The Unintended Effect of Tax Avoidance Crackdown on Corporate Innovation

To constrain the use of intangible assets in tax-motivated income shifting and thus crackdown on corporate tax avoidance, many U.S. state governments adopted addback statutes. Addback statutes require firms to add back intangible-related expenses paid to related parties in other states to the taxable income reported in the state taxable income. The addback reduces the benefits that firms and managers can gain from creating intangible assets such as patents. In this study, we examine the potential unintended effect of addback statutes on corporate innovation. First, we find that the adoption of addback statutes significantly reduces a firm’s innovation, measured by the number of patents or patent citations. Second, the “disappeared patents” resulting from tax avoidance crackdown do not seem to be of lower quality than other patents. Third, after a state adopts an addback statute, a firm with material subsidiaries in that state assigns fewer patents to subsidiaries in Delaware, where income generated by intangible assets is free of state income tax. Finally, affected firms do not have lower innovation prior to the adoption of addback statues. Overall, these findings suggest that the adoption of addback statutes impedes corporate innovation. Our study has important implications for policy makers who are interested in understanding the consequences of policies that constrain tax-motivated income shifting using intangibles and prevent income base erosion

The role that choice of model plays in predictions for epilepsy surgery

This is the final version. Available on open access from Nature Research via the DOI in this recordMathematical modelling has been widely used to predict the effects of perturbations to brain networks. An important example is epilepsy surgery, where the perturbation in question is the removal of brain tissue in order to render the patient free of seizures. Different dynamical models have been proposed to represent transitions to ictal states in this context. However, our choice of which mathematical model to use to address this question relies on making assumptions regarding the mechanism that defines the transition from background to the seizure state. Since these mechanisms are unknown, it is important to understand how predictions from alternative dynamical descriptions compare. Herein we evaluate to what extent three different dynamical models provide consistent predictions for the effect of removing nodes from networks. We show that for small, directed, connected networks the three considered models provide consistent predictions. For larger networks, predictions are shown to be less consistent. However consistency is higher in networks that have sufficiently large differences in ictogenicity between nodes. We further demonstrate that heterogeneity in ictogenicity across nodes correlates with variability in the number of connections for each node.Engineering and Physical Sciences Research Council (EPSRC)Medical Research Council (MRC)Epilepsy Research UKWellcome Trus

The Role of Excitability and Network Structure in the Emergence of Focal and Generalized Seizures

This is the final version. Available on open access from Frontiers Media via the DOI in this recordData Availability Statement: The code and synthetic networks generated are available upon request.Epileptic seizures are generally classified as either focal or generalized. It had been traditionally assumed that focal seizures imply localized brain abnormalities, whereas generalized seizures involve widespread brain pathologies. However, recent evidence suggests that large-scale brain networks are involved in the generation of focal seizures, and generalized seizures can originate in localized brain regions. Herein we study how network structure and tissue heterogeneities underpin the emergence of focal and widespread seizure dynamics. Mathematical modeling of seizure emergence in brain networks enables the clarification of the characteristics responsible for focal and generalized seizures. We consider neural mass network dynamics of seizure generation in exemplar synthetic networks and we measure the variance in ictogenicity across the network. Ictogenicity is defined as the involvement of network nodes in seizure activity, and its variance is used to quantify whether seizure patterns are focal or widespread across the network. We address both the influence of network structure and different excitability distributions across the network on the ictogenic variance. We find that this variance depends on both network structure and excitability distribution. High variance, i.e., localized seizure activity, is observed in networks highly heterogeneous with regard to the distribution of connections or excitabilities. However, networks that are both heterogeneous in their structure and excitability can underlie the emergence of generalized seizures, depending on the interplay between structure and excitability. Thus, our results imply that the emergence of focal and generalized seizures is underpinned by an interplay between network structure and excitability distribution.Medical Research Council (MRC)Epilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome TrustInnovate U

Revealing epilepsy type using a computational analysis of interictal EEG

This is the final version. Available from Nature Research via the DOI in this record.All materials (functional networks and code) are available upon request from the corresponding author.Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG.Medical Research Council (MRC)Wellcome TrustEpilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome Trus

Individualism, Collectivism, and Goal-Oriented Saving

This study examines how individualism (vs. collectivism) influences people's goal-oriented saving decisions. Three experimental studies show that the effect of individualism (collectivism) on people's propensity to save is contingent on the purpose of saving. People who are chronically or situationally high in individualist values (the "individualists") have a higher propensity to save for selfenhancing purposes (e.g., job transition or education) than do those who are high in collectivist values ("the collectivists"). When saving for self-enhancing purposes, the individualists also show a higher propensity to resist temptations for immediate gratifications than do the collectivists. However, the individualists and the collectivists do not differ in their propensity to save and to resist myopic temptations when saving for self-indulging purposes (e.g., saving for a vacation)

Revealing epilepsy type using a computational analysis of interictal EEG.

Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG

Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy

This is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this recordObjective: The effectiveness of intracranial electroencephalography (iEEG) to inform epilepsy surgery depends on where iEEG electrodes are implanted. This decision is informed by noninvasive recording modalities such as scalp EEG. Herein we propose a framework to interrogate scalp EEG and determine epilepsy lateralization to aid in electrode implantation. Methods: We use eLORETA to map source activities from seizure epochs recorded from scalp EEG and consider 15 regions of interest (ROIs). Functional networks are then constructed using the phase-locking value and studied using a mathematical model. By removing different ROIs from the network and simulating their impact on the network’s ability to generate seizures in silico, the framework provides predictions of epilepsy lateralization. We consider 15 individuals from the EPILEPSIAE database and study a total of 62 seizures. Results were assessed by taking into account actual intracranial implantations and surgical outcome. Results: The framework provided potentially useful information regarding epilepsy lateralization in 12 out of the 15 individuals (p=0.02, binomial test). Conclusions: Our results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization. Significance: The framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.Medical Research CouncilEpilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome TrustEngineering and Physical Sciences Research Council (EPSRC)Innovate UKEuropean Union’s Horizon 2020Alzheimer's SocietyMedical Research Counci

Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and minimize any risks to health or the environment posed by the presence of ENMs. Data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure–activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity and, most importantly, interpretation of the models they generate is often very difficult. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large datasets, are not particularly good at feature selection, can be relatively opaque to interpretation, and may not account for nonlinearity in the structure–property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four diverse literature datasets. We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with important advantages that it works with small datasets, automatically selects descriptors, and provides significantly improved interpretability of models