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Three-dimensional (3D) tomography is a powerful investigative tool for many scientific domains, going from materials science, to engineering, to medicine. Many factors may limit the 3D resolution, often spatially anisotropic, compromising the precision of the information retrievable. A neural network, designed for video-frame interpolation, is employed to enhance tomographic images, achieving cubic-voxel resolution. The method is applied to distinct domains: the investigation of the morphology of printed graphene nanosheets networks, obtained via focused ion beam-scanning electron microscope (FIB-SEM), magnetic resonance imaging of the human brain, and X-ray computed tomography scans of the abdomen. The accuracy of the 3D tomographic maps can be quantified through computer-vision metrics, but most importantly with the precision on the physical quantities retrievable from the reconstructions, in the case of FIB-SEM the porosity, tortuosity, and effective diffusivity. This work showcases a versatile image-augmentation strategy for optimizing 3D tomography acquisition conditions, while preserving the information content. This paper demonstrates that an algorithm designed for video augmentation can be efficiently used for 3D tomography reconstruction across the materials science and medical domain, namely across disciplines and length-scales. [ABSTRACT FROM AUTHOR]

Several gaps persist in haptic device development due to the multifaceted nature of the sense of touch. Existing gaps include challenges enhancing touch feedback fidelity, providing diverse haptic sensations, and ensuring wearability for delivering tactile stimuli to the fingertips. Here, we introduce the Bioinspired Adaptable Multiplanar Haptic system, offering mechanotactile/steady and vibrotactile pulse stimuli with adjustable intensity (up to 298.1 mN) and frequencies (up to 130 Hz). This system can deliver simultaneous stimuli across multiple fingertip areas. The paper includes a full characterisation of our system. As the device can play an important role in further understanding human touch, we performed human stimuli sensitivity and differentiation experiments to evaluate the capability of delivering mechano-vibrotactile, variable intensity, simultaneous, multiplanar and operator agnostic stimuli. Our system promises to accelerate the development of touch perception devices, providing painless, operator-independent data crucial for researching and diagnosing touch-related disorders. Challenges persist in haptic device development due to the complex nature of touch. The authors propose a Bioinspired Adaptable Multiplanar Haptic system providing multipoint and variable intensity pulse stimuli (0-130 Hz) across multiple fingertip areas, enabling deeper study of touch skin sensors. [ABSTRACT FROM AUTHOR]

Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in diverse applications, such as air pollution monitoring, disease tracking, and cloud-demand forecasting. As the scale of modern datasets increases, there is a growing need for statistical methods that are flexible enough to capture complex spatiotemporal dynamics and scalable enough to handle many observations. This article introduces the Bayesian Neural Field (BayesNF), a domain-general statistical model that infers rich spatiotemporal probability distributions for data-analysis tasks including forecasting, interpolation, and variography. BayesNF integrates a deep neural network architecture for high-capacity function estimation with hierarchical Bayesian inference for robust predictive uncertainty quantification. Evaluations against prominent baselines show that BayesNF delivers improvements on prediction problems from climate and public health data containing tens to hundreds of thousands of measurements. Accompanying the paper is an open-source software package (https://github.com/google/bayesnf) that runs on GPU and TPU accelerators through the Jax machine learning platform. Spatiotemporal data consisting of measurements gathered at different times and locations is challenging to analyse due to variability and noise impact across different scales. The authors propose a statistical approach that delivers models of large-scale spatiotemporal datasets applicable to data-analysis tasks of forecasting and interpolation. [ABSTRACT FROM AUTHOR]

Today's wireless environments, soft robotics, and space applications demand delicate design of devices with tunable performances and simple fabrication processes. Here we show strain-based adjustability of RF/microwave performance by applying frequency-selective patterns of conductive Ti3C2Tx MXene coatings on low-cost acetate substrates under ambient conditions. The tailored performances were achieved by applying frequency-selective patterns of thin Ti3C2Tx MXene coatings with high electrical conductivity as a replacement to metal on low-cost flexible acetate substrates under ambient conditions. Under quasi-axial stress, the Kirigami design enables displacements of individual resonant cells, changing the overall electromagnetic performance of a surface (i.e., array) within a simulated wireless channel. Two flexible Kirigami-inspired prototypes were implemented and tested within the S, C, and X (2-4 GHz, 4-8 GHz, and 8-12 GHz) microwave frequency bands. The resonant surface, having ~1/4 of the size of a standard A4 paper, was able to steer a beam of scattered waves from each resonator by ~25°. Under a strain of 22%, the resonant frequency of the wired co-planar resonator was shifted by 400 MHz, while the reflection coefficient changed by 158%. Deforming the geometry impacted the spectral response of the components across three arbitrary frequencies in the 4-10 GHz frequency range. With this proof of concept, we anticipate implementing thin films of MXenes on technologically relevant substrates, achieving multi-functionality through cost-effective and straightforward manufacturing. Modern communication applications may demand devices with tunable performances and simple fabrications. Here, we show strain dependent, adjustable RF/microwave performance by applying patterns of conductive Ti3C2Tx MXene coatings on low-cost acetate substrates in a straightforward coating process. [ABSTRACT FROM AUTHOR]

Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pretrainable geometric graph neural network, GearBind, and explores its potential in in silico affinity maturation. Leveraging multi-relational graph construction, multi-level geometric message passing and contrastive pretraining on mass-scale, unlabeled protein structural data, GearBind outperforms previous state-of-the-art approaches on SKEMPI and an independent test set. A powerful ensemble model based on GearBind is then derived and used to successfully enhance the binding of two antibodies with distinct formats and target antigens. ELISA EC50 values of the designed antibody mutants are decreased by up to 17 fold, and KD values by up to 6.1 fold. These promising results underscore the utility of geometric deep learning and effective pretraining in macromolecule interaction modeling tasks. Increasing the binding affinity of an antibody to its target antigen is key for antibody therapeutics. Here the authors report a pretrainable geometric graph neural network, GearBind, and explore its potential in in silico antibody affinity maturation. [ABSTRACT FROM AUTHOR]

The Ideal Plant Architecture 1 (IPA1) transcription factor promotes rice yield and immunity through phosphorylation at its amino acid residue Ser163 as a switch. Although phosphorylated IPA1 mimic, IPA1(S163D), directly targets the promoter of immune response gene WRKY45, it cannot activate its expression. Here, we identified a co-activator of IPA1(S163D), a RING-finger E3 ligase IPA1 interactor 7 (IPI7), which fine-tunes the transcriptional activity of IPA1 to timely promote plant immunity and simultaneously maintain growth for yield. IPI7 interacts with IPA1 and promotes K29-polyubiquitination of IPA1 in vitro and in vivo. However, the stability of IPA1 protein is not affected by IPI7-mediated ubiquitination. The IPI7-promoted K29-polyubiquitination of IPA1 is induced by Magnaporthe oryzae infection and required for phosphorylated IPA1 to transactivate WRKY45 expression for immune response but not for plain IPA1 to transactivate DENSE AND ERECT PANICLES 1 (DEP1) expression for panicle development. IPI7 knockout impairs IPA1-mediated immunity but not yield. Our study reveals that plants utilize non-proteolytic K29-ubiquitination as a response to pathogen infection to fine-tune IPA1 transactivation activity for promoting immunity.In this paper, the E3 ubiquitin ligase IPI7 is found to fine-tune the transactivation activity of IPA1 through non-hydrolyzed K29-ubiquitination chain, thereby timely regulating rice disease resistance during Magnaporthe oryzae infection. [ABSTRACT FROM AUTHOR]

In the initial version of our paper, we included Clean Water Act data for all 50 states, DC, and Puerto Rico. Similarly, the percent of Clean Water Act permittees in Significant Noncompliance drops from 6.01 to 3.37% and the average percent of permittees in Significant Noncompliance drops from 9.00 to 6.23%. To ensure that we did not misrepresent the level of Clean Water Act Significant Noncompliance in these states, we have corrected our article by removing these states from the Clean Water Act portion of our analysis. [Extracted from the article]

Benchmarks are crucial to measuring and steering progress in artificial intelligence (AI). However, recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, we introduce methodologies for creating condensed maps of the global dynamics of benchmark creation and saturation. We curate data for 3765 benchmarks covering the entire domains of computer vision and natural language processing, and show that a large fraction of benchmarks quickly trends towards near-saturation, that many benchmarks fail to find widespread utilization, and that benchmark performance gains for different AI tasks are prone to unforeseen bursts. We analyze attributes associated with benchmark popularity, and conclude that future benchmarks should emphasize versatility, breadth and real-world utility. Recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, the authors introduce methodologies for creating condensed maps of the global dynamics of benchmark. [ABSTRACT FROM AUTHOR]

Spectrally-selective monitoring of ultraviolet radiations (UVR) is of paramount importance across diverse fields, including effective monitoring of excessive solar exposure. Current UV sensors cannot differentiate between UVA, B, and C, each of which has a remarkably different impact on human health. Here we show spectrally selective colorimetric monitoring of UVR by developing a photoelectrochromic ink that consists of a multi-redox polyoxometalate and an e - donor. We combine this ink with simple components such as filter paper and transparency sheets to fabricate low-cost sensors that provide naked-eye monitoring of UVR, even at low doses typically encountered during solar exposure. Importantly, the diverse UV tolerance of different skin colors demands personalized sensors. In this spirit, we demonstrate the customized design of robust real-time solar UV dosimeters to meet the specific need of different skin phototypes. These spectrally-selective UV sensors offer remarkable potential in managing the impact of UVR in our day-to-day life.

Blue/deep-blue emission is crucial for organic optoelectronics but remains a formidable challenge in organic afterglow due to the difficulties in populating and stabilizing the high-energy triplet excited states. Here, a facile strategy to realize the efficient deep-blue organic afterglow is proposed via host molecules to sensitize the triplet exciton population of guest and water implement to suppress the non-radiative decays by matrices rigidification. A series of highly luminescent deep-blue (405–428 nm) organic afterglow materials with lifetimes up to 1.67 s and quantum yields of 46.1% are developed. With these high-performance water-responsive materials, lifetime-encrypted rewritable paper has been constructed for water-jet printing of high-resolution anti-counterfeiting patterns that can retain for a long time (>1 month) and be erased by dimethyl sulfoxide vapor in 15 min with high reversibility for many write/erase cycles. These results provide a foundation for the design of high-efficient blue/deep-blue organic afterglow and stimuli-responsive materials with remarkable applications. Though realizing organic materials with deep blue emission is attractive for next-generation display technologies, achieving this emission in afterglow molecules remains a challenge. Here, the authors report blue organic afterglow via a strategy involving guest sensitization and matrix rigidification. [ABSTRACT FROM AUTHOR]

The lack of data democratization and information leakage from trained models hinder the development and acceptance of robust deep learning-based healthcare solutions. This paper argues that irreversible data encoding can provide an effective solution to achieve data democratization without violating the privacy constraints imposed on healthcare data and clinical models. An ideal encoding framework transforms the data into a new space where it is imperceptible to a manual or computational inspection. However, encoded data should preserve the semantics of the original data such that deep learning models can be trained effectively. This paper hypothesizes the characteristics of the desired encoding framework and then exploits random projections and random quantum encoding to realize this framework for dense and longitudinal or time-series data. Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models. Healthcare data democratization is often hampered by privacy constraints governing the sensitive healthcare data. Here, the authors show that encoding healthcare data could be a potential solution for achieving healthcare democratization within the context of deep learning. [ABSTRACT FROM AUTHOR]

De novo chromosome synthesis is costly and time-consuming, limiting its use in research and biotechnology. Building synthetic chromosomes from natural components is an unexplored alternative with many potential applications. In this paper, we report CReATiNG (Cloning, Reprogramming, and Assembling Tiled Natural Genomic DNA), a method for constructing synthetic chromosomes from natural components in yeast. CReATiNG entails cloning segments of natural chromosomes and then programmably assembling them into synthetic chromosomes that can replace the native chromosomes in cells. We use CReATiNG to synthetically recombine chromosomes between strains and species, to modify chromosome structure, and to delete many linked, non-adjacent regions totaling 39% of a chromosome. The multiplex deletion experiment reveals that CReATiNG also enables recovery from flaws in synthetic chromosome design via recombination between a synthetic chromosome and its native counterpart. CReATiNG facilitates the application of chromosome synthesis to diverse biological problems. Building synthetic chromosomes from natural components is an unexplored alternative to de novo chromosome synthesis that may have many potential applications. In this paper, the authors report CReATiNG, a method for constructing synthetic chromosomes from natural components in yeast. [ABSTRACT FROM AUTHOR]

Generating sustainable electricity from ambient humidity and natural evaporation has attracted tremendous interest recently as it requires no extra mechanical energy input and is deployable across all weather and geography conditions. Here, we present a device prototype for enhanced power generation from ambient humidity. This prototype uses both heterogenous materials assembled from a LiCl-loaded cellulon paper to facilitate moisture adsorption and a carbon-black-loaded cellulon paper to promote water evaporation. Exposing such a centimeter-sized device to ambient humidity can produce voltages of around 0.78 V and a current of around 7.5 μA, both of which can be sustained for more than 10 days. The enhanced electric output and durability are due to the continuous water flow that is directed by evaporation through numerous, negatively charged channels within the cellulon papers. The voltage and current exhibit an excellent scaling behavior upon device integration to sufficiently power commercial devices including even cell phones. The results open a promising prospect of sustainable electricity generation based on a synergy between spontaneous moisture adsorption and water evaporation. Generating electricity from air opens a promising way for green energy harvesting. Here, authors present a prototype driven by the integration of moisture adsorption with evaporation to generate continuous electricity for a long duration. [ABSTRACT FROM AUTHOR]

Electrochemical conversion of CO2 to alcohols is one of the most challenging methods of conversion and storage of electrical energy in the form of high-energy fuels. The challenge lies in the catalyst design to enable its real-life implementation. Herein, we demonstrate the synthesis and characterization of a cobalt(III) triphenylphosphine corrole complex, which contains three polyethylene glycol residues attached at the meso-phenyl groups. Electron-donation and therefore reduction of the cobalt from cobalt(III) to cobalt(I) is accompanied by removal of the axial ligand, thus resulting in a square-planar cobalt(I) complex. The cobalt(I) as an electron-rich supernucleophilic d8-configurated metal centre, where two electrons occupy and fill up the antibonding dz2 orbital. This orbital possesses high affinity towards electrophiles, allowing for such electronically configurated metals reactions with carbon dioxide. Herein, we report the potential dependent heterogeneous electroreduction of CO2 to ethanol or methanol of an immobilized cobalt A3-corrole catalyst system. In moderately acidic aqueous medium (pH = 6.0), the cobalt corrole modified carbon paper electrode exhibits a Faradaic Efficiency (FE%) of 48 % towards ethanol production. Electrochemical conversion of carbon dioxide to ethanol is one of the most challenging energy conversion reactions. Here the authors show selective electroreduction of carbon dioxide to ethanol by using a functionalized cobalt A3-corrole catalyst immobilized on a carbon paper electrode. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD's efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression. Identifying rare cells is essential for advancing our understanding of complex biological systems and disease mechanisms. Here, authors propose scCAD, a method that combines cluster decomposition and anomaly detection to effectively identify rare cell types across diverse biological scenarios. [ABSTRACT FROM AUTHOR]

After an RNA polymerase reaches a terminator, instead of dissociating from the template, it may diffuse along the DNA and recommence RNA synthesis from the previous or a different promoter. Magnetic tweezers were used to monitor such secondary transcription and determine the effects of low forces assisting or opposing translocation, protein roadblocks, and transcription factors. Remarkably, up to 50% of Escherichia coli (E. coli) RNA polymerases diffused along the DNA after termination. Force biased the direction of diffusion (sliding) and the velocity increased rapidly with force up to 0.7 pN and much more slowly thereafter. Sigma factor 70 (σ70) likely remained associated with the DNA promoting sliding and enabling re-initiation from promoters in either orientation. However, deletions of the α-C-terminal domains severely limited the ability of RNAP to turn around between successive rounds of transcription. The addition of elongation factor NusG, which competes with σ70 for binding to RNAP, limited additional rounds of transcription. Surprisingly, sliding RNA polymerases blocked by a DNA-bound lac repressor could slowly re-initiate transcription and were not affected by NusG, suggesting a σ-independent pathway. Low forces effectively biased promoter selection suggesting a prominent role for topological entanglements that affect RNA polymerase translocation. Tiny forces cause E. coli RNA polymerase to slide along DNA after transcription termination at up to 700 bp/s. In this paper, the authors use magnetic tweezers to show that sliding complexes containing complete alpha C-terminal domains repeat transcription from protein roadblocks sites and promoters in either orientation. [ABSTRACT FROM AUTHOR]

Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum neural networks. It is natural to consider using a quantum processor based on microwave superconducting circuits to classify microwave signals that are analog—continuous in time. However, while there have been theoretical proposals of analog QRC, to date QRC has been implemented using the circuit model—imposing a discretization of the incoming signal in time. In this paper we show how a quantum superconducting circuit comprising an oscillator coupled to a qubit can be used as an analog quantum reservoir for a variety of classification tasks, achieving high accuracy on all of them. Our work demonstrates processing of ultra-low-power microwave signals within our superconducting circuit, a step towards achieving a quantum sensing-computational advantage on impinging microwave signals. Quantum reservoir computing might in principle give advantages in solving signal classification tasks, but current implementations usually incur in the digitalization bottleneck. Here, the authors demonstrate an implementation dealing directly with the analogue MW signals. [ABSTRACT FROM AUTHOR]

The fracture behaviour of artificial metamaterials often leads to catastrophic failures with limited resistance to crack propagation. In contrast, natural materials such as bones and ceramics possess microstructures that give rise to spatially controllable crack path and toughened material resistance to crack advances. This study presents an approach that is inspired by nature’s strengthening mechanisms to develop a systematic design method enabling damage-programmable metamaterials with engineerable microfibers in the cells that can spatially program the micro-scale crack behaviour. Machine learning is applied to provide an effective design engine that accelerate the generation of damage-programmable cells that offer advanced toughening functionality such as crack bowing, crack deflection, and shielding seen in natural materials; and are optimised for a given programming of crack path. This paper shows that such toughening features effectively enable crack-resisting mechanisms on the basis of the crack tip interactions, crack shielding, crack bridging and synergistic combinations of these mechanisms, increasing up to 1,235% absorbed fracture energy in comparison to conventional metamaterials. The proposed approach can have broad implications in the design of damage-tolerant materials, and lightweight engineering systems where significant fracture resistances or highly programmable damages for high performances are sought after.Due to the complex, stochastic nature, fracture is a key challenge of artificial materials causing catastrophic failures. The authors overcame this by designing metamaterials that program damage and translate crack-resisting mechanisms [ABSTRACT FROM AUTHOR]

Micro-/nano-robots (MNRs) have impressive potential in minimally invasive targeted therapeutics through blood vessels, which has disruptive impact to improving human health. However, the clinical use of MNRs has yet to happen due to intrinsic limitations, such as overcoming blood flow. These bottlenecks have not been empirically solved. To tackle them, a full understanding of MNR behaviors is necessary as the first step. The common movement principle of MNRs is corkscrew motion with a helical structure. The existing dynamic model is only applicable to standard helical MNRs. In this paper, we propose a dynamic model for general MNRs without structure limitations. Comprehensive simulations and experiments were conducted, which shows the validity and accuracy of our model. Such a model can serve as a reliable basis for the design, optimization, and control of MNRs and as a powerful tool for gaining fluid dynamic insights, thus accelerating the development of the field.Micro-/nano-robots (MNRs) hold potential for minimally invasive targeted therapies through blood vessels, but clinical use is hindered by challenges such as overcoming blood flow. This study proposes a dynamic model for general MNRs without structural limitations, validated through simulations and experiments, to facilitate their design, optimization, and control. [ABSTRACT FROM AUTHOR]

Synthetic droplets mimicking bio-soft matter droplets formed via liquid-liquid phase separation (LLPS) in living cells have recently been employed in nanobiotechnology for artificial cells, molecular robotics, molecular computing, etc. Temporally controlling the dynamics of synthetic droplets is essential for developing such bio-inspired systems because living systems maintain their functions based on the temporally controlled dynamics of biomolecular reactions and assemblies. This paper reports the temporal control of DNA-based LLPS droplets (DNA droplets). We demonstrate the timing-controlled division of DNA droplets via time-delayed division triggers regulated by chemical reactions. Controlling the release order of multiple division triggers results in order control of the multistep droplet division, i.e., pathway-controlled division in a reaction landscape. Finally, we apply the timing-controlled division into a molecular computing element to compare microRNA concentrations. We believe that temporal control of DNA droplets will promote the design of dynamic artificial cells/molecular robots and sophisticated biomedical applications.Controlling the dynamics of synthetic liquid-liquid phase separation droplets is essential for various bioinspired systems. Here, authors demonstrate temporally-controlled multi-step division of DNA-based liquid droplets, and develop a molecular computation system to compare miRNA concentrations. [ABSTRACT FROM AUTHOR]

This paper employs insights from earth science on the financial risk of project developments to present an economic theory of critical minerals. Our theory posits that back-ended critical mineral projects that have unaddressed technical and non-technical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors which we term the "back-ended risk premium". We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We posit that the back-ended risk premium may also reduce the gains in productivity expected from artificial intelligence (AI) technologies in the mining sector. Progress in AI may, however, lessen the back-ended risk premium itself by shortening the duration of mining projects and the required rate of investment by reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research. Vespignani and Smyth present an economic theory of risk in critical minerals they term the "back-ended risk premium." They apply AI approaches to reduce this risk premium and lower costs in energy transition. [ABSTRACT FROM AUTHOR]

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical results provide exact mapping algorithms from artificial to spiking neural networks with time-to-first-spike coding. In this paper we analyze in theory and simulation the learning dynamics of time-to-first-spike-networks and identify a specific instance of the vanishing-or-exploding gradient problem. While two choices of spiking neural network mappings solve this problem at initialization, only the one with a constant slope of the neuron membrane potential at threshold guarantees the equivalence of the training trajectory between spiking and artificial neural networks with rectified linear units. For specific image classification architectures comprising feed-forward dense or convolutional layers, we demonstrate that deep spiking neural network models can be effectively trained from scratch on MNIST and Fashion-MNIST datasets, or fine-tuned on large-scale datasets, such as CIFAR10, CIFAR100 and PLACES365, to achieve the exact same performance as that of artificial neural networks, surpassing previous spiking neural networks. Our approach accomplishes high-performance classification with less than 0.3 spikes per neuron, lending itself for an energy-efficient implementation. We also show that fine-tuning spiking neural networks with our robust gradient descent algorithm enables their optimization for hardware implementations with low latency and resilience to noise and quantization. To address challenges of training spiking neural networks (SNNs) at scale, the authors propose a scalable, approximation-free training method for deep SNNs using time-to-first-spike coding. They demonstrate enhanced performance and energy efficiency for neuromorphic hardware. [ABSTRACT FROM AUTHOR]

New strategies for converting signals between optical and microwave domains could play a pivotal role in advancing both classical and quantum technologies. Traditional approaches to optical-to-microwave transduction typically perturb or destroy the information encoded on intensity of the light field, eliminating the possibility for further processing or distribution of these signals. In this paper, we introduce an optical-to-microwave conversion method that allows for both detection and spectral analysis of microwave photonic signals without degradation of their information content. This functionality is demonstrated using an optomechanical waveguide integrated with a piezoelectric transducer. Efficient electromechanical and optomechanical coupling within this system permits bidirectional optical-to-microwave conversion with a quantum efficiency of up to −54.16 dB. Leveraging the preservation of the optical field envelope in intramodal Brillouin scattering, we demonstrate a multi-channel microwave photonic filter by transmitting an optical signal through a series of electro-optomechanical waveguide segments, each with distinct resonance frequencies. Such electro-optomechanical systems could offer flexible strategies for remote sensing, channelization, and spectrum analysis in microwave photonics. The authors showcase an optical-to-microwave conversion method using an optomechanical waveguide integrated with a piezoelectric transducer. The presented system allows bidirectional optical-to-microwave conversion with a quantum efficiency of up to—54.16 dB. [ABSTRACT FROM AUTHOR]

