Precipitation and Fire Effects on Flowering of a Rare Prairie Orchid

A small, isolated population of the threatened western prairie fringed orchid (Platanthera praeclara Sheviak & Bowles) occurs at Pipestone National Monument, Minnesota, in a mesic prairie that is periodically burned to control invasive cool-season grasses. During 1995-2004, monitoring counts of flowering orchids in the monument varied considerably for different years. Similar precipitation amounts in the spring and histories of burning suggest that fire and precipitation in the spring were not the causes of the variation. For the eight non-burn years in the monitoring record, we compared the number of flowering plants and the precipitation amounts during six growth stages of the orchid and found a 2-variab1e model (precipitation during senescence/bud development and precipitation in the dormant period) explained 77% of the annual variation in number of flowering plants. We also conducted a fire experiment in early May 2002, the typical prescribed burn period for the monument, and found that the frequency of flowering, vegetative, and absent plants observed in July did not differ between burned and protected locations of orchids. We used the model and forecasts of precipitation in the spring to develop provisional burn decision scenarios. We discussed management implications of the scenarios.

Particle formation during soy protein precipitation

The current success of soy foods is driving soy ingredient manufacturers to develop innovative products for food manufacturers. One such innovation is separating the soy proteins glycinin and beta-conglycinin to take advantage of their individual functional and nutritional properties. Precipitation by acidification is a low-cost method for separating these two proteins. Separation is achieved by preferentially precipitating glycinin at pH ~ 6 while beta-conglycinin remains in solution. Understanding the particle formation during protein precipitation is important as it can influence the efficiency of the precipitation process as well as subsequent downstream processes such as the particle-liquid separation step, usually achieved by centrifugation. Most of the previous soy protein precipitation studies are limited to precipitation at pH 4 as this is the pH range most commonly used in the commercial manufacturing of soy protein isolates. To date, there have been no studies on the particle formation during precipitation at pH > 5.Precipitation of soy protein is generally thought to occur by the rapid formation of primary particles in the size range of 0.1 - 0.3 microns followed by aggregation of these particles via collision to aggregates of size about 1 - 50 microns. The formation of the primary particles occurs on a time scale much shorter than that of the overall precipitation process (Nelson and Glatz, 1985). This study shows that precipitation of soy protein is indeed rapid. At high pH levels, binary liquid-liquid separation occurs forming a protein-rich heavy phase. The protein-rich phase appears as droplets which can be coalesced to form a uniform bulk layer under centrifugation forces. Upon lowering the pH level by the addition of acid, further protein is precipitated as amorphous material which binds the droplets together to form aggregates of amorphous precipitates. Liquid-liquid separation has been observed in many protein solutions but this phenomenon has only scarcely been reported in the literature for soy proteins. It presents an exciting opportunity for an innovative product. Features of the liquid-phase protein such as protein yield and purity will be characterized.

Data assimilation for precipitation nowcasting using Bayesian inference

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed

A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting

The generation of very short range forecasts of precipitation in the 0-6 h time window is traditionally referred to as nowcasting. Most existing nowcasting systems essentially extrapolate radar observations in some manner, however, very few systems account for the uncertainties involved. Thus deterministic forecast are produced, which have a limited use when decisions must be made, since they have no measure of confidence or spread of the forecast. This paper develops a Bayesian state space modelling framework for quantitative precipitation nowcasting which is probabilistic from conception. The model treats the observations (radar) as noisy realisations of the underlying true precipitation process, recognising that this process can never be completely known, and thus must be represented probabilistically. In the model presented here the dynamics of the precipitation are dominated by advection, so this is a probabilistic extrapolation forecast. The model is designed in such a way as to minimise the computational burden, while maintaining a full, joint representation of the probability density function of the precipitation process. The update and evolution equations avoid the need to sample, thus only one model needs be run as opposed to the more traditional ensemble route. It is shown that the model works well on both simulated and real data, but that further work is required before the model can be used operationally. © 2004 Elsevier B.V. All rights reserved.

