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Abstract The quality score of a biometric sample is intended to predict the sample’s degree of utility for biometric recognition. Different authors proposed different definitions for utility. A harmonized definition of utility would be useful to facilitate the comparison of biometric sample quality assessment algorithms. In this article, we compare different definitions of utility and apply them to both face image and fingerprint image data sets containing multiple samples per biometric instance and covering a wide range of potential quality issues. The results differ only slightly. We show that discarding samples with low utility scores results in rapidly declining false non-match rates. The obtained utility scores can be used as target labels for training biometric sample quality assessment algorithms and as baseline when summarizing utility-prediction performance in a single plot or even in a single figure of merit.

We study many-valued coalgebraic logics with semi-primal algebras of truth-degrees. We provide a systematic way to lift endofunctors defined on the variety of Boolean algebras to endofunctors on the variety generated by a semi-primal algebra. We show that this can be extended to a technique to lift classical coalgebraic logics to many-valued ones, and that (one-step) completeness and expressivity are preserved under this lifting. For specific classes of endofunctors, we also describe how to obtain an axiomatization of the lifted many-valued logic directly from an axiomatization of the original classical one. In particular, we apply all of these techniques to classical modal logic.

IntroductionThe increasing number of people living with dementia and its burden on families and systems particularly in low- and middle-income countries require comprehensive and efficient post-diagnostic management. This study aimed to explore the acceptability and efficacy of a multi-professional case management and psychoeducation model (North Macedonia Interprofessional Dementia Care, or NOMAD) delivered by mobile teams for people with dementia and their caregivers in North Macedonia.MethodWe conducted a two-arm randomized controlled trial comparing the intervention with treatment as usual. Participants were recruited from 12 general practitioner (GP) offices in the Skopje region. The NOMAD intervention included the delivery of a personalized care plan over four home visits to dyads of people with dementia and their caregivers by a team including a dementia nurse and a social worker, in collaboration with GPs and dementia experts, and the introduction of a caregiver manual. We assessed caregivers' depressive symptoms, burden, and quality of life and the neuropsychiatric symptoms, daily living activities, and service utilization of people with dementia at baseline and follow-up; we also assessed the acceptability of the intervention by analyzing case notes and attendance rates.ResultsOne hundred and twenty dyads were recruited and randomized to either the control (n = 60) or the intervention group (n = 60). At follow-up, caregivers in the intervention group had, on average, scores that were 2.69 lower for depressive symptoms (95% CI [−4.75, −0.62], p = 0.012), and people with dementia had, on average, 11.32 fewer neuropsychiatric symptoms (95% CI [−19.74, −2.90], p = 0.009) and used, on average, 1.81 fewer healthcare services (95% CI [−2.61, −1.00], p < 0.001) compared to the control group. The completion of the home visits was 100%, but the intervention's acceptability was underpinned by relationship building, GP competencies, and resources to support families with dementia. There were no differences in the caregivers' quality of life and burden levels or daily living activities in people with dementia. NOMAD is the first case management, non-pharmacological, and multi-professional intervention tested in North Macedonia.DiscussionThe trial showed that it is effective in reducing caregivers' depressive symptoms and neuropsychiatric symptoms in people with dementia and the burden on health and social care services, and it is acceptable for families. Implementing NOMAD in practice will require building primary care capacity and recognizing dementia as a national priority.

Objective Compared to late life dementia, Young Onset Dementia (YOD) has its own distinct challenges, including a lack of specialised and age-appropriate support services. Carers of people with YOD experience higher levels of psychological and physical symptoms, and lower quality of life. This study (RHAPSODY-Plus) assessed the acceptability and feasibility of combining RHAPSODY (Research to Assess Policies and Strategies for Dementia in the Young; a web-based information and skill-building programme for carers of people with YOD) with individually tailored support sessions with health professionals (a social worker and a clinical psychologist) provided via online videoconferencing. Methods Participants (n = 20) were informal carers aged over 18 years, who were caring for a person with YOD (either Alzheimer's disease or frontotemporal dementia type). Participants used the RHAPSODY programme for 4 weeks, then attended 2 support sessions. Participants and the health professionals then attended individual feedback sessions. Feedback was collected via open-ended and Likert-style questions. Results The majority of carers rated the RHAPSODY-Plus programme as good to very good, demonstrating a high level of acceptability. Positive feedback about the programme included being able to receive personal advice additionally to the information provided in RHAPSODY. The healthcare professionals also thought the programme was acceptable and beneficial for access to support. Some limitations in the feasibility of videoconferencing included network and technical issues and the loss of non-verbal communication. Conclusions This online pilot study had a high level of acceptability, demonstrating the potential of an individualised multi-modal intervention for carers of people with YOD which offers opportunities to overcome geographical and service access barriers.

BackgroundDeep neural networks are showing impressive results in different medical image classification tasks. However, for real-world applications, there is a need to estimate the network’s uncertainty together with its prediction. ObjectiveIn this review, we investigate in what form uncertainty estimation has been applied to the task of medical image classification. We also investigate which metrics are used to describe the effectiveness of the applied uncertainty estimation MethodsGoogle Scholar, PubMed, IEEE Xplore, and ScienceDirect were screened for peer-reviewed studies, published between 2016 and 2021, that deal with uncertainty estimation in medical image classification. The search terms “uncertainty,” “uncertainty estimation,” “network calibration,” and “out-of-distribution detection” were used in combination with the terms “medical images,” “medical image analysis,” and “medical image classification.” ResultsA total of 22 papers were chosen for detailed analysis through the systematic review process. This paper provides a table for a systematic comparison of the included works with respect to the applied method for estimating the uncertainty. ConclusionsThe applied methods for estimating uncertainties are diverse, but the sampling-based methods Monte-Carlo Dropout and Deep Ensembles are used most frequently. We concluded that future works can investigate the benefits of uncertainty estimation in collaborative settings of artificial intelligence systems and human experts. International Registered Report Identifier (IRRID)RR2-10.2196/11936

Choreographic approaches to message-passing applications can be regarded as an instance of the model-driven development principles. Choreographies specify interactions among distributed participants coordinating among themselves with message-passing at two levels of abstractions. A global view of the application is specified with a model that abstracts away from asynchrony while a local view of the application specifies the communication pattern of each participant. Noteworthy, the latter view can typically be algorithmically obtained by projection of the global view. A crucial element of this approach is to verify the so-called well-formed conditions on global views so that its projections realise a sound communication protocol. We introduce a novel local model, group interface automata, to represent the local view of choreographies and propose a new method to verify the well-formedness of global choreographies. We rely on a recently proposed semantics of global views formalised in terms of pomsets.

The design of agent-based models (ABMs) is often ad-hoc when it comes to defining their scope. In order for the inclusion of features such as network structure, location, or dynamic change to be justified, their role in a model should be systematically analysed. We propose a mechanism to compare and assess the impact of such features. In particular we are using techniques from software engineering and semantics to support the development and assessment of ABMs, such as graph transformations as semantic representations for agent-based models, feature diagrams to identify ingredients under consideration, and extension relations between graph transformation systems to represent model fragments expressing features.

Abstract Background The progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta1–42 (Aβ42), amyloid-beta1–40 (Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia. Methods We used 115 complete datasets from MCI patients of the “Dementia Competence Network”, a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%. Results Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aβ40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80–0.83, and the four-parameter combination from AUC 0.81–0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone. Conclusion A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials.

The relation between primary motor cortex (M1) activity and (muscular) force output has been studied extensively. Results from previous studies indicate that activity of a part of yet unidentified neurons in M1 are positively correlated with increased force levels. One considerable candidate causing this positive correlation could be circuits at supragranular layers. Here we tested this hypothesis and used the combination of H-reflexes with transcranial magnetic stimulation (TMS) to investigate laminar associations with force output in human subjects. Excitability of different M1 circuits were probed at movement onset and at peak torque while participants performed auxotonic contractions of the wrist with different torque levels. Only at peak torque we found a significant positive correlation between excitability of M1 circuits most likely involving neurons at supragranular layers and joint torque level. We argue that this finding may relate to the special role of upper layer circuits in integrating (force-related) afferent feedback and their connectivity with task-relevant pyramidal and also extrapyramidal pathways projecting to motoneurones in the spinal cord.