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1. Scan‐specific artifact reduction in k‐space (SPARK) neural networks synergize with physics‐based reconstruction to accelerate MRI

   https://doi.org/10.1002/mrm.29036  

   Arefeen, Yamin ; Beker, Onur ; Cho, Jaejin ; Yu, Heng ; Adalsteinsson, Elfar ; Bilgic, Berkin ( October 2021 , Magnetic Resonance in Medicine)

   To develop a scan‐specific model that estimates and corrects k‐space errors made when reconstructing accelerated MRI data.

   Scan‐specific artifact reduction in k‐space (SPARK) trains a convolutional‐neural‐network to estimate and correct k‐space errors made by an input reconstruction technique by back‐propagating from the mean‐squared‐error loss between an auto‐calibration signal (ACS) and the input technique’s reconstructed ACS. First, SPARK is applied to generalized autocalibrating partially parallel acquisitions (GRAPPA) and demonstrates improved robustness over other scan‐specific models, such as robust artificial‐neural‐networks for k‐space interpolation (RAKI) and residual‐RAKI. Subsequent experiments demonstrate that SPARK synergizes with residual‐RAKI to improve reconstruction performance. SPARK also improves reconstruction quality when applied to advanced acquisition and reconstruction techniques like 2D virtual coil (VC‐) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS region, and 2D/3D wave‐encoded imaging.

   SPARK yields SSIM improvement and 1.5 – 2× root mean squared error (RMSE) reduction when applied to GRAPPA and improves robustness to ACS size for various acceleration rates in comparison to other scan‐specific techniques. When applied to advanced reconstruction techniques such as residual‐RAKI, 2D VC‐GRAPPA and LORAKS, SPARK achieves up to 20% RMSE improvement. SPARK with 3D GRAPPA also improves RMSE performance by ~2×, SSIM performance, and perceived image quality without a fully sampled ACS region. Finally, SPARK synergizes with non‐Cartesian, 2D and 3D wave‐encoding imaging by reducing RMSE between 20% and 25% and providing qualitative improvements.

   SPARK synergizes with physics‐based acquisition and reconstruction techniques to improve accelerated MRI by training scan‐specific models to estimate and correct reconstruction errors in k‐space.

    

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2. How Well Do Multisatellite Products Capture the Space–Time Dynamics of Precipitation? Part II: Building an Error Model through Spectral System Identification

   https://doi.org/10.1175/JHM-D-22-0041.1  

   Guilloteau, Clement ; Foufoula-Georgiou, Efi ; Kirstetter, Pierre ; Tan, Jackson ; Huffman, George J. ( September 2022 , Journal of Hydrometeorology)

   Satellite precipitation products, as all quantitative estimates, come with some inherent degree of uncertainty. To associate a quantitative value of the uncertainty to each individual estimate, error modeling is necessary. Most of the error models proposed so far compute the uncertainty as a function of precipitation intensity only, and only at one specific spatiotemporal scale. We propose a spectral error model that accounts for the neighboring space–time dynamics of precipitation into the uncertainty quantification. Systematic distortions of the precipitation signal and random errors are characterized distinctively in every frequency–wavenumber band in the Fourier domain, to accurately characterize error across scales. The systematic distortions are represented as a deterministic space–time linear filtering term. The random errors are represented as a nonstationary additive noise. The spectral error model is applied to the IMERG multisatellite precipitation product, and its parameters are estimated empirically through a system identification approach using the GV-MRMS gauge–radar measurements as reference (“truth”) over the eastern United States. The filtering term is found to be essentially low-pass (attenuating the fine-scale variability). While traditional error models attribute most of the error variance to random errors, it is found here that the systematic filtering term explains 48% of the error variance at the native resolution of IMERG. This fact confirms that, at high resolution, filtering effects in satellite precipitation products cannot be ignored, and that the error cannot be represented as a purely random additive or multiplicative term. An important consequence is that precipitation estimates derived from different sources shall not be expected to automatically have statistically independent errors.

   Satellite precipitation products are nowadays widely used for climate and environmental research, water management, risk analysis, and decision support at the local, regional, and global scales. For all these applications, knowledge about the accuracy of the products is critical for their usability. However, products are not systematically provided with a quantitative measure of the uncertainty associated with each individual estimate. Various parametric error models have been proposed for uncertainty quantification, mostly assuming that the uncertainty is only a function of the precipitation intensity at the pixel and time of interest. By projecting satellite precipitation fields and their retrieval errors into the Fourier frequency–wavenumber domain, we show that we can explicitly take into account the neighboring space–time multiscale dynamics of precipitation and compute a scale-dependent uncertainty.

    

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3. A penalized likelihood for multispecies occupancy models improves predictions of species interactions

   https://doi.org/10.1002/ecy.3520  

   Clipp, Hannah L. ; Evans, Amber L. ; Kessinger, Brin E. ; Kellner, Kenneth ; Rota, Christopher T. ( September 2021 , Ecology)

   Multispecies occupancy models estimate dependence among multiple species of interest from patterns of co‐occurrence, but problems associated with separation and boundary estimates can lead to unreasonably large estimates of parameters and associated standard errors when species are rarely observed at the same site or when data are sparse. In this paper, we overcome these issues by implementing a penalized likelihood, which introduces a small bias in parameter estimates in exchange for a potentially large reduction in variance. We compare parameter estimates obtained from both penalized and unpenalized multispecies occupancy models fit to simulated data that exhibit various degrees of separation and to a real‐word data set of bird surveys with little apparent overlap between potentially interacting species. Our simulation results demonstrate that penalized multispecies occupancy models did not exhibit boundary estimates and produced lower bias, lower mean squared error, and improved inference relative to unpenalized models. When applied to real‐world data, our penalized multispecies occupancy model constrained boundary estimates and allowed for meaningful inference related to the interactions of two species of conservation concern. To facilitate the use of our penalized multispecies occupancy model, the techniques demonstrated in this paper have been integrated into theunmarkedpackage in R programing language.

    

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4. Estimating a Cosmological Mass Bias Parameter with Bootstrap Bandwidth Selection

   https://doi.org/10.1111/j.1467-9876.2010.00728.x  

   Loh, Ji Meng ; Jang, Woncheol ( July 2010 , Journal of the Royal Statistical Society Series C: Applied Statistics)

   We focus on selecting optimal bandwidths for non-parametric estimation of the two-point correlation function of a point pattern. We obtain these optimal bandwidths by using a bootstrap approach to select a bandwidth that minimizes the integrated squared error. The variance term is estimated by using a non-parametric spatial bootstrap, whereas the bias term is estimated with a plug-in approach using a pilot estimator of the two-point correlation function based on a parametric model. The choice of parametric model for the pilot estimator is very flexible. Depending on applications, parametric statistical point models, physical models or functional models can be used. We also explore the use of the procedure for selecting adaptive optimal bandwidths. We investigate the performance of the bandwidth selection procedure by using a simulation study. In our data example, we apply our method to a Sloan Digital Sky Survey galaxy cluster catalogue by using a pilot estimator based on the power law functional model in cosmology. The resulting non-parametric two-point correlation function estimate is then used to estimate a cosmological mass bias parameter that describes the relationship between the galaxy mass distribution and the underlying matter distribution.

    

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5. ISRES+: an improved evolutionary strategy for function minimization to estimate the free parameters of systems biology models

   https://doi.org/10.1093/bioinformatics/btad403  

   Bandodkar, Prasad ; Shaikh, Razeen ; Reeves, Gregory T ( July 2023 , Bioinformatics)

   Wren, Jonathan (Ed.)

   Mathematical models in systems biology help generate hypotheses, guide experimental design, and infer the dynamics of gene regulatory networks. These models are characterized by phenomenological or mechanistic parameters, which are typically hard to measure. Therefore, efficient parameter estimation is central to model development. Global optimization techniques, such as evolutionary algorithms (EAs), are applied to estimate model parameters by inverse modeling, i.e. calibrating models by minimizing a function that evaluates a measure of the error between model predictions and experimental data. EAs estimate model parameters “fittest individuals” by generating a large population of individuals using strategies like recombination and mutation over multiple “generations.” Typically, only a few individuals from each generation are used to create new individuals in the next generation. Improved Evolutionary Strategy by Stochastic Ranking (ISRES), proposed by Runnarson and Yao, is one such EA that is widely used in systems biology to estimate parameters. ISRES uses information at most from a pair of individuals in any generation to create a new population to minimize the error. In this article, we propose an efficient evolutionary strategy, ISRES+, which builds on ISRES by combining information from all individuals across the population and across all generations to develop a better understanding of the fitness landscape.

   ISRES+ uses the additional information generated by the algorithm during evolution to approximate the local neighborhood around the best-fit individual using linear least squares fits in one and two dimensions, enabling efficient parameter estimation. ISRES+ outperforms ISRES and results in fitter individuals with a tighter distribution over multiple runs, such that a typical run of ISRES+ estimates parameters with a higher goodness-of-fit compared with ISRES.

   Algorithm and implementation: Github—https://github.com/gtreeves/isres-plus-bandodkar-2022.

    

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