The active fraction of the EtOH extract of the stem of Annona glabra against acetylcholinesterase (AChE) was analyzed by combining HPLC microfractionation with a bioassay. The analytical-scale sample was fractionated by HPLC-DAD into 96-well microplates, which, after evaporation, were assayed against AChE. The active subfractions were scaled up by separation over semipreparative HPLC to give 20 compounds. Four of these, (7S,14S)-(-)-N-methyl-10-O-demethylxylopinine salt (3), S-(-)-7,8-didehydro-10-O-demethylxylopininium salt (10), S-(-)-7,8-didehydrocorydalminium salt (11), and 5-O-methylmarcanine D (17), were assigned as new natural products. In addition, compounds 10 and 11 represent the first natural occurrence of 7,8-didehydroprotoberberines. Compound 3, pseudocolumbamine (12), palmatine (15), and pseudopalmatine (16) showed anti-AChE IC50 values of 8.4, 5.0, 0.4, and 1.8 μM, respectively.
Abstract-In an H.264/AVC video encoder, integer motion estimation (IME) requires 74.29% computational complexity and 77.49% memory access and becomes the most critical component for low-power applications. According to our analysis, an optimal low-power IME engine should be a parallel hardware architecture supporting fast algorithms and efficient data reuse (DR). In this paper, a hardware-oriented fast algorithm is proposed with the intra-/inter-candidate DR considerations. In addition, based on the systolic array and 2-D adder tree architecture, a ladder-shaped search window data arrangement and an advanced searching flow are proposed to efficiently support inter-candidate DR and reduce latency cycles. According to the implementation results, 97% computational complexity is saved by the proposed fast algorithm. In addition, 77.6% memory bandwidth is further saved with the proposed DR techniques at architecture level. In the ultralow-power mode, the power consumption is 2.13 mW for real-time encoding CIF 30-fps videos at 13.5-MHz operating frequency.
SWLMs, and then shifted and reused in SRA. 87.5%A 2.8 to 67.2mW H.264 encoder is implemented on a memory power of IME is thus saved. 12.8mM2 die with 0.1I8 tm CMOS technology. The proposed Figure 2 shows the proposed fractional-pixel ME (FME) parallel architectures along with fast algorithms and data engine. Different from the sequential half-then-quarter reuse schemes enable 77.9% power savings. The power refinement algorithm, the proposed one-pass algorithm awareness is provided through a flexible system hierarchy searches 25 half-pixel/quarter-pixel candidates around the that supports content-aware algorithms and module-wise best integer-pixel candidate to facilitate parallel processing gated clock.and DR. reuse (DR) schemes should be implemented in the parallel scalability with the flexible encoder system and architectures for fast ME algorithms. Second, low power power-aware algorithm. Figure 3 shows the system techniques have to be integrated across different design architecture. The encoder has 3 macroblock (MB) pipeline levels. This is not easy because fast ME algorithms are stages: coarse-prediction, fine-prediction and block-engine, difficult to realize on parallel architectures due to their and three MBs are pipelined and simultaneously processed irregular and sequential natures. Furthermore, gated-clock with the power-aware algorithm shown in Fig. 4. A wide techniques at the circuit level cannot be effectively range of power scalability is achieved through this flexible supported without system-level considerations. Finally, to system thatadjuststheparametersofreconfigurablePEGs enable power scalability on an ASIC encoder, flexibility associated to all H.264 compression functionalities. Unlike must be explored on the system and module architectures the previous encoder system [1, 2], in which each pipeline along with a computationally scalable algorithm.controller is tightly coupled with the corresponding processing engines (PEGs) in each pipeline stage, our Low-Power Motion Estimation system hierarchy separates PEGs from pipeline controllers. Figure 4 shows the proposed integer-pixel ME (IME)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.