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This study demonstrates GHz-speed electro-optic modulation using ferroelectric nematic liquid crystals, offering dual-phase shifting, poling-free operation, low loss, and CMOS compatibility-enabling scalable hybrid silicon-organic photonics.

Imaging through complex media is challenging because scattering results in image blurring. By introducing a fingerprint operator and applying it to the measured reflection matrix, information on a target within a complex medium becomes accessible.

Carbon nanotube-based single photon emitters allow for room-temperature operation, but suffer from vanishing indistinguishability due to strong dephasing. Following a theoretical proposal, the authors tackle the problem experimentally by using a cavity to enhance the photon coherence time and the emission spectral density in the regime of incoherent good cavity-coupling.

Observing quantum effects in a mechanical oscillator requires it to be close to a pure quantum state, rather than a thermal mixture. Here a librational mode of a levitated nanoparticle is cooled close to its ground state without using cryogenics.

The authors showcase a photonic tensor core in TFLN platform that achieves a computational speed of 120 GOPS for neural networks, with capabilities of in-situ training that support exciting prospects of negative number multiplication. The tensor core can efficiently process 112  × 112-pixel images, potentially scaling up AI tasks and offering nanosecond latency without needing a digital processor.

Photonic-based instantaneous frequency measurement systems are usually bulky or require high optical power. Here, Burla et al. demonstrate an integrated measurement system that can identify frequency-varying signals in a dynamic fashion, without the need for fast measurement instrumentation.

An interpretability framework that compares how humans and deep neural networks process images has been presented. Their findings reveal that, unlike humans, deep neural networks focus more on visual properties than semantic ones, highlighting divergent representational strategies.

1000 Genomes imputation can increase the power of genome-wide association studies to detect genetic variants associated with human traits and diseases. Here, the authors develop a method to integrate and analyse low-coverage sequence data and SNP array data, and show that it improves imputation performance.

Biomonitoring of sweat from fingertips overcomes current limitations in time-resolved metabolomic profiling of humans and may prove to become a powerful, noninvasive tool for precision medicine. Here, in a feasibility study of short interval sampling of sweat from fingertips, the authors assay individual dynamic metabolic patterns of endogenous and exogenous molecules.

The authors numerically investigate the reservoir computing performance of vertical emitting two-mode semiconductor lasers and show the crucial impact of dynamic coupling, injection schemes and system timescales. A central finding is that high dimensional internal dynamics can only be utilized if an appropriate perturbation via the input is chosen.

The low carrier mobilities of TMDCs pose a challenge for applications in high-speed photodetection. Integrating vertical two-dimensional heterostructures with photonic waveguides allows the intrinsic speed limitations to be overcome and record-high photodetection bandwidths to be achieved.

The ultrafast optical dynamics of plasmon-quantum emitter systems have rarely been explored. Here, the authors study those dynamics by interferometrically probing nonlinear optical emission from porous gold nanosponges with zinc oxide emitters, allowing sum-frequency generation enhancement from individual hot spots.

Building on recent explainable AI techniques, this Article highlights the pervasiveness of Clever Hans effects in unsupervised learning and the substantial risks associated with these effects in terms of the prediction accuracy on new data.

Single-cell and spatial transcriptomic analysis of eight human heart tissues reveals the cellular profiles and tissue architecture of niches including the cardiac conduction system, and a new tool, drug2cell, identifies drug target expression.

Trans-ancestry meta-analysis of estimated glomerular filtration rate (eGFR) from 1,046,070 individuals identifies 264 associated loci, providing a resource of molecular targets for translational research of chronic kidney disease.

Authors present a centimeter-scale miniaturized Raman spectrometer using cheap nonstabilized laser diodes, densely packed optics, and non-cooled small sensors while the performance is comparable with expensive bulky research-grade Raman systems.

A vascular cell atlas integrating single-cell data of 19 organs and tissues from 62 donors identifies angiotypic and organotypic characteristics of endothelial and mural cells.

Light pulses with controllable parameters are desired for studying the fundamental properties of matter. Here the authors generate and use phase-manipulated and highly time-stable XUV pulse pairs to probe the coherent evolution and dephasing of XUV electronic coherences in helium and argon.

Understanding how people perceive and interpret uncertainty from large language models (LLMs) is crucial, as users often overestimate LLM accuracy, especially with default explanations. Steyvers et al. show that aligning LLM explanations with their internal confidence improves user perception.

The hippocampus in mammalian brain varies in size across individuals. Here, Hibar and colleagues perform a genome-wide association meta-analysis to find six genetic loci with significant association to hippocampus volume.

