Application of deep learning-based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time

OBJECTIVES To compare the image quality and diagnostic performance of conventional motion-corrected periodically rotated overlapping parallel line with enhanced reconstruction (PROPELLER) MRI sequences with post-processed PROPELLER MRI sequences using deep learning-based (DL) reconstructions. METHODS In this prospective study of 30 patients, conventional (19 min 18 s) and accelerated MRI sequences (7 min 16 s) using the PROPELLER technique were acquired. Accelerated sequences were post-processed using DL. The image quality and diagnostic confidence were qualitatively assessed by 2 readers using a 5-point Likert scale. Analysis of the pathological findings of cartilage, rotator cuff tendons and muscles, glenoid labrum and subacromial bursa was performed. Inter-reader agreement was calculated using Cohen's kappa statistic. Quantitative evaluation of image quality was measured using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). RESULTS Mean image quality and diagnostic confidence in evaluation of all shoulder structures were higher in DL sequences (p value = 0.01). Inter-reader agreement ranged between kappa values of 0.155 (assessment of the bursa) and 0.947 (assessment of the rotator cuff muscles). In 17 cases, thickening of the subacromial bursa of more than 2 mm was only visible in DL sequences. The pathologies of the other structures could be properly evaluated by conventional and DL sequences. Mean SNR (p value = 0.01) and CNR (p value = 0.02) were significantly higher for DL sequences. CONCLUSIONS The accelerated PROPELLER sequences with DL post-processing showed superior image quality and higher diagnostic confidence compared to the conventional PROPELLER sequences. Subacromial bursa can be thoroughly assessed in DL sequences, while the other structures of the shoulder joint can be assessed in conventional and DL sequences with a good agreement between sequences. KEY POINTS • MRI of the shoulder requires long scan times and can be hampered by motion artifacts. • Deep learning-based convolutional neural networks are used to reduce image noise and scan time while maintaining optimal image quality. The radial k-space acquisition technique (PROPELLER) can reduce the scan time and has potential to reduce motion artifacts. • DL sequences show a higher diagnostic confidence than conventional sequences and therefore are preferred for assessment of the subacromial bursa, while conventional and DL sequences show comparable performance in the evaluation of the shoulder joint

Data-Driven-Based Approach to Identifying Differentially Methylated Regions Using Modified 1D Ising Model

Background. DNA methylation is essential for regulating gene expression, and the changes of DNA methylation status are commonly discovered in disease. Therefore, identification of differentially methylation patterns, especially differentially methylated regions (DMRs), in two different groups is important for understanding the mechanism of complex diseases. Few tools exist for DMR identification through considering features of methylation data, but there is no comprehensive integration of the characteristics of DNA methylation data in current methods. Results. Accounting for the characteristics of methylation data, such as the correlation characteristics of neighboring CpG sites and the high heterogeneity of DNA methylation data, we propose a data-driven approach for DMR identification through evaluating the energy of single site using modified 1D Ising model. Applied to both simulated and publicly available datasets, our approach is compared with other popular methods in terms of performance. Simulated results show that our method is more sensitive than competing methods. Applied to the real data, our method can identify more common DMRs than DMRcate, ProbeLasso, and Wang’s methods with a high overlapping ratio. Also, the necessity of integrating the heterogeneity and correlation characteristics in identifying DMR is shown through comparing results with only considering mean or variance signals and without considering relationship of neighboring CpG sites, respectively. Through analyzing the number of DMRs identified in real data located in different genomic regions, we find that about 90% DMRs are located in CGI which always regulates the expression of genes. It may help us understand the functional effect of DNA methylation on disease

Analysis and Numerical Simulations of a Stochastic SEIQR Epidemic System with Quarantine-Adjusted Incidence and Imperfect Vaccination

This paper considers a high-dimensional stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with quarantine-adjusted incidence and the imperfect vaccination. The main aim of this study is to investigate stochastic effects on the SEIQR epidemic model and obtain its thresholds. We first obtain the sufficient condition for extinction of the disease of the stochastic system. Then, by using the theory of Hasminskii and the Lyapunov analysis methods, we show there is a unique stationary distribution of the stochastic system and it has an ergodic property, which means the infectious disease is prevalent. This implies that the stochastic disturbance is conducive to epidemic diseases control. At last, computer numerical simulations are carried out to illustrate our theoretical results

High-Order Degree and Combined Degree in Complex Networks

We define several novel centrality metrics: the high-order degree and combined degree of undirected network, the high-order out-degree and in-degree and combined out out-degree and in-degree of directed network. Those are the measurement of node importance with respect to the number of the node neighbors. We also explore those centrality metrics in the context of several best-known networks. We prove that both the degree centrality and eigenvector centrality are the special cases of the high-order degree of undirected network, and both the in-degree and PageRank algorithm without damping factor are the special cases of the high-order in-degree of directed network. Finally, we also discuss the significance of high-order out-degree of directed network. Our centrality metrics work better in distinguishing nodes than degree and reduce the computation load compared with either eigenvector centrality or PageRank algorithm

Dynamics of a stochastic SIS epidemic model with nonlinear incidence rates

Abstract In this paper, considering the impact of stochastic environment noise on infection rate, a stochastic SIS epidemic model with nonlinear incidence rate is proposed and analyzed. Firstly, for the corresponding deterministic system, the threshold which determines the extinction or permanence of the disease is obtained by analyzing the stability of the equilibria. Then, for the stochastic system, the global dynamics is investigated by using the theory of stochastic differential equations; especially the threshold dynamics is explored when the stochastic environment noise is small. The results show that the condition for the epidemic disease to go to extinction in the stochastic system is weaker than that of the deterministic system, which implies that stochastic noise has a significant impact on the spread of infectious diseases and the larger stochastic noise is conducive to controlling the epidemic diseases. To illustrate this phenomenon, we give some computer simulations with different intensities of the stochastic noise

Sensitivity of seed germination to temperature of a relict tree species from different origins along latitudinal and altitudinal gradients: implications for response to climate change

Key messageSeeds of a relict tree species collected from high latitudes were more sensitive to temperature and warming could accelerate germination.AbstractSeed germination is a crucial process in a plant life cycle and is highly vulnerable to environmental change. Studying among-population variation in seed germination in response to environmental and geographic gradients is an important tool, allowing us to understand how plants adapt to different environmental conditions and to predict population dynamics under future climate change. Here, we collected seeds of Euptelea pleiospermum, a relict broad-leaved tree species, from six provenances along latitudinal and altitudinal gradients across its distribution in China. We investigated variation in seed germination percentage and germination timing of seeds from these different origins (low, middle, and high latitudes/altitudes) at three incubation temperatures (15 degrees C, 20 degrees C and 25 degrees C). The key results were as follows: first, seeds collected from high latitudes were more sensitive to temperature and was likely to benefit from the higher incubation temperature with increasing germination percentage and shorter germination timing; second, for seeds across latitudes, germination percentage of central populations was lower than that of marginal populations; seed origin and its interaction with temperature were the major drivers of germination percentage variation; germination timing was significantly affected by incubation temperature, and warming could accelerate germination; third, for seeds across altitudes, both germination percentage and germination timing were not significantly affected by seed origin, incubation temperature, or their interaction. Our results indicate that climate warming may influence the population dynamics of relict tree species by altering their seed germination patterns, especially for the leading-edge populations along latitudinal gradient. It is vital to take inter-population variation across species' geographic distribution into account when estimating the impact of environmental changes on plant species' distribution and population persistence

Seed morphological traits and seed element concentrations of an endangered tree species displayed contrasting responses to waterlogging induced by extreme precipitation

Understanding how plant species respond to extreme climate events is crucial for planning management and conservation actions. As extreme precipitation accelerates, the waterlogging related to it is predicted to be more severe and frequent. To date, however, empirical studies addressing the effects of extreme precipitation-induced waterlogging on the seeds of wild plants are still scarce. In this study, we compared the size, mass and element concentration of seeds produced by non-inundated and inundated individuals of Sinojackia huangmeiensis, a critically endangered tree species with only one extant wild population. Compared to the seeds from non-inundated individuals, the seed length, seed width, and seed mass were all smaller for seeds from inundated individuals. However, the concentrations of four chemical elements in the seed displayed an opposite trend, except those elements (e.g., C, K, Ca, Mg, Al, Fe, Ni, B, Mo, and Cu) with no significant difference. Some toxic elements (e.g., Mn) accumulated in the seeds from inundated individuals, as well as some nucleic acid-protein elements (e.g., N and P) and enzymatic (e.g., Zn) elements. Our study provides rare empirical evidence that wild plants could respond to extreme precipitation-induced waterlogging by changing both seed morphological traits and element concentrations

Extinction and persistence of a stochastic SIRS epidemic model with saturated incidence rate and transfer from infectious to susceptible

Abstract In this paper, stochastic effect on the spread of the infectious disease with saturated incidence rate and the special transfer from infectious is discussed. The threshold dynamics is explored for the case of relatively small noise. Our results show that large noise will cause the elimination of the disease, which will help suppress the spread of the disease