A common rule for decision-making in animal collectives across species

A diversity of decision-making systems has been observed in animal collectives. In some species, choices depend on the differences of the numbers of animals that have chosen each of the available options, while in other species on the relative differences (a behavior known as Weber's law) or follow more complex rules. We here show that this diversity of decision systems corresponds to a single rule of decision-making in collectives. We first obtained a decision rule based on Bayesian estimation that uses the information provided by the behaviors of the other individuals to improve the estimation of the structure of the world. We then tested this rule in decision experiments using zebrafish (Danio rerio), and in existing rich datasets of argentine ants (Linepithema humile) and sticklebacks (Gasterosteus aculeatus), showing that a unified model across species can quantitatively explain the diversity of decision systems. Further, these results show that the different counting systems used by animals, including humans, can emerge from the common principle of using social information to make good decisions

Breast cancer analysis by confocal energy dispersive micro-XRD

In this work, the confocal energy dispersive micro-XRD technique has been employed to efficiently study differences between normal and malignant carcinomas in breast tissues. This technique has been implemented with low angular divergence glass monocapillaries in the excitation and detection channels. The microdiffractometer operates with a scattering angle of (20.3 ± 0.9)° that defines a cross section for analysis (0.178 mm × 0.175 mm), with a depth resolution of 1.18 mm. The obtained momentum transfer resolution between 3.9 and 10.9% was found to be highly useful to identify the scattering profiles of adipose tissues without any data processing. Differentiation between tissues with similar scattering profiles, such as fibroglandular and neoplastic tissues, has been achieved by processing the spectra within the framework of diffraction theory for scattering intensity. The obtained results allowed the development of a deterministic diagnostic model based on the evaluation of the depth profiles by confocal micro-XRD. In this model, the modulation of the scattering profiles caused by X-ray attenuation was analyzed to differentiate neoplastic tissues. The spatial resolution of the technique was the key aspect of the process, helping to detect variations in X-ray attenuation and to select uniform volume of analysis without superimposed scattering profiles.Fil: Escudero, Rodrigo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; ArgentinaFil: Cabral, María C.. Hospital Provincial Maternal Dr. Felipe Lucini; ArgentinaFil: Valladares, Mariana. Hospital Provincial Maternal Dr. Felipe Lucini; ArgentinaFil: Franco, María A.. Hospital Provincial Maternal Dr. Felipe Lucini; ArgentinaFil: Perez, Roberto Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentin

Diagnosis of Broken Rotor Bars during the Startup of Inverter-Fed Induction Motors Using the Dragon Transform and Functional ANOVA

[EN] A proper diagnosis of the state of an induction motor is of great interest to industry given the great importance of the extended use of this motor. Presently, the use of this motor driven by a frequency converter is very widespread. However, operation by means of an inverter introduces certain difficulties for a correct diagnosis, which results in a signal with higher harmonic content and noise level, which makes it difficult to perform a correct diagnosis. To solve these problems, this article proposes the use of a time-frequency technique known as Dragon Transform together with the functional ANOVA statistical technique to carry out a proper diagnosis of the state of the motor by working directly with the curves obtained from the application of the transform. A case study is presented showing the good results obtained by applying the methodology in which the state of the rotor bars of an inverter-fed motor is diagnosed considering three failure states and operating at different load levels.This research has been partially funded by the University of Valladolid.Fernández-Cavero, V.; García-Escudero, LA.; Pons Llinares, J.; Fernández-Temprano, MA.; Duque-Perez, O.; Morinigo-Sotelo, D. (2021). Diagnosis of Broken Rotor Bars during the Startup of Inverter-Fed Induction Motors Using the Dragon Transform and Functional ANOVA. Applied Sciences. 11(9):1-12. https://doi.org/10.3390/app1109376911211

Suppressed-scattering spectral windows for radiative cooling applications

The scattering of light by resonant nanoparticles is a key process for enhancing the solar reflectance in daylight radiative cooling. Here, we investigate the impact of material dispersion on the scattering performance of popular nanoparticles for radiative cooling applications. We show that, due to material dispersion, nanoparticles with a qualitatively similar response at visible frequencies exhibit fundamentally different scattering properties at infrared frequencies. It is found that dispersive nanoparticles exhibit suppressed-scattering windows, allowing for selective thermal emission within a highly reflective sample. The existence of suppressed-scattering windows solely depends on material dispersion, and they appear pinned to the same wavelength even in random composite materials and periodic metasurfaces. Finally, we investigate calcium-silicate-hydrate (CSH), the main phase of concrete, as an example of a dispersive host, illustrating that the co-design of nanoparticles and host allows for tuning of the suppressed-scattering windows. Our results indicate that controlled nanoporosities would enable concrete with daylight passive radiative cooling capabilities

European sea bass show behavioural resilience to near-future ocean acidification

Ocean acidification (OA)—caused by rising concentrations of carbon dioxide (CO₂)—is thought to be a major threat to marine ecosystems and has been shown to induce behavioural alterations in fish. Here we show behavioural resilience to near-future OA in a commercially important and migratory marine finfish, the Sea bass (Dicentrarchus labrax). Sea bass were raised from eggs at 19°C in ambient or near-future OA (1000 µatm pCO₂) conditions and n = 270 fish were observed 59–68 days post-hatch using automated tracking from video. Fish reared under ambient conditions, OA conditions, and fish reared in ambient conditions but tested in OA water showed statistically similar movement patterns, and reacted to their environment and interacted with each other in comparable ways. Thus our findings indicate behavioural resilience to near-future OA in juvenile sea bass. Moreover, simulated agent-based models indicate that our analysis methods are sensitive to subtle changes in fish behaviour. It is now important to determine whether the absences of any differences persist under more ecologically relevant circumstances and in contexts which have a more direct bearing on individual fitness

Size-Dependent Dissociation of Carbon Monoxide on Cobalt Nanoparticles

[[abstract]]In situ soft X-ray absorption spectroscopy (XAS) was employed to study the adsorption and dissociation of carbon monoxide molecules on cobalt nanoparticles with sizes ranging from 4 to 15 nm. The majority of CO molecules adsorb molecularly on the surface of the nanoparticles, but some undergo dissociative adsorption, leading to oxide species on the surface of the nanoparticles. We found that the tendency of CO to undergo dissociation depends critically on the size of the Co nanoparticles. Indeed, CO molecules dissociate much more efficiently on the larger nanoparticles (15 nm) than on the smaller particles (4 nm). We further observed a strong increase in the dissociation rate of adsorbed CO upon exposure to hydrogen, clearly demonstrating that the CO dissociation on cobalt nanoparticles is assisted by hydrogen. Our results suggest that the ability of cobalt nanoparticles to dissociate hydrogen is the main parameter determining the reactivity of cobalt nanoparticles in Fischer–Tropsch synthesis.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Arsenic exposure, diabetes-related genes and diabetes prevalence in a general population from Spain

Inorganic arsenic exposure may be associated with diabetes, but the evidence at low-moderate levels is not sufficient. Polymorphisms in diabetes-related genes have been involved in diabetes risk. We evaluated the association of inorganic arsenic exposure on diabetes in the Hortega Study, a representative sample of a general population from Valladolid, Spain. Total urine arsenic was measured in 1451 adults. Urine arsenic speciation was available in 295 randomly selected participants. To account for the confounding introduced by non-toxic seafood arsenicals, we designed a multiple imputation model to predict the missing arsenobetaine levels. The prevalence of diabetes was 8.3%. The geometric mean of total arsenic was 66.0 µg/g. The adjusted odds ratios (95% confidence interval) for diabetes comparing the highest with the lowest tertile of total arsenic were 1.76 (1.01, 3.09) and 2.14 (1.47, 3.11) before and after arsenobetaine adjustment, respectively. Polymorphisms in several genes including IL8RA, TXN, NR3C2, COX5A and GCLC showed suggestive differential associations of urine total arsenic with diabetes. The findings support the role of arsenic on diabetes and the importance of controlling for seafood arsenicals in populations with high seafood intake. Suggestive arsenic-gene interactions require confirmation in larger studies

Mesoscopic organization reveals the constraints governing C. elegans nervous system

One of the biggest challenges in biology is to understand how activity at the cellular level of neurons, as a result of their mutual interactions, leads to the observed behavior of an organism responding to a variety of environmental stimuli. Investigating the intermediate or mesoscopic level of organization in the nervous system is a vital step towards understanding how the integration of micro-level dynamics results in macro-level functioning. In this paper, we have considered the somatic nervous system of the nematode Caenorhabditis elegans, for which the entire neuronal connectivity diagram is known. We focus on the organization of the system into modules, i.e., neuronal groups having relatively higher connection density compared to that of the overall network. We show that this mesoscopic feature cannot be explained exclusively in terms of considerations, such as optimizing for resource constraints (viz., total wiring cost) and communication efficiency (i.e., network path length). Comparison with other complex networks designed for efficient transport (of signals or resources) implies that neuronal networks form a distinct class. This suggests that the principal function of the network, viz., processing of sensory information resulting in appropriate motor response, may be playing a vital role in determining the connection topology. Using modular spectral analysis, we make explicit the intimate relation between function and structure in the nervous system. This is further brought out by identifying functionally critical neurons purely on the basis of patterns of intra- and inter-modular connections. Our study reveals how the design of the nervous system reflects several constraints, including its key functional role as a processor of information.Comment: Published version, Minor modifications, 16 pages, 9 figure

Unravelling the effect of charge dynamics at the plasmonic metal/semiconductor interface for CO2 photoreduction

Sunlight plays a critical role in the development of emerging sustainable energy conversion and storage technologies. Light-induced CO2 reduction by artificial photosynthesis is one of the cornerstones to produce renewable fuels and environmentally friendly chemicals. Interface interactions between plasmonic metal nanoparticles and semiconductors exhibit improved photoactivities under a wide range of the solar spectrum. However, the photo-induced charge transfer processes and their influence on photocatalysis with these materials are still under debate, mainly due to the complexity of the involved routes occurring at different timescales. Here, we use a combination of advanced in situ and time-resolved spectroscopies covering different timescales, combined with theoretical calculations, to unravel the overall mechanism of photocatalytic CO2 reduction by Ag/TiO2 catalysts. Our findings provide evidence of the key factors determining the enhancement of photoactivity under ultraviolet and visible irradiation, which have important implications for the design of solar energy conversion materials