Effect of adhesion geometry and rigidity on cellular force distributions

The behaviour and fate of tissue cells is controlled by the rigidity and geometry of their adhesive environment, possibly through forces localized to sites of adhesion. We introduce a mechanical model that predicts cellular force distributions for cells adhering to adhesive patterns with different geometries and rigidities. For continuous adhesion along a closed contour, forces are predicted to be localized to the corners. For discrete sites of adhesion, the model predicts the forces to be mainly determined by the lateral pull of the cell contour. With increasing distance between two neighboring sites of adhesion, the adhesion force increases because cell shape results in steeper pulling directions. Softer substrates result in smaller forces. Our predictions agree well with experimental force patterns measured on pillar assays.Comment: 4 pages, Revtex with 4 figure

Numerical and semi-analytic core mass distributions in supersonic isothermal turbulence

We investigate the influence of the turbulence forcing on the mass distributions of gravitationally unstable cores by postprocessing data from simulations of non-selfgravitating isothermal supersonic turbulence with varying resolution. In one set of simulations solenoidal forcing is applied, while the second set uses purely compressive forcing to excite turbulent motions. From the resulting density field, we compute the mass distribution of gravitationally unstable cores by means of a clump-finding algorithm. Using the time-averaged probability density functions of the mass density, semi-analytic mass distributions are calculated from analytical theories. We apply stability criteria that are based on the Bonnor-Ebert mass resulting from the thermal pressure and from the sum of thermal and turbulent pressure. Although there are uncertainties in the application of the clump-finding algorithm, we find systematic differences in the mass distributions obtained from solenoidal and compressive forcing. Compressive forcing produces a shallower slope in the high-mass power-law regime compared to solenoidal forcing. The mass distributions also depend on the Jeans length resulting from the choice of the mass in the computational box, which is freely scalable for non-selfgravitating isothermal turbulence. Provided that all cores are numerically resolved and most cores are small compared to the length scale of the forcing, the normalised core mass distributions are found to be close to the semi-analytic models. Especially for the high-mass tails, the Hennebelle-Chabrier theory implies that the additional support due to turbulent pressure is important.Comment: 15 pages, 7 figures, submitted to A&

The role of actors in the policy design process: introducing design coalitions to explain policy output

Despite a renaissance of policy design thinking in public policy literature and a renewed interest in agency in the policy process literature, agency in the policy design process has, so far, not received systematic attention. Understanding the agency at play when designing policy, however, is crucial for better comprehension of policy design choices and varia- tion in policy design across cases. Here, we build on the hierarchical structure of design elements that constitute each policy and analyse how actors position themselves during a policy design process in relation to individual design elements. Our aim is to establish dif- ferent actors’ roles in shaping the policy output in an inductive, single-case study using the empirical case of the Swiss renewable energy feed-in tariff. Notably, we find agency in the form of coalitions which emerge around particular design elements. Based on our repre- sentative analysis, we derive the generalisable concept of design coalitions that we define as relational structures of actors who gather around and advocate for specific policy design elements during the policy design process. Policy design coalitions are dynamic through- out the design process and strategic and constitute the determinants in translating policy problems into final policy designs during policy designing. Our approach allows us to shed light on the role of agency in the policy design process in general

Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation

Optical coherence tomography (OCT) has become the most important imaging modality in ophthalmology. A substantial amount of research has recently been devoted to the development of machine learning (ML) models for the identification and quantification of pathological features in OCT images. Among the several sources of variability the ML models have to deal with, a major factor is the acquisition device, which can limit the ML model's generalizability. In this paper, we propose to reduce the image variability across different OCT devices (Spectralis and Cirrus) by using CycleGAN, an unsupervised unpaired image transformation algorithm. The usefulness of this approach is evaluated in the setting of retinal fluid segmentation, namely intraretinal cystoid fluid (IRC) and subretinal fluid (SRF). First, we train a segmentation model on images acquired with a source OCT device. Then we evaluate the model on (1) source, (2) target and (3) transformed versions of the target OCT images. The presented transformation strategy shows an F1 score of 0.4 (0.51) for IRC (SRF) segmentations. Compared with traditional transformation approaches, this means an F1 score gain of 0.2 (0.12).Comment: * Contributed equally (order was defined by flipping a coin) --------------- Accepted for publication in the "IEEE International Symposium on Biomedical Imaging (ISBI) 2019

Magnetization plateaux in an extended Shastry-Sutherland model

We study an extended two-dimensional Shastry-Sutherland model in a magnetic field where besides the usual Heisenberg exchanges of the Shastry-Sutherland model two additional SU(2) invariant couplings are included. Perturbative continous unitary transformations are used to determine the leading order effects of the additional couplings on the pure hopping and on the long-range interactions between the triplons which are the most relevant terms for small magnetization. We then compare the energy of various magnetization plateaux in the classical limit and we discuss the implications for the two-dimensional quantum magnet SrCu$_2$(BO$_3$)$_2$.Comment: 8 pages, Proceedings of the HFM2008 Conferenc

On the effectiveness of partial variance reduction in federated learning with heterogeneous data

Data heterogeneity across clients is a key challenge in federated learning. Prior works address this by either aligning client and server models or using control variates to correct client model drift. Although these methods achieve fast convergence in convex or simple non-convex problems, the performance in over-parameterized models such as deep neural networks is lacking. In this paper, we first revisit the widely used FedAvg algorithm in a deep neural network to understand how data heterogeneity influences the gradient updates across the neural network layers. We observe that while the feature extraction layers are learned efficiently by FedAvg, the substantial diversity of the final classification layers across clients impedes the performance. Motivated by this, we propose to correct model drift by variance reduction only on the final layers. We demonstrate that this significantly outperforms existing benchmarks at a similar or lower communication cost. We furthermore provide proof for the convergence rate of our algorithm.Comment: Accepted to CVPR 202

Global DNA hypomethylation prevents consolidation of differentiation programs and allows reversion to the embryonic stem cell state.

DNA methylation patterns change dynamically during mammalian development and lineage specification, yet scarce information is available about how DNA methylation affects gene expression profiles upon differentiation. Here we determine genome-wide transcription profiles during undirected differentiation of severely hypomethylated (Dnmt1⁻/⁻) embryonic stem cells (ESCs) as well as ESCs completely devoid of DNA methylation (Dnmt1⁻/⁻;Dnmt3a⁻/⁻;Dnmt3b⁻/⁻ or TKO) and assay their potential to transit in and out of the ESC state. We find that the expression of only few genes mainly associated with germ line function and the X chromosome is affected in undifferentiated TKO ESCs. Upon initial differentiation as embryoid bodies (EBs) wild type, Dnmt1⁻/⁻ and TKO cells downregulate pluripotency associated genes and upregulate lineage specific genes, but their transcription profiles progressively diverge upon prolonged EB culture. While Oct4 protein levels are completely and homogeneously suppressed, transcription of Oct4 and Nanog is not completely silenced even at late stages in both Dnmt1⁻/⁻ and TKO EBs. Despite late wild type and Dnmt1⁻/⁻ EBs showing a much higher degree of concordant expression, after EB dissociation and replating under pluripotency promoting conditions both Dnmt1⁻/⁻ and TKO cells, but not wild type cells rapidly revert to expression profiles typical of undifferentiated ESCs. Thus, while DNA methylation seems not to be critical for initial activation of differentiation programs, it is crucial for permanent restriction of developmental fate during differentiation

Characterization of PvuRts1I endonuclease as a tool to investigate genomic 5–hydroxymethylcytosine

In mammalian genomes a sixth base, 5-hydroxymethylcytosine ( hm C), is generated by enzymatic oxidation of 5-methylcytosine ( m C). This discovery has raised fundamental questions about the functional relevance of hm C in mammalian genomes. Due to their very similar chemical structure, discrimination of the rare hm C against the far more abundant m C is technically challenging and to date no methods for direct sequencing of hm C have been reported. Here, we report on a purified recombinant endonuclease, PvuRts1I, which selectively cleaves hm C-containing sequences. We determined the consensus cleavage site of PvuRts1I as hm CN 11–12 /N 9–10 G and show first data on its potential to interrogate hm C patterns in mammalian genomes

Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity

In federated learning, data heterogeneity is a critical challenge. A straightforward solution is to shuffle the clients' data to homogenize the distribution. However, this may violate data access rights, and how and when shuffling can accelerate the convergence of a federated optimization algorithm is not theoretically well understood. In this paper, we establish a precise and quantifiable correspondence between data heterogeneity and parameters in the convergence rate when a fraction of data is shuffled across clients. We prove that shuffling can quadratically reduce the gradient dissimilarity with respect to the shuffling percentage, accelerating convergence. Inspired by the theory, we propose a practical approach that addresses the data access rights issue by shuffling locally generated synthetic data. The experimental results show that shuffling synthetic data improves the performance of multiple existing federated learning algorithms by a large margin