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Received — 10 September 2026 ⏭

Codon-dependent translation of N1-ethylpseudouridine-modified mRNA reduces innate immune activation while preserving vaccine immunogenicity

✇Molecular Therapy Nucleic Acids
By: Jisang Park · GeonHee Lee · Haneul Lee · Ju Kim · Kyeongmi Kim · Eunsook Kim · Jiyoung Lee · Sangwook Lee · Jeehee Youn · Yong-Suk Jang
2 September 2026 at 08:00
Et1Ψ-modified mRNA shows UUU-dependent translational sensitivity, but targeted UUU-to-UUC recoding restores protein expression. With this sequence constraint addressed, Et1Ψ reduces early innate immune activation while preserving vaccine-induced humoral, cellular, and neutralizing responses, thereby establishing codon–modification compatibility as a practical design principle for mRNA therapeutics.
Received — 27 May 2026 ⏭

Spacetime Formation under Requirements: Contextual Realization and Form-Dependent Probability

✇cs.AI, q-bio.NC updates on arXiv.org
By: Song-Ju Kim
26 May 2026 at 12:00
arXiv:2605.23943v1 Announce Type: new Abstract: Quantum cognition often explains order effects, contextuality, and violations of the law of total probability by replacing classical probability with quantum probability on a fixed event structure. This paper proposes a different interpretation: quantum probability is the fixed-spacetime projection of contextual spacetime formation under finite-state requirements. The framework begins not with time, space, objects, or probabilities, but with requirements such as finite representational capacity, single-state semantic stability, context-sensitive intervention, avoidance of explicit context labels, coherent world-formation, and intersubjective transformability. When these requirements cannot be realized within a single global Boolean event structure, the mismatch appears, under fixed-spacetime projection, as noncommutativity, interference, and quantum-like probability. Building on prior single-state approaches to contextuality, we reinterpret classical contextual bookkeeping cost as the fixed-spacetime shadow of contextual spacetime formation. Auxiliary memory or context labels in a classical representation correspond, in this account, to holonomy-like mismatch among locally Boolean logic-worlds. The interference term is the cross term generated when locally classical realization contributions are nontrivially glued and projected back into a fixed classical spacetime form. The result is a transcendental-operational realist account: objecthood, eventhood, probability, and spacetime are treated as forms of realization under requirements, while objectivity is defined by invariants preserved across observer- and history-dependent spacetime formations.

Continual Speaker Identity Unlearning with Minimal Interference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jinju Kim · Yunsung Kang · Gyeong-Moon Park · Jong Hwan Ko
26 May 2026 at 12:00
arXiv:2605.25962v1 Announce Type: cross Abstract: Machine unlearning removes designated concepts or knowledge from pre-trained models. Recent work has extended this paradigm to speaker identity unlearning in zero-shot text-to-speech (ZS-TTS), the task of selectively erasing a model's ability to replicate a speaker's voice. Existing methods, however, quietly assume all unlearning requests arrive at once; an unrealistic assumption, since privacy-motivated removals arrive sequentially over time. We show this assumption breaks state-of-the-art methods: unlearning each new speaker fully revives previously unlearned speakers, reintroducing the very privacy risk unlearning was meant to eliminate. We present Cumulative ORThogonal Identity Suppression (CORTIS), the first framework for continual speaker identity unlearning in ZS-TTS that requires no access to previously-unlearned speaker data. CORTIS combines Fisher-information-based parameter masking, which localizes updates to speaker-relevant weights, with orthogonal projection against subspaces spanned by prior unlearning updates. With VoiceBox, CORTIS unlearns each requested speaker while keeping previously unlearned speakers forgotten across long request sequences, substantially outperforming sequential application of prior methods. The demo is available at https://cumulativeortis.github.io/ .

Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Donghwna Lee · Kirok Kim · Jisu Lee · Kyungha Min · Wooju Kim
26 May 2026 at 12:00
arXiv:2412.07333v2 Announce Type: replace-cross Abstract: Pose-Guided Person Image Synthesis (PGPIS) aims to generate human images in specified poses while preserving the identity and appearance of a source image. This technology facilitates diverse applications, including virtual try-on, digital avatars, animation, and sign language generation. Despite the high-quality results of recent diffusion-based PGPIS, these models typically depend on implicit feature aggregation within the denoising process. As a result, fine-grained texture preservation is limited, and even for the same identity, it is difficult to ensure consistent generation under variations in pose and source appearance. To address these limitations, we propose Fusion Embedding for PGPIS using a Diffusion Model (FPDM), the first framework that explicitly aligns fused source-pose embeddings with target image embeddings via contrastive learning, and subsequently employs the learned fusion embedding as a conditioning signal for generation. FPDM integrates an Image-Pose Fusion (IPF) module into our proposed Source-Enhanced Pose Fusion approach to learn a fusion embedding aligned with the target image. We then employ a conditional diffusion model guided by source appearance, target pose, and the learned fusion embedding. Experiments on the DeepFashion benchmark and the RWTH-PHOENIX-Weather 2014T dataset demonstrate competitive performance compared to existing methods in both quantitative and qualitative evaluations, with ablation studies confirming that explicit fusion embedding alignment substantially improves texture fidelity and consistency across pose and source appearance variations.
Received — 8 April 2026 ⏭
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Contextual Control without Memory Growth in a Context-Switching Task Song-Ju Kim
    arXiv:2604.03479v1 Announce Type: new Abstract: Context-dependent sequential decision making is commonly addressed either by providing context explicitly as an input or by increasing recurrent memory so that contextual information can be represented internally. We study a third alternative: realizing contextual dependence by intervening on a shared recurrent latent state, without enlarging recurrent dimensionality. To this end, we introduce an intervention-based recurrent architecture in which
     
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Contextual Control without Memory Growth in a Context-Switching Task

✇cs.AI, q-bio.NC updates on arXiv.org
By: Song-Ju Kim
7 April 2026 at 12:00
arXiv:2604.03479v1 Announce Type: new Abstract: Context-dependent sequential decision making is commonly addressed either by providing context explicitly as an input or by increasing recurrent memory so that contextual information can be represented internally. We study a third alternative: realizing contextual dependence by intervening on a shared recurrent latent state, without enlarging recurrent dimensionality. To this end, we introduce an intervention-based recurrent architecture in which a recurrent core first constructs a shared pre-intervention latent state, and context then acts through an additive, context-indexed operator. We evaluate this idea on a context-switching sequential decision task under partial observability. We compare three model families: a label-assisted baseline with direct context access, a memory baseline with enlarged recurrent state, and the proposed intervention model, which uses no direct context input to the recurrent core and no memory growth. On the main benchmark, the intervention model performs strongly without additional recurrent dimensions. We also evaluate the models using the conditional mutual information (I(C;O | S)) as a theorem-motivated operational probe of contextual dependence at fixed latent state. For task-relevant phase-1 outcomes, the intervention model exhibits positive conditional contextual information. Together, these results suggest that intervention on a shared recurrent state provides a viable alternative to recurrent memory growth for contextual control in this setting.

Molecular and Phenotypic Characterization of Fluid-Derived Patient-Derived Cell and Organoid Models in Advanced Gastric Cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Ye Jin Moon · Woo Sun Kwon · Chan Hee Park · Jinsoo Jang · Juin Park · Byeong Gyu Yoon · Han Byeol Mun · Namju Kim · Choong-Kun Lee · Hei Cheul Jeung · Su-Jin Shin · Tae Soo Kim · Sun Young Rha
6 April 2026 at 18:00

J Gastric Cancer. 2026 Apr;26(2):260-278. doi: 10.5230/jgc.2026.26.e19.

ABSTRACT

PURPOSE: Patient-derived cells (PDCs) and patient-derived organoids (PDOs) are complementary preclinical models widely used in translational cancer research. However, their molecular and functional differences have not been systematically characterized. This study established and analyzed paired PDC and PDO models derived from the same gastric cancer ascites to delineate platform-dependent molecular and functional profiles.

MATERIALS AND METHODS: Malignant ascites or pleural fluid obtained from 6 patients with advanced gastric cancer were used to establish paired PDC and PDO models. All pairs underwent comprehensive multi-omics profiling, integrating genomic, transcriptomic, and proteomic data. Phenotypic characterization included morphological, histological, proliferative, and cell cycle analyses. Drug sensitivity assays were performed using 4 chemotherapeutic agents commonly used to treat gastric cancer.

RESULTS: The 6 paired PDC and PDO models exhibited distinct morphological characteristics. Whole-genome analyses demonstrated high concordance among primary tumors, PDCs, and PDOs, confirming tumor representation across platforms. Multi-omics profiling identified platform-dependent molecular signatures; PDOs were enriched for extracellular matrix remodeling and stemness, whereas PDCs displayed proliferation- and immune-related signatures. Clinically relevant biomarkers, including HER2 and MET alterations, were concordant with primary tumors. Notably, drug responses differed between platforms and patients, indicating platform-dependent and patient-specific chemosensitivity.

CONCLUSIONS: Paired PDC and PDO models derived from the same patients preserved core patient-specific tumor characteristics while exhibiting distinct molecular and functional profiles. These findings underscore the culture platform as a critical determinant of experimental outcomes and therapeutic responses. Therefore, careful selection of an appropriate preclinical model is essential to accurately address biological questions and optimize precision oncology strategies.

PMID:41942359 | DOI:10.5230/jgc.2026.26.e19

Molecular and Phenotypic Characterization of Fluid-Derived Patient-Derived Cell and Organoid Models in Advanced Gastric Cancer

✇Omics in Gastric
By: Ye Jin Moon · Woo Sun Kwon · Chan Hee Park · Jinsoo Jang · Juin Park · Byeong Gyu Yoon · Han Byeol Mun · Namju Kim · Choong-Kun Lee · Hei Cheul Jeung · Su-Jin Shin · Tae Soo Kim · Sun Young Rha
6 April 2026 at 18:00

J Gastric Cancer. 2026 Apr;26(2):260-278. doi: 10.5230/jgc.2026.26.e19.

ABSTRACT

PURPOSE: Patient-derived cells (PDCs) and patient-derived organoids (PDOs) are complementary preclinical models widely used in translational cancer research. However, their molecular and functional differences have not been systematically characterized. This study established and analyzed paired PDC and PDO models derived from the same gastric cancer ascites to delineate platform-dependent molecular and functional profiles.

MATERIALS AND METHODS: Malignant ascites or pleural fluid obtained from 6 patients with advanced gastric cancer were used to establish paired PDC and PDO models. All pairs underwent comprehensive multi-omics profiling, integrating genomic, transcriptomic, and proteomic data. Phenotypic characterization included morphological, histological, proliferative, and cell cycle analyses. Drug sensitivity assays were performed using 4 chemotherapeutic agents commonly used to treat gastric cancer.

RESULTS: The 6 paired PDC and PDO models exhibited distinct morphological characteristics. Whole-genome analyses demonstrated high concordance among primary tumors, PDCs, and PDOs, confirming tumor representation across platforms. Multi-omics profiling identified platform-dependent molecular signatures; PDOs were enriched for extracellular matrix remodeling and stemness, whereas PDCs displayed proliferation- and immune-related signatures. Clinically relevant biomarkers, including HER2 and MET alterations, were concordant with primary tumors. Notably, drug responses differed between platforms and patients, indicating platform-dependent and patient-specific chemosensitivity.

CONCLUSIONS: Paired PDC and PDO models derived from the same patients preserved core patient-specific tumor characteristics while exhibiting distinct molecular and functional profiles. These findings underscore the culture platform as a critical determinant of experimental outcomes and therapeutic responses. Therefore, careful selection of an appropriate preclinical model is essential to accurately address biological questions and optimize precision oncology strategies.

PMID:41942359 | PMC:PMC13053824 | DOI:10.5230/jgc.2026.26.e19

Received — 4 April 2026 ⏭

Proteogenomic Analysis of Coronary Artery Calcification in Human Populations

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Bassim El-Sabawi · Xiaoning Huang · Phillip Lin · Mohammad Yaser Anwar · Michael Betti · Namju Kim · Andrew S Perry · B Lakshitha A Perera · Priya Gajjar · Laura A Colangelo · Kaushik Amancherla · Quanhu Sheng · Shilin Zhao · Lindsay Stolze · Eric Farber-Eger · Joshua M Landman · Patricia E Miller · Gabrielle Y Liu · Suman Das · Quinn S Wells · James G Terry · Donald Lloyd-Jones · Saumya Das · Sadiya S Khan · Kari E North · Jennifer Below · Matthew Nayor · Ravi Kalhan · John Jeffrey Carr · Eric R Gamazon · Ravi V Shah
2 April 2026 at 18:00

Arterioscler Thromb Vasc Biol. 2026 Apr 2. doi: 10.1161/ATVBAHA.125.324171. Online ahead of print.

ABSTRACT

BACKGROUND: Joint use of multiple molecular layers can be useful to prioritize targets for mechanistic studies. Application of coronary disease in large populations is an emerging field.

METHODS: We used reported circulating proteomic data (Somascan aptamer-based) from ≈3000 individuals in the CARDIA study (Coronary Artery Risk Development in Young Adults), measuring association with prevalent and 10-year incident coronary artery calcium (CAC) score. We used a multiparametric approach to prioritize circulating protein-CAC associations via genomics of circulating protein levels and coronary artery transcription.

RESULTS: Proteins linked to prevalent/incident CAC in CARDIA implicated pathogenic mechanisms of vascular disease, including fibrosis and inflammation (GDF-15 [growth/differentiation factor 15], CDCP1 [CUB domain-containing protein 1], GSN [gelsolin], THBS2 [thrombospondin-2], chemokines, RNAS6), oxidative lipid metabolism (CILP2), extracellular matrix remodeling and signaling (MMPs [matrix metalloproteinases], TIMP-1, integrins), calcification (Notch 1, ARHGAP36 [Rho GTPase-activating protein 36]), and metabolism (GIP [gastric inhibitory polypeptide]), as well as new proteins not previously reported. Using protein-wide association study genetic approaches, several targets with nominal evidence in CAC proteomics were associated with atherosclerosis or myocardial infarction in over 300K individuals, including PCSK9 (proprotein convertase subtilisin/kexin type 9) and APO C1. Finally, the coronary artery-specific transcriptome-wide association study of CAC yielded genes with previously implicated mechanistic roles in vascular homeostasis, inflammation, and metabolism, as well as genes without previously described function in CAC. Overlap across CAC proteomics and transcriptome-wide association study highlighted genes involved in vascular inflammation (S100A9), cardiac development (HES1), vessel wall structure (SPARCL1), and vascular dysfunction or plaque (NOTCH3, TNFSF12, S100A12).

CONCLUSIONS: These results report population-level multiomics in human coronary calcification, presenting a method to identify disease-relevant targets through integration of human genetic approaches with multiomics.

PMID:41924874 | DOI:10.1161/ATVBAHA.125.324171

Proteogenomic Analysis of Coronary Artery Calcification in Human Populations

✇Omics in Gastric
By: Bassim El-Sabawi · Xiaoning Huang · Phillip Lin · Mohammad Yaser Anwar · Michael Betti · Namju Kim · Andrew S Perry · B Lakshitha A Perera · Priya Gajjar · Laura A Colangelo · Kaushik Amancherla · Quanhu Sheng · Shilin Zhao · Lindsay Stolze · Eric Farber-Eger · Joshua M Landman · Patricia E Miller · Gabrielle Y Liu · Suman Das · Quinn S Wells · James G Terry · Donald Lloyd-Jones · Saumya Das · Sadiya S Khan · Kari E North · Jennifer Below · Matthew Nayor · Ravi Kalhan · John Jeffrey Carr · Eric R Gamazon · Ravi V Shah
2 April 2026 at 18:00

Arterioscler Thromb Vasc Biol. 2026 Apr 2. doi: 10.1161/ATVBAHA.125.324171. Online ahead of print.

ABSTRACT

BACKGROUND: Joint use of multiple molecular layers can be useful to prioritize targets for mechanistic studies. Application of coronary disease in large populations is an emerging field.

METHODS: We used reported circulating proteomic data (Somascan aptamer-based) from ≈3000 individuals in the CARDIA study (Coronary Artery Risk Development in Young Adults), measuring association with prevalent and 10-year incident coronary artery calcium (CAC) score. We used a multiparametric approach to prioritize circulating protein-CAC associations via genomics of circulating protein levels and coronary artery transcription.

RESULTS: Proteins linked to prevalent/incident CAC in CARDIA implicated pathogenic mechanisms of vascular disease, including fibrosis and inflammation (GDF-15 [growth/differentiation factor 15], CDCP1 [CUB domain-containing protein 1], GSN [gelsolin], THBS2 [thrombospondin-2], chemokines, RNAS6), oxidative lipid metabolism (CILP2), extracellular matrix remodeling and signaling (MMPs [matrix metalloproteinases], TIMP-1, integrins), calcification (Notch 1, ARHGAP36 [Rho GTPase-activating protein 36]), and metabolism (GIP [gastric inhibitory polypeptide]), as well as new proteins not previously reported. Using protein-wide association study genetic approaches, several targets with nominal evidence in CAC proteomics were associated with atherosclerosis or myocardial infarction in over 300K individuals, including PCSK9 (proprotein convertase subtilisin/kexin type 9) and APO C1. Finally, the coronary artery-specific transcriptome-wide association study of CAC yielded genes with previously implicated mechanistic roles in vascular homeostasis, inflammation, and metabolism, as well as genes without previously described function in CAC. Overlap across CAC proteomics and transcriptome-wide association study highlighted genes involved in vascular inflammation (S100A9), cardiac development (HES1), vessel wall structure (SPARCL1), and vascular dysfunction or plaque (NOTCH3, TNFSF12, S100A12).

CONCLUSIONS: These results report population-level multiomics in human coronary calcification, presenting a method to identify disease-relevant targets through integration of human genetic approaches with multiomics.

PMID:41924874 | DOI:10.1161/ATVBAHA.125.324171

Received — 2 April 2026 ⏭
Received — 26 March 2026 ⏭

Happiness is Sharing a Vocabulary: A Study of Transliteration Methods

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haeji Jung · Jinju Kim · Kyungjin Kim · Youjeong Roh · David R. Mortensen
25 March 2026 at 12:00
arXiv:2510.10827v2 Announce Type: replace-cross Abstract: Transliteration has emerged as a promising means to bridge the gap between various languages in multilingual NLP, showing promising results especially for languages using non-Latin scripts. We investigate the degree to which shared script, overlapping token vocabularies, and shared phonology contribute to performance of multilingual models. To this end, we conduct controlled experiments using three kinds of transliteration (romanization, phonemic transcription, and substitution ciphers) as well as orthography. We evaluate each model on three downstream tasks -- named entity recognition (NER), part-of-speech tagging (POS) and natural language inference (NLI) -- and find that romanization significantly outperforms other input types in 11 out of 12 evaluation settings, largely consistent with our hypothesis that it is the most effective approach. We further analyze how each factor contributed to the success, and suggest that having longer (subword) tokens shared with pre-trained languages leads to better utilization of the model.
Received — 14 March 2026 ⏭

Biomolecular profiling for noninvasive health monitoring

✇Nature Biotechnology - Issue - nature.com science feeds
By: Moon-Ju Kim · José A. Lasalde-Ramírez · Wenzheng Heng · Wei Gao
12 March 2026 at 08:00

Nature Biotechnology, Published online: 12 March 2026; doi:10.1038/s41587-026-03050-2

Kim et al. present mass spectrometry and wearable sensor technology as complementary approaches that, when integrated, can co-evolve to open new possibilities for noninvasive health monitoring.
Received — 5 March 2026 ⏭

CoBELa: Steering Transparent Generation via Concept Bottlenecks on Energy Landscapes

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sangwon Kim · Kyoungoh Lee · Jeyoun Dong · Kwang-Ju Kim
4 March 2026 at 13:00
arXiv:2507.08334v3 Announce Type: replace-cross Abstract: Generative concept bottleneck models aim to enable interpretable generation by routing synthesis through explicit, user-facing concepts. In practice, prior approaches often rely on non-explicit bottleneck representations (e.g., vision cues or opaque concept embeddings) or black-box decoders to preserve image quality, which weakens the transparency. We propose CoBELa (Concept Bottlenecks on Energy Landscapes), a decoder-free, energy-based framework that eliminates non-explicit bottleneck representations by conditioning generation entirely through per-concept energy functions over the latent space of a frozen pretrained generator-requiring no generator retraining and enabling post-hoc interpretation. Because these concept energies compose additively, CoBELa naturally supports compositional concept interventions: concept conjunction and negation are realized by summing or subtracting per-concept energy terms without additional training. A diffusion-scheduled energy guidance scheme further replaces expensive MCMC chains with more stable, scheduled denoising for efficient concept-steered sampling. Experiments on CelebA-HQ and CUB-200-2011 demonstrate improvements over prior concept bottleneck generative models, achieving 75.70%/82.42% concept accuracy and 6.47/5.37 FID, respectively, while enabling reliable multi-concept interventions.
Received — 25 February 2026 ⏭

Contextuality from Single-State Ontological Models: An Information-Theoretic No-Go Theorem

✇cs.AI, q-bio.NC updates on arXiv.org
By: Song-Ju Kim
24 February 2026 at 13:00
arXiv:2602.16716v2 Announce Type: replace Abstract: Contextuality is a central feature of quantum theory, traditionally understood as the impossibility of reproducing quantum measurement statistics using noncontextual ontological models. We consider classical ontological models constrained to reuse a single ontic state space across multiple interventions. We prove an information-theoretic no-go theorem showing that such models must incur an irreducible contextual information cost: contextual dependence cannot be fully mediated through the ontic state alone and requires additional contextual information beyond it. We provide a constructive example illustrating this obstruction and show that it arises solely from the requirement of ontic state reuse within a classical probability space. We further explain how quantum theory avoids this obstruction by relaxing the assumption that all measurement statistics arise from a single underlying classical ontic variable. These results identify contextuality as a fundamental information-theoretic constraint on classical ontological models and clarify its origin as a limitation on classical representations.
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