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Consensus case definitions for childhood tuberculosis have been proposed by an international expert panel, aiming to standardize the reporting of cases in research focusing on the diagnosis of intrathoracic tuberculosis in children. These definitions are intended for tuberculosis diagnostic evaluation studies of symptomatic children with clinical suspicion of intrathoracic tuberculosis, and were not intended to predefine inclusion criteria into such studies. Feedback from researchers suggested that further clarification was required and that these case definitions could be further improved. Particular concerns were the perceived complexity and overlap of some case definitions, as well as the potential exclusion of children with acute onset of symptoms or less severe disease. The updated case definitions proposed here incorporate a number of key changes that aim to reduce complexity and improve research performance, while maintaining the original focus on symptomatic children suspected of having intrathoracic tuberculosis. The changes proposed should enhance harmonized classification for intrathoracic tuberculosis disease in children across studies, resulting in greater comparability and the much-needed ability to pool study results.

Quantum neural networks (QNNs) have generated excitement around the possibility of efficiently analyzing quantum data. But this excitement has been tempered by the existence of exponentially vanishing gradients, known as barren plateau landscapes, for many QNN architectures. Recently, quantum convolutional neural networks (QCNNs) have been proposed, involving a sequence of convolutional and pooling layers that reduce the number of qubits while preserving information about relevant data features. In this work, we rigorously analyze the gradient scaling for the parameters in the QCNN architecture. We find that the variance of the gradient vanishes no faster than polynomially, implying that QCNNs do not exhibit barren plateaus. This result provides an analytical guarantee for the trainability of randomly initialized QCNNs, which highlights QCNNs as being trainable under random initialization unlike many other QNN architectures. To derive our results, we introduce a novel graph-based method to analyze expectation values over Haar-distributed unitaries, which will likely be useful in other contexts. Finally, we perform numerical simulations to verify our analytical results.

Perovskite minerals form an essential component of the Earth’s mantle, and synthetic crystals are ubiquitous in electronics, photonics, and energy technology. The extraordinary chemical diversity of these crystals raises the question of how many and which perovskites are yet to be discovered. Here we show that the “no-rattling” principle postulated by Goldschmidt in 1926, describing the geometric conditions under which a perovskite can form, is much more effective than previously thought and allows us to predict perovskites with a fidelity of 80%. By supplementing this principle with inferential statistics and internet data mining we establish that currently known perovskites are only the tip of the iceberg, and we enumerate 90,000 hitherto-unknown compounds awaiting to be studied. Our results suggest that geometric blueprints may enable the systematic screening of millions of compounds and offer untapped opportunities in structure prediction and materials design.

Comparing the brains of related species faces the challenges of establishing homologies whilst accommodating evolutionary specializations. Here we propose a general framework for understanding similarities and differences between the brains of primates. The approach uses white matter blueprints of the whole cortex based on a set of white matter tracts that can be anatomically matched across species. The blueprints provide a common reference space that allows us to navigate between brains of different species, identify homologous cortical areas, or to transform whole cortical maps from one species to the other. Specializations are cast within this framework as deviations between the species’ blueprints. We illustrate how this approach can be used to compare human and macaque brains.

Recent fruitful studies on rechargeable zinc-air battery have led to emergence of various bifunctional oxygen electrocatalysts, especially metal-based materials. However, their electrocatalytic configuration and evolution pathway during battery operation are rarely spotlighted. Herein, to depict the underlying behaviors, a concept named dynamic electrocatalyst is proposed. By selecting a bimetal nitride as representation, a current-driven “shell-bulk” configuration is visualized via time-resolved X-ray and electron spectroscopy analyses. A dynamic picture sketching the generation and maturation of nanoscale oxyhydroxide shell is presented, and periodic valence swings of performance-dominant element are observed. Upon maturation, zinc-air battery experiences a near two-fold enlargement in power density to 234 mW cm −2 , a gradual narrowing of voltage gap to 0.85 V at 30 mA cm −2 , followed by stable cycling for hundreds of hours. The revealed configuration can serve as the basis to construct future blueprints for metal-based electrocatalysts, and push zinc-air battery toward practical application. Interest in rechargeable Zn-air batteries has been renewed in recent years, however, their oxygen electrocatalysts remain not fully understood. Here the authors reveal the presence of a current-driven oxyhydroxide shell in a so-called dynamic eletrocatalyst that enables optimized battery performance.

Lithium-Niobate-On-Insulator (LNOI) is emerging as a promising platform for integrated quantum photonic technologies because of its high second-order nonlinearity and compact waveguide footprint. Importantly, LNOI allows for creating electro-optically reconfigurable circuits, which can be efficiently operated at cryogenic temperature. Their integration with superconducting nanowire single-photon detectors (SNSPDs) paves the way for realizing scalable photonic devices for active manipulation and detection of quantum states of light. Here we demonstrate integration of these two key components in a low loss (0.2 dB/cm) LNOI waveguide network. As an experimental showcase of our technology, we demonstrate the combined operation of an electrically tunable Mach-Zehnder interferometer and two waveguide-integrated SNSPDs at its outputs. We show static reconfigurability of our system with a bias-drift-free operation over a time of 12 hours, as well as high-speed modulation at a frequency up to 1 GHz. Our results provide blueprints for implementing complex quantum photonic devices on the LNOI platform. The combination of superconducting nanowire single photon detectors and electro-optically reconfigurable circuits in a cryogenic environment is notoriously difficult to reach. Here, the authors realise this on a Lithium-Niobate-On-Insulator platform, reaching high speed modulation at a frequency up to 1 GHz.

