Implication of GluR2 subunit of AMPA receptor in RGS14(414)-mediated memory enhancement

Ongoing quest for finding treatment against memory loss seen in aging and in many neurological and neurodegenerative diseases, so far has been unsuccessful and memory enhancers are seen as a potential remedy against this brain dysfunction. Recently, we showed that gene corresponding to a protein called regulator of G-protein signaling 14 of 414 amino acids (RGS14414) is a robust memory enhancer (Lopez-Aranda et al. 2009: Science). RGS14414-treatment in area V2 of visual cortex caused memory enhancement to such extent that it converted short-term object recognition memory (ORM) of 45min into long lasting long-term memory that could be traced even after many months. Now, through targeting of multiple receptors and molecules known to be involved in memory processing, we found that GluR2 subunit of AMPA receptor might be key to memory enhancement in RGS-animals. RGS14-animals showed a progressive increase in GluR2 protein expression while processing an object information which reached to highest level after 60min of object exposure, a time period required for conversion of short-term ORM into long-term memory in our laboratory set up. Normal rats could retain an object information in brain for 45min (short-term) and not for 60min. However, RGS-treated rats are able to retain the same information for 24h or longer (long-term). Therefore, highest expression of GluR2 subunit seen at 60min suggests that this protein might be key in memory enhancement and conversion to long-term memory in RGS-animals. In addition, we will also discuss the implication of Hebbian plasticity and interaction of brain circuits in memory enhancement.

Behavioral traits predicting cocaine-conditioned place reference in mice: role of anxiety adn the basolateral amygdala

Aims. The individual susceptibility to cocaine addiction, a factor of interest in the understanding and prevention of this disorder, may be predicted by certain behavioral traits. However, these are not usually taken into account in research, making it difficult to identify whether they are a cause or a consequence of drug use. Methods. Male C57BL/6J mice underwent a battery of behavioral tests (elevated plus maze, hole-board, novelty preference in the Y maze, episodic-like object recognition memory and forced swimming test), followed by a cocaine-conditioned place preference (CPP) training to assess the reinforcing effect of the drug. In a second study, we aimed to determine the existence of neurobiological differences between the mice expressing high or low CPP by studying the number of neurons in certain addiction-related structures: the medial prefrontal cortex, the basolateral amygdala and the ventral tegmental area. Results. Anxiety-like behaviors in the elevated plus maze successfully predicted the cocaine-CPP behavior, so that the most anxious mice were also more likely to search for cocaine in a CPP paradigm. In addition, these mice exhibited an increased number of neurons in the basolateral amygdala, a key structure in emotional response including anxiety expression, without differences in the others regions analyzed. Conclusions. Our results suggest a relevant role of anxiety as a psychological risk factor for cocaine vulnerability, with the basolateral amygdala as potential common neural center for both anxiety and addiction.

Characterization of the behavioural phenotype of calpain-knockout mice

Dissertação de Mestrado, Ciências Biomédicas, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2016

Cultural Heritage and Mobile Robotics Technologies

This paper reviews current research works at the authors’ Institutions to illustrate how mobile robotics and related technologies can be used to enhance economical fruition, control, protection and social impact of the cultural heritage. Robots allow experiencing on-line, from remote locations, tours at museums, archaeological areas and monuments. These solutions avoid travelling costs, increase beyond actual limits the number of simultaneous visitors, and prevent possible damages that can arise by over-exploitation of fragile environments. The same tools can be used for exploration and monitoring of cultural artifacts located in difficult to reach or dangerous areas. Examples are provided by the use of underwater robots in the exploration of deeply submerged archaeological areas. Besides, technologies commonly employed in robotics can be used to help exploring, monitoring and preserving cultural artifacts. Examples are provided by the development of procedures for data acquisition and mapping and by object recognition and monitoring algorithms.

ViDRILO: The Visual and Depth Robot Indoor Localization with Objects information dataset

In this article we describe a semantic localization dataset for indoor environments named ViDRILO. The dataset provides five sequences of frames acquired with a mobile robot in two similar office buildings under different lighting conditions. Each frame consists of a point cloud representation of the scene and a perspective image. The frames in the dataset are annotated with the semantic category of the scene, but also with the presence or absence of a list of predefined objects appearing in the scene. In addition to the frames and annotations, the dataset is distributed with a set of tools for its use in both place classification and object recognition tasks. The large number of labeled frames in conjunction with the annotation scheme make this dataset different from existing ones. The ViDRILO dataset is released for use as a benchmark for different problems such as multimodal place classification and object recognition, 3D reconstruction or point cloud data compression.

Epothilone-d rescues cognition and attenuates alzheimer’s disease-like pathology in APP/PS1 mice

AIMS: Cognitive decline in Alzheimer's disease (AD) patients has been linked to synaptic damage and neuronal loss. Hyperphosphorylation of tau protein destabilizes microtubules leading to the accumulation of autophagy/vesicular material and the generation of dystrophic neurites, thus contributing to axonal/synaptic dysfunction. In this study, we analyzed the effect of a microtubule-stabilizing compound in the progression of the disease in the hippocampus of APP751SL/PS1M146L transgenic model. METHODS: APP/PS1 mice (3 month-old) were treated with a weekly intraperitoneal injection of 2 mg/kg epothilone-D (Epo-D) for 3 months. Vehicle-injected animals were used as controls. Mice were tested on the Morris water maze, Y-maze and object-recognition tasks for memory performance. Abeta, AT8, ubiquitin and synaptic markers levels were analyzed by Western-blots. Hippocampal plaque, synaptic and dystrophic loadings were quantified by image analysis after immunohistochemical stainings. RESULTS: Epo-D treated mice exhibited a significant improvement in the memory tests compared to controls. The rescue of cognitive deficits was associated to a significant reduction in the AD-like hippocampal pathology. Levels of Abeta, APP and ubiquitin were significantly reduced in treated animals. This was paralleled by a decrease in the amyloid burden, and more importantly, in the plaque-associated axonal dystrophy pathology. Finally, synaptic levels were significantly restored in treated animals compared to controls. CONCLUSION: Epo-D treatment promotes synaptic and spatial memory recovery, reduces the accumulation of extracellular Abeta and the associated neuritic pathology in the hippocampus of APP/PS1 model. Therefore, microtubule stabilizing drugs could be considered therapeutical candidates to slow down AD progression. Supported by FIS-PI12/01431 and PI15/00796 (AG),FIS-PI12/01439 and PI15/00957(JV)

Guided Bus Rapid Transit

Electrical Bus Rapid Transit (eBRT) is a charging electrical public transport which brings a clean, high performance, and affordable cost alternative from the conventional traffic vehicles which work with combustion and hybrid technology. These buses charge the battery in every bus stop to arrive at the next station. But, this charging system needs an appropriate infrastructure called pantograph, and it requires a high precision bus location to maintain battery lifetime, energy saving and charging time. To overcome this issue Vicomtech and Datik has planned a project based on computer vision to help to the driver to locate the vehicle in the correct place. In this document, we present a mono camera bus driver guided fast algorithm because these vehicles embedded computers do not support high computation and precision operations. In addition to the frequent lane sign, there are more accurate geometric beacons painted on the road to bring metric information to the vision system. This method uses segmentation to binarize the image discriminating the background space. Besides it detects, tracks and counts different lane mark contours in addition to classify each special painted mark. Besides it does not need any calibration task to calculate longitudinal and cross distances because we know the lane mark sizes.

Altered oscillatory dynamics of CA1 parvalbumin-basket cells during theta-gamma rhythmopathies of temporal lobe epilepsy

Recent reports in human demonstrate a role of theta– gamma coupling in memory for spatial episodes and a lack of coupling in people experiencing temporal lobe epilepsy, but the mechanisms are unknown. Using multisite silicon probe recordings of epileptic rats engaged in episodic-like object recognition tasks, we sought to evaluate the role of theta– gamma coupling in the absence of epileptiform activities. Our data reveal a specific association between theta– gamma (30 – 60 Hz) coupling at the proximal stratum radiatum of CA1 and spatial memory deficits. We targeted the microcircuit mechanisms with a novel approach to identify putative interneuronal types in tetrode recordings (parvalbumin basket cells in particular) and validated classification criteria in the epileptic context with neurochemical identification of intracellularly recorded cells. In epileptic rats, putative parvalbumin basket cells fired poorly modulated at the falling theta phase, consistent with weaker inputs from Schaffer collaterals and attenuated gamma oscillations, as evaluated by theta-phase decomposition of current–source density signals. We propose that theta– gamma interneuronal rhythmopathies of the temporal lobe are intimately related to episodic memory dysfunction in this condition.

Into the looking glass: Literacy acquisition and mirror invariance in preschool and first-grade children

At what point in reading development does literacy impact object recognition and orientation processing? Is it specific to mirror images? To answer these questions, forty-six 5- to 7-year-old preschoolers and first graders performed two same–different tasks differing in the matching criterion-orientation-based versus shape-based (orientation independent)-on geometric shapes and letters. On orientation-based judgments, first graders out- performed preschoolers who had the strongest difficulty with mirrored pairs. On shape-based judgments, first graders were slower for mirrored than identical pairs, and even slower than preschoolers. This mirror cost emerged with letter knowledge. Only first graders presented worse shape-based judgments for mirrored and rotated pairs of reversible (e.g., b-d; b-q) than nonreversible (e.g., e-ә) letters, indicating readers’ difficulty in ignoring orientation contrasts relevant to letters.

Dynamic Match Analysis

Recent technological development has enabled research- ers to gather data from different performance scenarios while considering players positioning and action events within a specific time frame. This technology varies from global positioning systems to radio frequency devices and computer vision tracking, to name the most common, and aims to collect players’ time motion data and enable the dynamical analysis of performance. Team sports—and in particular, invasion games—present a complex dynamic by nature based on the interaction between 2 opposing sides trying to outperform 1 another. During match and training situations, players’ actions are coupled to their performance context at different interaction levels. As expected, ball, teammates’, and opponents’ positioning play an important role in this interaction process. But other factors, such as final score, teams’ development level, and players’ expertise, seem to affect the match dynamics. In this symposium, we will focus on how different constraints affect invasion games dynamics during both match and training situations. This relation will be established while underpinning the importance of these effects to game teaching and performance optimization. Regarding the match, different performance indicators based on spatial-temporal relations between players and teams will be presented to reveal the interaction processes that form the crucial component of game analysis. Considering the training, this symposium will address the relationship of small-sided games with full- sized matches and will present how players’ dynamical interaction affects different performance indicators.

Detecting social patterns within 20th century documentary photos: a deep learning based approach

The job of a historian is to understand what happened in the past, resorting in many cases to written documents as a firsthand source of information. Text, however, does not amount to the only source of knowledge. Pictorial representations, in fact, have also accompanied the main events of the historical timeline. In particular, the opportunity of visually representing circumstances has bloomed since the invention of photography, with the possibility of capturing in real-time the occurrence of a specific events. Thanks to the widespread use of digital technologies (e.g. smartphones and digital cameras), networking capabilities and consequent availability of multimedia content, the academic and industrial research communities have developed artificial intelligence (AI) paradigms with the aim of inferring, transferring and creating new layers of information from images, videos, etc. Now, while AI communities are devoting much of their attention to analyze digital images, from an historical research standpoint more interesting results may be obtained analyzing analog images representing the pre-digital era. Within the aforementioned scenario, the aim of this work is to analyze a collection of analog documentary photographs, building upon state-of-the-art deep learning techniques. In particular, the analysis carried out in this thesis aims at producing two following results: (a) produce the date of an image, and, (b) recognizing its background socio-cultural context,as defined by a group of historical-sociological researchers. Given these premises, the contribution of this work amounts to: (i) the introduction of an historical dataset including images of “Family Album” among all the twentieth century, (ii) the introduction of a new classification task regarding the identification of the socio-cultural context of an image, (iii) the exploitation of different deep learning architectures to perform the image dating and the image socio-cultural context classification.

Sperimentazione di Deep Metric Loss per Self-Supervised Information Retrieval Systems su CORD19.

Dopo lo sviluppo dei primi casi di Covid-19 in Cina nell’autunno del 2019, ad inizio 2020 l’intero pianeta è precipitato in una pandemia globale che ha stravolto le nostre vite con conseguenze che non si vivevano dall’influenza spagnola. La grandissima quantità di paper scientifici in continua pubblicazione sul coronavirus e virus ad esso affini ha portato alla creazione di un unico dataset dinamico chiamato CORD19 e distribuito gratuitamente. Poter reperire informazioni utili in questa mole di dati ha ulteriormente acceso i riflettori sugli information retrieval systems, capaci di recuperare in maniera rapida ed efficace informazioni preziose rispetto a una domanda dell'utente detta query. Di particolare rilievo è stata la TREC-COVID Challenge, competizione per lo sviluppo di un sistema di IR addestrato e testato sul dataset CORD19. Il problema principale è dato dal fatto che la grande mole di documenti è totalmente non etichettata e risulta dunque impossibile addestrare modelli di reti neurali direttamente su di essi. Per aggirare il problema abbiamo messo a punto nuove soluzioni self-supervised, a cui abbiamo applicato lo stato dell'arte del deep metric learning e dell'NLP. Il deep metric learning, che sta avendo un enorme successo soprattuto nella computer vision, addestra il modello ad "avvicinare" tra loro immagini simili e "allontanare" immagini differenti. Dato che sia le immagini che il testo vengono rappresentati attraverso vettori di numeri reali (embeddings) si possano utilizzare le stesse tecniche per "avvicinare" tra loro elementi testuali pertinenti (e.g. una query e un paragrafo) e "allontanare" elementi non pertinenti. Abbiamo dunque addestrato un modello SciBERT con varie loss, che ad oggi rappresentano lo stato dell'arte del deep metric learning, in maniera completamente self-supervised direttamente e unicamente sul dataset CORD19, valutandolo poi sul set formale TREC-COVID attraverso un sistema di IR e ottenendo risultati interessanti.

Concepts and methods for efficient decentralized learning in federated settings

Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.

Computational methods applied to undeciphered scripts from the Aegean and Cyprus: Linear A and Cypro-Minoan

The study of ancient, undeciphered scripts presents unique challenges, that depend both on the nature of the problem and on the peculiarities of each writing system. In this thesis, I present two computational approaches that are tailored to two different tasks and writing systems. The first of these methods is aimed at the decipherment of the Linear A afraction signs, in order to discover their numerical values. This is achieved with a combination of constraint programming, ad-hoc metrics and paleographic considerations. The second main contribution of this thesis regards the creation of an unsupervised deep learning model which uses drawings of signs from ancient writing system to learn to distinguish different graphemes in the vector space. This system, which is based on techniques used in the field of computer vision, is adapted to the study of ancient writing systems by incorporating information about sequences in the model, mirroring what is often done in natural language processing. In order to develop this model, the Cypriot Greek Syllabary is used as a target, since this is a deciphered writing system. Finally, this unsupervised model is adapted to the undeciphered Cypro-Minoan and it is used to answer open questions about this script. In particular, by reconstructing multiple allographs that are not agreed upon by paleographers, it supports the idea that Cypro-Minoan is a single script and not a collection of three script like it was proposed in the literature. These results on two different tasks shows that computational methods can be applied to undeciphered scripts, despite the relatively low amount of available data, paving the way for further advancement in paleography using these methods.

Mind the gaps: cognitive-inspired AI for high-level visual sensemaking. Towards abstract concept image classification

The abundance of visual data and the push for robust AI are driving the need for automated visual sensemaking. Computer Vision (CV) faces growing demand for models that can discern not only what images "represent," but also what they "evoke." This is a demand for tools mimicking human perception at a high semantic level, categorizing images based on concepts like freedom, danger, or safety. However, automating this process is challenging due to entropy, scarcity, subjectivity, and ethical considerations. These challenges not only impact performance but also underscore the critical need for interoperability. This dissertation focuses on abstract concept-based (AC) image classification, guided by three technical principles: situated grounding, performance enhancement, and interpretability. We introduce ART-stract, a novel dataset of cultural images annotated with ACs, serving as the foundation for a series of experiments across four key domains: assessing the effectiveness of the end-to-end DL paradigm, exploring cognitive-inspired semantic intermediaries, incorporating cultural and commonsense aspects, and neuro-symbolic integration of sensory-perceptual data with cognitive-based knowledge. Our results demonstrate that integrating CV approaches with semantic technologies yields methods that surpass the current state of the art in AC image classification, outperforming the end-to-end deep vision paradigm. The results emphasize the role semantic technologies can play in developing both effective and interpretable systems, through the capturing, situating, and reasoning over knowledge related to visual data. Furthermore, this dissertation explores the complex interplay between technical and socio-technical factors. By merging technical expertise with an understanding of human and societal aspects, we advocate for responsible labeling and training practices in visual media. These insights and techniques not only advance efforts in CV and explainable artificial intelligence but also propel us toward an era of AI development that harmonizes technical prowess with deep awareness of its human and societal implications.