Optimization of electron microscopy for human brains with long-term fixation and fixed-frozen sections.

BackgroundAbnormal connectivity across brain regions underlies many neurological disorders including multiple sclerosis, schizophrenia and autism, possibly due to atypical axonal organization within white matter. Attempts at investigating axonal organization on post-mortem human brains have been hindered by the availability of high-quality, morphologically preserved tissue, particularly for neurodevelopmental disorders such as autism. Brains are generally stored in a fixative for long periods of time (often greater than 10 years) and in many cases, already frozen and sectioned on a microtome for histology and immunohistochemistry. Here we present a method to assess the quality and quantity of axons from long-term fixed and frozen-sectioned human brain samples to demonstrate their use for electron microscopy (EM) measures of axonal ultrastructure.ResultsSix samples were collected from white matter below the superior temporal cortex of three typically developing human brains and prepared for EM analyses. Five samples were stored in fixative for over 10 years, two of which were also flash frozen and sectioned on a freezing microtome, and one additional case was fixed for 3 years and sectioned on a freezing microtome. In all six samples, ultrastructural qualitative and quantitative analyses demonstrate that myelinated axons can be identified and counted on the EM images. Although axon density differed between brains, axonal ultrastructure and density was well preserved and did not differ within cases for fixed and frozen tissue. There was no significant difference between cases in axon myelin sheath thickness (g-ratio) or axon diameter; approximately 70% of axons were in the small (0.25 μm) to medium (0.75 μm) range. Axon diameter and g-ratio were positively correlated, indicating that larger axons may have thinner myelin sheaths.ConclusionThe current study demonstrates that long term formalin fixed and frozen-sectioned human brain tissue can be used for ultrastructural analyses. Axon integrity is well preserved and can be quantified using the methods presented here. The ability to carry out EM on frozen sections allows for investigation of axonal organization in conjunction with other cellular and histological methods, such as immunohistochemistry and stereology, within the same brain and even within the same frozen cut section

Calculation of the Regularized Vacuum Energy in Cavity Field Theories

A novel technique based on Schwinger's proper time method is applied to the Casimir problem of the M.I.T. bag model. Calculations of the regularized vacuum energies of massless scalar and Dirac spinor fields confined to a static and spherical cavity are presented in a consistent manner. While our results agree partly with previous calculations based on asymptotic methods, the main advantage of our technique is that the numerical errors are under control. Interpreting the bag constant as a vacuum expectation value, we investigate potential cancellations of boundary divergences between the canonical energy and its bag constant counterpart in the fermionic case. It is found that such cancellations do not occur.Comment: 14 pages, 4 figures, accepted for publication in Eur.Phys.J.

Unrecognized Backscattering in Low Energy Beta Spectroscopy

We present studies on electron backscattering from the surface of plastic scintillator beta detectors. By using a setup of two detectors coaxial with a strong external magnetic field - one detector serving as primary detector, the other as veto-detector to detect backscattering - we investigate amount and spectrum of unrecognized backscattering, i.e. events where only one detector recorded a trigger signal. The implications are important for low energy particle physics experiments.Comment: 5 pages, 8 figures; v2: published NIM A versio

Spatially Dependent Parameter Estimation and Nonlinear Data Assimilation by Autosynchronization of a System of Partial Differential Equations

Given multiple images that describe chaotic reaction-diffusion dynamics, parameters of a PDE model are estimated using autosynchronization, where parameters are controlled by synchronization of the model to the observed data. A two-component system of predator-prey reaction-diffusion PDEs is used with spatially dependent parameters to benchmark the methods described. Applications to modelling the ecological habitat of marine plankton blooms by nonlinear data assimilation through remote sensing is discussed

GeMSE: A new Low-Background Facility for Meteorite and Material Screening

We are currently setting up a facility for low-background gamma-ray spectrometry based on a HPGe detector. It is dedicated to material screening for the XENON and DARWIN dark matter projects as well as to the characterization of meteorites. The detector will be installed in a medium depth ($\sim$620 m.w.e.) underground laboratory in Switzerland with several layers of shielding and an active muon-veto. The GeMSE facility will be operational by fall 2015 with an expected background rate of $\sim$250 counts/day (100-2700 keV).Comment: The following article appeared in AIP Conf. Proc. 1672, 120004 (2015) and may be found at http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.4928010. The muon spectrum in Figure 4 (left) was corrected due to a bug in the code. After correction the muon flux is reduced by a factor of about

Vacuum structure of a modified MIT Bag

An alternative to introducing and subsequently renormalizing classical parameters in the expression for the vacuum energy of the MIT bag for quarks is proposed in the massless case by appealing to the QCD trace anomaly and scale separation due to asymptotic freedom. The explicit inclusion of gluons implies an unrealistically low separation scale.Comment: 5 pages, 2 figure

A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking

Person re identification is a challenging retrieval task that requires matching a person's acquired image across non overlapping camera views. In this paper we propose an effective approach that incorporates both the fine and coarse pose information of the person to learn a discriminative embedding. In contrast to the recent direction of explicitly modeling body parts or correcting for misalignment based on these, we show that a rather straightforward inclusion of acquired camera view and/or the detected joint locations into a convolutional neural network helps to learn a very effective representation. To increase retrieval performance, re-ranking techniques based on computed distances have recently gained much attention. We propose a new unsupervised and automatic re-ranking framework that achieves state-of-the-art re-ranking performance. We show that in contrast to the current state-of-the-art re-ranking methods our approach does not require to compute new rank lists for each image pair (e.g., based on reciprocal neighbors) and performs well by using simple direct rank list based comparison or even by just using the already computed euclidean distances between the images. We show that both our learned representation and our re-ranking method achieve state-of-the-art performance on a number of challenging surveillance image and video datasets. The code is available online at: https://github.com/pse-ecn/pose-sensitive-embeddingComment: CVPR 2018: v2 (fixes, added new results on PRW dataset

Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model

Pedestrian attribute inference is a demanding problem in visual surveillance that can facilitate person retrieval, search and indexing. To exploit semantic relations between attributes, recent research treats it as a multi-label image classification task. The visual cues hinting at attributes can be strongly localized and inference of person attributes such as hair, backpack, shorts, etc., are highly dependent on the acquired view of the pedestrian. In this paper we assert this dependence in an end-to-end learning framework and show that a view-sensitive attribute inference is able to learn better attribute predictions. Our proposed model jointly predicts the coarse pose (view) of the pedestrian and learns specialized view-specific multi-label attribute predictions. We show in an extensive evaluation on three challenging datasets (PETA, RAP and WIDER) that our proposed end-to-end view-aware attribute prediction model provides competitive performance and improves on the published state-of-the-art on these datasets.Comment: accepted BMVC 201

Understanding Jet Scaling and Jet Vetos in Higgs Searches

Jet counting and jet vetos are crucial analysis tools for many LHC searches. We can understand their properties from the distribution of the exclusive number of jets. LHC processes tend to show either a distinct staircase scaling or a Poisson scaling, depending on kinematic cuts. We illustrate our approach in a detailed study of jets in weak boson fusion Higgs production.Comment: 5 pages, 4 figures, 1 table. Text clarified to reflect that we applied forward-backward tagging jet selectio