Microalgal lipid is one of the most promising feedstocks for biodiesel production. Chlorella appears to be a particularly good option, and nitrogen (N) starvation is an efficient environmental pressure used to increase lipid accumulation in Chlorella cells. The effects of N starvation of an oil-producing wild microalga, Chlorella sorokiniana C3, on lipid accumulation were investigated using thin layer chromatography (TLC), confocal laser scanning microscopy (CLSM) and flow cytometry (FCM). The results showed that N starvation resulted in lipid accumulation in C. sorokiniana C3 cells, oil droplet (OD) formation and significant lipid accumulation in cells were detected after 2 d and 8 d of N starvation, respectively. During OD formation, reduced photosynthetic rate, respiration rate and photochemistry efficiency accompanied by increased damage to PSII were observed, demonstrated by chlorophyll (Chl) fluorescence, 77K fluorescence and oxygen evolution tests. In the mean time the rate of cyclic electron transportation increased correspondingly to produce more ATP for triacylglycerols (TAGs) synthesis. And 0.5 d was found to be the turning point for the early stress response and acclimation of cells to N starvation. Increased level of membrane peroxidation was also observed during OD formation, and superoxide dismutase (SOD), peroxide dismutase (POD) and catalase (CAT) enzyme activity assays suggested impaired reactive oxygen species (ROS) scavenging ability. Significant neutral lipid accumulation was also observed by artificial oxidative stress induced by H2O2 treatment. These results suggested coupled neutral lipid accumulation and oxidative stress during N starvation in C. sorokiniana C3.

This paper introduces TIRAMISU, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. TIRAMISU introduces a scheduling language with novel commands to explicitly manage the complexities that arise when targeting these systems. The framework is designed for the areas of image processing, stencils, linear algebra and deep learning. TIRAMISU has two main features: it relies on a flexible representation based on the polyhedral model and it has a rich scheduling language allowing fine-grained control of optimizations. TIRAMISU uses a four-level intermediate representation that allows full separation between the algorithms, loop transformations, data layouts, and communication. This separation simplifies targeting multiple hardware architectures with the same algorithm. We evaluate TIRAMISU by writing a set of image processing, deep learning, and linear algebra benchmarks and compare them with state-of-the-art compilers and hand-tuned libraries. We show that TIRAMISU matches or outperforms existing compilers and libraries on different hardware architectures, including multicore CPUs, GPUs, and distributed machines.

The performance bottlenecks of graph applications depend not only on the algorithm and the underlying hardware, but also on the size and structure of the input graph. As a result, programmers must try different combinations of a large set of techniques, which make tradeoffs among locality, work-efficiency, and parallelism, to develop the best implementation for a specific algorithm and type of graph. Existing graph frameworks and domain specific languages (DSLs) lack flexibility, supporting only a limited set of optimizations.This paper introduces GraphIt, a new DSL for graph computations that generates fast implementations for algorithms with different performance characteristics running on graphs with different sizes and structures. GraphIt separates what is computed (algorithm) from how it is computed (schedule). Programmers specify the algorithm using an algorithm language, and performance optimizations are specified using a separate scheduling language. The algorithm language simplifies expressing the algorithms, while exposing opportunities for optimizations. We formulate graph optimizations, including edge traversal direction, data layout, parallelization, cache, NUMA, and kernel fusion optimizations, as tradeoffs among locality, parallelism, and work-efficiency. The scheduling language enables programmers to easily search through this complicated tradeoff space by composing together a large set of edge traversal, vertex data layout, and program structure optimizations. The separation of algorithm and schedule also enables us to build an autotuner on top of GraphIt to automatically find high-performance schedules. The compiler uses a new scheduling representation, the graph iteration space, to model, compose, and ensure the validity of the large number of optimizations. We evaluate GraphIt's performance with seven algorithms on graphs with different structures and sizes. GraphIt outperforms the next fastest of six state-of-the-art shared-memory frameworks (Ligra, Green-Marl, GraphMat, Galois, Gemini, and Grazelle) on 24 out of 32 experiments by up to 4.8×, and is never more than 43% slower than the fastest framework on the other experiments. GraphIt also reduces the lines of code by up to an order of magnitude compared to the next fastest framework.

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Abstract-Large-scale applications implemented in today's high performance graph frameworks heavily underutilize modern hardware systems. While many graph frameworks have made substantial progress in optimizing these applications, we show that it is still possible to achieve up to 5× speedups over the fastest frameworks by greatly improving cache utilization. Previous systems have applied out-of-core processing techniques from the memory/disk boundary to the cache/DRAM boundary. However, we find that blindly applying such techniques is ineffective because the much smaller performance gap between cache and DRAM requires new designs for achieving scalable performance and low overhead. We present Cagra, a cache optimized inmemory graph framework. Cagra uses a novel technique, CSR Segmenting, to break the vertices into segments that fit in last level cache, and partitions the graph into subgraphs based on the segments. Random accesses in each subgraph are limited to one segment at a time, eliminating the much slower random accesses to DRAM. The intermediate updates from each subgraph are written into buffers sequentially and later merged using a low overhead parallel cache-aware merge. Cagra achieves speedups of up to 5× for PageRank, Collaborative Filtering, Label Propagation and Betweenness Centrality over the best published results from state-of-the-art graph frameworks, including GraphMat, Ligra and GridGraph.

We report the rational design of coordination‐driven self‐assembly metal–organic nanostructures for multifunctional nanotheranostics. Zinc(II) coordination‐based nano‐formulations capable of loading indocyanine green (ICG) and therapeutic genes were prepared to achieve a fluorescence/photoacoustic imaging‐guided combination photo/gene therapy strategy. We showed the enhanced theranostic capability of zinc(II)‐dipicolylamine‐assisted assembly of ICG, as well as simultaneous targeted gene delivery in an experimental mouse model of cancer. Such a co‐assembly strategy provides a facile way to achieve combined therapeutic functions for personalized nanomedicine.

Although emerging evidence suggests that the pathogenesis of Parkinson’s disease (PD) is closely related to the aggregation of alpha-synuclein (α-syn) in the midbrain, the clearance of α-syn remains an unmet clinical need. Here, we develop a simple and efficient strategy for fabricating the α-syn nanoscavenger for PD via a reprecipitation self-assembly procedure. The curcumin analogue-based nanoscavenger (NanoCA) is engineered to be capable of a controlled-release property to stimulate nuclear translocation of the major autophagy regulator, transcription factor EB (TFEB), triggering both autophagy and calcium-dependent exosome secretion for the clearance of α-syn. Pretreatment of NanoCA protects cell lines and primary neurons from MPP+-induced neurotoxicity. More importantly, a rapid arousal intranasal delivery system (RA-IDDS) was designed and applied for the brain-targeted delivery of NanoCA, which affords robust neuroprotection against behavioral deficits and promotes clearance of monomer, oligomer, and aggregates of α-syn in the midbrain of an MPTP mouse model of PD. Our findings provide a clinically translatable therapeutic strategy aimed at neuroprotection and disease modification in PD.

We previously showed that both the linear photosynthetic electron transportation rate and the respiration rate dropped significantly during N starvation-induced neutral lipid accumulation in an oil-producing microalga, Chlorella sorokiniana, and proposed a possible role for cyclic electron flow (CEF) in ATP supply. In this study, we further exploited this hypothesis in both Chlorella sorokiniana C3 and the model green alga Chlamydomonas. We found that both the rate of CEF around photosystem I and the activity of thylakoid membrane-located ATP synthetase increased significantly during N starvation to drive ATP production. Furthermore, we demonstrated that the Chlamydomonas mutant pgrl1, which is deficient in PGRL1-mediated CEF, accumulated less neutral lipids and had reduced rates of CEF under N starvation. Further analysis revealed that Ca2+ signaling regulates N starvation-induced neutral lipid biosynthesis in Chlamydomonas by increasing calmodulin activity and boosting the expression of the calcium sensor protein that regulates Pgrl1-mediated CEF. Thus, Ca2+-regulated CEF supplies ATP for N starvation-induced lipid biosynthesis in green alga. The increased CEF may re-equilibrate the ATP/NADPH balance and recycle excess light energy in photosystems to prevent photooxidative damage, suggesting Ca2+-regulated CEF also played a key role in protecting and sustaining photosystems.