Non-verbal episodic memory deficits in primary progressive aphasias are highly predictive of underlying amyloid pathology

Diagnostic distinction of primary progressive aphasias (PPA) remains challenging, in particular for the logopenic (lvPPA) and nonfluent/agrammatic (naPPA) variants. Recent findings highlight that episodic memory deficits appear to discriminate these PPA variants from each other, as only lvPPA perform poorly on these tasks while having underlying amyloid pathology similar to that seen in amnestic dementias like Alzheimer’s disease (AD). Most memory tests are, however, language based and thus potentially confounded by the prevalent language deficits in PPA. The current study investigated this issue across PPA variants by contrasting verbal and non-verbal episodic memory measures while controlling for their performance on a language subtest of a general cognitive screen. A total of 203 participants were included (25 lvPPA; 29 naPPA; 59 AD; 90 controls) and underwent extensive verbal and non-verbal episodic memory testing, with a subset of patients (n = 45) with confirmed amyloid profiles as assessed by Pittsburgh Compound B and PET. The most powerful discriminator between naPPA and lvPPA patients was a non-verbal recall measure (Rey Complex Figure delayed recall), with 81% of PPA patients classified correctly at presentation. Importantly, AD and lvPPA patients performed comparably on this measure, further highlighting the importance of underlying amyloid pathology in episodic memory profiles. The findings demonstrate that non-verbal recall emerges as the best discriminator of lvPPA and naPPA when controlling for language deficits in high load amyloid PPA cases

Traditional ecological knowledge in the Peruvian Andes : practice, synergies, and sustainability

This thesis presents a theoretical discussion on the role of Traditional Ecological Knowledge (TEK) in livelihood activities and resilience strategies of the Indigenous peoples of the Peruvian Andes and the possibility of creating synergies with Western science. Using two case studies, from the Potato Park in Pisaq and the Chalakuy Maize Park in Lares, Cusco Region, it reviews how this ancestral knowledge is converted into practice by its holders to cultivate and protect the potato and maize varieties of the Andean highlands. The Quechua values of community, reciprocity, complementarity and solidarity are also considered, as they play an important role in the governance structures and the redistributive mechanisms of the parks. The study then examines how the collaboration with civil society and science practitioners has sparked innovation, improved the resilience of these communities to climate change and established the parks as Biocultural Heritage Territories for the protection of the Andean biodiversity. The analysis of the case studies demonstrates that TEK is a living, highly adaptable and valid source of information and practices of ecosystem management and climate-change adaptation for its holders. It may, however, be unsuitable to solve global sustainability problems due to its local and context-specific nature. The thesis concludes that TEK can, however, offer much-needed reflections on how to reconsider the anthropocentric view of Western science and capitalism, and rediscover a long-lost connection with our roots and a renewed respect for the natural world.M-D

Science Through the “Golden Security Triangle”: Information Security and Data Journeys in Data-intensive Biomedicine

This paper talks about ways in which infrastructure for biomedical data-intensive discovery is operationalized. Specifically, it is interested in information security solutions and how the processes of scientific research through data-intensive infrastructures are shaped by them. The implications of information security for big data biomedical research have not been discussed in depth by the extant IS literature. Yet, information security might exert a strong influence on the processes and outcomes of data sharing efforts. In this research-in-progress paper I present a developing, in-depth study of a leading information linkage infrastructure that is representative of the kind of opportunities that big data technologies are occasioning in the medical field. This research calls for IS to extend the discussion to consider, building on the empirical detail of intensive case studies, a whole range of relations between provisions for information security and the processes of scientific research and data work

Eye in the sky

This is the author accepted manuscriptAlan Turing Institut

Book Review: “Raw Data” is an Oxymoron

Book review of ’"Raw Data" is an Oxymoron’, by Lisa Gitelman (ed.), MIT Press

Till data do us part: Understanding data-based value creation in data-intensive infrastructures

