Berlin as the showdown city: Architectural symbolism in the capitol of the Cold War

In the wake of World War II, reconstruction of destroyed Berlin was the most important task for Germany’s occupying powers, the Soviet Union and the Western Allies. Corresponding to the heightening tensions of the Cold War, reconstruction of Berlin also asserted an ideological purpose, as new architecture was paired with new ideology. This thesis presents the significance of Berlin as an architectural “showdown city” between the Soviet Union and the West, and analyzes the following research question: to what extent does Berlin’s architectural development of main urban avenues, architectural centerpieces, and residential architecture during the Cold War reflect the polarized ideological dichotomy between the Soviet Union and the West? In each of these arenas, architectural design mirrored the Cold War’s competition between East and West, as this analysis juxtaposes Kurfurstendamm, Kaiser-Wilhelm-Gedachtnis-Kirche (Kaiser Wilhelm Memorial Church), and Hansaviertel in West Berlin to East Berlin’s Stalinallee, Fernsehturm (television tower), Marzahn, and Hellersdorf. With stark stylistic differences and overt ideological associations, both East and West Berlin mediated common urban circumstances of the post-World War II era, leaving an architectural legacy still visible in Berlin today

Interferon lambda protects the female reproductive tract against Zika virus infection

Zika virus infections can cause devastating congenital birth defects but the underlying interactions with the host immune system are not well understood. Here, the authors examine the immune basis of vaginal protection and susceptibility to Zika viral infection, and identify a hormonal dependent role for interferon-lambda-mediated protection against disease

IMM-BCP-01, a patient-derived anti-SARS-CoV-2 antibody cocktail, is active across variants of concern including Omicron BA.1 and BA.2

Monoclonal antibodies are an efficacious therapy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, rapid viral mutagenesis led to escape from most of these therapies, outlining the need for an antibody cocktail with a broad neutralizing potency. Using an unbiased interrogation of the memory B cell repertoire of patients with convalescent COVID-19, we identified human antibodies with broad antiviral activity in vitro and efficacy in vivo against all tested SARS-CoV-2 variants of concern, including Delta and Omicron BA.1 and BA.2. Here, we describe an antibody cocktail, IMM-BCP-01, that consists of three patient-derived broadly neutralizing antibodies directed at nonoverlapping surfaces on the SARS-CoV-2 Spike protein. Two antibodies, IMM20184 and IMM20190, directly blocked Spike binding to the ACE2 receptor. Binding of the third antibody, IMM20253, to its cryptic epitope on the outer surface of RBD altered the conformation of the Spike Trimer, promoting the release of Spike monomers. These antibodies decreased Omicron SARS-CoV-2 infection in the lungs of Syrian golden hamsters in vivo and potently induced antiviral effector response in vitro, including phagocytosis, ADCC, and complement pathway activation. Our preclinical data demonstrated that the three-antibody cocktail IMM-BCP-01 could be a promising means for preventing or treating infection of SARS-CoV-2 variants of concern, including Omicron BA.1 and BA.2, in susceptible individuals

Evaluation of predictions of the stochastic model of organelle production based on exact distributions

We present a reanalysis of the stochastic model of organelle production and show that the equilibrium distributions for the organelle numbers predicted by this model can be readily calculated in three different scenarios. These three distributions can be identified as standard distributions, and the corresponding exact formulae for their mean and variance can therefore be used in further analysis. This removes the need to rely on stochastic simulations or approximate formulae (derived using the fluctuation dissipation theorem). These calculations allow for further analysis of the predictions of the model. On the basis of this we question the extent to which the model can be used to conclude that peroxisome biogenesis is dominated by de novo production when Saccharomyces cerevisiae cells are grown on glucose medium

Neutralization, effector function and immune imprinting of Omicron variants

Currently circulating SARS-CoV-2 variants have acquired convergent mutations at hot spots in the receptor-binding domai

Ultrasonic Flaw Detection Using Neural Network Models and Statistical Analysis: Simulation Studies

Flaw detection problems in ultrasonic NDE can be considered as two-class classification problems, i.e., determining whether a flaw is present or not present. To be practical, a flaw classification method must be able to handle the uncertainties associated with interference from grain noise which leads to poor signal-to-noise ratios (SNR). In this work, the use of neural network models and statistical correlation is demonstrated for one such detection/classification problem. In particular, based on simulation studies, we wish to establish practical strategies in detecting weak volumetric flaw signals corrupted by high grain noise. An example of this type that is of recent interest is the detection of “hard-alpha” inclusions in aircraft titanium components [1]. Both the feasibility and reliability of using these classifiers are assessed. This effort was carried out in parallel with another study [2] where more traditional signal processing approaches were taken

Robust Online Hamiltonian Learning

In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Bayesian experimental design, and apply them to the problem of inferring the dynamical parameters of a quantum system. We design the algorithm with practicality in mind by including parameters that control trade-offs between the requirements on computational and experimental resources. The algorithm can be implemented online (during experimental data collection), avoiding the need for storage and post-processing. Most importantly, our algorithm is capable of learning Hamiltonian parameters even when the parameters change from experiment-to-experiment, and also when additional noise processes are present and unknown. The algorithm also numerically estimates the Cramer-Rao lower bound, certifying its own performance.Comment: 24 pages, 12 figures; to appear in New Journal of Physic