A New Heterobinuclear FeIIICuII Complex with a Single Terminal FeIII–O(phenolate) Bond. Relevance to Purple Acid Phosphatases and Nucleases

A novel heterobinuclear mixed valence complex [Fe^IIICu^II(BPBPMP)(OAc)_2]ClO_4, 1, with the unsymmetrical N_5O_2 donor ligand 2-bis[{(2-pyridylmethyl)aminomethyl}-6-{(2-hydroxybenzyl)(2-pyridylmethyl)} aminomethyl]-4-methylphenol (H_2BPBPMP) has been synthesized and characterized. A combination of data from mass spectrometry, potentiometric titrations, X-ray absorption and electron paramagnetic resonance spectroscopy, as well as kinetics measurements indicates that in ethanol/water solutions an [Fe^III-(nu)OH-Cu^IIOH_2]+ species is generated which is the likely catalyst for 2,4-bis(dinitrophenyl)phosphate and DNA hydrolysis. Insofar as the data are consistent with the presence of an Fe_III-bound hydroxide acting as a nucleophile during catalysis, 1 presents a suitable mimic for the hydrolytic enzyme purple acid phosphatase. Notably, 1 is significantly more reactive than its isostructural homologues with different metal composition (Fe^IIIM^II, where M^II is Zn^II, Mn^II, Ni^II,or Fe^II). Of particular interest is the observation that cleavage of double-stranded plasmid DNA occurs even at very low concentrations of 1 (2.5 nuM), under physiological conditions (optimum pH of 7.0), with a rate enhancement of 2.7 x 10^7 over the uncatalyzed reaction. Thus, 1 is one of the most effective model complexes to date, mimicking the function of nucleases

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

A new concept for insect damage evaluation based on plant physiological variables

The objective of this study was to determine the damage levels caused by Orthezia praelonga Douglas, 1891 and Leucoptera coffeella (Guérin-Mèneville 1842), on rangpur lime and Obatã coffee leaves, respectively. Measurements were based on a new concept for the evaluation of the following plant physiological parameters: photosynthesis, stomatal conductance, leaf temperature and transpiration, and internal concentration of CO2 (by infrared analyzer). A negative correlation between infestation level and photosynthesis was found, where the negative inflexion point of the curve was considered as a reference for damage levels. The control level for O. praelonga is below the 7-13% limit for damaged leaf area (40 to 70 scales per leaf), while for L. coffeella it is below the 26-36% limit for the same variable. Photosynthesis provided the best correlation for this type of analysis