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This paper is a call to action. By publishing concurrently across journals like an emergency bulletin, we are not merely making a plea for awareness about climate change. Instead, we are demanding immediate, tangible steps that harness the power of microbiology and the expertise of researchers and policymakers to safeguard the planet for future generations. [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]

The outbreak of novel coronavirus disease (COVID-19) has resulted in changes in productivity and daily life patterns, and as a result electricity consumption (EC) has also shifted. In this paper, we construct estimates of EC changes at the metropolitan level across the continental U.S., including total EC and residential EC during the initial two months of the pandemic. The total and residential data on the state level were broken down into the county level, and then metropolitan level EC estimates were aggregated from the counties included in each metropolitan statistical area (MSA). This work shows that the reduction in total EC is related to the shares of certain industries in an MSA, whereas regardless of the incidence level or economic structure, the residential sector shows a trend of increasing EC across the continental U.S. Since the MSAs account for 86% of the total population and 87% of the total EC of the continental U.S., the analytical result in this paper can provide important guidelines for future social-economic crises. In this paper the authors construct estimates of electricity consumption changes at the metropolitan level across the continental U.S., during the initial two months of the COVID-19 pandemic, demonstrating variations in electricity consumption patterns based on several factors. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic led to 231,841 deaths and 940,243 hospitalisations in England, by the end of March 2023. This paper calculates the real-time infection hospitalisation risk (IHR) and infection fatality risk (IFR) using the Office for National Statistics Coronavirus Infection Survey (ONS CIS) and the Real-time Assessment of Community Transmission Survey between November 2020 to March 2023. The IHR and the IFR in England peaked in January 2021 at 3.39% (95% Credible Intervals (CrI): 2.79, 3.97) and 0.97% (95% CrI: 0.62, 1.36), respectively. After this time, there was a rapid decline in the severity from infection, with the lowest estimated IHR of 0.32% (95% CrI: 0.27, 0.39) in December 2022 and IFR of 0.06% (95% CrI: 0.04, 0.08) in April 2022. We found infection severity to vary more markedly between regions early in the pandemic however, the absolute heterogeneity has since reduced. The risk from infection of SARS-CoV-2 has changed substantially throughout the COVID-19 pandemic with a decline of 86.03% (80.86, 89.35) and 89.67% (80.18, 93.93) in the IHR and IFR, respectively, since early 2021. From April 2022 until March 2023, the end of the ONS CIS study, we found fluctuating patterns in the severity of infection with the resumption of more normative mixing, resurgent epidemic waves, patterns of waning immunity, and emerging variants that have shown signs of convergent evolution. The severity of SARS-CoV-2 infection varied over the course of the pandemic due to factors such as changes in variant characteristics and population immunity from previous infection or vaccination. Here, the authors estimate infection hospitalisation and infection fatality rates in England over time from the start of the pandemic until March 2023. [ABSTRACT FROM AUTHOR]

Stimulator of interferon genes (STING) is a central component of the cytosolic nucleic acids sensing pathway and as such master regulator of the type I interferon response. Due to its critical role in physiology and its' involvement in a variety of diseases, STING has been a focus for drug discovery. Targeted protein degradation (TPD) has emerged as a promising pharmacology for targeting previously considered undruggable proteins by hijacking the cellular ubiquitin proteasome system (UPS) with small molecules. Here, we identify AK59 as a STING degrader leveraging HERC4, a HECT-domain E3 ligase. Additionally, our data reveals that AK59 is effective on the common pathological STING mutations, suggesting a potential clinical application of this mechanism. Thus, these findings introduce HERC4 to the fields of TPD and of compound-induced degradation of STING, suggesting potential therapeutic applications. In this paper, Mutlu et al. identifies a STING degrader, AK59, which inhibits downstream cGAS/STING activity through STING degradation employing a HECT-domain E3 ligase HERC4 and proteasomal ubiquitination pathway. [ABSTRACT FROM AUTHOR]

In nature, structural and functional materials often form programmed three-dimensional (3D) assembly to perform daily functions, inspiring researchers to engineer multifunctional 3D structures. Despite much progress, a general method to fabricate and assemble a broad range of materials into functional 3D objects remains limited. Herein, to bridge the gap, we demonstrate a freeform multimaterial assembly process (FMAP) by integrating 3D printing (fused filament fabrication (FFF), direct ink writing (DIW)) with freeform laser induction (FLI). 3D printing performs the 3D structural material assembly, while FLI fabricates the functional materials in predesigned 3D space by synergistic, programmed control. This paper showcases the versatility of FMAP in spatially fabricating various types of functional materials (metals, semiconductors) within 3D structures for applications in crossbar circuits for LED display, a strain sensor for multifunctional springs and haptic manipulators, a UV sensor, a 3D electromagnet as a magnetic encoder, capacitive sensors for human machine interface, and an integrated microfluidic reactor with a built-in Joule heater for nanomaterial synthesis. This success underscores the potential of FMAP to redefine 3D printing and FLI for programmed multimaterial assembly. A freeform multimaterial assembly process (FMAP) is demonstrated, synchronizing laser induction with 3D printing for seamlessly integrating multimaterials into 3D objects, enabling streamlined, flexible, and precise electronic device fabrication. [ABSTRACT FROM AUTHOR]

Alternative splicing events are a major causal mechanism for complex traits, but they have been understudied due to the limitation of short-read sequencing. Here, we generate a full-length isoform annotation of human immune cells from an individual by long-read sequencing for 29 cell subsets. This contains a number of unannotated transcripts and isoforms such as a read-through transcript of TOMM40-APOE in the Alzheimer's disease locus. We profile characteristics of isoforms and show that repetitive elements significantly explain the diversity of unannotated isoforms, providing insight into the human genome evolution. In addition, some of the isoforms are expressed in a cell-type specific manner, whose alternative 3'-UTRs usage contributes to their specificity. Further, we identify disease-associated isoforms by isoform switch analysis and by integration of several quantitative trait loci analyses with genome-wide association study data. Our findings will promote the elucidation of the mechanism of complex diseases via alternative splicing. This paper unveils the complexity of human immune cell splicing, highlighting cell-specific isoforms and establishing connections between alternative splicing and complex traits. These findings have implications for understanding diseases and the evolution of the genome. [ABSTRACT FROM AUTHOR]

Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH. Specifically, we model the attributes that affect the battery degradation from the perspective of empirical degradation and state space equations, and utilize neural networks to capture battery degradation dynamics. A general feature extraction method is designed to extract statistical features from a short period of data before the battery is fully charged, enabling our method applicable to different battery types and charge/discharge protocols. Additionally, we generate a comprehensive dataset consisting of 55 lithium-nickel-cobalt-manganese-oxide (NCM) batteries. Combined with three other datasets from different manufacturers, we use a total of 387 batteries with 310,705 samples to validate our method. The mean absolute percentage error (MAPE) is 0.87%. Our proposed PINN has demonstrated remarkable performance in regular experiments, small sample experiments, and transfer experiments when compared to alternative neural networks. This study highlights the promise of physics-informed machine learning for battery degradation modeling and SOH estimation. Reliable lithium-ion battery health assessment is vital for safety. Here, authors present a physics-informed neural network for accurate and stable state-of-health estimation, overcoming challenges of varied battery types and usage conditions. [ABSTRACT FROM AUTHOR]

Legged robots are well-suited for deployment in unstructured environments but require a unique control scheme specific for their design. As controllers optimised in simulation do not transfer well to the real world (the infamous sim-to-real gap), methods enabling quick learning in the real world, without any assumptions on the specific robot model and its dynamics, are necessary. In this paper, we present a generic method based on Central Pattern Generators, that enables the acquisition of basic locomotion skills in parallel, through very few trials. The novelty of our approach, underpinned by a mathematical analysis of the controller model, is to search for good initial states, instead of optimising connection weights. Empirical validation in six different robot morphologies demonstrates that our method enables robots to learn primary locomotion skills in less than 15 minutes in the real world. In the end, we showcase our skills in a targeted locomotion experiment.The authors present a machine learning approach to enable modular robots to learn to walk and follow objects within a short time. The method is based on bioinspired central pattern generators and is validated on six robots with different body shapes. [ABSTRACT FROM AUTHOR]

Point-scanning microscopy approaches are transforming super-resolution imaging. Despite achieving parallel high-speed imaging using multifocal techniques, efficient multicolor imaging methods with high-quality illumination are currently lacking. In this paper, we present for the first time Mechanical-scan-free multiColor Super-resolution Microscopy (MCoSM) with spot array illumination, which enables mechanical-scan-free super-resolution imaging with adjustable resolution and a good effective field-of-view based on spatial light modulators. Through 100–2,500 s super-resolution spot illumination with different effective fields of view for imaging, we demonstrate the adjustable capacity of MCoSM. MCoSM extends existing spectral imaging capabilities through a time-sharing process involving different color illumination with phase-shift scanning while retaining the spatial flexibility of super-resolution imaging with diffractive spot array illumination. To demonstrate the prospects of MCoSM, we perform four-color imaging of fluorescent beads at high resolution. MCoSM provides a versatile platform for studying molecular interactions in complex samples at the nanoscale level. Here, the authors use spot array illumination to enable mechanical-scan-free super-resolution microscopy with adjustable resolution and good effective field of view, demonstrating a platform for studying molecular interactions at the nanoscale. [ABSTRACT FROM AUTHOR]

In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning: VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell's mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited. While machine learning platforms can improve the assessment of Hematoxylin & Eosin (H&E) stained-tumour tissue images, current models typically require manual cell-type annotations in training. Here, the authors develop VOLTA, a self-supervised machine learning framework to improve cell representation learning in H&E images based on the cells environment [ABSTRACT FROM AUTHOR]

One of the most effective ways to advance the performance of quantum computers and quantum sensors is to increase the number of qubits or quantum resources in the system. A major technical challenge that must be solved to realize this goal for trapped-ion systems is scaling the delivery of optical signals to many individual ions. In this paper we demonstrate an approach employing waveguides and multi-mode interferometer splitters to optically address multiple 171Yb+ ions in a surface trap by delivering all wavelengths required for full qubit control. Measurements of hyperfine spectra and Rabi flopping were performed on the E2 clock transition, using integrated waveguides for delivering the light needed for Doppler cooling, state preparation, coherent operations, and detection. We describe the use of splitters to address multiple ions using a single optical input per wavelength and use them to demonstrate simultaneous Rabi flopping on two different transitions occurring at distinct trap sites. This work represents an important step towards the realization of scalable integrated photonics for atomic clocks and trapped-ion quantum information systems. A promising strategy for scaling trapped-ion-based quantum technologies is to use fully integrated optical waveguides to deliver light to numerous ions at multiple sites. Here, the authors. optically address three ions using on-chip waveguides to deliver three distinct wavelengths per ion, and perform Rabi flopping on each ion simultaneously. [ABSTRACT FROM AUTHOR]

An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in the brain depends only on information local to synapses. Deep networks, on the other hand, typically use non-local learning algorithms and are trained in an offline, non-noisy, independent, identically distributed setting. Understanding how neural networks learn under the same constraints as the brain is an open problem for neuroscience and neuromorphic computing. A standard approach to this problem has yet to be established. In this paper, we propose that discrete graphical models that learn via an online maximum a posteriori learning algorithm could provide such an approach. We implement this kind of model in a neural network called the Sparse Quantized Hopfield Network. We show our model outperforms state-of-the-art neural networks on associative memory tasks, outperforms these networks in online, continual settings, learns efficiently with noisy inputs, and is better than baselines on an episodic memory task. Brains and neuromorphic systems learn with local learning rules in online-continual learning scenarios. Designing neural networks that learn effectively under these conditions is challenging. The authors introduce a neural network that implements an effective, principled approach to local, online-continual learning on associative memory tasks. [ABSTRACT FROM AUTHOR]

Understanding projected changes in flooding across the contiguous United States (CONUS) helps increase our capability to adapt to and mitigate against this hazard. Here, we assess future changes in flooding across CONUS using outputs from 28 global climate models and four scenarios of the Coupled Model Intercomparison Project Phase 6. We find that CONUS is projected to experience an overall increase in flooding, especially under higher emission scenarios; there are subregional differences, with the Northeast and Southeast (Great Plains of the North and Southwest) showing higher tendency towards increasing (decreasing) flooding due to changes in flood processes at the seasonal scale. Moreover, even though trends may not be detected in the historical period, these projected future trends highlight the current needs for incorporating climate change in the future infrastructure designs and management of the water resources. This paper assesses future changes in flood magnitude across the conterminous United States based on multiple climate change scenarios. The results suggest that annual maximum peak discharge is projected to become more extreme under higher emission scenarios. [ABSTRACT FROM AUTHOR]

This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through LDDMM and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction. Omics data's diversity and high-dimensionality challenge integration across technologies and with imaging. Here, authors introduce mapping method xIV-LDDMM that estimates geometric and feature transformations to integrate tissue-scale atlases with molecular and cellular-scale data. [ABSTRACT FROM AUTHOR]

Optical interference filters (OIFs) are vital components for a wide range of optical and photonic systems. They are pivotal in controlling spectral transmission and reflection upon demand. OIFs rely on optical interference of the incident wave at multilayers, which are fabricated with nanometer precision. Here, we demonstrate that these requirements can be fulfilled by inkjet printing. This versatile technology offers a high degree of freedom in manufacturing, as well as cost-affordable and rapid-prototyping features from the micron to the meter scale. In this work, via rational ink design and formulation, OIFs were fully inkjet printed in ambient conditions. Longpass, shortpass, bandpass, and dichroic OIFs were fabricated, and precise control of the spectral response in OIFs was realized. Subsequently, customized lateral patterning of OIFs by inkjet printing was achieved. Furthermore, upscaling of the printed OIFs to A4 size (29.7 × 21.0 cm²) was demonstrated. Optical interference filters are multilayer structures for controlling the propagation of electromagnetic waves. Jin et al. have developed a method of via inkjet printing to fabricate optical interference filters with commercially relevant quality with remarkable A4 paper size (29.7 × 21.0 cm²) in ambient conditions. [ABSTRACT FROM AUTHOR]

Photoelectrochemical seawater splitting is a promising route for direct utilization of solar energy and abundant seawater resources for H2 production. However, the complex salinity composition in seawater results in intractable challenges for photoelectrodes. This paper describes the fabrication of a bilayer stack consisting of stainless steel and TiO2 as a cocatalyst and protective layer for Si photoanode. The chromium-incorporated NiFe (oxy)hydroxide converted from stainless steel film serves as a protective cocatalyst for efficient oxygen evolution and retarding the adsorption of corrosive ions from seawater, while the TiO2 is capable of avoiding the plasma damage of the surface layer of Si photoanode during the sputtering of stainless steel catalysts. By implementing this approach, the TiO2 layer effectively shields the vulnerable semiconductor photoelectrode from the harsh plasma sputtering conditions in stainless steel coating, preventing surface damages. Finally, the Si photoanode with the bilayer stack inhibits the adsorption of chloride and realizes 167 h stability in chloride-containing alkaline electrolytes. Furthermore, this photoanode also demonstrates stable performance under alkaline natural seawater for over 50 h with an applied bias photon-to-current efficiency of 2.62%. Here, the authors report a Si photoanode coated with a TiO2/stainless steel bilayer to inhibit chloride adsorption and facilitate stable alkaline seawater splitting for over 50 h with an applied bias photon-to-current efficiency of 2.62%. [ABSTRACT FROM AUTHOR]

Climate movements have gained momentum in recent years, aiming to create public awareness of the consequences of climate change through salient climate protests. This paper investigates whether concerns about climate change increase following demonstrative protests and confrontational acts of civil disobedience. Leveraging individual-level survey panel data from Germany, we exploit exogenous variations in the timing of climate protests relative to survey interview dates to compare climate change concerns in the days before and after a protest (N = 24,535). Following climate protests, we find increases in concerns about climate change by, on average, 1.2 percentage points. Further, we find no statistically significant evidence that concerns of any subpopulation decreased after climate protests. Lastly, the increase in concerns following protests is highest when concern levels before the protests are low. Climate movements aim to raise public awareness of climate change through protests, but their efficacy is debated. Here, the authors show that concerns about climate change increased in Germany after climate strikes and non-violent acts of civil disobedience. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, analysis of experimental results, and predicting evolution. Protein dynamics, crucial for life, are difficult and expensive to predict. This study shows that AI-based structure prediction methods can be modified for rapidly predicting the conformational landscapes of proteins, with strong correlations with experimentally-measured relative state populations. [ABSTRACT FROM AUTHOR]

Engineering biology (EngBio) is a dynamic field that uses gene editing, synthesis, assembly, and engineering to design new or modified biological systems. EngBio applications could make a significant contribution to achieving net zero greenhouse gas emissions. Yet, policy support will be needed if EngBio is to fulfil its climate mitigation potential. What form should such policies take, and what EngBio applications should they target? This paper reviews EngBio's potential climate contributions to assist policymakers shape regulations and target resources and, in so doing, to facilitate democratic deliberation on desirable futures. Engineering biology is a dynamic field that uses gene editing, synthesis, assembly, and engineering to design new or modified biological systems. Here the authors discuss the policy considerations and interventions needed to support a role for engineering biology in climate change mitigation. [ABSTRACT FROM AUTHOR]

Many surveys ask participants to retrospectively record their location of birth. This paper examines the accuracy of such data in the UK Biobank using a sample of full siblings. Comparison of reported birth locations for siblings with different age gaps allows us to estimate the probabilities of household moves and of misreported birth locations. Our first contribution is to show that there are inaccuracies in retrospective birth location data, showing a sizeable probability of misreporting, with 28% of birth coordinates, 16% of local districts and 6% of counties of birth being incorrectly reported. Our second contribution is to show that such error can lead to substantial attenuation bias when investigating the impacts of location-based exposures, especially when there is little spatial correlation and limited time variation in the exposure variable. Sibling fixed effect models are shown to be particularly vulnerable to the attenuation bias. Our third contribution is to highlight possible solutions to the attenuation bias and sensitivity analyses to the reporting error. Many surveys ask participants to retrospectively record their location of birth. Here, the authors find misreporting in retrospective birth location data in UK Biobank using data from siblings, which can lead to bias in estimates of the impact of location-based exposures. [ABSTRACT FROM AUTHOR]

Restoring tree cover changes albedo, which is the fraction of sunlight reflected from the Earth's surface. In most locations, these changes in albedo offset or even negate the carbon removal benefits with the latter leading to global warming. Previous efforts to quantify the global climate mitigation benefit of restoring tree cover have not accounted robustly for albedo given a lack of spatially explicit data. Here we produce maps that show that carbon-only estimates may be up to 81% too high. While dryland and boreal settings have especially severe albedo offsets, it is possible to find places that provide net-positive climate mitigation benefits in all biomes. We further find that on-the-ground projects are concentrated in these more climate-positive locations, but that the majority still face at least a 20% albedo offset. Thus, strategically deploying restoration of tree cover for maximum climate benefit requires accounting for albedo change and we provide the tools to do so. Restoring tree cover is a prominent climate solution but can cause global warming due to changes in albedo. This paper maps albedo and carbon changes from restoring tree cover to highlight where the greatest net climate benefits can be achieved. [ABSTRACT FROM AUTHOR]

The rise of antibiotic resistance is a critical public health concern, requiring an understanding of mechanisms that enable bacteria to tolerate antimicrobial agents. Bacteria use diverse strategies, including the amplification of drugresistance genes. In this paper, we showed that multicopy plasmids, often carrying antibiotic resistance genes in clinical bacteria, can rapidly amplify genes, leading to plasmid-mediated phenotypic noise and transient antibiotic resistance. By combining stochastic simulations of a computational model with high-throughput single-cell measurements of blaTEM-1 expression in Escherichia coli MG1655, we showed that plasmid copy number variability stably maintains populations composed of cells with both low and high plasmid copy numbers. This diversity in plasmid copy number enhances the probability of bacterial survival in the presence of antibiotics, while also rapidly reducing the burden of carrying multiple plasmids in drug-free environments. Our results further support the tenet that multicopy plasmids not only act as vehicles for the horizontal transfer of genetic information between cells but also as drivers of bacterial adaptation, enabling rapid modulation of gene copy numbers. Understanding the role of multicopy plasmids in antibiotic resistance is critical, and our study provides insights into how bacteria can transiently survive lethal concentrations of antibiotics. [ABSTRACT FROM AUTHOR]

Identifying sex differences in outcomes and toxicity between males and females in oncology clinical trials is important and has also been mandated by National Institutes of Health policies. Here we analyze the Trialtrove database, finding that, strikingly, only 472/89,221 oncology clinical trials (0.5%) had curated post-treatment sex comparisons. Among 288 trials with comparisons of survival, outcome, or response, 16% report males having statistically significant better survival outcome or response, while 42% reported significantly better survival outcome or response for females. The strongest differences are in trials of EGFR inhibitors in lung cancer and rituximab in non-Hodgkin's lymphoma (both favoring females). Among 44 trials with side effect comparisons, more trials report significantly lesser side effects in males (N = 22) than in females (N = 13). Thus, while statistical comparisons between sexes in oncology trials are rarely reported, important differences in outcome and toxicity exist. These considerable outcome and toxicity differences highlight the need for reporting sex differences more thoroughly going forward. The role of sex differences in response to cancer therapy remains unclear but this could be improved by reporting sex comparisons of outcomes in clinical trials. Here, the authors characterise the sex outcome comparisons in 89,221 interventional trials, finding that while comparisons were rare, important insights could be obtained. [ABSTRACT FROM AUTHOR]

Inertial Measurement Unit-based methods have great potential in capturing motion in large-scale and complex environments with many people. Sparse Inertial Measurement Unit-based methods have more research value due to their simplicity and flexibility. However, improving the computational efficiency and reducing latency in such methods are challenging. In this paper, we propose Fast Inertial Poser, which is a full body motion estimation deep neural network based on 6 inertial measurement units considering body parameters. We design a network architecture based on recurrent neural networks according to the kinematics tree. This method introduces human body shape information by the causality of observations and eliminates the dependence on future frames. During the estimation of joint positions, the upper body and lower body are estimated using separate network modules independently. Then the joint rotation is obtained through a well-designed single-frame kinematics inverse solver. Experiments show that the method can greatly improve the inference speed and reduce the latency while ensuring the reconstruction accuracy compared with previous methods. Fast Inertial Poser runs at 65 fps with 15 ms latency on an embedded computer, demonstrating the efficiency of the model.Inertial Measurement Units-based motion capture effective application in large scale and complex environments depends on improved efficiency and reduced latency. Here, authors propose a full body motion estimation deep neural network based on 6 IMUs, which runs at 65 fps with 15 ms latency on an embedded computer. [ABSTRACT FROM AUTHOR]

Ion trapping has been found to be responsible for the performance degradation in electrochromic oxide thin films, and a detrapping procedure was proved to be effective to rejuvenate the degraded films. Despite of the studies on ion trapping and detrapping, its dynamics remain largely unknown. Moreover, coloration mechanisms of electrochromic oxides are also far from clear, limiting the development of superior devices. Here, we visualize ion trapping and detrapping dynamics in a model electrochromic material, amorphous WO3. Specifically, formation of orthorhombic Li2WO4 during long-term cycling accounts for the origin of shallow traps. Deep traps are multiple-step-determined, composed of mixed W4+-Li2WO4, amorphous Li2WO4 and W4+-Li2O. The non-decomposable W4+-Li2WO4 couple is the origin of the irreversible traps. Furthermore, we demonstrate that, besides the typical small polaron hopping between W5+ ↔ W6+ sites, bipolaron hopping between W4+ ↔ W6+ sites gives rise to optical absorption in the short-wavelength region. Overall, we provide a general picture of electrochromism based on polaron hopping. Ion trapping and detrapping were demonstrated to also prevail in other cathodic electrochromic oxides. This work not only provides the ion trapping and detrapping dynamics of WO3, but also open avenues to study other cathodic electrochromic oxides and develop superior electrochromic devices with great durability. Ion trapping has been found to be responsible for the performance degradation in electrochromic oxide thin films. This paper visualizes ion trapping and detrapping dynamics, and provides a general picture of electrochromism in amorphous WO3. [ABSTRACT FROM AUTHOR]

Accelerating efforts for the Sustainable Development Goals requires understanding their synergies and trade-offs at the national and sub-national levels, which will help identify the key hurdles and opportunities to prioritize them in an indivisible manner for a country. Here, we present the importance of the 17 goals through synergy and trade-off networks. Our results reveal that 19 provinces show the highest trade-offs in SDG13 (Combating Climate Change) or SDG5 (Gender Equality) consistent with the national level, with other 12 provinces varying. 24 provinces show the highest synergies in SDG1 (No Poverty) or SDG6 (Clean Water and Sanitation) consistent with the national level, with the remaining 7 provinces varying. These common but differentiated SDG priorities reflect that to ensure a coordinated national response, China should pay more attention to the provincial situation, so that provincial governments can formulate more targeted policies in line with their own priorities towards accelerating sustainable development. The paper reveals areas of common and differentiated SDG priority at the national and subnational levels in China considering synergy and trade-off. The findings suggest that provincial governments should formulate more targeted policy aligning with national priority to achieve SDGs. [ABSTRACT FROM AUTHOR]

In May 2022, individuals infected with the monkeypox virus were detected in the UK without clear travel links to endemic areas. Understanding the clinical characteristics and infection severity of mpox is necessary for effective public health policy. The study period of this paper, from the 1st June 2022 to 30th September 2022, included 3,375 individuals that tested positive for the monkeypox virus. The posterior mean times from infection to hospital admission and length of hospital stay were 14.89 days (95% Credible Intervals (CrI): 13.60, 16.32) and 7.07 days (95% CrI: 6.07, 8.23), respectively. We estimated the modelled Infection Hospitalisation Risk to be 4.13% (95% CrI: 3.04, 5.02), compared to the overall sample Case Hospitalisation Risk (CHR) of 5.10% (95% CrI: 4.38, 5.86). The overall sample CHR was estimated to be 17.86% (95% CrI: 6.06, 33.11) for females and 4.99% (95% CrI: 4.27, 5.75) for males. A notable difference was observed between the CHRs that were estimated for each sex, which may be indicative of increased infection severity in females or a considerably lower infection ascertainment rate. It was estimated that 74.65% (95% CrI: 55.78, 86.85) of infections with the monkeypox virus in the UK were captured over the outbreak.Mpox cases without known travel links to endemic countries began to be detected in the UK in mid-2022. In this study, the authors characterise the severity of mpox cases in the UK and estimate the overall infection hospitalisation risk at ~4%. [ABSTRACT FROM AUTHOR]

Although ImmuneCells.Sig was not developed in the validation datasets, the reported predictive error on each validation dataset in the original paper is actually the training error from classification models trained on the same dataset. Based on this finding, they further identified a gene expression signature named ImmuneCells.Sig from the scRNA-seq datasets and a bulk gene expression dataset GSE78220[2] for the purpose of predicting response to immunotherapy. Comparisons with AUC values of 12 previously reported ICT response signatures show that ImmuneCells.Sig predicts the ICT outcomes of melanoma patients more accurately across the above four gene expression datasets of melanoma. Testing with random gene sets shows the effect of overfitting The ImmuneCells.Sig signature was discovered from the scRNA-seq datasets[9] and a bulk gene expression dataset GSE78220[2]. [Extracted from the article]

Locomotor activities can enhance learning, but the underlying circuit and synaptic mechanisms are largely unknown. Here we show that locomotion facilitates aversive olfactory learning in C. elegans by activating mechanoreceptors in motor neurons, and transmitting the proprioceptive information thus generated to locomotion interneurons through antidromic-rectifying gap junctions. The proprioceptive information serves to regulate experience-dependent activities and functional coupling of interneurons that process olfactory sensory information to produce the learning behavior. Genetic destruction of either the mechanoreceptors in motor neurons, the rectifying gap junctions between the motor neurons and locomotion interneurons, or specific inhibitory synapses among the interneurons impairs the aversive olfactory learning. We have thus uncovered an unexpected role of proprioception in a specific learning behavior as well as the circuit, synaptic, and gene bases for this function. Locomotion affects learning with unknown neural mechanisms. Here, authors show that locomotion enhances olfactory learning in C. elegans by transmitting proprioceptive information from mechanosensitive motor neurons to higher-order interneurons. [ABSTRACT FROM AUTHOR]

One well established HIF functional target from mammalian studies is pyruvate dehydrogenase kinase (PDK1), as upregulation of PDK1 by HIF reduces flux through the tricarboxylic acid cycle (TCA) and promotes anaerobic ATP generation by glycolysis[3]. B replying to b R. Comas-Ghierra et al. I Nature Communications i https://doi.org/10.1038/s41467-023-39510-5 (2023) In a recent paper, we showed that the hypoxia response (HR) pathway promotes gluconeogenesis in I Caenorhabditis elegans i through upregulation of PCK-1, a phosphoenolpyruvate carboxykinase (PEPCK)[1]. In summary, we agree with Comas-Ghierra et al. that malate dismutation is an additional important adaptation to hypoxia, but we want to highlight that the specific environmental and developmental context of HIF activation dictates the specific hypoxia response. Malate dehydrogenase, which is also upregulated by HIF-1, converts oxaloacetate into malate, which can enter the mitochondria to be converted to fumarate in the first step of malate dismutation. [Extracted from the article]

Despite persistent efforts in understanding the creativity of scientists over different career stages, little is known about the underlying dynamics of research topic switching that drives innovation. Here, we analyze the publication records of individual scientists, aiming to quantify their topic switching dynamics and its influence. We find that the co-citing network of papers of a scientist exhibits a clear community structure where each major community represents a research topic. Our analysis suggests that scientists have a narrow distribution of number of topics. However, researchers nowadays switch more frequently between topics than those in the early days. We also find that high switching probability in early career is associated with low overall productivity, yet with high overall productivity in latter career. Interestingly, the average citation per paper, however, is in all career stages negatively correlated with the switching probability. We propose a model that can explain the main observed features. How does a scientist's tendency to explore a variety of topics affect their career? Here, the authors analyze scientific publication data to understand how often scientists switch topics, how topic switching has changed over time, and how it relates to research productivity. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21448-1 [ABSTRACT FROM AUTHOR]

This paper explores the behaviour of the ubiquitous twisted pairs at high frequencies and wideband excitation of twisted pairs up to 12 GHz. Higher carrier frequencies on twisted pairs can enable the data rates required by the future communication networks; hence, the existing copper infrastructure can be utilised on the last mile complementing the fibre networks. In this paper, we show a fundamental limit on the operating frequency of twisted pairs beyond which twisted pairs start to radiate and behave like an antenna. To validate our theoretical derivations through measurements, we designed a microstrip balun to excite the differential mode on the twisted pairs. At the end, we demonstrate that the standard twisted pairs used in the UK can be used up to 5 GHz carrier frequency without any radiation effect and this upper-bound can be moved to higher frequencies by decreasing the twist lengths. This paper establishes a physical limit on the ability of the ubiquitous twisted pairs to support high speed internet. This limit due to radiation from the wire is theoretically explained as well as numerically simulated and experimentally measured. [ABSTRACT FROM AUTHOR]

Chemical bonds, including covalent and ionic bonds, endow semiconductors with stable electronic configurations but also impose constraints on their synthesis and lattice-mismatched heteroepitaxy. Here, the unique multi-scale van der Waals (vdWs) interactions are explored in one-dimensional tellurium (Te) systems to overcome these restrictions, enabled by the vdWs bonds between Te atomic chains and the spontaneous misfit relaxation at quasi-vdWs interfaces. Wafer-scale Te vdWs nanomeshes composed of self-welding Te nanowires are laterally vapor grown on arbitrary surfaces at a low temperature of 100 °C, bringing greater integration freedoms for enhanced device functionality and broad applicability. The prepared Te vdWs nanomeshes can be patterned at the microscale and exhibit high field-effect hole mobility of 145 cm2/Vs, ultrafast photoresponse below 3 μs in paper-based infrared photodetectors, as well as controllable electronic structure in mixed-dimensional heterojunctions. All these device metrics of Te vdWs nanomesh electronics are promising to meet emerging technological demands. The limited scalability of 1D semiconductors has restricted their large-area optoelectronic applications so far. Here, the authors report the low-temperature synthesis of wafer-scale van der Waals nanomeshes composed of self-welding Te nanowires on various substrates, showing improved transport and photoelectric properties. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Quantum interference between reaction pathways around a conical intersection (CI) is an ultrasensitive probe of detailed chemical reaction dynamics. Yet, for the hydrogen exchange reaction, the difference between contributions of the two reaction pathways increases substantially as the energy decreases, making the experimental observation of interference features at low energy exceedingly challenging. We report in this paper a combined experimental and theoretical study on the H + HD → H2 + D reaction at the collision energy of 1.72 eV. Although the roaming insertion pathway constitutes only a small fraction (0.088%) of the overall contribution, angular oscillatory patterns arising from the interference of reaction pathways were clearly observed in the backward scattering direction, providing direct evidence of the geometric phase effect at an energy of 0.81 eV below the CI. Furthermore, theoretical analysis reveals that the backward interference patterns are mainly contributed by two distinct groups of partial waves (J ~ 10 and J ~ 19). The well-separated partial waves and the geometric phase collectively influence the quantum reaction dynamics. In a combined experimental and theoretical study of the H + HD → H2 + D reaction at low collision energy (1.72 eV), the authors obtain detailed information on the quantum reaction dynamics surrounding a conical intersection. [ABSTRACT FROM AUTHOR]

Tunable generation of vortex beams holds relevance in various fields, including communications and sensing. In this paper, we demonstrate the feasibility of nonlinear spin-orbit interactions in thin films of materials with second-order nonlinear susceptibility. Remarkably, the nonlinear tensor can mix the longitudinal and transverse components of the pump field. We observe experimentally our theoretical predictions in the process of second-harmonic generation from a thin film of aluminum gallium arsenide, a material platform widely spread for its role in the advancement of active, nonlinear, and quantum photonic devices. In particular, we prove that a nonlinear thin film can be used to produce vector vortex beams of second-harmonic light when excited by circularly-polarized Gaussian beams.Spin-orbit interaction, and the associated phenomena, is commonly observed in crystalline structure pumped with circularly polarised beam. Here, the authors showed that this is not the case, and used nonlinear thin film to produce vortex beams of second-harmonic light. [ABSTRACT FROM AUTHOR]

