Publications

The ability to create complex arrays of organized nanostructures is crucial for many advanced technological applications. An extensively investigated methodology for producing such arrays is the directed self-assembly of block copolymers using topographically patterned substrates, where micron-scale features engraved in the substrate induce nanodomain alignment over macroscopic ranges. Most research thus far concentrated on the formation of ordered surface patterns through microphase separation of block copolymers in thin films. In this work, we demonstrate the utility of block copolymer micelles – soft, self-assembled, non-crosslinked entities – for the preparation of arrays with structural bi-modality. Systematic investigation of the influence of the substrate's topography on the micellar assembly at different concentrations revealed different structural behavior of micelles deposited on the plateaus and in the trenches, which is tunable by the topographic feature dimensions. The potential of this approach for effecting complex superstructures is demonstrated by employing the micelles to organize semiconductor nanorods.

How social complexity evolved remains a long-standing enigma. In most animal groups, social complexity is typically classified into a few discrete classes. This approach is oversimplified and constrains our inference of social evolution to a narrow trajectory consisting of transitions between classes. Such categorical classifications also limit quantitative studies on the molecular and environmental drivers of social complexity. The recent accumulation of relevant quantitative data has set the stage to overcome these limitations. Here, we propose a data-driven, high-dimensional approach for studying the full diversity of social phenotypes. We curated and analyzed a comprehensive dataset encompassing 17 social traits across 80 species and studied the evolution of social complexity in bees. We found that honey bees, stingless bees, and bumble bees underwent a major evolutionary transition ∼80 mya, inconsistent with the stepwise progression of the social ladder conceptual framework. This major evolutionary transition was followed by a phase of substantial phenotypic diversification of social complexity. Other bee lineages display a continuum of social complexity, ranging from solitary to simple societies, but do not reach the levels of social complexity seen in honey bees, stingless bees, and bumble bees. Bee evolution, therefore, provides a remarkable demonstration of a macroevolutionary process in which a major transition removed biological constraints and opened novel evolutionary opportunities, driving the exploration of the landscape of social phenotypes. Our approach can be extended to incorporate additional data types and readily applied to illuminate the evolution of social complexity in other animal groups.

We present a new approach to teaching the concept of osmotic pressure in physical chemistry courses. Our route is different from the traditional derivation that hinges on equating chemical potentials. Instead, we resort to the equivalent, yet more intuitive, concepts of mixing entropy and free energy and use their relation to the second law of thermodynamics. Our strategy emphasizes the role of entropically driven forces, which are a principal, yet underappreciated, theme in physical chemistry and biophysics. In doing so, we have extended the available examples of entropic forces that can be introduced to undergraduate chemistry and biology students.

Following the progressing internationalisation of social science research and the computational turn in the field, researchers are increasingly adopting computational text analysis (CTA) methods to compare textual data across multiple cases and languages. In these settings, it is not only the mapping between construct and measures that requires validation, but also the equivalence of this mapping across languages and cases. However, although the validation requirements in multilingual analyses exceed those in monolingual studies, current research shows that validation is often insufficiently and inconsistently addressed in comparative multilingual CTA. To support more robust comparative research, this article presents a framework for validating findings obtained from multilingual textual data. The framework outlines validation strategies for four key stages of a typical multilingual CTA workflow: corpus, input data, process, and output. It directly tackles the challenge of approaching equivalence across contexts and languages in these stages and moves beyond the common practice of identifying problems only at the final stage of research.

Can major cities accommodate the growing political polarization surrounding immigration? Attitudes of city residents toward immigrants vary widely, influenced by factors like urban diversity, labor market dynamics, and cultural identity. While some embrace immigrants as enriching urban life, others view them as threats to culture and economic stability. Using data from three original surveys from Berlin, Barcelona, and Paris (N = 1500), we explore how engagement in urban politics shapes these attitudes, considering national and non-national identities. Results indicate a correlation between political engagement and positive attitudes toward immigrants but also reveal how it can reinforce existing identity-based biases, potentially polarizing urban politics. Our comparative analysis highlights city-specific variations, with Berlin showing favorable attitudes alongside stronger polarization than Paris and Barcelona. These differences possibly stem from urban conditions, such as diversity, capital city status, and pro-immigrant policies. These findings highlight the central role of cities in shaping immigration politics—both as spaces that foster inclusive, a-national affiliations and as battlegrounds where national identity-driven polarization can intensify. In the conclusion, we discuss the implications of these findings for local governments, particularly how cities can respond to the growing political divides.

Optical imaging through scattering media is important in a variety of fields ranging from microscopy to autonomous vehicles. Although advanced wavefront shaping techniques have offered several breakthroughs in the past decade, current techniques still require a known guide star and a high-resolution spatial light modulator or a very large number of measurements and are limited in their correction field of view. Here we introduce a guide-star-free, non-invasive approach that can correct more than 190,000 scattered modes using only 25 incoherently compounded, holographically measured, scattered light fields, obtained under unknown random illuminations. This is achieved by computationally emulating an image-guided wavefront shaping experiment, where several virtual spatial light modulators are simultaneously optimized to maximize the reconstructed image quality. Our method shifts the burden from the physical hardware to a digital, naturally parallelizable computational optimization, leveraging state-of-the-art automatic differentiation tools. We demonstrate the flexibility and generality of this framework by applying it to imaging through various complex samples and imaging modalities, including epi-illumination, anisoplanatic multi-conjugate correction of highly scattering layers, lensless endoscopy in multicore fibres and acousto-optic tomography. The presented approach offers high versatility, effectiveness and generality for fast, non-invasive imaging in diverse applications.

This article examines the journalistic production of mediated political projections – media narratives about uncertain political futures, such as anticipated election outcomes and their implications. Despite the significance of prospective coverage in political journalism and its influence on political decision-making, there is limited understanding of journalists’ perceptions and textual expressions of political forecasting. Drawing on interviews with Israeli journalists and a computational text analysis of election coverage in France, Israel, and the U.S., this study aims to understand how journalists perceive, negotiate, and textually navigate political forecasting in their work – whether through their own projections or by mediating forecasts made by others. The findings reveal journalists’ deep ambivalence toward political forecasting and the resulting textual practices. We show how journalists attribute their engagement in forecasting to external pressures, while their reluctance stems from the inherent risks and challenges associated with political forecasting and its tension with their journalistic identity and professional values. To navigate this tension, they incorporate projections into conventional factual reporting or use non-committal language. Except in data journalism, assessing the likelihood of political scenarios is uncommon. Although these patterns are observed across countries and media types, prospective coverage is more prevalent in interventionist and accommodative journalistic cultures, with the rhetoric of facticity and certitude more common in broadcast news. We suggest that journalists’ reluctance to fully engage with the inherent uncertainties of political futures limits their ability to contribute effectively to public decision-making processes as societies navigate political futures.

This article examines the journalistic production of mediated political projections—media narratives about uncertain political futures, such as anticipated election outcomes and their implications. Despite the significance of prospective coverage in political journalism and its influence on political decision-making, there is limited understanding of journalists’ perceptions and textual expressions of political forecasting. Drawing on interviews with Israeli journalists and a computational text analysis of election coverage in France, Israel, and the U.S., this study aims to understand how journalists perceive, negotiate, and textually navigate political forecasting in their work—whether through their own projections or by mediating forecasts made by others. The findings reveal journalists’ deep ambivalence toward political forecasting and the resulting textual practices. We show how journalists attribute their engagement in forecasting to external pressures, while their reluctance stems from the inherent risks and challenges associated with political forecasting and its tension with their journalistic identity and professional values. To navigate this tension, they incorporate projections into conventional factual reporting or use non-committal language. Except in data journalism, assessing the likelihood of political scenarios is uncommon. Although these patterns are observed across countries and media types, prospective coverage is more prevalent in interventionist and accommodative journalistic cultures, with the rhetoric of facticity and certitude more common in broadcast news. We suggest that journalists’ reluctance to fully engage with the inherent uncertainties of political futures limits their ability to contribute effectively to public decision-making processes as societies navigate political futures.

Noninvasive optical imaging through complex scattering media presents a major challenge across multiple fields. State-of-the-art techniques, such as reflection matrix decomposition and neural networks, rely on multiple measurements with varying illumination within the sample decorrelation time, making their application challenging in rapidly varying dynamic media. Here, we show that due to commutativity property of the convolution operation, dynamic scattering in isoplanatic imaging is mathematically analogous to varying illumination in static media. This insight allows leveraging matrix-based approaches developed for static scattering to rapidly varying dynamic media. Specifically, we show that the covariance matrix of a set of scattered light camera frames captured through a dynamic scattering sample has the same mathematical form as the reflection matrix of a static medium, with the target object playing the scattering medium’s role. We demonstrate this concept by high-resolution diffraction-limited imaging through dynamic scattering across multiple modalities, from incoherent fluorescence microscopy to coherence-gated holographic reflection imaging.

The way digital news platforms represent migration issues can signifi-
cantly impact intergroup relations and policymaking. A recurring question in the
debate on the role of news platforms is whether they merely transmit informa-
tion on migration, or actively hype specific issues. Drawing on a comprehensive
set of socioeconomic statistics on migrants in Denmark, and employing a longitu-
dinal automated content analysis of migration news content, we utilize time-series
analysis to understand how four distinct categories of threat (security, economic,
cultural, and generalized) relate to socioeconomic data on terror attacks, migrant
crime levels, economic performance, and demographic trends. The results reveal a
direct effect of terror attacks, economic performance, and demographic trends on
migration news. We discuss the implications of socioeconomic and demographic
developments as factors in digital media content to understand the role of media
and substantiate contemporary debates