28 research outputs found
Promised Land? Immigration, Religiosity, and Space in Southern California
- Author
- Anderson B.
- Anzaldúa G.
- Bergmann S.
- Bergmann S.
- Bibler Coutin S.
- Budde M.L.
- Burke K.
- Campese G.
- Castillo J.
- Clara Irazábal
- Cunningham H.
- Dart J.
- Davey A.
- Dear M.
- Dyrness G.R.
- Díaz-Stevens A.M.
- Espín O.
- Gill J.
- Grace R. Dyrness
- Habermas J.
- Harvey D.
- Heelas P.
- Inge J.
- Ivakhiv A.
- Jenkins P.
- Jenkins P.
- Jenkins P.
- Kennel-Shank C.
- Kjellberg S.
- Kniss F.
- Kotin S.
- Lefebvre H.
- Lorentzen R.
- MacEoin G.
- Maduro O.
- Miller D.
- Miraftab F.
- Northcott M.
- Primavesi A.
- Salvatierra A.
- Sandercock L.
- Sheldrake P.
- Skinner D.E.
- Stevens-Arroyo A.M.
- Terry D.
- Ufford-Chase R.
- Vázquez M.A.
- Publication venue
- 'Columbia University Libraries/Information Services'
- Publication date
- 01/01/2010
- Field of study
This article looks at how immigrants and their supporters appropriate and use religious space and other public spaces for religious and socio-political purposes in Southern California. While the everyday living conditions of many immigrants, particularly the unauthorized Latino immigrants, force unto them an embodied disciplinarity that maintains spatialities of restricted citizenship, the public appropriations of space for and through religious practices allow for them -even if only momentarily -to express an embodied transgression. This practice in public space helps realize spaces of freedom and hope, however ephemerally. Potentially, these rehearsing exercises can help revert internalized disempowering subjectivities and create social empowerment. Negative stereotypes about immigrants held by the larger public can also be challenged through these spatial practices, as the public demonstrations make visible the invisible. We focus on “Posadas Without Borders” and “the New Sanctuary Movement,” considering both the role of progressive civic and religious institutions in supporting immigrants and the agency of the immigrants themselves. The theoretical analysis builds on concepts drawn from a conversation between geography and religious and theological studies. We use a triangulated methodological approach that includes observation and participant observation, content-analysis of multimedia, interviews, and intellectual advocacy for the immigrant movement. The cases discussed here show that progressive religious groups and coalitions can be important allies to progressive planners, geographers, and policy makers in advancing social and environmental justice for the disenfranchised. They also show that the theological underpinnings of such groups share a lot in common with planning epistemologies for the just city
Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries
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- Abad-Motos A.
- Abbas A.M.
- Abbas M.
- Abbasi M.
- Abbott T.
- Abdalaal I.
- Abdel-Galil K.
- Abdelemam R.
- Abdelgawad A.
- Abdelkarim M.
- Abdelkhalek M.
- Abdelmageed A.
- Abdelmawgoud S.
- Abdelmohsen S.
- Abdelrahman A.S.
- Abdelrahman M.
- Abdelwahab K.
- Abdou H.
- Abdulai D.R.
- Abdulkhaleq M.
- Abdullah B.
- Abdulsalam K.
- Abdulsalam S.
- Abdur-Rahman L.
- Abdus-Salam R.
- Abdussalam H.O.
- Abebrese J.T.
- Abeldaño A.
- Abou-Khalil J.
- Abou-Taleb H.
- Abozid A.
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- Abrate A.
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- Šantak G.
- Šimko K.
- Žatecký J.
- Publication venue
- Oxford University Press (OUP)
- Publication date
- 01/07/2023
- Field of study
Background
Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres.
Methods
This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries.
Results
In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia.
Conclusion
This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries
Religious involvement and group identification: The case of Hispanics in the United States
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- Publication venue
- 'Elsevier BV'
- Publication date
- Field of study
Promised Land? Immigration, Religiosity, and Space in Southern California
- Author
- Anderson B.
- Anzaldúa G.
- Bergmann S.
- Bergmann S.
- Bibler Coutin S.
- Budde M.L.
- Burke K.
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- Clara Irazábal
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- Stevens-Arroyo A.M.
- Terry D.
- Ufford-Chase R.
- Vázquez M.A.
