Development of an empirically based dynamic biomechanical strength model

The focus here is on the development of a dynamic strength model for humans. Our model is based on empirical data. The shoulder, elbow, and wrist joints are characterized in terms of maximum isolated torque, position, and velocity in all rotational planes. This information is reduced by a least squares regression technique into a table of single variable second degree polynomial equations determining the torque as a function of position and velocity. The isolated joint torque equations are then used to compute forces resulting from a composite motion, which in this case is a ratchet wrench push and pull operation. What is presented here is a comparison of the computed or predicted results of the model with the actual measured values for the composite motion

Correlation and prediction of dynamic human isolated joint strength from lean body mass

A relationship between a person's lean body mass and the amount of maximum torque that can be produced with each isolated joint of the upper extremity was investigated. The maximum dynamic isolated joint torque (upper extremity) on 14 subjects was collected using a dynamometer multi-joint testing unit. These data were reduced to a table of coefficients of second degree polynomials, computed using a least squares regression method. All the coefficients were then organized into look-up tables, a compact and convenient storage/retrieval mechanism for the data set. Data from each joint, direction and velocity, were normalized with respect to that joint's average and merged into files (one for each curve for a particular joint). Regression was performed on each one of these files to derive a table of normalized population curve coefficients for each joint axis, direction, and velocity. In addition, a regression table which included all upper extremity joints was built which related average torque to lean body mass for an individual. These two tables are the basis of the regression model which allows the prediction of dynamic isolated joint torques from an individual's lean body mass

Developing Children's Oral Health Assessment Toolkits Using Machine Learning Algorithm.

ObjectivesEvaluating children's oral health status and treatment needs is challenging. We aim to build oral health assessment toolkits to predict Children's Oral Health Status Index (COHSI) score and referral for treatment needs (RFTN) of oral health. Parent and Child toolkits consist of short-form survey items (12 for children and 8 for parents) with and without children's demographic information (7 questions) to predict the child's oral health status and need for treatment.MethodsData were collected from 12 dental practices in Los Angeles County from 2015 to 2016. We predicted COHSI score and RFTN using random Bootstrap samples with manually introduced Gaussian noise together with machine learning algorithms, such as Extreme Gradient Boosting and Naive Bayesian algorithms (using R). The toolkits predicted the probability of treatment needs and the COHSI score with percentile (ranking). The performance of the toolkits was evaluated internally and externally by residual mean square error (RMSE), correlation, sensitivity and specificity.ResultsThe toolkits were developed based on survey responses from 545 families with children aged 2 to 17 y. The sensitivity and specificity for predicting RFTN were 93% and 49% respectively with the external data. The correlation(s) between predicted and clinically determined COHSI was 0.88 (and 0.91 for its percentile). The RMSEs of the COHSI toolkit were 4.2 for COHSI (and 1.3 for its percentile).ConclusionsSurvey responses from children and their parents/guardians are predictive for clinical outcomes. The toolkits can be used by oral health programs at baseline among school populations. The toolkits can also be used to quantify differences between pre- and post-dental care program implementation. The toolkits' predicted oral health scores can be used to stratify samples in oral health research.Knowledge transfer statementThis study creates the oral health toolkits that combine self- and proxy- reported short forms with children's demographic characteristics to predict children's oral health and treatment needs using Machine Learning algorithms. The toolkits can be used by oral health programs at baseline among school populations to quantify differences between pre and post dental care program implementation. The toolkits can also be used to stratify samples according to the treatment needs and oral health status

Book Reviews

THE SUPREME COURT AND RELIGION. By Richard E. Morgan. New York: The Free Press, 1972. Pp. 216. $7.95. WHO RUNS CONGRESS? THE PRESIDENT, BIG BUSINESS, OR You? By Mark J. Green, James M. Fallows and David R. Zwick. New York: Bantam Books and Grossman Publishers, 1972. Pp. 307.$1.95. PRIVATE INTEREST AND PUBLIC GAIN: THE DARTMOUTH COLLEGE CASE, 1819. By Francis N. Stites. Amherst: The University of Massachusetts Press, 1972. Pp. 176. $9.50. JUSTICE IS THE CRIME: PRETRIAL DELAY IN FELONY CASES. By Lewis R. Katz, with Lawrence B. Litwin and Richard H. Bamberger. Cleveland: Case Western Reserve University Press, 1972. Pp. 386.$6.95

Computerized adaptive testing and short form development for child and adolescent oral health patient-reported outcomes measurement.

ObjectivesTo develop computerized adaptive testing (CAT) and short forms of self-report oral health measures that are predictive of both the children's oral health status index (COHSI) and the children's oral health referral recommendation (COHRR) scales, for children and adolescents, ages 8-17.Material and methodsUsing final item calibration parameters (discrimination and difficulty parameters) from the item response theory analysis, we performed post hoc CAT simulation. Items most frequently administered in the simulation were incorporated for possible inclusion in final oral health assessment toolkits, to select the best performing eight items for COHSI and COHRR.ResultsTwo previously identified unidimensional sets of self-report items consisting of 19 items for the COHSI and 22 items for the COHRR were administered through CAT resulting in eight-item short forms for both the COHSI and COHRR. Correlations between the simulated CAT scores and the full item bank representing the latent trait are r = .94 for COHSI and r = .96 for COHRR, respectively, which demonstrated high reliability of the CAT and short form.ConclusionsUsing established rigorous measurement development standards, the CAT and corresponding eight-item short form items for COHSI and COHRR were developed to assess the oral health status of children and adolescents, ages 8-17. These measures demonstrated good psychometric properties and can have clinical utility in oral health screening and evaluation and clinical referral recommendations

The Glucagon Receptor Is Required for the Adaptive Metabolic Response to Fasting

SummaryGlucagon receptor (Gcgr) signaling maintains hepatic glucose production during the fasting state; however, the importance of the Gcgr for lipid metabolism is unclear. We show here that fasted Gcgr−/− mice exhibit a significant increase in hepatic triglyceride secretion and fasting increases fatty acid oxidation (FAO) in wild-type (WT) but not in Gcgr−/− mice. Moreover fasting upregulated the expression of FAO-related hepatic mRNA transcripts in Gcgr+/+ but not in Gcgr−/− mice. Exogenous glucagon administration reduced plasma triglycerides in WT mice, inhibited TG synthesis and secretion, and stimulated FA beta oxidation in Gcgr+/+ hepatocytes. The actions of glucagon on TG synthesis and FAO were abolished in PPARα−/− hepatocytes. These findings demonstrate that the Gcgr receptor is required for control of lipid metabolism during the adaptive metabolic response to fasting