Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology.

Hearing impairment was recently identified as the most prominent risk factor for dementia. However, the mechanisms underlying the link between hearing impairment and dementia are still unclear. We investigated the association of hearing performance with cognitive function, brain structure and cerebrospinal fluid (CSF) proteins in cross-sectional, longitudinal, mediation and genetic association analyses across the UK Biobank (N = 165,550), the Chinese Alzheimer's Biomarker and Lifestyle (CABLE, N = 863) study, and the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 1770) database. Poor hearing performance was associated with worse cognitive function in the UK Biobank and in the CABLE study. Hearing impairment was significantly related to lower volume of temporal cortex, hippocampus, inferior parietal lobe, precuneus, etc., and to lower integrity of white matter (WM) tracts. Furthermore, a higher polygenic risk score (PRS) for hearing impairment was strongly associated with lower cognitive function, lower volume of gray matter, and lower integrity of WM tracts. Moreover, hearing impairment was correlated with a high level of CSF tau protein in the CABLE study and in the ADNI database. Finally, mediation analyses showed that brain atrophy and tau pathology partly mediated the association between hearing impairment and cognitive decline. Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology, and hearing impairment may reflect the risk for cognitive decline and dementia as it is related to bran atrophy and tau accumulation in brain. However, it is necessary to assess the mechanism in future animal studies. A full list of funding bodies that supported this study can be found in the Acknowledgements section. [Abstract copyright: Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.

Transcriptomics and Network Pharmacology Reveal the Protective Effect of Chaiqin Chengqi Decoction on Obesity-Related Alcohol-Induced Acute Pancreatitis via Oxidative Stress and PI3K/Akt Signaling Pathway

Obesity-related acute pancreatitis (AP) is characterized by increasing prevalence worldwide and worse clinical outcomes compared to AP of other etiologies. Chaiqin chengqi decoction (CQCQD), a Chinese herbal formula, has long been used for the clinical management of AP but its therapeutic actions and the underlying mechanisms have not been fully elucidated. This study has investigated the pharmacological mechanisms of CQCQD in a novel mouse model of obesity-related alcohol-induced AP (OA-AP). The mouse OA-AP model was induced by a high-fat diet for 12 weeks and subsequently two intraperitoneal injections of ethanol, CQCQD was administered 2 h after the first injection of ethanol. The severity of OA-AP was assessed and correlated with changes in transcriptomic profiles and network pharmacology in the pancreatic and adipose tissues, and further docking analysis modeled the interactions between compounds of CQCQD and their key targets. The results showed that CQCQD significantly reduced pancreatic necrosis, alleviated systemic inflammation, and decreased the parameters associated with multi-organ dysfunction. Transcriptomics and network pharmacology analysis, as well as further experimental validation, have shown that CQCQD induced Nrf2/HO-1 antioxidant protein response and decreased Akt phosphorylation in the pancreatic and adipose tissues. In vitro, CQCQD protected freshly isolated pancreatic acinar cells from H2O2-elicited oxidative stress and necrotic cell death. The docking results of AKT1 and the active compounds related to AKT1 in CQCQD showed high binding affinity. In conclusion, CQCQD ameliorates the severity of OA-AP by activating of the antioxidant protein response and down-regulating of the PI3K/Akt signaling pathway in the pancreas and visceral adipose tissue

Sentiment analysis based on combination of term weighting schemes and word vectors

Term weighting schemes are widely used in text mining tasks and supervised term weighting schemes have better performances on sentiment analysis task because the available labels of training documents make the learned model more discriminative. In this thesis, based on bag of words model, we introduced three supervised term weighting schemes and have shown their effectiveness for sentiment analysis in experiments. We also introduced the advanced word vectors technology and used the cosine similarity technique to measure intrinsic relationship between words to overcome the data sparsity problem. Based on term weighting schemes and word vectors technology, we proposed two kinds of ideas to utilize word vectors in sentiment analysis systems. The first idea lies that we combined word vectors and our introduced term weighting schemes by vector multiplication operation to generate effective document feature vectors. The second one is that, we applied these introduced supervised weighting schemes on bag of words models where binary term frequencies are the features and word vectors are used as a measure to correlate unknown test document words with training document words and predict the weights of unknown testing words. Our experiment results show supervised term weighting schemes and the intrinsic information among words discovered by word vectors can really improve the performance of sentiment analysis system jointly. Our methods outperform the state of the art methods on long-length document datasets and have competitive performances on short-length document datasets.Master of Science (Computer Control and Automation

Evaluation of public transportation station area accessibility based on walking perception

