Experiences and practices of caregiving for older persons living with dementia in African countries: A qualitative scoping review

Background The increasing awareness and diagnosis of dementia in Africa necessitate documentation of caregiving practices to understand local patterns and improve the quality of care. Caregiving in African communities is rooted in informal-communal social organization. This scoping review analyses caregiver characteristics and experiences, and practices of caregiving for persons living with dementia in Africa. Methods A total of 152 references were retrieved, with 64 references obtained from PubMed, 85 from AJOL, and three from Scopus. Based on the relevance of titles, 83 references were further retained from PubMed (64), AJOL (16), and Scopus (3). A rapid review of abstracts was done in Distiller SR, and finally, six relevant articles were content-analyzed using Atlas ti 8.4 qualitative analysis software. Results All six included studies were published between 2003 and 2018. Four themes were identified: article characteristics, caregiver characteristics, caregiver in context, and caregiver potentialities (challenges and opportunities). Studies reported findings from research conducted in four African countries: three from Nigeria, and one each from South Africa, Egypt, and Tanzania. Caregivers of persons living with dementia typically had eight years or less of formal education, were unpaid, and spent a daily average of 13 hours in caregiving. Cultural thresholds and individual caregiver differences underlie the interpretation of stressors across cultures. Caregivers lack the right training and information as well as support systems to improve their role performance and reduce accumulated stress. Conclusion In African countries, informational and educational platforms are essential for improved individual dementia caregiving, vis-à-vis strengthened roles of governments, and religious/traditional leaders and organizations.

Enhanced Local Patterns Using Deep Learning Techniques for ECG Based Identity Recognition System

Electrocardiogram (ECG) signals have distinct features of the electrical activity of the heart which are unique among individuals and have recently emerged as a potential biometric tool for human identification. The paper attempts to address the problem of ECG identification based on non-fiducial approach using unsupervised classifier and a Deep Learning approaches. This work investigates the ability of local binary pattern to extract the significant pattern/feature that describes the heartbeat activity for each person’s ECG and the use of staked autoencoders and deep belief network to further enhance the extracted features and classify them based on their heartbeat activity. The proposed approach is validated using experimental datasets from two publicly available databases MIT-BIH Normal Sinus Rhythm and ECG-ID and the results demonstrate the effectiveness of this approach for ECG-based human authentication.

MicNet Toolbox: visualizing and deconstructing a microbial network

Understanding both global and local patterns in the structure and interplay of microbial communities has been a fundamental question in ecological research. In this paper, we present a python toolbox that combines two emerging techniques that have been proposed as useful when analyzing compositional microbial data. On one hand, we introduce a visualization module that incorporates the use of UMAP, a recent dimensionality reduction technique that focuses on local patterns, and HDBSCAN, a clustering technique based on density. On the other hand, we have included a module that runs an enhanced version of the SparCC code, sustaining larger datasets than before, and we couple this with network theory analyses to describe the resulting co-occurrence networks, including several novel analyses, such as structural balance metrics and a proposal to discover the underlying topology of a co-occurrence network. We validated the proposed toolbox on 1) a simple and well described biological network of kombucha, consisting of 48 ASVs, and 2) using simulated community networks with known topologies to show that we are able to discern between network topologies. Finally, we showcase the use of the MicNet toolbox on a large dataset from Archean Domes, consisting of more than 2,000 ASVs. Our toolbox is freely available as a github repository (https://github.com/Labevo/MicNetToolbox), and it is accompanied by a web dashboard (http://micnetapplb-1212130533.us-east-1.elb.amazonaws.com) that can be used in a simple and straightforward manner with relative abundance data.

Pedestrian Paths as an Indicator of Legibility Aspects of Omani Traditional Settlements

The interest for studying the Omani built heritage is not recent. However, the published research about the subject since the early 1970s, is limited in number and sporadic in topics and territories investigated. Moreover, there was no or little interest in examining this built heritage from the point of view of its urban design typologies. This paper is examining the urban form of these settlements and linking it with legibility aspects. Legibility and its influence on users of urban spaces have been significant for many theorists in urban studies. In their point of view, behaviour patterns of pedestrians are strongly influenced by legibility perception of the spatial patterns in urban spaces. The current research aims at studying the legibility aspects of traditional Omani residential settlements in an objective evaluation represented by numerical approach. Through a descriptive and analytical method, the results will link the influence of urban forms with legibility and behaviour patterns of pedestrians, which are deeply affected by the perception of body and mind. The paper explored paths’ forms influence on legibility perception of pedestrian in some of traditional settlements in Oman that have a strong urban identification. Five case studies representing traditional Omani settlements were chosen and analysed with an innovative quantitative approach capable of discovering, evaluating deficiencies, and suggesting solutions to develop local concepts for paths in any of urban settlements. The findings disclose that the contest to adopt international approaches to solve local urban spaces has created pointless, despicable, and unused spaces, while adopting local patterns, features and solutions will enhance the legible image of local urban settlements.

Application of eDNA Metabarcoding for Detecting Anura on a Tropical Island

As anuran biodiversity quickly declines, it is important to understand local patterns of anuran occurrence. However, the limitations of traditional sampling methods make anuran biodiversity surveys inadequate. Tropical environments are rich in anuran species, which makes biodiversity measurements more difficult. Therefore, it is important to develop a rapid, inexpensive and nondestructive method to measure anuran biodiversity in tropical environments. We used eDNA metabarcoding to measure anuran diversity at 288 sites in 18 regions of Hainan Island. We also used traditional methods and compared the results with those obtained through the eDNA metabarcoding methods. We detected 9 anuran species by traditional sampling methods. We produced 626 million reads by eDNA metabarcoding and assigned them to 15 anuran species. Therefore, eDNA metabarcoding can be used for rapid and large-scale anuran biodiversity surveys.

Dyads, triads, and tetrads: a multivariate simulation approach to uncovering network motifs in social graphs

Motifs represent local subgraphs that are overrepresented in networks. Several disciplines document multiple instances in which motifs appear in graphs and provide insight into the structure and processes of these networks. In the current paper, we focus on social networks and examine the prevalence of dyad, triad, and symmetric tetrad motifs among 24 networks that represent six types of social interactions: friendship, legislative co-sponsorship, Twitter messages, advice seeking, email communication, and terrorist collusion. Given that the correct control distribution for detecting motifs is a matter of continuous debate, we propose a novel approach that compares the local patterns of observed networks to random graphs simulated from exponential random graph models. Our proposed technique can produce conditional distributions that control for multiple, lower-level structural patterns simultaneously. We find evidence for five motifs using our approach, including the reciprocated dyad, three triads, and one symmetric tetrad. Results highlight the importance of mutuality, hierarchy, and clustering across multiple social interactions, and provide evidence of “structural signatures” within different genres of graph. Similarities also emerge between our findings and those in other disciplines, such as the preponderance of transitive triads.