Is it possible to compare inhibitory and excitatory intracortical circuits in face and hand primary motor cortex?

Face muscles are important in a variety of different functions, such as feeding, speech and communication of non-verbal affective states, which require quite different patterns of activity from those of a typical hand muscle. We ask whether there are differences in their neurophysiological control that might reflect this. Fifteen healthy individuals were studied. Standard single- and paired-pulse transcranial magnetic stimulation (TMS) methods were used to compare intracortical inhibitory (short interval intracortical inhibition (SICI); cortical silent period (CSP)) and excitatory circuitries (short interval intracortical facilitation (SICF)) in two typical muscles, the depressor anguli oris (DAO), a face muscle, and the first dorsal interosseous (FDI), a hand muscle. TMS threshold was higher in DAO than in FDI. Over a range of intensities, resting SICF was not different between DAO and FDI, while during muscle activation SICF was stronger in FDI than in DAO (P = 0.012). At rest, SICI was stronger in FDI than in DAO (P = 0.038) but during muscle contraction, SICI was weaker in FDI than in DAO (P = 0.034). We argue that although many of the difference in response to the TMS protocols could result from the difference in thresholds, some, such as the reduction of resting SICI in DAO, may reflect fundamental differences in the physiology of the two muscle groups

Building the European Alien Species Information Network (EASIN): a novel approach for the exploration of distributed alien species data

The European Alien Species Information Network (EASIN; http://easin.jrc.ec.europa.eu) aims to facilitate the exploration of existing alien species information from distributed sources through a network of interoperable web services, and to assist the implementation of European policies on biological invasions. The network allows extraction of alien species information from online information systems for all species included in the EASIN catalogue. This catalogue was based on an inventory of reported alien species in Europe that was produced by reviewing and standardizing information from 43 online databases. It includes information on taxonomy, synonyms, common names, pathways of introduction, native range in Europe, and impact. EASIN catalogue entails the basic information needed to efficiently link to existing online databases and retrieve spatial information for alien species distribution in Europe. Using search functionality powered by a widget framework, it is possible to make a tailored selection of a subgroup of species based on various criteria (e.g., environment, taxonomy, pathways). Distribution maps of the selected species can be produced dynamically and downloaded by the user. The EASIN web tools and services follow internationally recognized standards and protocols, and can be utilized freely and independently by any website, while ownership of the data remains with its source, which is properly cited and linked.JRC.H.1-Water Resource

A machine learning approach to support decision in insider trading detection

Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to his/her own past trading history and on the present trading activity of his/her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids.Comment: 42 pages, 16 Figure

A model-agnostic algorithm for Bayes error determination in binary classification

This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with categorical features regardless of the model used. This limit, namely, the Bayes error, is completely independent of any model used and describes an intrinsic property of the dataset. The ILD algorithm thus provides important information regarding the prediction limits of any binary classification algorithm when applied to the considered dataset. In this paper, the algorithm is described in detail, its entire mathematical framework is presented and the pseudocode is given to facilitate its implementation. Finally, an example with a real dataset is given

One-dimensional convolutional neural networks design for fluorescence spectroscopy with prior knowledge : explainability techniques applied to olive oil fluorescence spectra

Optical spectra, and particularly fluorescence spectra, contain a large quantity of information about the substances and their interaction with the environment. It is of great interest, therefore, to try to extract as much of this information as possible, as optical measurements can be easy, non-invasive, and can happen in-situ making the data collection a very appealing method of gathering knowledge. Artificial neural networks are known for their feature extraction capabilities and are therefore well suited for this challenge. In this work, inspired by convolutional neural network (CNN) architectures in 2D and their success with images, a novel approach using one-dimensional convolutional neural networks (1D-CNN) is used to extract information on the measured spectra by using explainability techniques. The 1D-CNN architecture has as input the entire fluorescence spectrum and takes advantage in its design of prior knowledge about the instrumentation and sample characteristics as, for example, spectrometer resolution or the expected number of relevant features in the spectrum. Even if network performance is good, it remains an open question if the features used for the predictions make sense from a physical and chemical point of view and if they match what is known from existing studies. This work studies the output of the convolutional layers, known as feature maps, to understand which features the network has effectively used for the predictions, and thus which part of the measured spectra contains the relevant information about the phenomena at the basis of what has to be predicted. The proposed approach is demonstrated by applying it to the determination of the UV absorbance at 232 nm, K232, from fluorescence spectra using a dataset of 18 Spanish olive oils, which were chemically analyzed from certified laboratories. The 1D-CNN successfully predicts the parameter K232 and enables, by studying feature maps, the clear identification of the relevant spectral features. The main contributions of this work are two. Firstly, it describes how designing the neural network architecture with prior knowledge (spectrometer resolution, etc.) will help the network in learning features that have a clear connection to the chemical composition of the substances, and thus are clearly explainable. Secondly, it shows how, in the case of olive oil, the identified features match perfectly the relevant features known from existing previous studies, thus confirming that the network is learning from the underlying chemical process

Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil

One of the main challenges for olive oil producers is the ability to assess oil quality regularly during the production cycle. The quality of olive oil is evaluated through a series of parameters that can be determined, up to now, only through multiple chemical analysis techniques. This requires samples to be sent to approved laboratories, making the quality control an expensive, time-consuming process, that cannot be performed regularly and cannot guarantee the quality of oil up to the point it reaches the consumer. This work presents a new approach that is fast and based on low-cost instrumentation, and which can be easily performed in the field. The proposed method is based on fluorescence spectroscopy and one-dimensional convolutional neural networks and allows to predict five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters K270 and K232, and ethyl esters) from one single fluorescence spectrum obtained with a very fast measurement from a low-cost portable fluorescence sensor. The results indicate that the proposed approach gives exceptional results for quality determination through the extraction of the relevant physicochemical parameters. This would make the continuous quality control of olive oil during and after the entire production cycle a reality

Televisione e Infanzia. Rapporto sull’offerta televisiva per bambini in Italia

Il presente report restituisce i risultati della prima annualità (2011-2012) della ricerca denominata “Televisione e Infanzia”, realizzata da OssCom – Centro di ricerca sui media e la comunicazione dell’Università Cattolica del Sacro Cuore per conto della Fondazione per la Sussidiarietà, nel quadro delle attività di Focus in Media, al fine di ricostruire e descrivere l’offerta televisiva specificamente rivolta al pubblico dei bambini e dei preadolescenti

In Silico Analysis of the Multi-Targeted Mode of Action of Ivermectin and Related Compounds

Some clinical studies have indicated activity of ivermectin, a macrocyclic lactone, against COVID-19, but a biological mechanism initially proposed for this anti-viral effect is not applicable at physiological concentrations. This in silico investigation explores potential modes of action of ivermectin and 14 related compounds, by which the infectivity and morbidity of the SARS-CoV-2 virus may be limited. Binding affinity computations were performed for these agents on several docking sites each for models of (1) the spike glycoprotein of the virus, (2) the CD147 receptor, which has been identified as a secondary attachment point for the virus, and (3) the alpha-7 nicotinic acetylcholine receptor (α7nAChr), an indicated point of viral penetration of neuronal tissue as well as an activation site for the cholinergic anti-inflammatory pathway controlled by the vagus nerve. Binding affinities were calculated for these multiple docking sites and binding modes of each compound. Our results indicate the high affinity of ivermectin, and even higher affinities for some of the other compounds evaluated, for all three of these molecular targets. These results suggest biological mechanisms by which ivermectin may limit the infectivity and morbidity of the SARS-CoV-2 virus and stimulate an α7nAChr-mediated anti-inflammatory pathway that could limit cytokine production by immune cells

Exploration of Spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniques

This article belongs to the Special Issue Advanced Analysis Methods for Food Safety, Authenticity and Traceability AssessmentExtra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires advanced equipment and chemical knowledge of certified laboratories, and has therefore limited accessibility. In this work a minimalist, portable, and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing the classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential to substitute expensive and complex chemical analysis

Cooperation beyond development. Rethinking international aid for the self- determination of recipient communities.

Questo articolo propone un dibattito critico e costruttivo sui temi della cooperazione negli ambienti stessi della cooperazione, soprattutto sugli obiettivi reali e apparenti, sugli effetti sortiti inconsapevolmente, e sui vincitori e vinti dell’aiuto internazionale, il tutto visto da una prospettiva socio-economica mondiale. Sono qui presentati i primi esiti di un Seminario Partecipativo tenuto proprio su tali tematiche, articolato in quattro tavoli di lavoro: autodeterminazione e reciprocità, emergenza e sviluppo, formazione, co-progettazione / progettazione partecipata.A critical and constructive debate is proposed on and inside cooperation, specifically on the real and the apparent goals, on the unaware effects, and on the winners and losers of international aid, framed in a global socio-economic perspective. The first outcomes of a recent participatory workshop on such themes are hereby illustrated, divided in four working tables: self-determination and reciprocity, emergency and development, training, and co-design / participatory design