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

Skin moisturizing effects of panthenol-based formulations

Synopsis This study aims to evaluate the skin moisturizing effi cacy of formulations containing different concentrations of panthenol. Formulations supplemented with or without 0.5%, 1.0%, or 5.0% panthenol were applied daily to the forearms of healthy subjects. Skin conditions in terms of moisture and transepidermal water loss (TEWL) were analyzed before and after 15-and 30-day periods of application. The formulations were also applied after skin washing with sodium laureth sulphate (SLES) to evaluate the immediate effects on TEWL and skin moisture. Panthenol-containing formulations (1.0% and 5.0%) produced signifi cant decreases in TEWL after 30-day applications. In skin washed with SLES, signifi cant reduction of TEWL was evident two hours after application of formulations loaded with panthenol when compared with control and vehicle. It is concluded that skin integrity is maintained by the improved protective effect of 1.0% panthenol added to the formulation

Identification and complete sequencing of novel human transcripts through the use of mouse orthologs and testis cDNA sequences

The correct identification of all human genes, and their derived transcripts, has not yet been achieved, and it remains one of the major aims of the worldwide genomics community. Computational programs suggest the existence of 30,000 to 40,000 human genes. However, definitive gene identification can only be achieved by experimental approaches. We used two distinct methodologies, one based on the alignment of mouse orthologous sequences to the human genome, and another based on the construction of a high-quality human testis cDNA library, in an attempt to identify new human transcripts within the human genome sequence. We generated 47 complete human transcript sequences, comprising 27 unannotated and 20 annotated sequences. Eight of these transcripts are variants of previously known genes. These transcripts were characterized according to size, number of exons, and chromosomal localization, and a search for protein domains was undertaken based on their putative open reading frames. In silico expression analysis suggests that some of these transcripts are expressed at low levels and in a restricted set of tissues