Elevated emotion network connectivity is associated with fluctuations in depression.

Elevated emotion network connectivity is thought to leave people vulnerable to become and stay depressed. The mechanism through which this arises is however unclear. Here, we test the idea that the connectivity of emotion networks is associated with more extreme fluctuations in depression over time, rather than necessarily more severe depression. We gathered data from two independent samples of N = 155 paid students and N = 194 citizen scientists who rated their positive and negative emotions on a smartphone app twice a day and completed a weekly depression questionnaire for 8 wk. We constructed thousands of personalized emotion networks for each participant and tested whether connectivity was associated with severity of depression or its variance over 8 wk. Network connectivity was positively associated with baseline depression severity in citizen scientists, but not paid students. In contrast, 8-wk variance of depression was correlated with network connectivity in both samples. When controlling for depression variance, the association between connectivity and baseline depression severity in citizen scientists was no longer significant. We replicated these findings in an independent community sample (N = 519). We conclude that elevated network connectivity is associated with greater variability in depression symptoms. This variability only translates into increased severity in samples where depression is on average low and positively skewed, causing mean and variance to be more strongly correlated. These findings, although correlational, suggest that while emotional network connectivity could predispose individuals to severe depression, it could also be leveraged to bring about therapeutic improvements.

Jerne's "immune network theory", of interacting anti-idiotypic antibodies applied to immune responses during COVID-19 infection and after COVID-19 vaccination.

Niels Kaj Jerne has proposed the "immune network theory" of interactions among anti-idiotypic antibodies, able to interfere with humoral responses to certain antigens. After the occurrence of the primary generation of antibodies, against an antigenic epitope, idiotypes of these antibodies induce anti-idiotypic antibodies that modulate the intensity of the first response, and so on. Adverse effects following SARS-COV-2 COVID-19 vaccines are occasionally similar to the symptoms of COVID-19 infection. Rare events linked to SARS-CoV-2 vaccines also resemble some rarely reported COVID-19 complications. Safety data from product information by European Medicines Agency suggest that spectra do overlap for four main vaccines. The proposition is that vaccine events and COVID-19 complications are related to anti-idiotypic antibodies whose spatial shape can lead to interactions with ACE2 molecules, in individuals with a prolonged Spike protein synthesis. The vaccines target cells by their affinity to the vaccine vector, or to engulf lipid nanoparticles. Anti-idiotypic antibodies shaped similarly to the Spike protein possibly interact with ACE2 molecules and cause diverse signs and symptoms.

Design principles of PI(4,5)P2 clustering under protein-free conditions: Specific cation effects and calcium-potassium synergy.

Phosphatidylinositol 4,5-bisphosphate (PIP2) clustering is a key component in cell signaling, yet little is known about the atomic-level features of this phenomenon. Network-theoretic analysis of multimicrosecond atomistic simulations of PIP2 containing asymmetric bilayers under protein-free conditions, presented here, reveals how design principles of PIP2 clustering are determined by the specific cation effects. Ca2+ generates large clusters (6% are pentamer or larger) by adding existing PIP2 dimers formed by strong O‒Ca2+‒O bridging interactions of unprotonated P4/P5 phosphates. In contrast, monovalent cations (Na+ and K+) form smaller and less-stable clusters by preferentially adding PIP2 monomers. Despite having the same net charge, the affinity to P4/P5 is higher for Na+, while affinity toward glycerol P1 is higher for K+. Consequently, a mixture of K+ and Ca2+ (as would be produced by Ca2+ influx) synergistically yields larger and more stable clusters than Ca2+ alone due to the different binding preferences of these cations.

An inclusive anatomical network analysis of human craniocerebral topology.

The human brain's complex morphology is spatially constrained by numerous intrinsic and extrinsic physical interactions. Spatial constraints help to identify the source of morphological variability and can be investigated by employing anatomical network analysis. Here, a model of human craniocerebral topology is presented, based on the bony elements of the skull at birth and a previously designed model of the brain. The goal was to investigate the topological components fundamental to the craniocerebral geometric balance, to identify underlying phenotypic patterns of spatial arrangement, and to understand how these patterns might have influenced the evolution of human brain morphology. Analysis of the craniocerebral network model revealed that the combined structure of the body and lesser wings of the sphenoid bone, the parahippocampal gyrus, and the parietal and ethmoid bones are susceptible to sustain and apply major spatial constraints that are likely to limit or channel their morphological evolution. The results also showcase a high level of global integration and efficient diffusion of biomechanical forces across the craniocerebral system, a fundamental aspect of morphological variability in terms of plasticity. Finally, community detection in the craniocerebral system highlights the concurrence of a longitudinal and a vertical modular partition. The former reflects the distinct morphogenetic environments of the three endocranial fossae, while the latter corresponds to those of the basicranium and calvaria.

Centrality statistics of symptom networks of schizophrenia: a systematic review.

The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.

Biodiversity conservation, human-animal interactions, and zootherapy in ecological knowledge of Indonesian Healers.

We examined the entanglement of biodiversity conservation, human-animal interactions, zootherapy, and local beliefs among Sumatran Healers and their local community by completing an ethnography of 43 Indigenous Healers across 8 tribes in Bengkulu Province, Sumatra, Indonesia. Data collection tools were interviews, observations, videos, photographs, and a researcher journal. Of the 43 Healers, 30 used animals and mentioned 62 species. Of the animals identified, the International Union for Conservation of Nature Red List lists 34% (n = 21) as endangered, decreasing, or vulnerable, including Sumatran tiger (Panthera tigris sumatrae), Sumatran elephant (Elephas maximus sumatranus), and Sumatran rhinoceros (Dicerorhinus sumatrensis). Of the 30 Healers using animals, 50% (n = 15) practiced healing with at least one endangered, decreasing, or vulnerable animal. We defined 3 personas: Healer self-persona, Healer-imposed persona, and community-imposed persona. A persona represented a group's opinions and sentiments related to Healers killing animals for medicinal purposes. Using an iterative data analysis process, we grouped the data across the 3 personas into 5 themes: ease of killing and preparing animals, emotions related to killing animals, animal value, relationship to religion, and Healers are tricksters. The complexity of merging the identities of Healers and the community within an actor-network embodies the relationality of actions, interactions, and feelings among Healers, between Healers and animals, and between Healers and the community. Conservationists should be cognizant of Healers' medicinal use of animals, views of human-animal interactions, and zootherapy from all social and emotional perspectives. The data led to defining Indigenous Healer ecological knowledge components of zootherapy, human-animal interactions, and biodiversity conservation.

Conservación de la biodiversidad, interacciones humano­fauna y zooterapia dentro del conocimiento ecológico de los curanderos indonesios Resumen Analizamos la complejidad de la conservación de la biodiversidad, las interacciones humano­fauna, la zooterapia y las creencias locales de los curanderos y su comunidad local mediante una etnografía de 43 curanderos indígenas de ocho tribus en la provincia de Bengkulu en Sumatra, Indonesia. Usamos entrevistas, observaciones, videos, fotografías y una bitácora de investigador como herramientas de recolección de datos. De los 43 curanderos, 30 usaban animales y mencionaron 62 especies. El 34% (n = 21) de los animales identificados están catalogados como en peligro, en disminución o vulnerables en la Lista Roja de la Unión Internacional para la Conservación de la Naturaleza, incluidos el tigre de Sumatra (Panthera tigris sumatrae), el elefante de Sumatra (Elephas maximus sumatranus) y el rinoceronte de Sumatra (Dicerorhinus sumatrensis). De los 30 curanderos que usan animales, el 50% (n = 15) practica la sanación con al menos una especie en peligro, en disminución o vulnerable. Definimos tres percepciones: autopercepción de curandero, percepción impuesta de curandero y percepción impuesta de comunidad. Cada percepción representó las opiniones y sentimientos de un grupo con respecto a la matanza de animales por cuestiones medicinales. Usamos un proceso de análisis de datos repetitivos para agrupar la información de las tres percepciones en tres temas: facilidad para matar y preparar animales, emociones relacionadas con matar animales, valor del animal, relación con la religión y los curanderos son estafadores. La complejidad de combinar las identidades de los curanderos dentro de una red de actores representa cómo se relacionan las acciones, interacciones y sentimientos dentro de la comunidad de curanderos, entre los curanderos y los animales y entre los curanderos y la comunidad. Los conservacionistas deben ser conscientes desde todas las perspectivas sociales y emocionales del uso medicinal que los curanderos dan a los animales, las percepciones sobre las interacciones humano­fauna y la zooterapia. Esta información nos llevó a definir los componentes del conocimiento ecológico de los curanderos indígenas en materia de zooterapia, interacciones humano­fauna y conservación de la biodiversidad.

