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Synthetic Aperture Radar (SAR) interferometry is a formidable technique to monitor surface deformation with a millimeter detection resolution. This study applies the Persistent Scatter-Interferometric Synthetic Aperture Radar (PSInSARTM) technique to measure ground subsidence related to a twin-tunnel excavation in downtown Los Angeles, USA. The PSInSARTM technique is suitable for urban settings because urban areas have strong reflectors. The twin tunnels in downtown Los Angeles were excavated beneath a densely urbanized area with variable overburden depths. In practice, tunneling-induced ground settlement is dominantly vertical. The vertical deformation rate in this study is derived by combining Line of Sight (LOS) deformation velocities obtained from SAR images from both ascending and descending satellite orbits. Local and uneven settlements up to approximately 12 mm/year along the tunnel alignment are observed within the allowable threshold. No severe damages to aboveground structures were reported. Furthermore, ground movements mapped one year before tunnel construction indicate that no concentrated ground settlements pre-existed. A Machine Learning (ML)-based permutation feature importance method is used for a parametric study to identify dominant factors associated with the twin-tunneling induced uneven ground subsidence. Six parameters are selected to conduct the parametric study, including overburden thickness, i.e., the thickness of artificial fill and alluvium soils above the tunnel springline, the distance between the two tunnel centerlines, the depth to the tunnel springline, building height, the distance to the tunnel, and groundwater level. Results of the parametric analysis indicate that overburden thickness, i.e., the thickness of artificial fill and alluvium soils above the tunnel springline, is the dominant contributing factor, followed by the distance between tunnel centerlines, depth to the tunnel springline, and building height. Two parameters, the distance to the tunnel, and the groundwater level, play lesser essential roles than others. In addition, the geological profile provides comprehension of unevenly distributed ground settlements, which are geologically sensitive and more concentrated in areas with thick artificial fill and alluvium soils, low tunnel depth, and high groundwater levels.

Coal mining-induced ground subsidence is a severe hazard that can damage property, infrastructure, and the environment in the vicinity when the deformation is not negligible. The boundary of a mining-induced subsidence-affected zone refers to the area beyond which the ground subsidence is less concerned. Accurately measuring mining-induced ground deformation is essential for delineating the irregular boundary of the impacted area. This study employs multitemporal interferometric synthetic aperture radar (MT-InSAR) techniques, including differential InSAR (DInSAR), InSAR stacking, and interferometric point target analysis (IPTA), to analyze coal mine subsidence and delineate the boundaries of the mining-impacted zones. DInSAR accurately reconstructs, locates, and detects the trend in mining-induced subsidence and correlates well with documented mining operations. The InSAR stacking method maps the spatial variation of the ground’s average line-of-sight (LOS) velocity over the mining area, delineating the boundary of the impacted zone. IPTA analysis combining multilook and single-pixel phases achieves millimeter-level surface measurement above tunnel alignments and measures unevenly distributed deformation fields. This study considers an average of 4 cm per year of surface deformation in the LOS direction as the subsidence threshold value for delineating the boundary of the mining-induced subsidence-affected (MISA) zone during the active coal mining stage. Interestingly, there are twin transportation tunnels near the mining area. The twin tunnels completed before the coal mining activities started were functioning well, but damage was observed after the mining began. Our study reveals the tunnels are located within the InSAR-derived MISA zone, although the tunnels approach the MISA boundary. As direct signs of subsidence, ground fissures have been identified near the tunnels via field investigations and UAV photogrammetry. Furthermore, the derived distribution of ground fissures validates and verifies InSAR measurements. The integrated approach of MT-InSAR, UVA photogrammetry, and field investigation developed in this study can be applied to delineate the irregular boundary of the MISA zone and study the accumulating effects of mining-induced subsidence on the performance of infrastructure in areas proximate to coal mining activities.

The delivery of therapeutics to the central nervous system remains a major challenge in part due to the presence of the blood–brain barrier (BBB). Recently, cell-derived vesicles, particularly exosomes, have emerged as an attractive vehicle for targeting drugs to the brain, but whether or how they cross the BBB remains unclear. Here, we investigated the interactions between exosomes and brain microvascular endothelial cells (BMECs) in vitro under conditions that mimic the healthy and inflamed BBB in vivo . Transwell assays revealed that luciferase-carrying exosomes can cross a BMEC monolayer under stroke-like, inflamed conditions (TNF- α activated) but not under normal conditions. Confocal microscopy showed that exosomes are internalized by BMECs through endocytosis, co-localize with endosomes, in effect primarily utilizing the transcellular route of crossing. Together, these results indicate that cell-derived exosomes can cross the BBB model under stroke-like conditions in vitro . This study encourages further development of engineered exosomes as drug delivery vehicles or tracking tools for treating or monitoring neurological diseases.

