Understanding micro-processes of community building and mutual learning on Twitter: a ‘small data’ approach

This article contributes to an emerging field of ‘small data’ research on Twitter by presenting a case study of how teachers and students at a sixth-form college in the north of England used this social media platform to help construct a ‘community of practice’ that enabled micro-processes of recognition and mutual learning. Conducted as part of a broader action research project that focused on the ‘digital story circle’ as a site of, and for, narrative exchange and knowledge production, this study takes the form of a detailed analysis of a departmental Twitter account, combining basic quantitative metrics, close reading of selected Twitter data and qualitative interviews with teachers and students. Working with (and sometimes against) Twitter's platform architecture, teachers and students constructed, through distinct patterns of use, a shared space for dialogue that facilitated community building within the department. On the whole, they were able to overcome justified anxieties about professionalism and privacy; this was achieved by building on high levels of pre-existing trust among staff and by performing that mutual trust online through personal modes of communication. Through micro-processes of recognition and a breaking down of conventional hierarchies that affirmed students' agency as knowledge producers, the departmental Twitter account enabled mutual learning beyond curriculum and classroom. The significance of such micro-processes could only have been uncovered through the detailed scrutiny that a ‘small data’ approach to Twitter, in supplement to some obvious virtues of Big Data approaches, is particularly well placed to provide

Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe.

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project GenTORE and Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle

Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle.

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and United States of America), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs

Digital platforms and narrative exchange: hidden constraints, emerging agency

It is well known that narrative exchange takes distinctive forms in the digital age. Less understood are the digitally based processes and infrastructures that support or constrain the wider exchange of narrative materials. This article reports on research in a UK sixth form college with ambitions to expand its students’ digital skills. Our approach was to identify the preconditions (sometimes, but often not, involving fully formed narrative agency) that might support sustained narrative exchange. We call these conditions collectively ‘proto-agency’, and explore them as a way of establishing what a ‘digital story circle’ (not just a digital story) might be: that is, how new digital platforms and resources contribute to the infrastructures for narrative exchange and wider empowerment in a complex institutional context. During our fieldwork, interesting insights into the tensions around social media emerged. Only by understanding such forms of proto-agency can we begin to asses

Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph\u2014a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies

Constructing a digital storycircle: digital infrastructure and mutual recognition

Building on the principles of the digital storytelling movement, this article asks whether the narrative exchange within the ‘storycircles’ of storymakers created in face-to-face workshops can be further replicated by drawing on digital infrastructure in specific ways. It addresses this question by reporting on the successes and limitations of a five-stream project of funded action research with partners in north-west England that explored the contribution of digital infrastructure to processes of narrative exchange and the wider processes of mutual recognition that flow from narrative exchange. Three main dimensions of a digital storycircle are explored: multiplications, spatializations (or the building of narratives around sets of individual narratives), and habits of mutual recognition. Limitations relate to the factors of time, and levels of digital development and basic digital access