Cell Membranes Resist Flow.

The fluid-mosaic model posits a liquid-like plasma membrane, which can flow in response to tension gradients. It is widely assumed that membrane flow transmits local changes in membrane tension across the cell in milliseconds, mediating long-range signaling. Here, we show that propagation of membrane tension occurs quickly in cell-attached blebs but is largely suppressed in intact cells. The failure of tension to propagate in cells is explained by a fluid dynamical model that incorporates the flow resistance from cytoskeleton-bound transmembrane proteins. Perturbations to tension propagate diffusively, with a diffusion coefficient Dσ ∼0.024 µm2/s in HeLa cells. In primary endothelial cells, local increases in membrane tension lead only to local activation of mechanosensitive ion channels and to local vesicle fusion. Thus, membrane tension is not a mediator of long-range intracellular signaling, but local variations in tension mediate distinct processes in sub-cellular domains.

Cancer misinformation on social media.

Social media is widely used globally by patients, families of patients, health professionals, scientists, and other stakeholders who seek and share information related to cancer. Despite many benefits of social media for cancer care and research, there is also a substantial risk of exposure to misinformation, or inaccurate information about cancer. Types of misinformation vary from inaccurate information about cancer risk factors or unproven treatment options to conspiracy theories and public relations articles or advertisements appearing as reliable medical content. Many characteristics of social media networks-such as their extensive use and the relative ease it allows to share information quickly-facilitate the spread of misinformation. Research shows that inaccurate and misleading health-related posts on social media often get more views and engagement (e.g., likes, shares) from users compared with accurate information. Exposure to misinformation can have downstream implications for health-related attitudes and behaviors. However, combatting misinformation is a complex process that requires engagement from media platforms, scientific and health experts, governmental organizations, and the general public. Cancer experts, for example, should actively combat misinformation in real time and should disseminate evidence-based content on social media. Health professionals should give information prescriptions to patients and families and support health literacy. Patients and families should vet the quality of cancer information before acting upon it (e.g., by using publicly available checklists) and seek recommended resources from health care providers and trusted organizations. Future multidisciplinary research is needed to identify optimal ways of building resilience and combating misinformation across social media.

Physiological media advance cell culture experiments.

The metabolism plays a fundamental role in cellular signaling pathways, but commonly used cell culture media do not reflect physiological metabolite concentrations. The metabolic control hub mammalian target of rapamycin complex 1 (mTORC1) kinase is an illuminating example that it is about time to advance our cell culture to become more physiological and relevant.

Dynamic imaging of force chains in 3D granular media.

Granular media constitute the most abundant form of solid matter on Earth and beyond. When external forces are applied to a granular medium, the forces are transmitted through it via chains of contacts among grains-force chains. Understanding the spatial structure and temporal evolution of force chains constitutes a fundamental goal of granular mechanics. Here, we introduce an experimental technique, interference optical projection tomography, to study force chains in three-dimensional (3D) granular packs under triaxial shear loads and illustrate the technique with random assemblies of spheres and icosahedra. We find that, in response to an increasing vertical load, the pack of spheres forms intensifying vertical force chains, while the pack of icosahedra forms more interconnected force-chain networks. This provides microscopic insights into why particles with more angularity are more resistant to shear failure-the interconnected force-chain network is stronger (that is, more resilient to topological collapse) than the isolated force chains in round particles. The longer force chains with less branching in the pack of round particles are more likely to buckle, which leads to the macroscopic failure of the pack. This work paves the way for understanding the grain-scale underpinning of localized failure of 3D granular media, such as shear localization in landslides and stick-slip frictional motion in tectonic and induced earthquakes.

The effects of Facebook and Instagram on the 2020 election: A deactivation experiment.

We study the effect of Facebook and Instagram access on political beliefs, attitudes, and behavior by randomizing a subset of 19,857 Facebook users and 15,585 Instagram users to deactivate their accounts for 6 wk before the 2020 U.S. election. We report four key findings. First, both Facebook and Instagram deactivation reduced an index of political participation (driven mainly by reduced participation online). Second, Facebook deactivation had no significant effect on an index of knowledge, but secondary analyses suggest that it reduced knowledge of general news while possibly also decreasing belief in misinformation circulating online. Third, Facebook deactivation may have reduced self-reported net votes for Trump, though this effect does not meet our preregistered significance threshold. Finally, the effects of both Facebook and Instagram deactivation on affective and issue polarization, perceived legitimacy of the election, candidate favorability, and voter turnout were all precisely estimated and close to zero.

The offline roots of online hostility: Adult and childhood administrative records correlate with individual-level hostility on Twitter.

Reducing hostility in social media interactions is a key public concern. Most extant research emphasizes how online contextual factors breed hostility. Here, we take a different perspective and focus on the offline roots of hostility, that is, offline experiences and stable individual-level dispositions. Using a unique dataset of Danish Twitter users (N [Formula: see text] 4,931), we merge data from administrative government registries with a behavioral measure of online hostility. We demonstrate that individuals with more aggressive dispositions (as proxied by having many more criminal verdicts) are more hostile in social media conversations. We also find evidence that features of childhood environments predict online hostility. Time spent in foster care is a strong correlate, while other indicators of childhood instability (e.g., the number of moves and divorced parents) are not. Furthermore, people from more resourceful childhood environments-those with better grades in primary school and higher parental socioeconomic status-are more hostile on average, as such people are more politically engaged. These results offer an important reminder that much online hostility is rooted in offline experiences and stable dispositions. They also provide anuanced view of the core group of online aggressors. While these individuals display general antisocial personality tendencies by having many more criminal verdicts, they also come from resourceful backgrounds more often than not.

Social media influences National Park visitation.

Visitation to National Parks in the United States increased by more than 25% since 2010, rising from roughly 70 to 90 million annual visitors. Anecdotes suggest that this increase was driven by the advent of social media in the early-to-mid 2010s, generating a new form of exposure for parks, and has led to concerns about overcrowding and degradation of environmental quality. However, there is little empirical evidence on the role of social media in influencing recreation decisions. Here, I construct a dataset on social media exposure (SME) for each National Park and relate that exposure to changes in visitation over the last two decades. High SME parks see visitation increase by 16 to 22% relative to parks with less exposure, which comes with a concomitant increase in revenue. Low SME parks have no, or negative, changes in visitation. These estimates account for unobserved park heterogeneity and are based on an instrumental variables strategy that predicts exposure with a park's online popularity prior to the social media era. Additional analysis suggests that recent social media posts that include media attachments increase visitation, while posts with negative sentiment reduce visitation. These results provide insight for the National Park Service-which faces more than $22 billion in deferred maintenance costs and is considering policy options to manage demand-as well as for management of recreation on other public lands.

Correlation-informed ordered dictionary learning for imaging in complex media.

We propose a method for imaging in scattering media when large and diverse datasets are available. It has two steps. Using a dictionary learning algorithm the first step estimates the true Green's function vectors as columns in an unordered sensing matrix. The array data comes from many sparse sets of sources whose location and strength are not known to us. In the second step, the columns of the estimated sensing matrix are ordered for imaging using the multidimensional scaling algorithm with connectivity information derived from cross-correlations of its columns, as in time reversal. For these two steps to work together, we need data from large arrays of receivers so the columns of the sensing matrix are incoherent for the first step, as well as from sub-arrays so that they are coherent enough to obtain connectivity needed in the second step. Through simulation experiments, we show that the proposed method is able to provide images in complex media whose resolution is that of a homogeneous medium.

It matters what you see: Graphic media images of war and terror may amplify distress.

Media exposure to graphic images of violence has proliferated in contemporary society, particularly with the advent of social media. Extensive exposure to media coverage immediately after the 9/11 attacks and the Boston Marathon bombings (BMB) was associated with more early traumatic stress symptoms; in fact, several hours of BMB-related daily media exposure was a stronger correlate of distress than being directly exposed to the bombings themselves. Researchers have replicated these findings across different traumatic events, extending this work to document that exposure to graphic images is independently and significantly associated with stress symptoms and poorer functioning. The media exposure-distress association also appears to be cyclical over time, with increased exposure predicting greater distress and greater distress predicting more media exposure following subsequent tragedies. The war in Israel and Gaza, which began on October 7, 2023, provides a current, real-time context to further explore these issues as journalists often share graphic images of death and destruction, making media-based graphic images once again ubiquitous and potentially challenging public well-being. For individuals sharing an identity with the victims or otherwise feeling emotionally connected to the Middle East, it may be difficult to avoid viewing these images. Through a review of research on the association between exposure to graphic images and public health, we discuss differing views on the societal implications of viewing such images and advocate for media literacy campaigns to educate the public to identify mis/disinformation and understand the risks of viewing and sharing graphic images with others.

Harnessing elastic instabilities for enhanced mixing and reaction kinetics in porous media.

Turbulent flows have been used for millennia to mix solutes; a familiar example is stirring cream into coffee. However, many energy, environmental, and industrial processes rely on the mixing of solutes in porous media where confinement suppresses inertial turbulence. As a result, mixing is drastically hindered, requiring fluid to permeate long distances for appreciable mixing and introducing additional steps to drive mixing that can be expensive and environmentally harmful. Here, we demonstrate that this limitation can be overcome just by adding dilute amounts of flexible polymers to the fluid. Flow-driven stretching of the polymers generates an elastic instability, driving turbulent-like chaotic flow fluctuations, despite the pore-scale confinement that prohibits typical inertial turbulence. Using in situ imaging, we show that these fluctuations stretch and fold the fluid within the pores along thin layers ("lamellae") characterized by sharp solute concentration gradients, driving mixing by diffusion in the pores. This process results in a [Formula: see text] reduction in the required mixing length, a [Formula: see text] increase in solute transverse dispersivity, and can be harnessed to increase the rate at which chemical compounds react by [Formula: see text]-enhancements that we rationalize using turbulence-inspired modeling of the underlying transport processes. Our work thereby establishes a simple, robust, versatile, and predictive way to mix solutes in porous media, with potential applications ranging from large-scale chemical production to environmental remediation.

Illusory interparty disagreement: Partisans agree on what hate speech to censor but do not know it.

Whether and when to censor hate speech are long-standing points of contention in the US. The latest iteration of these debates entails grappling with content regulation on social media in an age of intense partisan polarization. But do partisans disagree about what types of hate speech to censor on social media or do they merely differ on how much hate speech to censor? And do they understand out-party censorship preferences? We examine these questions in a nationally representative conjoint survey experiment (participant N = 3,357; decision N = 40,284). We find that, although Democrats support more censorship than Republicans, partisans generally agree on what types of hate speech are most deserving of censorship in terms of the speech's target, source, and severity. Despite this substantial cross-party agreement, partisans mistakenly believe that members of the other party prioritize protecting different targets of hate speech. For example, a major disconnect between the two parties is that Democrats overestimate and Republicans underestimate the other party's willingness to censor speech targeting Whites. We conclude that partisan differences on censoring hate speech are largely based on free speech values and misperceptions rather than identity-based social divisions.

Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence.

Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out a dynamic analysis of coordinated behavior. To reach our goal, we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. We find that i) coordinated communities (CCs) feature variable degrees of temporal instability; ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; iii) some users exhibit distinct archetypal behaviors that have important practical implications; iv) content and network characteristics contribute to explaining why users leave and join CCs. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of CCs, and on the patterns of online influence.

Key language markers of depression on social media depend on race.

Depression has robust natural language correlates and can increasingly be measured in language using predictive models. However, despite evidence that language use varies as a function of individual demographic features (e.g., age, gender), previous work has not systematically examined whether and how depression's association with language varies by race. We examine how race moderates the relationship between language features (i.e., first-person pronouns and negative emotions) from social media posts and self-reported depression, in a matched sample of Black and White English speakers in the United States. Our findings reveal moderating effects of race: While depression severity predicts I-usage in White individuals, it does not in Black individuals. White individuals use more belongingness and self-deprecation-related negative emotions. Machine learning models trained on similar amounts of data to predict depression severity performed poorly when tested on Black individuals, even when they were trained exclusively using the language of Black individuals. In contrast, analogous models tested on White individuals performed relatively well. Our study reveals surprising race-based differences in the expression of depression in natural language and highlights the need to understand these effects better, especially before language-based models for detecting psychological phenomena are integrated into clinical practice.

4D microvelocimetry reveals multiphase flow field perturbations in porous media.

Many environmental and industrial processes depend on how fluids displace each other in porous materials. However, the flow dynamics that govern this process are still poorly understood, hampered by the lack of methods to measure flows in optically opaque, microscopic geometries. We introduce a 4D microvelocimetry method based on high-resolution X-ray computed tomography with fast imaging rates (up to 4 Hz). We use this to measure flow fields during unsteady-state drainage, injecting a viscous fluid into rock and filter samples. This provides experimental insight into the nonequilibrium energy dynamics of this process. We show that fluid displacements convert surface energy into kinetic energy. The latter corresponds to velocity perturbations in the pore-scale flow field behind the invading fluid front, reaching local velocities more than 40 times faster than the constant pump rate. The characteristic length scale of these perturbations exceeds the characteristic pore size by more than an order of magnitude. These flow field observations suggest that nonlocal dynamic effects may be long-ranged even at low capillary numbers, impacting the local viscous-capillary force balance and the representative elementary volume. Furthermore, the velocity perturbations can enhance unsaturated dispersive mixing and colloid transport and yet, are not accounted for in current models. Overall, this work shows that 4D X-ray velocimetry opens the way to solve long-standing fundamental questions regarding flow and transport in porous materials, underlying models of, e.g., groundwater pollution remediation and subsurface storage of CO2 and hydrogen.

People who share encounters with racism are silenced online by humans and machines, but a guideline-reframing intervention holds promise.

Are members of marginalized communities silenced on social media when they share personal experiences of racism? Here, we investigate the role of algorithms, humans, and platform guidelines in suppressing disclosures of racial discrimination. In a field study of actual posts from a neighborhood-based social media platform, we find that when users talk about their experiences as targets of racism, their posts are disproportionately flagged for removal as toxic by five widely used moderation algorithms from major online platforms, including the most recent large language models. We show that human users disproportionately flag these disclosures for removal as well. Next, in a follow-up experiment, we demonstrate that merely witnessing such suppression negatively influences how Black Americans view the community and their place in it. Finally, to address these challenges to equity and inclusion in online spaces, we introduce a mitigation strategy: a guideline-reframing intervention that is effective at reducing silencing behavior across the political spectrum.

Accurate prediction of discontinuous crack paths in random porous media via a generative deep learning model.

Pore structures provide extra freedoms for the design of porous media, leading to desirable properties, such as high catalytic rate, energy storage efficiency, and specific strength. This unfortunately makes the porous media susceptible to failure. Deep understanding of the failure mechanism in microstructures is a key to customizing high-performance crack-resistant porous media. However, solving the fracture problem of the porous materials is computationally intractable due to the highly complicated configurations of microstructures. To bridge the structural configurations and fracture responses of random porous media, a unique generative deep learning model is developed. A two-step strategy is proposed to deconstruct the fracture process, which sequentially corresponds to elastic deformation and crack propagation. The geometry of microstructure is translated into a scalar of elastic field as an intermediate variable, and then, the crack path is predicted. The neural network precisely characterizes the strong interactions among pore structures, the multiscale behaviors of fracture, and the discontinuous essence of crack propagation. Crack paths in random porous media are accurately predicted by simply inputting the images of targets, without inputting any additional input physical information. The prediction model enjoys an outstanding performance with a prediction accuracy of 90.25% and possesses a robust generalization capability. The accuracy of the present model is a record so far, and the prediction is accomplished within a second. This study opens an avenue to high-throughput evaluation of the fracture behaviors of heterogeneous materials with complex geometries.

January 6 arrests and media coverage do not remobilize conservatives on social media.

Social media's pivotal role in catalyzing social movements is widely acknowledged across scientific disciplines. Past research has predominantly explored social media's ability to instigate initial mobilization while leaving the question of its capacity to sustain these movements relatively uncharted. This study investigates the persistence of movement activity on Twitter and Gab following a substantial on-the-ground mobilization event catalyzed by social media-the StoptheSteal movement culminating in the January 6th Capitol attack. Our findings indicate that the online communities active in the January 6 mobilization did not display substantial remobilization in the subsequent year. These results highlight the fact that further exploration is needed to understand the factors shaping how and when movements are sustained by social media. In this regard, our study provides valuable insights for scientists across diverse disciplines, on how certain social media platforms may contribute to the evolving dynamics of collective action.

Multiscale drainage dynamics with Haines jumps monitored by stroboscopic 4D X-ray microscopy.

Slow multiphase flow in porous media is intriguing because its underlying dynamics is almost deterministic, yet depends on a hierarchy of spatiotemporal processes. There has been great progress in the experimental study of such multiphase flows, but three-dimensional (3D) microscopy methods probing the pore-scale fluid dynamics with millisecond resolution have been lacking. Yet, it is precisely at these length and time scales that the crucial pore-filling events known as Haines jumps take place. Here, we report four-dimensional (4D) (3D + time) observations of multiphase flow in a consolidated porous medium as captured in situ by stroboscopic X-ray micro-tomography. With a total duration of 6.5 s and 2 kHz frame rate, our experiments provide unprecedented access to the multiscale liquid dynamics. Our tomography strategy relies on the fact that Haines jumps, although irregularly spaced in time, are almost deterministic, and therefore repeatable during imbibition-drainage cycling. We studied the time-dependent flow pattern in a porous medium consisting of sintered glass shards. Exploiting the repeatability, we could combine the radiographic projections recorded under different angles during successive cycles into a 3D movie, allowing us to reconstruct pore-scale events, such as Haines jumps, with a spatiotemporal resolution that is two orders of magnitude higher than was hitherto possible. This high resolution allows us to explore the detailed interfacial dynamics during drainage, including fluid-front displacements and velocities. Our experimental approach opens the way to the study of fast, yet deterministic mesoscopic processes also other than flow in porous media.

Triggering interfacial instabilities during forced imbibition by adjusting the aspect ratio in depth-variable microfluidic porous media.

We present a comprehensive description of the aspect ratio impact on interfacial instability in porous media where a wetting liquid displaces a nonwetting fluid. Building on microfluidic experiments, we evidence imbibition scenarios yielding interfacial instabilities and macroscopic morphologies under different depth confinements, which were controlled by aspect ratio and capillary number. We report a phenomenon whereby a smaller aspect ratio of depth-variable microfluidic porous media and lower capillary number trigger interfacial instability during forced imbibition; otherwise, a larger aspect ratio of uniform-depth microfluidic porous media and higher capillary number will suppress the interfacial instability, which seemingly ignored or contradicts conventional expectations with compact and faceted growth during imbibition. Pore-scale theoretical analytical models, numerical simulations, as well as microfluidic experiments were combined for characteristics of microscopic interfacial dynamics and macroscopic displacement results as a function of aspect ratio, depth variation, and capillary number. Our results present a complete dynamic view of the imbibition process over a full range of regimes from interfacial stabilization to destabilization. We predict the mode of imbibition in porous media based on pore-scale interfacial behavior, which fits well with microfluidic experiments. The study provides insights into the role of aspect ratio in controlling interfacial instabilities in microfluidic porous media. The finding provides design or prediction principles for engineered porous media, such as microfluidic devices, membranes, fabric, exchange columns, and even soil and rocks concerning their desired immiscible imbibition behavior.

Sharing of misinformation is habitual, not just lazy or biased.

Why do people share misinformation on social media? In this research (N = 2,476), we show that the structure of online sharing built into social platforms is more important than individual deficits in critical reasoning and partisan bias-commonly cited drivers of misinformation. Due to the reward-based learning systems on social media, users form habits of sharing information that attracts others' attention. Once habits form, information sharing is automatically activated by cues on the platform without users considering response outcomes such as spreading misinformation. As a result of user habits, 30 to 40% of the false news shared in our research was due to the 15% most habitual news sharers. Suggesting that sharing of false news is part of a broader response pattern established by social media platforms, habitual users also shared information that challenged their own political beliefs. Finally, we show that sharing of false news is not an inevitable consequence of user habits: Social media sites could be restructured to build habits to share accurate information.