National Center for Mathematics and Interdisciplinary Sciences

This commentary explores the synergistic integration of Mongolian Mind-Body Interaction Therapy (MMBIT) with modern Systems Theory to offer a roadmap for advancing integrative psychosomatic care. By blending traditional Mongolian medical wisdom (Heyi/Xila/Badagan?triad) with interdisciplinary systems science, MMBIT provides a holistic framework for treating mind-body disorders through its Material-Energy-Information Triadic Interaction Model. This commentary highlights the theoretical foundations, clinical applications, and technological innovations of MMBIT, demonstrating its potential to transform integrative medicine and global healthcare practices.

In the literature on risk measures, cash subadditivity was proposed to replace cash additivity, motivated by the presence of stochastic or ambiguous interest rates and defaultable contingent claims. Cash subadditivity has been traditionally studied together with quasi-convexity, in a way similar to cash additivity with convexity. In this paper, we study cash-subadditive risk measures without quasi-convexity. One of our major results is that a general cash-subadditive risk measure can be represented as the lower envelope of a family of quasi-convex and cash-subadditive risk measures. Representation results of cash-subadditive risk measures with some additional properties are also examined. The notion of quasi-star-shapedness, which is a natural analogue of star-shapedness, is introduced, and we obtain a corresponding representation result via the lower envelope of normalized, quasi-convex, and cash-subadditive risk measures. Funding: J. Xia received financial support of the National Key R&D Program of China [Grant 2018YFA0703900] and the National Natural Science Foundation of China [Grants 12071146, 12431017, and 12471447]. X. Han, Q. Wang, and R. Wang received financial support from the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2024-03728 and CRC-2022-00141]. X. Han also received financial support from the National Natural Science Foundation of China [Grants 12301604, 12371471, and 12471449]. Q. Wang also received financial support from the Society of Actuaries through the James C. Hickman Scholar Doctoral Stipend.

Domain identification is a critical problem in spatially resolved transcriptomics data analysis, which aims to identify distinct spatial domains within a tissue that maintain both spatial continuity and expression consistency. The degree of coupling between expression data and spatial information in different datasets often varies significantly. Some regions have intact and clear boundaries, while others exhibit blurred boundaries with high intra-domain expression similarity. However, most domain identification methods do not adequately integrate expression and spatial information to flexibly identify different types of domains. To address these issues, we introduce Spot2vector, a computational framework that leverages a graph-enhanced autoencoder integrating zero-inflated negative binomial distribution modeling, combining both graph convolutional networks and graph attention networks to extract the latent embeddings of spots. Spot2vector encodes and integrates spatial and expression information, enabling effective identification of domains with diverse spatial patterns across spatially resolved transcriptomics data generated by different platforms. The decoders enable us to decipher the distribution and generation mechanisms of data while improving expression quality through denoising. Extensive validation and analyses demonstrate that Spot2vector excels in enhancing domain identification accuracy, effectively reducing data dimensionality, improving expression recovery and denoising, and precisely capturing spatial gene expression patterns.

ABSTRACTContinuous glucose monitoring (CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states (MAE = 3.7 mg/dL). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task (AUROC = 0.914 for type 2 diabetes (T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics, CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling (Pearson correlation coefficient = 0.763) and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the early warning of T2D and recommendations for lifestyle modification in diabetes management.

It is well known that there is a one-to-one correspondence between supersingular $ j $-invariants up to the action of $ \text{Gal}(\mathbb{F}_{p^2}/\mathbb{F}_p) $ and type classes of maximal orders in $ B_{p, \infty} $ by Deuring's theorem. Interestingly, we establish a one-to-one correspondence between $ \mathbb{F}_p $-isomorphism classes of supersingular elliptic curves and primitive reduced binary quadratic forms with discriminant $ -p $ or $ -16p $. Due to this correspondence and the fact that $ \mathbb{F}_p $-isogenies between elliptic curves could be represented by quadratic forms, we show that actions of these isogenies on supersingular elliptic curves over $ \mathbb{F}_p $ are compatible with the composition of quadratic forms. Based on these results, we reduce the security of CSIDH cryptosystem to computing this correspondence explicitly.

Although adeno-to-squamous transition (AST) has been observed in association with resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) in clinic, its causality, molecular mechanism and overcoming strategies remain largely unclear. We here demonstrate that squamous transition occurs concomitantly with TKI resistance in PC9-derived xenograft tumors. Perturbation of squamous transition via DNp63 overexpression or knockdown leads to significant changes in TKI responses, indicative of a direct causal link between squamous transition and TKI resistance. Integrative RNA-seq, ATAC-seq analyses and functional studies reveal that FOXA1 plays an important role in maintaining adenomatous lineage and contributes to TKI sensitivity. FOXM1 overexpression together with FOXA1 knockout fully recapitulates squamous transition and TKI resistance in both PC9 xenografts and patient-derived xenograft (PDX) models. Importantly, pharmacological inhibition of RAPGEF3 combined with EGFR TKI efficiently overcomes TKI resistance, especially in RAPGEF3high PDXs. Our findings provide novel mechanistic insights into squamous transition and therapeutic strategy to overcome EGFR TKI resistance in lung cancer.

Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.