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  1. 59001
    por Chabre, Yoann M., Roy, René
    Publicado 2010
    “…A systematic description of glycocluster and glycodendrimer synthesis follows, starting from the simplest architectures and ending in the most complex ones. Presentation of multivalent glycostructures of intermediate size and comprising, calix[n]arene, porphyrin, cyclodextrin, peptide, and carbohydrate scaffolds, has also been intercalated to better appreciate the growing synthetic complexity involved. …”
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    Design and Creativity in Synthesis of Multivalent Neoglycoconjugates
  2. 59002
    “…We design deep learning architectures allowing for reconstructing high-quality pCLE images directly from the irregularly sampled input data. …”
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    Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches
  3. 59003
    “…The sonographic features of DAs were carefully observed and other associated cardiovascular anomalies were also evaluated during the screening. The architectures of DAs and the whole cardiovascular system were observed and analyzed, and then the cast findings were compared with prenatal ultrasonic results. …”
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    Utility of modified vascular corrosion casting technique in the diagnosis of fetal ductus arteriosus abnormalities
  4. 59004
    “…Gene regulatory networks (GRNs) drive developmental and cellular differentiation, and variation in their architectures gives rise to morphological diversity. …”
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    Recurrent Loss of abaA, a Master Regulator of Asexual Development in Filamentous Fungi, Correlates with Changes in Genomic and Morphological Traits
  5. 59005
    por Karasaki, Takahiro, Moore, David A., Veeriah, Selvaraju, Naceur-Lombardelli, Cristina, Toncheva, Antonia, Magno, Neil, Ward, Sophia, Al Bakir, Maise, Watkins, Thomas B.K., Grigoriadis, Kristiana, Huebner, Ariana, Hill, Mark S., Frankell, Alexander M., Abbosh, Christopher, Gimeno-Valiente, Francisco, Saghafinia, Sadegh, Kanu, Nnennaya, Dietzen, Michelle, Pich, Oriol, Lim, Emilia, Martinez-Ruiz, Carlos, Black, James R.M., Puttick, Clare, Biswas, Dhruva, Campbell, Brittany B., Lee, Claudia, Colliver, Emma, Enfield, Katey S.S., Hessey, Sonya, Hiley, Crispin T., Zaccaria, Simone, Litchfield, Kevin, Birkbak, Nicolai J., Cadieux, Elizabeth Larose, Demeulemeester, Jonas, Van Loo, Peter, Adusumilli, Prasad S., Tan, Kay See, Cheema, Waseem, Sanchez-Vega, Francisco, Jones, David R., Rekhtman, Natasha, Travis, William D., Hackshaw, Allan, Marafioti, Teresa, Salgado, Roberto, Le Quesne, John, Nicholson, Andrew G., McGranahan, Nicholas, Swanton, Charles, Jamal-Hanjani, Mariam, Jamal-Hanjani, Mariam, McGranahan, Nicholas, Swanton, Charles, McGranahan, Nicholas, Birkbak, Nicolai J., Litchfield, Kevin, Zaccaria, Simone, Swanton, Charles, Karasaki, Takahiro, Moore, David A., Al Bakir, Maise, Campbell, Brittany B., Abbosh, Christopher, Hessey, Sonya, Hiley, Crispin T., Jamal-Hanjani, Mariam, Swanton, Charles, Hackshaw, Allan, Moore, David A., Nicholson, Andrew G., Le Quesne, John, Salgado, Roberto, Al Bakir, Maise, Karasaki, Takahiro, Veeriah, Selvaraju, Naceur-Lombardelli, Cristina, Toncheva, Antonia, Magno, Neil, Ward, Sophia, Karasaki, Takahiro, Moore, David A., Dietzen, Michelle, Karasaki, Takahiro, Grigoriadis, Kristiana, Frankell, Alexander M., Watkins, Thomas B.K., Lim, Emilia, Hill, Mark S., Campbell, Brittany B., Karasaki, Takahiro, Martinez-Ruiz, Carlos, Black, James R.M., Puttick, Clare, Biswas, Dhruva, Gimeno-Valiente, Francisco, Saghafinia, Sadegh, Kanu, Nnennaya, Karasaki, Takahiro, Huebner, Ariana, Al Bakir, Maise, Abbosh, Christopher, Frankell, Alexander M., Karasaki, Takahiro, Birkbak, Nicolai J., Swanton, Charles, Karasaki, Takahiro, Puttick, Clare, Pich, Oriol, Puttick, Clare, Lee, Claudia, Grigoriadis, Kristiana, Colliver, Emma, Enfield, Katey S.S., Hiley, Crispin T., McGranahan, Nicholas, Swanton, Charles, Karasaki, Takahiro, Hill, Mark S., Litchfield, Kevin, Hackshaw, Allan, Adusumilli, Prasad S., Tan, Kay See, Cheema, Waseem, Sanchez-Vega, Francisco, Jones, David R., Rekhtman, Natasha, Travis, William D., Moore, David A., Karasaki, Takahiro, Watkins, Thomas B.K., Frankell, Alexander M., Grigoriadis, Kristiana, Colliver, Emma, Enfield, Katey S.S., Cadieux, Elizabeth Larose, Demeulemeester, Jonas, Van Loo, Peter, Sanchez-Vega, Francisco, Jones, David R., Rekhtman, Natasha, Travis, William D., Marafioti, Teresa, Salgado, Roberto, Le Quesne, John, Nicholson, Andrew G., McGranahan, Nicholas, Jamal-Hanjani, Mariam, Swanton, Charles
    Publicado 2023
    “…Tumours with predominantly high-grade architectural patterns showed increased chromosomal complexity, with higher levels of loss of heterozygosity (LOH) and subclonal somatic copy number alterations (SCNAs). …”
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    Evolutionary characterisation of lung adenocarcinoma morphology in TRACERx
  6. 59006
    “…First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. …”
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    Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
  7. 59007
    “…CONCLUSIONS: FCNNs have promising potential in the analysis and segmentation of OC and OP video-endoscopic images. All tested FCNN architectures demonstrated satisfying outcomes in terms of diagnostic accuracy. …”
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    Deep Learning for Automatic Segmentation of Oral and Oropharyngeal Cancer Using Narrow Band Imaging: Preliminary Experience in a Clinical Perspective
  8. 59008
    “…The tumor cells were usually arranged in solid hypercellular sheets without a distinct architectural pattern. IHC showed expression of TLE1 (100%), CCNB3 (88%), BCOR (71%). …”
