Publications

There is increasing scholarly agreement about the key features of academically productive classroom dialogue, as well as about its role in student learning and growth. Yet, despite this emerging conceptual consensus, the ways in which it is measured and coded in quantitative research efforts vary significantly across settings, research teams, and studies. In order to communicate, compare and integrate findings from this rich body of empirical research and to further theory refinement, a more uniform approach to measuring classroom dialogue is needed. In this paper, we argue that for such an approach to succeed it should meet the following requirements: It should enable stable coding across settings and raters (reliability), be capable of capturing a wide variety of dialogue features (comprehensiveness) and enable different types of research questions and analyses (flexibility). Given the current conceptual maturity, as well as the vast number of quantitative studies that have accumulated in the last two decades, we believe that the field has sufficiently matured to achieve this goal. We selected seven well-known and validated coding frameworks for academically productive classroom dialogue. Through an iterative process of comparison, deconstruction, and application to classroom dialogue transcripts, we identified a set of nine elementary particles, so-called dialogue elements (DEs), that are common across the different coding categories and can be reliably coded at the conversational turn level. We then demonstrate how a much larger set of "compound" dialogue constructs can be identified post-coding by flagging co-occurrences of different DEs that recreate higher-order dialogue constructs. With the help of this Dialogue Elements to Compound Constructs Approach (DECCA), the majority of coding categories from each of the seven selected coding frameworks could be recreated. DECCA thus enables interrater reliability, while simultaneously maintaining the flexibility and comprehensiveness needed to enable research on a large variety of questions with a single methodological approach. The implications, limitations and for future research and theory are discussed.

Multiscale simulations have been established as a powerful tool to calculate and predict excitation energies in complex systems such as photoreceptor proteins. In these simulations the chromophore is typically treated using quantum mechanical (QM) methods while the protein and surrounding environment are described by a classical molecular mechanics (MM) force field. The electrostatic interactions between these regions are often treated using electrostatic embedding where the point charges in the MM region polarize the QM region. A more sophisticated treatment accounts also for the polarization of the MM region. In this work, the effect of such a polarizable embedding on excitation energies was benchmarked and compared to electrostatic embedding. This was done for two different proteins, the lipid membrane-embedded jumping spider rhodopsin and the soluble cyanobacteriochrome Slr1393g3. It was found that the polarizable embedding scheme produces absorption maxima closer to experimental values. The polarizable embedding scheme was also benchmarked against expanded QM regions and found to be in qualitative agreement. Treating individual residues as polarizable recovered between 50% and 71% of the QM improvement in the excitation energies, depending on the system. A detailed analysis of each amino acid residue in the chromophore binding pocket revealed that aromatic residues result in the largest change in excitation energy compared to the electrostatic embedding. Furthermore, the computational efficiency of polarizable embedding allowed it to go beyond the binding pocket and describe a larger portion of the environment, further improving the results.

Individual entities across levels of biological organization interact to reach collective decisions. In centralized neuronal networks, competing neural populations commonly accumulate information over time while increasing their own activity, and cross-inhibiting other populations until one group passes a given threshold. In social insects, there is good evidence for decisions mediated by positive feedbacks, but we found evidence for similar inhibitory signals only in honey bee (Apis mellifera) stop signals, and Pharaoh’s ant (Monomorium pharaonic) repellent pheromones, with only the former occasionally being used as cross-inhibition. We discuss whether these differences stem from insufficient research effort or represent genuine differences across levels of biological organization.

The current challenges of structural biophysics include determining the structure of large self-assembled complexes, resolving the structure of ensembles of complex structures and their mass fraction, and unraveling the dynamic pathways and mechanisms leading to the formation of complex structures from their subunits. Modern synchrotron solution X-ray scattering data enable simultaneous high-spatial and high-temporal structural data required to address the current challenges of structural biophysics. These data are complementary to crystallography, NMR, and cryo-TEM data. However, the analysis of solution scattering data is challenging; hence many different analysis tools, listed in the SAS Portal (http://smallangle.org/), were developed. In this review, we start by briefly summarizing classical X-ray scattering analyses providing insight into fundamental structural and interaction parameters. We then describe recent developments, integrating simulations, theory, and advanced X-ray scattering modeling, providing unique insights into the structure, energetics, and dynamics of self-assembled complexes. The structural information is essential for understanding the underlying physical chemistry principles leading to self-assembled supramolecular architectures and computational structural refinement.

Bumble bees are eusocial bees in which the division of labor in reproduction and in task performance changes during their annual life cycle. The queen monopolizes reproduction in young colonies, but at later stages some workers start to challenge the queen and lay their own unfertilized eggs. The division of colony maintenance and growth tasks relates to worker body size. Reproduction and task performance are regulated by multiple social signals of the queen, the workers, and the brood. Here we review recent studies suggesting that bumble bees use multiple sources of information to establish and maintain division of labor in both reproduction and in task performance. Juvenile hormone is an important neuroendocrine signal involved in the regulation of division of labor in reproduction but not in worker task performance. The reliance on multiple signals facilitate flexibility in face of changes in the social and geophysical environment. Data Availability No data were used for the research described in the article.

With more than 3000 international investment agreements (IIAs) worldwide, states negotiate similar agreements multiple times with numerous partners. Accordingly, many states have developed template agreements known as ‘Model bilateral investment treaties (BITs)’. Nevertheless, concluded IIAs commonly deviate from the corresponding Model BITs, albeit to varying degrees. Investigating this variation, we examine the impact of Model Countries and their Partner Countries’ investor–state dispute settlement (ISDS) experience. Specifically, we argue that the Model Country adopts changes sought by the Partner Country during the negotiation process in order to accommodate the latter’s preferences, which were shaped by lessons learned from ISDS cases. Empirically, we introduce novel measures of divergence between Model BITs and IIAs, based on the concept and scheme of state regulatory space, with respect to several key aspects of investment rules. Coding a large number of Model BITs and IIAs on these variables and controlling for a host of alternative explanations, we find that the higher number of investment claims filed against the Partner Country, but not the Model Country, is associated with greater divergence between the Model Country’s Model BITs and its IIAs. This effect is especially noticeable with respect to important substantive investment rules.

With more than 3,000 international investment agreements (IIAs) worldwide, states negotiate similar agreements multiple times with numerous partners. Accordingly, many states have developed template agreements known as ‘Model BITs.’ Nevertheless, concluded IIAs commonly deviate from the corresponding Model BITs, albeit to varying degrees. Investigating this variation, we examine the impact of Model Countries and their Partner Countries’ investor-state dispute settlement (ISDS) experience. Specifically, we argue that the Model Country adopts changes sought by the Partner Country during the negotiation process in order to accommodate the latter’s preferences, which were shaped by lessons learned from ISDS cases. Empirically, we introduce novel measures of divergence between Model BITs and IIAs, based on the concept and scheme of state regulatory space (SRS), with respect to several key aspects of investment rules. Coding a large number of Model BITs and IIAs on these variables and controlling for a host of alternative explanations, we find that the higher number of investment claims filed against the Partner Country, but not the Model Country, is associated with greater divergence between the Model Country’s Model BITs and its IIAs. This effect is especially noticeable with respect to important substantive investment rules.