395 research outputs found
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
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
Turnover intention of nurses in public hospitals and its association with quality of working life: a cross-sectional survey in six provinces in China
ObjectivesHigh turnover intention can exacerbate the workforce shortage of nurses. This study aimed to determine the level of turnover intention of public hospital nurses in China and its associated factors.MethodsA cross-sectional questionnaire survey of 2,863 nurses was conducted in 48 public hospitals across six provinces in mainland China, measuring the sociodemographic (gender, age, marital status, and monthly basic salary) and work characteristics (professional title, workload, night sleep deprivation, and workplace violence) of respondents, their quality of working life (QWL), and turnover intention. Multivariate logistic regression models were established to determine the association between QWL and turnover intention after adjustment for variations of the sociodemographic and work characteristics.ResultsOverall, 42.8% of respondents reported turnover intention. Higher QWL scores (AOR = 0.824 for job and career satisfaction, p < 0.001; AOR = 0.894 for professional pride, p < 0.001; AOR = 0.911 for balance between work and family, p < 0.05) were associated with lower turnover intention. Workplace violence was the strongest predictor of higher turnover intention (AOR = 3.003–4.767) amongst the sociodemographic and work characteristics, followed by an age between 30 and 40 years (AOR = 1.457 relative to <30 years), and night sleep deprivation (AOR = 1.391–1.808). Senior professional title had a protective effect (AOR = 0.417 relative to no title) on turnover intention.ConclusionHigh levels of turnover intention are evident across China in nurses employed by public hospitals, in particular in those aged between 30 and 40 years. Low QWL and poor work environment are significant predictors of turnover intention
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