URBAN CENTER. Una casa di vetro per le politiche urbane.

Nella cultura di governo della città, il termine "Urban Center" (o "Casa della città") designa una serie di strutture il cui denominatore comune risiede nello svolgimento di attività di servizio per le comunità urbane ai fini di soddisfare la crescente domenda di democrazia partecipativa e deliberativa nei processi di trasformazione degli insediamenti. Traendo spunto dalla storicizzazione del fenomeno e dal confronto tra i consolidati modelli statunitensi e le recenti esperienze in Italia, il volume si interroga sulla maturazione delle missioni dell' "Urban Center" nel passaggio da asettico spazio di informazione a luogo provilegiato per la costruzione trasparente di politiche urbane condivise. Il percorso logico del volume si sviluppa seguendo un fil rouge articolato in quattro parti. Il primo blocco si apre con due tematiche che costituiscono dialetticamente la cornice di riferimento entro cui può essere correttamente collocata la questione degli UC: l’urbanistica partecipata e il marketing urbano. Nella seconda parte attraverso lo studio di casi si ricostruisce il quadro delle articolate declinazioni statunitensi di Urban Center, consolidatesi in diversi decenni di storia. Sono strutture fortemente caratterizzate e autonome per stile, missioni, obiettivi, priorità, modalità operative, ma allo stesso tempo accomunate da un equilibrato mix di passione civile e pragmatismo professionale. Il terzo gruppo di saggi è dedicato alla condizione attuale e di prospettiva degli UC in Italia, delineando criticamente una sorta di “mappa dinamica” delle diverse strutture attivate e in divenire, caratterizzate per soggetti ispiratori, missioni “stili” e protagonismo degli attori coinvolti. Il cerchio delle riflessioni si chiude nella quarta parte discutendo la questione dell’innovazione di metodo per la costruzione di un UC sia attraverso la dimensione teoretica che le potenzialità operative. Testi in italiano e inglese di B. Monardo (curatore), M.C. Bizzarri, E. Carmagnani, M. Carta, F. Ceci, P. Colarossi, L. De Bonis, A. Dina, A. De Rossi, D. Filippi, A. Giorgi, P. Laconte, F. Lovato, L. J. Osmond, R. Shiffman, O Tommasi, A. Uttaro; postfazione di M. Ricci

Optimizing real time fMRI neurofeedback for therapeutic discovery and development

While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health, the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain–behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders

Facile Fabrication of Ultrafine Hollow Silica and Magnetic Hollow Silica Nanoparticles by a Dual-Templating Approach

The development of synthetic process for hollow silica materials is an issue of considerable topical interest. While a number of chemical routes are available and are extensively used, the diameter of hollow silica often large than 50 nm. Here, we report on a facial route to synthesis ultrafine hollow silica nanoparticles (the diameter of ca. 24 nm) with high surface area by using cetyltrimethylammmonium bromide (CTAB) and sodium bis(2-ethylhexyl) sulfosuccinate (AOT) as co-templates and subsequent annealing treatment. When the hollow magnetite nanoparticles were introduced into the reaction, the ultrafine magnetic hollow silica nanoparticles with the diameter of ca. 32 nm were obtained correspondingly. Transmission electron microscopy studies confirm that the nanoparticles are composed of amorphous silica and that the majority of them are hollow

Unravelling the mechanisms that determine the uptake and metabolism of magnetic single and multicore nanoparticles in a Xenopus laevis model.

Multicore superparamagnetic nanoparticles have been proposed as ideal tools for some biomedical applications because of their high magnetic moment per particle, high specific surface area and long term colloidal stability. Through controlled aggregation and packing of magnetic cores it is possible to obtain not only single-core but also multicore and hollow spheres with internal voids. In this work, we compare toxicological properties of single and multicore nanoparticles. Both types of particles showed moderate in vitro toxicity (MTT assay) tested in Hep G2 (human hepatocellular carcinoma) and Caco-2 (human colorectal adenocarcinoma) cells. The influence of surface chemistry in their biological behavior was also studied after functionalization with O,O′-bis(2-aminoethyl) PEG (2000 Da). For the first time, these nanoparticles were evaluated in a Xenopus laevis model studying their whole organism toxicity and their impact upon iron metabolism. The degree of activation of the metabolic pathway depends on the size and surface charge of the nanoparticles which determine their uptake. The results also highlight the potential of Xenopus laevis model bridging the gap between in vitro cell-based assays and rodent models for toxicity assessment to develop effective nanoparticles for biomedical applications

Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity

Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification.In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects.The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty

Using fMRI Brain Activation to Identify Cognitive States Associated with Perception of Tools and Dwellings

Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools, by analyzing the accompanying pattern of brain activity, measured with fMRI. The current research focused on identifying the cognitive state associated with a 4s viewing of an individual line drawing (1 of 10 familiar objects, 5 tools and 5 dwellings, such as a hammer or a castle). Here we demonstrate the ability to reliably (1) identify which of the 10 drawings a participant was viewing, based on that participant's characteristic whole-brain neural activation patterns, excluding visual areas; (2) identify the category of the object with even higher accuracy, based on that participant's activation; and (3) identify, for the first time, both individual objects and the category of the object the participant was viewing, based only on other participants' activation patterns. The voxels important for category identification were located similarly across participants, and distributed throughout the cortex, focused in ventral temporal perceptual areas but also including more frontal association areas (and somewhat left-lateralized). These findings indicate the presence of stable, distributed, communal, and identifiable neural states corresponding to object concepts

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies

Testing Simulation Theory with Cross-Modal Multivariate Classification of fMRI Data

The discovery of mirror neurons has suggested a potential neural basis for simulation and common coding theories of action perception, theories which propose that we understand other people's actions because perceiving their actions activates some of our neurons in much the same way as when we perform the actions. We propose testing this model directly in humans with functional magnetic resonance imaging (fMRI) by means of cross-modal classification. Cross-modal classification evaluates whether a classifier that has learned to separate stimuli in the sensory domain can also separate the stimuli in the motor domain. Successful classification provides support for simulation theories because it means that the fMRI signal, and presumably brain activity, is similar when perceiving and performing actions. In this paper we demonstrate the feasibility of the technique by showing that classifiers which have learned to discriminate whether a participant heard a hand or a mouth action, based on the activity patterns in the premotor cortex, can also determine, without additional training, whether the participant executed a hand or mouth action. This provides direct evidence that, while perceiving others' actions, (1) the pattern of activity in premotor voxels with sensory properties is a significant source of information regarding the nature of these actions, and (2) that this information shares a common code with motor execution

Molecular dynamics simulations and drug discovery

This review discusses the many roles atomistic computer simulations of macromolecular (for example, protein) receptors and their associated small-molecule ligands can play in drug discovery, including the identification of cryptic or allosteric binding sites, the enhancement of traditional virtual-screening methodologies, and the direct prediction of small-molecule binding energies. The limitations of current simulation methodologies, including the high computational costs and approximations of molecular forces required, are also discussed. With constant improvements in both computer power and algorithm design, the future of computer-aided drug design is promising; molecular dynamics simulations are likely to play an increasingly important role

Predicting Decisions in Human Social Interactions Using Real-Time fMRI and Pattern Classification

Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI) and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives