Numerical investigation of the partitioning phenomenon of carbon dioxide and multiple impurities in deep saline aquifers

The partitioning behaviours of CO2 with three kinds of common impurities, i.e., N2, CH4 and H2S, in the formation brine are investigated by numerical simulations. The results indicate that the effects of N2, CH4 or the mixture of N2 and CH4 at the same concentrations are generally similar. The leading gas front is usually made up of less soluble impurities, such as N2, CH4 or the mixture of N2 and CH4, while more soluble species such as H2S has dissolved preferentially in the formation brine. The separations between different gas species increase as the gas displacement front migrates forwards and contacts more of the aqueous phase. Compared with the partitioning results of the 98% CO2 and 2% H2S mixture, the results indicate that the inclusion of less soluble N2 and/or CH4 results in an earlier gas breakthrough and a longer delay between the breakthrough times of CO2 and H2S. The early breakthrough of the gas phase is mainly because that the addition of N2 and/or CH4 lowers the viscosity of the gas phase, resulting in a higher gas velocity than that of the CO2–H2S mixture. Meanwhile, the mobility ratio is higher and the gas mixture contacts the formation brine over a larger area, giving rise to more efficient stripping of the more soluble gas species like H2S and thus larger separations. In the meantime, with the same total concentrations of impurities (12%), when 2% H2S is contained in the CO2 streams, gas phase flows slower and thus the breakthrough time is later. Furthermore, the effects on the partitioning phenomenon are weaker with decreasing concentrations of N2 and/or CH4 (from 10% to 2%) with fixed concentrations of other impurity like H2S (2%). The migration distances and the separations between different gas species change linearly with time on the whole, as confirmed by a simulation in a longer model

Numerical simulations of pressure build up and salt precipitation during carbon dioxide storage in saline aquifers

The storage of large amounts of carbon dioxide (CO2) captured from fossil fuel fired power plants in deep saline aquifers can be an effective and promising measure for reducing the emissions of greenhouse gases. Massive CO2 injection into saline aquifers may cause multi-scale phenomena such as pressure buildup in a large scale, CO2 plume evolution in a medium scale and salt precipitation in a small scale. In this study, three-dimensional simulations are performed to investigate the propagation of pressure and the impact of salt precipitation on the process of large scale CO2 injection into the saline aquifers. Apart from the different scales of the processes, the numerical results show clearly different behaviours of the pressure changes in saline aquifers with different boundaries. Different types of salt precipitation occur adjacent to the injection well, presenting distinct impacts on the fluid flow. Affected by salt precipitation, the porosity and permeability are reduced, leading to declined transportation and degraded injectivity with different boundary conditions. The interplay between pressure buildup and solid saturation is compared in saline aquifers with different boundary conditions

Universal Domain Adaptation via Compressive Attention Matching

Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain without any prior knowledge about the label set. The challenge lies in how to determine whether the target samples belong to common categories. The mainstream methods make judgments based on the sample features, which overemphasizes global information while ignoring the most crucial local objects in the image, resulting in limited accuracy. To address this issue, we propose a Universal Attention Matching (UniAM) framework by exploiting the self-attention mechanism in vision transformer to capture the crucial object information. The proposed framework introduces a novel Compressive Attention Matching (CAM) approach to explore the core information by compressively representing attentions. Furthermore, CAM incorporates a residual-based measurement to determine the sample commonness. By utilizing the measurement, UniAM achieves domain-wise and category-wise Common Feature Alignment (CFA) and Target Class Separation (TCS). Notably, UniAM is the first method utilizing the attention in vision transformer directly to perform classification tasks. Extensive experiments show that UniAM outperforms the current state-of-the-art methods on various benchmark datasets

Designing Functional Carriage of High-Speed Medical Train – Systematic Analysis and Evaluation of Tasks, Functions and Flow Routes

This paper proposes a functional carriage design and an evaluation index system to improve the operational efficiency of high-speed medical trains. Hierarchical task analysis and human-machine-environment analysis were applied to model the transfer task and the functional modules of the medical train. The functional module configuration was obtained by performing a correlation analysis between the task and function. The relationship between carriages was elucidated by analysing material, personnel and information flow, and an optimal grouping diagram was obtained. Based on this design method, an innovative 6-carriage grouping design scheme was proposed. A functional evaluation index system for the carriage design was constructed, and the 6-carriage design was compared with the conventional 8-carriage design to verify the usability of the design method. The results showed that the 6-carriage high-speed trains can be flexibly configured to suit the changing task environment and are generally better than the 8-carriage design. This study provides theoretical and methodological support for constructing efficient and rational functional carriages for high-speed medical trains

Di’ao Xinxuekang Capsule, a Chinese Medicinal Product, Decreases Serum Lipids Levels in High-Fat Diet-Fed ApoE–/– Mice by Downregulating PCSK9

Numerous risk factors are responsible for the development of atherosclerosis, for which an increased serum level of low-density lipoprotein cholesterol (LDL-C) is a driving force. By binding to the low-density lipoprotein cholesterol receptor (LDLR) and inducing LDLR degradation, proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a key role in cholesterol homeostasis regulation. The inducement of PCSK9 expression is also an important reason for statin intolerance. The Di’ao Xinxuekang (DXXK) capsule extracted from Dioscorea nipponica Makino is a well-known traditional Chinese herbal medicinal product used in atherosclerotic cardiovascular disease. Although DXXK has been widely used in atherosclerotic cardiovascular treatment for nearly 30 years, studies on the potential mechanisms of the lipid-lowering effect are very limited. The purpose of the present study was to demonstrate the possible involvement of the PCSK9/LDLR signaling pathway in the lipid-lowering and antiatherosclerotic effect of DXXK in high-fat diet-fed ApoE–/– mice. The results showed that DXXK treatment alleviated hyperlipidemia, fat accumulation, and atherosclerosis formation in ApoE–/– mice. Furthermore, changes in the expression of PCSK9 mRNA in liver tissue and the circulating PCSK9 level in ApoE–/– mice were both reversed after DXXK treatment, and upregulation of LDLR in the liver was also detected in the protein level in DXXK-treated mice. Our study is the first to show that DXXK could alleviate lipid disorder and ameliorate atherosclerosis with downregulation of the PCSK9 in high-fat diet-fed ApoE–/– mice, suggesting that DXXK may be a potential novel therapeutic treatment and may support statin action in the treatment of atherosclerosis

NLTG Priors in Medical Image: Nonlocal TV-Gaussian (NLTG) prior for Bayesian inverse problems with applications to Limited CT Reconstruction

Bayesian inference methods have been widely applied in inverse problems, {largely due to their ability to characterize the uncertainty associated with the estimation results.} {In the Bayesian framework} the prior distribution of the unknown plays an essential role in the Bayesian inference, {and a good prior distribution can significantly improve the inference results.} In this paper, we extend the total~variation-Gaussian (TG) prior in \cite{Z.Yao2016}, and propose a hybrid prior distribution which combines the nonlocal total variation regularization and the Gaussian (NLTG) distribution. The advantage of the new prior is two-fold. The proposed prior models both texture and geometric structures present in images through the NLTV. The Gaussian reference measure also provides a flexibility of incorporating structure information from a reference image. Some theoretical properties are established for the NLTG prior. The proposed prior is applied to limited-angle tomography reconstruction problem with difficulties of severe data missing. We compute both MAP and CM estimates through two efficient methods and the numerical experiments validate the advantages and feasibility of the proposed NLTG prior

Designing Functional Carriage of High-Speed Medical Train – Systematic Analysis and Evaluation of Tasks, Functions and Flow Routes

This paper proposes a functional carriage design and an evaluation index system to improve the operational efficiency of high-speed medical trains. Hierarchical task analysis and human-machine-environment analysis were applied to model the transfer task and the functional modules of the medical train. The functional module configuration was obtained by performing a correlation analysis between the task and function. The relationship between carriages was elucidated by analysing material, personnel and information flow, and an optimal grouping diagram was obtained. Based on this design method, an innovative 6-carriage grouping design scheme was proposed. A functional evaluation index system for the carriage design was constructed, and the 6-carriage design was compared with the conventional 8-carriage design to verify the usability of the design method. The results showed that the 6-carriage high-speed trains can be flexibly configured to suit the changing task environment and are generally better than the 8-carriage design. This study provides theoretical and methodological support for constructing efficient and rational functional carriages for high-speed medical trains

High-throughput expression and purification of human solute carriers for structural and biochemical studies

Solute carriers (SLCs) are membrane transporters that import and export a range of endogenous and exogenous substrates, including ions, nutrients, metabolites, neurotransmitters, and pharmaceuticals. Despite having emerged as attractive therapeutic targets and markers of disease, this group of proteins is still relatively underdrugged by current pharmaceuticals. Drug discovery projects for these transporters are impeded by limited structural, functional, and physiological knowledge, ultimately due to the difficulties in the expression and purification of this class of membrane-embedded proteins. Here, we demonstrate methods to obtain high-purity, milligram quantities of human SLC transporter proteins using codon-optimized gene sequences. In conjunction with a systematic exploration of construct design and high-throughput expression, these protocols ensure the preservation of the structural integrity and biochemical activity of the target proteins. We also highlight critical steps in the eukaryotic cell expression, affinity purification, and size-exclusion chromatography of these proteins. Ultimately, this workflow yields pure, functionally active, and stable protein preparations suitable for high-resolution structure determination, transport studies, small-molecule engagement assays, and high-throughput in vitro screening

Structure and function of the SIT1 proline transporter in complex with the COVID-19 receptor ACE2

Proline is widely known as the only proteogenic amino acid with a secondary amine. In addition to its crucial role in protein structure, the secondary amino acid modulates neurotransmission and regulates the kinetics of signaling proteins. To understand the structural basis of proline import, we solved the structure of the proline transporter SIT1 in complex with the COVID-19 viral receptor ACE2 by cryo-electron microscopy. The structure of pipecolate-bound SIT1 reveals the specific sequence requirements for proline transport in the SLC6 family and how this protein excludes amino acids with extended side chains. By comparing apo and substrate-bound SIT1 states, we also identify the structural changes that link substrate release and opening of the cytoplasmic gate and provide an explanation for how a missense mutation in the transporter causes iminoglycinuria

Structure and function of the SIT1 proline transporter in complex with the COVID-19 receptor ACE2

Proline is widely known as the only proteogenic amino acid with a secondary amine. In addition to its crucial role in protein structure, the secondary amino acid modulates neurotransmission and regulates the kinetics of signaling proteins. To understand the structural basis of proline import, we solved the structure of the proline transporter SIT1 in complex with the COVID-19 viral receptor ACE2 by cryo-electron microscopy. The structure of pipecolate-bound SIT1 reveals the specific sequence requirements for proline transport in the SLC6 family and how this protein excludes amino acids with extended side chains. By comparing apo and substrate-bound SIT1 states, we also identify the structural changes that link substrate release and opening of the cytoplasmic gate and provide an explanation for how a missense mutation in the transporter causes iminoglycinuria