The Dynamic Roles of Red Blood Cell in Microcirculation

Erythrocytes (otherwise known as red blood cells (RBCs)), are the most common cell type in the body. They are responsible for oxygen (O2) transportation as well as carbon dioxide (CO2) exchange. Different from most cells, red cells have no nuclei in mammals due to the enucleation during the maturation. The structure of erythrocytes was shown to have a phospholipid bilayer membrane, membrane proteins and cell skeleton. It provides the stability that RBCs need for the circulation in the body systems. Also, this well-established structure makes it possible for them to accomplish ion and gas exchange, which therefore keeps the osmolality and pressure stable for extracellular and intracellular environment. Although a great variety of red cell characteristics have been investigated, the mechanism and kinetics of RBCs under certain environmental stimulation have not been well studied. In this work, we studied the development of cell membrane by testing the deformability change of erythrocytes during maturation. With the design of our microfluidic channels in ex vivo experiments, we then learned that RBC can work not only as O2 transporter but also as oxygen sensor itself. When oxygen level decrease, TBC membrane becomes softer and leads to blood flow increase eventually. We then investigated the mechanism of RBC membrane change on a molecular level to study the mechanism of RBC deformability change under hypoxia. We matched our findings in both in vivo and ex vivo experiments. Via in vivo experiments, we could even connect cerebral circulation to neuroactivity. Furthermore, the behavior of RBCs under hypoxia and in shear flow, such as the ATP release, was studied as well via ex vivo experiments. In the study, we focused on the mechanosensitive channel Piezo1 on RBC membrane and found the connection between this ion channel and RBC ATP release

Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes

Symptom information is primarily documented in free-text clinical notes and is not directly accessible for downstream applications. To address this challenge, information extraction approaches that can handle clinical language variation across different institutions and specialties are needed. In this paper, we present domain generalization for symptom extraction using pretraining and fine-tuning data that differs from the target domain in terms of institution and/or specialty and patient population. We extract symptom events using a transformer-based joint entity and relation extraction method. To reduce reliance on domain-specific features, we propose a domain generalization method that dynamically masks frequent symptoms words in the source domain. Additionally, we pretrain the transformer language model (LM) on task-related unlabeled texts for better representation. Our experiments indicate that masking and adaptive pretraining methods can significantly improve performance when the source domain is more distant from the target domain

Sun sensor design and test of a micro satellite

According to the requirement of small satellite, this paper designed a digital sun sensor which diaphragm is a V-shaped cross-section structure. Using Position Sensitive Detector (PSD) as the light detector, we designed the V-shaped cross-section structure based on the pinhole imaging principle. The sun sensor realized the accurate calculation for two axis sun angle of the sun sensor. The mechanical test, thermal test and testing of the sun sensor are designed and carried out. The mechanical test and thermal test results verify the stability of the sun sensor. Testing result shows that the detection angle can reach (120°)×(120°), and the attitude determination accuracy is better than 6” in the entire viewing field. The mass, volume and power consumption of the sun sensor is 0.177 kg, 78 mm×77 mm×21 mm and 0.25 W. The sun sensor has low power consumption, large viewing angle and high precision characteristics, which realized the sun sensor the miniaturization and meet the requirements of the micro satellite. Its performance has been verified in orbit

RegionBLIP: A Unified Multi-modal Pre-training Framework for Holistic and Regional Comprehension

In this work, we investigate extending the comprehension of Multi-modal Large Language Models (MLLMs) to regional objects. To this end, we propose to extract features corresponding to regional objects as soft prompts for LLM, which provides a straightforward and scalable approach and eliminates the need for LLM fine-tuning. To effectively extract regional features from regular image features and irregular point cloud features, we present a novel and unified position-assisted feature extraction module. Furthermore, training an MLLM from scratch is highly time-consuming. Thus, we propose incrementally extending existing pre-trained MLLMs to comprehend more modalities and the regional objects of those modalities. Specifically, we freeze the Q-Former from BLIP-2, an impressive MLLM, and optimize the modality-specific Lora parameters in Q-Former and LLM for each newly introduced modality. The freezing of the Q-Former eliminates the need for extensive pre-training on massive image-text data. The freezed Q-Former pre-trained from massive image-text data is also beneficial for the pre-training on image-region-text data. We name our framework RegionBLIP. We pre-train RegionBLIP on image-region-text, point-cloud-text, and point-cloud-region-text data. Experimental results verify that \Ours{} can preserve the image comprehension capability of BILP-2 and further gain a comprehension of the newly introduced point cloud modality and regional objects. The Data, Code, and Pre-trained models will be available at https://github.com/mightyzau/RegionBLIP

NF-Atlas: Multi-Volume Neural Feature Fields for Large Scale LiDAR Mapping

LiDAR Mapping has been a long-standing problem in robotics. Recent progress in neural implicit representation has brought new opportunities to robotic mapping. In this paper, we propose the multi-volume neural feature fields, called NF-Atlas, which bridge the neural feature volumes with pose graph optimization. By regarding the neural feature volume as pose graph nodes and the relative pose between volumes as pose graph edges, the entire neural feature field becomes both locally rigid and globally elastic. Locally, the neural feature volume employs a sparse feature Octree and a small MLP to encode the submap SDF with an option of semantics. Learning the map using this structure allows for end-to-end solving of maximum a posteriori (MAP) based probabilistic mapping. Globally, the map is built volume by volume independently, avoiding catastrophic forgetting when mapping incrementally. Furthermore, when a loop closure occurs, with the elastic pose graph based representation, only updating the origin of neural volumes is required without remapping. Finally, these functionalities of NF-Atlas are validated. Thanks to the sparsity and the optimization based formulation, NF-Atlas shows competitive performance in terms of accuracy, efficiency and memory usage on both simulation and real-world datasets

Suppression of <i>TREX1</i> deficiency-induced cellular senescence and interferonopathies by inhibition of DNA damage response

TREX1 encodes a major DNA exonuclease and mutations of this gene are associated with type I interferonopathies in human. Mice with Trex1 deletion or mutation have shortened life spans accompanied by a senescence-associated secretory phenotype. However, the contribution of cellular senescence in TREX1 deficiency-induced type I interferonopathies remains unknown. We found that features of cellular senescence present in Trex1−/− mice are induced by multiple factors, particularly DNA damage. The cGAS-STING and DNA damage response pathways are required for maintaining TREX1 deletion-induced cellular senescence. Inhibition of the DNA damage response, such as with Checkpoint kinase 2 (CHK2) inhibitor, partially alleviated progression of type I interferonopathies and lupus-like features in the mice. These data provide insights into the initiation and development of type I interferonopathies and lupus-like diseases, and may help inform the development of targeted therapeutics

Oxylipin transport by lipoprotein particles and its functional implications for cardiometabolic and neurological disorders

Lipoprotein metabolism is critical to inflammation. While the periphery and central nervous system (CNS) have separate yet connected lipoprotein systems, impaired lipoprotein metabolism is implicated in both cardiometabolic and neurological disorders. Despite the substantial investigation into the composition, structure and function of lipoproteins, the lipoprotein oxylipin profiles, their influence on lipoprotein functions, and their potential biological implications are unclear. Lipoproteins carry most of the circulating oxylipins. Importantly, lipoprotein-mediated oxylipin transport allows for endocrine signaling by these lipid mediators, long considered to have only autocrine and paracrine functions. Alterations in plasma lipoprotein oxylipin composition can directly impact inflammatory responses of lipoprotein metabolizing cells. Similar investigations of CNS lipoprotein oxylipins are non-existent to date. However, as APOE4 is associated with Alzheimer's disease-related microglia dysfunction and oxylipin dysregulation, ApoE4-dependent lipoprotein oxylipin modulation in neurological pathologies is suggested. Such investigations are crucial to bridge knowledge gaps linking oxylipin- and lipoprotein-related disorders in both periphery and CNS. Here, after providing a summary of existent literatures on lipoprotein oxylipin analysis methods, we emphasize the importance of lipoproteins in oxylipin transport and argue that understanding the compartmentalization and distribution of lipoprotein oxylipins may fundamentally alter our consideration of the roles of lipoprotein in cardiometabolic and neurological disorders

Robust Super-Resolution Imaging Based on a Ring Core Fiber with Orbital Angular Momentum

Single fiber imaging technology offers unique insights for research and inspection in difficult to reach and narrow spaces. In particular, ultra-compact multimode fiber (MMF) imaging, has received increasing interest over the past decade. However, MMF imaging will be seriously distorted when subjected to dynamic perturbations due to time-varying mode coupling, and the imaging of space objects via Gaussian beam will be relatively degraded at the edge due to insufficient contrast. Here, a robust super-resolution imaging method based on a ring core fiber (RCF) with orbital angular momentum (OAM) has been proposed and experimentally demonstrated. The OAM modes propagating in the RCF form a series of weakly-coupled mode groups, making our imaging system robust to external perturbations. In addition, a spiral phase plate is used as a vortex filter to produce OAM for edge enhancement, thus improving the image resolution. Furthermore, a few-shot U-Transformer neural network is proposed to enhance the resilience of the developed RCF-OAM imaging system against environmental perturbations. Finally, the developed RCF-OAM imaging system achieves biological image transmission, demonstrating the practicality of our scheme. This pioneering RCF OAM imaging system may have broad applications, potentially revolutionising fields such as biological imaging and industrial non-destructive testing