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

Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment

Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. Most existing AU detection works often treat face alignment as a preprocessing and handle the two tasks independently. In this paper, we propose a novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before. In particular, multi-scale shared features are learned firstly, and high-level features of face alignment are fed into AU detection. Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively. Finally, the assembled local features are integrated with face alignment features and global features for AU detection. Experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for AU detection.Comment: This paper has been accepted by ECCV 201

Presence of sensory nerve corpuscles in the human corpus and cervix uteri during pregnancy and labor as revealed by immunohistochemistry

BACKGROUND: The uterus is exposed to changes such as enlargement and distension during pregnancy and labor. In these processes and in the process of cervical ripening, proprioceptive information is likely to be of great importance. Therefore, we wanted to study the possible existence of sensory nerve corpuscles in uterine corpus and cervix during pregnancy and labor. Studies on this aspect have not previously been perfomed. METHODS: Biopsies were taken from the upper edge of the hysterotomy during caesarean section at term (n = 8), in labor (n = 5) and from the corresponding area in the non-pregnant uterus after hysterectomy (n = 7). Cervical biopsies were obtained transvaginally from the anterior cervical lip. Serial cryostat sections were prepared for immunohistochemistry using polyclonal antibodies against nerve growth factor receptor p75, protein gene product 9.5 and S-100. RESULTS: Structures with the characteristics of sensory nerve corpuscles were observed in several specimens after staining for p75, PGP 9.5 and S-100. They were observed in specimens of the non-pregnant corpus and cervix and also in specimens of the pregnant cervix before onset of labor. However, they were absent in all specimens during labor. CONCLUSION: Sensory corpuscles have here for the first time been detected in the human corpus and cervix uteri. Studies on the importance of the corpuscles in relation to the protective reflex actions that occur in the uterus during pregnancy should be performed in the future

What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae?

BACKGROUND: Most proteins interact with only a few other proteins while a small number of proteins (hubs) have many interaction partners. Hub proteins and non-hub proteins differ in several respects; however, understanding is not complete about what properties characterize the hubs and set them apart from proteins of low connectivity. Therefore, we have investigated what differentiates hubs from non-hubs and static hubs (party hubs) from dynamic hubs (date hubs) in the protein-protein interaction network of Saccharomyces cerevisiae. RESULTS: The many interactions of hub proteins can only partly be explained by bindings to similar proteins or domains. It is evident that domain repeats, which are associated with binding, are enriched in hubs. Moreover, there is an over representation of multi-domain proteins and long proteins among the hubs. In addition, there are clear differences between party hubs and date hubs. Fewer of the party hubs contain long disordered regions compared to date hubs, indicating that these regions are important for flexible binding but less so for static interactions. Furthermore, party hubs interact to a large extent with each other, supporting the idea of party hubs as the cores of highly clustered functional modules. In addition, hub proteins, and in particular party hubs, are more often ancient. Finally, the more recent paralogs of party hubs are underrepresented. CONCLUSION: Our results indicate that multiple and repeated domains are enriched in hub proteins and, further, that long disordered regions, which are common in date hubs, are particularly important for flexible binding

Extended Calculations of Spectroscopic Data: Energy Levels, Lifetimes and Transition rates for O-like ions from Cr XVII to Zn XXIII

Employing two state-of-the-art methods, multiconfiguration Dirac--Hartree--Fock and second-order many-body perturbation theory, the excitation energies and lifetimes for the lowest 200 states of the $2s^2 2p^4$, $2s 2p^5$, $2p^6$, $2s^2 2p^3 3s$, $2s^2 2p^3 3p$, $2s^2 2p^3 3d$, $2s 2p^4 3s$, $2s 2p^4 3p$, and $2s 2p^4 3d$ configurations, and multipole (electric dipole (E1), magnetic dipole (M1), and electric quadrupole (E2)) transition rates, line strengths, and oscillator strengths among these states are calculated for each O-like ion from Cr XVII to Zn XXIII. Our two data sets are compared with the NIST and CHIANTI compiled values, and previous calculations. The data are accurate enough for identification and deblending of new emission lines from the sun and other astrophysical sources. The amount of data of high accuracy is significantly increased for the $n = 3$ states of several O-like ions of astrophysics interest, where experimental data are very scarce

