Visualizations with statistical details: The 'ggstatsplot' approach

Graphical displays can reveal problems in a statistical model that might not be apparent from purely numerical summaries. Such visualizations can also be helpful for the reader to evaluate the validity of a model if it is reported in a scholarly publication/report. But, given the onerous costs involved, researchers can avoid preparing information-rich graphics and exploring several statistical approaches/tests available. The `ggstatsplot` package in R programming language [@base2021] provides a one-line syntax to enrich `ggplot2`-based visualizations with the results from statistical analysis embedded in the visualization itself. In doing so, the package helps researchers adopt a rigorous, reliable, and robust data exploratory and reporting workflow

Pharmacovigilance

A report on the ‘National Symposium on Pharmacovigilance – 2011’ held at KLE University’s JN Medical College, Belgaum on 12th April 201

Reputational and cooperative benefits of third-party compensation

Although third-party punishment helps sustain group cooperation, might victim compensation provide third-parties with superior reputational benefits? Across 24 studies (N = 21,296), we provide a comprehensive examination of the consequences of the choice between punishment and compensation. What do people infer from, and how do they respond to, the choice of punishment versus compensation? Across various contexts ranging from economic games, to workplace injustice, to people’s own personal experience of witnessing third-party responses in their daily lives, we find that compensating victims leads to greater reputational and partner choice benefits relative to punishing perpetrators. In fact, even people who themselves prefer to punish still prefer social partners who compensate. We also find that the signal that is sent via third-party compensating is an honest signal—people who choose to compensate rather than punish score lower on measures of trait Machiavellianism, narcissism, and psychopathy. Furthermore, we find that the personal decision of whether to compensate or punish is influenced by both injunctive and descriptive norms. These findings provide an extensive analysis of the causes and consequences of third-party responding to moral violations

Local predictability in a simple model of atmospheric balance

International audienceThe 2 degree-of-freedom elastic pendulum equations can be considered as the lowest order analogue of interacting low-frequency (slow) Rossby-Haurwitz and high-frequency (fast) gravity waves in the atmosphere. The strength of the coupling between the low and the high frequency waves is controlled by a single coupling parameter, e, defined by the ratio of the fast and slow characteristic time scales. In this paper, efficient, high accuracy, and symplectic structure preserving numerical solutions are designed for the elastic pendulum equation in order to study the role balanced dynamics play in local predictability. To quantify changes in the local predictability, two measures are considered: the local Lyapunov number and the leading singular value of the tangent linear map. It is shown, both based on theoretical considerations and numerical experiments, that there exist regions of the phase space where the local Lyapunov number indicates exceptionally high predictability, while the dominant singular value indicates exceptionally low predictability. It is also demonstrated that the local Lyapunov number has a tendency to choose instabilities associated with balanced motions, while the dominant singular value favors instabilities related to highly unbalanced motions. The implications of these findings for atmospheric dynamics are also discussed

Heuristics for simulated annealing search of active sub-networks in bio-molecular interaction networks

Different kinds of ‘omics’ data for several organisms and bio-molecular interaction networks (e.g. reconstructed networks of biochemical reactions and protein-protein physical interactions) are becoming very common nowadays. These bio-molecular networks are being used as a platform to integrate genome-scale ‘omics’ datasets. Identification of sub-networks in these large networks that show maximum collective response to a perturbation is one the interesting problems to solve by using an integrative analysis. Sub-networks can be hypothesized to represent significant collective biological activity due to the underlying interactions between the bio-molecules. The biological activity can be estimated in several ways- for example coordinated change in the expression level (e.g. mRNA). Identifying these regions reduce complexity of the network to be analyzed in greater detail by revealing the regions that are perturbed by a conditionremoving the interactions that are potentially false-positive and not related to the response under study. As the simulated annealing does not guarantee to find the global optimum and may lead to an incomplete picture of the biological phenomenon, we report a method to estimate the theoretical optimal score curve. The simulated annealing algorithm (SA) used in this study is a slightly modified version of the algorithm by Ideker et al.. Each node in the graph is associated with a binary variable turning the node visible or invisible and therefore inducing several sub-graphs. In the standard formulation, the initial solution is obtained by randomly attributing 0 or 1 to the nodes of the graph. Based in concepts described above, we propose an alternative initialization method to improve the performance of the simulated annealing algorithm.Systems Biology as a Driver for Industrial Biotechnology (SYSINBIO

Towards a biologically relevant description of phenotypes based on pathway analysis

In metabolic systems, the cellular network of reactions together with constraints on reversibility of enzymes determine the space of all possible steady-state phenotypes. In actuality, the cell does not invoke the large majority of those in given conditions. We propose a method in two steps to obtain a more precise description of cellular phenotypes through pathway analysis. The first step is based on a modified version of the concept of control effective flux (CEF) [1] and only requires the stoichiometric network. The second step is based on thermodynamic feasibility of reactions and requires measurements of concentrations and thermodynamic properties of the metabolites. CEFs represent the importance of each reaction for efficient and flexible operation of the entire metabolic network. We modified the concept to take into account the reaction directionality within the modes by splitting up the reversible reactions. We observed that directionality of the largest CEF -forward reaction at least two times larger than backward or vice versa- matches well with the measured reaction directions for growth on glucose, glycerol, and acetate as the sole carbon source. We also found that the modified CEFs are good predictors of intra-cellular fluxes for the central carbon metabolism of Escherichia coli and Sacharomyces cerevisiae. The proposed method allows a reduction of up to 51% out of 2706 modes for E. coli and up to 81% out of 191,083 modes for S. cerevisiae, so that only pathways are contained that carry flux matching the measured directions. An alternative reduction can be obtained by assigning reaction directionalities on thermodynamic grounds using anNET [2] and removing the pathways that contain infeasible reactions. The feasibility of the remaining pathways was checked by taking into account irreversibility of the pathways. Depending on the available measurements and its uncertainties, a reduction of up to 31% in the computed pathways was obtained for particular conditions, though no further reduction compared to the CEFs method. Overall, the largest reduction in the number of pathways was obtained using the stiochiometric network as the only input, thus without the requirement for measurements, towards a biologically relevant description of phenotypes

Integration of biomass functions of genome-scale metabolic models with experimental data reveals universally essential cofactors in prokaryotes

Knowledge of the core biochemical composition of the cell is critical for genome-scale metabolic modelling. In order to identify the universal core organic cofactors for prokaryotes, we performed a detailed analysis of biomass objective functions (BOFs) of 71 manually curated genome-scale prokaryotic models. These were then compared and integrated with the ModelSEED framework for biomass composition, experimental data on gene essentiality, curated enzyme-cofactor association data and a comprehensive survey of the literature. Surprisingly, no cofactor was present in all the BOFs analysed, including the important redox cofactor nicotinamide adenine dinucleotide (NAD) or its derivatives. Our results indicate not only the redox cofactors but also others such as coenzyme A, flavins and thiamin as universally essential for prokaryotes and therefore as important to include in the BOFs of future genome-scale models of prokaryotic organisms