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

Carbon footprint of beef cattle systems in the Southeast United States

Grasslands in the Southeast United States (SE US) cover 15.8 million ha and most of this area is dedicated to beef production systems. This region holds 6.3 million beef cows and 12.1 million cattle, including calves. Beef cattle systems in the SE US are mostly cow-calf based, and most of the greenhouse gas emission from cattle occurs during this phase (cow-calf) because of their forage-based diet. This review assessed the carbon footprint (C footprint) of beef cattle systems in the SE US and indicates possible ways to reduce it. Major emissions in beef cattle systems come from livestock enteric fermentation and greenhouse gases from excreta. Cow-calf systems in the SE US are typically low input, although they use some industrial fertilizers, machinery, and fossil fuel, which adds to the C footprint of the sector. There are opportunities to reduce the beef C footprint in the SE US by adopting climate-smart practices, including preservation of natural ecosystems that have potentially high carbon sequestration, afforestation, integration of forage legumes (and reduction of nitrogen fertilizer), use of slow-release fertilizers, and dietary interventions. In fact, depending on the level of adoption of some of these practices, it is possible to establish climate-neutral beef at the farm gate in the SE US. Beef is a key food for humans and has large economic effects. Development of climate-smart beef could create opportunities for a niche market that recognizes the environmental footprint of agricultural production and could incentivize producers to pursue those systems

A Combined Study Using Ligand-Based Design, Synthesis, and Pharmacological Evaluation of Analogues of the Acetaminophen Ortho-Regioisomer with Potent Analgesic Activity

A ligand-based drug design study was performed to acetaminophen regioisomers as analgesic candidates employing quantum chemical calculations at the DFT/B3LYP level of theory and the 6-31G* basis set. To do so, many molecular descriptors were used such as highest occupied molecular orbital, ionization potential, HO bond dissociation energies, and spin densities, which might be related to quench reactivity of the tyrosyl radical to give N-acetyl-p-benzosemiquinone-imine through an initial electron withdrawing or hydrogen atom abstraction. Based on this in silico work, the most promising molecule, orthobenzamol, was synthesized and tested. The results expected from the theoretical prediction were confirmed in vivo using mouse models of nociception such as writhing, paw licking, and hot plate tests. All biological results suggested an antinociceptive activity mediated by opioid receptors. Furthermore, at 90 and 120 min, this new compound had an effect that was comparable to morphine, the standard drug for this test. Finally, the pharmacophore model is discussed according to the electronic properties derived from quantum chemistry calculations.CAPESCNPqFAPESPAPARDUFPAPARD-UFP

Evaluating the effects of refined carbohydrate and fat diets with acute ethanol consumption using a mouse model of alcoholic liver injury

Alcoholism is a multifactorial and complex disorder responsible for 5.9% of deaths worldwide. Excessive consumption of ethanol (Et-OH) induces alcoholic liver disease (ALD), a condition comprising a spectrum of clinical signs and morphological changes, ranging from fatty liver (steatosis) to more severe forms of chronic liver injury. Secondary cofactors, such as nutritional and hepatotoxic comorbid conditions, can also contribute to liver disease development. Here we investigated the effects in the progression of ALD following short-term exposure to diet high in refined carbohydrates (HC), a high-sugar and -butter (HSB) hypercaloric diet and acute Et-OH consumption. HSB diet increased the body weight (BW) and adiposity independently of acute Et-OH consumption. HC diet did not affect BW but increased the adiposity, while acute Et-OH alone did not affect BW and adiposity. All groups of mice developed steatosis except the control group. Exposure to acute Et-OH and HSB diet increased the number of neutrophils and macrophages, and apoptosis in the liver. This combination also increased the number of circulating neutrophils and reduced mononuclear cells in the blood. Thus, short-term exposure to HSB diet and acute Et-OH intake is linked to increased liver injury. These findings offer important clues to understand the hepatic injuries associated with short exposure to hypercaloric diets and acute Et-OH.status: publishe