Transferring, translating and transforming knowledge: the role of brokering in healthcare networks

PURPOSE

This research examines how knowledge and information are managed within two care networks. We develop a conceptual framework drawing on the notion of brokering and the 3T framework, which is used to describe the relative complexity of boundaries (referred to in the framework as syntactic, semantic and pragmatic) as well as capabilities and processes required to exchange information within the network. Previous research on brokering has focused on healthcare managers and professionals, but this research extends to patients and caregivers. Understanding knowledge exchange and brokering practices in healthcare is critical to the delivery of effective services.

DESIGN/METHODOLOGY/APPROACH

For this case research, non-participant observation and experienced-based interviews were undertaken with healthcare professionals, patients and caregivers within two care networks.

FINDINGS

The findings reveal brokering roles occupied by healthcare professionals, patients and caregivers support the transfer, translation and transformation of knowledge and information across functional and organisational boundaries. Enablers and disablers to brokering and the exchange of knowledge and information are also identified.

RESEARCH LIMITATIONS/IMPLICATIONS

The study is limited to two care networks for long-term conditions within the UK. Further research opportunities exist to examine similar care networks that extend across professional and organisational boundaries.

PRACTICAL IMPLICATIONS

This research informs healthcare professionals of the brokering capabilities that occur within networks and the enabling and disabling factors to managing knowledge across boundaries.

ORIGINALITY/VALUE

This paper provides a conceptual framework that categorises how increased levels of knowledge and information exchange and brokering practices are managed within care networks.

Health decisions amidst controversy: Prenatal alcohol consumption and the unequal experience of influence and control in networks

Individuals avoid stigma by looking to members of their networks for guidance on how to behave. Health controversies complicate this process by exposing people to inconsistent norms, influence, and control within their networks. To understand this process, we meld perspectives on networks and social psychology. Using the case of light/moderate drinking during pregnancy, we examine how people make health decisions in the face of controversy. Through in-depth interviews with first-time pregnant individuals and their health care providers, we reveal that individuals' decisions about controversial health behaviors depend on 1) the consistency of the influence and control they encounter, 2) their status relative to enforcers, and 3) the strength of their ties to enforcers. These processes give people from higher social positions more power to resist influence and control. These findings illuminate how medical recommendations and public health interventions should account for the unequal experience of influence and control within networks.

Optimising frontline learning and engagement between consultant-led neonatal teams in the West Midlands: a survey on the utility of an augmented simulation training technique

Background

In England, neonatal care is delivered in operational delivery networks, comprising a combination of the Neonatal Intensive Care (NICU), Local-Neonatal (LNU) or Special-Care Units (SCU), based on their ability to care for babies with different degrees of illness or prematurity. With the development of network care pathways, the most premature and sickest are mostly triaged for delivery in services linked to NICU. This has created anxiety for teams in LNU and SCU. Less exposure to sicker babies has resulted in limited opportunities to maintain expertise for when these babies unexpectedly deliver at their centre and thereafter require transfer for care, to NICU. Simultaneously, LNU and SCU teams develop skills in the care of the less ill and premature baby which would also be of benefit to NICU teams. A need for mutual learning through inter-unit multidirectional collaborative learning and engagement (hereafter also called neonatal networking) between teams of different designations emerged. Here, neonatal networking is defined as collaboration, shared clinical learning and developing an understanding of local systems strengths and challenges between units of different and similar designations. We describe the responses to the development of a clinical and systems focussed platform for this engagement between different teams within our neonatal ODN.

Method

An interactive 1-day programme was developed in the West Midlands, focussing on a non-hierarchical, equal partnership between neonatal teams from different unit designations. It utilised simulation around clinical scenarios, with a slant towards consultant engagement. Four groups rotating through four clinical simulation scenarios were developed. Each group participated in a clinical simulation scenario, led by a consultant and supported by nurses and doctors in training together with facilitators, with a further ~two consultants, as observers within the group. All were considered learners. Consultant candidates took turns to be participants and observers in the simulation scenarios so that at the end of the day all had led a scenario. Each simulation-clinical debrief session was lengthened by a further ~ 20 min, during which freestyle discussion with all learners occurred. This was to promote further bonding, through multidirectional sharing, and with a systems focus on understanding the strengths and challenges of practices in different units. A consultant focus was adopted to promote a long-term engagement between units around shared care. There were four time points for this neonatal networking during the course of the day. Qualitative assessment and a Likert scale were used to assess this initiative over 4 years.

Results

One hundred fifty-five individuals involved in frontline neonatal care participated. Seventy-seven were consultants, supported by neonatal trainees, staff grade doctors, clinical fellows, advanced neonatal nurse practitioners and nurses in training. All were invited to participate in the survey. The survey response rate was 80.6%. Seventy-nine percent felt that this learning strategy was highly relevant; 96% agreed that for consultants this was appropriate adult learning. Ninety-eight percent agreed that consultant training encompassed more than bedside clinical management, including forging communication links between teams. Thematic responses suggested that this was a highly useful method for multi-directional learning around shared care between neonatal units.

Conclusion

Simulation, enhanced with systems focussed debrief, appeared to be an acceptable method of promoting multidirectional learning within neonatal teams of differing designations within the WMNODN.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41077-021-00181-1.

Multi-scale network targeting: A holistic systems-biology approach to cancer treatment

The vulnerabilities of cancer at the cellular and, recently, with the introduction of immunotherapy, at the tissue level, have been exploited with variable success. Evaluating the cancer system vulnerabilities at the organismic level through analysis of network topology and network dynamics can potentially predict novel anti-cancer drug targets directed at the macroscopic cancer networks. Theoretical work analyzing the properties and the vulnerabilities of the multi-scale network of cancer needs to go hand-in-hand with experimental research that uncovers the biological nature of the relevant networks and reveals new targetable vulnerabilities. It is our hope that attacking cancer on different spatial scales, in a concerted integrated approach, may present opportunities for novel ways to prevent treatment resistance.

Tracing the potential of networks to improve community cancer care: an in-depth single case study

Background

Despite overall declines in cancer mortality in the USA over the past three decades, many patients in community settings fail to receive evidence-based cancer care. Networks that link academic medical centers (AMCs) and community providers may reduce disparities by creating access to specialized expertise and care, but research on network effectiveness is mixed. The objective of this study was to identify factors related to whether and how an exemplar AMC network served to provide advice and referral access in community settings.

Methods

An embedded in–depth single case study design was employed to study a network in the Midwest USA that connects a leading cancer specialty AMC with community practices. The embedded case units were a subset of 20 patients with young-onset colorectal cancer or risk-related conditions and the providers involved in their care. The electronic health record (EHR) was reviewed from January 1, 1990, to February 28, 2018. Social network analysis identified care, advice, and referral relationships. Within-case process tracing provided detailed accounts of whether and how the network provided access to expert, evidence-based care or advice in order to identify factors related to network effectiveness.

Results

The network created access to evidence-based advice or care in some but not all case units, and there was variability in whether and how community providers engaged the network, including the path for referrals to the AMC and the way in which advice about an evidence-based approach to care was communicated from AMC specialists to community providers. Factors related to instances when the network functioned as intended included opportunities for both rich and lean communication between community providers and specialists, coordinated referrals, and efficient and adequately utilized documentation systems.

Conclusions

Network existence alone is insufficient to open up access to evidence-based expertise or care for patients in community settings. In-depth understanding of how this network operated provides insight into factors that support or inhibit the potential of networks to minimize disparities in access to evidence-based community cancer care, including both personal and organizational factors.

From DNA human sequence to the chromatin higher order organisation and its biological meaning: Using biomolecular interaction networks to understand the influence of structural variation on spatial genome organisation and its functional effect

The three-dimensional structure of the human genome has been proven to have a significant functional impact on gene expression. The high-order spatial chromatin is organised first by looping mediated by multiple protein factors, and then it is further formed into larger structures of topologically associated domains (TADs) or chromatin contact domains (CCDs), followed by A/B compartments and finally the chromosomal territories (CTs). The genetic variation observed in human population influences the multi-scale structures, posing a question regarding the functional impact of structural variants reflected by the variability of the genes expression patterns. The current methods of evaluating the functional effect include eQTLs analysis which uses statistical testing of influence of variants on spatially close genes. Rarely, non-coding DNA sequence changes are evaluated by their impact on the biomolecular interaction network (BIN) reflecting the cellular interactome that can be analysed by the classical graph-theoretic algorithms. Therefore, in the second part of the review, we introduce the concept of BIN, i.e. a meta-network model of the complete molecular interactome developed by integrating various biological networks. The BIN meta-network model includes DNA-protein binding by the plethora of protein factors as well as chromatin interactions, therefore allowing connection of genomics with the downstream biomolecular processes present in a cell. As an illustration, we scrutinise the chromatin interactions mediated by the CTCF protein detected in a ChIA-PET experiment in the human lymphoblastoid cell line GM12878. In the corresponding BIN meta-network the DNA spatial proximity is represented as a graph model, combined with the Proteins-Interaction Network (PIN) of human proteome using the Gene Association Network (GAN). Furthermore, we enriched the BIN with the signalling and metabolic pathways and Gene Ontology (GO) terms to assert its functional context. Finally, we mapped the Single Nucleotide Polymorphisms (SNPs) from the GWAS studies and identified the chromatin mutational hot-spots associated with a significant enrichment of SNPs related to autoimmune diseases. Afterwards, we mapped Structural Variants (SVs) from healthy individuals of 1000 Genomes Project and identified an interesting example of the missing protein complex associated with protein Q6GYQ0 due to a deletion on chromosome 14. Such an analysis using the meta-network BIN model is therefore helpful in evaluating the influence of genetic variation on spatial organisation of the genome and its functional effect in a cell.

Estimating Adult Death Rates From Sibling Histories: A Network Approach

Hundreds of millions of people live in countries that do not have complete death registration systems, meaning that most deaths are not recorded and that critical quantities, such as life expectancy, cannot be directly measured. The sibling survival method is a leading approach to estimating adult mortality in the absence of death registration. The idea is to ask survey respondents to enumerate their siblings and to report about their survival status. In many countries and periods, sibling survival data are the only nationally representative source of information about adult mortality. Although a vast amount of sibling survival data has been collected, important methodological questions about the method remain unresolved. To help make progress on this issue, we propose reframing the sibling survival method as a network sampling problem. This approach enables a formal derivation of statistical estimators for sibling survival data. Our derivation clarifies the precise conditions that sibling history estimates rely on, leads to internal consistency checks that can help assess data and reporting quality, and reveals important quantities that could potentially be measured to relax assumptions in the future. We introduce the R package siblingsurvival, which implements the methods we describe.

Inference of brain networks with approximate Bayesian computation - assessing face validity with an example application in Parkinsonism

This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental, in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependant dynamics, such as oscillatory bursts, in terms of the underlying neural circuits.

Socio-economic position and local solidarity in times of crisis. The COVID-19 pandemic and the emergence of informal helping arrangements in Germany

In this article we study the emergence of local solidarity in the wake of the COVID-19 crisis in Germany. The COVID-19 pandemic and ensuing lockdown measures have had far-reaching and quite diverse consequences for different social groups, and have increased the need for practical help, childcare, financial aid, but also emotional support to cope with the psychological consequences of social isolation. Hence, even individuals who are not traditionally receivers of informal help have suddenly become dependent on it. Existing research on volunteering, caregiving and donations has shown that the provision of help and volunteer work has a social gradient, and that social inequalities therein can partly be explained by reference to individuals' attitudes and social networks. Against this backdrop, we ask: (1) Has the COVID-19 pandemic sparked the emergence of a new local solidarity? (2) What types of help are provided, and to whom? (3) How does socio-economic position affect the provision of different forms of help during the COVID-19 crisis? (4) Which sociological mechanisms can explain these inequalities in helping? Using data from a topical online-survey based on a quota sample which was collected, during the heydays of the first lockdown in Germany, we find that one of two respondents engages in some sort of local solidarity. Depending on the recipient and the way of helping - up to half of these helping arrangements has newly emerged and does not build on already existing (pre-crisis) help-arrangements. Differences between income and educational groups can mostly be explained by attitudes and social networks. Embeddedness in formal networks is particularly important for extending help to previously unknown recipients in the community. This article contributes to the literature on the social origins of help and the initiation of social capital during crises in general, and the political discussion about solidarity in the COVID-19 pandemic in particular.

Picking the right metaphors for addressing microbial systems: economic theory helps understanding biological complexity

Any descriptive language is necessarily metaphoric and interpretative. Two somewhat overlapping-but not identical-languages have been thoroughly employed in the last decade to address the issue of regulatory complexity in biological systems: the terminology of network theory and the jargon of electric circuitry. These approaches have found many formal equivalences between the layout of extant genetic circuits and the architecture of man-made counterparts. However, these languages still fail to describe accurately key features of biological objects, in particular the diversity of signal-transfer molecules and the diffusion that is inherent to any biochemical system. Furthermore, current formalisms associated with networks and circuits can hardly face the problem of multi-scale regulatory complexity-from single molecules to entire ecosystems. We argue that the language of economic theory might be instrumental not only to portray accurately many features of regulatory networks, but also to unveil aspects of the biological complexity problem that remain opaque to other types of analyses. The main perspective opened by the economic metaphor when applied to control of microbiological activities is a focus on metabolism, not gene selfishness, as the necessary background to make sense of regulatory phenomena. As an example, we analyse and reinterpret the widespread phenomenon of catabolite repression with the formal frame of the consumer's choice theory.

Machine yearning: How advances in computational methods lead to new insights about reactions to loss

The loss of a loved one is a potentially traumatic event that can result in disparate outcomes and symptom patterns. Machine learning methods offer computational tools to probe this heterogeneity and understand grief psychopathology in its complexity. In this article, we examine the latest contributions to the scientific study of bereavement reactions garnered through the use of computational methods. We focus on findings originating from trajectory modeling studies, as well as the recent insights originating from the network analysis of prolonged grief symptoms. We also discuss applications of artificial intelligence for the accurate identification of major depression and post-traumatic stress, as examples for their potential applications to the study of loss reactions.

Scalable Estimation of Epidemic Thresholds via Node Sampling

Infectious or contagious diseases can be transmitted from one person to another through social contact networks. In today's interconnected global society, such contagion processes can cause global public health hazards, as exemplified by the ongoing Covid-19 pandemic. It is therefore of great practical relevance to investigate the network transmission of contagious diseases from the perspective of statistical inference. An important and widely studied boundary condition for contagion processes over networks is the so-called epidemic threshold. The epidemic threshold plays a key role in determining whether a pathogen introduced into a social contact network will cause an epidemic or die out. In this paper, we investigate epidemic thresholds from the perspective of statistical network inference. We identify two major challenges that are caused by high computational and sampling complexity of the epidemic threshold. We develop two statistically accurate and computationally efficient approximation techniques to address these issues under the Chung-Lu modeling framework. The second approximation, which is based on random walk sampling, further enjoys the advantage of requiring data on a vanishingly small fraction of nodes. We establish theoretical guarantees for both methods and demonstrate their empirical superiority.

Measuring the spatiotemporal evolution of accident hot spots

One reality of transportation systems is that vehicular accidents can happen practically anywhere and at any time. An increasing body of research suggests though that spatial and/or temporal dependencies (i.e., clusters or hot spots) among accidents likely exist. Along with understanding where and when such spatiotemporal dependencies may occur, another important facet to consider is the geographic extent or area associated with the hot spots. For example, an accident hot spot may involve a small, isolated portion of the transportation system or a much more expansive geographic area. Better delineation and quantification of the morphological characteristics of accident hotspots can provide valuable decision support for planning for accident hot spot mitigation and prevention. As the size and shape of accident hot spots may evolve over time, the capability to track such dynamics is vital, especially with respect to the identification of processes effecting hot spot occurrence as well as assessments of the efficacy of efforts to mitigate factors underlying hot spot development. To this end, a Geographical Information Systems (GIS) based framework is outlined to facilitate the analysis of the morphological characteristics of hot spots over time. The analysis framework is applied to a case study of vehicular accidents reported over a two-year period to demonstrate its practical utility. The application results indicate that patterns of change in hot spot morphology can be effectively quantified and a variety of informative spatial and temporal patterns can be detected.

Network Canvas: Key decisions in the design of an interviewer assisted network data collection software suite

Self-reported social network analysis studies are often complex and burdensome, both during the interview process itself, and when conducting data management following the interview. Through funding obtained from the National Institute on Drug Abuse (NIDA/NIH), our team developed the Network Canvas suite of software - a set of complementary tools that are designed to simplify the collection and storage of complex social network data, with an emphasis on usability and accessibility across platforms and devices, and guided by the practical needs of researchers. The suite consists of three applications: Architect: an application for researchers to design and export interview protocols; Interviewer: a touch-optimized application for loading and administering interview protocols to study participants; and Server: an application for researchers to manage the interview deployment process and export their data for analysis. Together, they enable researchers with minimal technological expertise to access a complete research workflow, by building their own network interview protocols, deploying these protocols widely within a variety of contexts, and immediately attaining the resulting data from a secure central location. In this paper, we outline the critical decisions taken in developing this suite of tools for the network research community. We also describe the work which guides our decision-making, including prior experiences and key discovery events. We focus on key design choices, taken for theoretical, philosophical, and pragmatic reasons, and outline their strengths and limitations.

Individual and partnership characteristics associated with consistent condom use in a cohort of cisgender men who have sex with men and transgender women in Nigeria

Background

This study reports on the individual and partnership characteristics that influence consistent condom use in cisgender men who have sex with men (MSM) and transgender women (TGW) attending trusted community centers that provide HIV prevention and treatment services in Nigeria.

Methods

Adults assigned male at birth who reported anal sex with male partners who enrolled between March 2013–2019 and had information about at least one male sexual partner were included in these analyses. At enrollment and follow-up visits every 3 months for up to 18 months, participants were administered detailed questionnaires that collected information about demographics, sexual practices, HIV risk behaviors, and characteristics and behaviors of their partners in the previous year (at enrollment) or the preceding 3 to 6-months (at follow-up visits). Logistic regression models with generalized estimating equations were used to assess the odds ratio (OR) and 95% confidence intervals (CI) of individual, partner, and partnership characteristics associated with consistent condom use (CCU). A participant was defined as consistently using condom if they reported always using condoms all the time they had insertive, receptive or both types of anal sex with a male partner.

Results

At the individual level, CCU was positively associated with higher education, disclosure of key population status to a healthcare worker and negatively associated with poor access to condoms. At the partner and partnership level, CCU was associated with partners with higher education (aOR: 1.36; 95% CI: 1.07–1.72), casual relationships (aOR: 1.22; 95% CI: 1.11–1.34) and relationships in which partners encouraged the participant to use condoms with other partners (aOR: 1.14; 95% CI: 1.02–1.28). Relationships in which the partner was married to a woman and/or the partner’s HIV status positive or unknown were negatively associated with CCU.

Conclusions

These findings suggest that individuals in relationships where partners were more open and encouraged safer sex were more likely to consistently use condoms. HIV prevention programs should consider leveraging communication to sexual partners to encourage condom use as this may support condom use with other sexual partners. Given sustained and growing HIV and STI epidemics among MSM and TGW, even with pre-exposure prophylaxis scale-up, it is crucial to continue to study optimal implementation strategies to increase condom use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11275-w.

Multivariate transcriptome analysis identifies networks and key drivers of chronic lymphocytic leukemia relapse risk and patient survival

Background

Chronic lymphocytic leukemia (CLL) is an indolent heme malignancy characterized by the accumulation of CD5⁺ CD19⁺ B cells and episodes of relapse. The biological signaling that influence episodes of relapse in CLL are not fully described. Here, we identify gene networks associated with CLL relapse and survival risk.

Methods

Networks were investigated by using a novel weighted gene network co-expression analysis method and examining overrepresentation of upstream regulators and signaling pathways within co-expressed transcriptome modules across clinically annotated transcriptomes from CLL patients (N = 203). Gene Ontology analysis was used to identify biological functions overrepresented in each module. Differential Expression of modules and individual genes was assessed using an ANOVA (Binet Stage A and B relapsed patients) or T-test (SF3B1 mutations). The clinical relevance of biomarker candidates was evaluated using log-rank Kaplan Meier (survival and relapse interval) and ROC tests.

Results

Eight distinct modules (M2, M3, M4, M7, M9, M10, M11, M13) were significantly correlated with relapse and differentially expressed between relapsed and non-relapsed Binet Stage A CLL patients. The biological functions of modules positively correlated with relapse were carbohydrate and mRNA metabolism, whereas negatively correlated modules to relapse were protein translation associated. Additionally, M1, M3, M7, and M13 modules negatively correlated with overall survival. CLL biomarkers BTK, BCL2, and TP53 were co-expressed, while unmutated IGHV biomarker ZAP70 and cell survival-associated NOTCH1 were co-expressed in modules positively correlated with relapse and negatively correlated with survival days.

Conclusions

This study provides novel insights into CLL relapse biology and pathways associated with known and novel biomarkers for relapse and overall survival. The modules associated with relapse and overall survival represented both known and novel pathways associated with CLL pathogenesis and can be a resource for the CLL research community. The hub genes of these modules, e.g., ARHGAP27P2, C1S, CASC2, CLEC3B, CRY1, CXCR5, FUT5, MID1IP1, and URAHP, can be studied further as new therapeutic targets or clinical markers to predict CLL patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01012-y.

Local network connectivity optimization: an evaluation of heuristics applied to complex spatial networks, a transportation case study, and a spatial social network

Optimizing global connectivity in spatial networks, either through rewiring or adding edges, can increase the flow of information and increase the resilience of the network to failures. Yet, rewiring is not feasible for systems with fixed edges and optimizing global connectivity may not result in optimal local connectivity in systems where that is wanted. We describe the local network connectivity optimization problem, where costly edges are added to a systems with an established and fixed edge network to increase connectivity to a specific location, such as in transportation and telecommunication systems. Solutions to this problem maximize the number of nodes within a given distance to a focal node in the network while they minimize the number and length of additional connections. We compare several heuristics applied to random networks, including two novel planar random networks that are useful for spatial network simulation research, a real-world transportation case study, and a set of real-world social network data. Across network types, significant variation between nodal characteristics and the optimal connections was observed. The characteristics along with the computational costs of the search for optimal solutions highlights the need of prescribing effective heuristics. We offer a novel formulation of the genetic algorithm, which outperforms existing techniques. We describe how this heuristic can be applied to other combinatorial and dynamic problems.

Temporal stability of the ventral attention network and general cognition along the Alzheimer's disease spectrum

Understanding the interrelationships of clinical manifestations of Alzheimer's disease (AD) and functional connectivity (FC) as the disease progresses is necessary for use of FC as a potential neuroimaging biomarker. Degradation of resting-state networks in AD has been observed when FC is estimated over the entire scan, however, the temporal dynamics of these networks are less studied. We implemented a novel approach to investigate the modular structure of static (sFC) and time-varying (tvFC) connectivity along the AD spectrum in a two-sample Discovery/Validation design (n = 80 and 81, respectively). Cortical FC networks were estimated across 4 diagnostic groups (cognitively normal, subjective cognitive decline, mild cognitive impairment, and AD) for whole scan (sFC) and with sliding window correlation (tvFC). Modularity quality (across a range of spatial scales) did not differ in either sFC or tvFC. For tvFC, group differences in temporal stability within and between multiple resting state networks were observed; however, these differences were not consistent between samples. Correlation analyses identified a relationship between global cognition and temporal stability of the ventral attention network, which was reproduced in both samples. While the ventral attention system has been predominantly studied in task-evoked designs, the relationship between its intrinsic dynamics at-rest and general cognition along the AD spectrum highlights its relevance regarding clinical manifestation of the disease.

Spontaneous eye movements during eyes-open rest reduce resting-state-network modularity by increasing visual-sensorimotor connectivity

During wakeful rest, individuals make small eye movements during fixation. We examined how these endogenously driven oculomotor patterns impact topography and topology of functional brain networks. We used a dataset consisting of eyes-open resting-state (RS) fMRI data with simultaneous eye tracking. The eye-tracking data indicated minor movements during rest, which correlated modestly with RS BOLD data. However, eye-tracking data correlated well with echo-planar imaging time series sampled from the area of the eye-orbit (EO-EPI), which is a signal previously used to identify eye movements during exogenous saccades and movie viewing. Further analyses showed that EO-EPI data were correlated with activity in an extensive motor and sensorimotor network, including components of the dorsal attention network and the frontal eye fields. Partialling out variance related to EO-EPI from RS data reduced connectivity, primarily between sensorimotor and visual areas. It also produced networks with higher modularity, lower mean connectivity strength, and lower mean clustering coefficient. Our results highlight new aspects of endogenous eye movement control during wakeful rest. They show that oculomotor-related contributions form an important component of RS network topology, and that those should be considered in interpreting differences in network structure between populations or as a function of different experimental conditions.

We studied how subtle eye movements made during fixation, in absence of any other task, are related to resting-state connectivity measured using fMRI. We used a dataset for which eye tracking and BOLD resting-state were acquired simultaneously. We correlated brain activity with both eye-tracking metrics as well as time series sampled from the area of the eye orbits (EO-EPI). Eye-tracking data correlated well with the EO-EPI data. Furthermore, EO-EPI correlated with BOLD signal in sensorimotor and visual brain systems. Removing variance related to EO-EPI reduced connectivity between sensorimotor and visual areas and resulted in more modular resting-state networks. Our findings show that oculomotor-related contributions are an important component of resting-state network topology, and that they can be studied using EPI data from the eye orbits.

Resting-state functional connectivity associated with gait characteristics in people with Parkinson's disease

INTRODUCTION

Parkinson's disease (PD) is a movement disorder caused by dysfunction in the basal ganglia (BG). Clinically relevant gait deficits, such as decreased velocity and increased variability, may be caused by underlying neural dysfunction. Reductions in resting-state functional connectivity (rs-FC) between networks have been identified in PD compared to controls; however, the association between gait characteristics and rs-FC of brain networks in people with PD has not yet been explored. The present study aimed to investigate these associations.

METHODS

Gait characteristics and rs-FC MRI data were collected for participants with PD (N = 50). Brain networks were identified from a set of seeds representing cortical, subcortical, and cerebellar regions. Gait outcomes were correlated with the strength of rs-FC within and between networks of interest. A stepwise regression analysis was also conducted to determine whether the rs-FC strength of brain networks, along with clinical motor scores, were predictive of gait characteristics.

RESULTS

Gait velocity was associated with rs-FC within the visual network and between motor and cognitive networks, most notably BG-thalamus internetwork rs-FC. The stepwise regression analysis showed strength of BG-thalamus internetwork rs-FC and clinical motor scores were predictive of gait velocity.

CONCLUSION

The results of the present study demonstrate gait characteristics are associated with functional organization of the brain at the network level, providing insight into the neural mechanisms of clinically relevant gait characteristics. This knowledge could be used to optimize the design of gait rehabilitation interventions for people with neurological conditions.

The fall of the innovation empire and its possible rise through open science

There is growing concern that the innovation system's ability to create wealth and attain social benefit is declining in effectiveness. This article explores the reasons for this decline and suggests a structure, the open science partnership, as one mechanism through which to slow down or reverse this decline. The article examines the empirical literature of the last century to document the decline. This literature suggests that the cost of research and innovation is increasing exponentially, that researcher productivity is declining, and, third, that these two phenomena have led to an overall flat or declining level of innovation productivity. The article then turns to three explanations for the decline - the growing complexity of science, a mismatch of incentives, and a balkanization of knowledge. Finally, the article explores the role that open science partnerships - public-private partnerships based on open access publications, open data and materials, and the avoidance of restrictive forms of intellectual property - can play in increasing the efficiency of the innovation system.

Ictal gamma-band interactions localize ictogenic nodes of the epileptic network in focal cortical dysplasia

OBJECTIVE

Epilepsy surgery fails in > 30% of patients with focal cortical dysplasia (FCD). The seizure persistence after surgery can be attributed to the inability to precisely localize the tissue with an endogenous potential to generate seizures. In this study, we aimed to identify the critical components of the epileptic network that were actively involved in seizure genesis.

METHODS

The directed transfer function was applied to intracranial EEG recordings and the effective connectivity was determined with a high temporal and frequency resolution. Pre-ictal network properties were compared with ictal epochs to identify regions actively generating ictal activity and discriminate them from the areas of propagation.

RESULTS

Analysis of 276 seizures from 30 patients revealed the existence of a seizure-related network reconfiguration in the gamma-band (25-170 Hz; p < 0.005) - ictogenic nodes. Unlike seizure onset zone, resecting the majority of ictogenic nodes correlated with favorable outcomes (p < 0.012).

CONCLUSION

The prerequisite to successful epilepsy surgery is the accurate identification of brain areas from which seizures arise. We show that in FCD-related epilepsy, gamma-band network markers can reliably identify and distinguish ictogenic areas in macroelectrode recordings, improve intracranial EEG interpretation and better delineate the epileptogenic zone.

SIGNIFICANCE

Ictogenic nodes localize the critical parts of the epileptogenic tissue and increase the diagnostic yield of intracranial evaluation.

Probabilistic mapping of human functional brain networks identifies regions of high group consensus

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain's large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show "core" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.

Indirect and direct effects of the subprime crisis on the real sector: labor market migration

The bursting of the US housing bubble in the second half of 2008 triggered an almost unprecedented systemic crisis in the world economy. The financial collapse quickly overflowed into the real economy and caused, among other effects, a sharp fall in the flow of world trade. Using export data from Brazilian municipalities, we show that the subprime crisis had a more significant effect on production and employment in exporting cities than municipalities more devoted to the domestic economy. We find that the manufacturing and construction sectors of exporting cities were the most affected during the crisis. However, exporting municipalities with a substantial share of services activities were more resilient to the external crisis. This difference is significant and sheds light on the debate on the effects of the crisis on Brazilian regions and cities. Using a unique business management dataset that contains firm-to-firm controls, we also find spillovers in the labor market from exporting to domestic-oriented cities through job reallocation. Our results suggest that workers migrate from exporting municipalities to other non-exporting municipalities within the same firm economic group.

Introduction of Network-Based Healthcare at Kaiser Permanente

Background:

Early 2014, Kaiser Permanente decided to adopt an innovative model for network-based allied healthcare for persons with Parkinson’s disease (PD), based on the principles of the Dutch ParkinsonNet.

Objective:

We present the interventions that were performed to implement this method at Kaiser Permanente and we show the first outcomes based on these interventions.

Methods:

In this study, 57 physical therapists, 18 speech therapists and 20 occupational therapists, as well as 13 medical centers across the state of California were included. Nine interventions were performed more or less simultaneously, including training and education of healthcare providers and patients, a train the trainer curriculum, organizing IT, streamlining referral processes and building networks.

Results:

At the start, less than 30% of the patients within the Southern California Region received specialized allied health treatment (consisting of, i.e., gait training, voice training or guidance in activities of daily life). After one year, almost 55% of patients received specialized allied health treatment. In the second year, this number increased to just under 67%, suggesting a sustained concentration of care (the second core component of networked care). This can be seen as a first indicator for successful implementation of the ParkinsonNet network at Kaiser Permanente.

Conclusions:

The importance of these findings lies in the fact that a healthcare innovation that proved effective in one country can be transferred successfully to another country and to another healthcare system.

Evolutionary bet-hedging in structured populations

As ecosystems evolve, species can become extinct due to fluctuations in the environment. This leads to the evolutionary adaption known as bet-hedging, where species hedge against these fluctuations to reduce their likelihood of extinction. Environmental variation can be either within or between generations. Previous work has shown that selection for bet-hedging against within-generational variation should not occur in large populations. However, this work has been limited by assumptions of well-mixed populations, whereas real populations usually have some degree of structure. Using the framework of evolutionary graph theory, we show that through adding competition structure to the population, within-generational variation can have a significant impact on the evolutionary process for any population size. This complements research using subdivided populations, which suggests that within-generational variation is important when local population sizes are small. Together, these conclusions provide evidence to support observations by some ecologists that are contrary to the widely held view that only between-generational environmental variation has an impact on natural selection. This provides theoretical justification for further empirical study into this largely unexplored area.

