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The observed increase in conjugation frequency was most obvious in the groups fed the combinations of DFM and phytogenic product, but merely up to 0.6 log units. Further, cecal samples were examined for ESBL-producing Enterobacteriaceae on five consecutive days in broilers aged 27-31 days. All samples derived from animals fed the experimental diet showed lower ESBL-prevalence than the control. It is concluded that Lactobacillus spp. and essential oils may help to reduce the prevalence of ESBL-harboring plasmids in broilers, while the effect on horizontal gene transfer is less obvious.Mouthpart structures were observed in four species of Largidae using scanning electron microscopy to investigate their morphological disparity, and linked to changes in feeding specialization. The examined species are pests that feed mainly on seeds and plant sap of forbs, shrubs, and trees. Their external mouthparts are described in detail for the first time herein. The cone-like labrum and four-segmented tube-like labium are shorter in Physopelta species than in Macrocheraia grandis (Grey). The labium surface in all studied species bears nine types of sensilla (St1-St2, Sb1-3, Sch, Sca1-2, Sm). The distributions of sensilla on particular labial segments varies among the studied species. The tripartite apex of the labium consists of two lateral lobes and an apical plate that is partly divided in Physopelta species, and not divided in Macrocheraia. Each lateral lobe possesses a sensillar field with 10 thick-walled uniporous sensilla basiconica, one multiporous sensillum styloconicum, and one long non-porous hrait more adapted for sucking sap from phloem or parenchymal cells.Visual inertial odometry (VIO) is the front-end of visual simultaneous localization and mapping (vSLAM) methods and has been actively studied in recent years. In this context, a time-of-flight (ToF) camera, with its high accuracy of depth measurement and strong resilience to ambient light of variable intensity, draws our interest. Thus, in this paper, we present a realtime visual inertial system based on a low cost ToF camera. The iterative closest point (ICP) methodology is adopted, incorporating salient point-selection criteria and a robustness-weighting function. In addition, an error-state Kalman filter is used and fused with inertial measurement unit (IMU) data. To test its capability, the ToF-VIO system is mounted on an unmanned aerial vehicle (UAV) platform and operated in a variable light environment. The estimated flight trajectory is compared with the ground truth data captured by a motion capture system. Real flight experiments are also conducted in a dark indoor environment, demonstrating good agreement with estimated performance. The current system is thus shown to be accurate and efficient for use in UAV applications in dark and Global Navigation Satellite System (GNSS)-denied environments.Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.Tibial plateau fractures (TPFs) are challenging, requiring complex open reduction and internal fixation (ORIF) and are often associated with complications including surgical site infections (SSIs). In 2007, we introduced a novel management protocol to treat TPFs which consisted of an angiosome- or perforator-sparing (APS) anterolateral approach followed by unrestricted weight bearing and range of motion. The primary aim of this retrospective study was to investigate complication rates and patient outcomes associated with our new management protocol. In total, 79 TPFs treated between 2004 and 2007 through a classic anterolateral surgical approach formed the "Classic Group"; while 66 TPFS treated between 2007 and 2013 formed the "APS Group". Fracture reduction, maintenance of reduction and patient-reported outcomes were assessed. There was a clinically important improvement in the infection incidence with the APS (1.5%) versus the Classic technique (7.6%) (1/66 versus 2/79 for superficial infections; 0/66 versus 4/79 for deep infections). Despite a more aggressive rehabilitation, there was no difference in the fracture reduction over time or the functional outcomes between both groups (p > 0.05). The APS anterolateral approach improved the rate of SSIs after TPFs without compromising fracture reduction and stabilisation. We continue to use this new management approach and early unrestricted weight bearing when treating amenable TPFs.To design an algorithm for detecting outliers over streaming data has become an important task in many common applications, arising in areas such as fraud detections, network analysis, environment monitoring and so forth. Due to the fact that real-time data may arrive in the form of streams rather than batches, properties such as concept drift, temporal context, transiency, and uncertainty need to be considered. In addition, data processing needs to be incremental with limited memory resource, and scalable. These facts create big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in