Search Results (13)

The post-modern era is characterized by a structural religious pluralism, whereby the public dimension of religion has re-emerged as a prominent feature after the relative obscurity of the modern period. In the context of Christianity, the contribution of religion to the common good has been reflected upon in the extensive and profound field of Public Theology. Since its inception in the 1980s, this vast area of studies has encompassed a wide range of topics, including politics, civil society, economics, social issues, and justice. However, the interreligious dimension of public theology appears to be less developed. This is linked, firstly, to the discovery of the public dimension of neighbouring religions and, secondly, to the clarification of the relationship between this and Christian Public Theology. It is evident that the focus of this discussion is the development of a Comparative Public Theology as Interreligious Public Theology. This is a theology that considers the comparative study of religions in a pluralistic context, with the public aspect as its background. On this basis, the contribution has two main aims. Firstly, to identify the field of interreligious public theology, and secondly to concretise it by exploring the field of education from an interreligious and public perspective, i.e., as a contribution to the common good. Full article

Power transmission lines transfer energy between power plants and substations by means of a linear chain of towers. These towers are often situated over extensive distances, sometimes in regions that are difficult to access. Wireless sensor networks present a viable solution for monitoring these long chains of towers due to their wide coverage, ease of installation and cost-effectiveness. The proposed LoRaBUS approach implements and analyses the benefits of a linear topology using a mixture of LoRa and LoRaWAN protocols. This approach is designed to enable automatic detection of nearby nodes, optimise energy consumption and provide a prioritised transmission mode in emergency situations. On remote, hard-to-reach towers, a prototype fire protection system was implemented and tested. The results demonstrate that LoRaBUS creates a self-configurable linear topology which proves advantageous for installation processes, node maintenance and troubleshooting node failures. The discovery process collects data from a neighbourhood to construct the network and to save energy. The network’s autonomous configuration can be completed within approximately 2 min. In addition, energy consumption is effectively reduced 25% by dynamically adjusting the transmission power based on the detected channel quality and the distance to the nearest neighbour nodes. Full article

The exponential growth in data traffic in the real world has drawn attention to the emerging computing technique called Fog Computing (FC) for offloading tasks in fault-free environments. This is a promising computing standard that offers higher computing benefits with a reduced cost, higher flexibility, and increased availability. With the increased number of tasks, the occurrence of faults increases and affects the offloading of tasks. A suitable mechanism is essential to rectify the faults that occur in the Fog network. In this research, the fault-tolerance (FT) mechanism is proposed based on cost optimization and fault minimization. Initially, the faulty nodes are identified based on the remaining residual energy with the proposed Priority Task-based Fault-Tolerance (PTFT) mechanism. The Minimum-Cost Neighbour Candidate Node Discovery (MCNCND) algorithm is proposed to discover the neighbouring candidate Fog access node that can replace the faulty Fog node. The Replication and Pre-emptive Forwarding (RPF) algorithm is proposed to forward the task information to the new candidate Fog access node for reliable transmission. These proposed mechanisms are simulated, analysed, and compared with existing FT methods. It is observed that the proposed FT mechanism improves the utilization of an active number of Fog access nodes. It also saved a residual energy of 1.55 J without replicas, compared to the 0.85 J of energy that is used without the FT method. Full article

Cell-to-cell communication is essential for the appropriate development and maintenance of homeostatic conditions in the central nervous system. Extracellular vesicles have recently come to the forefront of neuroscience as novel vehicles for the transfer of complex signals between neuronal cells. Extracellular vesicles are membrane-bound carriers packed with proteins, metabolites, and nucleic acids (including DNA, mRNA, and microRNAs) that contain the elements present in the cell they originate from. Since their discovery, extracellular vesicles have been studied extensively and have opened up new understanding of cell–cell communication; they may cross the blood–brain barrier in a bidirectional way from the bloodstream to the brain parenchyma and vice versa, and play a key role in brain–periphery communication in physiology as well as pathology. Neurons and glial cells in the central nervous system release extracellular vesicles to the interstitial fluid of the brain and spinal cord parenchyma. Extracellular vesicles contain proteins, nucleic acids, lipids, carbohydrates, and primary and secondary metabolites. that can be taken up by and modulate the behaviour of neighbouring recipient cells. The functions of extracellular vesicles have been extensively studied in the context of neurodegenerative diseases. The purpose of this review is to analyse the role extracellular vesicles extracellular vesicles in central nervous system cell communication, with particular emphasis on the contribution of extracellular vesicles from different central nervous system cell types in maintaining or altering central nervous system homeostasis. Full article

