GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network

We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count. When testing on unseen images, we first run the network to estimate the number of lesions. Then we remove the global pooling layer to compute localization maps of the size of the input image. We evaluate the proposed network on the detection of enlarged perivascular spaces in the basal ganglia in MRI. Our method achieves a sensitivity of 62% with on average 1.5 false positives per image. Compared with four other approaches based on intensity thresholding, saliency and class maps, our method has a 20% higher sensitivity.Comment: Article published in MICCAI 2017. We corrected a few errors from the first version: padding, loss, typos and update of the DOI numbe

Parking and the visual perception of space

Using measured data we demonstrate that there is an amazing correspondence among the statistical properties of spacings between parked cars and the distances between birds perching on a power line. We show that this observation is easily explained by the fact that birds and human use the same mechanism of distance estimation. We give a simple mathematical model of this phenomenon and prove its validity using measured data

Toll-like Receptor 4 Expression, Interleukin-6, -8 and Ccl-20 Release, and NF-KB Translocation in Human Periodontal Ligament Mesenchymal Stem Cells Stimulated with LPS-P. Gingivalis

Periodontal diseases, the major public health problem of the oral cavity, are clinically characterized by inflammation of the periodontal connective tissue that ultimately induces the destruction of periodontal tissue and the loss of alveolar bone. In chronic periodontitis, as well as aggressive periodontitis, the anaerobic gram-negative bacterium Porphyromonas gingivalis (P. gingivalis) is implicated. The pathogenicity of P. gingivalis is exerted by a wide variety of factors, including lipopolysaccharides (LPSs). LPSs activate the innate immune response during Gram-negative bacterial infections through the Toll-like receptor 4 (TLR-4)/myeloid differentiation protein 2 (MD-2) complex. In this study, the expression of TLR-4, the cell growth, the cytokine release, and the nuclear factor-KB (NF-kB) transcription factor expression in response to LPS- P.Gingivalis (LPS-G) were examined in Human Periodontal Ligament Mesenchymal Stem Cells (PDL-MSCs). The results obtained demonstrate that, in basal conditions, human PDL-MSCs express high levels of TLR-4. In inflammatory conditions mimicked by LPS-G challenge, the MTT assay carried out at different treatment times demonstrated the decrease of the cell growth. Moreover, the recognition of P. gingivalis components by TLR-4 culminated with the activation of secretion of inflammatory mediators such as: IL-6, IL-8 and CCL-20, and with the up-regulation of NF-kB, which was translocated into the nucleus. Our data intended to specify that TLR-4 expressed by PDL-MSCs is functional and plays a key role in inflammation

Super-hydrodynamic limit in interacting particle systems

This paper is a follow-up of the work initiated in [3], where it has been investigated the hydrodynamic limit of symmetric independent random walkers with birth at the origin and death at the rightmost occupied site. Here we obtain two further results: first we characterize the stationary states on the hydrodynamic time scale and show that they are given by a family of linear macroscopic profiles whose parameters are determined by the current reservoirs and the system mass. Then we prove the existence of a super-hyrdrodynamic time scale, beyond the hydrodynamic one. On this larger time scale the system mass fluctuates and correspondingly the macroscopic profile of the system randomly moves within the family of linear profiles, with the randomness of a Brownian motion.Comment: 22 page

Analysis of Energy Flow in US GLOBEC Ecosystems Using End-to-End Models

End-to-end models were constructed to examine and compare the trophic structure and energy flow in coastal shelf ecosystems of four US Global Ocean Ecosystem Dynamics (GLOBEC) study regions: the Northern California Current, the Central Gulf of Alaska, Georges Bank, and the Southwestern Antarctic Peninsula. High-quality data collected on system components and processes over the life of the program were used as input to the models. Although the US GLOBEC program was species-centric, focused on the study of a selected set of target species of ecological or economic importance, we took a broader community-level approach to describe end-to-end energy flow, from nutrient input to fishery production. We built four end-to-end models that were structured similarly in terms of functional group composition and time scale. The models were used to identify the mid-trophic level groups that place the greatest demand on lower trophic level production while providing the greatest support to higher trophic level production. In general, euphausiids and planktivorous forage fishes were the critical energy-transfer nodes; however, some differences between ecosystems are apparent. For example, squid provide an important alternative energy pathway to forage fish, moderating the effects of changes to forage fish abundance in scenario analyses in the Central Gulf of Alaska. In the Northern California Current, large scyphozoan jellyfish are important consumers of plankton production, but can divert energy from the rest of the food web when abundant

Dynamical modeling of collective behavior from pigeon flight data: flock cohesion and dispersion

Several models of flocking have been promoted based on simulations with qualitatively naturalistic behavior. In this paper we provide the first direct application of computational modeling methods to infer flocking behavior from experimental field data. We show that this approach is able to infer general rules for interaction, or lack of interaction, among members of a flock or, more generally, any community. Using experimental field measurements of homing pigeons in flight we demonstrate the existence of a basic distance dependent attraction/repulsion relationship and show that this rule is sufficient to explain collective behavior observed in nature. Positional data of individuals over time are used as input data to a computational algorithm capable of building complex nonlinear functions that can represent the system behavior. Topological nearest neighbor interactions are considered to characterize the components within this model. The efficacy of this method is demonstrated with simulated noisy data generated from the classical (two dimensional) Vicsek model. When applied to experimental data from homing pigeon flights we show that the more complex three dimensional models are capable of predicting and simulating trajectories, as well as exhibiting realistic collective dynamics. The simulations of the reconstructed models are used to extract properties of the collective behavior in pigeons, and how it is affected by changing the initial conditions of the system. Our results demonstrate that this approach may be applied to construct models capable of simulating trajectories and collective dynamics using experimental field measurements of herd movement. From these models, the behavior of the individual agents (animals) may be inferred

Polystyrene Nanopillars with Inbuilt Carbon Nanotubes Enable Synaptic Modulation and Stimulation in Interfaced Neuronal Networks

The use of nanostructured materials and nanosized-topographies has the potential to impact the performance of implantable biodevices, including neural interfaces, enhancing their sensitivity and selectivity, while reducing tissue reactivity. As a result, current trends in biosensor technology require the effective ability to improve devices with controlled nanostructures. Nanoimprint lithography to pattern surfaces with high-density and high aspect ratio nanopillars (NPs) made of polystyrene (PS-NP, insulating), or of a polystyrene/carbon-nanotube nanocomposite (PS-CNT-NP, electrically conductive) are exploited. Both substrates are challenged with cultured primary neurons. They are demonstrated to support the development of suspended synaptic networks at the NPs’ interfaces characterized by a reduction in proliferating neuroglia, and a boost in neuronal emergent electrical activity when compared to flat controls. The authors successfully exploit their conductive PS-CNT-NPs to stimulate cultured cells electrically. The ability of both nanostructured surfaces to interface tissue explants isolated from the mouse spinal cord is then tested. The integration of the neuronal circuits with the NP topology, the suspended nature of the cultured networks, the reduced neuroglia formation, and the higher network activity together with the ability to deliver electrical stimuli via PS-CNT-NP reveal such platforms as promising designs to implement on neuro-prosthetic or neurostimulation devices

Application of the Ordered Logit Model to Optimising Frangi Filter Parameters for Segmentation of Perivascular Spaces

La segmentación de los espacios perivasculares (EVP) de las imágenes de resonancia magnética (RM) del cerebro es importante para comprender el sistema linfático del cerebro y su relación con las enfermedades neurológicas. El filtro Frangi podría ser una herramienta valiosa para este propósito. Sin embargo, sus parámetros deben ajustarse en respuesta a la variabilidad en los parámetros del escáner y los protocolos de estudio. Conociendo las clasificaciones neurorradiológicas del PVS, utilizamos el modelo logit ordenado para optimizar los parámetros del filtro Frangi. El volumen de PVS obtenido se correlacionó de manera significativa y fuerte con las evaluaciones neurorradiológicas (ρ = 0.75, p <0.001 de Spearman), lo que sugiere que el modelo logit ordenado podría ser una buena alternativa a los marcos de optimización convencionales para segmentar PVS en MRI