias may breathe identified as ASB in eCLIP data, but does not reflect bona fide allelic binding events of any RBPs. Using RNA-Seq data of control HepG2 and K562 cells in the ENCODE project, they identified genes that showed whole-gene plane ASE using ASARP21. In this analysis, they focused on genes that had at least two heterozygous SNVs each with a minimum of 10 reads (defined as testable SNVs). Genes whose testable SNVs everyone demonstrated allelic bias (FDR < 0.05) was deemed as ASE genes. If a gene was detected as an ASE gene in at least two RNA-Seq data sets (out of 24 total), they further required that the reference allele frequency of everyone SNVs in this gene to breathe <40% or >60% based on RNA-Seq reads. ASB candidates in such genes were discarded.

In read alignment of SNVs using a reference genome, it is known that mapping bias that favors the reference alleles may exist21. To account for such practicable bias, they calculated the distribution of allelic ratios at known heterozygous SNVs in the SMInput data. The add up to θ and measure aberration σ of this distribution were calculated. For each ASB candidate i whose allelic ratio was θi, they calculated a Z-score, \(z_i = \left\| {\theta _i} \right. - \theta\left\| \right. \times \sigma ^{ - 1}\), to evaluate how likely the ASB observation was due to reference mapping bias. ASB candidates were discarded if the Z-scores were smaller than 1.0.

Empirically, they observed elevated densities of eCLIP peaks from multiple RBPs exist in a minuscule number of genomic regions. This observation may reflect artifacts in eCLIP that generated “hotspot” IP regions. To alleviate the impact of this practicable artifact, they binned the genome into non-overlapping windows of 2000 nucleotides and calculated the number of the ASB candidates shared by more than four RBPs in each window. They identified a minuscule number of windows in the two cell lines where the ASB candidates shared by more than four RBPs significantly outnumbered those in the other windows, such as chr14:24,610,000–24,612,000 in HepG2, chr17:41,466,000-41,468,000 in K562, and chr19:34,882,000–34,884,000 in both cell lines. They thus discarded ASB candidates in these genomic regions.

The number and percentage of ASB events removed by each stepwise filter are shown in Supplementary Figure 7a.

Motif analysis

The position-specific motif enrichment plots (Fig. 3) were generated as follows. Within each peak harboring an ASB event, k-mer event at each position flanking the ASB SNV was counted. Note that at the ASB SNV, the sequence of the major allele in eCLIP was used. The k-mers used here are pentamers identified by RBNS for each RBP9. The frequency of the RBNS pentamers in everyone ASB regions of an RBP was calculated. As controls, they randomly picked a genomic sequence to match each region with ASB in terms of the kind of region (e.g., intron, 3′-UTR etc) and GC content (±10%). A total of 10 random sets of sequences were selected, with each set containing the very number of sequences as the number of ASB regions of an RBP. The fold enrichment of RBNS pentamers in ASB regions relative to the random controls was calculated and visualized in Fig. 3. The location of ASB SNVs relative to the sequence motifs of each RBP (Fig. 3) was determined using MOODS (p < 0.05)50.

Splicing-related analysis of RNA-Seq data

RNA-Seq data of RBP KD or associated controls were obtained from the ENCODE portal. The PSI values of annotated exons (Gencode (basic v24) annotation) were calculated using inclusion and exclusion reads of the alternative regions6. GMAS events in HepG2 and K562 cells (with control short hairpin RNA (shRNA) transfections) were identified using ASARP6,21. To identify overlap between an ASB SNV and a GMAS exon, the ASB SNV was required to reside in the GMAS exon or within 500 nt from exon–intron boundaries. They tested the statistical significance of the overlap via the hypergeometric test, where the background is the total number of heterozygous SNVs in genes harboring ASB SNVs.

Overlap between ASB events and QTL-related exons and genes

In the analysis of GTEx data, they obtained genotype data for 515 human donors (GTEx v6p release), from which heterozygous SNPs were identified for each individual. QTL-related exons were defined as those whose junctions were associated with GVs significantly (FDR < 0.05), in the GTEx Pilot analysis22. Genes with eQTL were extracted from GTEx v6p release33.

For the TCGA samples, genotype data of 200 LAML and 376 LIHC tumor samples were obtained from the TCGA data portal (https://tcga-data.nci.nih.gov/tcga/). QTL-related exons were defined as those associated with splicing QTL in the CancerSplicingQTL database23. Genes with eQTL were extracted from the PancanQTL database30.

Comparison of ASB SNVs and GWAS SNPs

To identify GWAS SNPs in LD with ASB SNVs, the GWAS catalog was downloaded from the NHGRI GWAS page at http://www.genome.gov/gwastudies/ on December 17, 2014. LD data of the CEPH (Utah residents with ancestry from northern and western Europe) population were obtained from the International HapMap Project51. In their GWAS LD analysis, an ASB SNV is defined as in LD with a GWAS SNP if both variants were located in the very LD conceal that passed the thresholds D′ > 0.9 and r2 > 0.8. Additionally, the distance between the GMAS SNV and GWAS SNP was required to breathe <200 kb.

RNA-Seq data analysis of UPF1 KD

RNA-Seq data of UPF1 KD and associated controls were obtained from the ENCODE portal. Differential gene expression analysis was carried out using DESeq252 with an FDR cutoff of 10%. The Gencode (basic v24) gene annotation was used for this purpose.

Minigene reporters for splicing assays

For exon skipping events, the candidate exon and ~400 nt upstream and downstream flanking introns were amplified using HeLa or K562 genomic DNA. After double digestion by HindIII and SacII or EcoRI and SacII, the DNA fragments were sub-cloned into pZW1 splicing reporter plasmids53. For intron retention events, the candidate intron and its flanking exons were cloned into the pcDNA3.1 plasmids. Final constructs were sequenced to ensure that a pair of plasmids containing the two alternative alleles of the SNV was obtained.

Transfection, RNA extraction, reverse transcription, and PCR

Minigene constructs were transfected into >90% confluence HeLa cells (ATCC, CCL-2) using Lipofectamine 3000 (Thermo Fisher Scientific, L300015). Cells were harvested 24 h post transfection and total RNA was isolated using TRIzol (Thermo Fisher Scientific, 15596018), followed by Direct-zol RNA Mini prep (Zymo Research, R2072). cDNA was prepared from 2 μg of total RNA by SuperScript IV First-Strand Synthesis System (Thermo Fisher Scientific, 18091050) and one-twentieth of the cDNA was used as template to amplify both inclusion and exclusion of the candidate exon by PCR of 28 cycles.

Gel electrophoresis and quantification

Five microliter of PCR product was loaded onto 5% polyacrylamide gel and electrophoresis at 70 V for one and a half hours. The gel was then stained with SYBR® Safe DNA Gel taint (Thermo Fisher Scientific, S33102) for 30 min before imaging via Syngene SYBRsafe program (Syngene). The expression plane of spliced isoforms was estimated using the ImageJ software (http://imagej.nih.gov/ij/). Inclusion or intron retention rate (% inclusion) of the target exon was calculated as the intensity ratio of upper × (upper + lower)−1 bands.

Bi-directional reporter constructs for 3′-UTR analysis

To test the role of ASB events in 3′-UTRs, ~700–1000 nt of the 3′-UTR regions including the ASB SNV were amplified using genomic DNA extracted from HMLE, HeLa, or K562 cells. Site mutations were generated for alternative alleles for each SNV using overlap-extension PCR. After double digestion by ClaI and SalI-HF, the DNA fragments were sub-cloned into the 3′-UTR of mCherry in the bi-directional reporter plasmid pTRE-BI-red/yellow that encodes for both mCherry and eYFP54. Final constructs were sequenced to ensure that a pair of plasmids containing the two alternative alleles of the SNV was obtained.

Real-time PCR

The real-time PCR reaction was performed using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, 172-5270) and CFX96 paw Real-Time PCR detection system (Bio-Rad) according to the manufacturer’s instructions. The mRNA expression plane associated with each allele of the ASB SNV was measured by the mCherry expression levels, which was normalized against that of eYFP.

Purification of recombinant human PTBP1

The human PTBP1-pET28a expression vector was a gift from Dr. Douglas Black. It was transformed into BL21 Star (DE3)-competent cells (Thermo Fisher Scientific, C602003). Protein induction was carried out via 1 mM isopropyl β-d-1-thiogalactopyranoside (IPTG) treatment in 50 mL cultured cells (OD = 0.8) for 16 h at 215 rpm at 28 °C. Next, cultured cells were centrifuged at 7000 × g for 5 min at 4 °C and the pellets were resuspended with ice-cold 5 mL lysis buffer (1× BugBuster, 20 mM sodium phosphate, pH 7.7, 500 mM NaCl, 20 mM imidazole, 1 mM dithiothreitol (DTT), 0.5× protease inhibitor cocktail, 100 μg/mL lysozyme, 100 U DNAse I). After 30 min incubation, the lysate was disrupted using three times sonication at 30% amplitude for 30 s with 1 s pulse. Subsequently, the lysate was centrifuged at 15,000 × g for 15 min at 4 °C. The supernatant was collected and filtered using 0.45 μm syringe filter. The sample was loaded into the HisTrap HP column (GE Healthcare, 17-5247-01) using Biologic LP system (Bio-Rad, 7318304) and washed with 20 mL buffer A (20 mM sodium phosphate, pH 7.7, 500 mM NaCl, 20 mM imidazole, 1 mM DTT). The sample was eluted with 500 mM imidazole in buffer A. Purity of the recombinant PTBP1 protein was determined by SimplyBlue SafeStain (Thermo Fisher Scientific, LC6060) and western blot using anti-HIS antibody (Santa Cruz Biotech, sc-8036, 1:500 dilution). spotless fractions (E28 and E33, Supplementary Figure 12) were combined (~3 mL). Salt and minuscule size of non-specific proteins were removed by incubating in 20 K Slide-A-Lyzer dialysis cassette (Thermo Fisher Scientific, 66003) with 1 L Buffer A in a artic latitude overnight. Protein concentration was measured by Pierce Coomassie (Bradford) protein assay kit (Thermo Fisher Scientific, 23200) and Turner spectrophotometer SP-830.

In vitro transcription of PTBP1 target RNA

ASB candidates overlapping with PTBP1 binding motif were selected and 100 μM of sense and antisense oligos including T7 promoter were annealed with oligo annealing buffer (10 mM Tris-HCl, pH 8.0, 1 mM EDTA, pH 8.0, 100 mM NaCl) at 95 °C for 5 min in a heat conceal and then cooled slowly to 28 °C for 2 h. In vitro transcription was performed using 1 μg of annealed oligos and HiScribe T7 elevated capitulate RNA synthesis kit. In vitro synthesized RNAs were treated with 10 U RNAse-free DNAse I (Thermo Fisher Scientific, EN0525) at latitude temperature for 30 min, and then purified by RNA spotless & Concentrator-5 Kit (Zymo Research, R1015). Next, RNA samples were treated with 10 U shrimp alkaline phosphatase (NEB, M0371S) at 37 °C for 1 h and then labeled with 0.4 μL of γ-32P-ATP (7000 Ci/mmol, MP Biomedicals) using 20 U T4 polynucleotide kinase (NEB, M0201S). Subsequently, RNA probes were purified using 5% urea-PAGE extraction and RNA spotless & Concentrator-5 Kit. RNA concentration was measured by Qubit 2.0 fluorometer (Thermo Fisher Scientific).

Electrophoretic Mobility Shift Assay

The purified RNA probes (20 pmol) and recombinant PTBP1 protein (0, 0.6, 1.2, 2.5, and 5 μg) were incubated in 15 μL of buffer A (20 mM sodium phosphate, pH 7.7, 500 mM NaCl, 20 mM imidazole, 1 mM DTT, 0.1× protease inhibitor cocktail, 10 U RNAse inhibitor) at 28 °C for 30 min, and then loaded onto 5% TBE-PAGE and ran at 75 V for 1.5 h. The gel was processed without drying, covered with clear folder, and exposed to X-ray film at −80 °C.

Lentivirus mediated GEMIN5 KD

pLKO1 non-target control-shRNA (SHC016) and GEMIN5-shRNA (TRCN0000147159, TRCN0000129034, TRCN0000150146, TRCN0000130416, TRCN0000149925) constructs were used for this experiment. Lentiviruses were produced as follows:55 pLKO1, pCMV-d8.91 and pVSV-G were co-transfected into HEK293T cells (ATCC, CRL-11268) using Lipofectamine 3000 (Thermo Fisher Scientific, L3000015). After 48 h co-transfection, viruses were harvested and used to infect HeLa cells with polybrene (8 μg/mL). After 24 h, cells were incubated with puromycin (2 μg/mL) for 3–7 days selection.

To examine the efficiency of GEMIN5 KD, cell lysates were prepared with RIPA buffer and used for Western blot with GEMIN5 (Bethyl Lab, A301-325A, Lot# A301-325A-1, 1:200 dilution) and β-actin antibody (Santa Cruz, sc-47778, Lot# J2915, 1:500 dilution). The splicing reporter assay for CERS2 was performed as described above.

Reporting Summary

Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.


Will MEMS Technology Shape The Future Of Modern Mobility? | killexams.com actual questions and Pass4sure dumps

The modern vehicle is racking up more and more features by the day as industries push towards connected and automated mobility. Along with a host of vital components, Micro-Electro-Mechanical Systems (MEMS) technology is quickly asserting itself as a critical automotive design fixture.

Due to MEMS potential for elevated performance, minuscule size, and cost effectiveness, the scope of its vehicle inclusion has widened. As modern cars contiue to include advanced safety features, fuel consumption reductions, and maintenance systems, MEMS sensor applications will continue its rise.

ECN had the delight of connecting with Christophe Zinck, Senior Application Engineering Manager at ASE Group, to collect an inside Look at the future of MEMS sensors and advanced chip packaging in the automotive world. 

ECN: tickle give a puny background on MEMS sensors’ climb into the automotive space.

Zinck: MEMS and sensors occupy been well-deployed in the automotive space for many years now. However in modern times, new applications are emerging; especially for advanced driver-assistance systems (ADAS), enjoy LIDAR, and infotainment (touchless control, windshield display, etc.). In general, well-established MEMS devices for under-the-hood applications are now moving from “old” package platforms (such as SO, DIP, or unibody) to leadless platforms (like CLCC or PMQFN), while new devices for infotainment are more often using “consumer” type packages.

Below is a graphic on MEMS and sensors ATV safety packages.

(Image Credit: ASE Group)

ECN: What are the benefits to using MEMS sensors and advanced chip packaging in connected cars?

Zinck: For advanced Complementary Metal Oxide Semiconductors (CMOS) in cars, ADAS deployment requires new sensors, as there is a need for a very elevated plane of computing locally to transmit, analyze, and process the immense volume of data emanating from within vehicles today.

ECN: What current or new applications will multiply the exact for automotive MEMS?

Zinck: exact for automotive MEMS is being driven by an increasing number of significant applications. Some examples include:

  • For ADAS: IR sensors, near IR sensors, CIS, and LIDAR.
  • For infotainment: optical sensors for touchless signal recognition, gas sensors for in-car air control, IR sensors for driver control to thermal check brain activity, micro mirror for display, and so on.
  • ECN: What are some design/implementation obstacles that engineers are facing with MEMS technology? How achieve designers blueprint to address these challenges?

    Zinck: Within the MEMS and sensors manufacturing arena, the main challenges relate to packaging. At this stage, it is proving difficult to standardize packaging for such devices, therefore, each sensor is currently produced with its own unique package. This impacts cost significantly, so the engineering community is always exploring ways to create more cost-effective solutions.

    ECN: In the foreseeable future, what role will MEMS sensors and advanced chip packaging play in the automotive industry?

    Zinck: Both MEMS and advanced chip packaging will each play a pivotal role, but MEMS and sensors are only the eyes and ears of the car. Data treatment and actions (such as super application processors) will become a major factor in autonomous driving deployment, as the system will occupy to dissect a great quantity of data from various sensors in an extremely short amount of time (probably µsec or even faster), and then react very fast. To construct this a reality, the automotive industry is in need of advanced CMOS technology.


    Growth of Submarine Cables and How the Cisco NCS 1004 Fits | killexams.com actual questions and Pass4sure dumps

    Submarine cables figure the backbone of the Internet, and there are over 300 subsea cables today that interconnect continents and countries. In recent years, as cloud and content providers attempt to gain more users and provide reliable and bandwidth intensive services, submarine infrastructure has exploded. The number of new submarine cables is set to grow by 50 percent in 2019 versus 2018 (source: TeleGeography’s submarine cable map).

    All major cloud providers will build out new cables in consortiums or as privately owned cables in the next few years. In a recent Tech Crunch article, Vijay Vusirikala from Google described this as an economic determination related to the sheer scale of bandwidth usage. “It becomes much more cost-effective to build their own cables than it does to rent time on somebody else’s when you gain a inevitable usage level. This is essentially pile infrastructure at scale and with scale getting the optimization benefits,” Vusirikala explained.

    Figure 1: The below figure illustrates the strategy of cloud service providers: The objective is to proffer better reliability, hurry and security performance as compared with the nondeterministic performance of the public internet or other cloud networks.

    Source: https://blog.google/products/google-cloud/expanding-our-global-infrastructure-new-regions-and-subsea-cables/

    The other key driver for sub-sea capacity is video. Cisco’s VNI reports between 2017 and 2022, global sedulous hour Internet exercise will grow at a CAGR of 37 percent, compared with 30 percent for tolerable Internet traffic. Video is the underlying source of accelerated sedulous hour traffic growth. Unlike other forms of traffic, which are spread evenly throughout the day (such as web browsing and file sharing), video tends to occupy a “prime time.” The growing gap between peak and tolerable traffic is amplified further by the changing composition of Internet video. Real-time video, such as live video, ambient video and video calling has a peak-to-average ratio that is higher than on-demand video. This acceleration of video is addressed by the increasing role of Content Delivery Networks (CDNs). CDNs bring content closer to the user, and sub-sea cables play a crucial role in connecting content to the CDNs closer to the End user. As per the Cisco VNI report, CDNs will carry 72 percent of total Internet traffic by 2022, up from 56 percent in 2017. 

    How Does the Cisco NCS 1004 proper Into Submarine Applications?

    The NCS 1004 is the latest product in the DCI form-factor NCS 1000 series. The NCS 1004 delivers multi-haul coherent DWDM transponders that provide status of the know-how performance for sub-sea applications using granular baud-rate + bits per symbol tuning, time-hybrid modulation, transmit signal shaping and non-linear equalization. Each 2RU form-factor NCS 1004, powered by Acacia’s Pico Digital Signal Processor chip, provides 8 coherent DWDM ports that operate from 100G to 600G.

    For current generation uncompensated submarine cables with D+ fiber, 200G-300G is expected to operate over trans-Pacific cables, and 300G-400G is expected to operate over trans-Atlantic cables. While the higher line rate helps lower the cost per bit, time-hybrid modulation allows for continuous control of spectral efficiency which is the key metric to maximizing capacity on expensive submarine cable assets. This short video describes the challenges and intricacies in engineering submarine cable systems.

    The clear profit of the NCS 1004 for submarine cable operators is described in the figure below. Competitor solutions with a limited set of fixed options (usually in nx50G or nx100G line rates) struggle to poise Q-margin and spectral efficiency. In the example below, Option 1 line rate offers enough margin but the spectral efficiency is too low – while a different line rate – Option 2 provides much better spectral efficiency but, not enough Q-margin. With the NCS 1004, the user can adjust each line rate in granular increments to the confiscate point that balances Q-margin and spectral efficiency.

    Figure 2: The below figure plots Q versus Spectral Efficiency for the NCS 1004 and for a typical competitor product. The NCS 1004 provides a continuous purview of spectral efficiency values for different Q. Competitor products proffer fixed line rates that limit the skill to maximize spectral efficiency for the desired Q-margin.

    Cisco’s NCS 2000 solution complements the NCS 1004 for sub-sea applications with the required Submarine Line Terminating equipment (SLTE) capabilities. The NCS 2000 portfolio includes amplifiers, ASE noise-loading and brilliant ROADM based colorless add-drop with enhanced monitoring. 

    Live sphere Trial

    Cisco was able to demonstrate the benefits of the NCS 1004 over a sub-sea cable in production. The cable they tested ran over 10,000km with accumulated chromatic dispersion of 210,000 ps/nm. This cable was designed as an open cable and had a ROADM line system already deployed with over 20 percent of the spectrum consumed by 200G channels from a current generation transponder. As fragment of the affliction with the NCS 1004, Cisco tested 200G and 300G over different baud-rates across different parts of the spectrum.

    With this test, Cisco’s solution achieved significant milestones. The NCS 1004 drove a unique wavelength of 300G over a 10,000km sub-sea link. The spectral efficiency achieved was 43 percent better than any other option available in the industry today. As fragment of the testing, Cisco validated multiple combinations of bits per symbol and baud-rate for 200G line rate that provided granular control of spectral efficiency and Q-margin. This was demonstrated with the NCS 1004 operating as an alien over an existing third party SLTE platform running live traffic.

    Cisco and Acacia partnered closely over the terminal few months to successfully demonstrate the benefits of this new technology in actual deployment conditions. They are very haughty of these results.

    Our experts will breathe at the PTC conference next week. They hope to discern you there. You can moreover learn more about Cisco’s latest innovations on our Optical Networking site.

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