Today it is common to apply multiple potentially conflicting data sources to a given phylogenetic problem. At the same time, several different inference techniques are routinely employed instead of ...relying on just one. In view of both trends it is becoming increasingly important to be able to efficiently compare different sets of statistical values supporting (or conflicting with) the nodes of a given tree topology, and merging this into a meaningful representation. A tree editor supporting this should also allow for flexible editing operations and be able to produce ready-to-publish figures.
We developed TreeGraph 2, a GUI-based graphical editor for phylogenetic trees (available from http://treegraph.bioinfweb.info). It allows automatically combining information from different phylogenetic analyses of a given dataset (or from different subsets of the dataset), and helps to identify and graphically present incongruences. The program features versatile editing and formatting options, such as automatically setting line widths or colors according to the value of any of the unlimited number of variables that can be assigned to each node or branch. These node/branch data can be imported from spread sheets or other trees, be calculated from each other by specified mathematical expressions, filtered, copied from and to other internal variables, be kept invisible or set visible and then be freely formatted (individually or across the whole tree). Beyond typical editing operations such as tree rerooting and ladderizing or moving and collapsing of nodes, whole clades can be copied from other files and be inserted (along with all node/branch data and legends), but can also be manually added and, thus, whole trees can quickly be manually constructed de novo. TreeGraph 2 outputs various graphic formats such as SVG, PDF, or PNG, useful for tree figures in both publications and presentations.
TreeGraph 2 is a user-friendly, fully documented application to produce ready-to-publish trees. It can display any number of annotations in several ways, and permits easily importing and combining them. Additionally, a great number of editing- and formatting-operations is available.
Today a variety of phylogenetic file formats exists, some of which are well-established but limited in their data model, while other more recently introduced ones offer advanced features for metadata ...representation. Although most currently available software only supports the classical formats with a limited metadata model, it would be desirable to have support for the more advanced formats. This is necessary for users to produce richly annotated data that can be efficiently reused and make underlying workflows easily reproducible. A programming library that abstracts over the data and metadata models of the different formats and allows supporting all of them in one step would significantly simplify the development of new and the extension of existing software to address the need for better metadata annotation.
We developed the Java library JPhyloIO, which allows event-based reading and writing of the most common alignment and tree/network formats. It allows full access to all features of the nine currently supported formats. By implementing a single JPhyloIO-based reader and writer, application developers can support all of these formats. Due to the event-based architecture, JPhyloIO can be combined with any application data structure, and is memory efficient for large datasets. JPhyloIO is distributed under LGPL. Detailed documentation and example applications (available on http://bioinfweb.info/JPhyloIO/ ) significantly lower the entry barrier for bioinformaticians who wish to benefit from JPhyloIO's features in their own software.
JPhyloIO enables simplified development of new and extension of existing applications that support various standard formats simultaneously. This has the potential to improve interoperability between phylogenetic software tools and at the same time motivate usage of more recent metadata-rich formats such as NeXML or phyloXML.
Taxon identification is an important step in many plant ecological studies. Its efficiency and reproducibility might greatly benefit from partly automating this task. Image-based identification ...systems exist, but mostly rely on hand-crafted algorithms to extract sets of features chosen a priori to identify species of selected taxa. In consequence, such systems are restricted to these taxa and additionally require involving experts that provide taxonomical knowledge for developing such customized systems. The aim of this study was to develop a deep learning system to learn discriminative features from leaf images along with a classifier for species identification of plants. By comparing our results with customized systems like LeafSnap we can show that learning the features by a convolutional neural network (CNN) can provide better feature representation for leaf images compared to hand-crafted features.
We developed LeafNet, a CNN-based plant identification system. For evaluation, we utilized the publicly available LeafSnap, Flavia and Foliage datasets.
Evaluating the recognition accuracies of LeafNet on the LeafSnap, Flavia and Foliage datasets reveals a better performance of LeafNet compared to hand-crafted customized systems.
Given the overall species diversity of plants, the goal of a complete automatisation of visual plant species identification is unlikely to be met solely by continually gathering assemblies of customized, specialized and hand-crafted (and therefore expensive) identification systems. Deep Learning CNN approaches offer a self-learning state-of-the-art alternative that allows adaption to different taxa just by presenting new training data instead of developing new software systems.
•LeafNet is a plant identification system based on convolutional neural networks.•We used images of leaves to train and evaluate LeafNet on three datasets.•We achieved a better performance compared to hand-crafted customized systems.
We present the model and implementation of a workflow that blazes a trail in systematic biology for the re-usability of character data (data on any kind of characters of pheno- and genotypes of ...organisms) and their additivity from specimen to taxon level. We take into account that any taxon characterization is based on a limited set of sampled individuals and characters, and that consequently any new individual and any new character may affect the recognition of biological entities and/or the subsequent delimitation and characterization of a taxon. Taxon concepts thus frequently change during the knowledge generation process in systematic biology. Structured character data are therefore not only needed for the knowledge generation process but also for easily adapting characterizations of taxa. We aim to facilitate the construction and reproducibility of taxon characterizations from structured character data of changing sample sets by establishing a stable and unambiguous association between each sampled individual and the data processed from it. Our workflow implementation uses the European Distributed Institute of Taxonomy Platform, a comprehensive taxonomic data management and publication environment to: (i) establish a reproducible connection between sampled individuals and all samples derived from them; (ii) stably link sample-based character data with the metadata of the respective samples; (iii) record and store structured specimen-based character data in formats allowing data exchange; (iv) reversibly assign sample metadata and character datasets to taxa in an editable classification and display them and (v) organize data exchange via standard exchange formats and enable the link between the character datasets and samples in research collections, ensuring high visibility and instant re-usability of the data. The workflow implemented will contribute to organizing the interface between phylogenetic analysis and revisionary taxonomic or monographic work.
http://campanula.e-taxonomy.net/.
To determine the frequency of medication use in patients with dystonia enrolled in an international biorepository study.
In a cross-sectional analysis, we included 2,026 participants enrolled at 37 ...sites in the United States, Canada, Europe, and Australia through Project 1 of the Dystonia Coalition, an international biorepository study. The primary aim was to assess the frequency of medication classes recommended for treating patients with dystonia, and the secondary aim was to compare characteristics (disease type, age, sex, duration of disease, comorbid conditions, severity).
Querying the database for the presence of any medication for dystonia used (includes both injectable and oral therapy), we found 73% using medications (n = 1,488) and 27% using no dystonia medications (n = 538). Furthermore, 61% of the total sample used botulinum toxin (BoNT) therapy alone or in combination. Differences were found in medication use patterns by dystonia type, with the lowest oral medication use in focal dystonia and highest use in generalized dystonia; by region, with highest BoNT therapy rate reported in Italy and the lowest in the Northeast region of the United States; and by focal dystonia subtype, with highest BoNT therapy alone in blepharospasm and spasmodic dysphonia (49%) and lowest in other cranial dystonia (32%).
The majority of patients with dystonia enrolled in the Dystonia Coalition Project 1 were using medications to treat their dystonia. Overall, a complex picture of medication use patterns emerged, with factors such as region, disease duration, type of dystonia, disease severity, and psychiatric comorbidities all playing a significant role.
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Background: Symptom management is a cornerstone of quality oncology practice. ASCO established a Working Group to develop patient-reported outcome performance measures (PRO-PMs) for ...assessing symptom management. We describe multi-center testing funded by PCORI. Methods: Multi-stakeholder consensus and literature review identified 11 symptoms for testing as potential PRO-PMs. For these symptoms, questions from the NCI’s PRO-CTCAE tool were administered at 6 US academic and community oncology practices. Patients across cancer types completed questions electronically on days 5-15 of chemotherapy cycles. PRO-CTCAE mapped scores were dichotomized to delineate clinically meaningful thresholds (0-1 vs ≥2), and rates were tabulated between practices. Symptoms were selected to become PRO-PMs if clinically actionable and with prevalence ≥20%; between-practice variation was evaluated using χ
2
. Twelve candidate sociodemographic and clinical risk adjustment (RA) variables were evaluated via Akaike information criterion testing. Risk-adjusted PRO-PM rates were calculated using observed:expected ratios via generalized linear mixed modeling. Results: Among 653 enrolled patients, 607 (93%) completed questionnaires. Four of 11 symptoms met criteria for PRO-PM development: nausea, constipation, insomnia, pain. Four RA variables met inclusion criteria: age, gender, cancer type, insurance type. The Table shows raw and risk-adjusted rates of symptom burden (scores ≥2) for each PRO-PM across practices. Risk-adjustment yielded a modest impact on scores. Conclusions: Oncology PRO-PMs have been developed to quantify the burden of actionable symptoms at the practice level. Collection from patients is feasible. Further refinement is underway prior to submission for endorsement by the National Quality Forum.
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