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For generalist animals that have a broad range of food choices, the specific diet characteristics that are important for health and fitness are often unclear. Here, we examine whether individual variation in diet composition predicts nestling mass and fledge success, and we identify potential drivers of variation in diet composition, in a declining generalist aerial insectivore, the tree swallow (Tachycineta bicolor). We collected morphological measurements, feces, and fledge success information from adult and nestling swallows. We then used DNA metabarcoding of 429 fecal samples to characterize swallow diets. We focused on two diet characteristics that may be important for generalists: dietary diversity and prevalence of nutritionally important diet items, specifically aquatic insects, which are essential to tree swallows. We predicted that nestlings with more diverse diets that were composed of a higher proportion of aquatic arthropods would be more likely to survive to fledging, and that adults' phenotypes and their stage in the breeding season would be associated with both their own diets and the diets of their nestlings. Swallow nestling dietary diversity strongly predicted fledge success, but we found no link between dietary aquatic arthropod content and nestling fledging. Adult phenotype did not predict nestling diet, but during the provisioning period, adult females with lower body mass had more diverse diets. Because this study occurred during a single breeding season in one geographic area, future work should further explore the patterns observed here, and especially examine the importance of dietary diversity for wild generalist species across years and localities.
Non-steroidal anti-inflammatory drugs (NSAIDs) are pervasive environmental contaminants due to their frequent and widespread use, multiple paths of release into surface and ground water supply, diversity of the chemical class, and toxicity to aquatic and other non-target species. In particular, the 2-arylpropionic acid ("profen") class of NSAIDs poses significant risks to aquatic ecosystems due to incomplete removal during wastewater treatment. Current monitoring precludes high-frequency testing at point sources. Here, we present the engineering and application of a genetically encodable, protein-based biosensor for the detection of the NSAIDs ketoprofen and pranoprofen in wastewater effluent. We repurposed the plant hormone receptor PYR1 to bind selectively to profens using computational protein design, deep mutational scanning, and yeast 2-hybrid and yeast surface display screening. The resulting sensor, PYR1NSAID, has a nanomolar limit of detection for ketoprofen and panoprofen, and µM sensitivity to the NSAIDs ibuprofen, fenoprofen, tolmetin and diclofenac. We also demonstrated dose-responsive activity of our sensor in simulated wastewater matrices containing the common wastewater contaminants sulfamethoxazole, caffeine, acetaminophen, and 2,4-dichlorophenol using a split Nanoluc luminescence assay. PYR1NSAID is the first step towards a scalable, cost-effective alternative for real-time monitoring of pharmaceutical pollution.
Wireless communication technologies for bioelectronic implants enable remote monitoring for diagnosis and adaptive therapeutic intervention without the constraints of wired connections. However, wireless data uplink from millimeter-scale devices deep in the body struggles to achieve low power consumption while maintaining large misalignment tolerances. Here, we report a passive wireless backscatter communication system based on magnetoelectric transducers that consumes less than 0.3 pJ/bit and achieves less than 1E-6 bit error rate at a distance of 55 mm while tolerating a misalignment of 10 mm. Using this robust data uplink, we designed a wireless cardiac sensing node that can transmit electrocardiogram signals from the beating heart surface of a porcine model to a custom external transceiver using the magnetoelectric backscatter uplink. This reliable, near-zero-power communication method provides opportunities for next-generation bioelectronics to feature real-time physiological monitoring and closed-loop therapies while maintaining a small form factor and low power consumption.
Neural operators are promising surrogates for dynamical systems but when trained with standard L2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that combining operator learning with generative modeling overcomes this limitation. We consider three practical turbulent-flow challenges where conventional neural operators fail: spatio-temporal super-resolution, forecasting, and sparse flow reconstruction. For Schlieren jet super-resolution, an adversarially trained neural operator (adv-NO) reduces the energy-spectrum error by 15 × while preserving sharp gradients at neural operator-like inference cost. For 3D homogeneous isotropic turbulence, adv-NO trained on only 160 timesteps from a single trajectory forecasts accurately for five eddy-turnover times and offers 114 × wall-clock speed-up at inference than the baseline diffusion-based forecasters, enabling near-real-time rollouts. For reconstructing cylinder wake flows from highly sparse Particle Tracking Velocimetry-like inputs, a conditional generative model infers full 3D velocity and pressure fields with correct phase alignment and statistics. These advances enable accurate reconstruction and forecasting at low compute cost, bringing near-real-time analysis and control within reach in experimental and computational fluid mechanics.
Research on biological mechanisms and disease processes is limited by fragmented findings across unstructured text in publications. Question answering and hypothesis generation that can reason across multiple sources can overcome this limitation. However, Large language models (LLMs) are prone to inaccuracies and lack clear provenance to primary evidence. Retrieval augmented generation approaches that have provenance to the original source of evidence address these shortcomings. However, the response richness is dependent on the retrieval process design. Current approaches often fail to produce responses requiring multi-hop reasoning across multiple domains. To address this, we propose eGoT, which combines automated knowledge graph construction from biomedical literature with a novel graph-of-thoughts approach to query the knowledge base and construct comprehensive responses to natural language questions. Given a corpus of documents, eGoT first uses an LLM-based pipeline to identify and normalize entities and relationships and constructs graph and vector databases. Given an input question, eGoT performs multi-round LLM-based querying of the databases to construct a response. Benchmarking on datasets like MultiHopRAG, HotpotQA, and Ultradomain demonstrates eGoT's superiority over state-of-the-art retrieval methods, including HopRAG, SireRAG, HiRAG, and HippoRAG. We demonstrate eGoT on two biomedical use cases: (i) generate responses to domain expert-curated questions on small cell lung cancer using 1046 PubMed Central publications, and (ii) demonstrate eGoT's ability to find plausible connections between Lupus and climate factors (UV exposure) that affect disease trajectory. https://github.com/NNeuralDynamics/eGOT.git.
Tourniquet (TQ) application is a key component of extremity haemorrhage control in high-resource military and civilian trauma systems. In settings with prolonged evacuation times, TQs can cause complications including limb loss, rhabdomyolysis, or death, yet limited data exist on the scope and impact of this problem. We conducted a single-centre prospective observational analysis at the largest military hospital in Burkina Faso to describe injury epidemiology, TQ application practices and early outcomes among casualties with conflict-related injuries who underwent prehospital TQ application from January to July 2025. 100 casualties (N) were included in the analysis. Mean prehospital time was 11.9 hours. The most common mechanism of injury was gunshot wound (64.0%, n=64) followed by explosion (36.0%, n=36). 114 TQs were placed. 5.3% (n=6) of TQs were both medically necessary and appropriately placed. Of 24 amputations, 54.2% (n=13) were performed at an anatomical level more proximal than otherwise indicated due to TQ application and 50.0% (n=12) were associated with TQs that were not medically necessary. Mortality was 5.0% (n=5), three of which were attributable to TQ-associated complications (eg, rhabdomyolysis, cardiac arrest). TQs can reduce preventable death from extremity haemorrhage but can also have harmful consequences in settings with prolonged evacuation times. In our sample from the Sahel, a low proportion of prehospital TQs were medically necessary and appropriately placed, with substantial TQ-associated morbidity and mortality. Context-appropriate clinical algorithms and training programmes are needed to reduce these complications while upholding the role of appropriate TQ use for haemorrhage control in conflict.
Major Depressive Disorder (MDD) is a highly prevalent mental health condition, and a deeper understanding of its neurocognitive foundations is essential for identifying how core functions such as emotional and self-referential processing are affected. We investigate how depression symptoms alters the temporal dynamics of emotional processing by measuring neural responses to self-referential affective sentences using surface electroencephalography (EEG) in healthy and depressed individuals. Our results reveal significant group-level differences in neural activity during sentence viewing, suggesting disrupted integration of emotional and self-referential information in depression. Deep learning model trained on these responses achieves an area under the receiver operating curve (AUC) of 0.72 in distinguishing healthy from depressed participants, and 0.65 in differentiating depressed subgroups with and without suicidal ideation symptoms. Spatial ablations highlight anterior electrodes associated with semantic and affective processing as key contributors. These findings suggest stable, stimulus-driven neural signatures of depression symptoms that may inform future screening tools.
