Publications

Functional amyloid proteins are self-secreted by microbial cells that aggregate into extracellular networks and provide microbial colonies with mechanical stability and resistance to antibiotic treatment. In order to understand the formation mechanism of functional amyloid networks, their aggregation has been studied in vitro under different physical conditions, such as temperature, salt concentration, and pH. Typical aggregates' morphologies include fibers or plaques, the latter resembling amyloid aggregates in neurodegenerated brains. Here, we studied the pH-reduction-induced aggregation of TasA, an extracellular functional amyloid appearing as fibers in biofilms of the soil bacterium, Bacillus subtilis. We used turbidity and zeta potential measurements, electron microscopy, atomic force microscopy, and static light scattering measurements, to characterize the aggregates of TasA and to compare them with colloidal aggregates. We further studied the aggregation of TasA in the presence of negatively charged nanoparticles and showed that nanoparticles co-aggregated with TasA, and that the co-aggregation was hindered sterically. Based on these studies, we concluded that, similarly to colloidal aggregation, TasA aggregation occurs due to surface potential modulations and that the aggregation is followed by a rearrangement process. Shedding light on the aggregation mechanism of TasA, our results can be used for the design of TasA aggregation inhibitors and promoters.

Internal circadian clocks organize animal behavior and physiology and are entrained by ecologically relevant external time-givers such as light and temperature cycles. In the highly social honey bee, social time-givers are potent and can override photic entrainment, but the cues mediating social entrainment are unknown. Here, we tested whether substrate-borne vibrations and hive volatiles can mediate social synchronization in honey bees. We first placed newly emerged worker bees on the same or on a different substrate on which we placed cages with foragers entrained to ambient day-night cycles, while minimizing the spread of volatiles between cages. In the second experiment, we exposed young bees to constant airflow drawn from either a free-foraging colony or a similar-size control hive containing only heated empty honeycombs, while minimizing transfer of substrate-borne vibrations between cages. After 6 days, we isolated each focal bee in an individual cage in an environmental chamber and monitored her locomotor activity. We repeated each experiment 5 times, each trial with bees from a different source colony, monitoring a total of more than 1000 bees representing diverse genotypes. We found that bees placed on the same substrate as foragers showed a stronger phase coherence and a phase more similar to that of foragers compared with bees placed on a different substrate. In the second experiment, bees exposed to air drawn from a colony showed a stronger phase coherence and a phase more similar to that of foragers compared with bees exposed to air from an empty hive. These findings lend credence to the hypothesis that surrogates of activity entrain circadian rhythms and suggest that multiple social cues can act in concert to entrain social insect colonies to a common phase.

The rapid increase in “big data” of the post-genomic era makes it crucial to appropriately measure the level of social complexity in comparative studies. We argue that commonly-used qualitative classifications lump together species showing a broad range of social complexity, and falsely imply that social evolution always progresses along a single linear stepwise trajectory that can be deduced from comparing extant species. To illustrate this point, we compared widely-used social complexity measures in "primitively social" bumble bees with “advanced eusocial” stingless bees, honey bees, and attine ants. We find that a single species can have both higher and lower levels of complexity compared to other taxa, depending on the social trait measured. We propose that measuring the complexity of individual social traits switches focus from semantic discussions and offers several directions for progress. Firstly, quantitative social traits can be correlated with molecular, developmental, and physiological processes within and across lineages of social animals. This approach is particularly promising for identifying processes that influence or have been affected by social evolution. Secondly, key social complexity traits can be combined into multidimensional lineage-specific quantitative indices enabling fine scale comparison across species that are currently bundled within the same level of social complexity.

In this article we present an analysis of the concepts of fenxiang and gongxiang—the Mandarin words for ‘sharing’—in the context of Chinese social media. We do so through an interrogation of the words fenxiang and gongxiang as used by Chinese social media companies. Using the Internet Archive’s Wayback Machine, we created screenshots of 32 Chinese social network sites between 2000-2018 and tracked changes in the usage of fenxiang and gongxiang over time. The Mandarin translations in some ways operate like the English word, ‘sharing’. Fenxiang has the meaning of participating in social media, and gongxiang refers to technological aspects of sharing, while also conveying a sense of harmony. However, the interpersonal relations implied by fenxiang, and the political order implied by gongxiang, are quite different from those conveyed by ‘sharing’. Together, fenxiang and gongxiang construct a convergence of micro-level interpersonal harmony and macro-level social harmony. Thus, the language of sharing becomes the lens through which to observe the subtlety, complexity and idiosyncrasies of the Chinese internet. This article thus offers a new heuristic for understanding Chinese social media, while also pointing to an important facet of the discursive construction of Chinese social media. This implies a continuing need to de-westernize research into the internet and to identify cultural-specific meanings of social media.

