Dual dean entrainment with volume ratio modulation for efficient droplet co-encapsulation: Extreme single-cell indexing

The future of single cell diversity screens involves ever-larger sample sizes, dictating the need for higher throughput methods with low analytical noise to accurately describe the nature of the cellular system. Current approaches are limited by the Poisson statistic, requiring dilute cell suspensions and associated losses in throughput. In this contribution, we apply Dean entrainment to both cell and bead inputs, defining different volume packets to effect efficient co-encapsulation. Volume ratio scaling was explored to identify optimal conditions. This enabled the co-encapsulation of single cells with reporter beads at rates of ∼1 million cells per hour, while increasing assay signal-to-noise with cell multiplet rates of ∼2.5% and capturing ∼70% of cells. The method, called Pirouette coupling, extends our capacity to investigate biological systems.Jack Harrington, Luis Blay Esteban, Jonathan Butement, Andres F. Vallejo, Simon I. R. Lane, Bhavwanti Sheth, Maaike S. A. Jongen, Rachel Parker, Patrick S. Stumpf, Rosanna C. G. Smith, Ben D. MacArthur, Matthew J. J. Rose-Zerilli, Marta E. Polak, Tim Underwood and Jonathan Wes

A mathematical model of dynamic glioma-host interactions: receptor-mediated invasion and local proteolysis

We present a mathematical model of glioma spread based on cellular movement by receptor-mediated haptotaxis, local proteolysis of healthy tissue components by glioma-derived proteinases, malignant proliferative enhancement and host up-regulation of specific key extracellular matrix (ECM) components in response to the invading glioma. We subsequently consider the nature of glioma–host interactions as predicted by our model in order to test the hypothesis given in (Knott et al. (1998) that production of adhesive ECM components by the brain in response to the invading glioma may have the counter-intuitive effect of enhancing glioma invasion by assisting haptotactic migration. We suggest that host production of certain adhesive ECM chemicals can have a profound effect on both glioma invasion speed and the character of the glioma–host interface. In particular, we conclude that up-regulation of host ECM production in the vicinity of the glioma may produce a less diffuse glioma, providing clearer demarcation between glioma and healthy tissue, and thus improving the possibility of surgical resection within reasonable bounds

Mathematical modelling of skeletal repair

Tissue engineering offers significant promise as a viable alternative to current clinical strategies for replacement of damaged tissue as a consequence of disease or trauma. Since mathematical modelling is a valuable tool in the analysis of complex systems, appropriate use of mathematical models has tremendous potential for advancing the understanding of the physical processes involved in such tissue reconstruction. In this review, the potential benefits, and limitations, of theoretical modelling in tissue engineering applications are examined with specific emphasis on tissue engineering of bone. A central tissue engineering approach is the in vivo implantation of a biomimetic scaffold seeded with an appropriate population of stem or progenitor cells. This review will therefore consider the theory behind a number of key factors affecting the success of such a strategy including: stem cell or progenitor population expansion and differentiation ex vivo; cell adhesion and migration, and the effective design of scaffolds; and delivery of nutrient to avascular structures. The focus will be on current work in this area, as well as on highlighting limitations and suggesting possible directions for future work to advance health-care for all

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included “machine learning” or “artificial intelligence” and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types

Nested assemblages of Orthoptera species in the Netherlands: the importance of habitat features and life-history traits

Aim: Species communities often exhibit nestedness, the species found in speciespoor sites representing subsets of richer ones. In the Netherlands, where intensification of land use has led to severe fragmentation of nature, we examined the degree of nestedness in the distribution of Orthoptera species. An assessment was made of how environmental conditions and species life-history traits are related to this pattern, and how variation in sampling intensity across sites may influence the observed degree of nestedness. Location: The analysis includes a total of 178 semi-natural sites in the Pleistocene sand region of the Netherlands. Methods: A matrix recording the presence or absence of all Orthoptera species in each site was compiled using atlas data. Additionally, separate matrices were constructed for the species of suborders Ensifera and Caelifera. The degree of nestedness was measured using the binmatnest calculator. binmatnest uses an algorithm to sort the matrices to maximal nestedness. We used Spearman’s rank correlations to evaluate whether sites were sorted by area, isolation or habitat heterogeneity, and whether species were sorted by their dispersal ability, rate of development or degree of habitat specificity. Results: We found the Orthoptera assemblages to be significantly nested. The rank correlation between site order and sampling intensity was high. The degree of nestedness was lower, but remained significant when under- and oversampled sites were excluded from the analysis. Site order was strongly correlated with both size of sample site and number of habitat types per site. Rank correlations showed that species were probably ordered by variation in habitat specificity, rather than by variation in dispersal capacity or rate of development of the species. Main conclusions: Variation in sampling intensity among sites had a strong impact on the observed degree of nestedness. Nestedness in habitats may underlie the observed nestedness within the Orthoptera assemblages. Habitat heterogeneity is closely related to site area, which suggests that several large sites should be preserved, rather than many small sites. Furthermore, the results corroborate a focus of nature conservation policy on sites where rare species occur, as long as the full spectrum of habitat conditions and underlying ecological processes is secured