Seedling Development in Species of \u3ci\u3eChamaesyce\u3c/i\u3e (Euphorbiaceae) with Erect Growth Habits

Seedling development is described for Chamaesyce hirta, C. hypericifolia, and C. mesembrianthemifolia as discerned by light microscopy and scanning electron microscopy. Although these species ultimately develop erect to ascending growth habits, epicotyl development is limited to the production of a single pair ofleaves located immediately superjacent to and decussate with the cotyledons. The shoot system develops from one or more buds located in the axils of the cotyledons. In all respects, seedling ontogeny is very similar to that of previously studied prostrate species of Chamaesyce. Evidence from seedling ontogeny thus contradicts a hypothesis concerning homologies of plant form pertinent to the origin of Chamaesyce from Euphorbia that was first articulated by Roeper in 1824. These results support an alternative hypothesis based on proliferation of branches from the cotyledonary node in hypothetical ancestral elements within Euphorbia where this morphology can be found in perennial hemicryptophytes as well as certain annual species

MissForest - nonparametric missing value imputation for mixed-type data

Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a nonparametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple data sets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in data sets including different types of variables. In our comparative study missForest outperforms other methods of imputation especially in data settings where complex interactions and nonlinear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data.Comment: Submitted to Oxford Journal's Bioinformatics on 3rd of May 201

Graphle: Interactive exploration of large, dense graphs

<p>Abstract</p> <p>Background</p> <p>A wide variety of biological data can be modeled as network structures, including experimental results (e.g. protein-protein interactions), computational predictions (e.g. functional interaction networks), or curated structures (e.g. the Gene Ontology). While several tools exist for visualizing large graphs at a global level or small graphs in detail, previous systems have generally not allowed interactive analysis of dense networks containing thousands of vertices at a level of detail useful for biologists. Investigators often wish to explore specific portions of such networks from a detailed, gene-specific perspective, and balancing this requirement with the networks' large size, complex structure, and rich metadata is a substantial computational challenge.</p> <p>Results</p> <p>Graphle is an online interface to large collections of arbitrary undirected, weighted graphs, each possibly containing tens of thousands of vertices (e.g. genes) and hundreds of millions of edges (e.g. interactions). These are stored on a centralized server and accessed efficiently through an interactive Java applet. The Graphle applet allows a user to examine specific portions of a graph, retrieving the relevant neighborhood around a set of query vertices (genes). This neighborhood can then be refined and modified interactively, and the results can be saved either as publication-quality images or as raw data for further analysis. The Graphle web site currently includes several hundred biological networks representing predicted functional relationships from three heterogeneous data integration systems: <it>S. cerevisiae </it>data from bioPIXIE, <it>E. coli </it>data using MEFIT, and <it>H. sapiens </it>data from HEFalMp.</p> <p>Conclusions</p> <p>Graphle serves as a search and visualization engine for biological networks, which can be managed locally (simplifying collaborative data sharing) and investigated remotely. The Graphle framework is freely downloadable and easily installed on new servers, allowing any lab to quickly set up a Graphle site from which their own biological network data can be shared online.</p

Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions

Finely-tuned enzymatic pathways control cellular processes, and their dysregulation can lead to disease. Creating predictive and interpretable models for these pathways is challenging because of the complexity of the pathways and of the cellular and genomic contexts. Here we introduce Elektrum, a deep learning framework which addresses these challenges with data-driven and biophysically interpretable models for determining the kinetics of biochemical systems. First, it uses in vitro kinetic assays to rapidly hypothesize an ensemble of high-quality Kinetically Interpretable Neural Networks (KINNs) that predict reaction rates. It then employs a novel transfer learning step, where the KINNs are inserted as intermediary layers into deeper convolutional neural networks, fine-tuning the predictions for reaction-dependent in vivo outcomes. Elektrum makes effective use of the limited, but clean in vitro data and the complex, yet plentiful in vivo data that captures cellular context. We apply Elektrum to predict CRISPR-Cas9 off-target editing probabilities and demonstrate that Elektrum achieves state-of-the-art performance, regularizes neural network architectures, and maintains physical interpretability.Comment: 23 pages, 4 figure

Action following the discovery of a global association between the whole genome and adverse event risk in a clinical drug-development programme

Observation of adverse drug reactions during drug development can cause closure of the whole programme. However, if association between the genotype and the risk of an adverse event is discovered, then it might suffice to exclude patients of certain genotypes from future recruitment. Various sequential and non-sequential procedures are available to identify an association between the whole genome, or at least a portion of it, and the incidence of adverse events. In this paper we start with a suspected association between the genotype and the risk of an adverse event and suppose that the genetic subgroups with elevated risk can be identified. Our focus is determination of whether the patients identified as being at risk should be excluded from further studies of the drug. We propose using a utility function to determine the appropriate action, taking into account the relative costs of suffering an adverse reaction and of failing to alleviate the patient's disease. Two illustrative examples are presented, one comparing patients who suffer from an adverse event with contemporary patients who do not, and the other making use of a reference control group. We also illustrate two classification methods, LASSO and CART, for identifying patients at risk, but we stress that any appropriate classification method could be used in conjunction with the proposed utility function. Our emphasis is on determining the action to take rather than on providing definitive evidence of an association

Analysis of trends in the financial sector of the global fuel and energy complex

Understanding the global impact of energy on the economy and the financial sector is crucial for improving their interaction, especially within the fuel and energy complex (FEC). This study aims to identify the primary investment drivers for the financial sector within the FEC. It incorporates opinions, conclusions, and forecasts from leading international organizations that monitor the financial sector and the FEC. Through comprehensive analysis, key investment drivers were identified, including renewable and nuclear energy, risks associated with nuclear energy usage, and the impact of traditional fossil fuel sources. The analysis revealed distinct clusters of investment drivers that shape the future development of the financial sector within the FEC. The study determined the rank, median, and relative ranking of investment attractiveness for each cluster and investment vector. This information is valuable for finance and economics specialists and holds scientific significance for experts studying globalization and energy sector trends. The complex cluster analysis used provides a structured system of potential investment drivers for the development of the financial sector within the FEC. This framework is applicable to related studies relying on expert opinions and forecasts

Конкуренция в сфере налогообложения и формы ее проявления между субъектами Российской Федерации

The article considers competition in taxation as the condition for territories’ development and the forms of its implementation among regions. Studies of the theoretical aspects of competition in taxation emergence allowed concluding that primarily social relations are its basis. The author defines the concept of competition in taxation as the process of competitive privileges regulation while dealing with public law establishments to share the tax base by attracting mobile production factors and other advantages to achieve sustainable competitiveness. The author also adds her own features to the classification of competition in taxation. The application of this classification helps deeper understanding of this phenomenon in its versatility. Considering tax competition among the Russian Federation subjects in finance-budget sphere allowed seeing several stages in the development of competence in taxation among regions from its implementation through violence to the correct application of fiscal policy tools. The research revealed the main prerequisites of the development of regional competition in taxation in Russia, and provided the ways and measures of its regulation among the RF regions by the state. The duality of the implementation of regional taxation competition’s inner potential is demonstrated through the main directions of its ultimate impact via the fiscal and regulation functions. Considering the forms of the implementation of tax competition among the RF regions provided the opportunity to prioritize among the regional taxes, which allow influencing the competitive advantages of the territories in order to attract investors in their regions. The review of the regulations of all regional authorities allowed making a conclusion about the existence of different positions on participation in competition in taxation. The research demonstrated that most efficient and available forms of taxpayer involvement are establishing additional benefits on regional taxes, differentiation of the income tax rate (its regional part), and that most regions using the tools of competition in taxation bet on the increase of investment attractiveness of their territoryHighlights1. Competition in taxation is the process of regulation of competitive privileges in the process of social establishments interaction aimed at the sharing of tax bases at the expense of involving mobile production factors and other advantages in order to achieve and keep sustainable competitiveness2. It is expedient to add two more characteristics — the parameters’ size and the vector of the impact — to the tax competition classification3. The vertical tax competition by offering tax benefits has objective limitations at the present stage in the Russian Federation4. There are various positions among the subjects of the Russian Federation on participating in tax competition, most regions rely horizontal competition in taxation through the means that support investment activity at their territoryFor citation Troyanskaya M. A. Competition in Taxation and the Forms of its Implementation among the Subjects of the Russian Federation. Journal of Tax Reform, 2017, vol. 3, no. 3, pp. 182–198. DOI: http://dx.doi.org/10.15826/jtr.2017.3.3.039  Article info Received October 1, 2017; accepted November 13, 2017 В статье рассматриваются конкуренция в сфере налогообложения как условие развития территорий и формы ее проявления между регионами. Изучение теоретических аспектов возникновения налоговой конкуренции позволило сделать вывод о том, что ее базой в первую очередь являются общественные отношения. Сформулировано авторское определение конкуренции в сфере налогообложения как процесс регулирования конкурентных привилегий при взаимодействии публично-правовых образований за распределение налоговой базы за счет привлечения мобильных факторов производства и других преимуществ с целью достижения и сохранения устойчивой конкурентоспособности. Дополнена авторскими признаками видовая классификация налоговой конкуренции, практическое применение которой способствует глубинному пониманию данного явления во всей его многогранности. Рассмотрение содержания налоговой конкуренции субъектов РФ в сфере финансово-бюджетных отношений дало возможность определить, что налоговая конкуренция между регионами прошла в своем развитии несколько этапов — от реализации методом физического насилия до грамотного использования инструментов фискальной политики. В ходе исследования выявлены основные предпосылки развития региональной налоговой конкуренции в России и выделены методы и меры государственного регулирования налоговой конкуренции между субъектами РФ. Показана двойственность реализации внутреннего потенциала региональной конкуренции в сфере налогообложения посредством основных направлений ее конечного действия через фискальную и регулирующую функции. Рассмотрение форм проявления налоговой конкуренции между субъектами РФ дало возможность выделить приоритеты в отношении региональных налогов, которые позволяют оказывать влияние на конкурентные преимущества территорий с целью привлечения инвесторов в свои регионы. На основе обзора нормативно-правовых актов всех региональных органов власти сделан вывод о присутствии у субъектов РФ различных позиций по вопросу участия в конкуренции в сфере налогообложения. В работе показано, что наиболее действенными и доступными для регионов формами привлечения налогоплательщиков на свою территорию являются установление дополнительных льгот по региональным налогам, дифференциация ставки по налогу на прибыль (региональной части), большинство регионов при использовании инструментов налоговой конкуренции делают ставку на повышение инвестиционной привлекательности своей территорииОсновные положения1. Конкуренция в сфере налогообложения это процесс регулирования конкурентных привилегий при взаимодействии публично-правовых образований за распределение налоговой базы за счет привлечения мобильных факторов производства и других преимуществ с целью достижения и сохранения устойчивой конкурентоспособности2. Классификацию налоговой конкуренции следует дополнить  двумя признаками — размер параметров конкуренции  и вектор ее воздействия3. Вертикальная конкуренция  путем предоставление льгот по налогам на современном этапе в Российской Федерации имеет объективные ограничения4. У субъектов Российской Федерации наблюдаются различные тактики участия в налоговой конкуренции, при этом большинство регионов делают ставку на горизонтальную конкуренцию  с помощью мер, поддерживающие инвестиционную деятельность на своей территорииДля цитирования Троянская М. А. Конкуренция в сфере налогообложения и формы ее проявления между субъектами Российской Федерации / М. А. Троянская // Journal of Tax Reform. — 2017. — Т. 3, № 3. — С. 182–198. — DOI: http://dx.doi.org/10.15826/jtr.2017.3.3.039  Информация о статье Дата поступления 1 октября 2017 г.; дата принятия к печати 13 ноября 2017 г