Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative myelin water imaging using short TR adiabatic inversion recovery prepared echo-planar imaging (STAIR-EPI) sequence

Introduction Numerous techniques for myelin water imaging (MWI) have been devised to specifically assess alterations in myelin. The biomarker employed to measure changes in myelin content is known as the myelin water fraction (MWF). The short TR adiabatic inversion recovery (STAIR) sequence has recently been identified as a highly effective method for calculating MWF. The purpose of this study is to develop a new clinical transitional myelin water imaging (MWI) technique that combines STAIR preparation and echo-planar imaging (EPI) (STAIR-EPI) sequence for data acquisition. Methods Myelin water (MW) in the brain has shorter T1 and T2 relaxation times than intracellular and extracellular water. In the proposed STAIR-EPI sequence, a short TR (e.g., ≤300 ms) together with an optimized inversion time enable robust long T1 water suppression with a wide range of T1 values [i.e., (600, 2,000) ms]. The EPI allows fast data acquisition of the remaining MW signals. Seven healthy volunteers and seven patients with multiple sclerosis (MS) were recruited and scanned in this study. The apparent myelin water fraction (aMWF), defined as the signal ratio of MW to total water, was measured in the lesions and normal-appearing white matter (NAWM) in MS patients and compared with those measured in the normal white matter (NWM) in healthy volunteers. Results As seen in the STAIR-EPI images acquired from MS patients, the MS lesions show lower signal intensities than NAWM do. The aMWF measurements for both MS lesions (3.6 ± 1.3%) and NAWM (8.6 ± 1.2%) in MS patients are significantly lower than NWM (10 ± 1.3%) in healthy volunteers (P < 0.001). Discussion The proposed STAIR-EPI technique, which can be implemented in MRI scanners from all vendors, is able to detect myelin loss in both MS lesions and NAWM in MS patients.
Timing and style of deglaciation of northeastern Poland from cosmogenic 36 Cl dating of glacial and glaciofluvial deposits

Determining the age of glacial and glaciofluvial deposits is necessary to better understand the deglaciation of northeastern Poland. Cosmogenic 36 Cl, accumulated in boulders and matrix, suggests that inheritance and erosion affect cosmogenic inventories and must be taken into account when calculating exposure ages of landforms. A simple approach to detect and distinguish between the two effects, by comparing the inventories of 36 Cl in boulders and matrix, allows us to compute appropriate corrections to apparent (uncorrected) ages and to determine model (corrected) exposure ages of the deposits. Apparent cosmogenic 36 Cl ages fall in the range between 11 ky and 28 ky (1 ky = 1000 calibrated 36 Cl years), pointing, correctly, to the end of the last glaciation, and correlate with oxygen isotope stage 2 (OIS 2). Model ages of glacial and glaciofluvial deposits fall into one of three time intervals. The oldest erratics, 27-28 ky (Kruszki and Bachanowo 1), date the advance of the Weichselian ice sheet. They are older than the surrounding surfaces, which confirms the existence of nunataks during later phases of the last glaciation. The main belts of recessional moraines formed 19.7 ą 1.0 ky ago (Gremzdy Polskie) and 17.9 ą 1.3 ky ago (Łopuchowo 2 and Gulbieniszki), and the last ice melted 14.4 ą 1.0 ky ago (Łopuchowo 1). Erosional terraces in the Czarna Hancza valley were formed 14.7 ą 0.9 ky ago (Bachanowo 2) and 14.4 ą 1.0 ky ago (Bachanowo 3), probably by the melt waters from the last ice in the area. Our results suggest that different parts of the southern margin of the Fennoscandian Ice Sheet advanced and retreated independently, supporting the idea that the deposits at the southern margin of the Scandinavian Ice Sheet are not synchronous.
The effect of anthropogenic sulfate and soot aerosol on the clear sky planetary radiation budget

Carbonaceous soot within the troposphere can significantly modify the clear-sky radiative forcing. Using an extension to a simple radiation calculation and two model-derived sulfate aerosol data sets, the impact of an assumed soot/sulfate mass ratio of between 0.05 and 0.1 is examined. Fossil fuel derived soot causes a positive global-mean radiative forcing which for one data set ranges from +0.03 to +0.24Wm−2; the lower estimate is for an external mixture with a soot/sulfate ratio of 0.05 and the upper estimate is for an internal mixture and a soot/sulfate ratio of 0.10. These values compare to a global-mean radiative forcing of −0.34Wm−2 due to sulfate aerosol. Soot also significantly reduces the interhemispherical difference in the radiative forcing due to sulfate aerosol. The nature and amount of soot must be well established if the climatic role of tropospheric aerosols is to be fully understood.
Resting-State Functional Connectivity Difference in Alzheimer’s Disease and Mild Cognitive Impairment Using Threshold-Free Cluster Enhancement

The disruption of functional connectivity is one of the early events that occurs in the brains of Alzheimer’s disease (AD) patients. This paper reports a study on the clustering structure of functional connectivity in eight important brain networks in healthy, AD, and prodromal stage subjects. We used the threshold-free cluster enhancement (TFCE) method to explore the connectivity from resting-state functional MR images (rs-fMRIs). We conducted the study on a total of 32 AD, 32 HC, and 31 MCI subjects. We modeled the brain as a graph-based network to study these impairments, and pairwise Pearson’s correlation-based functional connectivity was used to construct the brain network. The study found that connections in the sensory motor network (SMN), dorsal attention network (DAN), salience network (SAN), default mode network (DMN), and cerebral network were severely affected in AD and MCI. The disruption in these networks may serve as potential biomarkers for distinguishing AD and MCI from HC. The study suggests that alterations in functional connectivity in these networks may contribute to cognitive deficits observed in AD and MCI. Additionally, a negative correlation was observed between the global clinical dementia rating (CDR) score and the Z-score of functional connectivity within identified clusters in AD subjects. These findings provide compelling evidence suggesting that the neurodegenerative disruption of functional magnetic resonance imaging (fMRI) connectivity is extensively distributed across multiple networks in individuals diagnosed with AD.
TEMPEREDNESS OF 𝐿 2 (Γ⧵𝐺) AND POSITIVE EIGENFUNCTIONS IN HIGHER RANK

Let 𝐺 = SO ∘ (𝑛,1) × SO ∘ (𝑛,1) and 𝑋 = ℍ 𝑛 × ℍ 𝑛 for 𝑛 ≥ 2 . For a pair (𝜋 1 ,𝜋 2 ) of non-elementary convex cocompact representations of a finitely generated group Σ into SO ∘ (𝑛,1) , let Γ = (𝜋 1 × 𝜋 2 )(Σ) . Denoting the bottom of the 𝐿 2 -spectrum of the negative Laplacian on Γ⧵𝑋 by 𝜆 0 , we show: (1) 𝐿 2 (Γ⧵𝐺) is tempered and 𝜆 0 = 12 (𝑛 − 1) 2 ; (2) There exists no positive Laplace eigenfunction in 𝐿 2 (Γ⧵𝑋) . In fact, analogues of (1)-(2) hold for any Anosov subgroup Γ in the product of at least two simple algebraic groups of rank one as well as for Hitchin subgroups Γ < PSL 𝑑 (ℝ) , 𝑑 ≥ 3 . Moreover, if 𝐺 is a semisimple real algebraic group of rank at least 2 , then (2) holds for any Anosov subgroup Γ of 𝐺 .
The Political Economy of the Smoot-Hawley Tariff

Economic histories of the interwar years view the Great Depression and the Smoot Hawley Tariff as inextricably bound up with one another. They assign a central role to the Depression in explaining the passage of the 1930 Tariff Act and at the same time emphasize the role of the tariff in the propogation of the Depression. This paper argues that popular accounts have conveyed what is at best an incomplete and at worst a misleading impression of the relationship between the tariff and the Depression. Rather than simply strengthening the hand of a Republican Executive predisposed toward protection or increasing the burden borne by a depressed agricultural sector, the uneven impact of the Depression occasioned the birth of a new protectionist coalition comprised of producers particularly hard hit by import competition: border agriculture and small-scale industry engaged in the production of speciality goods. Rather than leading to a dramatic across-the-board decline in the volume of U.S. imports, the tariff had very different effects across sectors. Rather than worsening the Great Depression by reducing foreign demands for U.S. exports, the direct macroeconomic effect of the tariff is likely to have been expansionary. This remains true even when feedbacks to the United States and foreign retaliation are analyzed. In any case, relative to the Depression, the direct macroeconomic effects of the tariff were small. If Smoot-Hawley had significant macroeconomic effects, these operated instead through its impact on the stability of the international monetary system and the efficiency of the international capital market.
Credit Booms, Labor Reallocation, and Productivity Growth ∗

We investigate how credit expansion aﬀects labor reallo-cation and labor productivity growth in a sample of over 20 advanced economies between 1979 and 2009. Using industry-level data, we decompose aggregate labor productivity growth for each country into a common and an allocation component. We then run country ﬁxed-eﬀects panel regressions to examine how credit expansion inﬂuences each of these components. Next, we run industry-level regressions to examine how the sensitivity of employment growth to labor productivity growth varies with the intensity of credit expansion. Both analyses lead to the conclusion that credit growth tends to reduce aggregate labor productivity growth. The evidence also suggests that during credit booms this reduction occurs through labor reallocations toward lower productivity growth sectors
QSM Reconstruction Challenge 2.0–Part 1: A Realistic in silico Head Phantom for MRI data simulation and evaluation of susceptibility mapping procedures

Purpose To create a realistic in-silico head phantom for the second QSM Reconstruction Challenge and for future evaluations of processing algorithms for Quantitative Susceptibility Mapping (QSM). Methods We created a whole-head tissue property model by segmenting and post-processing high-resolution, multi-parametric MRI data acquired from a healthy volunteer. We simulated the steady-state magnetization using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based post-processing. We demonstrated some of the phantom’s properties, including the possibility of generating phase data that do not evolve linearly with echo time due to partial volume effects or complex distributions of frequency shifts within the voxel. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the model, such as the inclusion of pathologies, as well as the simulation of a wide range of acquisition protocols. Results The brain-part of the phantom features realistic morphology combined with realistic spatial variations in relaxation and susceptibility values. Simulation code allows adjusting the following parameters and effects: repetition time and echo time, voxel size, background fields, and RF phase biases. Additionally, diffusion weighted imaging data of the phantom is provided allowing future investigations of tissue microstructure effects in phase and QSM algorithms. Conclusion The presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative MRI reconstruction algorithms.
Remote sensing ocean data analyses using fuzzy C-Means clustering

With the deep understanding and exploitation of the wide Ocean, There are more and more fine instrument installed or loaded on measuring ships or other marines. The high costs and complexity of corrosion place ever-increasing demands on the analyses of surrounding ocean environment. In this paper, the fuzzy C-Means clustering is used to analyze the surrounding ocean environment with remote sensing data. The studied ocean area is considered as a two dimensional gird or an image, and the fuzzy C-Means clustering technique is used to reveal the underlying relationship of the elements and segment the interrelated ocean in regions with similar spectral properties in the influence of instrument corrosion. The influence of the environment elements in instrument corrosion is studied and a priori spatial information is added to improving the segmentation result. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization and the segmentation could be accomplished. The calculation results show that the segmentation is accurate and reasonable. This ocean environment analysis fruit has used in real application and has proved to be valuable in ship instrument corrosion monitoring and the guide of other ocean activity.
Adjoint envelope tomography for scattering and absorption using radiative transfer theory

SUMMARY To investigate the small-scale elastic structure of the subsurface at length scales below the resolution limits of waveform tomography, envelopes of high-frequency scattered seismic waveforms have been used with a variety of approaches. However, a rigorous framework for the iterative inversion of seismogram envelopes to image heterogeneity and high-frequency attenuation comparable to full waveform inversion (FWI) is missing. We present the mathematical framework for an iterative full envelope inversion using forward and adjoint simulations of the radiative transfer equations, in full analogy to FWI that is based on the wave equation. The forward and adjoint problems are solved by modelling 2-D multiple non-isotropic scattering in a random elastic medium with spatially variable heterogeneity and attenuation using the Monte Carlo method. Sensitivity kernels are derived for the squared difference between the full observed and modelled envelopes which is iteratively minimized with the L-BFGS method. We apply this algorithm in numerical tests in the acoustic approximation and show that it is possible to image the spatial distribution of small-scale heterogeneity and attenuation in iterative inversions. Our analysis shows that the relative importance of scattering and attenuation anomalies needs to be considered when the model resolution is assessed. The inversions conﬁrm that the early coda is important for imaging the distribution of heterogeneity while later coda waves are more sensitive to intrinsic attenuation and we show that this dependency can be used to cope with the trade-off that exists between both material properties.
Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method

A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system’s state.
Temporal Risk Resolution: Utility versus Probability Weighting Approaches

5 This paper reports two experiments in which attitudes towards temporal risk resolution 6 is elicited from choices between two-outcome lotteries that pay out at some future ﬁxed 7 date and can be resolved either now or later. We show that matching probabilities provides 8 a simple method to measure attitudes towards temporal resolution –via the utility scale– 9 under Kreps and Porteus’ (1978) recursive expected utility. We also analyze our data using 10 a general recursive model that can reveal attitudes towards temporal risk resolution through 11 the utility scale and/or the probability weighting scale. In terms of goodness of ﬁt, as 12 well as of prediction accuracy, our results point to a better performance of the probability 13 weighting approach. More speciﬁcally, we show that individuals become less sensitive and 14 more pessimistic with respect to winning probabilities when lotteries are resolved later rather 15 than now. 16
Principles of Biophysical and Biochemical Characterization of Root Vegetables’ Bioactive Proteins

