A comparative analysis of “The rings of Saturn” and “Patience (After Sebald)”

Abstract: This work presents a comparative analysis of W. G. Sebald’s book The Rings of Saturn (1998) and Grant Gee’s film Patience (After Sebald) [2012]. As Sabine Wilke (2012) points out, Sebald did not provide a genre designation for three of his prose works, which means that he did not consider them as novels. Mixing up fact and fiction, his stories blur the line between literature and history, and hence establish a new form of “literary historiography” (WOLFF, 2014, p. 50). This hybridity is also manifested in Gee’s production, and this work explores in what aspects the latter can be considered an essay film, through the observation of questions of authorship and spectatorial experience. It argues that the book and the film are both based on a very similar concept of identity, according to which the writer and/or filmmaker’s I is but a tissue of citations from other texts. Likewise, it analyzes Sebald’s process of spatialization of time through the work of memory, and how this process is reflected in Gee’s inherently geographical approach, which emphasizes place and the mapping of consciousness. Key-words: Comparative literature; Intermedial studies; W. G. Sebal

Design, Synthesis and Biological Evaluation of Novel Triazole N-acylhydrazone Hybrids for Alzheimer's Disease

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder that involves different pathogenic mechanisms. In this regard, the goal of this study was the design and synthesis of new compounds with multifunctional pharmacological activity by molecular hybridization of structural fragments of curcumin and resveratrol connected by an N-acyl-hydrazone function linked to a 1,4-disubstituted triazole system. Among these hybrid compounds, derivative 3e showed the ability to inhibit acetylcholinesterase activity, the intracellular formation of reactive oxygen species as well as the neurotoxicity elicited by Aβ42 oligomers in neuronal SH-SY5Y cells. In parallel, compound 3e showed a good profile of safety and ADME parameters. Taken together, these results suggest that 3e could be considered a lead compound for the further development of AD therapeutics

Design, synthesis, and biological evaluation of new thalidomide–donepezil hybrids as neuroprotective agents targeting cholinesterases and neuroinflammation

A new series of eight multifunctional thalidomide–donepezil hybrids were synthesized based on the multi target-directed ligand strategy and evaluated as potential neuroprotective, cholinesterase inhibitors and anti neuroinflammatory agents against neurodegenerative diseases. A molecular hybridization approach was used for structural design by combining the N-benzylpiperidine pharmacophore of donepezil and the isoindoline 1,3-dione fragment from the thalidomide structure. The most promising compound, PQM-189 (3g), showed good AChE inhibitory activity with an IC50 value of 3.15 μM, which was predicted by docking studies as interacting with the enzyme in the same orientation observed in the AChE–donepezil complex and a similar profile of interaction. Additionally, compound 3g significantly decreased iNOS and IL-1β levels by 43% and 39%, respectively, after 24 h of incubation with lipopolysaccharide. In vivo data confirmed the ability of 3g to prevent locomotor impairment and changes in feeding behavior elicited by lipopolysaccharide. Moreover, the PAMPA assay evidenced adequate blood–brain barrier and gastrointestinal tract permeabilities with an Fa value of 69.8%. Altogether, these biological data suggest that compound 3g can treat the inflammatory process and oxidative stress resulting from the overexpression of iNOS and therefore the increase in reactive nitrogen species, and regulate the release of pro-inflammatory cytokines such as IL-1β. In this regard, compound PQM-189 (3g) was revealed to be a promising neuroprotective and anti-neuroinflammatory agent with an innovative thalidomide–donepezil-based hybrid molecular architectur

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost