Harvard University

Sample barcoding-based multiplex single-cell and single-nucleus sequencing (sc/sn-seq) offers substantial advantages by reducing costs, minimizing batch effects, and identifying artifacts, thereby advancing biological and biomedical research. Despite these benefits, universal sample barcoding has been hindered by challenges such as inhomogeneous expression of tagged biomolecules, limited tagging affinity, and insufficient genetic insertion. To overcome these limitations, we developed Toti-N-Seq, a universal sample multiplex method, by tagging Toti-N-glycan on cell surfaces or nuclear membranes via our engineered streptavidin–Fbs1 GYR variant fusion protein, which could be used not only for sc-seq but also for sn-seq. Instead of targeting lipids or proteins, we focused on targeting the ubiquitous N-glycans found on any species with accessible membranes, which minimizes the exchange between barcoded samples and avoids biased barcoding. Our technology can be broadly applied to multiple species and nearly all eukaryotic cell types, with an overall classification accuracy of 0.969 for sc-seq and of 0.987 for sn-seq. As a demonstration with clinical human peripheral blood mononuclear cells, our Toti-N-Seq achieved rapid one-step sample preparation (<3 min) for easily scaling up while keeping high fidelity of sample ratios, removing artifacts, and detecting rare cell populations (~0.5%). Consequently, we offer a versatile platform suitable for various cell types and applications.

Air quality modeling is critical for understanding PM2.5 sources and pollution dynamics, providing a scientific basis for regulatory and policy applications. This study presents a comparative evaluation of three chemical transport models (CTMs) for simulating surface-level PM2.5 and its chemical composition, including secondary aerosols, in Northeast Asia across the seasons of 2019. All CTMs reproduced the broad spatial and temporal patterns of PM2.5 reasonably well, supported by shared anthropogenic emissions and consistent meteorological inputs. However, notable discrepancies likely resulted from a combination of factors, including differences in chemical mechanisms, potentially outdated or inconsistent emission inventories for carbonaceous, sulfur, and nitrogenous compounds, and the presence or absence of process modules such as pcSOA in CMAQ and wet scavenging in WRF-Chem. CMAQ showed the most balanced performance, particularly in Korea, accurately simulating PM2.5 mass and chemical components with realistic seasonal variability in secondary aerosols. WRF-Chem, with online coupling of meteorology and atmospheric chemistry, effectively simulated temporal variability but unusually overestimated PM2.5 in summer. GEOS-Chem captured long-range transport and background concentrations of PM2.5 associated with biomass burning and dust, although these results are specific to the model configurations used in this study, and was limited in resolving urban-scale variability and detailed dust processes. Our findings highlight distinct model behaviors and emphasize the importance of carefully considering model characteristics relative to the specific research or policy objectives. Improving emission inventories, refining chemical and physical process representations, and advancing multi-model approaches may enhance model performance and support both scientific and policy objectives.

Simulations of critical phenomena, such as wildfires, epidemics, and ocean dynamics, are indispensable tools for decision-making. Many of these simulations are based on models expressed as Partial Differential Equations (PDEs). PDEs are invaluable inductive inference engines, as their solutions generalize beyond the particular problems they describe. Methods and insights acquired by solving the Navier–Stokes equations for turbulence can be very useful in tackling the Black-Scholes equations in finance. Advances in numerical methods, algorithms, software, and hardware over the last 60 years have enabled simulation frontiers that were unimaginable a couple of decades ago. However, there are increasing concerns that such advances are not sustainable. The energy demands of computers are soaring, while the availability of vast amounts of data and Machine Learning(ML) techniques are challenging classical methods of inference and even the need of PDE based forecasting of complex systems. I believe that the relationship between ML and PDEs needs to be reset. PDEs are not the only answer to modeling and ML is not necessarily a replacement, but a potent companion of human thinking. Algorithmic alloys of scientific computing and ML present a disruptive potential for the reliable and robust forecasting of complex systems. In order to achieve these advances, we argue for a rigorous assessment of their relative merits and drawbacks and the adoption of probabilistic thinking for developing complementary concepts between ML and scientific computing. The convergence of AI and scientific computing opens new horizons for scientific discovery and effective decision-making.

House mice (Mus musculus domesticus) are among the most widely studied laboratory models of mammalian social behaviour, yet we know relatively little about the ecology of their behaviours in natural environments. Here, we address this gap using radiotelemetry to track social interactions in a population of wild mice over 10 years, from 2013 to 2023, and interpret these interactions in the context of passive acoustic monitoring data collected from August 2022 to November 2023. Using automated vocal detection, we identify 1.3 million individual vocalizations and align them in time with continuously collected telemetry data recording social interactions between individually identifiable mice. We find that vocalization is seasonal and correlated with long-term dynamics in features of social groups. In addition, we find that vocalization is closely associated in time with entrances to and exits from those groups, occurs most often in the presence of pups, and is correlated with how much time pairs of mice spend together. This work identifies seasonal patterns in the vocalizations of wild mice and lays a foundation to investigate the social role of acoustic communication in wild populations of an important laboratory model organism.

When navigating complex environments, animals often combine multiple strategies to mitigate the effects of external disturbances. These modalities often correspond to different sources of information, leading to speed – accuracy trade-offs. Inspired by the intermittent reorientation strategy seen in the behaviour of the dung beetle, we consider the problem of the navigation strategy of a correlated random walker moving in two dimensions. We assume that the heading of the walker can be reoriented to the preferred direction by paying a fixed cost as it tries to maximize its total displacement in a fixed direction. Using optimal control theory, we derive analytically and confirm numerically the strategy that maximizes the walker’s speed, and show that the average time between reorientations scales inversely with the magnitude of the environmental noise. We then extend our framework to describe execution errors and sensory acquisition noise. As a result, we provide a range of testable predictions and suggest new experimental directions. Our approach may be amenable to other navigation problems involving multiple sensory modalities that require switching between egocentric and geocentric strategies.

Aim Understanding the geographic origin of lineages is critical to comprehending their biogeographical and evolutionary histories and the historical connections among biomes. In northeastern Brazil, the Caatinga dry forest represents the largest and most biologically diverse patch of Seasonally Dry Tropical Forest in the Neotropics. Here, using the endemic avian taxa of the Caatinga, we aim to (i) infer their biogeographical origins and timing of diversification, (ii) investigate the relationships with taxa from other Neotropical domains and (iii) understand the processes driving the evolution and diversification of the Caatinga endemic avifauna. Location Neotropical Region. Taxon Birds. Methods We obtained previously published calibrated phylogenies of 40 bird species endemic to the Caatinga to reconstruct their ancestral geographic ranges through the R package BioGeoBEARS to infer and highlight the origins, mode and tempo of evolution of the Caatinga avian endemics. Results Our results suggest that most avian endemics (21 taxa) are related to lineages from open habitats, including other dry forests or savannas; less than a quarter of the species (9 taxa) likely colonised the Caatinga from adjacent humid forests; we also highlight in situ origins (6 taxa), and cladogenetic events playing an important role in the colonisation of the domain. Although most of the endemics seem to represent new arrivals from adjacent habitats, we also detected relatively old lineages that likely occupied these dry landscapes since the Miocene. A correlation between the age, origins of Caatinga birds and their degree of threat was also found. Main Conclusions We provide a much needed framework for the evolution and biogeographic diversification of the Caatinga Dry Forest. The spatio‐temporal patterns recovered here suggest an evolutionary history influenced not only by strict vicariance events but also by episodes of dispersal that likely played an important role in the origin of its avifauna. These events were likely influenced by major climatic and geological events that formed ancient corridors, allowing connections between the Caatinga and both dry and humid forests in South America.

There are many benefits and costs that come from people and firms clustering together in space. Agglomeration economies, in particular, are the manifestation of centripetal forces that make larger cities disproportionately more wealthy than smaller cities, pulling together individuals and firms in close physical proximity. Measuring agglomeration economies, however, is not easy, and the identification of its causes is still debated. Such association of productivity with size can arise from interactions that are facilitated by cities (“positive externalities”), but also from more productive individuals moving in and sorting into large cities (“self-sorting”). Under certain circumstances, even pure randomness can generate increasing returns to scale. In this chapter, we discuss some of the empirical observations, models, measurement challenges, and open question associated with the phenomenon of agglomeration economies. Furthermore, we discuss the implications of urban complexity theory, and in particular urban scaling, for the literature in agglomeration economies.

Locomotor evolution in synapsids involved numerous functional shifts associated with the transition from sprawled to erect limb postures on the line to therian mammals. Given that bone structure frequently reflects functional requirements, this study investigated evolutionary changes in synapsid humerus and femur proportions as a lens to evaluate functional shifts through time. A total of 936 bones were measured, representing 330 species across the full 320+ million years of synapsid history. This dataset was used to test whether transformations in stylopod proportions are consistent with inferred changes in bone loading mechanics, alignment of joint and muscle forces, muscular control of the shoulder and hip, and differential support of body weight by the fore‐ and hindlimbs. As variation in bone dimensions may also correlate with bone or body size, this study first developed a novel approach for calculating species‐specific, size‐corrected measures of bone proportions. By disentangling the effect of body size from functional signals recorded in bone geometry, this then enabled a node‐to‐node appraisal of how bone allometry itself evolved through time. Ancestral state reconstruction of size‐corrected stylopod proportions reveals trends that broadly support many hypothesized shifts in locomotor biomechanics along the therian stem lineage. However, patterns of transformation are frequently complex, suggesting functional mosaicism, and stylopod proportions that typify therians as a whole are often not achieved until crown Theria itself. Several instances of temporary trend reversal are also inferred, particularly within non‐mammalian cynodonts, indicating greater functional or ecological diversification in this group.

Regulatory T cells (Tregs) maintain immune homeostasis and their adoptive transfer is being widely explored to mitigate inflammatory and autoimmune conditions. Here a biomaterial is developed to accumulate Tregs at a specific anatomic location to bypass the need for ex vivo Treg isolation and adoptive transfer. It is first shown that eliglustat, an FDA‐approved inhibitor of UDP‐glucose ceramide glucosyltransferase, promotes Tregs from both naïve and activated CD4⁺ T cells in vitro. Click‐crosslinked cryogels fabricated from alginate and collagen allow for a sustained release of CXCL10 or CXCL11, and when injected in subcutaneous tissues led to the enrichment of effector and memory T cells to the scaffolds. Loading eliglustat into these cryogels significantly enhances the local accumulation of Tregs in vivo. These findings demonstrate that eliglustat‐loaded cryogels offer a simple yet effective biomaterial strategy to boost Treg directly in vivo, potentially providing a targeted method to treat various inflammatory and autoimmune diseases.

An improved understanding of root vertical distribution is crucial for assessing plant-soil-atmosphere interactions and their influence on the land carbon sink. Here, we analyze a continental-scale dataset of fine roots reaching 2 meters depth, spanning from Alaskan tundra to Puerto Rican forests. Contrary to the expectation that fine root abundance decays exponentially with depth, we found root bimodality at ~20% of 44 sites, with secondary biomass peaks often below 1 m. Root bimodality was more likely in areas with low total fine root biomass and was more frequent in shrublands than grasslands. Notably, secondary peaks coincided with high soil nitrogen content at depth. Our analyses suggest that deep soil nutrients tend to be underexploited, while root bimodality offers plants a mechanism to tap into deep soil resources. Our findings add to the growing recognition that deep soil dynamics are systematically overlooked, and calls for more research attention to this deep frontier in the face of global environmental change.

A bstract The spectrum and dynamics of near-extremal black holes is strongly modified by quantum effects at low temperatures. When the extremal limit does not preserve any supersymmetry, the density of states goes to zero at extremality and no extremal black holes remain. However, when the extremal limit is supersymmetric, a large microscopic degeneracy survives and there is a gap to the first excited black hole visible from gravity. In this article we study large N quantum mechanical models where supersymmetry is explicitly broken, allowing us to interpolate between these two qualitatively different pictures. We propose and analyze deformations of $\mathcal{N}$ N = 2 SYK models with such a pattern of (super) symmetry breaking which violate the U(1) R -symmetry. These models feature a lifting of the BPS degeneracy and a closing of the spectral gap, and we further show that the large N soft effective action is given by a modification of the $\mathcal{N}$ N = 2 Schwarzian theory in which the U(1) R mode becomes massive.

Symmetry is a powerful tool for understanding phases of matter in equilibrium. Quantum circuits with measurements have recently emerged as a platform for novel states of matter intrinsically out of equilibrium. Can symmetry be used as an organizing principle for these novel states, their phases and phase transitions? In this work, we give an affirmative answer to this question in a simple adaptive monitored circuit, which hosts an ordering transition in addition to a separate entanglement transition, upon tuning a single parameter. Starting from a symmetry-breaking initial state, depending on the tuning parameter, the steady state could (i) remain symmetry-broken, (ii) exhibit the average symmetry in the ensemble of trajectories, or (iii) exhibit the exact symmetry for each trajectory. The ordering transition is mapped to the transition in a classical majority vote model, described by the Ising universality class, while the entanglement transition lies in the percolation class. Numerical simulations are further presented to support the analytical understandings.

Objectives Unplanned pneumonia readmissions increase patient morbidity, mortality and healthcare costs. Among pneumonia patients, the middle-aged and elderly (≥45 years old) have a significantly higher risk of readmission compared with the young. Given that the 14-day readmission rate is considered a healthcare quality indicator, this study is the first to develop survival machine learning (ML) models using emergency department (ED) data to predict 14-day readmission risk following pneumonia-related admissions. Design A retrospective multicentre cohort study. Setting This study used the Taipei Medical University Clinical Research Database, including data from patients at three affiliated hospitals. Participants 11 989 hospital admissions for pneumonia among patients aged ≥45 years admitted from 2014 to 2021. Primary and secondary outcome measures The dataset was randomly split into training (80%), validation (10%) and independent test (10%) sets. Input features included demographics, comorbidities, clinical events, vital signs, laboratory results and medical interventions. Four survival ML models—CoxNet, Survival Tree, Gradient Boosting Survival Analysis and Random Survival Forest—were developed and compared on the validation set. The best performance model was tested on the independent test set. Results The RSF model outperformed the other models. Validation on an independent test set confirmed the model’s robustness (C-index=0.710; AUC=0.693). The most important predictive features included creatinine levels, age, haematocrit levels, Charlson Comorbidity Index scores, and haemoglobin levels, with their predictive value changing over time. Conclusions The RSF model effectively predicts 14-day readmission risk among pneumonia patients. The ED data-based model allows clinicians to estimate readmission risk before ward admission or discharge from the ED, enabling timely interventions. Accurately predicting short-term readmission risk might also further support physicians in designing the optimal healthcare programme and controlling individual medical status to prevent readmissions.

As few as 34% of 3- and 4-year-olds in low- and middle-income countries (LMICs) receive adequate psychosocial stimulation from their caregivers (e.g., reading books, telling stories, playing). While previous research suggests that structural conditions within LMICs meaningfully influence caregivers’ capacities to engage in psychosocial stimulation of children, it remains unclear how such conditions may underlie both within-household and between-person changes in psychosocial stimulation over time. To address these gaps, the present study utilized longitudinal, repeated-measures data from two LMICs in the Multiple Indicator Cluster Surveys Plus, namely, Georgia and Mongolia. Household demographics and structural conditions were examined as predictors of caregivers’ engagement in psychosocial stimulation over time using longitudinal multilevel modeling ( N = 745). Results indicated that over time, caregivers’ engagement in psychosocial stimulation had a small, but significant decline. In addition, various household demographic factors (i.e., male child, larger household size) and structural conditions (i.e., living in a rural community, having fewer material resources, experiencing a recent decline in income, residing in Mongolia [compared to Georgia]) were associated with lower levels of caregiver psychosocial stimulation. These findings highlight how interventions to improve psychosocial stimulation in LMICs may be most effective if they are predicated on the multilevel contextual needs of families.

The process of development is accompanied by marked changes in the structure of the labor market. We lay out a broad set of stylized features that distinguish labor markets in developing countries from those in richer countries. We organize our review around one particularly striking difference: In poor countries, working-age individuals are employed in wage work only 20–50% of the time. There is evidence that this low wage employment reflects high levels of involuntary unemployment (often masked by self-employment) along with frictions such as wage rigidity, market power, and search-and-matching frictions. At the same time, there is growing documentation that workers prefer self-employment or unemployment to many of the wage jobs that are available to them, especially low-skill work in the formal sector. We offer evidence on several ways in which poverty itself can dampen labor supply, so that low labor supply may itself be an outcome of underdevelopment. Throughout our review, we highlight three core aspects of poverty—missing markets, the importance of social ties, and institutional irregularity—that are relevant for understanding how labor markets change in response to, and help facilitate, the process of development.

Children with attention-deficit/hyperactivity disorder (ADHD) typically experience social interaction problems. Neurocognitive functions, such as working memory and inhibitory control have been included in several studies of social problems in ADHD, with inconsistent findings emerging. Intraindividual reaction time variability (RTV) has been understudied as a factor explaining social problems in children with ADHD, despite being one of the most consistent cognitive impairments in the disorder. In the current study, we hypothesized that greater RTV would relate to greater parent-reported social problems and may reflect the negative impact of attentional fluctuations on social interactions in ADHD. We tested the association between RTV and social problems while accounting for the effects of working memory and inhibitory control in two independent samples of children with ADHD and typically developing (TD) peers. The neurocognitive functions were assessed through performance-based laboratory measures. Sample one, derived in Norway, included 41 children with ADHD and 35 TD children. Sample two, derived in the United States, included 73 children with ADHD and 26 TD children. Linear regression analyses, controlling for other relevant variables, indicated that greater RTV was associated with greater social problems across both samples. Our results support the view that attentional variability is linked to social problems in children with ADHD.

Objective To identify parenting profiles among Singaporean mothers of young children and to examine the longitudinal implications of these profiles for children's well‐being during middle childhood. Background The universality of traditional parenting styles (e.g., authoritative, authoritarian), established in Western contexts, and their implications for child well‐being, has been called into question, with studies conducted with Asian populations suggesting these styles may not be universally applicable. Method Our sample consisted of 411 mother–child dyads drawn from a prebirth cohort in Singapore, where mothers reported on their parenting at child age 4.5 years, and children reported on their evaluation of their mothers' parenting and their own depressive symptoms at ages 9 and 10 years, respectively. Results Three latent class profiles of maternal parenting emerged at child age 4.5 years: Supportive (high use of supportive practices and low use of harsh practices), Supportive‐Harsh (high use of both supportive and harsh practices), and Unsupportive (moderate use of both supportive and harsh practices). Children of Unsupportive mothers reported greater maternal indifference at age 9 years and depressive symptoms at age 10 years compared to children of Supportive or Supportive‐Harsh mothers. Children's evaluation of maternal indifference mediated the association between early maternal parenting profiles and later child depressive symptoms. Conclusion Our findings highlight the variation in parenting profiles in Singapore from well‐established parenting styles, and the greater implications of supportive, compared to harsh, parenting practices for children's well‐being.

BACKGROUND One-time atrial fibrillation (AF) screening trials in older adults have produced mixed results. In a secondary analysis of the VITAL-AF trial, we aimed to identify a subset of people in whom such screening is effective, using effect-based and risk-based approaches. METHODS The VITAL-AF trial was a cluster-randomized trial of 1-time, 30-second single-lead ECG screening during primary care visits. It enrolled adults aged ≥65 years in 16 Massachusetts General Hospital primary care practices (2018–2019). In this secondary analysis, we tested 2 approaches to identify subgroups where screening is effective. First, we developed an effect-based model using the T-learner, a causal inference approach that estimates screening effects by separately training 2 predictive models—one for screening and one for usual care—and then compares their predictions for each individual. Second, we used a validated AF risk model (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) to test for heterogeneous screening effectiveness. We assessed AF screening effectiveness by quartile of predicted effect and predicted AF risk and determined their correlation. RESULTS The study included 29 656 participants (mean±SD age 74±7 years, 59% female). In the highest quartile of predicted screening effect, AF diagnosis rates were higher in the screening versus the usual care group (4.00 versus 2.88 per 100 person-years, rate difference 1.12 [95% CI, 0.11–2.13] per 100 person-years). In the highest quartile of predicted AF risk, AF diagnosis rates were also higher in the screening versus the usual care group (5.55 versus 4.23 per 100 person-years, rate difference 1.32 [95% CI, 0.14–2.50] per 100 person-years). Predicted screening effect and predicted AF risk were weakly correlated (Spearman correlation coefficient, 0.23). CONCLUSIONS One-time screening was associated with increased AF diagnoses in the top quartile of both predicted screening effect and predicted AF risk. Because predicted effect and risk were only weakly correlated, future AF screening efforts should include both high-effect and high-risk individuals.

Objective Studies comparing post-traumatic distress (PTD) following physical and social stressors have primarily focused on the likelihood and severity of distress. We extend previous research by testing whether differences in PTD symptom networks are found for physical and social stressors diagnostically (using symptoms of PTSD) and trans-diagnostically (using symptom clusters from PTSD, depression, and social anxiety). Methods Participants included individuals with elevated levels of PTSD symptomatology who reported experiencing physical (n = 398) and social (n = 581) stressors. Symptom networks were analyzed to identify similarities and differences between the two stressor types. Replication analyses were conducted to verify previous findings, and transdiagnostic networks were examined to explore broader symptom interactions. Results The analysis replicated previously reported edges within the networks. Notably, while the global strength of the social stressors' symptom network exceeded that of physical stressors', the overall structure of the networks remained similar. However, significant differences emerged in the transdiagnostic networks, indicating distinct patterns of symptom interaction across stressor types. Conclusions These findings contribute to a nuanced understanding of how different stressors influence mental health. The observed similarities in symptom networks suggest common underlying mechanisms, while differences in transdiagnostic networks underscore the importance of considering broader symptom clusters in understanding event-activated psychopathology. This highlights the necessity of a transdiagnostic approach to comprehensively address the complexity of post-traumatic distress.