Towards a Functional Explanation of the Connectivity LGN - V1

The principles behind the connectivity between LGN and V1 are not well understood. Models have to explain two basic experimental trends: (i) the combination of thalamic responses is local and it gives rise to a variety of oriented Gabor-like receptive felds in V1 [1], and (ii) these filters are spatially organized in orientation maps [2]. Competing explanations of orientation maps use purely geometrical arguments such as optimal wiring or packing from LGN [3-5], but they make no explicit reference to visual function. On the other hand, explanations based on func- tional arguments such as maximum information transference (infomax) [6,7] usually neglect a potential contribution from LGN local circuitry. In this work we explore the abil- ity of the conventional functional arguments (infomax and variants), to derive both trends simultaneously assuming a plausible sampling model linking the retina to the LGN [8], as opposed to previous attempts operating from the retina. Consistently with other aspects of human vi- sion [14-16], additional constraints should be added to plain infomax to understand the second trend of the LGN-V1 con- nectivity. Possibilities include energy budget [11], wiring constraints [8], or error minimization in noisy systems, ei- ther linear [16] or nonlinear [14, 15]. In particular, consideration of high noise (neglected here) would favor the redundancy in the prediction (which would be required to match the relations between spatially neighbor neurons in the same orientation domain)

The match between molecular subtypes, histology and microenvironment of pancreatic cancer and its relevance for chemoresistance

In the last decade, several studies based on whole transcriptomic and genomic analyses of pancreatic tumors and their stroma have come to light to supplement histopathological stratification of pancreatic cancers with a molecular point-of-view. Three main molecular studies: Collisson et al. 2011, Moffitt et al. 2015 and Bailey et al. 2016 have found specific gene signatures, which identify different molecular subtypes of pancreatic cancer and provide a comprehensive stratification for both a personalized treatment or to identify potential druggable targets. However, the routine clinical management of pancreatic cancer does not consider a broad molecular analysis of each patient, due probably to the lack of target therapies for this tumor. Therefore, the current treatment decision is taken based on patients’ clinicopathological features and performance status. Histopathological evaluation of tumor samples could reveal many other attributes not only from tumor cells but also from their microenvironment specially about the presence of pancreatic stellate cells, regulatory T cells, tumor-associated macrophages, myeloid derived suppressor cells and extracellular matrix structure. In the present article, we revise the four molecular subtypes proposed by Bailey et al. and associate each subtype with other reported molecular subtypes. Moreover, we provide for each subtype a potential description of the tumor microenvironment that may influence treatment response according to the gene expression profile, the mutational landscape and their associated histolog

Derivatives and Inverse of a Linear-Nonlinear Multi-Layer Spatial Vision Model

Analyzing the mathematical properties of perceptually meaningful linear-nonlinear transforms is interesting because this computation is at the core of many vision models. Here we make such analysis in detail using a specific model [Malo & Simoncelli, SPIE Human Vision Electr. Imag. 2015] which is illustrative because it consists of a cascade of standard linear-nonlinear modules. The interest of the analytic results and the numerical methods involved transcend the particular model because of the ubiquity of the linear-nonlinear structure. Here we extend [Malo&Simoncelli 15] by considering 4 layers: (1) linear spectral integration and nonlinear brightness response, (2) definition of local contrast by using linear filters and divisive normalization, (3) linear CSF filter and nonlinear local con- trast masking, and (4) linear wavelet-like decomposition and nonlinear divisive normalization to account for orientation and scale-dependent masking. The extra layers were measured using Maximum Differentiation [Malo et al. VSS 2016]. First, we describe the general architecture using a unified notation in which every module is composed by isomorphic linear and nonlinear transforms. The chain-rule is interesting to simplify the analysis of systems with this modular architecture, and invertibility is related to the non-singularity of the Jacobian matrices. Second, we consider the details of the four layers in our particular model, and how they improve the original version of the model. Third, we explicitly list the derivatives of every module, which are relevant for the definition of perceptual distances, perceptual gradient descent, and characterization of the deformation of space. Fourth, we address the inverse, and we find different analytical and numerical problems in each specific module. Solutions are proposed for all of them. Finally, we describe through examples how to use the toolbox to apply and check the above theory. In summary, the formulation and toolbox are ready to explore the geometric and perceptual issues addressed in the introductory section (giving all the technical information that was missing in [Malo&Simoncelli 15])

Chaotic itinerancy, temporal segmentation and spatio-temporal combinatorial codes

We study a deterministic dynamics with two time scales in a continuous state attractor network. To the usual (fast) relaxation dynamics towards point attractors (``patterns'') we add a slow coupling dynamics that makes the visited patterns to loose stability leading to an itinerant behavior in the form of punctuated equilibria. One finds that the transition frequency matrix between patterns shows non-trivial statistical properties in the chaotic itinerant regime. We show that mixture input patterns can be temporally segmented by the itinerant dynamics. The viability of a combinatorial spatio-temporal neural code is also demonstrated

miRNA profiling during antigen-dependent T cell activation: A role for miR-132-3p

microRNAs (miRNAs) are tightly regulated during T lymphocyte activation to enable the establishment of precise immune responses. Here, we analyzed the changes of the miRNA profiles of T cells in response to activation by cognate interaction with dendritic cells. We also studied mRNA targets common to miRNAs regulated in T cell activation. pik3r1 gene, which encodes the regulatory subunits of PI3K p50, p55 and p85, was identified as target of miRNAs upregulated after T cell activation. Using 3'UTR luciferase reporter-based and biochemical assays, we showed the inhibitory relationship between miR-132-3p upregulation and expression of the pik3r1 gene. Our results indicate that specific miRNAs whose expression is modulated during T cell activation might regulate PI3K signaling in T cells.We thank Miguel Vicente-Manzanares for help with English editing and Almudena R. Ramiro for helpful discussions. We appreciate help from Gloria Martinez del Hoyo on DCs experiments set up. We also thank the CNIC Genomics, Bioinformatics and Cellomics Units for technical support. This work was supported by grants SAF2014-55579R from Ministerio de Economia y Competitividad-Spain, ERC-2011-AdG 294340-GENTRIS, CIBER CARDIOVASCULAR (FEDER and Instituto de Salud Carlos III), PIE-13-00041 and INDISNET S2011-BMD-2332 (F.S.M.). The Centro Nacional de Investigaciones Cardiovasculares (CNIC, Spain) is supported by the Ministerio de Economia y Competitividad-Spain and the Pro-CNIC Foundation.S

Nebulized ivermectin for COVID-19 and other respiratory diseases, a proof of concept, dose-ranging study in rats

Ivermectin is a widely used antiparasitic drug with known efcacy against several single-strain RNA viruses. Recent data shows signifcant reduction of SARS-CoV-2 replication in vitro by ivermectin concentrations not achievable with safe doses orally. Inhaled therapy has been used with success for other antiparasitics. An ethanol-based ivermectin formulation was administered once to 14 rats using a nebulizer capable of delivering particles with alveolar deposition. Rats were randomly assigned into three target dosing groups, lower dose (80–90 mg/kg), higher dose (110–140 mg/kg) or ethanol vehicle only. A toxicology profle including behavioral and weight monitoring, full blood count, biochemistry, necropsy and histological examination of the lungs was conducted. The pharmacokinetic profle of ivermectin in plasma and lungs was determined in all animals. There were no relevant changes in behavior or body weight. There was a delayed elevation in muscle enzymes compatible with rhabdomyolysis, that was also seen in the control group and has been attributed to the ethanol dose which was up to 11 g/kg in some animals. There were no histological anomalies in the lungs of any rat. Male animals received a higher ivermectin dose adjusted by adipose weight and reached higher plasma concentrations than females in the same dosing group (mean Cmax 86.2 ng/ml vs. 26.2 ng/ ml in the lower dose group and 152 ng/ml vs. 51.8 ng/ml in the higher dose group). All subjects had detectable ivermectin concentrations in the lungs at seven days post intervention, up to 524.3 ng/g for high-dose male and 27.3 ng/g for low-dose females. nebulized ivermectin can reach pharmacodynamic concentrations in the lung tissue of rats, additional experiments are required to assess the safety of this formulation in larger animals

Predicting aging-related decline in physical performance with sparse electrophysiological source imaging

Objective: We introduce a methodology for selecting biomarkers from activation and connectivity derived from Electrophysiological Source Imaging (ESI). Specifically, we pursue the selection of stable biomarkers associated with cognitive decline based on source activation and connectivity patterns of resting-state EEG theta rhythm, used as predictors of physical performance decline in aging individuals measured by a Gait Speed (GS) slowing. Methods: Our two-step methodology involves estimating ESI using flexible sparse-smooth-nonnegative models, from which activation ESI (aESI) and connectivity ESI (cESI) features are derived. The Stable Sparse Classifier method then selects potential biomarkers related to GS changes. Results and Conclusions: Our predictive models using aESI outperform traditional methods such as the LORETA family. The models combining aESI and cESI features provide the best prediction of GS changes. Potential biomarkers from activation/connectivity patterns involve orbitofrontal and temporal cortical regions. Significance: The proposed methodology contributes to the understanding of activation and connectivity of GS-related ESI and provides features that are potential biomarkers of GS slowing. Given the known relationship between GS decline and cognitive impairment, this preliminary work opens novel paths to predict the progression of healthy and pathological aging and might allow an ESI-based evaluation of rehabilitation programs