The upgrading of plastic waste is one of the grand challenges for the 21 st century owing to its disruptive impact on the environment. Here,w es howt he first example of the upgrading of various aromatic plastic wastes with C À Oand/or C À Cl inkages to arenes (75-85 %y ield) via catalytic hydrogenolysis over aR u/Nb 2 O 5 catalyst. This catalyst not only allows the selective conversion of single-component aromatic plastic,a nd more importantly,e nables the simultaneous conversion of am ixture of aromatic plastic to arenes.T he excellent performance is attributed to unique features including:(1) the small sized Ru clusters on Nb 2 O 5 ,whichprevent the adsorption of aromatic ring and its hydrogenation;(2) the strong oxygen affinity of NbO x species for C À Ob ond activation and Brønsted acid sites for CÀCb ond activation. This study offers ac atalytic path to integrate aromatic plastic waste back into the supply chain of plastic production under the context of circular economy.

Transposable elements (TE) usually take up a substantial portion of eukaryotic genome. Activities of TEs can cause genome instability or gene mutations that are harmful or even disastrous to the host. TEs also contribute to gene and genome evolution at many aspects. Part of miRNA genes in mammals have been found to derive from transposons while convincing evidences are absent for plants. We found that a considerable number of previously annotated plant miRNAs are identical or homologous to transposons (TE-MIR), which include a small number of bona fide miRNA genes that conform to generally accepted plant miRNA annotation rules, and hairpin derived siRNAs likely to be pre-evolved miRNAs. Analysis of these TE-MIRs indicate that transitions from the medium to high copy TEs into miRNA genes may undergo steps such as inverted repeat formation, sequence speciation and adaptation to miRNA biogenesis. We also identified initial target genes of the TE-MIRs, which contain homologous sequences in their CDS as consequence of cognate TE insertions. About one-third of the initial target mRNAs are supported by publicly available degradome sequencing data for TE-MIR sRNA induced cleavages. Targets of the TE-MIRs are biased to non-TE related genes indicating their penchant to acquire cellular functions during evolution. Interestingly, most of these TE insertions span boundaries between coding and non-coding sequences indicating their incorporation into CDS through alteration of splicing or translation start or stop signals. Taken together, our findings suggest that TEs in gene rich regions can form foldbacks in non-coding part of transcripts that may eventually evolve into miRNA genes or be integrated into protein coding sequences to form potential targets in a “temperate” manner. Thus, transposons may supply as resources for the evolution of miRNA-target interactions in plants.

The advent of readily available temporal imaging or time series volumetric (4D) imaging has become an indispensable component of treatment planning and adaptive radiotherapy (ART) at many radiotherapy centers. Deformable image registration (DIR) is also used in other areas of medical imaging, including motion corrected image reconstruction. Due to long computation time, clinical applications of DIR in radiation therapy and elsewhere have been limited and consequently relegated to offline analysis. With the recent advances in hardware and software, graphics processing unit (GPU) based computing is an emerging technology for general purpose computation, including DIR, and is suitable for highly parallelized computing. However, traditional general purpose computation on the GPU is limited because the constraints of the available programming platforms. As well, compared to CPU programming, the GPU currently has reduced dedicated processor memory, which can limit the useful working data set for parallelized processing. We present an implementation of the demons algorithm using the NVIDIA 8800 GTX GPU and the new CUDA programming language. The GPU performance will be compared with single threading and multithreading CPU implementations on an Intel dual core 2.4 GHz CPU using the C programming language. CUDA provides a C-like language programming interface, and allows for direct access to the highly parallel compute units in the GPU. Comparisons for volumetric clinical lung images acquired using 4DCT were carried out. Computation time for 100 iterations in the range of 1.8-13.5 s was observed for the GPU with image size ranging from 2.0 x 10(6) to 14.2 x 10(6) pixels. The GPU registration was 55-61 times faster than the CPU for the single threading implementation, and 34-39 times faster for the multithreading implementation. For CPU based computing, the computational time generally has a linear dependence on image size for medical imaging data. Computational efficiency is characterized in terms of time per megapixels per iteration (TPMI) with units of seconds per megapixels per iteration (or spmi). For the demons algorithm, our CPU implementation yielded largely invariant values of TPMI. The mean TPMIs were 0.527 spmi and 0.335 spmi for the single threading and multithreading cases, respectively, with <2% variation over the considered image data range. For GPU computing, we achieved TPMI =0.00916 spmi with 3.7% variation, indicating optimized memory handling under CUDA. The paradigm of GPU based real-time DIR opens up a host of clinical applications for medical imaging.