This work presents a new design space of Extended continuous class-F (ECCF) power amplifiers with resistive second harmonic impedance after including the effect of non-linear drain-to-source capacitance$C_{\mathbf {ds}}~_{\mathbf {}}$of a transistor. The expression of current waveforms is modified to include the impact of nonlinear drain to source capacitance in this mode. The analysis shows that this non-linear capacitance generates harmonic current, which alters the load trajectory on the Smith chart by achieving active and modified passive second harmonic loads. These active harmonic loads at the current reference plane of the transistor represent an active harmonic injection that increases the drain efficiency. Consequently, a design methodology is proposed to obtain passive loads for matching that ensures this active harmonic injection at the current reference plane of the transistor provides an efficiency improvement in the ECCF mode while maintaining the operation more than the octave frequency range. The proposed theory is validated by designing a power amplifier operating from 0.8-3.0 GHz (115.7% fractional bandwidth) with a measured drain efficiency of 57.24-70.3%, and an output power of 41.7-44.63 dBm. The power amplifier is also tested with modulated signals and linearized using digital predistortion to qualify spectral mask with a measured (Error Vector Magnitude) EVM of 1.54%.

MOTIVATION: Deep sequencing based ribosome footprint profiling can provide novel insights into the regulatory mechanisms of protein translation. However, the observed ribosome profile is fundamentally confounded by transcriptional activity. In order to decipher principles of translation regulation, tools that can reliably detect changes in translation efficiency in case-control studies are needed. RESULTS: We present a statistical framework and an analysis tool, RiboDiff, to detect genes with changes in translation efficiency across experimental treatments. RiboDiff uses generalized linear models to estimate the over-dispersion of RNA-Seq and ribosome profiling measurements separately, and performs a statistical test for differential translation efficiency using both mRNA abundance and ribosome occupancy. AVAILABILITY AND IMPLEMENTATION: RiboDiff webpage http://bioweb.me/ribodiff Source code including scripts for preprocessing the FASTQ data are available at http://github.com/ratschlab/ribodiff CONTACTS: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

Protein translation is a fundamental biochemical process and the regulation of this process in response to a variety of changes has been demonstrated to play a key role in cellular functional activity. Recently, the translational control of oncogenes is implicated in many cancers [ 1 ]. We recently reported a translation initiation factor eIF4A RNA helicase-dependent mechanism of translational control that contributes to oncogenesis and underlies the anticancer effects of drug silvestrol [ 2 ]. Inhibition of eIF4A with silvestrol has powerful therapeutic effects in vitro and in vivo . In this study, we developed novel computational tools, specifically be tailored to study high throughput ribosome footprint data (Ribo-seq) [ 3 ], to identify the genes featuring either one of the two changes between two experiment conditions: 1) translational efficiency (TE), and 2) ribosome occupancy distribution profile (ROD) on mRNA. In the parametric test of TE, we take RNA abundance and ribosome occupancy density into account in order to expeditiously identify differential translation efficiency. Whereas the non-parametric test of ROD [ 4 ] aims to identify differential occupancy profiles, such as ribosome stalling at specific sites even if overall translation efficiency remain unchanged. Using transcriptome-scale ribosome footprinting data of leukemia cell line, we defined drug-sensitive genes showing both decrease of translational efficiency (Figure 1A ) and accumulation of ribosome occupancy at 5'UTR (Figure 1B ). Among the most eIF4A-dependent transcripts are a number of oncogenes, super-enhancer associated transcription factors and epigenetic regulators. Computational methods revealed drug effects on protein translation . A Histogram of the ratio of TE in control and drug-treated samples. Red: genes with significant TE down and up regulation were identified based on the read count of Ribo-seq data. Blue and green: TE up and down genes defined by |Z score| > 2 as often used in other analyses. B Averaged distribution profile of ribosome occupancy of 62 drug-sensitive genes. Ribosome footprint coverages and transcript lengths were normalized. C Twelve-nucleotide motif that is highly enriched in 5' UTR of TE down and ROD positive genes. We suggested that the GQ structure is responsible for ribosome stalling in the 5' UTR [ 2 ]. Computational and statistical methodologies facilitate the discovery of the hallmark of eIF4A-dependent transcripts, namely 5'UTR sequence harbors the 12-mer guanine quartet (CGG) 4 motif associated with RNA G-quadruplex (GQ) structures (Figure 1C ). Our novel computational tools provide a fast, accurate solution to gain biological insights from Ribo-seq and RNA-seq data.