Antibody localization in horse, rabbit, and goat antilymphocyte sera

The localization of antibodies was studied in rabbit, goat, and horse ALS raised by weekly immunization with canine or human spleen cells for 4 to 12 weeks. A combination of analytic techniques was used including column chromatography, electrophoresis, immunoelectrophoresis, determination of protein concentration, and measurement of antibody titers. In the rabbit and goat ALS, virtually all of the leukoagglutinins and lymphocytotoxins were in the easily separable IgG; accidentally induced thromboagglutinins were in the same location. In the rabbit hemagglutinins were found in both the IgG and IgM, whereas in the goat these were almost exclusively in the IgM. The antiwhite cell antibodies were most widely distributed in the horse. The cytotoxins were primarily in the IgG, but the leukoagglutinins were most heavily concentrated in the T-equine globulin which consists mostly of IgA. By differential ammonium sulfate precipitation of a horse antidoglymphocyte serum, fractions were prepared that were rich in IgG and IgA. Both were able to delay the rejection of canine renal homografts, the IgA-rich preparation to a somewhat greater degree. The findings in this study have been discussed in relation to the refining techniques that have been used for the production of globulin from heterologous ALS. © 1970

Rectification by charging -- the physics of contact-induced current asymmetry in molecular conductors

We outline the qualitatively different physics behind charging-induced current asymmetries in molecular conductors operating in the weakly interacting self-consistent field (SCF) and the strongly interacting Coulomb Blockade (CB) regimes. A conductance asymmetry arises in SCF because of the unequal mean-field potentials that shift a closed-shell conducting level differently for positive and negative bias. A very different current asymmetry arises for CB due to the unequal number of open-shell excitation channels at opposite bias voltages. The CB regime, dominated by single charge effects, typically requires a computationally demanding many-electron or Fock space description. However, our analysis of molecular Coulomb Blockade measurements reveals that many novel signatures can be explained using a {{simpler}} orthodox model that involves an incoherent sum of Fock space excitations and {\it{hence treats the molecule as a metallic dot or an island}}. This also reduces the complexity of the Fock space description by just including various charge configurations only, thus partially underscoring the importance of electronic structure, while retaining the essence of the single charge nature of the transport process. We finally point out, however, that the inclusion of electronic structure and hence well-resolved Fock space excitations is crucial in some notable examples.Comment: 12 pages, 10 figure

Entropy, Optimization and Counting

In this paper we study the problem of computing max-entropy distributions over a discrete set of objects subject to observed marginals. Interest in such distributions arises due to their applicability in areas such as statistical physics, economics, biology, information theory, machine learning, combinatorics and, more recently, approximation algorithms. A key difficulty in computing max-entropy distributions has been to show that they have polynomially-sized descriptions. We show that such descriptions exist under general conditions. Subsequently, we show how algorithms for (approximately) counting the underlying discrete set can be translated into efficient algorithms to (approximately) compute max-entropy distributions. In the reverse direction, we show how access to algorithms that compute max-entropy distributions can be used to count, which establishes an equivalence between counting and computing max-entropy distributions

A combined experimental and computational fluid dynamics analysis of the dynamics of drop formation

This article presents a complementary experimental and computational investigation of the effect of viscosity and flowrate on the dynamics of drop formation in the dripping mode. In contrast to previous studies, numerical simulations are performed with two popular commercial computational fluid dynamics (CFD) packages, CFX and FLOW-3D, both of which employ the volume of fluid (VOF) method. Comparison with previously published experimental and computational data and new experimental results reported here highlight the capabilities and limitations of the aforementioned packages

Consistent Application of Maximum Entropy to Quantum-Monte-Carlo Data

Bayesian statistics in the frame of the maximum entropy concept has widely been used for inferential problems, particularly, to infer dynamic properties of strongly correlated fermion systems from Quantum-Monte-Carlo (QMC) imaginary time data. In current applications, however, a consistent treatment of the error-covariance of the QMC data is missing. Here we present a closed Bayesian approach to account consistently for the QMC-data.Comment: 13 pages, RevTeX, 2 uuencoded PostScript figure

Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.Comment: 8 pages, 3 figure

Computationally Efficient Implementation of Convolution-based Locally Adaptive Binarization Techniques

One of the most important steps of document image processing is binarization. The computational requirements of locally adaptive binarization techniques make them unsuitable for devices with limited computing facilities. In this paper, we have presented a computationally efficient implementation of convolution based locally adaptive binarization techniques keeping the performance comparable to the original implementation. The computational complexity has been reduced from O(W2N2) to O(WN2) where WxW is the window size and NxN is the image size. Experiments over benchmark datasets show that the computation time has been reduced by 5 to 15 times depending on the window size while memory consumption remains the same with respect to the state-of-the-art algorithmic implementation

Boltzmann-Shannon Entropy: Generalization and Application

The paper deals with the generalization of both Boltzmann entropy and distribution in the light of most-probable interpretation of statistical equilibrium. The statistical analysis of the generalized entropy and distribution leads to some new interesting results of significant physical importance.Comment: 5 pages, Accepted in Mod.Phys.Lett.

Depression predicts future emergency hospital admissions in primary care patients with chronic physical illness

PublishedObjective More than 15 million people currently suffer from a chronic physical illness in England. The objective of this study was to determine whether depression is independently associated with prospective emergency hospital admission in patients with chronic physical illness. Method 1860 primary care patients in socially deprived areas of Manchester with at least one of four exemplar chronic physical conditions completed a questionnaire about physical and mental health, including a measure of depression. Emergency hospital admissions were recorded using GP records for the year before and the year following completion of the questionnaire. Results The number of patients who had at least one emergency admission in the year before and the year after completion of the questionnaire were 221/1411 (15.7%) and 234/1398 (16.7%) respectively. The following factors were independently associated with an increased risk of prospective emergency admission to hospital; having no partner OR 1.49 (95% CI 1.04 to 2.15); having ischaemic heart disease OR 1.60 (95% CI 1.04 to 2.46); having a threatening experience OR 1.16 (95% CI 1.04 to 1.29) per experience; depression OR 1.58 (95% CI 1.04 to 2.40); emergency hospital admission in year prior to questionnaire completion OR 3.41 (95% CI (1.98 to 5.86). Conclusion To prevent potentially avoidable emergency hospital admissions, greater efforts should be made to detect and treat co-morbid depression in people with chronic physical illness in primary care, with a particular focus on patients who have no partner, have experienced threatening life events, and who have had a recent emergency hospital admission.National Institute for Health Research (NIHR