In microendoscopic discectomy for spinal disorders, bone cutting procedures are performed in tight spaces while observing a small portion of the target structures. Although optical tracking systems are able to measure the tip of the surgical tool during surgery, the poor shape information available during surgery makes accurate cutting difficult, even if preoperative computed tomography and magnetic resonance images are used for reference. Shape estimation and visualization of the target structures are essential for accurate cutting. However, time-varying shape changes during cutting procedures are still challenging issues for intraoperative navigation. This paper introduces a concept of endoscopic image augmentation that overlays shape changes to support bone cutting procedures. This framework handles the history of the location of the measured drill tip as a volume label and visualizes the remains to be cut overlaid on the endoscopic image in real time. A cutting experiment was performed with volunteers, and the feasibility of this concept was examined using a clinical navigation system. The efficacy of the cutting aid was evaluated with respect to the shape similarity, total moved distance of a cutting tool, and required cutting time. The results of the experiments showed that cutting performance was significantly improved by the proposed framework.
Genome-wide association studies (GWAS) of quantitative traits have detected numerous genetic associations, but they encounter difficulties in pinpointing prominent candidate genes and inferring gene networks. The present study used a systems genetics approach integrating GWAS results with external RNA-expression data to detect candidate gene networks in feed utilization and growth traits of Japanese Black cattle, which are matters of concern. A SNP coassociation network was derived from significant correlations between SNPs with effects estimated by GWAS across 7 phenotypic traits. The resulting network genes contained significant numbers of annotations related to the traits. Using bovine transcriptome data from a public database, an RNA coexpression network was inferred based on the similarity of expression patterns across different tissues. An intersection network was then generated by superimposing the SNP and RNA networks and extracting shared interactions. This intersection network contained 4 tissue-specific modules: nervous system, reproductive system, muscular system, and glands. To characterize the structure (topographical properties) of the 3 networks, their scale-free properties were evaluated, which revealed that the intersection network was the most scale-free. In the subnetwork containing the most connected transcription factors (URI1, ROCK2, and ETV6), most genes were widely expressed across tissues, and genes previously shown to be involved in the traits were found. Results indicated that the current approach might be used to construct a gene network that better reflects biological information, providing encouragement for the genetic dissection of economically important quantitative traits.
Telephone scams called "special scams" have become a major problem, with people being defrauded of nearly 30 billion yen every year in Japan. In this study, we used emotion analysis techniques based on speech analysis to detect whether victims were being deceived by analyzing multiple emotional parameters of victims in a special scam. In the verification, we first clarified the characteristics of the emotions generated in victims by two scam methods. Next, we improved the accuracy of detecting the risk of a victim being deceived by limiting the analysis section.
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