When and how java developers give up static type safety

The main goal of a static type system is to prevent certain kinds of errors from happening at run time. A type system is formulated as a set of constraints that gives any expression or term in a program a well-defined type. Besides detecting these kinds of errors, a static type system can be an invaluable maintenance tool, can be useful for documentation purposes, and can aid in generating more efficient machine code. However, there are situations when the developer has more information about the program that is too complex to explain in terms of typing constraints. To that end, programming languages often provide mechanisms that make the typing constraints less strict to permit more programs to be valid, at the expense of causing more errors at run time. These mechanisms are essentially two: Unsafe Intrinsics and Reflective Capabilities. We want to understand how and when developers give up these static constraints. This knowledge can be useful as: a) a recommendation for current and future language designers to make informed decisions, b) a reference for tool builders, e.g., by providing more precise or new refactoring analyses, c) a guide for researchers to test new language features, or to carry out controlled programming experiments, and d) a guide for developers for better practices. In this dissertation, we focus on the Unsafe API and cast operator---a subset of unsafe intrinsics and reflective capabilities respectively---in Java. We report two empirical studies to understand how these mechanisms---Unsafe API and cast operator---are used by Java developers when the static type system becomes too strict. We have devised usage patterns for both the Unsafe API and cast operator. Usage patterns are recurrent programming idioms to solve a specific issue. We believe that having usage patterns can help us to better categorize use cases and thus understand how those features are used

Factores psicosociales que influyen en la intención de los tomadores de decisión agropecuarios de la Pampa austral de Argentina de conservar las franjas de vegetación ribereñas

En áreas de intensificación agropecuaria, los tomadores de decisión son los responsables del uso de las tierras y de la conservación de las franjas de vegetación ribereñas. Estas, proporcionan numerosos servicios ecosistémicos a los seres humanos. A pesar de su importancia, cuando la calidad de los suelos lo permite, son convertidas en áreas de cultivo. El objetivo de este trabajo fue comprender la influencia relativa de distintos factores psicosociales, sobre las intenciones de conservar los ambientes ribereños. Se eligieron 50 explotaciones en la Pampa Austral de Argentina, se entrevistaron a los tomadores de decisión a los que se pidió completaran una encuesta de tipo estructurada. Las respuestas fueron codificadas, analizadas estadísticamente y se obtuvieron 3 modelos de ecuaciones estructurales. El modelo basado en los factores normativos mostró el mayor peso, el segundo mejor modelo, fue el cimentado en factores cognitivos, en tanto que, el fundado en factores emocionales fue el de menor representatividad. Se concluye que si bien los factores normativos fueron los que mejor explicaron las intenciones de conservación, representan cuellos de botella en los que no se puede influir y solo se puede intervenir a través de los factores cognitivos, promoviendo el conocimiento.In areas of agricultural intensification, decision makers are responsible for the land use and the conservation of riparian vegetation strips. These provide many ecosystem services to humans. Despite their importance, they are converted into crop areas when the quality of the soils allows it. The objective of this work was to understand the relative influence of different psycho-social factors on the intentions to conserve the riparian environments. Fifty decision makers of farms of the Southern Pampa of Argentina were interviewed in order to complete a structured survey. The responses were coded and statistically analyzed to determine three models of structural equations were obtained. A model based on normative factors showed the greatest weight. A second model, based on cognitive factors, was most adjusted than one based on emotional factors. It is concluded that although normative factors were the ones that best explained the intentions about the conservation. These factors represent bottlenecks that cannot be influenced and can only be intervened by cognitive factors based on the promotion of the knowledge.EEA BarrowFil: Giaccio, Gustavo Carlos Marí­a. Instituto Nacional de Tecnología Agropecuaria (INTA). Chacra Experimental Integrada Barrow; ArgentinaFil: Giaccio, Gustavo Carlos Marí­a. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mastrángelo, Matías Enrique. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Mastrángelo, Matías Enrique. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Aparicio, Virginia Carolina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Costa, Jose Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Laterra, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Laterra, Pedro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.Fil: Laterra, Pedro. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina

Major revision version 12.0 of the European AIDS Clinical Society guidelines 2023

Background The European AIDS Clinical Society (EACS) guidelines were revised in 2023 for the 19th time, and all aspects of HIV care were updated.Key Points of the Guidelines Update Version 12.0 of the guidelines recommend the same six first-line treatment options for antiretroviral treatment (ART)-naive adults as versions 11.0 and 11.1: tenofovir-based backbone plus an unboosted integrase inhibitor or doravirine; abacavir/lamivudine plus dolutegravir; or dual therapy with lamivudine or emtricitabine plus dolutegravir. The long-acting section has been expanded in the ART and drug-drug interaction (DDI) panels. Tables for preferred and alternative ART in children and adolescents have been updated, as has the section on prevention of vertical transmission, particularly with new guidance for breastfeeding. A new DDI table has been included for the ART and anti-infective drugs used for opportunistic infections, sexually transmitted infections, and other infectious conditions; lenacapavir has been included in all DDI tables. New sections on alcohol use and patient-reported outcome measures (PROMs) have been included in the comorbidity panel, in addition to updates on many relevant topics, such as new resource guidance for deprescribing in people with HIV. Other sections, including travel, cognitive impairment, cancer screening, sexual health, and diabetes have also been revised extensively. The algorithm for the management of acute hepatitis C virus infection has been removed, as current guidelines recommend immediate treatment of all people with recently acquired hepatitis C virus. Updates on vaccination for hepatitis B virus and recommendations for simplification to tenofovir-free two-drug regimens in people with isolated anti-hepatitis B core antibodies are provided. In the opportunistic infections and COVID-19 panel, guidance on the management of COVID-19 in people with HIV has been updated according to the most up-to-date evidence, and a new section on monkeypox has been added.Conclusions In 2023, the EACS guidelines were updated extensively and now include several new sections. The recommendations are available as a free app, in interactive web format, and as a pdf online

The HIV-1 reservoir landscape in persistent elite controllers and transient elite controllers

FUNDING. Instituto de Salud Carlos III (FI17/00186, FI19/00083, MV20/00057, PI18/01532, PI19/01127 and PI22/01796), Gilead Fellowships (GLD22/00147). NIH grants AI155171, AI116228, AI078799, HL134539, DA047034, MH134823, amfAR ARCHE and the Bill and Melinda Gates Foundation.BACKGROUND. Persistent controllers (PCs) maintain antiretroviral-free HIV-1 control indefinitely over time, while transient controllers (TCs) eventually lose virological control. It is essential to characterize the quality of the HIV reservoir in terms of these phenotypes in order to identify the factors that lead to HIV progression and to open new avenues toward an HIV cure. METHODS. The characterization of HIV-1 reservoir from peripheral blood mononuclear cells was performed using next-generation sequencing techniques, such as full-length individual and matched integration site proviral sequencing (FLIP-Seq; MIP-Seq). RESULTS. PCs and TCs, before losing virological control, presented significantly lower total, intact, and defective proviruses compared with those of participants on antiretroviral therapy (ART). No differences were found in total and defective proviruses between PCs and TCs. However, intact provirus levels were lower in PCs compared with TCs; indeed the intact/defective HIV-DNA ratio was significantly higher in TCs. Clonally expanded intact proviruses were found only in PCs and located in centromeric satellite DNA or zinc-finger genes, both associated with heterochromatin features. In contrast, sampled intact proviruses were located in permissive genic euchromatic positions in TCs. CONCLUSIONS. These results suggest the need for, and can give guidance to, the design of future research to identify a distinct proviral landscape that may be associated with the persistent control of HIV-1 without ART.Instituto de Salud Carlos III (FI17/00186, FI19/00083, MV20/00057, PI18/01532, PI19/01127, PI22/01796)Gilead Fellowships (GLD22/00147)NIH grants AI155171, AI116228, AI078799, HL134539, DA047034, MH134823, amfAR ARCHEBill and Melinda Gates Foundatio

Transdisciplinary studies in socio-ecosystems: Theoretical considerations and its application in Latin American contexts

