Temperature-sensitive protein–DNA dimerizers

Programmable DNA-binding polyamides coupled to short peptides have led to the creation of synthetic artificial transcription factors. A hairpin polyamide-YPWM tetrapeptide conjugate facilitates the binding of a natural transcription factor Exd to an adjacent DNA site. Such small molecules function as protein-DNA dimerizers that stabilize complexes at composite DNA binding sites. Here we investigate the role of the linker that connects the polyamide to the peptide. We find that a substantial degree of variability in the linker length is tolerated at lower temperatures. At physiological temperatures, the longest linker tested confers a "switch"-like property on the protein-DNA dimerizer, in that it abolishes the ability of the YPWM moiety to recruit the natural transcription factor to DNA. These observations provide design principles for future artificial transcription factors that can be externally regulated and can function in concert with the cellular regulatory circuitry

International Association for the Study of Lung Cancer Study of the Impact of Coronavirus Disease 2019 on International Lung Cancer Clinical Trials

Introduction: To evaluate the effects of the global coronavirus disease 2019 (COVID-19) pandemic on lung cancer trials, we surveyed investigators and collected aggregate enrollment data for lung cancer trials across the world before and during the pandemic. Methods: A Data Collection Survey collected aggregate monthly enrollment numbers from 294 global lung cancer trials for 2019 to 2020. A 64-question Action Survey evaluated the impact of COVID-19 on clinical trials and identified mitigation strategies implemented. Results: Clinical trial enrollment declined from 2019 to 2020 by 14% globally. Most reductions in enrollment occurred in April to June where we found significant decreases in individual site enrollment (p = 0.0309). Enrollment was not significantly different in October 2019 to December of 2019 versus 2020 (p = 0.25). The most frequent challenges identified by the Action Survey (N = 172) were fewer eligible patients (63%), decrease in protocol compliance (56%), and suspension of trials (54%). Patient-specific challenges included access to trial site (49%), ability to travel (54%), and willingness to visit the site (59%). The most frequent mitigation strategies included modified monitoring requirements (47%), telehealth visits (45%), modified required visits (25%), mail-order medications (25%), and laboratory (27%) and radiology (21%) tests at nonstudy facilities. Sites that felt the most effective mitigation strategies were telehealth visits (85%), remote patient-reported symptom collection (85%), off-site procedures (85%), and remote consenting (89%). Conclusions: The COVID-19 pandemic created many challenges for lung cancer clinical trials conduct and enrollment. Mitigation strategies were used and, although the pandemic worsened, trial enrollment improved. A more flexible approach may improve enrollment and access to clinical trials, even beyond the pandemic

Can we integrate ecological approaches to improve plant selection for green infrastructure?

Modern cities are dominated by impervious surfaces that absorb, store and release heat in summer, create large volumes of runoff and provide limited biodiversity habitat and poor air quality can also be a health issue. Future climate change, including more frequent and extreme weather events will likely exacerbate these issues. Green infrastructure such as parks, gardens, street trees and engineered technologies such as green roofs and walls, facades and raingardens can help mitigate these problems. This relies on selecting plants that can persist in urban environments and improve stormwater retention, cooling, biodiversity and air pollution. However, plant selection for green infrastructure is challenging where there is limited information on species tolerance to heat and water variability or how these species can deliver multiple benefits. Therefore, we draw on research to illustrate how plant performance for green infrastructure can be inferred from plant attributes (i.e., traits) or from analysis of their natural distribution. We present a new framework for plant selection for green infrastructure and use a case study to demonstrate how this approach has been used to select trees and shrubs for Australian cities. We have shown through the case study and examples, how plant traits and species’ natural distribution can be used to overcome the lack of information on tolerance to both individual and multiple stressors; and how species contribute to the provision of benefits such as stormwater retention, cooling, biodiversity and air pollution mitigation. We also discuss how planting design and species diversity can contribute to achieving multiple benefits to make the most of contested space in dense cities, and to also reduce the risk of failure in urban greening

AusTraits: a curated plant trait database for the Australian flora

INTRODUCTION AusTraits is a transformative database, containing measurements on the traits of Australia’s plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 250 distinct sources, describing > 400 plant traits and > 26,000 taxa. To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project’s GitHub repository, http://traitecoevo.github.io/austraits.build/. Further information on the project is available in the associated publication and at the project website austraits.org. Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 CONTRIBUTORS The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University. We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment, Land, Water and Planning, Victoria. AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their “Transformative data collections” (https://doi.org/10.47486/TD044) and “Data Partnerships” (https://doi.org/10.47486/DP720) programs; fellowship grants from Australian Research Council to Falster (FT160100113), Gallagher (DE170100208) and Wright (FT100100910), a grant from Macquarie University to Gallagher. The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS). ACCESSING AND USE OF DATA The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community. A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors: Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 In addition, we encourage users you to cite the original data sources, wherever possible. Note that under the license data may be redistributed, provided the attribution is maintained. The downloads below provide the data in two formats: austraits-3.0.2.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python. austraits-3.0.2.rds: data as compressed R object. Suitable for users of R (see below). Both objects contain all the data and relevant meta-data. AUSTRAITS R PACKAGE For R users, access and manipulation of data is assisted with the austraits R package. The package can both download data and provides examples and functions for running queries. STRUCTURE OF AUSTRAITS The compiled AusTraits database has the following main components: austraits ├── traits ├── sites ├── contexts ├── methods ├── excluded_data ├── taxanomic_updates ├── taxa ├── definitions ├── contributors ├── sources └── build_info These elements include all the data and contextual information submitted with each contributed datasets. A schema and definitions for the database are given in the file/component definitions, available within the download. The file dictionary.html provides the same information in textual format. Full details on each of these components and columns are contained within the definition. Similar information is available at http://traitecoevo.github.io/austraits.build/articles/Trait_definitions.html and http://traitecoevo.github.io/austraits.build/articles/austraits_database_structure.html. CONTRIBUTING We envision AusTraits as an on-going collaborative community resource that: Increases our collective understanding the Australian flora; and Facilitates accumulation and sharing of trait data; Builds a sense of community among contributors and users; and Aspires to fully transparent and reproducible research of the highest standard. As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at traitecoevo/austraits.build. Here are some of the ways you can contribute: Reporting Errors: If you notice a possible error in AusTraits, please post an issue on GitHub. Refining documentation: We welcome additions and edits that make using the existing data or adding new data easier for the community. Contributing new data: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at http://traitecoevo.github.io/austraits.build/articles/contributing_data.html