Monotone iteration scheme and its application to partial differential equation systems with mixed nonlocal and degenerate diffusions

A monotone iteration scheme for traveling waves based on ordered upper and lower solutions is derived for a class of nonlocal dispersal system with delay. Such system can be used to study the competition among nonlocally diffusive species and degenerately diffusive species. An example of such system is studied in detail. We show the existence of the traveling wave solutions for this system by this iteration scheme. In addition, we study the minimal wave speed, uniqueness, strict monotonicity and asymptotic behavior of the traveling wave solutions

Further Study on <i>C</i>-Eigenvalue Inclusion Intervals for Piezoelectric Tensors

The C-eigenpair of piezoelectric tensors finds applications in the area of the piezoelectric effect and converse piezoelectric effect. In this paper, we provide some characterizations of C-eigenvectors by exploring the structure of piezoelectric tensors, and establish sharp C-eigenvalue inclusion intervals via Cauchy–Schwartz inequality. Further, we propose the lower and upper bounds of the largest C-eigenvalue and evaluate the efficiency of the best rank-one approximation of piezoelectric tensors. Numerical examples are proposed to verify the efficiency of the obtained results

Further Study on C-Eigenvalue Inclusion Intervals for Piezoelectric Tensors

The C-eigenpair of piezoelectric tensors finds applications in the area of the piezoelectric effect and converse piezoelectric effect. In this paper, we provide some characterizations of C-eigenvectors by exploring the structure of piezoelectric tensors, and establish sharp C-eigenvalue inclusion intervals via Cauchy&ndash;Schwartz inequality. Further, we propose the lower and upper bounds of the largest C-eigenvalue and evaluate the efficiency of the best rank-one approximation of piezoelectric tensors. Numerical examples are proposed to verify the efficiency of the obtained results

High‐production dairy cattle exhibit different rumen and fecal bacterial community and rumen metabolite profile than low‐production cattle

Abstract Our aim was to simultaneously investigate the gut bacteria typical characteristic and conduct rumen metabolites profiling of high production dairy cows when compared to low‐production dairy cows. The bacterial differences in rumen fluid and feces were identified by 16S rDNA gene sequencing. The metabolite differences were identified by metabolomics profiling with liquid chromatography mass spectrometry (LC‐MS). The results indicated that the high‐production dairy cows presented a lower rumen bacterial richness and species evenness when compared to low‐production dairy cows. At the phylum level, the high‐production cows increased the abundance of Proteobacteria and decreased the abundance of Bacteroidetes, SR1, Verrucomicrobia, Euryarchaeota, Planctomycetes, Synergistetes, and Chloroflexi significantly (p < 0.05). At the genus level, the rumen fluid of the high‐production group was significantly enriched for Butyrivibrio, Lachnospira, and Dialister (p < 0.05). Meanwhile, rumen fluid of high‐production group was depleted for Prevotella, Succiniclasticum, Ruminococcu, Coprococcus,YRC22, CF231, 02d06, Anaeroplasma, Selenomonas, and Ruminobacter significantly (p < 0.05). A total of 92 discriminant metabolites were identified between high‐production cows and low‐production cows. Compared to rumen fluid of low‐production dairy cows, 10 differential metabolites were found up‐regulated in rumen fluid of high‐production dairy cows, including 6alpha‐Fluoropregn‐4‐ene‐3,20‐dione, 3‐Octaprenyl‐4‐hydroxybenzoate, disopyramide, compound III(S), 1,2‐Dimyristyl‐sn‐glycerol, 7,10,13,16‐Docosatetraenoic acid, ferrous lactate, 6‐Deoxyerythronolide B, vitamin D2, L‐Olivosyl‐oleandolide. The remaining differential metabolites were found down‐regulated obviously in high‐production cows. Metabolic pathway analyses indicated that most increased abundances of rumen fluid metabolites of high‐yield cows were related to metabolic pathways involving biosynthesis of unsaturated fatty acids, steroid biosynthesis, ubiquinone and other terpenoid‐quinone biosynthesis. Most down‐regulated metabolic pathways were relevant to nucleotide metabolism, energy metabolism, lipid metabolism and biosynthesis of some antibiotics

Gender-Specific Health-Adjusted Life Expectancy of Type 2 Diabetes Mellitus Among the Rural Elderly Population

Objectives: This study aimed to estimate the life expectancy (LE) and health-adjusted life expectancy (HALE) of type 2 diabetes mellitus (T2DM) among the rural elderly population.Methods: A total of 10,318 participants aged 65 to 79 were derived from the Henan Rural Cohort. The LE and HALE were calculated via the Sullivan method and multistate life table.Results: Among 10,318 subjects, 1,325 suffered from T2DM at the baseline, and 394 participants had newly-developed T2DM. The results from the Sullivan method showed that the LE, HALE, and HALE/LE were 17.98, 16.18 years, and 89.95% for men aged 65 to 69, and the corresponding estimates for women were 21.81, 18.73 years, and 85.86%, respectively. The LE, HALE and HALE/LE calculated via multistate life table were 19.86, 17.53 years, and 88.29% for men at aged 65, and the corresponding values for women were 25.01, 20.87 years, and 83.44%, respectively.Conclusion: Rural elderly women have a longer LE and HALE of T2DM, but they have lower quality of life than men. More attention should be paid to T2DM among rural elderly people, especially in women.Clinical Trial Registration: The Henan Rural Cohort Study has been registered at Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). Date of registration: 06 July 2015. http://www.chictr.org.cn/showproj.aspx?proj=11375

1D CNN-Based Intracranial Aneurysms Detection in 3D TOF-MRA

How to automatically detect intracranial aneurysms from Three-Dimension Time of Flight Magnetic Resonance Angiography (3D TOF MRA) images is a typical 3D image classification problem. Currently, the commonly used method is the Maximum Intensity Projection- (MIP-) based way. It transfers 3D classification into 2D case by projecting the 3D patch into 2D planes along different directions on the basis of voxel’s intensity. After then, the 2D Convolutional Neural Network (CNN) is established to do classification. It has been shown that the MIP-based method can reduce the demands for the samples and increase the computation efficiency. Meanwhile, the accuracy is comparable with that of 3D image classification. Inspired by the strategy of MIP, we want to further reduce the demands for samples and accelerate the training by transferring the 2D image classification into 1D case, i.e., we want to generate the 1D vectors from the MIP images and then establish a 1D CNN to do intracranial aneurysm detection and classification for 3D TOF MRA image. Specifically, our method first extracts a series of patches as the Region of Interests (ROIs) along the blood vessels from the original 3D TOF MRA 3D image. The corresponding MIP images of each ROI will be obtained through maximum intensity projecting. Then, we generate a series of 1D vectors by accumulating each MIP image along different directions. Meanwhile, a 1D CNN is established to detect aneurysms, in which, the input is the obtained 1D vectors and the output is the binary classification result denoting whether there are intracranial aneurysms in the considered patch. Generally, compared with 2D- and 3D-CNN, the 1D CNN-based way greatly accelerates the training and shows stronger robustness in the case of fewer samples. The efficiency of the proposed method outperforms the 2D CNN about 10 times in CPU training. Yet, their accuracies are close