Multimode fiber (MMF) which supports parallel transmission of spatially distributed information is a promising platform for remote imaging and capacity-enhanced optical communication. However, the variability of the scattering MMF channel poses a challenge for achieving long-term accurate transmission over long distances, of which static optical propagation modeling with calibrated transmission matrix or data-driven learning will inevitably degenerate. In this paper, we present a self-supervised dynamic learning approach that achieves long-term, high-fidelity transmission of arbitrary optical fields through unstabilized MMFs. Multiple networks carrying both long- and short-term memory of the propagation model variations are adaptively updated and ensembled to achieve robust image recovery. We demonstrate >99.9% accuracy in the transmission of 1024 spatial degree-of-freedom over 1 km length MMFs lasting over 1000 seconds. The long-term high-fidelity capability enables compressive encoded transfer of high-resolution video with orders of throughput enhancement, offering insights for artificial intelligence promoted diffusive spatial transmission in practical applications. This work introduces a cutting-edge technique to overcome dynamic scattering challenges in long-distance multimode fiber transmission, achieving >99.9% accuracy for 1024 modes over 1 km, hence promises applications in diverse scattering scenarios. [ABSTRACT FROM AUTHOR]

Capturing fine spatial, spectral, and temporal information of the scene is highly desirable in many applications. However, recording data of such high dimensionality requires significant transmission bandwidth. Current computational imaging methods can partially address this challenge but are still limited in reducing input data throughput. In this paper, we report a video-rate hyperspectral imager based on a single-pixel photodetector which can achieve high-throughput hyperspectral video recording at a low bandwidth. We leverage the insight that 4-dimensional (4D) hyperspectral videos are considerably more compressible than 2D grayscale images. We propose a joint spatial-spectral capturing scheme encoding the scene into highly compressed measurements and obtaining temporal correlation at the same time. Furthermore, we propose a reconstruction method relying on a signal sparsity model in 4D space and a deep learning reconstruction approach greatly accelerating reconstruction. We demonstrate reconstruction of 128 × 128 hyperspectral images with 64 spectral bands at more than 4 frames per second offering a 900× data throughput compared to conventional imaging, which we believe is a first-of-its kind of a single-pixel-based hyperspectral imager. The authors showcase a video-rate hyperspectral imager based on a single-pixel photodetector that can achieve high-throughput hyperspectral video recording at a low bandwidth. Specifically, they propose a joint spatial-spectral encoding scheme which can encode the scene into highly compressed single-pixel measurements and obtain temporal correlation at the same time. [ABSTRACT FROM AUTHOR]

Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers. Extracting scientific data from published research is a complex task required specialised tools. Here the authors present a scheme based on large language models to automatise the retrieval of information from text in a flexible and accessible manner. [ABSTRACT FROM AUTHOR]

This paper presents our findings on the recursive band gap engineering of chiral fermions in bilayer graphene doubly aligned with hBN. Using two interfering moiré potentials, we generate a supermoiré pattern that renormalizes the electronic bands of the pristine bilayer graphene, resulting in higher order fractal gaps even at very low energies. These Bragg gaps can be mapped using a unique linear combination of periodic areas within the system. To validate our findings, we use electronic transport measurements to identify the position of these gaps as a function of the carrier density. We establish their agreement with the predicted carrier densities and corresponding quantum numbers obtained using the continuum model. Our study provides strong evidence of the quantization of the momentum-space area of quasi-Brillouin zones in a minimally incommensurate lattice. It fills important gaps in the understanding of band structure engineering of Dirac fermions with a doubly periodic superlattice spinor potential.In moiré superlattices, a multitude of higher order Bragg gaps and van Hove singularities emerges as the band structure renormalizes. Here, the authors map these gaps uniquely to the recently predicted topological Bragg indices of the underlying supermoiré lattice. [ABSTRACT FROM AUTHOR]

Accurate estimation of protein–ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity predictions in minutes, making it suitable for practical applications in hit identification or lead optimization. The paper presents the universal QM-based scoring function that accurately and rapidly predicts protein-ligand binding affinities, outperforming current computational tools. This is demonstrated on the PL-REX experimental benchmark dataset. [ABSTRACT FROM AUTHOR]

Domain boundaries have been intensively investigated in bulk ferroelectric materials and two-dimensional materials. Many methods such as electrical, mechanical and optical approaches have been utilized to probe and manipulate domain boundaries. So far most research focuses on the initial and final states of domain boundaries before and after manipulation, while the microscopic understanding of the evolution of domain boundaries remains elusive. In this paper, we report controllable manipulation of the domain boundaries in two-dimensional ferroelectric In2Se3 with atomic precision using scanning tunneling microscopy. We show that the movements of the domain boundaries can be driven by the electric field from a scanning tunneling microscope tip and proceed by the collective shifting of atoms at the domain boundaries. Our density functional theory calculations reveal the energy path and evolution of the domain boundary movement. The results provide deep insight into domain boundaries in two-dimensional ferroelectric materials and will inspire inventive applications of these materials. Here, the authors realize controllable manipulation of polar domain boundaries in a two-dimensional ferroelectric material In2Se3. It reveals the origin of distinct behaviors for different domain boundaries in combination with density functional theory calculations. [ABSTRACT FROM AUTHOR]