Tropical and extra-tropical storms generate extreme waves, impacting both nearby and remote regions through swell propagation. Despite their devastating effects in tropical areas, the contribution of tropical cyclones (TCs) to global wave-induced coastal risk remains unknown. Here, we enable a quantitative assessment of TC's role in extreme waves approaching global coastlines, by designing twin oceanic wave simulations with and without realistic TC wind forcing. We find that TCs substantially contribute to extreme breaking heights in tropical regions (35-50% on average), reaching 100% in high-density TC areas like the North Pacific. TCs also impact remote TC-free regions, such as the equatorial Pacific experiencing in average 30% of its extreme wave events due to TCs. Interannual variability amplifies TC-induced wave hazards, notably during El Niño in the Central Pacific, and La Niña in the South China Sea, Caribbean Arc, and South Indian Ocean coastlines. This research offers critical insights for global risk management and preparedness. This paper shows that tropical cyclones contribute between 40% and 100% of the extreme wave events approaching coastlines in basins prone to tropical cyclone activity, while they can also impact remote areas via swell propagation across the ocean. [ABSTRACT FROM AUTHOR]

From power plants on land to bridges over the sea, safety-critical built environments require periodic inspections for detecting issues to avoid functional discontinuities of these installations. However, navigation paths in these environments are usually challenging as they often contain difficult-to-access spaces (near-millimetre and submillimetre-high gaps) and multiple domains (solid, liquid and even aerial). In this paper, we address these challenges by developing a class of Thin Soft Robots (TS-Robot: thickness, 1.7 mm) that can access narrow spaces and perform cross-domain multimodal locomotion. We adopted a dual-actuation sandwich structure with a tuneable Poisson's ratio tensioning mechanism for developing the TS-Robots driven by dielectric elastomers, providing them with two types of gaits (linear and undulating), remarkable output force (~ 41 times their weight) and speed (1.16 times Body Length/s and 13.06 times Body Thickness/s). Here, we demonstrated that TS-Robots can crawl, climb, swim and collaborate for transitioning between domains in environments with narrow entries. A family of thin, soft robots has been developed, utilising ultra-thin dielectric elastomer actuators and electroadhesive pads. These innovative robots can crawl, climb, swim, and collaborate for transitioning between domains in environments with narrow entries. [ABSTRACT FROM AUTHOR]

The deep ocean, a vast thermal reservoir, absorbs excess heat under greenhouse warming, which ultimately regulates the Earth's surface climate. Even if CO2 emissions are successfully reduced, the stored heat will gradually be released, resulting in a particular pattern of ocean warming. Here, we show that deep ocean warming will lead to El Niño-like ocean warming and resultant increased precipitation in the tropical eastern Pacific with southward shift of the intertropical convergence zone. Consequently, the El Niño-Southern Oscillation shifts eastward, intensifying Eastern Pacific El Niño events. In particular, the deep ocean warming could increase convective extreme El Niño events by 40 to 80% relative to the current climate. Our findings suggest that anthropogenic greenhouse warming will have a prolonged impact on El Niño variability through delayed deep ocean warming, even if CO2 stabilization is achieved. This paper shows that anthropogenic greenhouse warming will have a prolonged impact on El Niño variability through delayed deep ocean warming, even if CO2 stabilization is achieved. [ABSTRACT FROM AUTHOR]

Microstructural changes in conductive materials induced by electric current treatments, such as electromigration and electroplasticity, are critical in semiconductor and metal processing. However, owing to the inevitable thermal effect (Joule heating), the athermal effect on microstructural modifications remains obscure. This paper presents an approach of utilizing pre-micromachined structures, which obstruct current flow but maintain a thermal history similar to that of the matrix, effectively disentangling the thermal and athermal effects. A duplex stainless-steel material is selected to validate the feasibility of this method. Microstructural characterizations show that the athermal effect, especially the electron wind force (EWF), primarily governs the element diffusion and phase transformation in this study. Moreover, many σ phases (Cr-enriched) are precipitated in the micromachined structures, whereas no precipitation occurred in the matrix, suggesting that the directional EWF disrupts the Cr aggregation caused by Joule heating. Furthermore, we present a critical formula for determining the dimensions of micromachined structures of commonly used metallic materials. The proposed method may serve as an effective and powerful tool for unveiling the athermal effect on microstructural alterations. Understanding the impact of the athermal effect on microstructures is crucial for designing and manufacturing semiconductor and metal materials. Here, the authors propose a method using pre-micromachined structures to decouple thermal and athermal effects and experimentally validate its feasibility. [ABSTRACT FROM AUTHOR]

The dominant theoretical framework to account for reinforcement learning in the brain is temporal difference learning (TD) learning, whereby certain units signal reward prediction errors (RPE). The TD algorithm has been traditionally mapped onto the dopaminergic system, as firing properties of dopamine neurons can resemble RPEs. However, certain predictions of TD learning are inconsistent with experimental results, and previous implementations of the algorithm have made unscalable assumptions regarding stimulus-specific fixed temporal bases. We propose an alternate framework to describe dopamine signaling in the brain, FLEX (Flexibly Learned Errors in Expected Reward). In FLEX, dopamine release is similar, but not identical to RPE, leading to predictions that contrast to those of TD. While FLEX itself is a general theoretical framework, we describe a specific, biophysically plausible implementation, the results of which are consistent with a preponderance of both existing and reanalyzed experimental data. Reinforcement learning is essential for survival. In this paper, the authors explain why current machine learning models are hard to implement biologically, propose a biologically plausible framework, and show that it agrees with experiments. [ABSTRACT FROM AUTHOR]

Identifying groups of patients with similar disease progression patterns is key to understand disease heterogeneity, guide clinical decisions and improve patient care. In this paper, we propose a data-driven temporal stratification approach, ClusTric, combining triclustering and hierarchical clustering. The proposed approach enables the discovery of complex disease progression patterns not found by univariate temporal analyses. As a case study, we use Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease with a non-linear and heterogeneous disease progression. In this context, we applied ClusTric to stratify a hospital-based population (Lisbon ALS Clinic dataset) and validate it in a clinical trial population. The results unravelled four clinically relevant disease progression groups: slow progressors, moderate bulbar and spinal progressors, and fast progressors. We compared ClusTric with a state-of-the-art method, showing its effectiveness in capturing the heterogeneity of ALS disease progression in a lower number of clinically relevant progression groups. The authors proposed ClusTric, a temporal stratification approach to find disease progression groups. Applied to Amyotrophic Lateral Sclerosis, the method identifies four progression groups with distinguished characteristics. [ABSTRACT FROM AUTHOR]

Economic segregation is the geographical separation of people with different economic means. In this paper, we employ an archival study of attitudes in regions with varying degrees of economic segregation and a series of experimental studies measuring reactions to hypothetical levels of segregation to examine how segregation affects concerns about inequality. Combining correlational and experimental methods and examining attitudes about economic inequality in both the United States and South Africa, we show that when individuals of different means are segregated from each other, people are less likely to engage in economic comparisons and are therefore less concerned by inequality. Moreover, we find that this is true even when people are exposed to (and are aware of) the same levels of inequality, suggesting that segregation in and of itself affects attitudes about inequality. Our findings highlight the importance of economic segregation in shaping public attitudes about organizational and societal economic inequality. The authors show that real and hypothetical economic segregation is related to reduced concerns about inequality and that this is true even when holding constant both actual and perceived gaps between people of different economic means. [ABSTRACT FROM AUTHOR]

Many critical biological processes, like wound healing, require densely packed cell monolayers/tissues to transition from a jammed solid-like to a fluid-like state. Although numerical studies anticipate changes in the cell shape alone can lead to unjamming, experimental support for this prediction is not definitive because, in living systems, fluidization due to density changes cannot be ruled out. Additionally, a cell's ability to modulate its motility only compounds difficulties since even in assemblies of rigid active particles, changing the nature of self-propulsion has non-trivial effects on the dynamics. Here, we design and assemble a monolayer of synthetic cell-mimics and examine their collective behaviour. By systematically increasing the persistence time of self-propulsion, we discovered a cell shape-driven, density-independent, re-entrant jamming transition. Notably, we observed cell shape and shape variability were mutually constrained in the confluent limit and followed the same universal scaling as that observed in confluent epithelia. Dynamical heterogeneities, however, did not conform to this scaling, with the fast cells showing suppressed shape variability, which our simulations revealed is due to a transient confinement effect of these cells by their slower neighbors. Our experiments unequivocally establish a morphodynamic link, demonstrating that geometric constraints alone can dictate epithelial jamming/unjamming. Living cell collectives can jam and unjam through many pathways, yet the details remain elusive. Arora et al. design a monolayer of deformable cell-mimics composed of chiral active granular ellipsoids confined in flexible paper rings and show a re-entrant jamming transition mediated solely by cell shape change. [ABSTRACT FROM AUTHOR]

When processing language, the brain is thought to deploy specialized computations to construct meaning from complex linguistic structures. Recently, artificial neural networks based on the Transformer architecture have revolutionized the field of natural language processing. Transformers integrate contextual information across words via structured circuit computations. Prior work has focused on the internal representations ("embeddings") generated by these circuits. In this paper, we instead analyze the circuit computations directly: we deconstruct these computations into the functionally-specialized "transformations" that integrate contextual information across words. Using functional MRI data acquired while participants listened to naturalistic stories, we first verify that the transformations account for considerable variance in brain activity across the cortical language network. We then demonstrate that the emergent computations performed by individual, functionally-specialized "attention heads" differentially predict brain activity in specific cortical regions. These heads fall along gradients corresponding to different layers and context lengths in a low-dimensional cortical space. The extent to which transformer-based language models provide a good model of human brain activity during natural language comprehension is unclear. Here, the authors show that the internal transformations performed by the network predict brain activity in cortical language network as measured by fMRI. [ABSTRACT FROM AUTHOR]

Heat shuttling phenomenon is characterized by the presence of a non-zero heat flow between two bodies without net thermal bias on average. It was initially predicted in the context of nonlinear heat conduction within atomic lattices coupled to two time-oscillating thermostats. Recent theoretical works revealed an analog of this effect for heat exchanges mediated by thermal photons between two solids having a temperature dependent emissivity. In this paper, we present the experimental proof of this effect using systems made with composite materials based on phase change materials. By periodically modulating the temperature of one of two solids we report that the system akin to heat pumping with a controllable heat flow direction. Additionally, we demonstrate the effectiveness of a simultaneous modulation of two temperatures to control both the strength and direction of heat shuttling by exploiting the phase delay between these temperatures. These results show that this effect is promising for an active thermal management of solid-state technology, to cool down solids, to insulate them from their background or to amplify heat exchanges. Authors demonstrate a net heat flux between two objects at averagely zero temperature gradient, exploring the nonlinear thermal emissivity based on phase change materials. [ABSTRACT FROM AUTHOR]

Gut microbiomes of mammals carry a complex symbiotic assemblage of microorganisms. Feeding newborn infants milk from the mammary gland allows vertical transmission of the parental milk microbiome to the offspring's gut microbiome. This has benefits, but also has hazards for the host population. Using mathematical models, we demonstrate that biparental vertical transmission enables deleterious microbial elements to invade host populations. In contrast, uniparental vertical transmission acts as a sieve, preventing these invasions. Moreover, we show that deleterious symbionts generate selection on host modifier genes that keep uniparental transmission in place. Since microbial transmission occurs during birth in placental mammals, subsequent transmission of the milk microbiome needs to be maternal to avoid the spread of deleterious elements. This paper therefore argues that viviparity and the hazards from biparental transmission of the milk microbiome, together generate selection against male lactation in placental mammals. Lactation allows vertical microbiome transmission from the parent to the offspring in mammals. Here, the authors create a mathematical model to argue that uniparental transmission prevents the invasion of deleterious microbes and suggest that this may generate selection against male lactation. [ABSTRACT FROM AUTHOR]

Tuning the oxygen activity in perovskite oxides (ABO3) is promising to surmount the trade-off between activity and selectivity in redox reactions. However, this remains challenging due to the limited understanding in its activation mechanism. Herein, we propose the discovery that generating subsurface A-site cation (Lasub.) vacancy beneath surface Fe-O layer greatly improved the oxygen activity in LaFeO3, rendering enhanced methane conversion that is 2.9-fold higher than stoichiometric LaFeO3 while maintaining high syngas selectivity of 98% in anaerobic oxidation. Experimental and theoretical studies reveal that absence of Lasub.-O interaction lowered the electron density over oxygen and improved the oxygen mobility, which reduced the barrier for C-H bond cleavage and promoted the oxidation of C-atom, substantially boosting methane-to-syngas conversion. This discovery highlights the importance of A-site cations in modulating electronic state of oxygen, which is fundamentally different from the traditional scheme that mainly credits the redox activity to B-site cations and can pave a new avenue for designing prospective redox catalysts. Tuning the oxygen activity in perovskite oxides (ABO3) holds great potential for overcoming the trade-off between activity and selectivity in redox reactions. This paper highlights how A-site cations influence the electronic state of oxygen and how subsurface A-site vacancies enhance oxygen activity for improved methane conversion. [ABSTRACT FROM AUTHOR]

Plasmon polaritons, or plasmons, are coupled oscillations of electrons and electromagnetic fields that can confine the latter into deeply subwavelength scales, enabling novel polaritonic devices. While plasmons have been extensively studied in normal metals or semimetals, they remain largely unexplored in correlated materials. In this paper, we report infrared (IR) nano-imaging of thin flakes of CsV3Sb5, a prototypical layered Kagome metal. We observe propagating plasmon waves in real-space with wavelengths tunable by the flake thickness. From their frequency-momentum dispersion, we infer the out-of-plane dielectric function ϵ c that is generally difficult to obtain in conventional far-field optics, and elucidate signatures of electronic correlations when compared to density functional theory (DFT). We propose correlation effects might have switched the real part of ϵ c from negative to positive values over a wide range of middle-IR frequencies, transforming the surface plasmons into hyperbolic bulk plasmons, and have dramatically suppressed their dissipation. Plasmons polaritons, or collective excitations of electrons and electromagnetic fields, have been rarely studied in layered correlated materials. Shiravi et al. report hyperbolic plasmon polaritons in thin flakes of the Kagome metal CsV3Sb5 and discuss correlation effects on their formation and tunability. [ABSTRACT FROM AUTHOR]

Multiplayer games on graphs are at the heart of theoretical descriptions of key evolutionary processes that govern vital social and natural systems. However, a comprehensive theoretical framework for solving multiplayer games with an arbitrary number of strategies on graphs is still missing. Here, we solve this by drawing an analogy with the Balls-and-Boxes problem, based on which we show that the local configuration of multiplayer games on graphs is equivalent to distributing k identical co-players among n distinct strategies. We use this to derive the replicator equation for any n-strategy multiplayer game under weak selection, which can be solved in polynomial time. As an example, we revisit the second-order free-riding problem, where costly punishment cannot truly resolve social dilemmas in a well-mixed population. Yet, in structured populations, we derive an accurate threshold for the punishment strength, beyond which punishment can either lead to the extinction of defection or transform the system into a rock-paper-scissors-like cycle. The analytical solution also qualitatively agrees with the phase diagrams that were previously obtained for non-marginal selection strengths. Our framework thus allows an exploration of any multi-strategy multiplayer game on regular graphs. Evolutionary multiplayer games in structured populations illustrate a variety of phenomena in natural and social systems. This research provides a mathematical framework to analyze multiplayer games with an arbitrary number of strategies on regular graphs. [ABSTRACT FROM AUTHOR]

The meridional overturning circulation (MOC) in the ocean is a key player in the global climate system, while continental topography provides an essential backdrop to the system. In this study, we design a series of coupled model sensitivity experiments to investigate the influence of various mountain ranges on the global thermohaline circulation. The results highlight the influence of the Tibetan Plateau (TP) on the global thermohaline circulation. It emerges as a requisite for establishing the Atlantic MOC (AMOC) and a determining factor for the cessation of the Pacific MOC (PMOC). Additionally, the Antarctic continent plays a vital role in facilitating the TP to form the AMOC. While the formation of the AMOC cannot be attributed to any single mountain range, the TP alone can inhibit the PMOC's development. By modifying the global hydrological cycle, the TP is likely to have been crucial in molding the global thermohaline circulation. This paper presents quantitative evaluation of the role of different continental mountains in shaping the global meridional overturning circulation. The Tibetan Plateau is likely to have been crucial in molding the global thermohaline circulation. [ABSTRACT FROM AUTHOR]

Publicly available electric vehicle (EV) infrastructure is pivotal for the United States EV transition by 2030. Existing infrastructure lacks equitably distribution to low-income and underrepresented communities, impeding mass adoption. Our study, utilizing 2021 micro-level data from 121 million United States households, comprehensively examines income and racial disparities in EV infrastructure accessibility. Our analysis of national averages indicates that lower-income groups face less accessibility to public EV infrastructure in both urban and rural geographies. Black households experience less rural accessibility, but greater urban accessibility compared to White households conditioning on income. However, our localized analysis uncovers significant variations in accessibility gaps among counties, rural and urban settings, and dwelling types. While Black households experience greater urban accessibility nationally, a closer look at the county level reveals diminishing advantages. This study identifies areas with pronounced inequality and urgent needs for enhanced accessibility, emphasizing the necessity for tailored solutions by local governments to enhance equitable access to EV infrastructure. This paper finds that on average lower-income groups encounter reduced accessibility to public EV infrastructure in urban and rural areas. Black households have less rural accessibility, but greater urban accessibility compared to White households. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs), responsible for extreme weather conditions, are mid-latitude systems that can cause significant damage to coastal areas. While forecasting ARs beyond two weeks remains a challenge, past research suggests potential benefits may come from properly accounting for the changes in sea surface temperature (SST) through air–sea interactions. In this paper, we investigate the impact of ARs on SST over the North Pacific by analyzing 25 years of ocean reanalysis data using an SST budget equation. We show that in the region of strong ocean modification, ocean dynamics can offset over 100% of the anomalous SST warming that would otherwise arise from atmospheric forcing. Among all ocean processes, ageostrophic advection and vertical mixing (diffusion and entrainment) are the most important factors in modifying the SST tendency response. The SST tendency response to ARs varies spatially. For example, in coastal California, the driver of enhanced SST warming is the reduction in ageostrophic advection due to anomalous southerly winds. Moreover, there is a large region where the SST shows a warming response to ARs due to the overall reduction in the total clouds and subsequent increase in total incoming shortwave radiation. Strong air-sea interactions during atmospheric rivers often lead to modest upper ocean heat changes. The authors show that interior ocean dynamics are compensating for these air-sea exchanges. These findings can help improve subseasonal forecasts. [ABSTRACT FROM AUTHOR]

