The nuclear lamins form a karyoskeleton providing structural rigidity to the nucleus. One member of the lamin family, lamin A, is first synthesized as a 74 kDa precursor, prelamin A. After the endopeptidase and methylation reactions which occur after farnesylation of the CAAX-box cysteine, there is a second endoproteolysis that occurs 15 amino acids upstream from the C-terminal farnesylated cysteine residue. Studies with knockout mice have implicated the enzyme Zmpste24 (Face-1) as a suitable candidate to perform one or both of these proteolytic reactions. Evidence has been presented elsewhere establishing that Zmpste24 possesses a zinc-dependent CAAX endopeptidase activity. In the present study, we confirm this CAAX endopeptidase activity with recombinant, membrane-reconstituted Zmpste24 and show that it can accept a prelamin A farnesylated tetrapeptide as substrate. To monitor the second upstream endoproteolytic cleavage of prelamin A, we expressed a 33 kDa prelamin A C-terminal tail in insect cells. We demonstrate that this purified substrate possesses a C-terminal farnesylated and carboxyl-methylated cysteine and, therefore, constitutes a valid substrate for assaying the second endoproteolytic step in lamin A maturation. With this substrate, we demonstrate that insect cell membranes bearing recombinant Zmpste24 can also catalyse the second upstream endoproteolytic cleavage.

Biometrics can provide alternative methods for security than conventional authentication methods. There has been much research done in the field of biometrics, and efforts have been made to make them more easily usable in practice. The initial application for our work is a proof of concept for a system that would expedite some low-risk travellers’ arrival into the country while preserving the user’s privacy. This thesis focuses on the subset of problems related to the generation of cryptographic keys from noisy data, biometrics in our case. This thesis was built in two parts. In the first, we implemented a key generating quantization-based fuzzy extractor scheme for facial feature biometrics based on the work by Dodis et al. and Sutcu, Li, and Memon. This scheme was modified to increased user privacy, address some implementation-based issues, and add testing-driven changes to tailor it towards its expected real-world usage. We show that our implementation does not significantly affect the scheme's performance, while providing additional protection against malicious actors that may gain access to the information stored on a server where biometric information is stored. The second part consists of the creation of a process to automate the generation of deep datasets suitable for the testing of similar schemes. The process led to the creation of a larger dataset than those available for free online for minimal work, and showed that these datasets can be further expanded with only little additional effort. This larger dataset allowed for the creation of more representative recognition challenges. We were able to show that our implementation performed similarly to other non-commercial schemes. Further refinement will be necessary if this is to be compared to commercial applications.

Indiana University-Purdue University Indianapolis (IUPUI) / Frontonasal malformation, FNM, was first described by Hoppe in 1859. FNM is an anomaly that is characterized by ocular hypertelorism, broad nasal root, lack of a nasal tip, V-shaped hair, prolongation onto the forehead (widow's peak), anterior cranium bifidum occultum, median facial cleft affecting the nose, upper lip, and/or palate, and uni- or bilateral clefting of the ala nasi. The anomalies noted in FNM may be explained as a single malformation. If the nasal capsule fails to develop properly, the primitive brain vesicle fills the space normally occupied by the capsule, thus producing anterior cranium bifidum occultum, an arrest in the positioning of the eyes, and a lack of formation of the nasal tip. The condition presents clinically with variable expressions as sporadic cases and infrequently in familial cases. The present study is the first attempting to quantify and characterize FNM via anatomic radiographic measurements. The lateral (LA) and posterior-anterior (PA) cephalometric radiographs of twenty-four individuals, both sporadic and familial, with FNM were analyzed for comparison of linear and angular measurements with previously published data of a "normal," i.e. unaffected, population standard. Usual and customary cephalometric points were identified and located, then digitized into the computer. Twenty-nine measurements included the previously diagnosed anomalous features of hypertelorism, medial nasal cavity, and palatal shelves, as well as other facial features. The radiographs of individuals with FNM have anatomic features that are unusual and distinct to the specific malformation. The data from this research suggest that patients with FNM, regardless of a genetic or sporadic predisposition, have a midface deficiency in height and depth, an increased interorbital width with possible increased orbital socket width, and a longer zygomatic buttress. Also, the familial cases tend to have a flatter cranial base than the sporadic cases. Furthermore, the familial patients might be a different type of FNM since this subgroup shows narrower zygomatic widths. The patients with surgical procedures demonstrated improvement different from the growth of those patients who did not have surgery. The hypothesis that the facies of a patient with frontonasal malformation is different from the "normal" control population is supported by this research. The differences between the familial and sporadic patients tend to support the general theory that genetic predisposition is less severe than FNM that occurs randomly.

During the first year of life, infants’ capacities for face processing are shaped by experience with faces in their environment; a process known as perceptual narrowing. Perceptual narrowing has been found to lead to a decline in infants’ abilities to identify and differentiate faces of other races. In the current study, it is hypothesized that this decline may also lead to differential processing of emotion information in own- versus other-race faces. In the current research, we recorded electrophysiological data (Event-related potential; ERP) from 5- and 9-month-old infants while they were presented with paired emotion non-verbal sounds and faces. ERPs in response to the sounds suggest that both 5- and 9-month old infants differentiate happy and sad sounds. The pattern of results, however, is different across ages. ERPs in response to the faces suggest that whereas 5-month-olds exhibit differential responses to happy and sad faces for both the N290 and P400 components, 9-month-olds did not differentiate happy and sad faces. Nine-month old infants did exhibit a great P400 in response to own- relative to other-race faces. These results suggest that although both 5- and 9-month olds differentiate happy and sad emotional sounds, their processing of emotion faces differs.

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This study will present the Experimental, numerical and analytical characterizations of composite sandwich structures needed to optimize structure design. In this study, the effects of varying honeycomb core ribbon orientation and varying face sheet thickness’s have on the flexural behavior of honeycomb sandwich structures was investigated. Honeycomb sandwich panels were constructed using Hexcel 6367 A250-5H carbon fiber face sheets and Hexcel Nomex HRH-10-1/8-5 honeycomb cores. The mechanical properties of the constituent materials were discovered experimentally using ASTM standards and theoretical models using honeycomb mechanics and classical beam and plate theory are described. A failure mode map for loading under three point bending is developed from previous works by Triantafillou and Gibson26, showing the dependence of failure mode on face sheet to core thickness and honeycomb core ribbon orientation. Beam specimens are tested with the effects of Honeycomb core ribbon orientation and unequal face sheet thickness’s examined. Experimental data sufficiently agrees with theoretical predictions. A finite element model was developed in ABAQUS/CAE to validate experimental and analytical analysis and produced agreeable results. Optimal bending stiffness and strength with respect to minimum weight was analyzed. The results reveal an important role core ribbon orientation has in a sandwich beam’s bending behavior, and design of unequal ply count face sheets can produce higher stiffness to weight ratios than conventional symmetric sandwich structures of similar weight when subjected to a single static load.

No / Geometric modelling using Partial Differential Equations (PDEs) has been gradually recognised due to its smooth instinct, as well as the ability to generate a variety of geometric shapes by intuitively manipulating a relatively small set of PDE boundary curves. In this paper we explore and demonstrate the feasibility of the PDE method in facial geometry parameterisation. The geometry of a generic face is approximated by evaluating spectral solutions to a group of fourth order elliptic PDEs. Our PDE-based parameterisation scheme can produce and animate a high-resolution 3D face with a relatively small number of parameters. By taking advantage of parametric representation, the PDE method can use one fixed animation scheme to manipulate the facial geometry in varying Levels of Detail (LODs), without any further process.

Numerous techniques for 3D face reconstruction from a single image exist, making use of large facial databases. However, they commonly encounter quality issues due to the absence of information from alternate perspectives. For example, 3D reconstruction with a single front view input data has limited realism, particularly for profile views. We have observed that multiple-view 3D face reconstruction yields higher-quality models compared to single-view reconstruction. Based on this observation, we propose a novel pipeline that combines several deep-learning methods to enhance the quality of reconstruction from a single frontal view. Our method requires only a single image (front view) as input, yet it generates multiple realistic facial viewpoints using various deep-learning networks. These viewpoints are utilized to create a 3D facial model, significantly enhancing the 3D face quality. Traditional image-space editing has limitations in manipulating content and styles while preserving high quality. However, editing in the latent space, which is the space after encoding or before decoding in a neural network, offers greater capabilities for manipulating a given photo. Motivated by the ability of neural networks to generate 2D images from an extensive database and recognizing that multi-view 3D face reconstruction outperforms single-view approaches, we propose a new pipeline. This pipeline involves latent space manipulation by first finding a latent vector corresponding to a given image using the Generative Adversarial Network (GAN) inversion method. We then search for nearby latent vectors to synthesize multiple pose images from the provided input image, aiming to enhance 3D face reconstruction. The generated images are then fed into Diffusion models, another image synthesis network, to generate their respective profile views. The Diffusion model is known for producing more realistic large-angle variations of a given object than GAN models do. Subsequently, all these images (multi-view images) are fed into an Autoencoder, a neural network designed for 3D face model predictions, to derive the 3D structure of the face. Finally, the texture of the 3D face model is combined to enhance its realism, and certain areas of the 3D shape are refined to correct any unrealistic aspects. Our experimental results validate the effectiveness and efficiency of our method in reconstructing highly accurate 3D models of human faces from a single input (front view input) image. The reconstructed models retain high visual fidelity to the original image, even without the need for a 3D database.

Previous findings have discovered that self-related stimuli such as one’s own face relative to a familiar or an unknown face leads to enhanced neural processing. Preferential processing of the self-face has been observed at various event-related potential (ERP) components, both when stimuli were presented on a subliminal and supraliminal level. This study extends previous research on the relationship between self-preferential processing and awareness by examining whether the visual awareness negativity (VAN) component, a proposed early signature of visual awareness, is modulated by the self-face compared to a personally familiar face. By the use of backward masking paradigm, faces were presented on the left or the right side of the screen, after which participants performed localization and identification task, and rated their perceived awareness. We found more negative amplitude for supraliminal compared to subliminal faces, indexed by the VAN (180-280ms). Further, the self-face displayed more negative amplitude than the familiar-face, supporting findings of self-referential processing. However, no interaction between face identity and awareness was found. These findings suggest that preferential processing of the self-face does not depend on the level of awareness.