A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision

Convolutional neural networks (CNNs), which automatically learn features from raw data to approximate functions, are being increasingly applied to the end-to-end analysis of electroencephalographic (EEG) signals, especially for decoding brain states in brain-computer interfaces (BCIs). Nevertheless, CNNs introduce a large number of trainable parameters, may require long training times, and lack in interpretability of learned features. The aim of this study is to propose a CNN design for P300 decoding with emphasis on its lightweight design while guaranteeing high performance, on the effects of different training strategies, and on the use of post-hoc techniques to explain network decisions. The proposed design, named MS-EEGNet, learned temporal features in two different timescales (i.e., multi-scale, MS) in an efficient and optimized (in terms of trainable parameters) way, and was validated on three P300 datasets. The CNN was trained using different strategies (within-participant and within-session, within-participant and cross-session, leave-one-subject-out, transfer learning) and was compared with several state-of-the-art (SOA) algorithms. Furthermore, variants of the baseline MS-EEGNet were analyzed to evaluate the impact of different hyper-parameters on performance. Lastly, saliency maps were used to derive representations of the relevant spatio-temporal features that drove CNN decisions. MS-EEGNet was the lightest CNN compared with the tested SOA CNNs, despite its multiple timescales, and significantly outperformed the SOA algorithms. Post-hoc hyper-parameter analysis confirmed the benefits of the innovative aspects of MS-EEGNet. Furthermore, MS-EEGNet did benefit from transfer learning, especially using a low number of training examples, suggesting that the proposed approach could be used in BCIs to accurately decode the P300 event while reducing calibration times. Representations derived from the saliency maps matched the P300 spatio-temporal distribution, further validating the proposed decoding approach. This study, by specifically addressing the aspects of lightweight design, transfer learning, and interpretability, can contribute to advance the development of deep learning algorithms for P300-based BCIs

The impact of 1D seismostratigraphical amplification effects on probabilistic seismic hazard maps at regional scale: the case of Tuscany (Central Italy)

Seismic risk assessment at regional scale requires hazard estimates accounting for seismostratigraphical amplification effects. When detailed data related to the local subsoil configuration are lacking, these effects can be inferred from numerical simulations fed with information available on at regional scale. A key aspect concerns the implementation of these outcomes including relevant uncertainty into probabilistic seismic hazard estimates relative to standard subsoil conditions. A coherent approach is here proposed, which coherently accounts for the inherent probabilistic character of reference hazard estimates and of uncertain 1D seismostratographical amplification effects inferred from geological maps. The proposed approach has been applied in Central Italy relative PGA values corresponding to an exceedance probability of 10% in 50y. It is shown that accounting for uncertainty affecting amplification estimates is of main importance for correct implementation into PSHA. The outcome of this analysis is not expected to be considered for anti-seismic design of single structures, which requires detailed, and sound estimates of site effects at the proper scale. Anyway, these estimates may play a role for the preliminary identification of most critical situations along lifelines or outside inhabited areas where seismic microzonation studies are not available

Automatic identification of sites prone to topographic seismic amplification effects by the current seismic codes

Current seismic codes provide proxies to estimate seismic amplification effects expected in correspondence of some morphological features. To make possible any empirical validation of these proxies, these features must be univocally identified on the basis of an automatic procedure. To this purpose, based on geomorphological considerations, a GIS-based numerical approach has been developed. The results of a morphometric analysis allowed the correct identification and mapping of the landforms of concern, at a detail corresponding to the resolution of the available digital elevation model (DEM). Some case-studies are provided to show the feasibility of the proposed approach. © 2023 The Author

Modulations of Cortical Power and Connectivity in Alpha and Beta Bands during the Preparation of Reaching Movements

Planning goal-directed movements towards different targets is at the basis of common daily activities (e.g., reaching), involving visual, visuomotor, and sensorimotor brain areas. Alpha (8-13 Hz) and beta (13-30 Hz) oscillations are modulated during movement preparation and are implicated in correct motor functioning. However, how brain regions activate and interact during reaching tasks and how brain rhythms are functionally involved in these interactions is still limitedly explored. Here, alpha and beta brain activity and connectivity during reaching preparation are investigated at EEG-source level, considering a network of task-related cortical areas. Sixty-channel EEG was recorded from 20 healthy participants during a delayed center-out reaching task and projected to the cortex to extract the activity of 8 cortical regions per hemisphere (2 occipital, 2 parietal, 3 peri-central, 1 frontal). Then, we analyzed event-related spectral perturbations and directed connectivity, computed via spectral Granger causality and summarized using graph theory centrality indices (in degree, out degree). Results suggest that alpha and beta oscillations are functionally involved in the preparation of reaching in different ways, with the former mediating the inhibition of the ipsilateral sensorimotor areas and disinhibition of visual areas, and the latter coordinating disinhibition of the contralateral sensorimotor and visuomotor areas

Acidic microenvironment plays a key role in human melanoma progression through a sustained exosome mediated transfer of clinically relevant metastatic molecules

Background: Microenvironment cues involved in melanoma progression are largely unknown. Melanoma is highly influenced in its aggressive phenotype by the changes it determinates in its microenvironment, such as pH decrease, in turn influencing cancer cell invasiveness, progression and tissue remodelling through an abundant secretion of exosomes, dictating cancer strategy to the whole host. A role of exosomes in driving melanoma progression under microenvironmental acidity was never described. Methods: We studied four differently staged human melanoma lines, reflecting melanoma progression, under microenvironmental acidic pHs pressure ranging between pH 6.0-6.7. To estimate exosome secretion as a function of tumor stage and environmental pH, we applied a technique to generate native fluorescent exosomes characterized by vesicles integrity, size, density, markers expression, and quantifiable by direct FACS analysis. Functional roles of exosomes were tested in migration and invasion tests. Then we performed a comparative proteomic analysis of acid versus control exosomes to elucidate a specific signature involved in melanoma progression. Results: We found that metastatic melanoma secretes a higher exosome amount than primary melanoma, and that acidic pH increases exosome secretion when melanoma is in an intermediate stage, i.e. metastatic non-invasive. We were thus able to show that acidic pH influences the intercellular cross-talk mediated by exosomes. In fact when exposed to exosomes produced in an acidic medium, pH naïve melanoma cells acquire migratory and invasive capacities likely due to transfer of metastatic exosomal proteins, favoring cell motility and angiogenesis. A Prognoscan-based meta-analysis study of proteins enriched in acidic exosomes, identified 11 genes (HRAS, GANAB, CFL2, HSP90B1, HSP90AB1, GSN, HSPA1L, NRAS, HSPA5, TIMP3, HYOU1), significantly correlating with poor prognosis, whose high expression was in part confirmed in bioptic samples of lymph node metastases. Conclusions: A crucial step of melanoma progression does occur at melanoma intermediate -stage, when extracellular acidic pH induces an abundant release and intra-tumoral uptake of exosomes. Such exosomes are endowed with pro-invasive molecules of clinical relevance, which may provide a signature of melanoma advancement

Blockade of insulin-like growth factors increases efficacy of paclitaxel in metastatic breast cancer.

Breast cancer remains the leading cause of cancer death in women owing to metastasis and the development of resistance to established therapies. Macrophages are the most abundant immune cells in the breast tumor microenvironment and can both inhibit and support cancer progression. Thus, gaining a better understanding of how macrophages support cancer could lead to the development of more effective therapies. In this study, we find that breast cancer-associated macrophages express high levels of insulin-like growth factors 1 and 2 (IGFs) and are the main source of IGFs within both primary and metastatic tumors. In total, 75% of breast cancer patients show activation of insulin/IGF-1 receptor signaling and this correlates with increased macrophage infiltration and advanced tumor stage. In patients with invasive breast cancer, activation of Insulin/IGF-1 receptors increased to 87%. Blocking IGF in combination with paclitaxel, a chemotherapeutic agent commonly used to treat breast cancer, showed a significant reduction in tumor cell proliferation and lung metastasis in pre-clinical breast cancer models compared to paclitaxel monotherapy. Our findings provide the rationale for further developing the combination of paclitaxel with IGF blockers for the treatment of invasive breast cancer, and Insulin/IGF1R activation and IGF+ stroma cells as potential biomarker candidates for further evaluation

Mapping 1D seismic amplification effects in the range of periods of engineering interest based on geological data

Regional scale seismic hazard assessment including the effect of local seismo-stratigraphical conditions is a basic tool for seismic risk estimates. A novel physically based procedure is proposed for using geological maps to extensively estimate expected seismic amplification effects relative to spectral ordinates of main engineering interest (<0.8 s). Automatic GIS based analysis of geological maps, statistical data relative to the seismic/geotechnical properties of geological units and numerical modelling are combined to determine the probability distribution of expected amplification effects by accounting for uncertainty affecting the relevant parameters. To evaluate the feasibility of the proposed procedure, it has been applied to the Tuscany Region in Central Italy. Unbiasedness of outcomes has been tested by considering detailed microzonation studies available for the considered area. Results of the proposed approach could be easily implemented in extensive seismic risk analyses where detailed seismic microzonation studies are lacking

Green metrics in mechanochemistry

The development of new green methodologies and their broader adoption for promoting sustainable development in chemistry laboratories and industry play a significant role in society, due to the economic importance of chemistry and its widespread presence in everyday life. Therefore, a sustainable approach to chemistry contributes to the well-being of the worldwide population and complies with the United Nations Sustainable Development Goals (UN SDGs) and the European Green Deal. The review highlights how batch and continuous mechanochemical methods are an eco-friendly approach for organic synthesis, with a lower environmental footprint in most cases, compared to solution-based procedures. The assessment is objectively based on the use of green metrics (e.g., atom and real atom economy, E-factor, process mass intensity, material parameter recovery, Eco-scale, stoichiometric factor, etc.) and indicators (e.g. DOZN tool and life cycle assessment, LCA, studies) applied to organic transformations such as synthesis of the amide bond, carbamates, heterocycles, active pharmaceutical ingredients (APIs), porphyrins, porous organic polymers (POPs), metal- or acid-catalysed processes, multicomponent and condensation reactions, rearrangements, etc. The generalized absence of bulk solvents, the precise control over the stoichiometry (i.e., using agents in a stoichiometrically rather than in excess), and the more selective reactions enabling simplified work-up procedures are the distinctive factors, marking the superiority of mechanochemical processes over solution-based chemistry

A systematic review of studies on fine and coarse root traits measurement: towards the enhancement of urban forests monitoring and management

The analysis of fine and coarse roots' functional traits has the potential to reveal the performance of the root system, which is pivotal in tree growth, development, and failure in both natural and urban forest ecosystems. Furthermore, root traits may be a powerful indicator of tree resilience mechanisms. However, due to the inherent difficulties in measuring 'the hidden half,' and despite the recent advancements, the relationships among root functional traits and biotic and abiotic drivers still suffer from a lack of information. Thus, our study aimed to evidence knowledge milestones and gaps and to categorize, discuss, and suggest future directions for effective experimental designs in fine and coarse root studies. To this end, we conducted a systematic literature review supported by backward manual referencing based on 55 root functional traits and 136 plant species potentially suitable for afforestation and reforestation of natural and urban forest ecosystems. The majority of the 168 papers on fine and coarse root studies selected in our review focused predominantly on European natural contexts for a few plant species, such as Fagus sylvatica, Picea abies, Pinus sylvestris, and Pinus cembra, and root functional traits such as standing biomass, phenology production, turnover rate, and non-structural carbohydrates (NSC). Additionally, the analyzed studies frequently lack information and uniformity in experimental designs, measurements, and statistical analysis, highlighting the difficult integration and comparison of outcomes derived from different experiments and sites. Moreover, no information has been detected in selected literature about urban forest ecosystems, while most of the studies focus on natural forests. These biases observed during our literature analysis led us to give key indications for future experiment designs with fine and coarse roots involved, which may contribute to the building up of common protocols to boost the monitoring, managing, and planning of afforestation and reforestation projects