Interfacing cultured neurons to microtransducers arrays: A review of the neuro-electronic junction models

Microtransducer arrays, both metal microelectrodes and silicon-based devices, are widely used as neural interfaces to measure, extracellularly, the electrophysiological activity of excitable cells. Starting from the pioneering works at the beginning of the 70's, improvements in manufacture methods, materials, and geometrical shape have been made. Nowadays, these devices are routinely used in different experimental conditions (both in vivo and in vitro), and for several applications ranging from basic research in neuroscience to more biomedical oriented applications. However, the use of these micro-devices deeply depends on the nature of the interface (coupling) between the cell membrane and the sensitive active surface of the microtransducer. Thus, many efforts have been oriented to improve coupling conditions. Particularly, in the latest years, two innovations related to the use of carbon nanotubes as interface material and to the development of micro-structures which can be engulfed by the cell membrane have been proposed. In this work, we review what can be simulated by using simple circuital models and what happens at the interface between the sensitive active surface of the microtransducer and the neuronal membrane of in vitro neurons. We finally focus our attention on these two novel technological solutions capable to improve the coupling between neuron and micro-nano transducer

Angular rigidity in tetrahedral network glasses

A set of oxide and chalcogenide tetrahedral glasses are investigated using molecular dynamics simulations. It is shown that unlike stoichiometric selenides such as GeSe$_2$ and SiSe$_2$, germania and silica display large standard deviations in the associated bond angle distributions. Within bond-bending constraints theory, this pattern can be interpreted as a manifestation of {\it {broken}} (i.e. ineffective) oxygen bond-bending constraints. The same analysis reveals that the changes in the Ge composition affects mostly bending around germanium in binary Ge-Se systems, leaving Se-centred bending almost unchanged. In contrast, the corresponding Se twisting (quantified by the dihedral angle) depends on the Ge composition and is reduced when the system becomes rigid. Our results establishes the atomic-scale foundations of the phenomelogical rigidity theory, thereby profoundly extending its significance and impact on the structural description of network glasses.Comment: 5 pages, 4 figure

Learning for Optimization with Virtual Savant

Optimization problems arising in multiple fields of study demand efficient algorithms that can exploit modern parallel computing platforms. The remarkable development of machine learning offers an opportunity to incorporate learning into optimization algorithms to efficiently solve large and complex problems. This thesis explores Virtual Savant, a paradigm that combines machine learning and parallel computing to solve optimization problems. Virtual Savant is inspired in the Savant Syndrome, a mental condition where patients excel at a specific ability far above the average. In analogy to the Savant Syndrome, Virtual Savant extracts patterns from previously-solved instances to learn how to solve a given optimization problem in a massively-parallel fashion. In this thesis, Virtual Savant is applied to three optimization problems related to software engineering, task scheduling, and public transportation. The efficacy of Virtual Savant is evaluated in different computing platforms and the experimental results are compared against exact and approximate solutions for both synthetic and realistic instances of the studied problems. Results show that Virtual Savant can find accurate solutions, effectively scale in the problem dimension, and take advantage of the availability of multiple computing resources.Los problemas de optimización que surgen en múltiples campos de estudio demandan algoritmos eficientes que puedan explotar las plataformas modernas de computación paralela. El notable desarrollo del aprendizaje automático ofrece la oportunidad de incorporar el aprendizaje en algoritmos de optimización para resolver problemas complejos y de grandes dimensiones de manera eficiente. Esta tesis explora Savant Virtual, un paradigma que combina aprendizaje automático y computación paralela para resolver problemas de optimización. Savant Virtual está inspirado en el Sı́ndrome de Savant, una condición mental en la que los pacientes se destacan en una habilidad especı́fica muy por encima del promedio. En analogı́a con el sı́ndrome de Savant, Savant Virtual extrae patrones de instancias previamente resueltas para aprender a resolver un determinado problema de optimización de forma masivamente paralela. En esta tesis, Savant Virtual se aplica a tres problemas de optimización relacionados con la ingenierı́a de software, la planificación de tareas y el transporte público. La eficacia de Savant Virtual se evalúa en diferentes plataformas informáticas y los resultados se comparan con soluciones exactas y aproximadas para instancias tanto sintéticas como realistas de los problemas estudiados. Los resultados muestran que Savant Virtual puede encontrar soluciones precisas, escalar eficazmente en la dimensión del problema y aprovechar la disponibilidad de múltiples recursos de cómputo.Fundación Carolina Agencia Nacional de Investigación e Innovación (ANII, Uruguay) Universidad de Cádiz Universidad de la Repúblic