Single-cell RNA sequencing efforts have made large amounts of data available for transcriptomics research. Simon and colleagues develop a neural network embedding approach that avoids batch effects, such that it can rapidly and efficiently integrate large datasets from different studies.

Roger Milne and colleagues conduct a genome-wide association study for estrogen receptor (ER)-negative breast cancer combined with BRCA1 mutation carriers in a large cohort. They identify ten new risk variants and find high genetic correlation between breast cancer risk for BRCA1 mutation carriers and risk of ER-negative breast cancer in the general population.

Electro-optic modulators based on epitaxial barium titanate (BTO) integrated on silicon exhibit speeds up to 50 Gbit s^–1 while the Pockels coefficient of the BTO film is found to be approaching the bulk value.

In a GWAS study of 32,438 adults, the authors discovered five novel loci for intracranial volume and confirmed two known signals. Variants for intracranial volume were also related to childhood and adult cognitive function and to Parkinson's disease, and enriched near genes involved in growth pathways, including PI3K-AKT signaling.

Deep neural networks are a powerful tool for predicting protein function, but identifying the specific parts of a protein sequence that are relevant to its functions remains a challenge. An occlusion-based sensitivity technique helps interpret these deep neural networks, and can guide protein engineering by locating functionally relevant protein positions.

Heart failure is a complex syndrome that is associated with many different underlying risk factors. Here, to increase power, the authors jointly analyse cases of heart failure of different aetiologies in a genome-wide association study and identify 11 loci of which ten had not been previously reported.

Combining high-speed AFM, single molecule recognition force spectroscopy, and molecular dynamics simulations Zhu, Canena, Sikora et al. characterize the interaction dynamics of the trimeric spike protein of SARS-CoV-2 wt, and delta and omicron variants with its entry receptor ACE2. While delta variant increases avidity by multivalent binding to ACE2, omicron variant shows an extended binding lifetime.

The use of deep neural networks for the automated analysis of behavioural videos has emerged as a tool in neuroscience, medicine and psychology. Marks and colleagues present a pipeline capable of tracking and identifying animals, as well as classifying individual and interacting animal behaviour in video recordings and even in complex environments.

3D optical coherence microscopy of vascular networks enables quantitative analysis of flow dynamics and vessel connectivity at capillary resolution.

Christian Fuchsberger, Gonçalo Abecasis and colleagues describe a new web-based imputation service that enables rapid imputation of large numbers of samples and allows convenient access to large reference panels of sequenced individuals. Their state space reduction provides a computationally efficient solution for genotype imputation with no loss in imputation accuracy.

An excited two-level system emits a single photon, but in special circumstances it can emit two. The reason for this unexpected two-photon emission lies with modified Rabi oscillations.

A novel quantitative approach to identify intact glycopeptides from comparative proteomic data sets, allowing inference of complex glycan structures and direct mapping of them to sites within the associated proteins at the proteome scale.

Microrobots are usually too small to contain traditional computing substrates that could control their behaviour. Dekanovsky and colleagues have developed a microrobot swarm that removes hormonal pollutants when it senses a chemical signal in its environment.

The wealth of data generated by single-cell RNA sequencing can be used to identify gene sets across cells, as well as to identify specific cells. Lukassen and colleagues propose a method combining matrix factorization and variational auto encoders that can capture both cross-cell and cell-specific information.

With the aid of deep learning, the space of chemical molecules, such as candidates for drugs, can be constrained to find new bioactive molecules. A new open source tool can generate libraries of novel molecules with user defined properties.

Determining progress in adaptation to climate change is challenging, yet critical as climate change impacts increase. A stocktake of the scientific literature on implemented adaptation now shows that adaptation is mostly fragmented and incremental, with evidence lacking for its impact on reducing risk.

A single-center, observational cohort study assessing the mitochondrial bioenergetics of left ventricular tissue from patients undergoing coronary artery bypass grafting surgery suggests that viable myocardium has global alterations in bioenergetics and transcriptome without large regional differences between areas with or without inducible ischemia.

In order to complete the transition to the era of large-scale integration, silicon photonics will have to overcome several challenges. Here, the authors outline what these challenges are and what it will take to tackle them.

Magnons are oscillating magnetic waves that can be engineered using nanoscale structures and are expected to have useful applications in future information processing and storage devices. Here, the authors investigate magnetic superradiance as a possible mechanism limiting the lifetime of the magnonic excitations in thin films.