Biodegradation kinetics of 4-fluorocinnamic acid by a consortium of Arthrobacter and Ralstonia strains

Arthrobacter sp. strain G1 is able to grow on 4-fluorocinnamic acid (4-FCA) as sole carbon source. The organism converts 4-FCA into 4-fluorobenzoic acid (4-FBA) and utilizes the two-carbon side-chain for growth with some formation of 4-fluoroacetophenone as a dead-end side product. We also have isolated Ralstonia sp. strain H1, an organism that degrades 4-FBA. A consortium of strains G1 and H1 degraded 4-FCA with Monod kinetics during growth in batch and continuous cultures. Specific growth rates of strain G1 and specific degradation rates of 4-FCA were observed to follow substrate inhibition kinetics, which could be modeled using the kinetic models of Haldane–Andrew and Luong–Levenspiel. The mixed culture showed complete mineralization of 4-FCA with quantitative release of fluoride, both in batch and continuous cultures. Steady-state chemostat cultures that were exposed to shock loadings of substrate responded with rapid degradation and returned to steady-state in 10–15 h, indicating that the mixed culture provided a robust system for continuous 4-FCA degradation

A Monte Carlo method to estimate cell population heterogeneity from cell snapshot data

Variation is characteristic of all living systems. Laboratory techniques such as flow cytometry can probe individual cells, and, after decades of experimentation, it is clear that even members of genetically identical cell populations can exhibit differences. To understand whether variation is biologically meaningful, it is essential to discern its source. Mathematical models of biological systems are tools that can be used to investigate causes of cell-to-cell variation. From mathematical analysis and simulation of these models, biological hypotheses can be posed and investigated, then parameter inference can determine which of these is compatible with experimental data. Data from laboratory experiments often consist of “snapshots” representing distributions of cellular properties at different points in time, rather than individual cell trajectories. These data are not straightforward to fit using hierarchical Bayesian methods, which require the number of cell population clusters to be chosen a priori. Nor are they amenable to standard nonlinear mixed effect methods, since a single observation per cell is typically too few to estimate parameter variability. Here, we introduce a computational sampling method named “Contour Monte Carlo” (CMC) for estimating mathematical model parameters from snapshot distributions, which is straightforward to implement and does not require that cells be assigned to predefined categories. The CMC algorithm fits to snapshot probability distributions rather than raw data, which means its computational burden does not, like existing approaches, increase with the number of cells observed. Our method is appropriate for underdetermined systems, where there are fewer distinct types of observations than parameters to be determined, and where observed variation is mostly due to variability in cellular processes rather than experimental measurement error. This may be the case for many systems due to continued improvements in resolution of laboratory techniques. In this paper, we apply our method to quantify cellular variation for three biological systems of interest and provide Julia code enabling others to use this method

Cavity flow characteristics and applications to kidney stone removal

Ureteroscopy is a minimally invasive surgical procedure for the removal of kidney stones. A ureteroscope, containing a hollow, cylindrical working channel, is inserted into the patient's kidney. The renal space proximal to the scope tip is irrigated, to clear stone particles and debris, with a saline solution that flows in through the working channel. We consider the fluid dynamics of irrigation fluid within the renal pelvis, resulting from the emerging jet through the working channel and return flow through an access sheath. Representing the renal pelvis as a two-dimensional rectangular cavity, we investigate the effects of flow rate and cavity size on flow structure and subsequent clearance time of debris. Fluid flow is modelled with the steady incompressible Navier–Stokes equations, with an imposed Poiseuille profile at the inlet boundary to model the jet of saline, and zero-stress conditions on the outlets. The resulting flow patterns in the cavity contain multiple vortical structures. We demonstrate the existence of multiple solutions dependent on the Reynolds number of the flow and the aspect ratio of the cavity using complementary numerical simulations and particle image velocimetry experiments. The clearance of an initial debris cloud is simulated via solutions to an advection–diffusion equation and we characterise the effects of the initial position of the debris cloud within the vortical flow and the Péclet number on clearance time. With only weak diffusion, debris that initiates within closed streamlines can become trapped. We discuss a flow manipulation strategy to extract debris from vortices and decrease washout time

Sudden Depth

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Validation and psychometric properties of the commitment to hip protectors (C-HiP) index in long-term care providers of British Columbia, Canada: a cross-sectional survey

Abstract Background If worn during a fall, hip protectors substantially reduce risk for hip fracture. However, a major barrier to their clinical efficacy is poor user adherence. In long-term care, adherence likely depends on how committed care providers are to hip protectors, but empirical evidence is lacking due to the absence of a psychometrically valid assessment tool. Methods We conducted a cross-sectional survey in a convenience sample of 529 paid care providers. We developed the 15-item C-HiP Index to measure commitment, comprised of three subscales: affective, cognitive and behavioural. Responses were subjected to hierarchical factor analysis and internal consistency testing. Eleven experts rated the relevance and clarity of items on 4-point Likert scales. We performed simple linear regression to determine whether C-HiP Index scores were positively related to the question, “Do you think of yourself as a champion of hip protectors”, rated on a 5-point Likert scale. We examined whether the C-HiP Index could differentiate respondents: (i) who were aware of a protected fall causing hip fracture from those who were unaware; (ii) who agreed in the existence of a champion of hip protectors within their home from those who didn’t. Results Hierarchical factor analysis yielded two lower-order factors and a single higher-order factor, representing the overarching concept of commitment to hip protectors. Items from affective and cognitive subscales loaded highest on the first lower-order factor, while items from the behavioural subscale loaded highest on the second. We eliminated one item due to low factor matrix coefficients, and poor expert evaluation. The C-HiP Index had a Cronbach’s alpha of 0.96. A one-unit increase in championing was associated with a 5.2-point (p < 0.01) increase in C-HiP Index score. Median C-HiP Index scores were 4.3-points lower (p < 0.01) among respondents aware of a protected fall causing hip fracture, and 7.0-points higher (p < 0.01) among respondents who agreed in the existence of a champion of hip protectors within their home. Conclusions We offer evidence of the psychometric properties of the C-HiP Index. The development of a valid and reliable assessment tool is crucial to understanding the factors that govern adherence to hip protectors in long-term care