A study of the water molecule using frequency control over nuclear dynamics in resonant X ray scattering

In this combined theoretical and experimental study we report a full analysis of the resonant inelastic X ray scattering RIXS spectra of H 2O, D 2O and HDO. We demonstrate that electronically elastic RIXS has an inherent capability to map the potential energy surface and to perform vibrational analysis of the electronic ground state in multimode systems. We show that the control and selection of vibrational excitation can be performed by tuning the X ray frequency across core excited molecular bands and that this is clearly reflected in the RIXS spectra. Using high level ab initio electronic structure and quantum nuclear wave packet calculations together with high resolution RIXS measurements, we discuss in detail the mode coupling, mode localization and anharmonicity in the studied system

Investigation of Domain Formation in Sphingomyelin/Cholesterol/POPC Mixtures by Fluorescence Resonance Energy Transfer and Monte Carlo Simulations

We have recently proposed a phase diagram for mixtures of porcine brain sphingomyelin (BSM), cholesterol (Chol), and 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC) on the basis of kinetics of carboxyfluorescein efflux induced by the amphipathic peptide δ-lysin. Although that study indicated the existence of domains, phase separations in the micrometer scale have not been observed by fluorescence microscopy in BSM/Chol/POPC mixtures, though they have for some other sphingomyelins (SM). Here we examine the same BSM/Chol/POPC system by a combination of fluorescence resonance energy transfer (FRET) and Monte Carlo simulations. The results clearly demonstrate that domains are formed in this system. Comparison of the FRET experimental data with the computer simulations allows the estimate of lipid-lipid interaction Gibbs energies between SM/Chol, SM/POPC, and Chol/POPC. The latter two interactions are weakly repulsive, but the interaction between SM and Chol is favorable. Furthermore, those three unlike lipid interaction parameters between the three possible lipid pairs are sufficient for the existence of a closed loop in the ternary phase diagram, without the need to involve multibody interactions. The calculations also indicate that the largest POPC domains contain several thousand lipids, corresponding to linear sizes of the order of a few hundred nanometers

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, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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