Publications

Biofilms are surface-associated soft microbial communities, which may be either detrimental or beneficial to their hosting environment. They develop from single cells into mature colonies, that are composed of cells and sometimes (in Firmicutes phylum) spores, held together by an extracellular matrix (ECM) of secreted biomolecular components. Biofilm development is a dynamic process, during which cells organize into subgroups, creating functionally distinct regions in space. Specific examples of functional-spatial division in Bacillus subtilis biofilms include matrix and spore formation as well as water channels that form beneath wrinkles. An interesting question arising is whether the division of labor in biofilms is also reflected in the molecular-level order across whole biofilms. Using combined X-ray diffraction (XRD)/X-ray fluorescence (XRF), we studied the molecular order in intact biofilms across multiple length scales. We discovered that biofilms display a distinct spatio-temporal XRD signature that depends on highly ordered structures in spores and on cross β sheet structures in matrix components. Spore signal is found especially enhanced with water molecules and metal-ions signals along macroscopic wrinkles, known to act as water channels. Demonstrating in situ the link between molecular structures, metal ions distribution and division of labor across whole biofilms in time and space, this study provides new pivotal insight to the understanding biofilm development.

Stochastic density functional theory (sDFT) is becoming a valuable tool for studying ground-state properties of extended materials. The computational complexity of describing the Kohn–Sham orbitals is replaced by introducing a set of random (stochastic) orbitals leading to linear and often sub-linear scaling of certain ground-state observables at the account of introducing a statistical error. Schemes to reduce the noise are essential, for example, for determining the structure using the forces obtained from sDFT. Recently, we have introduced two embedding schemes to mitigate the statistical fluctuations in the electron density and resultant forces on the nuclei. Both techniques were based on fragmenting the system either in real space or slicing the occupied space into energy windows, allowing for a significant reduction in the statistical fluctuations. For chemical accuracy, further reduction of the noise is required, which could√be achieved by increasing the number of stochastic orbitals. However, the convergence is relatively slow as the statistical error scales as 1/ Nχ according to the central limit theorem, where Nχ is the number of random orbitals. In this paper, we combined the embedding schemes mentioned above and introduced a new approach that builds on overlapped fragments and energy windows. The new approach significantly lowers the noise for ground-state properties, such as the electron density, total energy, and forces on the nuclei, as demonstrated for a G-center in bulk silicon.

In the framework of a private-value auction first-price, we consider the seller as a player in a game with the buyers in which he has private information about their realized values. We ask whether the seller can benefit by using his private information strategically. We find that in fact, depending upon his information, set of signals, and commitment power the seller 5 indeed increase his revenue by strategic transmission of his information. We study mainly the case of partial truthful commitment (VC) in which the seller can commit to send only truthful (verifiable) messages. We show that in the case of two buyers with values distributed independently uniformly on [0,1], a seller informed of the private values of the buyers, can achieve a revenue close to 1/2 by sending verifiable messages (compared to 1/3 in the standard auction), and this is the largest revenue that he can reach with any signaling strategy and any level of commitment. The case studied here provides valuable insight into the issue of strategic use of information which applies more generally.

What role does news content play in explaining inter-party hostility? We argue that affective polarization is influenced by exposure to one of the most dominant ways to cover politics: strategy coverage. While previous studies have pointed to the negative consequences of covering politicians’ strategies and campaign tactics, we find that this reporting style decreases out-party hostility. Our findings are based on two separate studies: (1) a survey experiment and (2) a cross-sectional analysis that increases external validity by combining survey data with computational content analysis of the articles respondents were exposed to by their primary news sources throughout the 2016 US presidential campaign (415,604 articles from 157 American news outlets). The results demonstrate that despite the wide criticism of the tendency of journalists to focus on political strategies, such coverage may ease inter-party tensions in American politics.

Despite visions of social network technology (SNT) for collaborative knowledge construction, recent research in secondary schools suggest that students use these tools mainly for knowledge sharing of study-related artifacts. We extend these findings to higher education settings and report on two survey studies that map characteristics of students’ self-directed use of SNTs for study purposes, in undergraduate university programs (N = 264) and teacher training colleges (N = 449). The combined findings confirm that students use SNTs extensively for uploading, linking and downloading study-related artifacts in peer-directed SNT groups. They regard these practices positively and believe they improve academic achievements. Sharing was predicted by positive attitudes toward sharing and collectivist value orientations, motivated overall by prosocial reasons and less frequent in competitive study programs. Use of shared materials was associated with performance-avoidance achievement goals and lower GPA. Findings, directions for future research and implications are discussed in the context of learning theories, as well the knowledge sharing literature.

Cytokines such as interleukin-8 activate the immune system during infection and interact with sulfated glycosaminoglycans with specific sulfation patterns. In some cases, these interactions are mediated by metal ion binding which can be used to tune surface-based glycan-protein interactions. We evaluated the effect of both hyaluronan sulfation degree and Fe³⁺ on interleukin-8 binding by electrochemical impedance spectroscopy and surface characterizations. Our results show that sulfation degree and metal ion interactions have a synergistic effect in tuning the electrochemical response of the glycated surfaces to the cytokine.

We introduce a tempering approach with stochastic density functional theory (sDFT), labeled t-sDFT, which reduces the statistical errors in the estimates of observable expectation values. This is achieved by rewriting the electronic density as a sum of a "warm" component complemented by "colder" correction(s). Since the "warm" component is larger in magnitude but faster to evaluate, we use many more stochastic orbitals for its evaluation than for the smaller-sized colder correction(s). This results in a significant reduction of the statistical fluctuations and the bias compared to sDFT for the same computational effort. We the method's performance on large hydrogen-passivated silicon nanocrystals (NCs), finding a reduction in the systematic error in the energy by more than an order of magnitude, while the systematic errors in the forces are also quenched. Similarly, the statistical fluctuations are reduced by factors of around 4-5 for the total energy and around 1.5-2 for the forces on the atoms. Since the embedding in t-sDFT is fully stochastic, it is possible to combine t-sDFT with other variants of sDFT such as energy-window sDFT and embedded-fragmented sDFT.

We identify three gaps that limit the utility and obstruct the progress of computational text analysis methods (CTAM) for social science research. First, we contend that CTAM development has prioritized technological over validity concerns, giving limited attention to the operationalization of social scientific measurements. Second, we identify a mismatch between CTAMs’ focus on extracting specific contents and document-level patterns, and social science researchers’ need for measuring multiple, often complex contents in the text. Third, we argue that the dominance of English language tools depresses comparative research and inclusivity toward scholarly communities examining languages other than English. We substantiate our claims by drawing upon a broad review of methodological work in the computational social sciences, as well as an inventory of leading research publications using quantitative textual analysis. Subsequently, we discuss implications of these three gaps for social scientists’ uneven uptake of CTAM, as well as the field of computational social science text research as a whole. Finally, we propose a research agenda intended to bridge the identified gaps and improve the validity, utility, and inclusiveness of CTAM.