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This article reports the changes to virus taxonomy approved and ratified by the International Committee on Taxonomy of Viruses (ICTV) in March 2021. The entire ICTV was invited to vote on 290 taxonomic proposals approved by the ICTV Executive Committee at its meeting in October 2020, as well as on the proposed revision of the International Code of Virus Classification and Nomenclature (ICVCN). All proposals and the revision were ratified by an absolute majority of the ICTV members. Of note, ICTV mandated a uniform rule for virus species naming, which will follow the binomial 'genus-species' format with or without Latinized species epithets. The Study Groups are requested to convert all previously established species names to the new format. ICTV has also abolished the notion of a type species, i.e., a species chosen to serve as a name-bearing type of a virus genus. The remit of ICTV has been clarified through an official definition of ‘virus’ and several other types of mobile genetic elements. The ICVCN and ICTV Statutes have been amended to reflect these changes.

This article is a summary of the activities of the ICTV’s Bacterial and Archaeal Viruses Subcommittee for the years 2018 and 2019. Highlights include the creation of a new order, 10 families, 22 subfamilies, 424 genera and 964 species. Some of our concerns about the ICTV’s ability to adjust to and incorporate new DNA- and protein-based taxonomic tools are discussed.

The Executive Committee of the International Committee on Taxonomy of Viruses (ICTV) recognizes the need for a standardized nomenclature for virus species. This article sets out the case for establishing a binomial nomenclature and presents the advantages and disadvantages of different naming formats. The Executive Committee understands that adopting a binomial system would have major practical consequences, and invites comments from the virology community before making any decisions to change the existing nomenclature. The Executive Committee will take account of these comments in deciding whether to approve a standardized binomial system at its next meeting in October 2020. Note that this system would relate only to the formal names of virus species and not to the names of viruses.

The engineering classification of rock masses is the basis of rock engineering design and construction. We propose and apply a quick basic quality (BQ) classification method based on the standard BQ method of China to classify the quality grade of the rock mass around tunnels along the Ningguo-Huangshan Expressway during the construction period. Moreover, the joint continuity and surface roughness of the controlled key joint are added to the classification indices of the quick BQ method to address shortcomings of the standard BQ classification method. Therefore, an improved BQ classification method for rock mass is proposed. According to the BQ method, different personnel might select different values of correction coefficient that result in divergences in the result of rock mass classification. In order to solve this problem, the Genetic algorithm (GA) and support vector classification (SVC) coupling algorithm is introduced into the field of engineering rock mass classification. GA is used to automatically search for the optimal SVC parameters during the training process of samples. By training the classification samples of rock mass around a tunnel using the improved BQ method during the tunnel construction period, an intelligent SVC classification model is constructed with inputs based on eight classification indices and an output of the BQ quality grade. To verify the reliability and accuracy of the model, the SVC model is used to evaluate the quality grade of the rock mass around tunnel in other cross sections of the tunnels along the Ningguo-Huangshan Expressway. Only one section classification result differed from those of the improved BQ method in a total of 20 sections. In contrast, three section classification results based on the BP neural network (BPNN) model were inconsistent with those of the improved BQ method. Therefore, the proposed SVC model displays a higher rate of correct classification relative to that of the BPNN model. Meanwhile, the use of this SVC model can avoid the divergence among different people on the classification result of rock mass around a tunnel, which provides an effective new method for the rapid classification of rock mass around a tunnel during tunnel construction.

In the machine learning literature we can find numerous methods to solve classification problems. We propose two new performance measures to analyze such methods. These measures are defined by using the concept of proportional reduction of classification error with respect to three benchmark classifiers, the random and two intuitive classifiers which are based on how a non-expert person could realize classification simply by applying a frequentist approach. We show that these three simple methods are closely related to different aspects of the entropy of the dataset. Therefore, these measures account somewhat for entropy in the dataset when evaluating the performance of classifiers. This allows us to measure the improvement in the classification results compared to simple methods, and at the same time how entropy affects classification capacity. To illustrate how these new performance measures can be used to analyze classifiers taking into account the entropy of the dataset, we carry out an intensive experiment in which we use the well-known J48 algorithm, and a UCI repository dataset on which we have previously selected a subset of the most relevant attributes. Then we carry out an extensive experiment in which we consider four heuristic classifiers, and 11 datasets.

Rapid changes of marine ecosystems resulting from human activities and climate change, and the subsequent reported rise of infectious diseases in marine mammals, highlight the urgency for timely detection of unusual health events negatively affecting populations. Studies reporting pathological findings in the commonly stranded harbor porpoise ( Phocoena phocoena ) on North Atlantic coastlines are essential to describe new and emerging causes of mortality. However, such studies often cannot be used as long-term health surveillance tools due to analytical limitations. We tested 31 variables gained from stranding-, necropsy-, dietary- and marine debris data from 405 harbor porpoises using applied supervised and unsupervised machine learning techniques to explore and analyze this large dataset. We classified and cross-correlated the variables and characterized the importance of the different variables for accurately predicting cause-of-death categories, to allow trend assessment for good conservation decision. The variable ‘age class’ seemed most influential in determining cause-of-death categories, and it became apparent that juveniles died more often due to acute causes, including bycatch, grey-seal-predation and other trauma, while adults of infectious diseases. Neonates were found in summer, and mostly without prey in their stomach and more often stranded alive. The variables assigned as part of the external examination of carcasses, such as imprints from nets and lesions induced by predators, as well as nutritional condition were most important for predicting cause-of-death categories, with a model prediction accuracy of 75%. Future porpoise monitoring, and in particular the assessment of temporal trends, should predominantly focus on influential variables as determined in this study. Pathogen- and contaminant assessment data was not available for all cases, but would be an important step to further complete the dataset. This could be vital for drawing population-inferences and thus for long-term harbor porpoise population health monitoring as an early warning tool for population change.

In most classification tasks, there are observations that are ambiguous and therefore difficult to correctly label. Set-valued classifiers output sets of plausible labels rather than a single label, thereby giving a more appropriate and informative treatment to the labeling of ambiguous instances. We introduce a framework for multiclass set-valued classification, where the classifiers guarantee user-defined levels of coverage or confidence (the probability that the true label is contained in the set) while minimizing the ambiguity (the expected size of the output). We first derive oracle classifiers assuming the true distribution to be known. We show that the oracle classifiers are obtained from level sets of the functions that define the conditional probability of each class. Then we develop estimators with good asymptotic and finite sample properties. The proposed estimators build on existing single-label classifiers. The optimal classifier can sometimes output the empty set, but we provide two solutions to fix this issue that are suitable for various practical needs. Supplementary materials for this article are available online.

Binary classifiers are routinely evaluated with performance measures such as sensitivity and specificity, and performance is frequently illustrated with Receiver Operating Characteristics (ROC) plots. Alternative measures such as positive predictive value (PPV) and the associated Precision/Recall (PRC) plots are used less frequently. Many bioinformatics studies develop and evaluate classifiers that are to be applied to strongly imbalanced datasets in which the number of negatives outweighs the number of positives significantly. While ROC plots are visually appealing and provide an overview of a classifier's performance across a wide range of specificities, one can ask whether ROC plots could be misleading when applied in imbalanced classification scenarios. We show here that the visual interpretability of ROC plots in the context of imbalanced datasets can be deceptive with respect to conclusions about the reliability of classification performance, owing to an intuitive but wrong interpretation of specificity. PRC plots, on the other hand, can provide the viewer with an accurate prediction of future classification performance due to the fact that they evaluate the fraction of true positives among positive predictions. Our findings have potential implications for the interpretation of a large number of studies that use ROC plots on imbalanced datasets.

Current-day metagenomics analyses increasingly involve de novo taxonomic classification of long DNA sequences and metagenome-assembled genomes. Here, we show that the conventional best-hit approach often leads to classifications that are too specific, especially when the sequences represent novel deep lineages. We present a classification method that integrates multiple signals to classify sequences (Contig Annotation Tool, CAT) and metagenome-assembled genomes (Bin Annotation Tool, BAT). Classifications are automatically made at low taxonomic ranks if closely related organisms are present in the reference database and at higher ranks otherwise. The result is a high classification precision even for sequences from considerably unknown organisms.

Studying and protecting each and every living species on Earth is a major challenge of the 21 st century. Yet, most species remain unknown or unstudied, while others attract most of the public, scientific and government attention. Although known to be detrimental, this taxonomic bias continues to be pervasive in the scientific literature, but is still poorly studied and understood. Here, we used 626 million occurrences from the Global Biodiversity Information Facility (GBIF), the biggest biodiversity data portal, to characterize the taxonomic bias in biodiversity data. We also investigated how societal preferences and taxonomic research relate to biodiversity data gathering. For each species belonging to 24 taxonomic classes, we used the number of publications from Web of Science and the number of web pages from Bing searches to approximate research activity and societal preferences. Our results show that societal preferences, rather than research activity, strongly correlate with taxonomic bias, which lead us to assert that scientists should advertise less charismatic species and develop societal initiatives (e.g. citizen science) that specifically target neglected organisms. Ensuring that biodiversity is representatively sampled while this is still possible is an urgent prerequisite for achieving efficient conservation plans and a global understanding of our surrounding environment.