A network model of legal relations.

From at least the early twentieth century, legal scholars have recognized that rights and other legal relations inhere between individual legal actors, forming a vast and complex social network. Yet, no legal scholar has used the mathematical machinery of network theory to formalize these relationships. Here, we propose the first such approach by modelling a rudimentary, static set of real property relations using network theory. Then, we apply our toy model to measure the level of modularity-essentially, the community structure-among aggregations of these real property relations and associated actors. In so doing, we show that even for a very basic set of relations and actors, law may employ modular structures to manage complexity. Property, torts, contracts, intellectual property, and other areas of the law arguably reduce information costs in similar, quantifiable ways by chopping up the world of interactions between parties into manageable modules that are semi-autonomous. We also posit that our network science approach to jurisprudential issues can be adapted to quantify many other important aspects of legal systems. This article is part of the theme issue 'A complexity science approach to law and governance'.

A network analysis of exhaustion disorder symptoms throughout treatment.

BACKGROUND: Stress-induced Exhaustion Disorder (ED) is associated with work absenteeism and adverse health outcomes. Currently, little is known regarding how the symptoms of ED are interrelated and whether the patterns of symptoms influence treatment outcomes. To this end, the current study applied network analyses on ED patients participating in a multimodal intervention. METHODS: The first aim of the study was to explore the internal relationships between exhaustion symptoms and identify symptoms that were more closely related than others. A second aim was to examine whether the baseline symptom network of non-responders to treatment was more closely connected than the baseline symptom networks of responders, by comparing the sum of all absolute partial correlations in the respective groups' symptom network. This comparison was made based on the hypothesis that a more closely connected symptom network before treatment could indicate poorer treatment outcomes. Network models were constructed based on self-rated ED symptoms in a large sample of patients (n = 915) participating in a 24-week multimodal treatment program with a 12-month follow-up. RESULTS: The internal relations between self-rated exhaustion symptoms were stable over time despite markedly decreased symptom levels throughout participation in treatment. Symptoms of limited mental stamina and negative emotional reactions to demands were consistently found to be the most closely related to other ED symptoms. Meanwhile, sleep quality and irritability were weakly related to other exhaustion symptoms. The symptom network for the full sample became significantly more closely connected from baseline to the end of treatment and 12-month follow-up. The symptom network of non-responders to treatment was not found to be more closely connected than the symptom network of responders at baseline. CONCLUSIONS: The results of the current study suggest symptoms of limited mental stamina and negative emotional reactions to demands are central ED symptoms throughout treatment, while symptoms of irritability and sleep quality seem to have a weak relation to other symptoms of ED. The implications of these findings are discussed in relation to the conceptualization, assessment, and treatment of ED. TRIAL REGISTRATION: The clinical trial was registered on Clinicaltrials.gov 2017-12-02 (Identifier: NCT03360136).

Graph-theoretical chirality measure and chirality-property relations for chemical structures with multiscale mirror asymmetries.

Chirality is an essential geometric property unifying small molecules, biological macromolecules, inorganic nanomaterials, biological microparticles, and many other chemical structures. Numerous chirality measures have attempted to quantify this geometric property of mirror asymmetry and to correlate these measures with physical and chemical properties. However, their utility has been widely limited because these correlations have been largely notional. Furthermore, chirality measures also require prohibitively demanding computations, especially for chiral structures comprised of thousands of atoms. Acknowledging the fundamental problems with quantification of mirror asymmetry, including the ambiguity of sign-variable pseudoscalar chirality measures, we revisit this subject because of the significance of quantifying chirality for quantitative biomimetics and describing the chirality of nanoscale materials that display chirality continuum and scale-dependent mirror asymmetry. We apply the concept of torsion within the framework of differential geometry to the graph theoretical representation of chiral molecules and nanostructures to address some of the fundamental problems and practical limitations of other chirality measures. Chiral gold clusters and other chiral structures are used as models to elaborate a graph-theoretical chirality (GTC) measure, demonstrating its applicability to chiral materials with different degrees of chirality at different scales. For specific cases, we show that GTC provides an adequate description of both the sign and magnitude of mirror asymmetry. The direct correlations with macroscopic properties, such as chiroptical spectra, are enhanced by using the hybrid chirality measures combining parameters from discrete mathematics and physics. Taking molecular helices as an example, we established a direct relation between GTC and optical activity, indicating that this chirality measure can be applied to chiral metamaterials and complex chiral constructs.

Network ecology: Tie fitness in social context(s).

Social relations are embedded in material, cultural, and institutional settings that affect network dynamics and the resulting topologies. For example, romantic entanglements are subject to social and cultural norms, interfirm alliances are constrained by country-specific legislation, and adolescent friendships are conditioned by classroom settings and neighborhood effects. In short, social contexts shape social relations and the networks they give rise to. However, how and when they do so remain to be established. This paper presents network ecology as a general framework for identifying how the proximal environment shapes social networks by focusing interactions and social relations, and how these interactions and relations in turn shape the environment in which social networks form. Tie fitness is introduced as a metric that quantifies how well particular dyadic social relations would align with the setting. Using longitudinal networks collected on two cohorts each in 18 North American schools, i.e., 36 settings, we develop five generalizable observations about the time-varying fitness of adolescent friendship. Across all 252 analyzed networks, tie fitness predicted new tie formation, tie longevity, and tie survival. Dormant fit ties cluster in relational niches, thereby establishing a resource base for social identities competing for increased representation in the relational system.

Network-based forecasting of climate phenomena.

Network theory, as emerging from complex systems science, can provide critical predictive power for mitigating the global warming crisis and other societal challenges. Here we discuss the main differences of this approach to classical numerical modeling and highlight several cases where the network approach substantially improved the prediction of high-impact phenomena: 1) El Niño events, 2) droughts in the central Amazon, 3) extreme rainfall in the eastern Central Andes, 4) the Indian summer monsoon, and 5) extreme stratospheric polar vortex states that influence the occurrence of wintertime cold spells in northern Eurasia. In this perspective, we argue that network-based approaches can gainfully complement numerical modeling.

Emergent dual scaling of riverine biodiversity.

A prevailing paradigm suggests that species richness increases with area in a decelerating way. This ubiquitous power law scaling, the species-area relationship, has formed the foundation of many conservation strategies. In spatially complex ecosystems, however, the area may not be the sole dimension to scale biodiversity patterns because the scale-invariant complexity of fractal ecosystem structure may drive ecological dynamics in space. Here, we use theory and analysis of extensive fish community data from two distinct geographic regions to show that riverine biodiversity follows a robust scaling law along the two orthogonal dimensions of ecosystem size and complexity (i.e., the dual scaling law). In river networks, the recurrent merging of various tributaries forms fractal branching systems, where the prevalence of branching (ecosystem complexity) represents a macroscale control of the ecosystem's habitat heterogeneity. In the meantime, ecosystem size dictates metacommunity size and total habitat diversity, two factors regulating biodiversity in nature. Our theory predicted that, regardless of simulated species' traits, larger and more branched "complex" networks support greater species richness due to increased space and environmental heterogeneity. The relationships were linear on logarithmic axes, indicating power law scaling by ecosystem size and complexity. In support of this theoretical prediction, the power laws have consistently emerged in riverine fish communities across the study regions (Hokkaido Island in Japan and the midwestern United States) despite hosting different fauna with distinct evolutionary histories. The emergence of dual scaling law may be a pervasive property of branching networks with important implications for biodiversity conservation.

Opening up conversations: Collaborative working across sociomaterial contexts in nursing in London.

