An inquiry into the behavior of softwood plywood price

The small size of the average plywood mill, and the wide spread of markets, has fostered the growth of independent wholesalers, or jobbers, as the main distribution channel for softwood plywood. Jobbers buy directly from mills, or indirectly through sales agents representing mills, and in turn sell to retail outlets. Since World War II, there has been large growth in the number of jobbers. In l95l, jobbers handled 79.L per cent of the total output of the plywood industry. Of the remaining 20.6 per cent, 3.1 per cent was handled by sales branches, and 17.5 per cent was sold. either through brokers, or directly to commercial outlets by mills. (3, p. 7) There are only a small number of sales branches which are operated by large producing organizations. Sales branches maintain warehouses and a sales force to handle sales to retail outlets, and act basically as the wholesaler for those mills so represented. Brokers play a minor role in distribution, and are most active during periods of short supply. Brokers do not warehouse, but work rather, as contact men between buyers and sellers. The majority of brokers do not maintain constant relationships with any single mill, but deal where contacts can be made. Growth in the number of wholesalers has lead to increased price and non-price competition. Non-price competition has taken the form of broader services to the customer, and has caused the increased necessity of warehousing on the part of the jobber (6). A study conducted in 1953 revealed that 29 per cent of jobbers sales were made by direct mill shipment, while 71 per cent were made from jobbers inventories (3, p. 9). A survey of 900 jobbers, completed in 19S5, found that typical full-service jobber carried a 50 day inventory of ply-wood on hand (3, p. II). This inventory reduces the necessity of warehousing on the part of the producer and retailer, and plays a role in stabilization of seasonal fluctuation in both price and production of softwood plywood

Retroperitoneal cystic lymphangioma

A case of a large retroperitoneal tumor in a previously asymptomatic twenty-two-year-old white female is presented. A review of the literature confirms the rarity of this tumor. Its histologic and embryologic derivation as well as its subtle and bizarre method of presentation are discussed. The cause of such lesions is debatable, but primary cure can be accomplished by meticulous excision of the lesion or marsupialization. This seldom seen neoplasm must enter into the differential diagnosis of all retroperitoneal masses.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/22183/1/0000614.pd

Statistics in the service of science : don’t let the tail wag the dog

Statistical modeling is generally meant to describe patterns in data in service of the broader scientific goal of developing theories to explain those patterns. Statistical models support meaningful inferences when models are built so as to align parameters of the model with potential causal mechanisms and how they manifest in data. When statistical models are instead based on assumptions chosen by default, attempts to draw inferences can be uninformative or even paradoxical—in essence, the tail is trying to wag the dog. These issues are illustrated by van Doorn et al. (this issue) in the context of using Bayes Factors to identify effects and interactions in linear mixed models. We show that the problems identified in their applications (along with other problems identified here) can be circumvented by using priors over inherently meaningful units instead of default priors on standardized scales. This case study illustrates how researchers must directly engage with a number of substantive issues in order to support meaningful inferences, of which we highlight two: The first is the problem of coordination, which requires a researcher to specify how the theoretical constructs postulated by a model are functionally related to observable variables. The second is the problem of generalization, which requires a researcher to consider how a model may represent theoretical constructs shared across similar but non-identical situations, along with the fact that model comparison metrics like Bayes Factors do not directly address this form of generalization. For statistical modeling to serve the goals of science, models cannot be based on default assumptions, but should instead be based on an understanding of their coordination function and on how they represent causal mechanisms that may be expected to generalize to other related scenarios

Imprinting disorders: a group of congenital disorders with overlapping patterns of molecular changes affecting imprinted loci.

Congenital imprinting disorders (IDs) are characterised by molecular changes affecting imprinted chromosomal regions and genes, i.e. genes that are expressed in a parent-of-origin specific manner. Recent years have seen a great expansion in the range of alterations in regulation, dosage or DNA sequence shown to disturb imprinted gene expression, and the correspondingly broad range of resultant clinical syndromes. At the same time, however, it has become clear that this diversity of IDs has common underlying principles, not only in shared molecular mechanisms, but also in interrelated clinical impacts upon growth, development and metabolism. Thus, detailed and systematic analysis of IDs can not only identify unifying principles of molecular epigenetics in health and disease, but also support personalisation of diagnosis and management for individual patients and families.All authors are members of the EUCID.net network, funded by COST (BM1208). TE is funded by the German Ministry of research and education (01GM1513B). GPdN is funded by I3SNS Program of the Spanish Ministry of Health (CP03/0064; SIVI 1395/09), Instituto de Salud Carlos III (PI13/00467) and Basque Department of Health (GV2014/111017).This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s13148-015-0143-

Bayes factors for mixed models: A discussion

van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison