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Associative learning plays a fundamental role in the life of honey bees, especially in the context of foraging for food sources. This learning capacity can be investigated through controlled experiments conducted under laboratory, semi-natural, and near-natural conditions, to understand the general principles of learning and motivation. Honey bees can be trained to solve different elemental and non-elemental learning tasks by pairing a conditioned stimulus such as an odor with sucrose as an unconditioned stimulus and reward. Laboratory studies with restrained bees demonstrated that sucrose responsiveness is positively correlated with both elemental olfactory learning performance and responsiveness to stimuli of different sensory modalities, such as odors and visual stimuli. Here, we tested for the first time how responsiveness to sucrose and light is related to performance in elemental and non-elemental visual learning under free-flying conditions. Sensory responsiveness and learning proficiency did not correlate, nor did sucrose responsiveness correlate with responsiveness to light. These results indicate that relationships among responsiveness to sucrose and light and learning proficiency, as established under restrained laboratory conditions, may not translate to the natural behavior of bees in the field. This finding points toward the context-dependent importance of responsiveness to light and sucrose during associative learning under restrained or free-flying conditions.

The zebrafish is a laboratory species that gained increasing popularity the last decade in a variety of subfields of biology, including toxicology, ecology, medicine, and the neurosciences. An important phenotype often measured in these fields is behaviour. Consequently, numerous new behavioural apparati and paradigms have been developed for the zebrafish, including methods for the analysis of learning and memory in adult zebrafish. Perhaps the biggest obstacle in these methods is that zebrafish is particularly sensitive to human handling. To overcome this confound, automated learning paradigms have been developed with varying success. In this manuscript, we present a semi-automated home tank-based learning/memory test paradigm utilizing visual cues, and show that it is capable of quantifying classical associative learning performance in zebrafish. We demonstrate that in this task, zebrafish successfully acquire the association between coloured-light and food reward. The hardware and software components of the task are easy and cheap to obtain and simple to assemble and set up. The procedures of the paradigm allow the test fish to remain completely undisturbed by the experimenter for several days in their home (test) tank, eliminating human handling or human interference induced stress. We demonstrate that the development of cheap and simple automated home-tank-based learning paradigms for the zebrafish is feasible. We argue that such tasks will allow us to better characterize numerous cognitive and mnemonic features of the zebrafish, including elemental as well as configural learning and memory, which will, in turn, also enhance our ability to study neurobiological mechanisms underlying learning and memory using this model organism.

Spatial learning and memory have been studied for several decades. Analyses of these processes pose fundamental scientific questions but are also relevant from a biomedical perspective. The cellular, synaptic and molecular mechanisms underlying spatial learning have been intensively investigated, yet the behavioral mechanisms/strategies in a spatial task still pose unanswered questions. Spatial learning relies upon configural information about cues in the environment. However, each of these cues can also independently form part of an elemental association with the specific spatial position, and thus spatial tasks may be solved using elemental (single CS and US association) learning. Here, we first briefly review what we know about configural learning from studies with rodents. Subsequently, we discuss the pros and cons of employing a relatively novel laboratory organism, the zebrafish in such studies, providing some examples of methods with which both elemental and configural learning may be explored with this species. Last, we speculate about future research directions focusing on how zebrafish may advance our knowledge. We argue that zebrafish strikes a reasonable compromise between system complexity and practical simplicity and that adding this species to the studies with laboratory rodents will allow us to gain a better understanding of both the evolution of and the mechanisms underlying spatial learning. We conclude that zebrafish research will enhance the translational relevance of our findings.

In two experiments, rats were given intermixed or blocked preexposure to two similar compound stimuli, AX and BX. Following preexposure, conditioning trials took place in which AX (Experiment 1 ) or a novel compound stimulus NX (Experiment 2 ) was paired with a food-unconditioned stimulus in an appetitive Pavlovian preparation. Animals that were given alternated preexposure showed lower generalization from AX to BX (Experiment 1 ) and from NX to a new compound, ZX (Experiment 2 ), than animals that were given blocked preexposure, a perceptual learning and a perceptual learning transfer effect, respectively.

Density functional theory (DFT) calculations are widely used for material property prediction, but their computational cost can hinder the discovery of novel perovskites. This work explores machine learning (ML) as a faster alternative for predicting band gaps in complex perovskites, focusing on low-symmetry double and layered structures. We employ Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost) to predict both direct and indirect band gaps. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) metrics. Our results reveal SVR as the most effective general model for predicting band gaps in both double and layered perovskites. Interestingly, for double perovskites specifically, XGBoost achieves even higher accuracy when incorporating derivative discontinuity as a feature. Feature importance analysis identifies the standard deviation of valence charges (“Valence (std)”) as the most critical factor for band gap prediction across all studied perovskites. This research demonstrates the potential of ML for efficient and accurate band gap prediction in complex perovskites, accelerating material discovery efforts.

Continued progress in artificial intelligence (AI) and associated demonstrations of superhuman performance have raised the expectation that AI can revolutionize scientific discovery in general and materials science specifically. We illustrate the success of machine learning (ML) algorithms in tasks ranging from machine vision to game playing and describe how existing algorithms can also be impactful in materials science, while noting key limitations for accelerating materials discovery. Issues of data scarcity and the combinatorial nature of materials spaces, which limit application of ML techniques in materials science, can be overcome by exploiting the rich scientific knowledge from physics and chemistry using additional AI techniques such as reasoning, planning, and knowledge representation. The integration of these techniques in materials-intelligent systems will enable AI governance of the scientific method and autonomous scientific discovery.

Perovskites are a class of materials, known for their diverse structural, electronic, and optical properties. Band gap in perovskites is crucial in determining their suitability for applications such as solar cells, light‐emitting diodes, and photodetectors. By tuning the band gap through composition and structural modifications, perovskites can be optimized for specific optoelectronic and energy‐related applications, making them a versatile material in modern technology. Machine learning (ML) provides an efficient approach to predicting material band gaps by analyzing atomic and structural features, facilitating the discovery of materials with tailored electronic properties. This study employs adaptive boosting regression (ABR), random forest regression (RFR), and gradient boosting regression (GBR) for band gap prediction, alongside support vector machine (SVM), random forest classifier (RFC), and multilayer perceptron (MLP) for classifying compounds with zero and nonzero band gaps. Regression models are assessed using mean absolute error (MAE), mean squared error (MSE), and R 2 , while classification performance is evaluated based on accuracy, precision, recall, and F1‐score. ABR excels in predicting band gaps of inorganic perovskites, while RFC is the most effective model for classification. Feature analysis identifies the standard deviation of valence charges as the key predictor. This study underscores ML’s potential to accelerate perovskite discovery through accurate band gap predictions.

Previous studies have reported that atmospheric elemental carbon (EC) may pose potentially elevated toxicity when compared to total ambient fine particulate matter (PM 2.5 ). However, most research on EC has been conducted in the US and Europe, whereas China experiences significantly higher EC pollution levels. Investigating the health impact of EC exposure in China presents considerable challenges due to the absence of a monitoring network to document long-term EC levels. Despite extensive studies on total PM 2.5 in China over the past decade and a significant decrease in its concentration, changes in EC levels and the associated mortality burden remain largely unknown. In our study, we employed a combination of satellite remote sensing, available ground observations, machine learning techniques, and atmospheric big data to predict ground EC concentrations across China for the period 2005–2018, achieving a spatial resolution of 10 km. Our findings reveal that the national average annual mean EC concentration has remained relatively stable since 2005, even as total PM 2.5 levels have substantially decreased. Furthermore, we calculated the all-cause non-accidental deaths attributed to long-term EC exposure in China using baseline mortality data and pooled mortality risk from a cohort study. This analysis unveiled significant regional disparities in the mortality burden resulting from long-term EC exposure in China. These variations can be attributed to varying levels of effectiveness in EC regulations across different regions. Specifically, our study highlights that these regulations have been effective in mitigating EC-related health risks in first-tier cities. However, in regions characterized by a highconcentration of coal-power plants and industrial facilities, additional efforts are necessary to control emissions. This observation underscores the importance of tailoring environmental policies and interventions to address the specific challenges posed by varying emission sources and regional contexts.

Tires, bitumen, and road markings are important sources of traffic-derived carbonaceous wear particles and microplastic (MP) pollution. In this study, we further developed a machine-learning algorithm coupled to an automated scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDX) analytical approach to classify and quantify the relative number of the following subclasses contained in environmental road dust: tire wear particles (TWP), bitumen wear particles (BiWP), road markings, reflecting glass beads, metallics, minerals, and biogenic/organics. The method is non-destructive, rapid, repeatable, and enables information about the size, shape, and elemental composition of particles 2–125 µm. The results showed that the method enabled differentiation between TWP and BiWP for particles > 20 µm with satisfying results. Furthermore, the relative number concentration of the subclasses was similar in both analyzed size fractions (2–20 µm and 20–125 µm), with minerals as the most dominant subclass (2–20 µm x̄ = 78%, 20–125 µm x̄ = 74%) followed by tire and bitumen wear particles, TBiWP, (2–20 µm x̄ = 19%, 20–125 µm x̄ = 22%). Road marking wear, glass beads, and metal wear contributed to x̄ = 1%, x̄ = 0.1%, and x̄ = 1% in the 2–20-µm fraction and to x̄ = 0.5%, x̄ = 0.2%, and x̄ = 0.4% in the 20–125-µm fraction. The present results show that road dust appreciably consists of TWP and BiWP within both the coarse and the fine size fraction. The study delivers quantitative evidence of the importance of tires, bitumen, road marking, and glass beads besides minerals and metals to wear particles and MP pollution in traffic environments based on environmental (real-world) samples