Video-rate volumetric neuronal imaging using 3D targeted illumination

Fast volumetric microscopy is required to monitor large-scale neural ensembles with high spatio-temporal resolution. Widefield fluorescence microscopy can image large 2D fields of view at high resolution and speed while remaining simple and costeffective. A focal sweep add-on can further extend the capacity of widefield microscopy by enabling extended-depth-of-field (EDOF) imaging, but suffers from an inability to reject out-of-focus fluorescence background. Here, by using a digital micromirror device to target only in-focus sample features, we perform EDOF imaging with greatly enhanced contrast and signal-to-noise ratio, while reducing the light dosage delivered to the sample. Image quality is further improved by the application of a robust deconvolution algorithm. We demonstrate the advantages of our technique for in vivo calcium imaging in the mouse brain.This work was funded by the National Institutes of Health (R21EY026310) and the National Science Foundation (CBET-1508988). The authors wish to thank E. McCarthy and Prof. M.J. Baum for providing mouse brain slices used in this manuscript, and A. I. Mohammed for providing in vivo mouse brain samples in the early stages of this work. (R21EY026310 - National Institutes of Health; CBET-1508988 - National Science Foundation)Published versio

Apoptosis and antigen receptor function in T and B cells following exposure to herpes simplex virus

AbstractT cells are an essential component of the immune response against herpes simplex virus (HSV) infection. We previously reported that incubation of T cells with HSV-infected fibroblasts inhibits subsequent T cell antigen receptor signal transduction. In the current study, we found that incubation of T cells with HSV-infected fibroblasts also leads to apoptosis in exposed T cells. Apoptosis was observed in Jurkat cells, a T cell leukemia line, and also in CD4+ cells isolated from human peripheral blood mononuclear cells. Direct infection of these cells with HSV also resulted in apoptosis. Clinical isolates of both HSV type 1 and 2 induced apoptosis in infected T cells at comparable levels to cells infected with laboratory strains of HSV, suggesting an immune evasion mechanism that may be clinically relevant. Further understanding of these viral immune evasion mechanisms could be exploited for better management of HSV infection

Silicon-on-Insulator RF Filter Based on Photonic Crystal Functions for Channel Equalization

International audienceA compact silicon-on-insulator 2-tap interferometer is demonstrated as a channel equalizer. The radiofrequency filter is reconfigurable thanks to thermally-controlled photonic crystal couplers and delay lines. The channel fading of a dispersive fiber link supporting a directly modulated telecommunication signal is successfully compensated for using the interferometer, leading to eye diagram opening and the possibility to recover the bit-error-rate of a reference signal with less than 1-dB power penalty

Evaluation of a novel deep learning-based classifier for perifissural nodules

OBJECTIVES: To evaluate the performance of a novel convolutional neural network (CNN) for the classification of typical perifissural nodules (PFN). METHODS: Chest CT data from two centers in the UK and The Netherlands (1668 unique nodules, 1260 individuals) were collected. Pulmonary nodules were classified into subtypes, including "typical PFNs" on-site, and were reviewed by a central clinician. The dataset was divided into a training/cross-validation set of 1557 nodules (1103 individuals) and a test set of 196 nodules (158 individuals). For the test set, three radiologically trained readers classified the nodules into three nodule categories: typical PFN, atypical PFN, and non-PFN. The consensus of the three readers was used as reference to evaluate the performance of the PFN-CNN. Typical PFNs were considered as positive results, and atypical PFNs and non-PFNs were grouped as negative results. PFN-CNN performance was evaluated using the ROC curve, confusion matrix, and Cohen's kappa. RESULTS: Internal validation yielded a mean AUC of 91.9% (95% CI 90.6-92.9) with 78.7% sensitivity and 90.4% specificity. For the test set, the reader consensus rated 45/196 (23%) of nodules as typical PFN. The classifier-reader agreement (k = 0.62-0.75) was similar to the inter-reader agreement (k = 0.64-0.79). Area under the ROC curve was 95.8% (95% CI 93.3-98.4), with a sensitivity of 95.6% (95% CI 84.9-99.5), and specificity of 88.1% (95% CI 81.8-92.8). CONCLUSION: The PFN-CNN showed excellent performance in classifying typical PFNs. Its agreement with radiologically trained readers is within the range of inter-reader agreement. Thus, the CNN-based system has potential in clinical and screening settings to rule out perifissural nodules and increase reader efficiency. KEY POINTS: • Agreement between the PFN-CNN and radiologically trained readers is within the range of inter-reader agreement. • The CNN model for the classification of typical PFNs achieved an AUC of 95.8% (95% CI 93.3-98.4) with 95.6% (95% CI 84.9-99.5) sensitivity and 88.1% (95% CI 81.8-92.8) specificity compared to the consensus of three readers

Modulation of Hepatic Amyloid Precursor Protein and Lipoprotein Receptor-Related Protein 1 by Chronic Alcohol Intake: Potential Link Between Liver Steatosis and Amyloid-β

Heavy alcohol consumption is a known risk factor for various forms of dementia and the development of Alzheimer’s disease (AD). In this work, we investigated how intragastric alcohol feeding may alter the liver-to-brain axis to induce and/or promote AD pathology. Four weeks of intragastric alcohol feeding to mice, which causes significant fatty liver (steatosis) and liver injury, caused no changes in AD pathology markers in the brain [amyloid precursor protein (APP), presenilin], except for a decrease in microglial cell number in the cortex of the brain. Interestingly, the decline in microglial numbers correlated with serum alanine transaminase (ALT) levels, suggesting a potential link between liver injury and microglial loss in the brain. Intragastric alcohol feeding significantly affected two hepatic proteins important in amyloid-beta (Aβ) processing by the liver: 1) alcohol feeding downregulated lipoprotein receptor-related protein 1 (LRP1, ∼46%), the major receptor in the liver that removes Aβ from blood and peripheral organs, and 2) alcohol significantly upregulated APP (∼2-fold), a potentially important source of Aβ in the periphery and brain. The decrease in hepatic LRP1 and increase in hepatic APP likely switches the liver from being a remover or low producer of Aβ to an important source of Aβ in the periphery, which can impact the brain. The downregulation of LRP1 and upregulation of APP in the liver was observed in the first week of intragastric alcohol feeding, and also occurred in other alcohol feeding models (NIAAA binge alcohol model and intragastric alcohol feeding to rats). Modulation of hepatic LRP1 and APP does not seem alcohol-specific, as ob/ob mice with significant steatosis also had declines in LRP1 and increases in APP expression in the liver. These findings suggest that liver steatosis rather than alcohol-induced liver injury is likely responsible for regulation of hepatic LRP1 and APP. Both obesity and alcohol intake have been linked to AD and our data suggests that liver steatosis associated with these two conditions modulates hepatic LRP1 and APP to disrupt Aβ processing by the liver to promote AD

CANDELS: The progenitors of compact quiescent galaxies at z~2

We combine high-resolution HST/WFC3 images with multi-wavelength photometry to track the evolution of structure and activity of massive (log(M*) > 10) galaxies at redshifts z = 1.4 - 3 in two fields of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS). We detect compact, star-forming galaxies (cSFGs) whose number densities, masses, sizes, and star formation rates qualify them as likely progenitors of compact, quiescent, massive galaxies (cQGs) at z = 1.5 - 3. At z > 2 most cSFGs have specific star-formation rates (sSFR = 10^-9 yr^-1) half that of typical, massive SFGs at the same epoch, and host X-ray luminous AGN 30 times (~30%) more frequently. These properties suggest that cSFGs are formed by gas-rich processes (mergers or disk-instabilities) that induce a compact starburst and feed an AGN, which, in turn, quench the star formation on dynamical timescales (few 10^8 yr). The cSFGs are continuously being formed at z = 2 - 3 and fade to cQGs by z = 1.5. After this epoch, cSFGs are rare, thereby truncating the formation of new cQGs. Meanwhile, down to z = 1, existing cQGs continue to enlarge to match local QGs in size, while less-gas-rich mergers and other secular mechanisms shepherd (larger) SFGs as later arrivals to the red sequence. In summary, we propose two evolutionary scenarios of QG formation: an early (z > 2), fast-formation path of rapidly-quenched cSFGs that evolve into cQGs that later enlarge within the quiescent phase, and a slow, late-arrival (z < 2) path for SFGs to form QGs without passing through a compact state.Comment: Submitted to the Astrophysical Journal Letters, 6 pages, 4 figure

Lung cancer prediction by Deep Learning to identify benign lung nodules

INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity. METHODS: The LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.S. National Lung Screening Trial (NLST), and validated on CT scans containing 2106 nodules (205 lung cancers) detected in patients from from the Early Lung Cancer Diagnosis Using Artificial Intelligence and Big Data (LUCINDA) study, recruited from three tertiary referral centers in the UK, Germany and Netherlands. We pre-defined a benign nodule rule-out test, to identify benign nodules whilst maintaining a high sensitivity, by calculating thresholds on the malignancy score that achieve at least 99 % sensitivity on the NLST data. Overall performance per validation site was evaluated using Area-Under-the-ROC-Curve analysis (AUC). RESULTS: The overall AUC across the European centers was 94.5 % (95 %CI 92.6-96.1). With a high sensitivity of 99.0 %, malignancy could be ruled out in 22.1 % of the nodules, enabling 18.5 % of the patients to avoid follow-up scans. The two false-negative results both represented small typical carcinoids. CONCLUSION: The LCP-CNN, trained on participants with lung nodules from the US NLST dataset, showed excellent performance on identification of benign lung nodules in a multi-center external dataset, ruling out malignancy with high accuracy in about one fifth of the patients with 5-15 mm nodules