Physicists recently observed that realistic complex networks emerge as discrete samples from a continuous hyperbolic geometry enclosed in a circle: the radius represents the node centrality and the angular displacement between two nodes resembles their topological proximity. The hyperbolic circle aims to become a universal space of representation and analysis of many real networks. Yet, inferring the angular coordinates to map a real network back to its latent geometry remains a challenging inverse problem. Here, we show that intelligent machines for unsupervised recognition and visualization of similarities in big data can also infer the network angular coordinates of the hyperbolic model according to a geometrical organization that we term “angular coalescence.” Based on this phenomenon, we propose a class of algorithms that offers fast and accurate “coalescent embedding” in the hyperbolic circle even for large networks. This computational solution to an inverse problem in physics of complex systems favors the application of network latent geometry techniques in disciplines dealing with big network data analysis including biology, medicine, and social science.

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as productconsumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.

The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.

This systematic review aimed to establish the range and quality of clinical and preclinical evidence supporting the association of individual microRNAs, and the use of microRNA expression in the diagnosis and prognosis of ischaemic or haemorrhagic stroke. Electronic databases were searched from 1993 to October 2021, using key words relevant to concepts of stroke and microRNA. Studies that met specific inclusion and exclusion criteria were selected for data extraction. To minimise erroneous associations, findings were restricted to microRNAs reported to change in more than two independent studies. Of the papers assessed, 155 papers reported a change in microRNA expression observed in more than two independent studies. In ischaemic studies, two microRNAs were consistently differentially expressed in clinical samples (miR-29b & miR-146a) and four were altered in preclinical samples (miR-137, miR-146a, miR-181b & miR-223-3p). Across clinical and preclinical haemorrhagic studies, four microRNAs were downregulated consistently (miR-26a, miR-126, miR-146a & miR-155). Across included studies, miR-126 and miR-146a were the only two microRNAs to be differentially expressed in clinical and preclinical cohorts following ischaemic or haemorrhagic stroke. Further studies, employing larger populations with consistent methodologies, are required to validate the true clinical value of circulating microRNAs as biomarkers of ischaemic and haemorrhagic stroke.

Background: Face masks have been widely employed as a personal protective measure during the COVID-19 pandemic. However, concerns remain that masks create a false sense of security that reduces adherence to other public health measures, including social distancing. Purpose: This paper tested whether mask-wearing was negatively associated with social distancing compliance. Methods: In two studies, we combined video-observational records of public mask-wearing in two Dutch cities with a natural-experimental approach to evaluate the effect of an area-based mask mandate. Results: We found no observational evidence of an association between mask-wearing and social distancing (Study 1: p = .398; Study 2: p = .511), but found a positive link between crowding and social distancing violations (Study 1: p < .001; Study 2: p < .001). Our natural-experimental analysis showed that an area-based mask mandate did not significantly affect social distancing or crowding levels (Study 2: p = .781 and p = .126, respectively). Conclusions: Our results alleviate the concern that mask use reduces social distancing compliance or increases crowding levels. On the other hand, crowding reduction may be a viable strategy to mitigate social distancing violations.

Pink-pigmented methylotropic bacteria of the genus Methylobacterium inhabit the surfaces of plant organs. In bryophytes, these methylobacteria enhance cell growth, but the nature of this plant-microbe interaction is largely unknown. In this study, methylobacteria were isolated from the upper surface of the free-living thalli of the liverwort Marchantia polymorpha L. Identification of one strain by 16S ribosomal RNA (rRNA) gene-targeted polymerase chain reaction (PCR) and other data show that these microbes represent an undescribed species of the genus Methylobacterium (Methylobacterium sp.). The growth-promoting activity of these wild-type methylobacteria was tested and compared with that of the type strain Methylobacterium mesophilicum. Both types of methylobacteria stimulated surface expansion of isolated gemmae from Marchantia polymorpha by about 350%. When suspended in water, the liverwort-associated bacteria (Methylobacterium sp.) formed dense clusters of up to 600 cells. In liquid cultures of Methylobacterium mesophilicum, single cells were observed, but no clustering occurred. We suggest that the liverwort-associated methylobacteria are co-evolved symbionts of the plants: Cluster formation may be a behavior that enhances the survival of the epiphytic microbes during periods of drought of these desiccation-tolerant lower plants.

Intracerebral hemorrhage (ICH) is a deadly and debilitating type of stroke, caused by the rupture of cerebral blood vessels. To date, there are no restorative interventions approved for use in ICH patients, highlighting a critical unmet need. ICH shares some pathological features with other acute brain injuries such as ischemic stroke (IS) and traumatic brain injury (TBI), including the loss of brain tissue, disruption of the blood–brain barrier, and activation of a potent inflammatory response. New biomaterials such as hydrogels have been recently investigated for their therapeutic benefit in both experimental IS and TBI, owing to their provision of architectural support for damaged brain tissue and ability to deliver cellular and molecular therapies. Conversely, research on the use of hydrogels for ICH therapy is still in its infancy, with very few published reports investigating their therapeutic potential. Here, the published use of hydrogels in experimental ICH is commented upon and how approaches reported in the IS and TBI fields may be applied to ICH research to inform the design of future therapies is described. Unique aspects of ICH that are distinct from IS and TBI that should be considered when translating biomaterial‐based therapies between disease models are also highlighted.

ObjectivesCurrently there is a paucity of clinically available regenerative therapies for stroke. Extracellular vesicles (EV) have been investigated for their potential as modulators of regeneration in the poststroke brain. This systematic review and meta-analysis aims to provide a summary of the efficacy of therapeutic EVs in preclinical stroke models, to inform future research in this emerging field.MethodsStudies were identified by a comprehensive literature search of two online sources and subsequent screening. Studies using lesion volume or neurological score as outcome measures were included. Standardised mean difference (SMD) and 95% CIs were calculated using a restricted maximum likelihood random effects model. Publication bias was assessed with Egger’s regression and presented as funnel plots with trim and fill analysis. Subgroup analysis was performed to assess the effects of different study variables. Study quality and risk of bias were assessed using the CAMARADES checklist.ResultsA total of 20 publications were included in the systematic review, of which 19 were assessed in the meta-analysis (43 comparisons). Overall, EV interventions improved lesion volume (SMD: −1.95, 95% CI −2.72 to 1.18) and neurological scores (SMD: −1.26, 95% CI −1.64 to 0.87) compared with control groups. Funnel plots were asymmetrical suggesting publication bias, and trim and fill analysis predicted seven missing studies for lesion volume. Subgroup analysis suggested administration at 0–23 hours after stroke was the most effective timepoint for EV treatment. The median score on the CAMARADES checklist was 7 (IQR: 5–8).ConclusionsEVs may offer a promising new avenue for stroke therapies, as EV-based interventions had positive impacts on lesion volume and neurological score in preclinical stroke models.PROSPERO registration numberCRD42019134925.