Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells

Adherent cells exert traction forces on to their environment, which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures. This traction can be measured in a perturbation-free manner with traction force microscopy (TFM). In TFM, traction is usually calculated via the solution of a linear system, which is complicated by undersampled input data, acquisition noise, and large condition numbers for some methods. Therefore, standard TFM algorithms either employ data filtering or regularization. However, these approaches require a manual selection of filter- or regularization parameters and consequently exhibit a substantial degree of subjectiveness. This shortcoming is particularly serious when cells in different conditions are to be compared because optimal noise suppression needs to be adapted for every situation, which invariably results in systematic errors. Here, we systematically test the performance of new methods from computer vision and Bayesian inference for solving the inverse problem in TFM. We compare two classical schemes, L1- and L2-regularization, with three previously untested schemes, namely Elastic Net regularization, Proximal Gradient Lasso, and Proximal Gradient Elastic Net. Overall, we find that Elastic Net regularization, which combines L1 and L2 regularization, outperforms all other methods with regard to accuracy of traction reconstruction. Next, we develop two methods, Bayesian L2 regularization and Advanced Bayesian L2 regularization, for automatic, optimal L2 regularization. Using artificial data and experimental data, we show that these methods enable robust reconstruction of traction without requiring a difficult selection of regularization parameters specifically for each data set. Thus, Bayesian methods can mitigate the considerable uncertainty inherent in comparing cellular traction forces

Development and application of operational techniques for the inventory and monitoring of resources and uses for the Texas coastal zone

The author has identified the following significant results. Four LANDSAT scenes were analyzed for the Harbor Island area test sites to produce land cover and land use maps using both image interpretation and computer-assisted techniques. When evaluated against aerial photography, the mean accuracy for three scenes was 84% for the image interpretation product and 62% for the computer-assisted classification maps. Analysis of the fourth scene was not completed using the image interpretation technique, because of poor quality, false color composite, but was available from the computer technique. Preliminary results indicate that these LANDSAT products can be applied to a variety of planning and management activities in the Texas coastal zone

A study of detecting child pornography on smart phone

© Springer International Publishing AG 2018. Child Pornography is an increasingly visible rising cybercrime in the world today. Over the past decade, with rapid growth in smart phone usage, readily available free Cloud Computing storage, and various mobile communication apps, child pornographers have found a convenient and reliable mobile platform for instantly sharing pictures or videos of children being sexually abused. Within this new paradigm, law enforcement officers are finding that detecting, gathering, and processing evidence for the prosecution of child pornographers is becoming increasingly challenging. Deep learning is a machine learning method that models high-level abstractions in data and extracts hierarchical representations of data by using a deep graph with multiple processing layers. This paper presents a conceptual model of deep learning approach for detecting child pornography within the new paradigm by using log analysis, file name analysis and cell site analysis which investigate text logs of events that have happened in the smart phone at the scene of the crime using physical and logical acquisition to assists law enforcement officers in gathering and processing child pornography evidence for prosecution. In addition, this paper shows an illustrative example of logical and physical acquisition on smart phones using forensics tools

Institutional context: What elements shape how community occupational therapists think about their clients’ care?

Abstract : Clinical reasoning (CR) is the cognitive process that therapists use to plan, direct, perform and reflect on client care. Linked to intervention efficiency and quality, CR is a core competency that occurs within an institutional context (legal, regulatory, administrative and organisational elements). Because this context can shape how community therapists think about their clients’ care, its involvement in their CR could have a major impact on the interventions delivered. However, little is known about this involvement. Our study thus aimed to describe the elements of the institutional context involved in community therapists’ CR. From March 2012 to June 2014, we conducted an institutional ethnography (IE) inquiry in three Health and Social Services Centres in Québec (Canada). We observed participants and conducted semi-structured interviews with 10 occupational therapists. We also interviewed 12 secondary key informants (colleagues and managers) and collected administrative documents (n = 50). We analysed data using the IE process. Of the 13 elements of the institutional context identified, we found that four are almost constantly involved in participants’ CR. These four elements, that is, institutional procedures, organisation's basket of services, occupational therapists’ mandate and wait times for their services, restrictively shape CR. Specifically, occupational therapists restrict their representation of the client's situation and exploration of potential solutions to what is possible within the bounds of these four elements. In light of such restrictions on the way they think about their clients’ care, therapists should pay close attention to the elements of their own institutional context and how they are involved in their CR. Because of its potentially important impact on the future of professions (e.g. further restrictions on professionals’ role, reduced contribution to population health and well-being), this involvement of the institutional context in CR concerns all professionals, be they clinicians, educators, researchers or regulatory college officers

Simulating aerosol microphysics with the ECHAM/MADE GCM ? Part I: Model description and comparison with observations

