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Cellular growth is the result of passive physical constraints and active biological processes. Their interplay leads to the appearance of robust and ubiquitous scaling laws relating linearly cell size, dry mass, and nuclear size. Despite accumulating experimental evidence, their origin is still unclear. Here, we show that these laws can be explained quantitatively by a single model of size regulation based on three simple, yet generic, physical constraints defining altogether the Pump-Leak model. Based on quantitative estimates, we clearly map the Pump-Leak model coarse-grained parameters with the dominant cellular components. We propose that dry mass density homeostasis arises from the scaling between proteins and small osmolytes, mainly amino-acids and ions. Our model predicts this scaling to naturally fail, both at senescence when DNA and RNAs are saturated by RNA polymerases and ribosomes respectively, and at mitotic entry due to the counterion release following histone tail modifications. Based on the same physical laws, we further show that nuclear scaling results from a osmotic balance at the nuclear envelope and a large pool of metabolites, which dilutes chromatin counterions that do not scale during growth.

Cellular uptake through the lipid membranes plays an important role in adsorbing nutrients and fighting infection and can be used for drug delivery and nanomedicine developments. Endocytosis is one of the known pathways of the cellular uptake which associate with elastic deformation of the membrane wrapping around the foreign particle. The deformability of the membrane itself is strongly regulated by the presence of a cortical cytoskeleton placed underneath the membrane. It has been shown that size, shape, and orientation of the particles influence on their entry into the cell. Here, we study the role of particle local curvature in the cellular uptake by investigating the wrapping of an elastic membrane around a long cylindrical object with an elliptical cross section. The membrane itself is adhered to a substrate mimicking the cytoskeleton. Membrane wrapping proceeds differently whether the initial contact occurs at the particle's highly curved tip (prolate) or along its long side (oblate). We obtain a wrapping phase diagram as a function of the membrane-cytoskeleton and the membrane-target adhesion energy, which includes three distinct regimes of engulfment(unwrapped, partially wrapped, and fully wrapped), separated by two phase transitions. We also provide analytical expressions for the boundary between the different regimes which confirm that the transitions strongly depend on the orientation and aspect ratio of the particle.

The volume of adhered cells has been shown experimentally to decrease during spreading. This effect can be understood from the pump-leak model, which we have extended to include mechano-sensitive ion transporters. We identify a novel effect that has important consequences on cellular volume loss; cells that are swollen due to a modulation of ion transport rates are more susceptible to volume loss in response to a tension increase. This effect explains in a plausible manner the discrepancies between three recent, independent experiments on adhered cells, between which both the magnitude of the volume change and its dynamics varied substantially. We suggest that starved and synchronized cells in two of the experiments were in a swollen state and, consequently, exhibited a large volume loss at steady state. Non-swollen cells, for which there is a very small steady-state volume decrease, are still predicted to transiently lose volume during spreading due to a relaxing viscoelastic tension that is large compared with the steady-state tension. We elucidate the roles of cell swelling and surface tension in cellular volume regulation and discuss their possible microscopic origins.

The dimensions and compositions of cells are tightly regulated by active processes. This exquisite control is embodied in the robust scaling laws relating cell size, dry mass, and nuclear size. Despite accumulating experimental evidence, a unified theoretical framework is still lacking. Here, we show that these laws and their breakdown can be explained quantitatively by three simple, yet generic, physical constraints defining altogether the Pump and Leak model (PLM). Based on estimations, we clearly map the PLM coarse-grained parameters with the dominant cellular events they stem from. We propose that dry mass density homeostasis arises from the scaling between proteins and small osmolytes, mainly amino-acids and ions. Our theory predicts this scaling to naturally fail, both at senescence when DNA and RNAs are saturated by RNA polymerases and ribosomes respectively, and at mitotic entry due to the counterion release following histone tail modifications. We further show that nuclear scaling result from osmotic balance at the nuclear envelope (NE) and a large pool of metabolites, which dilutes chromatin counterions that do not scale during growth.

Many proteins that undergo sequential enzymatic modification in the Golgi cisternae are displayed at the plasma membrane as cell identity markers. The modified proteins, called glycans, represent a molecular code. The fidelity of this glycan code is measured by how accurately the glycan synthesis machinery realises the desired target glycan distribution for a particular cell type and niche. In this paper, we quantitatively analyse the tradeoffs between the number of cisternae and the number and specificity of enzymes, in order to synthesize a prescribed target glycan distribution of a certain complexity. We find that to synthesize complex distributions, such as those observed in real cells, one needs to have multiple cisternae and precise enzyme partitioning in the Golgi. Additionally, for fixed number of enzymes and cisternae, there is an optimal level of specificity of enzymes that achieves the target distribution with high fidelity. Our results show how the complexity of the target glycan distribution places functional constraints on the Golgi cisternal number and enzyme specificity., Comment: 30 pages

Federated Learning is a new area of distributed Machine Learning (ML) that emerged to deal with data privacy concerns. In this approach, each client has access to a local and private dataset. They only exchange the model weights and updates. This paper presents a Federated Learning (FL) approach to a cloud Tumor-Infiltrating Lymphocytes (TIL) application. The results show that the FL approach outperformed the centralized one in all evaluated ML metrics. It also reduced the execution time although the financial cost has increased.

International audience; This paper presents CEL, a new distributed eventual leader election algorithm for dynamic networks, which exploits topological information to improve the choice of a central leader and reduce message exchanges. The algorithm has a crosslayer neighbors detection, with a neighbor-aware mechanism, to improve the sharing of topological knowledge and elect a central leader faster. It uses a self-pruning mechanism based on topological knowledge, combined with probabilistic gossip, to improve the performance of broadcast propagation. Evaluations were conducted on the OMNeT++ environment, simulating realistic MANET with interference, collision, and messages loss. Using different parameters values, we have compared CEL to Gómez-Calzado et al. algorithm [1], on the Random Walk and the Truncated Lévy Walk mobility models. The results show better performances than [1], including fewer messages sent, shortest paths to the leader, and a more stable algorithm.

Mechanics has been a central focus of physical biology in the past decade. In comparison, the osmotic and electric properties of cells are less understood. Here we show that a parameter central to both the physics and the physiology of the cell, its volume, depends on a mechano-osmotic coupling. We found that cells change their volume depending on the rate at which they change shape, when they spread, migrate or are externally deformed. Cells undergo slow deformation at constant volume, while fast deformation leads to volume loss. We propose a mechano-sensitive pump and leak model to explain this phenomenon. Our model and experiments suggest that volume modulation depends on the state of the actin cortex and the coupling of ion fluxes to membrane tension. This mechano-osmotic coupling defines a membrane tension homeostasis module constantly at work in cells, causing volume fluctuations associated with fast cell shape changes, with potential consequences on cellular physiology.

We propose to demonstrate a network slice placement optimization solution based on Deep Reinforcement Learning (DRL), referred to as Heuristically-controlled DRL, which uses a heuristic to control the DRL algorithm convergence. The solution is adapted to realistic networks with large scale and under non-stationary traffic conditions (namely, the network load). We demonstrate the applicability of the proposed solution and its higher and stable performance over a non-controlled DRL-based solution. Demonstration scenarios include full online learning with multiple volatile network slice placement request arrivals., Comment: arXiv admin note: substantial text overlap with arXiv:2010.08295

Os provedores de nuvem oferecem vários serviços para processamento e armazenamento de dados. Alguns desses recursos são propensos a falhas ou revogações. Neste caso, a adoção de técnicas de tolerância a falhas é essencial para minimizar o impacto e o custo que estas falhas podem exercer na execução das aplicações e no orçamento dos usuários. Uma das soluções amplamente adotadas são as técnicas de checkpoint and recovery (CR), que gravam o estado atual da aplicação para que, caso ocorra falhas, esta seja reiniciada a partir do último estado gravado. Assim, essas abordagens exigem um local de armazenamento seguro e disponível ao longo de toda a execução. Felizmente, os serviços de nuvens também podem ser utilizados para este fim, uma vez que diferentes opções de armazenamento também são oferecidos. Neste trabalho, queremos avaliar três desses serviços: o Amazon Simple Storage Service (S3), o Amazon Elastic Block Store (EBS) e o Amazon Elastic File System (EFS). O objetivo é caracterizar e avaliar o desempenho desses serviços em relação a gravação e recuperação de checkpoints no contexto de falhas induzidas pela revogação de máquinas virtuais.

