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BackgroundAngiogenesis is an important protective mechanism after ischemic stroke (IS). We aimed to investigate the effect of insulin like growth factor 2 mRNA binding protein 2 (IGF2BP2) O-GlcNAcylation in angiogenesis after IS.MethodsIGF2BP2 O-GlcNAcylation and ubiquitination were detected by Co-immunoprecipitation. IGF2BP2 and PPP2CA mRNA binding relationship was verified by RNA immunoprecipitation (RIP) and RNA-pull down. Protein phosphatase 2 catalytic subunit alpha (PPP2CA) m6A modification level was measured by Methylated RIP. Cell viability, apoptosis and angiogenesis were assessed by Cell Counting Kit-8, TUNEL and tube formation assays. 2,3,5-Triphenyltetrazolium Chloride staining was used for infarct area, Nissl staining was used for neuronal damage.ResultsO-GlcNAc transferase (OGT) mediated O-GlcNAcylation of IGF2BP2 at the 162 serine site. O-GlcNAcylation stabilized IGF2BP2 by inhibiting its ubiquitination and degradation. O-GlcNAcylation of IGF2BP2 ameliorated brain injury in middle cerebral arterial occlusion/reperfusion (MCAO/R) rats by alleviating oxygen-glucose deprivation/reperfusion (OGD/R)-induced brain microvascular endothelial cell (BMEC) injury and promoting BMEC angiogenesis through the Hippo/Yes1-associated transcriptional regulator (YAP) signaling pathway. IGF2BP2 stabilized PPP2CA in an m6A-dependent manner. IGF2BP2 upregulated PPP2CA to reduce YAP phosphorylation and promote YAP nuclear translocation.ConclusionO-GlcNAcylation of IGF2BP2 reduces its ubiquitination and degradation, thereby stabilizing PPP2CA by m6A modification. Upregulated PPP2CA drives dephosphorylation and nuclear translocation of YAP, which promotes angiogenesis to alleviate the injury after IS.Graphical Abstract

Dyslipidemia is a leading risk factor for cardiovascular and cerebrovascular diseases. By collecting the blood lipid profiles among adult residents of Shenmu City in Shaanxi Province, China, we aim to assess and elucidate the prevalence and risk factors of dyslipidemia in this city. Stratified multistage sampling was used to survey 4,598 permanent adult residents in five areas of Shenmu (2 communities in the county seat, 2 in the southern area and 2 in the northern area) from September 2019 to December 2019. Questionnaire surveys and physical examinations were conducted. Data were analyzed using SPSS software version 26.0. The average level of total cholesterol (TC) is 4.47mmol/L, that of triglyceride (TG) 1.32mmol/L, high-density lipoprotein cholesterol (HDL-C) 1.27mmol/L, apolipoprotein A1 (ApoA1) 1.44g/L, low-density lipoprotein cholesterol (LDL-C) 2.7mmol/L and apolipoprotein B (ApoB) 0.97g/L. The prevalence of hypercholesterolemia (HTC), hypertriglyceridemia (HTG), low high-density lipoprotein (HDL-C) and high low-density lipoprotein (LDL-C) is 22.4%, 33.3%, 14.5%, and 5.81%, respectively, and the overall prevalence of dyslipidemia is 48.27%. Furthermore, blood lipid levels and prevalence of dyslipidemia vary by region, age, gender, occupation and educational level. Nine risk factors of dyslipidemia were identified, which are living in county seat or northern industrial area, increasing age, male, overweight or obesity, abdominal obesity, smoking, hypertension, abnormal glucose metabolism (pre-diabetes or diabetes) and hyperuricemia. The blood lipid levels and dyslipidemia prevalence of adults in Shenmu City are higher comparing to national averages of China. Combining risk factors of dyslipidemia, early detection and public health interventions are necessary in high-risk population for associated cardiovascular and cerebrovascular diseases prevention.

This article introduces a diffractive neural network-enabled spectral object detection approach (DNN-SOD) to efficiently process massive sky-based multidimensional light field data. DNN-SOD combines the novel exploitation of target spectral features with the intrinsic parallelism of optical computing to process multidimensional information efficiently. DNN-SOD detects targets by segmenting the spectral data cube and processing it with the DNN. The DNN maps spectral intensity to the designated area of the detector, then reconstructs spectral curves, and differentiates targets by comparing them with reference spectral signatures. Classification results from individual sub-spectral data cubes are compiled in sequence, enabling accurate target detection. Simulation results indicate that the architecture achieved an accuracy of 91.56% on the MNIST multi-spectral dataset and 84.27% on the infrared target multi-spectral dataset, validating its feasibility for target detection. This architecture represents an innovative outcome at the intersection of remote sensing and optical computing, significantly advancing the dissemination and practical adoption of optical computing in the field.

Planetary, lunar, and deep space exploration has become the frontier of remote sensing science, and three-dimensional (3D) positioning imaging technology is an important part of lunar and deep space exploration. This paper presents a novel passive 3D imaging method based on the photonics integrated interference computational imaging system. This method uses a photonics integrated interference imaging system with a complex lens array. The midpoints of the interference baselines formed by these lenses are not completely overlapped. The distance between the optical axis and the two lenses of the interference baseline are not equal. The system is used to obtain the complex coherence factor of the object space at a limited working distance, and the image evaluation optimization algorithm is used to obtain the clear images and 3D information of the targets of interest. The simulation results show that this method is effective for the working scenes with targets located at single or multiple limited working distances. The sharpness evaluation function of the target presents a good unimodality near its actual distance. The experimental results of the interference of broad-spectrum light show that the theoretical basis of this method is feasible.

