Network Of Connections Research Articles

Analysis of spatial correlation network of urban digital economy in China

ABSTRACT In the context of the digital economy, the structure of China's urban spatial correlation network is undergoing profound changes. Based on social network analysis, this study analyzes the overall network characteristics, city network status and influencing factors of the spatial network of digital economy in Chinese cities. The study finds that China's digital economy presents a complex network of spatial connections, with increasing network strength, a clear ‘core-edge’ hierarchy, and a stable network structure. The developed cities in the east are the center of the network, the less developed cities in the east undertake the function of network radiation, and the cities in the west are located at the edge of the network. Technological innovation, industrial structure, human capital and urbanization all contribute to the spatial relevance of the digital economy. This study helps cities work together to amplify the digital dividend and narrow the digital divide.

CRISPR spacers acquired from plasmids primarily target backbone genes, making them valuable for predicting potential hosts and host range.

In recent years, there has been a surge in metagenomic studies focused on identifying plasmids in environmental samples. Although these studies have unearthed numerous novel plasmids, enriching our understanding of their environmental roles, a significant gap remains: the scarcity of information regarding the bacterial hosts of these newly discovered plasmids. Furthermore, even when plasmids are identified within bacterial isolates, the reported host is typically limited to the original isolate, with no insights into alternative hosts or the plasmid's potential host range. Given that plasmids depend on hosts for their existence, investigating plasmids without the knowledge of potential hosts offers only a partial perspective. This study introduces a method for identifying potential hosts and host ranges for plasmids through alignment with CRISPR spacers. To validate the method, we compared the PLSDB plasmids database with the CRISPR spacers database, yielding host predictions for 46% of the plasmids. When compared with reported hosts, our predictions achieved 84% concordance at the family level and 99% concordance at the phylum level. Moreover, the method frequently identified multiple potential hosts for a plasmid, thereby enabling predictions of alternative hosts and the host range. Notably, we found that CRISPR spacers predominantly target plasmid backbone genes while sparing functional genes, such as those linked to antibiotic resistance, aligning with our hypothesis that CRISPR spacers are acquired from plasmid-specific regions rather than insertion elements from diverse sources. Finally, we illustrate the network of connections among different bacterial taxa through plasmids, revealing potential pathways for horizontal gene transfer.IMPORTANCEPlasmids are notorious for their role in distributing antibiotic resistance genes, but they may also carry and distribute other environmentally important genes. Since plasmids are not free-living entities and rely on host bacteria for survival and propagation, predicting their hosts is essential. This study presents a method for predicting potential hosts for plasmids and offers insights into the potential paths for spreading functional genes between different bacteria. Understanding plasmid-host relationships is crucial for comprehending the ecological and clinical impact of plasmids and implications for various biological processes.

PENGARUH MODAL SOSIAL TERHADAP KAPASITAS MANAJERIAL PETANI JAGUNG DI DESA UMBA KECAMATAN NAPANO KUSAMBI KABUPATEN MUNA BARAT

This study aims to ascertain the social capital and managerial capacity of farmers, as well as to determine the influence of the social model on the managerial capacity of corn farmers in Umba Village, Napano Kusambi Subdistrict, West Muna Regency. The research population comprises all individuals engaged in the cultivation of corn in Umba Village, amounting to a total of 32 individuals. The sample was selected using the census method, resulting in a sample size of 32 respondents for this study. Data were collected through observation, surveys, and structured interviews, with the use of a questionnaire. This study employed a quantitative approach. The variables under investigation are divided into two categories: social capital and managerial capacity among farmers. The data were subjected to two distinct forms of analysis. Initially, a quantitative descriptive analysis was conducted utilising the class interval formula to delineate the prevailing conditions pertaining to social capital and managerial capacity among maize farmers. Subsequently, a multiple linear regression analysis was employed to ascertain the influence of social capital on the managerial capacity of maize farmers. The multiple linear regression analysis is comprised of three tests: the T-test, F-test, and determination test. The findings indicate that the utilization of social capital among corn farmers in Umba Village is classified as favorable. This is evidenced by the presence of a robust level of trust, a commendable application of norms, and a relatively strong network of social connections. The managerial capacity of maize farmers is categorized as good, as evidenced by their effective maize farm planning and organization. However, there are areas for improvement, particularly in the dimensions of maize farming implementation and supervision, which remain in the poor category. Social capital has a significant positive effect on the managerial capacity of maize farmers, amounting to 29.1%, with 70.9% influenced by other factors.

Network statistics of the whole-brain connectome of Drosophila

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections1–3, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations4,5, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex (https://codex.flywire.ai) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Adaptive Autonomic and Neuroplastic Control in Diabetic Neuropathy: A Narrative Review.

Type 2 diabetes mellitus (T2DM) is a worldwide socioeconomic burden, and is accompanied by a variety of metabolic disorders, as well as nerve dysfunction referred to as diabetic neuropathy (DN). Despite a tremendous body of research, the pathogenesis of DN remains largely elusive. Currently, two schools of thought exist regarding the pathogenesis of diabetic neuropathy: a) mitochondrial-induced toxicity, and b) microvascular damage. Both mechanisms signify DN as an intractable disease and, as a consequence, therapeutic approaches treat symptoms with limited efficacy and risk of side effects. Here, we propose that the human body exclusively employs mechanisms of adaptation to protect itself during an adverse event. For this purpose, two control systems are defined, namely the autonomic and the neural control systems. The autonomic control system responds via inflammatory and immune responses, while the neural control system regulates neural signaling, via plastic adaptation. Both systems are proposed to regulate a network of temporal and causative connections which unravel the complex nature of diabetic complications. A significant result of this approach infers that both systems make DN reversible, thus opening the door to novel therapeutic applications.

Cognitive impairment in CKD patients: a guidance document by the CONNECT network

Abstract Cognitive impairment (CI) is a prevalent and debilitating complication in patients with chronic kidney disease (CKD). This position paper, developed by the CONNECT network, provides guidance on the epidemiology, risk factors, pathophysiology, diagnosis, and clinical management of CKD-related CI. CI is significantly more common in CKD patients compared to the general population, particularly those undergoing haemodialysis. The development of CI is influenced by a complex interplay of factors, including uremic neurotoxins, electrolytes and acid base disorders, anaemia, vascular damage, metabolic disturbances and comorbidities like diabetes and hypertension. Effective screening and diagnostic strategies are essential for early identification of CI, utilizing cognitive assessment tools, neuroimaging, and circulating biomarkers. The impact of various drug classes, including antiplatelet therapy, oral anticoagulants, lipid-lowering treatments, and antihypertensive drugs, on cognitive function is evaluated. Management strategies encompass pharmacological and non-pharmacological interventions, with recommendations for optimizing cognitive function while managing CKD-related complications. This guidance highlights the importance of addressing CI in CKD patients through early detection, careful medication management, and tailored therapeutic strategies to improve patient outcomes.

Mathematical discussion in classrooms as a technologically-supported activity fostering participation and inclusion

AbstractWhole-class mathematical discussion in a problem-solving activity is recognized as a powerful pedagogical activity but also a challenge for teachers who must consider several difficulties that learners might face, particularly in terms of an overload of Working Memory and Executive Functions. This study investigates how the use of a digital platform (Padlet) can support participatory and inclusive mathematical classroom discussion. We proposed a teaching experiment based on graphical tasks anticipating integral calculus to grade 13 students, and we examined how the use of the digital platform plays a role in the construction and interpretation of new mathematical objects emerging from the activity. The use of Instrumental Genesis and Double Instrumental Genesis frameworks allowed us to make the affordances of the tool emerge. As a result, we got evidence of how mathematical discussion may develop as a network of interactions, feedback, and connection of input and discuss examples of how active participation and inclusion are enhanced by the tool affordances. Indeed, the digital platform allowed easy interaction, with many ways to represent and express the ongoing evolution of personal and shared meanings and the possibility to manage the time of the activity. This fostered students’ participation and students which did not participate in previous discussions were actively engaged in it.

