Monitoring healing progression and characterizing the mechanical environment in preclinical models for bone tissue engineering

The treatment of large segmental bone defects remains a significant clinical challenge. Due to limitations surrounding the use of bone grafts, tissue-engineered constructs for the repair of large bone defects could offer an alternative. Before translation of any newly developed tissue engineering (TE) approach to the clinic, efficacy of the treatment must be shown in a validated preclinical large animal model. Currently, biomechanical testing, histology, and microcomputed tomography are performed to assess the quality and quantity of the regenerated bone. However, in vivo monitoring of the progression of healing is seldom performed, which could reveal important information regarding time to restoration of mechanical function and acceleration of regeneration. Furthermore, since the mechanical environment is known to influence bone regeneration, and limb loading of the animals can poorly be controlled, characterizing activity and load history could provide the ability to explain variability in the acquired data sets and potentially outliers based on abnormal loading. Many approaches have been devised to monitor the progression of healing and characterize the mechanical environment in fracture healing studies. In this article, we review previous methods and share results of recent work of our group toward developing and implementing a comprehensive biomechanical monitoring system to study bone regeneration in preclinical TE studies.

Improved optical character recognition by matched filtering

The effectiveness of linear matched filters for improved character discrimination in presence of random noise and poorly defined characters has been investigated. We have found that although the performance of the filter in presence of random noise is reasonably good (16 dB gain in signal-to-noise-ratio) its performance is poor when the unknown character is distorted (linear shift and rotation).

Personal health records as sources of productivity evidence

Capturing data from various data repositories and integrating them for productivity improvements is common in modern business organisations. With the well-accepted concept of achieving positive gains through investment in employee health and wellness, organisations have started to capture both employee health and non-health data as Employer Sponsored electronic Personal Health Records (ESPHRs). However, non-health related data in ESPHRs has hardly been taken into consideration with outcomes such as employee productivity potentially being suited for further validation and stimulation of ESPHR usage. Here we analyse selected employee demographic information (age, gender, marital status, and job grade) and health-related outcomes (absenteeism and presenteeism) of employees for evidence-based decision making. Our study considered demographic and health-related outcomes of 700 employees. Surprisingly, the analysis shows that employees with high sick leave rates are also high performers. A factor analysis shows 92% of the variance in the data can be explained by three factors, with the job grade capable of explaining 62% of the variance. Work responsibilities may drive employees to maintain high work performance despite signs of sickness, so ESPHRs should focus attention on high performers. This finding suggests new ways of extracting value from ESPHRs to support organisational health and wellness management to help assure sustainability in organisational productivity.

Quantization noise in binary holograms

The signal-to-noise (S/N) ratio in the reconstructed image from a binary hologram has been quantitatively related to the amplitude and phase quantization levels. The S/N ratio increases monotonically with increasing number of quantization levels. This observation is further supported by experimental results.