The SpaceX Inspiration4 mission provided a unique opportunity to study the impact of spaceflight on the human body. Biospecimen samples were collected from four crew members longitudinally before (Launch: L-92, L-44, L-3 days), during (Flight Day: FD1, FD2, FD3), and after (Return: R + 1, R + 45, R + 82, R + 194 days) spaceflight, spanning a total of 289 days across 2021-2022. The collection process included venous whole blood, capillary dried blood spot cards, saliva, urine, stool, body swabs, capsule swabs, SpaceX Dragon capsule HEPA filter, and skin biopsies. Venous whole blood was further processed to obtain aliquots of serum, plasma, extracellular vesicles and particles, and peripheral blood mononuclear cells. In total, 2,911 sample aliquots were shipped to our central lab at Weill Cornell Medicine for downstream assays and biobanking. This paper provides an overview of the extensive biospecimen collection and highlights their processing procedures and long-term biobanking techniques, facilitating future molecular tests and evaluations.As such, this study details a robust framework for obtaining and preserving high-quality human, microbial, and environmental samples for aerospace medicine in the Space Omics and Medical Atlas (SOMA) initiative, which can aid future human spaceflight and space biology experiments. Here the authors provide the biospecimen collection methodology from the SpaceX Inspiration4 mission, including venous blood, capillary blood, saliva, urine, stool, skin biopsy, body swab, and environmental swab samples. [ABSTRACT FROM AUTHOR]

Multi-stable structures attract great interest because they possess special energy landscapes with domains of attraction around the stable states. Consequently, multi-stable structures have the potential to achieve prescribed reconfiguration with only a few lightweight actuators (such as shape-memory alloy springs), and do not need constant actuation to be locked at a stable state. However, most existing multi-stability designs are based on assembling bi-stable unit cells, which contain multitudes of distractive stable states, diminishing the feasibility of reconfiguration actuation. Another type is by introducing prestress together with kinematic symmetry or nonlinearity to achieve multi-stability, but the resultant structure often suffers the lack of stiffness. To help address these challenges, we firstly introduce the constraints that a truss structure is simultaneously compatible at multiple (more than two) prescribed states. Then, we solve for the design of multi-stable truss structures, named multi-compatible structures in this paper, where redundant stable states are limited. Secondly, we explore minimum energy paths connecting the designed stable states, and compute for a simple and inaccurate pulling actuation guiding the structure to transform along the computed paths. Finally, we fabricated four prototypes to demonstrate that prescribed reconfigurations with easy-actuation have been achieved and applied a quadra-stable structure to the design of a variable stiffness gripper. Altogether, our full-cycle design approach contains multi-stability design, stiffness design, minimum-energy-path finding, and pulling actuation design, which highlights the potential for designing morphing structures with lightweight actuation for practical applications. Multi-stable structures are useful in soft robotics with fewer and lightweight actuators for rapid motion. Here, authors propose a full cycle design framework for morphing structures including multi-stability, stiffness design, minimum energy-path finding, and pulling actuation. [ABSTRACT FROM AUTHOR]

Silicon color centers have recently emerged as promising candidates for commercial quantum technology, yet their interaction with electric fields has yet to be investigated. In this paper, we demonstrate electrical manipulation of telecom silicon color centers by implementing novel lateral electrical diodes with an integrated G center ensemble in a commercial silicon on insulator wafer. The ensemble optical response is characterized under application of a reverse-biased DC electric field, observing both 100% modulation of fluorescence signal, and wavelength redshift of approximately 1.24 ± 0.08 GHz/V above a threshold voltage. Finally, we use G center fluorescence to directly image the electric field distribution within the devices, obtaining insight into the spatial and voltage-dependent variation of the junction depletion region and the associated mediating effects on the ensemble. Strong correlation between emitter-field coupling and generated photocurrent is observed. Our demonstration enables electrical control and stabilization of semiconductor quantum emitters. Si color centers offer promising quantum technology applications, but their interaction with electric fields has not been explored. Here the authors report electrical manipulation of telecom emitters in Si by fabricating lateral diodes with an integrated ensemble of G centers in commercial Si on insulator wafer. [ABSTRACT FROM AUTHOR]

We study mentorship in scientific collaborations, where a junior scientist is supported by potentially multiple senior collaborators, without them necessarily having formal supervisory roles. We identify 3 million mentor–protégé pairs and survey a random sample, verifying that their relationship involved some form of mentorship. We find that mentorship quality predicts the scientific impact of the papers written by protégés post mentorship without their mentors. We also find that increasing the proportion of female mentors is associated not only with a reduction in post-mentorship impact of female protégés, but also a reduction in the gain of female mentors. While current diversity policies encourage same-gender mentorships to retain women in academia, our findings raise the possibility that opposite-gender mentorship may actually increase the impact of women who pursue a scientific career. These findings add a new perspective to the policy debate on how to best elevate the status of women in science.Here, the authors study mentorship in scientific collaborations, and find that mentorship quality predicts the scientific impact of protégés post mentorship. Moreover, female protégés collaborating with male mentors become more impactful post mentorship than those who collaborate with female mentors. [ABSTRACT FROM AUTHOR]

It has long been a norm that researchers extract knowledge from literature to design materials. However, the avalanche of publications makes the norm challenging to follow. Text mining (TM) is efficient in extracting information from corpora. Still, it cannot discover materials not present in the corpora, hindering its broader applications in exploring novel materials, such as high-entropy alloys (HEAs). Here we introduce a concept of "context similarity" for selecting chemical elements for HEAs, based on TM models that analyze the abstracts of 6.4 million papers. The method captures the similarity of chemical elements in the context used by scientists. It overcomes the limitations of TM and identifies the Cantor and Senkov HEAs. We demonstrate its screening capability for six- and seven-component lightweight HEAs by finding nearly 500 promising alloys out of 2.6 million candidates. The method thus brings an approach to the development of ultrahigh-entropy alloys and multicomponent materials. The avalanche of publications challenges the norm that researchers extract knowledge from literature to design materials. Here the authors present a text-mining method that is implemented based on the abstracts of 6.4 million papers to enable the design of new high entropy alloys. [ABSTRACT FROM AUTHOR]