Pangasiids are an economically significant group of catfish, and many pangasiids are threatened in the wild from anthropogenic pressures, including increases in fishing pressure, habitat degradation, and improperly managed aquaculture practices. This study demonstrates the usage of DNA barcoding of the Cytochrome Oxidase subunit I (COI) gene as an identification tool in detecting potentially threatening invasive pangasiid species by establishing the diversity and phylogenetic relationship of Pangasiidae catfishes in Peninsular Malaysia. A neighbour-joining (NJ) dendrogram (Kimura-2-parameter model) generated five clades to represent distinct genera. Pangasius was further subdivided into two clades (Clade A: Pangasius bocourti-P. djambal and Clade B: P. nasutus-P. conchophilus). Given the marginal genetic divergence, indigenous and non-native species should be treated cautiously in allopatrically distributed species. The analysis used Automatic Barcode Gap Discovery (ABGD) and revealed barcode gaps between the intraspecific and interspecific distances. The sequences were partitioned into five groupings, corresponding with the species delineation based on the distribution of pairwise differences, which could not be differentiated using the NJ dendrogram. ABGD allows the recognition of one or two additional species using the recursive approach, but other taxonomic methods should be considered for a solid conclusion. DNA barcoding demonstrates the identification of closely related species, thus justifying its application towards the conservation of these fish. Full article

A narrative review on the interlinking effects of climate change and air pollution, and their impacts on human health in the Arabian Peninsula and its Neighbouring Regions (APNR) is provided. The APNR is experiencing the direct impacts of climate change through increasingly extreme temperatures in the summer season, increasing maximum and minimum temperatures, and increased frequency and severity of dust events. The region is also experiencing significant air pollution, of which particulate matter (PM), nitrogen dioxide (NO₂) and sulphur dioxide (SO₂) are of specific concern. Air pollution in the APNR is mainly caused by unprecedented industrial, population and motorization growth. The discovery of oil in the early 20th century has been the major economic driving force behind these changes. Climate change and air pollution impact human health in the region, primarily respiratory and cardiovascular health. Despite an increase in research capacity, research intensity was found to be inconsistent across the APNR countries, with Saudi Arabia, the UAE, Qatar and Iraq publishing more research articles than the other countries. In this review article, the existing research gaps in the region are investigated and the lack of synthesis between the interacting effects of air pollution and climate change upon human health is highlighted. Full article

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study. Full article

This concept paper reviews issues pertaining to parasitic and vector-borne infections, of humans, animals, or both, of topical relevance to the African continent as well as to neighbouring and interconnected geographies. This analysis is carried out through the “One Health” lens, being mindful of the central role of agriculture and livestock keeping in Africa’s sustainable development. The possible agricultural transformation that the continent may undergo to fulfil the rising demand for animal protein of its growing population, coupled with the ongoing climate changes, may lead to potentially enhanced interactions among humans, domesticated and wild animals, in a fast-changing environment. In this view, tackling parasitic conditions of livestock can prove being multidimensionally beneficial by improving animal health as well as communities’ food security, livelihood and public health. Accordingly, the value of applying the One Health approach to drug discovery and development in the fight against parasitic neglected tropical diseases and zoonoses, is also underscored. Overall, this article upholds the adoption of a holistic, global, interdisciplinary, multisectoral, harmonised and forward-looking outlook, encompassing both life and social sciences, when dealing with parasitic conditions of humans and animals, in Africa and beyond, in COVID-19 times and further. Full article

Vascular smooth muscle cells (VSMCs) provide vital contractile force within blood vessel walls, yet can also propagate cardiovascular pathologies through proliferative and pro-inflammatory activities. Such phenotypes are driven, in part, by the diverse effects of long non-coding RNAs (lncRNAs) on gene expression. However, lncRNA characterisation in VSMCs in pathological states is hampered by incomplete lncRNA representation in reference annotation. We aimed to improve lncRNA representation in such contexts by assembling non-reference transcripts in RNA sequencing datasets describing VSMCs stimulated in vitro with cytokines, growth factors, or mechanical stress, as well as those isolated from atherosclerotic plaques. All transcripts were then subjected to a rigorous lncRNA prediction pipeline. We substantially improved coverage of lncRNAs responding to pro-mitogenic stimuli, with non-reference lncRNAs contributing 21–32% for each dataset. We also demonstrate non-reference lncRNAs were biased towards enriched expression within VSMCs, and transcription from enhancer sites, suggesting particular relevance to VSMC processes, and the regulation of neighbouring protein-coding genes. Both VSMC-enriched and enhancer-transcribed lncRNAs were large components of lncRNAs responding to pathological stimuli, yet without novel transcript discovery 33–46% of these lncRNAs would remain hidden. Our comprehensive VSMC lncRNA repertoire allows proper prioritisation of candidates for characterisation and exemplifies a strategy to broaden our knowledge of lncRNA across a range of disease states. Full article

The process of knowledge discovery involves nowadays a major number of techniques. Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM) are some of the recent ones. the current research proposes a new hybrid and efficient tool to design prediction models called Scenarios Platform-Collaborative & Context-Aware Data Mining (SP-CCADM). Both CADM and CDM approaches are included in the new platform in a flexible manner; SP-CCADM allows the setting and testing of multiple configurable scenarios related to data mining at once. The introduced platform was successfully tested and validated on real life scenarios, providing better results than each standalone technique—CADM and CDM. Nevertheless, SP-CCADM was validated with various machine learning algorithms—k-Nearest Neighbour (k-NN), Deep Learning (DL), Gradient Boosted Trees (GBT) and Decision Trees (DT). SP-CCADM makes a step forward when confronting complex data, properly approaching data contexts and collaboration between data. Numerical experiments and statistics illustrate in detail the potential of the proposed platform. Full article

Pancreatic Ductal Adenocarcinoma (PDAC) is a particularly insidious and aggressive disease that causes significant mortality worldwide. The direct correlation between PDAC incidence, disease progression, and mortality highlights the critical need to understand the mechanisms by which PDAC cells rapidly progress to drive metastatic disease in order to identify actionable vulnerabilities. One such proposed vulnerability is epithelial mesenchymal plasticity (EMP), a process whereby neoplastic epithelial cells delaminate from their neighbours, either collectively or individually, allowing for their subsequent invasion into host tissue. This disruption of tissue homeostasis, particularly in PDAC, further promotes cellular transformation by inducing inflammatory interactions with the stromal compartment, which in turn contributes to intratumoural heterogeneity. This review describes the role of EMP in PDAC, and the preclinical target discovery that has been conducted to identify the molecular regulators and effectors of this EMP program. While inhibition of individual targets may provide therapeutic insights, a single ‘master-key’ remains elusive, making their collective interactions of greater importance in controlling the behaviours’ of heterogeneous tumour cell populations. Much work has been undertaken to understand key transcriptional programs that drive EMP in certain contexts, however, a collaborative appreciation for the subtle, context-dependent programs governing EMP regulation is needed in order to design therapeutic strategies to curb PDAC mortality. Full article

In this article, an Arrival and Departure Time Predictor (ADTP) for scheduling communication in opportunistic Internet of Things (IoT) is presented. The proposed algorithm learns about temporal patterns of encounters between IoT devices and predicts future arrival and departure times, therefore future contact durations. By relying on such predictions, a neighbour discovery scheduler is proposed, capable of jointly optimizing discovery latency and power consumption in order to maximize communication time when contacts are expected with high probability and, at the same time, saving power when contacts are expected with low probability. A comprehensive performance evaluation with different sets of synthetic and real world traces shows that ADTP performs favourably with respect to previous state of the art. This prediction framework opens opportunities for transmission planners and schedulers optimizing not only neighbour discovery, but the entire communication process. Full article

The sale of oil and gas is one of the most important components of the Syrian economy. Unfortunately, since the discovery of these resources, the Syrian people have not benefited from the revenues earned. This study deals with the development of oil and gas production and the geographical distribution of fields, as well as production control, deterioration of production, refining and selling mechanisms, and the resulting health and environmental impacts following Islamic State’s (IS) control of the majority of oilfields in Syria. Since summer 2015, IS controls 80% of the fields with a production of 65,000 barrels per day (b/d); the Assad regime controls 8% of fields with 10,000 b/d; and Kurdish forces dominate the remaining 12% with 25,000 b/d. IS depends on oil as a major source of financing for its military and civilian activities, and has also managed to set up an extensive network of middlemen in neighbouring territories and countries, with the aim of trading crude oil for cash and other resources. IS produces and exports within its areas of control and sells part of the oil to the Assad regime, and another portion to the liberated areas, as well as to Iraq and Turkey. Full article