Background
Nod‐like receptor family pyrin domain‐containing protein‐3 (NLRP3) complex inflammasome has potentially been shown to play an important role in the development of periodontitis and ...diabetes. The objective of this study was to analyze the association between serum and salivary NLRP3 concentrations in patients with periodontitis and type‐II diabetes mellitus (DM) and to evaluate whether this association was influenced by potential confounders.
Methods
: For the present study, a cohort of healthy controls (n = 32), and patients with periodontitis (n = 34), type‐II DM (n = 33), and a combination of periodontitis + type‐II DM (n = 34) were enrolled. Patients were characterized on the basis of their periodontal status and analyzed for demographic characteristics, serum mediators, and for serum and salivary concentrations of NLRP3. A uni‐ and multivariate model was established to analyze whether periodontitis, type‐II DM, and CRP influenced serum and salivary NLRP3 concentrations.
Results
In comparison to type‐II DM patients and healthy controls, patients with periodontitis (serum, P = 0.003; saliva P = 0.012) and periodontitis + type‐II DM (serum, P = 0.028; saliva, P = 0.003) had elevated serum and salivary NLRP3 concentrations. The multivariate regression model showed that periodontitis (P = 0.029) and HDL‐cholesterol (P = 0.012) were significant predictors of serum NLRP3 concentrations whereas periodontitis (P = 0.036) and CRP (P = 0.012) were significant predictors of salivary NLRP3.
Conclusion
The results of the present study showed that periodontitis and periodontitis + type‐II DM patients had higher serum and salivary NLRP3 concentrations in comparison to healthy controls and patients with type‐II DM. Periodontitis was demonstrated to be a significant predictor of both serum and salivary NLRP3 concentrations.
Polynomial multiplication is the basic and most computationally intensive operation in ring-learning with errors (ring-LWE) encryption and "somewhat" homomorphic encryption (SHE) cryptosystems. In ...this paper, the fast Fourier transform (FFT) with a linearithmic complexity of O(nlogn), is exploited in the design of a high-speed polynomial multiplier. A constant geometry FFT datapath is used in the computation to simplify the control of the architecture. The contribution of this work is three-fold. First, parameter sets which support both an efficient modular reduction design and the security requirements for ring-LWE encryption and SHE are provided. Second, a versatile pipelined architecture accompanied with an improved dataflow are proposed to obtain a high-speed polynomial multiplier. Third, the proposed architecture supports polynomial multiplications for different lengths n and moduli p. The experimental results on a Spartan-6 FPGA show that the proposed design results in a speedup of 3.5 times on average when compared with the state of the art. It performs a polynomial multiplication in the ring-LWE scheme (n=256,p=1049089) and the SHE scheme (n=1024,p=536903681) in only 6.3 μs and 33.1 μs, respectively.
The human brain can be seen as one of the most powerful processors in the world, and it has a very complex structure with different kinds of signals for monitoring organics, communicating to neurons, ...and reacting to different information, which allows large developments in observing human sleeping, revealing diseases, reflecting certain motivations of limbs, and other applications. Relative theory, algorithms, and applications also help us to build brain-computer interface (BCI) systems for different powerful functions. Therefore, we present a fast-reaction framework based on an extreme learning machine (ELM) with multiple layers for the ElectroEncephaloGram (EEG) signals classification in motor imagery, showing the advantages in both accuracy of classification and training speed compared with conventional machine learning methods. The experiments are performed on software with the dataset of BCI Competition II with fast training time and high accuracy. The final average results show an accuracy of 93.90% as well as a reduction of 75% of the training time as compared to conventional deep learning and machine learning algorithms for EEG signal classification, also showing its prospects of the improvement of the performance of the BCI system.
Using the neo-classical theory of production economics as the analytical framework, this book, first published in 2004, provides a unified and easily comprehensible, yet fairly rigorous, exposition ...of the core literature on data envelopment analysis (DEA) for readers based in different disciplines. The various DEA models are developed as nonparametric alternatives to the econometric models. Apart from the standard fare consisting of the basic input- and output-oriented DEA models formulated by Charnes, Cooper, and Rhodes, and Banker, Charnes, and Cooper, the book covers developments such as the directional distance function, free disposal hull (FDH) analysis, non-radial measures of efficiency, multiplier bounds, mergers and break-up of firms, and measurement of productivity change through the Malmquist total factor productivity index. The chapter on efficiency measurement using market prices provides the critical link between DEA and the neo-classical theory of a competitive firm. The book also covers several forms of stochastic DEA in detail.
Breast cancer is the leading type of cancer in women, causing nearly 600,000 deaths every year, globally. Although the tumors can be localized within the breast, they can spread to other body parts, ...causing more harm. Therefore, early diagnosis can help reduce the risks of this cancer. However, a breast cancer diagnosis is complicated, requiring biopsy by various methods, such as MRI, ultrasound, BI-RADS, or even needle aspiration and cytology with the suggestions of specialists. On certain occasions, such as body examinations of a large number of people, it is also a large workload to check the images. Therefore, in this work, we present an efficient and automatic diagnosis system based on the hierarchical extreme learning machine (H-ELM) for breast cancer ultrasound results with high efficiency and make a primary diagnosis of the images. To make it compatible to use, this system consists of PNG images and general medical software within the H-ELM framework, which is easily trained and applied. Furthermore, this system only requires ultrasound images on a small scale, of 28×28 pixels, reducing the resources and fulfilling the application with low-resolution images. The experimental results show that the system can achieve 86.13% in the classification of breast cancer based on ultrasound images from the public breast ultrasound images (BUSI) dataset, without other relative information and supervision, which is higher than the conventional deep learning methods on the same dataset. Moreover, the training time is highly reduced, to only 5.31 s, and consumes few resources. The experimental results indicate that this system could be helpful for precise and efficient early diagnosis of breast cancers with primary examination results.
Summary
The gut microbiome of vertebrates plays an integral role in host health by stimulating development of the immune system, aiding in nutrient acquisition and outcompeting opportunistic ...pathogens. Development of next‐generation sequencing technologies allows researchers to survey complex communities of microorganisms within the microbiome at great depth with minimal costs, resulting in a surge of studies investigating bacterial diversity of fishes. Many of these studies have focused on the microbial structure of economically significant aquaculture species with the goal of manipulating the microbes to increase feed efficiency and decrease disease susceptibility. The unravelling of intricate host–microbe symbioses and identification of core microbiome functions is essential to our ability to use the benefits of a healthy microbiome to our advantage in fish culture, as well as gain deeper understanding of bacterial roles in vertebrate health. This review aims to summarize the available knowledge on fish gastrointestinal communities obtained from metagenomics, including biases from sample processing, factors influencing assemblage structure, intestinal microbiology of important aquaculture species and description of the teleostean core microbiome.
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