Microscopic Investigation of Vortex Breakdown in a Dividing T-Junction Flow

3D-printed microfluidic devices offer new ways to study fluid dynamics. We present the first clear visualization of vortex breakdown in a dividing T-junction flow. By individual control of the inflow and two outflows, we decouple the effects of swirl and rate of vorticity decay. We show that even slight outflow imbalances can greatly alter the structure of vortex breakdown, by creating a net pressure difference across the junction. Our results are summarized in a dimensionless phase diagram, which will guide the use of vortex breakdown in T-junctions to achieve specific flow manipulation.Comment: 5 pages, 5 figure

Interferon-γ induces immunoproteasomes and the presentation of MHC I-associated peptides on human salivary gland cells.

A prominent histopathological feature of Sjögren's syndrome, an autoimmune disease, is the presence of lymphocytic infiltrates in the salivary and lachrymal glands. Such infiltrates are comprised of activated lymphocytes and macrophages, and known to produce multiple cytokines including interferon-gamma (IFN-γ). In this study, we have demonstrated that IFN-γ strongly induces the expression of immunoproteasome beta subunits (β1i, β2i and β5i) and immunoproteasome activity but conversely inhibits the expression of proteasome beta subunits (β1, β2 and β5) in human salivary gland (HSG) cells. Mass spectrometric analysis has revealed potential MHC I-associated peptides on the HSG cells, including a tryptic peptide derived from salivary amylase, due to IFN-γ stimulation. These results suggest that IFN-γ induces immunoproteasomes in HSG cells, leading to enhanced presentation of MHC I-associated peptides on cell surface. These peptide-presenting salivary gland cells may be recognized and targeted by auto-reactive T lymphocytes. We have also found that lactacystin, a proteasome inhibitor, inhibits the expression of β1 subunit in HSG cells and blocks the IFN-γ-induced expression of β1i and immunoproteasome activity. However, the expression of β2i and β5i in HSG cells is not affected by lactacystin. These results may add new insight into the mechanism regarding how lactacystin blocks the action of proteasomes or immunoproteasomes

An Experimental Realization of Quantum-vacuum Geometric Phases by Using the Gyrotropic-medium Optical Fiber

The connection between the quantum-vacuum geometric phases (which originates from the vacuum zero-point electromagnetic fluctuation) and the non-normal product procedure is considered in the present Letter. In order to investigate this physically interesting geometric phases at quantum-vacuum level, we suggest an experimentally feasible scheme to test it by means of a noncoplanarly curved fiber made of gyrotropic media. A remarkable feature of the present experimental realization is that one can easily extract the nonvanishing and nontrivial quantum-vacuum geometric phases of left- and/or right- handed circularly polarized light from the vanishing and trivial total quantum-vacuum geometric phases.Comment: 4 pages, Late

Geometric Phase and Helicity Inversion of Photons Propagating inside a Noncoplanarly Curved Optical Fiber

The Letter presents an exact expression for the non-adiabatic non-cyclic geometric phase of photons propagating inside a noncoplanarly curved optical fiber by using the Lewis-Riesenfeld invariant theory. It is shown that the helicity inversion of photons arises in the curved fiber. Since we have exactly solved the time-dependent Schr\"{o}dinger equation that governs the propagation of photons in a curved fiber and, moreover, the chronological product is not involved in this exact solution, our formulation therefore has several advantages over other treatments based on the classical Maxwell's theory and the Berry's adiabatic quantum theory. The potential application of helicity inversion of photons to information science is briefly suggested.Comment: 8 pages, Latex. accepted by Phys. Lett.

Reciprocal regulation between taurine and glutamate response via Ca2+- dependent pathways in retinal third-order neurons

Although taurine and glutamate are the most abundant amino acids conducting neural signals in the central nervous system, the communication between these two neurotransmitters is largely unknown. This study explores the interaction of taurine and glutamate in the retinal third-order neurons. Using specific antibodies, both taurine and taurine transporters were localized in photoreceptors and Off-bipolar cells, glutamatergic neurons in retinas. It is possible that Off-bipolar cells release juxtaposed glutamate and taurine to activate the third-order neurons in retina. The interaction of taurine and glutamate was studied in acutely dissociated third-order neurons in whole-cell patch-clamp recording and Ca2+ imaging. We find that taurine effectively reduces glutamate-induced Ca2+ influx via ionotropic glutamate receptors and voltage-dependent Ca2+ channels in the neurons, and the effect of taurine was selectively inhibited by strychnine and picrotoxin, but not GABA receptor antagonists, although GABA receptors are present in the neurons. A CaMKII inhibitor partially reversed the effect of taurine, suggesting that a Ca2+/calmodulin-dependent pathway is involved in taurine regulation. On the other hand, a rapid influx of Ca2+ through ionotropic glutamate receptors could inhibit the amplitude and kinetics of taurine-elicited currents in the third-order neurons, which could be controlled with intracellular application of BAPTA a fast Ca2+ chelator. This study indicates that taurine is a potential neuromodulator in glutamate transmission. The reciprocal inhibition between taurine and glutamate in the postsynaptic neurons contributes to computation of visual signals in the retinal neurons

An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches

Automatic cell segmentation by adaptive thresholding (ACSAT) for large-scale calcium imaging datasets

Advances in calcium imaging have made it possible to record from an increasingly larger number of neurons simultaneously. Neuroscientists can now routinely image hundreds to thousands of individual neurons. An emerging technical challenge that parallels the advancement in imaging a large number of individual neurons is the processing of correspondingly large datasets. One important step is the identification of individual neurons. Traditional methods rely mainly on manual or semimanual inspection, which cannot be scaled for processing large datasets. To address this challenge, we focused on developing an automated segmentation method, which we refer to as automated cell segmentation by adaptive thresholding (ACSAT). ACSAT works with a time-collapsed image and includes an iterative procedure that automatically calculates global and local threshold values during successive iterations based on the distribution of image pixel intensities. Thus, the algorithm is capable of handling variations in morphological details and in fluorescence intensities in different calcium imaging datasets. In this paper, we demonstrate the utility of ACSAT by testing it on 500 simulated datasets, two wide-field hippocampus datasets, a wide-field striatum dataset, a wide-field cell culture dataset, and a two-photon hippocampus dataset. For the simulated datasets with truth, ACSAT achieved >80% recall and precision when the signal-to-noise ratio was no less than ∼24 dB.DP2 NS082126 - NINDS NIH HHSPublished versio

Momentum-Resolved Inelastic X-ray Scattering as a Novel Tool to Study Charge Gap in Complex Insulators

We report particle-hole pair excitations in a cuprate insulator in the intermediate regimes of momentum-transfers using high energy inelastic x-ray scattering. The excitation spectra show dispersive features near the Mott edge which shed light on the momentum structure of the upper Hubbard band in cuprates. We briefly discuss the potential use of such a technique to study the momentum dependence of unoccupied bands and q-dependent charge fluctuations in complex insulators.Comment: 3 pages, 2 figures, Revise