[Biobanks, translational research and medical informatics]

When we think of medical research, one intuitively associates it with the analysis of study data collected for a specific research question or with the secondary use of patient data from routine care. However, these are not the only sources for answering scientific questions. Especially for translational research, tissue and liquid samples such as blood, DNA or other body fluids provide essential insights into disease pathogenesis, development of new therapies and treatment decisions. Access to these biomedical materials is provided by so-called biobanks. By collecting, characterizing, documenting and, if necessary, processing human biospecimens in accordance with high quality standards, they can support research of the causes of diseases, early diagnosis and the targeted treatment of diseases, or make a significant contribution to the investigation of common diseases.

White matter substrates of functional connectivity dynamics in the human brain

The contribution of structural connectivity to functional connectivity dynamics is still far from being elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two independent repositories. The tw-dFC maps were analyzed using independent component analysis, aiming at identifying spatially independent white matter components which support dynamic changes in functional connectivity. Each component consisted of a spatial map of white matter bundles that show consistent fluctuations in functional connectivity at their endpoints, and a time course representative of such functional activity. These components show high intra-subject, inter-subject, and inter-cohort reproducibility. We provided also converging evidence that functional information about white matter activity derived by this method can capture biologically meaningful features of brain connectivity organization, as well as predict higher-order cognitive performance.

Exploring Chinese students' issues and concerns of studying abroad amid COVID-19 pandemic: an actor-network perspective

Chinese students' issues and concerns of studying abroad amid the COVID-19 pandemic have been largely neglected in the tourism and hospitality education research. Based on the actor-network perspective, this paper explores the network of issues and concerns of Chinese students who have planned to study abroad. In-depth interviews, observations, and open-ended questionnaires are employed with a purposive sample of 16 hospitality students from a university in Macau. We identify five major concerns and issues namely health risk, dissatisfaction with online study, feeling difficult to get into a top university, discrimination of Chinese in the West, and perception of restricted opportunity to make a cross-cultural exchange.

Long-term patterns of an interconnected core marine microbiota

BACKGROUND

Ocean microbes constitute ~ 70% of the marine biomass, are responsible for ~ 50% of the Earth's primary production and are crucial for global biogeochemical cycles. Marine microbiotas include core taxa that are usually key for ecosystem function. Despite their importance, core marine microbes are relatively unknown, which reflects the lack of consensus on how to identify them. So far, most core microbiotas have been defined based on species occurrence and abundance. Yet, species interactions are also important to identify core microbes, as communities include interacting species. Here, we investigate interconnected bacteria and small protists of the core pelagic microbiota populating a long-term marine-coastal observatory in the Mediterranean Sea over a decade.

RESULTS

Core microbes were defined as those present in > 30% of the monthly samples over 10 years, with the strongest associations. The core microbiota included 259 Operational Taxonomic Units (OTUs) including 182 bacteria, 77 protists, and 1411 strong and mostly positive (~ 95%) associations. Core bacteria tended to be associated with other bacteria, while core protists tended to be associated with bacteria. The richness and abundance of core OTUs varied annually, decreasing in stratified warmers waters and increasing in colder mixed waters. Most core OTUs had a preference for one season, mostly winter, which featured subnetworks with the highest connectivity. Groups of highly associated taxa tended to include protists and bacteria with predominance in the same season, particularly winter. A group of 13 highly-connected hub-OTUs, with potentially important ecological roles dominated in winter and spring. Similarly, 18 connector OTUs with a low degree but high centrality were mostly associated with summer or autumn and may represent transitions between seasonal communities.

CONCLUSIONS

We found a relatively small and dynamic interconnected core microbiota in a model temperate marine-coastal site, with potential interactions being more deterministic in winter than in other seasons. These core microbes would be essential for the functioning of this ecosystem over the year. Other non-core taxa may also carry out important functions but would be redundant and non-essential. Our work contributes to the understanding of the dynamics and potential interactions of core microbes possibly sustaining ocean ecosystem function.

The interconnection between independent reactive control policies drives the stringency of local containment

The lack of medical treatments and vaccines upon the arrival of the SARS-CoV-2 virus has made non-pharmaceutical interventions the best allies in safeguarding human lives in the face of the COVID-19 pandemic. Here we propose a self-organized epidemic model with multi-scale control policies that are relaxed or strengthened depending on the extent of the epidemic outbreak. We show that optimizing the balance between the effects of epidemic control and the associated socio-economic cost is strongly linked to the stringency of control measures. We also show that non-pharmaceutical interventions acting at different spatial scales, from creating social bubbles at the household level to constraining mobility between different cities, are strongly interrelated. We find that policy functionality changes for better or worse depending on network connectivity, meaning that some populations may allow for less restrictive measures than others if both have the same resources to respond to the evolving epidemic.

Fear and hope in financial social networks: Evidence from COVID-19

This paper examines the diffusion of COVID-19 news on social media using a large sample of approximately 45 million tweets. Using textual analysis, I identify tweets containing COVID-19 news, and construct an index representing the intensity of Twitter discussions. Moreover, I use retweets and favorites as additional measures of investor attention to COVID-19. The results show that the intensity of Twitter discussions about COVID-19 (and about the treatment program) correspond to market returns. This suggests a role for financial social networks in transmitting information related to crises, such as COVID-19, and the resolution of crises.

Potential for cascading impacts of environmental change and policy on indigenous culture

Global environmental and societal changes threaten the cultures of indigenous peoples and local communities (IPLC). Despite the importance of IPLC worldviews and knowledge to sustaining human well-being and biodiversity, risks to these cultural resources are commonly neglected in environmental governance, in part because impacts can be indirect and therefore difficult to evaluate. Here, we investigate the connectivity of values associated with the relationship Ngātiwai (a New Zealand Māori tribe) have with their environment. We show that mapping the architecture of values-environment relationships enables assessment of how deep into culture the impacts of environmental change or policy can cascade. Our results detail how loss of access to key environmental elements could potentially have extensive direct and cascading impacts on the cultural values of Ngātiwai, including environmental responsibilities. Thus, considering only direct effects of environmental change or policy on cultural resources, or treating IPLC social-ecological relations simplistically, can severely underestimate threats to cultures.

The New System of Mexican Migration: The Role of Entry Mode-Specific Human and Social Capital

Between 2000 and 2020, undocumented migration declined, temporary labor migration rose, and legal permanent residents arrived at a steady pace-together creating a new system of Mexico-U.S. migration based on the circulation of legal temporary workers and permanent residents. Drawing on data from the U.S. Department of Homeland Security and the Mexican Migration Project, we specify multinomial event-history models to predict the likelihood of departure on first and later trips via four entry categories: no documents, noncompliant tourist visas, temporary work visas, and legal residence visas. The models reveal how the accumulation of entry mode-specific social and human capital powered a system of undocumented migration that emerged between 1965 and 1985, and how that system deteriorated from 1985 to 2000. After 2000, employers took advantage of new visa categories to recruit legal temporary workers, leading to the accumulation of migration-related human and social capital specific to that mode of entry and the emergence of a new system of Mexico-U.S. migration.

Smart network based portfolios

In this article we deal with the problem of portfolio allocation by enhancing network theory tools. We propose the use of the correlation network dependence structure in constructing some well-known risk-based models in which the estimation of the correlation matrix is a building block in the portfolio optimization. We formulate and solve all these portfolio allocation problems using both the standard approach and the network-based approach. Moreover, in constructing the network-based portfolios we propose the use of three different estimators for the covariance matrix: the sample, the shrinkage toward constant correlation and the depth-based estimators . All the strategies under analysis are implemented on three high-dimensional portfolios having different characteristics. We find that the network-based portfolio consistently performs better and has lower risk compared to the corresponding standard portfolio in an out-of-sample perspective.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10479-022-04675-7.

Characterizing the PrEP Continuum for Black and Latinx Sexual and Gender Minority Youth

Pre-exposure prophylaxis (PrEP) rollout efforts thus far have inadequately reached young people from underrepresented backgrounds. This study explores PrEP engagement among young Black and Latinx men who have sex with men, transgender women, and gender diverse individuals in three U.S. cities using an adapted PrEP continuum measure. We analyze data from the recruitment phase of an ongoing PrEP engagement intervention (n = 319) using partial proportional odds logistic regression. Participants reported high willingness and intention to use PrEP, yet most (82%) were not currently taking PrEP. Being insured (aOR = 2.95, 95%-CI = 1.60-5.49), having one or more PrEP users in one's sexual network (aOR = 4.19, 95%-CI = 2.61-6.79), and higher individual HIV risk scores (aOR = 1.62, 95%-CI = 1.34-1.97) were each associated with being further along the PrEP continuum. Strategies are needed to address barriers to healthcare access and leverage connections within social and sexual networks in order to bolster PrEP engagement among marginalized young people from diverse backgrounds.

Core-stability over networks with widespread externalities

The Covid-19 epidemic highlighted the significance of externalities: contacts with other people affect the chances of getting infected for our entire network. We study endogenous network formation where not only players or pairs but larger coalitions can, cooperatively change the network. We introduce a model for coalitional network stability for networks with widespread externalities. The network function form generalises the partition function form of cooperative games in allowing the network to be taken into account. The recursive core for network function form games generalises the recursive core for such environments. We present two simple examples to illustrate positive and negative externalities. The first is of a favour network and show that the core is nonempty when players must pay transfers to intermediaries; this simple setting also models economic situations such as airline networks. The second models social contacts during an epidemic and finds social bubbles as the solution.

A framework For brain atlases: Lessons from seizure dynamics

Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain’s function from its underlying structure. We show how network topology, structure–function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.

Brain-spinal cord interaction in long-term motor sequence learning in human: An fMRI study

The spinal cord is important for sensory guidance and execution of skilled movements. Yet its role in human motor learning is not well understood. Despite evidence revealing an active involvement of spinal circuits in the early phase of motor learning, whether long-term learning engages similar changes in spinal cord activation and functional connectivity remains unknown. Here, we investigated spinal-cerebral functional plasticity associated with learning of a specific sequence of visually-guided joystick movements (sequence task) over six days of training. On the first and last training days, we acquired high-resolution functional images of the brain and cervical cord simultaneously, while participants practiced the sequence or a random task while electromyography was recorded from wrist muscles. After six days of training, the subjects' motor performance improved in the sequence compared to the control condition. These behavioral changes were associated with decreased co-contractions and increased reciprocal activations between antagonist wrist muscles. Importantly, early learning was characterized by activation in the C8 level, whereas a more rostral activation in the C6-C7 was found during the later learning phase. Motor sequence learning was also supported by increased spinal cord functional connectivity with distinct brain networks, including the motor cortex, superior parietal lobule, and the cerebellum at the early stage, and the angular gyrus and cerebellum at a later stage of learning. Our results suggest that the early vs. late shift in spinal activation from caudal to rostral cervical segments synchronized with distinct brain networks, including parietal and cerebellar regions, is related to progressive changes reflecting the increasing fine control of wrist muscles during motor sequence learning.

A network SIRX model for the spreading of COVID-19

Infectious diseases, such as the current COVID-19, have a huge economic and societal impact. The ability to model its transmission characteristics is critical to minimize its impact. In fact, predicting how fast an infection is spreading could be a major factor in deciding on the severity, extent and strictness of the applied mitigation measures, such as the recent lockdowns. Even though modelling epidemics is a well studied subject, usually models do not include quarantine or other social measures, such as those imposed in the recent pandemic. The current work builds upon a recent paper by Maier and Brockmann (2020), where a compartmental SIRX model was implemented. That model included social or individual behavioural changes during quarantine, by introducing state X , in which symptomatic quarantined individuals are not transmitting the infection anymore, and described well the transmission in the initial stages of the infection. The results of the model were applied to real data from several provinces in China, quite successfully. In our approach we use a Monte-Carlo simulation model on networks. Individuals are network nodes and the links are their contacts. We use a spreading mechanism from the initially infected nodes to their nearest neighbours, as has been done previously. Initially, we find the values of the rate constants (parameters) the same way as in Maier and Brockmann (2020) for the confirmed cases of a country, on a daily basis, as given by the Johns Hopkins University. We then use different types of networks (random Erdős-Rényi, Small World, and Barabási-Albert Scale-Free) with various characteristics in an effort to find the best fit with the real data for the same geographical regions as reported in Maier and Brockmann (2020). Our simulations show that the best fit comes with the Erdős-Rényi random networks. We then apply this method to several other countries, both for large-size countries, and small size ones. In all cases investigated we find the same result, i.e. best agreement for the evolution of the pandemic with time is for the Erdős-Rényi networks. Furthermore, our results indicate that the best fit occurs for a random network with an average degree of the order of 〈 k 〉 ≈ 10-25, for all countries tested. Scale Free and Small World networks fail to fit the real data convincingly.

COVID-19 to go? The role of disasters and evacuation in the COVID-19 pandemic

Since the start of the pandemic, some U.S. communities have faced record storms, fires, and floods. Communities have confronted the increased challenge of curbing the spread of COVID-19 amid evacuation orders and short-term displacement that result from hazards. This raises the question of whether disasters, evacuations, and displacements have resulted in above-average infection rates during the COVID-19 pandemic. This study investigates the relationship between disaster intensity, sheltering-in-place, evacuation-related mobility, and contagion following Hurricane Zeta in Southeastern Louisiana and The Wildfires in Napa and Sonoma Counties, California, known as the Glass Fire. We draw on data from the county subdivision level and mapped and aggregated tallies of Facebook user movement from the Facebook Data for Good program's GeoInsights Portal. We test the effects of disasters, evacuation, and shelter-in-place behaviors on COVID-19 spread using panel data models, matched panel models, and synthetic control experiments. Our findings suggest associations between disaster intensity and higher rates of COVID-19 cases. We also find that while sheltering-in-place led to decreases in the spread of COVID-19, evacuation-related mobility did not result in our hypothesized surge of cases immediately after the disasters. The findings from this study aim to inform policymakers and scholars about how to better respond to disasters during multi-crisis events, such as offering hotel accommodations to evacuees instead of mass shelters and updating intake and accommodation procedures at shelters, such as administration temperature screenings, offering hand sanitizing stations, and providing isolated areas for ill evacuees.

Functional network connectivity and topology during naturalistic stimulus is altered in first-episode psychosis

BACKGROUND

Psychotic disorders have been suggested to derive from dysfunctional integration of signaling between brain regions. Earlier studies have found several changes in functional network synchronization as well as altered network topology in patients with psychotic disorders. However, studies have used mainly resting-state that makes it more difficult to link functional alterations to any specific stimulus or experience. We set out to examine functional connectivity as well as graph (topological) measures and their association to symptoms in first-episode psychosis patients during movie viewing. Our goal was to understand whole-brain functional dynamics of complex naturalistic information processing in psychosis and changes in brain functional organization related to symptoms.

METHODS

71 first-episode psychosis patients and 57 control subjects watched scenes from the movie Alice in Wonderland during 3 T fMRI. We compared functional connectivity and graph measures indicating integration, segregation and centrality between groups, and examined the association between topology and symptom scores in the patient group.

RESULTS

We identified a subnetwork with predominantly decreased links of functional connectivity in first-episode psychosis patients. The subnetwork was mainly comprised of nodes of and links between the cingulo-opercular, sensorimotor and default-mode networks. In topological measures, we observed between-group differences in properties of centrality.

CONCLUSIONS

Functional brain networks are affected during naturalistic information processing already in the early stages of psychosis, concentrated in salience- and cognitive control-related hubs and subnetworks. Understanding these aberrant dynamics could add to better targeted cognitive and behavioral interventions in the early stages of psychotic disorders.

Comment on the article "Spatially-extended nucleation-aggregation-fragmentation models for the dynamics of prion-like neurodegenerative protein-spreading in the brain and its connectome 486 (2020) 110102"

Considering the reaction schemes (20) and (21) in Fornari et al. (2020), the fragmentation terms F_i (Eq. (22) in Fornari et al. (2020)) are not correct. Thus, the fragmentation terms F_i (Eq. (12) in Fornari et al. (2020)) are also incorrect when the reaction schemes (10) and (11) in Fornari et al. (2020) are taken into account. Here, the correct equations will be presented.

Ankle Arthritis Networking: Getting the right treatment to the right patient first time

End stage ankle joint arthritis is a debilitating condition. Surgical treatment, most commonly ankle arthrodesis or fusion, can be highly effective. The authors outline the nature and prevalence of ankle arthritis and show that the frequency of each type of procedure varies geographically. They present data supporting the hypothesis that units performing ankle replacement more frequently tend to have better outcomes, both clinically and financially. Adoption of country-wide Ankle Arthritis Networks is proposed, ensuring that every patient seeing a foot and ankle orthopaedic surgeon has potential access to all treatment options whether their surgeon chooses to perform replacement or not. The case is made that establishment of Ankle Arthritis Networks will avoid the need for units to perform a low number of replacements per year, homogenise treatment availability across the country and enables the right patient to receive the right treatment first time. LEVEL OF EVIDENCE: IV.

Gene regulatory circuitry of plant-environment interactions: scaling from cells to the field

Plant growth and development is the product of layers of sensing and regulation that are modulated by multifactorial environmental cues. Innovations in genomics currently allow gene regulatory control to be quantified at multiple scales and high resolution in defined cell populations and even in individual cells or nuclei in plants. The application of these 'omic technologies in highly controlled, as well as field environments is revolutionizing the recognition of factors critical to spatial and temporal responses to single or multiple environmental cues. Within and pan-species comparisons illuminate deeply conserved circuitry and targets of selection. This knowledge can benefit the breeding and engineering of crops with greater resilience to climate variability and the ability to augment nutrition through plant-microbial interactions.

Land-use intensification differentially affects bacterial, fungal and protist communities and decreases microbiome network complexity

BACKGROUND

Soil microbial communities are major drivers of cycling of soil nutrients that sustain plant growth and productivity. Yet, a holistic understanding of the impact of land-use intensification on the soil microbiome is still poorly understood. Here, we used a field experiment to investigate the long-term consequences of changes in land-use intensity based on cropping frequency (continuous cropping, alternating cropping with a temporary grassland, perennial grassland) on bacterial, protist and fungal communities as well as on their co-occurrence networks.

RESULTS

We showed that land use has a major impact on the structure and composition of bacterial, protist and fungal communities. Grassland and arable cropping differed markedly with many taxa differentiating between both land use types. The smallest differences in the microbiome were observed between temporary grassland and continuous cropping, which suggests lasting effects of the cropping system preceding the temporary grasslands. Land-use intensity also affected the bacterial co-occurrence networks with increased complexity in the perennial grassland comparing to the other land-use systems. Similarly, co-occurrence networks within microbial groups showed a higher connectivity in the perennial grasslands. Protists, particularly Rhizaria, dominated in soil microbial associations, as they showed a higher number of connections than bacteria and fungi in all land uses.

CONCLUSIONS

Our findings provide evidence of legacy effects of prior land use on the composition of the soil microbiome. Whatever the land use, network analyses highlighted the importance of protists as a key element of the soil microbiome that should be considered in future work. Altogether, this work provides a holistic perspective of the differential responses of various microbial groups and of their associations to agricultural intensification.

PROTREC: A probability-based approach for recovering missing proteins based on biological networks

A novel network-based approach for predicting missing proteins (MPs) is proposed here. This approach, PROTREC (short for PROtein RECovery), dominates existing network-based methods - such as Functional Class Scoring (FCS), Hypergeometric Enrichment (HE), and Gene Set Enrichment Analysis (GSEA) - across a variety of proteomics datasets derived from different proteomics data acquisition paradigms: Higher PROTREC scores are much more closely correlated with higher recovery rates of MPs across sample replicates. The PROTREC score, unlike methods reporting p-values, can be directly interpreted as the probability that an unreported protein in a proteomic screen is actually present in the sample being screened. SIGNIFICANCE: Mass spectrometry (MS) has developed rapidly in recent years; however, an obvious proportion of proteins is still undetected, leading to missing protein problems. A few existing protein recovery methods are based on biological networks, but the performance is not satisfactory. We propose a new protein recovery method, PROTREC, a Bayesian-inspired approach based on biological networks, which shows exceptional performance across multiple validation strategies. It does not rely on peptide information, so it avoids the ambiguity issue that most protein assembly methods face.

analysis and computations

The COVID-19 pandemic has dramatically demonstrated the importance of labor to supply chain network activities from production to distribution with shortfalls in labor availability, for numerous reasons, resulting in product shortages and the reduction of profits of firms. Even as progress has been made through vaccinations, issues associated with labor are still arising. Increasing wages is a strategy to enhance labor productivity and, also to ameliorate, in part, labor shortages, but has not, until this work, been explored in a full supply chain network context. Specifically, in this paper, a game theory supply chain network model is constructed of firms competing in producing a substitutable, but differentiated, product, and seeking to determine their equilibrium product path flows, as well as hourly wages to pay their workers, under fixed labor amounts associated with links, and wage-responsive productivity factors. The theoretical and computational approach utilizes the theory of variational inequalities. We first introduce a model without wage bounds on links and then extend it to include wage bounds. Lagrange analysis is conducted for the latter model, which yields interesting insights, as well as an alternative variational inequality formulation. A series of numerical examples reveals that firms can gain in terms of profits by being willing to pay higher wages, resulting in benefits also for their workers, as well as consumers, who enjoy lower demand market prices for the products. However, sensitivity analysis should be conducted to determine the range of such wage bounds. Ultimately, we observed, that the profits may decrease and then stabilize. This work adds to the literature on the integration of concepts from economics and operations research for supply chain networks and also has policy implications.

The complex nature of financial market microstructure: the case of a stock market crash

This paper uses multivariate Hawkes processes to model the transactions behavior of the US stock market as measured by the 30 Dow Jones Industrial Average individual stocks before, during and after the 36-min May 6, 2010, Flash Crash. The basis for our analysis is the excitation matrix, which describes a complex network of interactions among the stocks. Using high-frequency transactions data, we find strong evidence of self- and asymmetrically cross-induced contagion and the presence of fragmented trading venues. Our findings have implications for stock trading and corresponding risk management strategies as well as stock market microstructure design.

Neural dynamics of mindfulness meditation and hypnosis explored with intracranial EEG: A feasibility study

PURPOSE

Intracranial electroencephalography (iEEG) offers a unique window on brain dynamics with excellent temporal and spatial resolution and is less prone to recording artefacts than surface EEG. This study used a within-subject design to explore the feasibility to compare iEEG data during mind wandering, mindfulness meditation and hypnosis.

RESULTS

Three patients who had iEEG for clinical monitoring and who were new to mindfulness meditation and hypnosis were able to enter these states. We found non-specific and wide-spread amplitude modulations. Data-driven connectivity analysis revealed widespread connectivity patterns that were common across the three conditions. These were predominant in the low frequencies (delta, theta and alpha) and characterised by positively correlated activity. Connectivity patterns that were unique to the three conditions predominated in the gamma band, one third of the correlations in these patterns were negative.

CONCLUSIONS

This study is the first to support the feasibility of a direct comparison of the neural correlates of mindfulness meditation and hypnosis using iEEG. These modulations may reflect the complex interplay between different known brain networks, and warrant further functional investigations in particular in the gamma band.

Gender inequality in relational position-taking: An analysis of intra-organizational job mobility networks

We conceptualize within-organization job mobility as a position-taking process, arguing that the structure and outcome of claims over positions are characteristics of organizational inequality regimes. Drawing on data from 10 distribution centers from a large U.S. firm, we examine gendered job mobility as the observed network of workers moving among jobs. Results from network analysis and meta-regression reveal that in the firm examined, workers tend to move between jobs with similar gender compositions, that mobility lattices tend to be more ladder-like for male-concentrated jobs but more circuitous for female-concentrated jobs, and that there is less upward mobility overall in organizations with higher levels of wage inequality. Both organization level inequalities and the relationship between positions within organizations condition mobility. While we do not observe discursive claims on positions, we argue that these are the underlying mechanisms driving gendered job mobility.

The connectional anatomy of the temporal lobe

The idea of a temporal lobe separated from the rest of the hemisphere by reason of its unique structural and functional properties is a clinically useful artifact. While the temporal lobe can be safely defined as the portion of the cerebrum lodged in the middle cranial fossa, the pattern of its connections is a more revealing description of its functional subdivisions and specific contribution to higher cognitive functions. This chapter provides an historical overview of the anatomy of the temporal lobe and an updated framework of temporal lobe connections based on tractography studies of human and nonhuman primates and patients with brain disorders. Compared to monkeys, the human temporal lobe shows a relatively increased connectivity with perisylvian frontal and parietal regions and a set of unique intrinsic connections, which may have supported the evolution of working memory, semantic representation, and language in our species. Conversely, the decreased volume of the anterior (limbic) interhemispheric temporal connections in humans is related to a reduced reliance on olfaction and a partial transference of functions from the anterior commissure to the posterior corpus callosum. Overall the novel data from tractography suggest a revision of current dual stream models for visual and auditory processing.

Soft Statistical Mechanics for Biology

The multilevel organization of nature is self-evident: proteins do interact among them to give rise to an organized metabolism and the same hierarchical organization is in action for gene expression, tissue and organ architectures, and ecological systems.The still more common approach to such state of affairs is to think that causally relevant events originate from the lower level in the form of perturbations, that climb up the hierarchy reaching the ultimate layer of macroscopic behavior (e.g., causing a specific disease). Such rigid bottom-up causative model is unable to offer realistic models of many biological phenomena.Complex network approach allows to uncover the nature of multilevel organization, but in order to operationally define the organization principles of biological systems, we need to go further and complement network approach with sensible measures of order and organization. These measures, while keeping their original physical meaning, must not impose theoretical premises not verifiable in biological frameworks. We will show here how relatively simple and largely hypothesis-free multidimensional statistics tools can satisfactorily meet these criteria.

Studying Protista WBR and Repair Using Physarum polycephalum

Physarum polycephalum is a protist slime mould that exhibits a high degree of responsiveness to its environment through a complex network of tubes and cytoskeletal components that coordinate behavior across its unicellular, multinucleated body. Physarum has been used to study decision making, problem solving, and mechanosensation in aneural biological systems. The robust generative and repair capacities of Physarum also enable the study of whole-body regeneration within a relatively simple model system. Here we describe methods for growing, imaging, quantifying, and sampling Physarum that are adapted for investigating regeneration and repair.

Network models to evaluate vaccine strategies towards herd immunity in COVID-19

Vaccination remains a critical element in the eventual solution to the COVID-19 public health crisis. Many vaccines are already being mass produced and supplied in many countries. However, the COVID-19 vaccination programme will be the biggest in history. Reaching herd immunity will require an unprecedented mass immunisation campaign that will take several months and millions of dollars. Using different network models, COVID-19 pandemic dynamics of different countries can be recapitulated such as in Italy. Stochastic computational simulations highlight that peak epidemic sizes in a population strongly depend on the network structure. Assuming a vaccine efficacy of at least 80% in a mass vaccination program, at least 70% of a given population should be vaccinated to obtain herd immunity, independently of the network structure. If the vaccine efficacy reports lower levels of efficacy in practice, then the coverage of vaccination would be needed to be even higher. Simulations suggest that the "Ring of Vaccination" strategy, vaccinating susceptible contact and contact of contacts, would prevent new waves of COVID -19 meanwhile a high percent of the population is vaccinated.

Functional brain network community structure in childhood: Unfinished territories and fuzzy boundaries

Adult cortex is organized into distributed functional communities. Yet, little is known about community architecture of children’s brains. Here, we uncovered the community structure of cortex in childhood using fMRI data from 670 children aged 9–11 years (48% female, replication sample n = 544, 56% female) from the Adolescent Brain and Cognitive Development study. We first applied a data-driven community detection approach to cluster cortical regions into communities, then employed a generative model-based approach called the weighted stochastic block model to further probe community interactions. Children showed similar community structure to adults, as defined by Yeo and colleagues in 2011, in early-developing sensory and motor communities, but differences emerged in transmodal areas. Children have more cortical territory in the limbic community, which is involved in emotion processing, than adults. Regions in association cortex interact more flexibly across communities, creating uncertainty for the model-based assignment algorithm, and perhaps reflecting cortical boundaries that are not yet solidified. Uncertainty was highest for cingulo-opercular areas involved in flexible deployment of cognitive control. Activation and deactivation patterns during a working memory task showed that both the data-driven approach and a set of adult communities statistically capture functional organization in middle childhood. Collectively, our findings suggest that community boundaries are not solidified by middle childhood.

Biosupramolecular networks: Taking inspiration from nature to create powerful synthetic platforms

Nature is predicated on the ability to process large number of parallel signals to produce specific downstream outputs. Biosupramolecular networks are beginning to allow such processing power in synthetic systems, particularly through harnessing the recognition power of biomolecules. Such systems can be summarised through the reductionist view of containing inputs, circuitry motifs and functional outputs, with each of these elements able to be readily combined in a modular approach. Through the inherent 'plug and play' nature of these systems the field continues to rapidly expand, providing a wealth of new smart diagnostic and therapeutic systems.

Integrating network theory into the study of integrated healthcare

Healthcare policy in the United States (U.S.) has focused on promoting integrated healthcare to combat fragmentation (e.g., 1993 Health Security Act, 2010 Affordable Care Act). Researchers have responded by studying coordination and developing typologies of integration. Yet, after three decades, research evidence for the benefits of coordination and integration are lacking. We argue that research efforts need to refocus in three ways: (1) use social networks to study relational coordination and integrated healthcare, (2) analyze integrated healthcare at three levels of analysis (micro, meso, macro), and (3) focus on clinical integration as the most proximate impact on patient outcomes. We use examples to illustrate the utility of such refocusing and present avenues for future research.

Resting State Network Connectivity is attenuated by fMRI acoustic noise

INTRODUCTION

During the past decades there has been an increasing interest in tracking brain network fluctuations in health and disease by means of resting state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI however does not provide the ideal environmental setting, as participants are continuously exposed to noise generated by MRI coils during acquisition of Echo Planar Imaging (EPI). We investigated the effect of EPI noise on resting state activity and connectivity using magnetoencephalography (MEG), by reproducing the acoustic characteristics of rs-fMRI environment during the recordings. As compared to fMRI, MEG has little sensitivity to brain activity generated in deep brain structures, but has the advantage to capture both the dynamic of cortical magnetic oscillations with high temporal resolution and the slow magnetic fluctuations highly correlated with BOLD signal.

METHODS

Thirty healthy subjects were enrolled in a counterbalanced design study including three conditions: a) silent resting state (Silence), b) resting state upon EPI noise (fMRI), and c) resting state upon white noise (White). White noise was employed to test the specificity of fMRI noise effect. The amplitude envelope correlation (AEC) in alpha band measured the connectivity of seven Resting State Networks (RSN) of interest (default mode network, dorsal attention network, language, left and right auditory and left and right sensory-motor). Vigilance dynamic was estimated from power spectral activity.

RESULTS

fMRI and White acoustic noise consistently reduced connectivity of cortical networks. The effects were widespread, but noise and network specificities were also present. For fMRI noise, decreased connectivity was found in the right auditory and sensory-motor networks. Progressive increase of slow theta-delta activity related to drowsiness was found in all conditions, but was significantly higher for fMRI . Theta-delta significantly and positively correlated with variations of cortical connectivity.

DISCUSSION

rs-fMRI connectivity is biased by unavoidable environmental factors during scanning, which warrant more careful control and improved experimental designs. MEG is free from acoustic noise and allows a sensitive estimation of resting state connectivity in cortical areas. Although underutilized, MEG could overcome issues related to noise during fMRI, in particular when investigation of motor and auditory networks is needed.

Neighborhoods, networks, and delivery methods

We examine the contribution of information transmission among pregnant women to geographic variation in C-sections in Lombardy, Italy. Defining networks as pregnant women living in the same municipality, we observe that if the incidence of C-sections within the womans network is one standard deviation higher over the 12 months preceding delivery, then her probability of delivering by C-section is 0.007 percentage points (3%) higher. This result is mainly a network effect on Italian women, while it arises from both network and neighborhood effects on foreign women. Both groups respond to additional information, such as the incidence of C-section complications. The selection of pregnant women across hospitals does not uniquely explain our results, which are robust to alternative sample selections and specifications.

Transcriptome dynamics in developing testes of domestic cats and impact of age on tissue resilience to cryopreservation

BACKGROUND

Fundamental knowledge of cellular and molecular mechanisms in developing testicular tissues is critical to better understand gonadal biology and responses to non-physiological conditions. The objective of our study was to (1) analyze transcriptome dynamics in developing testis of the domestic cat and (2) characterize age effects on the initial response of the tissue to vitrification. Tissues from adult and juvenile cats were processed for histology, DNA integrity, and RNA sequencing analyses before and after vitrification.

RESULTS

Transcriptomic findings enabled to further characterize juvenile period, distinguishing between early and late juvenile tissues. Changes in gene expression and functional pathways were extensive from early to late juvenile to adult development stages. Additionally, tissues from juvenile animals were more resilient to vitrification compared to adult counterparts, with early juvenile sample responding the least to vitrification and late juvenile sample response being closest to adult tissues.

CONCLUSIONS

This is the first study reporting comprehensive datasets on transcriptomic dynamic coupled with structural analysis of the cat testis according to the age and exposure to cryopreservation. It provides a comprehensive network of functional terms and pathways that are affected by age in the domestic cat and are either enriched in adult or juvenile testicular tissues.

Global and local community memberships for estimating spreading capability of nodes in social networks

The analysis of spreading processes within complex networks can offer many important insights for the application in contexts such as epidemics, information dissemination or rumours. Particularly, structural factors of the network which either contribute or hinder the spreading are of interest, as they can be used to control or predict such processes. In social networks, the community structure is especially relevant, as actors usually participate in different densely connected social groups which emerge from various contexts, potentially allowing them to inject the spreading process into many different communities quickly. This paper extends our recent findings on the community membership of nodes and how it can be used to predict their individual spreading capability (Krukowski and Hecking, in: Benito, Cherifi, Cherifi, Moro, Rocha, Sales-Pardo (eds) Complex networks & their applications IX. Springer, Cham, pp 408-419, 2021) by further evaluating it on additional networks (both real-world networks and artificially generated networks), while additionally introducing a new local measure to identify influential spreaders that-in contrast to most other measures, does not rely on knowledge of the global network structure. The results confirm our recent findings, showing that the community membership of nodes can be used as a predictor for their spreading capability, while also showing that especially the local measure proves to be a good predictor, effectively outperforming the global measure in many cases. The results are discussed with regard to real-world use cases, where knowledge of the global structure is often not given, yet a prediction regarding the spreading capability highly desired (e.g., contact-tracing apps).

It's a circular argument: Examining how a novel configuration impacts information flow in submarine control rooms

The continuing advancement of technology means that sociotechnical systems are primed for revolutionary changes to ways of working that can increase capability. It is critical to consider the unintended impact technology can have on human operators particularly regarding information flow and interactions within teams. Previous research revealed that the co-location of operator's dependent on each other for task relevant information can optimise information flow previously constrained by engineering considerations. The current work compared a novel circular configuration to that of a contemporary submarine control room. In the circular configuration, consoles faced inwards, permitting eye contact between operators, and three large screen displays were introduced to provide all operators with the same information. Ten teams participated in low and high demand dived tracking scenarios in a simulated submarine control room. All communications between operators were recorded in order to generate social, information, and task networks. These were statistically compared to networks generated from a baseline study of contemporary operation. Overall, the volume of verbal communications significantly reduced, information exchange was more structured, and the volume of tasks completed by operators significantly increased when operating in an inward facing circle configuration. The current work provides support for a data driven evidence-based approach to design that is information centric but endorsed by the end user to optimise performance and increase productivity. Implications of the work and future research ideas are discussed.

To utilize automation or not to utilize automation, that is the question: An evaluation of how drills and procedures impact optronics mast usage from a sociotechnical systems perspective

The delegation of tasks to a non-human agent in a sociotechnical system can extend human capabilities and performance. Effective performance is, however, reliant on a successful relationship between human operators and automation. Optronics is a partially automated system which has replaced periscope on board some modern submarine platforms, operating modes permit the completion of tasks either manually or utilizing automation. A reluctance to utilize automated functionality within the optronics system has been due to operator familiarity with legacy manual procedures based upon the use of a physical periscope. This highlights the gap that is prevalent between innovation, design, training and governance of automation utilization. The current work examined current (Control group) utilization of optronics technology using an expert population in a high fidelity simulator. Findings were utilized to guide the development of novel optronics specific standard operating procedures (Intervention group). Results indicate that automaton disuse was greatly reduced, which had had a positive overall impact on overall system performance with regard to productivity and accuracy. The current work highlights the importance of incorporating governance of use and training as part of an automation design and implementation program is critical to help 'maximize what you have'.

Prediction of the clinical and naming status after anterior temporal lobe resection in patients with epilepsy

By assessing the cognitive capital, neuropsychological evaluation (NPE) plays a vital role in the perioperative workup of patients with refractory focal epilepsy. In this retrospective study, we used cutting-edge statistical approaches to examine a group of 47 patients with refractory temporal lobe epilepsy (TLE), who underwent standard anterior temporal lobectomy (ATL). Our objective was to determine whether NPE may represent a robust predictor of the postoperative status, two years after surgery. Specifically, based on pre- and postsurgical neuropsychological data, we estimated the sensitivity of cognitive indicators to predict and to disentangle phenotypes associated with more or less favorable outcomes. Engel (ENG) scores were used to assess clinical outcome, and picture naming (NAM) performance to estimate naming status. Two methods were applied: (a) machine learning (ML) to explore cognitive sensitivity to postoperative outcomes; and (b) graph theory (GT) to assess network properties reflecting favorable vs. less favorable phenotypes after surgery. Specific neuropsychological indices assessing language, memory, and executive functions can globally predict outcomes. Interestingly, preoperative cognitive networks associated with poor postsurgical outcome already exhibit an atypical, highly modular and less densely interconnected configuration. We provide statistical and clinical tools to anticipate the condition after surgery and achieve a more personalized clinical management. Our results also shed light on possible mechanisms put in place for cognitive adaptation after acute injury of central nervous system in relation with surgery.

Uniqueness of weakly reversible and deficiency zero realizations of dynamical systems

A reaction network together with a choice of rate constants uniquely gives rise to a system of differential equations, according to the law of mass-action kinetics. On the other hand, different networks can generate the same dynamical system under mass-action kinetics. Therefore, the problem of identifying "the" underlying network of a dynamical system is not well-posed, in general. Here we show that the problem of identifying an underlying weakly reversible deficiency zero network is well-posed, in the sense that the solution is unique whenever it exists. This can be very useful in applications because from the perspective of both dynamics and network structure, a weakly reversible deficiency zero (WR₀) realization is the simplest possible one. Moreover, while mass-action systems can exhibit practically any dynamical behavior, including multistability, oscillations, and chaos, WR₀ systems are remarkably stable for any choice of rate constants: they have a unique positive steady state within each invariant polyhedron, and cannot give rise to oscillations or chaotic dynamics. We also prove that both of our hypotheses (i.e., weak reversibility and deficiency zero) are necessary for uniqueness.

Mapping covariance in brain FDG uptake to structural connectivity

PURPOSE

Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDG_cov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDG_cov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDG_cov and structural connectivity networks.

METHODS

We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDG_cov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking.

RESULTS

For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDG_cov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections.

CONCLUSION

Structural connectivity via white matter fiber tracts is a relevant substrate of FDG_cov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDG_cov connections.

Gut-microbiota-microglia-brain interactions in Alzheimer's disease: knowledge-based, multi-dimensional characterization

BACKGROUND

Interactions between the gut microbiota, microglia, and aging may modulate Alzheimer's disease (AD) pathogenesis but the precise nature of such interactions is not known.

METHODS

We developed an integrated multi-dimensional, knowledge-driven, systems approach to identify interactions among microbial metabolites, microglia, and AD. Publicly available datasets were repurposed to create a multi-dimensional knowledge-driven pipeline consisting of an integrated network of microbial metabolite-gene-pathway-phenotype (MGPPN) consisting of 34,509 nodes (216 microbial metabolites, 22,982 genes, 1329 pathways, 9982 mouse phenotypes) and 1,032,942 edges.

RESULTS

We evaluated the network-based ranking algorithm by showing that abnormal microglia function and physiology are significantly associated with AD pathology at both genetic and phenotypic levels: AD risk genes were ranked at the top 6.4% among 22,982 genes, P < 0.001. AD phenotypes were ranked at the top 11.5% among 9982 phenotypes, P < 0.001. A total of 8094 microglia-microbial metabolite-gene-pathway-phenotype-AD interactions were identified for top-ranked AD-associated microbial metabolites. Short-chain fatty acids (SCFAs) were ranked at the top among prioritized AD-associated microbial metabolites. Through data-driven analyses, we provided evidence that SCFAs are involved in microglia-mediated gut-microbiota-brain interactions in AD at both genetic, functional, and phenotypic levels.

CONCLUSION

Our analysis produces a novel framework to offer insights into the mechanistic links between gut microbial metabolites, microglia, and AD, with the overall goal to facilitate disease mechanism understanding, therapeutic target identification, and designing confirmatory experimental studies.

Music of metagenomics-a review of its applications, analysis pipeline, and associated tools

This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."

Mild traumatic brain injury impairs the coordination of intrinsic and motor-related neural dynamics

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MTBI is poorly understood and lacks objective diagnostic and prognostic tools.

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Abnormal neural oscillations are found in subjects with a history of mTBI.

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We identify transient bursts in MEG data using a Hidden Markov Model.

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We explain a deficit in beta connectivity and power in terms of transient bursts.

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Data-driven feature selection identifies symptom-relevant functional connections.

Mild traumatic brain injury (mTBI) poses a considerable burden on healthcare systems. Whilst most patients recover quickly, a significant number suffer from sequelae that are not accompanied by measurable structural damage. Understanding the neural underpinnings of these debilitating effects and developing a means to detect injury, would address an important unmet clinical need. It could inform interventions and help predict prognosis. Magnetoencephalography (MEG) affords excellent sensitivity in probing neural function and presents significant promise for assessing mTBI, with abnormal neural oscillations being a potential specific biomarker. However, growing evidence suggests that neural dynamics are (at least in part) driven by transient, pan-spectral bursting and in this paper, we employ this model to investigate mTBI. We applied a Hidden Markov Model to MEG data recorded during resting state and a motor task and show that previous findings of diminished intrinsic beta amplitude in individuals with mTBI are largely due to the reduced beta band spectral content of bursts, and that diminished beta connectivity results from a loss in the temporal coincidence of burst states. In a motor task, mTBI results in diminished burst amplitude, altered modulation of burst probability during movement, and a loss in connectivity in motor networks. These results suggest that, mechanistically, mTBI disrupts the structural framework underlying neural synchrony, which impairs network function. Whilst the damage may be too subtle for structural imaging to see, the functional consequences are detectable and persist after injury. Our work shows that mTBI impairs the dynamic coordination of neural network activity and proposes a potent new method for understanding mTBI.

Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity

Evidence of the non stationary behavior of functional connectivity (FC) networks has been observed in task based functional magnetic resonance imaging (fMRI) experiments and even prominently in resting state fMRI data. This has led to the development of several new statistical methods for estimating this time-varying connectivity, with the majority of the methods utilizing a sliding window approach. While computationally feasible, the sliding window approach has several limitations. In this paper, we circumvent the sliding window, by introducing a statistical method that finds change-points in FC networks where the number and location of change-points are unknown a priori. The new method, called cross-covariance isolate detect (CCID), detects multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. CCID allows for change-point detection in the presence of frequent changes of possibly small magnitudes, can assign change-points to one or multiple brain regions, and is computationally fast. In addition, CCID is particularly suited to task based data, where the subject alternates between task and rest, as it firstly attempts isolation of each of the change-points within subintervals, and secondly their detection therein. Furthermore, we also propose a new information criterion for CCID to identify the change-points. We apply CCID to several simulated data sets and to task based and resting state fMRI data and compare it to recent change-point methods. CCID may also be applicable to electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. Similar to other biological networks, understanding the complex network organization and functional dynamics of the brain can lead to profound clinical implications. Finally, the R package ccid implementing the method from the paper is available from CRAN.

MyoData: An expression knowledgebase at single cell/nucleus level for the discovery of coding-noncoding RNA functional interactions in skeletal muscle

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Regulation of gene expression through non-coding RNAs at single myofiber and nucleus resolution.

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Reinterpretation of KEGG pathways with microRNA and long non-coding RNA activities.

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miR-149, -214, and let-7e alter mitochondrial shape.

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The long non-coding RNA Pvt1 is a sponge for miR-27a.

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miR-208b regulates Sox6; miR-214 regulates both Sox6 and Slc16a3.

Non-coding RNAs represent the largest part of transcribed mammalian genomes and prevalently exert regulatory functions. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) can modulate the activity of each other. Skeletal muscle is the most abundant tissue in mammals. It is composed of different cell types with myofibers that represent the smallest complete contractile system. Considering that lncRNAs and miRNAs are more cell type-specific than coding RNAs, to understand their function it is imperative to evaluate their expression and action within single myofibers. In this database, we collected gene expression data for coding and non-coding genes in single myofibers and used them to produce interaction networks based on expression correlations. Since biological pathways are more informative than networks based on gene expression correlation, to understand how altered genes participate in the studied phenotype, we integrated KEGG pathways with miRNAs and lncRNAs. The database also integrates single nucleus gene expression data on skeletal muscle in different patho-physiological conditions. We demonstrated that these networks can serve as a framework from which to dissect new miRNA and lncRNA functions to experimentally validate. Some interactions included in the database have been previously experimentally validated using high throughput methods. These can be the basis for further functional studies. Using database information, we demonstrate the involvement of miR-149, -214 and let-7e in mitochondria shaping; the ability of the lncRNA Pvt1 to mitigate the action of miR-27a via sponging; and the regulatory activity of miR-214 on Sox6 and Slc16a3. The MyoData is available at https://myodata.bio.unipd.it.

Improving the continuity and coordination of ambulatory care through feedback and facilitated dialogue-a study protocol for a cluster-randomised trial to evaluate the ACD study (Accountable Care in Germany)

Background

Patients in Germany are free to seek care from any office-based physician and can always ask for multiple opinions on a diagnosis or treatment. The high density of physicians and the freedom to choose among them without referrals have led to a need for better coordination between the multiple health professionals treating any given patient. The objectives of this study are to (1) identify informal networks of physicians who treat the same patient population, (2) provide these physicians with feedback on their network and patients, using routine data and (3) give the physicians the opportunity to meet one another in facilitated network meetings.

Methods

The Accountable Care Deutschland (ACD) study is a prospective, non-blinded, cluster-randomised trial comprising a process and economic evaluation of informal networks among 12,525 GPs and office-based specialists and their 1.9 million patients. The units of allocation are the informal networks, which will be randomised either to the intervention (feedback and facilitated meetings) or control group (usual care). The informal networks will be generated by identifying connections between office-based physicians using complete datasets from the Regional Associations of Statutory Health Insurance (SHI) Physicians in Hamburg, Schleswig Holstein, North Rhine and Westphalia Lip, as well as data from three large statutory health insurers in Germany. The physicians will (a) receive feedback on selected indicators of their own treatment activity and that of the colleagues in their network and (b) will be invited to voluntary, facilitated network meetings by their Regional Association of SHI physicians. The primary outcome will be ambulatory-care-sensitive hospitalisations at baseline, at the end of the 2-year intervention period, and at six months and at 12 months after the end of the intervention period. Data will be analysed using the intention-to-treat principle. A pilot study preceded the ACD study.

Discussion

Cochrane reviews show that feedback can improve everyday medical practice by shedding light on previously unknown relationships. Providing physicians with information on how they are connected with their colleagues and what the outcomes are of care delivered within their informal networks can help them make these improvements, as well as strengthen their awareness of possible discontinuities in the care they provide.

Trial registration

German Clinical Trials Register DRKS00020884. Registered on 25 March 2020—retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-021-05584-z.

Patient participation in cancer network governance: a six-year case study

Background

Patient participation in decision-making has become a hallmark of responsive healthcare systems. Cancer networks in many countries have committed to involving people living with and beyond cancer (PLC) at multiple levels. However, PLC participation in network governance remains highly variable for reasons that are poorly understood. This study aims to share lessons learned regarding mechanisms that enable PLC participation in cancer network governance.

Methods

This multiple case study, using a qualitative approach in a natural setting, was conducted over six years in three local cancer networks within the larger national cancer network in Quebec (Canada), where PLC participation is prescribed by the Cancer Directorate. Data were collected from multiple sources, including individual and focus group interviews (n = 89) with policymakers, managers, clinicians and PLC involved in national and local cancer governance committees. These data were triangulated and iteratively analysed according to a framework based on functions of collaborative governance in the network context.

Results

We identify three main mechanisms that enable PLC participation in cancer network governance: (1) consistent emphasis on patient-centred care as a network objective; (2) flexibility, time and support to translate mandated PLC representation into meaningful participation; and (3) recognition of the distinct knowledge of PLC in decision-making. The shared vision of person-centred care facilitates PLC participation. The quality of participation improves through changes in how committee meetings are conducted, and through the establishment of a national committee where PLC can pool their experience, develop skills and establish a common voice on priority issues. PLC knowledge is especially valued around particular challenges such as designing integrated care trajectories and overcoming barriers to accessing care. These three mechanisms interact to enable PLC participation in governance and are activated to varying extents in each local network.

Conclusions

This study reveals that mandating PLC representation on governance structures is a powerful context element enabling participation, but that it also delineates which governance functions are open to influence from PLC participation. While the activation of mechanisms is context dependent, the insights from this study in Quebec are transferable to cancer networks in other jurisdictions seeking to embed PLC participation in decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06834-1.

Cannabinoids: Revealing their complexity and role in central networks of fear and anxiety

The first aim of the present review is to provide an in-depth description of the cannabinoids and their known effects at various neuronal receptors. It reveals that cannabinoids are highly diverse, and recent work has highlighted that their effects on the central nervous system (CNS) are surprisingly more complex than previously recognized. Cannabinoid-sensitive receptors are widely distributed throughout the CNS where they act as primary modulators of neurotransmission. Secondly, we examine the role of cannabinoid receptors at key brain sites in the control of fear and anxiety. While our understanding of how cannabinoids specifically modulate these networks is mired by their complex interactions and diversity, a plausible framework(s) for their effects is proposed. Finally, we highlight some important knowledge gaps in our understanding of the mechanism(s) responsible for their effects on fear and anxiety in animal models and their use as therapeutic targets in humans. This is particularly important for our understanding of the phytocannabinoids used as novel clinical interventions.