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We construct the Schwarzschild dynamical models for 11 early-type galaxies with the SAURON and Mitchell stellar IFUs out to 2–4Re, and construct dynamical models with combined stellar and H i kinematics for a subsample of four galaxies with H i velocity fields out to 10Re obtained from the Westerbork Synthesis Radio Telescope, thus robustly obtaining the dark matter content out to large radii for these galaxies. Adopting a generalized-NFW dark matter profile, we measure an NFW-like density cusp in the dark matter inner slopes for all sample galaxies, with a mean value of 1.00 ± 0.04 (rms scatter 0.15). The mean dark matter fraction for the sample is 0.2 within 1Re, and increases to 0.4 at 2Re, and 0.6 at 5Re. The dark matter fractions within 1Re of these galaxies are systematically lower than the predictions of both the TNG-100 and EAGLE simulations. For the dark matter fractions within 2Re and 5Re, 40 and 70 per cent galaxies are 1σ consistent with either the TNG-100 or the EAGLE predictions, while the remaining 60 and 30 per cent galaxies lie below the 1σ region. Combined with 36 galaxies with dark matter fractions measured out to 5Re in the literature, about 10 per cent of these 47 galaxies lie below the 3σ region of the TNG-100 or EAGLE predictions.

 

The detection of site-specific phosphorylation in the microtubule-associated protein tau is emerging as a means to diagnose and monitor the progression of Alzheimer’s Disease and other neurodegenerative diseases. However, there is a lack of phospho-specific monoclonal antibodies and limited validation of their binding specificity. Here, we report a novel approach using yeast biopanning against synthetic peptides containing site-specific phosphorylations. Using yeast cells displaying a previously validated phospho-tau (p-tau) single-chain variable region fragment (scFv), we show selective yeast cell binding based on single amino acid phosphorylation on the antigen. We identify conditions that allow phospho-specific biopanning using scFvs with a wide range of affinities (KD = 0.2 to 60 nM). Finally, we demonstrate the capability of screening large libraries by performing biopanning in 6-well plates. These results show that biopanning can effectively select yeast cells based on phospho-site specific antibody binding, opening doors for the facile identification of high-quality monoclonal antibodies.

 

Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.