Science Talks最新文献

Food choice is a multifaceted process influenced by various factors, including biological, economic, physical, social, and psychological determinants. Individuals' food choices directly impact their health, yielding both positive and negative outcomes depending on the context. For example, the pandemic-induced increase in time spent at home presented an opportunity for more cooking, assumed to yield dietary benefits and reduced stress. However, adverse consequences such as overeating have also been associated with stressful situations. This study explores the connection between cooking readiness, stress levels, and consumers' adherence to nutritionally recommended or non-recommended food choices. Analyzing cross-country data from 1074 consumers (Brazil, Denmark) using structural equation modeling, the findings indicate that those ‘ready to cook’ tend to make more nutritionally recommended food choices. This highlights the importance of providing consumers with the skills, motivation, and opportunity to cook as a means to encourage healthy eating. The reported stress level is linked to a higher frequency of non-recommended food choices, suggesting that unhealthy food serves as a coping mechanism during crises. Understanding coping strategies in populations exposed to acute stressors is crucial for defining actions to mitigate negative nutritional and psychosocial outcomes. The study underscores the potential of cooking motivations and abilities arising from the pandemic to persist and promote enduring healthy eating behaviors. The first wave of COVID-19 is considered a stressor scenario, emphasizing the relevance of addressing cooking readiness in tailored interventions.

This work investigates the impact of iron-based oxygen carriers (OCs) on green hydrogen production using a fixed-bed chemical looping (CL) process, aiming for industrial scalability. We conducted a comprehensive material screening of OCs comprising Fe₂O₃ with support materials (Al₂O₃, TiO₂, ZrO₂) at an 80/20 wt.-% ratio. Focus was placed on ZrO₂, pure and doped with CaO, MgO, and Y₂O₃, to examine their effects on redox efficiency and hydrogen production. Notably, ZrO₂ doped with MgO and Y₂O₃ achieved a specific hydrogen production over 12 molH₂/kgOC at a small scale, attributed to chemical inertness and porous morphology, enhancing cyclic stability over traditional TiO₂ and Al₂O₃ supports. Large-scale testing of the most promising OC compositions in a 250 g fixed-bed reactor for 100 cycles revealed that doping ZrO₂ with Y₂O₃ not only prevents phase transitions but also ensures higher cyclic stability among tested OCs. Our findings underscore the critical role of microscopic phenomena in the CL process's efficiency and introduce a novel approach for designing environmentally friendly OCs for effective hydrogen production.

Texture is one of the main attributes that determine food preferences and choices in children and adult population, especially in seniors. This study was performed to evaluate the influence of a variety of solid food textures on implicit and explicit emotions in schoolchildren and seniors.

A group of 50 children (5–12 years old) and 50 seniors (55–75 years old) were recruited to evaluate three texture-modified products designed to have the same sensory characteristics in terms of taste, flavour, and appearance, but textures that varied according to the International Dysphagia Diet Standardisation Initiative (IDDSI). After the consumption of each sample, liking was measured with a 7-point hedonic scale, and emotions were monitored at the three levels of cognitive processing: (i) cognitive with self-reported questionnaires (emoji for children and EsSense25 for seniors), (ii) physiological with the measure of Skin Conductance Response (SCR), and (iii) behavioural with facial coding.

Results showed that the three textures elicited a different implicit and explicit emotional response in children and in seniors. The methodology used allowed us to identify the emotional configurations elicited by the three solid samples in each group of consumers and the dynamics of the emotion due to the monitoring conducted in real-time with facial coding and SCR.

Individual dietary choices play a crucial role in shaping nutrition-related health outcomes, making it a focal point of ongoing research in sensory nutrition (Forde et al., 2015). Chemosensory function is often considered a key influencer of dietary choices and intake. However, empirical investigations into these sensory-diet links have yielded divergent results (McCrickerd & Forde, 2016; Abeywickrema et al., 2022; Ginieis et al., 2022). We thereby summarised recent findings from our lab, aimed at unravelling chemosensory connections to individual dietary choices and intake, incorporating data from four experiments. Employing robust psychophysical methods (i.e., signal detection theory), we systematically addressed critical questions: [1] the impact of olfactory and gustatory supra-threshold sensitivities on daily energy intake; [2] the relationships between individual chemosensory perception and dietary macronutrient composition; [3] the sensory drivers influencing food choices based on taste quality profiling. Our results indicate that individual olfactory and gustatory sensitivities contribute significantly to food choices (Abeywickrema et al., 2022), with only gustatory sensitivities directly linked to macronutrient intakes (Abeywickrema et al., 2023). Furthermore, profiling food choices based on taste qualities, rather than energy or nutrients, provides novel insights into the potential mechanism underpinning sensory influences on dietary behaviors. Our presentation offers compelling new evidence for the distinct roles of olfactory and gustatory senses in shaping individual food choices.

Morphing attacks keep threatening biometric systems, especially face recognition systems. Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown. At the same time, there is a constant concern regarding the lack of interpretability of deep learning models. Balancing performance and interpretability has been a difficult task for scientists. However, by leveraging domain information and proving some constraints, we have been able to develop IDistill, an interpretable method with state-of-the-art performance that provides information on both the identity separation on morph samples and their contribution to the final prediction. The domain information is learnt by an autoencoder and distilled to a classifier system in order to teach it to separate identity information. When compared to other methods in the literature it outperforms them in three out of five databases and is competitive in the remaining.

In this video article, accompanying the paper “An approach to learning the hierarchical organization of the frontal lobe”, we discuss a data driven approach to learning brain connectivity. Hierarchical models of brain connectivity are useful to understand how the brain can process sensory information, make decisions, and perform other high-level tasks. Despite extensive research, understanding the structure of the prefrontal cortex (PFC) remains a crucial challenge. In this work, we propose an approach to studying brain signals and uncovering characteristics of the underlying neural circuity, based on the mathematics of Gaussian processes and causal strengths. For discovering causations, we propose a metric referred to as double-averaged differential causal effect, which is a variant of the recently proposed differential causal effect, and it can be used as a principled measure of the causal strength between time series. We applied this methodology to study local field potential data from the frontal lobe, where the interest was in finding the causal relationship between the medial and lateral PFC areas of the brain. Our results suggest that the medial PFC causally influences the lateral PFC.

A growing body of research in the past decade has revealed that functional interaction between brain regions entail subject-specific idiosyncrasies that are highly replicable. As such, functional connectivity patterns can be seen as an individual's brain fingerprint, enabling their identification within a population, in health and disease. The conventional method involves constructing the functional connectome by treating brain regions as vertices and utilizing pairwise measures of statistical dependence, such as Pearson's correlation coefficient, between the regional time-courses as edge weights. However, by focusing on EEG data in our study, we propose an alternative approach to learn a sparse graph structure from an individual's EEG data using principles from graph signal processing. The inferred subject-specific graphs encode subtle instantaneous spatial relations between the ensemble set of EEG electrodes in such way that EEG maps are seen as smooth functions residing on the graph. We validated the inferred graphs on two publicly available EEG datasets, demonstrating that the learned graphs outperform correlation-based functional connectomes in fingerprinting performance. This talk provides an overview of our proposed method and related results, which was presented at the 2023 European Signal Processing Conference in Helsinki, Finland. The work was selected as the second-best student paper; aside from the talk, a poster was presented as part of the contest, segments of which can be found as figures in the present article.

This paper expands the concept of the power method to polynomial para-Hermitian matrices in order to extract the principal analytic eigenpair. The proposed technique involves repeatedly multiplying the para-Hermitian matrix by a polynomial vector, followed by an appropriate normalization of the resulting product in each iteration, under the assumption that the principal analytic eigenvalue spectrally majorises the remaining eigenvalues. To restrain the growth in polynomial order of the product vector, truncation is performed after normalization in each iteration. The effectiveness of this proposed method has been verified through simulation results on an ensemble of randomly generated para-Hermitian matrices, demonstrating superior performance compared to existing algorithms.

The rapid detection of biomarkers like nucleic acids and proteins in clinical samples can significantly enhance clinical outcomes in chronic diseases through early diagnosis and prevention. The Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) technology, coupled with the Cas protein, represents an innovative biosensing approach, renowned for its remarkable accuracy, specificity, and sensitivity. This technology has effectively harnessed the success in the detection of a broad range of analytes such as nucleic acids, proteins, et al. Cytokines are signally molecules between immune cells and biomarkers for many diseases. It is challenging to detect cytokines. In this study, taking advantage of magnetic beads and immunoassays, we developed a CRISPR/Cas12a-based immunosensors on magnetic beads, which exhibits rapid and highly sensitive detection of interleukin-6 (IL-6) down to 0.1 pg/mL within a compact reaction system excluding the need for additional sample purification or amplification steps. This breakthrough underscores its potential as a formidable alternative for future protein detection in both industrial and medical applications.

In this paper, we address the antenna array design problem at low signal-to-noise ratio (SNR). The Cramér-Rao bound (CRB) is the most commonly used criterion to solve the array optimization problem due to its computing simplicity and tightness in the asymptotical region. However, there exists a threshold SNR at which the estimation variance significantly deviates from the CRB. In this case, the CRB is no longer a tight bound. To address this issue, we propose the use of the Barankin Bound (BB) on the source location and source intensity in astrometry and photometry problems as an alternative optimization criterion. BB provides a mean square error (MSE)-optimal trade-off mainlobe width and sidelobe level of beampattern. The performance of the obtained array geometries is assessed and compared by evaluating the aforementioned bounds and the mean square error (MSE) on the estimation of source location and intensity. The simulation results illustrate that the BB-based criterion provides a trade-off between increasing the estimation accuracy and reducing the ambiguity.