Language difficulties in first year Science

A key goal of the study entitled ‘A cross-disciplinary approach to language support for first year students in the science disciplines’, funded by the Carrick Institute for Learning and Teaching in Higher Education, is to examine the role of language in the learning of science by first-year university students. The disciplines involved are Physics, Chemistry and Biology. This national project also aims to transfer active learning skills, which are widely used in language teaching, to the teaching of science in first year. The paper discusses the background to the study, reports on some of the preliminary results on the language difficulties faced by first year student cohorts in science from data collected in 2008, and describes the framework we have established for the organization and delivery of first year science courses to be implemented in semester one 2009

Embedding in-discipline language support for first year students in the sciences

This paper reports on a project which aims at addressing the need to cater for the language needs of a diverse student body (both domestic and international student body) by embedding strategic approaches to learning and teaching in first year sciences in tertiary education. These strategies consist of active learning skills which are widely used in language learning. The disciplines covered by the project are Biology, Chemistry and Physics and involves the University of Canberra (UC), University of Sydney (USyd), University of Tasmania (UTAS), University of Technology, Sydney (UTS) and University of Newcastle (Newcastle) in Australia. This project is funded by the Australian Learning and Teaching Council (ALTC). The paper discusses the background to the study and reports on results on the language difficulties faced by first year science student cohorts from data collected in 2008 as well as qualitative data was also collected on 2008 students’ attitudes towards online science learning. It will also report on the results on the implementation of the learning strategies at UTS and UTAS in Physics and Chemistry disciplines in 2009. Keywords: First year science teaching, role of language in science teaching, active learning skill

Integrating language learning practises in first year science disciplines

Student retention and progression rates are a matter of concern for most institutions in the higher education sector (Burton & Dowling, 2005;. Simpson, 2006;. Tinto & Pusser, 2006) in Australia. There is also a substantial body of literature concentrating on the first year experience at university (for example, in the Australian context, see Krause, Hartley, James, McInnis, & Centre for the Study of Higher Education. University of Melbourne, 2005). One of the particular concerns is that the diversity of the student body is rapidly increasing. Of course, with diversity comes with differentiated level of preparation for academic study within the student body

Liveness-Driven Random Program Generation

Randomly generated programs are popular for testing compilers and program analysis tools, with hundreds of bugs in real-world C compilers found by random testing. However, existing random program generators may generate large amounts of dead code (computations whose result is never used). This leaves relatively little code to exercise a target compiler's more complex optimizations. To address this shortcoming, we introduce liveness-driven random program generation. In this approach the random program is constructed bottom-up, guided by a simultaneous structural data-flow analysis to ensure that the generator never generates dead code. The algorithm is implemented as a plugin for the Frama-C framework. We evaluate it in comparison to Csmith, the standard random C program generator. Our tool generates programs that compile to more machine code with a more complex instruction mix.Comment: Pre-proceedings paper presented at the 27th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur, Belgium, 10-12 October 2017 (arXiv:1708.07854

The Effect of Multiple Imputation of Routine Pathology Variables on Laboratory Diagnosis of Hepatitis C Infection

Pathology tests are central to modern healthcare in terms of diagnosis and patient management. Aggregated pathology results provide opportunities for research into fundamental and applied questions in health and medicine, but data analytic challenges appear since test profiles vary between medical practitioners, resulting in missing data. In this study we provide an analytical investigation of the laboratory diagnosis of Hepatitis C (HCV) infection and focus on how to maximize the predictive value of routine pathology data. We recommend using the Influx - Outflux measures to help construct the imputation model when using multiple imputation. Data from 14,320 community-patients aged 15 - 100 years were accessed via ACT Pathology (The Canberra Hospital, Australia). Influx and Outflux were calculated to identify which variables were potentially powerful predictors of missing values. Available Case analysis and Multiple Imputation were used to accommodate missing values in the dataset. Logistic regression model and stepwise selection method were used for analysing the imputed datasets. The predictive power of all methods was compared. The predictive power of the models on multiply imputed data was similar to the power of the models based on complete data. The advantage of multiply imputed data was that it allowed for the inclusion of all the completed variables in the logistic models, thus identifying a broader selection of test results that could lead to the enhanced laboratory prediction of HCV. Multiple imputation is an important statistical resource allowing all individuals in a study to contribute whatever data they have supplied to the analysis. MI in combination with the values of Influx and Outflux identifies potential predictors of HepC infection. Variables age, gender and alanine aminotransferase have been shown to be strong laboratory predictors of HCV infection

Identification and characterization of a Ross River virus variant that grows persistently in macrophages, shows altered disease kinetics in a mouse model, and exhibits resistance to type I interferon

Alphaviruses, such as chikungunya virus, o'nyong-nyong virus, and Ross River virus (RRV), cause outbreaks of human rheumatic disease worldwide. RRV is a positive-sense single-stranded RNA virus endemic to Australia and Papua New Guinea. In this study, we sought to establish an in vitro model of RRV evolution in response to cellular antiviral defense mechanisms. RRV was able to establish persistent infection in activated macrophages, and a small-plaque variant (RRVPERS) was isolated after several weeks of culture. Nucleotide sequence analysis of RRV PERS found several nucleotide differences in the nonstructural protein (nsP) region of the RRV PERS genome. A point mutation was also detected in the E2 gene. Compared to the parent virus (RRV-T48), RRV PERS showed significantly enhanced resistance to beta interferon (IFN-β)-stimulated antiviral activity. RRV PERS infection of RAW 264.7 macrophages induced lower levels of IFN-β expression and production than infection with RRV-T48. RRV PERS was also able to inhibit type I IFN signaling. Mice infected with RRV PERS exhibited significantly enhanced disease severity and mortality compared to mice infected with RRV-T48. These results provide strong evidence that the cellular antiviral response can direct selective pressure for viral sequence evolution that impacts on virus fitness and sensitivity to alpha/beta IFN (IFN-α/β).Facultad de Ciencias Exacta

Fecal microbiota in client-owned obese dogs changes after weight loss with a high-fiber-high-protein diet

Background. The fecal microbiota from obese individuals can induce obesity in animal models. In addition, studies in humans, animal models and dogs have revealed that the fecal microbiota of subjects with obesity is different from that of lean subjects and changes after weight loss. However, the impact of weight loss on the fecal microbiota in dogs with obesity has not been fully characterized. Methods. In this study, we used 16S rRNA gene sequencing to investigate the differences in the fecal microbiota of 20 pet dogs with obesity that underwent a weight loss program. The endpoint of the weight loss program was individually tailored to the ideal body weight of each dog. In addition, we evaluated the qPCR based Dysbiosis Index before and after weight loss. Results. After weight loss, the fecal microbiota structure of dogs with obesity changed significantly (weighted ANOSIM; p = 0.016, R = 0.073), showing an increase in bacterial richness (p = 0.007), evenness (p = 0.007) and the number of bacterial species (p = 0.007). The fecal microbiota composition of obese dogs after weight loss was characterized by a decrease in Firmicutes (92.3% to 78.2%, q = 0.001), and increase in Bacteroidetes (1.4% to 10.1%, q = 0.002) and Fusobacteria (1.6% to 6.2%, q = 0.040). The qPCR results revealed an overall decrease in the Dysbiosis Index, driven mostly due to a significant decrease in E. coli (p = 0.030), and increase in Fusobacterium spp. (p = 0.017). Conclusion. The changes observed in the fecal microbiota of dogs with obesity after weight loss with a weight loss diet rich in fiber and protein were in agreement with previous studies in humans, that reported an increase of bacterial biodiversity and a decrease of the ratio Firmicutes/Bacteroidetes

Fecal Microbial and Metabolic Profiles in Dogs With Acute Diarrhea Receiving Either Fecal Microbiota Transplantation or Oral Metronidazole

The aim was to characterize differences in fecal consistency, and fecal microbiota and metabolome profiles in dogs with acute diarrhea (AD) treated with either fecal microbiota transplantation as enema (FMT;n = 11) or oral metronidazole (MET;n = 7) for 7 days. On days 0, 7, and 28 fecal samples were obtained. Fecal samples from healthy dogs (HC;n = 14) were used for comparison. Samples were analyzed by the previously validated qPCR based canine Dysbiosis Index (DI;increased values indicate microbiota dysbiosis) and 16S rRNA gene sequencing. The fecal metabolome was analyzed using a previously validated targeted canine assay for fecal unconjugated bile acids, and untargeted metabolomics. Fecal consistency improved significantly in dogs treated with FMT and MET by day 7 and day 28 (p < 0.01) compared to day 0. However, on day 28 fecal consistency was significantly better in FMT compared to MET (p = 0.040). At day 0, dogs with AD had an altered microbiota indicated by significantly increased DI, decreased alpha-diversity, and altered beta-diversity. In the FMT group, the DI decreased over time, while MET led to a significant increase in the dysbiosis index at day 7 and 28 compared to FMT. Sequencing data revealed that in FMT microbial diversity and beta-diversity was similar to HC at day 28, while in MET these parameters were still significantly different from HC. In dogs treated with FMT, a decrease in cholic acid and the percentage of primary bile acids was observed, whereas treatment with metronidazole led to an increase in cholic acid at day 7 and an increase in percentage of primary bile acids over time. Based on untargeted metabolomics, dogs with AD had an altered fecal metabolome compared to HC. Dogs treated with FMT clustered closer to HC at day 28, while dogs treated with MET did not. In this pilot study, dogs with AD had significant differences in fecal microbiota and metabolome profiles. Dogs treated with MET still had altered microbial and metabolic profiles at day 28 compared to dogs treated with FMT or healthy dogs

Identification of dimethylamine monooxygenase in marine bacteria reveals a metabolic bottleneck in the methylated amine degradation pathway

Methylated amines (MAs) are ubiquitous in the marine environment and their subsequent flux into the atmosphere can result in the formation of aerosols and ultimately cloud condensation nuclei. Therefore, these compounds have a potentially important role in climate regulation. Using Ruegeria pomeroyi as a model, we identified the genes encoding dimethylamine (DMA) monooxygenase (dmmABC) and demonstrate that this enzyme degrades DMA to monomethylamine (MMA). Although only dmmABC are required for enzyme activity in recombinant Escherichia coli, we found that an additional gene, dmmD, was required for the growth of R. pomeroyi on MAs. The dmmDABC genes are absent from the genomes of multiple marine bacteria, including all representatives of the cosmopolitan SAR11 clade. Consequently, the abundance of dmmDABC in marine metagenomes was substantially lower than the genes required for other metabolic steps of the MA degradation pathway. Thus, there is a genetic and potential metabolic bottleneck in the marine MA degradation pathway. Our data provide an explanation for the observation that DMA-derived secondary organic aerosols (SOAs) are among the most abundant SOAs detected in fine marine particles over the North and Tropical Atlantic Ocean