The characterization of root vegetables’ bioactive proteins biophysically and biochemically becomes imperative as they play an incredibly important role in the discovery, development, and formulation of bioactive proteins as biopharmaceutical products. This is because bioactive proteins differ in terms of size, molecular weights, structures, and physicochemical properties. Biophysical and biochemical characterization employs several techniques ranging from simple to complex procedures to give insight into proteins’ high-order structures, functions, and biochemical activities. Owing to the increasing awareness and acceptance of the use of peptides and proteins of root vegetable origin as treatment agents against some debilitatingly chronic diseases, researchers are now exploring an eco-innovative approach to reduce their loss by getting to structurally and functionally characterizing them. Several biophysical and biochemical tools are employed routinely for protein characterization and some of which are ultraviolet-visual (UV-Vis) spectroscopy, highperformance liquid chromatography (HPLC), circular dichroism (CD), intrinsic tryptophan fluorescence (ITF), differential scanning calorimetry (DSC), thermal shift assay (TSA), among others.
Virulence strategies of an insect herbivore and oomycete plant pathogen converge on a host E3 SUMO ligase

Pathogens and pests secrete proteins (effectors) to interfere with plant immunity through modification of host target functions and disruption of immune signalling networks. Importantly, molecular virulence strategies of distinct pathogens converge on a small set of regulators with central roles in plant immunity. The extent of convergence between pathogen and herbivorous insect virulence strategies is largely unexplored. We found that effectors from the oomycete pathogen, Phytophthora capsici, and the major aphid pest, Myzus persicae target the host immune regulator SIZ1, an E3 SUMO ligase. We show that the oomycete and aphid effector, which both contribute to virulence, feature different activities towards SIZ1. While M. persicae effector Mp64 increases SIZ1 protein levels, P. capsici effector CRN83_152 enhances SIZ1-E3 SUMO ligase activity in vivo. Loss of SIZ1 in host plants leads to reduced host susceptibility to aphids and an oomycete pathogen. Our results suggest convergence of distinct pathogen and pest virulence strategies on an E3 SUMO ligase to enhance host susceptibility.
Prediction of Earth arrival times of interplanetary southward magnetic field turnings

ACE magnetic field and velocity data, taken in L1 halo orbit, are used to predict the arrival time at the Wind spacecraft of sharp southward turnings of the interplanetary magnetic field. If such events are assumed to be tangential discontinuities, their orientations can be deduced from the cross product of the field immediately upstream and downstream of them. It is shown that such an assumption results in better predictions of arrival times than using minimum variance or simply assuming a sunward normal. Of the 70% of events observed at ACE that could be identified at Wind, 93% of arrival times were predicted within 10 min using the cross product, compared to 56% using minimum variance and 67% assuming a sunward normal. The most important factor controlling the likelihood of observing events at Wind was the angle of the discontinuity normal from the flow direction, although weak dependencies on solar wind speed, flow-perpendicular separation, and normal-perpendicular separation were also found. These results suggest that the cross product is an accurate estimator of discontinuity orientations and that using L1 monitor data, it would be possible to predict arrival times of around 86% of southward turnings at Earth with an accuracy of 10 min.
Optimal Cross-Sectional Spacing in Preissmann Scheme 1D Hydrodynamic Models

Choosing a suitable set of cross sections for the representation of the natural geometry of a river is important for the efficiency of one-dimensional (1D) hydraulic models, but only few guidelines are available for the selection of the most suitable distance between cross sections, depending on the hydraulic problem at hand. This issue is investigated by examining models of a ~55 km reach of the River Po, Italy, and a ~16 km reach of the River Severn, United Kingdom, for both of which high quality laser scanning altimetry are available. The high-resolution digital terrain models of the two river reaches enabled the construction of a series of hypothetical topographical ground surveys with different spacing between cross sections, which could be used as input to a standard 1D model (UNET). Both historical and synthetic flood events for the two river reaches were simulated, and the results were then analyzed to quantify the accuracy associated with each resolution and to assess how survey resolution impacts the performance of standard 1D models. The study results agree with the available suggestions in the literature and provide useful guidelines for 1D hydrodynamic modeling.
Development and Evaluation of Impact Statements for the Expanded Food and Nutrition Education Program (EFNEP)

Extension professionals often communicate program outcomes to external stakeholders using impact statements. We developed and evaluated four impact statements for the Expanded Food and Nutrition Education Program (EFNEP). We drafted the statements after conducting literature reviews for core content areas of EFNEP that include diet quality, food resource management, physical activity, and food safety. Subsequently, we evaluated the statements by facilitating expert panels made up of subject matter experts and communication professionals (n=14) from 12 Land-grant Universities. These impact statements aim to support EFNEP and other Extension professionals when communicating program value with key external stakeholders.