Study Of Symbolic Expressions In Peking Opera\u27scostumes And Lyrics

This thesis represents an analysis of symbolic expressions used to convey traditional Chinese cultural values in marital relations as expressed through costumes and lyrics in Peking Opera plays and performances. Two symbols, dragon and phoenix, were selected from the costume collection. Four symbols--bird, tiger, wild goose, and dragon--were selected from compilations of lyrics. These symbols were selected because they expressed Chinese core cultural values, an imperial ideology based on Confucian thoughts, which were practiced rigidly during Qing Dynasty (1644-1911). Modeling Theory is applied to argue that dragon and phoenix as visual symbols convey ideas about characters\u27 background, marital relationship, social status shifts, and socio-culturally desirable values. Social Drama Theory is employed to analyze the lyrics to understand how ideal images of husband and wife are constructed. The archetypes of Chinese traditional culture that have influenced Chinese thought and action for centuries are discovered and discussed

Estimating Causal Effects using a Multi-task Deep Ensemble

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks.Comment: 18 pages, 7 figures, 3 tables, published at the 40th International Conference on Machine Learning (ICML 2023

Sustainable building processes' challenges and strategies : the relative important index approach

Sustainability has been increasingly advocated by the global construction industry due to the need to minimise the industry's adverse impacts. An important area when focusing on sustainability is the issue of project management teams since they are involved from the project's inception to its completion. Many studies have investigated and advocated a wide range of sustainability practices within the construction industry. However, little attention has been geared towards construction project management teams when addressing the issues of sustainability. This study aims to provide an empirical analysis of the challenges and mitigating strategies for enhancing project management teams’ readiness in the adoption of sustainable building processes. It does so by undertaking an extensive critical review of literature resulting in the identification of sixteen challenges and sixteen mitigation strategies and conducted a cross-sectional survey among 200 Ghanaian construction industry professionals. Data obtained from the survey was analysed using descriptive statistics and relative importance index rankings. The study revealed that inadequate training and education, unfamiliarity with green technologies, and higher initial costs of green construction practices and materials are the key challenges that hinder project management teams’ implementation of sustainable building processes. The study further revealed the significant mitigation strategies such as educating stakeholders on the future benefits of green buildings, engaging personnel with green building background, and setting sustainable priorities and goals early in the feasibility study. The value of this paper is to help project management teams to understand these challenges and strategize to turn them into opportunities for the construction industry

Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environments

AU Humans: Please confirm that all heading levels are represented correctly are able to adapt to the fast-changing world by estimating : statistical regularities of the environment. Although fear can profoundly impact adaptive behaviors, the computational and neural mechanisms underlying this phenomenon remain elusive. Here, we conducted a behavioral experiment (n = 21) and a functional magnetic resonance imaging experiment (n = 37) with a novel cue-biased adaptation learning task, during which we simultaneously manipulated emotional valence (fearful/neutral expressions of the cue) and environmental volatility (frequent/infrequent reversals of reward probabilities). Across 2 experiments, computational modeling consistently revealed a higher learning rate for the environment with frequent versus infrequent reversals following neutral cues. In contrast, this flexible adjustment was absent in the environment with fearful cues, suggesting a suppressive role of fear in adaptation to environmental volatility. This suppressive effect was underpinned by activity of the ventral striatum, hippocampus, and dorsal anterior cingulate cortex (dACC) as well as increased functional connectivity between the dACC and temporal-parietal junction (TPJ) for fear with environmental volatility. Dynamic causal modeling identified that the driving effect was located in the TPJ and was associated with dACC activation, suggesting that the suppression of fear on adaptive behaviors occurs at the early stage of bottom-up processing. These findings provide a neuro-computational account of how fear interferes with adaptation to volatility during dynamic environments.</p

