Giardia duodenalis-induced G0/G1 intestinal epithelial cell cycle arrest and apoptosis involve activation of endoplasmic reticulum stress in vitro

Giardia duodenalis is a zoonotic intestinal protozoan parasite that may cause host diarrhea and chronic gastroenteritis, resulting in great economic losses annually and representing a significant public health burden across the world. However, thus far, our knowledge on the pathogenesis of Giardia and the related host cell responses is still extensively limited. The aim of this study is to assess the role of endoplasmic reticulum (ER) stress in regulating G0/G1 cell cycle arrest and apoptosis during in vitro infection of intestinal epithelial cells (IECs) with Giardia. The results showed that the mRNA levels of ER chaperone proteins and ER-associated degradation genes were increased and the expression levels of the main unfolded protein response (UPR)-related proteins (GRP78, p-PERK, ATF4, CHOP, p-IRE1, XBP1s and ATF6) were increased upon Giardia exposure. In addition, cell cycle arrest was determined to be induced by UPR signaling pathways (IRE1, PERK and ATF6) through upregulation of p21 and p27 levels and promotion of E2F1-RB complex formation. Upregulation of p21 and p27 expression was shown to be related to Ufd1-Skp2 signaling. Therefore, the cell cycle arrest was induced by ER stress when infected with Giardia. Furthermore, the apoptosis of the host cell was also assessed after exposure to Giardia. The results indicated that apoptosis would be promoted by UPR signaling (PERK and ATF6), but would be suppressed by the hyperphosphorylation of AKT and hypophosphorylation of JNK that were modulated by IRE1 pathway. Taken together, both of the cell cycle arrest and apoptosis of IECs induced by Giardia exposure involved the activation of the UPR signaling. The findings of this study will deepen our understanding of the pathogenesis of Giardia and the associated regulatory network

To explore the pathogenesis of Bell's palsy using diffusion tensor image

Abstract To explore the pathogenesis of Bell's palsy using the diffusion tensor image on 3.0 T MR. The healthy people and the patients with Bell's palsy underwent intraparotid facial nerve scanning by using the DTI and T1 structural sequence at 3.0 T MR. The raw DTI data were performed affine transformation and nonlinear registration in the common MNI152_T1 space and resampled to the 0.4 mm3 voxel size. A group of 4 spherical seed regions were placed on the intratemporal facial nerves in the common space, bilaterally and symmetrically. The DTI data in the common space were used to track the intratemporal facial nerve fibers by using TrackVis and its Diffusion Toolkit. Each tractography was used to construct the maximum probability map (MPM) according to the majority rule. The fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were calculated and extracted on the basis of MPM. For healthy people, there was no significant difference in FA, MD, RD and AD of bilateral facial nerves. For patients with Bell's palsy, there was no significant difference in AD, there was significant difference in FA, MD and RD between the affected nerve and the healthy nerve (P < 0.02). This study showed that the myelin sheath injury of the intratemporal facial nerve is the main cause of Bell's palsy. Most neural axons are not damaged. The results may explain the pathogenesis of the Bell's palsy, which is self-limited for most cases

The association between alcohol drinking and glycemic management among people with type 2 diabetes in China

Abstract Introduction To investigate the association between alcohol drinking and glycemic management among adult patients with type 2 diabetes in regional China. Materials and Methods In this cross‐sectional survey conducted in Nanjing Municipality of China in 2018, adult type 2 diabetes patients were randomly selected from urban and rural communities. The outcome variable was the glycemic management status. The explanatory measure was alcohol drinking. Mixed‐effects regression models were employed to estimate odds ratios (ORs) and 95% confidence interval (95% CI) for examining the associations of alcohol drinking with glycemic management among type 2 diabetes patients. Results Among the overall 5,663 participants, the glycemic management rate was 39.8% (95% CI = 38.5, 41.1), with 41.2% (95% CI = 39.7, 42.7), 43.9% (95% CI = 38.9, 48.8), and 34.1% (95% CI = 31.5, 36.7) for non‐drinkers, mild/moderate drinkers, and heavy drinkers, respectively. After adjustment for potential confounders and community‐level clustering effect, heavy and mild/moderate alcohol drinkers were at 0.76 (95% CI = 0.66, 0.89) and 1.04 (95% CI = 0.87, 1.28) times odds to have glycemia under control than non‐drinkers among the overall participants. Furthermore, when stratified separately by gender and use of anti‐diabetes agents, the scenario within men, either regular or irregular users of anti‐diabetes agents was the same as that for overall participants, while the association between alcohol drinking and glycemic management became non‐significant among women. Conclusions Heavy alcohol drinking might have a negative effect on glycemic management among patients with type 2 diabetes irrespective of the use of anti‐diabetes agents in regional China. This study has important public health implications regarding precision intervention on patients' glycemia control for type 2 diabetes management

Street-level air quality inference based on geographically context-aware random forest using opportunistic mobile sensor network

C

Graph-deep-learning-based inference of fine-grained air quality from mobile IoT sensors

Internet-of-Things (IoT) technologies incorporate a large number of different sensing devices and communication technologies to collect a large amount of data for various applications. Smart cities employ IoT infrastructures to build services useful for the administration of the city and the citizens. In this article, we present an IoT pipeline for acquisition, processing, and visualization of air pollution data over the city of Antwerp, Belgium. Our system employs IoT devices mounted on vehicles as well as static reference stations to measure a variety of city parameters, such as humidity, temperature, and air pollution. Mobile measurements cover a larger area compared to static stations; however, there is a tradeoff between temporal and spatial resolution. We address this problem as a matrix completion on graphs problem and rely on variational graph autoencoders to propose a deep learning solution for the estimation of the unknown air pollution values. Our model is extended to capture the correlation among different air pollutants, leading to improved estimation. We conduct experiments at different spatial and temporal resolution and compare with state-of-the-art methods to show the efficiency of our approach. The observed and estimated air pollution values can be accessed by interested users through a Web visualization tool designed to provide an air pollution map of the city of Antwerp

Spatiotemporal air quality inference of low-cost sensor data : evidence from multiple sensor testbeds

Recent advances in sensor and IoT technologies allow for denser and mobile air quality measurements. These measurements are still spatiotemporally sparse at city-level, but can be interpolated using data-driven techniques. This work presents validation results of two machine-learning models to infer air quality sensor data in both space and time. Temporal validation exercises are performed at available regulatory monitoring stations following the FAIRMODE protocol. Both models show scalable to different mobile datasets with comparable prediction performance for PM2.5 (R-2 = 0.68-0.75, MAE = 2.99-2.82 mu g m(- 3)) and NO2 (R-2 = 0.8-0.82, MAE = 8.81-9.83 mu gm(- 3)) in Utrecht and Antwerp. In Oakland (Atlanta), we observed a lower performance for NO2 (R-2 = 0.46-0.41, MAE = 4.06-5.07) and BC (R-2 = 0.31-0.28, MAE = 0.48-0.27), likely caused by the less representative monitoring coverage. Although comparable in terms of prediction performance, the Geographical Random Forest (GRF) model seems to achieve slightly better accuracies, while the correlations are typically higher for the Air Variational Graph Autoencoder (AVGAE) model. This work demonstrates the potential of data driven techniques for spatiotemporal air quality inference of complementary sensor data. The observed performance metrics approach current state-of-the-art chemical transport models in terms of performance while needing much lower resources, computational power, infrastructure and processing time.& nbsp;& nbsp

Fine-grained urban air quality mapping from sparse mobile air pollution measurements and dense traffic density

Urban air quality mapping has been widely applied in urban planning, air pollution control and personal air pollution exposure assessment. Urban air quality maps are traditionally derived using measurements from fixed monitoring stations. Due to high cost, these stations are generally sparsely deployed in a few representative locations, leading to a highly generalized air quality map. In addition, urban air quality varies rapidly over short distances (<1 km) and is influenced by meteorological conditions, road network and traffic flow. These variations are not well represented in coarse-grained air quality maps generated by conventional fixed-site monitoring methods but have important implications for characterizing heterogeneous personal air pollution exposures and identifying localized air pollution hotspots. Therefore, fine-grained urban air quality mapping is indispensable. In this context, supplementary low-cost mobile sensors make mobile air quality monitoring a promising alternative. Using sparse air quality measurements collected by mobile sensors and various contextual factors, especially traffic flow, we propose a context-aware locally adapted deep forest (CLADF) model to infer the distribution of NO2 by 100 m and 1 h resolution for fine-grained air quality mapping. The CLADF model exploits deep forest to construct a local model for each cluster consisting of nearest neighbor measurements in contextual feature space, and considers traffic flow as an important contextual feature. Extensive validation experiments were conducted using mobile NO2 measurements collected by 17 postal vans equipped with low-cost sensors operating in Antwerp, Belgium. The experimental results demonstrate that the CLADF model achieves the lowest RMSE as well as advances in accuracy and correlation, compared with various benchmark models, including random forest, deep forest, extreme gradient boosting and support vector regression

Explaining graph neural networks with topology-aware node selection : application in air quality inference

Graph neural networks (GNNs) have proven their ability in modelling graph-structured data in diverse domains, including natural language processing and computer vision. However, like other deep learning models, the lack of explainability is becoming a major drawback for GNNs, especially in health-related applications such as air pollution estimation, where a model’s predictions might directly affect humans’ health and habits. In this paper, we present a novel post-hoc explainability framework for GNN-based models. More concretely, we propose a novel topology-aware kernelised node selection method, which we apply over the graph structural and air pollution information. Thanks to the proposed model, we are able to effectively capture the graph topology and, for a certain graph node, infer its most relevant nodes. Additionally, we propose a novel topological node embedding for each node, capturing in a vector-shape the graph walks with respect to every other graph node. To prove the effectiveness of our explanation method, we include commonly employed evaluation metrics as well as fidelity, sparsity and contrastivity, and adapt them to evaluate explainability on a regression task. Extensive experiments on two real-world air pollution data sets demonstrate and visually show the effectiveness of the proposed method

Combining mobile air quality sensor data and machine learning for more fine-grained air quality assessments in urban areas

As conventional air quality monitoring networks are sparse, and recent advances in sensor and IoT technologies have revolutionized air quality monitoring applications for more-fine grained air quality mapping, more accurate personal exposure assessments, ... This presentation provides an overview of different mobile sensor testbeds deployed in Antwerp (BE), Utrecht (NL) and Oakland (US), used to train two different machine learning models; i.e. a Variational Graph Auto Encoder (AVGAE) and Geographical Random Forest (GRF) model with the aim of inferring the mobile data in both space and time. Moreover, we validated the prediction performance of the considered models at different regulatory station locations following the EU FAIRMODE protocol. Combining real-time air quality sensor data with data-driven modelling for fine-grained mapping of air quality in heterogeneous urban environments. The data-driven models show to perform while needing much lower resources, computational power, infrastructure and processing Time, when compared to the state-of-the-art physical models. Moreover, all Considered context information in this study is openly available and, therefore, scalable to any city worldwide