Widespread and major losses in multiple ecosystem services as a result of agricultural expansion in the Argentine Chaco

1.Where agriculture expands into tropical and subtropical forests, social–ecological impacts are typically strong. However, where and how frontier development impacts on ecosystem functioning and services is often unclear, including which services trade‐off against agricultural production. This constitutes a major barrier towards planning for more sustainable outcomes in deforestation frontiers. 2. Here we assessed spatiotemporal change in multiple ecosystem services in the Argentine Chaco, a global deforestation hotspot. We modelled and mapped five ecosystem functions (i.e. carbon storage in biomass, carbon storage in soil, erosion control, excess rainfall retention by vegetation and soil fertility) which together provide three ecosystem services (i.e. agricultural suitability, climate regulation and flood regulation) for 1985, 2000 and 2013. We then employed this information to identify and map: (a) main trade‐offs between ecosystem services and agricultural production, and (b) bundles of changes in ecosystem services through the use of Self‐Organizing Maps. 3. Our results highlight that land‐use changes since 1985 have led to widespread and drastic declines in ecosystem functions and services across the Argentine Chaco. Mean losses of ecosystem services ranged between 6% and 10% for flood regulation, climate regulation and agricultural suitability. The largest losses occurred in the Dry Chaco subregion between 2000 and 2013. 4. We find two main types of trade‐offs between regulating ecosystem services and agricultural production. Increases in crop and pasture production occurred along with large and moderate losses, respectively, in flood regulation and climate regulation over 20% of the region. 5. Our mapping of bundles identified five common patterns of change in ecosystem services, delineating areas of stable or degrading ecosystem service supply. This provides a powerful template for adaptive spatial planning. 6. Synthesis and applications. Using the Argentinean Chaco as an example, we demonstrate how combining fine‐scale land‐use maps with biophysical models provides deep insights into the spatiotemporal patterns of changes in ecosystem services, and their trade‐offs with agricultural production. The periodic updating of maps of trade‐offs and bundles of change in ecosystem services provides key inputs for the adaptive management of highly dynamic and threatened landscapes, such as those in tropical and subtropical deforestation frontiers.EEA BalcarceFil: Barral, María Paula. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Villarino, Sebastián Horacio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Levers, Christian. Humboldt-Universität zu Berlin. Geography Department; AlemaniaFil: Baumann, Mathias. Humboldt-Universität zu Berlin. Geography Department; AlemaniaFil: Kuemmerle, Tobias. Humboldt-Universität zu Berlin. Geography Department; AlemaniaFil: Mastrangelo, Matías Enrique. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

“¿Seres o Bioceres?” Memes on agribusiness and the dispute of senses between nature and biodiversity in Argentina

El objetivo de este artículo es analizar desde una perspectiva etnográfica, los distintos sentidos que construyen memes relacionados con el agronegocio en Argentina en relación con la naturaleza y la biodiversidad.Los memes son piezas de comunicación gráfica que se masificaron con la cultura de internet y que la ciencia social ha definido como textos multimodales puestos en circulación para ser intercambiados y replicados en la expresión y discusión pública.Se generó un universo de 75 memes recolectados en redes sociales (Facebook y Twitter) entre junio del año 2021 y septiembre de 2022. En una primera instancia realizamos un análisis de su autoría (emisores anónimos y nominales), circulación y contenido, agrupándolos en dos categorías referidas a diferentes instancias del vínculo sociedad-naturalezas: a) la disputa entre agronegocio y agroecología, donde el agronegocio, liderado por investigación científica (p. e. Bioceres), representa su dominación y destrucción y b) biodiversidad y agronegocio, donde el Estado se muestra inerme frente a la destrucción de la biodiversidad, a la vez que legitima el modelo agroindustrial.Se concluye que los memes sobre agronegocio son, para algunos grupos sociales, una herramienta comunicativa que, usando el humor esa “forma amable de la desesperación” interpelan al lector-consumidor, buscando su identificación con el ideario de lucha contra el agronegocio, denunciando la destrucción de la biodiversidad en el proceso de acumulación por desposesión.bstract The aim of this paper is to analyze, from an ethnographic point of view, different meanings of memes about agribusiness and biodiversity in Argentina. Memes are pieces of graphic communication that became widespread with internet culture and that social science has defined as multimodal texts put into circulation to be exchanged and replicated in public expression and discussion. We collected on social media (Facebook and Twitter) an universe of 75 memes between June 2021 and September 2022. We first analysed their authorship (anonymous and nominal issuers), circulation and content, grouping them into two categories referring to different instances of the society-natures link: a) the dispute between agribusiness and agroecology, where agribusiness, led by scientific research (e.g. Bioceres), represents its domination and destruction and b) biodiversity and agribusiness, where the state shows itself to be helpless in the face of the destruction of biodiversity, while at the same time legitimising the agro-industrial model. It is concluded that memes about agribusiness are, for some social groups, a communicative tool that, using humour, that "gentle form of desperation", interpellates the reader-consumer, seeking his or her identification with the ideology of struggle against agribusiness, reporting biodiversity collapse in the process of accumulation by dispossession.Fil: Pereyra, Horacio Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lucero, Paula. Universidad Nacional de La Plata; ArgentinaFil: Mastrangelo, Andrea Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Misiones; Argentin

Trade-offs between biodiversity and agriculture are moving targets in dynamic landscapes

1. Understanding how biodiversity responds to intensifying agriculture is critical to mitigating the trade-offs between them. These trade-offs are particularly strong in tropical and subtropical deforestation frontiers, yet it remains unclear how changing landscape context in such frontiers alters agriculture?biodiversity trade?offs.2. We focus on the Argentinean Chaco, a global deforestation hotspot, to explore how landscape context shapes trade-off curves between agricultural intensity and avian biodiversity. We use a space-for-time approach and integrate a large field dataset of bird communities (197 species, 234 survey plots), three agricultural intensity metrics (meat yield, energy yield and profit) and a range of environmental covariates in a hierarchical Bayesian occupancy framework.3. Woodland extent in the landscape consistently determines how individual bird species, and the bird community as a whole, respond to agricultural intensity.Many species switch in their fundamental response, from decreasing occupancy with increased agricultural intensity when woodland extent in the landscape is low (loser species), to increasing occupancy with increased agricultural intensity when woodland extent is high (winner species).4. This suggests that landscape context strongly mediates who wins and loses along agricultural intensity gradients. Likewise, where landscapes change, such as in deforestation frontiers, the very nature of the agriculture?biodiversity trade?offs can change as landscapes transformation progresses.5. Synthesis and applications. Schemes to mitigate agriculture?biodiversity trade?offs,such as land sparing or sharing, must consider landscape context. Strategies that are identified based on a snapshot of data risk failure in dynamic landscapes, particularly where agricultural expansion continues to reduce natural habitats. Rather than a single, fixed strategy, adaptive management of agriculture?biodiversity trade?offs is needed in such situations. Here we provide a toolset for considering changing landscape contexts when exploring such trade-offs. This can help to better align agriculture and biodiversity in tropical and subtropical deforestation frontiers.Fil: Macchi, Leandro. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Decarre, Julieta. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; ArgentinaFil: Goijman, Andrea Paula. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; ArgentinaFil: Mastrangelo, Matias Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Laboratorio de Agroecología; ArgentinaFil: Blendinger, Pedro Gerardo. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Gavier Pizarro, Gregorio. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Murray, Francisco. Instituto Nacional de Tecnología Agropecuaria. Centro Regional La Pampa-San Luis. Estación Experimental Agropecuaria San Luis. Agencia de Extensión Rural San Luis; ArgentinaFil: Piquer Rodríguez, María. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Semper Pascual, Asunción. Humboldt-universitat Zu Berlin. Geography Department.; AlemaniaFil: Kuemmerle, Tobias. Humboldt-universitat Zu Berlin. Geography Department.; Alemani