Promising Antifungal Targets Against Candida albicans Based on Ion Homeostasis

In recent decades, invasive fungal infections have been increasing significantly, contributing to high incidences and mortality in immunosuppressed patients. Candida albicans (C. albicans) is the most prevalent opportunistic fungal pathogen in humans that can cause severe and often fatal bloodstream infections. Current antifungal agents have several limitations, including that only a small number of classes of antifungals are available, certain of which have severe toxicity and high cost. Moreover, the emergence of drug resistance is a new limitation to successful patient outcomes. Therefore, the development of antifungals with novel targets is an essential strategy for the efficient management of C. albicans infections. It is widely recognized that ion homeostasis is crucial for all living cells. Many studies have identified that ion-signaling and transduction networks are central to fungal survival by regulating gene expression, morphological transition, host invasion, stress response, and drug resistance. Dysregulation of ion homeostasis rapidly mediates cell death, forming the mechanistic basis of a growing number of compounds that elicit antifungal activity. Most of the potent antifungals have been widely used in the clinic, and certain of them have low toxicity, meaning that they may be expected to be used as antifungal drugs in the future. Hence, we briefly summarize the homeostasis regulation of several important ions, potential antifungal targets based on these ion-signaling networks, and antifungal compounds based on the disruption of ion homeostasis. This summary will help in designing effective drugs and identifying new targets for combating fungal diseases

EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography

This paper introduces the Efficient Decoupled Masked Autoencoder (EDMAE), a novel self-supervised method for recognizing standard views in pediatric echocardiography. EDMAE introduces a new proxy task based on the encoder-decoder structure. The EDMAE encoder is composed of a teacher and a student encoder. The teacher encoder extracts the potential representation of the masked image blocks, while the student encoder extracts the potential representation of the visible image blocks. The loss is calculated between the feature maps output by the two encoders to ensure consistency in the latent representations they extract. EDMAE uses pure convolution operations instead of the ViT structure in the MAE encoder. This improves training efficiency and convergence speed. EDMAE is pre-trained on a large-scale private dataset of pediatric echocardiography using self-supervised learning, and then fine-tuned for standard view recognition. The proposed method achieves high classification accuracy in 27 standard views of pediatric echocardiography. To further verify the effectiveness of the proposed method, the authors perform another downstream task of cardiac ultrasound segmentation on the public dataset CAMUS. The experimental results demonstrate that the proposed method outperforms some popular supervised and recent self-supervised methods, and is more competitive on different downstream tasks.Comment: 15 pages, 5 figures, 8 tables, Published in Biomedical Signal Processing and Contro

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning

Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. Materials and methods: We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). We propose an echocardiography video-based atrial septal defect diagnosis system. In our model, we present a block random selection, maximal agreement decision and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. Results: We validate our model using our private dataset by five-cross validation. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score. Conclusion: The proposed model is multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors

Anti-BP180 Autoantibodies Are Present in Stroke and Recognize Human Cutaneous BP180 and BP180-NC16A

Objective: Current evidence has revealed a significant association between bullous pemphigoid (BP) and neurological diseases (ND), including stroke, but the incidence of BP autoantibodies in patients with stroke has not previously been investigated. Our study aimed to assess BP antigen-specific antibodies in stroke patients.Design: One hundred patients with stroke and 100 matched healthy controls were randomly selected for measurement of anti-BP180/BP230 IgG autoantibodies by enzyme-linked immunosorbent assay (ELISA), salt-split indirect immunofluorescence (IIF), and immunoblotting against human cutaneous BP180 and BP180-NC16A.Results: Anti-BP180 autoantibodies were found in 14 (14.0%) patients with stroke and 5 (5.0%) of controls by ELISA (p &lt; 0.05). Sera from 13 (13.0%) patients with stroke and 3 (3.0%) controls reacted with 180-kDa proteins from human epidermal extract (p &lt; 0.05). 11 (11.0%) of stroke and 2 (2.0%) of control sera recognized the human recombinant full length BP180 and NC16A (p &lt; 0.05). The anti-BP180-positive patients were significantly younger than the negative patients at the time of stroke (p &lt; 0.001).Conclusion: Development of anti-BP180 autoantibodies occurs at a higher frequency after stroke, suggesting BP180 as a relatively common autoantigen after stroke and providing novel insights into BP pathogenesis in aging

Ultrasound-Guided Attenuation Parameter May Replace B-mode Ultrasound in Diagnosing Nonalcoholic Fatty Liver Disease

Objective: To compare the diagnostic sensitivity and consistency of ultrasound-guided attenuation parameter (UGAP) with B-mode ultrasound in nonalcoholic fatty liver disease (NAFLD) patients, and explored their correlation with clinical indicators. Methods: Patients suspected of NAFLD from July to November 2021 were enrolled in this prospective study. After performing the B-mode ultrasound and UGAP examination, all patients were divided into four groups according to the grade of NAFLD obtained by two modalities, respectively. The diagnostic agreement of the two modalities were evaluated, and the diagnostic sensitivity was compared by the McNemar test. The correlation between clinical indicators and the attenuation coefficient (AC) of UGAP was analyzed by linear regression. Results: The intraclass correlation coefficient of UGAP was 0.958 (95%CI: 0.943,0.970), while the kappa value of B-mode ultrasound grading was 0.799 (95%CI: 0.686, 0.912). The diagnostic sensitivity of UGAP was higher than that of B-mode ultrasound (99.0% vs. 32%, P &lt; 0.001). BMI and TG can be distinguished in different grades of NAFLD diagnosed by B-mode ultrasound, while BMI, ALT, HDL, and Apo A can be distinguished in different grades of NAFLD diagnosed by UGAP. BMI (r = 0.502, P &lt; 0.001), ALT (r = 0. 396, P &lt; 0.001), TG (r = 0.418, P &lt; 0.001), HDL (r = -0. 359, P &lt; 0.001) and Apo A (r = -0.228, P = 0.020) were linearly correlated with the AC value of UGAP. Conclusions: Compared with the B-mode ultrasound, UGAP had a higher sensitivity and consistency in diagnosing NAFLD, and correlated well with some laboratory indicators, which may be more valuable in screening and diagnosis of NAFLD

p38β MAPK mediates ULK1-dependent induction of autophagy in skeletal muscle of tumor-bearing mice

Muscle wasting is the key manifestation of cancer-associated cachexia, a lethal metabolic disorder seen in over 50% of cancer patients. Autophagy is activated in cachectic muscle of cancer hosts along with the ubiquitin-proteasome pathway (UPP), contributing to accelerated protein degradation and muscle wasting. However, established signaling mechanism that activates autophagy in response to fasting or denervation does not seem to mediate cancer-provoked autophagy in skeletal myocytes. Here, we show that p38β MAPK mediates autophagy activation in cachectic muscle of tumor-bearing mice via novel mechanisms. Complementary genetic and pharmacological manipulations reveal that activation of p38β MAPK, but not p38α MAPK, is necessary and sufficient for Lewis lung carcinoma (LLC)-induced autophagy activation in skeletal muscle cells. Particularly, muscle-specific knockout of p38β MAPK abrogates LLC tumor-induced activation of autophagy and UPP, sparing tumor-bearing mice from muscle wasting. Mechanistically, p38β MAPK-mediated activation of transcription factor C/EBPβ is required for LLC-induced autophagy activation, and upregulation of autophagy-related genes LC3b and Gabarapl1. Surprisingly, ULK1 activation (phosphorylation at S555) by cancer requires p38β MAPK, rather than AMPK. Activated ULK1 forms a complex with p38β MAPK in myocytes, which is markedly increased by a tumor burden. Overexpression of a constitutively active p38β MAPK in HEK293 cells increases phosphorylation at S555 and other amino acid residues of ULK1, but not several of AMPK-mediated sites. Finally, ULK1 activation is abrogated in tumor-bearing mice with muscle-specific knockout of p38β MAPK. Thus, p38β MAPK appears a key mediator of cancer-provoked autophagy activation, and a therapeutic target of cancer-induced muscle wasting