Although the use of two "group-specific" probes targeting both the ITS1 and the ITS2 regions were required to identify T. soudanense, the other species were identified by single ITS1-or ITS2-targeted species-specific probes. There was good agreement between ITS sequencing and the RCA assay. Despite limited genetic variation between Trichophyton spp., the sensitive, specific RCA-based SNP detection assay showed potential as a simple, reproducible method for the rapid (2-h) identification of Trichophyton spp.

Shifts in rainfall and rising temperatures due to climate change poses a formidable challenge to the sustainability of broadacre crop yields in Western and SouthEastern Australia. Output from18 Global Climate Models (GCMs) for the Special Report on Emission Scenarios (SRES) A2 scenario was statistically downscaled to four contrasting locations. For the first time in these regions, bias corrected statistically downscaled climate data were employed to drive the Agricultural Production Systems Simulator (APSIM) crop model that integrates the effects of soil, crop phenotype, and management options for a quantitative comparison of crop yields and phenology under an historical and a plausible projected climate. The dynamic APSIM simulation model explore the implications of climate change across multiple locations and multiple time periods (1961-2010, 2030, 2060 and 2090) for multiple key crops (wheat, barley, lupin, canola, field pea) grown in three different types of soil. On average, the ensemble of downscaled GCM projections show a decrease in rainfall in the future at the four locations considered, with increased variability at two locations. At all locations and for five crops, future changes in both crop biomass and grain yield are strongly associated with changes in rainfall (P = 0.05 to P = 0.001). The overall rainfall amount is critical in determining yields but, equally, higher future temperatures can contribute to reducing crop productivity primarily due to advanced crop phenology. For example, for wheat cropping at Hamilton (a higher rainfall site), there is a significant advancement in median flowering date for 2030, 2060, and 2090 of 10, 18, and 29 days respectively with a significant 0.50% grain yield changes for each percentage change in rainfall compared to significant 0.90% grain yield changes in Cunderdin (a lower rainfall sites). At all sites except Hamilton, the change in crop grain yield is significantly correlated (P=0.001) with the percentage change in the future rainfall and the impact increased progressively from higher rainfall to lower rainfall sites. However, the magnitude of the change in crop phenology and yield were not significantly different between soil types. These results help to define regions of concern and their relative importance in the coming years. In this future climate the negative consequences for crop yields and advancement of phenology relative to baseline are not uniform across crops and locations. Of the crops studied-wheat, barley, lupin, canola and field pea-field pea is the most sensitive to the projected future climate changes, and the ensemble median changes in field pea yield range from a decrease of 12% to a decrease of 45%, depending on location. These results highlight the importance of research and policy to support strategies for adapting to climate change, such as advances in agronomy, soil moisture conservation, seasonal climate forecasting and breeding new crop varieties.

Global climate models (GCMs) are useful tools for assessing climate change impacts on temperature and rainfall. Although climate data from various GCMs have been increasingly used in climate change impact studies, GCMs configurations and module characteristics vary from one to another. Therefore, it is crucial to assess different GCMs to confirm the extent to which they can reproduce the observed temperature and rainfall. Rather than assessing the interdependence of each GCM, the purpose of this study is to compare the capacity of four different multi-model ensemble (MME) methods (random forest [RF], support vector machine [SVM], Bayesian model averaging [BMA] and the arithmetic ensemble mean [EM]) in reproducing observed monthly rainfall and temperature. Of these four methods, the RF and SVM demonstrated a significant improvement over EM and BMA in terms of performance criteria. The relative importance of each GCM based on the RF ensemble in reproducing rainfall and temperature could also be ranked. We compared the GCMs importance and Taylor skill score and found that their correlation was 0.95 for temperature and 0.54 for rainfall. Our results also demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. We conclude that machine learning MME could be efficient and useful with improved accuracy in reproducing historical climate variables.

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Highlights d B cell epitopes of SARS-CoV-2 are obtained using structurebased approaches d The predicted epitopes effectively induce robust antibody responses d D614 and G614 SARS-CoV-2 display different immunodominant epitopes d Epitopes on S protein elicit D614 and/or G614 SARS-CoV-2neutralizing antibodies

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HIV type 1 viral quasispecies were amplified by polymerase chain reaction (PCR) in the hypervariable V3 region of gp120 from six different regions of the brain (right and left frontal; right and left parietal; and right and left occipital) and from the peripheral blood mononuclear cells (PBMCs) of a patient who died of AIDS dementia complex (ADC). Cloning and sequencing of the entire V3 region suggested the presence of genetically unique sequences in different regions of the brain. In contrast, the blood-derived viral quasispecies carried homogeneous sequences that were characterized by a single octapeptide crest motif (HLGPGSAF), a motif important in viral fusion. The brain-derived viral strains showed extensive sequence heterogeneity and the presence of seven different octapeptide and four different tetrapeptide crest motifs (HIGPGRAF, RIGPGRAF, HIGPGSAI, HLGPGSAF, HIGPESAI, HLGPESAI, and YLRPGSAF). In addition, the brain-derived strains were also characterized by variable net V3 loop charge and hydrophilicity, along with distinct amino acid changes specific to different brain regions. Together, the sequence and phylogenetic analyses are unique in identifying the complexity of a viral quasispecies and its independent regional evolution within the brain compartment. Uniquely divergent viral strains were identified in the frontal regions and their presence was further supported by the presence of multinucleated giant cells (characteristic of HIV encephalopathy) predominantly in the left and right frontal regions. In summary, these analyses suggest that genetically different populations of HIV-1 may be present in different brain compartments and confirm that specific neurotropic variants may exist.