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Biometrics in the World of Electronic Borders
Get PDFYesRecently, the demand for border crossing has increased massively, with the aim to increase the processing and clearance speed at border crossing points (BCP). The attempt to improve travel convenience, Border Cross Point (BCP) output, and national security result in automated border control (ABC) with biometric technology having a major effect on the efficiency, and safety of the control processes. The border processing of BCP can be increased by automating biometric recognition and facilitated by clearance procedures. This paper discussed the two structures of an e-gate (ABC) and a prospective benefit of biometrics to the EU border in terms of accuracy, integrity, robustness, and efficiency. Challenges posed by biometrics in border control systems were identified and recommendations such as multimodal systems and smart systems with AI and machine learning were suggested to assist travelers to cross border points faster.European Union’s Horizon-MSCA-RISE-2019-2023, Marie Skłodowska-Curie, Research, and Innovation Staff Exchange (RISE), titled: Secure and Wireless Multimodal Biometric Scanning Device for Passenger Verification Targeting Land and Sea Border Contro
Cytochrome P450 Binding and Bioactivation of Tumor-targeted Duocarmycin Agents
No full textNoDuocarmycin natural products are promising anti-cancer cytotoxins but too potent for systemic use. Re-engineering of the duocarmycin scaffold has enabled the discovery of prodrugs designed for bioactivation by tissue-specific cytochrome P450 enzymes. Lead prodrugs bioactivated by both P450 isoforms CYP1A1 and CYP2W1 have shown promising results in xenograft studies, however to fully understand the potential of these agents it is desirable to compare dual-targeting compounds with isoform-selective analogs. Such redesign requires insight into the molecular interactions with these P450 enzymes. Herein binding and metabolism of the individual stereoisomers of the indole-based duocarmycin prodrug ICT2700 and a nontoxic benzofuran analog ICT2726 were evaluated with CYP1A1 and CYP2W1, revealing differences exploitable for drug design. While enantiomers of both compounds bound to and were metabolized by CYP1A1, the stereochemistry of the chloromethyl fragment was critical for CYP2W1 interactions. CYP2W1 differentially binds the S enantiomer of ICT2726 and its metabolite profile could potentially be used as a biomarker to identify CYP2W1 functional activity. In contrast to benzofuran-based ICT2726, CYP2W1 differentially binds the R isomer of the indole-based ICT2700 over the S stereoisomer. Thus the ICT2700 R configuration warrants further investigation as a scaffold to favor CYP2W1-selective bioactivation. Furthermore, structures of both duocarmycin S enantiomers with CYP1A1 reveal orientations correlating with nontoxic metabolites and further drug design optimization could lead to a decrease of CYP1A1 bioactivation. Overall, distinctive structural features present in the two P450 active sites can be useful for improving P450-and thus tissue-selective-bioactivation. Significance Statement Prodrug versions of the natural product duocarmycin can be metabolized by human tissue-specific cytochrome P450 enzymes 1A1 and 2W1 to form an ultrapotent cytotoxin and/or high affinity 2W1 substrates to potentially probe functional activity in situ The current work defines the binding and metabolism by both P450 enzymes to support the design of duocarmycins selectively activated by only one human P450 enzyme.National Institutes of Health and Yorkshire Cancer Research Program Grant (B381PA
Measuring activity engagement in old age: An exploratory factor analysis
No full textYesA growing body of literature suggests that higher engagement in a range of activities can be
beneficial for cognitive health in old age. Such studies typically rely on self-report questionnaires
to assess level of engagement. These questionnaires are highly heterogeneous
across studies, limiting generalisability. In particular, the most appropriate domains of activity
engagement remain unclear. The Victoria Longitudinal Study-Activity Lifestyle Questionnaire
comprises one of the broadest and most diverse collections of activity items, but
different studies report different domain structures. This study aimed to help establish a generalisable
domain structure of the Victoria Longitudinal Study-Activity Lifestyle Questionnaire.
The questionnaire was adapted for use in a sample of UK-based older adults (336
community-dwelling adults aged 65–92 with no diagnosed cognitive impairment). An exploratory
factor analysis was conducted on 29 items. The final model retained 22 of these items
in a six-factor structure. Activity domains were: Manual (e.g., household repairs), Intellectual
(e.g., attending a public lecture), Games (e.g., card games), Religious (e.g., attending religious
services), Exercise (e.g., aerobics) and Social (e.g., going out with friends). Given that
beneficial activities have the potential to be adapted into interventions, it is essential that
future studies consider the most appropriate measurement of activity engagement across
domains. The factor structure reported here offers a parsimonious and potentially useful
way for future studies to assess engagement in different kinds of activities
A Novel Data-based Stochastic Distribution Control for Non-Gaussian Stochastic Systems
Get PDFYesThis note presents a novel data-based approach to investigate the non-Gaussian stochastic distribution control problem. As the motivation of this note, the existing methods have been summarised regarding to the drawbacks, for example, neural network weights training for unknown stochastic distribution and so on. To overcome these disadvantages, a new transformation for dynamic probability density function is given by kernel density estimation using interpolation. Based upon this transformation, a representative model has been developed while the stochastic distribution control problem has been transformed into an optimisation problem. Then, data-based direct optimisation and identification-based indirect optimisation have been proposed. In addition, the convergences of the presented algorithms are analysed and the effectiveness of these algorithms has been evaluated by numerical examples. In summary, the contributions of this note are as follows: 1) a new data-based probability density function transformation is given; 2) the optimisation algorithms are given based on the presented model; and 3) a new research framework is demonstrated as the potential extensions to the existing s
Tackling the Covid-19 pandemic
Get PDFYesSince December 2019, a new type of coronavirus called novel coronavirus (2019-nCoV, or COVID-19) was identified in Wuhan, China and on March 11, 2020, the World Health Organization (WHO) has declared the novel coronavirus (COVID-19) outbreak a global pandemic. With more than 101,797,158 confirmed cases, resulting in 3,451,354 deaths as of May 21, 2021, the world faces an unprecedented economic, social, and health impact. The clinical spectrum of COVID-19 has a wide range of manifestations, ranging from an asymptomatic state or mild respiratory symptoms to severe viral pneumonia and acute respiratory distress syndrome. Several diagnostic methods are currently available for detecting the coronavirus in clinical, research, and public health laboratories. Some tests detect the infection directly by detecting the viral RNA using real time reverse transcriptase polymerase chain reaction (RT-PCR) and other tests detect the infection indirectly by detecting the host antibodies. Additional techniques are using medical imaging diagnostic tools such as X-ray and computed tomography (CT). Various approaches have been employed in the development of COVID-19 therapies. Some of these approaches use drug repurposing (eg Remdesivir and Dexamethasone) and combinational therapy (eg Lopinavir/Ritonavir), whilst others aim to develop anti-viral vaccines (eg mRNA and antibody). Additionally, health experts integrate data sharing, provide with guidelines and advice to minimize the effects of the pandemic. These guidelines include wearing masks, avoiding direct contact with infectious people, respiratory and personal hygiene
A Framework to Handle Uncertainties of Machine Learning Models in Compliance with ISO 26262
Get PDFYesAssuring safety and thereby certifying is a key challenge of
many kinds of Machine Learning (ML) Models. ML is one of the most
widely used technological solutions to automate complex tasks such as
autonomous driving, traffic sign recognition, lane keep assist etc. The
application of ML is making a significant contributions in the automotive
industry, it introduces concerns related to the safety and security of these
systems. ML models should be robust and reliable throughout and prove
their trustworthiness in all use cases associated with vehicle operation.
Proving confidence in the safety and security of ML-based systems and
there by giving assurance to regulators, the certification authorities, and
other stakeholders is an important task. This paper proposes a framework
to handle uncertainties of ML model to improve the safety level and
thereby certify the ML Models in the automotive industry
Mask wearing as a prosocial consumption behaviour during the COVID-19 pandemic: an application of the theory of reasoned action
Get PDFYesThis study adopts a theory of reasoned action approach to understand consumers’ mask wearing when shopping in the context of the COVID-19 pandemic. We investigated mask wearing while shopping as a prosocial consumption behaviour whereby self-oriented benefits and others-oriented benefits are added as proposed drivers of attitudes and perceived social norms. Empirical evidence from a survey in France and Germany confirms a strong effect of social norms on mask-wearing intentions. Moreover, altruistic benefits predict mask-wearing intentions, with attitude and subjective norms as mediators. In contrast, self-expression benefits of mask wearing only influence perceived social norms and not attitudes; this effect differs between the countries. Our findings guide scholars, policy makers and practitioners to steer consumers’ mask wearing as a prosocial behaviour.Received support from central internationalization funds of Universität Hamburg
The use and costs of paid and unpaid care for people with dementia longitudinal findings from the IDEAL cohort programme
Get PDFYesThe drivers of costs of care for people with dementia are not well understood and little is known on the costs of care for those with rarer dementias. To characterise use and costs of paid and unpaid care over time in a cohort of people with dementia living in Britain. To explore the relationship between cohort members’ demographic and clinical characteristics and service costs. Methods: We calculated costs of health and social services, unpaid care, and out-of-pocket expenditure for people with mild-to-moderate dementia participating in three waves of the IDEAL cohort (2014-2018). Latent growth curve modelling investigated associations between participants’ baseline sociodemographic and diagnostic characteristics and mean weekly service costs. Results: Data were available on use of paid and unpaid care by 1537 community-dwelling participants with dementia at Wave 1, 1199 at Wave 2, and 910 at Wave 3. In models of paid service costs, being female was associated with lower baseline costs and living alone was associated with higher baseline costs. Dementia subtype and caregiver status were associated with variations in baseline costs and the rate of change in costs, which was additionally influenced by age. Conclusion: Lewy body and Parkinson's disease dementias were associated with higher service costs at the outset, and Lewy body and frontotemporal dementias with more steeply increasing costs overall, than Alzheimer’s disease. Planners of dementia services should consider the needs of people with these relatively rare dementia subtypes as they may require more resources than people with more prevalent subtypes.The first phase of the IDEAL program was funded jointly by the Economic and Social Research Council (ESRC, United Kingdom) and the National Institute for Health Research (NIHR, United Kingdom) through grant ES/L001853/2
The effect of strategy game types on inhibition
Get PDFYesPast studies have shown evidence of transfer of learning in action video games, less so in other types e.g. strategy games. Further, the transfer of learning from games to inhibitory control has yet to be examined from the perspectives of time constraint and logic contradiction. We examined the effect of strategy games (puzzle, turn-based strategy ‘TBS’, real-time strategy ‘RTS’) on inhibition (response inhibition and distractor inhibition) and cerebral hemispheric activation over four weeks. We predicted that compared to RTS, puzzle and TBS games would (1) improve response and distractor inhibition, and (2) increase cerebral hemispheric activation demonstrating increased inhibitory control. A total of 67 non-habitual video game players (Mage = 21.63 years old, SD = 2.12) played one of three games; puzzle (n = 19), TBS (n = 24) or RTS (n = 24) for four weeks on their smartphones. Participants completed three inhibition tasks, working memory (WM), and had their tympanic membrane temperature (TMT) taken from each ear before and after playing the games. Results showed that only the puzzle game group showed an improved response inhibition while controlling for WM. There were no significant changes in the distractor inhibition tasks. We also found that there was an increase in left TMT while playing RTS, suggesting the presence of increased impulsivity in RTS. Our findings suggest that puzzle games involving logical contradiction could improve response inhibition, showing potential as a tool for inhibition training.Newton Fund Institutional Links grant ID: 331745333, under Newton-Ungku Omar Fund partnership. The grant is funded by the UK Department for Business, Energy and Industrial Strategy and Malaysian Industry-Government Group for High Technology (MIGHT) and delivered by the British CouncilResearch Development Fund Publication Prize Award winner, Dec 2021
Detecting Deepfake Videos using Digital Watermarking
Get PDFYesDeepfakes constitute fake content -generally in the form of video clips and other media formats such as images or audio- created using deep learning algorithms. With the rapid development of artificial intelligence (AI) technologies, the deepfake content is becoming more sophisticated, with the developed detection techniques proving to be less effective. So far, most of the detection techniques in the literature are based on AI algorithms and can be considered as passive. This paper presents a proof-of-concept deepfake detection system that detects fake news video clips generated using voice impersonation. In the proposed scheme, digital watermarks are embedded in the audio track of a video using a hybrid speech watermarking technique. This is an active approach for deepfake detection. A standalone software application can perform the detection of robust and fragile watermarks. Simulations are performed to evaluate the embedded watermark's robustness against common signal processing and video integrity attacks. As far as we know, this is one of the first few attempts to use digital watermarking for fake content detection.EIG CONCERT-Japan call to the project entitled “Detection of fake newS on SocIal MedIa pLAtfoRms� (DISSIMILAR) through grants PCI2020-120689-2 (Ministry of Science and Innovation, Spain) and JPMJSC20C3 (JST SICORP, Japan).; Spanish Government through RTI2018-095094-B-C22 “CONSENT