•We review the potential ecological impact of the fungal disease ash dieback.•953 ash-associated species were identified including 44 obligate species.•Compared to other tree species the ecological ...functioning of ash is very different.•The potential of alternative tree species to replace ash is assessed.•Management that does not remove infected ash is better for biodiversity.
The death of native trees caused by non-native pathogens is a global problem. An assessment of the potential ecological and conservation impacts of any tree disease should identify: (1) ecosystem functions associated with the tree species; (2) which species use the tree and how; (3) the suitability of alternative tree species to replace the threatened tree species; and (4) potential management options to mitigate or reduce the impact of the disease.
We assess the potential ecological impact of Hymenoscyphus pseudoalbidus (ash dieback) on Fraxinus excelsior in the UK. 953 species were identified as associated with F. excelsior trees: 12 birds, 28 mammals, 58 bryophytes, 68 fungi, 239 invertebrates, 548 lichens. Forty-four ‘obligate’ species were identified: 11 fungi, 29 invertebrates and 4 lichens; and 62 ‘highly associated’ species.
Off-setting the loss of ash with ‘alternative tree species’ may be one ‘solution’ to the biodiversity threat. No single alternative tree species can act as host for all ash-associated species but Quercus robur/petraea can host 69%. In an assessment of ecosystem function, when compared to other European deciduous tree species, F. excelsior interacts with the environment in a unique way, particularly in relation to nutrient cycling.
Exploration of different management scenarios in response to ash dieback indicated that management which did not remove infected F. excelsior trees was the best for ‘obligate’ and ‘highly associated’ species.
The results highlight wide-ranging ecological implications of ash dieback of relevance to other invasive pests and pathogens that are threatening the integrity of other tree species and woodland ecosystems.
Many restriction endonucleases are dimers that act symmetrically at palindromic DNA sequences, with each active site cutting one strand. In contrast, FokI acts asymmetrically at a non-palindromic ...sequence, cutting 'top' and 'bottom' strands 9 and 13 nucleotides downstream of the site. FokI is a monomeric protein with one active site and a single monomer covers the entire recognition sequence. To cut both strands, the monomer at the site recruits a second monomer from solution, but it is not yet known which DNA strand is cut by the monomer bound to the site and which by the recruited monomer. In this work, mutants of FokI were used to show that the monomer bound to the site made the distal cut in the bottom strand, whilst the recruited monomer made in parallel the proximal cut in the top strand. Procedures were also established to direct FokI activity, either preferentially to the bottom strand or exclusively to the top strand. The latter extends the range of enzymes for nicking specified strands at specific sequences, and may facilitate further applications of FokI in gene targeting.
Accuracy is an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety (which includes fairness and explainability), security, and ...provenance, are also critical elements to engender consumers' trust in a service. Many industries use transparent, standardized, but often not legally required documents called supplier's declarations of conformity (SDoCs) to describe the lineage of a product along with the safety and performance testing it has undergone. SDoCs may be considered multi-dimensional fact sheets that capture and quantify various aspects of the product and its development to make it worthy of consumers' trust. Inspired by this practice, we propose FactSheets to help increase trust in AI services. We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers. We suggest a comprehensive set of declaration items tailored to AI and provide examples for two fictitious AI services in the appendix of the paper.
Primary graft dysfunction (PGD) is a major cause of early mortality after lung transplant. We aimed to define objective estimates of PGD risk based on readily available clinical variables, using a ...prospective study of 11 centers in the Lung Transplant Outcomes Group (LTOG). Derivation included 1255 subjects from 2002 to 2010; with separate validation in 382 subjects accrued from 2011 to 2012. We used logistic regression to identify predictors of grade 3 PGD at 48/72 h, and decision curve methods to assess impact on clinical decisions. 211/1255 subjects in the derivation and 56/382 subjects in the validation developed PGD. We developed three prediction models, where low‐risk recipients had a normal BMI (18.5–25 kg/m2), chronic obstructive pulmonary disease/cystic fibrosis, and absent or mild pulmonary hypertension (mPAP<40 mmHg). All others were considered higher‐risk. Low‐risk recipients had a predicted PGD risk of 4–7%, and high‐risk a predicted PGD risk of 15–18%. Adding a donor‐smoking lung to a higher‐risk recipient significantly increased PGD risk, although risk did not change in low‐risk recipients. Validation demonstrated that probability estimates were generally accurate and that models worked best at baseline PGD incidences between 5% and 25%. We conclude that valid estimates of PGD risk can be produced using readily available clinical variables.
Using three clinical prediction models based on recipient and donor factors for the development of primary graft dysfunction (PGD) after lung transplantation, the authors demonstrate that elevated mean pulmonary arterial pressures, a diagnosis other than cystic fibrosis or chronic obstructive pulmonary disease, and obesity define high‐risk recipients, and addition of a lung from a smoking donor significantly increases PGD risk in the high‐risk group.
Here we present a phylogeny of beetles (Insecta: Coleoptera) based on DNA sequence data from eight nuclear genes, including six single‐copy nuclear protein‐coding genes, for 367 species representing ...172 of 183 extant families. Our results refine existing knowledge of relationships among major groups of beetles. Strepsiptera was confirmed as sister to Coleoptera and each of the suborders of Coleoptera was recovered as monophyletic. Interrelationships among the suborders, namely Polyphaga (Adephaga (Archostemata, Myxophaga)), in our study differ from previous studies. Adephaga comprised two clades corresponding to Hydradephaga and Geadephaga. The series and superfamilies of Polyphaga were mostly monophyletic. The traditional Cucujoidea were recovered in three distantly related clades. Lymexyloidea was recovered within Tenebrionoidea. Several of the series and superfamilies of Polyphaga received moderate to maximal clade support in most analyses, for example Buprestoidea, Chrysomeloidea, Coccinelloidea, Cucujiformia, Curculionoidea, Dascilloidea, Elateroidea, Histeroidea and Hydrophiloidea. However, many of the relationships within Polyphaga lacked compatible resolution under maximum‐likelihood and Bayesian inference, and/or lacked consistently strong nodal support. Overall, we recovered slightly younger estimated divergence times than previous studies for most groups of beetles. The ordinal split between Coleoptera and Strepsiptera was estimated to have occurred in the Early Permian. Crown Coleoptera appeared in the Late Permian, and only one or two lineages survived the end‐Permian mass extinction, with stem group representatives of all four suborders appearing by the end of the Triassic. The basal split in Polyphaga was estimated to have occurred in the Triassic, with the stem groups of most series and superfamilies originating during the Triassic or Jurassic. Most extant families of beetles were estimated to have Cretaceous origins. Overall, Coleoptera experienced an increase in diversification rate compared to the rest of Neuropteroidea. Furthermore, 10 family‐level clades, all in suborder Polyphaga, were identified as having experienced significant increases in diversification rate. These include most beetle species with phytophagous habits, but also several groups not typically or primarily associated with plants. Most of these groups originated in the Cretaceous, which is also when a majority of the most species‐rich beetle families first appeared. An additional 12 clades showed evidence for significant decreases in diversification rate. These clades are species‐poor in the Modern fauna, but collectively exhibit diverse trophic habits. The apparent success of beetles, as measured by species numbers, may result from their associations with widespread and diverse substrates – especially plants, but also including fungi, wood and leaf litter – but what facilitated these associations in the first place or has allowed these associations to flourish likely varies within and between lineages. Our results provide a uniquely well‐resolved temporal and phylogenetic framework for studying patterns of innovation and diversification in Coleoptera, and a foundation for further sampling and resolution of the beetle tree of life.
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the ...same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, present different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360 (http://aix360.mybluemix.net/), an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics. Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability. For data scientists and other users of the toolkit, we have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. We also discuss enhancements to bring research innovations closer to consumers of explanations, ranging from simplified, more accessible versions of algorithms, to tutorials and an interactive web demo to introduce AI explainability to different audiences and application domains. Together, our toolkit and taxonomy can help identify gaps where more explainability methods are needed and provide a platform to incorporate them as they are developed.
•Habitat within 100m of the nestbox was most influential on breeding success.•Breeding success was related to extensive tree canopy and tall hedgerows.•Tits may be useful indicators of quality for ...specific agri-environmental managements.
This study examined relationships between habitat and breeding success for two common bird species, the great tit Parus major and blue tit Cyanistes caeruleus. The aim was to determine the potential of these species to act as indicators of food resource availability for birds in managed semi-natural habitats on farmland and thus as a measure of the effectiveness of specific management practices under agri-environment schemes (AES). Breeding success was recorded for four years (2007–2010) using 90 nestboxes on arable farmland in central England. Habitat parameters were derived from high spatial resolution airborne Light Detection and Ranging (LiDAR) and hyperspectral data.
