Search for: All records

Measurements of oxygen and hydrogen stable isotope ratios (δ¹⁸O and δD) in meteoric waters provide insight to overlapping effects of evaporation, precipitation, and mixing on basin scale hydrology. This study of waters collected between 2016 and 2021 in the Turkana Basin, northern Kenya, uses δ¹⁸O and δD to understand water balance in Lake Turkana, a large, low‐latitude, alkaline desert lake. The Omo River, a major river system in the Ethiopian Highlands, is historically understood to provide approximately 90% of the water input to Lake Turkana. Discharge of the Omo is prohibitively difficult to measure, but stable isotope ratios in the lake may provide a meaningful method for monitoring the lake's response to changes in input. Precipitation in the Turkana Basin is low (<200 mm/year) with negligible rainfall on the lake's surface, and all water loss from the lake is evaporative. We compare new measurements with previous data from the region and records of lake height and precipitation from the same time period. We show that a Bayesian approach to modeling evaporation using atmospheric conditions and river δ¹⁸O and δD yields results consistent with published water balance models. Continued sampling of lake and meteoric waters in the Turkana Basin will be a useful way to monitor the lake's response to regional and global climate change.

 

Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is in- formative and important to understand motion mechanisms of body regions. Modeling such in- formation into the MRI reconstruction process produces temporally coherent image sequence and reduces imaging artifacts and blurring. However, existing deep learning based approaches neglect motion information during the reconstruction procedure, while traditional motion-guided methods are hindered by heuristic parameter tuning and long inference time. We propose a novel dynamic MRI reconstruction approach called MODRN that unitizes deep neural networks with motion in- formation to improve reconstruction quality. The central idea is to decompose the motion-guided optimization problem of dynamic MRI reconstruction into three components: dynamic reconstruc- tion, motion estimation and motion compensation. Extensive experiments have demonstrated the effectiveness of our proposed approach compared to other state-of-the-art approaches. 

We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate. This can prac- tically benefit patient due to reduced time of MRI scan, but it is also challenging since quality of reconstruction may be compromised. Currently, deep learning based methods dom- inate MRI reconstruction over traditional approaches such as Compressed Sensing, but they rarely show satisfactory performance in the case of low undersampled k-space data. One explanation is that these methods treat channel-wise fea- tures equally, which results in degraded representation ability of the neural network. To solve this problem, we propose a new model called MRI Cascaded Channel-wise Attention Network (MICCAN), highlighted by three components: (i) a variant of U-net with Channel-wise Attention (UCA) mod- ule, (ii) a long skip connection and (iii) a combined loss. Our model is able to attend to salient information by filtering irrelevant features and also concentrate on high-frequency in- formation by enforcing low-frequency information bypassed to the final output. We conduct both quantitative evaluation and qualitative analysis of our method on a cardiac dataset. The experiment shows that our method achieves very promis- ing results in terms of three common metrics on the MRI reconstruction with low undersampled k-space data. Code is public available 

A comprehensive study of the local and nonlocal amplitudes contributing to the decayB⁰→K^*0(→K⁺π⁻)μ⁺μ⁻is performed by analysing the phase-space distribution of the decay products. The analysis is based onppcollision data corresponding to an integrated luminosity of 8.4 fb⁻¹collected by the LHCb experiment. This measurement employs for the first time a model of both one-particle and two-particle nonlocal amplitudes, and utilises the complete dimuon mass spectrum without any veto regions around the narrow charmonium resonances. In this way it is possible to explicitly isolate the local and nonlocal contributions and capture the interference between them. The results show that interference with nonlocal contributions, although larger than predicted, only has a minor impact on the Wilson Coefficients determined from the fit to the data. For the local contributions, the Wilson Coefficient$$ {\mathcal{C}}_9 $$$C_9$, responsible for vector dimuon currents, exhibits a 2.1σdeviation from the Standard Model expectation. The Wilson Coefficients$$ {\mathcal{C}}_{10} $$$C_{10}$,$$ {\mathcal{C}}_9^{\prime } $$$C_9^{\prime }$and$$ {\mathcal{C}}_{10}^{\prime } $$$C_{10}^{\prime }$are all in better agreement than$$ {\mathcal{C}}_9 $$$C_9$with the Standard Model and the global significance is at the level of 1.5σ. The model used also accounts for nonlocal contributions fromB⁰→ K^*0[τ⁺τ⁻→ μ⁺μ⁻] rescattering, resulting in the first direct measurement of thebsττvector effective-coupling$$ {\mathcal{C}}_{9\tau } $$$C_{9\tau }$.