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While scientific researchers often aim for high productivity, prioritizing the quantity of publications may come at the cost of time and effort dedicated to individual research. It is thus important to examine the relationship between productivity and disruption for individual researchers. Here, we show that with the increase in the number of published papers, the average citation per paper will be higher yet the mean disruption of papers will be lower. In addition, we find that the disruption of scientists' papers may decrease when they are highly productive in a given year. The disruption of papers in each year is not determined by the total number of papers published in the author's career, but rather by the productivity of that particular year. Besides, more productive authors also tend to give references to recent and high-impact research. Our findings highlight the potential risks of pursuing productivity and aim to encourage more thoughtful career planning among scientists. [ABSTRACT FROM AUTHOR]

The present study examines the assumptions, modeling structure, and results of DICE-2023, the revised Dynamic Integrated Model of Climate and the Economy (DICE), updated to 2023. The revision contains major changes in the treatment of risk, the carbon and climate modules, the treatment of nonindustrial greenhouse gases, discount rates, as well as updates on all the major components. Noteworthy changes are a significant reduction in the target for the optimal (cost-beneficial) temperature path, a lower cost of reaching the 2 °C target, an analysis of the impact of the Paris Accord, and a major increase in the estimated social cost of carbon. [ABSTRACT FROM AUTHOR]

Scientific datasets play a crucial role in contemporary data-driven research, as they allow for the progress of science by facilitating the discovery of new patterns and phenomena. This mounting demand for empirical research raises important questions on how strategic data utilization in research projects can stimulate scientific advancement. In this study, we examine the hypothesis inspired by the recombination theory, which suggests that innovative combinations of existing knowledge, including the use of unusual combinations of datasets, can lead to high-impact discoveries. Focusing on social science, we investigate the scientific outcomes of such atypical data combinations in more than 30,000 publications that leverage over 5,000 datasets curated within one of the largest social science databases, Interuniversity Consortium for Political and Social Research. This study offers four important insights. First, combining datasets, particularly those infrequently paired, significantly contributes to both scientific and broader impacts (e.g., dissemination to the general public). Second, infrequently paired datasets maintain a strong association with citation even after controlling for the atypicality of dataset topics. In contrast, the atypicality of dataset topics has a much smaller positive impact on citation counts. Third, smaller and less experienced research teams tend to use atypical combinations of datasets in research more frequently than their larger and more experienced counterparts. Last, despite the benefits of data combination, papers that amalgamate data remain infrequent. This finding suggests that the unconventional combination of datasets is an underutilized but powerful strategy correlated with the scientific impact and broader dissemination of scientific discoveries. [ABSTRACT FROM AUTHOR]

Atomically dispersed catalysts are a promising alternative to platinum group metal catalysts for catalyzing the oxygen reduction reaction (ORR), while limited durability during the electrocatalytic process severely restricts their practical application. Here, we report an atomically dispersed Co-doped carbon−nitrogen bilayer catalyst with unique dual-axial Co–C bonds (denoted as Co/DACN) by a smart phenyl-carbon-induced strategy, realizing highly efficient electrocatalytic ORR in both alkaline and acidic media. The corresponding half-wave potential for ORR is up to 0.85 and 0.77 V (vs. reversible hydrogen electrode (RHE)) in 0.5 M H2SO4 and 0.1 M KOH, respectively, representing the best ORR activity among all non-noble metal catalysts reported to date. Impressively, the Zn–air battery (ZAB) equipped with Co/DACN cathode achieves outstanding durability after 1,688 h operation at 10 mA cm−2 with a high current density (154.2 mA cm−2) and a peak power density (210.1 mW cm−2). Density functional theory calculations reveal that the unique dual-axial cross-linking Co−C bonds of Co/DACN significantly enhance the stability during ORR and also facilitate the 4e− ORR pathway by forming a joint electron pool due to the improved interlayer electron mobility. We believe that axial engineering opens a broad avenue to develop high-performance heterogeneous electrocatalysts for advanced energy conversion and storage. [ABSTRACT FROM AUTHOR]

Carbon-based single-atom catalysts, a promising candidate in electrocatalysis, offer insights into electron-donating effects of metal center on adjacent atoms. Herein, we present a practical strategy to rationally design a model catalyst with a single zinc (Zn) atom coordinated with nitrogen and sulfur atoms in a multilevel carbon matrix. The Zn site exhibits an atomic interface configuration of ZnN4S1, where Zn's electron injection effect enables thermal-neutral hydrogen adsorption on neighboring atoms, pushing the activity boundaries of carbon electrocatalysts toward electrochemical hydrogen evolution to an unprecedented level. Experimental and theoretical analyses confirm the low-barrier Volmer-Tafel mechanism of proton reduction, while the multishell hollow structures facilitate the hydrogen evolution even at high current intensities. This work provides insights for understanding the actual active species during hydrogen evolution reaction and paves the way for designing high-performance electrocatalysts. [ABSTRACT FROM AUTHOR]

This article explores the benefits and challenges of using the first-person voice in scientific writing. The author shares their personal experience of having to change their writing style to conform to traditional norms. They argue that writing in the first person can enhance readability, conciseness, and accessibility of scientific articles. The article also addresses concerns about the use of first person in scientific papers and highlights the increasing acceptance of first-person pronouns in the field. The author encourages scientists to embrace first-person pronouns and move beyond outdated conventions to improve communication with readers. [Extracted from the article]

