Investigation of the SpinChem® rotating bed reactor: internally and externally mass transfer limited reactions

In questo lavoro lo SpinChem® rotating bed reactor è stato confrontato con un comune reattore slurry (stirred tank reactor), per diverse velocità di rotazione, usando due reazioni solido-liquido non-catalitiche: la rimozione di unaimpurità genotossica da una soluzione e una reazione di scambio ionicoope

Investigation of the potential of gelucire 44/14 for enhancing oral bioavailability using two model drugs [RS199.5. M211 2007 f rb].

Dua drug model yang termasuk dalam dua kumpulan Biopharmaceutics Classification System (BCS) yang berbeza, iaitu vancomycin (VCM), satu sebatian kelas III (keterlarutan tinggi dan ketelapan rendah), dan griseofulvin, satu drug kelas II (keterlarutan rendah and ketelapan tinggi) telah digunakan untuk menilai potensi satu pembawa gliserida berpoliglikol, iaitu Gelucire 44/14, untuk meningkatkan biokeperolehan oral. Two model drugs belonging to different groups of the Biopharmaceutics Classification System (BCS), which are vancomycin (VCM) as a class III drug (high solubility and low permeability) and griseofulvin (GF) as a class II drug (low solubility and high permeability), were employed to evaluate the potential of a polyglycolised glyceride carrier, namely Gelucire 44/14, to enhance their oral bioavailability

Deep learning-based EEG analysis: investigating P3 ERP components

The neural processing of incoming stimuli can be analysed from the electroencephalogram (EEG) through event-related potentials (ERPs). The P3 component is largely investigated as it represents an important psychophysiological marker of psychiatric disorders. This is composed by several subcomponents, such as P3a and P3b, reflecting distinct but interrelated sensory and cognitive processes of incoming stimuli. Due to the low EEG signal-to-noise-ratio, ERPs emerge only after an averaging procedure across trials and subjects. Thus, this canonical ERP analysis lacks in the ability to highlight EEG neural signatures at the level of single-subject and single-trial. In this study, a deep learning-based workflow is investigated to enhance EEG neural signatures related to P3 subcomponents already at single-subject and at single-trial level. This was based on the combination of a convolutional neural network (CNN) with an explanation technique (ET). The CNN was trained using two different strategies to produce saliency representations enhancing signatures shared across subjects or more specific for each subject and trial. Cross-subject saliency representations matched the signatures already emerging from ERPs, i.e., P3a and P3b-related activity within 350–400 ms (frontal sites) and 400–650 ms (parietal sites) post-stimulus, validating the CNN+ET respect to canonical ERP analysis. Single-subject and single-trial saliency representations enhanced P3 signatures already at the single-trial scale, while EEG-derived representations at single-subject and single-trial level provided no or only mildly evident signatures. Empowering the analysis of P3 modulations at single-subject and at single-trial level, CNN+ET could be useful to provide insights about neural processes linking sensory stimulation, cognition and behaviour

The Sensory-Cognitive Interplay: Insights into Neural Mechanisms and Circuits

Senses are our interface for acting in the external world. Consequently, sensory-motor information grounds and drives our higher cognitive processes. At the same time, we are impinged by a multitude of sensory inputs with variable saliency. It is therefore crucial that the process- ing of sensory inputs and motor signals is modulated by cognitive and executive mechanisms such as expectation, memory, attention, emotion, planning, monitoring. This is needed to highlight sensory information that is currently rel- evant for task goals, and to adapt motor control and behav- ior accordingly. The strict intertwining of sensory, motor, and cognitive functions is evidenced in aging and in neuro- logical disorders. Indeed, sensory-motor dysfunctions of- ten accompany higher-level dysfunctions in older popula- tions [1] and in neurological subjects (e.g., in dyslexia, at- tention deficit hyperactivity disorders, or autism spectrum disorders) [2,3] [...

