RESUMO
MOTIVATION: Generative Adversarial Nets (GAN) achieve impressive performance for text-guided editing of natural images. However, a comparable utility of GAN remains understudied for spatial transcriptomics (ST) technologies with matched gene expression and biomedical image data. RESULTS: We propose In Silico Spatial Transcriptomic editing that enables gene expression-guided editing of immunofluorescence images. Using cell-level spatial transcriptomics data extracted from normal and tumor tissue slides, we train the approach under the framework of GAN (Inversion). To simulate cellular state transitions, we then feed edited gene expression levels to trained models. Compared to normal cellular images (ground truth), we successfully model the transition from tumor to normal tissue samples, as measured with quantifiable and interpretable cellular features. AVAILABILITY AND IMPLEMENTATION: https://github.com/CTPLab/SST-editing.
Assuntos
Neoplasias , Transcriptoma , Humanos , Perfilação da Expressão Gênica , Inversão Cromossômica , Edição de GenesRESUMO
BACKGROUND: Numerous studies have shown that older women with endometrial cancer have a higher risk of recurrence and cancer-related death. However, it remains unclear whether older age is a causal prognostic factor, or whether other risk factors become increasingly common with age. We aimed to address this question with a unique multimethod study design using state-of-the-art statistical and causal inference techniques on datasets of three large, randomised trials. METHODS: In this multimethod analysis, data from 1801 women participating in the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials were used for statistical analyses and causal inference. The cohort included 714 patients with intermediate-risk endometrial cancer, 427 patients with high-intermediate risk endometrial cancer, and 660 patients with high-risk endometrial cancer. Associations of age with clinicopathological and molecular features were analysed using non-parametric tests. Multivariable competing risk analyses were performed to determine the independent prognostic value of age. To analyse age as a causal prognostic variable, a deep learning causal inference model called AutoCI was used. FINDINGS: Median follow-up as estimated using the reversed Kaplan-Meier method was 12·3 years (95% CI 11·9-12·6) for PORTEC-1, 10·5 years (10·2-10·7) for PORTEC-2, and 6·1 years (5·9-6·3) for PORTEC-3. Both overall recurrence and endometrial cancer-specific death significantly increased with age. Moreover, older women had a higher frequency of deep myometrial invasion, serous tumour histology, and p53-abnormal tumours. Age was an independent risk factor for both overall recurrence (hazard ratio [HR] 1·02 per year, 95% CI 1·01-1·04; p=0·0012) and endometrial cancer-specific death (HR 1·03 per year, 1·01-1·05; p=0·0012) and was identified as a significant causal variable. INTERPRETATION: This study showed that advanced age was associated with more aggressive tumour features in women with endometrial cancer, and was independently and causally related to worse oncological outcomes. Therefore, our findings suggest that older women with endometrial cancer should not be excluded from diagnostic assessments, molecular testing, and adjuvant therapy based on their age alone. FUNDING: None.
Assuntos
Neoplasias do Endométrio , Humanos , Feminino , Neoplasias do Endométrio/patologia , Neoplasias do Endométrio/mortalidade , Fatores Etários , Idoso , Pessoa de Meia-Idade , Prognóstico , Ensaios Clínicos Controlados Aleatórios como Assunto , Fatores de Risco , Recidiva Local de Neoplasia/patologia , Recidiva Local de Neoplasia/epidemiologia , Idoso de 80 Anos ou maisRESUMO
BACKGROUND: Modeling heterogeneous disease states by data-driven methods has great potential to advance biomedical research. However, a comprehensive analysis of phenotypic heterogeneity is often challenged by the complex nature of biomedical datasets and emerging imaging methodologies. METHODS: Here, we propose a novel GAN Inversion-enabled Latent Eigenvalue Analysis (GILEA) framework and apply it to in silico phenome profiling and editing. RESULTS: We show the performance of GILEA using cellular imaging datasets stained with the multiplexed fluorescence Cell Painting protocol. The quantitative results of GILEA can be biologically supported by editing of the latent representations and simulation of dynamic phenotype transitions between physiological and pathological states. CONCLUSION: In conclusion, GILEA represents a new and broadly applicable approach to the quantitative and interpretable analysis of biomedical image data. The GILEA code and video demos are available at https://github.com/CTPLab/GILEA.
Assuntos
Simulação por Computador , Humanos , Software , Fenótipo , Processamento de Imagem Assistida por Computador/métodos , Algoritmos , Fenômica/métodosRESUMO
Ba0.65Sr0.35TiO2 (BST) nanopowders doped with Er3+ were prepared by sol-gel method. The absorption spectrum and photoluminescence (PL) spectrum of Er3+ : BST nanopowders was measured at room temperature. Based on the Judd-Ofelt theory, the intensity parameters of Er3+ in BST nanopowders were determined, omega2 = 0.993 x 10(-20) cm2, omega4 = 1.665 x 10(-20) cm2 and omega = 0.540 x 10(-20) cm2, and then the values of the line strengths, radiative transition probabilities and branching ratios of Er3+ were calculated. According to the PL spectrum, the emission bands centered at about 522, 545, 654 and 851 nm corresponding to 2H(11/2)-->4S(3/2-->4I(15/2), 4F(9/2)-->4I(15/2), and 4S(3/2-->4I(13/2) transition were observed, and the emission properties were also discussed. The results show that the Er3+ : BST nanomaterials are prospective candidates for applications in new photoelectric devices.
RESUMO
Color demosaicing is a key image processing step aiming to reconstruct the missing pixels from a recorded raw image. On the one hand, numerous interpolation methods focusing on spatial-spectral correlations have been proved very efficient, whereas they yield a poor image quality and strong visible artifacts. On the other hand, optimization strategies, such as learned simultaneous sparse coding and sparsity and adaptive principal component analysis-based algorithms, were shown to greatly improve image quality compared with that delivered by interpolation methods, but unfortunately are computationally heavy. In this paper, we propose efficient regression priors as a novel, fast post-processing algorithm that learns the regression priors offline from training data. We also propose an independent efficient demosaicing algorithm based on directional difference regression, and introduce its enhanced version based on fused regression. We achieve an image quality comparable to that of the state-of-the-art methods for three benchmarks, while being order(s) of magnitude faster.