In recent years, cloud computing has grown in popularity as they give users easy and almost instantaneous access to different computational resources. Some cloud providers, like Amazon, took advantage of the growing popularity and offered their VMs in some different hiring types: on-demand, reserved, and spot. The last type is usually offered at lower prices but can be terminated by the provider at any time. To deal with those failures, checkpoint and recovery procedures are typically used. In this context, we propose and analyze checkpoint and recovery procedures using three different storage services from Amazon: Amazon Simple Storage Service (S3), Amazon Elastic Block Store (EBS) and Amazon Elastic File System (EFS), considering spot VMs. These procedures were built upon the HADS framework, designed to schedule bag-of-tasks applications to spot and on-demand VMs. Our results showed that EBS outperformed the other approaches in terms of time spent on recording a checkpoint. But it required more time in the recovery procedure. EFS presented checkpointing and recovery times close to EBS but with higher monetary costs than the other services. S3 proved to be the best option in terms of monetary cost but required a longer time for recording a checkpoint, individually. However, when concurrent checkpoints were analysed, which can occur in a real application with lots of tasks, in our tests, S3 outperformed EFS in terms of execution time also.

The design principles dictating the spatio-temporal organisation of eukaryotic cells, and in particular the mechanisms controlling the self-organisation and dynamics of membrane-bound organelles such as the Golgi apparatus, remain elusive. Although this organelle was discovered 120 years ago, such basic questions as whether vesicular transport through the Golgi occurs in an anterograde (from entry to exit) or retrograde fashion are still strongly debated. Here, we address these issues by studying a quantitative model of organelle dynamics that includes: de-novo compartment generation, inter-compartment vesicular exchange, and biochemical conversion of membrane components. We show that anterograde or retrograde vesicular transports are asymptotic behaviors of a much richer dynamical system. Indeed, the structure and composition of cellular compartments and the directionality of vesicular exchange are intimately linked. They are emergent properties that can be tuned by varying the relative rates of vesicle budding, fusion and biochemical conversion.

Motivated by the mechanics of dynamin-mediated membrane tube fission we analyse the stability of fluid membrane tubes subjected to shear flow in azimuthal direction. We find a novel helical instability driven by the membrane shear flow which results in a non-equilibrium steady state for the tube fluctuations. This instability has its onset at shear rates that may be physiologically accessible under the action of dynamin and could also be probed using in-vitro experiments on membrane nanotubes, e.g. using magnetic tweezers. We discuss how such an instability may play a role in the mechanism for dynamin-mediated membrane tube fission., Main Text: 5 pages, 3 figures + S.I. 14 pages, 6 figures (Accepted Version)

International audience; The protein structure prediction is one of the key problems in Structural Bioinformatics. The protein function is directly related to its conformation and the folding can provide to researchers better understandings about the protein roles in the cell. Several computational methods have been proposed over the last decades to tackle the problem. In this paper, we propose an ab initio algorithm with database information for the protein structure prediction problem. We do so by designing some versions of a multi-agent system that use concepts of dynamic distributed evolutionary algorithms to speed up and improve the optimization by better adapting the algorithm to the target protein. The dynamic strategy consists of auto-adapting the number of optimization agents according to the needs and current status of the optimization process. The system is able to scale in/out itself depending on some diversity criteria. The algorithms also take advantage of structural knowledge from the Protein Data Bank to better guide the search and constraint the state space. To validate our computational strategies, we tested them on a set of eight protein sequences. The obtained results were topologically compatible with the experimental correspondent ones, thus corroborating the promising performance of the strategies.

International audience; We propose an online heuristic algorithm for the problem of network slice placement optimization. The solution is adapted to support placement on large scale networks and integrates Edge-specific and URLLC constraints. We rely on an approach called the "Power of Two Choices" to build the heuristic. The evaluation results show the good performance of the heuristic that solves the problem in few seconds under a large scale scenario. The heuristic also improves the acceptance ratio of network slice placement requests when compared against a deterministic online Integer Linear Programming (ILP) solution.

Building an infrastructure for exascale applications requires, in addition to many other key components, a stable and efficient failure detector. This article describes the design and evaluation of a robust failure detector that can maintain and distribute the correct list of alive resources within proven and scalable bounds. The detection and distribution of the fault information follow different overlay topologies that together guarantee minimal disturbance to the applications. A virtual observation ring minimizes the overhead by allowing each node to be observed by another single node, providing an unobtrusive behavior. The propagation stage uses a nonuniform variant of a reliable broadcast over a circulant graph overlay network and guarantees a logarithmic fault propagation. Extensive simulations, together with experiments on the Titan Oak Ridge National Laboratory supercomputer, show that the algorithm performs extremely well and exhibits all the desired properties of an exascale-ready algorithm.

The failure detector abstraction has been used to solve agreement problems in asynchronous systems prone to crash failures, but so far it has mostly been used in static and complete networks. This paper aims to adapt existing failure detectors in order to solve agreement problems in unknown, dynamic systems. We are specifically interested in the k-set agreement problem. The problem of k-set agreement is a generalization of consensus where processes can decide up to k different values. Although some solutions to this problem have been proposed in dynamic networks, they rely on communication synchrony or make strong assumptions on the number of process failures. In this paper we consider unknown dynamic systems modeled using the formalism of Time-Varying Graphs, and extend the definition of the existing $\Pi \Sigma _{x,y}$ failure detector to obtain the $\Pi \Sigma _{\bot, x,y}$ failure detector, which is sufficient to solve k-set agreement in our model. We then provide an implementation of this new failure detector using connectivity and message pattern assumptions. Finally, we present an algorithm using $\Pi \Sigma _{\bot, x,y}$ to solve k-set agreement.

By contracting Virtual Machines on Amazon EC2, the user can opt for on-demand instances, which have high availability, or instances spot (preemptive), which are idle resources offered at a lower price, whose availability may vary throughout the run. Despite the economic advantage, if the demand for resources increases, the preemptive instances can be revoked by the provider without warning, and put in hibernation until the demand decreases. This work presents initial results and the formulation of the scheduling problem which considers hibernation-prone preemptive instances to minimize the monetary cost of execution.

Internalization of particles by cells plays a crucial role for adsorbing nutrients and fighting infection. Endocytosis is one of the most important mechanisms of particle uptake, which encompasses multiple pathways. Although endocytosis is a complex mechanism involving biochemical signaling and active force generation, the energetic cost associated with the large deformations of the cell membrane wrapping around a foreign particle is an important factor controlling this process, which can be studied using quantitative physical models. Of particular interest is the competition between membrane-cytoskeleton and membrane-target adhesion. This competitive adhesion mechanism can be reproduced to some extent by studying particle wrapping by a membrane adhered to a substrate. We propose a theoretical analysis of this process. Here, we explore the wrapping of a lipid membrane around a long cylindrical object in the presence of a substrate mimicking the cytoskeleton. Using discretization of the Helfrich elastic energy, which accounts for the membrane bending rigidity and surface tension, we obtain a wrapping phase diagram as a function of the membrane-cytoskeleton and the membrane-target adhesion energy, which includes unwrapped, partially wrapped and fully wrapped states. We provide an analytical expression for the boundary between the different regimes. While the transition to partial wrapping is independent of the membrane tension, the transition to full wrapping is very much influenced by the membrane tension. We also show that target wrapping may proceed in an asymmetric fashion in the full wrapping regime.

Extended version: https://hal.archives-ouvertes.fr/hal-01661127v3; International audience; Mutual exclusion is one of the fundamental problems in distributed computing but existing mutual exclusion algorithms are unadapted to the dynamics and lack of membership knowledge of current distributed systems (e.g., mobile ad-hoc networks, peer-to-peer systems, etc.). Additionally, in order to circumvent the impossibility of solving mutual exclusion in asynchronous message passing systems where processes can crash, some solutions include the use of (T + Σl) [3], which is the weakest failure detector to solve mutual exclusion in known static distributed systems. In this paper, we define a new failure detector TΣlr which is equivalent to (T + Σl) in known static systems, and prove that T Σlr is the weakest failure detector to solve mutual exclusion in unknown dynamic systems with partial memory losses. We consider that crashed processes may recover.

We introduce DeepMoD, a Deep learning based Model Discovery algorithm. DeepMoD discovers the partial differential equation underlying a spatio-temporal data set using sparse regression on a library of possible functions and their derivatives. A neural network is used as function approximator and its output is used to construct the function library, allowing to perform the sparse regression within the neural network. This construction makes it extremely robust to noise, applicable to small data sets, and, contrary to other deep learning methods, does not require a training set. We benchmark our approach on several physical problems such as the Burgers', Korteweg-de Vries and Keller-Segel equations, and find that it requires as few as O ( 10 2 ) samples and works at noise levels up to 75%. Motivated by these results, we apply DeepMoD directly on noisy experimental time-series data from a gel electrophoresis experiment and find that it discovers the advection-diffusion equation describing this system.

International audience; Hierarchical broadcast strategies based on trees are scalable since they distribute the workload among processes and disseminate messages in parallel. In this work we propose an efficient best-effort broadcast algorithm that employs multiple spanning trees to propagate messages that are bundled on tree intersections. The algorithm is autonomic in the sense that it employs dynamic trees rooted at the source process and which rebuild themselves after processes crash. Experimental results obtained with simulation are presented showing the performance to the algorithm in terms of the latency and the number and sizes of messages employed.

Cell membrane deformations are crucial for proper cell function. Specialized protein assemblies initiate inward or outward membrane deformations that turn into, for example, filopodia or endocytic intermediates. Actin dynamics and actin-binding proteins are involved in this process, although their detailed role remains controversial. We show here that a dynamic, branched actin network is sufficient, in absence of any membrane-associated proteins, to initiate both inward and outward membrane deformation. With actin polymerization triggered at the membrane of liposomes, we produce inward filopodia-like structures at low tension, while outward endocytosis-like structures are robustly generated regardless of tension. Our results are reminiscent of endocytosis in mammalian cells, where actin polymerization forces are required when membrane tension is increased, and in yeast, where they are always required to overcome the opposing turgor pressure. By combining experimental observations with physical modeling, we propose a mechanism for actin-driven endocytosis under high tension or high pressure conditions.

The proper sorting of membrane components by regulated exchange between cellular organelles is crucial to intra-cellular organisation. This process relies on the budding and fusion of transport vesicles, and should be strongly influenced by stochastic fluctuations considering the relatively small size of many organelles. We identify the perfect sorting of two membrane components initially mixed in a single compartment as a first passage process, and we show that the mean sorting time exhibits two distinct regimes as a function of the ratio of vesicle fusion to budding rates. Low ratio values lead to fast sorting, but results in a broad size distribution of sorted compartments dominated by small entities. High ratio values result in two well defined sorted compartments but is exponentially slow. Our results suggest an optimal balance between vesicle budding and fusion for the rapid and efficient sorting of membrane components, and highlight the importance of stochastic effects for the steady-state organisation of intra-cellular compartments., Accepted for publication. Phys. Rev. Lets. December 2017

Cloud platforms have emerged as a prominent environment to execute high performance computing (HPC) applications providing on-demand resources as well as scalability. They usually offer different classes of Virtual Machines (VMs) which ensure different guarantees in terms of availability and volatility, provisioning the same resource through multiple pricing models. For instance, in Amazon EC2 cloud, the user pays per hour for on-demand VMs while spot VMs are unused instances available for lower price. Despite the monetary advantages, a spot VM can be terminated, stopped, or hibernated by EC2 at any moment. Using both hibernation-prone spot VMs (for cost sake) and on-demand VMs, we propose in this paper a static scheduling for HPC applications which are composed by independent tasks (bag-of-task) with deadline constraints. However, if a spot VM hibernates and it does not resume within a time which guarantees the application's deadline, a temporal failure takes place. Our scheduling, thus, aims at minimizing monetary costs of bag-of-tasks applications in EC2 cloud, respecting its deadline and avoiding temporal failures. To this end, our algorithm statically creates two scheduling maps: (i) the first one contains, for each task, its starting time and on which VM (i.e., an available spot or on-demand VM with the current lowest price) the task should execute; (ii) the second one contains, for each task allocated on a VM spot in the first map, its starting time and on which on-demand VM it should be executed to meet the application deadline in order to avoid temporal failures. The latter will be used whenever the hibernation period of a spot VM exceeds a time limit. Performance results from simulation with task execution traces, configuration of Amazon EC2 VM classes, and VMs market history confirms the effectiveness of our scheduling and that it tolerates temporal failures.

The Internet of Things (IoT) generates massive streams of data which call for ever more efficient real time processing. Designing and implementing a big data service for the real time processing of such data requires an extensive knowledge of both input load and data distribution in order to provide a service which can cope with the workload. In this context, we study in this paper the challenges inherent to the real time processing of massive data flows from the IoT. We provide a detailed analysis of traces gathered from a well-known healthcare sport-oriented application in order to illustrate our conclusions from a big data perspective.

International audience; The distributed computing scenario is rapidly evolving for integrating self-organizing and dynamic wireless networks. Unreliable failure detectors (FDs) are classical mechanisms that provide information about process failures and can help systems to cope with the high dynamics of these networks. A number of failure detection algorithms have been proposed so far. Nonetheless, most of them assume a global knowledge about the membership as well as a fully communication connectivity; additionally, they are time-based, requiring that eventually some bound on the message transmission will permanently hold. These assumptions are no longer appropriate to the new scenario. This paper presents a new FD protocol that implements a new class of detectors, namely ⋄ SM, which adapts the properties of the ⋄ S class to a dynamic network with an unknown membership. It has the interesting feature of being time-free, so that it does not rely on timers to detect failures; moreover, it tolerates the mobility of nodes and message losses.

DHT-based P2P systems provide a fault-tolerant and scalable means to store data blocks in a fully distributed way. Unfortunately, recent studies have shown that if connection/disconnection frequency is too high, data blocks may be lost. This is true for most of the current DHT-based systems' implementations. To deal with this problem, it is necessary to build more efficient replication and maintenance mechanisms. In this article, we study the effect of churn on PAST, an existing DHT-based P2P system. We then propose solutions to enhance churn tolerance and evaluate them through discrete event simulation.

Cells remodel their plasma membrane and cytoskeleton during numerous physiological processes, including spreading and motility. Morphological changes require the cell to adjust its membrane tension on different timescales. While it is known that endo- and exocytosis regulate the cell membrane area in a timescale of 1 h, faster processes, such as abrupt cell detachment, require faster regulation of the plasma membrane tension. In this article, we demonstrate that cell blebbing plays a critical role in the global mechanical homeostasis of the cell through regulation of membrane tension. Abrupt cell detachment leads to pronounced blebbing (which slow detachment does not), and blebbing decreases with time in a dynamin-dependent fashion. Cells only start spreading after a lag period whose duration depends on the cell's blebbing activity. Our model quantitatively reproduces the monotonic decay of the blebbing activity and accounts for the lag phase in the spreading of blebbing cells.

Membrane traffic requires membrane deformation to generate vesicles and tubules. Strong evidence suggests that assembly of curvature-active proteins can drive such membrane shape changes. Well-documented pathways often involve protein scaffolds, in particular coats (clathrin or COP). However, membrane curvature should, in principle, be influenced by any protein binding asymmetrically on a membrane; large membrane morphological changes could result from their aggregation. In the case of Shiga toxin or viral matrix proteins, tubules and buds appear to result from the cargo-driven formation of protein-lipid nanodomains, showing that collective protein behaviour is crucial in the process. We argue here that a combination of in vitro experiments on giant unilamellar vesicles and theoretical modelling based on statistical physics is ideally suited to tackle these collective effects.

Clathrin seems to be dispensable for some endocytic processes and, in several instances, no cytosolic coat protein complexes could be detected at sites of membrane invagination. Hence, new principles must in these cases be invoked to account for the mechanical force driving membrane shape changes. Here we show that the Gb3 (glycolipid)-binding B-subunit of bacterial Shiga toxin induces narrow tubular membrane invaginations in human and mouse cells and model membranes. In cells, tubule occurrence increases on energy depletion and inhibition of dynamin or actin functions. Our data thus demonstrate that active cellular processes are needed for tubule scission rather than tubule formation. We conclude that the B-subunit induces lipid reorganization that favours negative membrane curvature, which drives the formation of inward membrane tubules. Our findings support a model in which the lateral growth of B-subunit–Gb3 microdomains is limited by the invagination process, which itself is regulated by membrane tension. The physical principles underlying this basic cargo-induced membrane uptake may also be relevant to other internalization processes, creating a rationale for conceptualizing the perplexing diversity of endocytic routes. An imaging study of an early step of bacterial toxin intake into cells — membrane invagination — reveals a cargo-induced mechanism that may also apply to other pathogens and more generally to other endocytosis events. The B subunit of Shiga toxin (from Shigella dysenteriae) is seen to enter cells via narrow tubular membrane invaginations. The toxin induces membrane reorganization prior to formation of tubular invaginations, which occurs independently of protein complexes (like clathrin) that have been ascribed membrane deforming capacities, and also when cellular energy is depleted. So membrane invagination relies on physical principles and can occur spontaneously, without the need for sophisticated cellular machinery. A study of endocytosis of Shigella toxin shows that it enters cells via narrow tubular membrane invaginations, with similar properties on cell and model membranes. The toxin induces membrane reorganisation before the formation of tubular invaginations.

Distributed Prefix Tree indexing structures on top of peer-to-peer overlays provide a scalable solution to support range queries and proximity queries for Big Data applications. However, the latency of current maintenance protocols impacts very negatively on main operations like data insertions. This paper presents a new maintenance protocol that anticipates every data insertion on provisional child nodes. A performance evaluation conducted on the Prefix Hash Tree and Free Split shows that Free Split significantly reduces maintenance overheads, and therefore improves query response time.

We propose a model for membrane-cortex adhesion that couples membrane deformations, hydrodynamics, and kinetics of membrane-cortex ligands. In its simplest form, the model gives explicit predictions for the critical pressure for membrane detachment and for the value of adhesion energy. We show that these quantities exhibit a significant dependence on the active acto-myosin stresses. The model provides a simple framework to access quantitative information on cortical activity by means of micropipette experiments. We also extend the model to incorporate fluctuations and show that detailed information on the stability of membrane-cortex coupling can be obtained by a combination of micropipette aspiration and fluctuation spectroscopy measurements.

Streaming video consumption has risen sharply over the last years. It has not only reshaped the Internet traffic, it has also changed the manner of watching videos. Users are progressively moving from the old-fashioned scheduled television to video-on-demand (VoD) services. As broadcasting future seems to be online, customers have become more sensitive to VoD quality, expecting ever-higher bitrates and lower rebuffering. In this context, average bitrate is a key quality of service (QoS) metric. Therefore, content delivery networks (CDNs) and content providers must be committed to enforce average bitrate through service-level agreement (SLA) contracts. Adaptive content replication is a promising technique towards this goal. However, this still offers a major challenge for CDN providers, particularly as they aim to avoid waste of resources. In this work, we introduce WiseReplica, an adaptive replication scheme for peer-assisted VoD systems that enforces the average bitrate for Internet videos. Using an accurate machine-learned ranking, WiseReplica saves storage and bandwidth from the vast majority of non-popular contents for the most watched videos. Simulations using YouTube traces suggest that our approach meets users expectations efficiently. Compared to caching, WiseReplica reduces the required replication degree for the most-watched videos by two orders of magnitude, and under heavy load, it increases the average bitrate by roughly 85 %.

Caveolae are Ω-shaped invaginations of the plasma membrane, found in many types of cells[1]. Caveolae are enriched in cholesterol, and have a membrane composition similar to that of lipid rafts[2]. In addition, caveolae show a high concentration of the protein caveolin, a hairpin-structured membrane protein possessing a hydrophobic domain (32 amino acids (AA)), flanked by two hydrophilic terminii (N-terminal: 101 AA, C-terminal: 43 AA)[3]. Both terminii of caveolin extend from the cytosolic side of the membrane, conferring a strong asymmetrical structure to the caveolar domain. The goal of the present chapter is to give an overview of the possible physical effects that can stem from both these characteristics of caveolae, namely the composition difference with the rest of the plasma membrane (“raft-aspect”) and their patent asymmetry (“hairy-aspect”).

As local-area workstation networks are widely available, the idea of offering a software distributed shared memory (SDSM) system across interconnects of clusters is quite an attractive alternative for compute-intensive applications. However, the higher cost of sending a message over an inter-cluster link compared to an intra-cluster one can limit applications' performance on a multi-cluster SDSM system. In this paper, we present the extensions that we have added to the SDSM TreadMarks, which provides the i>lazy release consistency (LRC) memory model, in order to adapt it to a loosely-coupled cluster-based platform. We have implemented a logical per-cluster cache that exploits cluster locality. By accessing the cache of its cluster, a processor can share data previously requested by a second processor of its cluster, thereby, minimizing, the cost of inter-cluster communication.

Membrane proteins diffuse within the membrane, form oligomers and supramolecular assemblies. Using high-speed atomic force microscopy, we present direct experimental measure of an in-membrane-plane interaction potential between membrane proteins. In purple membranes, ATP-synthase c-rings formed dimers that temporarily dissociated. C-ring dimers revealed subdiffusive motion, while dissociated monomers diffused freely. C-rings center-to-center distance probability distribution allowed the calculation and modeling of an in-membrane-plane energy landscape that presented repulsion at 80 Å, most stable dimer association at 103 Å (−3.5 kBT strength), and dissociation at 125 Å (−1 kBT strength). This first experimental data of nonlabeled membrane protein diffusion and the corresponding in-membrane-plane interaction energy landscape characterized membrane protein interaction with an attractive range of several kBT that reaches to a radius of ∼50 Å within the membrane plane.

We model theoretically the effect of localized forces on a fluid membrane anchored to a uniform elastic medium. We use this as a simple model for the plasma membrane of a cell. The atomic force microscope (AFM) has been used to apply such forces, but large membrane perturbations occurring in vivo are also treated within the same framework. Inclusions of this nature may include cell junctions, filipodia, caveolae, and similar membrane invaginations. The breakdown of linear elastic response, as observed by AFM, is predicted to occur for forces as small as 10pN. We estimate the position of this crossover and the subsequent nonlinear behavior and make encouraging quantitative comparison with experiments. Intrinsic membrane inclusions interact through their overlapping strain fields. For similar, point force-like inclusions at large separations, this yields an attractive potential that scales like the inverse of their separation. For membranes that are intrinsically stiff or under tension, the binding force between inclusions can depend on the properties of the membrane and may be large enough to induce aggregation of inclusions, as observed experimentally. For inclusions that fix the magnitude of the membrane deformation, rather than the applied force, we demonstrate the possibility of metastable states, corresponding to finite separations. Finally, we discuss briefly the case in which inclusions couple to the membrane in more complex ways, such as via a torque (twist). In such cases, the interaction scales like a higher power of the separation, depends on the orientation of the inclusions, and can have either sign.

This paper presents the design, implementation and performance evaluation of a software fault manager for distributed applications. Dubbed Star, it uses the natural redundancy existing in networks of workstations to offer a high level of fault tolerance. Fault management is transparent to the supported parallel applications. To improve the response time of fault-tolerant applications, Star implements non-blocking and incremental checkpointing to perform an efficient backup of process state. Moreover, Star is application independent, highly configurable. Star actually runs on top of SunOs and is easily portable to UNIX™-like operating systems. The current implementation is based on independent checkpointing and message logging. Measurements show the efficiency and the limits of this implementation. The challenge is to show that a software approach to fault tolerance can efficiently be implemented in a standard networked environment.