Relationships of breeding variables with a range of habitat variables, many of which were influenced by AES management, were evident for both species, despite strong interannual variation in breeding parameters. Relationships were strongest for models using habitat variables within a 100m radius of the nest, compared to values of 50 and 200m. Both species showed significant, positive relationships with the area and proximity of tree canopy and, for great tits especially, with hedgerow height and volume.
Therefore, tits may act as indicators of the quality of local habitat, particularly within-hedge trees and hedgerows, managed under agri-environmental provision, and provide insight into the spatial arrangement of AES options at the field scale.
AbstractRecent studies have suggested that among those receiving seasonal influenza vaccine (SIV), reduced immunogenicity is observed in recently vaccinated (RV; within the past season or 2) persons ...when compared with those not recently vaccinated (NRV). We performed a meta-analysis to assess the effect of recent immunization with SIV on serum H5 hemagglutination inhibition (HAI) antibody responses after influenza A/H5N1 vaccination using data from a series of randomized controlled trials. The primary outcome was seroconversion measured by HAI assays following receipt of 2 doses of H5N1 vaccine. The geometric mean titer (GMT) of serum HAI antibody after vaccination was the secondary outcome. Analyses were performed using propensity score (PS) matching. The PS for each individual in the meta-analysis cohort was calculated using logistic regression and covariates included age, gender, race, antigen dose, adjuvant, statin use and vaccine manufacturer. 2015 subjects enrolled in 7 clinical trials were eligible for inclusion in the meta-analysis cohort; among these, 915 (45%) were RV. 901 RV subjects were matched (1:1) with replacement to a subject who was NRV. Subjects who received SIV within the previous season were significantly less likely to seroconvert following H5N1 vaccination (adjusted odds ratio 0.76; 95%CI 0.60–0.96; p = 0.024), and the GMT was 18% higher among NRV subjects (GM ratio of HAI antibody 1.18; 95%CI 1.04–1.33; p = 0.008). Further work is needed to better define the effects of, and mechanisms contributing to, reduced immune responses to H5N1 vaccine among RV subjects.
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This ...paper introduces a new open source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license {https://github.com/ibm/aif360). The main objectives of this toolkit are to help facilitate the transition of fairness research algorithms to use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms. The package includes a comprehensive set of fairness metrics for datasets and models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. It also includes an interactive Web experience (https://aif360.mybluemix.net) that provides a gentle introduction to the concepts and capabilities for line-of-business users, as well as extensive documentation, usage guidance, and industry-specific tutorials to enable data scientists and practitioners to incorporate the most appropriate tool for their problem into their work products. The architecture of the package has been engineered to conform to a standard paradigm used in data science, thereby further improving usability for practitioners. Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking. A built-in testing infrastructure maintains code quality.
Patients with metastatic adenocarcinoma of unknown origin are a common clinical problem. Knowledge of the primary site is important for their management, but histologically, such tumors appear ...similar. Better diagnostic markers are needed to enable the assignment of metastases to likely sites of origin on pathologic samples.
Expression profiling of 27 candidate markers was done using tissue microarrays and immunohistochemistry. In the first (training) round, we studied 352 primary adenocarcinomas, from seven main sites (breast, colon, lung, ovary, pancreas, prostate and stomach) and their differential diagnoses. Data were analyzed in Microsoft Access and the Rosetta system, and used to develop a classification scheme. In the second (validation) round, we studied 100 primary adenocarcinomas and 30 paired metastases.
In the first round, we generated expression profiles for all 27 candidate markers in each of the seven main primary sites. Data analysis led to a simplified diagnostic panel and decision tree containing 10 markers only: CA125, CDX2, cytokeratins 7 and 20, estrogen receptor, gross cystic disease fluid protein 15, lysozyme, mesothelin, prostate-specific antigen, and thyroid transcription factor 1. Applying the panel and tree to the original data provided correct classification in 88%. The 10 markers and diagnostic algorithm were then tested in a second, independent, set of primary and metastatic tumors and again 88% were correctly classified.
This classification scheme should enable better prediction on biopsy material of the primary site in patients with metastatic adenocarcinoma of unknown origin, leading to improved management and therapy.
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