Public transportation (PT) often fails to provide door-to-door service. Passengers often have to walk a distance to reach their destination after getting off the public transportation station. Therefore, the walking accessibility of the station area directly affects the attractiveness of the PT. For walking, accurate calculation or prediction of accessibility should consider not only the objective distance, but also the environment and psychological perception factors of pedestrians. This paper aims to map the pedestrian perceived cost to the transportation environment to evaluate the walking accessibility of the public transportation station area accurately. From the perspective of psychological perception of walking environment, four key impedance factors are selected and a pedestrian perceived impedance model is established. Then an evaluation model of station area accessibility is set employing POIs (Point of Interests) based on the accumulative opportunity method. Finally, the case is given to show the application of the model. The results show that the number of crosswalks with signal lights, mixed use of sidewalk and non-motorized lane, the obstacle quantity and the vehicle entrance quantity on sidewalks can increase perceived impedance significantly. For example, pedestrians are willing to spend 4.21 extra minutes to adopt routes with one fewer obstacle per 100 meters. Within 10 minutes of walking time, walking perception has a greater impact on station area accessibility. The perceived walking time thresholds for evaluating bus and rail transit station area accessibility are recommended to be 15 minutes and 20 minutes, respectively. The evaluation results can provide a reliable basis for improving the walking network around public transportation station

Transcriptome Analysis of Beet Webworm Shows That Histone Deacetylase May Affect Diapause by Regulating Juvenile Hormone

The beet webworm (Loxostege sticticalis L.) is an important agricultural pest and can tolerate harsh environmental conditions by entering diapause. The diapause mechanism of beet webworm is unknown. Therefore, we conducted a transcriptomic study of the process from diapause induction to diapause release in beet webworms. The results revealed 393 gene modules closely related to the diapause of beet webworm. The hub gene of the red module was the HDACI gene, which acts through histone deacetylase (HDAC) enzymes. HDAC enzyme activity was regulated by the light duration and influenced the JH content under induced beet webworm diapause conditions (12 h light:12 h dark). In addition, transcriptomic data suggested that circadian genes may not be the key genes responsible for beet webworm diapause. However, we showed that the photoperiod affects HDAC enzyme activity, and HDAC can regulate the involvement of JH in beet webworm diapause. This study provided a new module for studying insect diapause and links histone acetylation and diapause at the transcriptome level

DVC-Net: a new dual-view context-aware network for emotion recognition in the wild

Emotion recognition in the wild (ERW) is a challenging task due to unknown and the unconstrained scenes in the wild environment. Different from previous approaches that use facial expression or posture for ERW, a growing number of researches are beginning to utilize contextual information to improve the performance of emotion recognition. In this paper, we propose a new dual-view context-aware network (DVC-Net) to fully explore the usage of contextual information from global and local views, and balance the individual features and context features by introducing the attention mechanism. The proposed DVC-Net consists of three parallel modules: (1) the body-aware stream to suppress the uncertainties of body gesture feature representation, (2) the global context-aware stream based on salient context to capture the global-level effective context, and (3) the local context-aware stream based on graph convolutional network to find the local discriminative features with emotional cues. Quantitative evaluations have been carried out on two in-the-wild emotion recognition datasets. The experimental results demonstrated that the proposed DVC-Net outperforms the state-of-the-art methods

An Occlusion-Aware Tracker With Local-Global Features Modeling in UAV Videos

Recently, tracking with unmanned aerial vehicle (UAVs) platforms has played significant roles in Earth observation tasks. However, target occlusion remains a challenging factor during the continuous tracking procedure. In particular, incomplete local appearance features can mislead the tracking network to produce inaccurate size and position estimations when the target is occluded. Furthermore, the tracking network lacks sufficient occlusion supervision information, which may lead to template degradation during template updating. To address these challenges, in this article, we design an occlusion-aware tracker with local-global features modeling, which contains two key components, namely the feature intrinsic association module (FIAM) and the feature verification module (FVM). Specifically, the FIAM divides the local features into blocks and utilizes the transformer network to explore the relative relationships among each subblock, which supplements the damaged local target features and assists the modeling for global target features. In addition, the FVM establishes a correlation measurement network between the target and the template. To precisely evaluate the occlusion status, masked samples with occlusion exceeding 50% are selected as negative samples for independent training, which ensures the purity of the target template. Qualitative and quantitative experiments are conducted on publicly available datasets, including UAV20 L, UAV123, and LaSOT. Qualitative and quantitative experiments have demonstrated the effectiveness of the proposed tracking algorithm over the other state-of-the-art trackers in occlusion scenarios