Creation of a line of "depressed" mice from a selection of breeders exhibiting a behavioral helplessness

Antidepressants are used since 40 years. All presently used antidepressants have a slow onset of action and do not improve all patients; thus, there is an absolute need for new antidepressants. A variety of animal models, often based upon the monoaminergic theory of depressive disorders, has been used to screen the current antidepressants. In fact, the main focus of most of these animal models has been to predict the antidepressant potential i.e. to establish predictive validity. However, the evaluation of such animal models should also consider face validity, i.e. how closely the model resembles the human condition, and this should help to identify innovating medicines. Antidepressants, when taken by a healthy person, induce nothing more than side effects, unrelated to an action on mood, whereas they alleviate depressive symptomatology in depressed patients. We have speculated that genetically selected animal models would be closer to the human clinical situation than models based on standard laboratory strains. We have depicted here that marked differences exist between strains of mice in the amount of immobility i.e. "spontaneous helplessness" observed in the tail suspension test, a method used to screen potential antidepressants. We have studied the behavioural characteristics of mice selectively bred for spontaneous high or low immobility scores in the tail suspension test. Hopefully, these selectively bred lines will provide a novel approach to investigate behavioural, neurochemical and neuroendocrine correlates of antidepressant action.     

Confidence intervals for uncommon but dramatic responses to treatment

A small literature discusses locally most powerful rank tests when only a fraction of treated subjects respond to treatment. The ranks used in these tests are very different from conventional ranks, being relatively flat for low responses and then rising steeply, and the associated tests are much more powerful than conventional rank tests when, indeed, only a small fraction of treated subjects exhibit dramatic responses. Because the tests are derived from considerations of local power, they do not yield a plausible family of models for effect, and therefore they do not yield confidence intervals for the magnitude of effect formed by inverting the tests. There is a similarity between these tests and another family of tests, originally motivated by different considerations involving peak performance in small subsets. Exploiting this similarity, a method for obtaining confidence statements is proposed. In the case of observational studies, sensitivity to unobserved bias from nonrandom assignment of treatments is also examined. Two examples are used as illustrations: (i) a study of smoking during pregnancy and its effects on birth weight, in which smokers are matched to six controls, and (ii) a matched pair study of damage to DNA among workers at aluminum production plants.     

Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class Model

Summary .  Latent class models have been recently developed for the joint analysis of a longitudinal quantitative outcome and a time to event. These models assume that the population is divided in  G  latent classes characterized by different risk functions for the event, and different profiles of evolution for the markers that are described by a mixed model for each class. However, the key assumption of conditional independence between the marker and the event given the latent classes is difficult to evaluate because the latent classes are not observed. Using a joint model with latent classes and shared random effects, we propose a score test for the null hypothesis of independence between the marker and the outcome given the latent classes versus the alternative hypothesis that the risk of event depends on one or several random effects from the mixed model in addition to the latent classes. A simulation study was performed to compare the behavior of the score test to other previously proposed tests, including situations where the alternative hypothesis or the baseline risk function are misspecified. In all the investigated situations, the score test was the most powerful. The methodology was applied to develop a prognostic model for recurrence of prostate cancer given the evolution of prostate-specific antigen in a cohort of patients treated by radiation therapy.     

Evolutionary Analyses of DNA Sequences Subject to Constraints on Secondary Structure

Evolutionary models appropriate for analyzing nucleotide sequences that are subject to constraints on secondary structure are developed. The models consider the evolution of pairs of nucleotides, and they incorporate the effects of base-pairing constraints on nucleotide substitution rates by introducing a new parameter to extensions of standard models of sequence evolution. To illustrate some potential uses of the models, a likelihood-ratio test is constructed for the null hypothesis that two (prespecified) regions of DNA evolve independently of each other. The sampling properties of the test are explored via simulation. The test is then incorporated into a heuristic method for identifying the location of unknown stems. The test and related procedures are applied to data from ribonuclease P RNA sequences of bacteria.     

Comparative in vitro study on the long term performance of cemented hip stems: validation of a protocol to discriminate between "good" and "bad" designs

The long-term clinical success of cemented hip stems is influenced both by the implant design, and by the surgical procedure. A methodology is proposed for discriminating between implant designs with different clinical outcomes. The protocol was designed with industrial pre-clinical validation in mind.Two cemented stem types were tested, one (Lubinus SPII) having good and the other (Müller Curved) having poor clinical outcomes. Three implants for each type were subjected to a mechanical in vitro test of one million loading cycles. Each cycle reproduced the load components of stair climbing. Interface shear micromotion was measured during the test in the direction of rotation and along the stem axis. The stem roughness before and after the test was compared. After the test, the cement mantles were retrieved and inspected through dye penetrants to detect evidences of micro-damage. For each specimen, the events of the loosening process were examined, based on the in vitro data available, so as to analyze the whole failure mechanism.The protocol developed was sensitive to the implant design, with significantly different results being found for the two stem types, both in terms of stem-cement micromotions, surface roughness alteration, and cement mantle damage. The information yielded by the three different investigation techniques was consistent for each of the two groups of specimens tested, allowing a better understanding of the failure process. In vitro inducible micromotion and permanent migration measurements, together with cement-stem interface fretting damage and cement fatigue damage, can help predicting the clinical performance of cemented stems.     

Neural network models for biological waste-gas treatment systems

This paper outlines the procedure for developing artificial neural network (ANN) based models for three bioreactor configurations used for waste-gas treatment. The three bioreactor configurations chosen for this modelling work were: biofilter (BF), continuous stirred tank bioreactor (CSTB) and monolith bioreactor (MB). Using styrene as the model pollutant, this paper also serves as a general database of information pertaining to the bioreactor operation and important factors affecting gas-phase styrene removal in these biological systems. Biological waste-gas treatment systems are considered to be both advantageous and economically effective in treating a stream of polluted air containing low to moderate concentrations of the target contaminant, over a rather wide range of gas-flow rates. The bioreactors were inoculated with the fungus Sporothrix variecibatus, and their performances were evaluated at different empty bed residence times (EBRT), and at different inlet styrene concentrations (C(i)). The experimental data from these bioreactors were modelled to predict the bioreactors performance in terms of their removal efficiency (RE, %), by adequate training and testing of a three-layered back propagation neural network (input layer-hidden layer-output layer). Two models (BIOF1 and BIOF2) were developed for the BF with different combinations of easily measurable BF parameters as the inputs, that is concentration (gm(-3)), unit flow (h(-1)) and pressure drop (cm of H(2)O). The model developed for the CSTB used two inputs (concentration and unit flow), while the model for the MB had three inputs (concentration, G/L (gas/liquid) ratio, and pressure drop). Sensitivity analysis in the form of absolute average sensitivity (AAS) was performed for all the developed ANN models to ascertain the importance of the different input parameters, and to assess their direct effect on the bioreactors performance. The performance of the models was estimated by the regression coefficient values (R(2)) for the test data set. The results obtained from this modelling work can be useful for obtaining important relationships between different bioreactor parameters and for estimating their safe operating regimes.     

Haemolytic anaemia after cisplatin treatment.

Normochromic or normocytic anaemia is a common side effect of treatment with cisplatin. Two patients treated with cisplatin 100 mg/m2 in combination with vinblastine, bleomycin, and actinomycin D developed haemolytic anaemia. Neither patient had evidence of haemolysis before treatment, and in both cases severe haemolytic anaemia developed after several courses of cisplatin and when the cancer had regressed almost completely. The importance of haemolysis in the development of anaemia after cisplatin treatment has not been investigated fully and further studies are needed.     

A new approach of fitting biomass dynamics models to data

A non-traditional approach of fitting dynamic resource biomass models to data is developed in this paper. A variational adjoint technique is used for dynamic parameter estimation. In the variational formulation, a cost function measuring the distance between the model solution and the observations is minimized. The data assimilation method provides a novel and computationally efficient procedure for combining all available information, i.e., the data and the model in the analysis of a resource system. This technique will be used to analyze data for the North-east Arctic cod stock. Two alternative population growth models: the logistic and the Gompertz model are used for estimating parameters of simple bioeconomic models by the method of constrained least squares. Estimates of the parameters of the models dynamics are reasonable and can be accepted. The main inference from the work is that the average fishing mortality is found to be significantly above the maximum sustainable yield value.     

Utilization of chlorhexidine gluconate to evaluate a nonhuman primate skin-degerming model

We have developed a nonhuman skin-degerming model to predict the in vivo effectiveness of topical antimicrobial formulations. The model incorporates a balanced, randomized, complete block design and uses the hands of anesthetized cynomolgus monkeys as treatment sites to measure product effectiveness. Two different 4% chlorhexidine gluconate formulations were evaluated in the primate model and then retested in a human skin-degerming model of identical design. Statistical analysis of the data revealed no significant differences between the two models with regard to the response exhibited by each to the test formulations.     

Abdominoplasty: a new concept and classification for treatment

From a study of the deformities of each layer of the abdominal wall, we have categorized five types of abdominoplasties. For each type, we used a different surgical technique, aiming to sculpture the abdomen and treat each layer according to the deformity present in each patient. Two hundred thirty-eight patients were treated with this method, and the results were judged good to excellent by the surgeons and patients because of the more natural appearance of the final results.     

X-ray structure of methanol dehydrogenase from<Emphasis Type="Italic"> Paracoccus denitrificans</Emphasis> and molecular modeling of its interactions with cytochrome<Emphasis Type="Italic"> c</Emphasis>-551i

The X-ray structure of methanol dehydrogenase (MEDH) from Paracoccus denitrificans (MEDH-PD) was determined at 2.5 A resolution using molecular replacement based on the structure of MEDH from Methylophilus methylotrophus W3A1 (MEDH-WA). The overall structures from the two bacteria are similar to each other except that the former has a longer C-terminal tail in each subunit and shows local differences in several insertion regions. The "X-ray sequence" of the segment alphaGly444-alphaLeu452 was established, including one insertion and seven replacements compared with the reported sequence. The primary electron acceptor of MEDH-PD is cytochrome c-551i (Cyt c551i). Based on the crystal structure of MEDH-PD and of the published structure of Cyt c551i, their interactions were investigated by molecular modeling. As a guide and starting point, the covalently attached cytochrome and PQQ domains of the alcohol dehydrogenase from Pseudomonas putida HK5 (ADH2B) were used. In the modeling, two molecules of Cyt c551i could be accommodated in their interaction with the MEDH heterotetramer in accordance with the two-fold molecular symmetry of the latter. Two models are proposed, in both of which electrostatic and hydrogen bonding interactions make major contributions to inter-protein binding. One of these models involves salt bridges from alphaArg99 of MEDH to the heme propionic acids of Cyt c551i and the other involves salt bridges from alphaArg426 of MEDH to Glu112 of Cyt c551i. Both involve salt bridges from alphaLys93 of MEDH to Asp75 of Cyt c551i. The size and nature of the cytochrome/quinoprotein heterodimer interfaces and calculations of electronic coupling and electron transfer rates favor one of these models over the other.     

An evolutionary interpretation of the “motivation to oviposit”

The ovipositional behavior of parasitoids and other insects is often described by phrases such as “motivation to oviposit” or “ovipositional drive”. This paper shows how an evolutionary (i.e. functional) interpretation can be given to such phrases. A detailed model for the parasitisation of Sycamore aphids by M. pseudoplatani is developed, using experiments by Collins and Dixon (1986). Two models are developed: i) one in which egg complement is the only state variable and ii) one in which egg complement and information concerning host densities are state variables. Comparisons of the behaviour of simulated parasitoids, using the decisions associated with the models, and the experiments suggest that both egg complement and information are important for the parasitoid's decision making. Accepting previously parasitized hosts may be optimal, and not simply an error in parasitoid perception. A number of other detailed predictions are made, such as the relative fitness of first and second eggs in superparasitized hosts and the nature of the memory of the parasitoid.     

A two-gene signature, SKI and SLAMF1, predicts time-to-treatment in previously untreated patients with chronic lymphocytic leukemia

We developed and validated a two-gene signature that predicts prognosis in previously-untreated chronic lymphocytic leukemia (CLL) patients. Using a 65 sample training set, from a cohort of 131 patients, we identified the best clinical models to predict time-to-treatment (TTT) and overall survival (OS). To identify individual genes or combinations in the training set with expression related to prognosis, we cross-validated univariate and multivariate models to predict TTT. We identified four gene sets (5, 6, 12, or 13 genes) to construct multivariate prognostic models. By optimizing each gene set on the training set, we constructed 11 models to predict the time from diagnosis to treatment. Each model also predicted OS and added value to the best clinical models. To determine which contributed the most value when added to clinical variables, we applied the Akaike Information Criterion. Two genes were consistently retained in the models with clinical variables: SKI (v-SKI avian sarcoma viral oncogene homolog) and SLAMF1 (signaling lymphocytic activation molecule family member 1; CD150). We optimized a two-gene model and validated it on an independent test set of 66 samples. This two-gene model predicted prognosis better on the test set than any of the known predictors, including ZAP70 and serum β2-microglobulin.     

A stochastic model of gene evolution with chaotic mutations

We develop here a new class of stochastic models of gene evolution in which the mutations are chaotic, i.e. a random subset of the 64 possible trinucleotides mutates at each evolutionary time t according to some substitution probabilities. Therefore, at each time t, the numbers and the types of mutable trinucleotides are unknown. Thus, the mutation matrix changes at each time t. The chaotic model developed generalizes the standard model in which all the trinucleotides mutate at each time t. It determines the occurrence probabilities at time t of trinucleotides which chaotically mutate according to three substitution parameters associated with the three trinucleotide sites. Two theorems prove that this chaotic model has a probability vector at each time t and that it converges to a uniform probability vector identical to that of the standard model. Furthermore, four applications of this chaotic model (with a uniform random strategy for the 64 trinucleotides and with a particular strategy for the three stop codons) allow an evolutionary study of the three circular codes identified in both eukaryotic and prokaryotic genes. A circular code is a particular set of trinucleotides whose main property is the retrieval of the frames in genes locally, i.e. anywhere in genes and particularly without start codons, and automatically with a window of a few nucleotides. After a certain evolutionary time and with particular values for the three substitution parameters, the chaotic models retrieve the main statistical properties of the three circular codes observed in genes. These applications also allow an evolutionary comparison between the standard and chaotic models.     

Transmembrane structure predictions with hydropathy index/charge two-dimensional trajectories of stochastic dynamical systems

A novel algorithm is proposed for predicting transmembrane protein secondary structure from two-dimensional vector trajectories consisting of a hydropathy index and formal charge of a test amino acid sequence using stochastic dynamical system models. Two prediction problems are discussed. One is the prediction of transmembrane region counts; another is that of transmembrane regions, i.e. predicting whether or not each amino acid belongs to a transmembrane region. The prediction accuracies, using a collection of well-characterized transmembrane protein sequences and benchmarking sequences, suggest that the proposed algorithm performs reasonably well. An experiment was performed with a glutamate transporter homologue from Pyrococcus horikoshii. The predicted transmembrane regions of the five human glutamate transporter sequences and observations based on the computed likelihood are reported.     

From sarcomere to cell: An efficient algorithm for linking mathematical models of muscle contraction

Two classes of mathematical framework have previously been developed to model active tension generation in contracting muscle. Cross-bridge models of muscle are biophysically based but computationally expensive to solve, and thus unsuitable for embedding in spatially distributed continuum representations. Fading memory models are computationally efficient but provide limited biophysical insight. In this study a novel computational method is proposed for coupling these two frameworks such that biophysical events can be determined and computational tractability maintained. Within the cross-bridge model, the functional forms of the distribution of cross-bridges, as a function of strain in each state, are approximated using the distribution moment approach. Using the variables of area, mean and standard deviation of each distribution, analytic expressions are developed to calculate the temporal dynamics of stiffness, tension and energy. A root finding method is employed to adjust the variables such that the temporal dynamics of the cross-bridge model match those of an equivalent fading memory model. The method is demonstrated for sinusoidal perturbations in length at two frequencies, with an approximate 30-fold increase in computational efficiency over a conventional technique for finding a solution to the cross-bridge model.     

Analysing treatment means in plant tissue culture research

Many researchers set up an experiment, make measurements, do an analysis of variance, calculate the mean response for each treatment, and then try to decide if the treatment means are significantly different and why. Much too frequently, Duncan's multiple range test is used to test differences among means. It is only one of a number of techniques that can be used to examine treatment means. Some researchers are unaware of the different techniques and that the interpretation of the results of an experiment is strongly influenced by the technique used i.e. using two different techniques might produce two different interpretations of the results. Selection of the appropriate technique to use for a particular experiment depends upon the nature of the treatments and the objectives of the research. This paper discusses four techniques (ranking treatment means, multiple comparison procedures, fitting response models, and using contrasts to make planned comparisons) that can be used to examine treatment means and presents examples of the use of each one for plant tissue culture research.Abbreviations (IAA) indole-3-acetic acid - (IBA) indole-3-butyric acid - (NAA) naphthalene-acetic acid - (2,4-D) 2, 4-dichlorophenoxyacetic acid - (BA) benzyladenine - (PBA) tetrahydropyranylbenzyladenine - (2iP) 2 isopentenyl adenine - (2iPA) 2 isopentenyl adenosine Journal Paper J-12661 of the Iowa Agriculture and Home Economics Experiment Station, Ames. Project 2210.     

Testing whether an identified treatment is best

We consider the problem of testing whether an identified treatment is better than each of K treatments. Suppose there are univariate test statistics Si that contrast the identified treatment with treatment i for i = 1, 2,...., K. The min test is defined to be the alpha-level procedure that rejects the null hypothesis that the identified treatment is not best when, for all i, Si rejects the one-sided hypothesis, at the alpha-level, that the identified treatment is not better than the ith treatment. In the normal case where Si are t statistics the min test is the likelihood ratio test. For distributions satisfying mild regularity conditions, if attention is restricted to test statistics that are monotone nondecreasing functions of Si, then regardless of their covariance structure the min test is an optimal alpha-level test. Tables of the sample size needed to achieve power .5, .8, .90, and .95 are given for the min test when the Si are Student's t and Wilcoxon.     

Atopic manifestations in the acquired immune deficiency syndrome: response to recombinant interferon gamma.

Six patients with the acquired immune deficiency syndrome (AIDS) had exacerbations or recurrences of previously quiescent atopic disease when they developed immunodeficiency. Four developed a different atopic illness from that suffered previously. Atopic symptoms developed within three months after the patients developed AIDS or during prodromal illness. Two of the patients were treated with recombinant interferon gamma: both showed a striking improvement in symptoms and cellular immunity. These results indicate that cellular immunity, through interferon gamma, may have a role in regulating atopic disease.