Search

Resting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations in the blood oxygen-level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD-derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high-resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI-derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel-wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.

According to the U.S. Supreme Court, the rising number of workplace retaliation claims is a problem, one warranting more stringent requirements for employees to successfully bring claims. The Court’s principal justification for this restrictive approach is a fear of “opening the floodgates” of litigation. This Article critically assesses the Court’s fear of opening the floodgates of retaliation claims, evaluates the Court’s evidence, and argues that such concerns are overstated and misplaced. Rather than a cause for concern, the rise in retaliation claims reflects rising intra-organizational conflict. Social scientists have demonstrated that, as the American workforce becomes more diverse, intra-organizational conflict increases, and the propensity for civil rights violations grows. In other words, claims are on the rise because retaliation is on the rise. Employment discrimination and other related statutes are aimed at mitigating the harms of this expected rise in intra-organizational conflict.The Article further argues that considerations of judicial economy are particularly misplaced in workplace retaliation cases. Retaliation protections are crucial to the private enforcement scheme Congress developed for civil rights laws generally and employment discrimination laws in particular. Attempting to limit judicial caseloads through restrictive interpretations of anti-retaliation laws eviscerates private enforcement, producing under- enforcement of these core civil rights protections. To remedy the Supreme Court’s wrong turn on retaliation, Congress should act. This Article proposes that Congress adopt a rule of construction mandating broad interpretation of all workplace anti-retaliation statutory provisions. This provision would strengthen critical civil rights safeguards for employees by restoring the optimal and essential function of retaliation provisions.

This year marks the fiftieth anniversary of Title IX of the Education Amendments of 1972, a gender-neutral statute that prohibits discrimination on the basis of sex in programs that receive federal financial assistance. This legislation has been nothing short of revolutionary, paving the way for equal opportunity for millions of women and men. This article covers the history, accomplishments, and future of Title IX. It is meant to spark an engaging classroom discussion on the law's impact.

To comprehensively model the macroeconomic impacts that result from changes in long-term energy-economy forecasts, the United States (U.S.) Department of Energy's National Energy Technology Laboratory (NETL) partnered with West Virginia University (WVU)'s Regional Research Institute to develop the NETL/WVU econometric input-output (ECIO) model. The NETL/WVU ECIO model is an impacts forecasting model that functions as an extension of the U.S. energy-economic models available from the U.S. Energy Information Administration's National Energy Modeling System (NEMS) and the U.S. Environmental Protection Agency's Market Allocation (MARKAL) model. The ECIO model integrates a macroeconomic econometric forecasting model and an input-output accounting framework along derived forecast scenarios detailing a baseline of the U.S. energy-economy and an alternative forecast on how power generation resources can meet future levels of energy demand to generate estimates of the impacts to gross domestic product, employment, and labor income. This manuscript provides an overview of the model design, assumptions, and standard outputs. Keywords: Energy-Economy Forecasting, National Energy Modeling System, Input-Output Model, Econometric Model, 1. INTRODUCTION Decision makers involved in energy policy are facing unprecedented challenges arising from globalization, decarbonization, and the advent of new energy technologies. Since the early 1970s energy-economic forecasting models [...]

Economic justice has long been a part of the civil rights agenda, and minimum labor standards statutes play a crucial role in eradicating the exploitation and subordination of historically marginalized workers. While statutes establishing labor standards are characterized as 'universal,' their effect has been anything but universal. Racial and ethnic minorities, women, and those at the intersection experience disproportionate violations of labor standards laws concerning minimum wage, overtime, and occupational safety and health. Through legislative maneuvering dating back to the New Deal era, Congress carved out many female workers and workers of color from core protections of minimum labor standards legislation. Due to the vigorous advocacy of civil rights groups, amendments to these statutes expanded coverage, making these statutes more inclusive of marginalized workers. Nevertheless, the exclusionary legacy of these New Deal era-laws lingers today. Black, Latinx, and female workers are more likely to be retaliated against for asserting rights or reporting employer misconduct pursuant to these statutes. Tracing the racial and gendered origins of exemptions to labor standards statutes from the early twentieth century to the present, this Article argues that, despite expanded coverage, female workers and workers of color remain largely excluded from 'universal' workplace protections. Although antiworker forces previously sought to thwart creation of legal rights for marginalized workers, contemporary antiworker campaigns seek to gut marginalized workers' protections through actual and threatened retaliation. Examination of the traditional rationales for employer retaliation reveals that the retaliation disparity is incongruent with these conventional motivations. This Article argues that securing compliance with both minimum labor standards and antiretaliation reform should be integral parts of the civil rights agenda., TABLE OF CONTENTS INTRODUCTION I. HISTORICAL EXCLUSIONS TO 'UNIVERSAL' LEGISLATION A. The Legal and Political Landscape of the New Deal B. The FLSA's New Deal Predecessor Statutes C. The FLSA's [...]

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.