This document serves as an introduction to a special feature on modeling for sustainability. It showcases recent advances in sustainability science and the integration of modeling with theory and data-focused approaches. The papers in this feature highlight innovative modeling methods and their applications in fields such as air quality, water resources, and energy systems. The authors emphasize the importance of equitable distribution and consider distributional issues in modeling. They also discuss the different stages of modeling practice, including defining the purpose, selecting components, analyzing interactions, and assessing interventions. The document concludes by emphasizing the potential of modeling to contribute to long-term sustainability goals. Additionally, there is a list of references to various scientific articles and studies related to topics such as water management, climate change, energy transition, and sustainability. These articles cover a range of subjects including modeling and decision-making in watershed management, the nexus between water, land, food, and climate, the impact of climate oscillations on water supply planning, and the social aspects of the energy transition. These articles provide valuable insights and research findings for library patrons conducting research on these specific topics. [Extracted from the article]

This document is a compilation of various scientific papers and preprints covering a wide range of topics, including protein folding, cell migration, machine learning, deep neural networks, neural scaling laws, representations and generalization in artificial and brain neural networks, and the neuron as a direct data-driven controller. The papers discuss different aspects of these subjects, providing a comprehensive overview of the current research in these fields. This document can be a valuable resource for library patrons conducting research on these topics, particularly in the fields of neuroscience and artificial intelligence. [Extracted from the article]

In the mid-1950s, Arthur Kornberg elucidated the enzymatic synthesis of DNA by DNA polymerase, for which he was recognized with the 1959 Nobel Prize in Physiology or Medicine. He then identified many of the proteins that cooperate with DNA polymerase to replicate duplex DNA of small bacteriophages. However, one major unanswered problem was understanding the mechanism and control of the initiation of chromosome replication in bacteria. In a seminal paper in 1981, Fuller, Kaguni, and Kornberg reported the development of a cell-free enzyme system that could replicate DNA that was dependent on the bacterial origin of DNA replication, oriC. This advance opened the door to a flurry of discoveries and important papers that elucidated the process and control of initiation of chromosome replication in bacteria. [ABSTRACT FROM AUTHOR]

Despite the long-standing calls for increased levels of interdisciplinary research as a way to address society's grand challenges, most science is still disciplinary. To understand the slow rate of convergence to more interdisciplinary research, we examine 154,021 researchers who received a PhD in a biomedical field between 1970 and 2013, measuring the interdisciplinarity of their articles using the disciplinary composition of references. We provide a range of evidence that interdisciplinary research is impactful, but that those who conduct it face early career impediments. The researchers who are initially the most interdisciplinary tend to stop publishing earlier in their careers--it takes about 8 y for half of the researchers in the top percentile in terms of initial interdisciplinarity to stop publishing, compared to more than 20 y for moderately interdisciplinary researchers (10th to 75th percentiles). Moreover, perhaps in response to career challenges, initially interdisciplinary researchers on average decrease their interdisciplinarity over time. These forces reduce the stock of interdisciplinary researchers who can train future cohorts. Indeed, new graduates tend to be less interdisciplinary than the stock of active researchers. We show that interdisciplinarity does increase over time despite these dampening forces because initially disciplinary researchers become more interdisciplinary as their careers progress. [ABSTRACT FROM AUTHOR]

Soft and biological matter come in a variety of shapes and geometries. When soft surfaces that do not fit into each other due to a mismatch in Gaussian curvatures form an interface, beautiful geometry-induced patterns are known to emerge. In this paper, we study the effect of geometry on the dynamical response of soft surfaces moving relative to each other. Using a simple experimental scheme, we measure friction between a highly bendable thin polymer sheet and a hydrogel substrate. At this soft and low-friction interface, we find a strong dependence of friction on the relative geometry of the two surfaces--a flat sheet experiences significantly larger friction on a spherical substrate than on flat or cylindrical substrate. We show that the stress developed in the sheet due to its geometrically incompatible confinement is responsible for the enhanced friction. This mechanism also leads to a transition in the nature of friction as the sheet radius is increased beyond a critical value. Our finding reveals a hitherto unnoticed mechanism based on an interplay between geometry and elasticity that may influence friction significantly in soft, biological, and nanoscale systems. In particular, it provokes us to reexamine our understanding of phenomena such as the curvature dependence of biological cell mobility. [ABSTRACT FROM AUTHOR]

Works of fiction play a crucial role in the production of cultural stereotypes. Concerning gender, a widely held presumption is that many such works ascribe agency to men and passivity to women. However, large-scale diachronic analyses of this notion have been lacking. This paper provides an assessment of agency attributions in 87,531 fiction works written between 1850 and 2010. It introduces a syntax-based approach for extracting networks of character interactions. Agency is then formalized as a dyadic property: Does a character primarily serve as an agent acting upon the other character or as recipient acted upon by the other character? Findings indicate that female characters are more likely to be passive in cross-gender relationships than their male counterparts. This difference, the gender agency gap, has declined since the 19th century but persists into the 21st. Male authors are especially likely to attribute less agency to female characters. Moreover, certain kinds of actions, especially physical and villainous ones, have more pronounced gender disparities. [ABSTRACT FROM AUTHOR]

In the past three decades, there has been a rise in young academy movements in the Global North and South. Such movements, in at least Germany and the Netherlands, have been shown to be quite effective in connecting scientific work with society. Likewise, these movements share a common goal of developing interdisciplinary collaboration among young scientists, which contributes to the growth of a nation's--but also global--scientific endeavors. This paper focuses on the young academy movement in the fourth-largest country hosting the biggest Muslim population in the world, which is also the third-most populous democracy: Indonesia. We observe that there has been rising awareness among the young generation of scientists in Indonesia of the need to advocate for the use of sciences in responding to upcoming and current multidimensional crises. Science advocacy can be seen in their peer-based identification of Indonesia's future challenges, encompassing the fundamental areas for scientific inquiry, discovery, and intervention. We focus on the Indonesian Young Academy of Sciences (ALMI) and its network of young scientists. We describe ALMI's science communication practice, specifically SAINS45 and Science for Indonesia's Biodiversity, and how they have been useful for policymakers, media, and school engagements. The article closes with a reflection on future directions for the young academy movement in Indonesia and beyond. [ABSTRACT FROM AUTHOR]

Long noncoding RNAs (lncRNAs) are transcribed elements increasingly recognized for their roles in regulating gene expression. Thus far, however, we have little understanding of how lncRNAs contribute to evolution and adaptation. Here, we show that a conserved lncRNA, ivory, is an important color patterning gene in the buckeye butterfly Junonia coenia. ivory overlaps with cortex, a locus linked to multiple cases of crypsis and mimicry in Lepidoptera. Along with a companion paper by Livraghi et al., we argue that ivory, not cortex, is the color pattern gene of interest at this locus. In J. coenia, a cluster of cis-regulatory elements (CREs) in the first intron of ivory are genetically associated with natural variation in seasonal color pattern plasticity, and targeted deletions of these CREs phenocopy seasonal phenotypes. Deletions of different ivory CREs produce other distinct phenotypes as well, including loss of melanic eyespot rings, and positive and negative changes in overall wing pigmentation. We show that the color pattern transcription factors Spineless, Bric-a-brac, and Ftz-f1 bind to the ivory promoter during wing pattern development, suggesting that they directly regulate ivory. This case study demonstrates how cis-regulation of a single noncoding RNA can exert diverse and nuanced effects on the evolution and development of color patterns, including modulating seasonally plastic color patterns. [ABSTRACT FROM AUTHOR]

Small bubbles in fluids rise to the surface due to Archimede's force. Remarkably, in turbulent flows this process is severely hindered by the presence of vortex filaments, which act as moving potential wells, dynamically trapping light particles and bubbles. Quantifying the statistical weights and roles of vortex filaments in turbulence is, however, still an outstanding experimental and computational challenge due to their small scale, fast chaotic motion, and transient nature. Here we show that, under the influence of a modulated oscillatory forcing, the collective bubble behavior switches from a dynamically localized to a delocalized state. Additionally, we find that by varying the forcing frequency and amplitude, a remarkable resonant phenomenon between light particles and small-scale vortex filaments emerges, likening particle behavior to a forced damped oscillator. We discuss how these externally actuated bubbles can be used as a type of microscopic probe to investigate the space-time statistical properties of the smallest turbulence scales, allowing to quantitatively measure physical characteristics of vortex filaments. We develop a superposition model that is in excellent agreement with the simulation data of the particle dynamics which reveals the fraction of localized/delocalized particles as well as characteristics of the potential landscape induced by vortices in turbulence. Our approach paves the way for innovative ways to accurately measure turbulent properties and to the possibility to control light particles and bubble motions in turbulence with potential applications to oceanography, medical imaging, drug/gene delivery, chemical reactions, wastewater treatment, and industrial mixing. [ABSTRACT FROM AUTHOR]

A major next step in hematopoietic stem cell (HSC) biology is to enhance our quantitative understanding of cellular and evolutionary dynamics involved in undisturbed hematopoiesis. Mathematical models have been and continue to be key in this respect, and are most powerful when parameterized experimentally and containing sufficient biological complexity. In this paper, we use data from label propagation experiments in mice to parameterize a mathematical model of hematopoiesis that includes homeostatic control mechanisms as well as clonal evolution. We find that nonlinear feedback control can drastically change the interpretation of kinetic estimates at homeostasis. This suggests that short-term HSC and multipotent progenitors can dynamically adjust to sustain themselves temporarily in the absence of long-term HSCs, even if they differentiate more often than they self-renew in undisturbed homeostasis. Additionally, the presence of feedback control in the model renders the system resilient against mutant invasion. Invasion barriers, however, can be overcome by a combination of age-related changes in stem cell differentiation and evolutionary niche construction dynamics based on a mutant-associated inflammatory environment. This helps us understand the evolution of e.g., TET2 or DNMT3A mutants, and how to potentially reduce mutant burden. [ABSTRACT FROM AUTHOR]

Creating opportunities for people to achieve socioeconomic mobility is a widely shared societal goal. Paradoxically, however, achieving this goal can pose a threat to high-socioeconomic-status (SES) people as they look to maintain their privileged positions in society for both them and their children. Two studies evaluate whether this threat manifests as "opportunity hoarding" in which high-SES parents adopt attitudes and behaviors aimed at shoring up their families' access to valuable educational and economic resources. The current paper provides converging evidence for this hypothesis across two studies conducted with 2,557 American parents. An initial correlational study demonstrated that believing that socioeconomic mobility is possible was associated with high-SES parents being more inclined to attempt to secure valuable educational and economic resources for their children, even when doing so came at the cost of low-SES families. Specifically, high-SES parents with stronger beliefs in socioeconomic mobility exhibited decreased support for redistributive policies and viewed engaging in discrete behaviors that would unfairly advantage their children (e.g., allowing them to misrepresent their identities on school and job applications) as more acceptable relative to both low-SES parents with similar beliefs and high-SES parents who were less optimistic about socioeconomic mobility. A subsequent experimental study established these relationships causally by comparing parents' responses to different types of socioeconomic mobility. Together, the current findings merge insights across psychology and economics to deepen understandings of the processes through which societal inequities emerge and persist, especially during times of apparently abundant opportunity. [ABSTRACT FROM AUTHOR]

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures. [ABSTRACT FROM AUTHOR]

Finding optimal bipartite matchings--e.g., matching medical students to hospitals for residency, items to buyers in an auction, or papers to reviewers for peer review--is a fundamental combinatorial optimization problem. We found a distributed algorithm for computing matchings by studying the development of the neuromuscular circuit. The neuromuscular circuit can be viewed as a bipartite graph formed between motor neurons and muscle fibers. In newborn animals, neurons and fibers are densely connected, but after development, each fiber is typically matched (i.e., connected) to exactly one neuron. We cast this synaptic pruning process as a distributed matching (or assignment) algorithm, where motor neurons "compete" with each other to "win" muscle fibers. We show that this algorithm is simple to implement, theoretically sound, and effective in practice when evaluated on real-world bipartite matching problems. Thus, insights from the development of neural circuits can inform the design of algorithms for fundamental computational problems. [ABSTRACT FROM AUTHOR]

This article provides an overview of the Flotillin cage, a protein complex found in various organisms, including humans. The Flotillin cage plays a role in several cellular processes, such as endocytosis, mitochondrial function, intracellular trafficking, and touch sensation. The study focuses on the structure of the Flotillin-1/Flotillin-2 complex, which forms a circular cage structure consisting of 22 heterodimers. The research paper cited, titled "In-situ architecture of the human prohibitin complex" by Lange et al., delves into the architecture and function of the human prohibitin complex, offering valuable insights. The citation provides links to access the full paper for further exploration. [Extracted from the article]

There is growing recognition of the potential of migration to contribute to climate-change adaptation. Yet, there is limited evidence to what degree, under what conditions, for whom, and with which limitations this is effectively the case. We argue that this results from a lack of recognition and systematic incorporation of sociospatiality--the nested, networked, and intersectional nature of migration-as-adaptation. Our central objective is to utilize the translocal social-resilience approach to overcome these gaps, to identify processes and structures that shape the social resilience of translocal livelihood systems, and to illustrate the mechanisms behind the multiplicity of possible resilience outcomes. Translocal livelihood constellations anchored in rural Thailand as well as in domestic and international destinations of Thai migrants serve as illustrative empirical cases. Data were gathered through a multisited and mixed-methods research design. This paper highlights the role of the distinct but interlinked situations and operational logics at places of origin and destination, as well as the different positionalities and resulting vulnerabilities, roles, commitments, and practices of individuals and households with regard to resilience. Based on the empirical results, the paper distills a generalized typology of five broad categories of resilience outcomes, which explicitly considers sociospatiality. Our approach helps to grasp the complexity of migration-as-adaptation and to avoid simplistic conclusions about the benefits and costs of migration for adaptation--both of which are necessary for sound, evidence-based, migration-as-adaptation policymaking. [ABSTRACT FROM AUTHOR]

The article titled "Ecology" has been retracted due to methodological and biological anomalies in the raw data. The authors state that the behavioral data related to aggressive spiders in the study shared duplicated sequences with spiders from a separate, concurrent study, rendering the results unreliable. The retraction was initially requested in February 2020 and was postponed due to an investigation by McMaster University. The authors now wish to retract the paper to correct the scientific record. [Extracted from the article]

Desired fertility measures are routinely collected and used by researchers and policy makers, but their self-reported nature raises the possibility of reporting bias. In this paper, we test for the presence of such bias by comparing responses to direct survey questions with indirect questions offering a varying, randomized, degree of confidentiality to respondents in a socioeconomically diverse sample of Nigerian women (N = 6,256). We find that women report higher fertility preferences when asked indirectly, but only when their responses afford them complete confidentiality, not when their responses are simply blind to the enumerator. Our results suggest that there may be fewer unintended pregnancies than currently thought and that the effectiveness of family planning policy targeting may be weakened by the bias we uncover. We conclude with suggestions for future work on how to mitigate reporting bias. [ABSTRACT FROM AUTHOR]

Transcription of eukaryotic protein-coding genes generates immature mRNAs that are subjected to a series of processing events, including capping, splicing, cleavage, and polyadenylation (CPA), and chemical modifications of bases. Alternative polyadenylation (APA) greatly contributes to mRNA diversity in the cell. By determining the length of the 3' untranslated region, APA generates transcripts with different regulatory elements, such as miRNA and RBP binding sites, which can influence mRNA stability, turnover, and translation. In the model plant Arabidopsis thaliana, APA is involved in the control of seed dormancy and flowering. In view of the physiological importance of APA in plants, we decided to investigate the effects of light/dark conditions and compare the underlying mechanisms to those elucidated for alternative splicing (AS). We found that light controls APA in approximately 30% of Arabidopsis genes. Similar to AS, the effect of light on APA requires functional chloroplasts, is not affected in mutants of the phytochrome and cryptochrome photoreceptor pathways, and is observed in roots only when the communication with the photosynthetic tissues is not interrupted. Furthermore, mitochondrial and TOR kinase activities are necessary for the effect of light. However, unlike AS, coupling with transcriptional elongation does not seem to be involved since light-dependent APA regulation is neither abolished in mutants of the TFIIS transcript elongation factor nor universally affected by chromatin relaxation caused by histone deacetylase inhibition. Instead, regulation seems to correlate with changes in the abundance of constitutive CPA factors, also mediated by the chloroplast. [ABSTRACT FROM AUTHOR]

Ethics standards reference the need for special consideration of vulnerable populations, such as pregnant women, incarcerated individuals, and minors. The concept of vulnerability is poorly conceptualized in the medical sciences where it originated, and its application to the social sciences is even more challenging. Social science researchers may unwittingly fail to appreciate preexisting vulnerabilities and indeed may be responsible for inducing new research-related vulnerability. In this paper, we present the first comprehensive coding of country-level vulnerability designations. Specifically, we coded all 355 official documents governing social/behavioral human subjects research for the 107 countries with such regulations and identified 68 distinct vulnerability categories. The data reveal substantial regional variation, overemphasis of categories derived from medical sciences, neglect of critical categories such as displacement, and likely heterogeneity within and across groups. The article provides a conceptual framework that shifts the problem away from static, enumerated categories toward emphasis on research-induced vulnerability. Based on our conceptualization and coding, we present a framework for assessing vulnerability and implementing appropriate protections. [ABSTRACT FROM AUTHOR]

An increasingly common viewpoint is that protein dynamics datasets reside in a nonlinear subspace of low conformational energy. Ideal data analysis tools should therefore account for such nonlinear geometry. The Riemannian geometry setting can be suitable for a variety of reasons. First, it comes with a rich mathematical structure to account for a wide range of geometries that can be modeled after an energy landscape. Second, many standard data analysis tools developed for data in Euclidean space can be generalized to Riemannian manifolds. In the context of protein dynamics, a conceptual challenge comes from the lack of guidelines for constructing a smooth Riemannian structure based on an energy landscape. In addition, computational feasibility in computing geodesics and related mappings poses a major challenge. This work considers these challenges. The first part of the paper develops a local approximation technique for computing geodesics and related mappings on Riemannian manifolds in a computationally feasible manner. The second part constructs a smooth manifold and a Riemannian structure that is based on an energy landscape for protein conformations. The resulting Riemannian geometry is tested on several data analysis tasks relevant for protein dynamics data. In particular, the geodesics with given start- and end-points approximately recover corresponding molecular dynamics trajectories for proteins that undergo relatively ordered transitions with medium-sized deformations. The Riemannian protein geometry also gives physically realistic summary statistics and retrieves the underlying dimension even for large-sized deformations within seconds on a laptop. [ABSTRACT FROM AUTHOR]

One of California's most pressing social and environmental challenges is the rapid expansion of the wildlands-urban interface (WUI). Multiple issues associated with WUI growth compared to more dense and compact urban form are of concern--including greatly increased fire risk, greenhouse gas emissions, and fragmentation of habitat. However, little is understood about the factors driving this growth in the first place and, specifically, its relationship to urban-regional housing dynamics. This paper connects work in urban social science, urban and regional planning, and natural sciences to highlight the potential role of housing crises in driving displacement from the urban core to relatively more affordable exurbs, and with this, WUI growth. We analyze this relationship in California, which leads the nation in lack of affordable housing, scale of WUI growth, and many associated WUI hazards, including wildfire. We offer three related arguments: first, that California's affordable housing crisis, with its effect of driving migration to exurban areas, should be recognized as a significant urban form-related sustainability challenge; second, that to understand this challenge scholars must expand the spatial scale and analytic toolkit of both urban and WUI analysis through relational, mixed methods research; and third, that political and programmatic efforts to address California's housing crisis should undergird efforts to address WUI growth and climate change. Ultimately, we argue that expanding access to affordable urban housing can produce a more sustainable and just urban form that mitigates WUI-related climate and environmental impacts and reduces the vulnerability of growing numbers of WUI residents living in harm's way. [ABSTRACT FROM AUTHOR]

Iron antimonide (FeSb2) has been investigated for decades due to its puzzling electronic properties. It undergoes the temperature-controlled transition from an insulator to an ill-defined metal, with a cross-over from diamagnetism to paramagnetism. Extensive efforts have been made to uncover the underlying mechanism, but a consensus has yet to be reached. While macroscopic transport and magnetic measurements can be explained by different theoretical proposals, the essential spectroscopic evidence required to distinguish the physical origin is missing. In this paper, through the use of X-ray absorption spectroscopy and atomic multiplet simulations, we have observed the mixed spin states of 3d6 configuration in FeSb2. Furthermore, we reveal that the enhancement of the conductivity, whether induced by temperature or doping, is characterized by populating the high-spin state from the low-spin state. Our work constitutes vital spectroscopic evidence that the electrical/magnetical transition in FeSb2 is directly associated with the spin-state excitation. [ABSTRACT FROM AUTHOR]

Biologically detailed models of brain circuitry are challenging to build and simulate due to the large number of neurons, their complex interactions, and the many unknown physiological parameters. Simplified mathematical models are more tractable, but harder to evaluate when too far removed from neuroanatomy/physiology. We propose that a multiscale model, coarse-grained (CG) while preserving local biological details, offers the best balance between biological realism and computability. This paper presents such a model. Generally, CG models focus on the interaction between groups of neurons--here termed "pixels"--rather than individual cells. In our case, dynamics are alternately updated at intra- and interpixel scales, with one informing the other, until convergence to equilibrium is achieved on both scales. An innovation is how we exploit the underlying biology: Taking advantage of the similarity in local anatomical structures across large regions of the cortex, we model intrapixel dynamics as a single dynamical system driven by "external" inputs. These inputs vary with events external to the pixel, but their ranges can be estimated a priori. Precomputing and tabulating all potential local responses speed up the updating procedure significantly compared to direct multiscale simulation. We illustrate our methodology using a model of the primate visual cortex. Except for local neuron-to-neuron variability (necessarily lost in any CG approximation) our model reproduces various features of large-scale network models at a tiny fraction of the computational cost. These include neuronal responses as a consequence of their orientation selectivity, a primary function of visual neurons. [ABSTRACT FROM AUTHOR]

In one of the first papers on the impact of early-life conditions on individuals' health in older age, Barker and Osmond [Lancet, 327, 1077-1081 (1986)] show a strong positive relationship between infant mortality rates in the 1920s and ischemic heart disease in the 1970s. We merge historical data on infant mortality rates to 370,000 individual records in the UK Biobank using information on local area and year of birth. We replicate the association between the early-life infant mortality rate and laterlife ischemic heart disease in our sample. We then go "beyond Barker," by showing considerable genetic heterogeneity in this association that is robust to within-area as well as within-family analyses. We find no association between the polygenic index and heart disease in areas with the lowest infant mortality rates, but a strong positive relationship in areas characterized by high infant mortality. These findings suggest that advantageous environments can cushion one's genetic disease risk. [ABSTRACT FROM AUTHOR]

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, is difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general. [ABSTRACT FROM AUTHOR]

Recent years witnessed the development of powerful generative models based on flows, diffusion, or autoregressive neural networks, achieving remarkable success in generating data from examples with applications in a broad range of areas. A theoretical analysis of the performance and understanding of the limitations of these methods remain, however, challenging. In this paper, we undertake a step in this direction by analyzing the efficiency of sampling by these methods on a class of problems with a known probability distribution and comparing it with the sampling performance of more traditional methods such as the Monte Carlo Markov chain and Langevin dynamics. We focus on a class of probability distribution widely studied in the statistical physics of disordered systems that relate to spin glasses, statistical inference, and constraint satisfaction problems. We leverage the fact that sampling via flow-based, diffusionbased, or autoregressive networks methods can be equivalently mapped to the analysis of a Bayes optimal denoising of a modified probability measure. Our findings demonstrate that these methods encounter difficulties in sampling stemming from the presence of a first-order phase transition along the algorithm's denoising path. Our conclusions go both ways: We identify regions of parameters where these methods are unable to sample efficiently, while that is possible using standard Monte Carlo or Langevin approaches. We also identify regions where the opposite happens: standard approaches are inefficient while the discussed generative methods work well. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "MDGA1 negatively regulates amyloid precursor protein-mediated synapse inhibition in the hippocampus." The authors discovered errors in the images included in previous publications, which were due to a lapse in attention during figure presentation. The corrected figures have been provided, and the authors state that these corrections do not change the conclusions of the paper. The document describes the methods and materials used in the study, including gel digestion, LC-MS/MS analysis, antibodies, expression vectors, and various assays and experiments. It also provides detailed information on the experimental procedures used to study the effects of a virus on synaptic changes, as well as the methods used to measure synaptic currents and conduct behavioral analyses in mice. The results of the experiments suggest that APP loss affects GABAergic synapse density, synaptic transmission, and excitability in specific types of interneurons in the hippocampus. [Extracted from the article]

This paper exploits the potential of Global Positioning System datasets sourced from mobile phones to estimate the racial composition of road users, leveraging data from their respective Census block group. The racial composition data encompasses approximately 46 million trips in the Chicago metropolitan region. The research focuses on the relationship between camera tickets and racial composition of drivers vs. police stops for traffic citations and the racial composition in these locations. Black drivers exhibit a higher likelihood of being ticketed by automated speed cameras and of being stopped for moving violations on roads, irrespective of the proportion of White drivers present. The research observes that this correlation attenuates as the proportion of White drivers on the road increases. The citation rate measured by cameras better matches the racial composition of road users on the links with cameras than do stops by police officers. This study therefore presents an important contribution to understanding racial disparities in moving violation stops, with implications for policy interventions and social justice reforms. [ABSTRACT FROM AUTHOR]

This document is a reply to a comment on a recent paper that investigated the relationship between race and language markers of depression. The comment raised two critiques of the prediction analysis. The authors of the paper provided details of their analyses, including sample sizes, validation metrics, and training and test set sizes. They also discussed the possibility that the language of Black individuals explains less of the variance in depression than the language of White individuals. The authors used a specific language model called BERT to evaluate the predictive performance of different feature sets. They acknowledged the suggestion to use a different model called RoBERTa but explained their reasons for prioritizing interpretable feature sets. The authors also noted that all models performed relatively poorly on data from Black individuals compared to White individuals. [Extracted from the article]

This article explores the emergence of AI as a new frontier in behavioral science. It discusses a study that compares the decision-making of AI chatbots, specifically ChatGPT-3 and ChatGPT-4, with that of humans using classical behavioral assessments. The study reveals that AI models have distinct preferences and behaviors, leading to the development of a new research direction called "AI Behavioral Science." The article also examines the potential benefits and challenges of studying AI behavior, including its impact on human decision-making, bias correction, and policy design. It emphasizes the need for a comprehensive behavioral assessment framework and the engineering of AI behavior. Furthermore, the article discusses the potential impact of AI integration on human behavior and culture, such as algorithmic bias, cognitive decline, and the promotion of equality. The document includes references to various research papers and studies on biased programmers, biased data, AI ethics, recommender systems, social media, news consumption, and polarization, making it a valuable resource for library patrons researching these specific topics. [Extracted from the article]

This document is a retraction notice published in the Proceedings of the National Academy of Sciences of the United States of America. The retraction is for a neuroscience paper titled "Neurexin-2 restricts synapse numbers and restrains the presynaptic release probability by an alternative splicing-dependent mechanism." The authors of the paper have retracted it due to discrepancies between the original raw data and the published data source file, as well as copy-paste errors and shifted data points in one of the figures. The retraction notice includes a thank you to Dr. Daniel Matus of Stanford University for independently analyzing the primary raw data. The article is distributed under a Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0. [Extracted from the article]

This document is a retraction notice from the authors of a medical sciences paper titled "Endothelial expression of hypoxia-inducible factor 1 protects the murine heart and aorta from pressure overload by suppression of TGF-β signaling." The authors state that they are retracting the paper due to concerns with the presentation of data in immunoblot assays. Specifically, there were issues with the use of different blots and splicing of lanes in the analysis of phosphorylated ERK1/2. The authors apologize for any inconvenience caused and note that they were unable to locate the raw data files from over 10 years ago. The retraction notice was published on February 26, 2024. [Extracted from the article]