Estudio cualicuantitativo de las variables sociales que definen escenarios de transmisión de la fiebre hemorrágica argentina en las provincias de Buenos Aires y Santa Fe, 2001-2010

The aim of this paper was to characterize transmission scenarios of Argentine Hemorrhagic Fever in the post-vaccination period (2001-2010). The study was made up of three phases. The first consisted of a quantitative analysis using the database of the Dr. Julio I. Maiztegui National Institute of Human Viral Diseases [Instituto Nacional de Enfermedades Virales Humanas] regarding the confirmed cases in the period of study (221 cases). Taking into account the transmission site and the known endemic area, cases were grouped into three hypothetical transmission scenarios, identified as: a) classical, b) emerging-reemerging, c) traveling. In the second phase, in order to test these hypotheses, in-depth interviews were carried out from August to September 2011 within an intentionally selected sample of patients distributed proportionally among the three hypotheses. Finally, in the third stage, the data obtained for each hypothetical scenario were grouped into three spatiotemporal scales: the microscale (subject), the mesoscale (locality) and macroscale (region). The results show that new transmission sites are associated with the social dynamics of cereal production and port-bound routes.El objetivo de este artículo es caracterizar los escenarios de transmisión de fiebre hemorrágica argentina (FHA) en el período de vacunación (2001-2010). El estudio constó de tres etapas. En la primera, se realizó un análisis cuantitativo de la base de datos del Instituto Nacional de Enfermedades Virales Humanas "Dr. Julio I. Maiztegui" (INEVH) de casos de FHA confirmados en el período (221 casos) que, sobre la base del lugar de transmisión y la zona endémica conocida, se agruparon según tres hipótesis de escenario: clásico, emergente-reemergente, y viajero. En la segunda etapa, para poner a prueba las hipótesis, se realizaron entrevistas en campo, entre agosto y octubre de 2011, a una muestra de selección intencional de pacientes distribuida proporcionalmente entre las tres hipótesis. Finalmente, en una tercera etapa, los datos generados para cada hipótesis de escenario se agruparon en tres escalas espacio-temporales: microescala (sujeto), mesoescala (localidad) y macroescala (región). Los resultados muestran que los nuevos lugares de transmisión estarían asociados a las dinámicas socioproductivas del cereal y las rutas al puerto

Microbiota Sensing by Mincle-Syk Axis in Dendritic Cells Regulates Interleukin-17 and -22 Production and Promotes Intestinal Barrier Integrity

We are grateful to members of the D.S. laboratory and Dr. E. Fernández-Malavé for discussions and critical reading of the manuscript. We appreciate the support of A. Tomás-Loba, G. Sabio, P. Martín, A. Tsilingiri, A.R. Ramiro, C.L. Abram, C.A. Lowell, J.M. García-Lobo, M. Molina, and M.C. Rodríguez for providing reagents and support. We thank the staff at the Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC) facilities for technical support. M.M.-L. received a Formación de Personal Universitario (FPU) fellowship (AP2010-5935) from the Spanish Ministerio de Educación. S.I. is funded by grant SAF2015-74561-JIN from the Spanish Ministerio de Ciencia, Innovación, y Universidades (MCIU) and Fondos Europeos de Desarrollo Regional (FEDER). G.D.B and D.M.R. are supported by the Wellcome Trust and the MRC Centre for Medical Mycology at the University of Aberdeen. S.L.L. is supported by the Swiss National Science Foundation (PP00P3_150758). Work in the D.S. laboratory is funded by the CNIC and grant SAF2016-79040-R from MCIU, the Agencia Estatal de Investigación, and FEDER; B2017/BMD-3733 Immunothercan-CM from Comunidad de Madrid; RD16/0015/0018-REEM from FIS-Instituto de Salud Carlos III, MCIU, and FEDER; the Acteria Foundation; the Constantes y Vitales prize (Atresmedia); La Marató de TV3 Foundation (201723); the European Commission (635122-PROCROP H2020), and the European Research Council (ERC-2016-Consolidator Grant 725091). The CNIC is supported by the MCIU and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence (SEV-2015-0505).Peer reviewedPublisher PD

