Gossypium hirsutum races are believed to be potential reservoirs of desirable traits, which can play crucial roles to overcome the existing narrow genetic base of modern Upland cotton cultivars. However, prior to utilizing the races in cotton improvement programs, understanding their genetic constitutions is needed. Thus, this study used molecular and morphological techniques to characterize 110 G. hirsutum germplasm including 109 semi-wild accessions and one Upland cotton cultivar, CRI12. 104 SSR markers detected a total of 795 alleles, with an average of 7.64 alleles per marker, ranging from 3 to 14, and average PIC value of 0.71. 96 of the markers were found to be highly informative, with polymorphism information content (PIC) value ≥0.50. Pairwise genetic similarity coefficient across the accessions ranged from 0.19 to 1.00, with an average value of 0.46. Morphological characterization was done using fiber length, fiber strength, micronaire, fiber uniformity index, and fiber elongation. Pairwise taxonomic distance within the accessions ranged from 0.17 to 3.41, with a mean of 1.33. The SSR and fiber quality traits data set based unweighted pair group method of arithmetic mean (UPGMA) analysis grouped the accessions into 7 and 12 distinct clusters, respectively that corresponds well with the results of principal component analysis (PCA). Our study revealed the existence of vast molecular and morphological diversities within the accessions and provided valuable information on each semi-wild accession for quick and better informed germplasm utilization in cotton breeding programs.

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Mechanical devices such as robots are widely adopted for limb rehabilitation. Due to the variety of human body parameters, the rehabilitation motion for different patients usually has its individual pattern, thus we adopt clustering-based machine learning technique to find a limited number of motion patterns for upper-limb rehabilitation, so that they could represent the large amount of those from people who have various body parameters. Using the regression motion of the clustering result as the target, in this paper we seek to apply kinematic-mapping-based motion synthesis framework to design a one-DOF mechanism such that it could lead the patients' upper limb through the target motion. Also, considering rehab training generally involves a large amount of repetition in daily basis, this paper has developed a rehab system with Unity3D based on Virtual Reality (VR). The proposed device and system could provide an immersive experience to the users, as well as the rehab motion data to the administrative staff for evaluation of users' status. The construction of the integrated system as well as the experimental trial of the prototype are presented in the end of this paper.

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