BACKGROUND: There is an often-held assumption that the engagement of clinicians and healthcare organizations in research improves healthcare performance at various levels. Previous reviews found up to 28 studies suggesting a positive association between the engagement of individuals and healthcare organizations in research and improvements in healthcare performance. The current study sought to provide an update.
METHODS: We updated our existing published systematic review by again addressing the question: Does research engagement (by clinicians and organizations) improve healthcare performance? The search covered the period 1 January 2012 to March 2024, in two phases. First, the formal updated search ran from 1 January 2012 to 31 May 2020, in any healthcare setting or country and focussed on English language publications. In this phase two searches identified 66 901 records. Later, a further check of key journals and citations to identified papers ran from May 2020 to March 2024. In total, 168 papers progressed to full-text appraisal; 62 were identified for inclusion in the update. Then we combined papers from our original and updated reviews.
RESULTS: In the combined review, the literature is dominated by papers from the United States (50/95) and mostly drawn from the Global North. Papers cover various clinical fields, with more on cancer than any other field; 86 of the 95 papers report positive results, of which 70 are purely positive and 16 positive/mixed, meaning there are some negative elements (i.e. aspects where there is a lack of healthcare improvement) in their findings.
CONCLUSIONS: The updated review collates a substantial pool of studies, especially when combined with our original review, which are largely positive in terms of the impact of research engagement on processes of care and patient outcomes. Of the potential engagement mechanisms, the review highlights the important role played by research networks. The review also identifies various papers which consider how far there is a \"dose effect\" from differing amounts of research engagement. Additional lessons come from analyses of equity issues and negative papers. This review provides further evidence of contributions played by systems level research investments such as research networks on processes of care and patient outcomes.

OBJECTIVE: For medically-refractory epilepsy patients, stereoelectroencephalography (sEEG) is a surgical method using intracranial recordings to identify brain networks participating in early seizure organization and propagation (i.e., the epileptogenic zone, EZ). If identified, surgical EZ treatment via resection, ablation or neuromodulation can lead to seizure-freedom. To date, quantification of sEEG data, including its visualization and interpretation, remains a clinical and computational challenge. Given elusiveness of physical laws or governing equations modelling complex brain dynamics, data science offers unique insight into identifying unknown patterns within high dimensional sEEG data. We apply here an unsupervised data-driven algorithm, Dynamic Mode Decomposition (DMD), to sEEG recordings from five focal epilepsy patients (three with temporal lobe, and two with cingulate epilepsy), who underwent subsequent resective or ablative surgery and became seizure free.
METHODS: DMD obtains a linear approximation of nonlinear data dynamics, generating coherent structures (\"modes\") defining important signal features, used to extract frequencies, growth rates and spatial structures. DMD was adapted to produce Dynamic Modal Maps (DMMs) across frequency sub-bands, capturing onset and evolution of epileptiform dynamics in sEEG data. Additionally, we developed a static estimate of EZ-localized electrode contacts, termed the Higher-Frequency Mode-based Norm Index (MNI). DMM and MNI maps for representative patient seizures were validated against clinical sEEG results and seizure-free outcomes following surgery.
RESULTS: DMD was most informative at higher frequencies, i.e. gamma (including high-gamma) and beta range, successfully identifying EZ contacts. Combined interpretation of DMM/MNI plots best identified spatiotemporal evolution of mode-specific network changes, with strong concordance to sEEG results and outcomes across all five patients. The method identified network attenuation in other contacts not implicated in the EZ.
CONCLUSIONS: This is the first application of DMD to sEEG data analysis, supporting integration of neuroengineering, mathematical and machine learning methods into traditional workflows for sEEG review and epilepsy surgical decision-making.

The acellular slime mold Physarum polycephalum is a large, unicellular amoeba, which, due to its huge size, is well suited to investigate chemotaxis and cellular locomotion. The myxomycete has an astonishing behavioral repertoire and is highly responsive to changes in its environment, which map to changes in its tubular network, internal cytoplasm flow, and cytoskeleton. The behavioral repertoire includes problem-solving, decision-making, and memory. P. polycephalum\'s chemo- and phototaxis are especially well studied. This chapter describes how to cultivate different morphotypes of P. polycephalum (micro-, meso-, and macroplasmodia). Furthermore, the setup of a chemotaxis experiment and the acquisition and analysis of chemotaxis data is described.

As health and health care systems continue to face massive challenges from local to global well-being, understanding the processes that lead to improvement or deterioration in human health has embraced a broad range of forces from genes to national cultures. Despite the many efforts to deploy a common framework that captures diverse drivers at scale, the common missing element is the absence of a flexible mechanism that can guide research within and across levels. This hinders both the cumulation of knowledge and the development of a scientific foundation for multiplex interventions. However, studies across disciplines using a wide variety of methods and measures have converged on \"connectedness\" as crucial to understanding how factors operate in the health space. More formally, a focus on the critical role of the network structure and content of key elements and how they interact, rather than just on the elements themselves, offers both a generalized theory of active factors within levels and the potential to theorize interactions across levels. One critical contemporary health crisis, suicide, is deployed to illustrate the Network Embedded Symbiome Framework. The wide range of health and health care research where networks have been implicated supports its potential but also cautions against inevitable limits that will require creative theorizing and data harmonization to move forward.

Functional movement disorder (FMD) is a common manifestation of functional neurological disorder. FMD can occur alongside other neurological conditions, but especially in patients with established Parkinson\'s disease (PD). An interesting observation emerging across cohort studies and case series is that FMD can precede the diagnosis of PD, suggesting that FMD may itself be a prodromal symptom of neurodegeneration. Such a notion would have significant clinical implications for the assessment and management of people with FMD, particularly with respect to decisions around the use of auxiliary investigations, counselling, and follow-up. In this Viewpoint we review the evidence concerning the temporal relationship between FMD and PD. We discuss the potential explanations and mechanisms for FMD as a prodromal symptom of PD, and highlight clinical considerations and important outstanding questions in the field. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

There is a growing interest in social behavior change with age,1,2,3,4,5 and the impacts of sociality on longevity,6,7,8 but current knowledge is broadly limited to primates, societies structured by dominance hierarchies, or single-sex studies. It is less clear how social aging patterns emerge in carnivores. The African lion (Panthera leo), a species that lives in egalitarian fission-fusion societies, presents an exceptional opportunity to examine social aging. Across felids, lions are unique in their dependence on conspecifics for many essential processes,9,10,11 and there is vast knowledge of lion behavioral ecology,10,11,12,13,14 including documented reproductive senescence in both sexes.14,15 Applying spatial-social network analyses across 30 years of data on the wild Serengeti lion population, we show that sex strongly modulates patterns of social aging and longevity. Group size increased with age for both sexes, but only males experienced significant changes in associate numbers (degree), specifically to females, which peaked in mid-life before declining. While aging females experienced declines in intra-sex connectivity (strength) and bond strength (mean strength), they peaked in both to males during mid-life. Male inter-sex strength also peaked in mid-life, while conversely their intra-sex strength and mean strength significantly dipped in mid-life. Although social associations were important for survival in both sexes, the investment diverged significantly: females\' overall network connectivity was key for longevity, while the number of associates was important for males. These findings illustrate important potential effects of social aging in a wild carnivore and demonstrate how these diverge strongly between the sexes.

UNASSIGNED: Phenomics, an interdisciplinary field that investigates the relationships between genomics and environmental factors, has significantly advanced plant breeding by offering comprehensive insights into plant traits from molecular to physiological levels. This study examines the global evolution, geographic distribution, collaborative efforts, and primary research hubs in plant phenomics from 2000 to 2021, using data derived from patents and scientific publications.
UNASSIGNED: The study utilized data from the EspaceNet and Lens databases for patents, and Web of Science (WoS) and Scopus for scientific publications. The final datasets included 651 relevant patents and 7173 peer-reviewed articles. Data were geocoded to assign country-level geographical coordinates and underwent multiple processing and cleaning steps using Python, Excel, R, and ArcGIS. Social network analysis (SNA) was conducted to assess collaboration patterns using Pajek and UCINET.
UNASSIGNED: Research activities in plant phenomics have increased significantly, with China emerging as a major player, filing nearly 70% of patents from 2010 to 2021. The U.S. and EU remain significant contributors, accounting for over half of the research output. The study identified around 50 global research hubs, mainly in the U.S. (36%), Western Europe (34%), and China (16%). Collaboration networks have become more complex and interdisciplinary, reflecting a strategic approach to solving research challenges.
UNASSIGNED: The findings underscore the importance of global collaboration and technological advancement in plant phenomics. China\'s rise in patent filings highlights its growing influence, while the ongoing contributions from the U.S. and EU demonstrate their continued leadership. The development of complex collaborative networks emphasizes the scientific community\'s adaptive strategies to address multifaceted research issues. These insights are crucial for researchers, policymakers, and industry stakeholders aiming to innovate in agricultural practices and improve crop varieties.

This study examined women\'s unrecognized roles in facilitating socio-economic interactions and clan networks in a patriarchal society. A qualitative research methodology was chosen. Situational observations, key interviews, and group discussions were applied as data-gathering tools. A thematic descriptive analysis method was used to examine the data that had been gathered. Accordingly, the study found that women are not participating in leading customary institutions and publicly due to the traditional patriarchal domination. However, the customary law shields women from various presumptions. It has been noted women\'s participation in maintaining social order, economic reciprocity, and resource sharing. Most importantly, women\'s continual control of household responsibilities and income-generating activities is essential to Afar society\'s survival. The study recommends that multifaceted interventions should be made to maintain women\'s role in supporting their traditional methods of engaging in maintaining clan networks. An intervention should mostly be better focused on women\'s activities as it will help to explore additional mechanisms that uplift either women by themselves or by the initial intervention system. Therefore, the study recommends incorporating pastoral women\'s roles into more extensive women\'s enclave empowerment policies and removing the existing sociocultural limitations to allow them to contribute more to pastoral livelihoods.

Socio-diversity, the variety of human opinions, ideas, behaviours and styles, has profound implications for social systems. While it fuels innovation, productivity and collective intelligence, it can also complicate communication and erode trust. So what mechanisms can influence it? This paper studies how fundamental characteristics of social networks can support or hinder socio-diversity. It employs models of cultural evolution, mathematical analysis and numerical simulations. We find that pronounced inequalities in the distribution of connections obstruct socio-diversity. By contrast, the prevalence of close-knit communities, a scarcity of long-range connections, and a significant tie density tend to promote it. These results open new perspectives for understanding how to change social networks to sustain more socio-diversity and, thereby, societal innovation, collective intelligence and productivity.

This study analyzes the perception and usage of ChatGPT based on the technology acceptance model (TAM). Conducting reticular analysis of coincidences (RAC) on a convenience survey among university students in the social sciences, this research delves into the perception and utilization of this artificial intelligence tool. The analysis considers variables such as gender, academic year, prior experience with ChatGPT, and the training provided by university faculty. The networks created with the statistical tool \"CARING\" highlight the role of perceived utility, credibility, and prior experience in shaping attitudes and behaviors toward this emerging technology. Previous experience, familiarity with video games, and programming knowledge were related to more favorable attitudes towards ChatGPT. Students who received specific training showed lower confidence in the tool. These findings underscore the importance of implementing training strategies that raise awareness among students about both the potential strengths and weaknesses of artificial intelligence in educational contexts.