Phylogenetic classification and the definition of taxon names

Taxon names should be founded on phylogenetic relationships. and the names defined on the basis of common ancestry. Definitions based on evolutionary relationships relate the names to a phylogeny, and while the inclusiveness of the name may change with changing hypotheses of monophyly, the actual name remains unaltered. The limits of the name arc fixed by pointing to a monophyletic clade, where group membership is determined by the relationship to this clade. and not to subjective decisions of taxon delineations. Since phylogenetic definitions unambiguously connect the name to a specified clade, and not to a type, the conventional type concept becomes superfluous. We furthermore consider the Linnean categories poorly suited to convey the information in evolutionary trees, and suggest that these categories are abandoned.     

Brassicaceae: Species checklist and database on CD-Rom

A species checklist has been prepared for the Brassicaceae (Cruciferae) family, providing the first updated list in over 70 years. The family, currently, includes 338 genera and 3709 species. The database contains approximately 14,000 taxonomic names (records). Taxon status and synonymy, taxon name, scientific authority, literature source and source verification, and the basionym are provided for each record. Electronic supplementary material to this article is available at and is accessible for authorized users.     

Sequencing of Australian wild rice genomes reveals ancestral relationships with domesticated rice

The related A genome species of the Oryza genus are the effective gene pool for rice. Here, we report draft genomes for two Australian wild A genome taxa: O. rufipogon‐like population, referred to as Taxon A, and O. meridionalis‐like population, referred to as Taxon B. These two taxa were sequenced and assembled by integration of short‐ and long‐read next‐generation sequencing (NGS) data to create a genomic platform for a wider rice gene pool. Here, we report that, despite the distinct chloroplast genome, the nuclear genome of the Australian Taxon A has a sequence that is much closer to that of domesticated rice (O. sativa) than to the other Australian wild populations. Analysis of 4643 genes in the A genome clade showed that the Australian annual, O. meridionalis, and related perennial taxa have the most divergent (around 3 million years) genome sequences relative to domesticated rice. A test for admixture showed possible introgression into the Australian Taxon A (diverged around 1.6 million years ago) especially from the wild indica/O. nivara clade in Asia. These results demonstrate that northern Australia may be the centre of diversity of the A genome Oryza and suggest the possibility that this might also be the centre of origin of this group and represent an important resource for rice improvement.     

Propositions for a character-state-based biological taxonomy

A new procedure for defining taxa upon a single character state is developed. It is centred on the designation of two specimens, belonging to two distinct species, exhibiting the same given character state, as type material, and referred to as ‘cladotypes’. A taxon name/definition designates a monophyletic group until one of the following assumptions is falsified: (1) the character state typified by cladotypes is homologous in individuals that are designated as cladotypes, and (2) cohesion mechanisms isolated individuals exhibiting the type‐character‐state from those that do not. A taxon defined by a character state that is found to be a combination of several character states is to be redefined upon a character state shared by its cladotypes. If several character states are available, the character state that makes the taxon the least inclusive taxon including cladotypic species (i.e., species to which belong cladotypes) is to be preferred. Taxon names designate obsolete phylogenetic hypotheses if the first assumption is falsified (such names are to be kept for this purpose, i.e., they are not to be recycled in another definition). Rules governing adaptation of previously erected names are proposed. Main cases of taxa synonymy involve definitions based on different pairs of cladotypes but referring to the same type‐character‐state; and definitions based on the same character‐state initially hypothesised as acquired by convergence in cladotypic species pairs, but later demonstrated as originating from a unique ancestor. Taxa could be synonyms if a permanent splitting event did not segregate individuals exhibiting a new character state, qualified as type‐character‐state, from individuals already assigned to a previously erected taxon. This procedure accommodates potentially any species concept, but is not tied to any; it is an extension of the composite species concept. Species are treated in a different way than other taxa: they are defined as sets of individuals belonging to the same evolving (segments of) metapopulation lineages as a holotype specimen, and do not need a defining character state.     

Apomorphy-based definition also pinpoints a node, and PhyloCode names prevent effective communication

Acceptable methods of defining taxon (or clade) names in the draft PhyloCode, or so-called phylogenetic nomenclature, are “node based,” “stem based,” and “apomorphy based.” All of them define a clade name by pinpointing a node; whereas node-based and stem-based definitions require two or more taxon “specifiers” to define names, an apomorphy-based definition requires two specifiers of different types; namely, a single-taxon specifier and a character specifier. The taxon specifier in an apomorphy-based definition is completely different from the “type” in the Linnaean system. Taxon (or clade) names in the PhyloCode are characterized in two entirely different manners: One is a name that does not change, either in its orthography or in the contents of the taxon referred to by it (or its meaning) over time; the other is a name that is just like a pure mark and thus has no meaning. Communication through such PhyloCode names is very ineffective or impossible.     

A nomenclature and data model to describe NMR experiments

Despite ongoing efforts in organising NMR information, there is no consistent and well-described generic standard for naming NMR experiments. The main reason for the absence of a universal naming system is that the information content of the coherence pathways is difficult to describe in full detail. We propose a system that describes the common and generic elements of the coherence pathways produced by pulse sequences. The system itself is formalised by an ‘NMR experiment protocol’ model, which is described in the Universal Modelling Language (UML) as part of the CCPN data model. Furthermore, normalized experiment names can be derived from this proposed model. We hope this article will stimulate discussion to organise the wealth of NMR experiments, and that by bringing this discussion into the public domain we can improve and expand our proposed system to include as much information and as many NMR experiments as possible. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.Joint first authors     

The family name as socio-cultural feature and genetic metaphor: from concepts to methods

A recent workshop entitled "The Family Name as Socio-Cultural Feature and Genetic Metaphor: From Concepts to Methods" was held in Paris in December 2010, sponsored by the French National Centre for Scientific Research (CNRS) and by the journal Human Biology. This workshop was intended to foster a debate on questions related to the family names and to compare different multidisciplinary approaches involving geneticists, historians, geographers, sociologists and social anthropologists. This collective paper presents a collection of selected communications.     

在墨尔本召开的第18届国际植物学大会做出的有关名称发表要求的变化——电子出版物对你意味着什么？

《国际植物命名法规》的修订由每六年一次的国际植物学大会（IBC）命名分会来决定。第18届国际植物学大会在澳大利亚墨尔本举行;命名分会于2011年7月18日至22日召开,其决议获得7月30日的全体会议通过。“墨尔本法规”有几个重要的变化,将影响新名称的发表。这些变化中的两个将在“墨尔本法规”出版前的几个月,即于2012年1月1日起生效。通过以移动文档格式（Portable Document Format;pdf）在线发表的具有国际标准连续出版物号（ISSN）或国际标准图书编号（ISBN）的电子出版物,将构成有效发表。新分类群名称的合格发表所必须的拉丁文描述或特征集要将更改为拉丁文或英文描述或特征集要。此外,自2013年1月1日起,被处理为真菌的生物的新名称必须在原始资料（某一名称合格发表时与之有关的所有资料）中引证一个由一家公认的存储库（例如MycoBank）签发的标识码,才构成合格发表。本文提供了有关电子出版物的新规则的草案文本,并概述了相应的最佳做法。     

Self-organizing system obtaining communication ability

As one of the synthetic approaches to brain functions, the possibility is discussed that two intellectual robots could make words for themselves and come to communicate with each other. Associatron, a model for associative memory with a neural network structure, is used as the memory in the brain, and information from the outer world is accumulated in it. Concepts regarding objects and attributes are extracted from the stored information, where the Associatron properties are utilized. The robots give specific names to the concepts and inter-change them with each other. When the robots have the same experience at the same time, they modify their own words according to what the other robot says. As they repeat common experiences, the differences between the words become smaller and smaller, and finally the robots agree on a word for each object. At this stage, the robots can exchange information with their words and each can act accordingly on the word from the other. This study shows that the self-organizing system with the above function can be constructed as a neural network model.     

Xylella fastidiosa subspecies: X. fastidiosa subsp. [correction] fastidiosa [correction] subsp. nov., X. fastidiosa subsp. multiplex subsp. nov., and X. fastidiosa subsp. pauca subsp. nov

Xylella fastidiosa, a fastidious bacterium causing disease in over 100 plant species, is classified as a single species, although genetic studies support multiple taxons. To determine the taxonomic relatedness among strains of X. fastidiosa, we conducted DNA-DNA relatedness assays and sequenced the 16S-23S intergenic spacer (ITS) region using 26 strains from 10 hosts. Under stringent conditions (Tm -15 degrees C), the DNA relatedness for most X. fastidiosa strains was *70%. However, at high stringency (Tm -8 degrees C), three distinct genotypes (A, B, and C) were revealed. Taxon A included strains from cultivated grape, alfalfa, almond (two), and maple, interrelated by 85% (mean); taxon B included strains from peach, elm, plum, pigeon grape, sycamore, and almond (one), interrelated by 84%; and taxon C included only strains from citrus, interrelated by 87%. The mean reciprocal relatedness between taxons A and B, A and C, and B and C, were 58, 41, and 45%, respectively. ITS results also indicated the same grouping; taxons A and B, A and C, and B and C had identities of 98.7, 97.9, and 99.2%, respectively. Previous and present phenotypic data supports the molecular data. Taxon A strains grow faster on Pierce's disease agar medium whereas B and C strains grow more slowly. Taxon B and C strains are susceptible to penicillin and resistant to carbenicillin whereas A strains are opposite. Each taxon can be differentiated serologically as well as by structural proteins. We propose taxons A, B, and C be named X. fastidiosa subsp. fastidiosa [correction] subsp. nov, subsp. multiplex, subsp. nov., and subsp. pauca, subsp. nov., respectively. The type strains of the subspecies are subsp. fastidiosa [correction] ICPB 50025 (= ATTC 35879T and ICMP 15197), subsp. multiplex ICPB 50039 (= ATTC 35871 and ICMP 15199), and subsp. pauca ICPB 50031 (= ICMP 15198).     

NetiNeti: discovery of scientific names from text using machine learning methods

ABSTRACT: BACKGROUND: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. RESULTS: We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing), a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE). NetiNeti performs better (precision = 98.9 % and recall = 70.5 %) compared to a popular dictionary based approach (precision = 97.5 % and recall = 54.3 %) on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central's full text articles annotated with scientific names, the precision and recall values are 98.5 % and 96.2 % respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages. Additionally, we present the comparison results of various machine learning algorithms on our annotated corpus. Naive Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms. CONCLUSIONS: We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org.     

A metamemory perspective on odor naming and identification

A metacognitive perspective is utilized to elucidate why it is so difficult to name common odors and what characterizes the subjective knowledge people have about their actual odor knowledge. Odor-naming failures are often accompanied by strong feelings of knowing (FOK) or feelings of imminent retrieval of what it is that smells. The paper's two experiments investigate FOK judgements and tip of the tongue (TOT) experiences for odor and person names. The data indicate that our inability to correctly name odors are typically not due to the often proposed uniquely poor association between odors and their proper names, but rather due to failures to identify the odors, that is, failures to know 'what it is'. It was also found that (i) TOT experiences are very unusual for odor names and more so than for person names; (ii) FOK judgements about odor names are significantly less predictive of later retrieval than equivalent judgements about names of persons; (iii) FOK judgements were highly correlated with the familiarity of the cue (odor or picture of famous person), rendering some support for the idea that FOK judgements are based on the perceived familiarity of the cue triggering the FOK; and (iv) the idea that FOK judgements are based on the amount of available information about the sought-for memory (accessibility theory) was also supported.     

Taxonome: a software package for linking biological species data

Online databases of biological information offer tremendous potential for evolutionary and ecological discoveries, especially if data are combined in novel ways. However, the different names and varied spellings used for many species present major barriers to linking data. Taxonome is a software tool designed to solve this problem by quickly and reproducibly matching biological names to a given reference set. It is available both as a graphical user interface (GUI) for simple interactive use, and as a library for more advanced functionality with programs written in Python. Taxonome also includes functions to standardize distribution information to a well‐defined set of regions, such as the TDWG World Geographical Scheme for Recording Plant Distributions. In combination, these tools will help biologists to rapidly synthesize disparate datasets, and to investigate large‐scale patterns in species traits.     

Automatic extraction of gene/protein biological functions from biomedical text

MOTIVATION: With the rapid advancement of biomedical science and the development of high-throughput analysis methods, the extraction of various types of information from biomedical text has become critical. Since automatic functional annotations of genes are quite useful for interpreting large amounts of high-throughput data efficiently, the demand for automatic extraction of information related to gene functions from text has been increasing. RESULTS: We have developed a method for automatically extracting the biological process functions of genes/protein/families based on Gene Ontology (GO) from text using a shallow parser and sentence structure analysis techniques. When the gene/protein/family names and their functions are described in ACTOR (doer of action) and OBJECT (receiver of action) relationships, the corresponding GO-IDs are assigned to the genes/proteins/families. The gene/protein/family names are recognized using the gene/protein/family name dictionaries developed by our group. To achieve wide recognition of the gene/protein/family functions, we semi-automatically gather functional terms based on GO using co-occurrence, collocation similarities and rule-based techniques. A preliminary experiment demonstrated that our method has an estimated recall of 54-64% with a precision of 91-94% for actually described functions in abstracts. When applied to the PUBMED, it extracted over 190 000 gene-GO relationships and 150 000 family-GO relationships for major eukaryotes.