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BACKGROUND/AIMS Recent research has attempted combinations of instruments to improve screening accuracy for mild cognitive impairment (MCI) and early Alzheimer's disease (AD). We compared Mini-Mental State Examination (MMSE), Immediate and Delayed Recall (Logical Memory I and II; LM-I and LM-II, respectively), a single-item informant report of memory(More)
BACKGROUND The treatment of the patient with pelvic fracture urethral disruption defects (PFUDD) remains controversial especially in pediatric urology. Debate continues in regarding the advisability of immediate repair versus delayed repair. The aim of this study was to analyze our experience in the outcomes of immediate and delayed repair of pelvic(More)
Centromere spreading (CS) of chromosomes and high occurrence of aberrations at centromeric region were observed in two papillary serous cystadenocarcinomas and one borderline papillary serous cystadenoma of the ovary. In the borderline tumor, CS of chromosome 12, trisomy of which had been reported as the sole abnomaly in benign ovarian tumors, was seen in(More)
It was reported recently that changing the TIR (translational initiation region) of lambda N gene resulted in the increasing expression of lambda N gene and it was regulated at translational level. According to the alignment, the leader sequence of lambda N gene had three parts: a code region for ORF lambda N, the upstream sequence of ORF lambda N and 17 bp(More)
Based on the complexity of mechanism and variation rules of thermal error on CNC machine, a new grey system model GM(1,1,α) combined with particle swarm optimization (PSO) was proposed and applied in thermal error modeling on CNC machine. Particle swarm optimization has very strong capacity to solve complicated multi-peaked optimum question. In the(More)
Noise in class labels of any training set can lead to poor classification results no matter what machine learning method is used. In this paper, we first present the problem of binary classification in the presence of random noise on the class labels, which we call class noise. To model class noise, a class noise rate is normally defined as a small(More)