Brexit and Competition Law: Future Directions of Domestic Enforcement

Reprinted from World Competition, Vol. 43, Issue 1, Spring 2020, 107-134, with permission of Kluwer Law International

Enhancing Biometric-Capsule-based Authentication and Facial Recognition via Deep Learning

In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based authentication systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based BioCapsule method. The BioCapsule method is provably secure, privacy-preserving, cancellable and flexible in its secure feature fusion design. In this work, we extend BioCapsule to face-based recognition. Moreover, we incorporate state-of-art deep learning techniques into a BioCapsule-based facial authentication system to further enhance secure recognition accuracy. We compare the performance of an underlying recognition system to the performance of the BioCapsule-embedded system in order to demonstrate the minimal effects of the BioCapsule scheme on underlying system performance. We also demonstrate that the BioCapsule scheme outperforms or performs as well as many other proposed secure biometric techniques

Action and Function of ASB Proteins in Compartment Size Regulation

One of the most important and defining processes during development is the pattern formation of the various compartments in embryos. In an effort to discover the participants involved in regulating compartment size, we identified, in Danio rerio (zebrafish) embryos, the ankyrin repeat and SOCS box-containing protein 11 (d-asb11) gene. We first showed that d-Asb11 is a key mediator of Delta-Notch Signaling, acting at the level of DeltaA ubiquitylation, important in fine-tuning the lateral inhibition gradients between DeltaA and Notch. We, then, isolated a zebrafish having a germline deletion of the d-Asb11 cullin box subdomain and showed that this deletion resulted in loss of d-Asb11 activity. As a consequence, the animals were defective for Notch signaling and proper cell fate specification within the neurogenic regions of zebrafish embryos. We also provided evidence that d-Asb11 is important in maintaining myogenic proliferation in the stem cell compartment of zebrafish embryos and muscle regenerative responses in adult animals. This finding is supported by the highly specific d-Asb11 expression found in proliferating satellite cells in zebrafish muscle. In addition, we have applied immunoaffinity chromatograpy followed by tandem mass spectrometry to identify human ASB11 interacting proteins. The data confirmed the role of ASB11 as a subs

A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the nerve structures can assist the early diagnosis of this disease. This paper proposes a robust, fast and fully automatic nerve segmentation and morphometric parameter quantification system for corneal confocal microscope images. The segmentation part consists of three main steps. First, a preprocessing step is applied to enhance the visibility of the nerves and remove noise using anisotropic diffusion filtering, specifically a Coherence filter followed by Gaussian filtering. Second, morphological operations are applied to remove unwanted objects in the input image such as epithelial cells and small nerve segments. Finally, an edge detection step is applied to detect all the nerves in the input image. In this step, an efficient algorithm for connecting discontinuous nerves is proposed. In the morphometric parameters quantification part, a number of features are extracted, including thickness, tortuosity and length of nerve, which may be used for the early diagnosis of diabetic polyneuropathy and when planning Laser-Assisted in situ Keratomileusis (LASIK) or Photorefractive keratectomy (PRK). The performance of the proposed segmentation system is evaluated against manually traced ground-truth images based on a database consisting of 498 corneal sub-basal nerve images (238 are normal and 260 are abnormal). In addition, the robustness and efficiency of the proposed system in extracting morphometric features with clinical utility was evaluated in 919 images taken from healthy subjects and diabetic patients with and without neuropathy. We demonstrate rapid (13 seconds/image), robust and effective automated corneal nerve quantification. The proposed system will be deployed as a useful clinical tool to support the expertise of ophthalmologists and save the clinician time in a busy clinical setting

Automated Quantification of Neuropad Improves Its Diagnostic Ability in Patients with Diabetic Neuropathy.

PublishedResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov'tNeuropad is currently a categorical visual screening test that identifies diabetic patients at risk of foot ulceration. The diagnostic performance of Neuropad was compared between the categorical and continuous (image-analysis (Sudometrics)) outputs to diagnose diabetic peripheral neuropathy (DPN). 110 subjects with type 1 and 2 diabetes underwent assessment with Neuropad, Neuropathy Disability Score (NDS), peroneal motor nerve conduction velocity (PMNCV), sural nerve action potential (SNAP), Deep Breathing-Heart Rate Variability (DB-HRV), intraepidermal nerve fibre density (IENFD), and corneal confocal microscopy (CCM). 46/110 patients had DPN according to the Toronto consensus. The continuous output displayed high sensitivity and specificity for DB-HRV (91%, 83%), CNFD (88%, 78%), and SNAP (88%, 83%), whereas the categorical output showed high sensitivity but low specificity. The optimal cut-off points were 90% for the detection of autonomic dysfunction (DB-HRV) and 80% for small fibre neuropathy (CNFD). The diagnostic efficacy of the continuous Neuropad output for abnormal DB-HRV (AUC: 91%, P = 0.0003) and CNFD (AUC: 82%, P = 0.01) was better than for PMNCV (AUC: 60%). The categorical output showed no significant difference in diagnostic efficacy for these same measures. An image analysis algorithm generating a continuous output (Sudometrics) improved the diagnostic ability of Neuropad, particularly in detecting autonomic and small fibre neuropathy.National Institute of Health (NIH)Juvenile Diabetes Research Foundation (JDRF

Small nerve fibre quantification in the diagnosis of diabetic sensorimotor polyneuropathy: comparing corneal confocal microscopy with intraepidermal nerve fibre density

OBJECTIVE: Quantitative assessment of small fiber damage is key to the early diagnosis and assessment of progression or regression of diabetic sensorimotor polyneuropathy (DSPN). Intraepidermal nerve fiber density (IENFD) is the current gold standard, but corneal confocal microscopy (CCM), an in vivo ophthalmic imaging modality, has the potential to be a noninvasive and objective image biomarker for identifying small fiber damage. The purpose of this study was to determine the diagnostic performance of CCM and IENFD by using the current guidelines as the reference standard. RESEARCH DESIGN AND METHODS: Eighty-nine subjects (26 control subjects and 63 patients with type 1 diabetes), with and without DSPN, underwent a detailed assessment of neuropathy, including CCM and skin biopsy. RESULTS: Manual and automated corneal nerve fiber density (CNFD) (P < 0.0001), branch density (CNBD) (P < 0.0001) and length (CNFL) (P < 0.0001), and IENFD (P < 0.001) were significantly reduced in patients with diabetes with DSPN compared with control subjects. The area under the receiver operating characteristic curve for identifying DSPN was 0.82 for manual CNFD, 0.80 for automated CNFD, and 0.66 for IENFD, which did not differ significantly (P = 0.14). CONCLUSIONS: This study shows comparable diagnostic efficiency between CCM and IENFD, providing further support for the clinical utility of CCM as a surrogate end point for DSPN

Consanguinity and willingness to perform premarital genetic screening in Sudan

Consanguineous marriage is prevalent in certain world regions due to cultural, economic, and social reasons. However, it can lead to negative consequences including an increased risk of genetic disorders in offspring. Premarital genetic screening (PMGS) is an important tool to identify and manage these risks before marriage. This study aimed to assess the magnitude of consanguineous marriage, knowledge of genetic diseases and PMGS, and attitudes and willingness to perform PMGS in Sudan. A national household survey was conducted using a multistage sampling technique, with a sample size of 2272 participants. Data were collected from December 2022 to March 2023 using an interviewer-administered questionnaire. A significant proportion of respondents (364/850, 42.8%) were married to consanguineal partners, with various types of relatedness. Moreover, 32.1% (242/755) of single respondents were planning to marry a close relative, signifying the likely persistence of consanguineous marriages in Sudan. The level of knowledge regarding genetic diseases and PMGS was relatively low in many states of Sudan, indicating the need for increased awareness interventions. A significant number of participants (85.2%) agreed that premarital screening is effective in reducing genetic diseases, whereas 71.2% supported the introduction of a mandatory PMGS program. Excluding married participants, 82.3% (1265/1537) of respondents were willing to perform PMGS, if implemented. These findings reflect the public positive attitude towards introducing the PMGS program and policies in Sudan and underscore the importance of addressing the knowledge gap of PMGS before such a potential implementation

Small nerve fibre quantification in the diagnosis of diabetic sensorimotor polyneuropathy: comparing corneal confocal microscopy with intraepidermal nerve fibre density

OBJECTIVE: Quantitative assessment of small fiber damage is key to the early diagnosis and assessment of progression or regression of diabetic sensorimotor polyneuropathy (DSPN). Intraepidermal nerve fiber density (IENFD) is the current gold standard, but corneal confocal microscopy (CCM), an in vivo ophthalmic imaging modality, has the potential to be a noninvasive and objective image biomarker for identifying small fiber damage. The purpose of this study was to determine the diagnostic performance of CCM and IENFD by using the current guidelines as the reference standard. RESEARCH DESIGN AND METHODS: Eighty-nine subjects (26 control subjects and 63 patients with type 1 diabetes), with and without DSPN, underwent a detailed assessment of neuropathy, including CCM and skin biopsy. RESULTS: Manual and automated corneal nerve fiber density (CNFD) (P < 0.0001), branch density (CNBD) (P < 0.0001) and length (CNFL) (P < 0.0001), and IENFD (P < 0.001) were significantly reduced in patients with diabetes with DSPN compared with control subjects. The area under the receiver operating characteristic curve for identifying DSPN was 0.82 for manual CNFD, 0.80 for automated CNFD, and 0.66 for IENFD, which did not differ significantly (P = 0.14). CONCLUSIONS: This study shows comparable diagnostic efficiency between CCM and IENFD, providing further support for the clinical utility of CCM as a surrogate end point for DSPN