Protein Mobility in the Cytoplasm of Escherichia coli

The rate of protein diffusion in bacterial cytoplasm may constrain a variety of cellular functions and limit the rates of many biochemical reactions in vivo. In this paper, we report noninvasive measurements of the apparent diffusion coefficient of green fluorescent protein (GFP) in the cytoplasm of Escherichia coli. These measurements were made in two ways: by photobleaching of GFP fluorescence and by photoactivation of a red-emitting fluorescent state of GFP (M. B. Elowitz, M. G. Surette, P. E. Wolf, J. Stock, and S. Leibler, Curr. Biol. 7:809-812, 1997). The apparent diffusion coefficient, Da, of GFP in E. coli DH5alpha was found to be 7.7 ± 2.5 µm^2/s. A 72-kDa fusion protein composed of GFP and a cytoplasmically localized maltose binding protein domain moves more slowly, with Da of 2.5 ± 0.6 µm^2/s. In addition, GFP mobility can depend strongly on at least two factors: first, Da is reduced to 3.6 ± 0.7 µm^2/s at high levels of GFP expression; second, the addition to GFP of a small tag consisting of six histidine residues reduces Da to 4.0 ± 2.0 µm^2/s. Thus, a single effective cytoplasmic viscosity cannot explain all values of Da reported here. These measurements have implications for the understanding of intracellular biochemical networks

TAN without a burn: Scaling Laws of DP-SGD

Differentially Private methods for training Deep Neural Networks (DNNs) have progressed recently, in particular with the use of massive batches and aggregated data augmentations for a large number of steps. These techniques require much more compute than their non-private counterparts, shifting the traditional privacy-accuracy trade-off to a privacy-accuracy-compute trade-off and making hyper-parameter search virtually impossible for realistic scenarios. In this work, we decouple privacy analysis and experimental behavior of noisy training to explore the trade-off with minimal computational requirements. We first use the tools of R\'enyi Differential Privacy (RDP) to show that the privacy budget, when not overcharged, only depends on the total amount of noise (TAN) injected throughout training. We then derive scaling laws for training models with DP-SGD to optimize hyper-parameters with more than a 100 reduction in computational budget. We apply the proposed method on CIFAR-10 and ImageNet and, in particular, strongly improve the state-of-the-art on ImageNet with a +9 points gain in accuracy for a privacy budget epsilon=8

The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning

We consider private federated learning (FL), where a server aggregates differentially private gradient updates from a large number of clients in order to train a machine learning model. The main challenge is balancing privacy with both classification accuracy of the learned model as well as the amount of communication between the clients and server. In this work, we build on a recently proposed method for communication-efficient private FL -- the MVU mechanism -- by introducing a new interpolation mechanism that can accommodate a more efficient privacy analysis. The result is the new Interpolated MVU mechanism that provides SOTA results on communication-efficient private FL on a variety of datasets

Isotope geochemistry and petrogenesis of peralkaline Middle Miocene ignimbrites from central Sonora: relationship with continental break-up and the birth of the Gulf of California

Middle Miocene peralkaline ignimbrites constitute a specific geodynamic marker of the early stage of opening of the Gulf of California, preserved either in central Sonora or the Puertecitos area, in Baja California. Very uniform ages (12-12.5 Ma) obtained on these rocks show that this volcanic episode corresponds to a specific stage in the tectonic evolution of the proto-gulf area. Field observations and slightly different Sr and Nd isotopic signatures support eruptions from several small volume magma batches rather than from a large-volume caldera forming event. Isotopic ratios help to constrain the petrogenesis of the peralkaline liquids by fractional crystallization of transitional basalts in a shallow reservoir, with slight contamination by Precambrian upper crustal material. Less differentiated glomeroporphyritic icelandites erupted at about 11 Ma, mark an increase in the magma production rate and highlight an easier access to the surface, illustrating an advanced stage in the weakening of the continental crust. The tilting of the Middle Tertiary sequences results from a major change in the tectonic regime, from E-W extension giving rise to N-S grabens, to NNW-SSE strike-slip motion that can be related to the transfer of Baja California from North America to the Pacific plate. The location of peralkaline volcanism coincides with the southern edge of the Precambrian crust and the southernmost extension of the California slab window at 12.5 Ma

Evaluating Privacy Leakage in Split Learning

Privacy-Preserving machine learning (PPML) can help us train and deploy models that utilize private information. In particular, on-device machine learning allows us to avoid sharing raw data with a third-party server during inference. On-device models are typically less accurate when compared to their server counterparts due to the fact that (1) they typically only rely on a small set of on-device features and (2) they need to be small enough to run efficiently on end-user devices. Split Learning (SL) is a promising approach that can overcome these limitations. In SL, a large machine learning model is divided into two parts, with the bigger part residing on the server side and a smaller part executing on-device, aiming to incorporate the private features. However, end-to-end training of such models requires exchanging gradients at the cut layer, which might encode private features or labels. In this paper, we provide insights into potential privacy risks associated with SL. Furthermore, we also investigate the effectiveness of various mitigation strategies. Our results indicate that the gradients significantly improve the attackers' effectiveness in all tested datasets reaching almost perfect reconstruction accuracy for some features. However, a small amount of differential privacy (DP) can effectively mitigate this risk without causing significant training degradation.Comment: 10 page

Careful Who You Trust: Studying the Pitfalls of Cross-Origin Communication

In the past, Web applications were mostly static and most of the content was provided by the site itself. Nowadays, they have turned into rich client-side experiences customized for the user where third parties supply a considerable amount of content, e.g., analytics, advertisements, or integration with social media platforms and external services. By default, any exchange of data between documents is governed by the Same-Origin Policy, which only permits to exchange data with other documents sharing the same protocol, host, and port. Given the move to a more interconnected Web, standard bodies and browser vendors have added new mechanisms to enable cross-origin communication, primarily domain relaxation, postMessages, and CORS. While prior work has already shown the pitfalls of not using these mechanisms securely (e.g., omitting origin checks for incoming postMessages), we instead focus on the increased attack surface created by the trust that is necessarily put into the communication partners. To that end, we report on a study of the Tranco Top 5,000 to measure the prevalence of cross-origin communication. By analyzing the interactions between sites, we build an interconnected graph of the trust relations necessary to run the Web. Subsequently, based on this graph, we estimate the damage that can be caused through real-world exploitability of existing client-side XSS flaws

Leptin directly stimulates thermogenesis in skeletal muscle

AbstractUsing a method involving repeated oxygen uptake (MO2) determinations in skeletal muscle ex vivo, the addition of leptin was found to increase MO2 in soleus muscles from lean mice. These effects were found to be inhibited by phosphatidylinositol 3-kinase inhibitors, absent in muscles from obese Leprdb mice which have the dysfunctional long form of leptin receptor, and blunted in muscles from diet-induced obese mice in the fed state but not during fasting. These findings indicate that leptin has direct thermogenic effects in skeletal muscle, and that these effects require both the long form of leptin receptors and phosphatidylinositol 3-kinase signalling

A note on the consistency of Hybrid Eulerian/Lagrangian approach to multiphase flows

The aim of the present paper is to introduce and to discuss inconsistencies errors that may arise when Eulerian and Lagrangian models are coupled for the simulations of turbulent poly-dispersed two-phase flows. In these hydrid models, two turbulence models are in fact implicitely used at the same time and it is essential to check that they are consistent, in spite of their apparent different formulations. This issue appears in particular in the case of very-small particles, or tracer-limit particles, and it is shown that coupling inconsistent turbulence models (Eulerian and Lagrangian) can result in non-physical results, notably for second-order fluid velocity moments. This problem is illustrated by some computations for fluid particles in a turbulent channel flow using several coupling strategies.Comment: 14 pages, 3 figure