Twinning as an Evolved Age-Dependent Physiological Mechanism: Evidence from Large Brazilian Samples

Multiple pregnancies occur in humans and other primates, which indicate that the twinning propensity is phylogenetically old. Factors such as decreased sexual dimorphism and size, rich and diverse nutrition and paternal care are related to multiple pregnancies in other animals. In human populations, despite its costs, twinning has a genetic basis and in Europe, Africa, and America, it was found that it increases mothers’ fitness. Here, we explore the hypothesis that twinning represents an evolved physiological mechanism, particularly in mothers of higher age, as an ‘all-or-nothing’ last chance strategy for reproduction just before menopause. We present decade-long, large-scale population data about maternities from the city of São Paulo and the entire country of Brazil that indicate a considerable main effect of advanced age in promoting twinning, particularly dizygotic (DZ) twinning, but also monozygotic (MZ) twinning and higher order maternities. We also show that socioeconomic status is an important contextual factor increasing twinning. Besides the theoretical implications, these datasets establish a Brazilian countrywide twinning rate of 9.39‰ and highlight an increasing historical trend. This chapter promotes the importance of integrating proximate patterns from human and nonhuman animals and evolutionary factors in order to reach a comprehensive view about twinning

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

Microscopic aspects of the colonization of Pyricularia oryzae on the rachis of wheat plants supplied with silicon

Considering the importance of blast, caused by Pyricularia oryzae, to reduce wheat yield, this study investigate how silicon (Si) could reduce the wheat blast symptoms in the rachis tissues using light microscopy and scanning electron microscopy. Wheat plants (cv. BR 18) were grown in hydroponic culture with either 0 (–Si) or 2 mM (+Si) of Si. Blast symptoms were very well developed on the spikes of the –Si plants, which showed intense discoloration in contrast with the spikes of the +Si plants. At 72 hours after inoculation (hai), fungal hyphae extensively colonized the epidermis and the collenchyma tissue in the radial direction in the rachis of the –Si plants. In the +Si plants, fungal hyphae colonized the epidermis and the collenchyma cells to a lesser extent than in the –Si plants. At 96 hai, fungal hyphae were observed in the epidermis, vascular bundles and cortical tissue in the rachis node of the -Si plants. In the +Si plants, a phenolic-like material was detected in the parenchyma with lower fungal colonization in comparison with the –Si plants. In scanning electron microscopy, fungal hyphae were scarcely observed in the upper epidermal, collenchyma and parenchyma cells in the rachis tissue of the +Si plants, whereas in the rachis tissue of the –Si plants, fungal hyphae extensively colonized the epidermis, collenchyma, parenchyma and vascular bundles

Microscopic aspects of the colonization of Pyricularia oryzae on the rachis of wheat plants supplied with silicon

Considering the importance of blast, caused by Pyricularia oryzae, to reduce wheat yield, this study investigate how silicon (Si) could reduce the wheat blast symptoms in the rachis tissues using light microscopy and scanning electron microscopy. Wheat plants (cv. BR 18) were grown in hydroponic culture with either 0 (–Si) or 2 mM (+Si) of Si. Blast symptoms were very well developed on the spikes of the –Si plants, which showed intense discoloration in contrast with the spikes of the +Si plants. At 72 hours after inoculation (hai), fungal hyphae extensively colonized the epidermis and the collenchyma tissue in the radial direction in the rachis of the –Si plants. In the +Si plants, fungal hyphae colonized the epidermis and the collenchyma cells to a lesser extent than in the –Si plants. At 96 hai, fungal hyphae were observed in the epidermis, vascular bundles and cortical tissue in the rachis node of the -Si plants. In the +Si plants, a phenolic-like material was detected in the parenchyma with lower fungal colonization in comparison with the –Si plants. In scanning electron microscopy, fungal hyphae were scarcely observed in the upper epidermal, collenchyma and parenchyma cells in the rachis tissue of the +Si plants, whereas in the rachis tissue of the –Si plants, fungal hyphae extensively colonized the epidermis, collenchyma, parenchyma and vascular bundles

Principles of target DNA cleavage and the role of Mg2+ in the catalysis of CRISPR-Cas9

At the core of the CRISPR-Cas9 genome-editing technology, the endonuclease Cas9 introduces site-specific breaks in DNA. However, precise mechanistic information to ameliorating Cas9 function is still missing. Here, multi-microsecond molecular dynamics, free-energy and multiscale simulations are combined with solution NMR and DNA cleavage experiments to resolve the catalytic mechanism of target DNA cleavage. We show that the conformation of an active HNH nuclease is tightly dependent on the catalytic Mg$^{2+}$, unveiling its cardinal structural role. This activated Mg$^{2+}$-bound HNH is consistently described through molecular simulations, solution NMR and DNA cleavage assays, revealing also that the protonation state of the catalytic H840 is strongly affected by active site mutations. Finally, ab-initio QM(DFT)/MM simulations and metadynamics establish the catalytic mechanism, showing that the catalysis is activated by H840 and completed by K866, rationalising DNA cleavage experiments. This information is critical to enhance the enzymatic function of CRISPR-Cas9 toward improved genome-editing