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

Calcium Ions Promote Formation of Amyloid β-Peptide (1–40) Oligomers Causally Implicated in Neuronal Toxicity of Alzheimer's Disease

Amyloid β-peptide (Aβ) is directly linked to Alzheimer's disease (AD). In its monomeric form, Aβ aggregates to produce fibrils and a range of oligomers, the latter being the most neurotoxic. Dysregulation of Ca2+ homeostasis in aging brains and in neurodegenerative disorders plays a crucial role in numerous processes and contributes to cell dysfunction and death. Here we postulated that calcium may enable or accelerate the aggregation of Aβ. We compared the aggregation pattern of Aβ(1–40) and that of Aβ(1–40)E22G, an amyloid peptide carrying the Arctic mutation that causes early onset of the disease. We found that in the presence of Ca2+, Aβ(1–40) preferentially formed oligomers similar to those formed by Aβ(1–40)E22G with or without added Ca2+, whereas in the absence of added Ca2+ the Aβ(1–40) aggregated to form fibrils. Morphological similarities of the oligomers were confirmed by contact mode atomic force microscopy imaging. The distribution of oligomeric and fibrillar species in different samples was detected by gel electrophoresis and Western blot analysis, the results of which were further supported by thioflavin T fluorescence experiments. In the samples without Ca2+, Fourier transform infrared spectroscopy revealed conversion of oligomers from an anti-parallel β-sheet to the parallel β-sheet conformation characteristic of fibrils. Overall, these results led us to conclude that calcium ions stimulate the formation of oligomers of Aβ(1–40), that have been implicated in the pathogenesis of AD

Transformative governance for linking forest and landscape restoration to human well-being in Latin America

Tree planting and reforestation are currently in the spotlight as strategies for solving global environmental degradation. Many ongoing large-scale initiatives have proposed restoring millions of hectares and planting a trillion trees to solve climate change and biodiversity loss. Forest and landscape restoration (FLR) is one of the approaches most frequently employed to support these initiatives. Currently, many FLR initiatives are implemented in developing countries through a top-down approach, not fully anchored to the social-ecological characteristics of landscapes (e.g. land use and tenure, values of local peoples, local livelihoods), and sometimes relegating human well-being to a secondary concern. Therefore, issues of social equity and legitimacy might hamper the effectiveness of FLR initiatives and projects regarding their environmental outcomes. In this perspective article, we present four challenges to better link FLR and human well-being in Latin America: (1) the high dependence of local communities and countries’ economies on natural resources, (2) conflicts over land tenure and access, (3) divergence in perceptions and values, and (4) the fragility of public institutions and policies. After describing these interrelated challenges, we discuss how to tackle them by implementing instruments and approaches recently organized under the concept of transformative governance. Finding an equitable and legitimate balance between global interests and urgency and increasing local well-being is the main challenge of FLR in Latin America, for which transformative governance is critical.Fil: Aguiar, Sebastián. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Mastrangelo, Matias Enrique. Universidad Nacional de Mar del Plata; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Balcarce; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Laboratorio de Agroecología; ArgentinaFil: Brancalion, Pedro H.S.. Universidade de Sao Paulo; BrasilFil: Meli, Paula. Universidad de La Frontera; Chil

Primary isolated osteoma cutis of the face

We report a healthy, 44-year-old woman presentingwith an at least a 20-year history of hardened papulesin the forehead region, extending to the scalp.The biopsy and histopathologic exam confirmed adiagnosis of osteoma cutis. We review the literaturereview and discuss the classification of the cutaneousossification process presented, along with the resultsof the surgical treatment