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1. Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

   https://doi.org/10.1371/journal.pcbi.1008379  

   Sun, Xiaoqiang ; Zhang, Ji ; Nie, Qing ( March 2021 , PLOS Computational Biology)

   Roy, Sushmita (Ed.)

   Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine. 

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2. Gradient descent algorithms for Bures-Wasserstein barycenters

   Chewi, Sinh ; Maunu, Tyler ; Rigollet, Philipp ; Stromme, Austin J. ( January 2020 , Proceedings of Machine Learning Research)

   Abernethy, Jacob ; Agarwal, Shivani (Ed.)

   We study first order methods to compute the barycenter of a probability distribution $P$ over the space of probability measures with finite second moment. We develop a framework to derive global rates of convergence for both gradient descent and stochastic gradient descent despite the fact that the barycenter functional is not geodesically convex. Our analysis overcomes this technical hurdle by employing a Polyak-Ł{}ojasiewicz (PL) inequality and relies on tools from optimal transport and metric geometry. In turn, we establish a PL inequality when $P$ is supported on the Bures-Wasserstein manifold of Gaussian probability measures. It leads to the first global rates of convergence for first order methods in this context. 

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3. A manifold two-sample test study: integral probability metric with neural networks

   https://doi.org/10.1093/imaiai/iaad018  

   Wang, Jie ; Chen, Minshuo ; Zhao, Tuo ; Liao, Wenjing ; Xie, Yao ( June 2023 , Information and Inference: A Journal of the IMA)

   Two-sample tests are important areas aiming to determine whether two collections of observations follow the same distribution or not. We propose two-sample tests based on integral probability metric (IPM) for high-dimensional samples supported on a low-dimensional manifold. We characterize the properties of proposed tests with respect to the number of samples $n$ and the structure of the manifold with intrinsic dimension $d$. When an atlas is given, we propose a two-step test to identify the difference between general distributions, which achieves the type-II risk in the order of $n^{-1/\max \{d,2\}}$. When an atlas is not given, we propose Hölder IPM test that applies for data distributions with $(s,\beta )$-Hölder densities, which achieves the type-II risk in the order of $n^{-(s+\beta )/d}$. To mitigate the heavy computation burden of evaluating the Hölder IPM, we approximate the Hölder function class using neural networks. Based on the approximation theory of neural networks, we show that the neural network IPM test has the type-II risk in the order of $n^{-(s+\beta )/d}$, which is in the same order of the type-II risk as the Hölder IPM test. Our proposed tests are adaptive to low-dimensional geometric structure because their performance crucially depends on the intrinsic dimension instead of the data dimension.

    

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4. Quantifying bankfull flow width using preserved bar clinoforms from fluvial strata

   https://doi.org/10.1130/G48729.1  

   Greenberg, Evan ; Ganti, Vamsi ; Hajek, Elizabeth ( May 2021 , Geology)

   Abstract Reconstruction of active channel geometry from fluvial strata is critical to constrain the water and sediment fluxes in ancient terrestrial landscapes. Robust methods—grounded in extensive field observations, numerical simulations, and physical experiments—exist for estimating the bankfull flow depth and channel-bed slope from preserved deposits; however, we lack similar tools to quantify bankfull channel widths. We combined high-resolution lidar data from 134 meander bends across 11 rivers that span over two orders of magnitude in size to develop a robust, empirical relation between the bankfull channel width and channel-bar clinoform width (relict stratigraphic surfaces of bank-attached channel bars). We parameterized the bar cross-sectional shape using a two-parameter sigmoid, defining bar width as the cross-stream distance between 95% of the asymptotes of the fit sigmoid. We combined this objective definition of the bar width with Bayesian linear regression analysis to show that the measured bankfull flow width is 2.34 ± 0.13 times the channel-bar width. We validated our model using field measurements of channel-bar and bankfull flow widths of meandering rivers that span all climate zones (R2 = 0.79) and concurrent measurements of channel-bar clinoform width and mud-plug width in fluvial strata (R2 = 0.80). We also show that the transverse bed slopes of bars are inversely correlated with bend curvature, consistent with theory. Results provide a simple, usable metric to derive paleochannel width from preserved bar clinoforms. 

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5. Type 2 diabetes mellitus accelerates brain aging and cognitive decline: Complementary findings from UK Biobank and meta-analyses

   https://doi.org/10.7554/eLife.73138  

   Antal, Botond ; McMahon, Liam P ; Sultan, Syed Fahad ; Lithen, Andrew ; Wexler, Deborah J ; Dickerson, Bradford ; Ratai, Eva-Maria ; Mujica-Parodi, Lilianne R ( May 2022 , eLife)

   Background: Type 2 diabetes mellitus (T2DM) is known to be associated with neurobiological and cognitive deficits; however, their extent, overlap with aging effects, and the effectiveness of existing treatments in the context of the brain are currently unknown. Methods: We characterized neurocognitive effects independently associated with T2DM and age in a large cohort of human subjects from the UK Biobank with cross-sectional neuroimaging and cognitive data. We then proceeded to evaluate the extent of overlap between the effects related to T2DM and age by applying correlation measures to the separately characterized neurocognitive changes. Our findings were complemented by meta-analyses of published reports with cognitive or neuroimaging measures for T2DM and healthy controls (HCs). We also evaluated in a cohort of T2DM-diagnosed individuals using UK Biobank how disease chronicity and metformin treatment interact with the identified neurocognitive effects. Results: The UK Biobank dataset included cognitive and neuroimaging data (N = 20,314), including 1012 T2DM and 19,302 HCs, aged between 50 and 80 years. Duration of T2DM ranged from 0 to 31 years (mean 8.5 ± 6.1 years); 498 were treated with metformin alone, while 352 were unmedicated. Our meta-analysis evaluated 34 cognitive studies (N = 22,231) and 60 neuroimaging studies: 30 of T2DM (N = 866) and 30 of aging (N = 1088). Compared to age, sex, education, and hypertension-matched HC, T2DM was associated with marked cognitive deficits, particularly in executive functioning and processing speed . Likewise, we found that the diagnosis of T2DM was significantly associated with gray matter atrophy, primarily within the ventral striatum , cerebellum , and putamen , with reorganization of brain activity (decreased in the caudate and premotor cortex and increased in the subgenual area , orbitofrontal cortex, brainstem, and posterior cingulate cortex ). The structural and functional changes associated with T2DM show marked overlap with the effects correlating with age but appear earlier, with disease duration linked to more severe neurodegeneration. Metformin treatment status was not associated with improved neurocognitive outcomes. Conclusions: The neurocognitive impact of T2DM suggests marked acceleration of normal brain aging. T2DM gray matter atrophy occurred approximately 26% ± 14% faster than seen with normal aging; disease duration was associated with increased neurodegeneration. Mechanistically, our results suggest a neurometabolic component to brain aging. Clinically, neuroimaging-based biomarkers may provide a valuable adjunctive measure of T2DM progression and treatment efficacy based on neurological effects. Funding: The research described in this article was funded by the W. M. Keck Foundation (to LRMP), the White House Brain Research Through Advancing Innovative Technologies (BRAIN) Initiative (NSFNCS-FR 1926781 to LRMP), and the Baszucki Brain Research Fund (to LRMP). None of the funding sources played any role in the design of the experiments, data collection, analysis, interpretation of the results, the decision to publish, or any aspect relevant to the study. DJW reports serving on data monitoring committees for Novo Nordisk. None of the authors received funding or in-kind support from pharmaceutical and/or other companies to write this article. 

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