Lipid nanodiscs are small synthetic lipid bilayer structures that are stabilized in solution by special circumscribing (or scaffolding) proteins or polymers. Because they create native-like environments for transmembrane proteins, lipid nanodiscs have become a powerful tool for structural determination of this class of systems when combined with cryo-electron microscopy or nuclear magnetic resonance. The elastic properties of lipid bilayers determine how the lipid environment responds to membrane protein perturbations, and how the lipid in turn modifies the conformational state of the embedded protein. However, despite the abundant use of nanodiscs in determining membrane protein structure, the elastic material properties of even pure lipid nanodiscs (i.e., without embedded proteins) have not yet been quantitatively investigated. A major hurdle is due to the inherently non-local treatment of the elastic properties of lipid systems implemented by most existing methods, both experimental and computational. In addition, these methods are best suited for very large “infinite” size lipidic assemblies, or ones that contain periodicity, in the case of simulations. We have previously described a computational analysis of molecular dynamics simulations designed to overcome these limitations, so that it allows quantification of the bending rigidity (K_C) and tilt moduli (κ_t) on a local scale even for finite, non-periodic systems, such as lipid nanodiscs. Here we use this computational approach to extract values of K_C and κ_t for a set of lipid nanodisc systems that vary in size and lipid composition. We find that the material properties of lipid nanodiscs are different from those of infinite bilayers of corresponding lipid composition, highlighting the effect of nanodisc confinement. Nanodiscs tend to show higher stiffness than their corresponding macroscopic bilayers, and moreover, their material properties vary spatially within them. For small-size MSP1 nanodiscs, the stiffness decreases radially, from a value that is larger in their center than the moduli of the corresponding bilayers by a factor of ~2-3. The larger nanodiscs (MSP1E3D1 and MSP2N2) show milder spatial changes of moduli that are composition dependent and can be maximal in the center or at some distance from it. These trends in moduli correlate with spatially varying structural properties, including the area per lipid and the nanodisc thickness. Finally, as has previously been reported, nanodiscs tend to show deformations from perfectly flat circular geometries to varying degrees, depending on size and lipid composition. The modulations of lipid elastic properties that we find should be carefully considered when making structural and functional inferences concerning embedded proteins.

There is an ongoing debate concerning the contribution of different aspects of empathy to achieving an accurate understanding of others. In this study, we aimed to better comprehend the roles of experience sharing and mentalizing using a modified empathic-accuracy task. We analyzed the unique contribution of each of these mechanisms with an explicit cognitive report as well as an affective physiological synchrony measurement. First, we recorded the emotional autobiographical stories told by participants (“targets”, N = 28). Then, the targets watched their own videos as their heart rate (HR) was measured, and they reported on both a continuous and a discrete emotion scale what they felt while relaying the story. Next, we collected HR data from new participants (“observers”, N = 72) as they similarly rated the targets' valence and discrete emotional states. In order to test the contribution of sensorimotor cues and contextual cues to empathic accuracy, observers viewed some videos with audio, others without audio, and listened to a third set of only the audio. We hypothesized that empathic accuracy—a cognitive measure that is a proxy for mentalizing and is operationalized by the correlation between a target's reported emotions and an observer's inference of those emotions—would be greater when linguistic information is present. We also hypothesized that physiological synchrony, a proxy for experience sharing, would be greater in the video-only condition, which was limited to sensorimotor cues to infer the other's emotional state. Indeed, we found that empathic accuracy was greater when auditory information was present, and that HR synchrony was more prevalent when visual cues were presented alone. Having both information streams together did not enhance accuracy, yet it was the only condition in which both behavioral empathic-accuracy measures correlated with HR synchrony. This study provides evidence that separate experience sharing and mentalizing pathways are active in the same task.

In December 2019, Ms. Lan Yun interviewed Leona Toker during her academic visit to Shanghai Jiao Tong University. In this interview, Toker approaches the concept of cultural remission and Gulag and Holocaust literature from an ethical perspective, exploring the complex relationship between literary forms and their ethical consequences. She claims that ethical criticism is coming back in new ways and that analysis of the ethics of form may take over from that of the ethics of character behavior as a potential orientation for future studies.

Painkillers are commonly used medications. Native peptide painkillers suffer from various pharmacological disadvantages, while small molecule painkillers like morphine are highly addictive. We present a general approach aimed to use backbone-cyclization to develop a peptidomimetic painkiller. Backbone-cyclization was applied to transform the linear peptide Tyr-Arg-Phe-Sar (TAPS) into an active backbone-cyclic peptide with improved drug properties. We designed and synthesized a focused backbone-cyclic TAPS library with conformational diversity, in which the members of the library have the generic name TAPS c(n-m) where n and m represent the lengths of the alkyl chains on the nitrogens of Gly and Arg, respectively. We used a combined screening approach to evaluate the pharmacological properties and the potency of the TAPS c(n-m) library. We focused on an in vivo active compound, TAPS c(2-6), which is metabolically stable and has the potential to become a peripheral painkiller being a full μ opioid receptor functional agonist. To prepare a large quantity of TAPS c(2-6), we optimized the conditions of the on-resin reductive alkylation step to increase the efficiency of its SPPS. NMR was used to determine the solution conformation of the peptide lead TAPS c(2-6).

Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56 – 0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1,002 authenticated Facebook users, alongside valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (.621 ≤ AUC ≤ .629), the MTM produced significantly improved prediction accuracy (.697 ≤ AUC ≤ .746), with substantially larger effect sizes (.729 ≤ d ≤ .936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools.

Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56 – 0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1,002 authenticated Facebook users, alongside clinically valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (.606 621 ≤ AUC ≤ .608629), the MTM produced significantly improved prediction accuracy (.690 697 ≤ AUC ≤ .759746), with substantially larger effect sizes (.701 729 ≤ d ≤ .994936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools.