Modulation of NKT cells and Th1/Th2 imbalance after α-GalCer treatment in progressive load-trained rats

Purpose: The purpose of this study was to determine whether α-galactosylceramide (α-GalCer), a synthetic glycolipid agonist of natural killer T (NKT) cells, can ameliorate exercise-induced immune imbalance. Methods: Eight-week-old female Sprague-Dawley rats were trained with a progressively increasing load for 9 weeks. At 36 h and at 7 d after training, groups of rats were euthanized. The whole blood was used to detect hemoglobin(Hb), plasma was analyzed for hormones testosterone(T) and corticosterone(C), and spleen was harvested for detecting NKT cells and interferon-γ (IFN-γ) and interleukin (IL)-4 producing cells. Results: Two-way analysis of variance (ANOVA) showed significant differences between training and time in Series 1. The results showed, at 36h after training, that the decrease in Hb, T and C concentration reflected overtraining or excessive exercise. At 7 d after training, NKT cell populations decreased, and a T helper 1/T helper 2 (Th1/Th2) lymphocyte imbalance occurred. In Series 2, α-galactosylceramide (α-GalCer), an NKT cell activator was found to enhance NKT cell numbers by 69% and shift the Th1/Th2 lymphocyte imbalance by observably decreasing the frequency of IL-4 secreting cells. Conclusion: These data showed that, in addition to Th1/Th2 self-regulation, α-GalCer played an important modulatory role in the exercise-induced Th1/Th2 lymphocyte imbalance, which may be correlative with NKT immunoregulatory cells

Intriguing generalization and simplicity of adversarially trained neural networks

Adversarial training has been the topic of dozens of studies and a leading method for defending against adversarial attacks. Yet, it remains unknown (a) how adversarially-trained classifiers (a.k.a "robust" classifiers) generalize to new types of out-of-distribution examples; and (b) what hidden representations were learned by robust networks. In this paper, we perform a thorough, systematic study to answer these two questions on AlexNet, GoogLeNet, and ResNet-50 trained on ImageNet. While robust models often perform on-par or worse than standard models on unseen distorted, texture-preserving images (e.g. blurred), they are consistently more accurate on texture-less images (i.e. silhouettes and stylized). That is, robust models rely heavily on shapes, in stark contrast to the strong texture bias in standard ImageNet classifiers (Geirhos et al. 2018). Remarkably, adversarial training causes three significant shifts in the functions of hidden neurons. That is, each convolutional neuron often changes to (1) detect pixel-wise smoother patterns; (2) detect more lower-level features i.e. textures and colors (instead of objects); and (3) be simpler in terms of complexity i.e. detecting more limited sets of concepts

How explainable are adversarially-robust CNNs?

Three important criteria of existing convolutional neural networks (CNNs) are (1) test-set accuracy; (2) out-of-distribution accuracy; and (3) explainability. While these criteria have been studied independently, their relationship is unknown. For example, do CNNs that have a stronger out-of-distribution performance have also stronger explainability? Furthermore, most prior feature-importance studies only evaluate methods on 2-3 common vanilla ImageNet-trained CNNs, leaving it unknown how these methods generalize to CNNs of other architectures and training algorithms. Here, we perform the first, large-scale evaluation of the relations of the three criteria using 9 feature-importance methods and 12 ImageNet-trained CNNs that are of 3 training algorithms and 5 CNN architectures. We find several important insights and recommendations for ML practitioners. First, adversarially robust CNNs have a higher explainability score on gradient-based attribution methods (but not CAM-based or perturbation-based methods). Second, AdvProp models, despite being highly accurate more than both vanilla and robust models alone, are not superior in explainability. Third, among 9 feature attribution methods tested, GradCAM and RISE are consistently the best methods. Fourth, Insertion and Deletion are biased towards vanilla and robust models respectively, due to their strong correlation with the confidence score distributions of a CNN. Fifth, we did not find a single CNN to be the best in all three criteria, which interestingly suggests that CNNs are harder to interpret as they become more accurate