The increasing number of approved nucleic acid therapeutics demonstrates the potential to treat diseases by targeting their genetic blueprints in vivo. Conventional treatments generally induce therapeutic effects that are transient because they target proteins rather than underlying causes. In contrast, nucleic acid therapeutics can achieve long-lasting or even curative effects via gene inhibition, addition, replacement or editing. Their clinical translation, however, depends on delivery technologies that improve stability, facilitate internalization and increase target affinity. Here, we review four platform technologies that have enabled the clinical translation of nucleic acid therapeutics: antisense oligonucleotides, ligand-modified small interfering RNA conjugates, lipid nanoparticles and adeno-associated virus vectors. For each platform, we discuss the current state-of-the-art clinical approaches, explain the rationale behind its development, highlight technological aspects that facilitated clinical translation and provide an example of a clinically relevant genetic drug. In addition, we discuss how these technologies enable the development of cutting-edge genetic drugs, such as tissue-specific nucleic acid bioconjugates, messenger RNA and gene-editing therapeutics. This Review provides an overview of four platform technologies that are currently used in the clinic for delivery of nucleic acid therapeutics, describing their properties, discussing technical advancements that led to clinical approval, and highlighting examples of approved genetic drugs that make use of these technologies.

Programmable shape-shifting materials can take different physical forms to achieve multifunctionality in a dynamic and controllable manner. Although morphing a shape from 2D to 3D via programmed inhomogeneous local deformations has been demonstrated in various ways, the inverse problem—finding how to program a sheet in order for it to take an arbitrary desired 3D shape—is much harder yet critical to realize specific functions. Here, we address this inverse problem in thin liquid crystal elastomer (LCE) sheets, where the shape is preprogrammed by precise and local control of the molecular orientation of the liquid crystal monomers. We show how blueprints for arbitrary surface geometries can be generated using approximate numerical methods and how local extrinsic curvatures can be generated to assist in properly converting these geometries into shapes. Backed by faithfully alignable and rapidly lockable LCE chemistry, we precisely embed our designs in LCE sheets using advanced top-down microfabrication techniques. We thus successfully produce flat sheets that, upon thermal activation, take an arbitrary desired shape, such as a face. The general design principles presented here for creating an arbitrary 3D shape will allow for exploration of unmet needs in flexible electronics, metamaterials, aerospace and medical devices, and more.

The advancements in generative AI inevitably raise concerns about the associated risks and safety implications, which, in return, catalyzes significant progress in AI safety. However, as this field continues to evolve, a critical question arises: are our current efforts aligned with the long-term goal of human history and civilization? This paper presents a blueprint for an advanced human society and leverages this vision to guide contemporary AI safety efforts. It outlines a future where the Internet of Everything becomes reality, and creates a roadmap of significant technological advancements towards this envisioned future. For each stage of the advancements, this paper forecasts potential AI safety issues that humanity may face. By projecting current efforts against this blueprint, we examine the alignment between the present efforts and the long-term needs. We also identify gaps in current approaches and highlight unique challenges and missions that demand increasing attention from AI safety practitioners in the 2020s, addressing critical areas that must not be overlooked in shaping a responsible and promising future of AI. This vision paper aims to offer a broader perspective on AI safety, emphasizing that our current efforts should not only address immediate concerns but also anticipate potential risks in the expanding AI landscape, thereby promoting a more secure and sustainable future in human civilization.

Background Identifying contextual factors that might support or hinder implementation of evidence-based mental health interventions for youth in low- and middle- income countries may improve implementation success by increasing the alignment of intervention implementation with local needs and resources. This study engaged community partners in Sierra Leone to: (a) investigate barriers and facilitators to implementing a mental health intervention within Sierra Leone’s schools; (b) develop an implementation blueprint to address identified implementation barriers; (c) explore the feasibility of using the implementation blueprint methodology in Sierra Leone. Methods We recruited Ministry of Education Officials ( n  = 2), teachers ( n  = 15) and principals ( n  =  15) in Sierra Leone to participate in needs assessment qualitative interviews. We used a rapid qualitative analysis approach to analyze data. Three team members summarized transcripts based on domains aligned with the structured research questions, organized themes into a matrix, and identified and discussed key themes to arrive at consensus. We then reconvened community partners to discuss implementation strategies that could address identified barriers. Participants ranked barriers according to high/low feasibility and high/low importance and selected implementation strategies for the blueprint. Results Qualitative results revealed several implementation barriers: teacher/parent/student buy-in; teacher motivation; scheduling time; limited funding; waning interest; daily hardships outside of school. Strategies selected included: develop/distribute educational materials; conduct education meetings/outreach; identify and prepare champions; access new funding. Conclusions Engaging community partners to develop an implementation blueprint for integration of a mental health intervention within Sierra Leone’s schools was feasible and may increase implementation effectiveness.