A Novel Blind Separation Method in Magnetic Resonance Images

A novel global search algorithm based method is proposed to separate MR images blindly in this paper. The key point of the method is the formulation of the new matrix which forms a generalized permutation of the original mixing matrix. Since the lowest entropy is closely associated with the smooth degree of source images, blind image separation can be formulated to an entropy minimization problem by using the property that most of neighbor pixels are smooth. A new dataset can be obtained by multiplying the mixed matrix by the inverse of the new matrix. Thus, the search technique is used to searching for the lowest entropy values of the new data. Accordingly, the separation weight vector associated with the lowest entropy values can be obtained. Compared with the conventional independent component analysis (ICA), the original signals in the proposed algorithm are not required to be independent. Simulation results on MR images are employed to further show the advantages of the proposed method

Expression of an engineered tandem-repeat starch-binding domain in sweet potato plants

In this study, the transgenic sweet potato Xu55-2 modified with an engineered tandem repeat of a family 20 starch binding domain (SBD2) was analyzed by Western dot blot to investigate whether SBD2 proteins are capable of granule-targeting during starch biosynthesis. Furthermore, the impact of SBD2 accumulation in granules on the physicochemical properties of the transgenic starches was also investigated. Our results demonstrate that the high levels of SBD2 protein could be accumulated in granules. The SBD2 expression affect granule morphology without altering the primary structure of the constituent starch molecules, suggesting that SBD2 could be used as an anchor for effector proteins to sweet potato starch granules during biosynthesis.Keywords: Sweet potato (Ipomoea batatas), tandem starch-binding domain, transgenic starch, granule morphology.African Journal of Biotechnology Vol. 12(41), pp. 5994-599

SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior

Novel View Synthesis (NVS) for street scenes play a critical role in the autonomous driving simulation. The current mainstream technique to achieve it is neural rendering, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). Although thrilling progress has been made, when handling street scenes, current methods struggle to maintain rendering quality at the viewpoint that deviates significantly from the training viewpoints. This issue stems from the sparse training views captured by a fixed camera on a moving vehicle. To tackle this problem, we propose a novel approach that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model along with complementary multi-modal data. Specifically, we first fine-tune a Diffusion Model by adding images from adjacent frames as condition, meanwhile exploiting depth data from LiDAR point clouds to supply additional spatial information. Then we apply the Diffusion Model to regularize the 3DGS at unseen views during training. Experimental results validate the effectiveness of our method compared with current state-of-the-art models, and demonstrate its advance in rendering images from broader views

Generic, Efficient and Isochronous Gaussian Sampling over the Integers

Gaussian sampling over the integers is one of the fundamental building blocks of lattice-based cryptography. Among the extensively used trapdoor sampling algorithms, it\u27s ineluctable until now. Under the influence of numerous side-channel attacks, it\u27s still challenging to construct a Gaussian sampler that is generic, efficient, and resistant to timing attacks. In this paper, our contribution is three-fold. First, we propose a secure, efficient exponential Bernoulli sampling algorithm. It can be applied to Gaussian samplers based on rejection samplings. We apply it to FALCON, a candidate of round 3 of the NIST post-quantum cryptography standardization project, and reduce its signature generation time by 13%-14%. Second, we develop an isochronous Gaussian sampler based on rejection sampling. Our Algorithm can securely sample from Gaussian distributions with different standard deviations and arbitrary centers. We apply it to PALISADE (S&P 2018), an open-source lattice cryptography library. During the online phase of trapdoor sampling, the running time of the G-lattice sampling algorithm is reduced by 44.12% while resisting timing attacks. Third, we improve the efficiency of the COSAC sampler (PQC 2020). The new COSAC sampler is 1.46x-1.63x faster than the original and has the lowest expected number of trials among all Gaussian samplers based on rejection samplings. But it needs a more efficient algorithm sampling from the normal distribution to improve its performance

Quick and efficient co-treatment of Zn2+/Ni2+ and CN- via the formation of Ni(CN)4 2- intercalated larger ZnAl-LDH crystals

The wide use of metal electroplating involving CN- necessitates the cost-effective treatment of both CN and metals (Zn, Cu, Ni etc.). In this research, we developed a novel strategy - Ni2+-assisted layered double hydroxide (LDH) precipitation - to simultaneously remove aqueous CN and Zn/Ni metals. The strategy is to convert CN-/Zn(CN)(4)(2-) to Ni(CN)(4)(2-) first, and then to quickly precipitate Ni(CN)(4)(2-)/CN- into LDH crystals. The conversion has been clearly evidenced by the change of CN characteristic FTIR bands of Zn-CN solution before and after adding Ni(NO3)(2). The intercalation and efficient removal of CN have also been confirmed through the formation of LDH crystals XRD and SEM. In particular, a set of optimized experimental factors has been obtained by investigating their effects on CN removal efficiency in the simulated tests. Remarkably, over 95% CN were removed with high removal efficiencies of metals. Our results thus suggest that the current strategy is a quick, efficient and promising way to simultaneously treat both Ni and metals/CN rich electroplating wastewaters. (C) 2014 Elsevier B.V. All rights reserved

Obesity-Related Genetic Variants and Hyperuricemia Risk in Chinese Men

Objective: Obesity/metabolic syndrome and hyperuricemia are clinically associated; however, the association of obesity/metabolic syndrome-related genetic variants with hyperuricemia is not clear. Therefore, we assessed this association in Chinese men diagnosed with hyperuricemia in comparison to a non-hyperuricemia group.Methods: We genotyped 47 single nucleotide polymorphisms (SNPs) previously identified to be associated with obesity or metabolic syndrome in 474 adult males (aged ≥ 18 years) using multiplex polymerase chain reaction. Multivariate logistic regression was used to investigate the association between the genetic variations and hyperuricemia. Stratified analyses were applied to further assess the associations.Results: The obesity-related SNP in MSRA rs545854 significantly affected serum uric acid levels. In addition, the G-allele of rs545854 was positively associated with the risk of hyperuricemia [odds ratio (OR) = 2.80, 95% confidence interval (CI) = 1.19–6.64, P = 0.0188]. After adjusting the model for body mass index and central obesity, rs545854 was shown to be an independent factor increasing the risk of hyperuricemia (OR = 2.81, 95%CI = 1.18–6.70, P = 0.0196). Stratified analyses also showed a significant association between rs545854 and hyperuricemia among meat eaters (OR = 2.62, 95%CI = 1.09–6.26, P = 0.0308).Conclusion: The obesity-related SNP rs545854 was correlated with the serum uric acid level and risk of hyperuricemia in a male Chinese population. Therefore, men carrying this SNP could benefit from limiting their meat consumption to prevent hyperuricemia. These findings suggest an underlying genetic link between obesity and hyperuricemia worthy of further exploration

Decadal soil carbon accumulation across Tibetan permafrost regions

Acknowledgements We thank the members of Peking University Sampling Teams (2001–2004) and IBCAS Sampling Teams (2013–2014) for assistance in field data collection. We also thank the Forestry Bureau of Qinghai Province and the Forestry Bureau of Tibet Autonomous Region for their permission and assistance during the sampling process. This study was financially supported by the National Natural Science Foundation of China (31670482 and 31322011), National Basic Research Program of China on Global Change (2014CB954001 and 2015CB954201), Chinese Academy of Sciences-Peking University Pioneer Cooperation Team, and the Thousand Young Talents Program.Peer reviewedPostprintPostprin