AGE-RELATED SARCOPENIA: AN ELECTROMYOGRAPHIC AND MECHANOMYOGRAPHYIC STUDY

The purpose of this study was to investigate the effects of age-related sarcopenia on muscle mass, relative muscle strength/power performance in the lower limbs, and the responses of electromyography (EMG) and mechanomyography (MMG) on the activation patterns of motor units under leg extension muscle power performance in the elderly. Subjects were healthy old (n=10, 64.5 ± 4.5 yrs) and young (n=10, 22.6 ± 2.8yrs) people. All subjects performed quadriceps maximal voluntary contraction (MVC) and fastest speed leg extension with different levels (75%, 60%, 45% 1RM), and 45% fatigue test to all-outThe results indicate the declines of muscle mass, neuromuscular performance and changes of MU activation patterns may result from age-related sarcopenia, and the age affects muscle power more than muscle strength

Fatigue safety monitoring and assessment of short and medium span concrete girder bridges

Concrete bridge is widely used in highway infrastructure in China, especially in short and medium span bridges. Concrete bridges are prone to fatigue failure under the coupled actions of repeated vehicles loads, environment and material degradation. In recent years, the traffic volume and vehicle weights of highway bridges have been continuously increasing, so concrete bridge fatigue problem becomes more serious. This paper introduces advanced fatigue safety monitoring techniques and fatigue performance assessment methods for short and medium span concrete girder bridges. Weigh-in-motion (WIM) system was used to record the real traffic volume, and then the acquired load spectrum was applied on typical concrete bridges through Matlab to analyze the fatigue performance of different bridge types. From the analysis results, several typical short and medium span concrete girder bridges are selected to conduct long-term service monitoring. The cross section types include hollow slab girder, T-girder and short box girder, and the structure types contain simple supported bridge and continuous girder bridge. WIM technique, dynamic strain monitoring technique and acoustic emission technique are used to monitor the key details. Fatigue performance is assessed and analyzed based on monitoring data, considering traffic increase, overload and corrosion factors

A Density Peak-Based Clustering Approach for Fault Diagnosis of Photovoltaic Arrays

Fault diagnosis of photovoltaic (PV) arrays plays a significant role in safe and reliable operation of PV systems. In this paper, the distribution of the PV systems’ daily operating data under different operating conditions is analyzed. The results show that the data distribution features significant nonspherical clustering, the cluster center has a relatively large distance from any points with a higher local density, and the cluster number cannot be predetermined. Based on these features, a density peak-based clustering approach is then proposed to automatically cluster the PV data. And then, a set of labeled data with various conditions are employed to compute the minimum distance vector between each cluster and the reference data. According to the distance vector, the clusters can be identified and categorized into various conditions and/or faults. Simulation results demonstrate the feasibility of the proposed method in the diagnosis of certain faults occurring in a PV array. Moreover, a 1.8 kW grid-connected PV system with 6×3 PV array is established and experimentally tested to investigate the performance of the developed method

Energy expenditure of type-specific sedentary behaviors estimated using sensewear mini armband: a metabolic chamber validation study among adolescents

SenseWear Mini Armband, an accelerometer with multiple physiological sensors, could be a practical means to estimate energy expenditure (EE) of children and adolescents, but its validity reported for these age groups has not been consistent within the literature. EE of twenty-six healthy Chinese 12-year-old adolescents was measured simultaneously using both SenseWear Mini Armband (SWMA) and metabolic chamber (MC) during a 16-hour stay in a MC. SWMA systematically underestimated the adolescents’ EE during sedentary behaviors, resting metabolic rate (RMR), basal metabolic rate (BMR), and total EE, with the absolute error rate ranging from 14.85% to 28.65%. The SWMA significantly underestimated EE compared with MC in Chinese adolescents. However, the amount of error can be reduced by applying correction equation proposed in this study