Debido a limitaciones para abordar la complejidad de la relación sociedad-naturaleza, los esfuerzos para solucionar los problemas ambientales han sido en general infructuosos. Aquí proponemos que el enfoque holístico de “socio-ecosistema” por parte de la academia, podría contribuir a disminuir estas limitaciones desde la adopción de cuatro cambios: i) ontológico, que presenta el concepto de “socio-ecosistemas”; ii) epistemológico, que propone a la transdisciplina como la forma de entenderlos, iii) metodológico, que sugiere intervenir en ellos de forma participativa y adaptativa y, iv) cambios institucionales que facilitarían la adopción de esta propuesta. Este planteamiento se complementa con la descripción de una experiencia transdiciplinaria en la cuenca del río San Juan Zitácuaro, México, en el contexto de un curso internacional de manejo de socio-ecosistemas.Given the difficulties to approach the complex relationship bettween society and nature, efforts to solve environmental problems have generally been unsuccessful. Here we suggest that a hollistic “socio-ecosystem” approach by the sciencies could help diminish these difficulties by embracing four kinds of changes: i) ontological, which introduces the concept of “socio-ecosystem”; ii) epistemological, which proposes transdiscipline as the way to understand them, iii) metholodogical, which suggests that in intervention in them must be participatory and adaptive, iv) institutional changes that would facilitate the adoption of this approach. This is then followed by a description of a transdisciplinary work experience in the Zitácuaro river basin, in Mexico, in the context of an international course on socio-ecosystem management.Fil: Ortega Uribe, Tamara. Universidad de Chile; ChileFil: Mastrangelo, Matias Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Villarroel Torrez, Daniel. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Piaz, Agustín Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Escuela de Humanidades. Centro de Estudios de Historia de la Ciencia y de la Técnica ; ArgentinaFil: Vallejos, María. 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: Saenz Ceja, Jesús Eduardo. Universidad Nacional Autónoma de México. Centro de Investigaciones en Ecosistemas; MéxicoFil: Gallego, Federico. Universidad de la República. Facultad de Ciencias; UruguayFil: Franquesa Soler, Monserrat. Instituto de Ecología; MéxicoFil: Calzada Peña, Leonardo. Universidad Nacional Autónoma de México; MéxicoFil: Espinosa Mellado, Noelia. Universidad de la Armada; MéxicoFil: Fiestas Flores, Jerico. Instituto de Estudios Peruanos; PerúFil: Gill Mairhofer, Luis R.. Ministerio de la Defensa Pública; ParaguayFil: González Espino, Zarahí. Instituto Superior de Tecnologías y Ciencias Aplicadas. Facultad de Medio Ambiente. Departamento de Meteorología; CubaFil: Luna Salguero, Betsabé Montserrat. Sociedad de Historia Natural Niparajá; MéxicoFil: Martinez Peralta, Claudia María. Comisión de Ecología y Desarrollo Sustentable del Estado de Sonora. Dirección General de Conservación; MéxicoFil: Ochoa, Olivia. Universidad Nacional Autónoma de México; MéxicoFil: Pérez Volkow,Lucía. No especifica;Fil: Sala, Juan Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: Sánchez Rose, Isabelle. Universidad Central de Venezuela; VenezuelaFil: Weeks, Madeline. University of Cambridge; Reino UnidoFil: Ávila García, Daniela. Universidad Nacional Autónoma de México; MéxicoFil: García Reyes, Isabel Bueno. Universidad Nacional Autónoma de México. Centro de Investigaciones en Ecosistemas; MéxicoFil: Carmona, Alejandra. Universidad Austral de Chile. Instituto de Economía Agraria; ChileFil: Castro Videla, Fernando Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; ArgentinaFil: Ferrer Gonzalez, César Sergio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; ArgentinaFil: Frank Buss, María Elisa. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Pampa. Facultad de Agronomía; ArgentinaFil: López Carapia, Gabriela. Universidad Nacional Autónoma de México; MéxicoFil: Núñez Cruz, Martha. Universidad Nacional Autónoma de México; MéxicoFil: Taboada Hermoza, Rossi. Universidad Nacional Mayor de San Marcos; PerúFil: Benet, Daniel. Alternare A. C.; MéxicoFil: Venegas, Ysmael. Alternare A. C.; MéxicoFil: Balvanera, Patricia. Universidad Nacional Autónoma de México. Centro de Investigaciones en Ecosistemas; MéxicoFil: Mwampamba, Tuyeni H.. Universidad Nacional Autónoma de México. Centro de Investigaciones en Ecosistemas; MéxicoFil: Lazos Chavero, Elena. Universidad Nacional Autónoma de México. Centro de Investigaciones en Ecosistemas; MéxicoFil: Noellemeyer, Elke Johanna. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Maass, Manuel. Universidad Nacional Autónoma de México. Centro de Investigaciones en Ecosistemas; Méxic

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

Infected pancreatic necrosis: outcomes and clinical predictors of mortality. A post hoc analysis of the MANCTRA-1 international study

: The identification of high-risk patients in the early stages of infected pancreatic necrosis (IPN) is critical, because it could help the clinicians to adopt more effective management strategies. We conducted a post hoc analysis of the MANCTRA-1 international study to assess the association between clinical risk factors and mortality among adult patients with IPN. Univariable and multivariable logistic regression models were used to identify prognostic factors of mortality. We identified 247 consecutive patients with IPN hospitalised between January 2019 and December 2020. History of uncontrolled arterial hypertension (p = 0.032; 95% CI 1.135-15.882; aOR 4.245), qSOFA (p = 0.005; 95% CI 1.359-5.879; aOR 2.828), renal failure (p = 0.022; 95% CI 1.138-5.442; aOR 2.489), and haemodynamic failure (p = 0.018; 95% CI 1.184-5.978; aOR 2.661), were identified as independent predictors of mortality in IPN patients. Cholangitis (p = 0.003; 95% CI 1.598-9.930; aOR 3.983), abdominal compartment syndrome (p = 0.032; 95% CI 1.090-6.967; aOR 2.735), and gastrointestinal/intra-abdominal bleeding (p = 0.009; 95% CI 1.286-5.712; aOR 2.710) were independently associated with the risk of mortality. Upfront open surgical necrosectomy was strongly associated with the risk of mortality (p < 0.001; 95% CI 1.912-7.442; aOR 3.772), whereas endoscopic drainage of pancreatic necrosis (p = 0.018; 95% CI 0.138-0.834; aOR 0.339) and enteral nutrition (p = 0.003; 95% CI 0.143-0.716; aOR 0.320) were found as protective factors. Organ failure, acute cholangitis, and upfront open surgical necrosectomy were the most significant predictors of mortality. Our study confirmed that, even in a subgroup of particularly ill patients such as those with IPN, upfront open surgery should be avoided as much as possible. Study protocol registered in ClinicalTrials.Gov (I.D. Number NCT04747990)

VMAD: an Advanced Dynamic Program Analysis & Instrumentation Framework

International audienceVMAD (Virtual Machine for Advanced Dynamic analysis) is a platform for advanced profiling and analysis of programs, consisting in a static component and a runtime system. The runtime system is organized as a set of decoupled modules, dedicated to specific instrumenting or optimizing operations, dynamically loaded when required. The program binary files handled by VMAD are previously processed at compile time to include all necessary data, instrumentation instructions and callbacks to the runtime system. For this purpose, the LLVM compiler has been extended to automatically generate multiple versions of the code, each of them tailored for the targeted instrumentation or optimization strategies. The compiler chooses the most suitable intermediate representation for each version, depending on the information to be acquired and on the optimizations to be applied. The control flow graph is adapted to include the new versions and to transfer the control to and from the runtime system, which is in charge of the execution flow orchestration. The strength of our system resides in its extensibility, as one can add support for various new profiling or optimization strategies, independently of the existing modules. VMAD's potential is illustrated by presenting several analysis and optimization applications dedicated to loop nests: instrumentation by sampling, dynamic dependence analysis, adaptive version selection

Adapting the Polyhedral Model as a Framework for Efficient Speculative Parallelization

International audienceIn this paper, we present a Thread-Level Speculation (TLS) framework whose main feature is to be able to speculatively parallelize a sequential loop nest in various ways, by re-scheduling its iterations. The transformation to be applied is selected at runtime with the goal of minimizing the number of rollbacks and maximizing performance. We perform code transformations by applying the polyhedral model that we adapted for speculative and runtime code parallelization. For this purpose, we designed a parallel code pattern which is patched by our runtime system according to the profiling information collected on some execution samples. Adaptability is ensured by considering chunks of code of various sizes, that are launched successively, each of which being parallelized in a different manner, or run sequentially, depending on the currently observed behavior for accessing memory