Economic Value of Original Non-Market Valuation Research

We describe a method to determine the net economic gain from conducting original research to estimate non-market benefits of public policy and demonstrate an application of this method. We provide a step-wise method to allow policy practitioners to make informed decisions about when there are expected net benefits to conducting or contracting for original research to estimate the benefits of a policy decision.Research and Development/Tech Change/Emerging Technologies,

The Time is Right for an Antarctic Biorepository Network

Antarctica is a central driver of the Earth’s climate and health. The Southern Ocean surrounding Antarctica serves as a major sink for anthropogenic CO2 and heat (1), and the loss of Antarctic ice sheets contributes significantly to sea level rise and will continue to do so as the loss of ice sheets accelerates, with sufficient water stores to raise sea levels by 58 m (2). Antarctica\u27s marine environment is home to a number of iconic species, and the terrestrial realm harbors a remarkable oasis for life, much of which has yet to be discovered (3). Distinctive oceanographic features of the Southern Ocean—including the Antarctic Circumpolar Current, the Antarctic Polar Front, and exceptional depths surrounding the continent—coupled with chronically cold temperatures have fostered the evolution of a vast number of uniquely coldadapted species, many of which are found nowhere else on the Earth (4). The Antarctic marine biota, for example, displays the highest level of species endemism on the Earth (5). However, warming, ocean acidification, pollution, and commercial exploitation threaten the integrity of Antarctic ecosystems (6). Understanding changes in the biota and its capacities for adaptation is imperative for establishing effective policies for mitigating the impacts of climate change and sustaining the Antarctic ecosystems that are vital to global health

Highly multiplexed, label-free proteoform imaging of tissues by individual ion mass spectrometry.

Imaging of proteoforms in human tissues is hindered by low molecular specificity and limited proteome coverage. Here, we introduce proteoform imaging mass spectrometry (PiMS), which increases the size limit for proteoform detection and identification by fourfold compared to reported methods and reveals tissue localization of proteoforms at <80-μm spatial resolution. PiMS advances proteoform imaging by combining ambient nanospray desorption electrospray ionization with ion detection using individual ion mass spectrometry. We demonstrate highly multiplexed proteoform imaging of human kidney, annotating 169 of 400 proteoforms of <70 kDa using top-down MS and a database lookup of ~1000 kidney candidate proteoforms, including dozens of key enzymes in primary metabolism. PiMS images reveal distinct spatial localizations of proteoforms to both anatomical structures and cellular neighborhoods in the vasculature, medulla, and cortex regions of the human kidney. The benefits of PiMS are poised to increase proteome coverage for label-free protein imaging of tissues

Dynamic Functional Connectivity Analysis Reveals Transient States of Dysconnectivity in Schizophrenia

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients.However, observed connectivity differences in schizophrenia have been inconsistent between studies,with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on amulti-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using slidingwindows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spendmuch less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical–subcortical antagonism (anticorrelations) and strong positive connectivity between sensory networks are those that showthe group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivitywith sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences

The Acute Satellite Cell Response and Skeletal Muscle Hypertrophy following Resistance Training

The extent of skeletal muscle hypertrophy in response to resistance training is highly variable in humans. The main objective of this study was to explain the nature of this variability. More specifically, we focused on the myogenic stem cell population, the satellite cell (SC) as a potential mediator of hypertrophy. Twenty-three males (aged 18–35 yrs) participated in 16 wk of progressive, whole body resistance training, resulting in changes of 7.9±1.6% (range of −1.9–24.7%) and 21.0±4.0% (range of −7.0 to 51.7%) in quadriceps volume and myofibre cross-sectional area (CSA), respectively. The SC response to a single bout of resistance exercise (80% 1RM), analyzed via immunofluorescent staining resulted in an expansion of type II fibre associated SC 72 h following exercise (pre: 11.3±0.9; 72 h: 14.8±1.4 SC/type II fibre; p<0.05). Training resulted in an expansion of the SC pool associated with type I (pre: 10.7±1.1; post: 12.1±1.2 SC/type I fibre; p<0.05) and type II fibres (pre: 11.3±0.9; post: 13.0±1.2 SC/type II fibre; p<0.05). Analysis of individual SC responses revealed a correlation between the relative change in type I associated SC 24 to 72 hours following an acute bout of resistance exercise and the percentage increase in quadriceps lean tissue mass assessed by MRI (r2 = 0.566, p = 0.012) and the relative change in type II associated SC following 16 weeks of resistance training and the percentage increase in quadriceps lean tissue mass assessed by MRI (r2 = 0.493, p = 0.027). Our results suggest that the SC response to resistance exercise is related to the extent of muscular hypertrophy induced by training

Economic Value of Original Non-Market Valuation Research

We describe a method to determine the net economic gain from conducting original research to estimate non-market benefits of public policy and demonstrate an application of this method. We provide a step-wise method to allow policy practitioners to make informed decisions about when there are expected net benefits to conducting or contracting for original research to estimate the benefits of a policy decision

Is Efficiency Analysis All There Is With Data Envelopment Analysis

Nonparametric cost frontier estimation has been commonly used to examine the relative efficiency of firms without critically examining the shape of the cost frontier. To examine the shape of the cost frontier has required additional estimation using parametric methods to recover potential cost savings from multi-product and product-specific economies of scale. This paper develops and tests a method for estimating multi-product and product-specific economies of scale using the nonparametric approach by evaluating the difference between scale calculations from an assumed cost frontier and those estimated using data envelopment analysis. The results demonstrated that the nonparametric approach is able to accurately estimate multi-product economies of scale and product-specific economies of scale under alternative inefficiency distributional assumptions

A Comparison of Parametric and Nonparametric Estimation Methods for Cost Frontiers and Economic Measures

This research examines the robustness of four different estimation approaches to evaluate their ability to estimate a “true” cost frontier and associated economic measures. The manuscript evaluates three parametric methods including a two-sided error system, OLS with only positive errors, and the stochastic frontier method. The fourth method is the nonparametric DEA method augmented to calculate multi-product and product-specific economies of scale. The robustness of the four estimation methods is examined using simulated data sets from two different distributions and two different observation quantity levels. The theoretical condition of curvature for the estimated cost functions was checked for the input price, and output quantity matrices. Calculation of the Eigenvalues revealed that all three parametric estimation methods violated curvature of either the price or quantity matrix, or both. Calculation of the estimated economic efficiency measures shows the parametric methods to be susceptible to distributional assumptions. However, the DEA method in all three simulations is fairly robust in estimating the “true” cost frontier and associated economic measures while maintaining curvature of the cost function

A Nonparametric Approach to Multi-product and Product-specific Scale Economies, Economies of Scope, and Cost Efficiency for Kansas Farms

Use of nonparametric frontier estimation has been shown to accurately estimate cost frontiers, cost efficiency, and economies of scope in industries. This research uses the nonparametric approach to estimate scope and efficiency as well as multi-product and product-specific economies of scale for Kansas Farms. We find that small farms have a large cost savings advantage through exploiting economies of scale up to $500k in gross revenues. Large farms however have the best opportunity for cost savings through improving efficiency. We also find that the nonparametric approach can estimate an accurate cost frontier from a single year’s data while the parametric approach may require up to 20 years of price variability to generate a reliable frontier