Fine and ultrafine particle exposure: Health effects and biomarkers

Particle, especially fine and ultrafine particle exposure has been linked to lower airway infections, asthma, chronic obstructive pulmonary disease (COPD), ischemic heart disease, stroke and cancer. The underlying mechanisms are under active investigation, and are still not fully understood. Several mechanisms have been proposed such as oxidative stress, inflammatory response and genotoxicity.Different biomarkers have been developed to investigate the mechanisms. This thesis includes exposure to fine and ultrafine particles from three different exposure sources: diesel exhaust, asphalt fumes and welding fumes. The main aim is to investigate adverse health effectscaused by airborne fine and ultrafine particle exposure, and to analyze biomarkers that are hypothesized to be in the causal pathway from exposure to pulmonary and cardiovascular disease.The thesis is based on three studies (four papers): i) a human experimental exposure study with 18 volunteers; ii) a field study with 167 asphalt workers and 100 controls; iii) a field study with 101 welders and 127 controls. We investigated airway symptoms and lung function as health outcomes,and measured different biomarkers: cytokines (as biomarkers for inflammatory response), mitochondrial DNA copy number (as biomarker for oxidative stress) and telomere length (as biomarker for genotoxicity) to explore mechanisms.The exposure levels in our studies were lower than the current occupational exposure limits. However, we still found exposure related eyes and airway irritation and transient decrease in lung function. Changes in cytokines after exposures were not statistically clear, but may indicate a mild inflammatory response. Higher mitochondrial DNA copy number together with lower methylation suggests possible exposure related oxidative stress. No difference in telomere length was found between exposure groups and controls, but telomere length was positively associated with PAH metabolites, indicating more PAH exposure was associated with longer telomere length.This thesis shows health effects and change of biomarkers under low to moderate exposure to particles. Although the effects seem to be in the compensatory stage, reconsideration is still called for regarding current occupational exposure limits

Efficient Residual Dense Block Search for Image Super-Resolution

Although remarkable progress has been made on single image super-resolution due to the revival of deep convolutional neural networks, deep learning methods are confronted with the challenges of computation and memory consumption in practice, especially for mobile devices. Focusing on this issue, we propose an efficient residual dense block search algorithm with multiple objectives to hunt for fast, lightweight and accurate networks for image super-resolution. Firstly, to accelerate super-resolution network, we exploit the variation of feature scale adequately with the proposed efficient residual dense blocks. In the proposed evolutionary algorithm, the locations of pooling and upsampling operator are searched automatically. Secondly, network architecture is evolved with the guidance of block credits to acquire accurate super-resolution network. The block credit reflects the effect of current block and is earned during model evaluation process. It guides the evolution by weighing the sampling probability of mutation to favor admirable blocks. Extensive experimental results demonstrate the effectiveness of the proposed searching method and the found efficient super-resolution models achieve better performance than the state-of-the-art methods with limited number of parameters and FLOPs

Deep Generative Models on 3D Representations: A Survey

Generative models, as an important family of statistical modeling, target learning the observed data distribution via generating new instances. Along with the rise of neural networks, deep generative models, such as variational autoencoders (VAEs) and generative adversarial network (GANs), have made tremendous progress in 2D image synthesis. Recently, researchers switch their attentions from the 2D space to the 3D space considering that 3D data better aligns with our physical world and hence enjoys great potential in practice. However, unlike a 2D image, which owns an efficient representation (i.e., pixel grid) by nature, representing 3D data could face far more challenges. Concretely, we would expect an ideal 3D representation to be capable enough to model shapes and appearances in details, and to be highly efficient so as to model high-resolution data with fast speed and low memory cost. However, existing 3D representations, such as point clouds, meshes, and recent neural fields, usually fail to meet the above requirements simultaneously. In this survey, we make a thorough review of the development of 3D generation, including 3D shape generation and 3D-aware image synthesis, from the perspectives of both algorithms and more importantly representations. We hope that our discussion could help the community track the evolution of this field and further spark some innovative ideas to advance this challenging task