an incremental fashion, especially in the streaming environment. To address these problems, we first propose C_KDE_WR, which uses sliding window and kernel function to process the streaming data online, and reports its results demonstrating high throughput on handling real-time streaming data, implemented in a CUDA framework on Graphics Processing Unit (GPU). We also presenttively. Experimental results show that C_LOF can overcome the masquerading problem, which often exists in outlier detection on streaming data. We provide complexity analysis and report experiment results on the accuracy of both C_KDE_WR and C_LOF algorithms in order to evaluate their effectiveness as well as their efficiencies.This paper focuses on the time series prediction problem for underflow concentration of deep cone thickener. It is commonly used in the industrial sedimentation process. In this paper, we introduce a dual attention neural network method to model both spatial and temporal features of the data collected from multiple sensors in the thickener to predict underflow concentration. The concentration is the key factor for future mining process. This model includes encoder and decoder. Their function is to capture spatial and temporal importance separately from input data, and output more accurate prediction. We also consider the domain knowledge in modeling process. Several supplementary constructed features are examined to enhance the final prediction accuracy in addition to the raw data from sensors. To test the feasibility and efficiency of this method, we select an industrial case based on Industrial Internet of Things (IIoT). This Tailings Thickener is from FLSmidth with multiple sensors. The comparative results support this method has favorable prediction accuracy, which is more than 10% lower than other time series prediction models in some common error indices. We also try to interpret our method with additional ablation experiments for different features and attention mechanisms. By employing mean absolute error index to evaluate the models, experimental result reports that enhanced features and dual-attention modules reduce error of fitting ~5% and ~11%, respectively.Chinese children are facing health challenges brought by chronic non-communicable diseases, such as physical problems and psychological related health problems. Childhood represents a critical life period when the long-term dietary and lifestyle behaviors are formed. It is necessary to survey the prevalence of suboptimal health status (SHS) among Chinese children and to research the relationship between SHS and lifestyles. This study aimed to examine the prevalence of SHS among Chinese children using a large-scale population survey sample covering school students and nonstudent children, and clarified the relationships between SHS and lifestyle factors using multi-level models controlled for the cluster effect of location and the confounding effect of demographics. Multi-level generalized estimating equation models were used to examine the relationships between SHS and lifestyle factors. Prevalence ratios (PR) and 95% confidence intervals (CI) were used to assess the strength of these relationships. Of the 29of SHS. SHS has become a serious public health challenge for Chinese children. Unhealthy lifestyles were closely associated with SHS. Implementation of preventative strategies are needed to reduce the potential SHS burden associated with these widespread high-risk unhealthy lifestyle behaviors.The main purpose of this study was to compare the lifetime prevalence of anxiety disorders among foreign-born and Canadian-born adults in middle and later life. Using baseline data of the Canadian Longitudinal Study on Aging (2010-2015), multivariable binary logistic regression was conducted to investigate anxiety diagnosis and immigrant status, while controlling for socio-economic, health-related, and nutrition covariates. Of 26,991 participants (49.3% men, 82.5% Canadian born, 58.5% aged 45-65 years), the overall prevalence of self-reported physician diagnosis of anxiety disorders was 8.5%, with immigrants being lower than Canadian-born respondents (6.4% vs. 9.3%, p 1/day; aOR = 1.55, 95% CI 1.12-1.15) (p less then 0.05). PROTAC Linker chemical Targeting socio-economic and nutritional risk factors may reduce the burden of anxiety disorders in middle and late adulthood.Prader-Willi syndrome (PWS) is a genetic disorder characterized by specific physical and behavioral abnormalities and considered the most commonly known genetic cause of morbid obesity in children. Recent studies indicate that patients suffering from this syndrome have significant problems in skill acquisition, muscle force, cardiovascular fitness, and activity level. In this study, we report an obese adolescent PWS patient of poor aerobic fitness compared with 13 obesity adolescents, and great improvement in cardiopulmonary exercise test (CPET) outcomes of the PWS patient measured after two weeks of physical exercise training programs.