Filling the gap. Human cranial remains from Gombore II (Melka Kunture, Ethiopia; ca. 850 ka) and the origin of Homo heidelbergensis

African archaic humans dated to around 1,0 Ma share morphological affinities with Homo ergaster and appear distinct in cranio-dental morphology from those of the Middle Pleistocene that are referred to Homo heidelbergensis. This observation suggests a taxonomic and phylogenetic discontinuity in Africa that ranges across the Matuyama/Brunhes reversal (780 ka). Yet, the fossil record between roughly 900 and 600 ka is notoriously poor. In this context, the Early Stone Age site of Gombore II, in the Melka Kunture formation (Upper Awash, Ethiopia), provides a privileged case-study. In the Acheulean layer of Gombore II, somewhat more recent than 875±10 ka, two large cranial fragments were discovered in 1973 and 1975 respectively: a partial left parietal (Melka Kunture 1) and a right portion of the frontal bone (Melka Kunture 2), which probably belonged to the same cranium. We present here the first detailed description and computer-assisted reconstruction of the morphology of the cranial vault pertaining to these fossil fragments. Our analysis suggest that the human fossil specimen from Gombore II fills a phenetic gap between Homo ergaster and Homo heidelbergensis. This appears in agreement with the chronology of such a partial cranial vault, which therefore represents at present one of the best available candidates (if any) for the origin of Homo heidelbergensis in Africa

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

Design and experimental set-up of hydrogen based microgrid: characterization of components and control system development

In this study, the implementation of a hydrogen microgrid is investigated, considering the integration of H2 production, storage, and energy conversion to feed a typical end-user. A remote control system has been realized through LabVIEW software, allowing to monitor real-time all the devices and analyze their performances. The integrated system is composed of a PEM electrolyzer (325 W), a storage system based on metal hydrides (two tanks, 54 g of hydrogen each, 1.08 wt%) and an energy converter (PEM Fuel Cell stack, 200 W). A programmable electronic load was used to set a power demand throughout the year, simulating an end-user. Data collected from each component of the micro-grid were used to characterize the energetic performance of the devices, focusing on the H2 production via electrolyzer, charging cycles of the H2 storage system, and energy conversion efficiency of the FC stack. Finally, the global efficiency of the microgrid is computed. Even though the system is realized in laboratory scale, this circumstance will not invalidate the significance of the configuration due to modularity of all the technologies that can be easily scaled up to realistic scales

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

Delirium risk factors analysis post proximal femur fracture surgery in elderly

Background and aim: The increase in the average-age and in the percentage of elderly people implies an exponential increase in fractures of the proximal femur. A common consequence of hip fracture in elderly patients is delirium, characterized by cognitive confusion or a lethargic-type condition. Predisposing factors have been identified, but risk factors assessment useful for managing clinical intervention, has not received unanimous consent. This work aims to identify the potential risk factors for delirium in the elderly operated for hip fracture. Methods: In this prospective observational study, we included 83 patients aged ≥65 years. Patients undergoing osteosynthesis of the femur and hip replacement for fractures were included. Patients already delusional in the pre-operative period were excluded. At the time, deadlines T0 (pre-operative), and T1,T3,T7 post-operative day, delirium, hematic parameters, blood transfusions, were assessed. Results: Level of delirium was assessed obtaining 80% not delusional and 20% delusional. Glycemia and hemoglobin were not found to be risk factors, although they are known to influence cognitive status; we hypothesize they should be considered predisposing factors. Comorbidities such as atrial fibrillation and Chronic Obstructive Pulmonary Disease were found associated with delirium. The most advanced age, anxiolytic drugs, the use of benzodiazepine as anaesthetic, the time surgical waiting, were found significantly associated with delirium. Conclusions: Taken together, findings of this prospective observational study showed that environmental and metabolic risk factors might contribute to make elderly susceptible to develop postoperative delirium following hip surgery. Thus, these patients should be adequately assessed and monitored. (www.actabiomedica.it)

Regional monitoring plan for the detection of allergens in food from Campania Region. First year monitoring results

Food allergens are substances able to induce an abnormal immunological response in sensitive individuals. The presence of allergens in food must be reported in tables (Directive 2003/89/EC). In this study we report the data of a monitoring plan carried out in the Campania Region during the 2012 for the detection of allergens (ovoalbumin and β-lattoglobulin) in food of different origin. The analisys were performed by means of ELISA assays. The percentage of analyzes with the presence of allegens not declared on the label is 4.3%, out of a total of 208 analyzes. It is therefore important to continue monitoring activities by the competent Authorities

Presentation of peptides by cultured monocytes or activated T cells allows specific priming of human cytotoxic T lymphocytes in vitro

The conditions favouring effective specific cytotoxic T lymphocyte (CTL) priming have been exploited to set up a simple and reproducible method to induce a primary CTL response in vitro. We report that cultured monocytes, as well as activated T cells, pulsed with exogenous HLA-A2 binding immunogenic peptides, can induce primary peptide-specific CTL responses in vitro in a Th-independent manner. Primary viral peptide-induced CTL were HLA-A2 restricted, and recognized both peptide-pulsed target cells and targets infected with recombinant vaccinia virus expressing viral endogenous antigens. In addition, both cultured monocytes and activated T cells primed peptide-specific CD8+ T cells depleted from the CD45RO+ memory cell fraction. The efficiency of CTL priming by monocytes was dependent upon the strong up-regulation of class I, adhesion and co-stimulatory molecules occurring spontaneously upon in vitro culture. The inability of unseparated peripheral blood mononuclear cells to mount a peptide-specific CTL response could be reverted by direct co-stimulation of responding CD8+ T cells by soluble B7.1 or a stimulatory anti-CD28 antibody, that allowed a specific response to take place. Although co-stimulation via the B7-CD28 interaction appeared sufficient to trigger CTL responses, It was not essential for CTL priming, since neither anti-B7.1 mAb nor soluble CTLA-4 inhibited induction of primary CTL response. This new method for induction of specific CD8+ T cell response in vitro may be exploited in adoptive immunotherapy in cancer or in HIV-infected patient

Celle a combustibile microbiche terrestri: uno strumento efficace nel recupero di suoli contaminati e nella produzione di energia.

Una cella a combustibile microbica (MFC) è un sistema bio-elettrochimico che utilizza un microrganismo attivo come biocatalizzatore per la produzione di elettricità. Essa è costituita da due comparti, uno anodico ed uno catodico, separati da una membrana di scambio protonico. L’energia chimica di legame, disponibile grazie alla presenza di un substrato biodegradabile, viene trasformata direttamente in energia elettrica per azione microbica, che catalizza la rimozione degli elettroni dal substrato. I batteri presenti nella camera anodica, o comunque nel mezzo in cui è immerso l’anodo, sono in grado di convertire un’enorme varietà di substrati organici (acetato, glucosio, cellulosa, reflui di varia origine, contaminanti organici) in CO2, acqua ed energia. Tra le MFC, le Celle a Combustibile Microbiche Terrestri (Terrestrial Microbial Fuel Cells - TMFC), hanno come elettrolita il suolo. Esso è una matrice molto più complessa rispetto all’acqua, variando nella composizione granulometrica, nella capacita di ritenzione idrica, nella capacità di scambio cationico, nonché nella distribuzione dei contaminanti; pertanto le TMFC sono dei dispositivi di cui è ancora necessario esplorare tutte le potenzialità di applicazione per il recupero di suoli contaminati