COGNITIVE ORGANISATION OF LITERARY IMAGE: METAPHORICAL FRAMING

Topicality of studying the artistic image, its ontological status and complex of functions in the paradigm of modern linguistics is determined by the need to understand the specifics of an artistic text as a result of the verbal and mental activity of the author and the receptive-interpretative activity of the reader in the process of aesthetic communication. An artistic image embodies the main qualities of an artistic text – its conceptual inability to reflect objective reality within the framework of conventionality or life-like strategies. A literary text appeals to the network of associative connections that arise for the addressee of an aesthetic statement in the process of perceiving such a text on the basis of everyday and spiritual experience, background knowledge, including those determined by the general cultural outlook of the individual. In the process of research, it was proven that an artistic image arises in the process of abstraction and in an artistic text can be realised through metaphors, as a result of which the reader gets the opportunity to decode implicit meanings with the help of visual representations and verbalise them. Based on the texts of novels by Vladimir Orlov “Danilov, the Violist”, “Pharmacist” and “Shevrikuka” it is established that the cognitive organisation of an artistic image is determined by metaphorical framing, which forms the foundation of artistic fiction: there is a transfer by similarity in relation to situations, events, characteristics of characters represented in artistic text, and knowledge, values and meanings that form the foundation of the addressee’s worldview.

An improved bridge reference network for 3-D SAR tomography based on regional growing strategy

ABSTRACT The utilization of synthetic aperture radar tomography (TomoSAR) technology for the three-dimensional imaging of urban areas has garnered significant attention. In the case of multi-pass spaceborne TomoSAR, eliminating the atmospheric phase screen (APS) is a critical step. At present, a two-tier network method is widely employed for APS removal from extensive regions. However, challenges persist in the construction of first-tier reference networks using the Delaunay triangulation and bridge reference network methods, such as low network coverage ratios and linear correlation problems associated with near-parallel bridges. Consequently, a notable portion of the inversion results for buildings or artificial structures are either absent or characterized by low accuracy. To fully harness the observational information from all small connecting networks, this study introduces an improved bridge reference network construction method based on a region-growing strategy. This method merges small subnetworks with surrounding reference networks using high-quality bridge connections to maximize the coverage of the largest reference network. To validate the feasibility and effectiveness of the proposed approach, 27 TerraSAR-X images, which were collected in Shenzhen, China were utilized to construct a reference network and estimate building heights. The results clearly indicate that the proposed method effectively overcomes the limitations of existing methods.

IoT Attacks Detection Using Supervised Machine Learning Techniques

In recent times, the growing significance of Internet of Things (IoT) devices in people's lives is undeniable, driven by their myriad benefits. However, these devices confront cybersecurity threats akin to traditional network devices, as they depend on networks for connectivity and synchronization. Artificial Intelligence (AI) techniques, specifically Machine Learning (ML) and Deep Learning (DL), have demonstrated notable reliability in the field of cyberattack detection. This study focuses on detecting Flood and Brute Force cyberattacks using Machine Learning (ML) and Deep Learning (DL) models. The primary emphasis lies in identifying traffic features that significantly detect these types of attacks. The experimental study incorporates eight models: Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Gradient Boosting (GB), Naïve Bayes (NB), and Artificial Neural Network (ANN). Two sets of experiments were conducted, with the first set involving six features and the subsequent set, after feature selection, focusing on a reduced set of three features. The evaluation of the proposed model's efficiency and performance relied on metrics such as Accuracy, Precision, Recall, and F1-score. Remarkably, all proposed models exhibited high performance in both sets of experiments. However, the Gradient Boosting (GB) classifier suppressed others, attaining an impressive accuracy level of 95.94% and 95.28% in the sets with six features and three features, respectively. Doi: 10.28991/HIJ-2024-05-03-01 Full Text: PDF

Too much focus on your health might be bad for your health: Reddit user's communication style predicts their Long COVID likelihood.

Long Covid is a chronic disease that affects more than 65 million people worldwide, characterized by a wide range of persistent symptoms following a Covid-19 infection. Previous studies have investigated potential risk factors contributing to elevated vulnerability to Long Covid. However, research on the social traits associated with affected patients is scarce. This study introduces an innovative methodological approach that allows us to extract valuable insights directly from patients' voices. By analyzing written texts shared on social media platforms, we aim to collect information on the psychological aspects of people who report experiencing Long Covid. In particular, we collect texts of patients they wrote BEFORE they were afflicted with Long Covid. We examined the differences in communication style, sentiment, language complexity, and psychological factors of natural language use among the profiles of 6.107 Reddit users, distinguishing between those who claim they have never contracted Covid -19, those who claim to have had it, and those who claim to have experienced Long Covid symptoms. Our findings reveal that people in the Long Covid group frequently discussed health-related topics before the pandemic, indicating a greater focus on health-related concerns. Furthermore, they exhibited a more limited network of connections, lower linguistic complexity, and a greater propensity to employ emotionally charged expressions than the other groups. Using social media data, we can provide a unique opportunity to explore potential risk factors associated with Long Covid, starting from the patient's perspective.

Streams of conscious visual experience

Consciousness, a cornerstone of human cognition, is believed to arise from complex neural interactions. Traditional views have focused on localized fronto-parietal networks or broader inter-regional dynamics. In our study, we leverage advanced fMRI techniques, including the novel Functionnectome framework, to unravel the intricate relationship between brain circuits and functional activity shaping visual consciousness. Our findings underscore the importance of the superior longitudinal fasciculus within the fronto-parietal fibers, linking conscious perception with spatial neglect. Additionally, our data reveal the critical contribution of the temporo-parietal fibers and the splenium of the corpus callosum in connecting visual information with conscious representation and their verbalization. Central to these networks is the thalamus, posited as a conductor in synchronizing these interactive processes. Contrasting traditional fMRI analyses with the Functionnectome approach, our results emphasize the important explanatory power of interactive mechanisms over localized activations for visual consciousness. This research paves the way for a comprehensive understanding of consciousness, highlighting the complex network of neural connections that lead to awareness.

Scaffold Infiltrated Cathodes for Low-Temperature Solid Oxide Fuel Cells.

Lowering the operating temperature of solid oxide fuel cells (SOFCs) and electrolysis cells (SOECs) to reduce system cost and increase lifetime is the key to widely deploy this highly efficient energy technology, but the high cathode polarization losses at low temperatures limit overall cell performance. Here we demonstrate that by engineering a universal ceria-based scaffold with infiltrated nanoscale electrocatalysts, a low cathode polarization <0.25 Ω·cm2 with remarkably high performance 1 W/cm2 at 550 °C is achieved. The combination of low processing and operating temperature restrains the nanosized electrocatalysts, not only allowing fast oxygen transport but also providing an essential electronically connective network to facilitate electrochemical reactions without requiring the high-temperature processing of a separate cathode layer. Moreover, excellent SOFC durability was demonstrated for over 500 h. This work shows a promising pathway to reduce processing/system costs with all scalable ceramic processing techniques for the future development of low-temperature SOFCs and SOECs.

Theta burst stimulation on the fronto-cerebellar connective network promotes cognitive processing speed in the simple cognitive task.

The fronto-cerebellar functional network has been proposed to subserve cognitive processing speed. This study aims to elucidate how the long-range frontal-to-cerebellar effective connectivity contributes to faster speed. In total, 60 healthy participants were randomly allocated to three five-daily sessions of transcranial magnetic stimulation conditions, namely intermittent theta-burst stimulation (iTBS, excitatory), continuous theta-burst stimulation (CTBS, inhibitory), or a sham condition. The sites of the stimulations were the right pre-supplementary motor area (RpSMA), medial cerebellar vermis VI (MCV6), and vertex, respectively. Performances in two reaction time tasks were recorded at different time points. Post-stimulation speeds revealed marginal decreases in the simple but not complex task. Nevertheless, participants in the excitatory RpSMA and inhibitory MCV6 conditions showed direct and negative path effects on faster speeds compared to the sham condition in the simple reaction time (SRT) task (β = -0.320, p = 0.045 and β = -0.414, p = 0.007, respectively). These path effects were not observed in the SDMT task. RpSMA and MCV6 were involved in promoting the path effects of faster reaction times on simple cognitive task. This study offers further evidence to support their roles within the long-range frontal-to-cerebellar connectivity subserving cognitive processing speed. The enhancement effects, however, are likely limited to simple rather than complex mental operations.

Gene co-expression network analysis for porcine intramuscular fatty acid composition

In pigs, meat quality depends markedly on the fatty acid (FA) content and composition of the intramuscular fat, which is partly determined by the gene expression in this tissue. The aim of this work was to identify the link between muscle gene expression and its FA composition. In an (Iberian × Duroc) × Duroc backcrossed pig population, we identified modules of co-expressed genes, and correlation analyses were performed for each of them versus the phenotypes, finding four relevant modules. Two of the modules were positively correlated with saturated FAs (SFAs) and monounsaturated FAs (MUFAs), while negatively correlated with polyunsaturated FAs (PUFAs) and the omega-6/omega-3 ratio. The gene-enrichment analysis showed that these modules had over-representation of pathways related with the biosynthesis of unsaturated FAs, the Peroxisome proliferator-activated receptor signalling pathway and FA elongation. The two other relevant modules were positively correlated with PUFA and the n-6/n-3 ratio, but negatively correlated with SFA and MUFA. In this case, they had an over-representation of pathways related with fatty and amino acid degradation, and with oxidative phosphorylation. Using a graphical Gaussian model, we inferred a network of connections between the genes within each module. The first module had 52 genes with 87 connections, and the most connected genes were ADIPOQ, which is related with FA oxidation, and ELOVL6 and FABP4, both involved in FA metabolism. The second module showed 196 genes connected by 263 edges, being FN1 and MAP3K11 the most connected genes. On the other hand, the third module had 161 genes connected by 251 edges and ATG13 was the top neighbouring gene, while the fourth module had 224 genes and 655 connections, and its most connected genes were related with mitochondrial pathways. Overall, this work successfully identified relevant muscle gene networks and modules linked with FA composition, providing further insights on how the physiology of the pigs influences FA composition.

Networking people for goal attainment: Psychosocial processes facilitating resilience of care-leavers in South Africa

This article aims to deepen and theorize our understanding of the psychosocial processes young people who are transitioning out of residential care engage in, specifically networking people for goal attainment. A qualitative approach was used to collect data from 31 South African residential care-leavers who were one to seven years out of care, aged 17–26 years. Using a grounded theory method of data analysis this study aims to fill the gap in theoretically informed studies by constructing indigenous theories to explain the care-leaving journey. This paper presents findings on (1) the need to elicit social connections; (2) honing networking skills in eliciting social connections; and (3) the strength in weak ties. The implications for practice point to the necessity of fostering a sense of community among care-leavers prior to their transition since this would eventually promote the development of youth resilience. Thus, to support young people’s reintegration, social workers and child and youth care providers should cooperatively assist them to cultivate and broaden their networks of social connections and work proactively on building social skills for those purposes.

Identifying Dominant Actors of Ferdy Sambo's Case Network on Social Media X/Twitter Using Social Network Analysis for Public Relations Strategy

The Indonesian National Police (Polri) has experienced ups and downs in building a positive image in interacting with the public. This decline in trust is caused by the emergence of various issues that show the low performance of the police. In the Ferdy Sambo case study, the performance and integrity of the police is at stake and the sensitivity of the police to meet public expectations. One solution to im-prove the image is through an effective public relations strategy. However, to develop it, a deep un-derstanding of the characteristics and interaction patterns between social media through social net-work analysis is required. This research aims to identify influential X/Twitter actors in the case study of Inspector General Ferdy Sambo by applying the centrality method in Social Network User Analysis. The results of centrality analysis on the network show a wide variety of centrality levels. The @Zaindamai account dominates with the highest Degree Centrality value of 0.426829, indicating the number of connections in the network. The main role in disseminating information is held by @Zaindamai with the highest Betweenness Centrality value of 0.325748, indicating its role in connect-ing various networks. @Rizkynu46127931 stands out in Closeness Centrality with a high value of 0.497791, signifying quick and efficient access to all parts of the social network. In addition, @Rizkynu46127931 has significant influence in the network based on the highest Eigenvector Cen-trality of 0.245625. This centrality value forms the basis for formulating a more focused public relations strategy, improving the efficiency of communication with stakeholders, and designing a more concrete public relations plan.

The weighted multi-scale connections networks for macrodispersivity estimation

Macrodispersivity is critical for predicting solute behaviors with dispersive transport models. Conventional methods of estimating macrodispersivity usually need to solve flow equations and are time-consuming. Convolutional neural networks (CNN) have recently been proven capable of efficiently mapping the hydraulic conductivity field and macrodispersivity. However, the mapping accuracy still needs further improvement. In this paper, we present a new network shortcut connection style called weighted multi-scale connections (WMC) for convolutional neural networks to improve mapping accuracy. We provide empirical evidence showing that the WMC can improve the performance of CNN in macrodispersivity estimation by implementing the WMC in CNNs (CNN without short-cut connections, ResNet, and DenseNet), and evaluating them on datasets of macrodispersivity estimation. For the CNN without short-cut connections, the WMC can improve the estimating R2 by at least 3% on three datasets of conductivity fields. For ResNet18, the WMC improved the estimated R2 by an average of 2.5% on all three datasets. For ResNet34, the WMC improved the estimated R2 by an average of 5.6%. For ResNet50, the WMC improved the estimated R2 by an average of 16%. For ResNet101, the WMC improved the estimating R2 by an average of 30%. For DenseNets, the improved estimated R2 ranges from 0.5% to 5%. The WMC can strengthen feature propagation of different sizes and alleviate the vanishing-gradient issue. Moreover, it can be implemented to any CNN with down-sampling layers or blocks.

Cultivating Resilience and Success: Support Systems for Novice Teachers in Diverse Contexts

This qualitative study explores the support systems that foster resilience and success among novice teachers in diverse sociocultural contexts within Israeli teacher training colleges. Semistructured interviews were conducted with a sample of 51 novice teachers from various educational backgrounds. The findings reveal a complex network of support encompassing formal institutional structures, such as mentorship programs and induction workshops, as well as informal networks of familial support and cultural connections. Utilizing a holistic framework that integrates social support theory, social connection theory, and self-determination theory, the study highlights the interplay between external support and novice teachers’ intrinsic motivations, which drive their pursuit of autonomy, competence, and relatedness. The key findings underscore the crucial role of comprehensive induction and mentoring programs that address novice teachers’ emotional, psychological, and professional needs. These programs should provide emotional support, opportunities for collaboration and peer learning, and personalized guidance from experienced mentors. The study also emphasizes the significance of supportive school cultures, meaningful mentoring relationships, and the strength of informal support networks in promoting novice teachers’ well-being and effectiveness. The implications of this research are far-reaching, offering valuable insights for the global educational community on developing supportive ecosystems that enhance novice teachers’ resilience, job satisfaction, and retention rates. By articulating the essential role of both formal and informal support systems, this study contributes significantly to the discourse on teacher education and addresses a crucial gap in the literature on support mechanisms across diverse educational settings.

Neutrophils in the Focus: Impact on Neuroimmune Dynamics and the Gut–Brain Axis

The growing field of gut–brain axis research offers significant potential to revolutionize medical practices and improve human well-being. Neutrophils have emerged as key players in gut–brain inflammation, contributing to the relocation of inflammatory cells from the gut to the brain and exacerbating neuroinflammation in conditions, such as inflammatory bowel disease and neurodegenerative diseases. The intricate network of molecular and functional connections that interlinks the brain with the gastrointestinal system is characterized by complex signaling pathways. Understanding the complex interplay among the microbiota, gut, and brain offers unparalleled opportunities to develop novel therapeutic interventions for neurological disorders and improve overall health outcomes. The aim of this review was to comprehensively summarize current knowledge and future perspectives regarding the multifaceted role of neutrophils and their impact on the neuroimmune dynamics in the context of the gut–brain axis.