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  1. Cuomo, Christina A (Ed.)
    Abstract Supraglacial pools are prevalent on debris-covered mountain glaciers, yet only limited information is available on the microbial communities within these habitats. Our research questions for this preliminary study were: (1) What microbes occur in supraglacial pool sediments of monsoonal Tibet?; (2) Which abiotic and biotic habitat variables have the most influence on the microbial community structure?; and (3) Does microbial composition of supraglacial pool sediments differ from that of glacial-melt stream pool sediments? We collected microbial samples for 16S rRNA sequencing and invertebrates for enumeration and identification and measured 14 abiotic variables from 46 supraglacial pools and nine glacial-melt stream pools in 2018 and 2019. Generalized linear model analyses, small sample Akaike information criterion, and variable importance scores were used to identify the best predictor variables of microbial community structure. Multi-response permutation procedure (MRPP) was used to compare taxa composition between supraglacial pools and stream pools. The most abundant phyla in supraglacial pool sediments were Proteobacteria, Actinobacteria, Bacteroidota, Chloroflexi, and Cyanobacteria. Genera richness, indicator genera richness, andPolaromonasrelative abundance were best predicted by Chironomidae larvae abundance. more » « less
    Free, publicly-accessible full text available September 3, 2025
  2. In this article, we develop CausalEGM, a deep learning framework for nonlinear dimension reduction and generative modeling of the dependency among covariate features affecting treatment and response. CausalEGM can be used for estimating causal effects in both binary and continuous treatment settings. By learning a bidirectional transformation between the high-dimensional covariate space and a low-dimensional latent space and then modeling the dependencies of different subsets of the latent variables on the treatment and response, CausalEGM can extract the latent covariate features that affect both treatment and response. By conditioning on these features, one can mitigate the confounding effect of the high dimensional covariate on the estimation of the causal relation between treatment and response. In a series of experiments, the proposed method is shown to achieve superior performance over existing methods in both binary and continuous treatment settings. The improvement is substantial when the sample size is large and the covariate is of high dimension. Finally, we established excess risk bounds and consistency results for our method, and discuss how our approach is related to and improves upon other dimension reduction approaches in causal inference.

     
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    Free, publicly-accessible full text available June 4, 2025
  3. Debris-covered glaciers (DCGs) are globally distributed and thought to contain greater microbial diversity than clean surface continental glaciers, but the ecological characteristics of microbial communities on the surface of DCGs have remained underexplored. Here, we investigated bacterial and fungal diversity and co-occurrence networks on the supraglacial debris habitat of two DCGs (Hailuogou and Dagongba Glaciers) in the southeastern Tibetan Plateau. We found that the supraglacial debris harbored abundant microbes with Proteobacteria occupying more than half (51.5%) of the total bacteria operational taxonomic units. The composition, diversity, and co-occurrence networks of both bacterial and fungal communities in the debris were significantly different between Hailuogou Glacier and Dagongba Glacier even though the glaciers are geographically adjacent within the same mountain range. Bacteria were more diverse in the debris of the Dagongba Glacier, where a lower surface velocity and thicker debris layer allowed the supraglacial debris to continuously weather and accumulate nutrients. Fungi were more diverse in the debris of the Hailuogou Glacier, which experiences a wetter monsoonal climate, is richer in calcium, has greater debris instability, and greater ice velocity than the Dagongba Glacier. These factors may provide ideal conditions for the dispersal and propagation of fungi spores on the Hailuogou Glacier. In addition, we found an obvious gradient of bacterial diversity along the supraglacial debris transect on the Hailuogou Glacier. Bacterial diversity was lower where debris cover was thin and scattered and became more diverse near the glacial terminus in thick, slow-moving debris. No such increasing bacterial pattern was detected on the Dagongba Glacier, which implies a positive relationship of debris age, thickness, and weathering on bacterial diversity. Additionally, a highly connected bacterial co-occurrence network with low modularity was found in the debris of the Hailuogou Glacier. In contrast, debris from the Dagongba Glacier exhibited less connected but more modularized co-occurrence networks of both bacterial and fungal communities. These findings indicate that less disturbed supraglacial debris conditions are crucial for microbes to form stable communities on DCGs. 
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  4. Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort , for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring. 
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  5. null (Ed.)
    Density estimation is one of the fundamental problems in both statistics and machine learning. In this study, we propose Roundtrip, a computational framework for general-purpose density estimation based on deep generative neural networks. Roundtrip retains the generative power of deep generative models, such as generative adversarial networks (GANs) while it also provides estimates of density values, thus supporting both data generation and density estimation. Unlike previous neural density estimators that put stringent conditions on the transformation from the latent space to the data space, Roundtrip enables the use of much more general mappings where target density is modeled by learning a manifold induced from a base density (e.g., Gaussian distribution). Roundtrip provides a statistical framework for GAN models where an explicit evaluation of density values is feasible. In numerical experiments, Roundtrip exceeds state-of-the-art performance in a diverse range of density estimation tasks. 
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  7. null (Ed.)
    Hydropower is the largest renewable energy source for electricity generation in the world, with numerous benefits in terms of: environment protection (near-zero air pollution and climate impact), cost-effectiveness (long-term use, without significant impacts of market fluctuation), and reliability (quickly respond to surge in demand). However, the effectiveness of hydropower plants is affected by multiple factors such as reservoir capacity, rainfall, temperature and fluctuating electricity demand, and particularly their complicated relationships, which make the prediction/recommendation of station operational output a difficult challenge. In this paper, we present DeepHydro, a novel stochastic method for modeling multivariate time series (e.g., water inflow/outflow and temperature) and forecasting power generation of hydropower stations. DeepHydro captures temporal dependencies in co-evolving time series with a new conditioned latent recurrent neural networks, which not only considers the hidden states of observations but also preserves the uncertainty of latent variables. We introduce a generative network parameterized on a continuous normalizing flow to approximate the complex posterior distribution of multivariate time series data, and further use neural ordinary differential equations to estimate the continuous-time dynamics of the latent variables constituting the observable data. This allows our model to deal with the discrete observations in the context of continuous dynamic systems, while being robust to the noise. We conduct extensive experiments on real-world datasets from a large power generation company consisting of cascade hydropower stations. The experimental results demonstrate that the proposed method can effectively predict the power production and significantly outperform the possible candidate baseline approaches. 
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  8. Abstract

    Although many porous materials, including metal–organic frameworks (MOFs), have been reported to selectively adsorb C2H2in C2H2/CO2separation processes, CO2‐selective sorbents are much less common. Here, we report the remarkable performance ofMFU‐4(Zn5Cl4(bbta)3, bbta=benzo‐1,2,4,5‐bistriazolate) toward inverse CO2/C2H2separation. The MOF facilitates kinetic separation of CO2from C2H2, enabling the generation of high purity C2H2(>98 %) with good productivity in dynamic breakthrough experiments. Adsorption kinetics measurements and computational studies show C2H2is excluded fromMFU‐4by narrow pore windows formed by Zn−Cl groups. Postsynthetic F/Clligand exchange was used to synthesize an analogue (MFU‐4‐F) with expanded pore apertures, resulting in equilibrium C2H2/CO2separation with reversed selectivity compared toMFU‐4.MFU‐4‐Falso exhibits a remarkably high C2H2adsorption capacity (6.7 mmol g−1), allowing fuel grade C2H2(98 % purity) to be harvested from C2H2/CO2mixtures by room temperature desorption.

     
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