153 research outputs found
Microstructure reconstruction of 2D/3D random materials via diffusion-based deep generative models
Microstructure reconstruction serves as a crucial foundation for establishing
Process-Structure-Property (PSP) relationship in material design. Confronting
the limitations of variational autoencoder and generative adversarial network
within generative modeling, this study adopted the denoising diffusion
probability model (DDPM) to learn the probability distribution of
high-dimensional raw data and successfully reconstructed the microstructures of
various composite materials, such as inclusion materials, spinodal
decomposition materials, chessboard materials, fractal noise materials, and so
on. The quality of generated microstructure was evaluated using quantitative
measures like spatial correlation functions and Fourier descriptor. On this
basis, this study also successfully achieved the regulation of microstructure
randomness and the generation of gradient materials through continuous
interpolation in latent space using denoising diffusion implicit model (DDIM).
Furthermore, the two-dimensional microstructure reconstruction is extended to
three-dimensional framework and integrates permeability as a feature encoding
embedding. This enables the conditional generation of three-dimensional
microstructures for random porous materials within a defined permeability
range. The permeabilities of these generated microstructures were further
validated through the application of the Boltzmann method
MixReorg: Cross-Modal Mixed Patch Reorganization is a Good Mask Learner for Open-World Semantic Segmentation
Recently, semantic segmentation models trained with image-level text
supervision have shown promising results in challenging open-world scenarios.
However, these models still face difficulties in learning fine-grained semantic
alignment at the pixel level and predicting accurate object masks. To address
this issue, we propose MixReorg, a novel and straightforward pre-training
paradigm for semantic segmentation that enhances a model's ability to
reorganize patches mixed across images, exploring both local visual relevance
and global semantic coherence. Our approach involves generating fine-grained
patch-text pairs data by mixing image patches while preserving the
correspondence between patches and text. The model is then trained to minimize
the segmentation loss of the mixed images and the two contrastive losses of the
original and restored features. With MixReorg as a mask learner, conventional
text-supervised semantic segmentation models can achieve highly generalizable
pixel-semantic alignment ability, which is crucial for open-world segmentation.
After training with large-scale image-text data, MixReorg models can be applied
directly to segment visual objects of arbitrary categories, without the need
for further fine-tuning. Our proposed framework demonstrates strong performance
on popular zero-shot semantic segmentation benchmarks, outperforming GroupViT
by significant margins of 5.0%, 6.2%, 2.5%, and 3.4% mIoU on PASCAL VOC2012,
PASCAL Context, MS COCO, and ADE20K, respectively
Hypericin Inhibit Alpha-Coronavirus Replication by Targeting 3CL Protease
The porcine epidemic diarrhea virus (PEDV) is an Alphacoronavirus (α-CoV) that causes high mortality in infected piglets, resulting in serious economic losses in the farming industry. Hypericin is a dianthrone compound that has been shown as an antiviral activity on several viruses. Here, we first evaluated the antiviral effect of hypericin in PEDV and found the viral replication and egression were significantly reduced with hypericin post-treatment. As hypericin has been shown in SARS-CoV-2 that it is bound to viral 3CLpro, we thus established a molecular docking between hypericin and PEDV 3CLpro using different software and found hypericin bound to 3CLpro through two pockets. These binding pockets were further verified by another docking between hypericin and PEDV 3CLpro pocket mutants, and the fluorescence resonance energy transfer (FRET) assay confirmed that hypericin inhibits the PEDV 3CLpro activity. Moreover, the alignments of α-CoV 3CLpro sequences or crystal structure revealed that the pockets mediating hypericin and PEDV 3CLpro binding were highly conserved, especially in transmissible gastroenteritis virus (TGEV). We then validated the anti-TGEV effect of hypericin through viral replication and egression. Overall, our results push forward that hypericin was for the first time shown to have an inhibitory effect on PEDV and TGEV by targeting 3CLpro, and it deserves further attention as not only a pan-anti-α-CoV compound but potentially also as a compound of other coronaviral infections
Wright-Fisher diffusion bridges
The trajectory of the frequency of an allele which begins at at time and is known to have frequency at time can be modelled by the bridge process of the Wright-Fisher diffusion. Bridges when are particularly interesting because they model the trajectory of the frequency of an allele which appears at a time, then is lost by random drift or mutation after a time . The coalescent genealogy back in time of a population in a neutral Wright-Fisher diffusion process is well understood. In this paper we obtain a new interpretation of the coalescent genealogy of the population in a bridge from a time . In a bridge with allele frequencies of 0 at times 0 and the coalescence structure is that the population coalesces in two directions from to and to such that there is just one lineage of the allele under consideration at times and .
The genealogy in Wright-Fisher diffusion bridges with selection is more complex than in the neutral model, but still with the property of the population branching and coalescing in two directions from time . The density of the frequency of an allele at time is expressed in a way that shows coalescence in the two directions.
A new algorithm for exact simulation of a neutral Wright-Fisher bridge is derived. This follows from knowing the density of the frequency in a bridge and exact simulation from the Wright-Fisher diffusion. The genealogy of the neutral Wright-Fisher bridge is also modelled by branching P\'olya urns, extending a representation in a Wright-Fisher diffusion. This is a new very interesting representation that relates Wright-Fisher bridges to classical urn models in a Bayesian setting.
This paper is dedicated to the memory of Paul Joyce
Neuroprotective effect of arctigenin via upregulation of P-CREB in mouse primary neurons and human SH-SY5Y neuroblastoma cells.
Arctigenin (Arc) has been shown to act on scopolamine-induced memory deficit mice and to provide a neuroprotective effect on cultured cortical neurons from glutamate-induced neurodegeneration through mechanisms not completely defined. Here, we investigated the neuroprotective effect of Arc on H89-induced cell damage and its potential mechanisms in mouse cortical neurons and human SH-SY5Y neuroblastoma cells. We found that Arc prevented cell viability loss induced by H89 in human SH-SY5Y cells. Moreover, Arc reduced intracellular beta amyloid (Aβ) production induced by H89 in neurons and human SH-SY5Y cells, and Arc also inhibited the presenilin 1(PS1) protein level in neurons. In addition, neural apoptosis in both types of cells, inhibition of neurite outgrowth in human SH-SY5Y cells and reduction of synaptic marker synaptophysin (SYN) expression in neurons were also observed after H89 exposure. All these effects induced by H89 were markedly reversed by Arc treatment. Arc also significantly attenuated downregulation of the phosphorylation of CREB (p-CREB) induced by H89, which may contribute to the neuroprotective effects of Arc. These results demonstrated that Arc exerted the ability to protect neurons and SH-SY5Y cells against H89-induced cell injury via upregulation of p-CREB
Administration of Intranasal Insulin During Cardiopulmonary Resuscitation Improves Neurological Outcomes After Cardiac Arrest
INTRODUCTION: Over 325,000 people die from cardiac arrest each year. Prognosis is poor and survivors typically experience persistent neurologic deficits. Currently, neuroprotective treatments to reduce brain injury in cardiac arrest survivors are limited and ineffective. This study evaluates the potential neuroprotection induced by high dose intranasal insulin (HD-IN-I) in a rodent model of asphyxial cardiac arrest.
METHODS: Male Long Evans rats were block randomized to sham-operated controls or 8-minute asphyxial cardiac arrest treated with placebo or HD-IN-I at the onset of CPR. To investigate mechanism of action, hippocampi were collected 30 minutes post-ROSC and analyzed by Western blot for phosphorylation of Akt. To assess long-term functional outcomes, neurobehavioral evaluation was conducted using neurologic function scores daily and Barnes maze, Rotarod, and passive avoidance on days 7-10 post-ROSC. Histologic quantification of surviving hippocampal CA1 pyramidal neurons was also conducted.
RESULTS: Hippocampal phospho-Akt/total Akt ratio increased 2-fold in the placebo group and 5.7-fold in HD-IN-I group relative to shams (p \u3c 0.05). Rats treated with HD-IN-I had significantly improved performance on Rotarod, Barnes maze, and passive avoidance (p \u3c 0.05). HD-IN-I had no significant effect on ROSC rate, 10-day survival, systemic glycemic response, or on the number of surviving CA1 pyramidal neurons compared to placebo treatment.
DISCUSSION: This study is the first to demonstrate that HD-IN-I administered at the onset of CPR, causes phosphorylation of brain Akt and results in significant neuroprotection. This primary work strongly suggests that intranasal insulin could be the first highly effective neuroprotective treatment for cardiac arrest patients
Computed tomography guided electromagnetic navigation system in percutaneous laser ablation for treating primary lung cancer: a case report
BackgroundThe majority of patients of lung cancer have already lost the chance of surgery at the time of diagnosis. Percutaneous local thermal ablation is a precise minimally invasive technique and a viable alternative to surgical treatment. Compared with radiofrequency ablation and microwave ablation, percutaneous laser ablation for the treatment of lung tumors is less commonly used and reported, especially for primary lung cancer.Case presentationA 63-year-old male patient with mixed pulmonary nodules selected computed tomography-guided electromagnetic navigation system for percutaneous biopsy and laser ablation therapy. The puncture point was determined through Computed tomography scanning, along with the placement of the electromagnetic navigation system locators. After rapid on-site evaluation and pathological examination of the puncture tissue specimen, the diagnosis of lung adenocarcinoma was confirmed. A 980-nanometer wavelength semiconductor laser fiber was inserted into the appropriate position guided by the electromagnetic navigation system. Subsequently, a power of 7 watt was applied to ablate the tumor for 30 seconds, then pause for 60 seconds before repeating the procedure. Positron emission tomography-Computed tomography examination was performed 1 month after operation, suggesting complete response of the tumor.ConclusionHere, we present a case of percutaneous laser ablation treatment for primary lung cancer guided by computed tomography-electromagnetic navigation system. As a more precise, shorter duration, impedance-independent, safe and effective minimally invasive thermal ablation method, it is expected to gain wider application and become a novel alternative for surgical treatment
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