Integrative paleophysiology of the metriorhynchoid Pelagosaurus typus (Pseudosuchia, Thalattosuchia).

Paleophysiology is an emergent discipline. Organismic (integrative) approaches seem more appropriate than studies focusing on the variation of specific features because traits are tightly related in actual organisms. Here, we used such an organismic approach (including lifestyle, thermometabolism, and hunting behavior) to understand the paleobiology of the lower Jurassic (Toarcian) thalattosuchian metriorhynchoid Pelagosaurus typus. First, we show that the lifestyle (aquatic, amphibious, terrestrial) has an effect on the femoral compactness profiles in amniotes. The profile of Pelagosaurus indicates that it was amphibious, with a foraging activity in shallow marine environments (as suggested by the presence of salt glands) and thermoregulatory basking behavior in land (as suggested by the presence of osteoderms with highly developed ornamentation). As for the thermometabolism, we show that the mass-independent resting metabolic rate of Pelagosaurus is relatively high compared to the sample of extant ectothermic amniotes, but analysis of vascular canal diameter and inferences of red blood cell size refute the hypothesis suggesting incipient endothermy. Finally, the foraging behavior was inferred using two proxies. Pelagosaurus had a mass-independent maximum metabolic rate and an aerobic scope higher than those measured in the almost motionless Iguana iguana, similar to those measured in the sit-and-wait predator Crocodylus porosus but lower than those quantified in the active hunter Varanus gouldii. These results suggest that Pelagosaurus may have had a hunting behavior involving a slow sustained swimming or a patient waiting in shallow waters, and may have caught preys like gharials, using fast sideways sweeping motions of the head.

Quantitative Online Monitoring of an Immobilized Enzymatic Network by Ion Mobility-Mass Spectrometry.

The forward design of in vitro enzymatic reaction networks (ERNs) requires a detailed analysis of network kinetics and potentially hidden interactions between the substrates and enzymes. Although flow chemistry allows for a systematic exploration of how the networks adapt to continuously changing conditions, the analysis of the reaction products is often a bottleneck. Here, we report on the interface between a continuous stirred-tank reactor, in which an immobilized enzymatic network made of 12 enzymes is compartmentalized, and an ion mobility-mass spectrometer. Feeding uniformly 13C-labeled inputs to the enzymatic network generates all isotopically labeled reaction intermediates and products, which are individually detected by ion mobility-mass spectrometry (IMS-MS) based on their mass-to-charge ratios and inverse ion mobilities. The metabolic flux can be continuously and quantitatively monitored by diluting the ERN output with nonlabeled standards of known concentrations. The real-time quantitative data obtained by IMS-MS are then harnessed to train a model of network kinetics, which proves sufficiently predictive to control the ERN output after a single optimally designed experiment. The high resolution of the time-course data provided by this approach is an important stepping stone to design and control sizable and intricate ERNs.

Chemical reservoir computation in a self-organizing reaction network.

Chemical reaction networks, such as those found in metabolism and signalling pathways, enable cells to process information from their environment1,2. Current approaches to molecular information processing and computation typically pursue digital computation models and require extensive molecular-level engineering3. Despite considerable advances, these approaches have not reached the level of information processing capabilities seen in living systems. Here we report on the discovery and implementation of a chemical reservoir computer based on the formose reaction4. We demonstrate how this complex, self-organizing chemical reaction network can perform several nonlinear classification tasks in parallel, predict the dynamics of other complex systems and achieve time-series forecasting. This in chemico information processing system provides proof of principle for the emergent computational capabilities of complex chemical reaction networks, paving the way for a new class of biomimetic information processing systems.

Infrared Imaging Using Thermally Stable HgTe/CdS Nanocrystals.

Transferring nanocrystals (NCs) from the laboratory environment toward practical applications has raised new challenges. HgTe appears as the most spectrally tunable infrared colloidal platform. Its low-temperature synthesis reduces the growth energy cost yet also favors sintering. Once coupled to a read-out circuit, the Joule effect aggregates the particles, leading to a poorly defined optical edge and large dark current. Here, we demonstrate that CdS shells bring the expected thermal stability (no redshift upon annealing, reduced tendency to form amalgams, and preservation of photoconduction after an atomic layer deposition process). The electronic structure of these confined particles is unveiled using k.p self-consistent simulations showing a significant exciton binding energy of ∼200 meV. After shelling, the material displays a p-type behavior that favors the generation of photoconductive gain. The latter is then used to increase the external quantum efficiency of an infrared imager, which now reaches 40% while presenting long-term stability.

Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems.

The intricate and complex features of enzymatic reaction networks (ERNs) play a key role in the emergence and sustenance of life. Constructing such networks in vitro enables stepwise build up in complexity and introduces the opportunity to control enzymatic activity using physicochemical stimuli. Rational design and modulation of network motifs enable the engineering of artificial systems with emergent functionalities. Such functional systems are useful for a variety of reasons such as creating new-to-nature dynamic materials, producing value-added chemicals, constructing metabolic modules for synthetic cells, and even enabling molecular computation. In this review, we offer insights into the chemical characteristics of ERNs while also delving into their potential applications and associated challenges.

The Electronic Impact of Light-Induced Degradation in CsPbBr3 Perovskite Nanocrystals at Gold Interfaces.

The understanding of the interfacial properties in perovskite devices under irradiation is crucial for their engineering. In this study we show how the electronic structure of the interface between CsPbBr3 perovskite nanocrystals (PNCs) and Au is affected by irradiation of X-rays, near-infrared (NIR), and ultraviolet (UV) light. The effects of X-ray and light exposure could be differentiated by employing low-dose X-ray photoelectron spectroscopy (XPS). Apart from the common degradation product of metallic lead (Pb0), a new intermediate component (Pbint) was identified in the Pb 4f XPS spectra after exposure to high intensity X-rays or UV light. The Pbint component is determined to be monolayer metallic Pb on-top of the Au substrate from underpotential deposition (UPD) of Pb induced from the breaking of the perovskite structure allowing for migration of Pb2+.

Vegetation enhances curvature-driven dynamics in meandering rivers.

Stabilization of riverbanks by vegetation has long been considered necessary to sustain single-thread meandering rivers. However, observation of active meandering in modern barren landscapes challenges this assumption. Here, we investigate a globally distributed set of modern meandering rivers with varying riparian vegetation densities, using satellite imagery and statistical analyses of meander-form descriptors and migration rates. We show that vegetation enhances the coefficient of proportionality between channel curvature and migration rates at low curvatures, and that this effect wanes in curvier channels irrespective of vegetation density. By stabilizing low-curvature reaches and allowing meanders to gain sinuosity as channels migrate laterally, vegetation quantifiably affects river morphodynamics. Any causality between denser vegetation and higher meander sinuosity, however, cannot be inferred owing to more frequent avulsions in modern non-vegetated environments. By illustrating how vegetation affects channel mobility and floodplain reworking, our findings have implications for assessing carbon stocks and fluxes in river floodplains.

On the origins of endothermy in amniotes.

A recent study showed evidence that endothermy was ancestral for amniotes using a variety of proxies and a large sample of taxa. However, it did not include numerous crucial taxa. We reevaluated this hypothesis using a large sample of early amniotes and tetrapodomorphs. We inferred the probability of endothermy for each taxon using a model constructed through phylogenetic logistic regressions and using the size of their bone vascular cavities. An ancestral state reconstruction, based on these inferences, was performed to assess the probability of an ancestral endothermy at the node Amniota. Most outgroups were recovered as ectothermic, as is the node Amniota. Our results contradict the hypothesis of an ancestral endothermy and support several independent acquisitions. We discuss that endothermy should be regarded as a collection of acquisitions forming an "endothermic engine" and that studies aimed at inferring endothermy should consider as many of these features as possible.

Iterative design of training data to control intricate enzymatic reaction networks.

Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency. Our workflow scales with the complexity of the system and enables the optimization of previously unobtainable network outputs.

Direct Analysis of Complex Reaction Mixtures: Formose Reaction.

Complex reaction mixtures, like those postulated on early Earth, present an analytical challenge because of the number of components, their similarity, and vastly different concentrations. Interpreting the reaction networks is typically based on simplified or partial data, limiting our insight. We present a new approach based on online monitoring of reaction mixtures formed by the formose reaction by ion-mobility-separation mass-spectrometry. Monitoring the reaction mixtures led to large data sets that we analyzed by non-negative matrix factorization, thereby identifying ion-signal groups capturing the time evolution of the network. The groups comprised ≈300 major ion signals corresponding to sugar-calcium complexes formed during the formose reaction. Multivariate analysis of the kinetic profiles of these complexes provided an overview of the interconnected kinetic processes in the solution, highlighting different pathways for sugar growth and the effects of different initiators on the initial kinetics. Reconstructing the network's topology further, we revealed so far unnoticed fast retro-aldol reaction of ketoses, which significantly affects the initial reaction dynamics. We also detected the onset of sugar-backbone branching for C6  sugars and cyclization reactions starting for C5  sugars. This top-down analytical approach opens a new way to analyze complex dynamic mixtures online with unprecedented coverage and time resolution.

Structural and Magnetic Properties of a Drop-Cast C54H34Br4CuO4 ß-Diketonato Complex Film on a Graphite Surface.

The structural and magnetic properties of a drop-cast film of flat C54H34Br4CuO4, a ß-diketonato complex functionalized with bromine atoms, on a graphite surface are investigated using scanning tunneling microscopy, synchrotron X-ray absorption spectroscopy, and X-ray magnetic circular dichroism. Experimental measurements reveal that the Cu-complexes preferentially lay flat on the graphite surface. The magnetic hysteresis loops show that the organic thin film remains paramagnetic at 2 K with an easy axis of magnetization perpendicular to the graphite surface and is therefore perpendicular to the plane of the Cu-complex skeleton.

Inside a nanocrystal-based photodiode using photoemission microscopy.

As nanocrystal-based devices gain maturity, a comprehensive understanding of their electronic structure is necessary for further optimization. Most spectroscopic techniques typically examine pristine materials and disregard the coupling of the active material to its actual environment, the influence of an applied electric field, and possible illumination effects. Therefore, it is critical to develop tools that can probe device in situ and operando. Here, we explore photoemission microscopy as a tool to unveil the energy landscape of a HgTe NC-based photodiode. We propose a planar diode stack to facilitate surface-sensitive photoemission measurements. We demonstrate that the method gives direct quantification of the diode's built-in voltage. Furthermore, we discuss how it is affected by particle size and illumination. We show that combining SnO2 and Ag2Te as electron and hole transport layers is better suited for extended-short-wave infrared materials than materials with larger bandgaps. We also identify the effect of photodoping over the SnO2 layer and propose a strategy to overcome it. Given its simplicity, the method appears to be of utmost interest for screening diode design strategies.

An Engineered Probiotic Platform for Cancer Epitope-Independent Targeted Radionuclide Therapy of Solid Tumors.

Targeted radionuclide therapy (TRT) is an emerging therapeutic modality for the treatment of various solid cancers. Current approaches rely on the presence of cancer-specific epitopes and receptors against which a radiolabeled ligand is systemically administered to specifically deliver cytotoxic doses of α and ß particles to tumors. In this proof-of-concept study, tumor-colonizing Escherichia coli Nissle 1917 (EcN) is utilized to deliver a bacteria-specific radiopharmaceutical to solid tumors in a cancer-epitope independent manner. In this microbe-based pretargeted approach, the siderophore-mediated metal uptake pathway is leveraged to selectively concentrate copper radioisotopes, 64 Cu and 67 Cu, complexed to yersiniabactin (YbT) in the genetically modified bacteria. 64 Cu-YbT facilitates positron emission tomography (PET) imaging of the intratumoral bacteria, whereas 67 Cu-YbT delivers a cytotoxic dose to the surrounding cancer cells. PET imaging with 64 Cu-YbT reveals persistence and sustained growth of the bioengineered microbes in the tumor microenvironment. Survival studies with 67 Cu-YbT reveals significant attenuation of tumor growth and extends survival of both MC38 and 4T1  tumor-bearing mice harboring the microbes. Tumor response to this pretargeted approach correlates with promising anti-tumor immunity, with noticeable CD8+ T:Treg cell ratio. Their strategy offers a pathway to target and ablate multiple solid tumors independent of their epitope and receptor phenotype.

Mapping the Energy Landscape from a Nanocrystal-Based Field Effect Transistor under Operation Using Nanobeam Photoemission Spectroscopy.

As the field of nanocrystal-based optoelectronics matures, more advanced techniques must be developed in order to reveal the electronic structure of nanocrystals, particularly with device-relevant conditions. So far, most of the efforts have been focused on optical spectroscopy, and electrochemistry where an absolute energy reference is required. Device optimization requires probing not only the pristine material but also the material in its actual environment (i.e., surrounded by a transport layer and an electrode, in the presence of an applied electric field). Here, we explored the use of photoemission microscopy as a strategy for operando investigation of NC-based devices. We demonstrate that the method can be applied to a variety of materials and device geometries. Finally, we show that it provides direct access to the metal-semiconductor interface band bending as well as the distance over which the gate effect propagates in field-effect transistors.

Computing Arithmetic Functions Using Immobilised Enzymatic Reaction Networks.

Living systems use enzymatic reaction networks to process biochemical information and make decisions in response to external or internal stimuli. Herein, we present a modular and reusable platform for molecular information processing using enzymes immobilised in hydrogel beads and compartmentalised in a continuous stirred tank reactor. We demonstrate how this setup allows us to perform simple arithmetic operations, such as addition, subtraction and multiplication, using various concentrations of substrates or inhibitors as inputs and the production of a fluorescent molecule as the readout.

Spatiotemporal analysis of malaria incidence in Côte d'Ivoire from 2015 to 2019.

BACKGROUND: The collection of malaria cases over time allows the identification of areas with the highest incidence. Our objective was to characterize the spatial distribution of malaria in Côte d'Ivoire from 2015 to 2019 at the health district level. METHODS: Data on the number of reported malaria cases confirmed by rapid diagnostic test (RDT) in the general population, the number of patients attending medical consultations and the total population by health district and year were collected from the National Malaria Control Program in Côte d'Ivoire. Crude and adjusted incidence rates were estimated for each health district and year. Adjusted incidence rates were used to perform global (Moran's index) and local indicators of spatial autocorrelation (LISA) analyses. RESULTS: Between 2015 and 2019, mean crude incidence rates increased from 155.5‰ to 229.8‰. We observed significant heterogeneity in malaria incidence rates across the study period and within a given year. The overall Moran index showed spatial autocorrelation for every year analysed except 2017. The LISA analysis showed that the health districts with high incidence rates were concentrated in the western zone of Côte d'Ivoire. CONCLUSIONS: The use of spatial analyses to identify the areas with the highest malaria incidence rates is a relevant approach to optimize control measures in targeted areas.

A distinct ripple-formation regime on Mars revealed by the morphometrics of barchan dunes.

Sand mobilized by wind forms decimeter-scale impact ripples and decameter-scale or larger dunes on Earth and Mars. In addition to those two bedform scales, orbital and in situ images revealed a third distinct class of larger meter-scale ripples on Mars. Since their discovery, two main hypotheses have been proposed to explain the formation of large martian ripples-that they originate from the growth in wavelength and height of decimeter-scale ripples or that they arise from the same hydrodynamic instability as windblown dunes or subaqueous bedforms instead. Here we provide evidence that large martian ripples form from the same hydrodynamic instability as windblown dunes and subaqueous ripples. Using an artificial neural network, we characterize the morphometrics of over a million isolated barchan dunes on Mars and analyze how their size and shape vary across Mars' surface. We find that the size of Mars' smallest dunes decreases with increasing atmospheric density with a power-law exponent predicted by hydrodynamic theory, similarly to meter-size ripples, tightly bounding a forbidden range in bedform sizes. Our results provide key evidence for a unifying model for the formation of subaqueous and windblown bedforms on planetary surfaces, offering a new quantitative tool to decipher Mars' atmospheric evolution.

Double-crowned 2D semiconductor nanoplatelets with bicolor power-tunable emission.

Nanocrystals (NCs) are now established building blocks for optoelectronics and their use as down converters for large gamut displays has been their first mass market. NC integration relies on a combination of green and red NCs into a blend, which rises post-growth formulation issues. A careful engineering of the NCs may enable dual emissions from a single NC population which violates Kasha's rule, which stipulates that emission should occur at the band edge. Thus, in addition to an attentive control of band alignment to obtain green and red signals, non-radiative decay paths also have to be carefully slowed down to enable emission away from the ground state. Here, we demonstrate that core/crown/crown 2D nanoplatelets (NPLs), made of CdSe/CdTe/CdSe, can combine a large volume and a type-II band alignment enabling simultaneously red and narrow green emissions. Moreover, we demonstrate that the ratio of the two emissions can be tuned by the incident power, which results in a saturation of the red emission due to non-radiative Auger recombination that affects this emission much stronger than the green one. Finally, we also show that dual-color, power tunable, emission can be obtained through an electrical excitation.