Bacterial community structure alterations within the colorectal cancer gut microbiome

BACKGROUND

Colorectal cancer is a leading cause of cancer-related deaths worldwide. The human gut microbiome has become an active area of research for understanding the initiation, progression, and treatment of colorectal cancer. Despite multiple studies having found significant alterations in the carriage of specific bacteria within the gut microbiome of colorectal cancer patients, no single bacterium has been unequivocally connected to all cases. Whether alterations in species carriages are the cause or outcome of cancer formation is still unclear, but what is clear is that focus should be placed on understanding changes to the bacterial community structure within the cancer-associated gut microbiome.

RESULTS

By applying a novel set of analyses on 252 previously published whole-genome shotgun sequenced fecal samples from healthy and late-stage colorectal cancer subjects, we identify taxonomic, functional, and structural changes within the cancer-associated human gut microbiome. Bacterial association networks constructed from these data exhibited widespread differences in the underlying bacterial community structure between healthy and colorectal cancer associated gut microbiomes. Within the cancer-associated ecosystem, bacterial species were found to form associations with other species that are taxonomically and functionally dissimilar to themselves, as well as form modules functionally geared towards potential changes in the tumor-associated ecosystem. Bacterial community profiling of these samples revealed a significant increase in species diversity within the cancer-associated gut microbiome, and an elevated relative abundance of species classified as originating from the oral microbiome including, but not limited to, Fusobacterium nucleatum, Peptostreptococcus stomatis, Gemella morbillorum, and Parvimonas micra. Differential abundance analyses of community functional capabilities revealed an elevation in functions linked to virulence factors and peptide degradation, and a reduction in functions involved in amino-acid biosynthesis within the colorectal cancer gut microbiome.

CONCLUSIONS

We utilize whole-genome shotgun sequenced fecal samples provided from a large cohort of late-stage colorectal cancer and healthy subjects to identify a number of potentially important taxonomic, functional, and structural alterations occurring within the colorectal cancer associated gut microbiome. Our analyses indicate that the cancer-associated ecosystem influences bacterial partner selection in the native microbiota, and we highlight specific oral bacteria and their associations as potentially relevant towards aiding tumor progression.

Inference and Analysis of SPIEC-EASI Microbiome Networks

Network analysis facilitates examination of the interactions between different populations in a community. It can provide a range of metrics describing the social characteristics of each population and emergent structural properties of the community, which may be used to address novel ecological questions. Using a publicly available dataset, this chapter provides point-by-point code and instructions to infer and analyze a SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference) network using free, open source software (R and Gephi).

Denoising large-scale biological data using network filters

Background

Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation.

Results

We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or “filtered” to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 43% compared to using unfiltered data.

Conclusions

Network filters are a general way to denoise biological data and can account for both correlation and anti-correlation between different measurements. Furthermore, we find that partitioning a network prior to filtering can significantly reduce errors in networks with heterogenous data and correlation patterns, and this approach outperforms existing diffusion based methods. Our results on proteomics data indicate the broad potential utility of network filters to applications in systems biology.

Supplementary Information

The online version supplementary material available at 10.1186/s12859-021-04075-x.

Dermatologic Research Potential of the Observational Health Data Sciences and Informatics (OHDSI) Network

BACKGROUND

The Observational Health Data Sciences and Informatics (OHDSI) network enables access to billions of deidentified, standardized health records and built-in analytics software for observational health research, with numerous potential applications to dermatology. While the use of the OHDSI has increased steadily over the past several years, review of the literature reveals few studies utilizing OHDSI in dermatology. To our knowledge, the University of Colorado School of Medicine is unique in its use of OHDSI for dermatology big data research.

SUMMARY

A PubMed search was conducted in August 2020, followed by a literature review, with 24 of the 72 screened articles selected for inclusion. In this review, we discuss the ways OHDSI has been used to compile and analyze data, improve prediction and estimation capabilities, and inform treatment guidelines across specialties. We also discuss the potential for OHDSI in dermatology - specifically, ways that it could reveal adherence to available guidelines, establish standardized protocols, and ensure health equity. Key Messages: OHDSI has demonstrated broad utility in medicine. Adoption of OHDSI by the field of dermatology would facilitate big data research, allow for examination of current prescribing and treatment patterns without clear best practice guidelines, improve the dermatologic knowledge base and, by extension, improve patient outcomes.

Stakeholder mapping in animal health surveillance: A comparative assessment of networks in intensive dairy cattle and extensive sheep production in Australia

The capacity to rapidly identify and respond to suspicion of animal disease is fundamental to protecting the integrity of the Australian livestock industry. An incursion of a nationally significant endemic, emerging or exotic animal disease could be disruptive and economically damaging for the industry, broader community and national economy. To counter this potential threat, a surveillance system that includes general and targeted activities exists at a jurisdictional and national level. Such a system requires a collaborative effort from all involved to work towards a common goal, reflecting the notion of shared responsibility. As in all systems, the animal health surveillance system can be enhanced or constrained by the relationships of the players involved. This study focusses on two livestock industries, dairy cattle and sheep, exploring the interrelationships between all stakeholders, and their role within the Australian animal health surveillance system. A stakeholder mapping exercise was undertaken, including a depiction of the perceived level of stakeholder interest and influence on producers' animal health surveillance practices and/or the surveillance system. Results from these activities were expanded upon through interviews. The findings reveal complex networks and a system that is, at times, constrained by institutional and individual barriers such as communication between and within stakeholders, and uncertainty about the consequences of reporting a suspected emergency disease. Whilst these challenges have the potential to negatively impact the robustness of the animal disease surveillance system, the study also provides clear evidence of strong and effective relationships amongst many of the key individuals and organisations.

[Institutions of evidence-based medicine in Germany : Who is behind it and who does what?]

BACKGROUND

In recent decades, a number of institutions have been established to address the development, application, and dissemination of evidence-based medicine (EBM) in Germany.

OBJECTIVES

The aim of the paper is to give an overview of important EBM institutions in Germany and to determine their tasks and goals.

MATERIALS AND METHODS

Internet research was conducted to identify important German EBM institutions.

RESULTS

EBM institutions in Germany can be divided into institutions of self-administration and independent institutions in the health care system. Common tasks and goals are mainly the improvement of health care, the dissemination of evidence-based knowledge and the further development and teaching of EBM methods.

CONCLUSIONS

Both self-governing institutions and independent institutions have taken up EBM. They ensure that EBM becomes an integral part of everyday medical practice.

