Objective To investigate the surgical methods and clinical effect of repairing forefoot soft tissue defects with free peroneal artery perforator flap in elderly patients.Methods From June,2011 to April,2015,17 cases of forefoot soft tissue defects repaired with free peroneal artery perforator flap in elderly patients.There were 10 cases of male and female in 7 cases with an average age of 65.8 years old ranging from 60 to 74 years.Causes of injury:traffic accident in 7 cases,heavy crushing in 9 cases,electrical bums in 1 case.Injury part:6 cases on the left side and 11 cases on the right side.Metatarsus and phalanges fracture in 9 cases,tendon injury in 5 cases.Defect area:3.0 cm × 4.0 cm-6.3 cm × 11.2 cm.Results All flaps survived.All wounds were primary healing.Skin graft survived for the foot flap donor site,and no complicated with infection.All patients were followed up from 8 to 36 months with an average of 17.6 months.The appearance of flaps were good,slight bloated.The texture and color of the flaps were close to the recipient site.Flap feel were good.Accortling to (AOFAS)criteria system,the AOFAS score of last follow-up was (77.5±13.2).Excellent in 6 cases,good in 9 cases,fair in 2 cases.VAS score was (2.6±0.4).Conclusion The free peroneal artery perforator flap with the advantages of vascular anatomy constant,blood supply is reliable,thickness moderate,etc.It is a useful clinical method to repair forefoot soft tissue defects in elderly patients.

Along with the develoipment of high-throughput sequencing technologies, both sample size and SNP number are increasing rapidly in genome-wide association studies (GWAS), and the associated computation is more challenging than ever. Here, we present a memory-efficient, visualization-enhanced, and parallel-accelerated R package called"rMVP"to address the need for improved GWAS computation. rMVP can 1) effectively process large GWAS data, 2) rapidly evaluate population structure, 3) efficiently estimate variance components by Efficient Mixed-Model Association eX-pedited (EMMAX), Factored Spectrally Transformed Linear Mixed Models (FaST-LMM), and Haseman-Elston (HE) regression algorithms, 4) implement parallel-accelerated association tests of markers using general linear model (GLM), mixed linear model (MLM), and fixed and random model circulating probability unification (FarmCPU) methods, 5) compute fast with a globally efficient design in the GWAS processes, and 6) generate various visualizations of GWAS-related information. Accelerated by block matrix multiplication strategy and multiple threads, the association test methods embedded in rMVP are significantly faster than PLINK, GEMMA, and FarmCPU_pkg. rMVP is freely available at https://github.com/xiaolei-lab/rMVP.