Objective To address the issue of motion misclassification caused by individual gait differences and fixed time window data extraction in motion intention recognition for intelligent lower limb prostheses,this study proposes a motion intention recognition method based on adaptive temporal alignment.Methods In lower limb motion analysis,for continuous gait cycle data,inter-class variability across different steady-state modes was utilized to detect gait pattern consistency through inter-cycle frame differencing.For samples identified as single steady-state modes,the dynamic time warping algorithm was introduced to align adjacent mo-tion sequences,thereby reducing individual variability.Haar wavelet 4-level decomposition was applied to ex-tract low-frequency coefficients for feature vector construction,and classification was performed using a support vector machine.The experimental protocol was designed as follows:three inertial measurement units were used to collect lower limb acceleration and angular velocity data from subjects performing thirteen locomotion modes.The test subjects included ten healthy participants and one transtibial amputee.The locomotion modes consisted of five steady-state modes(level walking,stair ascent,stair descent,ramp ascent,and ramp descent)and eight transition modes(mutual transitions between level walking and stair ascent/descent,as well as ramp ascent/de-scent).Results Simulation tests on ten healthy individuals and one amputee showed recognition accuracies of 99.24%and 100%for five steady-state modes,and 98.51%and 89.11%for all thirteen motion modes,respectively.Conclusion This study proposes an adaptive temporal alignment-based motion intention recognition method.The pro-posed approach effectively reduces the interference of individual gait variability on feature representation,en-hances the consistency and discriminability of gait features,and ultimately improves recognition performance.

Objective To address the issue of motion misclassification caused by individual gait differences and fixed time window data extraction in motion intention recognition for intelligent lower limb prostheses,this study proposes a motion intention recognition method based on adaptive temporal alignment.Methods In lower limb motion analysis,for continuous gait cycle data,inter-class variability across different steady-state modes was utilized to detect gait pattern consistency through inter-cycle frame differencing.For samples identified as single steady-state modes,the dynamic time warping algorithm was introduced to align adjacent mo-tion sequences,thereby reducing individual variability.Haar wavelet 4-level decomposition was applied to ex-tract low-frequency coefficients for feature vector construction,and classification was performed using a support vector machine.The experimental protocol was designed as follows:three inertial measurement units were used to collect lower limb acceleration and angular velocity data from subjects performing thirteen locomotion modes.The test subjects included ten healthy participants and one transtibial amputee.The locomotion modes consisted of five steady-state modes(level walking,stair ascent,stair descent,ramp ascent,and ramp descent)and eight transition modes(mutual transitions between level walking and stair ascent/descent,as well as ramp ascent/de-scent).Results Simulation tests on ten healthy individuals and one amputee showed recognition accuracies of 99.24%and 100%for five steady-state modes,and 98.51%and 89.11%for all thirteen motion modes,respectively.Conclusion This study proposes an adaptive temporal alignment-based motion intention recognition method.The pro-posed approach effectively reduces the interference of individual gait variability on feature representation,en-hances the consistency and discriminability of gait features,and ultimately improves recognition performance.

Objective To identify the characteristics of HCoV-OC43genotype in recent years.Methods Nucleic acids were extracted from HCoV-OC43-positive respiratory specimens collected from 2013 to 2015.The cDNA were generated by reverse transcription.Specific primers were used to amplify the full length of spike (S),RNA dependent RNA polymerase (RdRp) and Nucleocapsid (N) genes.The sequences assembling,phylogenic analyzing and amino acids alignments were performed by using Mega 6.0 software.Results The genes were successful amplified from 16 HCoV-OC43 clinical samples.Of the 16 strains,1 from 2013,all 8 from 2014 and 3 from 2015 belonged to genotype D,and 3 from 2015 belonged to genotype B,and 1 from 2015 belonged to genotype E named by China,E (CH).The coding polymorphisms were found in S protein,with 9 sites in B and 17 sites in D genotypes were between chronologically distinct strains.Accumulations of amino acids mutations were observed during the evolution of B genotype,while reverse and successive changes were found in D genotypes.Conclusions Multiple genotypes of HCoV-OC43 co-circulated,with dominant epidemic of D genotype.The genetic drift in S gene plays an important role in the epidemic of specific genotype of HCoV-OC43.Our results provide insights to clarify the molecular epidemic characteristics of HCoV-OC43.

OBJECTIVETo confirm the existence of point mutations in the SSU rDNA variable regions of 5 Leishmania donovani (L.d.) isolates from different epidemic foci in China.

METHODSSpecific SSU rDNA fragments from nuclear DNA of 7 Leishmania species/isolates were amplified by PCR and then cloned into pGEM(R)-T Easy Vectors. After that, the specific fragments were sequenced by an automated DNA sequencer.

RESULTSSequence analysis showed that the amplified DNA fragments of 7 Leishmania species/isolates were all 392 bp in length. All 5 point mutations were located in two unique sequence blocks (UQ-I and UQ-II), and no insertions or deletions were found. The identities of comparison of Leishmania in GeneBank were more than 98%.

CONCLUSIONFive point mutations exist in the SSU rDNA variable region of 5 L.d. isolates from different epidemic foci of visceral leishmaniasis (VL) in China. Sequence differences of the SSU rDNA variable region exist among L.d. isolates from different foci.

Animals ; DNA, Protozoan ; chemistry ; DNA, Ribosomal ; chemistry ; Humans ; Leishmania donovani ; genetics ; Leishmaniasis, Visceral ; parasitology ; Point Mutation ; Polymerase Chain Reaction