The Continuity of Statutory and Constitutional Interpretation: An Essay for Phil Frickey

This Essay seeks to honor Phil by exploring the contributions of his Legal Process approach to a problem near and dear to his heart: the uses and legitimacy of canons of statutory construction. I focus, as Phil did in his most recent work, on the canon of constitutional avoidance—that is, the rule that courts should construe statutes to avoid significant ―doubt as to their constitutionality. This Essay largely supports Phil‘s defense of the avoidance canon, but links that defense to another set of canons that Phil has criticized: the various clear statement rules of statutory construction that Phil and Bill Eskridge memorably labeled ―quasi-constitutional law. These rules require that Congress make its intent especially clear when it legislates in areas of particular constitutional sensitivity—for example, by intruding on the prerogatives of the states. This Essay proceeds in three parts. Part I develops two problems in statutory construction—the canon of constitutional avoidance and judge-made clear statement rules—by reference to some major cases decided in the Supreme Court‘s 2008 Term. Part II elaborates the Legal Process School‘s approach to these sorts of problems of canonical construction, with particular emphasis on Professor Frickey‘s work in this vein. Part III then develops the central Legal Process insight that rules of construction are part of constitutional interpretation as a means of interpreting and protecting the broader structural aspects of the Constitution, namely, federalism and separation of powers

Who are Taxable?—Basic Problems in Definition Under the Illinois Retailers’ Occupation Tax Act

Identification of non-linear systems is a challenge, due to the richness of both model structures and estimation approaches. As a case study, in this paper we test a number of methods on a data set collected from an electrical circuit at the Free University of Brussels. These methods are based on black box and grey box model structures or on a mixture of them, which are all implemented in a forthcoming Matlab toolbox. The results of this case study illustrate the importance of the use of custom (user defined) regressors in a black box model. Based on physical knowledge or on insights gained through experience, such custom regressors allow to build efficient models with a relatively simple model structure.

“Traditional” Resource Uses and Activities: Articulating Values and Examining Conflicts in Alaska

The paper describes additions to the MATLAB system identification toolbox, that handle also the estimation of nonlinear models. Both structured grey-box models and general, flexible black-box models are covered. The idea is that the look and feel of the syntax, and the graphical user interface should be as close as possible to the linear case

Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity

We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.Comment: 13 pages, 5 tables, 15 figure

Interobserver reproducibility of perineural invasion of prostatic adenocarcinoma in needle biopsies

Numerous studies have shown a correlation between perineural invasion (PNI) in prostate biopsies and outcome. The reporting of PNI varies widely in the literature. While the interobserver variability of prostate cancer grading has been studied extensively, less is known regarding the reproducibility of PNI. A total of 212 biopsy cores from a population-based screening trial were included in this study (106 with and 106 without PNI according to the original pathology reports). The glass slides were scanned and circulated among four pathologists with a special interest in urological pathology for assessment of PNI. Discordant cases were stained by immunohistochemistry for S-100 protein. PNI was diagnosed by all four observers in 34.0% of cases, while 41.5% were considered to be negative for PNI. In 24.5% of cases, there was a disagreement between the observers. The kappa for interobserver variability was 0.67-0.75 (mean 0.73). The observations from one participant were compared with data from the original reports, and a kappa for intraobserver variability of 0.87 was achieved. Based on immunohistochemical findings among discordant cases, 88.6% had PNI while 11.4% did not. The most common diagnostic pitfall was the presence of bundles of stroma or smooth muscle. It was noted in a few cases that collagenous micronodules could be mistaken for a nerve. The distance between cancer and nerve was another cause of disagreement. Although the results suggest that the reproducibility of PNI may be greater than that of prostate cancer grading, there is still a need for improvement and standardization

Carbonic anhydrase activity of dinuclear CuII complexes with patellamide model ligands

The dicopper(ii) complexes of six pseudo-octapeptides, synthetic analogues of ascidiacyclamide and the patellamides, found in ascidians of the Pacific and Indian Oceans, are shown to be efficient carbonic anhydrase model complexes with k up to 7.3 × 10 s (uncatalyzed: 3.7 × 10 s; enzyme-catalyzed: 2 × 10 -1.4 × 10 s) and a turnover number (TON) of at least 1700, limited only by the experimental conditions used. So far, no copper-based natural carbonic anhydrases are known, no faster model systems have been described and the biological role of the patellamide macrocycles is so far unknown. The observed CO hydration rates depend on the configuration of the isopropyl side chains of the pseudo-octapeptide scaffold, and the naturally observed R*,S*, R*,S* geometry is shown to lead to more efficient catalysts than the S*,S*,S*,S* isomers. The catalytic efficiency also depends on the heterocyclic donor groups of the pseudo-octapeptides. Interestingly, the dicopper(ii) complex of the ligand with four imidazole groups is a more efficient catalyst than that of the close analogue of ascidiacyclamide with two thiazole and two oxazoline rings. The experimental observations indicate that the nucleophilic attack of a Cu- coordinated hydroxide at the CO carbon center is rate determining, i.e. formation of the catalyst-CO adduct and release of carbonate/bicarbonate are relatively fast processes

The human secretome

The proteins secreted by human cells (collectively referred to as the secretome) are important not only for the basic understanding of human biology but also for the identification of potential targets for future diagnostics and therapies. Here, we present a comprehensive analysis of proteins predicted to be secreted in human cells, which provides information about their final localization in the human body, including the proteins actively secreted to peripheral blood. The analysis suggests that a large number of the proteins of the secretome are not secreted out of the cell, but instead are retained intracellularly, whereas another large group of proteins were identified that are predicted to be retained locally at the tissue of expression and not secreted into the blood. Proteins detected in the human blood by mass spectrometry-based proteomics and antibody-based immuno-assays are also presented with estimates of their concentrations in the blood. The results are presented in an updated version 19 of the Human Protein Atlas in which each gene encoding a secretome protein is annotated to provide an open-access knowledge resource of the human secretome, including body-wide expression data, spatial localization data down to the single-cell and subcellular levels, and data about the presence of proteins that are detectable in the blood

A new framework for cortico-striatal plasticity: behavioural theory meets In vitro data at the reinforcement-action interface

Operant learning requires that reinforcement signals interact with action representations at a suitable neural interface. Much evidence suggests that this occurs when phasic dopamine, acting as a reinforcement prediction error, gates plasticity at cortico-striatal synapses, and thereby changes the future likelihood of selecting the action(s) coded by striatal neurons. But this hypothesis faces serious challenges. First, cortico-striatal plasticity is inexplicably complex, depending on spike timing, dopamine level, and dopamine receptor type. Second, there is a credit assignment problem—action selection signals occur long before the consequent dopamine reinforcement signal. Third, the two types of striatal output neuron have apparently opposite effects on action selection. Whether these factors rule out the interface hypothesis and how they interact to produce reinforcement learning is unknown. We present a computational framework that addresses these challenges. We first predict the expected activity changes over an operant task for both types of action-coding striatal neuron, and show they co-operate to promote action selection in learning and compete to promote action suppression in extinction. Separately, we derive a complete model of dopamine and spike-timing dependent cortico-striatal plasticity from in vitro data. We then show this model produces the predicted activity changes necessary for learning and extinction in an operant task, a remarkable convergence of a bottom-up data-driven plasticity model with the top-down behavioural requirements of learning theory. Moreover, we show the complex dependencies of cortico-striatal plasticity are not only sufficient but necessary for learning and extinction. Validating the model, we show it can account for behavioural data describing extinction, renewal, and reacquisition, and replicate in vitro experimental data on cortico-striatal plasticity. By bridging the levels between the single synapse and behaviour, our model shows how striatum acts as the action-reinforcement interface

A System Identification Software Tool for General MISO ARX-Type of Model Structures

The typical system identification procedure requires powerful and versatile software means. In this paper we describe and exemplify the use of a prototype identi#cation software tool, applicable for the rather broad class of multi input single output model structures with regressors that are formed by delayed in- and outputs. Interesting special instances of this model structure category include, e.g., linear ARX and many semi-physical structures, feed-forward neural networks, radial basis function networks, hinging hyperplanes, certain fuzzy structures, etc., as well as anyhybrid obtained by combining an arbitrary number of such approaches