Xie Zhuo Tiao Zhi formula ameliorates chronic alcohol-induced liver injury in mice

This study aimed to evaluate the protective role and potential mechanisms of Xie Zhuo Tiao Zhi decoction (XZTZ) on alcohol-associated liver disease (ALD). XZTZ significantly alleviated alcohol-induced liver dysfunction, based on histological examinations and biochemical parameters after 4-week administration. Mechanically, alcohol-stimulated hepatic oxidative stress was ameliorated by XZTZ, accompanied by the improvement of Nrf2/Keap1 expression and alcohol-activated phosphorylation of pro-inflammatory transcription factors, including JNK, P38, P65, and IκBα, were rescued by XZTZ. In conclusion, XZTZ demonstrates potential in alleviating alcohol-induced liver injury, oxidative stress, and inflammation possibly through modulation of Nrf2/Keap1 and MAPKs/NF-κB signaling pathways, suggesting its potential as a therapeutic option for patients with alcoholic liver disease

Vibrational spectroscopy and microwave dielectric properties of AY2Si3O10 (A=Sr, Ba) ceramics for 5G applications

AY2Si3O10 (A = Sr, Ba) trisilicate ceramics were synthesized by traditional high temperature solid state reaction method. X-ray diffraction patterns and Rietveld refinement revealed that AY2Si3O10 (A = Sr, Ba) ceramics belonged to triclinic and monoclinic crystal systems with Pī and P21/m space groups, respectively. The vibrational modes of [SiO4] tetrahedra, [YO6] octahedra and [(Sr/Ba)O8] polyhedra were analyzed by Raman spectroscopy. The infrared spectroscopy fitting analysis was used to determine intrinsic dielectric properties. Excellent microwave dielectric properties were measured for SrY2Si3O10 and BaY2Si3O10 with ɛr = 9.3, Qf = 64100 GHz, τf = −31 ppm/°C and ɛr = 9.5, Qf = 65600 GHz, τf = −28 ppm/°C, respectively. Both trisilicate ceramics are considered potential candidates for 5G and mm wave technology, provided τf can be further tuned

A coupled 3D isogeometric and discrete element approach for modelling interactions between structures and granular matters

A three-dimensional (3D) isogeometric/discrete-element coupling method is presented for modelling contact/impact between structures and particles. This method takes advantages of the geometry smoothness and exactness of isogeometric analysis (IGA) for continuous solid media and the effectiveness and flexibility of the discrete element method (DEM) for particulate matters. The coupling procedure for handling interactions between IGA elements and discrete elements (DEs) includes global search, local search and interaction calculation. In the global search, the CGRID method is modified to detect potential contact pairs between IGA elements and DEs based on their bounding box representations. The strong convex hull property of a NURBS control mesh plays an important part in the bounding box representation of IGA elements. In the local search, the proposed approach treats each spherical DE centroid as a slave node and the contact surface of each IGA element as the master surface. The projection of a DE centroid onto an IGA element contact surface is solved by modifying the simplex method and Brent iterations. The contact force between an IGA element and a DE is determined from their penetration by using a (nonlinear) penalty function based method. The whole coupled system is solved by the explicit time integration within a updated Lagrangian scheme. Finally, three impact examples, including the impact of two symmetric bars, a tube onto a footing strip, and an assembly of granular particles to a tailor rolled blank, are simulated in elastic regime to assess the accuracy and applicability of the proposed method

An epithelial-immune circuit amplifies inflammasome and IL-6 responses to SARS-CoV-2

Elevated levels of cytokines IL-1β and IL-6 are associated with severe COVID-19. Investigating the underlying mechanisms, we find that while primary human airway epithelia (HAE) have functional inflammasomes and support SARS-CoV-2 replication, they are not the source of IL-1β released upon infection. In leukocytes, the SARS-CoV-2 E protein upregulates inflammasome gene transcription via TLR2 to prime, but not activate, inflammasomes. SARS-CoV-2-infected HAE supply a second signal, which includes genomic and mitochondrial DNA, to stimulate leukocyte IL-1β release. Nuclease treatment, STING, and caspase-1 inhibition but not NLRP3 inhibition blocked leukocyte IL-1β release. After release, IL-1β stimulates IL-6 secretion from HAE. Therefore, infection alone does not increase IL-1β secretion by either cell type. Rather, bi-directional interactions between the SARS-CoV-2-infected epithelium and immune bystanders stimulates both IL-1β and IL-6, creating a pro-inflammatory cytokine circuit. Consistent with these observations, patient autopsy lungs show elevated myeloid inflammasome gene signatures in severe COVID-19., IL-1β and IL-6 are increased in severe COVID-19. Examining the underlying mechanisms, Barnett et al. show that SARS-CoV-2 E protein primes, and DNA from infected epithelial cells activates, inflammasome-dependent IL-1β secretion from leukocytes, which in turn stimulates IL-6 release from epithelial cells

Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins

Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response

Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins

Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response. Statins are effectively used to prevent and manage cardiovascular disease, but patient response to these drugs is highly variable. Here, the authors identify two new genes associated with the response of LDL cholesterol to statins and advance our understanding of the genetic basis of drug response

Long-Term Forecasting Using MAMTF: A Matrix Attention Model Based on the Time and Frequency Domains

There are many time series forecasting methods, but there are few research methods for long-term multivariate time series forecasting, which are mainly dominated by a series of forecasting models developed on the basis of a transformer. The aim of this study is to perform forecasting for multivariate time series data and to improve the forecasting accuracy of the model. In the recent past, it has appeared that the prediction effect of linear models surpasses that of the family of self-attention mechanism models, which encourages us to look for new methods to solve the problem of long-term multivariate time series forecasting. In order to overcome the problem that the temporal order of information is easily broken in the self-attention family and that it is difficult to capture information on long-distance data using recurrent neural network models, we propose a matrix attention mechanism, which is able to weight each previous data point equally without breaking the temporal order of the data, so that the overall data information can be fully utilized. We used the matrix attention mechanism as the basic module to construct the frequency domain block and time domain block. Since complex and variable seasonal component features are difficult to capture in the time domain, mapping them to the frequency domain reduces the complexity of the seasonal components themselves and facilitates data feature extraction. Therefore, we use the frequency domain block to extract the seasonal information with high randomness and poor regularity to help the model capture the local dynamics. The time domain block is used to extract the smooth floating trend component information to help the model capture long-term change patterns. This also improves the overall prediction performance of the model. It is experimentally demonstrated that our model achieves the best prediction results on three public datasets and one private dataset

A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism

Traffic flow forecasting, as an integral part of intelligent transportation systems, plays a critical part in traffic planning. Previous studies have primarily focused on short-term traffic flow prediction, paying insufficient attention to long-term prediction. In this study, we propose a hybrid model that utilizes variational mode decomposition (VMD) and the auto-correlation mechanism for long-term prediction. In view of the periodic and stochastic characteristics of traffic flow, VMD is able to decompose the data into intrinsic mode functions with different frequencies, which in turn helps the model extract the internal features of the data and better capture the changes of traffic flow data in the cycle. Additionally, we improve the residual structure by adding a convolutional layer to propose a correction module and use it together with the auto-correlation mechanism to jointly build an encoder and decoder to extract features from different data components (intrinsic mode functions) and fuse the extracted features for output. To meet the requirements of long-term forecasting, we set the traffic flow forecast length to 4 levels: 96, 192, 336, and 720. We validated our model using the departure statistics dataset of a taxi parking lot at Beijing Capital International Airport and achieved the best prediction performance in terms of mean squared error and mean absolute error, compared to the baseline model

Effect of Bacterial Anaerobic Degradation on Pore Structure and Fractal Characteristics of Bituminous Coal

In order to study the effect of bacterial anaerobic degradation on coal pores and fractal characteristics, the development of coal pores was characterized by high-pressure mercury injection and low-temperature liquid nitrogen adsorption. Menger model and FHH model were used to analyze the fractal characteristics. The results showed that after anaerobic degradation of bacteria, the micro-pores, transitional pores and meso-pores of residual coal were reduced, and the specific surface area was reduced more significantly. But at the same time, the large pore volume was increased. Besides, the dimension was obviously reduced, the surface roughness was reduced, and the pore development tended to be simple. These results showed that after anaerobic degradation, the coalbed methane adsorption capacity of bituminous coal reduced, while the seepage capacity partly increased

The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data

With the rapid development of the remote sensing monitoring and computer vision technology, the deep learning method has made a great progress to achieve applications such as earth observation, climate change and even space exploration. However, the model trained on existing data cannot be directly used to handle the new remote sensing data, and labeling the new data is also time-consuming and labor-intensive. Unsupervised Domain Adaptation (UDA) is one of the solutions to the aforementioned problems of labeled data defined as the source domain and unlabeled data as the target domain, i.e., its essential purpose is to obtain a well-trained model and tackle the problem of data distribution discrepancy defined as the domain shift between the source and target domain. There are a lot of reviews that have elaborated on UDA methods based on natural data, but few of these studies take into consideration thorough remote sensing applications and contributions. Thus, in this paper, in order to explore the further progress and development of UDA methods in remote sensing, based on the analysis of the causes of domain shift, a comprehensive review is provided with a fine-grained taxonomy of UDA methods applied for remote sensing data, which includes Generative training, Adversarial training, Self-training and Hybrid training methods, to better assist scholars in understanding remote sensing data and further advance the development of methods. Moreover, remote sensing applications are introduced by a thorough dataset analysis. Meanwhile, we sort out definitions and methodology introductions of partial, open-set and multi-domain UDA, which are more pertinent to real-world remote sensing applications. We can draw the conclusion that UDA methods in the field of remote sensing data are carried out later than those applied in natural images, and due to the domain gap caused by appearance differences, most of methods focus on how to use generative training (GT) methods to improve the model’s performance. Finally, we describe the potential deficiencies and further in-depth insights of UDA in the field of remote sensing