The Hill functions, Hh(x) = xh/(1 + xh), have been widely used in biology for over a century but, with the exception of H1, they have had no justification other than as a convenient fit to empirical data. Here, we show that they are the universal limit for the sharpness of any input-output response arising from a Markov process model at thermodynamic equilibrium. Models may represent arbitrary molecular complexity, with multiple ligands, internal states, conformations, coregulators, etc, under core assumptions that are detailed in the paper. The model output may be any linear combination of steady-state probabilities, with components other than the chosen input ligand held constant. This formulation generalizes most of the responses in the literature. We use a coarse-graining method in the graph-theoretic linear framework to show that two sharpness measures for input-output responses fall within an effectively bounded region of the positive quadrant, Ωm ⊂ (R+)², for any equilibrium model with m input binding sites. Ωm exhibits a cusp which approaches, but never exceeds, the sharpness of Hm, but the region and the cusp can be exceeded when models are taken away from thermodynamic equilibrium. Such fundamental thermodynamic limits are called Hopfield barriers, and our results provide a biophysical justification for the Hill functions as the universal Hopfield barriers for sharpness. Our results also introduce an object, Ωm, whose structure may be of mathematical interest, and suggest the importance of characterizing Hopfield barriers for other forms of cellular information processing. [ABSTRACT FROM AUTHOR]

Nanoparticles tethered with vasculature-binding epitopes have been used to deliver the drug into injured or diseased tissues via the bloodstream. However, the extent that blood flow dynamics affects nanoparticle retention at the target site after adhesion needs to be better understood. This knowledge gap potentially underlies significantly different therapeutic efficacies between animal models and humans. An experimentally validated mathematical model that accurately simulates the effects of blood flow on nanoparticle adhesion and retention, thus circumventing the limitations of conventional trial-and-error-based drug design in animal models, is lacking. This paper addresses this technical bottleneck and presents an integrated mathematical method that derives heavily from a unique combination of a mechanics-based dispersion model for nanoparticle transport and diffusion in the boundary layers, an asperity model to account for surface roughness of endothelium, and an experimentally calibrated stochastic nanoparticle-cell adhesion model to describe nanoparticle adhesion and subsequent retention at the target site under external flow. PLGA-b-HA nanoparticles tethered with VHSPNKK peptides that specifically bind to vascular cell adhesion molecules on the inflamed vascular wall were investigated. The computational model revealed that larger particles perform better in adhesion and retention at the endothelium for the particle sizes suitable for drug delivery applications and within physiologically relevant shear rates. The computational model corresponded closely to the in vitro experiments which demonstrates the impact that model-based simulations can have on optimizing nanocarriers in vascular microenvironments, thereby substantially reducing in vivo experimentation as well as the development costs. [ABSTRACT FROM AUTHOR]

Closing the achievement gap for minority students in higher education requires addressing the lack of belonging these students experience. This paper introduces a psychological intervention that strategically targets key elements within the learning environment to foster the success of minority students. The intervention sought to enhance Palestinian minority student's sense of belonging by increasing the presence of their native language. We tested the effectiveness of the intervention in two field experiments in Israel (n > 20,000), at the height of the COVID-19 pandemic when all classes were held via Zoom. Lecturers in the experimental condition added a transcript of their names in Arabic to their default display (English/Hebrew only). Our findings revealed a substantial and positive impact on Palestinian student's sense of belonging, class participation, and overall grades. In experiment 1, Palestinian student's average grade increased by 10 points. In experiment 2, there was an average increase of 4 points among Palestinian students' semester grade. Our intervention demonstrates that small institutional changes when carefully crafted can have a significant impact on minority populations. These results have significant implications for addressing educational disparities and fostering inclusive learning environment. [ABSTRACT FROM AUTHOR]

This document is a correction to the supporting information for a medical sciences article titled "MEK1 mutations confer resistance to MEK and B-RAF inhibition." The authors acknowledge an error in one of the images in the supporting information, but assure that it does not affect the experiment's results or the overall paper. The corrected version of the supporting information is available online. The article provides detailed information on various aspects of the study, including cell lines, MEK1 expression constructs, mutagenesis screen, sequencing, growth inhibition assays, Western blot analysis, kinase assays, and colony formation assays. The document includes figures and tables presenting the experimental findings. [Extracted from the article]

In this paper, we present findings from four separate studies using different data sources and methods to examine Chinese attitudes toward the United States amid the COVID-19 pandemic. The empirical results consistently indicate a marked and significant decline in Chinese attitudes toward the US between late 2019 and the end of 2022. Using a quasi-experimental design and granular survey data that exploit daily variations in public opinion, we offer additional evidence that the decline in Chinese attitudes toward the United States followed a distinct pattern not true for Chinese attitudes toward other countries. Specifically, the rise in Chinese unfavorability toward the United States closely corresponded to the heightened Chinese attention to the pandemic's progression in the United States. These results collectively suggest a causal effect of COVID-19, shedding light on how public health crises, international relations, and media jointly shape the increasing enmity between the two great powers. [ABSTRACT FROM AUTHOR]

The reaction kinetics of photocatalytic CO2 reduction is highly dependent on the transfer rate of electrons and protons to the CO2 molecules adsorbed on catalytic centers. Studies on uncovering the proton effect in catalysts on photocatalytic activity of CO2 reduction are significant but rarely reported. In this paper, we, from the molecular level, revealed that the photocatalytic activity of CO2 reduction is closely related to the proton availability in catalysts. Specifically, four dinuclear Co(II) complexes based on Robson-type ligands with different number of carboxylic groups (-nCOOH; n = 0, 2, 4, 6) were designed and synthesized. All these complexes show photocatalytic activity for CO2 reduction to CO in a water-containing system upon visible-light illumination. Interestingly, the CO yields increase positively with the increase of the carboxylic-group number in dinuclear Co(II) complexes. The one containing -6COOH shows the best photocatalytic activity for CO2 reduction to CO, with the TON value reaching as high as 10,294. The value is 1.8, 3.4, and 7.8 times higher than those containing -4COOH, -2COOH, and -0COOH, respectively. The high TON value also makes the dinuclear Co(II) complex with -6COOH outstanding among reported homogeneous molecular catalysts for photocatalytic CO2 reduction. Control experiments and density functional theory calculation indicated that more carboxylic groups in the catalyst endow the catalyst with more proton relays, thus accelerating the proton transfer and boosting the photocatalytic CO2 reduction. This study, at a molecular level, elucidates that more carboxylic groups in catalysts are beneficial for boosting the reaction kinetics of photocatalytic CO2 reduction. [ABSTRACT FROM AUTHOR]

Molecular chaperones assist in protein refolding by selectively binding to proteins in their nonnative states. Despite progress in creating artificial chaperones, these designs often have a limited range of substrates they can work with. In this paper, we present molecularly imprinted flexible polymer nanoparticles (nanoMIPs) designed as customizable biomimetic chaperones. We used model proteins such as cytochrome c, laccase, and lipase to screen polymeric monomers and identify the most effective formulations, offering tunable charge and hydrophobic properties. Utilizing a dispersed phase imprinting approach, we employed magnetic beads modified with destabilized whole-protein as solid-phase templates. This process involves medium exchange facilitated by magnetic pulldowns, resulting in the synthesis of nanoMIPs featuring imprinted sites that effectively mimic chaperone cavities. These nanoMIPs were able to selectively refold denatured enzymes, achieving up to 86.7% recovery of their activity, significantly outperforming control samples. Mechanistic studies confirmed that nanoMIPs preferentially bind denatured rather than native enzymes, mimicking natural chaperone interactions. Multifaceted analyses support the functionality of nanoMIPs, which emulate the protective roles of chaperones by selectively engaging with denatured proteins to inhibit aggregation and facilitate refolding. This approach shows promise for widespread use in protein recovery within biocatalysis and biomedicine. [ABSTRACT FROM AUTHOR]

This paper studies the effect of air pollution on voting outcomes. We use data from 60 federal and state elections in Germany from 2000 to 2018 and exploit plausibly exogenous fluctuations in ambient air pollution within counties across election dates. Higher air pollution on election day shifts votes away from incumbent parties and toward opposition parties. An increase in the concentration of particulate matter (PM10) by 10 g/m3--around two within-county SDs--reduces the vote share of incumbent parties by two percentage points, which is equivalent to 4% of the mean vote share. We generalize these findings by documenting similar effects with data from a weekly opinion poll and a large-scale panel survey. We provide further evidence that poor air quality leads to more negative emotions such as anger, worry, and unhappiness, which, in turn, may reduce the support for the political status quo. Overall, these results suggest that poor air quality affects decision-making in the population at large, including consequential political decisions. [ABSTRACT FROM AUTHOR]

G protein- coupled receptors (GPCRs) transduce the effects of many neuromodulators including dopamine, serotonin, epinephrine, acetylcholine, and opioids. The localization of synthetic or endogenous GPCR agonists impacts their action on specific neuronal pathways. In this paper, we show a series of single- protein chain integrator sensors that are highly modular and could potentially be used to determine GPCR agonist localization across the brain. We previously engineered integrator sensors for the mu- and kappa- opioid receptor agonists called M- and K- Single- chain Protein- based Opioid Transmission Indicator Tool (SPOTIT), respectively. Here, we engineered red versions of the SPOTIT sensors for multiplexed imaging of GPCR agonists. We also modified SPOTIT to create an integrator sensor design platform called SPOTIT for all GPCRs (SPOTall). We used the SPOTall platform to engineer sensors for the beta 2- adrenergic receptor (B2AR), the dopamine receptor D1, and the cholinergic receptor muscarinic 2 agonists. Finally, we demonstrated the application of M- SPOTIT and B2AR- SPOTall in detecting exogenously administered morphine, isoproterenol, and epinephrine in the mouse brain via locally injected viruses. The SPOTIT and SPOTall sensor design platform has the potential for unbiased agonist detection of many synthetic and endogenous neuromodulators across the brain. [ABSTRACT FROM AUTHOR]

Over the past few years, machine learning models have significantly increased in size and complexity, especially in the area of generative AI such as large language models. These models require massive amounts of data andcomputecapacitytotrain,totheextentthatconcerns over the training data (such as protected or private content)cannotbepracticallyaddressedbyretrainingthe model"fromscratch"withthequestionabledataremoved or altered. Furthermore, despite significant efforts and controls dedicated to ensuring that training corpora are properly curated and composed, the sheer volume re- quiredmakesitinfeasibletomanuallyinspecteachdatum comprising a training corpus. One potential approach to training corpus data defects is model disgorgement, by which we broadly mean the elimination or reduction of not only any improperly used data, but also the effects of improperly used data on any component of an ML model. Model disgorgement techniques can be used to address a wide range of issues, such as reducing bias or toxicity, increasing fidelity, and ensuring responsible use of intellectual property. In this paper, we survey the land- scape of model disgorgement methods and introduce a taxonomyofdisgorgementtechniquesthatareapplicable to modern ML systems. In particular, we investigate the variousmeaningsof"removingtheeffects"ofdataonthe trained model in a way that does not require retraining from scratch [ABSTRACT FROM AUTHOR]