Community biology labs are locally organized spaces for research, tinkering and innovation, which are important for improving the accessibility of biological research and the transferability of scientific knowledge. These labs promote citizen science by providing resources and education to community members. For community labs to deliver consistent and reliable results, they would ideally be based on an adaptive and robust foundation: an Enterprise Systems Thinking (EST) framework. This paper follows a descriptive methodology to apply EST to conceptualize the optimal functioning of community biology labs. EST approaches can increase the overall understanding of the community lab system’s context and performance. This supportive tool can aid in successful stakeholder engagement and communications within the lab’s complex structure. It is also adaptive and can be adjusted as Community Bio labs expand in scale and are newly introduced to local communities. The result of this paper is the development of a framework that may help enhance existing community laboratory organizational approaches so that they may provide consistent accessibility, innovation and education to local communities.

A biofilm refers to an intricate community of microorganisms firmly attached to surfaces and enveloped within a self-generated extracellular matrix. Machine learning (ML) methodologies have been harnessed across diverse facets of biofilm research, encompassing predictions of biofilm formation, identification of pivotal genes and the formulation of novel therapeutic approaches. This investigation undertook a bibliographic analysis focused on ML applications in biofilm research, aiming to present a comprehensive overview of the field’s current status. Our exploration involved searching the Web of Science database for articles incorporating the term “machine learning biofilm,” leading to the identification and analysis of 126 pertinent articles. Our findings indicate a substantial upswing in the publication count concerning ML in biofilm over the last decade, underscoring an escalating interest in deploying ML techniques for biofilm investigations. The analysis further disclosed prevalent research themes, predominantly revolving around biofilm formation, prediction and control. Notably, artificial neural networks and support vector machines emerged as the most frequently employed ML techniques in biofilm research. Overall, our study furnishes valuable insights into prevailing trends and future trajectories within the realm of ML applied to biofilm research. It underscores the significance of collaborative efforts between biofilm researchers and ML experts, advocating for interdisciplinary synergy to propel innovation in this domain.

The paper uses the material and conceptual figure of dust and matter out of place to amplify more-than-human perspectives of time, to trace the changing orientations and ethos of a site. Dust contains a complex mixture of inorganic and organic material, made up of an exuberance of microbial life such as Penicillium, Aspergillus and Cladosporium and around 20 other fungal sources. We are interested in dust as a material and metaphorical device to situate and critique temporality and the way we narrate and investigate the past and future, from a non-human, microbial point of view. Dust implies residual matter, a contradiction to order often associated with dirt. It indicates something that needs to be removed, or rearranged, something that is “out of place,” an element that does not fit. Dust also indicates time and space and signals movement and life: dust hosts a medley of non-human particles and microbial communities that engage in their own worldmaking practices. The paper brings together methods of “un-cleaning” with archival research and spatial methods of 3D scanning, modelling and mapping, as an opportunity to decentre human hubris and explore the ways in which non-humans have and continue to inhabit “our” spaces.

This systematic review evaluates the use of Normothermic Machine Perfusion (NMP) as a testbed for developing peripheral nerve and muscle interfaces for bionic prostheses. Our findings suggest that NMP offers a viable alternative to traditional models, with significant implications for future research and clinical applications. A literature search was performed using Ovid MEDLINE (1946 to October 2023), revealing 559 abstracts.

No studies using nerve and/or muscle electrodes for the testing or development of bionic interface technologies were identified, except for one conference abstract. NMP could serve as a test bed for future development of interface biocompatibility, selectivity, stability and data transfer, whilst complying with ethical practices and potentially offering greater relevance for human translation. Implemention of machine perfusion requires experienced personnel. Encompassing artificial intelligence alongside machine learning will provide a significant contribution to advancing interface technologies for multiple neurological disorders.