Author Correction: The measurement of partisan sorting for 180 million voters
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Optical sorting: past, present and future
Optical sorting combines optical tweezers with diverse techniques, including optical spectrum, artificial intelligence (AI) and immunoassay, to endow unprecedented capabilities in particle sorting. In comparison to other methods such as microfluidics, acoustics and electrophoresis, optical sorting offers appreciable advantages in nanoscale precision, high resolution, non-invasiveness, and is becoming increasingly indispensable in fields of biophysics, chemistry, and materials science. This review aims to offer a comprehensive overview of the history, development, and perspectives of various optical sorting techniques, categorised as passive and active sorting methods. To begin, we elucidate the fundamental physics and attributes of both conventional and exotic optical forces. We then explore sorting capabilities of active optical sorting, which fuses optical tweezers with a diversity of techniques, including Raman spectroscopy and machine learning. Afterwards, we reveal the essential roles played by deterministic light fields, configured with lens systems or metasurfaces, in the passive sorting of particles based on their varying sizes and shapes, sorting resolutions and speeds. We conclude with our vision of the most promising and futuristic directions, including AI-facilitated ultrafast and bio-morphology-selective sorting. It can be envisioned that optical sorting will inevitably become a revolutionary tool in scientific research and practical biomedical applications.
Inoculation reduces social media engagement with affectively polarized content in the UK and US
The generation and distribution of hyper-partisan content on social media has gained millions of exposure across platforms, often allowing malevolent actors to influence and disrupt democracies. The spread of this content is facilitated by real users’ engaging with it on platforms. The current study tests the efficacy of an ‘inoculation’ intervention via six online survey-based experiments in the UK and US. Experiments 1–3 (total N = 3276) found that the inoculation significantly reduced self-reported engagement with polarising stimuli. However, Experiments 4–6 (total N = 1878) found no effects on participants’ self-produced written text discussing the topic. The implications of these findings are discussed in the context of the literature on polarisation and previous interventions to reduce engagement with disinformation.
Informational ecosystems partially explain differences in socioenvironmental conceptual associations between U.S. American racial groups
Social groups represent a collective identity defined by a distinct consensus of concepts (e.g., ideas, values, and goals) whose structural relationship varies between groups. Here we set out to measure how a set of inter-concept semantic associations, comprising what we refer to as a concept graph, covaries between established social groups, based on racial identity, and how this effect is mediated by information ecosystems, contextualized as news sources. Group differences among racial identity (278 Black and 294 white Americans) and informational ecosystems (Left- and Right- leaning news sources) are present in subjective judgments of how the meaning of concepts such as healthcare, police, and voting relate to each other. These racial group differences in concept graphs were partially mediated by the bias of news sources that individuals get their information from. This supports the idea of groups being defined by common conceptual semantic relationships that partially arise from shared information ecosystems.
Generative language models exhibit social identity biases
Social identity biases, particularly the tendency to favor one’s own group (ingroup solidarity) and derogate other groups (outgroup hostility), are deeply rooted in human psychology and social behavior. However, it is unknown if such biases are also present in artificial intelligence systems. Here we show that large language models (LLMs) exhibit patterns of social identity bias, similarly to humans. By administering sentence completion prompts to 77 different LLMs (for instance, ‘We are…’), we demonstrate that nearly all base models and some instruction-tuned and preference-tuned models display clear ingroup favoritism and outgroup derogation. These biases manifest both in controlled experimental settings and in naturalistic human–LLM conversations. However, we find that careful curation of training data and specialized fine-tuning can substantially reduce bias levels. These findings have important implications for developing more equitable artificial intelligence systems and highlight the urgent need to understand how human–LLM interactions might reinforce existing social biases.
Ultra-sensitive fluorescence-activated droplet single-cell sorting based on Tetramer-HCR-EvaGreen amplification
The current single-cell analysis technologies such as fluorescence-activated cell sorting (FACS) and fluorescence-activated droplet sorting (FADS) could decipher the cellular heterogeneity but were constrained by low sorting performance and cell viability. Here, an ultra-sensitive single-cell sorting platform has been developed by integrating the FADS technology with Tetramer-HCR-EvaGreen (THE) fluorescence signal amplification. The THE system produced much higher fluorescence signal than that of the single Tetramer or Tetramer-HCR signal amplification. Upon application to target MCF-7 cells, the platform exhibited high efficacy and selectivity while maintaining more than 95% cell viability. The THE-FADS achieved sorting efficiencies of 55.5% and 50.3% with purities of 91% and 85% for MCF-7 cells in PBS solutions and simulated serum samples, respectively. The sorted MCF-7 cells showed similar proliferation together with CK19 and EGFR mRNA expression compared with the control cells. The established THE-FADS showed the promising prospects to cellular heterogeneity understanding and personalized medicine.
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