EARLY HEPATIC RECURRENCE AFTER COLORECTAL CANCER LIVER METASTASES SURGERY: A SINGLE PROSPECTIVE CENTRE STUDY

Liver resection, combined with modern chemotherapy, is considered the standard treatment for patients with resectable CRLM. However, the recurrence of hepatic metastasis after liver resection remains a concern worldwide. About twenty years ago, important studies overwhelmed the historical concept that 1.0-cm margin was not an absolute requirement for a curative approach in the treatment of patients with colorectal cancer liver metastases. This is a prospective observational study, performed at the Oncological Surgery, Hospital Policlinic San Martino, Genoa, Italy from 1st April 2014 to the 1st June 2019. Patients undergoing primary hepatic resection for colorectal liver metastasis with curative intent and having a minimum follow-up period of 6 months were included. Several clinical, pathological, and surgical factors have been tested for correlation with early recurrence and disease-free survival (DFS) in univariate analyses with a specific focus on the impact of resection margin depth. Microscopically and in line with the histological reports, the widths were stratified as coincidental margins if the tumor was in contact with the surgical margin (0 mm); widths of less than, or equal to, 1 mm or greater than 1 mm. During the follow up period, recurrence after liver resection was documented in 24 patients (48%). Early recurrence (within 6 months after liver resection) occurred in 11 patients (22% of the sample and 46% of the total recurrences), including 4 patients (36%) with liver-only recurrence and 7 patients (63%) with systemic recurrence (with or without liver recurrence). One-year and two-year mortality were 12% and 22%, respectively. According to univariate analysis, no significant differences were found in early recurrence and DFS between gender, location of the primary tumor, number and size of resected liver metastases, growth pattern and KRAS wild type. Time of diagnosis of liver metastases was the only significant prognostic factor for both DFS and for early recurrence. Moreover, histological grade of primary tumor (G2:33% vs. G3:86% vs. G4:100%; p<0.040) and synchronous presentation of liver metastases (80% vs. 20%; p<0.037) were associated with shorter DFS. No significant differences were found in the early recurrence rates and DFS in R1 versus R0 patients and even between the stratification of surgical margin size. Indeed, patients with wider-margin groups showed similar trend of recurrence in comparison with the narrow-margin group. Additionally, there was a slightly significant association between the severity of postoperative complication and the occurrence of a recurrence disease (p<0.08). In conclusion, in the present study, the lack of association between R1 status and DFS or early recurrence disease suggested that R1 margin status may be a surrogate indicator of advanced and/or more extensive disease. Even exploratory in nature, the present study suggests that the tumor biology (in term of grading and synchronous metastasis) rather than R1 resection was associated recurrence disease. So, up to date, the preferred surgical technique should be a parenchymal-sparing non-anatomic resection using modern surgical devices to keep as much liver parenchyma as possible. Furthermore, the risk of an R1 resection should not be considered a contraindication to surgery with curative intent, as neoadjuvant chemotherapy may destroy peripheral micrometastases before liver resection, minimizing consequently the residual micro-metastatic disease

Urban mobility data analysis in Montevideo, Uruguay

Transportation systems play a major role in modern urban contexts, where citizens are expected to travel in order to engage in social and economic activities. Understanding the interaction between citizens and transportation systems is crucial for policy-makers that aim to improve mobility in a city. Within the novel paradigm of smart cities, modern urban transportation systems incorporate technologies that generate huge volumes of data in real time, which can be processed to extract valuable information about the mobility of citizens. This thesis studies the public transportation system of Montevideo, Uruguay, following an urban data analysis approach. A thorough analysis of the transportation system and its usage is outlined, which combines several sources of urban data. The analyzed data includes the location of each bus of the transportation system as well as every ticket sold using smart cards during 2015, accounting for over 150 GB of raw data. Furthermore, origin-destination matrices, which describe mobility patterns in the city, are generated by processing geolocalized bus ticket sales data. For this purpose, a destination estimation algorithm is implemented following methodologies from the related literature. The computed results are compared to the ndings of a recent mobility survey, where the proposed approach arises as a viable alternative to obtain up-to-date mobility information. Finally, a visualization web application is presented, which allows conveying the aggregated information in an intuitive way to stakeholders

Improved modelling of liquid GeSe2_2: the impact of the exchange-correlation functional

The structural properties of liquid GeSe$_2$ are studied by using first-principles molecular dynamics in conjuncton with the Becke, Lee, Yang and Parr (BLYP) generalized gradient approximation for the exchange and correlation energy. The results on partial pair correlation functions, coordination numbers, bond angle distributions and partial structure factors are compared with available experimental data and with previous first-principle molecular dynamics results obtained within the Perdew and Wang (PW) generalized gradient approximation for the exchange and correlation energy. We found that the BLYP approach substantially improves upon the PW one in the case of the short-range properties. In particular, the Ge$-$Ge pair correlation function takes a more structured profile that includes a marked first peak due to homopolar bonds, a first maximum exhibiting a clear shoulder and a deep minimum, all these features being absent in the previous PW results. Overall, the amount of tetrahedral order is significantly increased, in spite of a larger number of Ge$-$Ge homopolar connections. Due to the smaller number of miscoordinations, diffusion coefficients obtained by the present BLYP calculation are smaller by at least one order of magnitude than in the PW case.Comment: 6 figure

Modeling the three-dimensional connectivity of in vitro cortical ensembles coupled to Micro-Electrode Arrays

: Nowadays, in vitro three-dimensional (3D) neuronal networks are becoming a consolidated experimental model to overcome most of the intrinsic limitations of bi-dimensional (2D) assemblies. In the 3D environment, experimental evidence revealed a wider repertoire of activity patterns, characterized by a modulation of the bursting features, than the one observed in 2D cultures. However, it is not totally clear and understood what pushes the neuronal networks towards different dynamical regimes. One possible explanation could be the underlying connectivity, which could involve a larger number of neurons in a 3D rather than a 2D space and could organize following well-defined topological schemes. Driven by experimental findings, achieved by recording 3D cortical networks organized in multi-layered structures coupled to Micro-Electrode Arrays (MEAs), in the present work we developed a large-scale computational network model made up of leaky integrate-and-fire (LIF) neurons to investigate possible structural configurations able to sustain the emerging patterns of electrophysiological activity. In particular, we investigated the role of the number of layers defining a 3D assembly and the spatial distribution of the connections within and among the layers. These configurations give rise to different patterns of activity that could be compared to the ones emerging from real in vitro 3D neuronal populations. Our results suggest that the introduction of three-dimensionality induced a global reduction in both firing and bursting rates with respect to 2D models. In addition, we found that there is a minimum number of layers necessary to obtain a change in the dynamics of the network. However, the effects produced by a 3D organization of the cells is somewhat mitigated if a scale-free connectivity is implemented in either one or all the layers of the network. Finally, the best matching of the experimental data is achieved supposing a 3D connectivity organized in structured bundles of links located in different areas of the 2D network

Approximate Entropy of Spiking Series Reveals Different Dynamical States in Cortical Assemblies

Self-organized criticality theory proved that information transmission and computational performances of neural networks are optimal in critical state. By using recordings of the spontaneous activity originated by dissociated neuronal assemblies coupled to Micro-Electrode Arrays (MEAs), we tested this hypothesis using Approximate Entropy (ApEn) as a measure of complexity and information transfer. We analysed 60 min of electrophysiological activity of three neuronal cultures exhibiting either sub-critical, critical or super-critical behaviour. The firing patterns on each electrode was studied in terms of the inter-spike interval (ISI), whose complexity was quantified using ApEn. We assessed that in critical state the local complexity (measured in terms of ApEn) is larger than in sub- and super-critical conditions (mean ± std, ApEn about 0.93 ± 0.09, 0.66 ± 0.18, 0.49 ± 0.27, for the cultures in critical, sub-critical and super-critical state, respectively—differences statistically significant). Our estimations were stable when considering epochs as short as 5 min (pairwise cross- correlation of spatial distribution of mean ApEn of 94 ± 5%). These preliminary results indicate that ApEn has the potential of being a reliable and stable index to monitor local information transmission in a neuronal network during maturation. Thus, ApEn applied on ISI time series appears to be potentially useful to reflect the overall complex behaviour of the neural network, even monitoring a single specific location

A new simulation environment to model spontaneous and evoked activity of large-scale neuronal networks coupled to micro-electrode arrays

Introduction The use of neuronal cultures coupled to Micro-Electrode Array (MEA) is becoming a widely used and recognized experimental model for studying basic properties of information processing in neuronal systems [1]. However, the electrophysiological activity of such large-scale neuronal networks is recorded only by tens/hundreds microelectrodes. This undersampling results in a lack of information. Thus the development of a new simulation environment able to reproduce the electrophysiological behavior typically found in these preparations offers a valid help to better understand the actual dynamics. In this work, we present the main features of our software showing the simulation results of the spontaneous and evoked activity of a high-connected network