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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

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

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

Western Diet-Fed ApoE Knockout Male Mice as an Experimental Model of Non-Alcoholic Steatohepatitis

One of the consequences of the Western lifestyle and high-fat diet is non-alcoholic fatty liver disease (NAFLD) and its aggressive form, non-alcoholic steatohepatitis (NASH), which can progress to cirrhosis and hepatocellular carcinoma (HCC) and is rapidly becoming the leading cause of end-stage liver disease or liver transplantation. Currently, rodent NASH models lack significant aspects of the full NASH spectrum, representing a major problem for NASH research. Therefore, this work aimed to characterize a fast rodent model with all characteristic features of NASH. Eight-week-old male ApoE KO mice were fed with Western diet (WD), high fatty diet (HFD) or normal chow (Chow) for 7 weeks. Whole-body fat was increased by ~2 times in WD mice and HFD mice and was associated with increased glucose intolerance, hepatic triglycerides, and plasma ALT and plasma AST compared with Chow mice. WD mice also showed increased galectin-3 expression compared with Chow or HFD mice and increased plasma cholesterol compared with Chow mice. WD and HFD displayed increased hepatic fibrosis and increased F4/80 expression. WD mice also displayed increased levels of plasma MCP-1. Hepatic inflammatory markers were evaluated, and WD mice showed increased levels of TNF-α, MCP-1, IL-6 and IFN-γ. Taken together, these data demonstrated that the ApoE KO mouse fed with WD is a great model for NASH research, once it presents the fundamental parameters of the disease, including hepatic steatosis, fibrosis, inflammation, and metabolic syndrome

Taurine Supplementation Restores Glucose And Carbachol-induced Insulin Secretion In Islets From Low-protein Diet Rats: Involvement Of Ach-m3r, Synt 1 And Snap-25 Proteins.

Isolated islets from low-protein (LP) diet rats showed decreased insulin secretion in response to glucose and carbachol (Cch). Taurine (TAU) increases insulin secretion in rodent islets with a positive effect upon the cholinergic pathway. Here, we investigated the effect of TAU administration upon glucose tolerance and insulin release in rats fed on a normal protein diet (17%) without (NP) or with 2.5% of TAU in their drinking water (NPT), and LP diet fed rats (6%) without (LP) or with TAU (LPT). Glucose tolerance was found to be higher in LP, compared to NP rats. However, plasma glucose levels, during ipGTT, in LPT rats were similar to those of controls. Isolated islets from LP rats secreted less insulin in response to increasing glucose concentrations (2.8-22.2 mmol/L) and to 100 μmol/L Cch. This lower secretion was accompanied by a reduction in Cch-induced internal Ca(2+) mobilization. TAU supplementation prevents these alterations, as judged by the higher secretion induced by glucose or Cch in LPT islets. In addition, Ach-M3R, syntaxin 1 and synaptosomal associated protein of 25 kDa protein expressions in LP were lower than in NP islets. The expressions of these proteins in LPT were normalized. Finally, the sarcoendoplasmatic reticulum Ca(2+)-ATPase 3 protein expression was higher in LPT and NPT, compared with controls. In conclusion, TAU supplementation to LP rats prevented alterations in glucose tolerance as well as in insulin secretion from isolated islets. The latter effect involves the normalization of the cholinergic pathway, associated with the preservation of exocytotic proteins.23306-1

Taurine Supplementation Enhances Nutrient-induced Insulin Secretion In Pancreatic Mice Islets.

Taurine (TAU), a naturally occurring sulfur-containing amino acid, is found at high concentrations in plasma and mammalian tissues and regulates osmolarity, ion channel activity, and glucose homeostasis. Several reports have shown that physiological plasma TAU levels seem to be important for adequate beta (beta)-cell function and insulin action, since low concentrations of TAU in the plasma have been reported in the pre-diabetic and diabetic states. Glucose tolerance and insulin sensitivity were investigated in mice supplemented with 2% (w/v) TAU in their drinking water for 30 days, as well as the insulin secretion from isolated islets stimulated by glucose or L-leucine. TAU-supplemented mice demonstrated improved glucose tolerance and higher insulin sensitivity, compared to controls (CTL). In addition, their islets secreted more insulin in response to high concentrations of glucose and L-leucine. L-[U-(14)C]leucine oxidation was higher in TAU than in CTL islets, whereas D-[U-(14)C]glucose oxidation, ATP levels, glucose transporter (GLUT) 2 and glucokinase (GCK) protein expressions were similar in both types of islets. The L-type beta(2) subunit voltage-sensitive Ca(2+) channel protein, as well as (45)Ca uptake, were significantly higher in TAU-supplemented than CTL islets. In addition, islets from TAU-supplemented mice secreted more glucagon than CTL islets at low glucose. TAU supplementation improves glucose tolerance and insulin sensitivity in mice, as well as insulin secretion from isolated islets. The latter effect seems to be, at least in part, dependent on a better Ca(2+) handling by the islets.25370-