International audienceThe aerosol dynamics module MADE has been coupled to the general circulation model ECHAM4 to simulate the chemical composition, number concentration, and size distribution of the global submicrometer aerosol. The present publication describes the new model system ECHAM4/MADE and presents model results in comparison with observations. The new model is able to simulate the full life cycle of particulate matter and various gaseous precursors including emissions of primary particles and trace gases, advection, convection, diffusion, coagulation, condensation, nucleation of sulfuric acid vapor, aerosol chemistry, cloud processing, and size-dependent dry and wet deposition. Aerosol components considered are sulfate (SO4), ammonium (NH4), nitrate (NO3), black carbon (BC), particulate organic matter (POM), sea salt, mineral dust, and aerosol liquid water. The model is numerically efficient enough to allow long term simulations, which is an essential requirement for application in general circulation models. In order to evaluate the results obtained with this new model system, calculated mass concentrations, particle number concentrations, and size distributions are compared to observations. The intercomparison shows, that ECHAM4/MADE is able to reproduce the major features of the geographical patterns, seasonal cycle, and vertical distributions of the basic aerosol parameters. In particular, the model performs well under polluted continental conditions in the northern hemispheric lower and middle troposphere. However, in comparatively clean remote areas, e.g. in the upper troposphere or in the southern hemispheric marine boundary layer, the current model version tends to underestimate particle number concentrations

Helicity sensitive terahertz radiation detection by dual-grating-gate high electron mobility transistors

We report on the observation of a radiation helicity sensitive photocurrent excited by terahertz (THz) radiation in dual-grating-gate (DGG) InAlAs/InGaAs/InAlAs/InP high electron mobility transistors (HEMT). For a circular polarization the current measured between source and drain contacts changes its sign with the inversion of the radiation helicity. For elliptically polarized radiation the total current is described by superposition of the Stokes parameters with different weights. Moreover, by variation of gate voltages applied to individual gratings the photocurrent can be defined either by the Stokes parameter defining the radiation helicity or those for linear polarization. We show that artificial non-centrosymmetric microperiodic structures with a two-dimensional electron system excited by THz radiation exhibit a dc photocurrent caused by the combined action of a spatially periodic in-plane potential and spatially modulated light. The results provide a proof of principle for the application of DGG HEMT for all-electric detection of the radiation's polarization state.Comment: 7 pages, 4 figure

Legionella shows a diverse secondary metabolism dependent on a broad spectrum Sfp-type phosphopantetheinyl transferase

Several members of the genus Legionella cause Legionnaires' disease, a potentially debilitating form of pneumonia. Studies frequently focus on the abundant number of virulence factors present in this genus. However, what is often overlooked is the role of secondary metabolites from Legionella. Following whole genome sequencing, we assembled and annotated the Legionella parisiensis DSM 19216 genome. Together with 14 other members of the Legionella, we performed comparative genomics and analysed the secondary metabolite potential of each strain. We found that Legionella contains a huge variety of biosynthetic gene clusters (BGCs) that are potentially making a significant number of novel natural products with undefined function. Surprisingly, only a single Sfp-like phosphopantetheinyl transferase is found in all Legionella strains analyzed that might be responsible for the activation of all carrier proteins in primary (fatty acid biosynthesis) and secondary metabolism (polyketide and non-ribosomal peptide synthesis). Using conserved active site motifs, we predict some novel compounds that are probably involved in cell-cell communication, differing to known communication systems. We identify several gene clusters, which may represent novel signaling mechanisms and demonstrate the natural product potential of Legionella

Legionella shows a diverse secondary metabolism dependent on a broad spectrum Sfp-type phosphopantetheinyl transferase

Several members of the genus Legionella cause Legionnaires' disease, a potentially debilitating form of pneumonia. Studies frequently focus on the abundant number of virulence factors present in this genus. However, what is often overlooked is the role of secondary metabolites from Legionella. Following whole genome sequencing, we assembled and annotated the Legionella parisiensis DSM 19216 genome. Together with 14 other members of the Legionella, we performed comparative genomics and analysed the secondary metabolite potential of each strain. We found that Legionella contains a huge variety of biosynthetic gene clusters (BGCs) that are potentially making a significant number of novel natural products with undefined function. Surprisingly, only a single Sfp-like phosphopantetheinyl transferase is found in all Legionella strains analyzed that might be responsible for the activation of all carrier proteins in primary (fatty acid biosynthesis) and secondary metabolism (polyketide and non-ribosomal peptide synthesis). Using conserved active site motifs, we predict some novel compounds that are probably involved in cell-cell communication, differing to known communication systems. We identify several gene clusters, which may represent novel signaling mechanisms and demonstrate the natural product potential of Legionella