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    Detection of BCOR gene rearrangement in Ewing-like sarcoma: an important diagnostic tool
  9. 59009
    “…Despite possessing generally similar active-site architectures to the archetypal FgrGalOx, FgrAAO and FoxAAO have weak activity on carbohydrates, but instead efficiently oxidize specific aryl alcohols. …”
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    Two Fusarium copper radical oxidases with high activity on aryl alcohols
  10. 59010
    “…Transfer learning was carried out on Indian dataset using popular deep neural networks, which includes DenseNet, ResNet50, and ResNet18 network architectures to classify CXRs into three categories. …”
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    Predicting COVID-19 pneumonia severity on chest X-ray with convolutional neural network: A retrospective study
  11. 59011
    “…We furthermore offer specialised data loading procedures and privacy budget accounting based on the Gaussian Differential Privacy framework, as well as automated modification of the user-supplied neural network architectures to ensure DP-conformity of its layers. …”
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    Medical imaging deep learning with differential privacy
  12. 59012
    por Kristianingsih, Ruth, MacLean, Dan
    Publicado 2021
    “…RESULTS: We created a training set of manually curated effector sequences from PHI-Base and used these to train a range of model architectures for classifying bacteria, fungal and oomycete sequences. …”
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    Accurate plant pathogen effector protein classification ab initio with deepredeff: an ensemble of convolutional neural networks
  13. 59013
    “…The animal's body weights and relative organ weights were found to be partially restored. Tissue architectures of the pancreas and the kidney were remarkably improved by low doses of CMJE. …”
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    The Antioxidative Role of Natural Compounds from a Green Coconut Mesocarp Undeniably Contributes to Control Diabetic Complications as Evidenced by the Associated Genes and Biochemical Indexes
  14. 59014
    “…Three disease-related traits were measured and associated with different sets of host genes for each strain. The genetic architectures of these traits differed among viral strains and, in the case of the more specialized virus, also varied along the duration of infection. …”
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    A genome-wide association study identifies Arabidopsis thaliana genes that contribute to differences in the outcome of infection with two Turnip mosaic potyvirus strains that differ in their evolutionary history and degree of host specialization
  15. 59015
    “…Lobular histology (adjusted OR 2.86, 95% CI 1.68–4.87), large tumor size (per millimeter increase, adjusted OR 1.05, 95% CI 1.03–1.07), high (>75%) mammographic density (adjusted OR 3.61, 95% CI 1.07–12.12), and the presence of microcalcifications (adjusted OR 4.45, 95% CI 2.69–7.37) and architectural distortions (adjusted OR 1.85, 95% CI 1.01–3.40) were independently associated with positive resection margins after BCS. …”
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    The Impact of Preoperative Breast MRI on Surgical Margin Status in Breast Cancer Patients Recalled at Biennial Screening Mammography: An Observational Cohort Study
  16. 59016
    por Touzani, Yassine, Douzi, Khadija
    Publicado 2021
    “…Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are among the most widely used types of RNNs, given their suitability for sequential data. …”
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    An LSTM and GRU based trading strategy adapted to the Moroccan market
  17. 59017
    “…The results further our understanding of the evolutionary architectures of tortricid mitogenomes and provide a basis for future studies of population genetics and phylogenetic investigations in this group. …”
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    The mitochondrial genomes of Tortricidae: nucleotide composition, gene variation and phylogenetic performance
  18. 59018
    “…Triangle pore scaffolds significantly increased osteoblast mineralisation (1.5-fold) when compared to square architectures (P ≤ 0.001). Interestingly, EV yield was also significantly enhanced on these higher permeability structures (P ≤ 0.001), in particular (2.2-fold) for the larger pore structures (1000 µm). …”
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    Development of a Bone-Mimetic 3D Printed Ti6Al4V Scaffold to Enhance Osteoblast-Derived Extracellular Vesicles’ Therapeutic Efficacy for Bone Regeneration
  19. 59019
    “…To address these classification tasks, we evaluated 6 representative state-of-the-art deep network architectures on 3 different public datasets: (I) Chest X-ray dataset of the Radiological Society of North America (RSNA); (II) Covid-19 Image Data Collection; (III) SIRM dataset of the Italian Society of Medical Radiology. …”
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    Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images
  20. 59020
    “…The 2-class classification task was divided into two subtasks: benign vs. borderline & malignant (task A), borderline vs. malignant (task B). Five DCNN architectures, namely VGG16, GoogLeNet, ResNet34, MobileNet, and DenseNet, were trained and model performance before and after transfer learning was tested. …”
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    Application of Deep Convolutional Neural Networks for Discriminating Benign, Borderline, and Malignant Serous Ovarian Tumors From Ultrasound Images
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