Biochemical and cytological interactions between callose synthase and microtubules in the tobacco pollen tube

Key message: The article concerns the association between callose synthase and cytoskeleton by biochemical and ultrastructural analyses in the pollen tube. Results confirmed this association and immunogold labeling showed a colocalization. Abstract: Callose is a cell wall polysaccharide involved in fundamental biological processes, from plant development to the response to abiotic and biotic stress. To gain insight into the deposition pattern of callose, it is important to know how the enzyme callose synthase is regulated through the interaction with the vesicle-cytoskeletal system. Actin filaments likely determine the long-range distribution of callose synthase through transport vesicles but the spatial/biochemical relationships between callose synthase and microtubules are poorly understood, although experimental evidence supports the association between callose synthase and tubulin. In this manuscript, we further investigated the association between callose synthase and microtubules through biochemical and ultrastructural analyses in the pollen tube model system, where callose is an essential component of the cell wall. Results by native 2-D electrophoresis, isolation of callose synthase complex and far-western blot confirmed that callose synthase is associated with tubulin and can therefore interface with cortical microtubules. In contrast, actin and sucrose synthase were not permanently associated with callose synthase. Immunogold labeling showed colocalization between the enzyme and microtubules, occasionally mediated by vesicles. Overall, the data indicate that pollen tube callose synthase exerts its activity in cooperation with the microtubular cytoskeleton

Review: Animal model and the current understanding of molecule dynamics of adipogenesis

Among several potential animal models that can be used for adipogenic studies, Wagyu cattle is the one that presents unique molecular mechanisms underlying the deposit of substantial amounts of intramuscular fat. As such, this review is focused on current knowledge of such mechanisms related to adipose tissue deposition using Wagyu cattle as model. So abundant is the lipid accumulation in the skeletal muscles of these animals that in many cases, the muscle cross-sectional area appears more white (adipose tissue) than red (muscle fibers). This enhanced marbling accumulation is morphologically similar to that seen in numerous skeletal muscle dysfunctions, disease states and myopathies; this might indicate cross-similar mechanisms between such dysfunctions and fat deposition in Wagyu breed. Animal models can be used not only for a better understanding of fat deposition in livestock, but also as models to an increased comprehension on molecular mechanisms behind human conditions. This revision underlies some of the complex molecular processes of fat deposition in animals

Empowerment or Engagement? Digital Health Technologies for Mental Healthcare

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare

An empirical analysis of patents flows and R&D flows around the world

In this article, we empirically investigate the effect of Research and Development (R&D) flows on patent flows around the world. We do this using an unbalanced panel consisting primarily of Organization for Economic Co-operation and Development (OECD) countries that have both patent and R&D expenditure information broken down by domestic and foreign sources. Our analysis shows that even among a fairly homogeneous group of countries, the sources of patents and R&D differ substantially. Using a dynamic panel framework, we find that domestic R&D per capita increases domestic patents per capita only for the European Patent Convention (EPC) countries that already have a decentralized approach to innovation. Foreign R&D per capita increases foreign patents per capita in all countries even though foreign R&D constitutes a very small fraction of total R&D. We find that some of these differences can be attributed to the locations of the patent applications, including those to the European Patent Office (EPO), United States Patent and Trademark Office (USPTO) and triadic patent applications to the EPO, USPTO and Japan Patent Office (JPO) simultaneously

Microsimulation as a tool for evaluating redistribution policies

During the last twenty years, microsimulation models have been increasingly applied in qualitative and quantitative analysis of public policies. This paper provides a discussion on microsimulation techniques and their theoretical background as a tool for the analysis of public policies with particular attention to redistribution and social policies. Basic principles in using microsimulation models and interpreting their results are analyzed, with particular emphasis on tax incidence, redistribution and poverty analysis. Social welfare analysis permitted by microsimulation techniques is also discussed. Finally, the paper points to limits of present approaches and directions for future research.Au cours des vingt dernières années, l'utilisation des modèles de microsimulation des politiques de redistribution n'a cessé de croître. Cet article offre un rapide survol de ces modèles, l'accent étant mis sur les développements récents dans ce domaine de l'économie appliquée et sur quelques directions de recherche future

A Simple Method to Account for Measurement Errors in Revealed Preference Tests

Revealed preference tests are widely used in empirical applications of consumer rationality. These are static tests, and consequently, lack ability to handle measurement errors in the data. This paper extends and generalizes existing procedures that account for measurement errors in revealed preference tests. In particular, it introduces a very efficient method to implement these procedures, which make them operational for large data sets. The paper illustrates the new method for both classical and Berkson measurement errors models

The ethics of digital well-being: a multidisciplinary perspective

This chapter serves as an introduction to the edited collection of the same name, which includes chapters that explore digital well-being from a range of disciplinary perspectives, including philosophy, psychology, economics, health care, and education. The purpose of this introductory chapter is to provide a short primer on the different disciplinary approaches to the study of well-being. To supplement this primer, we also invited key experts from several disciplines—philosophy, psychology, public policy, and health care—to share their thoughts on what they believe are the most important open questions and ethical issues for the multi-disciplinary study of digital well-being. We also introduce and discuss several themes that we believe will be fundamental to the ongoing study of digital well-being: digital gratitude, automated interventions, and sustainable co-well-being

Have State Renewable Portfolio Standards Really Worked? Synthesizing Past Policy Assessments to Build an Integrated Econometric Analysis of RPS effectiveness in the U.S.

Renewable portfolio standards (RPS) are the most popular U.S. state-level policies for promoting deployment of renewable electricity (RES-E). While several econometric studies have estimated the effect of RPS on in-state RES-E deployment, results are contradictory. We reconcile these studies and move toward a definitive answer to the question of RPS effectiveness. We conduct an analysis using time series cross sectional regressions - including the most nuanced controls for policy design features to date - and nonparametric matching analysis. We find that higher RPS stringency does not necessarily drive more RES-E deployment. We examine several RPS design features and market characteristics (including REC unbundling, RPS in neighboring states, out-of-state renewable energy purchases) that may explain the gap between effective and ineffective policies. We also investigate other RES-E policies and technology-specific effects. Ultimately, we show that RPS effectiveness is largely explained by a combination of policy design, market context, and inter-state trading effects

Agricultural Biotechnology's Complementary Intellectual Assets

We formulate and test a hypothesis to explain the dramatic restructuring experienced recently by the plant breeding and seed industry. The reorganization can be explained in part by the desire to exploit complementarities between intellectual assets needed to create genetically modified organisms. This hypothesis is tested using data on agricultural biotechnology patents, notices for field tests of genetically modified organisms, and firm characteristics. The presence of complementarities is identified with a positive covariance in the unexplained variation of asset holdings. Results indicate that coordination of complementary assets have increased under the consolidation of the industry

Endogeneity in Panel Data Models with Time-Varying and Time-Fixed Regressors: To IV or Not IV?

We analyse the problem of parameter inconsistency in panel data econometrics due to the correlation of exogenous variables with the error term. A common solution in this setting is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981). However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to systematically compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the Hausman- Taylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, the non-IV rival performs equally well or even better especially in terms of estimating variable coefficients for time- fixed regressors. Moreover, the non-IV method tends to have a smaller root mean square error (rmse) than both Hausman-Taylor models with perfect and imperfect knowledge about the underlying correlation between r.h.s variables and residual term. This indicates that it is generally more efficient. The results are roughly robust for various combinations in the time and cross-section dimension of the data