Vacuum-UV spectroscopy of interstellar ice analogs. II. Absorption cross-sections of nonpolar ice molecules

Dust grains in cold circumstellar regions and dark-cloud interiors at 10-20 K are covered by ice mantles. A nonthermal desorption mechanism is invoked to explain the presence of gas-phase molecules in these environments, such as the photodesorption induced by irradiation of ice due to secondary ultraviolet photons. To quantify the effects of ice photoprocessing, an estimate of the photon absorption in ice mantles is required. In a recent work, we reported the vacuum-ultraviolet (VUV) absorption cross sections of nonpolar molecules in the solid phase. The aim was to estimate the VUV-absorption cross sections of nonpolar molecular ice components, including CH4, CO2, N2, and O2. The column densities of the ice samples deposited at 8 K were measured in situ by infrared spectroscopy in transmittance. VUV spectra of the ice samples were collected in the 120-160 nm (10.33-7.74 eV) range using a commercial microwave-discharged hydrogen flow lamp. We found that, as expected, solid N2 has the lowest VUV-absorption cross section, which about three orders of magnitude lower than that of other species such as O2, which is also homonuclear. Methane (CH4) ice presents a high absorption near Ly-alpha (121.6 nm) and does not absorb below 148 nm. Estimating the ice absorption cross sections is essential for models of ice photoprocessing and allows estimating the ice photodesorption rates as the number of photodesorbed molecules per absorbed photon in the ice.Comment: 9 pages, 6 figures, 7 table

Protein-mediated DNA Loop Formation and Breakdown in a Fluctuating Environment

Living cells provide a fluctuating, out-of-equilibrium environment in which genes must coordinate cellular function. DNA looping, which is a common means of regulating transcription, is very much a stochastic process; the loops arise from the thermal motion of the DNA and other fluctuations of the cellular environment. We present single-molecule measurements of DNA loop formation and breakdown when an artificial fluctuating force, applied to mimic a fluctuating cellular environment, is imposed on the DNA. We show that loop formation is greatly enhanced in the presence of noise of only a fraction of $k_B T$, yet find that hypothetical regulatory schemes that employ mechanical tension in the DNA--as a sensitive switch to control transcription--can be surprisingly robust due to a fortuitous cancellation of noise effects

Ontogeny of Human IgE-expressing B Cells and Plasma Cells

BACKGROUND: IgE‐expressing (IgE(+)) plasma cells (PCs) provide a continuous source of allergen‐specific IgE that is central to allergic responses. The extreme sparsity of IgE(+) cells in vivo has confined their study almost entirely to mouse models. OBJECTIVE: To characterize the development pathway of human IgE(+) PCs and to determine the ontogeny of human IgE(+) PCs. METHODS: To generate human IgE(+) cells, we cultured tonsil B cells with IL‐4 and anti‐CD40. Using FACS and RT‐PCR, we examined the phenotype of generated IgE(+) cells, the capacity of tonsil B‐cell subsets to generate IgE(+) PCs and the class switching pathways involved. RESULTS: We have identified three phenotypic stages of IgE(+) PC development pathway, namely (i) IgE(+)germinal centre (GC)‐like B cells, (ii) IgE(+) PC‐like ‘plasmablasts’ and (iii) IgE(+) PCs. The same phenotypic stages were also observed for IgG1(+) cells. Total tonsil B cells give rise to IgE(+) PCs by direct and sequential switching, whereas the isolated GC B‐cell fraction, the main source of IgE(+) PCs, generates IgE(+) PCs by sequential switching. PC differentiation of IgE(+) cells is accompanied by the down‐regulation of surface expression of the short form of membrane IgE (mIgE(S)), which is homologous to mouse mIgE, and the up‐regulation of the long form of mIgE (mIgE(L)), which is associated with an enhanced B‐cell survival and expressed in humans, but not in mice. CONCLUSION: Generation of IgE(+) PCs from tonsil GC B cells occurs mainly via sequential switching from IgG. The mIgE(L)/mIgE(S) ratio may be implicated in survival of IgE(+) B cells during PC differentiation and allergic disease

The Cerenkov effect revisited: from swimming ducks to zero modes in gravitational analogs

We present an interdisciplinary review of the generalized Cerenkov emission of radiation from uniformly moving sources in the different contexts of classical electromagnetism, superfluid hydrodynamics, and classical hydrodynamics. The details of each specific physical systems enter our theory via the dispersion law of the excitations. A geometrical recipe to obtain the emission patterns in both real and wavevector space from the geometrical shape of the dispersion law is discussed and applied to a number of cases of current experimental interest. Some consequences of these emission processes onto the stability of condensed-matter analogs of gravitational systems are finally illustrated.Comment: Lecture Notes at the IX SIGRAV School on "Analogue Gravity" in Como, Italy from May 16th-21th, 201

Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis

Transfer learning represents a recent paradigm shift in the way we build artificial intelligence (AI) systems. In contrast to training task-specific models, transfer learning involves pre-training deep learning models on a large corpus of data and minimally fine-tuning them for adaptation to specific tasks. Even so, for 3D medical imaging tasks, we do not know if it is best to pre-train models on natural images, medical images, or even synthetically generated MRI scans or video data. To evaluate these alternatives, here we benchmarked vision transformers (ViTs) and convolutional neural networks (CNNs), initialized with varied upstream pre-training approaches. These methods were then adapted to three unique downstream neuroimaging tasks with a range of difficulty: Alzheimer's disease (AD) and Parkinson's disease (PD) classification, "brain age" prediction. Experimental tests led to the following key observations: 1. Pre-training improved performance across all tasks including a boost of 7.4% for AD classification and 4.6% for PD classification for the ViT and 19.1% for PD classification and reduction in brain age prediction error by 1.26 years for CNNs, 2. Pre-training on large-scale video or synthetic MRI data boosted performance of ViTs, 3. CNNs were robust in limited-data settings, and in-domain pretraining enhanced their performances, 4. Pre-training improved generalization to out-of-distribution datasets and sites. Overall, we benchmarked different vision architectures, revealing the value of pre-training them with emerging datasets for model initialization. The resulting pre-trained models can be adapted to a range of downstream neuroimaging tasks, even when training data for the target task is limited