To support the development of the National Trauma Research Repository (NTRR), a multidisciplinary workgroup focused on prehospital trauma care used a consensus-driven approach to review established data elements and recommend basic common data elements (CDEs) for inclusion in the NTRR data dictionary. A 13-member workgroup of military and civilian trauma researchers and data scientists located and reviewed databases, codebooks, data collection forms, and published articles for data elements relevant to prehospital trauma care. Identified data elements were reviewed in a three-round Delphi survey and during monthly workgroup meetings. Consensus during the Delphi survey was determined with an 80% agreement threshold. Twenty-one sources were reviewed for prehospital data elements. Following three rounds and workgroup discussions, 52 elements (84%) reached consensus for inclusion, two elements (3%) were excluded, and eight elements (13%) did not reach consensus. The Delphi process proved effective in achieving expert consensus on basic CDEs for prehospital trauma care research. The resulting standardized basic CDEs will improve data harmonization and support consistent data collection in the NTRR. These CDEs represent an initial framework and serve as a foundational starting point for prehospital data collection within the NTRR. As researchers use the NTRR, the list of CDEs will grow and evolve to meet the needs of the trauma research community.
The spatiotemporal structure of muscle coordination emerges from the collaboration and competition among cortical, brainstem and spinal pathways onto motor neuron pools, each continuously shaped by task demands, limb position and descending tract integrity. Here, we used galvanic vestibular stimulation (GVS) to investigate whether brainstem vestibular output is disrupted in stroke survivors with right hemiparesis (n = 14) compared with age-matched control subjects (n = 14). We estimated this via intermuscular coherence among neck and arm muscles during both rest and a reaching-like (i.e., reaching) movement with the shoulder in neutral and abducted positions, each with no stimulation, sham stimulation or GVS. Previous work showed that young adults exhibit increased coherence between neck muscles with GVS at rest, as do our control subjects. Surprisingly, we saw the same pattern in stroke survivors only on their paretic side, with reduced coherence in their non-paretic side. During reaching, the paretic side did not show changes in coherence between arm or neck muscles, in comparison to control subjects, with and without GVS, and importantly, even lower coherence with the abducted shoulder. Given that GVS did not exacerbate intermuscular coherence in any muscle pair during the reaching tasks and that coherence was even reduced during shoulder abduction, our findings provide evidence to exclude increased brainstem vestibular output as a dominant contributor to pathological synergies during voluntary movement following stroke. In addition, the decreased coherence between neck muscles of the non-paretic side during GVS at rest suggests changes in ipsilesional brainstem vestibular output. This highlights opportunities to consider unexplored contralesional brainstem-spinal pathways, in addition to downregulated ipsilesional vestibular projections, in neurorehabilitation strategies following stroke.
Influenza A virus populations contain substantial genetic diversity. A major contributor to this diversity is the ubiquitous production of deletion-containing viral genomes (DelVGs) - viral RNAs with large internal deletions. DelVGs directly compete with wild-type (WT) genomes and have been implicated in modulating disease severity. However, the specific functional and genetic interactions between DelVGs and WT genomes remain poorly understood. To examine how DelVGs and WT genomes co-evolve, we serially passage influenza A virus and use next-generation sequencing to build a longitudinal profile of DelVG emergence and dynamics. Highly diverse repertoires of DelVGs observed in early passages undergo sharp contractions in overall diversity, leaving only one or two DelVGs that persist at high frequency. We also identify a recurring substitution that significantly enhances DelVG replication and interference potency. These findings reveal DelVGs to be dynamic genomic elements that are subject to unique selection forces and actively shape viral population dynamics.
Maladaptive emotion dynamics and processes, such as emotional inflexibility and dominance of negative emotions, are characteristic of depression. The extent to which these abnormalities persist following depressive episodes, and represent a vulnerability factor for recurrent depressive episodes, remains unknown. The current study investigated whether emotion dynamics predict depressive symptoms in individuals with remitted depression (rMDD) and healthy controls (HC). 98 adults (HC: n = 50; rMDD: n = 48) completed a three-week ecological momentary assessment protocol, in which they responded to two items probing positive emotions and three items probing negative emotions six times daily. Contemporaneous and temporal networks were constructed using multilevel vector autoregressive models. Density was calculated as a measure of emotional inflexibility and In- and Out-Expected Influence were calculated as measures of centrality. Linear regression models examined if density predicted clinical outcomes at the 6-month follow-up assessment. Individuals with rMDD had significantly denser temporal emotional networks than HC participants. Groups also showed differential patterns of the most influential nodes in temporal and contemporaneous networks. Greater temporal density and contemporaneous density was significantly associated with increased depressive symptoms at the 6-month follow-up, assessed through both clinician-rated and self-report measures. Abnormalities in emotion dynamics persist following remission from depression and can be used to predict future depressive symptoms, suggesting these may represent a vulnerability factor for depression. Future research should study if interventions based on emotion networks targeting maladaptive emotional processes are able to prevent future depression.
Frequency combs have revolutionized metrology, ranging and optical clocks1, motivating substantial efforts on the development of chip-scale comb sources2,3. Some on-chip comb sources exist and have been implemented through electro-optic modulation4,5, mode-locked lasers6,7, quantum cascade lasers8-10 or soliton formation by Kerr nonlinearity11,12. However, widespread deployment of on-chip comb sources has remained elusive, as they still require radiofrequency sources, high-Q (high-quality factor) resonators or complex stabilization schemes while facing efficiency challenges. Here, we demonstrate an on-chip frequency comb source based on the integration of a lithium niobate nanophotonic circuit with a semiconductor laser that can alleviate these challenges. We show the formation of temporal topological solitons in an on-chip nanophotonic parametric oscillator with quadratic nonlinearity and low finesse. These solitons, independent of the dispersion regime, consist of phase defects separating two π-out-of-phase continuous wave solutions at the signal frequency, which is half the input pump frequency13,14. We use on-chip cross-correlation for temporal measurements and confirm formation of topological solitons as short as 60 fs around 2 μm, in agreement with a generalized parametrically forced Ginzburg-Landau theory15-17. Moreover, we demonstrate a proof-of-concept turn-key operation of a hybrid-integrated source of topological frequency comb. Topological solitons are potential candidates for the development of integrated comb sources, which are dispersion-sign agnostic and do not require high-Q resonators or high-speed modulators, and can provide access to hard-to-reach spectral regions, including mid-infrared regions18.
Hepatocellular carcinoma (HCC) screening targets patients with cirrhosis but lacks risk stratification and misses non-cirrhotic cases. Thus, we evaluated whether LIver cancer RIsk Computation (LIRIC) models built from routinely collected electronic health record (EHR) data can predict 3-year HCC risk in general and cirrhosis populations and generalize across diverse US and international cohorts. We conducted a multicenter study using longitudinal EHR data from 64 US and 29 ex-US (Latin America/Asia-Pacific) healthcare organizations. Individuals aged ≥40 years at cohort identification and without previous HCC were included; cases were identified using ICD codes 6-36 months prediagnosis. US models were developed in general and cirrhosis cohorts (46,679 cases; 1,128,202 controls), internally externally validated by site, race/ethnicity, and time, and evaluated in a simulated 'silent' deployment. We benchmarked model-derived risk against population incidence using standardized incidence ratios (SIRs). External validation applied US models to ex-US cohorts; region-specific ex-US models were also trained. Logistic regression and neural networks (a liver risk computation logistic regression model and a liver risk computation neural network model) were compared using AUC, calibration, and geometric mean of overestimation (GMOE) risk ratios. In the US general population, LIRICNN achieved AUC 0.93 (95% CI 0.9218-0.9289) using 46 features. Internal-external AUCs averaged 0.93 across sites and race/ethnicity strata, with good calibration (GMOE 0.89; 95% CI 0.862-0.911). At a screening threshold corresponding to SIR ≈ 31, sensitivity was 48% and specificity was 97%. US-trained models applied to ex-US data yielded lower AUCs (0.84), but region-specific retraining restored performance (AUC 0.94). LIRIC models accurately stratified HCC risk using routine EHR data in both general and cirrhosis populations and could be adapted for international settings via local retraining. These results support LIRIC as a scalable foundation for risk-based HCC surveillance strategies and future prospective implementation studies. Current HCC screening targets patients with cirrhosis but lacks risk stratification and misses non-cirrhotic cases; therefore, we evaluated whether LIRIC models built from routinely collected EHR data can predict 3-year HCC risk in general and cirrhosis populations and generalize across diverse US and international cohorts. These findings are important for hepatologists, primary care clinicians, and health systems because they show that routine EHR data can support accurate and scalable HCC risk stratification across diverse populations. In practice, LIRIC could serve as a foundation for risk-based HCC surveillance strategies and future prospective implementation studies using data already collected in routine care. Given that this was a retrospective modeling study and international performance required local retraining, these findings should be interpreted as supporting future implementation rather than immediate universal adoption.
Ultra-low linewidth widely tunable lasers capable of emission by design from the visible to shortwave infrared are important building blocks for a range of precision applications including quantum sensing and computing, timekeeping, metrology, optical clocks, and fiber sensing. Importantly, integration of precision tunable lasers in a CMOS foundry compatible platform that can support higher level integration with other components, such as low loss silicon nitride (Si3N4), is an important step towards full system on chip solutions. Integration of the III-V gain material with the Si3N4 tunable cavity is a critical step towards this goal and must be achieved through a low-cost, manufacturable, and reliable process. However, this co-integration has remained challenging due to tight alignment tolerances and mode mismatches between the semiconductor and silicon nitride waveguides. 3D-printed photonic wire bonding (PWB) offers a robust approach to hybrid integration due to the relaxation of waveguide alignment tolerances and the inherent low-loss mode matching. In this work, we demonstrate a narrow linewidth PWB-integrated Si3N4 external cavity tunable laser (ECTL) with a 3.75-7.77 Hz fundamental linewidth measured across a 60 nm tuning range and a 1.27 kHz integral linewidth: a reduction of nearly three orders of magnitude in fundamental linewidth compared with previously reported PWB-integrated ECTLs in Si3N4. The PWB process has the potential to realize reliable and manufacturable tunable lasers on-chip with the performance of table-top fiber lasers. These results establish photonic wirebonding as a viable integration pathway for precision photonic systems, enabling portable, scalable, and cost-effective solutions for quantum, low-noise microwave, and sensing applications.
Reward processing deficits are prominent in major depressive disorder (MDD) and contribute to appetitive phenotypes: hyperphagic (HyperMDD) and hypophagic (HypoMDD). However, few studies have examined neurobiological processes underlying these phenotypes, and whether they stem from aberrant responsivity specific to food or rewards more broadly. This study probed group differences in functional connectivity during anticipation and outcome phases of food vs. monetary rewards across appetitive MDD phenotypes. 14 unmedicated HyperMDDs, 20 unmedicated HypoMDDs, and 36 healthy controls completed food and monetary incentive delay tasks while undergoing fMRI. Psychophysiological interaction analyses examined functional connectivity changes during relevant task conditions/reward processing components: anticipation, successful reward outcome, and failed reward outcomes. Findings revealed dissociable patterns of fronto-striatal and salience network connectivity during anticipatory reward processing among groups, reflecting differential top-down control of food versus monetary cues. HypoMDD was associated with heightened connectivity to food cues and negative connectivity to monetary cues, while HyperMDD exhibited the opposite patterns. MDD groups also differed in connectivity during reward outcome: HypoMDD demonstrated increased functional connectivity for successful food outcome and reduced functional connectivity for successful money outcome whereas HyperMDD demonstrated reduced connectivity during successful food outcome and increased connectivity during successful money reward outcome. For failed reward outcomes, only HyperMDD demonstrated reduced connectivity for failed food outcomes, and increased connectivity for failed money outcomes. These findings suggest that appetitive MDD phenotypes are distinguished by opposing circuitry profiles that bias responsivity toward food versus monetary rewards, patterns that may help guide the development of phenotype- and reward-specific interventions.
Ice accretion poses a formidable challenge for transparent surfaces in cold environments, such as windows, solar cells, and vehicle windshields. It is commonly understood that increasing surface roughness typically increases the forces required for the removal of ice from a substrate due to an increase in the ice-solid interfacial area. We introduce a novel and scalable laser-based technique to fabricate wave-like micro-patterns on glass surfaces, which defy this conventional understanding. Our results show that these patterns can strategically guide crack propagation at the ice-glass interface, which significantly lowers the forces required for ice detachment, while preserving substrate transparency. Interestingly, once these micro-patterns are present, variations in their amplitude and wavelength do not significantly impact the forces required for ice detachment. We present a comprehensive theoretical framework that explains these results, outlines the design principles for pattern fabrication to enable facile ice-shedding in all directions, and support the model with experimental validation. Overall, this work offers a scalable strategy to create high-performance, ice-shedding glass and reassesses the role of surface roughness in facilitating passive ice detachment.
BACKGROUND: The rumen harbors a diverse and dynamic microbiome vital in digesting vegetation into metabolic byproducts for energy and general biological function. Although previous studies have reported connections between the rumen and the overall health of the sheep, the exact biological process by which this occurs is not well understood. Therefore, our study aimed to quantify sheep rumen metabolites to determine if enriched biological pathways are differentiable across phenotypic features of sex, age, and weight. RESULTS: We collected and quantified metabolites of rumen samples from sixteen sheep using liquid chromatography-tandem mass spectrometry. We performed a series of univariate and multivariate statistical analyses to interpret the rumen metabolomics data. To identify metabolic pathways associated with the phenotypic features of sex, weight, and age, we used MetaboAnalyst, which identified amino acid metabolism as a distinguishing factor. Among the pathways, phenylalanine metabolism emerged as a key pathway differentiating sheep based on sex and age. Additionally, phenylalanine, tyrosine, and tryptophan biosynthesis were exclusively associated with age. In univariate linear models, we also discovered that these amino acid and protein pathways were associated with weight by age-corrected effect. Finally, we identified arginine and proline biosynthesis as a pathway linked to metabolites with weight. CONCLUSION: Our study identified differential pathways based on the sex, age, and weight features of sheep. Metabolites produced by the rumen may act as an indicator for sheep health and other ruminants. These findings encourage further investigation of the differentially produced metabolites to assess overall sheep health.
The family of quantized anomalous Hall effects provides remarkable electronic properties-for example, current flow perpendicular to the voltage and, in some cases, dissipationless edge states even with zero applied magnetic field, B-but their development is limited by their realization only at very low temperatures. The state of the art in magnetically-doped topological insulators (TIs) currently allows quantized Hall conductivities to persist up to temperatures of several Kelvin. An alternative approach, proximity-coupled TI/magnet heterostructures made using a chemically separate magnet, has up until now been more limited, with Hall quantization either completely absent or present only below 100 mK at B = 0. Here, we demonstrate one in the family of quantized anomalous Hall effects, the parity anomaly state (with Hall conductivity e2/2h) at temperatures up to 10 K in TI/magnet bilayers made by mechanical assembly of van der Waals layers. This represents an enhancement by a factor of 100 compared to previous proximity-coupled heterostructures grown by deposition.
Proteins offer a molecular design space to create bespoke ligands for the separation of critical metals like rare earth elements (REs). However, data-intensive approaches to tune metalloprotein selectivity are constrained by the low-throughput nature of existing characterization methods. Here we invented an assay called 'SpyTag-Catcher Immobilization of Lanmodulin for Assaying Metal-Binding Selectivity' (SpyCI-LAMBS) to measure metalloprotein selectivity en masse. This 96-format workflow was used to study the selectivity of 621 lanmodulin (LanM) orthologs for 15 REs, revealing eight distinct selectivity profiles based on sequence-to-function analyses. We discovered >200 LanMs with stronger selectivity against low-value LaIII relative to the prototypical LanM. This includes a LanM that can perform a challenging one-stage separation of PrIII from LaIII with up to >99.9 mol% purity and 83% yield. SpyCI-LAMBS is a powerful tool that can rapidly collect high-fidelity selectivity data to inform metal ion separations and machine-learning-assisted metalloprotein design.