Orthogonalized Lattice Enumeration for Solving SVP

In 2014, the orthogonalized integer representation was proposed independently by Ding et al. using genetic algorithm and Fukase et al. using sampling technique to solve SVP. Their results are promising. In this paper, we consider sparse orthogonalized integer representations for shortest vectors and propose a new enumeration method, called orthognalized enumeration, by integrating such a representation. Furthermore, we present a mixed BKZ method, called MBKZ, by alternately applying orthognalized enumeration and other existing enumeration methods. Compared to the existing ones, our methods have greater efficiency and achieve exponential speedups both in theory and in practice for solving SVP. Implementations of our algorithms have been tested to be effective in solving challenging lattice problems. We also develop some new technique to reduce enumeration space which has been demonstrated to be efficient experimentally, though a quantitative analysis about its success probability is not available

An inverse dynamics method for railway vehicle systems

The wheel–rail action will obviously be increased during the vehicles in high-speed operation state. However, in many practical cases, direct measurement of the wheel–rail contact forces cannot be performed with traditional procedures and transducers. An inverse mathematical dynamic model for the estimation of wheel–rail contact forces from measured accelerations was developed. The inverse model is a non-iteration recurrence method to identify the time history of input excitation based on the dynamic programming equation. Furthermore, the method overcomes the weakness of large fluctuations which exist in current inverse techniques. Based on the inverse dynamic model, a high-speed vehicle multibody model with twenty-seven Degree of Freedoms (DOFs) is established. With the measured responses as input, the inverse vehicle model can not only identify the responses in other parts of vehicle, but also identify the vertical and lateral wheel–rail forces respectively. Results from the inverse model were compared with experiment data. In a more complex operating condition, the inverse model was also compared with results from simulations calculated by SIMPACK. First published online: 22 May 201

Effects of plastic debris on the biofilm bacterial communities in lake water

Increasing discharge of plastic debris into aquatic ecosystems and the worsening ecological risks have received growing attention. Once released, plastic debris could serve as a new substrate for microbes in waters. The complex relationship between plastics and biofilms has aroused great interest. To confirm the hypothesis that the presence of plastic in water affects the composition of biofilm in natural state, in situ biofilm culture experiments were conducted in a lake for 40 days. The diversity of biofilm attached on natural (cobble stones (CS) and wood) and plastic substrates (Polyethylene terephthalate (PET) and Polymethyl methacrylate (PMMA)) were compared, and the community structure and composition were also analyzed. Results from high-throughput sequencing of 16S rRNA showed that the diversity and species richness of biofilm bacterial communities on natural substrate (observed species of 1353~1945, Simpson index of 0.977~0.989 and Shannon–Wiener diversity index of 7.42~8.60) were much higher than those on plastic substrates (observed species of 900~1146, Simpson index of 0.914~0.975 and Shannon–Wiener diversity index of 5.47~6.99). The NMDS analyses were used to confirm the taxonomic significance between different samples, and Anosim (p = 0.001, R = 0.892) and Adonis (p = 0.001, R = 808, F = 11.19) demonstrated that this classification was statistically rigorous. Different dominant bacterial communities were found on plastic and natural substrates. Alphaproteobacterial, Betaproteobacteria and Synechococcophycideae dominated on the plastic substrate, while Gammaproteobacteria, Phycisphaerae and Planctomycetia played the main role on the natural substrates. The bacterial community structure of the two substrates also showed significant difference which is consistent with previous studies using other polymer types. Our results shed light on the fact that plastic debris can serve as a new habitat for biofilm colonization, unlike natural substrates, pathogens and plastic-degrading microorganisms selectively attached to plastic substrates, which affected the bacterial community structure and composition in aquatic environment. This study provided a new insight into understanding the potential impacts of plastics serving as a new habitat for microbial communities in freshwater environments. Future research should focus on the potential impacts of plastic-attached biofilms in various aquatic environments and the whole life cycle of plastics (i.e., from plastic fragments to microplastics) and also microbial flock characteristics using microbial plastics in the natural environment should also be addressed

Molecular epidemiology and antimicrobial resistance patterns of carbapenem-resistant Acinetobacter baumannii isolates from patients admitted at ICUs of a teaching hospital in Zunyi, China

BackgroundCarbapenem-resistant Acinetobacter baumannii (CRAB) has emerged as a predominant strain of healthcare-associated infections worldwide, particularly in intensive care units (ICUs). Therefore, it is imperative to study the molecular epidemiology of CRAB in the ICUs using multiple molecular typing methods to lay the foundation for the development of infection prevention and control strategies. This study aimed to determine the antimicrobial susceptibility profile, the molecular epidemiology and conduct homology analysis on CRAB strains isolated from ICUs.MethodsThe sensitivity to various antimicrobials was determined using the minimum inhibitory concentration (MIC) method, Kirby-Bauer disk diffusion (KBDD), and E-test assays. Resistance genes were identified by polymerase chain reaction (PCR). Molecular typing was performed using multilocus sequence typing (MLST) and multiple-locus variable-number tandem repeat analysis (MLVA).ResultsAmong the 79 isolates collected, they exhibited high resistance to various antimicrobials but showed low resistance to levofloxacin, trimethoprim-sulfamethoxazole, and tetracyclines. Notably, all isolates of A. baumannii were identified as multidrug-resistant A. baumannii (MDR-AB). The blaOXA-51-like, adeJ, and adeG genes were all detected, while the detection rates of blaOXA-23-like (97.5%), adeB (93.67%), blaADC (93.67%), qacEΔ1-sul1 (84.81%) were higher; most of the Ambler class A and class B genes were not detected. MLST analysis on the 79 isolates identified five sequence types (STs), which belonged to group 3 clonal complexes 369. ST1145Ox was the most frequently observed ST with a count of 56 out of 79 isolates (70.89%). MLST analysis for non-sensitive tigecycline isolates, which were revealed ST1145Ox and ST1417Ox as well. By using the MLVA assay, the 79 isolates could be grouped into a total of 64 distinct MTs with eleven clusters identified in them. Minimum spanning tree analysis defined seven different MLVA complexes (MCs) labeled MC1 to MC6 along with twenty singletons. The locus MLVA-AB_2396 demonstrated the highest Simpson’s diversity index value at 0.829 among all loci tested in this study while also having one of the highest variety of tandem repeat species.ConclusionThe molecular diversity and clonal affinities within the genomes of the CRAB strains were clearly evident, with the identification of ST1144Ox, ST1658Ox, and ST1646Oxqaq representing novel findings