Precipitation casting of drug-loaded microporous PCL matrices:incorporation of progesterone by co-dissolution

Microporous, poly(ε-caprolactone) (PCL) matrices were loaded with progesterone by precipitation casting using co-solutions of PCL and progesterone in acetone. Progesterone loadings up to 32% w/w were readily achieved by increasing the drug content of the starting PCL solution. The kinetics of steroid release in PBS at 37°C over 10 days could be described effectively by a diffusional release model although the Korsmeyer-Peppas model indicated the involvement of multiple release phenomena. The diffusion rate constant (D) increased from 8 to 24 μg/mg matrix/day0.5 as the drug loading increased from 3.6 to 12.4% w/w. A total cumulative release of 75%-95% indicates the high efficiency of steroid delivery. Increasing the matrix density from 0.22 to 0.39 g/cm3, by increasing the starting PCL solution concentration, was less effective in changing drug release kinetics. Retention of anti-proliferative activity of released steroid was confirmed using cultures of breast cancer epithelial (MCF-7) cells. Progesterone released from PCL matrices into PBS at 37°C over 14 days retarded the growth of MCF-7 cells by a factor of at least 3.5 compared with progesterone-free controls. These findings recommend further investigation of precipitation-cast PCL matrices for delivery of bioactive molecules such as anti-proliferative agents from implanted, inserted or topical devices.

Delivery of the antibiotic gentamicin sulphate from precipitation cast matrices of polycaprolactone

Microporous, poly(ε-caprolactone) (PCL) matrices were loaded with the aminoglycoside antibiotic, gentamicin sulphate (GS) using the precipitation casting technique by suspension of powder in the PCL solution prior to casting. Improvements in drug loading from 1.8% to 6.7% w/w and distribution in the matrices were obtained by pre-cooling the suspension to 4°C. Gradual release of approximately 80% of the GS content occurred over 11 weeks in PBS at 37°C and low amounts of antibiotic were measured up to 20 weeks. The kinetics of release could be described effectively by the Higuchi model with the diffusion rate constant (D) increasing from of 1.7 to 5.1 μg/mg matrix/day0.5 as the drug loading increased from 1.4% to 8.3% w/w. GS-loaded PCL matrices retained anti-bacterial activity after immersion in PBS at 37°C over 14 days as demonstrated by inhibition of growth of S. epidermidis in culture. These findings recommend further investigation of precipitation-cast PCL matrices for delivery of hydrophilic molecules such as anti-bacterial agents from implanted, inserted or topical devices. © 2005 Elsevier B.V. All rights reserved.

Precipitation studies on alloys from the system copper chromium zirconium

The precipitation reactions occurring in a series of copper-based alloys selected from the system copper-chromium-zirconium have been studied by resistometric and metallographic techniques. A survey of the factors influencing the development of copper-based alloys for high strength, high conductivity applications is followed by a more general review of contemporary materials, and illustrates that the most promising alloys are those containing chromium and zirconium. The few systematic attempts to study alloys from this system have been collated, discussed, and used as a basis for the selection of four alloy compositions viz:- Cu - 0.4% Cr Cu - 0.24. Zr Cu - 0. 3% Cr - 0.1% Zr Cu - 0.2% Cr - 0.2% Zr A description of the experimental techniques used to study the precipitation behaviour of these materials is preceeded by a discussion of the currently accepted theories relating to precipitate nucleation and growth. The experimental results are presented and discussed for each of the alloys independently, and are then treated jointly to obtain an overall assessment of the way in which the precipitation kinetics, metallography and mechanical properties vary with alloy composition and heat treatment. The metastable solid solution of copper-chromium is found to decompose by the rejection of chromium particles which maintain a coherent interface and a Kurdjumov-Sachs type crystallographic orientation relationship with the copper matrix. The addition of 0.1% zirconium to the alloy retards the rate of transformation by a factor of ten and modifies the dispersion characteristics of the precipitate without markedly altering the morphology. Further additions of zirconium lead to the growth of stacking faults during ageing, which provide favourable nucleation sites for the chromium precipitate. The partial dislocations bounding such stacking faults are also found to provide mobile heterogeneous nucleation sources for the precipitation reactions occurring in copper-zirconium.