Sperm-binding fibronectin type II-module proteins are genetically linked and functionally related

Fibronectin type II (Fn2) module-containing proteins in the male genital tract are characterized by different numbers of Fn2 modules. Predominantly two classes exist which are distinct by having either two or four Fn2 modules. Minor variants with three Fn2 modules were also found in the human and the porcine epididymis. To reveal their relationship, mRNAs and proteins of representatives of these classes were studied in human, in Sus scrofa, and in rodents. Adult boars expressed members of both classes, i.e. ELSPBP1 and pB1, in subsequent regions of the epididymis, and both were under androgenic control. Human and rodent epididymides, on the other hand, alternatively contained only representatives of one of these two classes, i.e. ELSPBP1 in the human and two different pB1-related counterparts in rodents. ELSPBP1 and pB1-related genomic sequences were closely linked in chromosomal regions HSA 19q and SSC 6 q11-q21; conserved synteny between these regions is well established. On the other hand, in a syntenic region on mouse chromosome 7, ELSPBP1-related sequences were lacking. Tight binding to the sperm membrane via a choline-mediated mechanism was a common feature of the two classes of Fn2-module proteins, suggesting related function(s). However, differences in their regionalized expression patterns along the male genital tract as well as in association sites on the sperm surface suggested a species-specific sequential order in sperm binding.

A pain assessment scale for population-based studies: Development and validation of the Pain Module of the Standard Evaluation Questionnaire

The objectives of this study were to develop and validate a tool for assessing pain in population-based observational studies and to develop three subscales for back/neck, upper extremity and lower extremity pain. Based on a literature review, items were extracted from validated questionnaires and reviewed by an expert panel. The initial questionnaire consisted of a pain manikin and 34 items relating to (i) intensity of pain in different body regions (7 items), (ii) pain during activities of daily living (18 items) and (iii) various pain modalities (9 items). Psychometric validation of the initial questionnaire was performed in a random sample of the German-speaking Swiss population. Analyses included tests for reliability, correlation analysis, principal components factor analysis, tests for internal consistency and validity. Overall, 16,634 of 23,763 eligible individuals participated (70%). Test-retest reliability coefficients ranged from 0.32 to 0.97, but only three coefficients were below 0.60. Subscales were constructed combining four items for each of the subscales. Item-total coefficients ranged from 0.76 to 0.86 and Cronbach's alpha were 0.75 or higher for all subscales. Correlation coefficients between subscales and three validated instruments (WOMAC, SPADI and Oswestry) ranged from 0.62 to 0.79. The final Pain Standard Evaluation Questionnaire (SEQ Pain) included 28 items and the pain manikin and accounted for the multidimensionality of pain by assessing pain location and intensity, pain during activity, triggers and time of onset of pain and frequency of pain medication. It was found to be reliable and valid for the assessment of pain in population-based observational studies.

COEFPLOT: Stata module to plot regression coefficients and other results

coefplot plots results from estimation commands or Stata matrices. Results from multiple models or matrices can be combined in a single graph. The default behavior of coefplot is to draw markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce various other types of graphs.

ADDPLOT: Stata module to add twoway plot objects to an existing twoway graph

addplot adds twoway plot objects to an existing twoway graph. This is useful if you want to add additional objects such as titles or extra data points to a twoway graph after it has been created. Most of what addplot can do, can also be done by rerunning the original graph command including additional options or plot statements. addplot, however, might be useful if you have to modify a graph for which you cannot rerun the original command, for example, because you only have the graph file but not the data that were used to create the graph. Furthermore, addplot can do certain things that would be difficult to achieve in a single graph command (e.g. customizing individual subgraphs within a by-graph). addplot also provides a substitute for some of the functionality of the graph editor.

ROBREG: Stata module providing robust regression estimators

robreg provides a number of robust estimators for linear regression models. Among them are the high breakdown-point and high efficiency MM-estimator, the Huber and bisquare M-estimator, and the S-estimator, each supporting classic or robust standard errors. Furthermore, basic versions of the LMS/LQS (least median of squares) and LTS (least trimmed squares) estimators are provided. Note that the moremata package, also available from SSC, is required.

Training Module for ALS Moderators: Report on conceptual and methodological aspects of the first workshop in Pemba Metuge, Cabo Delgado, Mozambique, April 2005

Report on conceptual and methodological aspects of the first workshop for training moderators and trainers in ALS and sustainable management of natural resources, in Pemba Metuge, Cabo Delgado, Mozambique, April 2005.

PSHARE: Stata module to compute and graph percentile shares

-pshare- computes and graphs percentile shares from individual level data. Percentile shares are often used in inequality research to study the distribution of income or wealth. They are defined as differences between Lorenz ordinates of the outcome variable. Technically, the observations are sorted in increasing order of the outcome variable and the specified percentiles are computed from the running sum of the outcomes. Percentile shares are then computed as differences between percentiles, divided by total outcome. pshare requires moremata to be installed on the system; see ssc describe moremata.