Fine-Grained Emotion Recognition Using Brain-Heart Interplay Measurements and eXplainable Convolutional Neural Networks

Emotion recognition from electro-physiological signals is an important research topic in multiple scientific domains. While a multimodal input may lead to additional information that increases emotion recognition performance, an optimal processing pipeline for such a vectorial input is yet undefined. Moreover, the algorithm performance often compromises between the ability to generalize over an emotional dimension and the explainability associated with its recognition accuracy. This study proposes a novel explainable artificial intelligence architecture for a 9-level valence recognition from electroencephalographic (EEG) and electrocardiographic (ECG) signals. Synchronous EEG-ECG information are combined to derive vectorial brain-heart interplay features, which are rearranged in a sparse matrix (image) and then classified through an explainable convolutional neural network. The proposed architecture is tested on the publicly available MAHNOB dataset also against the use of vectorial EEG input. Results, also expressed in terms of confusion matrices, outperform the current state of the art, especially in terms of recognition accuracy. In conclusion, we demonstrate the effectiveness of the proposed approach embedding multimodal brain-heart dynamics in an explainable fashion

From local counterfactuals to global feature importance: efficient, robust, and model-agnostic explanations for brain connectivity networks

Background: Explainable artificial intelligence (XAI) is a technology that can enhance trust in mental state classifications by providing explanations for the reasoning behind artificial intelligence (AI) models outputs, especially for high-dimensional and highly-correlated brain signals. Feature importance and counterfactual explanations are two common approaches to generate these explanations, but both have drawbacks. While feature importance methods, such as shapley additive explanations (SHAP), can be computationally expensive and sensitive to feature correlation, counterfactual explanations only explain a single outcome instead of the entire model. Methods: To overcome these limitations, we propose a new procedure for computing global feature importance that involves aggregating local counterfactual explanations. This approach is specifically tailored to fMRI signals and is based on the hypothesis that instances close to the decision boundary and their counterfactuals mainly differ in the features identified as most important for the downstream classification task. We refer to this proposed feature importance measure as Boundary Crossing Solo Ratio (BoCSoR), since it quantifies the frequency with which a change in each feature in isolation leads to a change in classification outcome, i.e., the crossing of the model's decision boundary. Results and conclusions: Experimental results on synthetic data and real publicly available fMRI data from the Human Connect project show that the proposed BoCSoR measure is more robust to feature correlation and less computationally expensive than state-of-the-art methods. Additionally, it is equally effective in providing an explanation for the behavior of any AI model for brain signals. These properties are crucial for medical decision support systems, where many different features are often extracted from the same physiological measures and a gold standard is absent. Consequently, computing feature importance may become computationally expensive, and there may be a high probability of mutual correlation among features, leading to unreliable results from state-of-the-art XAI methods

Improving Emotion Recognition Systems by Exploiting the Spatial Information of EEG Sensors

Electroencephalography (EEG)-based emotion recognition is gaining increasing importance due to its potential applications in various scientific fields, ranging from psychophysiology to neuromarketing. A number of approaches have been proposed that use machine learning (ML) technology to achieve high recognition performance, which relies on engineering features from brain activity dynamics. Since ML performance can be improved by utilizing 2D feature representation that exploits the spatial relationships among the features, here we propose a novel input representation that involves re-arranging EEG features as an image that reflects the top view of the subject’s scalp. This approach enables emotion recognition through image-based ML methods such as pre-trained deep neural networks or "trained-from-scratch" convolutional neural networks. We have employed both of these techniques in our study to demonstrate the effectiveness of our proposed input representation. We also compare the recognition performance of these methods against state-of-the-art tabular data analysis approaches, which do not utilize the spatial relationships between the sensors. We test our proposed approach using two publicly available benchmark datasets for EEG-based emotion recognition tasks, namely DEAP and MAHNOB-HCI. Our results show that the "trained-from-scratch" convolutional neural network outperforms the best approaches in the literature, achieving 97.8% and 98.3% accuracy in valence and arousal classification on MAHNOB-HCI, and 91% and 90.4% on DEAP, respectively

Fibronectin-binding protein B (FnBPB) from Staphylococcus aureus protects against the antimicrobial activity of histones

Staphylococcus aureus is a Gram-positive bacterium that can cause both superficial and deep-seated infections. Histones released by neutrophils kill bacteria by binding to the bacterial cell surface and causing membrane damage. We postulated that cell wall–anchored proteins protect S. aureus from the bactericidal effects of histones by binding to and sequestering histones away from the cell envelope. Here, we focused on S. aureus strain LAC and by using an array of biochemical assays, including surface plasmon resonance and ELISA, discovered that fibronectin-binding protein B (FnBPB) is the main histone receptor. FnBPB bound all types of histones, but histone H3 displayed the highest affinity and bactericidal activity and was therefore investigated further. H3 bound specifically to the A domain of recombinant FnBPB with a K D of 86 nM, 20-fold lower than that for fibrinogen. Binding apparently occurred by the same mechanism by which FnBPB binds to fibrinogen, because FnBPB variants defective in fibrinogen binding also did not bind H3. An FnBPB-deletion mutant of S. aureus LAC bound less H3 and was more susceptible to its bactericidal activity and to neutrophil extracellular traps, whereas an FnBPB-overexpressing mutant bound more H3 and was more resistant than the WT. FnBPB bound simultaneously to H3 and plasminogen, which after activation by tissue plasminogen activator cleaved the bound histone. We conclude that FnBPB provides a dual immune-evasion function that captures histones and prevents them from reaching the bacterial membrane and simultaneously binds plasminogen, thereby promoting its conversion to plasmin to destroy the bound histone

Maiorca wheat malt: A comprehensive analysis of physicochemical properties, volatile compounds, and sensory evaluation in brewing process and final product quality

This study explores the potential of Maiorca wheat malt as an alternative ingredient in beer production, investigating its impact on the brewing process and beer quality at different recipe contents (50 %, 75 %, 100 %). The study encompasses a comprehensive analysis of key malt parameters, revealing Maiorca malt's positive influence on maltose, glucose, filterability, extract, free amino nitrogen, and fermentability. Notably, the malt exhibited heightened levels of α-amylase and β-amylase enzymes compared to conventional commercial malt. Furthermore, the analysis of aroma compounds and subsequent sensory evaluations unveiled a significant correlation between the proportion of Maiorca malt in the formulation and intensified estery, fruity, malty, honey, complemented by a reduction in attributes such as aromatic compounds, phenolic, yeasty, sulfury, oxidized, and solvent-like odors. This research underscores the favorable contribution of Maiorca wheat malt to enhancing both the brewing process and final beer quality, highlighting its potential as an innovative ingredient in brewing practices

Cherry Tomato Drying: Sun Versus Convective Oven

Solar drying and convective oven drying of cherry tomatoes (Solanum lycopersicum) were compared. The changes in the chemical parameters of tomatoes and principal drying parameters were recorded during the drying process. Drying curves were fitted to several mathematical models, and the effects of air temperature during drying were evaluated by multiple regression analyses, comparing to previously reported models. Models for drying conditions indicated a final water content of 30% (semidry products) and 15% (dry products) was achieved, comparing sun-drying and convective oven drying at three different temperatures. After 26–28 h of sun drying, the tomato tissue had reached a moisture content of 15%. However, less drying time, about 10–11 h, was needed when starting with an initial moisture content of 92%. The tomato tissue had high ORAC and polyphenol content values after convective oven drying at 60 °C. The dried tomato samples had a satisfactory taste, color and antioxidant values

Adapting American Hop (<i>Humulus lupulus</i> L.) Varieties to Mediterranean Sustainable Agriculture: A Trellis Height Exploration

In recent years, Italy’s craft beer industry has seen remarkable growth, fostering the local production of key ingredients, notably hops. However, a research gap exists in exploring open-field hop productivity in typical Mediterranean climates using low-trellis systems. This study addressed this gap by evaluating the productive performances of “Cascade” and “Chinook” hop varieties on “V” trellis systems at different heights (2.60, 3.60, and 4.60 m above ground) in inner Sicily’s Mediterranean climate and soil conditions. The results highlighted the significant impact of trellis height on various parameters, with Cascade displaying exceptional adaptability to low-trellis farming. Key factors like stem and leaf weight emerged as crucial drivers of cone yield, emphasizing their significance in hop cultivation. The distinct responses of Cascade and Chinook varieties to varying trellis heights underscored the need for tailored approaches, offering valuable insights for optimizing hop cultivation practices in semi-arid climates

New development opportunities for the craft brewing segment: the case study of a micro-malthouse

In Italy in the past few years, the number of small breweries penetrating the craft beer sector has grown exponentially. Craft producers intend to give a strong added value and a local character to their production in different ways. One of these is the use of malt derived from small batches of local cereals and pseudo cereals. The aim of this study is the assessment of investment profitability, through a cost-benefit analysis (CBA), for a compact and a modular micro-malting plant in Sicily (Southern Italy). The CBA for a micro-malthouse was carried out considering both installation and operating costs. Net present value (NPV), discounted benefit-cost ratio (DBCR) and internal rate of return (IRR) highlight the feasibility of an investment in a compact 2-tonnes micro-malthouse. Sensitivity analysis shows positive results of the above financial indices up to a 15% increase in the raw material costs, while with a 10% reduction of malt selling price, the same indices start being negative

Preliminary evaluation of durum wheat (Triticum Turgidum Subsp Durum) during malting process

© 2018 AACC International. Background and objectives: Effects of 45 and 70°C final malt drying temperature on a traditional Italian durum wheat (SM45, SM70) were evaluated for the malt quality parameters and the wort characteristics when employed in rate of 40% with commercial barley malt (BM), using a common wheat (CWM) as a control test. Findings: Drying temperatures and wheat genotypes were major contributors to variability in malt quality parameters. SM45 and SM70 were characterized by reduced protein and starch degradation, lower solubility for beta-glucans (BG), and high levels of water-extractable arabinoxylans (WEAX) compared to CWM. Alpha- and beta-amylases, endo-β-glucanases, and endo-1,4-β-d-xylanase activities detected on SM45 were higher than SM70 and CWM, likely due to the combined effects of the cultivar characteristics and the low temperatures used during the kilning phase. When SM4540% and SM7040% were used, the derived worts have had lower color, FAN levels, saccharification time, beta-glucans (WBG), and viscosity than CWM40%. Conclusions: Malting conditions and genotypes affect the malt quality attributes, mainly in terms of extractable compounds and enzyme activities. The use in mashing of 40% of durum wheat malt results in low viscosity and reduced availability of BG. Significance and novelty: These first results indicate that durum wheat malt has good characteristics and can be suitable for brewing purposes.status: publishe