Background and aims ‘Earth-air’ refers to air in the vadose zone (VZ). Barometric pumping results in the rising and falling of earth-air in the VZ. Earth-air vertical movement (EVM) has an important effect on water transport in the VZ. However, phreatic water is an important source of water in the Loess Plateau, the effect of earth-air on water content is unclear. Methods This paper aims to reveal the effect of earth-air on the water content in the VZ of loessal soil by calculating the amount of earth-air and measuring volumetric water content (VWC), temperature, and relative humidity (RH). Results Our results showed that the variation of the VWC in the Loess Plateau is directly proportional to the amount of EVM. It is the sum of water vapor gains and losses from earth-air. Also, the amount of water in the earth-air is positively correlated with the frequency of the fluctuations in the atmospheric pressure (AP), thickness of the loess layer, aerated porosity, and gradient of the soil water vapor concentration. The correlation of recorded every 10 min for 3 years between the calculated VWC of the soil and the monitored values is 0.76. Conclusion This study reveals a new way in which soil water migrates in the Loess Plateau and furthers our understanding of the spatiotemporal distribution mechanism. It also provides a scientific basis for the utilization of scarce water resources in semi-arid areas such as the Loess Plateau region.

Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational efficiency due to the closest point computation. Most implementations of the ICP algorithm attempt to deal with this issue by modifying correspondence or adding coarse registration. However, this leads to sacrificing the accuracy rate or adding the algorithm complexity. This paper proposes a hierarchical optimization approach that includes improved voxel filter and Multi-Scale Voxelized Generalized-ICP (MVGICP) for 3D point cloud registration. By combining traditional voxel sampling with point density, the outlier filtering and downsample are successfully realized. Through multi-scale iteration and avoiding closest point computation, MVGICP solves the local minimum problem and optimizes the operation efficiency. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of outlier filtering and registration performance.

The waveguide chip with a phase measurement function has garnered significant attention in the field of imaging optics, emerging as a crucial component in optical interferometric imaging systems. Enhancing the working bandwidth of these waveguide chips is essential for improving the imaging quality of interferometric systems. However, most existing designs primarily focus on narrow bands, with no reported research on broadband designs. This paper introduces a novel broadband waveguide chip design that incorporates a phase measurement function. We explore the fundamental structure and working principle of this innovative design. Fabricated on a silicon substrate, the chip features a silicon dioxide cladding layer and a germanium-doped silicon dioxide core layer, strategically optimized for performance. Utilizing the Beam Propagation Method (BPM), we conduct detailed simulations to determine the optimal device parameters. The simulation results demonstrate the effectiveness of our design, showing a phase measurement deviation of approximately 5° at a center wavelength of 1550 nm across a 300 nm wavelength range. The loss of the device is approximately 0.8 dB. These findings provide a solid foundation for future experimental implementations and fabrications, offering both a theoretical framework and technical reference for advancing the practical use of broadband waveguide chips with phase measurement functions in optical interferometric imaging.

The liver is vulnerable to oxidative stress-induced damage, which leads to many diseases, including alcoholic liver disease (ALD). Liver disease endanger people’s health, and the incidence of ALD is increasing; therefore, prevention is very important. 7-phloro-eckol (7PE) is a seaweed polyphenol, which was isolated from Ecklonia cava in a previous study. In this study, the antioxidative stress effect of 7PE on HepG2/CYP2E1 cells was evaluated by alcohol-induced cytotoxicity, DNA damage, and expression of related inflammation and apoptosis proteins. The results showed that 7PE caused alcohol-induced cytotoxicity to abate, reduced the amount of reactive oxygen species (ROS) and nitric oxide (NO), and effectively inhibited DNA damage in HepG2/CYP2E1 cells. Additionally, the expression levels of glutathione (GSH), superoxide dismutase (SOD), B cell lymphoma 2 (Bcl-2), and Akt increased, while γ-glutamyltransferase (GGT), Bcl-2 related x (Bax), cleaved caspase-3, cleaved caspase-9, nuclear factor-κB (NF-κB), and JNK decreased. Finally, molecular docking proved that 7PE could bind to BCL-2 and GSH protein. These results indicate that 7PE can alleviate the alcohol-induced oxidative stress injury of HepG2 cells and that 7PE may have a potential application prospect in the future development of antioxidants.

Egocentric activity recognition in first-person video (FPV) requires fine-grained matching of the camera wearer’s action and the objects being operated. The traditional method used for third-person action recognition does not suffice because of (1) the background ego-noise introduced by the unstructured movement of the wearable devices caused by body movement; (2) the small-sized and fine-grained objects with single scale in FPV. Size compensation is performed to augment the data. It generates a multi-scale set of regions, including multi-size objects, leading to superior performance. We compensate for the optical flow to eliminate the camera noise in motion. We developed a novel two-stream convolutional neural network-recurrent attention neural network (CNN-RAN) architecture: spatial temporal attention on compensation information (STAC), able to generate generic descriptors under weak supervision and focus on the locations of activated objects and the capture of effective motion. We encode the RGB features using a spatial location-aware attention mechanism to guide the representation of visual features. Similar location-aware channel attention is applied to the temporal stream in the form of stacked optical flow to implicitly select the relevant frames and pay attention to where the action occurs. The two streams are complementary since one is object-centric and the other focuses on the motion. We conducted extensive ablation analysis to validate the complementarity and effectiveness of our STAC model qualitatively and quantitatively. It achieved state-of-the-art performance on two egocentric datasets.