Destacados

Cada año, un comité de expertos debe acometer una ardua tarea: de entre todas las publicaciones de ICREA, debe escoger unas cuantas que destaquen del resto. Es todo un reto: a veces los debates se acaloran, y siempre son difíciles, pero acaba saliendo una lista con las mejors publicaciones del año. No se concede ningún premio, y el único reconocimiento adicional es el honor de ser resaltado en la web de ICREA. Cada publicación tiene algo especial, ya sea una solución especialmente elegante, un éxito espectacular en los medios de comunicación o la simple fascinación por una idea del todo nueva. Independientemente de la razón, se trata de los mejores de los mejores y, como tales, nos complace compartirlos aquí.

LIST OF SCIENTIFIC HIGHLIGHTS

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  • The large-scale structure of the Universe (2022)

    Miquel Pascual, Ramon (IFAE)

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    The large-scale structure of the Universe

    Large galaxy surveys provide detailed information about the large-scale structure of the Universe, which, in turn, helps understand its geometry, composition, evolution and fate. Particularly relevant is the "weak gravitational lensing" effect, by which the measured shape and orientation of distant galaxies are slightly distorted by the gravitational pull of the masses between them and us, thus providing a measurement of the distribution of the intervening matter, be it visible or dark. 

    The Dark Energy Survey (DES) is one such galaxy survey, an international collaboration of 400 scientists from 25 institutions in 7 countries that has surveyed an eighth of the sky using DECam, a 570-megapixel camera installed at the 4-meter Blanco telescope in the Cerro Tololo Inter-American Observatory in Chile. The IFAE group was responsible for the design and production of most of the read-out electronics for the 74 CCDs in DECam.

    Using half of its final data set, DES has measured the shape and orientation of over 100 million distant galaxies ([1], led by an IFAE PhD student), together with their distances to us ([2], led by two IFAE PhD students). Combining these with the measurement of the positions of a sample of 11 million nearby galaxies, DES has produced stringent constraints on the mean matter density in the Universe and its level of inhomogeneity ([3]; figure). In the figure, we compare the determination of these quantities by DES in the current Universe with the extrapolation to the current Universe of the measurements performed by Planck, an ESA satellite, in the early Universe, as probed by the Cosmic Microwave Background radiation, a relic from the Big Bang. This extrapolation assumes the prevailing Lambda Cold Dark Matter (/\CDM) cosmological model, with Einstein's cosmological constant as dark energy. The agreement between the DES and Planck results is fair, but not perfect, which might (or not) point to deficiencies in the /\CDM model. More data are needed to elucidate this issue.

  • Breakthrough sensitivity in the detection of magnetic fields (2022)

    Mitchell, Morgan W. (ICFO)

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    Breakthrough sensitivity in the detection of magnetic fields

    Sensitive detection of magnetic fields has applications ranging from brain imaging to space exploration, and many technogologies have been developed for these many applications. Until now, all have been limited in sensitivity by the so-called "energy resolution limit," which constrains the combined spatial, temporal, and field resolution of the sensor. The in-practice limit was suspiciously close to hbar, the fundamental constant that defines the scale of the quantum world. In 2020 we analyzed the energy resolution limit (Mitchell et al. Rev. Mod. Phys. 2020), and concluded that exotic sensor types could beat this "limit."  In 2022 we employed a spinor Bose-Einstein condensate, to do just that, beating all previous sensors by almost two orders of magnitude, and more importantly showing that quantum physics does not place a hard limit on the sensitivity of field measurements (Palacios et al. PNAS 2022).

  • Diabetes opens the door to SARS-CoV-2 infection in the human kidney (2022)

    Montserrat Pulido, Núria (IBEC)

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    Diabetes opens the door to SARS-CoV-2 infection in the human kidney

    We previously showed that human kidney organoids represent an amenable model for investigating the direct interaction between SARS-CoV-2 and human kidney tissue. Despite the important of those findings, established kidney organoid models could not recapitulate critical kidney pathophysiology observed in patients with diabetes. To address this issue and to investigate the role of direct kidneyinfection by SARS-CoV-2 in the increased disease severity in patients with diabetes, we developed a novel human kidney organoid culture that mimics human kidney tissue exposed to diabetic conditions. Our approach allowed us to explore the potential mechanisms underlying the increased disease severity in patients with COVID-19 and diabetes.

  • Zipping graphene nanostructures with atomic precision (2022)

    Mugarza Ezpeleta, Aitor (ICN2)

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    Zipping graphene nanostructures with atomic precision

    By stacking single atom layers of 2D materials one by one as if they were Lego pieces, heterostructures with atomically precise interfaces can be fabricated. Here, the pristine properties of each layer coexist with emerging interfacial quantum phenomena that is the focus of a rising field of research. For instance, controlling such interfaces with atomic precision can turn graphene bilayers into superconductors, or exciton superlattices. Adding layers of different properties basically at will also brings tunability of properties and multifunctionality that can be applied in optoelectronic, sensing or memory devices.

    When moving to the two-dimensional analogues, however, controlling the lateral interfaces with atomic precision becomes a real challenge due to the stronger covalent bonding in this dimension.  As consequence, one cannot simply stack one-dimensional stripes laterally by existing fabrication techniques, and their bottom-up epitaxial growth becomes very limited and hard to bring to the nanoscale.

    We have recently overcome this challenge by developing a bottom-up synthetic strategy where, in a kind of Lego chemistry, two type of molecular building blocks turn into an interdigitated array of two different graphene nanoribbon components forming atomically precise interfaces. For the first time, we demonstrate that not only the atomic, but also the electronic interface can be brought down to the single bond limit. The resulting interface also hosts nanometer scale heterogeneous pores, altogether expected to promote interesting phenomena such has the formation of interibbon exciton superlattices or an efficient photocatalytic splitting of water for hydrogen generation.

  • Multitemporal Lidar: A New Workflow for the Detection of Subtle Topographic Features in Dense Forest Areas (2022)

    Orengo Romeu, Hector A (ICAC)
    Belarte Franco, Maria Carme (ICAC)

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    Multitemporal Lidar: A New Workflow for the Detection of Subtle Topographic Features in Dense Forest Areas

    GIAP-ICAC researchers publish a new study in Land, showcasing the potential of multitemporal lidar-derived digital terrain models for the detection of subtle archaeological features under perennial dense forest. The study directed by Prof. Hector Orengo (ICREA-ICAC) provides a way to co-register and filter several point clouds to increase the resolution of a DTM and, by doing so, improve the detection of subtle topographic features even in complex areas were perennial forest and shrubs combine with abrupt slopes.

    This method has the potential to radically improve archaeological survey and the detection and analysis of microtopographic archaeological features. More importantly, it can be employed for the combination and filtering of not just lidar but also different types of legacy point data such as DGPS, total station, and photogrammetry-derived point clouds. As such it provides an important new basis for the integration of data, which up to know had rarely been combined, that can be used for the development of accurate high-resolution DTMs. 

  • The need for speed: Fast yet accurate computational enzyme design  (2022)

    Osuna Oliveras, Sílvia (UdG)

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    The need for speed: Fast yet accurate computational enzyme design 

    Life could not be sustained without the presence of enzymes, which are responsible for accelerating the chemical reactions in a biologically compatible timescale. Enzymes present other advantageous features such as high specificity and selectivity, plus they operate under very mild biological conditions. Inspired by these extraordinary characteristics, many scientists wondered about the possibility of designing new enzymes for industrially relevant targets. Unfortunately, none of the current enzyme design strategies can rapidly design tailor-made enzymes exhibiting high levels of activity at a reduced cost. This is limiting the general routine application of enzyme catalysis in industry, and thus the chemical manufacturing competitiveness.

    Our goal is to develop a fast yet accurate computational enzyme design approaches for allowing the routine design of highly efficient enzymes. We combine computational chemistry, deep learning, graph theory, and computational geometry for controlling the complexity of enzyme catalysis and for developing a new computational pipeline to capture the chemical steps and conformational changes that take place along the enzyme catalytic cycle. Instead of relying on computationally expensive Molecular Dynamics (MD) simulations, we tuned the recently developed neural network AlphaFold2 (which is able to predict the three-dimensional structure of enzymes with high precision) for estimating the conformational flexibility of different enzyme variants.(1) Additionally, thanks to our new developed correlation-based methods focused on exploiting allostery operating in some enzymes,(2,3) we can predict mutations located at the enzyme active site (where the reaction happens), but also at positions far away from the reaction center.