CLOUD COMPUTING BASED TECHNOLOGY INNOVATION

This paper attempts to define the concept of commodity computing. It is essentially about entrepreneurship based on the cloud computing technology. It is about how to build commercialized systems on the top of cloud computing technology (such as Azure) offered by public cloud computing vendors such as Microsoft. Current cloud computing technology has made it practical and financially attractive for an entrepreneurs to develop innovative IT services or products for a third party based on existing cloud computing offerings. A “Cloud Computing Commercialization Model” is proposed in this paper, with aims to build a bridge between the cloud computing technology and the technology-based entrepreneurship. More specifically, this paper explores how to develop customizable enterprise networks with existing technology available in a public cloud; these customizable enterprise networks have the commercial potentials to be delivered to any firm or organizations in many ways similar to a commodity like electricity

Specificity of S9.6 Antibody in the Detection of R-loops

https://openworks.mdanderson.org/sumexp22/1108/thumbnail.jp

High yield expression of an AHL-lactonase from Bacillus sp. B546 in Pichia pastoris and its application to reduce Aeromonas hydrophila mortality in aquaculture

<p>Abstract</p> <p>Background</p> <p><it>Aeromonas hydrophila </it>is a serious pathogen and can cause hemorrhagic septicemia in fish. To control this disease, antibiotics and chemicals are widely used which can consequently result in "superbugs" and chemical accumulation in the food chain. Though vaccine against <it>A. hydrophila </it>is available, its use is limited due to multiple serotypes of this pathogen and problems of safety and efficacy. Another problem with vaccination is the ability to apply it to small fish especially in high numbers. In this study, we tried a new way to attenuate the <it>A. hydrophila </it>infection by using a quorum quenching strategy with a recombinant AHL-lactonase expressed in <it>Pichia pastoris</it>.</p> <p>Results</p> <p>The AHL-lactonase (AiiA_B546) from <it>Bacillus </it>sp. B546 was produced extracellularly in <it>P. pastoris </it>with a yield of 3,558.4 ± 81.3 U/mL in a 3.7-L fermenter when using 3-oxo-C8-HSL as the substrate. After purification with a HiTrap Q Sepharose column, the recombinant homogenous protein showed a band of 33.6 kDa on SDS-PAGE, higher than the calculated molecular mass (28.14 kDa). Deglycosylation of AiiA_B546with Endo H confirmed the occurrence of <it>N</it>-glycosylation. The purified recombinant AiiA_B546showed optimal activity at pH 8.0 and 20°C, exhibited excellent stability at pH 8.0-12.0 and thermal stability at 70°C, was firstly confirmed to be significantly protease-resistant, and had wide substrate specificity. In application test, when co-injected with A. <it>hydrophila </it>in common carp, recombinant AiiA_B546decreased the mortality rate and delayed the mortality time of fish.</p> <p>Conclusions</p> <p>Our results not only indicate the possibility of mass-production of AHL-lactonase at low cost, but also open up a promising foreground of application of AHL-lactonase in fish to control <it>A. hydrophila </it>disease by regulating its virulence. To our knowledge, this is the first report on heterologous expression of AHL-lactonase in <it>P. pastoris </it>and attenuating <it>A. hydrophila </it>virulence by co-injection with AHL-lactonase.</p

Tuning the correlated color temperature of white LED with a guest-host liquid crystal

We demonstrate an electro-optic method to tune the correlated color temperature (CCT) of white light-emitting-diode (WLED) with a color conversion film, consisting of fluorescent dichroic dye doped in a liquid crystal host. By controlling the molecular reorientation of dichroic dyes, the power ratio of the transmitted blue and red lights of the white light can be accurately manipulated, resulting in different CCT. In a proof-of-concept experiment, we showed that the CCT of a yellow phosphor-converted WLED can be tuned from 3200 K to 4100 K. With further optimizations, the tuning range could be enlarged to 2500 K with fairly good color performance: luminous efficacy of radiation (LER) \u3e 300 lm/W, color rendering index (CRI) \u3e 75, and Duv \u3c 0.005. Besides, the operation voltage is lower than 5 V and good angular color uniformity is achieved with remote-phosphor coating. This approach is promising for next generation smart lighting

Tera-sample-per-second arbitrary waveform generation in the synthetic dimension

The synthetic dimension opens new horizons in quantum physics and topological photonics by enabling new dimensions for field and particle manipulations. The most appealing property of the photonic synthetic dimension is its ability to emulate high-dimensional optical behavior in a unitary physical system. Here we show that the photonic synthetic dimension can transform technical problems in photonic systems between dimensionalities, providing unexpected solutions to technical problems that are otherwise challenging. Specifically, we propose and experimentally demonstrate a photonic Galton board (PGB) in the temporal synthetic dimension, in which the temporal high-speed challenge is converted into a spatial fiber-optic length matching problem, leading to the experimental generation of tera-sample-per-second arbitrary waveforms. Limited by the speed of the measurement equipment, waveforms with sampling rates of up to 341.53 GSa/s are recorded. Our proposed PGB operating in the temporal synthetic dimension breaks the speed limit in a physical system, bringing arbitrary waveform generation into the terahertz regime. The concept of dimension conversion offers possible solutions to various physical dimension-related problems, such as super-resolution imaging, high-resolution spectroscopy, time measurement, etc

An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO) algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR)

Point-PC: Point Cloud Completion Guided by Prior Knowledge via Causal Inference

Point cloud completion aims to recover raw point clouds captured by scanners from partial observations caused by occlusion and limited view angles. Many approaches utilize a partial-complete paradigm in which missing parts are directly predicted by a global feature learned from partial inputs. This makes it hard to recover details because the global feature is unlikely to capture the full details of all missing parts. In this paper, we propose a novel approach to point cloud completion called Point-PC, which uses a memory network to retrieve shape priors and designs an effective causal inference model to choose missing shape information as additional geometric information to aid point cloud completion. Specifically, we propose a memory operating mechanism where the complete shape features and the corresponding shapes are stored in the form of ``key-value'' pairs. To retrieve similar shapes from the partial input, we also apply a contrastive learning-based pre-training scheme to transfer features of incomplete shapes into the domain of complete shape features. Moreover, we use backdoor adjustment to get rid of the confounder, which is a part of the shape prior that has the same semantic structure as the partial input. Experimental results on the ShapeNet-55, PCN, and KITTI datasets demonstrate that Point-PC performs favorably against the state-of-the-art methods

Boosting freshwater fish conservation with high-resolution distribution mapping across a large territory

The lack of high-resolution distribution maps for freshwater species across large extents fundamentally challenges biodiversity conservation worldwide. We devised a simple framework to delineate the distributions of freshwater fishes in a high-resolution drainage map based on stacked species distribution models and expert information. We applied this framework to the entire Chinese freshwater fish fauna (>1600 species) to examine high-resolution biodiversity patterns and reveal potential conflicts between freshwater biodiversity and anthropogenic disturbances. The correlations between spatial patterns of biodiversity facets (species richness, endemicity, and phylogenetic diversity) were all significant (r = 0.43–0.98, p < 0.001). Areas with high values of different biodiversity facets overlapped with anthropogenic disturbances. Existing protected areas (PAs), covering 22% of China's territory, protected 25–29% of fish habitats, 16–23% of species, and 30–31% of priority conservation areas. Moreover, 6–21% of the species were completely unprotected. These results suggest the need for extending the network of PAs to ensure the conservation of China's freshwater fishes and the goods and services they provide. Specifically, middle to low reaches of large rivers and their associated lakes from northeast to southwest China hosted the most diverse species assemblages and thus should be the target of future expansions of the network of PAs. More generally, our framework, which can be used to draw high-resolution freshwater biodiversity maps combining species occurrence data and expert knowledge on species distribution, provides an efficient way to design PAs regardless of the ecosystem, taxonomic group, or region considered.Strategic Priority Research Program of Chinese Academy of Sciences XDB31000000Second Tibetan PlateauScientific Expedition Program 2019QZKK0304, 2019QZKK05010102National Key Research and Devel-opment Program of China 2021YFC3200300103National Natural Science Foundation of China 32070436, 4207744

An Effective Conversation-Based Botnet Detection Method

A botnet is one of the most grievous threats to network security since it can evolve into many attacks, such as Denial-of-Service (DoS), spam, and phishing. However, current detection methods are inefficient to identify unknown botnet. The high-speed network environment makes botnet detection more difficult. To solve these problems, we improve the progress of packet processing technologies such as New Application Programming Interface (NAPI) and zero copy and propose an efficient quasi-real-time intrusion detection system. Our work detects botnet using supervised machine learning approach under the high-speed network environment. Our contributions are summarized as follows: (1) Build a detection framework using PF_RING for sniffing and processing network traces to extract flow features dynamically. (2) Use random forest model to extract promising conversation features. (3) Analyze the performance of different classification algorithms. The proposed method is demonstrated by well-known CTU13 dataset and nonmalicious applications. The experimental results show our conversation-based detection approach can identify botnet with higher accuracy and lower false positive rate than flow-based approach

Targeted disruption of MCPIP1/Zc3h12a results in fatal inflammatory disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141347/1/imcb201311.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141347/2/imcb201311-sup-0001.pd