Temporality, vulnerability, and energy justice in household low carbon innovations

Decarbonisation and innovation will change the affordability of different domestic energy services. This has the potential to alleviate vulnerability to fuel poverty, but it could create new injustices unless the risks are preempted and actively mitigated. In this paper, we ask: In what ways can emerging low-carbon innovations at the household scale complement, and complicate, achieving energy justice objectives? Drawing from four empirical case studies in the United Kingdom, the paper highlights different risks that come from different types of innovation required to tackle different decarbonisation challenges. More specifically, it assesses four particular household innovations—energy service contracts, electric vehicles, solar photovoltaic (PV) panels, and low carbon heating—selected for their fit with a typology of incremental vs. radical technology and modest vs. substantial changes in user practices. It shows how in each case, such innovations come with a collection of opportunities but also threats. In doing so, the paper seeks to unveil the “political economy” of low-carbon innovations, identifying particular tensions alongside who wins and who loses, as well as the scope and temporality of those consequences

Volumetric reconstruction from printed films: Enabling 30 year longitudinal analysis in MR neuroimaging

Hospitals often hold historical MR image data printed on films without being able to make it accessible to modern image processing techniques. Having the possibility to recover geometrically consistent, volumetric images from scans acquired decades ago will enable more comprehensive, longitudinal studies to understand disease progressions. In this paper, we propose a consistent framework to reconstruct a volumetric representation from printed films holding thick single-slice brain MR image acquisitions dating back to the 1980's. We introduce a flexible framework based on semi-automatic slice extraction, followed by automated slice-to-volume registration with inter-slice transformation regularisation and slice intensity correction. Our algorithm is robust against numerous detrimental effects being present in archaic films. A subsequent, isotropic total variation deconvolution technique revitalises the visual appearance of the obtained volumes. We assess the accuracy and perform the validation of our reconstruction framework on a uniquely long-term MRI dataset where a ground-truth is available. This method will be used to facilitate a robust longitudinal analysis spanning 30 years of MRI scans

A thirty year clinical and MRI observational study of multiple sclerosis and clinically isolated syndromes

OBJECTIVE: Clinical outcomes in multiple sclerosis (MS) are highly variable. We aim to determine the long-term clinical outcomes in MS, and to identify early prognostic features of these outcomes. METHODS: 132 people presenting with a clinically isolated syndrome (CIS) were prospectively recruited between 1984-87, and followed up clinically and radiologically 1, 5, 10, 14, 20 and now 30 years later. All available notes and magnetic resonance imaging (MRI) scans were reviewed, and MS was defined according to the 2010 McDonald criteria. RESULTS: Clinical outcome data was obtained in 120 participants at 30 years. Eighty were known to have developed MS by 30 years. Expanded disability status scale (EDSS) scores were available in 107 participants, of whom 77 had MS: thirty-two (42%) remained fully ambulatory (EDSS ≤3.5) all of whom had relapsing-remitting MS (RRMS), three (4%) had RRMS and EDSS >3.5, 26 (34%) had secondary progressive MS (all had EDSS >3.5), and MS contributed to death in 16 (20%). Of those with MS, 11 have been treated with a DMT. The strongest early predictors (within 5 years of presentation) of secondary progressive MS (SPMS) at 30 years were presence of baseline infratentorial lesions and deep white matter lesions at one year. INTERPRETATION: Thirty years after onset, in a largely untreated cohort, there was a divergence of MS outcomes; some people accrued substantial disability early on, while others ran a more favourable long-term course. These outcomes could, in part, be predicted by radiological findings from within a year of first presentation

A 30-Year Clinical and Magnetic Resonance Imaging Observational Study of Multiple Sclerosis and Clinically Isolated Syndromes

OBJECTIVE: Clinical outcomes in multiple sclerosis (MS) are highly variable. We aim to determine the long-term clinical outcomes in MS, and to identify early prognostic features of these outcomes. METHODS: One hundred thirty-two people presenting with a clinically isolated syndrome were prospectively recruited between 1984 and 1987, and followed up clinically and radiologically 1, 5, 10, 14, 20, and now 30 years later. All available notes and magnetic resonance imaging scans were reviewed, and MS was defined according to the 2010 McDonald criteria. RESULTS: Clinical outcome data were obtained in 120 participants at 30 years. Eighty were known to have developed MS by 30 years. Expanded Disability Status Scale (EDSS) scores were available in 107 participants, of whom 77 had MS; 32 (42%) remained fully ambulatory (EDSS scores ≤3.5), all of whom had relapsing-remitting MS (RRMS), 3 (4%) had RRMS and EDSS scores >3.5, 26 (34%) had secondary progressive MS (all had EDSS scores >3.5), and MS contributed to death in 16 (20%). Of those with MS, 11 received disease-modifying therapy. The strongest early predictors (within 5 years of presentation) of secondary progressive MS at 30 years were presence of baseline infratentorial lesions and deep white matter lesions at 1 year. INTERPRETATION: Thirty years after onset, in a largely untreated cohort, there was a divergence of MS outcomes; some people accrued substantial disability early on, whereas others ran a more favorable long-term course. These outcomes could, in part, be predicted by radiological findings from within 1 year of first presentation. ANN NEUROL 2020;87:63-74

The role of pontine lesion location in differentiating multiple sclerosis from vascular risk factor-related small vessel disease

Background: Differentiating multiple sclerosis (MS) from vascular risk factor (VRF)-small vessel disease (SVD) can be challenging. Objective and Methods: In order to determine whether or not pontine lesion location is a useful discriminator of MS and VRF-SVD, we classified pontine lesions on brain magnetic resonance imaging (MRI) as central or peripheral in 93 MS cases without VRF, 108 MS patients with VRF and 43 non-MS cases with VRF. Results: MS without VRF were more likely to have peripheral pons lesions (31.2%, 29/93) than non-MS with VRF (0%, 0/43) (Exp(B) = 29.8; 95% confidence interval (CI) = (1.98, 448.3); p = 0.014) but there were no significant differences regarding central pons lesions between MS without VRF (5.4%, 5/93) and non-MS with VRF patients (16.3%, 7/43) (Exp(B) = 0.89; 95% CI = (0.2, 3.94); p = 0.87). The presence of peripheral pons lesions discriminated between MS and VRF-SVD with 100% (95% CI = (91.8, 100)) specificity. The proportion of peripheral pons lesions in MS with VRF (30.5%, 33/108) was similar to that seen in MS without VRF (31.2%, 29/93, p = 0.99). Central lesions occurred in similar frequency in MS with VRF (8.3%, 9/108) and non-MS with VRF (16.3%, 7/43, p = 0.15). Conclusion: Peripheral pons lesion location is a good discriminator of MS from vascular lesions

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2–3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100 × to 1000 × improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼ 1011 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine

Crowdcloud: A Crowdsourced System for Cloud Infrastructure

The widespread adoption of truly portable, smart devices and Do-It-Yourself computing platforms by the general public has enabled the rise of new network and system paradigms. This abundance of wellconnected, well-equipped, affordable devices, when combined with crowdsourcing methods, enables the development of systems with the aid of the crowd. In this work, we introduce the paradigm of Crowdsourced Systems, systems whose constituent infrastructure, or a significant part of it, is pooled from the general public by following crowdsourcing methodologies. We discuss the particular distinctive characteristics they carry and also provide their “canonical” architecture. We exemplify the paradigm by also introducing Crowdcloud, a crowdsourced cloud infrastructure where crowd members can act both as cloud service providers and cloud service clients. We discuss its characteristic properties and also provide its functional architecture. The concepts introduced in this work underpin recent advances in the areas of mobile edge/fog computing and co-designed/cocreated systems

MRI Pattern Recognition in Multiple Sclerosis Normal-Appearing Brain Areas

Objective Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing­remitting type) in lesioned areas, areas of normal­appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques. Methods A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information. Results Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10−13). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10−7). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10−10). Interpretation We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale