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

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

Recent developments and challenges in the application of fungal laccase for the biodegradation of textile dye pollutants

According to the European Environment Agency, the textile industry is responsible for 20% of global water pollution due to dyeing and finishing products, thus facing severe environmental challenges. It is essential to design more biocompatible and sustainable treatment processes capable of removing dyes from industrial wastewater to fight this environmental hazard. Chemical industries must change traditional chemical-based concepts to more environmentally friendly and greener processes to remove pollutants, including dyes. Enzymatic bioremediation is a smart tool and a promising alternative for environmental pollutant degradation. The use of enzymes in dye decolourization makes the process a green and clean alternative to conventional chemical treatments. Moreover, enzymemediated biocatalysis decreases the formation of toxic by-products compared to chemical reactions. The most used enzyme for the decolourization of dyes is laccase. Laccase is a multicopper oxidase found in diverse organisms such as fungi. It promotes the oxidation of phenolic compounds and has a wide range of substrate specificity, making it a promising enzyme for removing different dyes used by the textile industry, including recalcitrant aromatic dyes. The present article gives a comprehensive revision of textile dye decolourization, its types, recent developments in laccase-mediated dye bioremediation technologies, the mechanism of biocatalysis, and their limitations and challenges. Emphasis on the chemical pathways of laccase reaction mechanisms for dye bioremediation processes is also provided. In addition, a brief overview of textile industries and the respective traditional treatment processes for textile wastewater is also presented.publishe

Use of Solar Panels for Shade for Holstein Heifers

Animal Agrivoltaics combines electric energy generation, animal thermal comfort, and sustainable production at the same time. This model of production can foster the sustainable intensification of dairy production in tropical areas where solar irradiance is high and nearly constant throughout the year. In this study, we propose Animal Agrivoltaics as an alternative practice to reduce the heat load and eCH4 emissions from dairy heifers in tropical areas. To attest this hypothesis, (1) the meteorological data and the behavioral and physiological responses of the animals were integrated in order to determine the benefits provided by the shade from the solar panels on the thermoregulation of the dairy heifers, and (2) measurements of the enteric methane emissions were taken to determine the potential of the solar panels to offset the GHG. Seven crossbred Holstein heifers (7/8, Holstein × Gyr) with a mean body weight of 242 kg (SD = 53.5) were evaluated in a paddock shaded with ten modules of solar panels. Miniature temperature loggers were used to record the body surface, skin and vaginal temperatures of the heifers every five minutes. The respiratory rate and the shade-use behavior were also monitored by two observers. These measurements were taken from 08:00 to 17:00 h for 18 consecutive days. After completing the field study, the heifers underwent for assessments of the daily oscillations of eCH4 emission using a flow-through respirometry system. The use of shade by the heifers was progressively increased (p < 0.01) with an increasing level of solar irradiance. Lying and ruminating were more likely (p < 0.01) to occur when the heifers were in the shade, especially when the solar irradiance exceeded 500 W m−2. Between 10:00 and 14:00 h, the heifers benefited from the shade produced by the solar panels, with a reduction of 40% in the radiant heat load. With an increasing intensity of solar irradiance, body surface temperature, skin temperature and respiratory rate of the heifers in the shade were lower (p < 0.01) compared to when they were exposed to the sun. The heifers had a daily methane emission total of 63.5 g per animal−1 or 1.7 kg of CO2-eq. Based on this emission rate and the amount of CO2-eq that was not emitted to the atmosphere due to the electricity generated by solar panels, 4.1 m2 of panels per animal (nominal power = 335 W) would be expected to obtain a net-zero eCH4 emission. Over a period of one year (from September 2018 to August 2019), a set of ten photovoltaic panels used in the study produced 4869.4 kWh of electricity, thereby saving US $970.00 or US$48.00 per m2 of solar panel. Based on the results of this study, it can be concluded that use of Animal Agrivoltaics, in addition to producing electricity, has significant potential benefit in providing better thermal comfort to cattle, as well as offsetting the enteric methane emissions released into the environment. In addition, the system would provide extra income to farmers, as well as a potential source of energy micro-generation

Insect-specific viruses regulate vector competence in <em>Aedes aegypti</em> mosquitoes via expression of histone H4

Aedes aegypti and Aedes albopictus are major mosquito vectors for arthropod-borne viruses (arboviruses) such as dengue (DENV) and Zika (ZIKV) viruses. Mosquitoes also carry insect-specific viruses (ISVs) that may affect the transmission of arboviruses. Here, we analyzed the global virome in urban Aedes mosquitoes and observed that two insect-specific viruses, Phasi Charoen-like virus (PCLV) and Humaita Tubiacanga virus (HTV), were the most prevalent in A. aegypti worldwide except for African cities, where transmission of arboviruses is low. Spatiotemporal analysis revealed that presence of HTV and PCLV led to a 200% increase in the chances of having DENV in wild mosquitoes. In the laboratory, we showed that HTV and PCLV prevented downregulation of histone H4, a previously unrecognized proviral host factor, and rendered mosquitoes more susceptible to DENV and ZIKV. Altogether, our data reveals a molecular basis for the regulation of A. aegypti vector competence by highly prevalent ISVs that may impact how we analyze the risk of arbovirus outbreaks

Untargeted Metabolomics Insights into Newborns with Congenital Zika Infection

Zika virus (ZIKV), an emerging virus belonging to the Flaviviridae family, causes severe neurological clinical complications and has been associated with Guillain-Barré syndrome, fetal abnormalities known collectively as congenital Zika syndrome, and microcephaly. Studies have shown that ZIKV infection can alter cellular metabolism, directly affecting neural development. Brain growth requires controlled cellular metabolism, which is essential for cell proliferation and maturation. However, little is known regarding the metabolic profile of ZIKV-infected newborns and possible associations related to microcephaly. Furthering the understanding surrounding underlying mechanisms is essential to developing personalized treatments for affected individuals. Thus, metabolomics, the study of the metabolites produced by or modified in an organism, constitutes a valuable approach in the study of complex diseases. Here, 26 serum samples from ZIKV-positive newborns with or without microcephaly, as well as controls, were analyzed using an untargeted metabolomics approach involving gas chromatography–mass spectrometry (GC-MS). Significant alterations in essential and non-essential amino acids, as well as carbohydrates (including aldohexoses, such as glucose or mannose) and their derivatives (urea and pyruvic acid), were observed in the metabolic profiles analyzed. Our results provide insight into relevant metabolic processes in patients with ZIKV and microcephaly

Mosquito vector competence for dengue is modulated by insect-specific viruses

International audienceAedes aegypti and A. albopictus mosquitoes are the main vectors for dengue virus (DENV) and other arboviruses, including Zika virus (ZIKV). Understanding the factors that affect transmission of arboviruses from mosquitoes to humans is a priority because it could inform public health and targeted interventions. Reasoning that interactions among viruses in the vector insect might affect transmission, we analysed the viromes of 815 urban Aedes mosquitoes collected from 12 countries worldwide. Two mosquito-specific viruses, Phasi Charoen-like virus (PCLV) and Humaita Tubiacanga virus (HTV), were the most abundant in A. aegypti worldwide. Spatiotemporal analyses of virus circulation in an endemic urban area revealed a 200% increase in chances of having DENV in wild A. aegypti mosquitoes when both HTV and PCLV were present. Using a mouse model in the laboratory, we showed that the presence of HTV and PCLV increased the ability of mosquitoes to transmit DENV and ZIKV to a vertebrate host. By transcriptomic analysis, we found that in DENV-infected mosquitoes, HTV and PCLV block the downregulation of histone H4, which we identify as an important proviral host factor in vivo