On the nature of damage involved in liquid-holding recovery in diploid yeast after gamma- and alpha-irradiation.

Cells surviving after liquid-holding recovery following gamma- and alpha-irradiations are found to be slightly more sensitive to a second series of radiation doses. Further, the shoulder on the gamma survival curve of the pre-irradiated and liquid-held cells disappears. The shoulder and sensitivity are restored only when these cells are grown in broth before the second series of doses. In addition to this, liquid-holding recovery reduces progressively if the cells after irradiation are incubated in broth for different periods of time before holding. These observations suggest that: (1) the so-called potentially lethal damage may consitute that part of the sub-lethal damage which interact with one another to form lethal damage; (2) during liquid-holding, the interaction among sub-lethal damage transforming them to the status of lethal damage is inhibited; (3) the 'recovered' cells are saturated with sub-lethal damage, the repair of which will be completed only when the cells are placed in a nutrient medium. The inhibitory process is not a passive one, but requires energy metabolism.     

Forecasting and chronic illness

Biomedical data in the form of series of observations made on a single process at regular intervals constitute a discrete time series and are eligible for time series methods of analysis. The models yielded by this analysis provide the framework within which exponential smoothing methods may operate on the data to provide recurrent forecasts of future states of the process. Because the forecasts may be made on an individual basis and are sensitive to the past behavior of the individual process, the methods are presented as being potentially of great utility in the management of chronic and progressive illnesses. When incorporated into automated testing and diagnostic systems, the forecasting method will provide the capability of making prognoses for large numbers of individuals, quickly, routinely and reproducibly.     

Recovery of biologically produced 3‐hydroxypropionaldehyde and its dehydrated product acrolein

3‐Hydroxypropionaldehyde (3‐HPA), which can be derived from biomass, is an important precursor for low‐cost, large‐volume acrolein‐based chemicals like acrylic acid and acrylamide with a wide range of applications. In order to find an efficient process for isolating 3‐HPA from fermentation broth, we comparatively investigated several separation methods including precipitation with hydrazides, immobilization with amines, reactive extraction with thiols, extraction with hydrophilic solvents, and reactive distillation as acrolein. It turned out that the reactive distillation is the most efficient method for in situ recovery of 3‐HPA as acrolein. In a reactive distillation process at 37°C and Hammett acidity H₀ = –1, the aldehyde concentration was reduced to 6 ± 1 mM in the transformation medium and increased to 1866 ± 146 mM in the distillate. The yield was 96 ± 8%. These experimental results are close to the calculated ideal equilibrium results assuming total dehydration of 3‐HPA to acrolein. The main advantages of the reactive distillation process are that the recovery, purification, and concentration of acrolein are carried out in one step and the process is well suited for large‐scale production at low costs.     

Analysis of simulated and experimental fluorescence recovery after photobleaching. Data for two diffusing components.

Fluorescence recovery after photobleaching has been a popular technique to quantify the lateral mobility of membrane components. A variety of analysis methods have been used to determine the lateral diffusional mobility, D. However, many of these methods suffer from the drawbacks that they are not able to discern two-component diffusion (i.e., three-point fit), cannot solve for two components (linearization procedures), and do not perform well at low signal-to-noise. To overcome these limitations, we have adopted the approach of fitting fluorescence recovery after photobleaching curves by the full series solution using a Marquardt algorithm. Using simulated data of one or two diffusing components, determinations of the accuracy and reliability of the method with regard to extraction of diffusion parameters and the differentiation of one- versus two-component recovery curves were made under a variety of conditions comparable with those found in actual experimental situations. The performance of the method was also examined in experiments on artificial liposomes and fibroblast membranes labeled with fluorescent lipid and/or protein components. Our results indicate that: 1) the method was capable of extracting one- and two-component D values over a large range of conditions; 2) the D of a one-component recovery can be measured to within 10% with a small signal (100 prebleach photon counts per channel); 3) a two-component recovery requires more than 100-fold greater signal level than a one-component recovery for the same error; and 4) for two-component fits, multiple recovery curves may be needed to provide adequate signal to achieve the desired level of confidence in the fitted parameters and in the differentiation of one- and two-component diffusion.     

Consistency of estimators of population scaled parameters using composite likelihood

Composite likelihood methods have become very popular for the analysis of large-scale genomic data sets because of the computational intractability of the basic coalescent process and its generalizations: It is virtually impossible to calculate the likelihood of an observed data set spanning a large chromosomal region without using approximate or heuristic methods. Composite likelihood methods are approximate methods and, in the present article, assume the likelihood is written as a product of likelihoods, one for each of a number of smaller regions that together make up the whole region from which data is collected. A very general framework for neutral coalescent models is presented and discussed. The framework comprises many of the most popular coalescent models that are currently used for analysis of genetic data. Assume data is collected from a series of consecutive regions of equal size. Then it is shown that the observed data forms a stationary, ergodic process. General conditions are given under which the maximum composite estimator of the parameters describing the model (e.g. mutation rates, demographic parameters and the recombination rate) is a consistent estimator as the number of regions tends to infinity.     

昆虫病原线虫感染期幼虫恢复发育的研究进展

昆虫病原线虫的感染期幼虫（infective juvenile,IJ）是其一生中唯一具有侵染能力和可自由生活于寄主体外的虫态,一般滞育不取食,体外包裹着已经蜕去的第2龄幼虫的表皮,对外界不良环境的耐受能力强,又称为耐受态幼虫(dauer juvenile,DJ),类似于秀丽隐杆线虫Caenorhabditis elegans的耐受态幼虫。在食物信息的诱导下,感染期幼虫脱鞘,释放出共生细菌,恢复取食并继续发育,这个过程称为感染期幼虫的恢复（IJ recovery）。这个过程是发生在寄生性线虫入侵寄主时的发育过程,对于成功寄生是必要的,在线虫的产业化培养中发挥着重要作用,感染期线虫的恢复率及其发育的同步性直接影响了线虫的产量。本文概述了感染期线虫的恢复发育过程,并对诱导感染期线虫恢复发育的食物信号（food signals）、恢复的影响因素及其检测手段进行了综述,同时讨论了未来的研究方向。     

A model of neural network for spatiotemporal pattern recognition

A model of neural network to recognize spatiotemporal patterns is presented. The network consists of two kinds of neural cells: P-cells and B-cells. A P-cell generates an impulse responding to more than one impulse and embodies two special functions: short term storage (STS) and heterosynaptic facilitation (HSF). A B-cell generates several impulses with high frequency as soon as it receives an impulse. In recognizing process, an impulse generated by a P-cell represents a recognition of stimulus pattern, and triggers the generation of impulses of a B-cell. Inhibitory impulses with high frequency generated by a B-cell reset the activities of all P-cells in the network.Two examples of spatiotemporal pattern recognition are presented. They are achieved by giving different values to the parameters of the network. In one example, the network recognizes both directional and non-directional patterns. The selectivities to directional and non-directional patterns are realized by only adjusting excitatory synaptic weights of P-cells. In the other example, the network recognizes time series of spatial patterns, where the lengths of the series are not necessarily the same and the transitional speeds of spatial patterns are not always the same. In both examples, the HSF signal controls the total activity of the network, which contributes to exact recognition and error recovery. In the latter example, it plays a role to trigger and execute the recognizing process. Finally, we discuss the correspondence between the model and physiological findings.     

On the controllability of continuous fermentation processes

Simulation may be used as a powerful tool for accelerating bioprocess design. This paper demonstrates the use of simulations in exploring the nature and impact of the interactions that exist in a typical bioprocess for the recovery of an intracellular protein. The study shows that an integrated approach to design must be adopted in order to achieve acceptable process designs. Data from a fed-batch fermentation, with verified models for cell harvesting, cell disruption and cell debris removal have been integrated to demonstrate the consequence of process design and operating decisions on the resulting process performance. The trade-offs between product recovery and the extent of cell debris removal for a range of operating conditions have been represented through a series of windows of operation which show how process conditions must be altered in order for given process performance levels to be realised. The capacity to account for process performance including the impact of interactions is seen as a pre-requisite for rigorous bioprocess sequence design and optimisation.     

Optimal synthesis of chromatographic trains for downstream protein processing

Downstream bioprocessing and especially chromatographic steps, commonly used for the purification of multicomponent systems, are significant cost drivers in the production of therapeutic proteins. There has been an increased interest in the development of systematic methods for the design of such processes, and the appropriate selection of a series of chromatographic steps is still a major challenge to be addressed. Several models have been developed previously but have assumed that 100% recovery of the desired product is obtained at each chromatographic step. In this work, a mathematical framework is proposed, based on mixed integer optimisation techniques, that removes this assumption and allows full flexibility on the position of retention time cut-points, between which the desired product fraction is collected. The proposed model is demonstrated on three example protein mixtures, each containing up to 13 contaminants and selecting from a set of up to 21 candidate steps. The proposed model results in a reduction of one to three chromatographic steps over solutions that no losses are allowed.     

Comparison of activity and conformation changes during refolding of urea-denatured creatine kinase

The course of the recovery of the enzymatic activity and the native conformation during the renaturation of urea-denatured creatine kinase (ATP:creatine N-phosphotransferase, EC 2.7.3.2) has been studied. Under suitable conditions, an activity recovery of 95% can be obtained and the reactivation follows a triphasic course. The initial two phases are relatively fast, whereas the slow phase takes some 24 h to reach completion. The recovery of the native conformation has been followed by changes in fluorescence, ultraviolet absorption and in exposed SH groups and has been shown to be a biphasic process. Both the reactivation and the refolding processes are independent of protein concentrations within a certain range, showing that the dimerization of the enzyme molecule is not rate-limiting. A comparison of the rate constants for the refolding of the molecule with those for the recovery of its catalytic activity shows that these are not synchronized and the activity recovery approaches completion after the refolding and dimerization of the subunits so far as can be detected by the methods employed. The final stage of refolding with complete activity recovery probably involves subtle conformational changes of the dimeric enzyme molecule not detectable by the physiochemical methods used in the present study.     

The effect of temporary hypoxia on prostaglandin synthesis in mouse brain cell cultures during development

Cultures of dissociated brain cells of new born mice represent a model for the study of brain development. One and two weeks old, they correspond in regard to oligodendrocyte differentiation to about the developmental stage of a human newborn and a six months old infant respectively. Such cultures were used to establish the developmental prostaglandin pattern and to study early and late recovery of prostaglandin synthesis from temporary hypoxia. Basal and bradykinin stimulated prostaglandin release were examined. Most prominently in stimulated release, the developmental prostaglandin pattern at one week showed a prevalence of PGE2 (33 +/- 4%) over PGD2 (12 +/- 5%), which in two weeks old cultures changes to an opposite distribution (PGE2 10 +/- 4%; PGD2 25 +/- 6%). This change goes parallel with the number and differentiation of oligodendrocytes. During the first day post hypoxia, imposed at the end of one week, the production of 6 oxo PGF1 alpha, PGE2, PGD2 and TXB2 was significantly decreased in two study series and increased compared to control in another. Since the arachidonic acid uptake was the same in all three series, this differential observation indicates differential early recovery. 8 days post hypoxia (late recovery), PG release was not different from control, indicating complete recovery at that time. During early recovery from hypoxia on 14 days old cultures, basal PG release was not significantly different from control, however bradykinin stimulated release was significantly inhibited in all three series. This may indicate that mainly regulatory influences on PG release in older cultures are compromised by hypoxia.(ABSTRACT TRUNCATED AT 250 WORDS)     

Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities

Background

Many methods for the genetic analysis of mastitis use a cross-sectional approach, which omits information on, e.g., repeated mastitis cases during lactation, somatic cell count fluctuations, and recovery process. Acknowledging the dynamic behavior of mastitis during lactation and taking into account that there is more than one binary response variable to consider, can enhance the genetic evaluation of mastitis.

Methods

Genetic evaluation of mastitis was carried out by modeling the dynamic nature of somatic cell count (SCC) within the lactation. The SCC patterns were captured by modeling transition probabilities between assumed states of mastitis and non-mastitis. A widely dispersed SCC pattern generates high transition probabilities between states and vice versa. This method can model transitions to and from states of infection simultaneously, i.e. both the mastitis liability and the recovery process are considered. A multilevel discrete time survival model was applied to estimate breeding values on simulated data with different dataset sizes, mastitis frequencies, and genetic correlations.

Results

Correlations between estimated and simulated breeding values showed that the estimated accuracies for mastitis liability were similar to those from previously tested methods that used data of confirmed mastitis cases, while our results were based on SCC as an indicator of mastitis. In addition, unlike the other methods, our method also generates breeding values for the recovery process.

Conclusions

The developed method provides an effective tool for the genetic evaluation of mastitis when considering the whole disease course and will contribute to improving the genetic evaluation of udder health.     

Interaction between model membranes and a new class of surfactants with antioxidant function.

The effect of two series of amphiphilic quaternary ammonium salts on some properties of phospholipid membranes was studied. The compounds of one series, N-benzyl-N,N-dimethyl-N-alkyl ammonium bromides, exert a destructive effect on membranes and are treated as reference compounds. The compounds of the other series, N-(3,5-di-t-butyl-4-hydroxy)benzyl-N,N-dimethyl-N-alkyl ammonium bromides, are derivatives of the former ones, exhibit antioxidant properties, and do only relatively slight damage to the membranes. The aim of the work was to explain the difference in molecular interaction with membranes between the two kinds of hydrophobic compounds. Thermodynamic methods, a new mixing technique, and monolayer and quantum calculation methods were used. It has been shown that the antioxidant molecules are less hydrophobic than those of the reference compounds and disturb the membrane organization to a lesser extent. On the basis of monolayer data, we suggest that the studied antioxidant behaves like a substitutional impurity, whereas the reference behaves like an interstitial one.     

Granger causality between multiple interdependent neurobiological time series: blockwise versus pairwise methods

Granger causality is becoming an important tool for determining causal relations between neurobiological time series. For multivariate data, there is often the need to examine causal relations between two blocks of time series, where each block could represent a brain region of interest. Two alternative methods are available. In the pairwise method, bivariate autoregressive models are fit to all pairwise combinations involving one time series from the first block and one from the second. The total Granger causality between the two blocks is then derived by summing pairwise causality values from each of these models. This approach is intuitive but computationally cumbersome. Theoretically, a more concise method can be derived, which we term the blockwise Granger causality method. In this method, a single multivariate model is fit to all the time series, and the causality between the two blocks is then computed from this model. We compare these two methods by applying them to cortical local field potential recordings from monkeys performing a sensorimotor task. The obtained results demonstrate consistency between the two methods and point to the significance potential of utilizing Granger causality analysis in understanding coupled neural systems.     

Revisited measles and chickenpox dynamics through orthogonal transformation

The question addressed is whether or not childhood epidemics such as measles and chickenpox are characterized by low-dimensional chaos. We propose a new method for the detection and extraction of hidden periodic components embedded in an irregular cyclical series, and study the characterization of the epidemiological series in terms of the characteristic features or periodicity attributes of the extracted components. It is shown that the measles series possesses two periodic components each having a period of one year. Both the periodic components have time-varying pattern, and the process is nonlinear and deterministic; there is no evidence of strong chaoticity in the measles dynamics. The chickenpox series has one seasonal component with stable pattern, and the process is deterministic but linear, and hence non-chaotic. We also propose surrogate generators based on null hypotheses relating to the variability of the periodicity attributes to analyse the dynamics in the epidemic series. The process dynamics is also studied using seasonally forced SEIR epidemic model, and the characterization performance of the proposed schemes is assessed.     

A new method for detecting neural interconnectivity

We propose a class of counting process models for analyzing firing times of an ensemble of neurons. We allow the counting process intensities to be unspecified, unknown functions of the times passed since the most recent firings. Under this assumption we derive a class of statistics with their respective thresholds as well as graphical methods for detecting neural connectivity. We introduce a model under which detection is shown to be certain for long series of observations. We suggest ways to classify interactions as inhibition or excitation and to estimate their strengths. The power of the proposed methods is compared by simulating observations from artificial networks. By analyzing empirically obtained series we obtain results which are consistent with those obtained from cross-correlation-based methods but in addition obtain new insights on further aspects of the interactions. Received: 7 February 1996 / Accepted in revised form: 5 March 1997     

Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data.     

Strategy for selection of methods for separation of bioparticles from particle mixtures

The desired product of bioprocesses is often produced in particulate form, either as an inclusion body (IB) or as a crystal. Particle harvesting is then a crucial and attractive form of product recovery. Because the liquid phase often contains other bioparticles, such as cell debris, whole cells, particulate biocatalysts or particulate by-products, the recovery of product particles is a complex process. In most cases, the particulate product is purified using selective solubilization or extraction. However, if selective particle recovery is possible, the already high purity of the particles makes this downstream process more favorable. This work gives an overview of typical bioparticle mixtures that are encountered in industrial biotechnology and the various driving forces that may be used for particle-particle separation, such as the centrifugal force, the magnetic force, the electric force, and forces related to interfaces. By coupling these driving forces to the resisting forces, the limitations of using these driving forces with respect to particle size are calculated. It shows that centrifugation is not a general solution for particle-particle separation in biotechnology because the particle sizes of product and contaminating particles are often very small, thus, causing their settling velocities to be too low for efficient separation by centrifugation. Examples of such separation problems are the recovery of IBs or virus-like particles (VLPs) from (microbial) cell debris. In these cases, separation processes that use electrical forces or fluid-fluid interfaces show to have a large potential for particle-particle separation. These methods are not yet commonly applied for large-scale particle-particle separation in biotechnology and more research is required on the separation techniques and on particle characterization to facilitate successful application of these methods in industry.