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Medical device technology develops rapidly, and the life cycle of a medical device is much shorter than drugs. It is necessary to evaluate the safety and effectiveness of a medical device in a timely manner to keep up with technology flux. Bayesian methods provides an efficient approach to addressing this challenge. In this article, we review the characteristics of the Bayesian approach and some Bayesian designs that were commonly used in medical device regulatory setting, including Bayesian adaptive design, Bayesian diagnostic design, Bayesian multiregional design, and Bayesian label expansion study. We illustrate these designs with medical devices approved by the US Food and Drug Administration (FDA). We also review several innovative Bayesian information borrowing methods, and briefly discuss the challenges and future directions of the Bayesian application in medical device trials. Our objective is to promote the use of the Bayesian approach to accelerate the development of innovative medical devices and their accessibility to patients for effective disease diagnoses and treatments.

BACKGROUND Identifying the occurrence form of iodine in soil and the law of iodine migration and transformation in soil is of great significance for evaluating the biogeochemical behavior of iodine and preventing human iodine deficiency diseases. OBJECTIVES To review the source, content, speciation, transportation, and transformation of iodine in soils; and briefly summarize the transportation of iodine between soil and plants. METHODS The source, content, speciation, transportation, and transformation characteristics of iodine in soils were reviewed. The influences of soil types, environmental factors, organic matter, microbial activity, pH, and Eh on iodine dynamics were summarized. Great emphasis was laid on the main factors affecting the sorption of iodine in soil, which is the key process regulating soil iodine mobility and bioavailability. RESULTS The dry and wet deposition of the atmosphere, the weathering of the soil parent rock, and the absorption and release of plants are the main sources of iodine in soil. Iodine contents in soil typically range from 1 to 5mg/kg. Organic iodine is the dominant form of iodine in soil due to the close association of iodine with organic matter, which is the critical factor affecting soil iodine dynamics. Sorption is the key process regulating soil iodine mobility and bioavailability. Under acidic conditions, hydroxyl groups on soil mineral surface are protonated, and thus facilitating iodine sorption via electrostatic interaction. CONCLUSIONS Due to the complex composition of soil, establishing a complete soil iodine database is the basis for exploring soil iodine related aspects. It is necessary to further seek better soil iodine analysis methods. Studying the adsorption mechanism of soil iodine and the effect of organic matter and microorganisms on iodine in soil are also an important direction for future research.

Feature selection is a challenging step in the field of data mining, because there are many local optimal solutions in a feature space. Feature selection can be considered an optimization problem, which requires as few feature combinations as possible and high accuracy. The binary symbiotic organism search (BSOS) algorithm is proposed in this paper. It maps the symbiotic organism search algorithm from a continuous space to a discrete space using an adaptive S-shaped transfer function and can be used to search for the optimal feature subset in a feature selection space. The proposed BSOS algorithm is evaluated using 19 datasets from the UCI repository. First, the results of four basic S-shaped transfer functions are compared with those of the adaptive S-shaped transfer function. Additionally, the experimental results are compared with the results obtained by the popular binary grasshopper optimization, binary gray wolf optimization, traditional binary particle swarm optimization, and binary differential evolution algorithms, which are also employed for feature selection in the existing literature. The experimental results show that the BSOS algorithm can find the fewest number of features in most datasets and achieve a high classification accuracy. Moreover, the experiments also show that the BSOS algorithm is still at a disadvantage in handling low-dimensional datasets and attains low sensitivity in hyperdimensional datasets.

The hot deformation characteristics of a AZ81E magnesium alloy are studied in the RANGE temperatures from 340 to 430 °C and strain rates ranging between 0.003 and 3.0 s−1 utilizing hot compression tests. Combining Arrhenius equation and Zener-Hollomon parameter, the high temperature flow stress model is proposed and the average activation energy is 166.15 kJ/mol. Processing maps for hot working are developed on the basis of the efficiency of power dissipation with changing temperatures and strain rates. The power dissipation map reveals that the optimum hot working domain is the temperature range of 390–400 °C and strain rate range of 0.03–0.3 s−1.

BackgroundThe polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk.MethodsWe constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model.ResultsThe deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms.ConclusionOur results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.

In this study, we propose a MOOC Analytic Statistical Visual model (MOOC-ASV) to explore students' engagement in MOOC courses and predict their performance on the basis of their behaviors logged as big data in MOOC platforms. The model has multifunctions, which performs on visually analyzing learners' data by state-of-the-art techniques. The model presents several visual analyses, which exhibit learners' interaction patterns with course videos and also statistical summaries of associated events for each video. The results showed positive indicators that would enable instructors to identify learners dropping out based on their interactions with videos. The model gives feedback into course instructors and developers to review and modify the course content, especially video content, which ultimately lead to improving the course quality and outcomes.

In this paper we analyze the existing defects of hierarchical and nonhierarchical path finding algorithm respectively in detail and present a hierarchical path finding algorithm based on the heuristic directing search approach which is used for promoting the nodes of lower level to higher level in the road network. The improved heuristic-hierarchical path finding algorithm was tested on road network of Xi'an city with a two-level hierarchy. The experimental results show that the heuristic based hierarchical path finding algorithm has been found to be over 25 times faster than the Dijkstra's algorithm and the ratio of the highest level road obtained by heuristic based hierarchical path finding algorithm is twice as much as the Dijkstra's algorithm. This means that the hierarchical path finding algorithm is suitable for the status of long-distance between origin and destination ( O - D ). We also propose a novel yet simple path finding strategy based on a threshold, that is, if the distance of O - D is less than the threshold which was obtained by means of statistical analysis the nonhierarchical path finding algorithm will be used, otherwise we use hierarchical path finding algorithm to find a near-optimal or satisfactory path in city road network. The testing results of Xi'an city road network show that the roads which were found out using this new path finding strategy are easy to be adopted by drivers.