With the arrival of 5G networks, cellular networks are moving in the direction of diversified, broadband, integrated, and intelligent networks. At the same time, the popularity of various smart terminals has led to an explosive growth in cellular traffic. Accurate network traffic prediction has become an important part of cellular network intelligence. In this context, this paper proposes a deep learning method for space-time modeling and prediction of cellular network communication traffic. First, we analyze the temporal and spatial characteristics of cellular network traffic from Telecom Italia. On this basis, we propose a hybrid spatiotemporal network (HSTNet), which is a deep learning method that uses convolutional neural networks to capture the spatiotemporal characteristics of communication traffic. This work adds deformable convolution to the convolution model to improve predictive performance. The time attribute is introduced as auxiliary information. An attention mechanism based on historical data for weight adjustment is proposed to improve the robustness of the module. We use the dataset of Telecom Italia to evaluate the performance of the proposed model. Experimental results show that compared with the existing statistics methods and machine learning algorithms, HSTNet significantly improved the prediction accuracy based on MAE and RMSE.

Hypoxia can cause a variety of diseases, including ischemic stroke and neurodegenerative diseases. Within a certain range of partial pressure of oxygen, cells can respond to changes in oxygen. Changes in oxygen concentration beyond a threshold will cause damage or even necrosis of tissues and organs, especially for the central nervous system. Therefore, it is very important to find appropriate measures to alleviate damage. MiRNAs can participate in the regulation of hypoxic responses in various types of cells. MiRNAs are involved in regulating hypoxic responses in many types of tissues by activating the hypoxia-inducible factor (HIF) to affect angiogenesis, glycolysis and other biological processes. By analyzing differentially expressed miRNAs in hypoxia and hypoxia-related studies, as well as the HT22 neuronal cell line under hypoxic stress, we found that the expression of miR-18a was changed in these models. MiR-18a could regulate glucose metabolism in HT22 cells under hypoxic stress by directly regulating the 3’UTR of the Hif1a gene. As a small molecule, miRNAs are easy to be designed into small nucleic acid drugs, so this study can provide a theoretical basis for the research and treatment of nervous system diseases caused by hypoxia. Graphical Abstract

In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we proposed KANR, a knowledge graph-enhanced attention aggregation network for making recommendations. This is an end-to-end deep learning model using knowledge graph embedding to enhance the attention aggregation network for making recommendations. It consists of three main parts. The first is the attention aggregation network, which collect the user’s interaction history and captures the user’s preference for each item. The second is the knowledge graph-embedded model, which aims to integrate the knowledge. The semantic information of the nodes and edges in the graph is mapped to the low-dimensional vector space. The final part is the information interaction unit, which is used for fusing the features of two vectors. Experiments showed that our model achieved a stable improvement compared to the baseline model in making recommendations for movies, books, and music.

In this experiment, inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) was used to determine the content of 30 elements in rice from six places of production and to explore the relationship between the multielement content in rice and the producing area. The contents of Ca, P, S, Zn, Cu, Fe, Mn, K, Mg, Na, Ge, Sb, Ba, Ti, V, Se, As, Sr, Mo, Ni, Co, Cr, Al, Li, Cs, Pb, Cd, B, In, and Sn in rice were determined by ICP-MS/MS in the SQ and MS/MS mode. By passing H2, O2, He, and NH3/He reaction gas into the ICP-MS/MS, respectively, the interference was eliminated by means of in situ mass spectrometry and mass transfer. The detection limit of each element was 0.0000662–0.144 mg/kg, and the limit of quantification was in the range of 0.000221–0.479 mg/kg, the linear correlation coefficient was greater or equal to 0.9987 (R2 ≥ 0.9987), and the detection results had low detection limit and great linear regression. Recovery of the method was in the range of 80.6% to 110.5% with spike levels of 0.10–100.00 mg/kg, and relative standard deviations were lower than 10%. For the multielement content of rice from different producing areas, the principal component factor analysis can get six principal component factors, 87.878% cumulative contribution rate, and the distribution of the principal component scores of each element and different producing areas. Based on the multielement content and cluster analysis, the samples were accurately divided into two major categories and six subcategories according to the places of production, which proved that there was a significant correlation between the multielement content in rice and the place of production, so that the place of rice origin can be traced.

Background Mechanical and biophysical properties of the cellular microenvironment regulate cell fate decisions. Mesenchymal stem cell (MSC) fate is influenced by past mechanical dosing (memory), but the mechanisms underlying this process have not yet been well defined. We have yet to understand how memory affects specific cell fate decisions, such as the differentiation of MSCs into neurons, adipocytes, myocytes, and osteoblasts. Results We study a minimal gene regulatory network permissive of multi-lineage MSC differentiation into four cell fates. We present a continuous model that is able to describe the cell fate transitions that occur during differentiation, and analyze its dynamics with tools from multistability, bifurcation, and cell fate landscape analysis, and via stochastic simulation. Whereas experimentally, memory has only been observed during osteogenic differentiation, this model predicts that memory regions can exist for each of the four MSC-derived cell lineages. We can predict the substrate stiffness ranges over which memory drives differentiation; these are directly testable in an experimental setting. Furthermore, we quantitatively predict how substrate stiffness and culture duration co-regulate the fate of a stem cell, and we find that the feedbacks from the differentiating MSC onto its substrate are critical to preserve mechanical memory. Strikingly, we show that re-seeding MSCs onto a sufficiently soft substrate increases the number of cell fates accessible. Conclusions Control of MSC differentiation is crucial for the success of much-lauded regenerative therapies based on MSCs. We have predicted new memory regions that will directly impact this control, and have quantified the size of the memory region for osteoblasts, as well as the co-regulatory effects on cell fates of substrate stiffness and culture duration. Taken together, these results can be used to develop novel strategies to better control the fates of MSCs in vitro and following transplantation.

Changes in climate and land use are causing grasslands to suffer increasingly from abiotic stresses, including soil salinization. Silicon (Si) amendment has been frequently proposed to improve plant resistance to multiple biotic and abiotic stresses and increase ecosystem productivity while controlling the biogeochemical carbon (C) cycle. However, the effects of Si on plant C distribution and accumulation in salt-suffering grasslands are still unclear. In this study, we investigated how salt ions affected major elemental composition in plants and whether Si enhanced biomass C accumulation in grassland species in situ . In samples from the margins of salt lakes, our results showed that the differing distance away from the shore resulted in distinctive phytocoenosis, including halophytes and moderately salt-tolerant grasses, which are closely related to changing soil properties. Different salinity (Na+/K+, ranging from 0.02 to 11.8) in plants caused negative effects on plant C content that decreased from 53.9 to 29.2% with the increase in salinity. Plant Si storage [0.02–2.29 g Si m–2 dry weight (dw)] and plant Si content (0.53 to 2.58%) were positively correlated with bioavailable Si in soils (ranging from 94.4 to 192 mg kg–1). Although C contents in plants and phytoliths were negatively correlated with plant Si content, biomass C accumulation (1.90–83.5 g C m–2 dw) increased due to the increase of Si storage in plants. Plant phytolith-occluded carbon (PhytOC) increased from 0.07 to 0.28‰ of dry mass with the increase of Si content in moderately salt-tolerant grasses. This study demonstrates the potential of Si in mediating plant salinity and C assimilation, providing a reference for potential manipulation of long-term C sequestration via PhytOC production and biomass C accumulation in Si-accumulator dominated grasslands.

Wetlands are one of the most crucial ecosystems for regulating carbon sequestration and mitigating global climate change. However, the disturbance to carbon dynamics caused by microplastics (MPs) in wetlands cannot be overlooked. This review explores the impacts of MPs on the carbon cycles within wetland ecosystems, focusing on the underlying physicochemical and microbial mechanisms. The accumulation of MPs in wetland sediments can severely destabilize plant root functions, disrupting water, nutrient, and oxygen transport, thereby reducing plant biomass development. Although MPs may temporarily enhance carbon storage, they ultimately accelerate the mineralization of organic carbon, leading to increased atmospheric carbon dioxide emissions and undermining long-term carbon sequestration. A critical aspect of this process involves shifts in microbial community structures driven by selective microbial colonization on MPs, which affect organic carbon decomposition and methane production, thus posing a threat to greenhouse gas emissions. Notably, dissolved organic matter derived from biodegradable MPs can promote the photoaging of coexisting MPs, enhancing the release of harmful substances from aged MPs and further impacting microbial-associated carbon dynamics due to disrupted metabolic activity. Therefore, it is imperative to deepen our understanding of the adverse effects and mechanisms of MPs on wetland health and carbon cycles. Future strategies should incorporate microbial regulation and ecological engineering techniques to develop effective methodologies aimed at maintaining the sustainable carbon sequestration capacity of wetlands affected by MP contamination.