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Cationic water-soluble deep cavitands enable hierarchical assembly-based recognition, optical detection and extraction of perfluoroalkyl substances (PFAS) in aqueous solution. Recognition of the PFAS occurs at the lower rim crown of the cavitand, which triggers self-aggregation of a PFAS-cavitand complex, allowing extraction from water. In addition, when paired with an indicator dye that can be bound in the cavity of the host molecule, the PFAS-cavitand association causes a significant (>20-fold at micromolar [PFAS]) enhancement of dye fluorescence due to conformational rearrangement of the fluxional cavitand AMI, allowing optical sensing of PFAS. The cavitands are water-soluble, and the detection and recognition occur in purely aqueous solution. The association is most effective for long chain sulfonate PFAS, and as such, selective optical detection of perfluorooctanesulfonate is possible, with a LOD = 130 nM in buffered water, and 500 nM in real-world samples such as polluted canal water. By pairing the AMI host with multiple dyes in an array-based format, full discrimination of five other PFAS can be achieved at micromolar concentration via differential sensing. In addition, the aggregation process allows extraction of PFAS from solution, and a 99% reduction of PFOS concentration in water is possible with a single treatment of an equimolar concentration of AMI cavitand. The hierarchical nature of the cavitand recognition system allows both selective, sensitive optical detection and extraction of PFAS from water with a single scaffold. 

Abstract Hyporheic zones regulate biogeochemical processes in streams and rivers, but high spatiotemporal heterogeneity makes it difficult to predict how these processes scale from individual reaches to river basins. Recent work applying allometric scaling (i.e., power‐law relationships between size and function) to river networks provides a new paradigm for understanding cumulative hyporheic biogeochemical processes. We used previously published model predictions of reach‐scale hyporheic aerobic respiration to explore patterns in allometric scaling across two climatically divergent basins with differing characteristics in the Pacific Northwest, United States. In the model, hydrologic exchange fluxes (HEFs) regulate hyporheic respiration, so we examined how HEFs might influence allometric scaling of respiration. We found consistent scaling behaviors where HEFs were either very low or very high, but differences between basins when HEFs were moderate. Our findings provide initial model‐generated hypotheses for factors influencing allometric scaling of hyporheic respiration. These hypotheses can be used to optimize new data generation efforts aimed at developing predictive understanding of allometries that can, in turn, be used to scale biogeochemical dynamics across watersheds. 

In this work, we tackle two widespread challenges in real applications for time series forecasting that have been largely understudied: distribution shifts and missing data. We propose SpectraNet, a novel multivariate time-series forecasting model that dynamically infers a latent space spectral decomposition to capture current temporal dynamics and correlations on the recent observed history. A Convolution Neural Network maps the learned representation by sequentially mixing its components and refining the output. Our proposed approach can simultaneously produce forecasts and interpolate past observations and can, therefore, greatly simplify production systems by unifying imputation and forecasting tasks into a single model. SpectraNet achieves SoTA performance simultaneously on both tasks on five benchmark datasets, compared to forecasting and imputation models, with up to 92% fewer parameters and comparable training times. On settings with up to 80% missing data, SpectraNet has average performance improvements of almost 50% over the second-best alternative. 

Multivariate time series anomaly detection has become an active area of research in recent years, with Deep Learning models outperforming previous approaches on benchmark datasets. Among reconstruction-based models, most previous work has focused on Variational Autoencoders and Generative Adversarial Networks. This work presents DGHL, a new family of generative models for time series anomaly detection, trained by maximizing the observed likelihood by posterior sampling and alternating back-propagation. A top-down Convolution Network maps a novel hierarchical latent space to time series windows, exploiting temporal dynamics to encode information efficiently. Despite relying on posterior sampling, it is computationally more efficient than current approaches, with up to 10x shorter training times than RNN based models. Our method outperformed current state-of-the-art models on four popular benchmark datasets. Finally, DGHL is robust to variable features between entities and accurate even with large proportions of missing values, settings with increasing relevance with the advent of IoT. We demonstrate the superior robustness of DGHL with novel occlusion experiments in this literature. Our code is available at https://github. com/cchallu/dghl. 

We report an improved measurement of the valence $u$and $d$quark distributions from the forward-backward asymmetry in the Drell-Yan process using $8.6{\mathrm{fb}}^{-1}$of data collected with the D0 detector in $p\overline{p}$collisions at $\sqrt{s}=1.96$. This analysis provides the values of new structure parameters that are directly related to the valence up and down quark distributions in the proton. In other experimental results measuring the quark content of the proton, $d$quark contributions are mixed with those from other quark flavors. In this measurement, the $u$and $d$quark contributions are separately extracted by applying a factorization of the QCD and electroweak portions of the forward-backward asymmetry. Published by the American Physical Society2024