Distinguishing Posed and Spontaneous Smiles by Facial Dynamics

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial Behavior Analysi

Blur-Robust Face Recognition via Transformation Learning

Abstract. This paper introduces a new method for recognizing faces degraded by blur using transformation learning on the image feature. The basic idea is to transform both the sharp images and blurred im-ages to a same feature subspace by the method of multidimensional s-caling. Different from the method of finding blur-invariant descriptors, our method learns the transformation which both preserves the mani-fold structure of the original shape images and, at the same time, en-hances the class separability, resulting in a wide applications to various descriptors. Furthermore, we combine our method with subspace-based point spread function (PSF) estimation method to handle cases of un-known blur degree, by applying the feature transformation correspond-ing to the best matched PSF, where the transformation for each PSF is learned in the training stage. Experimental results on the FERET database show the proposed method achieve comparable performance a-gainst the state-of-the-art blur-invariant face recognition methods, such as LPQ and FADEIN.

Novel endosomolytic compounds enable highly potent delivery of antisense oligonucleotides

The therapeutic and research potentials of oligonucleotides (ONs) have been hampered in part by their inability to effectively escape endosomal compartments to reach their cytosolic and nuclear targets. Splice-switching ONs (SSOs) can be used with endosomolytic small molecule compounds to increase functional delivery. So far, development of these compounds has been hindered by a lack of high-resolution methods that can correlate SSO trafficking with SSO activity. Here we present in-depth characterization of two novel endosomolytic compounds by using a combination of microscopic and functional assays with high spatiotemporal resolution. This system allows the visualization of SSO trafficking, evaluation of endosomal membrane rupture, and quantitates SSO functional activity on a protein level in the presence of endosomolytic compounds. We confirm that the leakage of SSO into the cytosol occurs in parallel with the physical engorgement of LAMP1-positive late endosomes and lysosomes. We conclude that the new compounds interfere with SSO trafficking to the LAMP1-positive endosomal compartments while inducing endosomal membrane rupture and concurrent ON escape into the cytosol. The efficacy of these compounds advocates their use as novel, potent, and quick-acting transfection reagents for antisense ONs

Novel endosomolytic compounds enable highly potent delivery of antisense oligonucleotides

The therapeutic and research potentials of oligonucleotides (ONs) have been hampered in part by their inability to effectively escape endosomal compartments to reach their cytosolic and nuclear targets. Splice-switching ONs (SSOs) can be used with endosomolytic small molecule compounds to increase functional delivery. So far, development of these compounds has been hindered by a lack of high-resolution methods that can correlate SSO trafficking with SSO activity. Here we present in-depth characterization of two novel endosomolytic compounds by using a combination of microscopic and functional assays with high spatiotemporal resolution. This system allows the visualization of SSO trafficking, evaluation of endosomal membrane rupture, and quantitates SSO functional activity on a protein level in the presence of endosomolytic compounds. We confirm that the leakage of SSO into the cytosol occurs in parallel with the physical engorgement of LAMP1-positive late endosomes and lysosomes. We conclude that the new compounds interfere with SSO trafficking to the LAMP1-positive endosomal compartments while inducing endosomal membrane rupture and concurrent ON escape into the cytosol. The efficacy of these compounds advocates their use as novel, potent, and quick-acting transfection reagents for antisense ONs

Bioactive glass induced osteogenic differentiation of human adipose stem cells is dependent on cell attachment mechanism and mitogen-activated protein kinases

Bioactive glasses (BaGs) are widely utilised in bone tissue engineering (TE) but the molecular response of cells to BaGs is poorly understood. To elucidate the mechanisms of cell attachment to BaGs and BaG-induced early osteogenic differentiation, we cultured human adipose stem cells (hASCs) on discs of two silica-based BaGs S53P4 (23.0 Na2O - 20.0 CaO - 4.0 P2O5 - 53.0 SiO2 (wt-%)) and 1-06 (5.9 Na2O - 12.0 K2O - 5.3 MgO - 22.6 CaO - 4.0 P2O5 - 0.2 B2O3 - 50.0 SiO2) in the absence of osteogenic supplements. Both BaGs induced early osteogenic differentiation by increasing alkaline phosphatase activity (ALP) and the expression of osteogenic marker genes RUNX2a and OSTERIX. Based on ALP activity, the slower reacting 1-06 glass was a stronger osteoinducer. Regarding the cell attachment, cells cultured on BaGs had enhanced integrinβ1 and vinculin production, and mature focal adhesions were smaller but more dispersed than on cell culture plastic (polystyrene). Focal adhesion kinase (FAK), extracellular signal-regulated kinase (ERK1/2) and c-Jun N-terminal kinase (JNK)-induced c-Jun phosphorylations were upregulated by glass contact. Moreover, the BaG-stimulated osteoinduction was significantly reduced by FAK and mitogen-activated protein kinase (MAPK) inhibitors, indicating an important role for FAK and MAPKs in the BaG-induced early osteogenic commitment of hASCs. Upon indirect insert culture, the ions released from the BaG discs could not reproduce the observed cellular changes, which highlighted the role of direct cell-BaG interactions in the osteopotential of BaGs. These findings gave valuable insight into the mechanism of BaG-induced osteogenic differentiation and therefore provided knowledge to aid the future design of new functional biomaterials to meet the increasing demand for clinical bone TE treatments

RegiSTORM: channel registration for multi-color stochastic optical reconstruction microscopy

Background: Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions. Results: We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error (TRE) with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software. Conclusions: The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at https://github.com/oystein676/RegiSTORM.git and DOI:10.5281/zenodo.5509861 (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux)