A modular and synthetic biosynthesis platform for de novo production of diverse halogenated tryptophan-derived molecules

Halogen-containing molecules are ubiquitous in modern society and present unique chemical possibilities. As a whole, de novo fermentation and synthetic pathway construction for these molecules remain relatively underexplored and could unlock molecules with exciting new applications in industries ranging from textiles to agrochemicals to pharmaceuticals. Here, we report a mix-and-match co-culture platform to de novo generate a large array of halogenated tryptophan derivatives in Escherichia coli from glucose. First, we engineer E. coli to produce between 300 and 700 mg/L of six different halogenated tryptophan precursors. Second, we harness the native promiscuity of multiple downstream enzymes to access unexplored regions of metabolism. Finally, through modular co-culture fermentations, we demonstrate a plug-and-play bioproduction platform, culminating in the generation of 26 distinct halogenated molecules produced de novo including precursors to prodrugs 4-chloro- and 4-bromo-kynurenine and new-to-nature halogenated beta carbolines.

Photonic-electronic integrated circuit-based coherent LiDAR engine

Chip-scale integration is a key enabler for the deployment of photonic technologies. Coherent laser ranging or FMCW LiDAR, a perception technology that benefits from instantaneous velocity and distance detection, eye-safe operation, long-range, and immunity to interference. However, wafer-scale integration of these systems has been challenged by stringent requirements on laser coherence, frequency agility, and the necessity for optical amplifiers. Here, we demonstrate a photonic-electronic LiDAR source composed of a micro-electronic-based high-voltage arbitrary waveform generator, a hybrid photonic circuit-based tunable Vernier laser with piezoelectric actuators, and an erbium-doped waveguide amplifier. Importantly, all systems are realized in a wafer-scale manufacturing-compatible process comprising III-V semiconductors, silicon nitride photonic integrated circuits, and 130-nm SiGe bipolar complementary metal-oxide-semiconductor (CMOS) technology. We conducted ranging experiments at a 10-meter distance with a precision level of 10 cm and a 50 kHz acquisition rate. The laser source is turnkey and linearization-free, and it can be seamlessly integrated with existing focal plane and optical phased array LiDAR approaches.

Multisensory flicker modulates widespread brain networks and reduces interictal epileptiform discharges

Modulating brain oscillations has strong therapeutic potential. Interventions that both non-invasively modulate deep brain structures and are practical for chronic daily home use are desirable for a variety of therapeutic applications. Repetitive audio-visual stimulation, or sensory flicker, is an accessible approach that modulates hippocampus in mice, but its effects in humans are poorly defined. We therefore quantified the neurophysiological effects of flicker with high spatiotemporal resolution in patients with focal epilepsy who underwent intracranial seizure monitoring. In this interventional trial (NCT04188834) with a cross-over design, subjects underwent different frequencies of flicker stimulation in the same recording session with the effect of sensory flicker exposure on local field potential (LFP) power and interictal epileptiform discharges (IEDs) as primary and secondary outcomes, respectively. Flicker focally modulated local field potentials in expected canonical sensory cortices but also in the medial temporal lobe and prefrontal cortex, likely via resonance of stimulated long-range circuits. Moreover, flicker decreased interictal epileptiform discharges, a pathological biomarker of epilepsy and degenerative diseases, most strongly in regions where potentials were flicker-modulated, especially the visual cortex and medial temporal lobe. This trial met the scientific goal and is now closed. Our findings reveal how multi-sensory stimulation may modulate cortical structures to mitigate pathological activity in humans.

An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

Functionalizing tandem mass tags for streamlining click-based quantitative chemoproteomics

Mapping the ligandability or potential druggability of all proteins in the human proteome is a central goal of mass spectrometry-based covalent chemoproteomics. Achieving this ambitious objective requires high throughput and high coverage sample preparation and liquid chromatography-tandem mass spectrometry analysis for hundreds to thousands of reactive compounds and chemical probes. Conducting chemoproteomic screens at this scale benefits from technical innovations that achieve increased sample throughput. Here we realize this vision by establishing the silane-based cleavable linkers for isotopically-labeled proteomics-tandem mass tag (sCIP-TMT) proteomic platform, which is distinguished by early sample pooling that increases sample preparation throughput. sCIP-TMT pairs a custom click-compatible sCIP capture reagent that is readily functionalized in high yield with commercially available TMT reagents. Synthesis and benchmarking of a 10-plex set of sCIP-TMT reveal a substantial decrease in sample preparation time together with high coverage and high accuracy quantification. By screening a focused set of four cysteine-reactive electrophiles, we demonstrate the utility of sCIP-TMT for chemoproteomic target hunting, identifying 789 total liganded cysteines. Distinguished by its compatibility with established enrichment and quantification protocols, we expect sCIP-TMT will readily translate to a wide range of covalent chemoproteomic applications.

DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology

Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.

De novo design of pH-responsive self-assembling helical protein filaments

Biological evolution has led to precise and dynamic nanostructures that reconfigure in response to pH and other environmental conditions. However, designing micrometre-scale protein nanostructures that are environmentally responsive remains a challenge. Here we describe the de novo design of pH-responsive protein filaments built from subunits containing six or nine buried histidine residues that assemble into micrometre-scale, well-ordered fibres at neutral pH. The cryogenic electron microscopy structure of an optimized design is nearly identical to the computational design model for both the subunit internal geometry and the subunit packing into the fibre. Electron, fluorescent and atomic force microscopy characterization reveal a sharp and reversible transition from assembled to disassembled fibres over 0.3 pH units, and rapid fibre disassembly in less than 1 s following a drop in pH. The midpoint of the transition can be tuned by modulating buried histidine-containing hydrogen bond networks. Computational protein design thus provides a route to creating unbound nanomaterials that rapidly respond to small pH changes.

Passive frequency comb generation at radiofrequency for ranging applications

Optical frequency combs, featuring evenly spaced spectral lines, have been extensively studied and applied to metrology, signal processing, and sensing. Recently, frequency comb generation has been also extended to MHz frequencies by harnessing nonlinearities in microelectromechanical membranes. However, the generation of frequency combs at radio frequencies (RF) has been less explored, together with their potential application in wireless technologies. In this work, we demonstrate an RF system able to wirelessly and passively generate frequency combs. This circuit, which we name quasi-harmonic tag (qHT), offers a battery-free solution for far-field ranging of unmanned vehicles (UVs) in GPS-denied settings, and it enables a strong immunity to multipath interference, providing better accuracy than other RF approaches to far-field ranging. Here, we discuss the principle of operation, design, implementation, and performance of qHTs used to remotely measure the azimuthal distance of a UV flying in an uncontrolled electromagnetic environment. We show that qHTs can wirelessly generate frequency combs with μWatt-levels of incident power by leveraging the nonlinear interaction between an RF parametric oscillator and a high quality factor piezoelectric microacoustic resonator. Our technique for frequency comb generation opens new avenues for a wide range of RF applications beyond ranging, including timing, computing and sensing.

q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics

Unlocking the full dimensionality of single-cell RNA sequencing data (scRNAseq) is the next frontier to a richer, fuller understanding of cell biology. We introduce q-diffusion, a framework for capturing the coexpression structure of an entire library of genes, improving on state-of-the-art analysis tools. The method is demonstrated via three case studies. In the first, q-diffusion helps gain statistical significance for differential effects on patient outcomes when analyzing the CALGB/SWOG 80405 randomized phase III clinical trial, suggesting precision guidance for the treatment of metastatic colorectal cancer. Secondly, q-diffusion is benchmarked against existing scRNAseq classification methods using an in vitro PBMC dataset, in which the proposed method discriminates IFN-γ stimulation more accurately. The same case study demonstrates improvements in unsupervised cell clustering with the recent Tabula Sapiens human atlas. Finally, a local distributional segmentation approach for spatial scRNAseq, driven by q-diffusion, yields interpretable structures of human cortical tissue.

Ecological countermeasures to prevent pathogen spillover and subsequent pandemics

Substantial global attention is focused on how to reduce the risk of future pandemics. Reducing this risk requires investment in prevention, preparedness, and response. Although preparedness and response have received significant focus, prevention, especially the prevention of zoonotic spillover, remains largely absent from global conversations. This oversight is due in part to the lack of a clear definition of prevention and lack of guidance on how to achieve it. To address this gap, we elucidate the mechanisms linking environmental change and zoonotic spillover using spillover of viruses from bats as a case study. We identify ecological interventions that can disrupt these spillover mechanisms and propose policy frameworks for their implementation. Recognizing that pandemics originate in ecological systems, we advocate for integrating ecological approaches alongside biomedical approaches in a comprehensive and balanced pandemic prevention strategy.

Profiling the proximal proteome of the activated μ-opioid receptor

The μ-opioid receptor (μOR) represents an important target of therapeutic and abused drugs. So far, most understanding of μOR activity has focused on a subset of known signal transducers and regulatory molecules. Yet μOR signaling is coordinated by additional proteins in the interaction network of the activated receptor, which have largely remained invisible given the lack of technologies to interrogate these networks systematically. Here we describe a proteomics and computational approach to map the proximal proteome of the activated μOR and to extract subcellular location, trafficking and functional partners of G-protein-coupled receptor (GPCR) activity. We demonstrate that distinct opioid agonists exert differences in the μOR proximal proteome mediated by endocytosis and endosomal sorting. Moreover, we identify two new μOR network components, EYA4 and KCTD12, which are recruited on the basis of receptor-triggered G-protein activation and might form a previously unrecognized buffering system for G-protein activity broadly modulating cellular GPCR signaling.

The direction of theta and alpha travelling waves modulates human memory processing

To support a range of behaviours, the brain must flexibly coordinate neural activity across widespread brain regions. One potential mechanism for this coordination is a travelling wave, in which a neural oscillation propagates across the brain while organizing the order and timing of activity across regions. Although travelling waves are present across the brain in various species, their potential functional relevance has remained unknown. Here, using rare direct human brain recordings, we demonstrate a distinct functional role for travelling waves of theta- and alpha-band (2-13 Hz) oscillations in the cortex. Travelling waves propagate in different directions during separate cognitive processes. In episodic memory, travelling waves tended to propagate in a posterior-to-anterior direction during successful memory encoding and in an anterior-to-posterior direction during recall. Because travelling waves of oscillations correspond to local neuronal spiking, these patterns indicate that rhythmic pulses of activity move across the brain in different directions for separate behaviours. More broadly, our results suggest a fundamental role for travelling waves and oscillations in dynamically coordinating neural connectivity, by flexibly organizing the timing and directionality of network interactions across the cortex to support cognition and behaviour.

Digital twins in medicine

Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.

Synthetic microbe-to-plant communication channels

Plants and microbes communicate to collaborate to stop pests, scavenge nutrients, and react to environmental change. Microbiota consisting of thousands of species interact with each other and plants using a large chemical language that is interpreted by complex regulatory networks. In this work, we develop modular interkingdom communication channels, enabling bacteria to convey environmental stimuli to plants. We introduce a "sender device" in Pseudomonas putida and Klebsiella pneumoniae, that produces the small molecule p-coumaroyl-homoserine lactone (pC-HSL) when the output of a sensor or circuit turns on. This molecule triggers a "receiver device" in the plant to activate gene expression. We validate this system in Arabidopsis thaliana and Solanum tuberosum (potato) grown hydroponically and in soil, demonstrating its modularity by swapping bacteria that process different stimuli, including IPTG, aTc and arsenic. Programmable communication channels between bacteria and plants will enable microbial sentinels to transmit information to crops and provide the building blocks for designing artificial consortia.

Multi-night cortico-basal recordings reveal mechanisms of NREM slow-wave suppression and spontaneous awakenings in Parkinson's disease

Sleep disturbance is a prevalent and disabling comorbidity in Parkinson's disease (PD). We performed multi-night (n = 57) at-home intracranial recordings from electrocorticography and subcortical electrodes using sensing-enabled Deep Brain Stimulation (DBS), paired with portable polysomnography in four PD participants and one with cervical dystonia (clinical trial: NCT03582891). Cortico-basal activity in delta increased and in beta decreased during NREM (N2 + N3) versus wakefulness in PD. DBS caused further elevation in cortical delta and decrease in alpha and low-beta compared to DBS OFF state. Our primary outcome demonstrated an inverse interaction between subcortical beta and cortical slow-wave during NREM. Our secondary outcome revealed subcortical beta increases prior to spontaneous awakenings in PD. We classified NREM vs. wakefulness with high accuracy in both traditional (30 s: 92.6 ± 1.7%) and rapid (5 s: 88.3 ± 2.1%) data epochs of intracranial signals. Our findings elucidate sleep neurophysiology and impacts of DBS on sleep in PD informing adaptive DBS for sleep dysfunction.

Enhancing detection of topological order by local error correction

The exploration of topologically-ordered states of matter is a long-standing goal at the interface of several subfields of the physical sciences. Such states feature intriguing physical properties such as long-range entanglement, emergent gauge fields and non-local correlations, and can aid in realization of scalable fault-tolerant quantum computation. However, these same features also make creation, detection, and characterization of topologically-ordered states particularly challenging. Motivated by recent experimental demonstrations, we introduce a paradigm for quantifying topological states-locally error-corrected decoration (LED)-by combining methods of error correction with ideas of renormalization-group flow. Our approach allows for efficient and robust identification of topological order, and is applicable in the presence of incoherent noise sources, making it particularly suitable for realistic experiments. We demonstrate the power of LED using numerical simulations of the toric code under a variety of perturbations. We subsequently apply it to an experimental realization, providing new insights into a quantum spin liquid created on a Rydberg-atom simulator. Finally, we extend LED to generic topological phases, including those with non-abelian order.

Forum on immune digital twins: a meeting report

Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.

Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN

Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, interspecies genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes' biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN can detect functionally related genes coexpressed across species, redefining differential expression for cross-species analysis. Applying SATURN to three species whole-organism atlases and frog and zebrafish embryogenesis datasets, we show that SATURN can effectively transfer annotations across species, even when they are evolutionarily remote. We also demonstrate that SATURN can be used to find potentially divergent gene functions between glaucoma-associated genes in humans and four other species.

Asymmetric imaging through engineered Janus particle obscurants using a Monte Carlo approach for highly asymmetric scattering media

The advancement of imaging systems has significantly ameliorated various technologies, including Intelligence Surveillance Reconnaissance Systems and Guidance Systems, by enhancing target detection, recognition, identification, positioning, and tracking capabilities. These systems can be countered by deploying obscurants like smoke, dust, or fog to hinder visibility and communication. However, these counter-systems affect the visibility of both sides of the cloud. In this sense, this manuscript introduces a new concept of a smoke cloud composed of engineered Janus particles to conceal the target image on one side while providing clear vision from the other. The proposed method exploits the unique scattering properties of Janus particles, which selectively interact with photons from different directions to open up the possibility of asymmetric imaging. This approach employs a model that combines a genetic algorithm with Discrete Dipole Approximation to optimize the Janus particles' geometrical parameters for the desired scattering properties. Moreover, we propose a Monte Carlo-based approach to calculate the image formed as photons pass through the cloud, considering highly asymmetric particles, such as Janus particles. The effectiveness of the cloud in disguising a target is evaluated by calculating the Probability of Detection (PD) and the Probability of Identification (PID) based on the constructed image. The optimized Janus particles can produce a cloud where it is possible to identify a target more than 50% of the time from one side (PID > 50%) while the target is not detected more than 50% of the time from the other side (PD < 50%). The results demonstrate that the Janus particle-engineered smoke enables asymmetric imaging with simultaneous concealment from one side and clear visualization from the other. This research opens intriguing possibilities for modern obscurant design and imaging systems through highly asymmetric and inhomogeneous particles besides target detection and identification capabilities in challenging environments.

Mesoscopic calcium imaging in a head-unrestrained male non-human primate using a lensless microscope

Mesoscopic calcium imaging enables studies of cell-type specific neural activity over large areas. A growing body of literature suggests that neural activity can be different when animals are free to move compared to when they are restrained. Unfortunately, existing systems for imaging calcium dynamics over large areas in non-human primates (NHPs) are table-top devices that require restraint of the animal's head. Here, we demonstrate an imaging device capable of imaging mesoscale calcium activity in a head-unrestrained male non-human primate. We successfully miniaturize our system by replacing lenses with an optical mask and computational algorithms. The resulting lensless microscope can fit comfortably on an NHP, allowing its head to move freely while imaging. We are able to measure orientation columns maps over a 20 mm2 field-of-view in a head-unrestrained macaque. Our work establishes mesoscopic imaging using a lensless microscope as a powerful approach for studying neural activity under more naturalistic conditions.

Ammonia-oxidizing bacteria and archaea exhibit differential nitrogen source preferences

Ammonia-oxidizing microorganisms (AOM) contribute to one of the largest nitrogen fluxes in the global nitrogen budget. Four distinct lineages of AOM: ammonia-oxidizing archaea (AOA), beta- and gamma-proteobacterial ammonia-oxidizing bacteria (β-AOB and γ-AOB) and complete ammonia oxidizers (comammox), are thought to compete for ammonia as their primary nitrogen substrate. In addition, many AOM species can utilize urea as an alternative energy and nitrogen source through hydrolysis to ammonia. How the coordination of ammonia and urea metabolism in AOM influences their ecology remains poorly understood. Here we use stable isotope tracing, kinetics and transcriptomics experiments to show that representatives of the AOM lineages employ distinct regulatory strategies for ammonia or urea utilization, thereby minimizing direct substrate competition. The tested AOA and comammox species preferentially used ammonia over urea, while β-AOB favoured urea utilization, repressed ammonia transport in the presence of urea and showed higher affinity for urea than for ammonia. Characterized γ-AOB co-utilized both substrates. These results reveal contrasting niche adaptation and coexistence patterns among the major AOM lineages.

Sentinel cells programmed to respond to environmental DNA including human sequences

Monitoring environmental DNA can track the presence of organisms, from viruses to animals, but requires continuous sampling of transient sequences from a complex milieu. Here we designed living sentinels using Bacillus subtilis to report the uptake of a DNA sequence after matching it to a preencoded target. Overexpression of ComK increased DNA uptake 3,000-fold, allowing for femtomolar detection in samples dominated by background DNA. This capability was demonstrated using human sequences containing single-nucleotide polymorphisms (SNPs) associated with facial features. Sequences were recorded with high efficiency and were protected from nucleases for weeks. The SNP could be determined by sequencing or in vivo using CRISPR interference to turn on reporter expression in response to a specific base. Multiple SNPs were recorded by one cell or through a consortium in which each member recorded a different sequence. Sentinel cells could surveil for specific sequences over long periods of time for applications spanning forensics, ecology and epidemiology.

Anti-CRISPR Anopheles mosquitoes inhibit gene drive spread under challenging behavioural conditions in large cages

CRISPR-based gene drives have the potential to spread within populations and are considered as promising vector control tools. A doublesex-targeting gene drive was able to suppress laboratory Anopheles mosquito populations in small and large cages, and it is considered for field application. Challenges related to the field-use of gene drives and the evolving regulatory framework suggest that systems able to modulate or revert the action of gene drives, could be part of post-release risk-mitigation plans. In this study, we challenge an AcrIIA4-based anti-drive to inhibit gene drive spread in age-structured Anopheles gambiae population under complex feeding and behavioural conditions. A stochastic model predicts the experimentally-observed genotype dynamics in age-structured populations in medium-sized cages and highlights the necessity of large-sized cage trials. These experiments and experimental-modelling framework demonstrate the effectiveness of the anti-drive in different scenarios, providing further corroboration for its use in controlling the spread of gene drive in Anopheles.

A multiplexed, confinable CRISPR/Cas9 gene drive can propagate in caged Aedes aegypti populations

Aedes aegypti is the main vector of several major pathogens including dengue, Zika and chikungunya viruses. Classical mosquito control strategies utilizing insecticides are threatened by rising resistance. This has stimulated interest in new genetic systems such as gene drivesHere, we test the regulatory sequences from the Ae. aegypti benign gonial cell neoplasm (bgcn) homolog to express Cas9 and a separate multiplexing sgRNA-expressing cassette inserted into the Ae. aegypti kynurenine 3-monooxygenase (kmo) gene. When combined, these two elements provide highly effective germline cutting at the kmo locus and act as a gene drive. Our target genetic element drives through a cage trial population such that carrier frequency of the element increases from 50% to up to 89% of the population despite significant fitness costs to kmo insertions. Deep sequencing suggests that the multiplexing design could mitigate resistance allele formation in our gene drive system.

Embryos assist morphogenesis of others through calcium and ATP signaling mechanisms in collective teratogen resistance

Information for organismal patterning can come from a variety of sources. We investigate the possibility that instructive influences for normal embryonic development are provided not only at the level of cells within the embryo, but also via interactions between embryos. To explore this, we challenge groups of embryos with disruptors of normal development while varying group size. Here, we show that Xenopus laevis embryos are much more sensitive to a diverse set of chemical and molecular-biological perturbations when allowed to develop alone or in small groups, than in large groups. Keeping per-embryo exposure constant, we find that increasing the number of exposed embryos in a cohort increases the rate of survival while incidence of defects decreases. This inter-embryo assistance effect is mediated by short-range diffusible signals and involves the P2 ATP receptor. Our data and computational model emphasize that morphogenesis is a collective phenomenon not only at the level of cells, but also of whole bodies, and that cohort size is a crucial variable in studies of ecotoxicology, teratogenesis, and developmental plasticity.

Carbon-nanotube field-effect transistors for resolving single-molecule aptamer-ligand binding kinetics

Small molecules such as neurotransmitters are critical for biochemical functions in living systems. While conventional ultraviolet-visible spectroscopy and mass spectrometry lack portability and are unsuitable for time-resolved measurements in situ, techniques such as amperometry and traditional field-effect detection require a large ensemble of molecules to reach detectable signal levels. Here we demonstrate the potential of carbon-nanotube-based single-molecule field-effect transistors (smFETs), which can detect the charge on a single molecule, as a new platform for recognizing and assaying small molecules. smFETs are formed by the covalent attachment of a probe molecule, in our case a DNA aptamer, to a carbon nanotube. Conformation changes on binding are manifest as discrete changes in the nanotube electrical conductance. By monitoring the kinetics of conformational changes in a binding aptamer, we show that smFETs can detect and quantify serotonin at the single-molecule level, providing unique insights into the dynamics of the aptamer-ligand system. In particular, we show the involvement of G-quadruplex formation and the disruption of the native hairpin structure in the conformational changes of the serotonin-aptamer complex. The smFET is a label-free approach to analysing molecular interactions at the single-molecule level with high temporal resolution, providing additional insights into complex biological processes.

Flexible switch matrix addressable electrode arrays with organic electrochemical transistor and pn diode technology

Due to their effective ionic-to-electronic signal conversion and mechanical flexibility, organic neural implants hold considerable promise for biocompatible neural interfaces. Current approaches are, however, primarily limited to passive electrodes due to a lack of circuit components to realize complex active circuits at the front-end. Here, we introduce a p-n organic electrochemical diode using complementary p- and n-type conducting polymer films embedded in a 15-μm -diameter vertical stack. Leveraging the efficient motion of encapsulated cations inside this polymer stack and the opposite doping mechanisms of the constituent polymers, we demonstrate high current rectification ratios ([Formula: see text]) and fast switching speeds (230 μs). We integrate p-n organic electrochemical diodes with organic electrochemical transistors in the front-end pixel of a recording array. This configuration facilitates the access of organic electrochemical transistor output currents within a large network operating in the same electrolyte, while minimizing crosstalk from neighboring elements due to minimized reverse-biased leakage. Furthermore, we use these devices to fabricate time-division-multiplexed amplifier arrays. Lastly, we show that, when fabricated in a shank format, this technology enables the multiplexing of amplified local field potentials directly in the active recording pixel (26-μm diameter) in a minimally invasive form factor with shank cross-sectional dimensions of only 50×8 [Formula: see text].

Motion and teleportation of polar bubbles in low-dimensional ferroelectrics

Electric bubbles are sub-10nm spherical vortices of electric dipoles that can spontaneously form in ultra-thin ferroelectrics. While the static properties of electric bubbles are well established, little to nothing is known about the dynamics of these particle-like structures. Here, we reveal pathways to realizing both the spontaneous and controlled dynamics of electric bubbles in ultra-thin Pb(Zr0.4Ti0.6)O3 films. In low screening conditions, we find that electric bubbles exhibit thermally-driven chaotic motion giving rise to a liquid-like state. In the high screening regime, we show that bubbles remain static but can be continuously displaced by a local electric field. Additionally, we predict and experimentally demonstrate the possibility of bubble teleportation - a process wherein a bubble is transferred to a new location via a single electric field pulse of a PFM tip. Finally, we attribute the discovered phenomena to the hierarchical structure of the energy landscape.

A system-on-chip microwave photonic processor solves dynamic RF interference in real time with picosecond latency

Radio-frequency interference is a growing concern as wireless technology advances, with potentially life-threatening consequences like interference between radar altimeters and 5 G cellular networks. Mobile transceivers mix signals with varying ratios over time, posing challenges for conventional digital signal processing (DSP) due to its high latency. These challenges will worsen as future wireless technologies adopt higher carrier frequencies and data rates. However, conventional DSPs, already on the brink of their clock frequency limit, are expected to offer only marginal speed advancements. This paper introduces a photonic processor to address dynamic interference through blind source separation (BSS). Our system-on-chip processor employs a fully integrated photonic signal pathway in the analogue domain, enabling rapid demixing of received mixtures and recovering the signal-of-interest in under 15 picoseconds. This reduction in latency surpasses electronic counterparts by more than three orders of magnitude. To complement the photonic processor, electronic peripherals based on field-programmable gate array (FPGA) assess the effectiveness of demixing and continuously update demixing weights at a rate of up to 305 Hz. This compact setup features precise dithering weight control, impedance-controlled circuit board and optical fibre packaging, suitable for handheld and mobile scenarios. We experimentally demonstrate the processor's ability to suppress transmission errors and maintain signal-to-noise ratios in two scenarios, radar altimeters and mobile communications. This work pioneers the real-time adaptability of integrated silicon photonics, enabling online learning and weight adjustments, and showcasing practical operational applications for photonic processing.

Phase transitions in 2D multistable mechanical metamaterials via collisions of soliton-like pulses

In recent years, mechanical metamaterials have been developed that support the propagation of an intriguing variety of nonlinear waves, including transition waves and vector solitons (solitons with coupling between multiple degrees of freedom). Here we report observations of phase transitions in 2D multistable mechanical metamaterials that are initiated by collisions of soliton-like pulses in the metamaterial. Analogous to first-order phase transitions in crystalline solids, we observe that the multistable metamaterials support phase transitions if the new phase meets or exceeds a critical nucleus size. If this criterion is met, the new phase subsequently propagates in the form of transition waves, converting the rest of the metamaterial to the new phase. More interestingly, we numerically show, using an experimentally validated model, that the critical nucleus can be formed via collisions of soliton-like pulses. Moreover, the rich direction-dependent behavior of the nonlinear pulses enables control of the location of nucleation and the spatio-temporal shape of the growing phase.

Remotely sensing potential climate change tipping points across scales

Potential climate tipping points pose a growing risk for societies, and policy is calling for improved anticipation of them. Satellite remote sensing can play a unique role in identifying and anticipating tipping phenomena across scales. Where satellite records are too short for temporal early warning of tipping points, complementary spatial indicators can leverage the exceptional spatial-temporal coverage of remotely sensed data to detect changing resilience of vulnerable systems. Combining Earth observation with Earth system models can improve process-based understanding of tipping points, their interactions, and potential tipping cascades. Such fine-resolution sensing can support climate tipping point risk management across scales.

Tethered spinal cord tension assessed via ultrasound elastography in computational and intraoperative human studies

BACKGROUND

Tension in the spinal cord is a trademark of tethered cord syndrome. Unfortunately, existing tests cannot quantify tension across the bulk of the cord, making the diagnostic evaluation of stretch ambiguous. A potential non-destructive metric for spinal cord tension is ultrasound-derived shear wave velocity (SWV). The velocity is sensitive to tissue elasticity and boundary conditions including strain. We use the term Ultrasound Tensography to describe the acoustic evaluation of tension with SWV.

METHODS

Our solution Tethered cord Assessment with Ultrasound Tensography (TAUT) was utilized in three sub-studies: finite element simulations, a cadaveric benchtop validation, and a neurosurgical case series. The simulation computed SWV for given tensile forces. The cadaveric model with induced tension validated the SWV-tension relationship. Lastly, SWV was measured intraoperatively in patients diagnosed with tethered cords who underwent treatment (spinal column shortening). The surgery alleviates tension by decreasing the vertebral column length.

RESULTS

Here we observe a strong linear relationship between tension and squared SWV across the preclinical sub-studies. Higher tension induces faster shear waves in the simulation (R2 = 0.984) and cadaveric (R2 = 0.951) models. The SWV decreases in all neurosurgical procedures (p < 0.001). Moreover, TAUT has a c-statistic of 0.962 (0.92-1.00), detecting all tethered cords.

CONCLUSIONS

This study presents a physical, clinical metric of spinal cord tension. Strong agreement among computational, cadaveric, and clinical studies demonstrates the utility of ultrasound-induced SWV for quantitative intraoperative feedback. This technology is positioned to enhance tethered cord diagnosis, treatment, and postoperative monitoring as it differentiates stretched from healthy cords.

Compact and wideband nanoacoustic pass-band filters for future 5G and 6G cellular radios

Over recent years, the surge in mobile communication has deepened global connectivity. With escalating demands for faster data rates, the push for higher carrier frequencies intensifies. The 7-20 GHz range, located between the 5G sub-6 GHz and the mm-wave spectra, provides an excellent trade-off between network capacity and coverage, and constitutes a yet-to-be-explored range for 5G and 6G applications. This work proposes a technological platform able to deliver CMOS-compatible, on-chip multi-frequency, low-loss, wide-band, and compact filters for cellular radios operating in this range by leveraging the micro-to-nano scaling of acoustic electromechanical resonators. The results showcase the first-ever demonstrated low insertion loss bank of 7 nanoacoustic passband filters in the X-band. Most of the filters showcase fractional bandwidths above 3% and sub-dB loss per stage in an extremely compact form factor, enabling the manufacturing of filters and duplexers for the next generation of mobile handsets operating in the X-band and beyond.

Terahertz parametric amplification as a reporter of exciton condensate dynamics

Condensates are a hallmark of emergence in quantum materials such as superconductors and charge density waves. Excitonic insulators are an intriguing addition to this library, exhibiting spontaneous condensation of electron-hole pairs. However, condensate observables can be obscured through parasitic coupling to the lattice. Here we employ nonlinear terahertz spectroscopy to disentangle such obscurants through measurement of the quantum dynamics. We target Ta2NiSe5, a putative room-temperature excitonic insulator in which electron-lattice coupling dominates the structural transition (Tc = 326 K), hindering identification of excitonic correlations. A pronounced increase in the terahertz reflectivity manifests following photoexcitation and exhibits a Bose-Einstein condensation-like temperature dependence well below the Tc, suggesting an approach to monitor the exciton condensate dynamics. Nonetheless, dynamic condensate-phonon coupling remains as evidenced by peaks in the enhanced reflectivity spectrum at select infrared-active phonon frequencies, indicating that parametric reflectivity enhancement arises from phonon squeezing. Our results highlight that coherent dynamics can drive parametric stimulated emission.

An orthogonalized PYR1-based CID module with reprogrammable ligand-binding specificity

Plants sense abscisic acid (ABA) using chemical-induced dimerization (CID) modules, including the receptor PYR1 and HAB1, a phosphatase inhibited by ligand-activated PYR1. This system is unique because of the relative ease with which ligand recognition can be reprogrammed. To expand the PYR1 system, we designed an orthogonal '*' module, which harbors a dimer interface salt bridge; X-ray crystallographic, biochemical and in vivo analyses confirm its orthogonality. We used this module to create PYR1*MANDI/HAB1* and PYR1*AZIN/HAB1*, which possess nanomolar sensitivities to their activating ligands mandipropamid and azinphos-ethyl. Experiments in Arabidopsis thaliana and Saccharomyces cerevisiae demonstrate the sensitive detection of banned organophosphate contaminants using living biosensors and the construction of multi-input/output genetic circuits. Our new modules enable ligand-programmable multi-channel CID systems for plant and eukaryotic synthetic biology that can empower new plant-based and microbe-based sensing modalities.

Octopus-inspired deception and signaling systems from an exceptionally-stable acene variant

Multifunctional platforms that can dynamically modulate their color and appearance have attracted attention for applications as varied as displays, signaling, camouflage, anti-counterfeiting, sensing, biomedical imaging, energy conservation, and robotics. Within this context, the development of camouflage systems with tunable spectroscopic and fluorescent properties that span the ultraviolet, visible, and near-infrared spectral regions has remained exceedingly challenging because of frequently competing materials and device design requirements. Herein, we draw inspiration from the unique blue rings of the Hapalochlaena lunulata octopus for the development of deception and signaling systems that resolve these critical challenges. As the active material, our actuator-type systems incorporate a readily-prepared and easily-processable nonacene-like molecule with an ambient-atmosphere stability that exceeds the state-of-the-art for comparable acenes by orders of magnitude. Devices from this active material feature a powerful and unique combination of advantages, including straightforward benchtop fabrication, competitive baseline performance metrics, robustness during cycling with the capacity for autonomous self-repair, and multiple dynamic multispectral operating modes. When considered together, the described exciting discoveries point to new scientific and technological opportunities in the areas of functional organic materials, reconfigurable soft actuators, and adaptive photonic systems.

Real-time photonic blind interference cancellation

mmWave devices can broadcast multiple spatially-separated data streams simultaneously in order to increase data transfer rates. Data transfer can, however, be compromised by interference. Photonic blind interference cancellation systems offer a power-efficient means of mitigating interference, but previous demonstrations of such systems have been limited by high latencies and the need for regular calibration. Here, we demonstrate real-time photonic blind interference cancellation using an FPGA-photonic system executing a zero-calibration control algorithm. Our system offers a greater than 200-fold reduction in latency compared to previous work, enabling sub-second cancellation weight identification. We further investigate key trade-offs between system latency, power consumption, and success rate, and we validate sub-Nyquist sampling for blind interference cancellation. We estimate that photonic interference cancellation can reduce the power required for digitization and signal recovery by greater than 74 times compared to the digital electronic alternative.

Docking for EP4R antagonists active against inflammatory pain

The lipid prostaglandin E2 (PGE2) mediates inflammatory pain by activating G protein-coupled receptors, including the prostaglandin E2 receptor 4 (EP4R). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce nociception by inhibiting prostaglandin synthesis, however, the disruption of upstream prostanoid biosynthesis can lead to pleiotropic effects including gastrointestinal bleeding and cardiac complications. In contrast, by acting downstream, EP4R antagonists may act specifically as anti-inflammatory agents and, to date, no selective EP4R antagonists have been approved for human use. In this work, seeking to diversify EP4R antagonist scaffolds, we computationally dock over 400 million compounds against an EP4R crystal structure and experimentally validate 71 highly ranked, de novo synthesized molecules. Further, we show how structure-based optimization of initial docking hits identifies a potent and selective antagonist with 16 nanomolar potency. Finally, we demonstrate favorable pharmacokinetics for the discovered compound as well as anti-allodynic and anti-inflammatory activity in several preclinical pain models in mice.

Metabolic tagging of extracellular vesicles and development of enhanced extracellular vesicle based cancer vaccines

As key mediators of cellular communication, extracellular vesicles (EVs) have been actively explored for diagnostic and therapeutic applications. However, effective methods to functionalize EVs and modulate the interaction between EVs and recipient cells are still lacking. Here we report a facile and universal metabolic tagging technology that can install unique chemical tags (e.g., azido groups) onto EVs. The surface chemical tags enable conjugation of molecules via efficient click chemistry, for the tracking and targeted modulation of EVs. In the context of tumor EV vaccines, we show that the conjugation of toll-like receptor 9 agonists onto EVs enables timely activation of dendritic cells and generation of superior antitumor CD8+ T cell response. These lead to 80% tumor-free survival against E.G7 lymphoma and 33% tumor-free survival against B16F10 melanoma. Our study yields a universal technology to generate chemically tagged EVs from parent cells, modulate EV-cell interactions, and develop potent EV vaccines.

Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics

The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity. These low-dimensional embeddings are just as effective, if not better, than raw data for tasks such as identifying bacterial strains, predicting traits like antibiotic resistance, and predicting community dynamics. Additionally, we demonstrate that essential dynamical information of these systems can be captured using far fewer variables than traditional mechanistic models. Our work suggests that machine learning can enable the creation of concise representations of high-dimensional microbiome dynamics to facilitate data analysis and gain new biological insights.

Systematic mining of the human microbiome identifies antimicrobial peptides with diverse activity spectra

Human-associated bacteria secrete modified peptides to control host physiology and remodel the microbiota species composition. Here we scanned 2,229 Human Microbiome Project genomes of species colonizing skin, gastrointestinal tract, urogenital tract, mouth and trachea for gene clusters encoding RiPPs (ribosomally synthesized and post-translationally modified peptides). We found 218 lanthipeptides and 25 lasso peptides, 70 of which were synthesized and expressed in E. coli and 23 could be purified and functionally characterized. They were tested for activity against bacteria associated with healthy human flora and pathogens. New antibiotics were identified against strains implicated in skin, nasal and vaginal dysbiosis as well as from oral strains selectively targeting those in the gut. Extended- and narrow-spectrum antibiotics were found against methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococci. Mining natural products produced by human-associated microbes will enable the elucidation of ecological relationships and may be a rich resource for antimicrobial discovery.

A dynamic partitioning mechanism polarizes membrane protein distribution

The plasma membrane is widely regarded as the hub of the numerous signal transduction activities. Yet, the fundamental biophysical mechanisms that spatiotemporally compartmentalize different classes of membrane proteins remain unclear. Using multimodal live-cell imaging, here we first show that several lipid-anchored membrane proteins are consistently depleted from the membrane regions where the Ras/PI3K/Akt/F-actin network is activated. The dynamic polarization of these proteins does not depend upon the F-actin-based cytoskeletal structures, recurring shuttling between membrane and cytosol, or directed vesicular trafficking. Photoconversion microscopy and single-molecule measurements demonstrate that these lipid-anchored molecules have substantially dissimilar diffusion profiles in different regions of the membrane which enable their selective segregation. When these diffusion coefficients are incorporated into an excitable network-based stochastic reaction-diffusion model, simulations reveal that the altered affinity mediated selective partitioning is sufficient to drive familiar propagating wave patterns. Furthermore, normally uniform integral and lipid-anchored membrane proteins partition successfully when membrane domain-specific peptides are optogenetically recruited to them. We propose "dynamic partitioning" as a new mechanism that can account for large-scale compartmentalization of a wide array of lipid-anchored and integral membrane proteins during various physiological processes where membrane polarizes.

Cortical reactivation of spatial and non-spatial features coordinates with hippocampus to form a memory dialogue

Episodic memories comprise diverse attributes of experience distributed across neocortical areas. The hippocampus is integral to rapidly binding these diffuse representations, as they occur, to be later reinstated. However, the nature of the information exchanged during this hippocampal-cortical dialogue remains poorly understood. A recent study has shown that the secondary motor cortex carries two types of representations: place cell-like activity, which were impaired by hippocampal lesions, and responses tied to visuo-tactile cues, which became more pronounced following hippocampal lesions. Using two-photon Ca2+ imaging to record neuronal activities in the secondary motor cortex of male Thy1-GCaMP6s mice, we assessed the cortical retrieval of spatial and non-spatial attributes from previous explorations in a virtual environment. We show that, following navigation, spontaneous resting state reactivations convey varying degrees of spatial (trajectory sequences) and non-spatial (visuo-tactile attributes) information, while reactivations of non-spatial attributes tend to precede reactivations of spatial representations surrounding hippocampal sharp-wave ripples.

Molecular dissection of the soluble photosynthetic antenna from the cryptophyte alga Hemiselmis andersenii

Cryptophyte algae have a unique phycobiliprotein light-harvesting antenna that fills a spectral gap in chlorophyll absorption from photosystems. However, it is unclear how the antenna transfers energy efficiently to these photosystems. We show that the cryptophyte Hemiselmis andersenii expresses an energetically complex antenna comprising three distinct spectrotypes of phycobiliprotein, each composed of two αβ protomers but with different quaternary structures arising from a diverse α subunit family. We report crystal structures of the major phycobiliprotein from each spectrotype. Two-thirds of the antenna consists of open quaternary form phycobiliproteins acting as primary photon acceptors. These are supplemented by a newly discovered open-braced form (~15%), where an insertion in the α subunit produces ~10 nm absorbance red-shift. The final components (~15%) are closed forms with a long wavelength spectral feature due to substitution of a single chromophore. This chromophore is present on only one β subunit where asymmetry is dictated by the corresponding α subunit. This chromophore creates spectral overlap with chlorophyll, thus bridging the energetic gap between the phycobiliprotein antenna and the photosystems. We propose that the macromolecular organization of the cryptophyte antenna consists of bulk open and open-braced forms that transfer excitations to photosystems via this bridging closed form phycobiliprotein.

Electrocatalytic on-site oxygenation for transplanted cell-based-therapies

Implantable cell therapies and tissue transplants require sufficient oxygen supply to function and are limited by a delay or lack of vascularization from the transplant host. Previous exogenous oxygenation strategies have been bulky and had limited oxygen production or regulation. Here, we show an electrocatalytic approach that enables bioelectronic control of oxygen generation in complex cellular environments to sustain engineered cell viability and therapy under hypoxic stress and at high cell densities. We find that nanostructured sputtered iridium oxide serves as an ideal catalyst for oxygen evolution reaction at neutral pH. We demonstrate that this approach exhibits a lower oxygenation onset and selective oxygen production without evolution of toxic byproducts. We show that this electrocatalytic on site oxygenator can sustain high cell loadings (>60k cells/mm3) in hypoxic conditions in vitro and in vivo. Our results showcase that exogenous oxygen production devices can be readily integrated into bioelectronic platforms, enabling high cell loadings in smaller devices with broad applicability.

A unified approach of detecting phase transition in time-varying complex networks

Deciphering the non-trivial interactions and mechanisms driving the evolution of time-varying complex networks (TVCNs) plays a crucial role in designing optimal control strategies for such networks or enhancing their causal predictive capabilities. In this paper, we advance the science of TVCNs by providing a mathematical framework through which we can gauge how local changes within a complex weighted network affect its global properties. More precisely, we focus on unraveling unknown geometric properties of a network and determine its implications on detecting phase transitions within the dynamics of a TVCN. In this vein, we aim at elaborating a novel and unified approach that can be used to depict the relationship between local interactions in a complex network and its global kinetics. We propose a geometric-inspired framework to characterize the network's state and detect a phase transition between different states, to infer the TVCN's dynamics. A phase of a TVCN is determined by its Forman-Ricci curvature property. Numerical experiments show the usefulness of the proposed curvature formalism to detect the transition between phases within artificially generated networks. Furthermore, we demonstrate the effectiveness of the proposed framework in identifying the phase transition phenomena governing the training and learning processes of artificial neural networks. Moreover, we exploit this approach to investigate the phase transition phenomena in cellular re-programming by interpreting the dynamics of Hi-C matrices as TVCNs and observing singularity trends in the curvature network entropy. Finally, we demonstrate that this curvature formalism can detect a political change. Specifically, our framework can be applied to the US Senate data to detect a political change in the United States of America after the 1994 election, as discussed by political scientists.

Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex

We used a dynamical systems perspective to understand decision-related neural activity, a fundamentally unresolved problem. This perspective posits that time-varying neural activity is described by a state equation with an initial condition and evolves in time by combining at each time step, recurrent activity and inputs. We hypothesized various dynamical mechanisms of decisions, simulated them in models to derive predictions, and evaluated these predictions by examining firing rates of neurons in the dorsal premotor cortex (PMd) of monkeys performing a perceptual decision-making task. Prestimulus neural activity (i.e., the initial condition) predicted poststimulus neural trajectories, covaried with RT and the outcome of the previous trial, but not with choice. Poststimulus dynamics depended on both the sensory evidence and initial condition, with easier stimuli and fast initial conditions leading to the fastest choice-related dynamics. Together, these results suggest that initial conditions combine with sensory evidence to induce decision-related dynamics in PMd.

Systematic functional interrogation of SARS-CoV-2 host factors using Perturb-seq

Genomic and proteomic screens have identified numerous host factors of SARS-CoV-2, but efficient delineation of their molecular roles during infection remains a challenge. Here we use Perturb-seq, combining genetic perturbations with a single-cell readout, to investigate how inactivation of host factors changes the course of SARS-CoV-2 infection and the host response in human lung epithelial cells. Our high-dimensional data resolve complex phenotypes such as shifts in the stages of infection and modulations of the interferon response. However, only a small percentage of host factors showed such phenotypes upon perturbation. We further identified the NF-κB inhibitor IκBα (NFKBIA), as well as the translation factors EIF4E2 and EIF4H as strong host dependency factors acting early in infection. Overall, our study provides massively parallel functional characterization of host factors of SARS-CoV-2 and quantitatively defines their roles both in virus-infected and bystander cells.

Accelerating science with human-aware artificial intelligence

Artificial intelligence (AI) models trained on published scientific findings have been used to invent valuable materials and targeted therapies, but they typically ignore the human scientists who continually alter the landscape of discovery. Here we show that incorporating the distribution of human expertise by training unsupervised models on simulated inferences that are cognitively accessible to experts dramatically improves (by up to 400%) AI prediction of future discoveries beyond models focused on research content alone, especially when relevant literature is sparse. These models succeed by predicting human predictions and the scientists who will make them. By tuning human-aware AI to avoid the crowd, we can generate scientifically promising 'alien' hypotheses unlikely to be imagined or pursued without intervention until the distant future, which hold promise to punctuate scientific advance beyond questions currently pursued. By accelerating human discovery or probing its blind spots, human-aware AI enables us to move towards and beyond the contemporary scientific frontier.

Proteomic and genetic analyses of influenza A viruses identify pan-viral host targets

Influenza A Virus (IAV) is a recurring respiratory virus with limited availability of antiviral therapies. Understanding host proteins essential for IAV infection can identify targets for alternative host-directed therapies (HDTs). Using affinity purification-mass spectrometry and global phosphoproteomic and protein abundance analyses using three IAV strains (pH1N1, H3N2, H5N1) in three human cell types (A549, NHBE, THP-1), we map 332 IAV-human protein-protein interactions and identify 13 IAV-modulated kinases. Whole exome sequencing of patients who experienced severe influenza reveals several genes, including scaffold protein AHNAK, with predicted loss-of-function variants that are also identified in our proteomic analyses. Of our identified host factors, 54 significantly alter IAV infection upon siRNA knockdown, and two factors, AHNAK and coatomer subunit COPB1, are also essential for productive infection by SARS-CoV-2. Finally, 16 compounds targeting our identified host factors suppress IAV replication, with two targeting CDK2 and FLT3 showing pan-antiviral activity across influenza and coronavirus families. This study provides a comprehensive network model of IAV infection in human cells, identifying functional host targets for pan-viral HDT.