A Computational Model of the Lexical-Semantic System Based on a Grounded Cognition Approach

This work presents a connectionist model of the semantic-lexical system based on grounded cognition. The model assumes that the lexical and semantic aspects of language are memorized in two distinct stores. The semantic properties of objects are represented as a collection of features, whose number may vary among objects. Features are described as activation of neural oscillators in different sensory-motor areas (one area for each feature) topographically organized to implement a similarity principle. Lexical items are represented as activation of neural groups in a different layer. Lexical and semantic aspects are then linked together on the basis of previous experience, using physiological learning mechanisms. After training, features which frequently occurred together, and the corresponding word-forms, become linked via reciprocal excitatory synapses. The model also includes some inhibitory synapses: features in the semantic network tend to inhibit words not associated with them during the previous learning phase. Simulations show that after learning, presentation of a cue can evoke the overall object and the corresponding word in the lexical area. Moreover, different objects and the corresponding words can be simultaneously retrieved and segmented via a time division in the gamma-band. Word presentation, in turn, activates the corresponding features in the sensory-motor areas, recreating the same conditions occurring during learning. The model simulates the formation of categories, assuming that objects belong to the same category if they share some features. Simple exempla are shown to illustrate how words representing a category can be distinguished from words representing individual members. Finally, the model can be used to simulate patients with focalized lesions, assuming an impairment of synaptic strength in specific feature areas

Neural Networks and Connectivity among Brain Regions

As is widely understood, brain functioning depends on the interaction among several neural populations, which are linked via complex connectivity circuits and work together (in antagonistic or synergistic ways) to exchange information, synchronize their activity, adapt plastically to external stimuli or internal requirements, and more generally to participate in solving multifaceted cognitive tasks [...]

Possible mechanisms underlying tilt aftereffect in the primary visual cortex: A critical analysis with the aid of simple computational models

AbstractA mathematical model of orientation selectivity in a single hypercolumn of the primary visual cortex developed in a previous work [Ursino, M., & La Cara, G.-E. (2004). Comparison of different models of orientation selectivity based on distinct intracortical inhibition rules. Vision Research, 44, 1641–1658] was used to analyze the possible mechanisms underlying tilt aftereffect (TAE). Two alternative models are considered, based on a different arrangement of intracortical inhibition (an anti-phase model in which inhibition is in phase opposition with excitation, and an in-phase model in which inhibition has the same phase arrangement as excitation but wider orientation selectivity). Different combinations of parameter changes were tested to explain TAE: a threshold increase in excitatory and inhibitory cortical neurons (fatigue), a decrease in intracortical excitation, an increase or a decrease in intracortical inhibition, a decrease in thalamo-cortical synapses. All synaptic changes were calculated on the basis of Hebbian (or anti-Hebbian) rules. Results demonstrated that the in-phase model accounts for several literature results with different combinations of parameter changes requiring: (i) a depressive mechanism to neurons with preferred orientation close to the adaptation orientation (fatigue of excitatory cortical neurons, and/or depression of thalamo-cortical synapses directed to excitatory neurons, and/or depression of intracortical excitatory synapses); (ii) a facilitatory mechanism to neurons with preferred orientation far from the adaptation orientation (fatigue of inhibitory cortical neurons, and/or depression of thalamo-cortical synapses directed to inhibitory neurons, and/or depression of intracortical inhibitory synapses). By contrast, the anti-phase model appeared less suitable to explain experimental data

A Semantic Model to Study Neural Organization of Language in Bilingualism

A neural network model of object semantic representation is used to simulate learning of new words from a foreign language. The network consists of feature areas, devoted to description of object properties, and a lexical area, devoted to words representation. Neurons in the feature areas are implemented as Wilson-Cowan oscillators, to allow segmentation of different simultaneous objects via gamma-band synchronization. Excitatory synapses among neurons in the feature and lexical areas are learned, during a training phase, via a Hebbian rule. In this work, we first assume that some words in the first language (L1) and the corresponding object representations are initially learned during a preliminary training phase. Subsequently, second-language (L2) words are learned by simultaneously presenting the new word together with the L1 one. A competitive mechanism between the two words is also implemented by the use of inhibitory interneurons. Simulations show that, after a weak training, the L2 word allows retrieval of the object properties but requires engagement of the first language. Conversely, after a prolonged training, the L2 word becomes able to retrieve object per se. In this case, a conflict between words can occur, requiring a higher-level decision mechanism