Much of the literature on value creation in social media-based infrastructures has largely neglected the pivotal role of data and their processes. This paper tries to move beyond this limitation and discusses data-based value creation in data-intensive infrastructures, such as social media, by focusing on processes of data generation, use and reuse, and on infrastructure development activities. Building on current debates in value theory, the paper develops a multidimensional value framework to interrogate the data collected in an embedded ethnographical case study of the development of PatientsLikeMe, a social media network for patients. It asks when, and where, value is created from the data, and what kinds of value are created from them, as they move through the data infrastructure; and how infrastructure evolution relates to, and shapes, existing data-based value creation practices. The findings show that infrastructure development can have unpredictable consequences for data-based value creation, shaping shared practices in complex ways and through a web of interdependent situations. The paper argues for an understanding of infrastructural innovation that accounts for the situational interdependencies of data use and reuse. Uniquely positioned, the paper demonstrates the importance of research that looks critically into processes of data use in infrastructures to keep abreast of the social consequences of developments in big data and data analytics aimed at exploiting all kinds of digital traces for multiple purposes.This research is funded by the European Research Council (ERC) under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement number 335925

Science Through the “Golden Security Triangle”: Information Security and Data Journeys in Data-intensive Biomedicine.

37th International Conference on Information Systems, Dublin, Ireland, 11-14 December 2016This is the author accepted manuscript. The final version is available from the Association for Information Systems via the URL in this record.This paper talks about ways in which infrastructure for biomedical data-intensive discovery is operationalized. Specifically, it is interested in information security solutions and how the processes of scientific research through data-intensive infrastructures are shaped by them. The implications of information security for big data biomedical research have not been discussed in depth by the extant IS literature. Yet, information security might exert a strong influence on the processes and outcomes of data sharing efforts. In this research-in-progress paper I present a developing, in-depth study of a leading information linkage infrastructure that is representative of the kind of opportunities that big data technologies are occasioning in the medical field. This research calls for IS to extend the discussion to consider, building on the empirical detail of intensive case studies, a whole range of relations between provisions for information security and the processes of scientific research and data work.This research is funded by the European Research Council under the European Union's 7th Framework Programme (FP7/2007-2013) / ERC grant agreement n° 335925

Book Review: Big Data: A Revolution That Will Transform How We Live, Work, and Think

Book review of "Big Data: A Revolution That Will Transform How We Live, Work and Think", by Kenneth Cukier and Viktor Mayer-Schonberger, John Murra

Where Health and Environment Meet: The Use of Invariant Parameters for Big Data Analysis

The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on “data mash-ups” – that is the linking of data from epidemiology, biomedicine, climate and environmental science, which is typically achieved by holding one or more basic parameters, such as geolocation, as invariant. We argue that this strategy works best when epidemiologists interpret localisation procedures through an idiographic perspective that recognises their context-dependence and supports a critical evaluation of the epistemic value of geolocation data whenever they are used for new research purposes. Approaching invariants as strategic constructs can foster data linkage and re-use, and support carefully-targeted predictions in ways that can meaningfully inform public health. At the same time, it explicitly signals the limitations in the scope and applicability of the original datasets incorporated into big data collections, and thus the situated nature of data linkage exercises and their predictive power

Where Health and Environment Meet: The Use of Invariant Parameters for Big Data Analysis

The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on “data mash-ups” – that is the linking of data from epidemiology, biomedicine, climate and environmental science, which is typically achieved by holding one or more basic parameters, such as geolocation, as invariant. We argue that this strategy works best when epidemiologists interpret localisation procedures through an idiographic perspective that recognises their context-dependence and supports a critical evaluation of the epistemic value of geolocation data whenever they are used for new research purposes. Approaching invariants as strategic constructs can foster data linkage and re-use, and support carefully-targeted predictions in ways that can meaningfully inform public health. At the same time, it explicitly signals the limitations in the scope and applicability of the original datasets incorporated into big data collections, and thus the situated nature of data linkage exercises and their predictive power