Faster Bootstrapping with Multiple Addends

As an important cryptographic primitive in cloud computing and outsourced computation, fully homomorphic encryption (FHE) is an animated area of modern cryptography. However, the efficiency of FHE has been a bottleneck that impeding its application. According to Gentry’s blueprint, bootstrapping, which is used to decrease ciphertext errors, is the most important process in FHE. However, bootstrapping is also the most expensive process that affecting the efficiency of the whole system. Firstly, we notice that, hundreds of serial homomorphic additions take most of the time of bootstrapping. We made use of the properties of Boolean circuit to reduce the number of serial homomorphic additions by two-thirds, and thus constructed an efficient FHE scheme with bootstrapping in 10ms. Secondly, the most expensive parts in our bootstrapping, EHCM and addition operations of multiple matrices, can be accelerated by parallel. This parallel may accelerate the bootstrapping. At last, we found a set of more efficient combination of parameters. As a result, our security parameter level is 128 bits and the correctness is elevated, compared with TFHE scheme in ASIACRYPT 2016. Experiments show that the running time of our bootstrapping is 10ms, which is only 52 percent of TFHE, and is less than CGGI17

Liposome-Based Delivery Systems in Plant Polysaccharides

Plant polysaccharides consist of many monosaccharide by α- or β-glycosidic bond which can be extracted by the water, alcohol, lipophile liquid from a variety of plants including Cordyceps sinensis, astragalus, and mushrooms. Recently, many evidences illustrate that natural plant polysaccharides possess various biological activities including strengthening immunity, lowering blood sugar, regulating lipid metabolism, antioxidation, antiaging, and antitumour. Plant polysaccharides have been widely used in the medical field due to their special features and low toxicity. As an important drug delivery system, liposomes can not only encapsulate small-molecule compound but also big-molecule drug; therefore, they present great promise for the application of plant polysaccharides with unique physical and chemical properties and make remarkable successes. This paper summarized the current progress in plant polysaccharides liposomes, gave an overview on their experiment design method, preparation, and formulation, characterization and quality control, as well as in vivo and in vitro studies. Moreover, the potential application of plant polysaccharides liposomes was prospected as well

Extensive beam test study of prototype MRPCs for the T0 detector at the CSR external-target experiment

The CSR External-target Experiment (CEE) will be the first large-scale nuclear physics experiment device at the Cooling Storage Ring (CSR) of the Heavy-Ion Research Facility in Lanzhou (HIRFL) in China. A new T0 detector has been proposed to measure the multiplicity, angular distribution and timing information of charged particles produced in heavy-ion collisions at the target region. Multi-gap resistive plate chamber (MRPC) technology was chosen as part of the construction of the T0 detector, which provides precision event collision times (T0) and collision geometry information. The prototype was tested with hadron and heavy-ion beams to study its performance. By comparing the experimental results with a Monte Carlo simulation, the time resolution of the MRPCs are found to be $\sim$ 50 ps or better. The timing performance of the T0 detector, including both detector and readout electronics, we found to fulfil the requirements of the CEE.Comment: 12 pages, 36 figure

Comparison of Three Molecular Simulation Approaches for Cyclodextrin-Ibuprofen Complexation

Cyclodextrins are widely used for the solubilisation of poorly soluble drugs in the formulations. However, current cyclodextrin formulation development strongly depends on trial-and-error in the laboratory, which is time-consuming and high cost. The aim of this research was to compare three modeling approaches (Docking, molecular dynamics (MD), and quantum mechanics (QM)) for cyclodextrin/drug complexation. Ibuprofen was used as a model drug. Binding free energy from three simulation methods was calculated as an important parameter to compare with the experimental results. Docking results from AutoDock Vina program showed that the scoring of complexation capability between ibuprofen and cyclodextrins is alpha (α), gamma (γ), beta (β), and HP-beta-cyclodextrins, which indicated similar ranking with the results from phase, solubility diagram experiments. MD simulation indicated that ibuprofen could form the stable complexes with β-, γ-, and HP-β-cyclodextrins, but not for alpha cyclodextrin. Binding free energies from the MD simulation for β-, γ-, and HP-β-cyclodextrins were −3.67, −0.67, and −3.87 kcal/mol, individually. The enthalpies of QM simulation for β-, γ-, and HP-β-cyclodextrins were −17.22, −14.75, and −20.28 kcal/mol, respectively. Results from all three modeling approaches showed similar ranking between ibuprofen and four cyclodextrin molecules as the experimental data. However, MD simulation with entropy calculation had the closest value to experimental data for β and HP-beta-cyclodextrins. Thus, MD simulation with MM-PBSA method may be fit to in silico screen for cyclodextrin formulations

Prioritization of feasible physiological parameters in drought tolerance evaluation in sorghum: a grey relational analysis

Abstract Identification and evaluation of drought tolerant germplasm is the primary step for sorghum (Sorghum bicolor L. Moench) breeding and utilization under drought conditions. The objective of this study was to use a grey relational analysis to investigate the role of feasible physiological parameters in evaluating drought tolerance in sorghum. Four sorghum varieties were cultivated in pots with two water treatments, including normal watering (75-80% of the soil moisture capacity) and water deficit (45-50% of the soil moisture capacity), which occurred at jointing stage, anthesis and filling stage, respectively. Drought tolerance index of yield was used as the key indicator to evaluate sorghum performance under drought. The grey relational degree of the investigated parameters decreased in the order of transpiration rate, stomatal conductance, photosynthetic rate, soluble sugar content, proline content, relative water content, activity of catalase, activity of superoxide dismutase and activity of peroxidase, implying that drought tolerance for guaranteeing sorghum yield formation was the most related to gas exchange parameters. Water content was a very sensitive parameter of plant growth under drought stress and was more important as compared to the activities of antioxidant enzymes. Results of this research suggested that feasible physiological parameters could be used in the evaluation of drought tolerance to improve the efficiency and accuracy of selection

Lemur: Harmonizing Natural Language and Code for Language Agents

We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents. The evolution from language chat models to functional language agents demands that models not only master human interaction, reasoning, and planning but also ensure grounding in the relevant environments. This calls for a harmonious blend of language and coding capabilities in the models. Lemur and Lemur-Chat are proposed to address this necessity, demonstrating balanced proficiencies in both domains, unlike existing open-source models that tend to specialize in either. Through meticulous pre-training using a code-intensive corpus and instruction fine-tuning on text and code data, our models achieve state-of-the-art averaged performance across diverse text and coding benchmarks among open-source models. Comprehensive experiments demonstrate Lemur's superiority over existing open-source models and its proficiency across various agent tasks involving human communication, tool usage, and interaction under fully- and partially- observable environments. The harmonization between natural and programming languages enables Lemur-Chat to significantly narrow the gap with proprietary models on agent abilities, providing key insights into developing advanced open-source agents adept at reasoning, planning, and operating seamlessly across environments. https://github.com/OpenLemur/Lemu

A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks

Online optimal energy management of plug-in hybrid electric vehicles has been continually investigated for better fuel economy. This paper proposed a predictive energy management strategy based on multi neural networks for a multi-mode plug-in hybrid electric vehicle. To attain it, firstly, the offline optimal results prepared for knowledge learning are derived by dynamic programming and Pontryagin’s minimum principle. Then, the mode recognition neural network is trained based on the optimal results of dynamic programming and the recurrent neural network is firstly exploited to realize online co-state estimation application. Consequently, the velocity prediction-based online model predictive control framework is established with the co-state correction and slacked constraints to solve the real-time optimal control sequence. A series of numerical simulation results validate that the optimal performance yielded from global optimal strategy can be exploited online to attain the satisfied cost reduction, compared with equivalent consumption minimum strategy, with the assistance of estimated real time co-state and slacked reference. In addition, the computation duration of proposed algorithm decreases by 23.40%, compared with conventional Pontryagin’s minimum principle-based model predictive control scheme, thereby proving its online application potential

Lack of Association of Two Common Polymorphisms rs2910164 and rs11614913 with Susceptibility to Hepatocellular Carcinoma: A Meta-Analysis

BACKGROUND: Single nucleotide polymorphisms (SNPs) in microRNA-coding genes may participate in the process of carcinogenesis by altering the expression of tumor-related microRNAs. It has been suggested that two common SNPs rs2910164 in miR-146a and rs11614913 in miR-196a2 are associated with susceptibility to hepatocellular carcinoma (HCC). However, published results are inconsistent and inconclusive. In the present study, we performed a meta-analysis to systematically summarize the possible association between the two SNPs and the risk for HCC. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a search of case-control studies on the associations of SNPs rs2910164 and/or rs11614913 with susceptibility to HCC in PubMed, EMBASE, ISI Web of Science, Cochrane Central Register of Controlled Trials, ScienceDirect, Wiley Online Library and Chinese National Knowledge Infrastructure databases. Data from eligible studies were extracted for meta-analysis. HCC risk associated with the two polymorphisms was estimated by pooled odds ratios (ORs) and 95% confidence intervals (95% CIs). 5 studies on rs2910164 and 4 studies on rs11614913 were included in our meta-analysis. Our results showed that neither allele frequency nor genotype distribution of the two polymorphisms was associated with risk for HCC in all genetic models. Similarly, subgroup analysis in Chinese population showed no association between the two SNPs and the susceptibility to HCC. CONCLUSIONS/SIGNIFICANCE: This meta-analysis suggests that two common SNPs rs2910164 and rs11614913 are not associated with the risk of HCC. Well-designed studies with larger sample size and more ethnic groups are required to further validate the results