TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Exchange rate policy among trading partners: Does it pay to be different?

Most models of monetary coordination overlook two important aspects of exchange rate regimes in developing countries: countries generally peg to a single currency, and they may or may not adopt the same exchange rate regime as many of their trading partners, especially during periods of financial instability (such as the 1990s). This paper develops a model in which two trading partners initially peg their currency to that of a “large” country. Then we ask: does it matter if these countries adopt different currency regimes? We show that under certain circumstances the choice of a trading partner to float can impair the economic performance of the economy which maintains a hard peg. In other words, countries that maintain a pegged exchange rate can suffer welfare losses if their trading partners switch to more flexible forms of exchange rates. To test the empirical impact of these “third country” effects, we develop a new index of exchange regime “similarity” across trading partners using a variation of the de jure exchange rate regime derived from the IMF\u27s Annual Report on Exchange Rate Arrangements and Restrictions. Estimates based on panels of 23 and 154 countries show the decision of one\u27s trading partners to adopt “different” (more flexible) regimes imposes a statistically significant cost in terms of slower real growth and higher interest rates. Terms of trade shocks also impact pegged and different economies more, suggesting that flexible rate countries can shift some of the burden of adjustment to less flexible trading partners. The policy implications of these results are straight-forward: when trading partners float, the benefits of a pegged regimes diminish. An example of this phenomenon is Argentina during the late 1990s. Post 1994 both Argentina and Brazil linked their currencies to the dollar. In 1998 Brazil switched to floating rate regime while Argentina ignored the decision of her trading partner at considerable cost in lost output. The empirical results of this paper show that these “third country”, effects are common to other countries as well

Improving SDG Classification Precision Using Combinatorial Fusion

Combinatorial fusion algorithm (CFA) is a machine learning and artificial intelligence (ML/AI) framework for combining multiple scoring systems using the rank-score characteristic (RSC) function and cognitive diversity (CD). When measuring the relevance of a publication or document with respect to the 17 Sustainable Development Goals (SDGs) of the United Nations, a classification scheme is used. However, this classification process is a challenging task due to the overlapping goals and contextual differences of those diverse SDGs. In this paper, we use CFA to combine a topic model classifier (Model A) and a semantic link classifier (Model B) to improve the precision of the classification process. We characterize and analyze each of the individual models using the RSC function and CD between Models A and B. We evaluate the classification results from combining the models using a score combination and a rank combination, when compared to the results obtained from human experts. In summary, we demonstrate that the combination of Models A and B can improve classification precision only if these individual models perform well and are diverse

Epitope spreading toward wild-type melanocyte-lineage antigens rescues suboptimal immune checkpoint blockade responses

Although immune checkpoint inhibitors (ICIs), such as anti-programmed cell death protein-1 (PD-1), can deliver durable antitumor effects, most patients with cancer fail to respond. Recent studies suggest that ICI efficacy correlates with a higher load of tumor-specific neoantigens and development of vitiligo in patients with melanoma. Here, we report that patients with low melanoma neoantigen burdens who responded to ICI had tumors with higher expression of pigmentation-related genes. Moreover, expansion of peripheral blood CD8+ T cell populations specific for melanocyte antigens was observed only in patients who responded to anti-PD-1 therapy, suggesting that ICI can promote breakdown of tolerance toward tumor-lineage self-antigens. In a mouse model of poorly immunogenic melanomas, spreading of epitope recognition toward wild-type melanocyte antigens was associated with markedly improved anti-PD-1 efficacy in two independent approaches: introduction of neoantigens by ultraviolet (UV) B radiation mutagenesis or the therapeutic combination of ablative fractional photothermolysis plus imiquimod. Complete responses against UV mutation-bearing tumors after anti-PD-1 resulted in protection from subsequent engraftment of melanomas lacking any shared neoantigens, as well as pancreatic adenocarcinomas forcibly overexpressing melanocyte-lineage antigens. Our data demonstrate that somatic mutations are sufficient to provoke strong antitumor responses after checkpoint blockade, but long-term responses are not restricted to these putative neoantigens. Epitope spreading toward T cell recognition of wild-type tumor-lineage self-antigens represents a common pathway for successful response to ICI, which can be evoked in neoantigen-deficient tumors by combination therapy with ablative fractional photothermolysis and imiquimod

TRY plant trait database - enhanced coverage and open access

10.1111/gcb.14904GLOBAL CHANGE BIOLOGY261119-18