Monitoring Water and Energy Cycles at Climate Scale in the Third Pole Environment (CLIMATE-TPE)

A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and for predicting the possible changes in water resources in South and East Asia. This paper reports the following results: (1) A platform of in situ observation stations is briefly described for quantifying the interactions in hydrosphere-pedosphere-atmosphere-cryosphere-biosphere over the Tibetan Plateau. (2) A multiyear in situ L-Band microwave radiometry of land surface processes is used to develop a new microwave radiative transfer modeling system. This new system improves the modeling of brightness temperature in both horizontal and vertical polarization. (3) A multiyear (2001–2018) monthly terrestrial actual evapotranspiration and its spatial distribution on the Tibetan Plateau is generated using the surface energy balance system (SEBS) forced by a combination of meteorological and satellite data. (4) A comparison of four large scale soil moisture products to in situ measurements is presented. (5) The trajectory of water vapor transport in the canyon area of Southeast Tibet in different seasons is analyzed, and (6) the vertical water vapor exchange between the upper troposphere and the lower stratosphere in different seasons is presented

Ultraviolet light A irradiation induces immunosuppression associated with the generation of reactive oxygen species in human neutrophils

Ultraviolet blood irradiation has been used as a physical therapy to treat many nonspecific diseases in clinics; however, the underlying mechanisms remain largely unclear. Neutrophils, the first line of host defense, play a crucial role in a variety of inflammatory responses. In the present work, we investigated the effects of ultraviolet light A (UVA) on the immune functions of human neutrophils at the single-cell level by using an inverted fluorescence microscope. N-Formyl-methionyl-leucyl-phenylalanine (FMLP), a classic physiological chemotactic peptide, was used to induce a series of immune responses in neutrophils in vitro. FMLP-induced calcium mobilization, migration, and phagocytosis in human neutrophils was significantly blocked after treatment with 365nm UVA irradiation, demonstrating the immunosuppressive effects of UVA irradiation on neutrophils. Similar responses were also observed when the cells were pretreated with H2O2, a type of reactive oxygen species (ROS). Furthermore, UVA irradiation resulted in an increase in NAD(P)H, a member of host oxidative stress in cells. Taken together, our data indicate that UVA irradiation results in immunosuppression associated with the production of ROS in human neutrophils

The national COPD screening programme in China: rationale and design

Background COPD is the most prevalent chronic respiratory disease in China. It is estimated that there is a large, as-yet undetected, high-risk population who will develop in COPD in future. Methods and design In this context, a nationwide COPD screening programme was launched on 9 October 2021. This multistage sequential screening programme incorporates a previously validated questionnaire (i.e. COPD Screening Questionnaire) and pre- and post-bronchodilator spirometry to target the COPD high-risk population. The programme plans to recruit 800 000 participants (eligible age 35–75 years) from 160 districts or counties of 31 provinces, autonomous regions or municipalities across China. The filtered COPD high-risk population and early-detected COPD patients will receive integrated management and be followed-up for ≥1 year. Discussion This is the first large-scale prospective study to determine the net benefit of mass screening for COPD in China. Whether the smoking cessation rate, morbidity, mortality and health status of individuals at high risk of COPD could be improved along with this systematic screening programme will be observed and validated. Moreover, the diagnostic accuracy, cost-effectiveness and superiority of the screening programme will also be assessed and discussed. The programme marks a remarkable achievement in the management of chronic respiratory disease in China

Carbon dioxide exchange between an alpine steppe ecosystem and the atmosphere on the Nam Co area of the Tibetan Plateau

International audienc

A novel brain inception neural network model using EEG graphic structure for emotion recognition

Purpose EEG analysis of emotions is greatly significant for the diagnosis of psychological diseases and brain-computer interface (BCI) applications. However, the applications of EEG brain neural network for emotion classification are rarely reported and the accuracy of emotion recognition for cross-subject tasks remains a challenge. Thus, this paper proposes to design a domain invariant model for EEG-network based emotion identification. Methods A novel brain-inception-network based deep learning model is proposed to extract discriminative graph features from EEG brain networks. To verify its efficiency, we compared our proposed method with some commonly used methods and three types of brain networks. In addition, we also compared the performance difference between the EEG brain network and EEG energy distribution for emotion recognition. Result One public EEG-based emotion dataset (SEED) was utilized in this paper, and the classification accuracy of leave-one-subject-out cross-validation was adopted as the comparison index. The classification results show that the performance of the proposed method is superior to those of the other methods mentioned in this paper. Conclusion The proposed method can capture discriminative structural features from the EEG network, which improves the emotion classification performance of brain neural networks

Characteristics of the Water Vapor Transport in the Canyon Area of the Southeastern Tibetan Plateau

Changes in the surface fluxes cause changes in the annular flow field over a region, and they affect the transport of water vapor. To study the influence of the changes in the surface flux on the water vapor transport in the upper layer in the canyon area of southeastern Tibet, in this study, the water vapor transport characteristics were analyzed using the HYSPLIT_v4 backward trajectory model at Danka and Motuo stations in the canyons in the southeastern Tibetan Plateau from November 2018 to October 2019. Then, using ERA-5 reanalysis data from 1989 to 2019 and the characteristics of the high-altitude water vapor transportation, the impact of the surface flux changes on the water vapor transportation was analyzed using singular value decomposition (SVD). The results show that the main sources of the water vapor in the study area were from the west and southwest during the non-Asian monsoon (non-AMS), while there was mainly southwest air flow and a small amount of southeast air flow in the lower layer during the Asian monsoon (AMS) at the stations in southeastern Tibet. The water vapor transmission channel of the westward airflow is higher than 3000 m, and the water vapor transmission channel of the southwestward and southeastward airflow is about 2000 m. The sensible heat and latent heat are negatively correlated with water vapor flux divergence. The southwest boundary of southeastern Tibet is a key area affecting water vapor flux divergence. When the sensible heat and latent heat exhibit downward trends during the non-Asian monsoon season, the eastward water vapor flux exhibits an upward trend. During the Asian monsoon season, when the sensible heat and latent heat in southeastern Tibet increase as a whole, the eastward water vapor flux in the total-column of southeastern Tibet increases

A hybrid graph network model for ASD diagnosis based on resting-state EEG signals

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder and early diagnosis is crucial for effective treatment. Stable and effective biomarkers are essential for understanding the underlying causes of the disorder and improving diagnostic accuracy. Electroencephalography (EEG) signals have proven to be reliable biomarkers for diagnosing ASD. Extracting stable connectivity patterns from EEG signals helps ensure robustness in ASD diagnostic systems. In this study, we propose a hybrid graph convolutional network framework called Rest-HGCN, which utilizes resting-state EEG signals to capture differential patterns of brain connectivity between normal children and ASD patients using graph learning strategies. The Rest-HGCN combines brain network analysis techniques and data-driven strategies to extract discriminative graph features from resting-state EEG signals. By automatically extracting differential graph patterns from these signals, the Rest-HGCN achieves reliable ASD diagnosis. To evaluate the performance of Rest-HGCN, we conducted ASD diagnosis experiments using k-fold cross-validation on the public ABC-CT resting EEG dataset. The proposed Rest-HGCN model achieved accuracies of 87.12 % and 85.32 % in single-subject and cross-experiment analyses, respectively. The results suggest that Rest-HGCN can effectively capture discriminant graph patterns from resting EEG signals and achieve robust ASD diagnosis. This may provide an effective and convenient tool for clinical ASD diagnosis