Biological Monitoring of Pyrethroid Exposure of Pest Control Workers in Japan: Dong WANG, et al. Department of Radiological and MedicalLaboratory Sciences, Nagoya University Graduate School of Medicine-Synthetic pyrethroids such as cypermethrin, deltamethrin and permethrin, which are usually used in pest control operations, are metabolized to 3-phenoxybenzoic acid (3-PBA) and excreted in urine. Though 3-PBA can be used to assess exposure to pyrethroids, there are few reports describing urinary 3-PBA levels in Japan. This study aimed to investigate the seasonal variation of the exposure levels of pyrethroids and the concentration of urinary 3-PBA among pest control operators (PCOs) in Japan. The study subjects were 78 and 66 PCOs who underwent a health examination in December 2004 and in August 2005, respectively. 3-PBA was determined using gas chromatography-mass spectrometry. The geometric mean concentration of urinary 3-PBA in winter (3.9 µg/ g creatinine) was significantly lower than in summer (12.2 µg/g creatinine) (p<0.05). Geometric mean concentrations of urinary 3-PBA in the spraying workers and the not-spraying workers within 2 d before the survey were 5.4 µg/g creatinine and 0.9 µg/g creatinine for winter with a significant difference between the groups (p<0.05), and 12.3 µg/g creatinine and 8.7 µg/ g creatinine for summer (p>0.05), respectively. A significant association of 3-PBA levels and pyrethroid Field Studyspraying was thus observed only in winter. In conclusion, the results of the present study show that the exposure level of pyrethroids among PCOs in Japan assessed by monitoring urinary 3-PBA was higher than that reported in the UK but comparable to that in Germany. Further research should be accumulated to establish an occupational reference value in Japan. (J Occup Health 2007; 49: 509-514)

The non-inferiority of the Thopaz system compared to a standard system was verified statistically. With advantages of providing objective data as a small portable system, in addition to the quietness of the unit and the ease of setup and operation, Thopaz system is likely to become mainstream in postoperative management in general thoracic surgery.

We report a 53-year-old man with metastases of alveolar soft-part sarcoma originated from the mediastinum. He was hospitalized due to lower extremities’ paralysis. Computed tomography scan findings revealed multiple nodules of bilateral lungs, swollen mediastinal lymph nodes, and osteolysis of thoracic vertebrae. We performed spinal decompression and biopsy from vertebra. And, we finally diagnosed this case as metastases of mediastinal alveolar soft-part sarcoma which was removed 10 years ago. Alveolar soft-part sarcoma is rare tumor accounted for 0.5%–1.0% of soft tissue sarcoma that often occurs primarily in the lower extremities and trunk. It is difficult to distinguish between alveolar soft-part sarcoma and paraganglioma, renal cell carcinoma and granular cell tumor morphologically. Periodic acid–Schiff stain and immunohistochemical staining of ASPL-TFE3 are useful in making a definitive diagnosis of alveolar soft-part sarcoma. This case is a rare case of alveolar soft-part sarcoma originated in the mediastinum with local recurrence and distant metastases 10 years after the initial surgery.

We deal with the problem of detecting anomalous objects at the pixel level for the purpose of picking anomalous objects up by the robot in the soil recycling process. A previous convex detection method does not distinguish anomalous objects and soil with stones, which makes it impossible for the robot to pick them up. In this paper, we focus on that we can get a lot of data of soil and stones although there are few data of anomalous objects. We get color images and depth maps of soil and stones by using a RGBD camera. Using the color images and depth maps, GAN is trained to estimate the depth map from the color image. GAN cannot estimate the depth map of anomalous objects because there are few training data of anomalous objects. Then, we can detect anomalous objects by comparing the real depth map and the depth map estimated from color image by using GAN. Experimental results show that our method can detect anomalous objects 2.4 times more accurately than the previous method can do. Moreover, we confirm that our method enables the robot to estimate grasping position of anomalous objects more accurately.

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