The perfect in spoken British English

The English perfect construction involves the perfect auxiliary have followed by a verb in the past participle form. It occurs in several subtypes according to the inflectional form of the auxiliary. The most frequently occurring is the present perfect, as in She has seen them. The other subtypes are the past perfect (She had seen them), the infinitival perfect (She must have seen them) and the -ing-participial perfect (Having seen them, she went home). All subtypes typically function to express anteriority (i.e. pastness relative to a reference point), although further semantic complexities have led to varying treatments of the perfect, for example as an aspect or a secondary tense system. Research on the English perfect has revealed considerable variation in use both diachronically, across longer historical periods, and synchronically, across regions and dialects. Recent trends in perfect usage are therefore of interest. The study presented here investigates this topic with regard to spoken standard British English. Much previous work has focused mainly or exclusively on the present perfect. However, here we investigate all inflectional subtypes of the perfect in terms of frequency changes over time. The findings on the present perfect are compared with those of other researchers. We then focus in more detail on the past perfect and infinitival perfect, to seek explanations for the frequency changes observed

A Surprising Similarity Between Holographic CFTs and a Free Fermion in (2+1)(2+1) Dimensions

We compare the behavior of the vacuum free energy (i.e. the Casimir energy) of various $(2+1)$-dimensional CFTs on an ultrastatic spacetime as a function of the spatial geometry. The CFTs we consider are a free Dirac fermion, the conformally-coupled scalar, and a holographic CFT, and we take the spatial geometry to be an axisymmetric deformation of the round sphere. The free energies of the fermion and of the scalar are computed numerically using heat kernel methods; the free energy of the holographic CFT is computed numerically from a static, asymptotically AdS dual geometry using a novel approach we introduce here. We find that the free energy of the two free theories is qualitatively similar as a function of the sphere deformation, but we also find that the holographic CFT has a remarkable and mysterious quantitative similarity to the free fermion; this agreement is especially surprising given that the holographic CFT is strongly-coupled. Over the wide ranges of deformations for which we are able to perform the computations accurately, the scalar and fermion differ by up to 50% whereas the holographic CFT differs from the fermion by less than one percent.Comment: 16+8 pages, 13 figures. v2: References added, minor edit

Trust, guilds and kinship in London, 1330-1680

How was trust created and reinforced between the inhabitants of medieval and early modern cities? And how did the social foundations of trusting relationships change over time? Current research highlights the role of kinship, neighbourhood and associations, particularly guilds, in creating ‘relationships of trust’ and social capital in the face of high levels of migration, mortality and economic volatility, but tells us little about their relative importance or how they developed. We uncover a profound shift in the contribution of family and guilds to trust networks among the middling and elite of one of Europe’s major cities, London, over three centuries, from the 1330s to the 1680s. We examine the networks of sureties created to secure the inheritances of children whose fathers died while they were minors, surviving in the records of London’s Orphans Court. Our analysis of almost fifteen thousand networks evaluates the presence of trusting relationships connected with guild membership, family and place over several centuries. We show a profound increase in the role of kinship – a re-embedding of trust within the family - and a decline of the importance of shared guild membership in connecting Londoner’s who secured orphans’ inheritances together. We suggest these developments are best explained as a result of the impact of the Reformation on the form and intensity of sociability fostered by guilds and the enormous growth of the metropolis

Trust, guilds and kinship in London, 1330-1680

How was trust created and reinforced between the inhabitants of medieval and early modern cities? And how did the social foundations of trusting relationships change over time? Current research highlights the role of kinship, neighbourhood and associations, particularly guilds, in creating ‘relationships of trust’ and social capital in the face of high levels of migration, mortality and economic volatility, but tells us little about their relative importance or how they developed. We uncover a profound shift in the contribution of family and guilds to trust networks among the middling and elite of one of Europe’s major cities, London, over three centuries, from the 1330s to the 1680s. We examine almost 15,000 networks of sureties created to secure orphans’ inheritances to measure the presence of trusting relationships connected by guild membership, family and place. We uncover a profound increase in the role of kinship – a re-embedding of trust within the family - and a decline of the importance of shared guild membership in connecting Londoner’s who secured orphans’ inheritances together. These developments indicate a profound transformation in the social fabric of urban society

Rocznik Lubuski (t.31, cz.2): Pogranicze Lubusko-Brandenburskie po II wojnie światowej

Pogranicze Lubusko-Brandenburskie po II wojnie światowejPod redakcją:<br> Czesława Osękowskiego<br>i RobertaSkobelskieg

Individual characteristics associated with road traffic collisions and healthcare seeking in Low- and Middle-Income Countries and territories

Incidence of road traffic collisions (RTCs), types of users involved, and healthcare requirement afterwards are essential information for efficient policy making. We analysed individual-level data from nationally representative surveys conducted in low- or middle-income countries (LMICs) between 2008-2019. We describe the weighted incidence of non-fatal RTC in the past 12 months, type of road user involved, and incidence of traffic injuries requiring medical attention. Multivariable logistic regressions were done to evaluate associated sociodemographic and economic characteristics, and alcohol use. Data were included from 90,790 individuals from 15 countries or territories. The non-fatal RTC incidence in participants aged 24-65 years was 5.2% (95% CI: 4.6-5.9), with significant differences dependent on country income status. Drivers, passengers, pedestrians and cyclists composed 37.2%, 40.3%, 11.3% and 11.2% of RTCs, respectively. The distribution of road user type varied with country income status, with divers increasing and cyclists decreasing with increasing country income status. Type of road users involved in RTCs also varied by the age and sex of the person involved, with a greater proportion of males than females involved as drivers, and a reverse pattern for pedestrians. In multivariable analysis, RTC incidence was associated with younger age, male sex, being single, and having achieved higher levels of education; there was no association with alcohol use. In a sensitivity analysis including respondents aged 18-64 years, results were similar, however, there was an association of RTC incidence with alcohol use. The incidence of injuries requiring medical attention was 1.8% (1.6-2.1). In multivariable analyses, requiring medical attention was associated with younger age, male sex, and higher wealth quintile. We found remarkable heterogeneity in RTC incidence, the type of road users involved, and the requirement for medical attention after injuries depending on country income status and socio-demographic characteristics. Targeted data-informed approaches are needed to prevent and manage RTCs

Clinical impact of genomic testing in patients with suspected monogenic kidney disease

Purpose: To determine the diagnostic yield and clinical impact of exome sequencing (ES) in patients with suspected monogenic kidney disease. Methods: We performed clinically accredited singleton ES in a prospectively ascertained cohort of 204 patients assessed in multidisciplinary renal genetics clinics at four tertiary hospitals in Melbourne, Australia. Results: ES identified a molecular diagnosis in 80 (39%) patients, encompassing 35 distinct genetic disorders. Younger age at presentation was independently associated with an ES diagnosis (p < 0.001). Of those diagnosed, 31/80 (39%) had a change in their clinical diagnosis. ES diagnosis was considered to have contributed to management in 47/80 (59%), including negating the need for diagnostic renal biopsy in 10/80 (13%), changing surveillance in 35/80 (44%), and changing the treatment plan in 16/80 (20%). In cases with no change to management in the proband, the ES result had implications for the management of family members in 26/33 (79%). Cascade testing was subsequently offered to 40/80 families (50%). Conclusion: In this pragmatic pediatric and adult cohort with suspected monogenic kidney disease, ES had high diagnostic and clinical utility. Our findings, including predictors of positive diagnosis, can be used to guide clinical practice and health service design

Soil organic carbon stocks in native forest of Argentina: a useful surrogate for mitigation and conservation planning under climate variability