- Publication venue
- 'SAGE Publications'
- Publication date
- Field of study
Improving Access to Health Care Among African-American, Asian and Pacific Islander, and Latino Lesbian, Gay, and Bisexual Populations
- Author
- A. Madison
- A. Singer
- A.M. Avery
- A.M. Barbara
- American Association of Medical Colleges Division of Community and Minority Programs
- American Medical Association
- B. Gerbert
- B. Greene
- B. Shatz
- C.A. Gomez
- C.L. Broman
- C.L. Chng
- C.L. Chng
- C.L. Chng
- C.M. Anderson
- Centers for Disease Control
- Council of Scientific Affairs American Medical Association
- D. Chin
- D. Doutrich
- D.C. Wallace
- D.J. Malebranche
- D.L. Higgins
- D.P. Wheeler
- D.R. Williams
- D.R. Williams
- E. Klonoff
- E.M. Maccio
- E.M. Rogers
- E.S. Morales
- Gay and Lesbian Medical Association
- H. Palacio
- H. Yoshikawa
- H. Yoshikawa
- H. Yoshikawa
- H.A. Neville
- H.L. Dalton
- I.H. Meyer
- I.H. Meyer
- J. Blanchard
- J. Woodyard
- J.A. Kelly
- J.A. Parés-Avila
- J.H. Jones
- J.L. Peterson
- K. Siegel
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- L. Dean
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- M. Phillip
- M.B. Blank
- M.G. Shedlin
- M.J. Eliason
- N. Krieger
- N. Krieger
- P.A. Wilson
- P.E. Stevens
- R. Capps
- R. Capps
- R. Clark
- R. Myrick
- R.G. Rumbaut
- R.J. Mancoske
- R.L. Klitzman
- R.M. Díaz
- R.M. Díaz
- R.M. Díaz
- R.P. Cabaj
- S. Brotman
- S. Saha
- S.A. Caldwell
- S.A. Dowd
- S.B. Thomas
- S.D. Cochran
- S.D. Cochran
- S.E. Gilman
- S.F. Morin
- S.J. Semple
- S.M. Kegeles
- T.A. Richards
- T.C. Mills
- V.M. Mays
- V.N. Gamble
- W. Jiang
- W. Zimmerman
- W.G. Hawkeswood
- Y.A. Amirkhanian
- Publication venue
- 'Springer Science and Business Media LLC'
- Publication date
- 01/01/2007
- Field of study
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with Machine Learning
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- Stevens G
- Sánchez A.G
- Sánchez H. Domínguez
- Tallada-Crespí P
- Taylor A.N
- Tenti M
- Teplitz H.I
- Tereno I
- Testera G
- Tewes M
- Teyssier R
- Toba Y
- Toft S
- Toledo-Moreo R
- Torradeflot F
- Tortora C
- Tosi S
- Troja A
- Tucci M
- Tutusaus I
- Valentijn E.A
- Valenziano L
- Valieri C
- Valiviita J
- Vassallo T
- Vergani D
- Veropalumbo A
- Viel M
- Vigo J.A. Escartin
- Walmsley M
- Walton Nicholas A
- Wang L
- Wang Y
- Weller J
- Wiesmann M
- Zacchei A
- Zamorani G
- Zinchenko I.A
- Zoubian J
- Zucca E
- Publication venue
- HAL CCSD
- Publication date
- 21/02/2024
- Field of study
International audienceThe Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes such as disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowdsourcing. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images
Progress in Brucella vaccine development
- Author
- A. A. Oñate
- A. Al-Mariri
- A. B. Bandara
- A. B. den Hartigh
- A. Cabrera
- A. Cloeckaert
- A. Contreras-Rodriguez
- A. Eker
- A. F. Haag
- A. H. el Idrissi
- A. I. Dueñas
- A. I. Radwan
- A. J. Winter
- A. K. Bhattacharjee
- A. M. Arenas-Gamboa
- A. M. Arenas-Gamboa
- A. M. Arenas-Gamboa
- A. M. Arenas-Gamboa
- A. M. Whatmore
- A. Minas
- A. P. Cardena
- A. Rafiei
- A. Tibor
- A. W. Confer
- A. W. Taylor
- A.M. Arenas-Gamboa
- B. Arellano-Reynoso
- B. Clapp
- B. Clapp
- B. M. Scurlock
- C. A. Manthei
- C. A. Velikovsky
- C. Chacón-Díaz
- C. G. Trant
- C. García-Carrillo
- C. K. Mingle
- C. M. Marín
- C. Muñoz-Montesino
- Centers for Disease ControlPrevention
- D. A. Ashford
- D. González
- D. H. Yu
- D. O’Callaghan
- D. S. Davis
- D. S. Davis
- D. S. Pontes
- D. Shi
- E. Barquero-Calvo
- E. Buyukcangaz
- E. D. Ebel
- E. Herrera
- E. Hofer
- E. J. Young
- E. Jelastopulu
- E. Kurar
- E. Moreno
- E. Pishva
- E. Zowghi
- F. Bäckhed
- F. Entessar
- F. J. Sangari
- F. J. Sangari
- F. Mukherjee
- F. P. Poester
- F. Roth
- F. W. Beckett
- G. A. Kojouri
- G. Briones
- G. C. Walker
- G. G. Alton
- G. G. Alton
- G. G. Schurig
- G. K. Dzata
- G. Martínez de Tejada
- G. P. Ferguson
- G. Pappas
- G. Pappas
- G. Pappas
- G. Rajashekara
- G. T. Fosgate
- G. T. Robertson
- G. W. J. Pugh Jr
- H. Van Campen
- I. H. Borts
- I. Moriyón
- I. U. Diju
- J. Cassataro
- J. Dornand
- J. E. Ugalde
- J. H. Wyckoff 3rd
- J. J. Treanor
- J. M. Blasco
- J. M. Blasco
- J. M. Verger
- J. P. Lavigne
- J. Zinsstag
- K. A. Pasquevich
- L. D. Smith
- L. Petrovska
- M. A. Ascón
- M. A. Fiorentino
- M. A. Pourbagher
- M. Almirón
- M. B. Barrio
- M. Banai
- M. C. Ferrero
- M. D. Diptee
- M. D. Edmonds
- M. D. Edmonds
- M. Eschenbrenner
- M. G. Stevens
- M. G. Stevens
- M. G. Stevens
- M. G. Stevens
- M. J. Castaño
- M. J. Corbel
- M. L. Boschiroli
- M. M. Levine
- M. N. Xavier
- M. Osorio
- M. P. Franco
- M. P. Jiménez de Bagués
- M. P. Jiménez de Bagüés
- M. V. Delpino
- M. V. Palmer
- M. V. Palmer
- M. W. Valderas
- N. Bosseray
- N. Bosseray
- N. E. Schlabritz-Loutsevitch
- N. F. Cheville
- N. F. Cheville
- N. F. Cheville
- N. F. Cheville
- N. J. Commander
- O. R. Crasta
- P. Barrionuevo
- P. C. Baldi
- P. H. Elzer
- P. Kaushik
- P. M. Muñoz
- P. Rajasekaran
- P. S. Chain
- Q. M. Abu Shaqra
- R. Adone
- R. B. Alcantara
- R. C. Galindo
- R. Conde-Alvarez
- R. Da Costa Martins
- R. Fensterbank
- R. I. Wise
- R. Keller
- R. M. Roop 2nd
- R. M. Roop 2nd
- R. R. Graves
- R. Vemulapalli
- R. W. Phillips
- S. Audic
- S. Baloglu
- S. Burkhardt
- S. C. Olsen
- S. C. Olsen
- S. C. Olsen
- S. C. Olsen
- S. C. Olsen
- S. C. Olsen
- S. Cespedes
- S. Gulsun
- S. J. Smither
- S. Kim
- S. Loisel-Meyer
- S. M. Halling
- S. S. Elberg
- S. S. Teske
- T. J. Kreeger
- T. J. Kreeger
- T. J. Stabel
- T. J. Stabel
- T. J. Stabel
- T. M. Silva
- T. M. Silva
- V. Caporale
- V. Foulongne
- V. L. Atluri
- V. R. Lord
- W. E. Cook
- W. H. Hall
- W. W. Spink
- X. Xin
- X. Yang
- X. Yang
- X. Yang
- X. Yang
- Y. K. Li
- Y. Wang
- Y. Yang
- Y. Zhan
- Z. Bercovich
- Z. Memish
- Z. Zhao
- Publication venue
- 'Springer Science and Business Media LLC'
- Publication date
- Field of study
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with Machine Learning
- Author
- Aghanim N
- Akrami Y
- Allevato V
- Altieri B
- Amara A
- Andreon S
- Anselmi S
- Auricchio N
- Aussel B
- Baccigalupi C
- Baldi M
- Ballardini M
- Bardelli S
- Bender R
- Biviano A
- Bodendorf C
- Bolzonella M
- Bonino D
- Borgani S
- Borlaff A.S
- Boucaud A
- Bozzo E
- Branchini E
- Brescia M
- Bretonnière H
- Brinchmann J
- Bruton S
- Burigana C
- Cabanac R
- Calabro A
- Camera S
- Capobianco V
- Cappi A
- Carbone C
- Carretero J
- Carvalho C.S
- Casas S
- Castellano M
- Castignani G
- Castro T
- Cavuoti S
- Cañas-Herrera G
- Chambers K.C
- Cimatti A
- Colodro-Conde C
- Congedo G
- Conselice C.J
- Conversi L
- Copin Y
- Coupon J
- Courbin F
- Courtois H.M
- Cropper M
- Cucciati O
- da Silva A
- Davini S
- de Lucia G
- Degaudenzi H
- Delli Veneri M
- Desprez G
- Di Domizio S
- Di Ferdinando D
- Di Giorgio A.M
- Dinis J
- Dole H
- Dubath F
- Duc P.-A
- Dupac X
- Dusini S
- Díaz-Sánchez A
- Escoffier S
- Farina M
- Farinelli R
- Farrens S
- Ferrero I
- Ferriol S
- Finelli F
- Fotopoulou S
- Frailis M
- Franceschi E
- Franzetti P
- Fumana M
- Gabarra L
- Galeotta S
- Ganga K
- García-Bellido J
- Garilli B
- Gaztanaga E
- George K
- Giacomini F
- Gillis B
- Giocoli C
- Gozaliasl G
- Graciá-Carpio J
- Grazian A
- Gregorio A
- Grupp F
- Guinet D
- Hall A
- Haugan S.V.H
- Hildebrandt H
- Holmes W
- Hook I
- Hormuth F
- Hornstrup A
- Hudelot P
- Huertas-Company M
- Jahnke K
- Kajava J.J.E
- Kansal V
- Karagiannis D
- Keihänen E
- Kermiche S
- Kiessling A
- Kilbinger M
- Kirkpatrick C.C
- Kruk S
- Kubik B
- Kuchner U
- Kunz M
- Kurki-Suonio H
- Kümmel M
- La Marca A
- Laureijs R
- Legrand L
- Ligori S
- Lilje P.B
- Lindholm V
- Lloro I
- Loureiro A
- Macias-Perez J
- Magliocchetti M
- Mainetti G
- Maiorano E
- Mancini A. Spurio
- Mansutti O
- Maoli R
- Marcin S
- Margalef-Bentabol B
- Marggraf O
- Markovic K
- Marleau F.R
- Martinelli M
- Martinet N
- Martins C.J.A.P
- Marulli F
- Massey R
- Matthew S
- Maturi M
- Mauri N
- Maurin L
- Maurogordato S
- Medinaceli E
- Mei S
- Mellier Y
- Meneghetti M
- Merlin E
- Metcalf R.B
- Meylan G
- Migliaccio M
- Monaco P
- Moresco M
- Morgante G
- Moscardini L
- Munari E
- Munoz A. Jimenez
- Nadathur S
- Neissner C
- Niemi S.-M
- Nucita A.A
- Padilla C
- Paltani S
- Pasian F
- Pedersen K
- Peel A
- Percival W.J
- Pettorino V
- Pezzotta A
- Pires S
- Polenta G
- Poncet M
- Popa L.A
- Popa V
- Porciani C
- Potter D
- Pozzetti L
- Pöntinen M
- Raison F
- Rebolo R
- Reimberg P
- Renzi A
- Rhodes J
- Riccio G
- Rocci P.-F
- Romelli E
- Roncarelli M
- Rossetti E
- Saglia R
- Sakr Z
- Sapone D
- Sartoris B
- Schirmer M
- Schneider A
- Schneider P
- Scottez V
- Secroun A
- Sefusatti E
- Seidel G
- Sereno M
- Serrano S
- Simon P
- Sirignano C
- Sirri G
- Stanco L
- Stanford S.A
- Starck J.-L
- Steinwagner J
- Stevens G
- Sánchez A.G
- Sánchez H. Domínguez
- Tallada-Crespí P
- Taylor A.N
- Tenti M
- Teplitz H.I
- Tereno I
- Testera G
- Tewes M
- Teyssier R
- Toba Y
- Toft S
- Toledo-Moreo R
- Torradeflot F
- Tortora C
- Tosi S
- Troja A
- Tucci M
- Tutusaus I
- Valentijn E.A
- Valenziano L
- Valieri C
- Valiviita J
- Vassallo T
- Vergani D
- Veropalumbo A
- Viel M
- Vigo J.A. Escartin
- Walmsley M
- Walton Nicholas A
- Wang L
- Wang Y
- Weller J
- Wiesmann M
- Zacchei A
- Zamorani G
- Zinchenko I.A
- Zoubian J
- Zucca E
- Publication venue
- HAL CCSD
- Publication date
- 21/02/2024
- Field of study
International audienceThe Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes such as disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowdsourcing. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images
Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight
- Publication venue
- Publication date
- 01/01/2021
- Field of study
From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions. © Copyright