Diet-induced changes in titer support a discrete response of Wolbachia-associated plastic recombination in Drosophila melanogaster

Plastic recombination in Drosophila melanogaster has been associated with a variety of extrinsic and intrinsic factors such as temperature, starvation, and parasite infection. The bacterial endosymbiont Wolbachia pipientis has also been associated with plastic recombination in D. melanogaster. Wolbachia infection is pervasive in arthropods and this infection induces a variety of phenotypes in its hosts, the strength of which can depend on bacterial titer. Here we test the hypothesis that the magnitude of Wolbachia-associated plastic recombination in D. melanogaster depends on titer. To manipulate titer, we raised Wolbachia-infected and uninfected flies on diets that have previously been shown to increase or decrease Wolbachia titer relative to controls. We measured recombination in treated and control individuals using a standard backcrossing scheme with two X-linked visible markers. Our results recapitulate previous findings that Wolbachia infection is associated with increased recombination rate across the yellow-vermillion interval of the X chromosome. Our data show no significant effect of diet or diet by Wolbachia interactions on recombination, suggesting that diet-induced changes in Wolbachia titer have no effect on the magnitude of plastic recombination. These findings represent one of the first steps toward investigating Wolbachia-associated plastic recombination and demonstrate that the phenotype is a discrete response rather than a continuous one.

Differential Expression of Long Non-Coding RNA (lncRNA) in Mediterranean Mussel (Mytilus galloprovincialis) Hemocytes under Immune Stimuli

The Mediterranean mussel is one of the most economically relevant bivalve mollusk species in Europe and China. The absence of massive mortalities and their resistance to pathogens affecting other cultured bivalves has been under study in recent years. The transcriptome response of this species to different immune stimuli has been extensively studied, and even the complexity of its genome, which has recently been sequenced, has been suggested as one of the factors contributing to this resistance. However, studies concerning the non-coding RNA profiles remain practically unexplored—especially those corresponding to the lncRNAs. To the best of our knowledge, this is the second characterization and study of lncRNAs in this bivalve species. In this work, we identified the potential repertoire of lncRNAs expressed in mussel hemocytes, and using RNA-Seq we analyzed the lncRNA profile of mussel hemocytes stimulated in vitro with three different immune stimuli: LPS, poly I:C, and β-glucans. Compared to unstimulated hemocytes, LPS induced the highest modulation of lncRNAs, whereas poly I:C and β-glucans induced a similar discrete response. Based on the potential cis-regulatory activity of the lncRNAs, we identified the neighboring protein-coding genes of the regulated lncRNAs to estimate—at least partially—the processes in which they are implicated. After applying correlation analyses, it seems that—especially for LPS—the lncRNAs could participate in the regulation of gene expression, and substantially contribute to the immune response.

Parametric modeling of quantile regression coefficient functions with count data

Applying quantile regression to count data presents logical and practical complications which are usually solved by artificially smoothing the discrete response variable through jittering. In this paper, we present an alternative approach in which the quantile regression coefficients are modeled by means of (flexible) parametric functions. The proposed method avoids jittering and presents numerous advantages over standard quantile regression in terms of computation, smoothness, efficiency, and ease of interpretation. Estimation is carried out by minimizing a “simultaneous” version of the loss function of ordinary quantile regression. Simulation results show that the described estimators are similar to those obtained with jittering, but are often preferable in terms of bias and efficiency. To exemplify our approach and provide guidelines for model building, we analyze data from the US National Medical Expenditure Survey. All the necessary software is implemented in the existing R package .

THE RELATIONSHIP BETWEEN PSYCHOLOGICAL CHARACTERISTICS AND BURNOUT SYNDROME AMONG DEPARTMENTAL UNIVERSITIES TEACHERS

The article deals with the results of the study on burnout syndrome among teachers with various work experiences in university. The aim of the study is the review of burnout psychological patterns among teachers with various work experiences in university. The main hypothesis of the study is based on the assumption that the teachers whose work experience in departmental universities is more than 10 years are more vulnerable to the burnout syndrome development. In order to attain the envisaged goals and to test the hypothesis there were used empirical methods: differential diagnosis of decreased functioning by A. Leonova and S. Velichkovskaya, diagnosis of professional burnout by K. Maslach and S. Jackson adapted by N.E. Vodop’yanova, technique for diagnosing the burnout syndrome level by V.V. Boiko. Group comparison of teachers with various work experiences in university has shown that long term professional activity leads to burnout syndrome development: the most part of examined teachers are characterized by high level of burnout syndrome which structure is observed in high intensity of resistance and exhaustion phases and such syndromes as inadequate emotional discrete response; resignation or depersonalization, emotional and moral disorientation; psychosomatic and vegetative disorders.

Discrete Choice Data with Unobserved Heterogeneity: A Conditional Binary Quantile Model

In political science, data with heterogeneous units are used in many studies, such as those involving legislative proposals in different policy areas, electoral choices by different types of voters, and government formation in varying party systems. To disentangle decision-making mechanisms by units, traditional discrete choice models focus exclusively on the conditional mean and ignore the heterogeneous effects within a population. This paper proposes a conditional binary quantile model that goes beyond this limitation to analyze discrete response data with varying alternative-specific features. This model offers an in-depth understanding of the relationship between the explanatory and response variables. Compared to conditional mean-based models, the conditional binary quantile model relies on weak distributional assumptions and is more robust to distributional misspecification. The model also relaxes the assumption of the independence of irrelevant alternatives, which is often violated in practice. The method is applied to a range of political studies to show the heterogeneous effects of explanatory variables across the conditional distribution. Substantive interpretations from counterfactual scenarios are used to illustrate how the conditional binary quantile model captures unobserved heterogeneity, which extant models fail to do. The results point to the risk of averaging out the heterogeneous effects across units by conditional mean-based models.

Distributed Control Strategy for Smart Home Appliances Considering the Discrete Response Characteristics of the On/Off Loads

With the development of smart home technology, more and more electrical appliances can participate in demand response, providing support for active power balance of the power grid. However, the conventional centralized control method faces vast amounts of electrical appliances, resulting in problems such as communication congestion and dimension curse. This paper proposes a distributed control strategy for electrical appliances based on a multi-agent consensus algorithm. Considering the discrete response characteristics of the on/off loads, a priority ranking mechanism is established, and the customer cost function is established by a fitting method. Based on the incremental cost consensus (ICC) algorithm, the optimal power allocation of customers is realized through distributed control. Simulation and analysis of the examples verify the effectiveness of the proposed strategy.

Statistical estimation of conditional Shannon entropy

The new estimates of the conditional Shannon entropy are introduced in the framework of the model describing a discrete response variable depending on a vector of d factors having a density w.r.t. the Lebesgue measure in ℝd. Namely, the mixed-pair model (X, Y ) is considered where X and Y take values in ℝd and an arbitrary finite set, respectively. Such models include, for instance, the famous logistic regression. In contrast to the well-known Kozachenko–Leonenko estimates of unconditional entropy the proposed estimates are constructed by means of the certain spacial order statistics (or k-nearest neighbor statistics where k = kn depends on amount of observations n) and a random number of i.i.d. observations contained in the balls of specified random radii. The asymptotic unbiasedness and L2-consistency of the new estimates are established under simple conditions. The obtained results can be applied to the feature selection problem which is important, e.g., for medical and biological investigations.