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The Prairie region of Canada is an agriculture-intensive area and is strategic to Canada's food security. Moisture deficit due to extreme temperatures leading to high evapotranspiration can have significant impacts on water availability, resulting in poor crop yield. The primary objective of this study is to quantify the spatial structure and dependency of extreme temperatures by employing Max-Stable Process (MSP) modeling on daily annual maximum temperature data spanning 1970–2020. The spatial trend surface of the marginal parameters of the Spatial Generalized Extreme Value (SGEV) shows that geographical coordinates, topography due to the Rocky Mountains, and proximity (Euclidean distance) to Hudson Bay are important covariates in capturing the spatial trend of extreme temperatures. Furthermore, through the SGEV, important products such as the point-wise return periods and levels were derived using the best selected model as determined by Takeuchi's information criteria. The return levels show that the southern portion of the Canadian Prairies shows a consistent increase in extreme temperature for all the return periods. The results show that all the return periods mentioned above have extreme temperatures exceeding 37 °C in the southern portion of the Canadian Prairies. An unconditional simulation using the fitted MSP model provided various realizations of temperature extremes. The results from this study provide important insights into extreme temperatures in this important region, where water resources management is crucial. This study will be beneficial to the hydrologists, water resource specialists, climate change scientists and policy makers involved in the monitoring of extreme events in the Canadian Prairies.

Summary: Bromodomain-containing protein 8 (BRD8) is a subunit of the NuA4/TIP60-histone acetyltransferase complex. Although BRD8 has been considered to act as a co-activator of the complex, its biological role remains to be elucidated. Here, we uncovered that BRD8 accumulates in colorectal cancer cells through the inhibition of ubiquitin-dependent protein degradation by the interaction with MRG domain binding protein. Transcriptome analysis coupled with genome-wide mapping of BRD8-binding sites disclosed that BRD8 transactivates a set of genes independently of TIP60, and that BRD8 regulates the expression of multiple subunits of the pre-replicative complex in concert with the activator protein-1. Depletion of BRD8 induced cell-cycle arrest at the G1 phase and suppressed cell proliferation. We have also shown that the bromodomain of BRD8 is indispensable for not only the interaction with histone H4 or transcriptional regulation but also its own protein stability. These findings highlight the importance of bromodomain as a therapeutic target.

Many real-world systems are profitably described as complex networks that grow over time. Preferential attachment and node fitness are two simple growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are statistical packages for estimating various parametric forms of the preferential attachment function, there is no such package implementing non-parametric estimation procedures. The non-parametric approach to the estimation of the preferential attachment function allows for comparatively finer-grained investigations of the "rich-get-richer" phenomenon that could lead to novel insights in the search to explain certain nonstandard structural properties observed in real-world networks. This paper introduces the R package PAFit, which implements non-parametric procedures for estimating the preferential attachment function and node fitnesses in a growing network, as well as a number of functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure scalability to large-scale networks. In this paper, we first introduce the main functionalities of PAFit through simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks. The results indicate the joint presence of "richget-richer" and "fit-get-richer" phenomena in the collaboration network. The estimated attachment function is observed to be near-linear, which we interpret as meaning that the chance an author gets a new collaborator is proportional to their current number of collaborators. Furthermore, the estimated author fitnesses reveal a host of familiar faces from the complex networks community among the field's topmost fittest network scientists.

Roseburia and Eubacterium species of the human gut microbiota play an important role in the maintaince of human health, partly by producing butyrate, the main energy source of our colonic epithelial cells. However, our knowledge of the biochemistry and physiology of these bacteria has been limited by a lack of genetic manipulation techniques. Conjugative transposons previously introduced into Roseburia species could not be easily modified, greatly limiting their applicability as genetic modification platforms. Modular plasmid shuttle vectors have previously been developed for Clostridium species, which share a taxonomic order with Roseburia and Eubacterium, raising the possibility that these vectors could be used in these organisms. Here, we describe an optimized conjugation protocol enabling the transfer of autonomously replicating plasmids from an E. coli donor strain into Roseburia inulinivorans and Eubacterium rectale. The modular nature of the plasmids and their ability to be maintained in the recipient bacterium by autonomous replication makes them ideal for investigating heterologous gene expression, and as a platform for other genetic tools including antisense RNA silencing or mobile group II interon gene disruption strategies.

In this paper, we describe how the hypergeometric test can be used to determine whether a given theme of interest occurs in a storyset at a frequency more than would be expected by chance. By a storyset we mean simply a list of stories defined according to a common attribute (e.g., author, movement, period). The test works roughly as follows: Given a background storyset and a sub-storyset of interest, the test determines whether a given theme is over-represented in the sub-storyset, based on comparing the proportions of stories in the sub-storyset and background storyset featuring the theme. A storyset is said to be “enriched” for a theme with respect to a particular background storyset, when the theme is identified as being significantly over-represented by the test. Furthermore, we introduce here a toy dataset consisting of 280 manually themed Star Trek television franchise episodes. As a proof of concept, we use the hypergeometric test to analyze the Star Trek stories for enriched themes. The hypergeometric testing approach to theme enrichment analysis is implemented for the Star Trek thematic dataset in the R package stoRy. A related R Shiny web application can be found at https://github.com/theme-ontology/shiny-apps. RésuméDans cet article, nous décrivons la manière dont le test hypergéométrique peut être employé pour déterminer si un thème d’intérêt donné se produit dans une gamme d’histoires à une fréquence qui est plus élevée que ce à quoi on s’attendrait avec le hasard. Par gamme d’histoires, nous entendons simplement une liste d’histoires regroupées selon un attribut commun (par exemple : auteur, mouvement, période). Le test fonctionne à peu près comme ceci : Après avoir reçu une gamme d’histoires en contexte et une sous-gamme d’histoires d’intérêt, le test détermine si le thème donné est surreprésenté dans la sous-gamme d’histoires, en comparant le taux d’histoires dans la sous-gamme d’histoires et dans la gamme d’histoires en contexte qui présentent le thème en question. Une gamme d’histoires est considérée comme « enrichie » pour un thème par rapport à une gamme d’histoires en contexte particulière lorsque le test identifie le thème comme notablement surreprésenté. Par ailleurs, nous présentons ici une base de données de jouets qui se compose de 280 épisodes de la franchise Star Trek qui ont été manuellement organisés par thème. Comme preuve de concept, nous nous servons du test hypergéométrique pour analyser les histoires de Star Trek avec des thèmes enrichis. La méthode de test hypergéométrique pour l’analyse de thèmes enrichis est réalisée pour la base de données thématique de Star Trek dans le logiciel R stoRy. Une application Web similaire de R Shiny se trouve sur le site-Web suivant : https://github.com/theme-ontology/shiny-apps. Mots-clés: analyse d’enrichissement; test hypergéométrique; analyse de surreprésentation; Star Trek; ontologie de thème

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

Preferential attachment is a stochastic process that has been proposed to explain certain topological features characteristic of complex networks from diverse domains. The systematic investigation of preferential attachment is an important area of research in network science, not only for the theoretical matter of verifying whether this hypothesized process is operative in real-world networks, but also for the practical insights that follow from knowledge of its functional form. Here we describe a maximum likelihood based estimation method for the measurement of preferential attachment in temporal complex networks. We call the method PAFit, and implement it in an R package of the same name. PAFit constitutes an advance over previous methods primarily because we based it on a nonparametric statistical framework that enables attachment kernel estimation free of any assumptions about its functional form. We show this results in PAFit outperforming the popular methods of Jeong and Newman in Monte Carlo simulations. What is more, we found that the application of PAFit to a publically available Flickr social network dataset yielded clear evidence for a deviation of the attachment kernel from the popularly assumed log-linear form. Independent of our main work, we provide a correction to a consequential error in Newman's original method which had evidently gone unnoticed since its publication over a decade ago.

The problem of reconstructing large-scale, gene regulatory networks from gene expression data has garnered considerable attention in bioinformatics over the past decade with the graphical modeling paradigm having emerged as a popular framework for inference. Analysis in a full Bayesian setting is contingent upon the assignment of a so-called structure prior-a probability distribution on networks, encoding a priori biological knowledge either in the form of supplemental data or high-level topological features. A key topological consideration is that a wide range of cellular networks are approximately scale-free, meaning that the fraction, , of nodes in a network with degree is roughly described by a power-law with exponent between and . The standard practice, however, is to utilize a random structure prior, which favors networks with binomially distributed degree distributions. In this paper, we introduce a scale-free structure prior for graphical models based on the formula for the probability of a network under a simple scale-free network model. Unlike the random structure prior, its scale-free counterpart requires a node labeling as a parameter. In order to use this prior for large-scale network inference, we design a novel Metropolis-Hastings sampler for graphical models that includes a node labeling as a state space variable. In a simulation study, we demonstrate that the scale-free structure prior outperforms the random structure prior at recovering scale-free networks while at the same time retains the ability to recover random networks. We then estimate a gene association network from gene expression data taken from a breast cancer tumor study, showing that scale-free structure prior recovers hubs, including the previously unknown hub SLC39A6, which is a zinc transporter that has been implicated with the spread of breast cancer to the lymph nodes. Our analysis of the breast cancer expression data underscores the value of the scale-free structure prior as an instrument to aid in the identification of candidate hub genes with the potential to direct the hypotheses of molecular biologists, and thus drive future experiments.