Near the end of their lives, low-mass and intermediate-mass stars expel metal-enriched material in winds and outflows, ultimately producing planetary nebulae1,2. The ejected material is expected to fragment and mix into the interstellar medium (ISM), but this final assimilation step has been difficult to observe directly3,4. Here we report evidence for this process in the form of 22 bow shocks in the eastern outskirts of the Helix Nebula, detected in Hα emission with the partially completed MOTHRA telescope. Unlike the large-scale wind-ISM bow shocks commonly observed around evolved stars5-7, the shocks are compact and associated with individual clumps of gas. Going outward from the central star, the radius of curvature Rc decreases by a factor of roughly 102 over the radial range r = 0.4-1.4 pc. This is accompanied by a morphological transition from thin, well-defined bows to fuzzy, patchy structures. We interpret these changes as progressive stripping and fragmentation of asymptotic giant branch-shell remnants as they interact with the ISM. The slope of the observed Rc-r relation implies a loss of fragment coherence on a timescale of roughly 104 years, providing a rare direct constraint on the timescale for disruption and entrainment of fragmented stellar ejecta into the ISM8,9.

Disrupted reward processing, including reward learning, is a prominent feature of various neuropsychiatric disorders. Such abnormalities are particularly consequential in major depressive disorder, in which anhedonia (reduced reactivity to pleasurable stimuli) predicts a host of negative outcomes, including poor response to treatments, worse disease course, impaired psychosocial functioning, and suicide. Despite substantial research in this area, there are no approved treatments for anhedonia; reasons for such modest progress include the use of vastly disparate approaches to assess anhedonic behaviors in laboratory animals and humans (leading to poor translation) and overreliance on clinical scales in humans, which generally fail to parse important subdomains of reward processing. To address these and other gaps, in 2005 we published the first report evaluating the Probabilistic Reward Task (PRT) in individuals with elevated depressive symptoms. Rooted in signal detection theory, this task allows objective assessment of reward learning, i.e., a subject's propensity to modulate behavior as a function of reward. Critically, functionally identical PRT versions have been developed for mice, rats, non-human primates, and humans, and data across species are analyzed using identical formulas and computational models. In this review, we summarize 20 years of PRT findings, organized according to several forms of validity, including construct, clinical (diagnostic), prognostic, predictive, susceptibility/risk, biological, and external validity. In addition, we summarize the PRT's psychometrics and context of use. The rich body of findings across species, conditions, and samples provides a foundation for future work that will contribute to the development of much-needed treatments targeting anhedonia.

Historically, aperiodic neural activity (i.e., the 1/f exponent and offset of electroencephalogram (EEG) power spectra) has been treated as neural noise. However, recent work has highlighted associations among aperiodic activity, aging, cognition, and psychopathology. While depression has been associated with cognitive deficits and dysregulation of excitatory/inhibitory neural mechanisms linked to aperiodic activity, to date, few studies have compared aperiodic activity in adults with and without depression, and none have evaluated aperiodic activity as a function of depressive chronicity. Here, individuals with depression (n=72) and healthy controls (n=34) completed a resting state EEG recording from which aperiodic activity estimates were extracted. We show significant group differences in aperiodic exponent and offset across central and posterior regions, such that, as hypothesized, these parameters were lower for depressed individuals relative to controls. Follow-up analyses clarified that this effect was driven by the number of lifetime depressive episodes experienced. Future research is needed to understand how depression heterogeneity impacts aperiodic activity.

Long COVID has been an important health concern in children and adolescents, yet factors associated with its development remain incompletely understood. Selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) are widely prescribed for pediatric neuropsychiatric conditions and may influence immune and autonomic pathways involved in postinfectious symptoms. Here we show associations between SSRI/SNRI use and long coronavirus disease (COVID)-related outcomes in a retrospective cohort of 110,955 children and adolescents with pre-existing neuropsychiatric conditions across 37 US health systems participating in the National Institutes of Health Researching COVID to Enhance Recovery consortium. SSRI/SNRI use was not associated with clinician-recorded long COVID diagnosis but showed heterogeneous associations with individual symptoms. Lower risks were observed for some symptoms, including fever, chills and hair loss, whereas higher risks were observed for neurological and systemic outcomes, including postural orthostatic tachycardia syndrome, cognitive dysfunction and fatigue. These findings suggest that antidepressant exposure may be associated with differing post-COVID symptom patterns in youth and warrant further investigation.

Research and clinical practice on childhood maltreatment largely rely on retrospective self-reports. However, self-reports are often considered unreliable due to concerns about memory biases and shifting subjective appraisals. Here, in a meta-analysis of 49 studies including n = 38,332 individuals followed for an average of 2.4 years (range = 2 months to 12 years), we found that retrospective self-reports of maltreatment are overall highly stable (r = 0.79). However, stability was lower in population-representative samples than in clinical or convenience samples, for neglect compared with abuse, and in children compared with adults. In children, but not adults, stability declined with longer follow-up. These findings challenge the view that retrospective self-reports are inherently unstable, although further research is needed to investigate long-term stability. Reshaping trauma-related appraisals may require deliberate intervention and might be most effective during childhood when memories appear more malleable.

Depression has been linked to reduced size of subcortical regions and abnormal functional connectivity in frontal and default mode networks. However, recent meta-analyses have failed to identify significant converging correlates of depression across the literature such that a conclusive mapping of the neuroimaging correlates of depression remains elusive. We leveraged 23,417 participants across 6 population datasets to comprehensively establish the neuroimaging correlates of depression. We found reductions in gray matter volume/cortical surface area associated with depression in the frontal cortex, anterior cingulate and insula, confirming previous studies showing the importance of prefrontal and default mode regions in depression. Our findings demonstrate multiple surprising results, including a lack of depression correlates in subcortical brain regions and significant depression correlates in somatomotor and visual regions. Overall, these results shed new light on key brain regions involved in the pathophysiology of depression, updating our understanding of the neuroimaging correlates of depression symptoms.

The long-term consequences of attention deficit hyperactivity disorder (ADHD) in females remain underrecognized both in research and clinical practice. Multimorbidity (two or more long-term conditions) is increasingly viewed as a key outcome yet its prevalence, complexity and sociodemographic determinants are understudied in this group. Here, in this longitudinal cohort study, we examined the combined influence of childhood socioeconomic deprivation and ADHD diagnosis on multimorbidity risk in women in early adulthood (that is, aged 18-32 years) and used latent class analysis to explore clustering patterns. Three findings emerged: females with childhood ADHD had a significantly higher risk of adult multimorbidity; childhood ADHD and socioeconomic deprivation increased this risk both in isolation and synergistically, with 39% of the multimorbidity burden attributable to their interaction; and distinct multimorbidity clusters emerged, with the most adverse marked by psychiatric complexity. These results highlight girls with ADHD from disadvantaged backgrounds as a high-risk group requiring earlier, integrated care.

The biological mechanisms underlying major neuropsychiatric disorders remain largely elusive. Given the frequent association of immune dysregulation with these conditions, we used blood-derived multi-omics data from 1,274 healthy adolescents in the IMAGEN cohort to identify transdiagnostic biomarkers and mechanisms that could inform diagnosis and treatment. We first conducted genome-wide analyses to identify single nucleotide polymorphisms associated with DNA methylation and gene expression, with findings replicated in external datasets. These quantitative trait loci were further explored through Mendelian randomization analyses across 6 neuropsychiatric disorders (attention deficit hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia and insomnia), leading to the identification of 73 putatively causal CpG sites and 62 genes that were either unique to individual disorders or, as in the case of MRPL2, shared among the disorders. Notably, the identified genes were significantly enriched in pathways linked to both psychiatric and autoimmune diseases, suggesting a shared genomic architecture between autoimmune and neuropsychiatric disorders. Two-step Mendelian randomization and colocalization analyses revealed potential transdiagnostic regulatory pathways, in which the expression of three genes (MAD1L1, MRPL2 and HLA-DRB1) mediated the effects of CpG methylation on schizophrenia and insomnia. Specifically, DNA methylation at cg06770790 repressed MRPL2, which was putatively causal for insomnia (β = -0.38, P = 1.29 × 10-4) and schizophrenia (β = -0.38, P = 1 × 10-4). Conversely, increased expression of MAD1L1 and HLA-DRB1, driven by methylation at several CpG sites, was potentially causal for schizophrenia. Our findings highlight key molecular mechanisms and genes implicated in neuropsychiatric disorders, offering promising new targets for therapeutic intervention.

Although adverse childhood experiences (ACEs) increase risk for mental illness at the population level, existing ACEs screens are less helpful in forecasting individual outcomes, suggesting they may not capture significant elements of childhood adversity. We have previously identified unpredictable parental and household experiences as an ACE that portends poorer cognitive and mental health. However, the contribution of unpredictability to established ACEs in real-world settings is unknown. Here, leveraging existing ACEs screening in California, we added the five-item Questionnaire on Unpredictability in Childhood (QUIC-5) in 19 pediatric clinics spanning broad sociodemographic constituencies and compared in ~30,000 children the link of each screen with mental health diagnoses. Scores on either the ACEs or QUIC-5 associated with probabilities of depression, externalizing symptoms, sleep disorders, anxiety and somatic symptoms. Each screen provided unique contributions and combining them often doubled the strength of associations. For depression and sleep disorders, the QUIC-5 identified vulnerable individuals missed by ACEs screen, improving risk detection and facilitating future interventions.

Disease heterogeneity and commonality pose significant challenges to precision medicine, as traditional approaches frequently focus on single disease entities and overlook shared mechanisms across conditions. Inspired by pan-cancer and multi-organ research, we introduce the concept of "pan-disease" to investigate the heterogeneity and shared etiology in brain, eye, and heart diseases. Leveraging individual-level data from 129,340 participants, as well as summary-level data, curated from the MULTI consortium, we applied a weakly-supervised deep learning model (Surreal-GAN) to multi-organ imaging, genetic, proteomic, and RNA-seq data, identifying 11 AI-derived biomarkers, called Multi-organ AI Endophenotypes (MAEs), for the brain (Brain 1-6), eye (Eye 1-3), and heart (Heart 1-2), respectively. We found Brain 3 to be a risk factor for Alzheimer's disease (AD) progression and mortality, whereas Brain 5 was protective against AD progression. Crucially, in data from an anti-amyloid AD drug (solanezumab), heterogeneity in cognitive decline trajectories was observed across treatment groups. At week 240, patients with lower brain 1-3 expression had slower cognitive decline, whereas patients with higher expression had faster cognitive decline. A multi-layer causal pathway pinpointed Brain 1 as a mediational endophenotype linking the FLRT2 protein to migraine, exemplifying novel therapeutic targets and pathways. Additionally, genes associated with Eye 1 and Eye 3 were enriched in cancer drug-related gene sets with causal links to specific cancer types and proteins. Finally, Heart 1 and Heart 2 had the highest mortality risk and unique medication history profiles, with Heart 1 showing favorable responses to antihypertensive medications and Heart 2 to digoxin treatment. The 11 MAEs provide novel AI dimensional representations for precision medicine and highlight the potential of AI-driven patient stratification for disease risk monitoring, clinical trials, and drug discovery.

Quantum error correction (QEC) will likely be required to realize the full potential of quantum computing, but comes with daunting hardware overheads and demands low gate errors on the physical qubits1-4. These requirements can be eased by engineering qubits with a strong error hierarchy, in which the most common noise channels are also the easiest to correct. Erasure qubits can achieve this when detectable leakage errors out of the computational subspace dominate over the residual Pauli errors5-11, resulting in higher thresholds and improved scaling with code distance5,12,13. In practice, these advantages come to fruition only if the error hierarchy is preserved as much as possible throughout all gates and operations. Here we design and realize a two-qubit entangling gate for dual-rail cavity qubits, a type of erasure qubit encoded in a pair of superconducting microwave cavities7. Our experimental demonstration confirms that the error hierarchy is largely preserved during the gate. The gate is fast (about 500 ns duration) and shows low erasure rates of approximately 0.5% per gate, remaining Pauli errors below 0.1%, and a strong bias towards dephasing errors, in which bit-flips are practically non-existent at the 10-6 level. These results enable a faster path to error-corrected systems that rapidly suppress errors as they scale; a claim we support with our detailed surface code simulations.

Gene expression is regulated by transcription factors (TFs), which recognize specific DNA sequence motifs. Several hundred putative human TFs, identified mainly by an apparent DNA-binding domain, lack known binding motifs1. Furthermore, even for well-characterized TFs, it remains controversial the degree to which motifs accurately reflect binding sites in living cells2. Here we describe a systematic effort ('Codebook') to determine the sequence specificity of 332 putative and poorly characterized human TFs. More than 4,000 independent experiments, encompassing multiple in vitro and in vivo assays, produced motifs for just over half (177; 53%) of the TFs, of which most are associated with only a single protein. These results extend the vocabulary of sequence recognition encoded by human TFs by around 130 distinct motifs. Moreover, binding motifs identified in vitro are strongly enriched in cellular binding sites. Collectively, the data reveal tens of thousands of previously unknown, conserved and direct TF-binding sites across the human genome. These sites are concentrated in promoter regions and are predictive of gene expression. In summary, this new codebook provides an important step forward in decoding the human genome.

Anaemia is a major global health burden that affects one-quarter of the human population and annually accounts for over 50 million years of healthy life lost1. It arises from nutritional iron deficiency, hereditary disorders (including thalassaemia and sickle cell disease) and malaria, and is characterized by haemoglobin imbalances2. Haem-the active component of haemoglobin-is both essential and potentially toxic, which necessitates tight control of levels. However, the molecular circuitry that monitors haem levels remains obscure. The cytosolic eIF2α kinase HRI counteracts anaemia amid iron deficiency or thalassaemia3,4 by acting as a gatekeeper of translation during erythroid differentiation, which has been attributed to its haem-binding ability5. Here we uncover that haem scarcity is sensed inside mitochondria through an OMA1-DELE1 axis. Mechanistically, haem deficiency triggers OMA1-dependent mitochondrial release of DELE1. In the cytosol, DELE1 releases inhibitory haem from HRI, which enables modifications in a crucial disordered segment of the kinase. We demonstrate that this sensor-actuator operates across human tissues, including erythroid progenitors, and is evolutionarily conserved down to bloodless invertebrates, thus predating the emergence of haemoglobin-based oxygen transport. Notably, pharmacological manipulation of this system enhances fetal globin expression-a central therapeutic objective in haemoglobinopathies. Together, these results reveal a primordial sentinel system that safeguards against haem-related toxicity from the single-cell to the organismic scale.

Polyvinyl chloride (PVC) is a thermoplastic used ubiquitously in households and industry owing to its light weight, mechanical strength, durability, low cost and ultraviolet and fire resistance1,2. With a production volume of about 60 million tonnes annually2, post-consumer and post-industrial PVC waste pose marked environmental challenges, such as leaching chlorohydrocarbons and additives, which contaminate groundwater and soil1-4. To address the formidable challenge of recycling PVC and achieving carbon circularity, valorization into high-value products is essential to mitigate the associated costs and offer high financial incentives for reusing plastic waste5-14. Here we report a method for upcycling PVC into high-value lubricants with controllable viscosities. Using AlCl3 at a mild temperature of 70 °C, PVC undergoes dechlorination, alkylation and chain scission, producing vinyl-derived polyalphaolefins (vPAO). PVC serves as an effective template for the alkylation of α-olefins of various chain lengths, producing vPAO with limited short branches in the backbone without the need for metallocene catalysts essential to current PAO technology. The PVC-derived lubricants exhibit tunable molar masses, kinematic viscosities at 100 °C (KV100 ≈ 14.9-26.3 centistokes), a low coefficient of friction (COF ≈ 0.08-0.15) and a high viscosity index (VI up to 130). This work highlights an economical approach to using PVC as a low-cost feedstock to synthesize high-value lubricants with superior tribological properties, meeting the demand for sustainability in both the plastic and lubricant industries.

Spatial proteomics technologies have transformed our understanding of complex tissue architecture in cancer but present unique challenges for computational analysis1. Each study uses a different marker panel and protocol, and most methods are tailored to single cohorts, which limits knowledge transfer and robust biomarker discovery. Here we present Virtual Tissues (VirTues), a general-purpose foundation model for spatial proteomics that learns marker-aware, multi-scale representations of proteins, cells, niches and tissues directly from multiplex imaging data. From a single pretrained backbone, VirTues supports marker reconstruction, cell segmentation and typing, niche annotation, spatial biomarker discovery and patient stratification, including zero-shot annotation across heterogeneous panels and datasets. In triple-negative breast cancer, VirTues-derived biomarkers predict anti-PD-L1 chemo-immunotherapy response2 and stratify disease-free survival in an independent cohort3, outperforming state-of-the-art biomarkers derived from the same datasets and current clinical stratification schemes.

Phenotypic plasticity is a hallmark of cancer1; however the molecular switches required for cell-fate reprogramming are poorly understood. During intestinal wound-healing and colorectal cancer (CRC) metastasis, differentiated cells can dynamically dedifferentiate into an intestinal stem cell (ISC) state to drive epithelial regeneration and metastatic outgrowth2-10. Here we show that the RNA-binding protein ZFP36L2, which is mutated in 5-10% of CRC11-15, is a pivotal stress-responsive orchestrator of dynamic dedifferentiation. In mouse colon regeneration models, ZFP36L2 ablation inhibits dedifferentiation, ISC gene expression and function and impairs intestinal regeneration. In human CRC, loss of ZFP36L2 function abrogates metastatic seeding and the outgrowth of LGR5+ canonical metastases while promoting lineage plasticity and non-canonical differentiation into heterogeneous cell states. Mechanistically, ZFP36L2 binds to stress-associated mRNAs that contain AU-rich 3' untranslated regions, which induces the formation of dynamic biomolecular condensates associated with mRNA degradation and termination of the stress response. Together, these data show that ZFP36L2 acts as an important molecular switch that couples stress sensing with phenotypic plasticity. This in turn drives cellular dedifferentiation essential for re-establishing the ISC state during wound healing and metastasis. In ZFP36L2-deficient CRC, the inability to re-enter the LGR5+ state during metastatic outgrowth promotes non-canonical lineage plasticity, which is associated with poor clinical outcomes.

Targeted protein degradation is a powerful pharmacological strategy that harnesses the ubiquitin proteasome system to eliminate disease-relevant proteins, including otherwise undruggable proteins1. Here we report an unbiased and broadly applicable platform for the systematic discovery of molecular glues across diverse E3 ligases. Using multiplexed mass spectrometry-based chemical screening, we identified M12, a molecular glue that reprogrammes the E3 ligase DCAF11 to degrade DDX18. Mechanistically, M12 functions as a prodrug that is activated through glutathione S-transferase-mediated glutathionylation. The glutathione moiety binds to an evolutionary conserved glutathione-binding site on DCAF11, and the exposed M12 moiety facilitates neo-substrate recruitment. We demonstrate that this glutathione-dependent mechanism readily enables targeted degradation of a range of proteins. Collectively, these findings establish that metabolically activated compounds can redirect E3 ligase function, thereby expanding the scope of targeted protein degradation and chemically induced proximity.

Understanding how adverse life events trigger stress-related behavioural changes remains an unresolved challenge. The amygdala is integral to emotion and stress responses1 and comprises astrocytes, neurons and other cells. Here we show that amygdala astrocytes contribute to stress-related behaviours through signalling mechanisms related to their primary cilia2. Amygdala astrocytes are altered during stress at the protein and gene expression level, display reduced expression of molecules related to primary cilia3,4 and have morphologically short primary cilia5,6. G protein-coupled receptors (GPCRs) are central to astrocyte7 and primary cilia2,8,9 function. Therefore, we speculated that GPCR signalling activation might be beneficial in stress-related behavioural disorders. We identified amygdala astrocyte GPCRs as regulators of responses following stress. Chemogenetics and targeting of native sphingosine-1-phosphate receptor 1 (S1PR1) GPCRs led to the restoration of astrocyte primary cilia length, corrected molecular alterations and improved stress-related behaviours. Cilium-related genes were abundantly expressed in human amygdala astrocytes, with many displaying disrupted expression in stress-related brain disorders. S1PR1 was also highly expressed in amygdala astrocytes from human tissue. Selective genetic disruption of amygdala astrocyte primary cilia in mice altered some stress-related behaviours and gene expression of astrocytes and parenchymal cells. These data confirm that astrocytic cilia have important roles in this brain nucleus. In summary, amygdala astrocytes and their primary cilia are disrupted during stress, and their restoration is accompanied by stress-related molecular and behavioural improvements. Astrocyte primary cilia-related mechanisms may therefore provide new treatment strategies for stress-related and other brain disorders.

Structural anisotropy is ubiquitous in nature and governs orientation-dependent properties and functions1,2. Recreating biomimetic anisotropy in hydrogels-water-rich soft materials widely used in biomedicine-has proven challenging3. Here we report a phenomenon termed shear-extensional in situ particle-to-fibre transformation (SHIFT) and demonstrate the SHIFT printing of highly aligned hydrogel microfibres (5-30 μm in diameter). In SHIFT, particle-embedded hydrogel is extruded in a controlled manner, in which the discrete particles and surrounding matrix experience opposite phase transitions, concurrently transforming particles into well-aligned subvoxel microfibres. SHIFT establishes a methodology for creating anisotropy from droplets, which is dominated by extensional flow of sol-gel two-phase systems. It constitutes a versatile upstream manufacturing approach that can be readily adapted into three-dimensional printing and microfibre spinning scenarios in a highly accessible manner. The pronounced structural anisotropy enables the formation of exceptionally long myotubes in vitro and accelerates regeneration in volumetric muscle loss. By implementing an in situ subvoxel manufacturing process within flow, SHIFT expands the ability to process soft materials.

Two-dimensional (2D) superconductors are emerging platforms supporting both strongly correlated physics and quantum information science1,2. Their reduced dimensionality, atomically flat interfaces and high crystallinity are particularly attractive for realizing compact lumped-element devices in superconducting circuits3-5. However, large-scale synthesis of monolayer 2D superconductors remains challenging as they are easily oxidized in air6. Here we report an 'encapsulation epitaxy' mechanism that enables the growth of large-area (more than 1 inch), air-stable, monolayer niobium diselenide (NbSe2) films (1L-NbSe2) and explore their potential for superconducting quantum circuits. This work represents a distinct growth phenomenon in which a 2D encapsulation layer, such as graphene or hexagonal boron nitride, pre-deposited on a 3D substrate (for example, SiO2 or Si3N4) simultaneously serves as a template for the epitaxial growth of 1L-NbSe2 underneath it at the encapsulation-substrate interface and as a protective capping layer against ambient degradation. The as-grown 1L-graphene/NbSe2 heterostructures exhibit robust superconductivity (superconducting transition temperature Tc ≈ 1 K) and enhanced charge density waves (CDWs; CDW transition temperature TCDW ≈ 177 K). We further demonstrate the integration of 1L-NbSe2 into superconducting circuits by developing oxidation-free transfer and superconducting edge-contact techniques. The 1L-NbSe2 in these circuits feature a measured kinetic inductance LK ≈ 0.7 nH □-1, making it suitable for quantum circuits requiring elements with high kinetic inductance. This encapsulation-epitaxy methodology enables the production of air-stable 2D superconductors and van der Waals heterostructures, holding promise for wafer-scale, monolithic fabrication of superconducting quantum circuitry.