A placebo-controlled trial of folic acid and betaine in identical twins with Angelman syndrome.

BackgroundAngelman syndrome (AS) is a neurodevelopmental disorder that is caused by maternal genetic deficiency of a gene that encodes E6-AP ubiquitin-protein ligase (gene symbol UBE3A) mapping to chromosome 15q11-q13. AS leads to stiff and jerky gait, excess laughter, seizures, and severe intellectual disability. In some parts of the brain, the paternally inherited UBE3A gene is subject to genomic imprinting by the action of the UBE3A-antisense transcript (UBE3A-ATS) on the paternally inherited allele. Consequently, only the maternally inherited UBE3A gene is expressed in mature neurons. AS occurs due to deletions of the maternal 15q11 - 13 region, paternal uniparental disomy (UPD), imprinting center defects, mutations in the maternal UBE3A gene, or other unknown genetic malfunctions that result in a silenced maternal UBE3A gene in the specific imprinted regions of the brain.ResultsA potential treatment strategy for AS is to increase methylation of UBE3A-ATS to promote expression of the paternal UBE3A gene and thus ameliorate the clinical phenotypes of AS. We treated two sets of male identical twins with class I deletions with a 1 year treatment trial of either betaine and folic acid versus placebo. We found no statistically significant changes in the clinical parameters tested at the end of the 1 year trial, nor did we find any significant adverse events.ConclusionsThis study tested the hypothesis that by increasing the methylation of the UBE3A-antisense transcript in Angelman syndrome to promote expression of the silenced paternal UBE3A gene we may ameliorate the clinical phenotypes of AS. We treated two sets of identical twins with placebo versus betaine and folic acid. Although this study represented a novel approach to treating Angelman syndrome, the differences in the developmental testing results was not significant. This paper also discusses the value of monozygotic twin studies in minimizing confounding variables and its utility in conducting small treatment studies.Trial registrationNCT00348933 . Registered 6 July 2006

Learning from Millions of 3D Scans for Large-scale 3D Face Recognition

Deep networks trained on millions of facial images are believed to be closely approaching human-level performance in face recognition. However, open world face recognition still remains a challenge. Although, 3D face recognition has an inherent edge over its 2D counterpart, it has not benefited from the recent developments in deep learning due to the unavailability of large training as well as large test datasets. Recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans cannot be sourced from the web causing a bottleneck in the development of deep 3D face recognition networks and datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing. We also propose the first deep CNN model designed specifically for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises 1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this dataset, our network already outperforms state of the art face recognition by over 10%. We fine tune our network on the gallery set to perform end-to-end large scale 3D face recognition which further improves accuracy. Finally, we show the efficacy of our method for the open world face recognition problem.Comment: 11 page

Emergenesis: Genetic traits that may not run in familes.

Traits that are influenced by a configuration--rather than by a simple sum-- of polymorphic genes may not be seen to be genetic unless one studies monozygotic twins (who share all their genes and thus all gene configurations) because such “emergenic” traits will tend not to run in families. Personal idiosyncrasies that have been found to be surprisingly concordant among MZ twins separated in infancy and reared apart may be emergenic traits. More speculatively, important human traits like leadership, genius in its many manfestations, being an eflective therapist or parent, as well as certain psychopathological syndromes, may also be emergenic. These ideas re-emphasize the importance of the role played in human aflairs by genetic variation

Training methods for facial image comparison: a literature review

This literature review was commissioned to explore the psychological literature relating to facial image comparison with a particular emphasis on whether individuals can be trained to improve performance on this task. Surprisingly few studies have addressed this question directly. As a consequence, this review has been extended to cover training of face recognition and training of different kinds of perceptual comparisons where we are of the opinion that the methodologies or findings of such studies are informative. The majority of studies of face processing have examined face recognition, which relies heavily on memory. This may be memory for a face that was learned recently (e.g. minutes or hours previously) or for a face learned longer ago, perhaps after many exposures (e.g. friends, family members, celebrities). Successful face recognition, irrespective of the type of face, relies on the ability to retrieve the to-berecognised face from long-term memory. This memory is then compared to the physically present image to reach a recognition decision. In contrast, in face matching task two physical representations of a face (live, photographs, movies) are compared and so long-term memory is not involved. Because the comparison is between two present stimuli rather than between a present stimulus and a memory, one might expect that face matching, even if not an easy task, would be easier to do and easier to learn than face recognition. In support of this, there is evidence that judgment tasks where a presented stimulus must be judged by a remembered standard are generally more cognitively demanding than judgments that require comparing two presented stimuli Davies & Parasuraman, 1982; Parasuraman & Davies, 1977; Warm and Dember, 1998). Is there enough overlap between face recognition and matching that it is useful to look at the literature recognition? No study has directly compared face recognition and face matching, so we turn to research in which people decided whether two non-face stimuli were the same or different. In these studies, accuracy of comparison is not always better when the comparator is present than when it is remembered. Further, all perceptual factors that were found to affect comparisons of simultaneously presented objects also affected comparisons of successively presented objects in qualitatively the same way. Those studies involved judgments about colour (Newhall, Burnham & Clark, 1957; Romero, Hita & Del Barco, 1986), and shape (Larsen, McIlhagga & Bundesen, 1999; Lawson, Bülthoff & Dumbell, 2003; Quinlan, 1995). Although one must be cautious in generalising from studies of object processing to studies of face processing (see, e.g., section comparing face processing to object processing), from these kinds of studies there is no evidence to suggest that there are qualitative differences in the perceptual aspects of how recognition and matching are done. As a result, this review will include studies of face recognition skill as well as face matching skill. The distinction between face recognition involving memory and face matching not involving memory is clouded in many recognition studies which require observers to decide which of many presented faces matches a remembered face (e.g., eyewitness studies). And of course there are other forensic face-matching tasks that will require comparison to both presented and remembered comparators (e.g., deciding whether any person in a video showing a crowd is the target person). For this reason, too, we choose to include studies of face recognition as well as face matching in our revie

ANALYSIS OF FACIAL MARKS TO DISTINGUISH BETWEEN IDENTICAL TWINS USING NOVEL METHOD

Reliable and accurate verification of people is extremely important in a number of business transactions as well as access to privileged information. The biometrics-based methods assume that the physical characteristics of an individual (as captured by a sensor) used for verification are sufficiently unique to distinguish one person from another. But the increase in twin births has created a requirement for biometric systems to accurately determine the identity of a person who has an identical twin. Identical twins have the closest genetics-based relationship and, therefore, the maximum similarity between fingerprints is expected to be found among identical twins. They can’t be discriminated based on DNA. As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. Identical twin face recognition is a difficult task due to the existence of a high degree of correlation in overall facial appearance. In this paper, we study the usability of facial marks as biometric signatures to distinguish between identical twins. We propose a multi scale automatic facial mark detector based on a gradient-based operator known as the fast radial symmetry transform. The transform detects bright or dark regions with high radial symmetry at different scales. Next, the detections are tracked across scales to determine the prominence of facial marks. Extensive experiments are performed both on manually annotated and on automatically detected facial marks to evaluate the usefulness of facial marks as biometric signatures. The results of our analysis signify the usefulness of the distribution of facial marks as a biometric signature

Identical Twins as a Facial Similarity Benchmark for Human Facial Recognition

The problem of distinguishing identical twins and non-twin look-alikes in automated facial recognition (FR) applications has become increasingly important with the widespread adoption of facial biometrics. Due to the high facial similarity of both identical twins and look-alikes, these face pairs represent the hardest cases presented to facial recognition tools. This work presents an application of one of the largest twin datasets compiled to date to address two FR challenges: 1) determining a baseline measure of facial similarity between identical twins and 2) applying this similarity measure to determine the impact of doppelgangers, or look-alikes, on FR performance for large face datasets. The facial similarity measure is determined via a deep convolutional neural network. This network is trained on a tailored verification task designed to encourage the network to group together highly similar face pairs in the embedding space and achieves a test AUC of 0.9799. The proposed network provides a quantitative similarity score for any two given faces and has been applied to large-scale face datasets to identify similar face pairs. An additional analysis which correlates the comparison score returned by a facial recognition tool and the similarity score returned by the proposed network has also been performed

Classification via Incoherent Subspaces

This article presents a new classification framework that can extract individual features per class. The scheme is based on a model of incoherent subspaces, each one associated to one class, and a model on how the elements in a class are represented in this subspace. After the theoretical analysis an alternate projection algorithm to find such a collection is developed. The classification performance and speed of the proposed method is tested on the AR and YaleB databases and compared to that of Fisher's LDA and a recent approach based on on $\ell_1$ minimisation. Finally connections of the presented scheme to already existing work are discussed and possible ways of extensions are pointed out.Comment: 22 pages, 2 figures, 4 table

Identification of Identical Twins using Face Recognition with Results

Face recognition is a process used to identify or verify the person based on digital image from unique face of humans. Face recognition is based on individual and unique person identification. This process fully based on comparing the image with other person image for identification. Face Recognition is typically used in security systems and can be compared with other biometrics such as fingerprint or iris recognition systems. Here, the major problem is to identify twins. To overcome this problem we can use different facial recognition algorithms. The facial recognition algorithms should be able to identify the similar-looking individuals or identical Twins with accurate classification. In the proposed system, image of a person is given as a input then different features of image were extracted by using the Gabor and LBP algorithms. Extracted Features of both the images are compared and then classified using multi-SVM classifier. Based on classification method, the persons were identified to be identical twins or they were identified to be same person or not twins. After Identification, Performance of the process is measured

Congenital anomalies from a physics perspective. The key role of "manufacturing" volatility

Genetic and environmental factors are traditionally seen as the sole causes of congenital anomalies. In this paper we introduce a third possible cause, namely random "manufacturing" discrepancies with respect to ``design'' values. A clear way to demonstrate the existence of this component is to ``shut'' the two others and to see whether or not there is remaining variability. Perfect clones raised under well controlled laboratory conditions fulfill the conditions for such a test. Carried out for four different species, the test reveals a variability remainder of the order of 10%-20% in terms of coefficient of variation. As an example, the CV of the volume of E.coli bacteria immediately after binary fission is of the order of 10%. In short, ``manufacturing'' discrepancies occur randomly, even when no harmful mutation or environmental factors are involved. Not surprisingly, there is a strong connection between congenital defects and infant mortality. In the wake of birth there is a gradual elimination of defective units and this screening accounts for the post-natal fall of infant mortality. Apart from this trend, post-natal death rates also have humps and peaks associated with various inabilities and defects.\qL In short, infant mortality rates convert the case-by-case and mostly qualitative problem of congenital malformations into a global quantitative effect which, so to say, summarizes and registers what goes wrong in the embryonic phase. Based on the natural assumption that for simple organisms (e.g. rotifers) the manufacturing processes are shorter than for more complex organisms (e.g. mammals), fewer congenital anomalies are expected. Somehow, this feature should be visible on the infant mortality rate. How this conjecture can be tested is outlined in our conclusion.Comment: 43 pages, 9 figure

Twin identification over viewpoint change: A deep convolutional neural network surpasses humans

Deep convolutional neural networks (DCNNs) have achieved human-level accuracy in face identification (Phillips et al., 2018), though it is unclear how accurately they discriminate highly-similar faces. Here, humans and a DCNN performed a challenging face-identity matching task that included identical twins. Participants (N=87) viewed pairs of face images of three types: same-identity, general imposter pairs (different identities from similar demographic groups), and twin imposter pairs (identical twin siblings). The task was to determine whether the pairs showed the same person or different people. Identity comparisons were tested in three viewpoint-disparity conditions: frontal to frontal, frontal to 45-degree profile, and frontal to 90-degree profile. Accuracy for discriminating matched-identity pairs from twin-imposters and general imposters was assessed in each viewpoint-disparity condition. Humans were more accurate for general-imposter pairs than twin-imposter pairs, and accuracy declined with increased viewpoint disparity between the images in a pair. A DCNN trained for face identification (Ranjan et al., 2018) was tested on the same image pairs presented to humans. Machine performance mirrored the pattern of human accuracy, but with performance at or above all humans in all but one condition. Human and machine similarity scores were compared across all image-pair types. This item-level analysis showed that human and machine similarity ratings correlated significantly in six of nine image-pair types [range r=0.38 to r=0.63], suggesting general accord between the perception of face similarity by humans and the DCNN. These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN