PNAS nexus最新文献

The primary function of biological membranes is to enable compartmentalization among cells and organelles. Loss of integrity by the formation of membrane pores would trigger uncontrolled depolarization or influx of toxic compounds, posing a fatal threat to living cells. How the lipid complexity of biological membranes enables mechanical stability against pore formation while, simultaneously, allowing for ongoing membrane remodeling is largely enigmatic. We performed molecular dynamics simulations of eight complex lipid membranes including the plasma membrane and membranes of the organelles endoplasmic reticulum, Golgi, lysosome, and mitochondrion. To quantify the mechanical stability of these membranes, we computed the free energy of transmembrane pore nucleation as well as the line tension of the rim of open pores. Our simulations reveal that complex biological membranes are remarkably stable, however, with the plasma membrane standing out as exceptionally stable, which aligns with its crucial role as a protective layer. We observe that sterol content is a key regulator for biomembrane stability, and that lateral sorting among lipid mixtures influences the energetics of membrane pores. A comparison of 25 model membranes with varying sterol content, tail length, tail saturation, and head group type shows that the pore nucleation free energy is mostly associated with the lipid tilt modulus, whereas the line tension along the pore rim is determined by the lipid intrinsic curvature. Together, our study provides an atomistic and energetic view on the role of lipid complexity in biomembrane stability.

Discovering manufacturing processes has been largely experienced-based. We propose a shift to a systematic approach driven by dependencies between energy inputs and performance outputs. Uncovering these dependencies across diverse process classes requires a universal language that characterizes process inputs and performances. Traditional manufacturing languages, with their individualized syntax and terminology, hinder the characterization across varying length scales and energy inputs. To enable the evaluation of process dependencies, we propose a broad manufacturing language that facilitates the characterization of diverse process classes, which include energy inputs, tool-material interactions, material compatibility, and performance outputs. We analyze the relationships between these characteristics by constructing a dataset of over 50 process classes, which we use to train a variational autoencoder (VAE) model. This generative model encodes our dataset into a 2D latent space, where we can explore, select, and generate processes based on desired performances and retrieve the corresponding process characteristics. After verifying the dependencies derived from the VAE model match with existing knowledge on manufacturing processes, we demonstrate the usefulness of using the model to discover new potential manufacturing processes through three illustrative cases.

We present an ultrafast thermodynamics framework to model heat generation and entropy production in laser-driven ferromagnetic systems. By establishing a connection between the magnetic field strength of the laser pulse and magnetization dynamics, we model time-dependent entropy production rates and deduce the associated heat dissipation in epitaxial and polycrystalline FeNi and CoFeB thin films. Our theoretical predictions are validated by comparison to experimental magnetization dynamics data, shedding light on thermodynamic processes on picosecond timescales. Crucially, we incorporate recently observed inertial spin dynamics, to describe their impact on heat generation in pump-probe experiments. As such, this formalism provides novel insights into controlling heat production in magnetic systems and contributes to advancing the understanding of nonequilibrium thermodynamics in magnetic systems, with implications for future experimental protocols in spintronics and nanotechnology.

The diffusion of high-yielding crop varieties has been a key driver for agricultural productivity. This study examines the relationship between the adoption of high-yielding crop varieties of two staple crops-wheat and maize-and infant mortality in rural China. Using data from 1954 to 1987, we find a significant reduction in infant mortality linked to high-yielding crop varieties diffusion, an association that remains robust even after excluding the Great Famine years. We investigate potential mechanisms driving this relationship, including increased grain production, improved infant nutrition, and changes in maternal characteristics. Additionally, our analysis unveils a spectrum of heterogeneous relationships between high-yielding crop varieties adoption and infant mortality across factors such as infant gender, maternal characteristics, and policy regulation. These findings reaffirm the positive and lasting benefits of dissemination of high-yielding crop varieties for human welfare and provide valuable policy insights for developing nations grappling with food and nutritional insecurity.

Misinformation disrupts our information ecosystem, adversely affecting individuals and straining social cohesion and democracy. Understanding what causes online (mis)information to (re)appear is crucial for fortifying our information ecosystem. We analyzed a large-scale Twitter (now "X") dataset of about 2 million tweets across 123 fact-checked stories. Previous research suggested a falsehood effect (false information reappears more frequently) and an ambiguity effect (ambiguous information reappears more frequently). However, robust indicators for their existence remain elusive. Using polynomial statistical modeling, we compared a falsehood model, an ambiguity model, and a dual effect model. The data supported the dual effect model ( $13.76$ times as likely as a null model), indicating both ambiguity and falsehood promote information reappearance. However, evidence for ambiguity was stronger: the ambiguity model was $6.6$ times as likely as the falsehood model. Various control checks affirmed the ambiguity effect, while the falsehood effect was less stable. Nonetheless, the best-fitting model explained $<7\%$ of the variance, indicating that (i) the dynamics of online (mis)information are complex and (ii) falsehood effects may play a smaller role than previous research has suggested. These findings underscore the importance of understanding the dynamics of online (mis)information, though our focus on fact-checked stories may limit the generalizability to the full spectrum of information shared online. Even so, our results can inform policymakers, journalists, social media platforms, and the public in building a more resilient information environment, while also opening new avenues for research, including source credibility, cross-platform applicability, and psychological factors.

Smartphones enable people to access the online world from anywhere at any time. Despite the benefits of this technology, there is growing concern that smartphone use could adversely impact cognitive functioning and mental health. Correlational and anecdotal evidence suggests that these concerns may be well-founded, but causal evidence remains scarce. We conducted a month-long randomized controlled trial to investigate how removing constant access to the internet through smartphones might impact psychological functioning. We used a mobile phone application to block all mobile internet access from participants' smartphones for 2 weeks and objectively track compliance. This intervention specifically targeted the feature that makes smartphones "smart" (mobile internet) while allowing participants to maintain mobile connection (through texts and calls) and nonmobile access to the internet (e.g. through desktop computers). The intervention improved mental health, subjective well-being, and objectively measured ability to sustain attention; 91% of participants improved on at least one of these outcomes. Mediation analyses suggest that these improvements can be partially explained by the intervention's impact on how people spent their time; when people did not have access to mobile internet, they spent more time socializing in person, exercising, and being in nature. These results provide causal evidence that blocking mobile internet can improve important psychological outcomes, and suggest that maintaining the status quo of constant connection to the internet may be detrimental to time use, cognitive functioning, and well-being.

Human trisomy 21, responsible for Down syndrome, is the most prevalent genetic cause of cognitive impairment and remains a key focus for prenatal and preimplantation diagnosis. However, research directed toward eliminating supernumerary chromosomes from trisomic cells is limited. The present study demonstrates that allele-specific multiple chromosome cleavage by clustered regularly interspaced palindromic repeats Cas9 can achieve trisomy rescue by eliminating the target chromosome from human trisomy 21 induced pluripotent stem cells and fibroblasts. Unlike previously reported allele-nonspecific strategies, we have developed a comprehensive allele-specific (AS) Cas9 target sequence extraction method that efficiently removes the target chromosome. The temporary knockdown of DNA damage response genes increases the chromosome loss rate, while chromosomal rescue reversibly restores gene signatures and ameliorates cellular phenotypes. Additionally, this strategy proves effective in differentiated, nondividing cells. We anticipate that an AS approach will lay the groundwork for more sophisticated medical interventions targeting trisomy 21.

The World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern on 2020 January 31. However, rumors of a "mysterious virus" had already been circulating in China in 2019 December, possibly preceding the first confirmed COVID-19 case. Understanding how awareness about an emerging pandemic spreads through society is vital not only for enhancing disease surveillance, but also for mitigating demand shocks and social inequities, such as shortages of personal protective equipment and essential supplies. Here we leverage a massive e-commerce dataset comprising 150 billion online queries and purchase records from 94 million people to detect the traces of early awareness and public response during the cryptic transmission period of COVID-19. Our analysis focuses on identifying information gaps across different demographic cohorts, revealing significant social inequities and the role of cultural factors in shaping awareness diffusion and response behaviors. By modeling awareness diffusion in heterogeneous social networks and analyzing online shopping behavior, we uncover the evolving characteristics of vulnerable populations. Our findings expand the theoretical understanding of awareness spread and social inequality in the early stages of a pandemic, highlighting the critical importance of e-commerce data and social network data in effectively and timely addressing future pandemic challenges. We also provide actionable recommendations to better manage and mitigate dynamic social inequalities in public health crises.

The rebound curve remains the most prevalent model for conceptualizing, measuring, and explaining resilience for engineering and community systems by tracking the functional robustness and recovery of systems over time. (It also goes by many names, including the resilience curve, the resilience triangle, and the system functionality curve, among others.) Despite longstanding recognition that resilience is more than rebound, the curve remains highly used, cited, and taught. In this article, we challenge the efficacy of this model for resilience and identify fundamental shortcomings in how it handles system function, time, dynamics, and decisions - the key elements that make up the curve. These oversimplifications reinforce misconceptions about resilience that are unhelpful for understanding complex systems and are potentially dangerous for guiding decisions. We argue that models of resilience should abandon the use of this curve and instead be reframed to open new lines of inquiry that center on improving adaptive capacity in complex systems, rather than on functional rebound. We provide a list of questions to help future researchers communicate these limitations and address any implications on recommendations derived from its use.

Several comorbidities of sickle cell disease (SCD) originate from red blood cell (RBC) instability, chronic inflammation, and oxidative stress. Development of scalable, cost-effective therapeutics suitable for chronic administration to prevent, attenuate, and perhaps reverse the consequences of RBC instability is needed. Curcumin has many of these attributes as a safe compound with antisickling, antiinflammatory, and antioxidant properties, but its translational potential has been constrained due to limited bioavailability from oral administration. The present study demonstrates the rapid and high bioavailability of a novel topical/transdermal (TD) curcumin gel formulation in the plasma and blood cells and its effectiveness in humanized sickle cell mice in: (i) ameliorating features of sickle cell pain hypersensitivity and axonal injury; (ii) reducing multiple manifestations of RBC instability including evidence of decreased hemolysis (reduced lactate dehydrogenase levels), enhanced RBC ATP levels along with decreased oxidative damage; (iii) decreasing multiple proinflammatory cytokines including interleukin-6, monocyte chemoattractant protein-1, granulocyte-macrophage colony-stimulating factor, and activation, normal T cell expressed and secreted protein in skin secretome; and (iv) reducing mast cell degranulation and activation. Our data suggest that an easy-to-use novel TD curcumin gel formulation has the potential to ameliorate chronic pain, improve RBC stability, and reduce inflammatory consequences of SCD.