Multiple Inertial Measurement Unit Combination and Location for Center of Pressure Prediction in Gait.

Center of pressure (COP) during a gait cycle indicates crucial information with regard to fall risk such as balance capacity. The drawbacks of conventional research instruments include inconvenient use during activities of daily living and expensive costs. The present study illustrates the promising fall-relevant information predicted by acceleration and angular velocity data from different placement sensors with machine learning techniques. This approach is inspired by the emerging machine learning technique, specifically the long short-term memory (LSTM), which is often used in time series data and aims to decrease the burden of the user while using the novel wearable technology. The Jaccard similarity coefficient, which implies the consistency of profile alignment between prediction and real situation, achieved 94% accuracy in the walking direction. Furthermore, the number of sensors used and the placement influenced the feasibility of an application. The outcome revealed that the accuracy could exceed 90% with only one sensor placed on the foot in the walking direction, and the toe would be the best location for sensor placement. To examine the performance of machine learning, the current study employed two parameters from different perspectives. One is a commonly used parameter, which represented the error, and the other investigated the similarity between the prediction and ground truth. From a similarity perspective, the parameter can be used as a metric to assess the consistency of profile alignment.

Parallel Mining of Gene Expression Differences of Nucleotide Excision Repair Gene XPA in Human Skin Microarrays based on GEO Database / 昆明医科大学学报

Objective To make a parallel mining the data of expression differences of a crucial gene XPA involved in nucleotide excision repair pathway of human skin microarrays by bioinformatics from the system level.Methods Using the ScanGEO, the data of microarrays which included the significant differences expression level of XPA were screened and analyzed from 59 human skin samples in the GEO database. Results There were 7 samples with the down-regulated expression of XPA: cutaneous malignant melanoma, epidermal injury model, DNA damage and UV radiation, foreskin fibroblast response to Toxoplasma gondii RH type 1 (ROP5) mutant infection, interleukin-20 subfamily cytokines effect on epidermal keratinocytes, Egr-1 overexpression effect on skin fibroblasts in vitro: time course, in vitro model for inflammatory dendritic cells.Present expression down. Conclusion Based on the GEO database and ScanGEO, high-throughput shared data can be screened and analyzed efficiently.