272 research outputs found
Static compression regulates OPG expression in periodontal ligament cells via the CAMK II pathway
Objective This study aimed to investigate the potential role of CAMK II pathway in the compression-regulated OPG expression in periodontal ligament cells (PDLCs). Material and Methods The PDL tissue model was developed by 3-D culturing human PDLCs in a thin sheet of poly lactic-co-glycolic acid (PLGA) scaffolds, which was subjected to static compression of 25 g/cm2 for 3, 6 and 12 h, with or without treatment of KN-93. After that, the expression of OPG, RANKL and NFATC2 was investigated through real-time PCR and western blot analysis. Results After static compression, the NFATC2 and RANKL expression was significantly up-regulated, while partially suppressed by KN-93 for 6 and 12 h respectively. The OPG expression was significantly down-regulated by compression in 3 h, started to elevate in 6 h, and significantly up-regulated in 12 h. The up-regulation after 12 h was significantly suppressed by KN-93. Conclusions Long-term static compression increases OPG expression in PDLCs, at least partially, via the CAMK II pathway
Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models
Fine-tuning pre-trained vision-language models (VLMs), e.g., CLIP, for the
open-world generalization has gained increasing popularity due to its practical
value. However, performance advancements are limited when relying solely on
intricate algorithmic designs for a single model, even one exhibiting strong
performance, e.g., CLIP-ViT-B/16. This paper, for the first time, explores the
collaborative potential of leveraging much weaker VLMs to enhance the
generalization of a robust single model. The affirmative findings motivate us
to address the generalization problem from a novel perspective, i.e., ensemble
of pre-trained VLMs. We introduce three customized ensemble strategies, each
tailored to one specific scenario. Firstly, we introduce the zero-shot
ensemble, automatically adjusting the logits of different models based on their
confidence when only pre-trained VLMs are available. Furthermore, for scenarios
with extra few-shot samples, we propose the training-free and tuning ensemble,
offering flexibility based on the availability of computing resources. The
proposed ensemble strategies are evaluated on zero-shot, base-to-new, and
cross-dataset generalization, achieving new state-of-the-art performance.
Notably, this work represents an initial stride toward enhancing the
generalization performance of VLMs via ensemble. The code is available at
https://github.com/zhiheLu/Ensemble_VLM.git.Comment: Technical repor
Design of an Aircraft Rolling Bearings Platform and its Thermal Performance Evaluation
The thermal instability is one crucial factor leading to low bearing operation performance. This paper presents a novel experiment device for thermal performance investigation of an aircraft rolling bearings. A bidirectional fixing structure was designed to balance the spindle thermal deformation. The hydraulic loading was used and the oil injection manner was adopted in the new device. Experimental test was conducted using the new device and experimental results were compared with the calculation based on the temperature and thermal nodes theory. The comparison demonstrates that the temperature distribution trends between the theoretical and experimental results remained the same; specifically, the error between the theoretical and experimental results was 1.0 % under the condition of 200 kg load and 2250 rpm driving speed. Consequently, the analysis result shows that the new device is feasible and reliable to provide precise thermal characteristics for the aircraft rolling bearings
Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement
In terms of 3D imaging speed and system cost, the single-camera system
projecting single-frequency patterns is the ideal option among all proposed
Fringe Projection Profilometry (FPP) systems. This system necessitates a robust
spatial phase unwrapping (SPU) algorithm. However, robust SPU remains a
challenge in complex scenes. Quality-guided SPU algorithms need more efficient
ways to identify the unreliable points in phase maps before unwrapping.
End-to-end deep learning SPU methods face generality and interpretability
problems. This paper proposes a hybrid method combining deep learning and
traditional path-following for robust SPU in FPP. This hybrid SPU scheme
demonstrates better robustness than traditional quality-guided SPU methods,
better interpretability than end-to-end deep learning scheme, and generality on
unseen data. Experiments on the real dataset of multiple illumination
conditions and multiple FPP systems differing in image resolution, the number
of fringes, fringe direction, and optics wavelength verify the effectiveness of
the proposed method.Comment: 26 page
Prediction Calibration for Generalized Few-shot Semantic Segmentation
Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image
pixel into either base classes with abundant training examples or novel classes
with only a handful of (e.g., 1-5) training images per class. Compared to the
widely studied Few-shot Semantic Segmentation FSS, which is limited to
segmenting novel classes only, GFSS is much under-studied despite being more
practical. Existing approach to GFSS is based on classifier parameter fusion
whereby a newly trained novel class classifier and a pre-trained base class
classifier are combined to form a new classifier. As the training data is
dominated by base classes, this approach is inevitably biased towards the base
classes. In this work, we propose a novel Prediction Calibration Network PCN to
address this problem. Instead of fusing the classifier parameters, we fuse the
scores produced separately by the base and novel classifiers. To ensure that
the fused scores are not biased to either the base or novel classes, a new
Transformer-based calibration module is introduced. It is known that the
lower-level features are useful of detecting edge information in an input image
than higher-level features. Thus, we build a cross-attention module that guides
the classifier's final prediction using the fused multi-level features.
However, transformers are computationally demanding. Crucially, to make the
proposed cross-attention module training tractable at the pixel level, this
module is designed based on feature-score cross-covariance and episodically
trained to be generalizable at inference time. Extensive experiments on
PASCAL- and COCO- show that our PCN outperforms the
state-the-the-art alternatives by large margins.Comment: Technical Repor
GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph
Adapter-style efficient transfer learning (ETL) has shown excellent
performance in the tuning of vision-language models (VLMs) under the low-data
regime, where only a few additional parameters are introduced to excavate the
task-specific knowledge based on the general and powerful representation of
VLMs. However, most adapter-style works face two limitations: (i) modeling
task-specific knowledge with a single modality only; and (ii) overlooking the
exploitation of the inter-class relationships in downstream tasks, thereby
leading to sub-optimal solutions. To mitigate that, we propose an effective
adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual
adapter by explicitly modeling the dual-modality structure knowledge (i.e., the
correlation of different semantics/classes in textual and visual modalities)
with a dual knowledge graph. In particular, the dual knowledge graph is
established with two sub-graphs, i.e., a textual knowledge sub-graph, and a
visual knowledge sub-graph, where the nodes and edges represent the
semantics/classes and their correlations in two modalities, respectively. This
enables the textual feature of each prompt to leverage the task-specific
structure knowledge from both textual and visual modalities, yielding a more
effective classifier for downstream tasks. Extensive experimental results on 11
benchmark datasets reveal that our GraphAdapter significantly outperforms
previous adapter-based methods. The code will be released at
https://github.com/lixinustc/GraphAdapterComment: Accepted by NeurIPS 2023. The manuscript will be further revised
based on the review
Dental-Derived Mesenchymal Stem Cells: State of the Art
Mesenchymal stem cells (MSCs) could be identified in mammalian teeth. Currently, dental-derived MSCs (DMSCs) has become a collective term for all the MSCs isolated from dental pulp, periodontal ligament, dental follicle, apical papilla, and even gingiva. These DMSCs possess similar multipotent potential as bone marrow-derived MSCs, including differentiation into cells that have the characteristics of odontoblasts, cementoblasts, osteoblasts, chondrocytes, myocytes, epithelial cells, neural cells, hepatocytes, and adipocytes. Besides, DMSCs also have powerful immunomodulatory functions, which enable them to orchestrate the surrounding immune microenvironment. These properties enable DMSCs to have a promising approach in injury repair, tissue regeneration, and treatment of various diseases. This review outlines the most recent advances in DMSCs’ functions and applications and enlightens how these advances are paving the path for DMSC-based therapies
Potential Application of Copper Aspirinate in Preventing and Treating Thromboembolic Diseases
The efficacy of copper aspirinate against thrombotic diseases has been tested
in animal models. The results show that copper aspirinate, following ig pretreatment
for 7 days at 0.012mmol/kg markedly prolonged the bleeding time and inhibited the
mortality induced by arachidonic acid (AA) in mice. On cereral ischemia model
pretreatment with 0.018mmol/kg copper aspirinate ig significantly increased
survival of animals and the density of intact hippocampal CA1 cells and decreased
brain calcium concentration. Its anticerebral ischemia activity was superior to or
equal to nimodipine. It is, therefore, suggested that copper aspirinate is very promising
in becoming an antithrombotic drug in preventing and treating thrombotic diseases
Swollen lymph nodes may not be clinical manifestations of chronic myeloid leukemia: case report and revision of literature
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