Organization, Maturation, and Plasticity of Multisensory Integration: Insights from Computational Modeling Studies

In this paper, we present two neural network models – devoted to two specific and widely investigated aspects of multisensory integration – in order to evidence the potentialities of computational models to gain insight into the neural mechanisms underlying organization, development, and plasticity of multisensory integration in the brain. The first model considers visual–auditory interaction in a midbrain structure named superior colliculus (SC). The model is able to reproduce and explain the main physiological features of multisensory integration in SC neurons and to describe how SC integrative capability – not present at birth – develops gradually during postnatal life depending on sensory experience with cross-modal stimuli. The second model tackles the problem of how tactile stimuli on a body part and visual (or auditory) stimuli close to the same body part are integrated in multimodal parietal neurons to form the perception of peripersonal (i.e., near) space. The model investigates how the extension of peripersonal space – where multimodal integration occurs – may be modified by experience such as use of a tool to interact with the far space. The utility of the modeling approach relies on several aspects: (i) The two models, although devoted to different problems and simulating different brain regions, share some common mechanisms (lateral inhibition and excitation, non-linear neuron characteristics, recurrent connections, competition, Hebbian rules of potentiation and depression) that may govern more generally the fusion of senses in the brain, and the learning and plasticity of multisensory integration. (ii) The models may help interpretation of behavioral and psychophysical responses in terms of neural activity and synaptic connections. (iii) The models can make testable predictions that can help guiding future experiments in order to validate, reject, or modify the main assumptions

The Representation of Objects in the Brain, and Its Link with Semantic Memory and Language: a Conceptual Theory with the Support of a Neurocomputational Model

Recognition of objects, their representation and retrieval in memory and the link of this representation with words is a hard cognitive problem, which can be summarized with the term “lexico-semantic memory”. Several recent cognitive theories suggest that the semantic representation of objects is a distributed process, which engages different brain areas in the sensory and motor regions. A further common hypothesis is that each region is organized by conceptual features, that are highly correlated, and neurally contiguous. These theories may be useful to explain the results of clinical tests on patients with lesions of the brain, who exhibit deficits in recognizing objects from words or in evoking words from objects, or to explain the use of appropriate words in bilingual subjects. The study of the cognitive aspects of lexico-semantic memory representation may benefit from the use of mathematical models and computer simulations. Aim of this chapter is to describe a theoretical model of the lexico-semantic system, which can be used by cognitive neuroscientists to summarize conceptual theories into a rigorous quantitative framework, to test the ability of these theories to reproduce real pieces of behavior in healthy and pathological subjects, and to suggest new hypotheses for subsequent testing. The chapter is structured as follows: first the basic assumptions on cognitive aspects of the lexico-semantic memory model are clearly presented; the same aspects are subsequently illustrated via the results of computer simulations using abstract object representations as input to the model. Equations are then reported in an Appendix for readers interested to mathematical issues. The model is based on the following main assumptions: i) an object is represented as a collection of features, topologically ordered according to a similarity principle in different brain areas; ii) the features belonging to the same object are linked together via a Hebbian process during a phase in which objects are presented individually; iii) features are described via neural oscillators in the gamma band. As a consequence, different object representations can be maintained simultaneously in memory, via synchronization of the corresponding features (binding and segmentation problem); iv) words are represented in a lexical area devoted to recognition of words from phonemes; v) words in the lexical area and the features representing objects are linked together via a Hebbian mechanism during a learning phase in which a word is presented together with the corresponding object; vi) the same object representation can be associated to two alternative words (for instance to represent bilinguism). In this case, the two words are connected via inhibitory synapses, to implement a competition among them. vii) the choice of words is further selected by an external inhibitory control system, which suppresses words which do not correspond to the present objective (for instance to choose between alternative languages). Several exempla of model possibilities are presented, with the use of abstract words. These exempla comprehend: the possibility to retrieve objects and words even in case of incomplete or corrupted information on object features; the possibility to establish a semantic link between words with superimposed features; the process of learning a second language (L2) with the support of a language previously known (L1) to represent neurocognitive aspects of bilinguism