Multiplication stimulating activity (MSA) can substitute for insulin to stimulate the secretion of testicular transferrin by cultured sertoli cells

Both MSA and insulin were found to stimulate transferrin production in cultured Sertoli cells to the same extent in the absence or presence of follitropin, testosterone, or retinol. Sertoli cells were responsive to 30-fold lower concentrations of MSA than of insulin. MSA and insulin together stimulated transferrin secretion to the same extent as either hormone alone. These results, and what is presently understood about the relationship of MSA and insulin, suggest that insulin can substitute for the action of an MSA-like peptide in the stimulation of testicular transferrin secretion by Sertoli cells

Sertoli Cells Synthesize and Secrete a Ceruloplasmin-Like Protein

Sertoli cells synthesize and secrete a ceruloplasmin-like protein (testicular ceruloplasmin) that is immunologically similar to serum ceruloplasmin. Rat serum ceruloplasmin was purified and an antiserum was produced to the purified protein which specifically immunoprecipitated a 130,000 dalton protein from rat serum. This ceruloplasmin antiserum was found to also immunoprecipitate a 130,000 dalton protein synthesized and secreted by Sertoli cells. The presence of a protease inhibitor, phenylmethylsulfonyl fluoride (PMSF), was required during the immunoprecipitation procedure to prevent the proteolytic degradation of testicular ceruloplasmin. Immunoprecipitation of proteins secreted by Sertoli cells with an antiserum to rat serum proteins was found to precipitate two proteins, testicular ceruloplasmin and testicular transferrin

A User-Friendly Introduction to Link-Probit-Normal Models

Probit-normal models have attractive properties compared to logit-normal models. In particular, they allow for easy specification of marginal links of interest while permitting a conditional random effects structure. Moreover, programming fitting algorithms for probit-normal models can be trivial with the use of well-developed algorithms for approximating multivariate normal quantiles. In typical settings, the data cannot distinguish between probit and logit conditional link functions. Therefore, if marginal interpretations are desired, the default conditional link should be the most convenient one. We refer to models with a probit conditional link an arbitrary marginal link and a normal random effect distribution as link-probit-normal models. In this manuscript we outline these models and discuss appropriate situations for using multivariate normal approximations. Unlike other manuscripts in this area that focus on very general situations and implement Markov chain or MCEM algorithms, we focus on simpler, random intercept settings and give a collection of user-friendly examples and reproducible code. Marginally, the link-probit-normal model is obtained by a non-linear model on a discretized multivariate normal distribution, and thus can be thought of as a special case of discretizing a multivariate T distribution (as the degrees of freedom go to infinity). We also consider the larger class of multivariate T marginal models and illustrate how these models can be used to closely approximate a logit link

On the classification of North American Chelostoma (Hymenoptera: Megachilidae)

A new subgenus of Chelostoma Latreille is established for the New World group historically placed in Foveosmia Warncke.  These species, placed herein in Neochelostoma Engel & Griswold, new subgenus, are differentiated from the Palearctic Foveosmia and a modified key is provided to the subgenera of Chelostoma

Exploring the Social Impacts of a Summer Camp for Youth With Tourette Syndrome

Although a wealth of research exists documenting the positive social outcomes promoted by summer camps, research specifically examining youths with Tourette Syndrome (TS) within the camp context is lacking. This study utilized a phenomenological approach to explore the social impacts of a weeklong camp specifically for youths with TS, involving focus groups with 18 campers aged 10–16, interviews with 10 staff members, and participant observations compiled by the researcher. Multiple themes and sub-themes concerning the social impacts of the camp experience were identified, including (a) relatedness (not alone and self-assurance); (b) social development (friendships, optimism, educational experience, and bullying); (c) programmatic outcomes (unique program opportunities and cabin bonding); and (d) various implications for professional practice and future research are discussed

Analysis of Sertoli Cell-Secreted Proteins by Two-Dimensional Gel Electrophoresis

The [35S]methionine-labeled proteins secreted from cultured Sertoli cells were analyzed by two-dimensional gel electrophoresis and fluorography. Major polypeptides which were resolved by this procedure were designated by number and further analyzed. Many of these major polypeptides appeared as a series of spots which corresponded to charge isomers. Two of these polypeptides (5 and 6) were shown to be acidic, glycosylated and to comprise the subunits of a dimeric protein of molecular weight 70,000. Some of the polypeptides (4a and 5a) were shown to be secreted from testicular peritubular cells which contaminated the Sertoli cell cultures. However, many of the polypeptides (1,2,3,4,5,5b and 6) were specifically secreted from the Sertoli cells. The fluorogram of the secreted polypeptides obtained from cultured Sertoli cells from 20- or 60-day-old rats were similar to each other but differed from the pattern of polypeptides which were secreted from cultures of Sertoli cells from 10-day-old rats. Polypeptide 3 was identified by immunoprecipitation as testicular transferrin and the synthesis of polypeptide 3 was stimulated when the Sertoli cells were cultured in the presence of follicle-stimulating hormone (FSH), insulin, testosterone and retinol

ON MARGINALIZED MULTILEVEL MODELS AND THEIR COMPUTATION

Clustered data analysis is characterized by the need to describe both systematic variation in a mean model and cluster-dependent random variation in an association model. Marginalized multilevel models embrace the robustness and interpretations of a marginal mean model, while retaining the likelihood inference capabilities and flexible dependence structures of a conditional association model. Although there has been increasing recognition of the attractiveness of marginalized multilevel models, there has been a gap in their practical application arising from a lack of readily available estimation procedures. We extend the marginalized multilevel model to allow for nonlinear functions in both the mean and association aspects. We then formulate marginal models through conditional specifications to facilitate estimation with mixed model computational solutions already in place. We illustrate this approach on a cerebrovascular deficiency crossover trial