L∞-norm and energy quantization for the planar Lane–Emden problem with large exponent

For any smooth bounded domain (Formula presented.), we consider positive solutions to (Formula presented.)which satisfy the uniform energy bound (Formula presented.)for (Formula presented.). We prove convergence to (Formula presented.) as (Formula presented.) of the (Formula presented.)-norm of any solution. We further deduce quantization of the energy to multiples of (Formula presented.), thus completing the analysis performed in De Marchis et al. (J Fixed Point Theory Appl 19:889–916, 2017)

Produção de mudas de espécies lenhosas.

Planejamento das instalações para produção de mudas; Técnicas de produção de mudas.bitstream/CNPF-2009-09/43217/1/doc130.pd

Influência de diferentes relações de reguladores de crescimento no alongamento in vitro de microestacas de Grevillea robusta (Cunn.)

Organizado por Patrícia Póvoa de Mattos, Celso Garcia Auer, Paulo César Botosso e Rejane Stumpf Sberze

reply to the letter to the editor by C.Nicolazzo et al.: "circulating cell-free DNA and circulating tumor cells as prognostic and predictive biomarkers in advanced non-small cell lung cancer patients treated with first-line chemotherapy"

n/a

The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible

Propagação vegetativa de E. benthamii x E. dunnii por miniestaquia.

bitstream/item/40278/1/Doc183.pd

Clinical and biological significance of miR-23b and miR-193a in human hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common cancer of the liver with a very poor prognosis. The dysregulation of microRNAs (miRs) is indeed implicated in HCC onset and progression. In this study, we have evaluated the expression of miR-23b and miR-193a in a large cohort of 59 and 67 HCC patients, respectively. miR-23b and miR-193a resulted significantly down-regulated in primary HCCs compared to their matched peritumoral counterparts. Furthermore, patients with higher miR-193a expression exhibited longer OS and DFS, suggesting that miR-193a may be a molecular prognostic factor for HCC patients. Since the regulation of miRs by DNA methylation may occur in human cancers, we verified whether the down-modulation of miR-23b and miR-193a in HCC tissues could be related to DNA methylation. An inverse trend between miR-23b expression and DNA methylation was observed, indicating that miR-23b can be epigenetically regulated. By contrast, the down-regulation of miR-193a was not mediated by DNA methylation. To verify the potential role of miR-23b and miR-193a as responsive molecular targets in vitro, we used the inhibitor of DNA methylation 5-aza-dC to restore miR-23b expression level in combination with miR-193a transfection. The combined treatment led to a significant inhibition of cellular proliferation and migration. Taken together, our findings provide evidence that miR-23b and miR-193a may be molecular diagnostic and prognostic factors for HCC; furthermore, miR-23b and miR-193a are responsive molecular targets for limiting HCC cell aggressiveness in combination with the epigenetic drug 5-aza-dC. Moreover, our results provide new advances in the epigenetic regulation of these miRs in HCC