Giulio Iannello

Confocal light sheet microscopy: micron-scale neuroanatomy of the entire mouse brain

Elucidating the neural pathways that underlie brain function is one of the greatest challenges in neuroscience. Light sheet based microscopy is a cutting edge method to map cerebral circuitry through optical sectioning of cleared mouse brains. However, the image contrast provided by this method is not sufficient to resolve and reconstruct the entire neuronal network. Here we combined the advantages of light sheet illumination and confocal slit detection to increase the image contrast in real time, with a frame rate of 10 Hz. In fact, in confocal light sheet microscopy (CLSM), the out-of-focus and scattered light is filtered out before detection, without multiple acquisitions or any post-processing of the acquired data. The background rejection capabilities of CLSM were validated in cleared mouse brains by comparison with a structured illumination approach. We show that CLSM allows reconstructing macroscopic brain volumes with sub-cellular resolution. We obtained a comprehensive map of Purkinje cells in the cerebellum of L7-GFP transgenic mice. Further, we were able to trace neuronal projections across brain of thy1-GFP-M transgenic mice. The whole-brain high-resolution fluorescence imaging assured by CLSM may represent a powerful tool to navigate the brain through neuronal pathways. Although this work is focused on brain imaging, the macro-scale high-resolution tomographies affordable with CLSM are ideally suited to explore, at micron-scale resolution, the anatomy of different specimens like murine organs, embryos or flies.

Extensible visualization and analysis for multidimensional images using Vaa3D

Open-Source 3D Visualization-Assisted Analysis (Vaa3D) is a software platform for the visualization and analysis of large-scale multidimensional images. In this protocol we describe how to use several popular features of Vaa3D, including (i) multidimensional image visualization, (ii) 3D image object generation and quantitative measurement, (iii) 3D image comparison, fusion and management, (iv) visualization of heterogeneous images and respective surface objects and (v) extension of Vaa3D functions using its plug-in interface. We also briefly demonstrate how to integrate these functions for complicated applications of microscopic image visualization and quantitative analysis using three exemplar pipelines, including an automated pipeline for image filtering, segmentation and surface generation; an automated pipeline for 3D image stitching; and an automated pipeline for neuron morphology reconstruction, quantification and comparison. Once a user is familiar with Vaa3D, visualization usually runs in real time and analysis takes less than a few minutes for a simple data set.

Aggregation of Classifiers for Staining Pattern Recognition in Antinuclear Autoantibodies Analysis

Indirect immunofluorescence is currently the recommended method for the detection of antinuclear autoantibodies (ANA). The diagnosis consists of both estimating the fluorescence intensity and reporting the staining pattern for positive wells only. Since resources and adequately trained personnel are not always available for these tasks, an evident medical demand is the development of computer-aided diagnosis (CAD) tools that can support the physician decisions. In this paper, we present a system that classifies the staining pattern of positive wells on the strength of the recognition of their cells. The core of the CAD is a multiple expert system (MES) based on the one-per-class approach devised to label the pattern of single cells. It employs a hybrid approach since each composing binary module is constituted by an ensemble of classifiers combined by a fusion rule. Each expert uses a set of stable and effective features selected from a wide pool of statistical and spectral measurements. In this framework, we present a novel parameter that measures the reliability of the final classification provided by the MES. This feature is used to introduce a reject option that allows to reduce the error rate in the recognition of the staining pattern of the whole well. The approach has been evaluated on 37 wells, for a total of 573 cells. The measured performance shows a low overall error rate (2.7%-5.8%), which is below the observed intralaboratory variability.

A versatile clearing agent for multi-modal brain imaging

Extensive mapping of neuronal connections in the central nervous system requires high-throughput µm-scale imaging of large volumes. In recent years, different approaches have been developed to overcome the limitations due to tissue light scattering. These methods are generally developed to improve the performance of a specific imaging modality, thus limiting comprehensive neuroanatomical exploration by multi-modal optical techniques. Here, we introduce a versatile brain clearing agent (2,2′-thiodiethanol; TDE) suitable for various applications and imaging techniques. TDE is cost-efficient, water-soluble and low-viscous and, more importantly, it preserves fluorescence, is compatible with immunostaining and does not cause deformations at sub-cellular level. We demonstrate the effectiveness of this method in different applications: in fixed samples by imaging a whole mouse hippocampus with serial two-photon tomography; in combination with CLARITY by reconstructing an entire mouse brain with light sheet microscopy and in translational research by imaging immunostained human dysplastic brain tissue.

Indirect immunofluorescence in autoimmune diseases: Assessment of digital images for diagnostic purpose

Background: The recommended method for antinuclear antibodies (ANA) detection is indirect immunofluorescence (IIF). To pursue a high image quality without artefacts and reduce interobserver variability, this study aims at evaluating the reliability of automatically acquired digital images of IIF slides for diagnostic purposes. Methods: Ninety‐six sera were screened for ANA by IIF on HEp‐2 cells. Two expert physicians looking at both the fluorescence microscope and the digital images on computer monitor performed a blind study to evaluate fluorescence intensity and staining pattern. Cohens kappa was used as an agreement evaluator between methods and experts. Results: Considering fluorescence intensity, there is a substantial agreement between microscope and monitor analysis in both physicians. Agreement between physicians was substantial at the microscope and perfect at the monitor. Considering IIF pattern, there was a substantial and moderate agreement between microscope and monitor analysis in both physicians. Kappa between physicians was substantial both at the microscope and at the monitor. Conclusions: These preliminary results suggest that digital media is a reliable tool to help physicians in detecting autoantibodies in IIF. Our data represent a first step to validate the use of digital images, thus offering an opportunity for standardizing and automatizing the detection of ANA by IIF.

Vertical handoff performance in heterogeneous networks

We study the problem of handoffs in heterogeneous (both wired and wireless) networks. We first present a testbed that integrates multiple network technologies (Ethernet LAN, WiFi and GPRS cellular data network) to provide seamless connectivity to mobile hosts. We then propose a model to analyze the performance of vertical handoffs as well as experimental measures to validate the model. Finally, we discuss the advantages of handoff detection and triggering through link layer mechanisms. An implementation of handoffs? link layer triggering is presented, and its performance is compared with layer-3 handoff triggering.

Seamless internetworking of WLANs and cellular networks: architecture and performance issues in a Mobile IPv6 scenario

We review the problem of network mobility and internetworking between heterogeneous data networks and present an approach to the integration of WLAN and cellular networks based on loose coupling and the use of emerging mobility protocols. The handoff performance of such an approach is studied, at the network and transport levels, in a realistic scenario along with the impact on global performance of transport protocols. Finally, a method of eliminating any packet loss at the network layer during handoff is presented and evaluated.

Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis

Three-dimensional (3D) bioimaging, visualization and data analysis are in strong need of powerful 3D exploration techniques. We develop virtual finger (VF) to generate 3D curves, points and regions-of-interest in the 3D space of a volumetric image with a single finger operation, such as a computer mouse stroke, or click or zoom from the 2D-projection plane of an image as visualized with a computer. VF provides efficient methods for acquisition, visualization and analysis of 3D images for roundworm, fruitfly, dragonfly, mouse, rat and human. Specifically, VF enables instant 3D optical zoom-in imaging, 3D free-form optical microsurgery, and 3D visualization and annotation of terabytes of whole-brain image volumes. VF also leads to orders of magnitude better efficiency of automated 3D reconstruction of neurons and similar biostructures over our previous systems. We use VF to generate from images of 1,107 Drosophila GAL4 lines a projectome of a Drosophila brain.

Joint end-to-end loss-delay hidden Markov model for periodic UDP traffic over the Internet

Performance of real-time applications on network communication channels is strongly related to losses and temporal delays. Several studies showed that these network features may be correlated and exhibit a certain degree of memory such as bursty losses and delays. The memory and the statistical dependence between losses and temporal delays suggest that the channel may be well modeled by a hidden Markov model (HMM) with appropriate hidden variables that capture the current state of the network. In this paper, an HMM is proposed that shows excellent performance in modeling typical channel behaviors in a set of real packet links. The system is trained with a modified version of the Expectation-Maximization (EM) algorithm. Hidden-state analysis shows how the state variables characterize channel dynamics. State-sequence estimation is obtained by the use of Viterbi algorithm. Real-time modeling of the channel is the first step to implement adaptive communication strategies.

A multiple expert system for classifying fluorescent intensity in antinuclear autoantibodies analysis

At the present, Indirect Immunofluorescence (IIF) is the recommended method for the detection of antinuclear autoantibodies (ANA). IIF diagnosis requires both the estimation of the fluorescent intensity and the description of the staining pattern, but resources and adequately trained personnel are not always available for these tasks. In this respect, an evident medical demand is the development of computer-aided diagnosis (CAD) tools that can offer a support to physician decision. In this paper we first propose a strategy to reliably label the image data set by using the diagnoses performed by different physicians, and then we present a system to classify the fluorescent intensity. Such a system adopts a multiple expert system architecture (MES), based on the classifier selection paradigm. Two different selection rules are presented and, given the application domain, the convenience of using one of them is analyzed. Different sets of operating points are determined, making the recognition system suited to application in daily practice and in a wide spectrum of scenarios. The measured performance on an annotated database of IIF images shows a low overall miss rate (<1.5%, 0.00% of false negative).