Recognition of major histocompatibility complex antigens on cultured human biliary epithelial cells by alloreactive lymphocytes

We have developed an in vitro system to study the interactions between biliary epithelium and lymphocytes using cultured human biliary epithelial cells. No class II antigens were detected by immunoperoxidase staining of the normal biliary epithelial cells, but alloactivated lymphocyte culture supernatants were able to induce class II expression. The activity of the supernatants was blocked with an anti‐γ‐interferon monoclonal antibody. In addition, recombinant human γ‐interferon alone induced the expression of class II antigens and increased the intensity of class I staining of cultured biliary epithelial cells. Biliary epithelial cell‐induced proliferation of alloreactive T lymphocytes demonstrated that the major histocompatibility complex molecules carry functional lymphocyteactivating determinants. The recognition of major histocompatibility complex determinants was confirmed by monoclonal antibody‐blocking studies and by stimulation of an alloreactive T‐cell clone. However, the biliary epithelial cells were much less potent stimulators than arterial endothelial cells tested in the same assay system. (HEPATOLOGY 1991;13:239–246). Copyright © 1991 American Association for the Study of Liver Disease

On Maximum Weight Clique Algorithms, and How They Are Evaluated

Maximum weight clique and maximum weight independent set solvers are often benchmarked using maximum clique problem instances, with weights allocated to vertices by taking the vertex number mod 200 plus 1. For constraint programming approaches, this rule has clear implications, favouring weight-based rather than degree-based heuristics. We show that similar implications hold for dedicated algorithms, and that additionally, weight distributions affect whether certain inference rules are cost-effective. We look at other families of benchmark instances for the maximum weight clique problem, coming from winner determination problems, graph colouring, and error-correcting codes, and introduce two new families of instances, based upon kidney exchange and the Research Excellence Framework. In each case the weights carry much more interesting structure, and do not in any way resemble the 200 rule. We make these instances available in the hopes of improving the quality of future experiments

Optimizing the kidney exchange problem with a budget for simultaneous crossmatch tests

Many people in the world suffer from end stage renal disease, which has transplantation as the most effective form of treatment. However, kidneys obtained from deceased donors are not nearly enough to meet demand and willing living donors may display incompatibilities with their intended recipient. Kidney Exchange Programs have emerged as an attempt to answer the transplant shortage and bypass these incompatibility issues between donor-patient pairs. The process of selecting the pairs participating in the transplantation plan requires optimization models, one of which is presented in the current paper. We focus on maximizing the expected number of transplants taking into account that only a given number of actual incompatibility (crossmatch) tests can be made. We present an integer programming model to address this problem when recourse is feasible and we compare computationally its outcomes with other two approaches in 150 instances.INCT-EN - Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção(UID/CEC/00319/2019

Genetic Variation Patterns of “Algarrobos” from the “Great American Chaco” (Prosopis alba, P. nigra, P. hassleri, P. fiebrigii, P. ruscifolia, P. chilensis, and P. flexuosa)

Instituto de Fisiología y Recursos Genéticos VegetalesFil: Vega, Carmen Delcira . Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Fisiología y Recursos Genéticos Vegetales; ArgentinaFil:Vega, Carmen Delcira. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Estudios Agropecuarios (UDEA); ArgentinaFil: Aguilar, Dana. Universidad Nacional de Córdoba. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Multidisciplinario de Biología Vegetal ( IMBIV). Laboratorio Ecología Evolutiva – Biología Floral; ArgentinaFil: Bessega, Cecilia. Universidad de Buenos Aires. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecología, Genética y Evolución de Buenos Aires (IEGEBA). Departamento de Ecología, Genética y Evolución. Laboratorio de Genética; ArgentinaFil: Teich, Ingrid. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Fisiología y Recursos Genéticos Vegetales; ArgentinaFil: Teich, Ingrid. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Estudios Agropecuarios (UDEA); ArgentinaFil: Acosta, María Cristina. Universidad Nacional de Córdoba. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Multidisciplinario de Biología Vegetal ( IMBIV). Laboratorio Ecología Evolutiva – Biología Floral; ArgentinaFil: Cosacov, Andrea. Universidad Nacional de Córdoba. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Multidisciplinario de Biología Vegetal (IMBIV). Laboratorio de Ecología Evolutiva - Biología Floral; ArgentinaFil: Ewens, Mauricio. Universidad Católica de Santiago del Estero. Estación Experimental Fernández; ArgentinaFil: Vilardi, Juan. Universidad de Buenos Aires. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecología, Genética y Evolución de Buenos Aires (IEGEBA). Departamento de Ecología, Genética y Evolución. Laboratorio de Genética; ArgentinaFil: Sérsic, Alicia N. Universidad Nacional de Córdoba. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Multidisciplinario de Biología Vegetal ( IMBIV). Laboratorio Ecología Evolutiva – Biología Floral; ArgentinaFil: Verga, Anibal. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria La Rioja. Agencia De Extensión Rural La Rioja; Argentin