Geography, death and finitude

publication-status: PublishedRomanillos J L, 2011. The definitive, peer-reviewed and edited version of this article is published Environment and Planning A, 2011, Vol. 43, Issue 11, pp. 2533 – 2553 DOI: 10.1068/a4474Copyright © 2011 PionDespite growing interest in the geographies of death, loss, and remembrance, comparatively little geographical research has been devoted either to the historical and cultural practices of death, or to an adequate conceptualisation of finitude. Responding to these absences, in this paper I argue for the importance of the notion of finitude within the history and philosophy of geographical thought. Situating finitude initially in the context of the work of Torsten Hägerstrand and Richard Hartshorne, the notion is argued to be both productive of a geographical ethics, and as epistemologically constitutive of phenomenological apprehensions of ‘earth’ and ‘world’. In order to better grasp the sense and genealogy of finitude, I turn to the work of Martin Heidegger, Michel Foucault, and Georges Bataille. These authors are drawn upon precisely because their writings present powerful conceptual frameworks which demonstrate the intimate relations between spatiality, death, and finitude. At the same time, their writings are critically interrogated in the light of perhaps the most important aspect of the conceptual history of finitude: the way in which it has been articulated as a site of anthropocentric distinction. I argue for a critical deconstruction of this anthropocentric basis to finitude; a deconstruction which raises a series of profound questions over the ethics, normativities, and understandings of responsibility shaping contemporary ethical geographies of the human and nonhuman. In so doing, I demonstrate the geographical importance of the notion of finitude for a variety of arenas of debate which include: phenomenological understandings of spatiality; the biopolitical boundaries drawn between human and animal; and contemporary theorisations of corporeality, materiality, and hospitality

Pattern matching oriented photodetector image sensor with programmable interconnection between pixels

We present a photodetector sensor that is able to perform preprocessing operations on the focal plane. Each pixel can be connected with any of its neighbors in order to implement detection zones defined by software. The output current of the sensor is a customizable weighted sum of the currents sourced at the defined detection zones. This characteristic leads to applications related to pattern matching. Two examples are shown in our work: one is related to speckle correlation for real-time vibration detection, and the other one is an alternative image recording method that is the first step to an on-hardware compressed sensing technique.Fil: Calarco, Nicolás Ezequiel. Universidad Nacional del Comahue. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia. Instituto de Investigación En Tecnologías y Ciencias de la Ingeniería. Universidad Nacional del Comahue. Instituto de Investigación En Tecnologías y Ciencias de la Ingeniería; ArgentinaFil: Lipovetzky, José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; ArgentinaFil: Lutenberg, Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Perez Quintian, Luis Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia. Instituto de Investigación En Tecnologías y Ciencias de la Ingeniería. Universidad Nacional del Comahue. Instituto de Investigación En Tecnologías y Ciencias de la Ingeniería; Argentina. Universidad Nacional del Comahue. Facultad de Ingeniería; Argentin

Self-aligning CMOS photodetector sensor for application on an NDB-based optical encoder

In this work, a complementary metal–oxide–semiconductor (CMOS) photodetector specifically designed to be used in a non-diffracting-beam (NDB) optical encoder is introduced. The sensor is aimed to address the critical alignment of the NDB-based optical encoder head and consists of a hexagonal array of photodiodes that can be interconnected among themselves. The photodiodes’ interconnection is configured by means of static random access memory (SRAM) cells and transistors that operate as configurable connection switches between each photodiode and three of its neighbor photodiodes. This feature of the photodetector makes it possible to implement a search algorithm in order to find the photodiode where the NDB center impinges and then configure the appropriate detectivity pattern centered on that photodiode. It is shown that the new sensor design remarkably simplifies the alignment of the system.Fil: Calarco, Nicolás Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación En Tecnologías y Ciencias de la Ingeniería. Universidad Nacional del Comahue. Instituto de Investigación En Tecnologías y Ciencias de la Ingeniería; Argentina. Universidad Nacional del Comahue. Facultad de Ingeniería; ArgentinaFil: Mombello, Lucas. Universidad Nacional del Comahue. Facultad de Ingeniería; ArgentinaFil: Lipovetzky, José. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; ArgentinaFil: Lutenberg, Ariel. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Perez Quintián, Fernando. Universidad Nacional del Comahue. Facultad de Ingeniería; Argentin

Perception of evidence-based practice and the professional environment of Primary Health Care nurses in the Spanish context: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>The study of the factors that encourage evidence-based clinical practice, such as structure, environment and professional skills, has contributed to an improvement in quality of care. Nevertheless, most of this research has been carried out in a hospital context, neglecting the area of primary health care. The main aim of this work was to assess the factors that influence an evidence-based clinical practice among nursing professionals in Primary Health Care.</p> <p>Methods</p> <p>A multicentre cross-sectional study was designed, taking the 619 Primary Care staff nurses at the Balearic Islands’ Primary Health Care Service, as the study population. The methodology applied consisted on a self-administered survey using the instruments <it>Evidence-Based Practice Questionnaire (EBPQ)</it> and <it>Nursing Work Index (NWI)</it>.</p> <p>Results</p> <p>Three hundred and seventy seven surveys were received (60.9% response rate). Self-assessment of skills and knowledge, obtained 66.6% of the maximum score. The <it>Knowledge/Skills</it> factor obtained the best scores among the staff with shorter professional experience. There was a significant difference in the <it>Attitude</it> factor (p = 0.008) in favour of nurses with management functions, as opposed to clinical nurses.</p> <p>Multivariate analysis showed a significant positive relationship between NWI and level of evidence-based practice (p < 0,0001).</p> <p>Conclusions</p> <p>Institutions ought to undertake serious reflection on the lack of skills of senior nurses about Evidence-Based Clinical Practice, even when they have more professional experience. Leadership emerge as a key role in the transferral of knowledge into clinical practice.</p

Tractography dissection variability

Funding Information: This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Université de Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Center from the National Institute of Child Health and Human Development (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the support of the Cluster of Excellence Matters of Activity. Image Space Material funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group's processing was performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Fundação para a Ciência e a Tecnologia within CINTESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Science Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Research Council ofAustralia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32-MH103213 to William Hetrick (Indiana University). CL is partly supported by NIH grants P41 EB027061 and P30 NS076408 “Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children's Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children's Hospital Foundation, Murdoch Children's Research Institute, The University of Melbourne Department of Paediatrics, and the Victorian Government's Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. LC acknowledges support from CONACYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID-FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions. Funding Information: This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Universit? de Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Center from the National Institute of Child Health and Human Development (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the support of the Cluster of Excellence Matters of Activity. Image Space Material funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany?s Excellence Strategy ? EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group's processing was performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Funda??o para a Ci?ncia e a Tecnologia within CINTESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Science Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Research Council of Australia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32-MH103213 to William Hetrick (Indiana University). CL is partly supported by NIH grants P41 EB027061 and P30 NS076408 ?Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children's Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children's Hospital Foundation, Murdoch Children's Research Institute, The University of Melbourne Department of Paediatrics, and the Victorian Government's Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. LC acknowledges support from CONACYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID-FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions. Publisher Copyright: © 2021White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols foreach fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.Peer reviewe