Water Balance Analysis of Hulun Lake, a Semi-Arid UNESCO Wetland, Using Multi-Source Data

Hulun Lake is the largest lake in northeastern China, and its basin is located in China and Mongolia. This research aims to analyze the dynamic changes in the water volume of Hulun Lake and to estimate the groundwater recharge of the lake during the past 60 years. Multi-source data were used, and water-level-data-interpolation extrapolation, water-balance equations, and other methods were applied. The proportion of the contribution of each component to the quantity of water in Hulun Lake during the last 60 years was accurately calculated. Evaporation loss was the main component in the water loss in Hulun Lake. In the last 60 years, the average annual runoff into the lake was about 1.202 billion m3, and it was the factor with the largest variation range and the leading factor affecting the changes in the quantity of water in Hulun Lake. There was groundwater recharge in Hulun Lake for a long period, and the average annual groundwater recharge was about 776 million m3 (excluding leakage). The contribution ratio of the river water, groundwater, and precipitation to the recharging of Hulun Lake was about 5:3:2. The changes in the quantity of water in Hulun Lake are affected by climate change and human activities in China and Mongolia, especially those in Mongolia

Association of Toll-Like Receptor 4 Gene Polymorphism and Expression with Urinary Tract Infection Types in Adults

Background: Innate immunity of which Toll-like receptor (TLR) 4 and CXCR1 are key elements plays a central role in the development of urinary tract infection (UTI). Although the relation between the genetics of TLR4 and CXCR1 and UTI is investigated partly, the polymorphisms and expression of TLR4 and CXCR1 in different types of UTI in adults are not extremely clear. Methodology/Principal Findings: This study investigates the presence of TLR4 A (896) G and CXCR1 G (2608) C polymorphisms in 129 UTI patients using RFLP-PCR. Gene and allelic prevalence were compared with 248 healthy controls. Flow cytometry was used to detect TLR4 and CXCR1 expression in the monocytes of UTI patients and healthy controls. TLR4 (896) AG genotype and TLR4 (896) G allele had higher prevalence in UTI (especially in acute cystitis and urethritis) patients, whereas CXCR1 (2608) GC genotype and CXCR1 (2608) C allele had lower prevalence in UTI patients than controls. TLR4 expression was significantly lower in chronic UTI patients than in acute pyelonephritis or healthy controls. CXCR1 expression was similar in both controls and patients. TLR4 expression in chronic UTI patients after astragalus treatment was higher than pre-treatment. Conclusions: The results indicate the relationship between the carrier status of TLR4 (896) G alleles and the development of UTI, especially acute cystitis and urethritis, in adults. TLR4 expression levels are correlated with chronic UTI

EST analysis of gene expression in the tentacle of Cyanea capillata

AbstractJellyfish, Cyanea capillata, has an important position in head patterning and ion channel evolution, in addition to containing a rich source of toxins. In the present study, 2153 expressed sequence tags (ESTs) from the tentacle cDNA library of C. capillata were analyzed. The initial ESTs consisted of 198 clusters and 818 singletons, which revealed approximately 1016 unique genes in the data set. Among these sequences, we identified several genes related to head and foot patterning, voltage-dependent anion channel gene and genes related to biological activities of venom. Five kinds of proteinase inhibitor genes were found in jellyfish for the first time, and some of them were highly expressed with unknown functions

Pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning

To realize automation and high accuracy of pedestal looseness extent recognition for rotating machinery, a novel pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning dimension reduction is proposed. Firstly, the pedestal looseness extent of rotating machinery is characterized by vibration signal of rotating machinery and its spectrum, then the time-frequency features are extracted from vibration signal to construct the origin looseness extent feature set. Secondly, the algorithm of looseness sensitivity index is designed to filter out the non-sensitive feature and poor sensitivity feature from the origin looseness extent feature set, avoiding the interference of non-sensitive and poor sensitivity feature. The sensitive features are selected to construct the looseness extent sensitive feature set, which has stronger characterization capabilities than the origin looseness extent feature set. Moreover, an effective manifold learning method called linear local tangent space alignment (LLTSA) is introduced to compress the looseness extent sensitive feature set into the low-dimensional looseness extent sensitive feature set. Finally, the low-dimensional looseness extent sensitive feature set is inputted into weight K nearest neighbor classifier (WKNNC) to recognize the different pedestal looseness extents of rotating machinery, the WKNNC’s recognition accuracy is more stable compared with that of a k nearest neighbor classification (KNNC). At the same time, the pedestal looseness extent recognition of rotating machinery is realized. The feasibility and validity of the present method are verified by successful pedestal looseness extent recognition application in a rotating machinery

Context De-confounded Emotion Recognition

Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios. Concretely, the harmful bias is a confounder that misleads existing models to learn spurious correlations based on conventional likelihood estimation, significantly limiting the models' performance. To tackle the issue, this paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a tailored causal graph. Then, we propose a Contextual Causal Intervention Module (CCIM) based on the backdoor adjustment to de-confound the confounder and exploit the true causal effect for model training. CCIM is plug-in and model-agnostic, which improves diverse state-of-the-art approaches by considerable margins. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our CCIM and the significance of causal insight.Comment: Accepted by CVPR 2023. CCIM is available at https://github.com/ydk122024/CCI