Using social media to support small group learning

Abstract Background Medical curricula are increasingly using small group learning and less didactic lecture-based teaching. This creates new challenges and opportunities in how students are best supported with information technology. We explored how university-supported and external social media could support collaborative small group working on our new undergraduate medical curriculum. Methods We made available a curation platform (Scoop.it) and a wiki within our virtual learning environment as part of year 1 Case-Based Learning, and did not discourage the use of other tools such as Facebook. We undertook student surveys to capture perceptions of the tools and information on how they were used, and employed software user metrics to explore the extent to which they were used during the year. Results Student groups developed a preferred way of working early in the course. Most groups used Facebook to facilitate communication within the group, and to host documents and notes. There were more barriers to using the wiki and curation platform, although some groups did make extensive use of them. Staff engagement was variable, with some tutors reviewing the content posted on the wiki and curation platform in face-to-face sessions, but not outside these times. A small number of staff posted resources and reviewed student posts on the curation platform. Conclusions Optimum use of these tools depends on sufficient training of both staff and students, and an opportunity to practice using them, with ongoing support. The platforms can all support collaborative learning, and may help develop digital literacy, critical appraisal skills, and awareness of wider health issues in society

Prime Focus Spectrograph - Subaru's future -

The Prime Focus Spectrograph (PFS) of the Subaru Measurement of Images and Redshifts (SuMIRe) project has been endorsed by Japanese community as one of the main future instruments of the Subaru 8.2-meter telescope at Mauna Kea, Hawaii. This optical/near-infrared multi-fiber spectrograph targets cosmology with galaxy surveys, Galactic archaeology, and studies of galaxy/AGN evolution. Taking advantage of Subaru's wide field of view, which is further extended with the recently completed Wide Field Corrector, PFS will enable us to carry out multi-fiber spectroscopy of 2400 targets within 1.3 degree diameter. A microlens is attached at each fiber entrance for F-ratio transformation into a larger one so that difficulties of spectrograph design are eased. Fibers are accurately placed onto target positions by positioners, each of which consists of two stages of piezo-electric rotary motors, through iterations by using back-illuminated fiber position measurements with a wide-field metrology camera. Fibers then carry light to a set of four identical fast-Schmidt spectrographs with three color arms each: the wavelength ranges from 0.38 {\mu}m to 1.3 {\mu}m will be simultaneously observed with an average resolving power of 3000. Before and during the era of extremely large telescopes, PFS will provide the unique capability of obtaining spectra of 2400 cosmological/astrophysical targets simultaneously with an 8-10 meter class telescope. The PFS collaboration, led by IPMU, consists of USP/LNA in Brazil, Caltech/JPL, Princeton, & JHU in USA, LAM in France, ASIAA in Taiwan, and NAOJ/Subaru.Comment: 13 pages, 11 figures, submitted to "Ground-based and Airborne Instrumentation for Astronomy IV, Ian S. McLean, Suzanne K. Ramsay, Hideki Takami, Editors, Proc. SPIE 8446 (2012)

Comparing very low birth weight versus very low gestation cohort methods for outcome analysis of high risk preterm infants

Background: Compared to very low gestational age (\u3c32 weeks, VLGA) cohorts, very low birth weight (\u3c1500 g; VLBW) cohorts are more prone to selection bias toward small-for-gestational age (SGA) infants, which may impact upon the validity of data for benchmarking purposes. Method: Data from all VLGA or VLBW infants admitted in the 3 Networks between 2008 and 2011 were used. Two-thirds of each network cohort was randomly selected to develop prediction models for mortality and composite adverse outcome (CAO: mortality or cerebral injuries, chronic lung disease, severe retinopathy or necrotizing enterocolitis) and the remaining for internal validation. Areas under the ROC curves (AUC) of the models were compared. Results: VLBW cohort (24,335 infants) had twice more SGA infants (20.4% vs. 9.3%) than the VLGA cohort (29,180 infants) and had a higher rate of CAO (36.5% vs. 32.6%). The two models had equal prediction power for mortality and CAO (AUC 0.83), and similarly for all other cross-cohort validations (AUC 0.81-0.85). Neither model performed well for the extremes of birth weight for gestation (\u3c1500 g and ≥32 weeks, AUC 0.50-0.65; ≥1500 g and \u3c32 weeks, AUC 0.60-0.62). Conclusion: There was no difference in prediction power for adverse outcome between cohorting VLGA or VLBW despite substantial bias in SGA population. Either cohorting practises are suitable for international benchmarking

Complex Reorganization and Predominant Non-Homologous Repair Following Chromosomal Breakage in Karyotypically Balanced Germline Rearrangements and Transgenic Integration

We defined the genetic landscape of balanced chromosomal rearrangements at nucleotide resolution by sequencing 141 breakpoints from cytogenetically-interpreted translocations and inversions. We confirm that the recently described phenomenon of “chromothripsis” (massive chromosomal shattering and reorganization) is not unique to cancer cells but also occurs in the germline where it can resolve to a karyotypically balanced state with frequent inversions. We detected a high incidence of complex rearrangements (19.2%) and substantially less reliance on microhomology (31%) than previously observed in benign CNVs. We compared these results to experimentally-generated DNA breakage-repair by sequencing seven transgenic animals, and revealed extensive rearrangement of the transgene and host genome with similar complexity to human germline alterations. Inversion is the most common rearrangement, suggesting that a combined mechanism involving template switching and non-homologous repair mediates the formation of balanced complex rearrangements that are viable, stably replicated and transmitted unaltered to subsequent generations

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License