AIM: To discuss nurses' use of networks to address nursing recruitment and retention in London, UK. DESIGN: Qualitative evaluation of the Capital Nurse programme reporting on 30 narrative interviews with executive, clinical and student nurses in 2019. RESULTS: Executive nurses within the Capital Nurse programme recognized the importance of sociomaterial contexts in the health and social care system in London and worked strategically across these contexts to achieve change. Supported through the Capital Nurse programme, executive nurses from health organizations across London initiated collaborative working to improve recruitment and retention. Primarily by designing and delivering sociomaterial products (organizational and educational) to support nurses to build a career in London. Drawing on ideas from actor network theory, in particular sociomaterial contexts, nurses' actions at all levels to develop and sustain networks to address nursing recruitment and retention across the NHS in London are described. CONCLUSIONS: Capital Nurse supported collaborative working both within single organizations and across organizations in London. There is evidence of change in how nurses across the capital work together to improve patient care, improve recruitment and retention. Findings may resonate with nurses in other settings who seek to address the problem of recruitment and retention. They show how nurses coming together in networks to effect changes in practice can work successfully. IMPACT: Nurses' use of networks led to novel models of communication and action to address the problems of recruitment and retention in London. We argue that sociomateriality should be considered outside the clinical practice setting, as part of nurses' professional development and organizational practice, that is how they plan their career, how they address recruitment and retention, how they communicate across organizations about nursing issues. NO PATIENT OR PUBLIC CONTRIBUTION: This was an evaluation of a staff development project in London, which sought to elicit nurses' experiences of participation in Capital Nurse.

Reassembling nursing in the digital age: An actor-network theory perspective.

This article explores the application of actor-network theory (ANT) to the nursing profession, proposing a novel perspective in understanding nursing in the context of modern digital healthcare. Traditional grand nursing theories, while foundational, often fail to encapsulate the dynamic and complex nature of nursing, particularly in an era of rapid technological advancements and shifting societal dynamics. ANT, with its emphasis on the relationships between human and nonhuman actors, offers a framework to understand nursing beyond traditional paradigms. This article makes two key arguments: first, that nursing can be viewed as a highly organised social assemblage, where both human (nurses, patients and policymakers) and nonhuman actors (technologies, medical equipment, institutional policies) play a crucial role, and second, that ANT can be used to enhance existing nursing theory to better understand the role of technology in nursing practice. The article considers how ANT can provide a more holistic and adaptable model for describing the nursing profession, particularly in an era where technology plays an integral role in healthcare delivery. It discusses the implications of viewing nursing through ANT, highlighting the need for nursing education and practice to adapt to the interconnected and technologically advanced nature of modern healthcare. The article also acknowledges the limitations of ANT, particularly its potential oversimplification of the complex ethical dimensions inherent in nursing and its focus on observable phenomena.

Distinction of Chaos from Randomness Is Not Possible from the Degree Distribution of the Visibility and Phase Space Reconstruction Graphs.

We investigate whether it is possible to distinguish chaotic time series from random time series using network theory. In this perspective, we selected four methods to generate graphs from time series: the natural, the horizontal, the limited penetrable horizontal visibility graph, and the phase space reconstruction method. These methods claim that the distinction of chaos from randomness is possible by studying the degree distribution of the generated graphs. We evaluated these methods by computing the results for chaotic time series from the 2D Torus Automorphisms, the chaotic Lorenz system, and a random sequence derived from the normal distribution. Although the results confirm previous studies, we found that the distinction of chaos from randomness is not generally possible in the context of the above methodologies.

Streamflow Prediction Using Complex Networks.

The reliable prediction of streamflow is crucial for various water resources, environmental, and ecosystem applications. The current study employs a complex networks-based approach for the prediction of streamflow. The approach consists of three major steps: (1) the formation of a network using streamflow time series; (2) the calculation of the clustering coefficient (CC) as a network measure; and (3) the use of a clustering coefficient-based nearest neighbor search procedure for streamflow prediction. For network construction, each timestep is considered as a node and the existence of link between any node pair is identified based on the difference (distance) between the streamflow values of the nodes. Different distance threshold values are used to identify the critical distance threshold to form the network. The complex networks-based approach is implemented for the prediction of daily streamflow at 142 stations in the contiguous United States. The prediction accuracy is quantified using three statistical measures: correlation coefficient (R), normalized root mean square error (NRMSE), and Nash-Sutcliffe efficiency (NSE). The influence of the number of neighbors on the prediction accuracy is also investigated. The results, obtained with the critical distance threshold, reveal that the clustering coefficients for the 142 stations range from 0.799 to 0.999. Overall, the prediction approach yields reasonably good results for all 142 stations, with R values ranging from 0.05 to 0.99, NRMSE values ranging from 0.1 to 12.3, and the NSE values ranging from -0.89 to 0.99. An attempt is also made to examine the relationship between prediction accuracy and the catchment characteristics/streamflow statistical properties (drainage area, mean flow, coefficient of variation of flow). The results suggest that the prediction accuracy does not have much of a relationship with the drainage area and the mean streamflow values, but with the coefficient of variation of flow. The outcomes from this study are certainly promising regarding the application of complex networks-based concepts for the prediction of streamflow (and other hydrologic) time series.

A comprehensive anatomical network analysis of human brain topology.

A network approach to the macroscopic anatomy of the human brain can be used to model physical interactions among regions in order to study their topological properties, as well as the topological properties of the overall system. Here, a comprehensive model of human brain topology is presented, based on traditional macroanatomical divisions of the whole brain, which includes its subcortical regions. The aim was to localise anatomical elements that are essential for the geometric balance of the brain, as to identify underlying phenotypic patterns of spatial arrangement and understand how these patterns may influence brain morphology in ontogeny and phylogeny. The model revealed that the parahippocampal gyrus, the anterior lobe of the cerebellum and the ventral portion of the midbrain are subjected to major topological constraints that are likely to limit or channel their morphological evolution. The present model suggests that the brain can be divided into a superior and an inferior morphological block, linked with extrinsic topological constraints imposed by the surrounding braincase. This information should be considered duly both in ontogenetic and phylogenetic studies of primate neuroanatomy.

Seizure forecasting: Where do we stand?

A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures-ICTALS 2022-convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long-suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi-day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large-scale network disorder yielded novel perspectives on the pre-ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure-forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well-being.

The unique role of smartphone addiction and related factors among university students: a model based on cross-sectional and cross-lagged network analyses.

Smartphone addiction is a global problem affecting university students. Previous studies have explored smartphone addiction and related factors using latent variables. In contrast, this study examines the role of smartphone addiction and related factors among university students using a cross-sectional and cross-lagged panel network analysis model at the level of manifest variables. A questionnaire method was used to investigate smartphone addiction and related factors twice with nearly six-month intervals among 1564 first-year university students (M = 19.14, SD = 0.66). The study found that procrastination behavior, academic burnout, self-control, fear of missing out, social anxiety, and self-esteem directly influenced smartphone addiction. Additionally, smartphone addiction predicted the level of self-control, academic burnout, social anxiety, and perceived social support among university students. Self-control exhibited the strongest predictive relationship with smartphone addiction. Overall, self-control, self-esteem, perceived social support, and academic burnout were identified as key factors influencing smartphone addiction among university students. Developing prevention and intervention programs that target these core influencing factors would be more cost-effective.

A network-based explanation of why most COVID-19 infection curves are linear.

Many countries have passed their first COVID-19 epidemic peak. Traditional epidemiological models describe this as a result of nonpharmaceutical interventions pushing the growth rate below the recovery rate. In this phase of the pandemic many countries showed an almost linear growth of confirmed cases for extended time periods. This new containment regime is hard to explain by traditional models where either infection numbers grow explosively until herd immunity is reached or the epidemic is completely suppressed. Here we offer an explanation of this puzzling observation based on the structure of contact networks. We show that for any given transmission rate there exists a critical number of social contacts, [Formula: see text], below which linear growth and low infection prevalence must occur. Above [Formula: see text] traditional epidemiological dynamics take place, e.g., as in susceptible-infected-recovered (SIR) models. When calibrating our model to empirical estimates of the transmission rate and the number of days being contagious, we find [Formula: see text] Assuming realistic contact networks with a degree of about 5, and assuming that lockdown measures would reduce that to household size (about 2.5), we reproduce actual infection curves with remarkable precision, without fitting or fine-tuning of parameters. In particular, we compare the United States and Austria, as examples for one country that initially did not impose measures and one that responded with a severe lockdown early on. Our findings question the applicability of standard compartmental models to describe the COVID-19 containment phase. The probability to observe linear growth in these is practically zero.