Genome sequencing and analysis of the paclitaxelproducing endophytic fungus \u3cem\u3ePenicillium aurantiogriseum\u3c/em\u3e NRRL 62431

Background Paclitaxel (Taxol™) is an important anticancer drug with a unique mode of action. The biosynthesis of paclitaxel had been considered restricted to the Taxus species until it was discovered in Taxomyces andreanae, an endophytic fungus of T. brevifolia. Subsequently, paclitaxel was found in hazel (Corylus avellana L.) and in several other endophytic fungi. The distribution of paclitaxel in plants and endophytic fungi and the reported sequence homology of key genes in paclitaxel biosynthesis between plant and fungi species raises the question about whether the origin of this pathway in these two physically associated groups could have been facilitated by horizontal gene transfer. Results The ability of the endophytic fungus of hazel Penicillium aurantiogriseum NRRL 62431 to independently synthesize paclitaxel was established by liquid chromatography-mass spectrometry and proton nuclear magnetic resonance. The genome of Penicillium aurantiogriseum NRRL 62431 was sequenced and gene candidates that may be involved in paclitaxel biosynthesis were identified by comparison with the 13 known paclitaxel biosynthetic genes in Taxus. We found that paclitaxel biosynthetic gene candidates in P. aurantiogriseum NRRL 62431 have evolved independently and that horizontal gene transfer between this endophytic fungus and its plant host is unlikely. Conclusions Our findings shed new light on how paclitaxel-producing endophytic fungi synthesize paclitaxel, and will facilitate metabolic engineering for the industrial production of paclitaxel from fungi

The diploid genome sequence of an Asian individual

Here we present the first diploid genome sequence of an Asian individual. The genome was sequenced to 36-fold average coverage using massively parallel sequencing technology. We aligned the short reads onto the NCBI human reference genome to 99.97% coverage, and guided by the reference genome, we used uniquely mapped reads to assemble a high-quality consensus sequence for 92% of the Asian individual's genome. We identified approximately 3 million single-nucleotide polymorphisms (SNPs) inside this region, of which 13.6% were not in the dbSNP database. Genotyping analysis showed that SNP identification had high accuracy and consistency, indicating the high sequence quality of this assembly. We also carried out heterozygote phasing and haplotype prediction against HapMap CHB and JPT haplotypes (Chinese and Japanese, respectively), sequence comparison with the two available individual genomes (J. D. Watson and J. C. Venter), and structural variation identification. These variations were considered for their potential biological impact. Our sequence data and analyses demonstrate the potential usefulness of next-generation sequencing technologies for personal genomics

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis

ObjectivesThe abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms.MethodsResting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test.ResultsWe found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus.ConclusionThe decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR.Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR-DCD-15006096

Whole exome sequencing identifies frequent somatic mutations in cell-cell adhesion genes in chinese patients with lung squamous cell carcinoma

Lung squamous cell carcinoma (SQCC) accounts for about 30% of all lung cancer cases. Understanding of mutational landscape for this subtype of lung cancer in Chinese patients is currently limited. We performed whole exome sequencing in samples from 100 patients with lung SQCCs to search for somatic mutations and the subsequent target capture sequencing in another 98 samples for validation. We identified 20 significantly mutated genes, including TP53, CDH10, NFE2L2 and PTEN. Pathways with frequently mutated genes included those of cell-cell adhesion/Wnt/Hippo in 76%, oxidative stress response in 21%, and phosphatidylinositol-3-OH kinase in 36% of the tested tumor samples. Mutations of Chromatin regulatory factor genes were identified at a lower frequency. In functional assays, we observed that knockdown of CDH10 promoted cell proliferation, soft-agar colony formation, cell migration and cell invasion, and overexpression of CDH10 inhibited cell proliferation. This mutational landscape of lung SQCC in Chinese patients improves our current understanding of lung carcinogenesis, early diagnosis and personalized therapy

A Security Risk Assessment Method Based on Improved FTA-IAHP for Train Position System

The positioning system based on satellite navigation can meet the requirements of CTCS-4 train control, improve the transportation efficiency, reduce the operation and maintenance costs, which is the trend of train positioning system in the future, and the security risk assessment is of great significance to the future application of this system. In this paper, combined with the self-developed train positioning system based on satellite navigation, and an improved fault tree-interval analytic hierarchy process (FTA-IAHP) method for evaluating the safety risk of train positioning system is proposed. Firstly, a security risk assessment model based on FTA-IAHP is established by combining FTA and IAHP. Secondly, two judgment matrices are constructed by using the basic events and structural importance based on FTA, and the IAHP model based on expert scoring, the difference between FTA and IAHP is adjusted by combining the weighting factor. The new method of trial of weighting can determine the degree of each factor in the system fault. This method has great significance to the safety design and protection of the new train positioning system based on satellite navigation