Learning Interpretable BEV Based VIO without Deep Neural Networks

Monocular visual-inertial odometry (VIO) is a critical problem in robotics and autonomous driving. Traditional methods solve this problem based on filtering or optimization. While being fully interpretable, they rely on manual interference and empirical parameter tuning. On the other hand, learning-based approaches allow for end-to-end training but require a large number of training data to learn millions of parameters. However, the non-interpretable and heavy models hinder the generalization ability. In this paper, we propose a fully differentiable, and interpretable, bird-eye-view (BEV) based VIO model for robots with local planar motion that can be trained without deep neural networks. Specifically, we first adopt Unscented Kalman Filter as a differentiable layer to predict the pitch and roll, where the covariance matrices of noise are learned to filter out the noise of the IMU raw data. Second, the refined pitch and roll are adopted to retrieve a gravity-aligned BEV image of each frame using differentiable camera projection. Finally, a differentiable pose estimator is utilized to estimate the remaining 3 DoF poses between the BEV frames: leading to a 5 DoF pose estimation. Our method allows for learning the covariance matrices end-to-end supervised by the pose estimation loss, demonstrating superior performance to empirical baselines. Experimental results on synthetic and real-world datasets demonstrate that our simple approach is competitive with state-of-the-art methods and generalizes well on unseen scenes

Enhanced magnetoresistance in NiFe/GaAs/Fe hybrid magnon valve

The magnon valve (MV), which consists of a one spacer layer sandwiched between two ferromagnetic layers, is a potential spintronic device. The operation principle of the magnon valve depends on magnon current propagating between the two magnetic layers. More specifically, the magnon current is induced in one ferromagnetic layer and then injects magnons into the other ferromagnetic layer through the spacer layer. During this process, the magnetization of the injected ferromagnetic layer is changed, leading to the different relative magnetic orientations of the two magnetic layers. Here, we investigated the electromagnetic property of the NiFe/GaAs/Fe magnon valve assisted by microwaves with various frequencies. We find that the magnetoresistance (MR) of the magnon valve increases up to 40% when applying an external 3.4GHz microwave. The increase in the magnetoresistance results from the magnon current propagating between the two ferromagnetic layers. The magnons induced by the external microwave share the same phase, and thus the magnon current can penetrate into a 70 μm thick GaAs by coherent propagation

A new partial task offloading method in a cooperation mode under multi-constraints for multi-UE

In Multi-access Edge Computing (MEC), to deal with multiple user equipment (UE)’s task offloading problem of parallel relationships under the multi-constraints, this paper proposes a cooperation partial task offloading method (named CPMM), aiming to reduce UE's energy and computation consumption, while meeting the task completion delay as much as possible. CPMM first studies the task offloading of single-UE and then considers the task offloading of multi-UE based on single-UE task offloading. CPMM uses the critical path algorithm to divide the modules into key and non-key modules. According to some constraints of UE-self when offloading tasks, it gives priority to non-key modules for offloading and uses the evaluation decision method to select some appropriate key modules for offloading. Based on fully considering the competition between multiple UEs for communication resources and MEC service resources, CPMM uses the weighted queuing method to alleviate the competition for communication resources and uses the branch decision algorithm to determine the location of module offloading by BS according to the MEC servers’ resources. It achieves its goal by selecting reasonable modules to offload and using the cooperation of UE, MEC, and Cloud Center to determine the execution location of the modules. Extensive experiments demonstrate that CPMM obtains superior performances in task computation consumption reducing around 6% on average, task completion delay reducing around 5% on average, and better task execution success rate than other similar methods

Biogenic Synthesis of Novel Functionalized Selenium Nanoparticles by Lactobacillus casei ATCC 393 and Its Protective Effects on Intestinal Barrier Dysfunction Caused by Enterotoxigenic Escherichia coli K88

Selenium (Se) is an essential element for human and animal health. Biogenic selenium nanoparticles (SeNPs) by microorganism possess unique physical and chemical properties and biological activities compared with inorganic Se and organic Se. The study was conducted to investigate the mainly biological activities of SeNPs by Lactobacillus casei ATCC 393 (L. casei 393). The results showed that L. casei 393 transformed sodium selenite to red SeNPs with the size of 50–80 nm, and accumulated them intracellularly. L. casei 393-SeNPs promoted the growth and proliferation of porcine intestinal epithelial cells (IPEC-J2), human colonic epithelial cells (NCM460), and human acute monocytic leukemia cell (THP-1)-derived macrophagocyte. L. casei 393-SeNPs significantly inhibited the growth of human liver tumor cell line-HepG2, and alleviated diquat-induced IPEC-J2 oxidative damage. Moreover, in vivo and in vitro experimental results showed that administration with L. casei 393-SeNPs protected against Enterotoxigenic Escherichia coli K88 (ETEC K88)-caused intestinal barrier dysfunction. ETEC K88 infection-associated oxidative stress (glutathione peroxidase activity, total superoxide dismutase activity, total antioxidant capacity, and malondialdehyde) was ameliorated in L. casei 393-SeNPs-treated mice. These findings suggest that L. casei 393-SeNPs with no cytotoxicity play a key role in maintaining intestinal epithelial integrity and intestinal microflora balance in response to oxidative stress and infection