Policy Risk and Private Investment in Ontario’s Wind Power Sector

Even though governments may adopt favourable regulatory policies for renewable power generation, their ability to encourage private sector investment depends also on the presence of regulatory governance institutions that provide credible long-term commitments to potential investors. In the case of Ontario we contend that, despite large market potential and comparatively strong regulatory incentive policies, weak regulatory governance is one factor that has accounted for the challenges in attracting and implementing large scale private investment in power generation at a reasonable cost. We find empirical support for our arguments in a unique survey of 63 wind power firms that assessed private sector opinions about the investment environment for renewable energy in Ontario. Compared to a range of factors, firms rated the stability of regulatory policy among the weakest aspects of Ontario?s business environment. However, policy stability ranked among the most important factors in firms? assessments of the attractiveness of alternative jurisdictions in their location decisions. Subsequent interviews revealed that firms have responded to this risk in Ontario by explicitly pricing it into wind project financial models – implying higher wind power prices for ratepayers – and by directing investment funds to other jurisdictions. We argue that policy stability in Ontario may be improved by devolving greater decision-making authority to regulatory agencies in the energy sector and by strengthening their institutional independence.

Functional equivalence of stem cell and stem cell-derived extracellular vesicle transplantation to repair the irradiated brain.

Cranial radiotherapy, although beneficial for the treatment of brain tumors, inevitably leads to normal tissue damage that can induce unintended neurocognitive complications that are progressive and debilitating. Ionizing radiation exposure has also been shown to compromise the structural integrity of mature neurons throughout the brain, an effect believed to be at least in part responsible for the deterioration of cognitive health. Past work has shown that cranially transplanted human neural stem cells (hNSCs) or their extracellular vesicles (EVs) afforded long-term beneficial effects on many of these cognitive decrements. To provide additional insight into the potential neuroprotective mechanisms of cell-based regenerative strategies, we have analyzed hippocampal neurons for changes in structural integrity and synaptic remodeling after unilateral and bilateral transplantation of hNSCs or EVs derived from those same cells. Interestingly, hNSCs and EVs similarly afforded protection to host neurons, ameliorating the impact of irradiation on dendritic complexity and spine density for neurons present in both the ipsilateral and contralateral hippocampi 1 month following irradiation and transplantation. These morphometric improvements were accompanied by increased levels of glial cell-derived growth factor and significant attenuation of radiation-induced increases in postsynaptic density protein 95 and activated microglia were found ipsi- and contra-lateral to the transplantation sites of the irradiated hippocampus treated with hNSCs or hNSC-derived EVs. These findings document potent far-reaching neuroprotective effects mediated by grafted stem cells or EVs adjacent and distal to the site of transplantation and support their potential as therapeutic agents to counteract the adverse effects of cranial irradiation

Event-based modelling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data

OBJECTIVE: Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multi-centre cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features. METHODS: We extracted regional measures of cortical thickness, surface area and subcortical brain volumes from T1-weighted (T1W) MRI scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1,625 healthy controls from 25 centres. Features with a moderate case-control effect size (Cohen's d≥0.5) were used to train an Event-Based Model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age of onset and anti-seizure medicine (ASM) resistance. RESULTS: In MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume and, finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated to duration of illness (Spearman's ρ=0.293, p=7.03x10-16 ), age of onset (ρ=-0.18, p=9.82x10-7 ) and ASM resistance (AUC=0.59, p=0.043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM stage zero, which represents MTLE-HS with mild or non-detectable abnormality on T1W MRI. SIGNIFICANCE: From cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing