Month: April 2024

Abstract

Community detection is a ubiquitous problem in applied network analysis, however efficient techniques do not yet exist for all types of network data. Directed and weighted networks are an example, where the different information encoded by link weights and the possibly high graph density can cause difficulties for some approaches. Here we present an algorithm based on Voronoi partitioning generalized to deal with directed weighted networks. As an added benefit, this method can directly employ edge weights that represent lengths, in contrast to algorithms that operate with connection strengths, requiring ad-hoc transformations of length data. We demonstrate the method on inter-areal brain connectivity, air transportation networks, and several social networks. We compare the performance with several other well-known algorithms, applying them on a set of randomly generated benchmark networks. The algorithm can handle dense graphs where weights are the main factor determining communities. The hierarchical structure of networks can also be detected, as shown for the brain. Its time efficiency is comparable or even outperforms some of the state-of-the-art algorithms, the part with the highest time-complexity being Dijkstra’s shortest paths algorithm ( \({\mathcal {O}}(|E| + |V|\log |V|)\) ).

International: NPC and DIFC sign MoU on data protection  DataGuidance

Abstract

This study investigates the dynamic linkages between financial development, economic growth, urbanization, trade openness, and a more reliable environmental indicator (ecological footprint) in ten ECOWAS nations from 1980 to 2022. The study applied the Pedroni and Kao cointegration tests to confirm the equilibrium tie-in between the variables. The study used the pool mean group (PMG) method to draw empirical inferences. In addition, the study also employed the ordinary least square (OLS), Fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS) methods to confirm the robustness of the PMG estimates. The long-run estimates indicate that financial development, economic growth, and trade openness have a significant positive impact on the ecological footprint, while urbanization affects the ecological footprint negatively. The results of the country analysis reveal that financial development leads to a depletion in the ecological footprint in Sierra Leone, Togo, and Cote d’Ivoire, but it increases in Niger. Furthermore, we discover that economic development is not eco-friendly and increases the ecological footprint in Benin, Ghana, and Nigeria. We offer policy recommendations, discuss limitations, and suggest future research directions for our study.

Abstract

This study investigates the dynamic linkages between financial development, economic growth, urbanization, trade openness, and a more reliable environmental indicator (ecological footprint) in ten ECOWAS nations from 1980 to 2022. The study applied the Pedroni and Kao cointegration tests to confirm the equilibrium tie-in between the variables. The study used the pool mean group (PMG) method to draw empirical inferences. In addition, the study also employed the ordinary least square (OLS), Fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS) methods to confirm the robustness of the PMG estimates. The long-run estimates indicate that financial development, economic growth, and trade openness have a significant positive impact on the ecological footprint, while urbanization affects the ecological footprint negatively. The results of the country analysis reveal that financial development leads to a depletion in the ecological footprint in Sierra Leone, Togo, and Cote d’Ivoire, but it increases in Niger. Furthermore, we discover that economic development is not eco-friendly and increases the ecological footprint in Benin, Ghana, and Nigeria. We offer policy recommendations, discuss limitations, and suggest future research directions for our study.

Abstract

Pre-injured lungs are prone to injury progression in response to mechanical ventilation. Heterogeneous ventilation due to (micro)atelectases imparts injurious strains on open alveoli (known as volutrauma). Hence, recruitment of (micro)atelectases by positive end-expiratory pressure (PEEP) is necessary to interrupt this vicious circle of injury but needs to be balanced against acinar overdistension. In this study, the lung-protective potential of alveolar recruitment was investigated and balanced against overdistension in pre-injured lungs. Mice, treated with empty vector (AdCl) or adenoviral active TGF-β1 (AdTGF-β1) were subjected to lung mechanical measurements during descending PEEP ventilation from 12 to 0 cmH₂O. At each PEEP level, recruitability tests consisting of two recruitment maneuvers followed by repetitive forced oscillation perturbations to determine tissue elastance (H) and damping (G) were performed. Finally, lungs were fixed by vascular perfusion at end-expiratory airway opening pressures (Pao) of 20, 10, 5 and 2 cmH₂O after a recruitment maneuver, and processed for design-based stereology to quantify derecruitment and distension. H and G were significantly elevated in AdTGF-β1 compared to AdCl across PEEP levels. H was minimized at PEEP = 5–8 cmH₂O and increased at lower and higher PEEP in both groups. These findings correlated with increasing septal wall folding (= derecruitment) and reduced density of alveolar number and surface area (= distension), respectively. In AdTGF-β1 exposed mice, 27% of alveoli remained derecruited at Pao = 20 cmH₂O. A further decrease in Pao down to 2 cmH₂O showed derecruitment of an additional 1.1 million alveoli (48%), which was linked with an increase in alveolar size heterogeneity at Pao = 2–5 cmH₂O. In AdCl, decreased Pao resulted in septal folding with virtually no alveolar collapse. In essence, in healthy mice alveoli do not derecruit at low PEEP ventilation. The potential of alveolar recruitability in AdTGF-β1 exposed mice is high. H is optimized at PEEP 5–8 cmH₂O. Lower PEEP folds and larger PEEP stretches septa which results in higher H and is more pronounced in AdTGF-β1 than in AdCl. The increased alveolar size heterogeneity at Pao = 5 cmH₂O argues for the use of PEEP = 8 cmH₂O for lung protective mechanical ventilation in this animal model.

Abstract

Pre-injured lungs are prone to injury progression in response to mechanical ventilation. Heterogeneous ventilation due to (micro)atelectases imparts injurious strains on open alveoli (known as volutrauma). Hence, recruitment of (micro)atelectases by positive end-expiratory pressure (PEEP) is necessary to interrupt this vicious circle of injury but needs to be balanced against acinar overdistension. In this study, the lung-protective potential of alveolar recruitment was investigated and balanced against overdistension in pre-injured lungs. Mice, treated with empty vector (AdCl) or adenoviral active TGF-β1 (AdTGF-β1) were subjected to lung mechanical measurements during descending PEEP ventilation from 12 to 0 cmH₂O. At each PEEP level, recruitability tests consisting of two recruitment maneuvers followed by repetitive forced oscillation perturbations to determine tissue elastance (H) and damping (G) were performed. Finally, lungs were fixed by vascular perfusion at end-expiratory airway opening pressures (Pao) of 20, 10, 5 and 2 cmH₂O after a recruitment maneuver, and processed for design-based stereology to quantify derecruitment and distension. H and G were significantly elevated in AdTGF-β1 compared to AdCl across PEEP levels. H was minimized at PEEP = 5–8 cmH₂O and increased at lower and higher PEEP in both groups. These findings correlated with increasing septal wall folding (= derecruitment) and reduced density of alveolar number and surface area (= distension), respectively. In AdTGF-β1 exposed mice, 27% of alveoli remained derecruited at Pao = 20 cmH₂O. A further decrease in Pao down to 2 cmH₂O showed derecruitment of an additional 1.1 million alveoli (48%), which was linked with an increase in alveolar size heterogeneity at Pao = 2–5 cmH₂O. In AdCl, decreased Pao resulted in septal folding with virtually no alveolar collapse. In essence, in healthy mice alveoli do not derecruit at low PEEP ventilation. The potential of alveolar recruitability in AdTGF-β1 exposed mice is high. H is optimized at PEEP 5–8 cmH₂O. Lower PEEP folds and larger PEEP stretches septa which results in higher H and is more pronounced in AdTGF-β1 than in AdCl. The increased alveolar size heterogeneity at Pao = 5 cmH₂O argues for the use of PEEP = 8 cmH₂O for lung protective mechanical ventilation in this animal model.

Abstract

Background

European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens.

Methods

We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software.

Results

Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics.

Conclusions

The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

Abstract

Background

European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens.

Methods

We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software.

Results

Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics.

Conclusions

The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

Abstract

The image segmentation is an important stage in automatic detection, identification and quantification of various type of parasite eggs in microscopic images. Different types of parasites, such as protozoa, helminths (worms) and ectoparasites and their eggs, can be observed under a microscope in human and animal faeces, blood or urine samples. Microscopic images often contain multiple kinds of parasite eggs as well as other sample impurities or debris. The size, colour, and texture of parasite eggs may also vary in the images depending on the sample collection and image acquisition process, making the segmentation process challenging. Over the years, various segmentation methods such as thresholding, edge detection, wathershed, etc. have been utilised by the researchers in this field. However, to the best of our knowledge, no survey works on the segmentation approaches for parasite eggs in various types of microscopic images have been reported yet. As a result, we explored a number of research reports and prepared a detail review of various segmentation techniques employed in previous works on automatic detection and identification of parasite eggs in different types of microscopic images. The aim of the study is to assist future researchers in identify the suitable strategies for effectively segmenting parasite egg images.

Abstract

The image segmentation is an important stage in automatic detection, identification and quantification of various type of parasite eggs in microscopic images. Different types of parasites, such as protozoa, helminths (worms) and ectoparasites and their eggs, can be observed under a microscope in human and animal faeces, blood or urine samples. Microscopic images often contain multiple kinds of parasite eggs as well as other sample impurities or debris. The size, colour, and texture of parasite eggs may also vary in the images depending on the sample collection and image acquisition process, making the segmentation process challenging. Over the years, various segmentation methods such as thresholding, edge detection, wathershed, etc. have been utilised by the researchers in this field. However, to the best of our knowledge, no survey works on the segmentation approaches for parasite eggs in various types of microscopic images have been reported yet. As a result, we explored a number of research reports and prepared a detail review of various segmentation techniques employed in previous works on automatic detection and identification of parasite eggs in different types of microscopic images. The aim of the study is to assist future researchers in identify the suitable strategies for effectively segmenting parasite egg images.