Collective diffusion in sheared colloidal suspensions

Collective diffusivity in a suspension of rigid particles in steady linear viscous flows is evaluated by investigating the dynamics of the time correlation of long-wavelength density fluctuations. In the absence of hydrodynamic interactions between suspended particles in a dilute suspension of identical hard spheres, closed-form asymptotic expressions for the collective diffusivity are derived in the limits of low and high Péclet numbers, where the Péclet number Pe = gamma-dot a^2/D0 with gamma-dot being the shear rate and D0 = kB T/6πη a is the Stokes–Einstein diffusion coefficient of an isolated sphere of radius a in a fluid of viscosity η. The effect of hydrodynamic interactions is studied in the analytically tractable case of weakly sheared (Pe « 1) suspensions. For strongly sheared suspensions, i.e. at high Pe, in the absence of hydrodynamics the collective diffusivity Dc = 6 Ds∞, where Ds∞ is the long-time self-diffusivity and both scale as φ gamma-dot a^2$, where φ is the particle volume fraction. For weakly sheared suspensions it is shown that the leading dependence of collective diffusivity on the imposed flow is proportional to D0 φPe Ê, where Ê is the rate-of-strain tensor scaled by gamma-dot, regardless of whether particles interact hydrodynamically. When hydrodynamic interactions are considered, however, correlations of hydrodynamic velocity fluctuations yield a weakly singular logarithmic dependence of the cross-gradient-diffusivity on k at leading order as ak → 0 with k being the wavenumber of the density fluctuation. The diagonal components of the collective diffusivity tensor, both with and without hydrodynamic interactions, are of O(φPe2), quadratic in the imposed flow, and finite at k = 0. At moderate particle volume fractions, 0.10 ≤ φ ≤ 0.35, Brownian Dynamics (BD) numerical simulations in which there are no hydrodynamic interactions are performed and the transverse collective diffusivity in simple shear flow is determined via time evolution of the dynamic structure factor. The BD simulation results compare well with the derived asymptotic estimates. A comparison of the high-Pe BD simulation results with available experimental data on collective diffusivity in non-Brownian sheared suspensions shows a good qualitative agreement, though hydrodynamic interactions prove to be important at moderate concentrations

Text Embeddings Reveal (Almost) As Much As Text

How much private information do text embeddings reveal about the original text? We investigate the problem of embedding \textit{inversion}, reconstructing the full text represented in dense text embeddings. We frame the problem as controlled generation: generating text that, when reembedded, is close to a fixed point in latent space. We find that although a na\"ive model conditioned on the embedding performs poorly, a multi-step method that iteratively corrects and re-embeds text is able to recover $92\%$ of $32\text{-token}$ text inputs exactly. We train our model to decode text embeddings from two state-of-the-art embedding models, and also show that our model can recover important personal information (full names) from a dataset of clinical notes. Our code is available on Github: \href{https://github.com/jxmorris12/vec2text}{github.com/jxmorris12/vec2text}.Comment: Accepted at EMNLP 202

Tree Prompting: Efficient Task Adaptation without Fine-Tuning

Prompting language models (LMs) is the main interface for applying them to new tasks. However, for smaller LMs, prompting provides low accuracy compared to gradient-based finetuning. Tree Prompting is an approach to prompting which builds a decision tree of prompts, linking multiple LM calls together to solve a task. At inference time, each call to the LM is determined by efficiently routing the outcome of the previous call using the tree. Experiments on classification datasets show that Tree Prompting improves accuracy over competing methods and is competitive with fine-tuning. We also show that variants of Tree Prompting allow inspection of a model's decision-making process.Comment: Both first authors contributed equally; accepted to EMNLP 202

Tracking circadian rhythms of bone mineral deposition in murine calvarial organ cultures

Osteoblasts, which orchestrate the deposition of small apatite crystals through the expression of nucleating proteins, have been shown to also express clock genes associated with the circadian signaling pathway. We hypothesized that protein‐mediated bone mineralization may be linked to circadian oscillator mechanisms functioning in peripheral bone tissue. In this study, Per1 expression in ex vivo neonatal murine calvaria organ cultures was monitored for 6 days using a Per1 ‐ luciferase transgene as a bioluminescent indicator of clock function. Fluctuations in Per1 expression had a period of 25 ± 4 hours ( n  = 14) with early expression at CT09:59 ± 03:37 (CT = circadian time). We also established the kinetics of mineral deposition in developing bone by using noninvasive Raman microscopy to track mineral accumulation in calvarial tissue. The content and quality of newly deposited mineral was continually examined at the interparietal bone/fontanel boundary for a period of 6 days with 1‐hour temporal resolution. Using this approach, mineralization over time exhibited bursts of mineral deposition followed by little or no deposition, which was recurrent with a periodicity of 26.8 ± 9.6 hours. As many as six near‐daily mineralization events were observed in the calvaria before deposition ceased. Earliest mineralization events occurred at CT16:51 ± 03:45, which is 6 hours behind Per1 expression. These findings are consistent with the hypothesis that mineralization in developing bone tissue is regulated by a local circadian oscillator mechanism.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99084/1/jbmr1924.pd

Australian Antarctic Division contributions to Atmospheric Science in the Australian Antarctic Science Strategic Plan 2011/12 – 2020/21

第3回極域科学シンポジウム/第36回極域宙空圏シンポジウム 11月26日（月） 国立極地研究所 2階大会議

Molecular Recognition of Insulin by a Synthetic Receptor

The discovery of molecules that bind tightly and selectively to desired proteins continues to drive innovation at the interface of chemistry and biology. This paper describes the binding of human insulin by the synthetic receptor cucurbit[7]uril (Q7) in vitro. Isothermal titration calorimetry and fluorescence spectroscopy experiments show that Q7 binds to insulin with an equilibrium association constant of 1.5 × 106 M−1 and with 50−100-fold selectivity versus proteins that are much larger but lack an N-terminal aromatic residue, and with \u3e1000-fold selectivity versus an insulin variant lacking the N-terminal phenylalanine (Phe) residue. The crystal structure of the Q7·insulin complex shows that binding occurs at the N-terminal Phe residue and that the N-terminus unfolds to enable binding. These findings suggest that site-selective recognition is based on the properties inherent to a protein terminus, including the unique chemical epitope presented by the terminal residue and the greater freedom of the terminus to unfold, like the end of a ball of string, to accommodate binding. Insulin recognition was predicted accurately from studies on short peptides and exemplifies an approach to protein recognition by targeting the terminus

Bostonia: The Boston University Alumni Magazine. Volume 10

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs