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Background Oesophageal cancer is one of the most deadly forms of cancer worldwide. Long non-coding RNAs (lncRNAs) are often found to have important regulatory roles. Objective To assess the lncRNA expression profile of oesophageal squamous cell carcinoma (OSCC) and identify prognosis-related lncRNAs. Method LncRNA expression profiles were studied by microarray in paired tumour and normal tissues from 119 patients with OSCC and validated by qRT-PCR. The 119 patients were divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random Forest supervised classification algorithm and a nearest shrunken centroid algorithm, then validated in a test group and further, in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by multivariable Cox regression analysis. Results LncRNAs showed significantly altered expression in OSCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885.1, XLOC_013014 and ENST00000547963.1) which classified the patients into two groups with significantly different overall survival (median survival 19.2 months vs >60 months, p<0.0001). The signature was applied to the test group (median survival 21.5 months vs >60 months, p=0.0030) and independent cohort (median survival 25.8 months vs >48 months, p=0.0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for patients with OSCC. Stratified analysis suggested that the signature was prognostic within clinical stages. Conclusions Our results suggest that the three-lncRNA signature is a new biomarker for the prognosis of patients with OSCC, enabling more accurate prediction of survival.

The formation of biomolecular condensates mediated by a coupling of associative and segregative phase transitions plays a critical role in controlling diverse cellular functions in nature. This has inspired the use of phase transitions to design synthetic systems. While design rules of phase transitions have been established for many synthetic intrinsically disordered proteins, most efforts have focused on investigating their phase behaviors in a test tube. Here, we present a rational engineering approach to program the formation and physical properties of synthetic condensates to achieve intended cellular functions. We demonstrate this approach through targeted plasmid sequestration and transcription regulation in bacteria and modulation of a protein circuit in mammalian cells. Our approach lays the foundation for engineering designer condensates for synthetic biology applications. Dai et al. present a streamlined approach for the design and engineering of synthetic biomolecular condensates for controlling different cellular processes, such as gene flow, transcriptional regulation and modulation of protein circuits.

Neuromorphic electronics emulate the computational principles of biological neural systems, offering low-power, adaptive, and parallel signal processing capabilities for next-generation intelligent systems. When integrated with stretchable platforms, neuromorphic devices gain the mechanical compliance necessary to interface seamlessly with soft, dynamic biological environments, enabling applications in wearable computing, bioelectronic skins, and implantable artificial intelligence. This review provides a comprehensive overview of recent progress in stretchable neuromorphic electronics, covering device architectures, material design strategies, underlying neuromorphic mechanisms, and novel applications. We also discuss key challenges and outline future research directions toward advancing the performance, integration, and translational potential of stretchable neuromorphic systems. Ultimately, we aim to provide a foundational resource to guide the co-design of materials, devices, and systems toward autonomous, skin-conformal neuromorphic intelligence. A systematic review of stretchable neuromorphic electronics from materials, mechanisms, and performance to applications. Summary of stretchable strategies for neuromorphic electronics. Summary of unique stretchable neuromorphic applications and implementations for future human-integrated intelligence.

Thermal nanophotonics enables fundamental breakthroughs across technological applications from energy technology to information processing 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 – 11 . From thermal emitters to thermophotovoltaics and thermal camouflage, precise spectral engineering has been bottlenecked by trial-and-error approaches. Concurrently, machine learning has demonstrated its powerful capabilities in the design of nanophotonic and meta-materials 12 , 13 , 14 , 15 , 16 , 17 – 18 . However, it remains a considerable challenge to develop a general design methodology for tailoring high-performance nanophotonic emitters with ultrabroadband control and precise band selectivity, as they are constrained by predefined geometries and materials, local optimization traps and traditional algorithms. Here we propose an unconventional machine learning-based paradigm that can design a multitude of ultrabroadband and band-selective thermal meta-emitters by realizing multiparameter optimization with sparse data that encompasses three-dimensional structural complexity and material diversity. Our framework enables dual design capabilities: (1) it automates the inverse design of a vast number of possible metastructure and material combinations for spectral tailoring; (2) it has an unprecedented ability to design various three-dimensional meta-emitters by applying a three-plane modelling method that transcends the limitations of traditional, flat, two-dimensional structures. We present seven proof-of-concept meta-emitters that exhibit superior optical and radiative cooling performance surpassing current state-of-the-art designs. We provide a generalizable framework for fabricating three-dimensional nanophotonic materials, which facilitates global optimization through expanded geometric freedom and dimensionality and a comprehensive materials database. An unconventional machine learning-based inverse design framework enables the generation of ultrabroadband and band-selective thermal meta-emitters with complex 3D architectures and diverse material compositions.

The human immunodeficiency virus (HIV) infected approximately 1.1 million individuals in 2024. There is no effective vaccine or cure, and funding cuts in resource-limited settings threaten treatment access. Cost-effective and widely available prevention strategies, such as oral emtricitabine/tenofovir disoproxil fumarate pre-exposure prophylaxis (FTC/TDF-PrEP), are therefore essential. Current PrEP guidelines differ between cisgender women and men who have sex with men (MSM), based on mechanistic differences in tissue-level pharmacokinetics (PK) at vaginal vs. colorectal exposure sites. To test these mechanistic hypothesis, we use data from major FTC/TDF-PrEP trials to establish PrEP efficacy when used in MSM. We independently predict efficacy utilizing different PK-matrices in a mechanistic model, simulate each clinical trial informed by adherence data and compare the predictions with clinical efficacy estimates. With this combined approach, two of the five trials (HPTN 083, DISCOVER) yield sufficient statistical power to conclude that rectal tissue pharmacokinetics do not predict PrEP efficacy in MSM. In contrast, PBMC-based predictions agree with clinical PrEP efficacy and support the suitability of on-demand use of oral PrEP in MSM. When combining our findings with recent results on suitable pharmacokinetic markers in women, our work suggests that adherence requirements for cisgender women and MSM may not differ. Authors examine data from five trials on PrEP and found colorectal tissue drug levels do not predict observed clinical PrEP efficacy in men who have sex with men (MSM). Further, adherence requirements between cisgender women and MSM may not differ.

Rosa rugosa, commonly known as rugged rose, is a perennial ornamental shrub. It produces beautiful flowers with a mild fragrance and colorful seed pods. Unlike many other cultivated roses, R. rugosa adapts to a wide range of habitat types and harsh environmental conditions such as salinity, alkaline, shade, drought, high humidity, and frigid temperatures. Here, we produced and analyzed a high-quality genome sequence for R. rugosa to understand its ecology, floral characteristics and evolution. PacBio HiFi reads were initially used to construct the draft genome of R. rugosa, and then Hi-C sequencing was applied to assemble the contigs into 7 chromosomes. We obtained a 382.6 Mb genome encoding 39,704 protein-coding genes. The genome of R. rugosa appears to be conserved with no additional whole-genome duplication after the gamma whole-genome triplication (WGT), which occurred ~100 million years ago in the ancestor of core eudicots. Based on a comparative analysis of the high-quality genome assembly of R. rugosa and other high-quality Rosaceae genomes, we found a unique large inverted segment in the Chinese rose R. chinensis and a retroposition in strawberry caused by post-WGT events. We also found that floral development- and stress response signaling-related gene modules were retained after the WGT. Two MADS-box genes involved in floral development and the stress-related transcription factors DREB2A-INTERACTING PROTEIN 2 (DRIP2) and PEPTIDE TRANSPORTER 3 (PTR3) were found to be positively selected in evolution, which may have contributed to the unique ability of this plant to adapt to harsh environments. In summary, the high-quality genome sequence of R. rugosa provides a map for genetic studies and molecular breeding of this plant and enables comparative genomic studies of Rosa in the near future.

Biomolecular condensates composed of proteins and RNA are one approach by which cells regulate post-transcriptional gene expression. Their formation typically involves the phase separation of intrinsically disordered proteins with a target mRNA, sequestering the mRNA into a liquid condensate. This sequestration regulates gene expression by modulating translation or facilitating RNA processing. Here we engineer synthetic condensates using a fusion of an RNA-binding protein, the human Pumilio2 homology domain (Pum2), and a synthetic intrinsically disordered protein, an elastin-like polypeptide (ELP), that can bind and sequester a target mRNA transcript. In protocells, sequestration of a target mRNA largely limits its translation. Conversely, in Escherichia coli , sequestration of the same target mRNA increases its translation. We characterize the Pum2–ELP condensate system using microscopy, biophysical and biochemical assays, and RNA sequencing. This approach enables the modulation of cell function via the formation of synthetic biomolecular condensates that regulate the expression of a target protein. Formation of biomolecular condensates composed of proteins and RNA facilitates the regulation of gene expression by modulating translation or facilitating RNA processing. Now, synthetic ribonucleoprotein granules created with engineered intrinsically disordered proteins selectively sequester mRNA and enhance protein translation in cells. These highly liquid-like condensates exchange biomolecules across the cell and facilitate target mRNA and ribosome partitioning.

On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on road traffic monitoring datasets in the Beijing–Tianjin–Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than other machine learning models on most occasions in this study. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. NOx, fine particulate matter (PM2.5), and black carbon (BC) emissions from heavy-duty trucks (HDTs) generally have a higher emission intensity on the highways connecting to regional ports. The model found a general reduction in light-duty passenger vehicles when traffic restrictions were implemented but a much more spatially heterogeneous impact on HDTs, with some road links experiencing up to 40 % increases in the HDT traffic volume. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, thereby providing a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.

Background Understanding social determinants of health (SDoH) factors contributing to suicide incidents is crucial for early intervention and prevention. However, data-driven approaches to this goal face challenges such as long-tailed factor distributions, analyzing pivotal stressors preceding suicide incidents, and limited model explainability. Methods We present a multi-stage large language model framework to enhance SDoH factor extraction from unstructured text. Our approach was compared to other state-of-the-art language models (i.e., pre-trained BioBERT and GPT-3.5-turbo) and reasoning models (i.e., DeepSeek-R1). We also evaluated how the model’s explanations help people annotate SDoH factors more quickly and accurately. The analysis included both automated comparisons and a pilot user study. Results We show that our proposed framework demonstrates performance boosts in the overarching task of extracting SDoH factors and in the finer-grained tasks of retrieving relevant context. Additionally, we show that fine-tuning a smaller, task-specific model achieves comparable or better performance with reduced inference costs. The multi-stage design not only enhances extraction but also provides intermediate explanations, improving model explainability. Conclusions Our approach improves both the accuracy and transparency of extracting suicide-related SDoH from unstructured texts. These advancements have the potential to support early identification of individuals at risk and inform more effective prevention strategies. Plain language summary Social determinants of health (SDoH) are the circumstances in which people are born, grow, live, work, and age that can have an impact on their health and well-being. We aimed to improve how SDoH factors that contribute to suicide incidents are identified. We developed a computational large language model framework that can extract suicide-related SDoH factors from unstructured text. Our approach was evaluated against other advanced language models and found to perform better in extracting SDoH factors and explaining its decisions. By making the SDoH factor extraction process more accurate and transparent, this work can help experts more quickly recognize individuals at risk of suicide and support better prevention strategies. Wang et al. present a multi-stage large language model framework designed to improve the extraction of social determinants of health (SDoH) factors from unstructured text. It results in more accurate, transparent, and efficient annotation as demonstrated through comparative analyses and user studies.