Evolutionary graph theory derived from eco-evolutionary dynamics

A biologically motivated individual-based framework for evolution in network-structured populations is developed that can accommodate eco-evolutionary dynamics. This framework is used to construct a network birth and death model. The evolutionary graph theory model, which considers evolutionary dynamics only, is derived as a special case, highlighting additional assumptions that diverge from real biological processes. This is achieved by introducing a negative ecological feedback loop that suppresses ecological dynamics by forcing births and deaths to be coupled. We also investigate how fitness, a measure of reproductive success used in evolutionary graph theory, is related to the life-history of individuals in terms of their birth and death rates. In simple networks, these ecologically motivated dynamics are used to provide new insight into the spread of adaptive mutations, both with and without clonal interference. For example, the star network, which is known to be an amplifier of selection in evolutionary graph theory, can inhibit the spread of adaptive mutations when individuals can die naturally.

Social Network Interventions for HIV Transmission Elimination

Purpose of Review

Network interventions for HIV prevention represent a potential area for growth in a globalizing world, where persons are more easily connected to one another through social media and networking applications. The basic tenets of network interventions such as (1) selection of a change agent, (2) segmentation, (3) induction, and (4) alteration represent myriad ways to structure network interventions for HIV prevention with the potential for large public health impact.

Recent Findings

Recent studies have employed the use of social networking websites such as Facebook to identify key persons to recruit others and disseminate information aimed at decreasing HIV transmission and improving safe sex practices among groups who are more vulnerable to HIV acquisition. Many of these interventions have successfully decreased HIV risk behaviors as well as decreased the spread of HIV among intervention cohorts.

Summary

Network interventions for HIV prevention provide more opportunities to reach populations who have not been reached through typical efforts employed in clinical and public health settings, though they are not currently widely employed by the public health community and other stakeholders.

In the hands of a few: Disaster recovery committee networks

When disaster strikes, urban planners often rely on feedback and guidance from committees of officials, residents, and interest groups when crafting reconstruction policy. Focusing on recovery planning committees after Japan's 2011 earthquake, tsunami, and nuclear disasters, we compile and analyze a dataset on committee membership patterns across 39 committees with 657 members. Using descriptive statistics and social network analysis, we examine 1) how community representation through membership varied among committees, and 2) in what ways did committees share members, interlinking members from certain interests groups. This study finds that community representation varies considerably among committees, negatively related to the prevalence of experts, bureaucrats, and business interests. Committee membership overlap occurred heavily along geographic boundaries, bridged by engineers and government officials. Engineers and government bureaucrats also tend to be connected to more members of the committee network than community representatives, giving them prized positions to disseminate ideas about best practices in recovery. This study underscores the importance of diversity and community representation in disaster recovery planning to facilitate equal participation, information access, and policy implementation across communities.

Understanding the Influences Behind Parents' Asthma Decision-Making: A Qualitative Exploration of the Asthma Network of Parents with Children with Asthma

Introduction

Interventions aimed at optimizing parents’ ability to manage their children’s asthma could be strengthened by better understanding the networks that influence these parents’ choices when managing asthma. This study aimed to explore the asthma networks of parents of children with asthma—specifically to gain insights into whom parents select to be within their networks and why; how individuals within parents’ networks influence the way in which they manage their children’s asthma medications, and factors driving the development of these networks.

Methods

A qualitative research methodology utilizing semi-structured interviews with parents of children with asthma was employed to fulfil the objectives of this study.

Results

Twenty-six face-to-face interviews with parents of children with asthma were conducted, recorded, and transcribed. Transcriptions were independently coded for concepts and themes by the research team. Asthma medications was a dominant theme identified, and revealed that parents actively sought advice and support from a series of complex and multidimensional relationships with people and resources in their health network. These not only included health care professionals (HCPs) but also personal connections, lay individuals, and resources. The composition and development of these asthma networks occurred over time and were determined by several key factors: satisfaction with their HCP provider; need for information; convenience; trust and support; self-confidence in management; and parents’ perceptions of their children’s asthma severity.

Conclusions

By exploring parents’ asthma network, this study uncovers the complex relationship between HCPs, family and friends of parents of children with asthma, and provides new insight into the intimate and parallel influence they have on parent’s decision-making.

Brains, complex systems and therapeutic opportunities in epilepsy

The treatment of epilepsy remains extremely challenging for the thirty percent of people that do not become seizure free. This is despite the introduction of multiple new drugs over that last several decades, highlighting the need for new approaches to identifying novel therapeutic strategies. Conceptualizing the brain as a complex adaptive system and applying the tools that are used in addressing such systems provides an opportunity for expanding the space in which to search for new therapies. Epilepsy has long been considered a network disease at the level of whole brain connectivity, but the application of the concepts to gene and protein expression networks as well as to the dynamic behaviors of microcircuits has been underexplored. These levels of the brain complex adaptive system will be reviewed and a case made for the epilepsy community to embrace these concepts in order to reap to enormous potential rewards.

A city cluster risk-based approach for Sars-CoV-2 and isolation barriers based on anonymized mobile phone users' location data

Given the recent outbreak of Sars-CoV-2, several countries started to seek different strategies to control contamination and minimize fatalities, which are usually the primary objectives for all strategies. Secondary objectives are related to economic factors, therefore ensuring that society would be able is to keep its essential activities and avoid supply disruptions. This paper presents an application of anonymized mobile phone users' location data to estimate population flow amongst cities with an origin-destination matrix. The work includes a clustering analysis of cities, which may enable policymakers (and epidemiologists) to develop public policies giving the appropriate consideration for each set of cities within a Province or State. Risk measures are included to analyze the severity of the spread among the clusters, which can be ranked. Then, intelligence can be obtained from the analysis, and some clusters could be isolated to avoid contagion while keeping their economic activities. Therefore, this analysis is reproducible for other states of Brazil and other countries and can be adapted for districts within a city, especially considering the possibility of a second wave COVID-19 pandemic.

The importance of surgeons and their peers in adoption and diffusion of innovation: An observational study of laparoscopic colectomy adoption and diffusion in England

Little is known about the role of clinicians in accounting for adoption and diffusion of medical innovations, especially within the English National Health System. This study examines the importance of surgical consultants and their work-based networks on the diffusion of an important innovation, minimally invasive elective laparoscopic colectomy for colorectal cancer. The study used linked patient-level and workforce data on 260,110 elective colectomies and 1288 consultants between 2000 and 2014, to examine adoption of laparoscopic colectomy pre- and post-introduction of clinical guidelines and total share of colectomies performed laparoscopically by adopters. Laparoscopy as a share of elective colectomy increased from 0% in 2000 to 53% in 2014. Surgeons, rather than hospitals, were the principal agents accounting for the increase and explain 46.6% of the variance in laparoscopic colectomy use. Female surgeons, surgeons trained outside the United Kingdom, and recent graduates had higher rates of laparoscopy adoption. More experienced surgeons and surgeons with more peers who perform laparoscopy were more likely to adopt, adopt early and have greater use of laparoscopy. Targeting clinicians, rather than hospitals, is central to increasing adoption and diffusion of new medical technologies.

Summarizing RNA-Seq Data or Differentially Expressed Genes Using Gene Set, Network, or Pathway Analysis

The main purpose of pathway or gene set analysis methods is to provide mechanistic insight into the large amount of data produced in high-throughput studies. These tools were developed for gene expression analyses, but they have been rapidly adopted by other high-throughput techniques, becoming one of the foremost tools of omics research.Currently, according to different biological questions and data, we can choose among a vast plethora of methods and databases. Here we use two published examples of RNAseq datasets to approach multiple analyses of gene sets, networks and pathways using freely available and frequently updated software. Finally, we conclude this chapter by presenting a survival pathway analysis of a multiomics dataset. During this overview of different methods, we focus on visualization, which is a fundamental but challenging step in this computational field.

Approaches for network based drug discovery

Molecular network-based studies have gained tremendous importance in biomedical research. Several such advanced technologies in molecular biology have evolved in the past decade and have contributed to building up enormous molecular data. These molecular networks gained much significance among researchers triggering widespread use of experimental and computational tools. This interest led researchers to compile data of biomolecules systematically and to develop various computational tools for analyzing data. In the present scenario, an enormous amount of molecular network databases are available which can be accessed freely by the public. This is the central focus of this article.

Classification of Long Noncoding RNAs by k-mer Content

K-mer based comparisons have emerged as powerful complements to BLAST-like alignment algorithms, particularly when the sequences being compared lack direct evolutionary relationships. In this chapter, we describe methods to compare k-mer content between groups of long noncoding RNAs (lncRNAs), to identify communities of lncRNAs with related k-mer contents, to identify the enrichment of protein-binding motifs in lncRNAs, and to scan for domains of related k-mer contents in lncRNAs. Our step-by-step instructions are complemented by Python code deposited in Github. Though our chapter focuses on lncRNAs, the methods we describe could be applied to any set of nucleic acid sequences.

RNA Sequencing Analyses for Deciphering Potato Molecular Responses

Understanding the molecular mechanisms of potato development and responses to environmental stressors is of utmost importance for achieving stable crop yields. RNA sequencing (RNA-Seq) provides an insight into responses of all of the organism genes to the environmental and developmental cues and thus provides insights into underlying modes of action. In this chapter, we guide a researcher through some of the most important steps in the analysis of transcriptomics data. The initial topic of experimental design is followed by a more wet-lab-oriented section on RNA-Seq sample preparation. Next, we present intermediate steps of data retrieval, quality control, mapping, and differential expression of the dataset and a section on how to expose your data to the public (i.e., public repositories) and make it findable, accessible, interoperable, and reusable (FAIR). In the last four sections, we describe specific tools or Web applications, which ease the exploration of generated results in the context of their gene function and network-based visualizations, specifically GoMapMan, GSEA, DiNAR, and Biomine Explorer. All sections are accompanied by potato dataset examples and include general hints and tricks, as well as potato specificities that one should be aware of.

Why is Complexity Science valuable for reaching the goals of the UN 2030 Agenda?

The goals and targets included in the 2030 Agenda compiled by the United Nations want to stimulate action in areas of critical importance for humanity and the Earth. These goals and targets regard everyone on Earth from both the health and economic and social perspectives. Reaching these goals means to deal with Complex Systems. Therefore, Complexity Science is undoubtedly valuable. However, it needs to extend its scope and focus on some specific objectives. This article proposes a development of Complexity Science that will bring benefits for achieving the United Nations' aims. It presents a list of the features shared by all the Complex Systems involved in the 2030 Agenda. It shows the reasons why there are certain limitations in the prediction of Complex Systems' behaviors. It highlights that such limitations raise ethical issues whenever new technologies interfere with the dynamics of Complex Systems, such as human beings and the environment. Finally, new methodological approaches and promising research lines to face Complexity Challenges included in the 2030 Agenda are put forward.

Engineering of Biological Pathways: Complex Formation and Signal Transduction

The rational in silico design of interface mutations within protein complexes is a synthetic biology tool that enables-when introduced into biological systems-the artificial rewiring of biological pathways. Here we describe the three-dimensional structure-based design of "rewiring" mutations using the FoldX force field. Specifically, we provide the protocol for the design and selection of interface mutations in three Ras-effector complex structures (PDB entries 3KUD, 4K81, and 6AMB). Ras mutations that impair binding to some but not all interacting partners are selected.

Advances in the agent-based modeling of economic and social behavior

In this review we discuss advances in the agent-based modeling of economic and social systems. We show the state of the art of the heuristic design of agents and how behavioral economics and laboratory experiments have improved the modeling of agent behavior. We further discuss how economic networks and social systems can be modeled and we discuss novel methodology and data sources. Lastly, we present an overview of estimation techniques to calibrate and validate agent-based models and show avenues for future research.

Improving practice through collaboration: Early experiences from the multi-site Spinal Cord Injury Implementation and Evaluation Quality Care Consortium

CONTEXT

Dedicated implementation efforts are critical to bridging the gaps between current practices and best practices. A quality improvement collaborative (QIC), the Spinal Cord Injury Implementation and Evaluation Quality Care Consortium (SCI IEQCC), was established to meet this need, bringing together a network of clinicians and administrators to systematically improve the quality and equity of tertiary spinal cord injury or disease (SCI/D) rehabilitation care in Ontario, Canada.

METHODS

Clinicians and leaders from five tertiary SCI/D rehabilitation centers and two not-for-profit SCI/D advocacy groups comprised a network dedicated to supporting implementation of the SCI-High quality indicators in prioritized domains of SCI rehabilitation and related best practices by: (1) building capacity through implementation science education of frontline clinicians; (2) providing resources and support to empower frontline clinicians to lead quality improvement efforts within their institutions; (3) promoting wider learning through a network for sharing ideas, efforts, and experiences; and (4) collecting indicator data to facilitate provincial evaluation of goal attainment.

RESULTS

Network members and sites collaborated to implement best practices within six priority domains; in 18 months, significant progress has been made in emotional wellbeing, sexual health, walking, and wheeled mobility despite disruptions due to the COVID-19 pandemic. These efforts encompass heterogeneous challenges and strategies, ranging from developing clinical skills programs, to streamlining processes, to manipulating physical space.

CONCLUSION

A QIC targeting SCI/D rehabilitation demonstrates promise for advancing the implementation of best practices, building implementation science capacity across multiple sites, and for promoting collaboration amongst SCI/D rehabilitation centers and organizational partners.