Expansion of Protein Domain Repeats

Many proteins, especially in eukaryotes, contain tandem repeats of several domains from the same family. These repeats have a variety of binding properties and are involved in protein–protein interactions as well as binding to other ligands such as DNA and RNA. The rapid expansion of protein domain repeats is assumed to have evolved through internal tandem duplications. However, the exact mechanisms behind these tandem duplications are not well-understood. Here, we have studied the evolution, function, protein structure, gene structure, and phylogenetic distribution of domain repeats. For this purpose we have assigned Pfam-A domain families to 24 proteomes with more sensitive domain assignments in the repeat regions. These assignments confirmed previous findings that eukaryotes, and in particular vertebrates, contain a much higher fraction of proteins with repeats compared with prokaryotes. The internal sequence similarity in each protein revealed that the domain repeats are often expanded through duplications of several domains at a time, while the duplication of one domain is less common. Many of the repeats appear to have been duplicated in the middle of the repeat region. This is in strong contrast to the evolution of other proteins that mainly works through additions of single domains at either terminus. Further, we found that some domain families show distinct duplication patterns, e.g., nebulin domains have mainly been expanded with a unit of seven domains at a time, while duplications of other domain families involve varying numbers of domains. Finally, no common mechanism for the expansion of all repeats could be detected. We found that the duplication patterns show no dependence on the size of the domains. Further, repeat expansion in some families can possibly be explained by shuffling of exons. However, exon shuffling could not have created all repeats

Influence of pregnancy and labor on the occurrence of nerve fibers expressing the capsaicin receptor TRPV1 in human corpus and cervix uteri

<p>Abstract</p> <p>Background</p> <p>Cervical ripening is a prerequisite for a normal obstetrical outcome. This process, including labor, is a painful event that shares features with inflammatory reactions where peripheral nociceptive pathways are involved. The capsaicin and heat receptor TRPV1 is a key molecule in sensory nerves involved in peripheral nociception, but little is known regarding its role in the pregnant uterus. Therefore, the aim of this study was to investigate human corpus and cervix uteri during pregnancy and labor and non-pregnant controls for the presence of TRPV1.</p> <p>Methods</p> <p>We have investigated human uterine corpus and cervix biopsies at term pregnancy and parturition. Biopsies were taken from the upper edge of the hysterotomy during caesarean section at term (n = 8), in labor (n = 8) and from the corresponding area in the non-pregnant uterus after hysterectomy (n = 8). Cervical biopsies were obtained transvaginally from the anterior cervical lip. Serial frozen sections were examined immunohistochemically using specific antibodies to TRPV1 and nerve markers (neurofilaments/peripherin).</p> <p>Results</p> <p>In cervix uteri, TRPV1-immunoreactive fibers were scattered throughout the stroma and around blood vessels, and appeared more frequent in the sub-epithelium. Counts of TRPV1-immunoreactive nerve fibers were not significantly different between the three groups. In contrast, few TRPV1-immunoreactive fibers were found in nerve fascicles in the non-pregnant corpus, and none in the pregnant corpus.</p> <p>Conclusion</p> <p>In this study, TRPV1 innervation in human uterus during pregnancy and labor is shown for the first time. During pregnancy and labor there was an almost complete disappearance of TRPV1 positive nerve fibers in the corpus. However, cervical innervation remained throughout pregnancy and labor. The difference in TRPV1 innervation between the corpus and the cervix is thus very marked. Our data suggest that TRPV1 may be involved in pain mechanisms associated with cervical ripening and labor. Furthermore, these data support the concept that cervix uteri may be the major site from which labor pain emanates. Our findings also support the possibility of developing alternative approaches to treat labor pain.</p

Volatility distribution in the S&P500 Stock Index

We study the volatility of the S&P500 stock index from 1984 to 1996 and find that the volatility distribution can be very well described by a log-normal function. Further, using detrended fluctuation analysis we show that the volatility is power-law correlated with Hurst exponent $\alpha\cong0.9$.Comment: 6 pages, 5 figure

Near-Infrared Fluorescence Image-Guided Surgery in Esophageal and Gastric Cancer Operations

Background: Near-infrared fluorescence image-guided surgery helps surgeons to see beyond the classical eye vision. Over the last few years, we have witnessed a revolution which has begun in the field of image-guided surgery. Purpose, and Research design: Fluorescence technology using indocyanine green (ICG) has shown promising results in many organs, and in this review article, we wanted to discuss the 6 main domains where fluorescence image-guided surgery is currently used for esophageal and gastric cancer surgery. Study sample and data collection: Visualization of lymphatic vessels, tumor localization, fluorescence angiography for anastomotic evaluation, thoracic duct visualization, tracheal blood flow analysis, and sentinel node biopsy are discussed. Conclusions: It seems that this technology has already found its place in surgery. However, new possibilities and research avenues in this area will probably make it even more important in the near future