Triggered by advances in atomic-layer exfoliation and growth techniques, along with the identification of a wide range of extraordinary physical properties in self-standing films consisting of one or a few atomic layers, two-dimensional (2D) materials such as graphene, transition metal dichalcogenides (TMDs), and other van der Waals (vdW) crystals now constitute a broad research field expanding in multiple directions through the combination of layer stacking and twisting, nanofabrication, surface-science methods, and integration into nanostructured environments. Photonics encompasses a multidisciplinary subset of those directions, where 2D materials contribute remarkable nonlinearities, long-lived and ultraconfined polaritons, strong excitons, topological and chiral effects, susceptibility to external stimuli, accessibility, robustness, and a completely new range of photonic materials based on layer stacking, gating, and the formation of moiré patterns. These properties are being leveraged to develop applications in electro-optical modulation, light emission and detection, imaging and metasurfaces, integrated optics, sensing, and quantum physics across a broad spectral range extending from the far-infrared to the ultraviolet, as well as enabling hybridization with spin and momentum textures of electronic band structures and magnetic degrees of freedom. The rapid expansion of photonics with 2D materials as a dynamic research arena is yielding breakthroughs, which this Roadmap summarizes while identifying challenges and opportunities for future goals and how to meet them through a wide collection of topical sections prepared by leading practitioners.

Introduction Ovarian serous cystadenocarcinoma (SCA), a deadly gynecologic cancer, often goes undetected until the late stages. Tissue proteomics unveils disease heterogeneity, enhancing tumor classification and enabling personalized treatments tailored to individual expression profiles. Material and methods Tissue samples from 46 serous ovarian tumors were quantified using label-free liquid chromatography-tandem mass spectrometry. We identified 80 proteins differentiating SCA from borderline tumors, 277 distinguishing SCA from benign tumors, and 195 between borderline and benign tumors. Ingenuity pathway analysis revealed increased cell proliferation and RNA processing in SCA and borderline tumors compared to benign tumors, with SCA showing greater oxidative phosphorylation than borderline tumors. Results Our comparative analysis indicates that upregulated (ASS1 – argininosuccinate synthase 1, CAPS, PPA1, BCAT1, MCM4) and downregulated proteins (MUC5B, SLC4A1, tenascin-XB – TNXB, carbonic anhydrase 1, hemoglobin β) may offer a robust panel for distinguishing SCA from benign and borderline ovarian tumors, potentially aiding in early diagnosis and disease monitoring. The cancer-associated proteins pyridoxal dependent decarboxylase domain containing 1 (AUC: 0.83, 95% CI: 0.66–1), GFPT1 (AUC: 0.84, CI: 0.70–0.89), and HYOU1 (AUC: 0.84, CI: 0.70–0.98) significantly differentiated between low-grade (LGSCA) and high-grade serous cystadenocarcinoma (HGSCA). Low-grade SCA showed significantly greater levels of MZB1 (log2 fold change (FC): –1.951, p-value: 0.0258), CRABP2 (FC: –2.34, p-value: 0.0016), and BCAM (FC: –1.945, p-value: 0.0197) than borderline cancers. Conclusions Argininosuccinate synthase 1 and TNXB showed potential as markers of disease progression. Elevated ASS1 was observed in borderline, LGSCA, and HGSCA tumors compared to benign tumors, while TNXB levels progressively declined from benign to borderline, LGSCA, and HGSCA tumors. Our study pinpoints critical biomarkers in serous ovarian tumors for HGSCA progression.

For the detection of somatic structural variation (SV) in cancer genomes, long-read sequencing is advantageous over short-read sequencing with respect to mappability and variant phasing. However, most current long-read SV detection methods are not developed for the analysis of tumor genomes characterized by complex rearrangements and heterogeneity. Here, we present Severus, a breakpoint graph-based algorithm for somatic SV calling from long-read cancer sequencing. Severus works with matching normal samples, supports unbalanced cancer karyotypes, can characterize complex multibreak SV patterns and produces haplotype-specific calls. On a comprehensive multitechnology cell line panel, Severus consistently outperforms other long-read and short-read methods in terms of SV detection F1 score (harmonic mean of the precision and recall). We also illustrate that compared to long-read methods, short-read sequencing systematically misses certain classes of somatic SVs, such as insertions or clustered rearrangements. We apply Severus to several clinical cases of pediatric leukemia/lymphoma, revealing clinically relevant cryptic rearrangements missed by standard genomic panels. Complex structural variations and rearrangements in cancer are identified using long-read sequencing.

Spatial transcriptomics has revolutionized our understanding of tissue organization by simultaneously capturing gene expression and spatial localization within intact tissues. However, analyzing these increasingly complex datasets requires specialized expertise across computational biology, statistics, and biological context. To address this challenge, we introduce the Spatial Transcriptomics AI Agent (STAgent), an autonomous multimodal agentic AI that integrates multimodal large language models (LLMs) with specialized computational tools to transform weeks-long analysis tasks into minutes of automated processing. Unlike conventional machine learning approaches that are limited to narrow, predefined tasks, STAgent leverages the emergent capabilities of multimodal LLMs – such as flexible reasoning, contextual understanding, and cross-modal integration – which allow it to adapt to novel data, execute multi-step analyses, and generate biologically meaningful insights with minimal human input. STAgent enables autonomous deep research through integrated capabilities, including dynamic code generation for complex analytical workflows, visual reasoning for interpreting spatial patterns, real-time retrieval of relevant peer-reviewd scientific literature, and synthesis of comprehensive, actionable reports. We applied STAgent to investigate the in vivo maturation of human stem cell-derived pancreatic cells (SC-pancreas) transplanted into immunodeficient mice. We generated single-cell spatial transcriptomics data spanning multiple developmental timepoints. STAgent autonomously (1) identified the maturation of initially scattered endocrine cells into well-defined islet-like structures, with predominantly peripheral α-cells surrounding β-cell cores supported by an expanding mesenchymal network; (2) revealed strengthening endocrine-endocrine cell interactions over time and, through context-aware gene set analysis, uncovered spatially resolved biological processes driving maturation; (3) unlike traditional analytical approaches, STAgent offers mechanistic explanations of spatial patterns, contextualizing findings with relevant literatures and developing cohesive insights into human pancreatic development. This agentic approach establishes a new paradigm in spatial transcriptomics analysis by substantially lowering the expertise barrier and reducing analysis time, accelerating biological and biomedical discovery.

The accumulation of electrochemically produced bubbles is inevitable in gas-evolving reactions and can induce potential losses by theoretically increasing activation, concentration, and ohmic overpotentials. These effects are often either overstated or completely neglected in the literature, which complicates the accurate analysis of experimental results for gas evolution reactions. This study systematically identifies and quantifies the overpotential losses induced by bubbles by combining experimental results for hydrogen (HER) and oxygen evolution reactions (OER), obtained using the rotating disk electrode (RDE) technique, with simulations based on a two-dimensional transmission line model. Our results show that ohmic overpotential is the primary cause of apparent activity loss due to bubbles in RDE. This effect leads to catalyst activity misestimates exceeding 2 orders of magnitude, and Tafel slope errors of 100% at higher currents if left uncorrected. By identifying these effects, this work provides a robust framework for mitigating inaccuracies and improving the characterization of electrocatalysts for gas evolution reactions.

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024 (Second Terrestrial Very-Long-Baseline Atom Interferometry Workshop, Imperial College, April 2024), building on the initial discussions during the inaugural workshop held at CERN in March 2023 (First Terrestrial Very-Long-Baseline Atom Interferometry Workshop, CERN, March 2023). Like the summary of the first workshop (Abend et al. in AVS Quantum Sci. 6:024701, 2024), this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions (Memorandum of Understanding for the Terrestrial Very Long Baseline Atom Interferometer Study).

This article is concerned with qualitative and quantitative refinements of the concepts of the log-convexity and log-concavity of positive sequences. A new class of tempered sequences is introduced, its basic properties are established and several interesting examples are provided. The new class extends the class of log-balanced sequences by including the sequences of similar growth rates, but of the opposite log-behavior. Special attention is paid to the sequences defined by two- and three-term linear recurrences with constant coefficients. For the special cases of generalized Fibonacci and Lucas sequences, we graphically illustrate the domains of their log-convexity and log-concavity. For an application, we establish the concyclicity of the points a2na2n+1,1a2n+1 for some classes of Horadam sequences (an) with positive terms.

Climate change is predicted to drive geographical range shifts that will result in changes in species diversity and functional composition and have potential repercussions for ecosystem functioning. However, the effect of these changes on species composition and functional diversity (FD) remains unclear, especially for mammals, specifically bats. We used species distribution models and a comprehensive ecological and morphometrical trait database to estimate how projected future climate and land‐use changes could influence the distribution, composition, and FD of the European bat community. Future bat assemblages were predicted to undergo substantial shifts in geographic range and trait structure. Range suitability decreased substantially in southern Europe and increased in northern latitudes. Our findings highlight the potential for climate change to drive shifts in bat FD, which has implications for ecosystem function and resilience at a continental scale. It is important to incorporate FD in conservation strategies. These efforts should target species with key functional traits predicted to be lost and areas expected to experience losses in FD. Conservation strategies should include habitat and roost protection, enhancing landscape connectivity, and international monitoring to preserve bat populations and their ecosystem services.

Background: Undernutrition disorder is a prevalent comorbidity (up to 25%) in type 2 diabetes (T2D) patients which significantly compromises their health. We aimed to assess the association between single nucleotide polymorphysms (SNPs) adiponectin (ADIPOQ) +276 (G/T) and resistin (RETN) −420 (C/G) with the risk of developing T2D and undernutrition in patients with T2D. Methods: The research was conducted as prospective case-control study among 106 patients with T2D and 106 healthy control individuals in the territory of the Bosnia and Herzegovina from Sep 1st 2022 to May 1st 2023. For assessing the nutritional status, the mini nutritional assessment (MNA) was used. DNA analysis was carried out by restriction fragment length polymorphism-polymerase chain reaction (RFLP-PCR) method. The data were analyzed using chi-square test, t-test for independent samples and binary multivariate logistic regression. Results: The research included 212 subjects of which 124 (58.5%) were male. The mean age of the subjects was 68.48±4,67 yr. Almost 20% of subjects were undernourished, significantly more T2D patients when compared to controls (33% vs. 6.6%; P<0.001). ADIPOQ +276 GT genotype was identified as significant predictor of T2D (OR: 3.454; 95% CI: 1.400–8.521; P=0.007) and undernutrition disorder (OR: 3.453; 95% CI: 1.331–8.961; P=0.011) in T2D population, while the presence of RETN −420 CG genotype had protective effect against occurrence of T2D (OR: 0.353; 95% CI: 0.144–0.867; P=0.023). However, RETN genotypes were not associated with undernutrition disorder. Conclusion: ADIPOQ +276 gene polymorphism represent a significant predictor for development of T2D and undernutrition disorder in T2D population, while RETN −420 gene polymorphism was identified as a significant factor associated with a reduced risk for T2D, but was not associated with undernutrition.

Human cathelicidin LL‐37 derivative, the 24‐mer SAAP‐148, is highly effective in vitro in eradicating multidrug‐resistant bacteria without inducing resistance. SAAP‐148 has a high cationic charge (+11) and 46% hydrophobicity, which, once the peptide folds into an alpha helix, forms a wide hydrophobic face. This highly amphipathic nature facilitates on the one hand its insertion into the membrane's fatty acyl chain region and on the other hand it´s interaction with anionic membrane components, which aids in killing bacteria. However, the contributions of the secondary and quaternary structures have not been thoroughly investigated so far. To address this, we applied circular dichroism, NMR spectroscopy, X‐ray scattering, AlphaFold 3 protein folding software, and molecular dynamics simulations. Our results reveal that SAAP‐148 adopts a stable hexameric bundle composed of three parallel dimers, that together form a hydrophobic core of aromatic side chain residues. The hexameric structure is retained at the membrane interface, whereby, MD simulation studies indicated the formation of a fiber‐like structure in the presence of anionic membranes. This certainly seems plausible, as oligomers are stabilized by aromatic residues, and the exposure of positively charged side chains on the surface likely facilitates the transition of the peptide into fibrils on anionic membranes.