A comparative study of splint effectiveness in limiting forearm rotation

Study Design: Case Study Series.---------- Introduction: Restriction of forearm rotation may be required for effective management and rehabilitation of the upper limb after trauma.---------- Purpose of the Study: To compare the effectiveness of four splints in restricting forearm rotation.---------- Methods: Muenster, Sugartong, antipronation distal radioulnar joint (DRUJ), and standard wrist splints were fabricated for five healthy participants. Active range of motion (AROM) in forearm pronation and supination was measured with a goniometer for each splint, at the initial point of sensory feedback and during exertion of maximal force.---------- Results: Repeated-measures analysis of variance indicated significant differences between splints for all four AROM measures. Post hoc paired t-tests showed that the Sugartong splint was significantly more restrictive in pronation than the Muenster splint. The antipronation DRUJ splint provided significantly greater restriction in pronation than the standard wrist splint. No splints immobilized the forearm completely.---------- Conclusions: The Sugartong splint is recommended for maximal restriction in pronation, but individual patient characteristics require consideration in splint choice.

Action research as foresight methodology

To the action researcher, who laboriously spends his or her hours working within the local contexts of communities or organisations to co-generate meaningful research, and who’s theories are hardened on the anvil of creating meaningful social change; futures studies might seem the discipline the most peripheral to its interests, and the most ill equipped to deal with the local and intimate domain of community existence. To the futurist, who laboriously spends his or her hours understanding the nuances of history and social change, who through persistent work, begins to make sense of the weak signals and the subtle shifts, action research would seem as simply an auxiliary field, inappropriate for understanding the greater scheme. I invite the reader, however, whether they belong to one camp or the other, to let go of their respective disciplinary perspectives, and see both belonging to each other. [Introduction] .

From critique to cultural recovery: critical futures studies and casual layered analysis

Critical futures studies is not about the careers of a few scholars, rather it is about projects that transcend the narrow boundaries of the self. This biographical monograph examines the life and work of Richard Slaughter and Sohail Inayatullah.

Modeling meat yield based on measurements of body traits in genetically improved giant freshwater prawn (GFP) Macrobrachium rosenbergii

A single-generation dataset consisting of 1,730 records from a selection program for high growth rate in giant freshwater prawn (GFP, Macrobrachium rosenbergii) was used to derive prediction equations for meat weight and meat yield. Models were based on body traits [body weight, total length and abdominal width (AW)] and carcass measurements (tail weight and exoskeleton-off weight). Lengths and width were adjusted for the systematic effects of selection line, male morphotypes and female reproductive status, and for the covariables of age at slaughter within sex and body weight. Body and meat weights adjusted for the same effects (except body weight) were used to calculate meat yield (expressed as percentage of tail weight/body weight and exoskeleton-off weight/body weight). The edible meat weight and yield in this GFP population ranged from 12 to 15 g and 37 to 45 %, respectively. The simple (Pearson) correlation coefficients between body traits (body weight, total length and AW) and meat weight were moderate to very high and positive (0.75–0.94), but the correlations between body traits and meat yield were negative (−0.47 to −0.74). There were strong linear positive relationships between measurements of body traits and meat weight, whereas relationships of body traits with meat yield were moderate and negative. Step-wise multiple regression analysis showed that the best model to predict meat weight included all body traits, with a coefficient of determination (R 2) of 0.99 and a correlation between observed and predicted values of meat weight of 0.99. The corresponding figures for meat yield were 0.91 and 0.95, respectively. Body weight or length was the best predictor of meat weight, explaining 91–94 % of observed variance when it was fitted alone in the model. By contrast, tail width explained a lower proportion (69–82 %) of total variance in the single trait models. It is concluded that in practical breeding programs, improvement of meat weight can be easily made through indirect selection for body trait combinations. The improvement of meat yield, albeit being more difficult, is possible by genetic means, with 91 % of the variation in the trait explained by the body and carcass traits examined in this study.