Background The nationally determined contribution (NDC) presented by Argentina within the framework of the Paris Agreement is aligned with the decisions made in the context of the United Nations Framework Convention on Climate Change (UNFCCC) on the reduction of emissions derived from deforestation and forest degradation, as well as forest carbon conservation (REDD+). In addition, climate change constitutes one of the greatest threats to forest biodiversity and ecosystem services. However, the soil organic carbon (SOC) stocks of native forests have not been incorporated into the Forest Reference Emission Levels calculations and for conservation planning under climate variability due to a lack of information. The objectives of this study were: (i) to model SOC stocks to 30 cm of native forests at a national scale using climatic, topographic and vegetation as predictor variables, and (ii) to relate SOC stocks with spatial–temporal remotely sensed indices to determine biodiversity conservation concerns due to threats from high inter‑annual climate variability. Methods We used 1040 forest soil samples (0–30 cm) to generate spatially explicit estimates of SOC native forests in Argentina at a spatial resolution of approximately 200 m. We selected 52 potential predictive environmental covariates, which represent key factors for the spatial distribution of SOC. All covariate maps were uploaded to the Google Earth Engine cloud‑based computing platform for subsequent modelling. To determine the biodiversity threats from high inter‑annual climate variability, we employed the spatial–temporal satellite‑derived indices based on Enhanced Vegetation Index (EVI) and land surface temperature (LST) images from Landsat imagery. Results SOC model (0–30 cm depth) prediction accounted for 69% of the variation of this soil property across the whole native forest coverage in Argentina. Total mean SOC stock reached 2.81 Pg C (2.71–2.84 Pg C with a probability of 90%) for a total area of 460,790 km2, where Chaco forests represented 58.4% of total SOC stored, followed by Andean Patagonian forests (16.7%) and Espinal forests (10.0%). SOC stock model was fitted as a function of regional climate, which greatly influenced forest ecosystems, including precipitation (annual mean precipitation and precipitation of warmest quarter) and temperature (day land surface temperature, seasonality, maximum temperature of warmest month, month of maximum temperature, night land surface temperature, and monthly minimum temperature). Biodiversity was influenced by the SOC levels and the forest regions. Conclusions In the framework of the Kyoto Protocol and REDD+, information derived in the present work from the estimate of SOC in native forests can be incorporated into the annual National Inventory Report of Argentina to assist forest management proposals. It also gives insight into how native forests can be more resilient to reduce the impact of biodiversity loss.EEA Santa CruzFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Gaitan, Juan José. Universidad Nacional de Luján. Buenos Aires; Argentina.Fil: Gaitan, Juan José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Mastrangelo, Matias Enrique. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Mastrangelo, Matias Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Nosetto, Marcelo Daniel. Universidad Nacional de San Luis. Instituto de Matemática Aplicada San Luis. Grupo de Estudios Ambientales; Argentina.Fil: Nosetto, Marcelo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Villagra, Pablo Eugenio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Villagra, Pablo Eugenio. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Balducci, Ezequiel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Yuto; Argentina.Fil: Pinazo, Martín Alcides. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Eclesia, Roxana Paola. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina.Fil: Von Wallis, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Villarino, Sebastián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Villarino, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Alaggia, Francisco Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Alaggia, Francisco Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Gonzalez-Polo, Marina. Universidad Nacional del Comahue; Argentina.Fil: Gonzalez-Polo, Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. INIBIOMA; Argentina.Fil: Manrique, Silvana M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Energía No Convencional. CCT Salta‑Jujuy; Argentina.Fil: Meglioli, Pablo A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Meglioli, Pablo A. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Rodríguez‑Souilla, Julián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina.Fil: Mónaco, Martín H. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Chaves, Jimena Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina.Fil: Medina, Ariel. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Gasparri, Ignacio. Universidad Nacional de Tucumán. Instituto de Ecología Regional; Argentina.Fil: Gasparri, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Alvarez Arnesi, Eugenio. Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina.Fil: Alvarez Arnesi, Eugenio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Barral, María Paula. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Barral, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Von Müller, Axel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel Argentina.Fil: Pahr, Norberto Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Uribe Echevarría, Josefina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Quimilí; Argentina.Fil: Fernandez, Pedro Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Famaillá; Argentina.Fil: Fernandez, Pedro Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecología Regional; Argentina.Fil: Morsucci, Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Morsucci, Marina. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Lopez, Dardo Ruben. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Lopez, Dardo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Cellini, Juan Manuel. Universidad Nacional de la Plata (UNLP). Facultad de Ciencias Naturales y Museo. Laboratorio de Investigaciones en Maderas; Argentina.Fil: Alvarez, Leandro M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Alvarez, Leandro M. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Barberis, Ignacio Martín. Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Barberis, Ignacio Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Colomb, Hernán Pablo. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Colomb, Hernán. Administración de Parques Nacionales (APN). Parque Nacional Los Alerces; Argentina.Fil: La Manna, Ludmila. Universidad Nacional de la Patagonia San Juan Bosco. Centro de Estudios Ambientales Integrados (CEAI); Argentina.Fil: La Manna, Ludmila. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Barbaro, Sebastian Ernesto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; Argentina.Fil: Blundo, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecología Regional; Argentina.Fil: Blundo, Cecilia. Universidad Nacional de Tucumán. Tucumán; Argentina.Fil: Sirimarco, Marina Ximena. Universidad Nacional de Mar del Plata. Grupo de Estudio de Agroecosistemas y Paisajes Rurales (GEAP); Argentina.Fil: Sirimarco, Marina Ximena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Cavallero, Laura. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Zalazar, Gualberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Zalazar, Gualberto. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina