Exiles in British sociology

We have all seen them, foreheads wrinkled like a ploughed field, pastel-shaded check summer shirts worn in winter, desks festooned with yellowed index cards covered in hieroglyphics, books like yours only in plainer covers and read more carefully, filthy cigarettes, an accent growing thicker with age. But we have all seen them too, the luxuriant thatch at seventy, the jacket and tie, the tidy desk, the London club and the house in the country, the pipe, the disdain for small talk made all the more intimidating by an English acquired somewhere between grammar school and Oxford. Self-contained in a way only the uprooted can be, mysterious because you never knew what questions to ask them, emissaries from worlds they have lost and you have never known: the Polish gentry, the central European peasantry, Jewish merchants, German workers and, most puzzling of all, the continental European middle class

A calibration protocol for population-specific accelerometer cut-points in children

PurposeTo test a field-based protocol using intermittent activities representative of children\u27s physical activity behaviours, to generate behaviourally valid, population-specific accelerometer cut-points for sedentary behaviour, moderate, and vigorous physical activity.MethodsTwenty-eight children (46% boys) aged 10&ndash;11 years wore a hip-mounted uniaxial GT1M ActiGraph and engaged in 6 activities representative of children\u27s play. A validated direct observation protocol was used as the criterion measure of physical activity. Receiver Operating Characteristics (ROC) curve analyses were conducted with four semi-structured activities to determine the accelerometer cut-points. To examine classification differences, cut-points were cross-validated with free-play and DVD viewing activities.ResultsCut-points of &le;372, &gt;2160 and &gt;4806 counts&bull;min&minus;1 representing sedentary, moderate and vigorous intensity thresholds, respectively, provided the optimal balance between the related needs for sensitivity (accurately detecting activity) and specificity (limiting misclassification of the activity). Cross-validation data demonstrated that these values yielded the best overall kappa scores (0.97; 0.71; 0.62), and a high classification agreement (98.6%; 89.0%; 87.2%), respectively. Specificity values of 96&ndash;97% showed that the developed cut-points accurately detected physical activity, and sensitivity values (89&ndash;99%) indicated that minutes of activity were seldom incorrectly classified as inactivity.ConclusionThe development of an inexpensive and replicable field-based protocol to generate behaviourally valid and population-specific accelerometer cut-points may improve the classification of physical activity levels in children, which could enhance subsequent intervention and observational studies.<br /

A ROC analysis-based classification method for landslide susceptibility maps

[EN] A landslide susceptibility map is a crucial tool for landuse spatial planning and management in mountainous areas. An essential issue in such maps is the determination of susceptibility thresholds. To this end, the map is zoned into a limited number of classes. Adopting one classification system or another will not only affect the map's readability and final appearance, but most importantly, it may affect the decision-making tasks required for effective land management. The present study compares and evaluates the reliability of some of the most commonly used classification methods, applied to a susceptibility map produced for the area of La Marina (Alicante, Spain). A new classification method based on ROC analysis is proposed, which extracts all the useful information from the initial dataset (terrain characteristics and landslide inventory) and includes, for the first time, the concept of misclassification costs. This process yields a more objective differentiation of susceptibility levels that relies less on the intrinsic structure of the terrain characteristics. The results reveal a considerable difference between the classification methods used to define the most susceptible zones (in over 20% of the surface) and highlight the need to establish a standard method for producing classified susceptibility maps. The method proposed in the study is particularly notable for its consistency, stability and homogeneity, and may mark the starting point for consensus on a generalisable classification method.Cantarino-Martí, I.; Carrión Carmona, MÁ.; Goerlich-Gisbert, F.; Martínez Ibáñez, V. (2018). A ROC analysis-based classification method for landslide susceptibility maps. Landslides. 1-18. doi:10.1007/s10346-018-1063-4S118Armstrong MP, Xiao N, Bennett DA (2003) Using genetic algorithms to create multicriteria class intervals for choropleth maps. Ann Assoc Am Geogr 93(3):595–623. https://doi.org/10.1111/1467-8306.9303005Atkinson P, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385. https://doi.org/10.1016/S0098-3004(97)00117-9Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31. https://doi.org/10.1016/j.geomorph.2004.06.010Baeza C, Lantada N, Amorim S (2016) Statistical and spatial analysis of landslide susceptibility maps with different classification systems. Environ Earth Science 75:1318. https://doi.org/10.1007/s12665-016-6124-1Basofi A, Fariza A, Ahsan AS, Kamal IM (2015) A comparison between natural and head/tail breaks in LSI (landslide susceptibility index) classification for landslide susceptibility mapping: a case study in Ponorogo, East Java, Indonesia. 2015 International Conference on Science in Information Technology, pp 337–342Cantarino I (2013) Elaboración y validación de un modelo jerárquico derivado de SIOSE. Revista de Teledetección 39:5–21Carrara A, Crosta GB, Frattini P (2008) Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology 94(3–4):353–378. https://doi.org/10.1016/j.geomorph.2006.10.033Chacón J, Irigaray C, Fernández T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65(4):341–411Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472COPUT (1998) Lithology, exploitation of industrial rocks and landslide risk in the Valencian Community. Thematic Mapping Series. Department of Public Works of the Valencian Regional GovernmentDrummond C, Holte RC (2006) Cost curves: an improved method for visualizing classifier performance. Mach Learn 65(1):95–130Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51(2):241–256. https://doi.org/10.1007/s00254-006-0322-1Evans IS (1977) The selection of class intervals. Transactions of the Institute of British Geographers. Contemp Cartograph 2(1):98–124. https://doi.org/10.2307/622195Fleiss JL, Levin B, Paik MC (2003) Statistical methods for rates and proportions, Book Series: Wiley Series in Probability and Statistics. John Wiley & Sons. Print ISBN: 9780471526292. doi: https://doi.org/10.1002/0471445428Foody GM (2004) Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogramm Eng Remote Sens 70(5):627–633Fotheringham AS, Brunsdon C, Charlton M (2000) Quantitative geography: perspectives on spatial data analysis. SAGE Publications, Thousand Oaks 270 ppFrattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111(1–4):62–72. https://doi.org/10.1016/j.enggeo.2009.12.004Geisser S (1998) Comparing two tests used for diagnostic or screening processes. Stat Probability Lett 40:113–119Greiner M, Pfeiffer D, Smith RD (2000) Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 45:23–41Günther A, Reichenbach P, Malet JP, van den Eeckhaut M, Hervás J, Dashwood C, Guzzetti F (2013) Tier-based approaches for landslide susceptibility assessment in Europe. Landslides 10:529–546. https://doi.org/10.1007/s10346-012-0349-1Günther A, Van Den Eeckhaut M, Malet J-P, Reichenbach P, Hervás J (2014) Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology 224:69–85Gupta RP, Kanungo DP, Arora MK, Sarkar S (2008) Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps. Int J Appl Earth Obs Geoinf 10(3):330–341. https://doi.org/10.1016/j.jag.2008.01.003Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184. https://doi.org/10.1016/j.geomorph.2006.04.007Hervás J (2017) El inventario de movimientos de ladera de España ALISSA: Metodología y análisis preliminar. In: Alonso E, Corominas J, Hürlimann M (Eds.), Taludes 2017. Proc. IX Simposio Nacional sobre Taludes y Laderas Inestables, Santander, 27–30 June 2017. CIMNE, Barcelona, pp. 629–639Jaedicke C, Van Den Eeckhaut M, Nadim F et al (2014) Identification of landslide hazard and risk ‘hotspots’ in Europe. Bull Eng Geol Environ 73:325. https://doi.org/10.1007/s10064-013-0541-0Jenks GF (1967) The data model concept in statistical mapping. Int Yearbook Cartograph 7:186–190Jiang B (2013) Head/tail breaks: a new classification scheme for data with a heavy-tailed distribution. Prof Geogr 65(3):482–494. https://doi.org/10.1080/00330124.2012.700499Kiang MY (2003) A comparative assessment of classification methods. Decis Support Syst 35(4):441–454. https://doi.org/10.1016/S0167-9236(02)00110-0Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174Langping L, Hengxing L, Changbao G, Yongshuang Z, Quanwen L, Yuming W (2017) A modified frequency ratio method for landslide susceptibility assessment. Landslides 14:727–741. https://doi.org/10.1007/s10346-016-0771-xLee S (2007) Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surf Process Landforms 32:2133–2148. https://doi.org/10.1002/esp.1517Liu C, Frazier P, Kumar L (2007) Comparative assessment of the measures of thematic classification accuracy. Remote Sens Environ 107(4):606–616. https://doi.org/10.1016/j.rse.2006.10.010López-Ratón M, Rodríguez-Álvarez MX, Cadarso-Suárez C, Gude-Sampedro F (2014) Optimal cutpoints: an R package for selecting optimal cutpoints in diagnostic tests. J Stat Softw 61(8):4Malet JP, Puissant A, Mathieu A, Van Den Eeckhaut M, Fressard M (2013) Integrating spatial multi-criteria evaluation and expert knowledge for country-scale landslide susceptibility analysis: application to France. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice. Springer, Berlin. https://doi.org/10.1007/978-3-642-31325-7_40McGee S (2002) Simplifying likelihood ratios. J Gen Intern Med 17:647–650Metz C (1978) Basic principles of ROC analysis. Semin Nucl Med VIII(4):183–198Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C (2006) Global landslide and avalanche hotspots. Landslides 3:159–173. https://doi.org/10.1007/s10346-006-0036-1Ohlmacher G, Davis J (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69(3–4):331–343. https://doi.org/10.1016/S0013-7952(03)00069-3Powell RL, Matzke N, de Souza C Jr, Clark M, Numata I, Hess LL, Roberts DA (2004) Sources of error accuracy assessment of thematic land-cover maps in the Brazilian Amazon. Remote Sens Environ 90(2):221–234. https://doi.org/10.1016/j.rse.2003.12.007Saaty T (1980) The analytic hierarchy process. McGraw Hill, New YorkSmits PC, Dellepiane SG, Schowengerdt RA (1999) Quality assessment of image classification algorithms for land-cover mapping: a review and proposal for a cost-based approach. Int J Remote Sens 20:1461–1486Stehman SV, Czaplewski RL (1998) Design and analysis of thematic map accuracy assessment: fundamental principles. Remote Sens Environ 64:331–344Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293Van Den Eeckhaut M, Hervás J, Jaedicke C, Malet J-P, Montanarella L, Nadim F (2012) Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data. Landslides 8:357–369Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. Natural hazards. UNESCO, ParisZhu X (2016) GIS for environmental applications. Routledge, Abingdon, p 490Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561–57

Nonlinear complexity analysis of brain fMRI signals in schizophrenia

Peer reviewedPublisher PD

Image perception and interpretation of abnormalities; can we believe our eyes? Can we do something about it?

The radiologist’s visual impression of images is transmitted, via non-visual means (the report), to the clinician. There are several complex steps from the perception of the images by the radiologist to the understanding of the impression by the clinician. With a process as complex as this, it is no wonder that errors in perception, cognition, interpretation, transmission and understanding are very common. This paper reviews the processes of perception and error generation and possible strategies for minimising them

Why Are There Social Gradients in Preventative Health Behavior? A Perspective from Behavioral Ecology

Background: Within affluent populations, there are marked socioeconomic gradients in health behavior, with people of lower socioeconomic position smoking more, exercising less, having poorer diets, complying less well with therapy, using medical services less, ignoring health and safety advice more, and being less health-conscious overall, than their more affluent peers. Whilst the proximate mechanisms underlying these behavioral differences have been investigated, the ultimate causes have not. Methodology/Principal Findings: This paper presents a theoretical model of why socioeconomic gradients in health behavior might be found. I conjecture that lower socioeconomic position is associated with greater exposure to extrinsic mortality risks (that is, risks that cannot be mitigated through behavior), and that health behavior competes for people’s time and energy against other activities which contribute to their fitness. Under these two assumptions, the model shows that the optimal amount of health behavior to perform is indeed less for people of lower socioeconomic position. Conclusions/Significance: The model predicts an exacerbatory dynamic of poverty, whereby the greater exposure of poor people to unavoidable harms engenders a disinvestment in health behavior, resulting in a final inequality in health outcomes which is greater than the initial inequality in material conditions. I discuss the assumptions of the model, and it

Serum IL-6: a candidate biomarker for intracranial pressure elevation following isolated traumatic brain injury

<p>Abstract</p> <p>Background</p> <p>Increased intracranial pressure (ICP) is a serious, life-threatening, secondary event following traumatic brain injury (TBI). In many cases, ICP rises in a delayed fashion, reaching a maximal level 48-96 hours after the initial insult. While pressure catheters can be implanted to monitor ICP, there is no clinically proven method for determining a patient's risk for developing this pathology.</p> <p>Methods</p> <p>In the present study, we employed antibody array and Luminex-based screening methods to interrogate the levels of inflammatory cytokines in the serum of healthy volunteers and in severe TBI patients (GCS≤8) with or without incidence of elevated intracranial pressure (ICP). De-identified samples and ELISAs were used to confirm the sensitivity and specificity of IL-6 as a prognostic marker of elevated ICP in both isolated TBI patients, and polytrauma patients with TBI.</p> <p>Results</p> <p>Consistent with previous reports, we observed sustained increases in IL-6 levels in TBI patients irrespective of their ICP status. However, the group of patients who subsequently experienced ICP ≥ 25 mm Hg had significantly higher IL-6 levels within the first 17 hours of injury as compared to the patients whose ICP remained ≤20 mm Hg. When blinded samples (n = 22) were assessed, a serum IL-6 cut-off of <5 pg/ml correctly identified 100% of all the healthy volunteers, a cut-off of >128 pg/ml correctly identified 85% of isolated TBI patients who subsequently developed elevated ICP, and values between these cut-off values correctly identified 75% of all patients whose ICP remained ≤20 mm Hg throughout the study period. In contrast, the marker had no prognostic value in predicting elevated ICP in polytrauma patients with TBI. When the levels of serum IL-6 were assessed in patients with orthopedic injury (n = 7) in the absence of TBI, a significant increase was found in these patients compared to healthy volunteers, albeit lower than that observed in TBI patients.</p> <p>Conclusions</p> <p>Our results suggest that serum IL-6 can be used for the differential diagnosis of elevated ICP in isolated TBI.</p

Characterization of the Single Stranded DNA Binding Protein SsbB Encoded in the Gonoccocal Genetic Island

Background: Most strains of Neisseria gonorrhoeae carry a Gonococcal Genetic Island which encodes a type IV secretion system involved in the secretion of ssDNA. We characterize the GGI-encoded ssDNA binding protein, SsbB. Close homologs of SsbB are located within a conserved genetic cluster found in genetic islands of different proteobacteria. This cluster encodes DNA-processing enzymes such as the ParA and ParB partitioning proteins, the TopB topoisomerase, and four conserved hypothetical proteins. The SsbB homologs found in these clusters form a family separated from other ssDNA binding proteins. Methodology/Principal Findings: In contrast to most other SSBs, SsbB did not complement the Escherichia coli ssb deletion mutant. Purified SsbB forms a stable tetramer. Electrophoretic mobility shift assays and fluorescence titration assays, as well as atomic force microscopy demonstrate that SsbB binds ssDNA specifically with high affinity. SsbB binds single-stranded DNA with minimal binding frames for one or two SsbB tetramers of 15 and 70 nucleotides. The binding mode was independent of increasing Mg 2+ or NaCl concentrations. No role of SsbB in ssDNA secretion or DNA uptake could be identified, but SsbB strongly stimulated Topoisomerase I activity

A rapid screening tool for psychological distress in children 3--6years old: results of a validation study.

International audienceABSTRACT: BACKGROUND: The mental health needs of young children in humanitarian contexts often remain unaddressed. The lack of a validated, rapid and simple tool for screening combined with few mental health professionals able to accurately diagnose and provide appropriate care mean that young children remain without care. Here, we present the results of the principle cross-cultural validation of the "Psychological Screening for Young Children aged 3 to 6" (PSYCAa3-6). The PSYCa 3--6 is a simple scale for children 3 to 6 years old administered by non-specialists, to screen young children in crises and thereby refer them to care if needed. METHODS: This study was conducted in Maradi, Niger. The scale was translated into Hausa, using corroboration of independent translations. A cross-cultural validation was implemented using quantitative and qualitative methods. A random sample of 580 mothers or caregivers of children 3 to 6 years old were included. The tool was psychometrically examined and diagnostic properties were assessed comparing the PSYCa 3--6 against a clinical interview as the gold standard. RESULTS: The PSYCa 3--6 Hausa version demonstrated good concurrent validity, as scores correlated with the gold standard and the Clinical Global Impression Severity Scale (CGI-S) [rho = 0.41, p-value = 0.00]. A reduction procedure was used to reduce the scale from 40 to 22 items. The test-retest reliability of the PSYCa 3--6 was found to be high (ICC 0.81, CI95% [0.68; 0.89]). In our sample, although not the purpose of this study, approximately 54 of 580 children required subsequent follow-up with a psychologist. CONCLUSIONS: To our knowledge, this is the first validation of a screening scale for children 3 to 6 years old with a cross-cultural validation component, for use in humanitarian contexts. The Hausa version of the PSYCa 3--6 is a reliable and a valuable screening tool for psychological distress. Further studies to replicate our findings and additional validations of the PSYCa 3--6 in other populations may help improve the delivery of mental health care to children