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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. 

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. 

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⁻/Cl⁻ligand 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.

 

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⁻/Cl⁻ligand 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.

 

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. 

Relative contributions to the zonal mean meridional heat transport by the climatological annual mean, climatological annual variation, synoptic, intra‐seasonal and lower‐frequency motions were examined based on the ERA‐Interim reanalysis data for the period of 1981–2015. The meridional heat transport analysed in this study only includes the component related to meridional wind and temperature. In the tropics, the climatological annual mean circulations dominate the long‐term mean meridional heat transport, while the interaction between the climatological annual mean temperature and the seasonal anomalous flow largely contributes to the seasonal variation of the meridional heat transport. In the middle latitudes, the climatological annual mean circulations and transient eddies (mostly synoptic and intra‐seasonal eddies) are of roughly equal importance in the poleward heat transport, leading to the maximum poleward heat transport around 50°N/S. The upper‐ and lower‐tropospheric heat transports by the climatological annual mean circulations appear opposite, with the magnitude of the lower‐tropospheric transport being greater. The preferred maximum zonal mean heat transport at 50°N by the climatological mean flow is attributed to the maximum zonal mean low‐level southerly in situ. The preferred peak latitude of the mid‐latitude poleward heat transport by synoptic eddies near 50°N arises from the combined effect of the strong synoptic‐scale meridional wind and temperature variabilities in situ and their in‐phase relationship. The heat transport by tropical cyclones (TCs) was estimated by applying a statistical relationship between TC intensity and the vertically integrated temperature averaged over the TC core region derived from high‐resolution Weather Research and Forecasting (WRF) model simulations. For northern hemisphere summer, TCs contribute about 35% of the total heat transport in the active TC regions, suggesting that TCs play a critical role in the regional meridional heat transport.

 

We choose the high-performance nonfullerene acceptor ITIC-Th as an example, and incorporate electron-donating methoxy and electron-withdrawing F groups onto the terminal group 1,1-dicyanomethylene-3-indanone (IC) to construct a small library of four fused-ring electron acceptors. With this series, we systematically investigate the effects of the substituents on the end-groups on the electronic properties, charge transport, film morphology, and photovoltaic properties of the ITIC-Th series. The electron-withdrawing ability increases from methoxylated to unsubstituted, fluorinated, and difluorinated IC, leading to a downshift of energy levels and a redshift of absorption spectra. Optimized organic solar cells based on the ITIC-Th series show power conversion efficiencies ranging from 8.88% to 12.1%.