45,858 research outputs found
A comment on "Intergenerational equity: sup, inf, lim sup, and lim inf"
No full textWe reexamine the analysis of Chambers (Social Choice and Welfare, 2009), that produces a characterization of a family of social welfare functions in the context of intergenerational equity: namely, those that coincide with either the sup, inf, lim sup, or lim inf rule. Reinforcement, ordinal covariance, and monotonicity jointly identify such class of rules. We show that the addition of a suitable axiom to this three properties permits to characterize each particular rule. A discussion of the respective distinctive properties is provided.Social welfare function; Intergenerational equity; Lim sup ; Lim inf
Multiple functions of LIM domain-binding CLIM/NLI/Ldb cofactors during zebrafish development
No full textThe crucial involvement of CLIM/NLI/Ldb cofactors for the exertion of the biological activity of LIM homeodomain transcription factors (LIM-HD) has been demonstrated. In this paper we show that CLIM cofactors are widely expressed during zebrafish development with high protein levels in specific neuronal cell types where LIM-HD proteins of the Isl class are synthesized. The overexpression of a dominant-negative CLIM molecule (DN-CLIM) that contains the LIM interaction domain (LID) during early developmental stages of zebrafish embryos results in an impairment of eye and midbrain-hindbrain boundary (MHB) development and disturbances in the formation of the anterior midline. On a cellular level we show that the outgrowth of peripheral but not central axons from Rohon Beard (RB) and trigeminal sensory neurons is inhibited by DN-CLIM overexpression. We demonstrate a further critical role of CLIM cofactors for axonal outgrowth of motor neurons. Additionally, DN-CLIM overexpression causes an increase of Isl-protein expression levels in specific neuronal cell types, likely due to a protection of the DN-CLIM/LIM-HD complex from proteasomal degradation. Our results demonstrate multiple roles of the CLIM cofactor family for the development of entire organs, axonal outgrowth of specific neurons and protein expression levels
Interpretare la LIM
No full textIl capitolo presenta le forme di utilizzo della LIM secondo la prospettiva delle competenze digitali
Four and a half LIM protein 1C (FHL1C)
No full textFour-and-a-half LIM domain protein 1 isoform A (FHL1A) is predominantly expressed in skeletal and cardiac muscle. Mutations in the FHL1 gene are causative for several types of hereditary myopathies including X-linked myopathy with postural muscle atrophy (XMPMA). We here studied myoblasts from XMPMA patients. We found that functional FHL1A protein is completely absent in patient myoblasts. In parallel, expression of FHL1C is either unaffected or increased. Furthermore, a decreased proliferation rate of XMPMA myoblasts compared to controls was observed but an increased number of XMPMA myoblasts was found in the G(0)/G(1) phase. Furthermore, low expression of K(v1.5), a voltage-gated potassium channel known to alter myoblast proliferation during the G(1) phase and to control repolarization of action potential, was detected. In order to substantiate a possible relation between K(v1.5) and FHL1C, a pull-down assay was performed. A physical and direct interaction of both proteins was observed in vitro. In addition, confocal microscopy revealed substantial colocalization of FHL1C and K(v1.5) within atrial cells, supporting a possible interaction between both proteins in vivo. Two-electrode voltage clamp experiments demonstrated that coexpression of K(v1.5) with FHL1C in Xenopus laevis oocytes markedly reduced K(+) currents when compared to oocytes expressing K(v1.5) only. We here present the first evidence on a biological relevance of FHL1C
Strumenti di valutazione e progettazione delle attività con la LIM
No full textIl capitolo presenta le nuove forme di comunicazione introdotte dalla LIM e le organizzazioni didattiche innovative relative a progettazione e valutazione
D-LIM: a Neural Network for Interpretable Gene-Gene Interactions
No full textRecent advances in gene editing can produce large genotype-fitness maps for targeted genes, yet predicting the effects of mutations between genes remains challenging. Indeed, biochemical models require knowledge of underlying parameters and interactions, whereas machine learning methods typically lack interpretability, as they do not link model parameters to biological quantities. We introduce D-LIM, a neural network that infers low-dimensional fitness landscapes directly from mutation-fitness data. The distinctive feature of D-LIM is that it assumes genes act through independent gene-specific molecular phenotypes whose nonlinear interactions determine fitness. When this assumption holds, the model yields accurate predictions and interpretable effective phenotypes. Conversely, failure reveals that a low-dimensional model is insufficient. Applied to deep mutational scanning of metabolic pathways, protein-protein interactions, and yeast environmental adaptation, D-LIM achieves state-ofthe-art predictive accuracy. The inferred phenotype-fitness landscapes reveal whether epistatic interactions can be captured by a low-dimensional continuous model and identify potential trade-offs. Moreover, D-LIM estimates mutational effects on the effective phenotypes, enabling weak extrapolation beyond the training domain. D-LIM demonstrates how simple structure constraints in a neural network can help inference and hypothesis generation in biology.</div
D-LIM: A neural network for interpretable gene–gene interactions
No full textInternational audienceRecent advances in gene editing can produce large genotype-fitness maps for targeted genes, yet predicting the effects of mutations between genes remains challenging. Indeed, biochemical models require knowledge of underlying parameters and interactions, whereas machine learning methods typically lack interpretability, as they do not link model parameters to biological quantities. We introduce D-LIM, a neural network that infers low-dimensional fitness landscapes directly from mutation-fitness data. The distinctive feature of D-LIM is that it assumes genes act through independent gene-specific molecular phenotypes whose nonlinear interactions determine fitness. When this assumption holds, the model yields accurate predictions and interpretable effective phenotypes. Conversely, failure reveals that a low-dimensional model is insufficient. Applied to deep mutational scanning of metabolic pathways, protein-protein interactions, and yeast environmental adaptation, D-LIM achieves state-ofthe-art predictive accuracy. The inferred phenotype-fitness landscapes reveal whether epistatic interactions can be captured by a low-dimensional continuous model and identify potential trade-offs. Moreover, D-LIM estimates mutational effects on the effective phenotypes, enabling weak extrapolation beyond the training domain. D-LIM demonstrates how simple structure constraints in a neural network can help inference and hypothesis generation in biology.</div
Strumenti di valutazione e progettazione delle attività con la LIM
No full textIl capitolo presenta le nuove forme di comunicazione introdotte dalla LIM e le organizzazioni didattiche innovative relative a progettazione e valutazione
Andiamo alla lavagna! Integrare la LIM in classe
No full textNel volume viene analizzato il "fenomeno" LIM da diversi punti di vista: i vantaggi per gli studenti e i docenti, le nuove dinamiche che lo strumento introduce nel contesto classe, i risultati dei primi studi sulla qualità dell'apprendimento con la LIM. Una parte metodologica, descrive l'utilizzo possibile in classe
Andiamo alla lavagna! Integrare la LIM in classe
No full textNel volume viene analizzato il "fenomeno" LIM da diversi punti di vista: i vantaggi per gli studenti e i docenti, le nuove dinamiche che lo strumento introduce nel contesto classe, i risultati dei primi studi sulla qualità dell'apprendimento con la LIM. Una parte metodologica, descrive l'utilizzo possibile in classe
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