Global Analysis of Predicted G Protein-Coupled Receptor Genes in the Filamentous Fungus, Neurospora crassa.

G protein-coupled receptors (GPCRs) regulate facets of growth, development, and environmental sensing in eukaryotes, including filamentous fungi. The largest predicted GPCR class in these organisms is the Pth11-related, with members similar to a protein required for disease in the plant pathogen Magnaporthe oryzae. However, the Pth11-related class has not been functionally studied in any filamentous fungal species. Here, we analyze phenotypes in available mutants for 36 GPCR genes, including 20 Pth11-related, in the model filamentous fungus Neurospora crassa. We also investigate patterns of gene expression for all 43 predicted GPCR genes in available datasets. A total of 17 mutants (47%) possessed at least one growth or developmental phenotype. We identified 18 mutants (56%) with chemical sensitivity or nutritional phenotypes (11 uniquely), bringing the total number of mutants with at least one defect to 28 (78%), including 15 mutants (75%) in the Pth11-related class. Gene expression trends for GPCR genes correlated with the phenotypes observed for many mutants and also suggested overlapping functions for several groups of co-transcribed genes. Several members of the Pth11-related class have phenotypes and/or are differentially expressed on cellulose, suggesting a possible role for this gene family in plant cell wall sensing or utilization

Fungicide Resistance Management in West Australia’s Wheatbelt

Barley growers from the West Australia Wheatbelt were invited to share information on their fungicide resistance management strategies. The study aimed to identify gaps in growers’ knowledge about issues like fungicide resistance and the objective and/or perceived obstacles and constraints associated with the management of fungal epidemics. To gather this information, we used a case study approach and co-designed the survey in collaboration with industry stakeholders. Socio-economic data was collected using in-depth phone interviews (which made up 82% of the responses) and self-administered questionnaires (which accounted for 18%). The data included both qualitative and quantitative responses. This data covered several aspects: growers’ demographic details, barley production statistics, current knowledge and understanding about fungicide resistance, current agronomic practices, willingness to pay to mitigate fungicide resistance risk, types of fungicide resistance management extension services growers currently use, the reasons for their preferences and additional types of fungicide resistance management extension services growers would like to access in the future

Ensemble Distillation for Unsupervised Constituency Parsing

We investigate the unsupervised constituency parsing task, which organizes words and phrases of a sentence into a hierarchical structure without using linguistically annotated data. We observe that existing unsupervised parsers capture differing aspects of parsing structures, which can be leveraged to enhance unsupervised parsing performance. To this end, we propose a notion of "tree averaging," based on which we further propose a novel ensemble method for unsupervised parsing. To improve inference efficiency, we further distill the ensemble knowledge into a student model; such an ensemble-then-distill process is an effective approach to mitigate the over-smoothing problem existing in common multi-teacher distilling methods. Experiments show that our method surpasses all previous approaches, consistently demonstrating its effectiveness and robustness across various runs, with different ensemble components, and under domain-shift conditions.Comment: Accepted by International Conference on Learning Representations (ICLR) 202

Responsible AI Considerations in Text Summarization Research: A Review of Current Practices

AI and NLP publication venues have increasingly encouraged researchers to reflect on possible ethical considerations, adverse impacts, and other responsible AI issues their work might engender. However, for specific NLP tasks our understanding of how prevalent such issues are, or when and why these issues are likely to arise, remains limited. Focusing on text summarization -- a common NLP task largely overlooked by the responsible AI community -- we examine research and reporting practices in the current literature. We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022. We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals. We also discuss current evaluation practices and consider how authors discuss the limitations of both prior work and their own work. Overall, we find that relatively few papers engage with possible stakeholders or contexts of use, which limits their consideration of potential downstream adverse impacts or other responsible AI issues. Based on our findings, we make recommendations on concrete practices and research directions