Atomic-scale factors that control the rate capability of nanostructured amorphous Si for high-energy-density batteries

Nanostructured Si is the most promising high-capacity anode material to substantially increase the energy density of Li-ion batteries. Among the remaining challenges is its low rate capability as compared to conventional materials. To understand better what controls the diffusion of Li in the amorphous Li-Si alloy, we use a novel machine-learning potential trained on more than 40,000 ab-initio calculations and nanosecond-scale molecular dynamics simulations, to visualize for the first time the delithiation of entire LiSi nanoparticles. Our results show that the Si host is not static but undergoes a dynamic rearrangement from isolated atoms, to chains, and clusters, with the Li diffusion strongly governed by this Si rearrangement. We find that the Li diffusivity is highest when Si segregates into clusters, so that Li diffusion proceeds via hopping between the Si clusters. The average size of Si clusters and the concentration range over which Si clustering occurs can thus function as design criteria for the development of rate-improved anodes based on modified Si.Comment: 31 pages, 3 main figures, 14 supplementary figures, 1 main table, 1 supplementary tabl

Scaling laws governing stochastic growth and division of single bacterial cells

Uncovering the quantitative laws that govern the growth and division of single cells remains a major challenge. Using a unique combination of technologies that yields unprecedented statistical precision, we find that the sizes of individual Caulobacter crescentus cells increase exponentially in time. We also establish that they divide upon reaching a critical multiple ($\approx$1.8) of their initial sizes, rather than an absolute size. We show that when the temperature is varied, the growth and division timescales scale proportionally with each other over the physiological temperature range. Strikingly, the cell-size and division-time distributions can both be rescaled by their mean values such that the condition-specific distributions collapse to universal curves. We account for these observations with a minimal stochastic model that is based on an autocatalytic cycle. It predicts the scalings, as well as specific functional forms for the universal curves. Our experimental and theoretical analysis reveals a simple physical principle governing these complex biological processes: a single temperature-dependent scale of cellular time governs the stochastic dynamics of growth and division in balanced growth conditions.Comment: Text+Supplementar

A general mechanism for signal propagation in the nicotinic acetylcholine receptor family

Nicotinic acetylcholine receptors (nAChRs) modulate synaptic activity in the central nervous system. The α7 subtype, in particular, has attracted considerable interest in drug discovery as a target for several conditions, including Alzheimer’s disease and schizophrenia. Identifying agonist-induced structural changes underlying nAChR activation is fundamentally important for understanding biological function and rational drug design. Here, extensive equilibrium and nonequilibrium molecular dynamics simulations, enabled by cloud-based high-performance computing, reveal the molecular mechanism by which structural changes induced by agonist unbinding are transmitted within the human α7 nAChR. The simulations reveal the sequence of coupled structural changes involved in driving conformational change responsible for biological function. Comparison with simulations of the α4β2 nAChR subtype identifies features of the dynamical architecture common to both receptors, suggesting a general structural mechanism for signal propagation in this important family of receptors

CellCognition : time-resolved phenotype annotation in high-throughput live cell imaging

Author Posting. © The Authors, 2010. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Methods 7 (2010): 747-754, doi:10.1038/nmeth.1486.Fluorescence time-lapse imaging has become a powerful tool to investigate complex dynamic processes such as cell division or intracellular trafficking. Automated microscopes generate time-resolved imaging data at high throughput, yet tools for quantification of large-scale movie data are largely missing. Here, we present CellCognition, a computational framework to annotate complex cellular dynamics. We developed a machine learning method that combines state-of-the-art classification with hidden Markov modeling for annotation of the progression through morphologically distinct biological states. The incorporation of time information into the annotation scheme was essential to suppress classification noise at state transitions, and confusion between different functional states with similar morphology. We demonstrate generic applicability in a set of different assays and perturbation conditions, including a candidate-based RNAi screen for mitotic exit regulators in human cells. CellCognition is published as open source software, enabling live imaging-based screening with assays that directly score cellular dynamics.Work in the Gerlich laboratory is supported by Swiss National Science Foundation (SNF) research grant 3100A0-114120, SNF ProDoc grant PDFMP3_124904, a European Young Investigator (EURYI) award of the European Science Foundation, an EMBO YIP fellowship, and a MBL Summer Research Fellowship to D.W.G., an ETH TH grant, a grant by the UBS foundation, a Roche Ph.D. fellowship to M.H.A.S, and a Mueller fellowship of the Molecular Life Sciences Ph.D. program Zurich to M.H. M.H. and M.H.A.S are fellows of the Zurich Ph.D. Program in Molecular Life Sciences. B.F. was supported by European Commission’s seventh framework program project Cancer Pathways. Work in the Ellenberg laboratory is supported by a European Commission grant within the Mitocheck consortium (LSHG-CT-2004-503464). Work in the Peter laboratory is supported by the ETHZ, Oncosuisse, SystemsX.ch (LiverX) and the SNF

Network analysis of human glaucomatous optic nerve head astrocytes

<p>Abstract</p> <p>Background</p> <p>Astrocyte activation is a characteristic response to injury in the central nervous system, and can be either neurotoxic or neuroprotective, while the regulation of both roles remains elusive.</p> <p>Methods</p> <p>To decipher the regulatory elements controlling astrocyte-mediated neurotoxicity in glaucoma, we conducted a systems-level functional analysis of gene expression, proteomic and genetic data associated with reactive optic nerve head astrocytes (ONHAs).</p> <p>Results</p> <p>Our reconstruction of the molecular interactions affected by glaucoma revealed multi-domain biological networks controlling activation of ONHAs at the level of intercellular stimuli, intracellular signaling and core effectors. The analysis revealed that synergistic action of the transcription factors AP-1, vitamin D receptor and Nuclear Factor-kappaB in cross-activation of multiple pathways, including inflammatory cytokines, complement, clusterin, ephrins, and multiple metabolic pathways. We found that the products of over two thirds of genes linked to glaucoma by genetic analysis can be functionally interconnected into one epistatic network via experimentally-validated interactions. Finally, we built and analyzed an integrative disease pathology network from a combined set of genes revealed in genetic studies, genes differentially expressed in glaucoma and closely connected genes/proteins in the interactome.</p> <p>Conclusion</p> <p>Our results suggest several key biological network modules that are involved in regulating neurotoxicity of reactive astrocytes in glaucoma, and comprise potential targets for cell-based therapy.</p

Goat Milk Nutritional Quality Software-Automatized Individual Curve Model Fitting, Shape Parameters Calculation and Bayesian Flexibility Criteria Comparison

SPSS syntax was described to evaluate the individual performance of 49 linear and non-linear models to fit the milk component evolution curve of 159 Murciano-Granadina does selected for genotyping analyses. Peak and persistence for protein, fat, dry matter, lactose, and somatic cell counts were evaluated using 3107 controls (3.91 ± 2.01 average lactations/goat). Best-fit (adjusted R2) values (0.548, 0.374, 0.429, and 0.624 for protein, fat, dry matter, and lactose content, respectively) were reached by the five-parameter logarithmic model of Ali and Schaeffer (ALISCH), and for the three-parameter model of parabolic yield-density (PARYLDENS) for somatic cell counts (0.481). Cross-validation was performed using the Minimum Mean-Square Error (MMSE). Model comparison was performed using Residual Sum of Squares (RSS), Mean-Squared Prediction Error (MSPE), adjusted R2 and its standard deviation (SD), Akaike (AIC), corrected Akaike (AICc), and Bayesian information criteria (BIC). The adjusted R2 SD across individuals was around 0.2 for all models. Thirty-nine models successfully fitted the individual lactation curve for all components. Parametric and computational complexity promote variability-capturing properties, while model flexibility does not significantly (p > 0.05) improve the predictive and explanatory potential. Conclusively, ALISCH and PARYLDENS can be used to study goat milk composition genetic variability as trustable evaluation models to face future challenges of the goat dairy industry

EEG Characterization of Sensorimotor Networks: Implications in Stroke

The purpose of this dissertation was to use electroencephalography (EEG) to characterize sensorimotor networks and examine the effects of stroke on sensorimotor networks. Sensorimotor networks play an essential role in completion of everyday tasks, and when damaged, as in stroke survivors, the successful completion of seemingly simple motor tasks becomes fantasy. When sensorimotor networks are impaired as a result of stroke, varying degrees of sensorimotor deficits emerge, most often including loss of sensation and difficulty generating upper extremity movements. Although sensory therapies, such as the application of tendon vibration, have been shown to reduce the sensorimotor deficits after stroke, the underlying sensorimotor mechanisms associated with such improvements are unknown. While sensorimotor networks have been studied extensively, unanswered questions still surround their role in basic control paradigms and how their role changes after stroke. EEG provides a way to probe the high-speed temporal dynamics of sensorimotor networks that other more common imaging modalities lack. Sensorimotor network function was examined in controls during a task designed to differentiate potential mechanisms of arm stabilization and determine to what degree the sensorimotor network is involved. After sensorimotor network function was characterized in controls, we examined the effect of stroke on the sensorimotor network during rest and described the reorganization that occurs. Lastly, we explored tendon vibration as a sensory therapy for stroke survivors and determined if sensorimotor network mechanisms underlie improvements in arm tracking performance due to wrist tendon vibration. We observed cortical activity and connectivity that suggests sensorimotor networks are involved in the control of arm stability, cortical networks reorganize to more asymmetric, local networks after stroke, and tendon vibration normalizes sensorimotor network activity and connectivity during motor control after stroke. This dissertation was among the first studies using EEG to characterize the high-speed temporal dynamics of sensorimotor networks following stroke. This new knowledge has led to a better understanding of how sensorimotor networks function under ordinary circumstances as well as extreme situations such as stroke and revealed previously unknown mechanisms by which tendon vibration improves motor control in stroke survivors, which will lead to better therapeutic approaches